paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3be3da2f-e293-4bd8-9546-77968c11260d | neural-posterior-estimation-with | 2207.05636 | null | https://arxiv.org/abs/2207.05636v1 | https://arxiv.org/pdf/2207.05636v1.pdf | Neural Posterior Estimation with Differentiable Simulators | Simulation-Based Inference (SBI) is a promising Bayesian inference framework that alleviates the need for analytic likelihoods to estimate posterior distributions. Recent advances using neural density estimators in SBI algorithms have demonstrated the ability to achieve high-fidelity posteriors, at the expense of a large number of simulations ; which makes their application potentially very time-consuming when using complex physical simulations. In this work we focus on boosting the sample-efficiency of posterior density estimation using the gradients of the simulator. We present a new method to perform Neural Posterior Estimation (NPE) with a differentiable simulator. We demonstrate how gradient information helps constrain the shape of the posterior and improves sample-efficiency. | ['Eric Aubourg', 'Benjamin Remy', 'Alexandre Boucaud', 'François Lanusse', 'Justine Zeghal'] | 2022-07-12 | null | null | null | null | ['physical-simulations'] | ['miscellaneous'] | [ 1.48571972e-02 -2.22011745e-01 2.03540787e-01 -2.78109372e-01
-8.01430941e-01 -2.64419079e-01 6.57826483e-01 -1.59402072e-01
-5.98656476e-01 1.38583755e+00 -3.58255446e-01 -5.47686636e-01
-1.61325455e-01 -7.77962327e-01 -9.10484970e-01 -7.44058669e-01
-1.11440249e-01 5.03009796e-01 2.98455417e-01 2.85195291e-01
3.64519060e-01 7.03679681e-01 -1.54295182e+00 -7.01365173e-01
9.95119572e-01 7.57179499e-01 1.56795159e-01 8.69568408e-01
2.34356970e-01 3.00167292e-01 -9.30646360e-01 -2.91473721e-03
-2.85670102e-01 -3.71878356e-01 3.75409983e-02 -7.52010107e-01
4.61391686e-03 -6.47482693e-01 -1.18724681e-01 1.02560341e+00
6.14196420e-01 2.63237804e-01 1.18719149e+00 -1.04138172e+00
9.24882218e-02 2.06530169e-01 -5.79514563e-01 2.33882964e-01
6.11992516e-02 2.72235155e-01 2.70989060e-01 -6.91069305e-01
2.98360616e-01 1.24146867e+00 9.33822453e-01 3.20306033e-01
-1.50634110e+00 -8.01062942e-01 -3.28625202e-01 -1.35413632e-01
-1.77841425e+00 -4.01591808e-01 5.23395002e-01 -4.14082408e-01
8.67856979e-01 -1.53615683e-01 7.54776478e-01 9.95207846e-01
6.35864615e-01 4.02151376e-01 9.68097031e-01 -3.27456087e-01
8.86847138e-01 2.49948632e-02 5.22026829e-02 4.22605783e-01
6.47243142e-01 4.88211125e-01 -5.71592093e-01 -4.68478262e-01
1.17213190e+00 -6.16446197e-01 -2.15630248e-01 -2.76044369e-01
-6.01539135e-01 8.59771848e-01 -2.56935526e-02 -2.35426620e-01
-3.73887718e-01 7.10891664e-01 -1.21920183e-02 -1.66772038e-01
3.23899776e-01 2.09707752e-01 -7.69740492e-02 -6.21617436e-01
-1.03339052e+00 6.72608852e-01 9.90403473e-01 7.12894678e-01
4.85992312e-01 3.56683642e-01 -1.09934919e-01 5.02435446e-01
7.71081805e-01 1.07124829e+00 -1.32913426e-01 -1.24526978e+00
-6.55177794e-03 -2.30053067e-01 8.08584988e-01 -4.98378307e-01
-2.31010631e-01 -5.31382382e-01 -7.67057896e-01 5.01890182e-01
6.11328661e-01 -6.26269639e-01 -9.36980069e-01 1.77434349e+00
4.66596067e-01 2.25208774e-01 -8.93645808e-02 6.01694465e-01
-4.32787836e-03 1.09388804e+00 3.94581780e-02 -1.85710624e-01
6.70775473e-01 -2.14782834e-01 -6.54483795e-01 -5.52475713e-02
3.85619956e-03 -3.65989357e-01 8.11781585e-01 3.78516883e-01
-1.28178358e+00 -2.13796318e-01 -1.28930104e+00 4.15874869e-01
-6.52492642e-02 -3.32206115e-02 5.08504212e-01 1.06382704e+00
-9.46983993e-01 8.89764071e-01 -1.31011558e+00 1.50949478e-01
5.06829262e-01 4.27891642e-01 3.49433064e-01 2.46710867e-01
-9.03462410e-01 1.05442262e+00 2.37457111e-01 2.86159161e-02
-1.11175203e+00 -1.10688508e+00 -6.48590326e-01 3.22753638e-01
4.59962562e-02 -5.10155976e-01 1.33561218e+00 -2.76172832e-02
-2.11770105e+00 -2.19175979e-01 -3.46175909e-01 -5.56368113e-01
5.24710357e-01 -1.32108212e-01 -9.20765623e-02 2.02802062e-01
-2.45279923e-01 6.45958960e-01 9.40750420e-01 -9.56685483e-01
-1.97600007e-01 1.83630306e-02 -5.23483038e-01 -9.43820029e-02
1.54745013e-01 -2.79576898e-01 -2.60495424e-01 -9.84348804e-02
-1.04576759e-01 -7.41555691e-01 -1.82749182e-01 2.03761235e-01
2.36593857e-02 8.67455229e-02 6.39126480e-01 -6.25735760e-01
7.78373063e-01 -1.83579850e+00 -2.57454306e-01 4.23533887e-01
-6.45717308e-02 2.61111259e-01 2.46407986e-01 3.53741139e-01
6.30713522e-01 -9.72023308e-02 -3.70092392e-01 -1.87586740e-01
2.27998663e-02 9.39518288e-02 -3.50022405e-01 5.72111368e-01
3.06775510e-01 5.98002613e-01 -7.90494800e-01 -4.51515377e-01
4.93626982e-01 9.27286625e-01 -6.92726672e-01 3.66658092e-01
-1.69984534e-01 7.61428595e-01 -3.13868165e-01 2.95610309e-01
7.98868835e-01 -3.26387465e-01 2.76649594e-01 -7.83386081e-02
-2.61212796e-01 3.54459316e-01 -1.21104848e+00 1.11972117e+00
-4.73205447e-01 5.36628008e-01 4.25013602e-01 -8.97840977e-01
8.00051689e-01 9.71152857e-02 4.00921591e-02 -1.99473128e-01
2.74161577e-01 2.16677502e-01 9.92008206e-03 7.11035356e-02
2.13758722e-01 -4.51305270e-01 7.29433745e-02 3.56697321e-01
2.27871478e-01 -7.23135769e-01 2.34480917e-01 1.44996285e-01
5.30991793e-01 4.73647952e-01 3.07521045e-01 -7.38721371e-01
7.65526518e-02 -3.52399945e-01 3.42040509e-01 1.22069836e+00
5.33816479e-02 2.52696604e-01 4.23705399e-01 2.21522748e-01
-1.19369745e+00 -1.67283905e+00 -5.05563796e-01 2.87729889e-01
7.42800534e-02 -8.31807405e-02 -6.82187200e-01 5.90699986e-02
1.71765685e-01 1.06475723e+00 -2.66811997e-01 -1.30058542e-01
-3.65536571e-01 -1.05620897e+00 4.72160459e-01 5.70644200e-01
3.59359860e-01 -3.21363986e-01 -7.69689560e-01 3.19525957e-01
1.99474454e-01 -6.66306138e-01 1.09705940e-01 2.83957243e-01
-1.00426126e+00 -4.17689383e-01 -9.24261868e-01 -4.81889322e-02
4.67349112e-01 -4.01040912e-01 7.80555785e-01 -2.25821555e-01
-1.74411863e-01 3.22502524e-01 3.58044863e-01 -5.31238794e-01
-5.76432526e-01 -1.26754403e-01 2.22178429e-01 -5.85136771e-01
4.85308394e-02 -1.01825964e+00 -3.05360049e-01 1.20246552e-01
-5.30180573e-01 1.46618962e-01 2.73224115e-01 7.57104397e-01
3.52142155e-01 -2.16450263e-02 7.32581556e-01 -3.10680896e-01
6.74769759e-01 -3.95884246e-01 -1.64069188e+00 1.59859881e-01
-5.67155421e-01 4.56657171e-01 5.51616967e-01 -5.29040813e-01
-1.41277039e+00 -3.65361392e-01 -3.24360609e-01 -1.71942055e-01
1.92047939e-01 5.92301309e-01 1.10088952e-01 -3.65411043e-01
4.95644838e-01 8.90322402e-02 2.79274374e-01 -4.42116052e-01
-3.60601991e-02 4.74368721e-01 5.78767061e-01 -9.07936454e-01
5.58642447e-01 2.49592170e-01 6.06152713e-01 -1.02554059e+00
-7.11353123e-01 1.11011453e-01 -2.10358873e-01 -4.38169837e-01
3.89191806e-01 -6.89602792e-01 -1.15825748e+00 4.80318457e-01
-8.04574847e-01 -5.83029270e-01 -1.42736048e-01 9.17429626e-01
-6.33323908e-01 3.86083245e-01 -5.79175413e-01 -1.40575767e+00
-1.74884930e-01 -9.60554898e-01 7.82715201e-01 6.56890213e-01
-2.30221882e-01 -9.00833130e-01 1.88039169e-01 -3.92689705e-01
7.65074134e-01 4.80064452e-02 6.37656569e-01 2.07787082e-01
-6.36104345e-01 -1.23595081e-01 -2.80093461e-01 3.59231681e-02
-4.25420582e-01 4.06428277e-01 -9.38947380e-01 -2.97050804e-01
9.67838615e-02 -2.44442374e-01 4.86739606e-01 1.04607499e+00
1.01627445e+00 1.24355145e-01 -4.81961966e-01 6.35595977e-01
1.38721287e+00 2.52485812e-01 5.48059106e-01 -2.98918456e-01
1.96483776e-01 1.20433792e-01 3.89407188e-01 7.31920779e-01
-1.83800429e-01 4.13128287e-01 -1.19189106e-01 3.59150380e-01
1.70757711e-01 -4.88130301e-01 1.95875168e-01 6.21662140e-01
1.23235226e-01 -3.35892677e-01 -9.13696527e-01 2.98680961e-01
-1.61453557e+00 -6.48650408e-01 5.11239842e-02 2.39736509e+00
9.79279578e-01 2.03150183e-01 -5.79598770e-02 -1.06763661e-01
7.57348239e-01 -4.73980010e-01 -8.12031806e-01 -1.28528371e-01
3.70752960e-01 5.27589798e-01 6.61455989e-01 9.00239289e-01
-6.37120426e-01 4.41590399e-01 8.24959278e+00 1.08239996e+00
-8.10406268e-01 4.82851602e-02 4.02370304e-01 -2.48868778e-01
-1.95413187e-01 7.67948553e-02 -1.22844601e+00 7.30525672e-01
1.59109175e+00 -2.90079951e-01 4.35113907e-01 6.11273110e-01
3.40990901e-01 -9.81739938e-01 -8.97119880e-01 8.03723633e-01
-4.19913381e-01 -1.42427850e+00 -2.38346353e-01 9.38612670e-02
5.65737665e-01 2.42248438e-02 -8.83547962e-03 3.90150189e-01
4.30821329e-01 -1.02038908e+00 6.04452491e-01 7.63900757e-01
8.77401590e-01 -9.63010907e-01 6.15326166e-01 5.02006888e-01
-7.17953086e-01 2.98358828e-01 -5.33653855e-01 -2.69055039e-01
7.89144874e-01 9.70619202e-01 -8.62635791e-01 -1.12110339e-02
4.96176541e-01 9.84345302e-02 4.64840159e-02 1.31607854e+00
-1.66443959e-01 1.05996990e+00 -1.19803441e+00 -5.76403141e-01
2.02115495e-02 -5.31038463e-01 6.48595035e-01 1.01394749e+00
5.92585862e-01 -1.22053832e-01 -1.59946322e-01 1.46121800e+00
2.71403998e-01 -5.75513422e-01 -2.74849206e-01 -2.26983368e-01
8.61315668e-01 7.17086554e-01 -7.03956842e-01 -3.89436841e-01
3.89929116e-02 2.56963164e-01 1.86749965e-01 4.79274511e-01
-1.01417351e+00 -4.39215869e-01 3.71208906e-01 -6.86410069e-03
4.30410177e-01 -6.27442479e-01 -2.59567916e-01 -8.29012036e-01
-3.54225487e-01 -2.98154652e-01 -6.20301180e-02 -9.12724972e-01
-1.02589130e+00 -3.27429950e-01 7.47640133e-01 -5.51585495e-01
-7.28208065e-01 -5.80980182e-01 -4.88625407e-01 1.21968937e+00
-1.19770670e+00 -4.18366700e-01 1.39460564e-01 7.01930225e-02
-1.44368291e-01 9.77342799e-02 6.57083750e-01 2.92460453e-02
-4.19513285e-01 4.61131901e-01 6.69702649e-01 -3.63125026e-01
1.21483661e-01 -9.54585910e-01 4.60876673e-01 7.49194324e-01
-4.69707996e-01 7.20516086e-01 1.20920157e+00 -9.25641596e-01
-1.46533787e+00 -3.06079000e-01 1.75539732e-01 -9.34019536e-02
7.19761431e-01 -3.45686197e-01 -8.37269902e-01 2.90639102e-01
-2.13907763e-01 -4.41744745e-01 3.43387514e-01 2.30014071e-01
2.34206736e-01 6.81050727e-03 -1.32706439e+00 7.28130579e-01
3.59559983e-01 -3.21065038e-01 -2.51821220e-01 -1.19038135e-01
2.63831794e-01 -2.49181911e-01 -8.91312361e-01 5.59911549e-01
8.26692283e-01 -8.17640185e-01 9.71449673e-01 2.01006725e-01
-1.45669937e-01 -4.59623337e-01 1.54764533e-01 -1.07815146e+00
1.40369117e-01 -7.77414739e-01 -5.08862436e-01 1.03198111e+00
2.33106002e-01 -8.53243351e-01 7.80893326e-01 7.63350964e-01
2.82743186e-01 -3.56905609e-01 -1.21261704e+00 -1.06225002e+00
2.48638377e-01 -5.20054460e-01 3.51979971e-01 6.93147853e-02
-6.86483383e-02 1.84625804e-01 -2.29359508e-01 2.45588779e-01
1.08738112e+00 -1.90798447e-01 5.68030119e-01 -1.15494609e+00
-6.60545170e-01 -3.80170256e-01 -1.18894041e-01 -1.33420241e+00
-9.63322520e-02 -1.53295502e-01 3.82613957e-01 -1.14251888e+00
1.88871637e-01 -2.91040778e-01 1.43962920e-01 -1.90512910e-01
-1.09884575e-01 1.22046918e-01 -2.66603917e-01 -1.33483857e-01
-6.66685626e-02 8.67618561e-01 8.59607935e-01 3.88863772e-01
3.18140872e-02 2.59136455e-03 2.79913712e-02 7.35846162e-01
9.59748447e-01 -7.14097202e-01 -5.51554739e-01 -2.09286250e-02
2.48304904e-01 3.36527288e-01 5.53569436e-01 -1.29547429e+00
2.55408168e-01 -1.06522448e-01 6.68937147e-01 -1.08809090e+00
6.94657624e-01 -2.53697902e-01 2.81030416e-01 5.24491727e-01
-7.40740970e-02 -3.80088419e-01 5.55069387e-01 6.50914669e-01
1.75359726e-01 -8.24818134e-01 1.18653822e+00 2.30567813e-01
-5.32548651e-02 -1.25681847e-01 -1.20356238e+00 5.70675060e-02
4.60601270e-01 6.96723014e-02 -2.56315172e-02 -6.94504261e-01
-3.93055767e-01 -1.08243162e-02 5.00915527e-01 -6.33042634e-01
3.70644122e-01 -8.86479020e-01 -3.69331032e-01 1.82400584e-01
-6.00462973e-01 3.40830348e-03 1.57942742e-01 5.72380662e-01
-7.13665128e-01 1.61584318e-01 -4.87016812e-02 -7.92692542e-01
-6.70502245e-01 2.48444285e-02 5.46492755e-01 -9.96964499e-02
-6.26345277e-01 8.20343494e-01 -1.13637321e-01 -2.79572785e-01
2.31633440e-01 -1.30846933e-01 3.08382422e-01 -6.20726526e-01
6.14971638e-01 6.09191418e-01 -3.76532763e-01 1.90747783e-01
-1.85021609e-01 2.67077684e-01 1.49482131e-01 -8.30004871e-01
1.04176486e+00 -5.39366715e-02 1.46244586e-01 5.92492461e-01
8.86837900e-01 -2.24285007e-01 -1.87343788e+00 3.72413367e-01
-1.68673977e-01 -4.48903233e-01 5.95406890e-01 -7.73523331e-01
-3.80845219e-01 1.00233233e+00 4.80967909e-01 -2.05581337e-01
5.57924449e-01 -3.46627682e-01 5.23441136e-01 5.67150712e-01
5.57592034e-01 -1.21273243e+00 -3.47268641e-01 5.06676555e-01
3.80999833e-01 -6.83577478e-01 3.83011669e-01 -2.28764758e-01
-4.38614301e-02 9.95266497e-01 3.05015594e-01 -3.36274981e-01
9.94815707e-01 9.01932895e-01 -5.69345593e-01 4.60600257e-02
-4.70526308e-01 3.17262292e-01 9.90105942e-02 8.22148740e-01
1.99488267e-01 -3.59912217e-02 -2.04092503e-01 2.76319116e-01
-3.90866771e-02 2.38902375e-01 5.11142254e-01 1.02958632e+00
-6.44866884e-01 -8.08615625e-01 -4.08055156e-01 5.10395169e-01
-2.97527701e-01 -8.33202079e-02 3.28684181e-01 7.11809158e-01
-6.31502271e-01 8.50211978e-01 2.44669884e-01 3.80274206e-01
-4.04595554e-01 -1.16291856e-02 9.59351957e-01 -8.65810588e-02
7.05482364e-02 2.22287431e-01 2.23803610e-01 -3.37307751e-01
-1.92558929e-01 -6.68756902e-01 -1.08498454e+00 -6.27789795e-01
-4.71912950e-01 3.86447370e-01 1.01745963e+00 1.00551379e+00
2.47366741e-01 2.65794039e-01 1.09725699e-01 -1.21760988e+00
-9.08680856e-01 -9.52795148e-01 -7.19548941e-01 -4.41696882e-01
5.54246716e-02 -1.02251506e+00 -5.76418400e-01 -3.81648093e-01] | [6.8207244873046875, 3.9060211181640625] |
16797a4c-cc19-41aa-9124-a3622cb04594 | cross-attention-based-style-distribution-for | 2208.00712 | null | https://arxiv.org/abs/2208.00712v1 | https://arxiv.org/pdf/2208.00712v1.pdf | Cross Attention Based Style Distribution for Controllable Person Image Synthesis | Controllable person image synthesis task enables a wide range of applications through explicit control over body pose and appearance. In this paper, we propose a cross attention based style distribution module that computes between the source semantic styles and target pose for pose transfer. The module intentionally selects the style represented by each semantic and distributes them according to the target pose. The attention matrix in cross attention expresses the dynamic similarities between the target pose and the source styles for all semantics. Therefore, it can be utilized to route the color and texture from the source image, and is further constrained by the target parsing map to achieve a clearer objective. At the same time, to encode the source appearance accurately, the self attention among different semantic styles is also added. The effectiveness of our model is validated quantitatively and qualitatively on pose transfer and virtual try-on tasks. | ['Qingli Li', 'Changxin Gao', 'Li Sun', 'Xinyuan Chen', 'Mingyu Yin', 'Xinyue Zhou'] | 2022-08-01 | null | null | null | null | ['pose-transfer'] | ['computer-vision'] | [ 5.89737147e-02 -4.46443520e-02 -1.64475422e-02 -5.68343699e-01
-2.51767725e-01 -3.95311654e-01 5.05698800e-01 -3.55272740e-01
-1.67625949e-01 3.84110510e-01 3.58118117e-01 5.16175568e-01
1.08627699e-01 -8.16193104e-01 -6.09228015e-01 -4.92779821e-01
5.22112072e-01 4.97698069e-01 1.99285939e-01 -5.27051508e-01
-3.98404300e-02 1.25790477e-01 -1.28136051e+00 3.51043433e-01
9.59929883e-01 9.91517782e-01 4.51396674e-01 3.14295769e-01
-2.30233774e-01 4.31632310e-01 -7.23049283e-01 -5.16361594e-01
1.53490052e-01 -7.71410406e-01 -4.59746212e-01 2.56075650e-01
6.39368236e-01 -2.59576350e-01 -2.58568734e-01 1.25930560e+00
5.43736935e-01 1.20165974e-01 5.61810136e-01 -1.11803019e+00
-9.69570220e-01 4.18595701e-01 -7.42967010e-01 -1.28655106e-01
6.27803087e-01 2.85632402e-01 7.32044339e-01 -4.99347836e-01
5.80597103e-01 1.63046503e+00 1.54084608e-01 8.37068498e-01
-1.20187616e+00 -8.67456853e-01 7.05537438e-01 1.28725812e-01
-1.09914482e+00 2.43270304e-02 1.14547086e+00 -3.46560776e-01
-2.14139675e-03 2.70099819e-01 1.05788422e+00 1.29634559e+00
2.15473309e-01 7.26799548e-01 1.20462263e+00 -3.05550784e-01
7.22200796e-02 1.75807968e-01 -1.35802746e-01 8.74549747e-01
8.02153274e-02 -4.14987234e-03 -7.12382674e-01 3.42362791e-01
1.17639983e+00 -1.83507994e-01 -4.49354321e-01 -6.54884517e-01
-1.26226950e+00 6.20998323e-01 6.37191057e-01 7.24689513e-02
-2.42493991e-02 2.18712032e-01 3.51480693e-01 5.89830913e-02
3.18555087e-01 4.67206836e-01 -5.11705637e-01 2.32382119e-01
-3.11668545e-01 3.28774065e-01 4.00450319e-01 1.16546607e+00
5.94990551e-01 1.24974720e-01 -6.15108013e-01 9.41898823e-01
5.40040433e-01 7.67770350e-01 3.96770328e-01 -8.38935196e-01
5.25974214e-01 7.16526926e-01 1.07010625e-01 -1.26689136e+00
-3.52719098e-01 -5.01344621e-01 -5.39264560e-01 2.48820707e-01
3.98925215e-01 -2.27615237e-02 -1.13691175e+00 2.29748464e+00
4.28987056e-01 -2.15054780e-01 -4.63272870e-01 1.40794063e+00
8.21044564e-01 4.68549699e-01 3.25897813e-01 5.93441352e-02
1.57016289e+00 -9.12573278e-01 -8.45940650e-01 -6.44332826e-01
-7.12431967e-02 -5.35975277e-01 1.46160901e+00 1.32121950e-01
-8.98507535e-01 -8.57304931e-01 -9.88570988e-01 -2.37031043e-01
3.42888571e-03 4.55009371e-01 5.62293649e-01 5.26707292e-01
-7.82875180e-01 3.34683895e-01 -6.61581218e-01 -4.40786600e-01
1.83547899e-01 4.29595500e-01 -6.61734864e-02 1.23937167e-01
-1.31380081e+00 7.85163760e-01 1.58132166e-01 8.39437321e-02
-6.02754414e-01 -5.98068595e-01 -9.47418928e-01 -1.22944139e-01
8.25021863e-02 -1.07232022e+00 8.52008224e-01 -1.51477396e+00
-1.85425615e+00 9.60702479e-01 9.44556072e-02 2.45603725e-01
6.06551409e-01 -3.38350743e-01 -3.53020579e-01 2.05830652e-02
2.48539433e-01 1.08954823e+00 1.07538617e+00 -1.30397737e+00
-4.79871899e-01 -6.93787456e-01 2.28904128e-01 7.57965326e-01
-4.27545190e-01 -1.55776367e-01 -1.16558003e+00 -9.67218995e-01
6.76556453e-02 -8.95264208e-01 -5.35431653e-02 2.43386284e-01
-3.90271753e-01 1.22446656e-01 5.58263183e-01 -7.73458064e-01
9.55550253e-01 -2.11919880e+00 9.32320118e-01 1.47658840e-01
9.32328105e-02 -2.77802855e-01 -1.75722674e-01 -8.74572322e-02
6.69279993e-02 -2.11600453e-01 2.80198697e-02 -2.19487548e-01
-1.28958926e-01 1.17288403e-01 -3.06073837e-02 1.91517621e-01
5.78822978e-02 8.62125814e-01 -9.07803297e-01 -5.26654065e-01
3.02579015e-01 5.54444253e-01 -6.25483751e-01 3.66713226e-01
-3.41542453e-01 8.51350069e-01 -8.27552617e-01 3.69675547e-01
6.21099651e-01 -4.98432815e-02 2.11011216e-01 -7.94170618e-01
2.97208935e-01 -2.22629130e-01 -9.46940958e-01 2.08289218e+00
-5.75608552e-01 2.33610496e-01 8.61295536e-02 -2.82765508e-01
1.03603947e+00 -1.09717108e-01 3.44889253e-01 -8.60513151e-01
5.26134789e-01 -1.61377415e-01 -3.62795107e-02 -3.24692219e-01
3.02554816e-01 -1.73028469e-01 -3.68223608e-01 5.64415492e-02
3.07276715e-02 -9.61972326e-02 -3.98399681e-02 -7.37612993e-02
1.39061570e-01 6.02128804e-01 -3.76473181e-02 -4.55991715e-01
5.20617545e-01 -2.69084960e-01 6.62966549e-01 2.29523838e-01
-1.39745191e-01 7.59168208e-01 4.12721694e-01 -4.40950871e-01
-8.19353938e-01 -1.24464238e+00 1.39641359e-01 1.36942983e+00
8.11224520e-01 -2.56637216e-01 -1.09315562e+00 -8.04024577e-01
-4.89197336e-02 4.56800908e-01 -9.64247644e-01 -5.61486781e-01
-5.30080676e-01 -3.07787418e-01 1.08113796e-01 6.81733727e-01
7.23937333e-01 -1.15954494e+00 -4.53625232e-01 -5.02346121e-02
-3.35210592e-01 -7.68913388e-01 -1.15254891e+00 -4.83589083e-01
-5.41066706e-01 -8.69784236e-01 -9.03782129e-01 -7.01369762e-01
8.41316104e-01 -1.54034272e-01 9.41959679e-01 -3.68083447e-01
-1.56683743e-01 3.16892087e-01 -3.10017467e-01 -2.19103441e-01
-1.55010223e-01 1.70553997e-02 2.22171769e-02 3.96418184e-01
-5.49126230e-02 -4.31747884e-01 -8.95995378e-01 3.86806995e-01
-5.51564276e-01 5.04214942e-01 3.66414189e-01 6.81086063e-01
4.99018282e-01 -4.26914483e-01 2.24087998e-01 -6.49159610e-01
5.55448413e-01 6.39745686e-03 -4.42700207e-01 3.07115853e-01
-1.13920167e-01 1.50622696e-01 5.13104618e-01 -5.88528991e-01
-1.26435113e+00 1.38699472e-01 4.15700162e-03 -6.10582769e-01
-4.40536216e-02 -5.40124923e-02 -9.32078362e-01 2.65519898e-02
3.81376117e-01 1.65127665e-02 1.18675619e-01 -4.26906317e-01
6.24547303e-01 2.72551924e-01 4.77034181e-01 -8.27720165e-01
7.22324014e-01 3.89105827e-01 -3.21330369e-01 -3.83347541e-01
-1.04894078e+00 1.87581658e-01 -6.86966419e-01 -5.04239738e-01
1.29772639e+00 -7.72234619e-01 -7.26070106e-01 6.21922672e-01
-1.13733017e+00 -4.26255286e-01 -1.47945732e-01 3.22264791e-01
-4.68967766e-01 8.54008123e-02 -5.32862365e-01 -4.10044163e-01
-2.30741978e-01 -1.40918958e+00 1.42626417e+00 3.99036825e-01
-3.99542004e-01 -9.35142636e-01 -1.38470888e-01 2.63047308e-01
1.73873931e-01 2.27846429e-01 7.80284464e-01 1.52903825e-01
-5.01583815e-01 1.57513544e-01 -3.86171371e-01 3.31563465e-02
3.80446017e-01 -2.01431900e-01 -6.89197659e-01 -3.81693184e-01
-3.28885466e-01 -6.51634261e-02 6.27118647e-01 5.30937433e-01
1.22196734e+00 -1.60072923e-01 -1.82940289e-01 9.50539112e-01
1.29425073e+00 2.72176415e-01 5.18828869e-01 2.53514707e-01
1.28367722e+00 8.41957927e-01 6.05878949e-01 2.05276534e-01
3.93860132e-01 1.05011404e+00 1.68246463e-01 -3.38372946e-01
-3.53077501e-01 -6.26308143e-01 2.71095812e-01 4.95253235e-01
-8.50981250e-02 -1.13158017e-01 -5.19903719e-01 1.05466127e-01
-1.62216687e+00 -8.23333561e-01 2.53956318e-01 2.25649405e+00
8.24032426e-01 9.76213887e-02 1.46557942e-01 -5.14391005e-01
8.40167522e-01 2.00837836e-01 -6.32405162e-01 -1.96651250e-01
1.15983851e-01 1.37839746e-02 2.53551930e-01 5.11955738e-01
-1.01845169e+00 1.05856144e+00 6.01064777e+00 7.80911565e-01
-1.23814893e+00 -1.94583498e-02 6.64183557e-01 -1.20499782e-01
-5.44036508e-01 -4.08503234e-01 -6.93238616e-01 6.96127474e-01
3.01124994e-02 -1.16612799e-01 4.89238679e-01 7.15980232e-01
1.33019835e-01 1.53362498e-01 -1.03492069e+00 1.09910679e+00
2.85691470e-01 -8.91344905e-01 5.53453445e-01 -1.76511288e-01
6.38951302e-01 -6.37952387e-01 4.28933144e-01 3.11007947e-01
1.09149083e-01 -7.13089883e-01 1.17642128e+00 5.60269833e-01
1.24725199e+00 -8.92065108e-01 3.56894612e-01 -2.62742937e-02
-1.31915379e+00 -8.40337500e-02 -1.07914925e-01 1.12793609e-01
1.65396556e-01 1.37471497e-01 -3.05026807e-02 4.90029663e-01
6.25651658e-01 5.50089359e-01 -6.68257177e-01 5.47913730e-01
-3.67440939e-01 -5.87963983e-02 5.53771220e-02 -3.74035956e-03
-2.35870734e-01 -4.29191589e-01 4.89158541e-01 9.46527898e-01
1.08238660e-01 -9.74992812e-02 4.52140719e-01 1.03122520e+00
1.28916517e-01 2.73211092e-01 -3.44750106e-01 3.43677163e-01
3.44847202e-01 1.09244013e+00 -5.33915997e-01 -1.17855288e-01
-1.19338103e-01 1.51846492e+00 3.22523296e-01 5.05997300e-01
-1.14823818e+00 -1.90495387e-01 8.91517520e-01 1.90184787e-01
3.53795812e-02 -2.22759284e-02 -5.07088900e-01 -1.22559381e+00
-9.68252867e-02 -7.73519099e-01 2.42081955e-01 -1.00336981e+00
-1.25574207e+00 7.13626981e-01 1.21960029e-01 -1.25834882e+00
1.91293195e-01 -5.85177541e-01 -5.58242142e-01 1.04370558e+00
-8.18257868e-01 -1.41768825e+00 -6.47081494e-01 4.83929634e-01
5.85646689e-01 -1.17096670e-01 5.62820435e-01 2.48792216e-01
-7.49207556e-01 8.81583273e-01 -5.12220919e-01 2.17927203e-01
8.77292693e-01 -1.15245152e+00 2.41933987e-01 5.15123010e-01
-1.68347925e-01 7.24993885e-01 8.36721241e-01 -7.78080702e-01
-1.07825208e+00 -1.23025548e+00 2.02141643e-01 -4.46302205e-01
2.94075280e-01 -4.29994434e-01 -6.96141243e-01 5.27195156e-01
1.35011151e-01 -2.25876540e-01 2.58565038e-01 4.18123379e-02
-4.03563440e-01 -4.47262675e-01 -9.21519101e-01 8.80160213e-01
1.29411387e+00 -4.10934269e-01 -4.17548388e-01 1.69266500e-02
8.67449820e-01 -8.18656445e-01 -6.07072234e-01 2.66331822e-01
6.95468068e-01 -8.26874316e-01 9.88421679e-01 -4.75746512e-01
6.92738473e-01 -4.53171104e-01 5.96772544e-02 -1.51960230e+00
-6.29236519e-01 -1.29763409e-01 2.46022701e-01 1.37719464e+00
2.41294578e-01 -3.41040015e-01 7.33282864e-01 5.15778899e-01
-5.62111959e-02 -6.41464889e-01 -6.12156153e-01 -4.09286261e-01
-1.30040154e-01 -4.64485399e-02 8.18565667e-01 7.07458198e-01
-2.34279409e-01 5.82395613e-01 -5.40639699e-01 1.54027894e-01
7.52573192e-01 3.56225580e-01 7.45406568e-01 -9.53017294e-01
-4.99090254e-01 -5.55020452e-01 -4.01545137e-01 -1.15095782e+00
1.41292900e-01 -6.80291772e-01 -7.22878100e-03 -1.51631141e+00
2.49622762e-01 -3.87900025e-01 -2.57261902e-01 2.65734166e-01
-4.59670752e-01 3.08549106e-01 4.66468900e-01 7.63006881e-02
-3.81872535e-01 8.33391309e-01 1.94098759e+00 -1.98611528e-01
-3.24915081e-01 -1.25111356e-01 -8.32261980e-01 7.18650103e-01
8.40095401e-01 2.33024284e-02 -7.48914540e-01 -7.22525716e-01
-1.08723536e-01 1.03521071e-01 4.75324690e-01 -9.01181161e-01
-2.59472758e-01 -3.67723078e-01 7.09155142e-01 -2.66104102e-01
5.40069878e-01 -5.95002532e-01 9.41167921e-02 4.77551848e-01
-5.73448241e-01 -1.18636623e-01 7.16036782e-02 7.01022804e-01
6.56756088e-02 4.12497133e-01 1.03671098e+00 -1.47430211e-01
-7.11291194e-01 5.91128528e-01 2.58161038e-01 6.69688210e-02
1.01458538e+00 -2.78199255e-01 5.84448390e-02 -4.22205716e-01
-9.87681925e-01 3.21156055e-01 7.76185215e-01 7.56192446e-01
5.85697293e-01 -1.79207027e+00 -5.32248318e-01 4.88865852e-01
3.58807832e-01 -1.81109056e-01 4.98022944e-01 5.06916344e-01
-4.01998371e-01 -3.74615677e-02 -6.83319807e-01 -5.53455234e-01
-1.18690872e+00 6.21657610e-01 6.97357059e-01 5.56206144e-02
-5.75959325e-01 9.13964331e-01 9.93126214e-01 -3.52931023e-01
8.20370838e-02 -1.54142708e-01 -3.75644326e-01 -1.79867238e-01
4.91074800e-01 1.27729684e-01 -4.88389939e-01 -8.43491316e-01
-2.14887366e-01 1.08858109e+00 1.56109817e-02 -2.04497516e-01
7.83120334e-01 -3.82234722e-01 -2.10759733e-02 3.52096975e-01
1.01732540e+00 1.84626296e-01 -1.67259049e+00 1.37055352e-01
-6.31792724e-01 -7.46978283e-01 -2.43248031e-01 -8.91323328e-01
-1.44936633e+00 7.43533909e-01 7.45404661e-01 -4.97860730e-01
1.16249084e+00 1.71958417e-01 5.86433649e-01 -4.04284567e-01
7.11813271e-01 -1.03261089e+00 3.87686402e-01 2.27983132e-01
1.21832204e+00 -8.76744032e-01 -3.26544791e-01 -8.14202845e-01
-8.88316035e-01 7.99318433e-01 1.31710565e+00 -1.31922230e-01
2.45484546e-01 1.40437707e-01 2.48951674e-01 -1.78948373e-01
-1.65822059e-01 -6.11139201e-02 6.81594551e-01 9.38602149e-01
4.72043455e-01 2.81363428e-01 -2.54099250e-01 6.37404680e-01
-2.17533186e-01 -2.89948761e-01 -1.20654672e-01 2.40458846e-01
-2.44203538e-01 -1.14853024e+00 -4.75914747e-01 1.54990807e-01
2.19666846e-02 3.03307205e-01 -4.46551949e-01 5.84824979e-01
5.41249096e-01 4.76470023e-01 1.38109341e-01 -6.22734368e-01
6.07114375e-01 -2.93718100e-01 8.30731690e-01 -6.95084870e-01
-4.60032225e-01 2.43418410e-01 -7.47737959e-02 -7.00887859e-01
-1.13326542e-01 -4.30821568e-01 -1.16670430e+00 -1.06979810e-01
1.66467729e-03 -2.52305627e-01 2.01067775e-01 6.87256336e-01
2.64678597e-01 9.04076815e-01 6.13608956e-01 -9.70755696e-01
-2.25331336e-01 -7.93433130e-01 -5.89251161e-01 8.24283004e-01
-6.80789277e-02 -1.02450430e+00 1.09163724e-01 2.45787781e-02] | [12.057969093322754, -0.9352614879608154] |
372761a5-6aee-4641-a267-fd0cca885eeb | a-corpus-based-study-of-corporate-image | null | null | https://aclanthology.org/2022.csrnlp-1.3 | https://aclanthology.org/2022.csrnlp-1.3.pdf | A Corpus-based Study of Corporate Image Represented in Corporate Social Responsibility Report: A Case Study of China Mobile and Vodafone | By examination of the high-frequency nouns, verbs, and keywords, the present study probes into the similarities and differences of corporate images represented in Corporate Social Responsibility (CSR) reports of China Mobile and Vodafone. The results suggest that: 1) both China Mobile and Vodafone prefer using some positive words, like improve, support and service to shape a positive, approachable and easy-going corporate image, and an image of prioritizing the environmental sustainability and the well-being of people; 2) CSR reports of China Mobile contain the keywords poverty and alleviation, which means China Mobile is pragmatic, collaborative and active to assume the responsibility for social events; 3) CSR reports of Vodafone contain keywords like privacy, women and global as well as some other countries, which shows Vodafone is enterprising, globalized and attentive to the development of women; 4) these differences might be related to the ideology and social culture of Chinese and British companies. This study may contribute to understanding the function of CSR report and offer helpful implications for broadening the research of corporate image. | ['Liang Xu', 'Xing Chen'] | null | null | null | null | csrnlp-lrec-2022-6 | ['culture'] | ['speech'] | [-3.30832213e-01 2.99478918e-01 -4.96882290e-01 6.02343716e-02
1.91406891e-01 -2.87524939e-01 6.89922392e-01 4.12086815e-01
-3.59433949e-01 4.37054306e-01 9.98201072e-01 -2.51630008e-01
-2.48764202e-01 -7.71249354e-01 -1.20546492e-02 -6.85775280e-01
5.48703015e-01 -3.23552549e-01 -1.16162300e-01 -7.21996427e-01
7.14750707e-01 9.03430060e-02 -9.59715962e-01 -1.97727755e-01
5.30953228e-01 4.41045105e-01 1.84108630e-01 1.98131830e-01
-5.77224232e-02 9.88173962e-01 -3.79565626e-01 -6.60984576e-01
-3.69587898e-01 -3.01234931e-01 -4.36508745e-01 2.28765279e-01
-8.98423344e-02 -2.51402557e-01 3.61979395e-01 1.23355699e+00
2.69532382e-01 -3.13585073e-01 2.12960318e-01 -7.29348183e-01
-1.16256142e+00 6.45053327e-01 -3.77392948e-01 8.40017125e-02
-1.02086663e-01 -4.87152152e-02 1.01196420e+00 -5.78327775e-01
1.11496615e+00 1.21693933e+00 5.02492726e-01 -1.30936746e-02
-7.89111018e-01 -8.10379624e-01 2.73999989e-01 -3.76608402e-01
-1.10619450e+00 -4.38567102e-01 3.16418231e-01 -6.36489332e-01
8.35805595e-01 5.68105876e-01 9.32186007e-01 4.86344248e-01
4.01758194e-01 -2.16695949e-01 1.03737628e+00 -4.50150669e-01
-9.81122777e-02 6.86765790e-01 -2.51347095e-01 7.64353946e-02
6.72200084e-01 -5.79168022e-01 -1.14761598e-01 -3.65464278e-02
5.51136434e-01 2.50701606e-01 -2.21634120e-01 1.91427201e-01
-1.62014198e+00 1.07822645e+00 3.09894174e-01 9.44643795e-01
-6.01777732e-01 3.08286875e-01 3.76799434e-01 -1.55681639e-03
6.05248392e-01 2.70350784e-01 1.68615490e-01 -5.70284724e-01
-1.21010303e-01 -1.14189915e-01 5.56516349e-01 3.87806565e-01
2.19609261e-01 2.10057616e-01 3.90216321e-01 5.98805130e-01
5.70529640e-01 7.85802543e-01 -1.07110083e-01 -1.44678617e+00
4.90418583e-01 5.69531620e-01 3.84111330e-02 -1.70201111e+00
-2.00776998e-02 -8.52644086e-01 -1.72135025e-01 -1.46497205e-01
-1.22998394e-01 -4.31972463e-03 2.05133408e-01 1.37120509e+00
-1.04275517e-01 -7.53547490e-01 3.36845189e-01 7.27747262e-01
6.06688619e-01 6.66567385e-01 4.73562449e-01 -5.50861955e-01
1.23835564e+00 -3.96200806e-01 -1.06780052e+00 -3.95048469e-01
7.50716746e-01 -9.41764414e-01 1.04916167e+00 -9.71643925e-02
-9.37418580e-01 -2.07696363e-01 -8.87871623e-01 1.70602724e-01
-1.37506709e-01 4.41358685e-02 5.82228959e-01 7.87500739e-01
-6.39702976e-01 3.26266080e-01 -4.85246122e-01 -1.00108492e+00
4.69437659e-01 -1.35517865e-01 -3.61525744e-01 4.55531478e-02
-5.06477773e-01 6.95702434e-01 1.33315489e-01 1.74816907e-03
2.24496033e-02 -4.44800146e-02 -6.52530253e-01 -7.93740749e-02
2.93525636e-01 -2.02087715e-01 7.72398949e-01 -1.53976929e+00
-5.99916041e-01 1.09937751e+00 2.90149480e-01 -1.43346235e-01
1.72387734e-01 -3.04975539e-01 -8.51229370e-01 2.43615299e-01
6.35762155e-01 4.71347004e-01 -9.72018018e-02 -1.13105249e+00
-8.16718400e-01 -5.81600308e-01 1.81756094e-01 1.60490125e-01
-8.26822937e-01 8.57989132e-01 1.86480969e-01 -4.56525505e-01
3.47179204e-01 -6.22434735e-01 -9.91713479e-02 -3.13033283e-01
-3.51353168e-01 -1.58517901e-02 8.11659694e-01 -8.43614280e-01
1.42582428e+00 -2.41969538e+00 -7.70274282e-01 -1.17345437e-01
2.05279246e-01 -2.25672260e-01 4.99427468e-01 9.64567542e-01
2.97489792e-01 7.66209245e-01 4.99525338e-01 3.85230601e-01
2.45985924e-03 6.50169104e-02 -9.75656435e-02 5.83738208e-01
6.14326559e-02 4.60064083e-01 -1.06147826e+00 -2.84937948e-01
-4.01933283e-01 4.62467432e-01 -4.06866759e-01 -8.54966998e-01
5.60976207e-01 2.68226713e-01 -5.65787613e-01 7.28836715e-01
5.74020088e-01 -3.89613897e-01 7.69301534e-01 1.46539524e-01
-1.08894527e+00 3.20805818e-01 -7.55396903e-01 3.39126170e-01
-1.02416717e-01 8.23441386e-01 4.22836572e-01 -3.03652525e-01
1.06456649e+00 2.40214750e-01 7.85543770e-02 -1.20704520e+00
2.79726177e-01 4.66383934e-01 -7.85111338e-02 -4.91288006e-01
8.88678670e-01 -2.78064281e-01 -1.09995484e-01 4.14760053e-01
-9.40456569e-01 -7.89831355e-02 -2.68198967e-01 3.53881896e-01
3.79276067e-01 1.66399673e-01 4.60986763e-01 -4.64611322e-01
2.44580001e-01 -1.36153191e-01 9.49066460e-01 -2.01021686e-01
-3.58056039e-01 4.81421873e-02 7.56005287e-01 -2.48038974e-02
-8.07956040e-01 -4.13406312e-01 5.43173142e-02 8.37067246e-01
4.69898045e-01 -1.22985564e-01 -4.38838184e-01 -5.84503524e-02
-3.95977318e-01 1.13446879e+00 2.27044653e-02 2.44881630e-01
-3.16296339e-01 -4.02447313e-01 -2.13142827e-01 2.46544868e-01
9.45792615e-01 -5.96059382e-01 -9.26230311e-01 1.07521549e-01
1.55329080e-02 -1.13197899e+00 -3.30919951e-01 -1.79528713e-01
-8.30133855e-01 -9.04314220e-01 -3.66530806e-01 -6.60158217e-01
7.52128482e-01 6.96352303e-01 7.17768490e-01 2.35814855e-01
5.06968856e-01 2.82856524e-01 -1.12120427e-01 -7.02584147e-01
-6.51717007e-01 -4.43766922e-01 -2.50209868e-01 -1.72567233e-01
1.17602825e-01 -3.84753883e-01 -2.82468587e-01 1.24553151e-01
-4.58450407e-01 1.53964356e-01 1.62364155e-01 9.50789005e-02
4.28522855e-01 3.49277437e-01 8.32818925e-01 -8.83235395e-01
5.36648750e-01 -7.85676301e-01 6.42260686e-02 -2.03245074e-01
-7.71143496e-01 -1.06938457e+00 -1.34857520e-01 -7.65084773e-02
-1.14906728e+00 -7.62408674e-01 1.20917298e-01 4.00519222e-01
5.03623784e-01 3.87676984e-01 -2.21717507e-01 1.13706291e-01
3.03280562e-01 -2.37617567e-01 3.33039701e-01 -8.65516141e-02
-1.39892787e-01 7.59359956e-01 2.07774043e-01 4.98344526e-02
3.70281905e-01 8.03219795e-01 -3.76285613e-01 -9.14002597e-01
-3.28053206e-01 -3.76338273e-01 6.81616515e-02 -7.03432143e-01
1.30599964e+00 -1.31434429e+00 -7.71458507e-01 1.60784319e-01
-8.25364530e-01 7.29148984e-02 -8.62918869e-02 1.00413167e+00
-7.02828616e-02 1.44841567e-01 -4.11191195e-01 -1.05981576e+00
-1.58509627e-01 -6.31441772e-01 2.57649213e-01 3.93506974e-01
-5.28264225e-01 -1.08322072e+00 -4.12899107e-01 7.59158075e-01
6.04850590e-01 7.83308148e-01 1.09869778e+00 -3.56347412e-01
-5.97562611e-01 5.72149409e-03 -3.52001280e-01 5.69128156e-01
5.68885505e-01 1.14065669e-01 -2.57310331e-01 -1.09285833e-02
4.52987403e-01 -1.55616403e-01 1.43228054e-01 -4.71661165e-02
1.23683676e-01 -8.96964669e-01 -2.64043719e-01 -2.59418875e-01
1.79346442e+00 4.84491616e-01 9.08861279e-01 1.12786853e+00
3.37480098e-01 9.54583108e-01 1.22076643e+00 5.11455178e-01
9.50239480e-01 3.27434957e-01 7.63042152e-01 -7.45616108e-02
1.87712193e-01 -1.00604706e-01 6.58128619e-01 1.03252947e+00
-3.98022443e-01 2.96496540e-01 -8.52456093e-01 7.96709478e-01
-1.29868865e+00 -1.00664926e+00 -8.21143031e-01 1.82610595e+00
2.96849161e-01 3.35771888e-01 7.17268363e-02 2.06056852e-02
8.49474013e-01 3.97573322e-01 1.62902370e-01 -6.35874867e-01
-1.14422321e-01 -4.62597370e-01 6.54041171e-01 6.91993460e-02
-5.13480723e-01 4.72412884e-01 5.84470987e+00 3.27342927e-01
-1.20783234e+00 3.17239344e-01 9.61268544e-01 8.04240108e-02
-1.01569498e+00 3.66973817e-01 -6.87803090e-01 2.20211163e-01
5.60015023e-01 -4.30689663e-01 -3.79473627e-01 7.40852058e-01
7.18759060e-01 -3.47470969e-01 2.51289876e-03 3.07240576e-01
-9.08910558e-02 -1.39929748e+00 -6.22286797e-01 5.63780427e-01
7.39333749e-01 -3.62313449e-01 1.93411425e-01 -1.93923265e-01
-2.48711258e-01 -4.65448111e-01 1.51478660e+00 3.93633038e-01
3.99801195e-01 -7.97741711e-01 7.84266770e-01 1.42634705e-01
-4.29653436e-01 -3.78312618e-01 -1.75911114e-01 -5.24573505e-01
1.42167792e-01 2.48780116e-01 -4.94314194e-01 1.78029209e-01
7.61948705e-01 6.78043485e-01 -3.99840921e-01 3.08555931e-01
1.12345450e-01 6.41963959e-01 2.83308446e-01 -1.62429094e-01
3.62312704e-01 -5.84360301e-01 7.10635841e-01 7.70001948e-01
5.49189270e-01 -1.53905258e-01 -4.74630415e-01 7.34098911e-01
4.43284363e-01 3.68227452e-01 -4.48434263e-01 -1.02765620e+00
6.23453140e-01 9.62628543e-01 -1.27717030e+00 2.61026412e-01
-6.79380715e-01 1.36957720e-01 -4.03128922e-01 2.26022452e-01
-5.73174536e-01 -1.89227104e-01 4.12290305e-01 1.03290915e+00
3.66226405e-01 -2.75379360e-01 -3.03334087e-01 -3.62408906e-01
-2.69073516e-01 -7.79090345e-01 -9.83818099e-02 -5.50015688e-01
-4.03860718e-01 1.17630124e-01 -2.32416302e-01 -9.23879623e-01
5.16164958e-01 -7.45203048e-02 -5.57773411e-01 3.73112172e-01
-1.15723217e+00 -1.18065929e+00 8.85801464e-02 -3.06609631e-01
-4.41995300e-02 -3.93833183e-02 5.52843273e-01 2.47949213e-01
-4.22785997e-01 -1.64019242e-01 1.56736031e-01 -4.14343089e-01
5.13103008e-01 -4.15382355e-01 -1.06801331e-01 5.06726086e-01
-6.04041100e-01 6.18187904e-01 5.09592533e-01 -8.31084549e-01
-1.01197612e+00 -6.13168061e-01 1.67850816e+00 -3.15172486e-02
9.05354798e-01 2.84488052e-01 -2.44843081e-01 5.75846970e-01
6.43879235e-01 -7.36972272e-01 7.71315694e-01 6.15906790e-02
2.42073730e-01 -3.87192875e-01 -1.22321796e+00 7.25885510e-01
7.74524212e-01 -2.38981560e-01 2.83137709e-03 3.78905714e-01
9.54905093e-01 2.84007818e-01 -8.80226493e-01 -5.05912244e-01
2.10567445e-01 -1.29845059e+00 6.50396347e-01 -8.43345299e-02
3.57468873e-01 -1.54510126e-01 -5.34000039e-01 -4.44855213e-01
-4.13296461e-01 -5.21920323e-01 9.26080406e-01 1.84983194e+00
1.90741166e-01 -1.20468712e+00 1.66574940e-01 5.81684649e-01
-3.73920858e-01 -3.88153821e-01 -7.50378728e-01 -3.02327961e-01
-1.46838024e-01 -1.30702570e-01 6.03793442e-01 1.36860776e+00
-2.21765801e-01 -1.48361102e-01 -6.30648658e-02 -2.90450424e-01
1.55738920e-01 -1.38065919e-01 4.78274643e-01 -1.12114561e+00
1.09950371e-01 -2.46332541e-01 -1.75927922e-01 6.85742125e-02
-4.86949474e-01 -4.09715205e-01 -8.14474463e-01 -1.90086710e+00
3.51519972e-01 -4.94149774e-01 2.48384267e-01 7.51397014e-02
5.11123121e-01 8.90643522e-02 7.08833575e-01 6.06580853e-01
-4.36570317e-01 1.75649881e-01 1.68025517e+00 6.85116500e-02
-1.76834360e-01 -1.10064045e-01 -2.05079103e+00 8.30555081e-01
6.09558403e-01 -4.89580572e-01 -2.54387856e-01 8.16265214e-03
1.35587966e+00 8.33932012e-02 4.91894722e-01 -6.93593621e-01
-1.76928967e-01 -7.79292226e-01 -1.02179103e-01 -3.06289196e-01
4.16072793e-02 -9.47865903e-01 8.45763206e-01 1.01590037e+00
3.13507556e-03 9.91895869e-02 -3.70758712e-01 3.28493640e-02
-3.83302480e-01 -1.53995082e-01 4.63087231e-01 -6.23785853e-02
-3.55403900e-01 -6.51796639e-01 -4.45665807e-01 -1.95864275e-01
1.28328311e+00 -9.05659735e-01 -7.27775455e-01 -8.55502009e-01
-2.98062831e-01 -5.55861136e-03 9.16164398e-01 3.64267319e-01
3.24826062e-01 -1.31410408e+00 -6.11036718e-01 -4.55888480e-01
-1.65023968e-01 -5.09914219e-01 2.12715849e-01 1.28371453e+00
-7.35705256e-01 3.12265217e-01 -1.51936397e-01 6.15550876e-02
-1.19217873e+00 -1.40293136e-01 -7.78467879e-02 2.75906026e-01
-3.93945366e-01 5.73189855e-01 3.43123704e-01 5.79955056e-02
-2.22401455e-01 6.55935556e-02 -5.59782743e-01 6.88332856e-01
3.34086031e-01 9.86988366e-01 -6.82490826e-01 -1.34394300e+00
-5.75621068e-01 3.86660248e-01 3.17319185e-01 1.12591192e-01
1.61240637e+00 -6.33032143e-01 -8.48484993e-01 5.29619575e-01
9.85178888e-01 6.74397349e-01 -6.24180734e-01 4.63485986e-01
2.00665697e-01 -9.79319274e-01 -1.97232887e-03 -5.04218280e-01
-9.35871780e-01 3.34289938e-01 4.96951520e-01 4.89412338e-01
1.02221000e+00 1.47864133e-01 4.99609411e-01 -2.63358891e-01
3.47189963e-01 -1.63723624e+00 1.77556962e-01 -2.46745534e-02
1.07939649e+00 -5.97066700e-01 2.02818260e-01 -7.06400871e-01
-1.23363614e+00 9.35222626e-01 3.63768250e-01 3.53670776e-01
5.18394530e-01 -2.72794634e-01 5.15360460e-02 -4.39654499e-01
-5.39579988e-01 1.89521283e-01 -6.05058968e-01 4.51162040e-01
7.03115761e-01 3.59101772e-01 -1.11299193e+00 8.32518578e-01
-5.18619940e-02 -9.65474173e-02 8.85207236e-01 7.30578423e-01
-7.29135156e-01 -8.44049990e-01 -5.99542201e-01 8.70769545e-02
-1.14547944e+00 1.76246300e-01 -4.38743293e-01 1.25248837e+00
6.06254935e-01 9.25116122e-01 4.34439659e-01 6.35026842e-02
1.18303970e-01 -7.11410224e-01 -1.88016623e-01 -4.11588103e-01
-7.87354887e-01 6.49594009e-01 1.03920937e+00 -8.54350999e-02
-7.92415619e-01 -1.03414321e+00 -1.47077429e+00 -6.02953494e-01
-2.23639175e-01 4.18730713e-02 9.26271796e-01 6.28293872e-01
4.81246978e-01 3.16987932e-01 6.08020127e-01 8.73162821e-02
-1.68704614e-01 -6.10541046e-01 -1.04292023e+00 -1.86259672e-01
-1.24639407e-01 -6.91681802e-02 -2.36913651e-01 -2.53327101e-01] | [9.074763298034668, 6.255584716796875] |
918e6ff8-cb22-4fae-beba-f52a999d8cb5 | recurrent-autoregressive-networks-for-online | 1711.02741 | null | http://arxiv.org/abs/1711.02741v2 | http://arxiv.org/pdf/1711.02741v2.pdf | Recurrent Autoregressive Networks for Online Multi-Object Tracking | The main challenge of online multi-object tracking is to reliably associate
object trajectories with detections in each video frame based on their tracking
history. In this work, we propose the Recurrent Autoregressive Network (RAN), a
temporal generative modeling framework to characterize the appearance and
motion dynamics of multiple objects over time. The RAN couples an external
memory and an internal memory. The external memory explicitly stores previous
inputs of each trajectory in a time window, while the internal memory learns to
summarize long-term tracking history and associate detections by processing the
external memory. We conduct experiments on the MOT 2015 and 2016 datasets to
demonstrate the robustness of our tracking method in highly crowded and
occluded scenes. Our method achieves top-ranked results on the two benchmarks. | ['Yu Xiang', 'Silvio Savarese', 'Kuan Fang', 'Xiaocheng Li'] | 2017-11-07 | null | null | null | null | ['online-multi-object-tracking'] | ['computer-vision'] | [-3.33035678e-01 -7.14316130e-01 -1.64254323e-01 -1.53366119e-01
-4.51513737e-01 -5.20862162e-01 5.98617256e-01 -4.02487874e-01
-3.31524938e-01 3.44980210e-01 1.19983569e-01 1.91817075e-01
6.67417720e-02 -5.58230579e-01 -9.33754623e-01 -6.23364091e-01
-4.18190747e-01 3.54610622e-01 7.46907532e-01 5.79160631e-01
-3.40982229e-01 4.89551842e-01 -1.42781639e+00 3.03544432e-01
1.90798968e-01 1.02111185e+00 3.71149123e-01 1.04554200e+00
7.78697655e-02 1.29190993e+00 -4.60275769e-01 -3.52057070e-01
1.94015130e-01 -1.69458613e-01 -2.19968945e-01 1.75424322e-01
6.89439595e-01 -5.44526219e-01 -1.16696060e+00 9.37452018e-01
1.22087196e-01 2.94905841e-01 6.44795299e-01 -1.51220477e+00
-9.72133636e-01 3.06995451e-01 -5.55012941e-01 7.27943361e-01
-6.05442263e-02 5.22836924e-01 7.03951120e-01 -8.94149959e-01
8.08614910e-01 1.35622728e+00 7.91790187e-01 4.38785315e-01
-8.86716485e-01 -7.30095565e-01 7.07786679e-01 1.92591339e-01
-1.39898884e+00 -2.16396064e-01 2.71483719e-01 -9.41003799e-01
8.41732323e-01 -2.59957403e-01 7.20463514e-01 1.11885977e+00
5.16287208e-01 9.24058378e-01 4.80589628e-01 3.58287841e-01
-1.55418471e-01 -3.33287179e-01 4.00077969e-01 8.48490715e-01
2.70404220e-01 4.86827344e-01 -6.75065696e-01 -3.32723349e-01
7.94719338e-01 5.68371475e-01 1.96573213e-01 -1.35056227e-01
-1.12365651e+00 6.05996847e-01 4.86937493e-01 -1.78157482e-02
-5.98082244e-01 7.66953051e-01 2.72785306e-01 2.55616486e-01
5.00145435e-01 -4.03886020e-01 -1.88034654e-01 5.16326092e-02
-1.00058615e+00 2.46765003e-01 4.53266442e-01 1.33429635e+00
5.77527285e-01 2.66382694e-01 -8.05346251e-01 2.92383939e-01
6.65421426e-01 1.02917039e+00 1.77890643e-01 -8.90637159e-01
1.39196694e-01 3.75077039e-01 3.60792607e-01 -9.17208016e-01
-1.26427382e-01 -5.32581270e-01 -5.50537646e-01 3.23623084e-02
3.07249367e-01 -1.70276687e-01 -1.15562809e+00 1.82609439e+00
4.10391390e-01 1.10395682e+00 -1.62440866e-01 8.03322196e-01
9.30713356e-01 7.78013289e-01 5.10984600e-01 -2.91990280e-01
1.15397525e+00 -1.21496129e+00 -7.72183061e-01 -1.95051804e-01
1.00473158e-01 -5.93290031e-01 8.25892687e-02 -1.80392206e-01
-1.15152347e+00 -1.01798761e+00 -6.86605334e-01 2.04720870e-01
-1.10581718e-01 5.20560324e-01 4.07557130e-01 6.20143779e-04
-9.99186516e-01 5.20634949e-01 -1.43151009e+00 -1.06002271e-01
4.28099185e-01 2.04675630e-01 -7.25353658e-02 -1.14793861e-02
-7.50585437e-01 5.02791047e-01 3.67942452e-01 3.41060609e-01
-1.63985634e+00 -7.26512849e-01 -7.98599839e-01 -1.81355670e-01
1.94086477e-01 -1.00102746e+00 1.32926440e+00 -6.08895719e-01
-1.15410149e+00 6.82607710e-01 -7.40314901e-01 -8.69012833e-01
6.71860754e-01 -6.02023780e-01 -6.23219609e-01 -1.10556841e-01
1.58519477e-01 6.00639045e-01 1.17502141e+00 -1.01985848e+00
-8.72775733e-01 -2.99349099e-01 -4.25610632e-01 -3.12488109e-01
2.67864745e-02 1.91959128e-01 -1.04735994e+00 -7.89212048e-01
-5.04654013e-02 -1.20689261e+00 -1.98420256e-01 2.58184150e-02
-1.07541561e-01 -3.84884983e-01 1.33722985e+00 -6.77316487e-01
1.19844389e+00 -2.15858173e+00 2.09523678e-01 -1.17439449e-01
3.18606317e-01 3.31639856e-01 -1.49308443e-01 -9.35398694e-03
2.69932747e-01 -3.55000198e-01 4.29305732e-01 -6.70744181e-01
-9.71732587e-02 2.48775631e-01 -7.83722222e-01 6.72816932e-01
1.90027878e-01 1.32254422e+00 -9.92076039e-01 -5.62685788e-01
5.20308129e-02 6.80325270e-01 -1.25015184e-01 5.22974432e-01
-5.54706573e-01 6.58464789e-01 -5.12648821e-01 5.44673204e-01
4.50037897e-01 -7.30895400e-01 -7.53782988e-02 -1.72455460e-01
-2.04417124e-01 -2.12053850e-01 -1.06460178e+00 1.48673189e+00
1.15148142e-01 7.41555095e-01 -2.64520139e-01 2.55669970e-02
7.45569646e-01 3.24226081e-01 6.07933342e-01 -4.11180973e-01
1.37394533e-01 -1.62522435e-01 -2.59253114e-01 -3.42637420e-01
7.93957829e-01 3.14547777e-01 1.80592030e-01 4.62848336e-01
3.46705228e-01 9.24507618e-01 2.09885046e-01 3.10390025e-01
1.24230075e+00 3.24012846e-01 -2.18362302e-01 3.42724562e-01
1.60422012e-01 -1.34430498e-01 9.06143188e-01 9.99153733e-01
-4.19931740e-01 3.67482573e-01 -1.94780931e-01 -7.65446901e-01
-9.12689388e-01 -1.45847321e+00 3.07235092e-01 1.38238502e+00
1.88464954e-01 -4.75709051e-01 4.45478447e-02 -5.51778734e-01
1.97654277e-01 1.18390165e-01 -7.03216374e-01 -1.40417889e-01
-8.18567514e-01 -4.27665412e-01 4.82724875e-01 1.04210985e+00
2.88116068e-01 -1.18589854e+00 -7.43692696e-01 4.10718858e-01
-7.35958591e-02 -1.37916589e+00 -7.83273280e-01 -4.23451364e-01
-7.34382510e-01 -9.08936083e-01 -8.24995279e-01 -5.70801735e-01
4.42585856e-01 4.11243349e-01 1.12816155e+00 1.10745735e-01
-3.53510708e-01 6.58199310e-01 -4.14934643e-02 -3.60457897e-01
-2.31879905e-01 -2.97617048e-01 1.27345994e-01 2.17445403e-01
2.67381847e-01 -2.43471831e-01 -5.31617880e-01 4.24225956e-01
-4.94929731e-01 -2.25846007e-01 3.83617550e-01 4.00671154e-01
9.64242339e-01 -3.19915056e-01 2.54192263e-01 -4.54890549e-01
-4.01531085e-02 -7.38296926e-01 -1.04039764e+00 4.54061657e-01
-9.03134886e-03 -1.41858101e-01 3.54988724e-02 -9.01424706e-01
-9.47536528e-01 4.38530684e-01 3.08651000e-01 -1.31287146e+00
9.67453420e-02 1.83962613e-01 2.73564667e-01 1.06489308e-01
9.19975787e-02 3.92293125e-01 -3.01272422e-01 -4.33169216e-01
4.54363197e-01 -5.33152595e-02 1.03010952e+00 -3.67796540e-01
9.63203788e-01 7.97607303e-01 -6.34475127e-02 -4.86675233e-01
-1.09687722e+00 -7.36155212e-01 -6.51680350e-01 -7.20850170e-01
1.09139693e+00 -1.43916059e+00 -1.01298094e+00 5.31420708e-01
-1.35137093e+00 -4.11060363e-01 -1.63018346e-01 6.15218282e-01
-4.46839005e-01 2.66858507e-02 -9.66282666e-01 -9.93076682e-01
-3.06934744e-01 -8.83067250e-01 1.16992712e+00 6.22254729e-01
7.65232146e-02 -1.09311569e+00 4.50550050e-01 -1.74048930e-01
3.01343530e-01 3.80097777e-01 -6.48656813e-03 -2.48237148e-01
-1.27649343e+00 -2.23976970e-01 -1.66768774e-01 -1.43874630e-01
-1.06515512e-01 2.83112258e-01 -8.35477114e-01 -6.76495790e-01
-2.72613853e-01 -7.92867169e-02 1.35256553e+00 7.82877266e-01
8.34400535e-01 -1.10871501e-01 -8.54381800e-01 5.88061392e-01
1.36838973e+00 1.72413960e-01 3.40202749e-01 -1.88084431e-02
8.39950442e-01 8.68488997e-02 7.23616421e-01 4.25158590e-01
5.07686198e-01 7.00354278e-01 2.51576990e-01 2.09338307e-01
-3.85955095e-01 -4.66755867e-01 7.42765069e-01 6.93248689e-01
-1.44255221e-01 -2.73580968e-01 -8.09195101e-01 7.61702657e-01
-2.35984731e+00 -1.44709730e+00 -2.69559294e-01 1.99676061e+00
2.39748538e-01 1.73753083e-01 5.09701669e-01 -9.52591896e-01
9.97807205e-01 2.55513668e-01 -8.58927071e-01 5.24267673e-01
-2.72610873e-01 -2.00122550e-01 5.16954064e-01 2.56812632e-01
-1.45885456e+00 1.06580079e+00 7.02933598e+00 2.80809700e-01
-9.75567043e-01 4.63302195e-01 1.28668711e-01 -3.54447573e-01
4.49309260e-01 -1.17844976e-01 -1.46717119e+00 7.03678548e-01
1.15826356e+00 -2.10973412e-01 -2.18503494e-02 9.09897804e-01
1.15697123e-01 1.71100467e-01 -9.91080582e-01 8.34160626e-01
-7.31448531e-02 -1.52743006e+00 6.63283244e-02 3.16704102e-02
8.47907305e-01 6.88350677e-01 4.04283166e-01 4.45075214e-01
8.08970094e-01 -8.20754886e-01 9.13538277e-01 1.24373984e+00
3.16360950e-01 -4.68698770e-01 2.98841178e-01 3.03802758e-01
-2.02991414e+00 -8.93443003e-02 -4.23206240e-01 1.32465512e-01
5.63150823e-01 2.11266860e-01 -4.46637213e-01 5.09426296e-01
9.76906717e-01 1.19037557e+00 -7.14577556e-01 1.54578078e+00
-6.79855719e-02 6.75656676e-01 -1.26398876e-01 3.45310897e-01
1.75605744e-01 -3.48011218e-02 8.70772183e-01 1.36136925e+00
3.07582945e-01 4.37826663e-02 7.25752711e-01 1.05807126e+00
-5.13587259e-02 -5.67530274e-01 -6.29346251e-01 -1.33438811e-01
5.13215542e-01 1.28590500e+00 -6.46129072e-01 -6.01836681e-01
-5.08534968e-01 7.42744386e-01 4.81469721e-01 5.06729245e-01
-1.31258595e+00 3.66355598e-01 9.35100734e-01 -3.02233636e-01
8.99007499e-01 -5.53255975e-01 4.18584645e-01 -1.09591329e+00
-9.87489056e-03 -1.86077043e-01 8.26510131e-01 -8.18692446e-01
-1.55925477e+00 6.18466020e-01 -2.76228666e-01 -1.41725230e+00
-2.90922225e-01 -3.54236305e-01 -7.58592546e-01 7.61937141e-01
-1.23798895e+00 -1.47714138e+00 -3.40207160e-01 6.09517217e-01
5.30361056e-01 -1.53532609e-01 3.53058219e-01 4.63861465e-01
-6.45452678e-01 2.86502928e-01 3.03578470e-02 5.71522713e-01
6.07076108e-01 -9.06879425e-01 6.77633226e-01 1.10074532e+00
4.90139037e-01 6.68378651e-01 7.26438224e-01 -1.23535192e+00
-1.53750896e+00 -1.91107261e+00 5.26541770e-01 -8.68728817e-01
8.01896811e-01 -1.08522415e-01 -1.20676970e+00 1.33176076e+00
5.96457049e-02 5.67697763e-01 3.99689585e-01 -1.35088861e-01
-4.23852146e-01 2.37180352e-01 -4.17008579e-01 1.83671683e-01
1.19728208e+00 -6.76909268e-01 -5.02507567e-01 2.60724515e-01
9.65317488e-01 -7.65328288e-01 -9.14220095e-01 3.07640076e-01
6.97819173e-01 -4.56433088e-01 1.16405571e+00 -1.05310798e+00
-5.05546443e-02 -7.40481853e-01 4.96109761e-02 -8.95241141e-01
-7.92648733e-01 -5.33149540e-01 -1.01865911e+00 1.23848784e+00
1.86184019e-01 -1.20481580e-01 7.97015905e-01 5.12342393e-01
-4.99146245e-02 -4.13222909e-01 -5.84466338e-01 -1.20651484e+00
-3.15987766e-01 -4.37296242e-01 4.39526498e-01 4.62737679e-01
-9.48110580e-01 1.27468854e-02 -9.16002989e-01 7.36920834e-01
8.35464418e-01 6.00357771e-01 9.56183314e-01 -1.22644031e+00
-4.32485878e-01 -1.87251329e-01 -6.16699517e-01 -1.26867783e+00
4.41450894e-01 -7.21760452e-01 1.95424885e-01 -1.28854036e+00
5.97999930e-01 -3.85100655e-02 -3.86901438e-01 2.33612105e-01
-3.85775000e-01 2.36002237e-01 4.82809514e-01 5.69025338e-01
-1.51391518e+00 5.90201139e-01 8.29268813e-01 -3.16715896e-01
-1.18814133e-01 2.84221947e-01 1.23420335e-01 7.57132053e-01
3.37060630e-01 -8.08203340e-01 -4.12765928e-02 -5.85059524e-01
-9.09703970e-02 1.34735614e-01 8.71576309e-01 -1.15418267e+00
8.84184837e-01 4.66309078e-02 7.09213853e-01 -1.44583237e+00
5.45803428e-01 -6.44842267e-01 7.44991302e-01 6.73278153e-01
-1.93647444e-01 5.43473959e-01 2.65695751e-01 1.33863056e+00
-1.29795626e-01 4.51917171e-01 5.81880569e-01 8.78769979e-02
-1.19342744e+00 1.04337442e+00 -1.57382756e-01 1.87922176e-03
1.10764170e+00 1.07746981e-01 -4.00111705e-01 -2.05630556e-01
-1.15054786e+00 6.15598798e-01 3.29187661e-01 8.61358464e-01
7.06660032e-01 -1.70701981e+00 -6.91453040e-01 -1.28643870e-01
3.47888581e-02 -3.31949115e-01 5.02677023e-01 8.98667336e-01
1.42694920e-01 4.13617432e-01 -1.70163915e-01 -1.11169469e+00
-1.48084104e+00 8.33075464e-01 3.74750704e-01 -2.46774122e-01
-8.69790554e-01 8.26762915e-01 2.81448275e-01 2.15807095e-01
5.12003958e-01 -7.18831941e-02 -1.78000644e-01 -2.14146078e-01
1.01845193e+00 4.82169628e-01 -5.05442142e-01 -1.08004797e+00
-2.84772068e-01 4.99568224e-01 -1.12817883e-01 -3.32427658e-02
1.19993234e+00 -1.16161607e-01 8.45819488e-02 8.15602124e-01
9.72146571e-01 -4.97522563e-01 -1.84030259e+00 -7.47543931e-01
1.41427353e-01 -5.96559465e-01 -2.00387940e-01 -3.02876323e-01
-1.26813567e+00 4.77912694e-01 7.44422138e-01 -1.68178111e-01
6.85730398e-01 3.04653645e-01 9.10956562e-01 3.15205187e-01
4.28927779e-01 -7.03789473e-01 2.79085189e-01 7.55301833e-01
6.45594060e-01 -1.09813523e+00 -2.71517873e-01 -1.12154774e-01
-6.48936152e-01 8.21204066e-01 7.13439643e-01 -2.74215013e-01
8.13685596e-01 3.84274244e-01 -1.66311428e-01 -3.89021724e-01
-1.17488527e+00 -5.00100911e-01 5.95436275e-01 4.13620025e-01
1.30174384e-01 2.28626803e-02 4.70954418e-01 5.20599782e-01
3.27513039e-01 2.25509077e-01 -7.90020674e-02 9.46270585e-01
-4.09317315e-01 -6.58789515e-01 -4.30726111e-01 3.46161425e-01
-3.40392143e-01 3.74940217e-01 -2.58339137e-01 5.53817809e-01
8.42178613e-02 8.47211540e-01 3.92628998e-01 -4.02234823e-01
2.01416776e-01 -2.08863337e-02 4.83319342e-01 -4.89656568e-01
-5.13970017e-01 2.22455889e-01 -4.31111097e-01 -8.42001557e-01
-5.80266237e-01 -9.05351281e-01 -1.26223731e+00 -2.62958437e-01
-1.35119379e-01 -1.56665757e-01 2.77042270e-01 9.23562586e-01
6.09140098e-01 7.90070534e-01 1.70798391e-01 -8.33994269e-01
-2.52995849e-01 -8.45570803e-01 -3.04064691e-01 3.85539860e-01
6.16586626e-01 -9.81397450e-01 1.49559051e-01 3.65225315e-01] | [6.301468849182129, -2.0810887813568115] |
af45a4ac-c57f-459b-9fde-ecad2a01e570 | collaborative-multi-bs-power-management-for | 2304.07976 | null | https://arxiv.org/abs/2304.07976v1 | https://arxiv.org/pdf/2304.07976v1.pdf | Collaborative Multi-BS Power Management for Dense Radio Access Network using Deep Reinforcement Learning | Network energy efficiency is a main pillar in the design and operation of wireless communication systems. In this paper, we investigate a dense radio access network (dense-RAN) capable of radiated power management at the base station (BS). Aiming to improve the long-term network energy efficiency, an optimization problem is formulated by collaboratively managing multi-BSs radiated power levels with constraints on the users traffic volume and achievable rate. Considering stochastic traffic arrivals at the users and time-varying network interference, we first formulate the problem as a Markov decision process (MDP) and then develop a novel deep reinforcement learning (DRL) framework based on the cloud-RAN operation scheme. To tackle the trade-off between complexity and performance, the overall optimization of multi-BSs energy efficiency with the multiplicative complexity constraint is modeled to achieve nearoptimal performance by using a deep Q-network (DQN). In DQN,each BS first maximizes its individual energy efficiency, and then cooperates with other BSs to maximize the overall multiBSs energy efficiency. Simulation results demonstrate that the proposed algorithm can converge faster and enjoy a network energy efficiency improvement by 5% and 10% compared with the benchmarks of the Q-learning and sleep schemes, respectively. | ['Naofal Al-Dhahir', 'Zhendong Wang', 'Haoran Wei', 'Jianpo Liu', 'Jun Li', 'Wen Chen', 'Yuchao Chang'] | 2023-04-17 | null | null | null | null | ['q-learning'] | ['methodology'] | [-4.46282744e-01 2.07316086e-01 -3.20315391e-01 2.89145917e-01
-3.72387350e-01 -2.64616251e-01 -1.58571631e-01 -2.82858700e-01
-3.42016965e-01 1.07057607e+00 -2.10893750e-01 -5.78087807e-01
-7.12400079e-01 -1.06533897e+00 -2.35705271e-01 -1.47630668e+00
-3.34724069e-01 1.69909462e-01 -3.98116320e-01 1.13640130e-01
-2.15968981e-01 4.92874771e-01 -7.31222272e-01 -9.89153266e-01
1.04461420e+00 1.34789109e+00 4.52741504e-01 5.38188398e-01
2.75493175e-01 6.61065221e-01 -6.16707802e-01 -8.01266506e-02
2.12458134e-01 -6.14747524e-01 -4.59221363e-01 5.17678559e-02
-1.04722130e+00 -3.33415329e-01 -5.18253565e-01 9.66063857e-01
1.09243155e+00 4.20833886e-01 1.59581482e-01 -1.71423781e+00
-2.36056402e-01 5.43932617e-01 -6.41049564e-01 4.34052706e-01
-2.77223945e-01 4.56226766e-02 8.63273382e-01 -3.07854086e-01
-1.02158234e-01 7.34870434e-01 8.35500807e-02 5.69785953e-01
-8.79323423e-01 -9.75038052e-01 -4.48142849e-02 5.09711862e-01
-1.46426058e+00 -4.51240689e-01 7.91791439e-01 3.87978792e-01
6.19396210e-01 2.37585813e-01 1.09292018e+00 4.20048505e-01
5.22391558e-01 5.92158437e-01 6.48737788e-01 -4.61167902e-01
9.34626937e-01 -3.28721404e-01 -5.45135796e-01 5.34637332e-01
3.66276443e-01 2.32225448e-01 1.52001809e-02 -3.50067914e-02
7.08291650e-01 -4.14773077e-02 -2.85451207e-02 -5.30779839e-01
-7.21107423e-01 7.14506805e-01 6.59634650e-01 4.32090551e-01
-7.49450028e-01 5.72307110e-01 -2.54272103e-01 -9.65262018e-03
6.83990270e-02 1.99114844e-01 -3.07018608e-01 -3.12091529e-01
-1.08619177e+00 -2.70017207e-01 8.50489438e-01 1.03588617e+00
5.04326999e-01 3.51697564e-01 -4.13979024e-01 6.54246032e-01
5.02898335e-01 8.02644908e-01 1.29243270e-01 -1.29262555e+00
1.11015253e-01 -5.22575229e-02 3.63168597e-01 -6.38983309e-01
-5.70440888e-01 -1.29091942e+00 -1.47739685e+00 -1.42653257e-01
-2.53169358e-01 -1.04354799e+00 -4.64378446e-01 1.75785148e+00
2.64519840e-01 1.90930188e-01 1.21461667e-01 7.24412620e-01
1.14791483e-01 1.17056096e+00 1.35783523e-01 -1.26538408e+00
1.20564151e+00 -9.75889027e-01 -1.13128281e+00 -1.69588611e-01
1.28212258e-01 -1.58135995e-01 1.51422396e-01 1.94168016e-01
-1.69077802e+00 -1.70077026e-01 -1.13678074e+00 6.19167566e-01
1.04966879e-01 -3.70743051e-02 1.79521993e-01 1.03711522e+00
-1.25781822e+00 3.02337874e-02 -9.69519079e-01 -1.73216671e-01
7.09613919e-01 6.74470544e-01 7.07222819e-01 5.15843965e-02
-1.03018987e+00 5.81871629e-01 3.96305561e-01 2.11484164e-01
-1.08598983e+00 -6.17947757e-01 -3.24526250e-01 6.33053064e-01
7.47713208e-01 -1.00806212e+00 1.34759140e+00 -5.33371687e-01
-1.97710621e+00 -1.95681348e-01 5.99334799e-02 -2.25288734e-01
2.50349969e-01 2.22626567e-01 -6.08982444e-01 1.57115370e-01
-1.85435191e-01 1.58998653e-01 4.73771811e-01 -1.14978719e+00
-8.00031900e-01 -1.74244672e-01 2.05686256e-01 4.71876949e-01
-5.82513511e-01 -2.81940192e-01 -4.38635588e-01 -6.07629478e-01
-2.46186838e-01 -9.85659063e-01 -6.59874141e-01 -3.65558803e-01
-1.70183867e-01 -3.07604551e-01 8.38643432e-01 -4.26050276e-01
1.47630537e+00 -1.98027122e+00 4.34826285e-01 5.36065578e-01
7.83871859e-02 -7.63361827e-02 5.93767986e-02 1.05729602e-01
4.10562068e-01 1.64512411e-01 3.16278078e-02 -1.36080295e-01
-8.43839049e-02 5.56541502e-01 5.00111639e-01 2.93062210e-01
-4.04310882e-01 7.92073011e-01 -1.05436182e+00 -4.89076048e-01
2.05407977e-01 2.59393245e-01 -5.38677454e-01 5.98822415e-01
-2.38881875e-02 3.98530215e-01 -7.71267653e-01 4.64540422e-01
8.01788092e-01 -4.31080550e-01 4.98021394e-01 -2.59915203e-01
-4.38085459e-02 -4.49446857e-01 -1.24655151e+00 1.63874125e+00
-1.15694118e+00 2.56502032e-01 8.23248684e-01 -1.37339127e+00
2.66802192e-01 4.30628210e-01 1.15111530e+00 -1.16509604e+00
2.62901813e-01 8.95255730e-02 1.18013605e-01 -3.20787817e-01
1.22690499e-01 -4.95400548e-01 2.33783834e-02 4.38004762e-01
1.69102848e-01 2.12988868e-01 5.99491708e-02 3.13654505e-02
1.26154006e+00 -4.92776901e-01 3.94415677e-01 -5.20998299e-01
6.26495719e-01 -6.80064440e-01 9.30337310e-01 6.03360772e-01
-3.83708298e-01 -4.96381253e-01 2.51945764e-01 2.29290888e-01
-6.81980193e-01 -1.21856785e+00 1.27799273e-01 1.22769523e+00
6.41900003e-01 1.79512873e-01 -7.37023711e-01 -3.27450305e-01
-4.80936378e-01 9.91744936e-01 -1.18497074e-01 -3.06441396e-01
-2.50467747e-01 -1.23694170e+00 -1.05377644e-01 1.42600730e-01
8.99526179e-01 -7.22252905e-01 -9.00677145e-01 5.20925760e-01
-3.44735593e-01 -1.10282731e+00 -3.79321486e-01 6.36879802e-01
-5.04092813e-01 -4.84577417e-01 -6.65627122e-01 -6.20389938e-01
5.21318078e-01 2.21434042e-01 1.05150437e+00 -7.16750175e-02
5.02785295e-02 4.95293170e-01 -9.54990238e-02 -3.18312883e-01
-1.50752380e-01 4.55389798e-01 -3.96168455e-02 -1.36591390e-01
-3.11240822e-01 -7.29673922e-01 -1.11286032e+00 4.41874802e-01
-7.95407832e-01 -5.19752353e-02 9.52435970e-01 4.55330968e-01
5.32448173e-01 9.65368390e-01 9.62855041e-01 -4.59813476e-02
4.66919810e-01 -8.48220706e-01 -7.18089163e-01 3.61964017e-01
-6.74820483e-01 8.11525136e-02 7.19363928e-01 1.19315796e-01
-1.18217993e+00 -2.05092296e-01 -1.81243688e-01 -1.17955871e-01
4.91797894e-01 1.63415164e-01 -7.72632182e-01 -1.34784088e-01
-2.00914472e-01 4.15277302e-01 -2.36391276e-01 1.73427105e-01
3.09903353e-01 6.01327896e-01 1.98287979e-01 -5.32115638e-01
9.87126589e-01 1.28917441e-01 6.10479951e-01 -6.92723691e-01
-9.55598712e-01 -2.46342123e-01 6.18998259e-02 -5.90052009e-01
7.27220833e-01 -1.16469991e+00 -1.18173397e+00 6.64855763e-02
-6.45416260e-01 -3.47940087e-01 -2.30681166e-01 4.15264070e-01
-7.54885018e-01 8.63552466e-02 -9.02310461e-02 -1.17219508e+00
-6.50118113e-01 -8.00375879e-01 3.51025671e-01 7.21737683e-01
6.79060757e-01 -9.22071040e-01 -2.18673423e-02 1.08178064e-01
7.75816917e-01 2.18253627e-01 7.76738524e-01 1.14787824e-01
-6.15610003e-01 2.96723276e-01 -1.65525114e-03 2.92580098e-01
2.84026921e-01 -4.40957278e-01 -5.72775662e-01 -8.86463404e-01
1.37979895e-01 6.82544038e-02 1.66635901e-01 7.97078133e-01
1.69623172e+00 -5.78868449e-01 -4.54934239e-01 6.74046159e-01
1.96141255e+00 6.11518562e-01 4.48371977e-01 7.69323558e-02
2.17487141e-01 -7.28395209e-03 4.37484562e-01 1.05941820e+00
6.29803896e-01 5.25036633e-01 1.04668474e+00 -1.13942206e-01
3.99598032e-01 2.46208385e-01 2.76410073e-01 7.76940107e-01
-9.35510173e-02 -8.99559200e-01 -4.86711770e-01 2.28759363e-01
-1.82135546e+00 -9.49059069e-01 4.62052524e-01 2.00518394e+00
5.01355588e-01 8.06247368e-02 2.25027665e-01 2.46043012e-01
6.13762617e-01 6.69838712e-02 -9.66926813e-01 -2.57629484e-01
1.27353892e-01 1.85470209e-01 8.66944611e-01 1.49478257e-01
-6.51860237e-01 1.85607389e-01 5.40854740e+00 1.03690195e+00
-8.22003007e-01 4.90095198e-01 8.60122263e-01 -5.91964543e-01
-2.69228309e-01 -4.24352556e-01 -2.47603387e-01 9.02600288e-01
1.54362500e+00 -6.21565282e-01 1.07196820e+00 5.20874023e-01
1.02205312e+00 -2.51803756e-01 -6.83673263e-01 1.12993586e+00
-4.49721783e-01 -1.08039808e+00 -4.98770207e-01 5.17156661e-01
9.29253519e-01 1.48253748e-03 -2.11120486e-01 5.65791011e-01
4.86325294e-01 -8.09039235e-01 5.16136587e-01 6.86991334e-01
5.34266770e-01 -1.54037929e+00 7.74141014e-01 5.78325689e-01
-1.35173881e+00 -1.01502228e+00 -1.79372370e-01 2.06339210e-01
3.23857963e-01 9.76168394e-01 -1.23732373e-01 9.49952304e-01
6.58765316e-01 3.43576759e-01 1.90425515e-01 1.22528148e+00
-4.88772988e-02 5.95467806e-01 -3.67969990e-01 -2.96524048e-01
2.36145914e-01 -4.35392082e-01 3.89663130e-01 7.28212714e-01
7.17060149e-01 4.87664610e-01 2.43452236e-01 6.66789114e-01
-6.14759803e-01 -1.70773804e-01 2.61104226e-01 1.78648353e-01
8.59022319e-01 1.78484356e+00 -6.80401742e-01 -8.68127495e-02
-3.58921945e-01 7.75526226e-01 -1.40961394e-01 5.51724613e-01
-1.12264812e+00 -3.87657076e-01 6.77413046e-01 -2.62001425e-01
3.96396369e-01 -3.63345146e-01 -1.94199443e-01 -5.73804677e-01
-7.55656734e-02 -1.63590580e-01 2.94816047e-01 -5.04768848e-01
-8.40192795e-01 8.90579596e-02 -3.43896210e-01 -1.13959610e+00
1.77346408e-01 -6.60823658e-02 -8.59626591e-01 4.93274838e-01
-1.95700276e+00 -6.00002170e-01 -5.18417120e-01 5.03272831e-01
3.72970581e-01 -3.08481557e-03 4.86669332e-01 5.93955696e-01
-1.14728153e+00 4.13720608e-01 7.06395149e-01 -2.41997004e-01
-2.20987409e-01 -1.32104194e+00 -6.14574194e-01 8.52165759e-01
-5.72398305e-01 -2.19514161e-01 5.34023285e-01 1.18866071e-01
-1.81147349e+00 -1.27639461e+00 2.90623633e-03 4.35429662e-01
4.17311460e-01 -1.89412951e-01 -7.38966763e-02 -4.55955304e-02
7.50928402e-01 3.37431461e-01 9.15252030e-01 -4.57367033e-01
9.20588434e-01 -5.57975590e-01 -1.36933041e+00 5.45820057e-01
1.06036079e+00 2.23382413e-01 2.83955842e-01 2.68463224e-01
6.86155260e-01 -6.55784085e-02 -1.00822365e+00 1.75693423e-01
2.05610380e-01 -4.32733625e-01 7.24587381e-01 -1.29344478e-01
-1.45840690e-01 -2.07654864e-01 -1.70555025e-01 -1.78826630e+00
-6.45997941e-01 -1.06887209e+00 -9.20758247e-01 1.08761752e+00
4.18836437e-02 4.01607761e-03 8.79641294e-01 2.29565769e-01
1.43270746e-01 -9.37035263e-01 -1.46709371e+00 -8.81388724e-01
8.64778534e-02 -2.07606763e-01 5.99101484e-01 1.99108928e-01
-4.96616215e-02 3.97800624e-01 -1.48717850e-01 6.67272210e-01
9.52784181e-01 -2.24550217e-01 1.82121828e-01 -8.79118383e-01
-1.97398052e-01 -5.17393291e-01 2.33972669e-01 -1.12248147e+00
8.55052918e-02 -7.37407744e-01 2.37823769e-01 -1.91642523e+00
2.11538374e-01 -5.39936364e-01 -8.68473947e-01 1.21433638e-01
5.51059551e-04 -2.35132083e-01 1.67641029e-01 -2.11308494e-01
-1.17581022e+00 1.29837000e+00 1.12405896e+00 -5.70526421e-02
-2.07123429e-01 5.25316060e-01 -8.10415030e-01 2.81563312e-01
8.16551805e-01 -4.36980397e-01 -6.72041059e-01 -1.93702579e-01
2.45661914e-01 8.32179666e-01 3.04787024e-03 -1.36253536e+00
4.55513000e-01 -5.33465028e-01 2.87880927e-01 -5.40646970e-01
8.15822929e-02 -1.54760718e+00 7.77306631e-02 9.29878175e-01
1.05876632e-01 -1.93788022e-01 -1.57456055e-01 9.89446402e-01
2.95470506e-01 8.08429942e-02 1.05095077e+00 1.33071989e-01
-3.93755555e-01 6.68703258e-01 -8.13979805e-01 -1.26813333e-02
1.63568091e+00 5.78341819e-03 2.04886943e-01 -5.81401885e-01
-7.77142346e-01 1.08555663e+00 -2.50781626e-01 1.86033264e-01
3.59051466e-01 -1.38906145e+00 -3.70895565e-01 -2.93123722e-01
-3.78104091e-01 -1.59362927e-01 5.30529737e-01 7.40921438e-01
-1.17002986e-02 3.47575396e-01 -1.83087662e-01 -3.63475412e-01
-6.86849594e-01 3.31728280e-01 1.01598954e+00 -6.27095759e-01
2.46335924e-01 4.64542955e-01 -4.48781133e-01 4.03384455e-02
2.64759183e-01 -6.71080798e-02 -6.12565465e-02 -1.10498458e-01
1.01899332e-03 9.70588028e-01 -1.60523299e-02 -1.85540449e-02
-4.10386264e-01 1.72395751e-01 6.27716243e-01 1.54102698e-01
1.52501774e+00 -7.62919247e-01 -1.11588396e-01 -2.56840944e-01
1.11169159e+00 -1.78253233e-01 -1.11377549e+00 -2.32580960e-01
-1.73161685e-01 -7.99843743e-02 9.12454009e-01 -9.19093788e-01
-1.67728758e+00 2.19236314e-01 1.15372956e+00 5.58476686e-01
1.73101318e+00 -1.31577522e-01 9.75950062e-01 3.71355057e-01
5.05984306e-01 -1.50840127e+00 2.82725841e-01 1.56575754e-01
3.08991373e-01 -9.19359326e-01 -7.09936768e-02 1.22539036e-01
1.25768716e-02 1.10374117e+00 6.77108705e-01 3.03502947e-01
1.15226233e+00 2.58323342e-01 -7.54885137e-01 1.23507455e-01
-6.72487915e-01 -2.28966132e-01 -3.48502100e-01 2.69319147e-01
-3.69088328e-03 3.89572144e-01 -3.32476377e-01 7.23228216e-01
8.42462033e-02 1.12393394e-01 4.67643917e-01 9.91722763e-01
-6.81695759e-01 -9.71564531e-01 -1.05489634e-01 6.10422790e-01
-5.28167844e-01 3.08188349e-01 7.46926248e-01 2.21322998e-01
4.19760436e-01 1.43448842e+00 1.39622584e-01 3.01620760e-03
3.41102213e-01 -5.84147513e-01 1.80389836e-01 -3.12295347e-01
-8.38961825e-02 1.66973203e-01 -1.64194331e-01 -4.50753868e-01
-6.25423193e-01 -3.97216052e-01 -1.33077335e+00 -4.13554370e-01
-4.65011925e-01 6.61246479e-01 8.42794657e-01 1.24166465e+00
5.22813261e-01 1.22265220e+00 1.56487668e+00 -4.20289636e-01
-4.84458327e-01 -5.44960141e-01 -8.70203495e-01 -6.82613492e-01
4.79320765e-01 -4.38928515e-01 -3.65301847e-01 -5.56484997e-01] | [5.91804313659668, 1.640189290046692] |
e2538118-3161-411b-a4c9-ebd7e793986c | cgdtest-a-constrained-gradient-descent | 2304.01826 | null | https://arxiv.org/abs/2304.01826v1 | https://arxiv.org/pdf/2304.01826v1.pdf | CGDTest: A Constrained Gradient Descent Algorithm for Testing Neural Networks | In this paper, we propose a new Deep Neural Network (DNN) testing algorithm called the Constrained Gradient Descent (CGD) method, and an implementation we call CGDTest aimed at exposing security and robustness issues such as adversarial robustness and bias in DNNs. Our CGD algorithm is a gradient-descent (GD) method, with the twist that the user can also specify logical properties that characterize the kinds of inputs that the user may want. This functionality sets CGDTest apart from other similar DNN testing tools since it allows users to specify logical constraints to test DNNs not only for $\ell_p$ ball-based adversarial robustness but, more importantly, includes richer properties such as disguised and flow adversarial constraints, as well as adversarial robustness in the NLP domain. We showcase the utility and power of CGDTest via extensive experimentation in the context of vision and NLP domains, comparing against 32 state-of-the-art methods over these diverse domains. Our results indicate that CGDTest outperforms state-of-the-art testing tools for $\ell_p$ ball-based adversarial robustness, and is significantly superior in testing for other adversarial robustness, with improvements in PAR2 scores of over 1500% in some cases over the next best tool. Our evaluation shows that our CGD method outperforms competing methods we compared against in terms of expressibility (i.e., a rich constraint language and concomitant tool support to express a wide variety of properties), scalability (i.e., can be applied to very large real-world models with up to 138 million parameters), and generality (i.e., can be used to test a plethora of model architectures). | ['Vijay Ganesh', 'Piyush Jha', 'Guanting Pan', 'Laura Graves', 'Vineel Nagisetty'] | 2023-04-04 | null | null | null | null | ['dnn-testing'] | ['adversarial'] | [-1.89462468e-01 -6.21430203e-02 -7.13118985e-02 -3.14832717e-01
-5.05049050e-01 -1.19482732e+00 7.92561293e-01 -4.52897280e-01
-5.79244673e-01 7.61840761e-01 -2.19041288e-01 -9.73609626e-01
-1.58521578e-01 -8.61155152e-01 -9.89125848e-01 -4.21517044e-01
-2.91101545e-01 3.38567525e-01 5.28858244e-01 -4.15227592e-01
-2.78330535e-01 8.81795704e-01 -1.38011658e+00 -1.09427728e-01
6.10077977e-01 1.10775161e+00 -5.93273282e-01 6.73934221e-01
4.83325541e-01 6.16812706e-01 -8.60749424e-01 -8.95216405e-01
6.05775237e-01 4.23145369e-02 -5.69113314e-01 -6.72622800e-01
8.24611783e-01 -6.45133734e-01 -4.97317374e-01 1.29287493e+00
9.41788614e-01 -1.14408098e-01 9.72285122e-02 -1.70340323e+00
-3.89037818e-01 6.98533714e-01 8.96002501e-02 8.87012854e-02
1.77337110e-01 7.66416550e-01 9.23341453e-01 -3.50422651e-01
7.56035805e-01 1.35299480e+00 7.42474973e-01 1.15306056e+00
-1.31355751e+00 -9.88841414e-01 1.39491171e-01 -4.15468104e-02
-8.49289417e-01 -4.72713172e-01 4.35852617e-01 -4.13936049e-01
1.22581255e+00 3.85675490e-01 2.27724060e-01 1.76121497e+00
2.02254668e-01 5.73935986e-01 9.95716691e-01 5.47799431e-02
5.03596067e-01 2.09666435e-02 3.03772725e-02 7.48591542e-01
2.51939148e-01 6.61034882e-01 -5.42702749e-02 -3.51422936e-01
6.01347566e-01 -5.01436234e-01 -1.96917742e-01 -2.02821583e-01
-9.16615844e-01 8.45158815e-01 1.97877273e-01 -2.10193560e-01
2.96141803e-01 4.04088795e-01 8.82405698e-01 6.72605038e-01
3.84650007e-02 7.12428451e-01 -8.41189086e-01 -1.06678146e-03
-5.81038833e-01 6.30579889e-01 1.14580023e+00 9.23155546e-01
3.35339427e-01 5.44086993e-01 -2.17544734e-01 5.83494365e-01
2.62345850e-01 4.79044318e-01 1.36996180e-01 -1.01346898e+00
6.73739433e-01 2.11434811e-01 -2.55700618e-01 -5.92621326e-01
-2.70558178e-01 -4.34323519e-01 -8.98755312e-01 1.04911196e+00
4.42574888e-01 -8.37706327e-01 -1.07121134e+00 2.38799214e+00
1.59986287e-01 1.32406175e-01 3.52569580e-01 7.54011273e-01
8.80849242e-01 2.95192540e-01 -7.13719651e-02 2.66053706e-01
9.90014374e-01 -4.55442876e-01 1.78445838e-02 -2.88615525e-01
3.68197918e-01 -2.71553993e-01 1.16651034e+00 6.42101467e-01
-1.10867262e+00 -4.11690474e-01 -1.22196317e+00 3.13317686e-01
-4.76899922e-01 -5.93869328e-01 6.18158758e-01 1.13887680e+00
-1.29052842e+00 7.02849388e-01 -7.19699204e-01 -1.33530116e-02
5.82688272e-01 6.61601901e-01 -6.43800676e-01 -1.41127914e-01
-1.48449862e+00 8.79633367e-01 4.77491796e-01 -2.16963008e-01
-1.45223618e+00 -9.46866572e-01 -1.00421524e+00 -2.38117784e-01
2.87974209e-01 -6.35830879e-01 1.11542737e+00 -6.44998908e-01
-1.51852095e+00 6.83129311e-01 6.40388787e-01 -7.16641009e-01
1.02796090e+00 -7.85593539e-02 -7.26052403e-01 -1.27287179e-01
-3.57256919e-01 8.26028824e-01 5.89364648e-01 -9.68283892e-01
-2.18786120e-01 4.71251458e-02 7.33043373e-01 -2.15770662e-01
-2.93711364e-01 3.34255517e-01 -5.13342977e-01 -6.71075344e-01
-4.25859481e-01 -9.89926815e-01 -1.04911610e-01 4.18609023e-01
-8.30610931e-01 8.64238814e-02 1.05526876e+00 -2.80345559e-01
7.93516278e-01 -2.24513054e+00 5.98959327e-02 5.02749145e-01
1.66905493e-01 6.08071744e-01 -3.99644017e-01 1.12368755e-01
-3.50141764e-01 6.18012965e-01 -3.13217968e-01 -2.38736551e-02
5.07765710e-01 4.51667249e-01 -4.49316800e-01 3.85074407e-01
4.42470491e-01 9.16400671e-01 -6.08965456e-01 -1.58520922e-01
1.69910379e-02 3.03126216e-01 -8.77707481e-01 9.48941633e-02
-7.26881087e-01 2.62359202e-01 -2.05225229e-01 7.30939567e-01
7.52689779e-01 4.37165797e-01 -1.11766331e-01 1.21025555e-01
2.59298563e-01 1.09875359e-01 -1.13442826e+00 1.27791107e+00
-2.10498646e-01 7.09443688e-01 1.40323669e-01 -5.01477540e-01
7.27938950e-01 2.81357795e-01 -1.14346743e-01 -6.35041893e-01
9.05279890e-02 1.12564966e-01 3.08596849e-01 -2.00503707e-01
-1.12238914e-01 4.22928594e-02 -2.52341360e-01 2.59452194e-01
2.24716052e-01 -3.54426116e-01 2.90432781e-01 8.47647414e-02
1.58212948e+00 -5.43203317e-02 -2.13159353e-01 -2.90246069e-01
3.94502223e-01 -3.24550629e-01 6.92057192e-01 9.92668390e-01
-1.37609214e-01 3.25063795e-01 1.08440936e+00 -3.90562981e-01
-9.86828268e-01 -1.35127556e+00 -1.69675335e-01 9.51431930e-01
-2.46916905e-01 5.45825576e-03 -8.54929388e-01 -8.96919847e-01
1.93411946e-01 9.09326494e-01 -6.81322098e-01 -3.81959051e-01
-4.54501122e-01 -6.15130901e-01 1.47954750e+00 6.08238041e-01
8.47498238e-01 -1.27875304e+00 -4.49751943e-01 -2.14179650e-01
4.34352726e-01 -1.27435648e+00 -1.87636465e-01 3.52121919e-01
-6.77628040e-01 -1.05524611e+00 -2.09646270e-01 -5.30303240e-01
4.28182155e-01 -6.72917008e-01 1.29517233e+00 9.35848951e-02
-2.27254093e-01 1.27380058e-01 -1.24122344e-01 -5.22718132e-01
-7.22332835e-01 -3.04818779e-01 3.07735533e-01 -6.14330947e-01
-1.64141119e-01 -7.21443772e-01 -2.45607361e-01 5.36610186e-01
-1.36954796e+00 -4.97363150e-01 3.43433380e-01 7.68752038e-01
3.79544705e-01 2.94465065e-01 3.39071959e-01 -1.11430180e+00
8.24600101e-01 -3.86086315e-01 -1.18184328e+00 1.55197933e-01
-4.94806856e-01 8.44659060e-02 1.01699233e+00 -8.06038976e-01
-4.32914376e-01 -4.25389856e-01 -6.90604806e-01 -5.59529066e-01
-2.77656019e-01 3.56505603e-01 -8.07663023e-01 -4.99966562e-01
1.04734874e+00 -1.42399520e-01 -1.50272012e-01 -1.67522162e-01
3.85146350e-01 -7.66793713e-02 6.58710659e-01 -1.01603818e+00
1.19427872e+00 5.29904328e-02 1.97125524e-01 -2.18971014e-01
-2.63939172e-01 5.46737552e-01 -2.26122085e-02 6.53827330e-04
5.51991045e-01 -6.63236558e-01 -9.86794770e-01 8.97069871e-01
-8.85148525e-01 -8.36087227e-01 -2.59906620e-01 1.95447892e-01
-3.99299085e-01 2.38328740e-01 -8.10136974e-01 -4.42425698e-01
-2.77604818e-01 -1.55640924e+00 3.67856741e-01 5.68038085e-03
-3.56296822e-02 -9.58792329e-01 6.10629432e-02 -1.26030862e-01
4.69315648e-01 8.14874172e-01 1.30294645e+00 -1.12333155e+00
-3.02318692e-01 -3.93720418e-01 -1.67674661e-01 8.75645161e-01
-4.94962364e-01 1.25246957e-01 -1.09106803e+00 -4.68054622e-01
-1.67115897e-01 -7.50638902e-01 6.51748598e-01 3.84306051e-02
1.26024199e+00 -6.28359735e-01 2.15104520e-01 1.03974426e+00
1.45971024e+00 2.36047748e-02 1.01431382e+00 5.37323534e-01
5.48815370e-01 2.43260711e-01 1.07848786e-01 1.65911525e-01
-2.66267151e-01 5.21591663e-01 1.19175684e+00 -5.41987121e-02
1.15449116e-01 -2.18357760e-02 6.63397133e-01 -2.21363783e-01
3.11306506e-01 -6.02406204e-01 -9.53518033e-01 1.58108503e-01
-1.48717999e+00 -8.66024375e-01 3.42503399e-01 1.92789948e+00
1.17125404e+00 8.00403118e-01 1.39674559e-01 1.89259917e-01
3.47318441e-01 7.41885006e-02 -1.06280124e+00 -8.37499142e-01
-4.38000053e-01 6.04336262e-01 5.66269398e-01 4.57817733e-01
-1.10050035e+00 1.02720928e+00 6.51186466e+00 8.02400172e-01
-9.67506588e-01 -1.24344885e-01 5.68596780e-01 -2.05248460e-01
-4.68055487e-01 -3.08675081e-01 -8.14246833e-01 4.08832610e-01
8.44758451e-01 1.52295828e-01 6.92358971e-01 9.11612511e-01
-2.77208775e-01 4.10235852e-01 -1.38903451e+00 4.66865033e-01
-2.13289201e-01 -1.23254228e+00 3.03210542e-02 1.96314715e-02
6.15752041e-01 4.29160714e-01 4.15418118e-01 3.63512278e-01
1.09801829e+00 -1.25122643e+00 9.41536903e-01 1.05990700e-01
1.21448755e+00 -9.93496299e-01 8.87110949e-01 2.56474167e-01
-4.65621650e-01 -1.43150210e-01 -2.40149811e-01 1.83937550e-01
-3.07941228e-01 4.59589154e-01 -5.45291722e-01 2.62099773e-01
8.63857567e-01 1.29111320e-01 -4.37139302e-01 8.40975583e-01
-6.06842101e-01 6.87388778e-01 -5.03248394e-01 9.89269018e-02
3.61491978e-01 2.98939258e-01 6.30653203e-01 1.33761191e+00
8.60766470e-02 -1.87068105e-01 -1.02622598e-01 8.85916710e-01
-4.23974961e-01 -5.69867909e-01 -7.01884508e-01 1.53354511e-01
4.89355505e-01 9.72760975e-01 -2.42337897e-01 4.37709689e-02
-1.16537154e-01 7.36755371e-01 1.60480484e-01 5.01665771e-01
-1.22263753e+00 -5.18621564e-01 1.23165917e+00 -2.41960108e-01
3.31086487e-01 -1.59327567e-01 -9.43239331e-02 -8.67704511e-01
2.31680468e-01 -1.53616285e+00 4.09144670e-01 -5.91555059e-01
-1.39998007e+00 8.33837390e-01 6.99146241e-02 -9.86849904e-01
-3.56422096e-01 -1.15133905e+00 -8.24233532e-01 9.72750366e-01
-1.28798211e+00 -1.02105570e+00 6.15083389e-02 1.01877677e+00
-6.00266270e-03 -5.95355153e-01 1.03654516e+00 2.31073722e-01
-8.02680671e-01 1.36974561e+00 -1.61753610e-01 5.98102689e-01
4.55947876e-01 -1.18149173e+00 9.62816238e-01 1.19117951e+00
3.18114250e-03 6.04104936e-01 7.48578489e-01 -4.96201903e-01
-1.40015090e+00 -1.29362679e+00 1.57194093e-01 -4.95500416e-01
8.97942841e-01 -6.79467320e-01 -5.54804385e-01 7.21598387e-01
-1.11552343e-01 3.62846524e-01 4.90439236e-01 -1.00292064e-01
-1.12532210e+00 -1.54827207e-01 -1.70218623e+00 1.10357118e+00
1.21226048e+00 -5.93876898e-01 -1.84713483e-01 2.49846160e-01
1.06489778e+00 -7.81843007e-01 -8.18818450e-01 5.97986937e-01
5.69424391e-01 -1.17028141e+00 1.09615207e+00 -8.64708602e-01
4.16101843e-01 -2.91436553e-01 -4.88174856e-01 -1.11141121e+00
-5.33644184e-02 -8.66380692e-01 -8.17802250e-02 1.22492945e+00
7.61620581e-01 -1.03620243e+00 7.04735398e-01 8.88962626e-01
-3.13752502e-01 -8.19933414e-01 -1.11938465e+00 -1.04449117e+00
2.98419058e-01 -1.02293766e+00 7.62392879e-01 8.89792323e-01
-3.77016902e-01 -3.52819771e-01 -1.40926227e-01 4.12866116e-01
4.26759243e-01 -6.61961794e-01 7.14688599e-01 -1.06031048e+00
-6.34482682e-01 -6.57495737e-01 -7.79611528e-01 -4.66014951e-01
5.48021615e-01 -8.79212379e-01 -1.59759387e-01 -9.66575444e-01
-2.93969840e-01 -4.36483026e-01 -3.84092093e-01 9.99295354e-01
2.25935817e-01 3.37415874e-01 1.67987913e-01 -2.69793659e-01
-2.66077638e-01 -7.43903569e-04 8.28250527e-01 -2.82333225e-01
8.57823864e-02 9.38742161e-02 -8.56153905e-01 7.76650548e-01
8.81346345e-01 -4.29765671e-01 -5.81224918e-01 -6.97780788e-01
3.91161978e-01 -2.77213424e-01 8.18212748e-01 -1.34564173e+00
1.52711719e-01 -1.60085887e-01 4.71315533e-01 -6.01612255e-02
2.56387711e-01 -7.90777147e-01 1.71121821e-01 5.75533986e-01
-2.85688043e-01 1.77874342e-01 8.91333163e-01 2.49412283e-01
1.37285188e-01 -7.83695281e-02 8.60010624e-01 8.92474055e-02
-7.55996943e-01 5.45070350e-01 -1.13054805e-01 4.37593102e-01
9.56992388e-01 -1.47223651e-01 -7.97865748e-01 -2.11753651e-01
-4.52453405e-01 2.82470226e-01 6.21339798e-01 3.27574223e-01
7.47544408e-01 -1.07116568e+00 -7.55015373e-01 5.76879621e-01
5.60096726e-02 -4.06986587e-02 -1.40167713e-01 1.67778462e-01
-7.07936645e-01 -3.29916179e-02 -4.24797982e-01 -2.79660612e-01
-1.19158053e+00 4.64500487e-01 6.08157098e-01 -2.95942575e-01
-5.92012107e-01 1.26120377e+00 -1.26546964e-01 -7.59497225e-01
6.86398685e-01 -2.76059419e-01 2.65935838e-01 -4.09364611e-01
3.24158370e-01 1.52185857e-01 2.32217774e-01 -1.21046342e-01
-5.75144529e-01 3.02742541e-01 2.71950066e-02 -3.96366775e-01
1.24402976e+00 8.67066264e-01 -1.15597770e-01 -1.90033495e-01
1.05752969e+00 -3.47217917e-02 -1.46077585e+00 8.61500055e-02
-2.73710966e-01 -1.13424726e-01 -1.56665921e-01 -1.43953788e+00
-1.30260050e+00 6.25988364e-01 5.60674548e-01 9.70796645e-02
1.26060390e+00 -2.09578067e-01 5.63129961e-01 7.43812919e-01
3.35338682e-01 -6.75685465e-01 -6.55587763e-02 7.84762204e-01
8.55362236e-01 -9.15359795e-01 -9.98466164e-02 -4.13672514e-02
-5.55355966e-01 8.69515061e-01 7.88488746e-01 -2.59339541e-01
6.87535167e-01 7.89448321e-01 1.70307249e-01 1.31590560e-01
-7.69560277e-01 1.87003940e-01 1.42199963e-01 8.84826243e-01
-1.14566483e-01 -1.63135439e-01 3.70760679e-01 4.41292167e-01
-4.97904330e-01 -2.65786648e-01 2.22724721e-01 7.24947393e-01
-2.38027563e-03 -1.26061261e+00 -1.67289987e-01 3.79425734e-01
-7.54413605e-01 -2.38977671e-01 -4.45837826e-01 1.24657512e+00
3.58286142e-01 9.36820388e-01 -3.18035662e-01 -7.37037420e-01
5.72092593e-01 -1.10294029e-01 3.48998845e-01 -4.57751542e-01
-9.33315217e-01 -5.22330463e-01 3.81558269e-01 -8.87828410e-01
2.32464552e-01 -3.02267462e-01 -9.35377181e-01 -5.71734250e-01
-1.30642146e-01 -1.26050174e-01 6.99809670e-01 7.82666206e-01
7.38171637e-02 4.58969712e-01 4.25931960e-01 -6.22429550e-01
-8.39273453e-01 -5.74576855e-01 -3.51879656e-01 3.34358543e-01
4.61953938e-01 -3.23630542e-01 -5.69144666e-01 -4.67061549e-01] | [5.708015441894531, 7.850716590881348] |
6db7bf4f-ee32-45df-9670-1a9f5d27a69a | semi-supervised-segmentation-of-salt-bodies | 1904.04445 | null | https://arxiv.org/abs/1904.04445v3 | https://arxiv.org/pdf/1904.04445v3.pdf | Semi-Supervised Segmentation of Salt Bodies in Seismic Images using an Ensemble of Convolutional Neural Networks | Seismic image analysis plays a crucial role in a wide range of industrial applications and has been receiving significant attention. One of the essential challenges of seismic imaging is detecting subsurface salt structure which is indispensable for identification of hydrocarbon reservoirs and drill path planning. Unfortunately, exact identification of large salt deposits is notoriously difficult and professional seismic imaging often requires expert human interpretation of salt bodies. Convolutional neural networks (CNNs) have been successfully applied in many fields, and several attempts have been made in the field of seismic imaging. But the high cost of manual annotations by geophysics experts and scarce publicly available labeled datasets hinder the performance of the existing CNN-based methods. In this work, we propose a semi-supervised method for segmentation (delineation) of salt bodies in seismic images which utilizes unlabeled data for multi-round self-training. To reduce error amplification during self-training we propose a scheme which uses an ensemble of CNNs. We show that our approach outperforms state-of-the-art on the TGS Salt Identification Challenge dataset and is ranked the first among the 3234 competing methods. | ['Yauhen Babakhin', 'Hirotoshi Kitamura', 'Artsiom Sanakoyeu'] | 2019-04-09 | null | null | null | null | ['geophysics', 'seismic-imaging'] | ['miscellaneous', 'miscellaneous'] | [ 3.41741264e-01 -1.27768010e-01 2.10255384e-01 -4.41445380e-01
-8.41689646e-01 -4.88372803e-01 4.15847659e-01 2.42338419e-01
-7.75989115e-01 2.64054149e-01 -8.87543336e-02 -4.60256070e-01
1.11521527e-01 -8.51727366e-01 -6.15263760e-01 -9.52010989e-01
1.95618290e-02 6.35258853e-01 7.31743753e-01 -2.48909444e-01
6.99478626e-01 5.95111370e-01 -1.23908508e+00 5.69208004e-02
7.84656405e-01 1.00829852e+00 5.73593020e-01 3.35062653e-01
-2.05505475e-01 6.02667212e-01 -3.03930461e-01 -3.93451869e-01
4.07504737e-01 -2.06111819e-01 -8.13540936e-01 8.55483636e-02
3.10227066e-01 -1.39092445e-01 -8.68838206e-02 1.30083132e+00
7.06630349e-01 -8.04213155e-03 7.15080857e-01 -6.84325814e-01
-2.96036869e-01 8.83973539e-01 -7.80084491e-01 2.93094575e-01
-5.48665166e-01 1.68249890e-01 8.95975769e-01 -1.13933027e+00
1.35256499e-01 6.87837839e-01 8.48348141e-01 3.54593694e-01
-6.28308773e-01 -8.13691318e-01 -2.30324090e-01 4.82545570e-02
-1.23842633e+00 -4.80797350e-01 7.48431146e-01 -5.48043489e-01
5.81159353e-01 2.63513140e-02 4.56548899e-01 4.69059289e-01
-2.73441017e-01 8.81560981e-01 1.22111464e+00 -2.56944299e-01
4.21635389e-01 -1.59534350e-01 -8.71428326e-02 7.06473589e-01
3.70851547e-01 -3.31838042e-01 -4.67402756e-01 8.11422691e-02
9.17337537e-01 -2.97583602e-02 -1.94671810e-01 7.78993312e-03
-1.28849185e+00 8.24173033e-01 5.35710216e-01 4.86105770e-01
-3.98186505e-01 2.86750108e-01 4.50684905e-01 -3.30248028e-02
4.89573091e-01 6.55059695e-01 -4.18910712e-01 -2.96748113e-02
-1.19837320e+00 3.61510009e-01 4.75167334e-01 4.32374775e-01
8.14239919e-01 3.57304335e-01 4.96923089e-01 8.02109480e-01
4.62835848e-01 4.61508393e-01 5.01698315e-01 -5.34654140e-01
6.43125653e-01 6.94277406e-01 7.66894519e-02 -6.51090562e-01
-4.63604629e-01 -2.28664950e-01 -1.13160145e+00 3.86823326e-01
6.01641655e-01 -4.33579348e-02 -1.16335070e+00 8.81286502e-01
1.80319086e-01 3.54977876e-01 2.67417520e-01 9.34753776e-01
9.56716895e-01 4.87152100e-01 -4.32600174e-03 3.57927352e-01
1.32862210e+00 -7.68017471e-01 -3.43793780e-01 -5.03383040e-01
3.63838643e-01 -5.56435287e-01 8.05314302e-01 4.06923205e-01
-6.99152470e-01 -2.85294563e-01 -1.17413974e+00 2.86304861e-01
-5.19046724e-01 1.78864837e-01 6.24467194e-01 8.18932354e-01
-7.30888307e-01 7.02044427e-01 -1.18895102e+00 -1.70354500e-01
7.65582383e-01 4.74415779e-01 -2.96791852e-01 1.56106442e-01
-9.96006668e-01 7.84818053e-01 3.61814201e-01 7.64582336e-01
-1.08448696e+00 -4.62602317e-01 -6.89324737e-01 -1.83327660e-01
4.64810401e-01 2.11976528e-01 1.24344385e+00 -4.99523550e-01
-1.16108811e+00 7.62726486e-01 1.68741256e-01 -4.37575459e-01
6.99422061e-01 -1.54289484e-01 -1.03153244e-01 1.13496318e-01
1.73933487e-02 4.19707775e-01 6.94123209e-01 -1.36662674e+00
-6.87844276e-01 -4.08027411e-01 -3.34359497e-01 4.04873490e-02
-2.45112792e-01 1.82699695e-01 -4.89941537e-01 -5.40777683e-01
7.19631374e-01 -6.92716122e-01 -7.29916632e-01 -1.01686925e-01
-5.62986612e-01 -1.82517260e-01 9.01056170e-01 -7.89547980e-01
7.52188087e-01 -1.98982525e+00 -1.42303914e-01 3.39370996e-01
2.73376346e-01 4.40726250e-01 2.54901350e-01 1.68010548e-01
7.63290077e-02 2.33837739e-01 -9.72241402e-01 -4.05753225e-01
-1.75396968e-02 1.62933975e-01 -1.39702618e-01 5.62195599e-01
2.81054378e-01 6.79485857e-01 -8.90917063e-01 -4.07679558e-01
1.91925436e-01 2.52335578e-01 -6.06293008e-02 2.20053732e-01
-1.95289329e-01 8.41791451e-01 -5.38789690e-01 1.04349840e+00
8.57199848e-01 -2.79542834e-01 -1.00069575e-01 -2.00363442e-01
-4.91054475e-01 9.86992493e-02 -1.26320755e+00 1.41805065e+00
-3.70375142e-02 4.19348598e-01 1.45034222e-02 -1.34652531e+00
1.23114765e+00 3.32285911e-01 3.52770180e-01 -5.69099665e-01
1.53197542e-01 8.75731468e-01 1.72218364e-02 -8.77004325e-01
7.03589320e-01 -2.79838890e-01 1.21118814e-01 2.99341351e-01
-4.22695309e-01 -5.01649976e-01 1.32673129e-01 -9.66725051e-02
8.55070770e-01 1.63058639e-02 -8.14297795e-02 -2.49216020e-01
4.11303610e-01 4.94019799e-02 4.86676663e-01 5.58138967e-01
-9.05274972e-02 9.13227260e-01 1.61814779e-01 -7.09444106e-01
-1.20377600e+00 -7.20254123e-01 -2.23525554e-01 7.10706890e-01
1.75280675e-01 1.97006598e-01 -7.18759894e-01 -6.48396254e-01
-3.64085310e-03 1.38603732e-01 -6.74002051e-01 3.60561907e-01
-7.85850227e-01 -1.03967261e+00 9.48127210e-01 9.90346253e-01
9.59291518e-01 -1.39688575e+00 -8.96449268e-01 3.77723873e-01
-7.91356936e-02 -1.06275165e+00 1.47042751e-01 5.75741172e-01
-9.06388938e-01 -1.24475241e+00 -1.06349421e+00 -9.17710602e-01
7.86913514e-01 -3.02031487e-02 7.82303631e-01 1.46717682e-01
-1.64628044e-01 -3.92095000e-01 -4.66151237e-01 -7.27928698e-01
-4.19760793e-01 4.14356947e-01 -2.31537372e-01 6.58315122e-02
2.61432081e-01 -2.36721829e-01 -6.87170386e-01 4.55798775e-01
-1.01242864e+00 -1.87184557e-01 6.99855924e-01 6.41659379e-01
4.06103313e-01 1.68869540e-01 8.05130482e-01 -1.12227356e+00
1.60929635e-01 -3.35115969e-01 -8.04789782e-01 9.67544466e-02
-2.45103672e-01 -8.87505189e-02 2.83095568e-01 9.91823375e-02
-9.74031568e-01 3.99547726e-01 -6.42245054e-01 -1.11222558e-01
-2.39722565e-01 8.92259002e-01 -6.87390158e-04 -4.53771710e-01
4.60372508e-01 1.58095151e-01 -1.64050341e-01 -6.66468918e-01
-2.63147742e-01 8.98873806e-01 6.90146506e-01 -4.70720381e-01
8.08994591e-01 6.29575253e-01 1.52446121e-01 -1.08026600e+00
-8.43553543e-01 -7.33522594e-01 -7.12485135e-01 -1.88581526e-01
1.12352300e+00 -8.21557879e-01 -6.47285342e-01 1.01671648e+00
-8.56906414e-01 -3.64508003e-01 5.78874303e-03 2.92874902e-01
-7.06092827e-03 6.04191244e-01 -4.92647797e-01 -1.02339244e+00
-4.44951117e-01 -1.53070509e+00 1.06026495e+00 3.16829950e-01
2.09407285e-01 -6.76624179e-01 -1.13785870e-01 5.42891502e-01
6.45898819e-01 2.22616181e-01 4.61473584e-01 -7.63549864e-01
-7.82413960e-01 -2.72648364e-01 -4.22263086e-01 3.25520515e-01
1.88105285e-01 -1.46135911e-01 -1.21535087e+00 -2.45984569e-02
-2.48415455e-01 -5.47255516e-01 1.37891030e+00 2.92376876e-01
1.04833329e+00 3.27703476e-01 -8.37782212e-03 3.99817199e-01
1.49654007e+00 2.80675203e-01 7.11494505e-01 6.65140212e-01
8.55530977e-01 6.52224720e-01 4.57893521e-01 3.98984104e-01
2.17557505e-01 9.29040611e-02 8.68971527e-01 -3.65288526e-01
4.53777283e-01 2.88119406e-01 -1.22313023e-01 7.30180562e-01
-4.51816857e-01 -1.77586712e-02 -1.33886778e+00 1.01288354e+00
-1.78737664e+00 -7.07796395e-01 -2.76834309e-01 1.94382727e+00
5.96166730e-01 2.12614536e-01 -9.26494747e-02 5.23152232e-01
6.23185456e-01 1.44064620e-01 -5.03159642e-01 2.82106608e-01
-2.05961794e-01 3.75396967e-01 8.81592155e-01 7.46659562e-02
-1.59823859e+00 8.88699055e-01 5.65241385e+00 4.25518781e-01
-1.22945499e+00 -4.12027985e-02 7.29718328e-01 8.04212868e-01
2.78903488e-02 -2.58076727e-01 -7.94950843e-01 2.45315135e-01
4.22416806e-01 6.36664212e-01 -1.59054101e-01 7.09502220e-01
1.41377240e-01 -2.89384872e-01 -6.89976335e-01 6.97467744e-01
-1.50542051e-01 -1.58810496e+00 -4.16004121e-01 -1.56408891e-01
8.82156014e-01 5.29769421e-01 -8.61671492e-02 -2.18973815e-01
2.46534571e-01 -8.94094229e-01 8.55263233e-01 2.37550914e-01
5.62631488e-01 -6.53509915e-01 1.11176968e+00 1.03063010e-01
-1.50148141e+00 -1.72242941e-03 -4.37097967e-01 2.25929901e-01
2.76120901e-01 6.18176460e-01 -6.99692309e-01 4.57615256e-01
9.71496463e-01 6.11116230e-01 -4.51240778e-01 1.44776702e+00
-2.98541963e-01 9.10576224e-01 -4.65516090e-01 -8.51083174e-02
6.01845980e-01 -4.17141110e-01 4.73571420e-02 1.22817397e+00
3.43284428e-01 2.56587863e-02 3.43921751e-01 6.36494815e-01
-1.56348854e-01 5.69219850e-02 -2.75118381e-01 -3.37079078e-01
2.34012231e-01 1.12879801e+00 -1.36962116e+00 -3.43855321e-01
-3.46497744e-01 4.30214673e-01 -9.60955843e-02 -9.16168559e-04
-4.55440372e-01 -5.20122051e-01 2.35339329e-01 -1.44741433e-02
5.56160748e-01 -2.79270381e-01 -5.92179120e-01 -8.87161374e-01
-7.45180175e-02 -8.02734613e-01 2.25450814e-01 -4.98385429e-01
-1.18963861e+00 5.98606765e-01 -3.38788599e-01 -1.20962536e+00
2.69308329e-01 -7.49065399e-01 -8.15809667e-01 8.83422315e-01
-1.94655323e+00 -1.25676584e+00 -6.97610438e-01 1.61632091e-01
7.26370573e-01 -1.98642507e-01 5.55138171e-01 5.53045452e-01
-6.09152675e-01 1.88351676e-01 1.03375211e-01 6.98113680e-01
5.16353548e-01 -1.36704671e+00 8.21219802e-01 9.92476761e-01
-1.01044893e-01 2.24467799e-01 5.85132360e-01 -8.06802690e-01
-1.30623507e+00 -1.28455341e+00 6.48023784e-01 4.22206558e-02
7.61973917e-01 -3.05577014e-02 -1.32416630e+00 5.18937767e-01
5.38061485e-02 1.20850638e-01 4.28751528e-01 -4.30771232e-01
-1.59051344e-01 1.69793032e-02 -9.34368074e-01 3.04155797e-01
4.78189230e-01 -2.93945253e-01 -3.09775949e-01 3.42412382e-01
1.26288459e-01 -5.91053426e-01 -5.58461249e-01 6.21807098e-01
3.05968583e-01 -7.94003308e-01 7.79258132e-01 -5.32377400e-02
5.65528393e-01 -5.91038823e-01 -1.32307008e-01 -9.36655819e-01
1.34549022e-01 -1.22313388e-01 4.90570754e-01 1.02985048e+00
5.55653632e-01 -5.28607666e-01 1.56081057e+00 3.32966983e-01
-5.95819473e-01 -6.40177250e-01 -7.94981182e-01 -5.41686714e-01
-4.61535249e-03 -4.52955335e-01 5.57124496e-01 1.15690660e+00
-3.39652359e-01 -1.89653143e-01 -2.98707664e-01 5.48540413e-01
8.08183312e-01 -2.44959779e-02 5.89214444e-01 -1.35995471e+00
-1.46392122e-01 -4.14905518e-01 -3.16280812e-01 -9.38936710e-01
-2.21946493e-01 -6.67709589e-01 6.41186297e-01 -1.70737278e+00
-9.80214477e-02 -7.98946381e-01 -2.44240358e-01 8.54056001e-01
-1.27046227e-01 7.26083755e-01 -1.61601275e-01 2.67784536e-01
-3.09252173e-01 2.67105907e-01 1.09431934e+00 -3.95785213e-01
-2.64345901e-03 7.86565319e-02 -2.63736635e-01 8.43473792e-01
1.01238465e+00 -4.69688177e-01 1.29333928e-01 -6.21691644e-01
5.72940588e-01 -2.22560689e-01 2.85860509e-01 -9.65213180e-01
4.77137327e-01 1.84755679e-02 1.73901811e-01 -1.02573669e+00
1.22427441e-01 -7.11953938e-01 1.77397430e-02 6.32454574e-01
-7.60109276e-02 -4.03379975e-03 -1.55155519e-02 5.12951791e-01
-4.01960790e-01 -7.53185034e-01 9.63202715e-01 -5.87069452e-01
-1.04680490e+00 3.57103229e-01 -4.52755839e-01 -2.90275723e-01
5.79357088e-01 -2.53063828e-01 -2.78044902e-02 1.20831840e-01
-3.92708004e-01 1.67640209e-01 1.57785639e-01 7.21183643e-02
8.27520072e-01 -7.08191454e-01 -7.47350693e-01 2.08424971e-01
2.23729491e-01 8.28633249e-01 1.93843663e-01 8.46563399e-01
-1.22070503e+00 -1.94530040e-01 3.15070227e-02 -7.06796169e-01
-9.97108698e-01 -2.19710290e-01 3.76732558e-01 -2.17048749e-01
-7.92971134e-01 1.19196475e+00 -7.44698718e-02 -3.90397131e-01
2.76758641e-01 -4.28724378e-01 -5.52867174e-01 3.27703953e-02
4.84191716e-01 3.49395931e-01 4.43713993e-01 -5.71377039e-01
-1.61233708e-01 7.07740426e-01 -1.02652475e-01 6.77707195e-02
1.84174716e+00 3.06804091e-01 -1.91822425e-01 3.63120914e-01
7.98971236e-01 -4.12193924e-01 -1.19706404e+00 -3.24214876e-01
4.09981102e-01 -4.52725053e-01 1.67853400e-01 -4.97008443e-01
-1.58887947e+00 1.31251419e+00 4.87208426e-01 1.16476558e-01
9.45551932e-01 -4.65382189e-02 8.68864655e-01 7.73204148e-01
3.16261411e-01 -1.02522874e+00 2.21838728e-01 5.09551466e-01
5.83050132e-01 -1.79062736e+00 -8.60391930e-02 -3.86359155e-01
-6.84031427e-01 1.26381850e+00 5.22495627e-01 -1.29894197e-01
6.77582502e-01 5.68319261e-01 4.78830427e-01 -6.41445220e-01
1.69879839e-01 -1.45978555e-01 -3.13286297e-02 2.45299980e-01
3.40726465e-01 -2.62004405e-01 9.96174216e-02 3.60236734e-01
1.18961178e-01 -2.34304935e-01 4.46075231e-01 1.51404703e+00
-7.87598312e-01 -9.06826973e-01 -4.76154476e-01 6.43952727e-01
-6.81275964e-01 -1.39908984e-01 -1.07080117e-01 4.66824114e-01
1.24011673e-01 5.43181598e-01 2.78516579e-02 -1.58728257e-01
2.30616108e-01 -6.35361001e-02 7.29533061e-02 -5.32629371e-01
-9.54348862e-01 2.67493516e-01 2.75648460e-02 1.14009142e-01
-8.51822019e-01 -7.63247907e-01 -1.43073058e+00 1.36108100e-01
-5.41038930e-01 2.57339418e-01 1.04782999e+00 1.16614199e+00
-3.58204246e-01 3.34377140e-01 6.30290568e-01 -1.20704067e+00
-5.49024940e-01 -9.43634033e-01 -9.10300016e-01 3.54360163e-01
2.44124681e-01 -5.37228882e-01 -2.91256934e-01 3.63613725e-01] | [7.224246978759766, 2.028026819229126] |
5505fd1f-15d6-4721-99a4-4b16fc616cdc | can-gan-learn-topological-features-of-a-graph | 1707.06197 | null | http://arxiv.org/abs/1707.06197v1 | http://arxiv.org/pdf/1707.06197v1.pdf | Can GAN Learn Topological Features of a Graph? | This paper is first-line research expanding GANs into graph topology
analysis. By leveraging the hierarchical connectivity structure of a graph, we
have demonstrated that generative adversarial networks (GANs) can successfully
capture topological features of any arbitrary graph, and rank edge sets by
different stages according to their contribution to topology reconstruction.
Moreover, in addition to acting as an indicator of graph reconstruction, we
find that these stages can also preserve important topological features in a
graph. | ['Pin-Yu Chen', 'Min Hwan Oh', 'Toyotaro Suzumura', 'Hal Cooper', 'Weiyi Liu', 'Sailung Yeung'] | 2017-07-19 | null | null | null | null | ['graph-reconstruction'] | ['graphs'] | [ 2.34715715e-01 6.06424689e-01 -1.86341003e-01 9.56814438e-02
-1.90135181e-01 -1.02173841e+00 6.22930110e-01 -3.22169550e-02
2.63180137e-01 7.17491210e-01 3.21558088e-01 -2.83442676e-01
-2.65707105e-01 -1.48326254e+00 -7.01353490e-01 -3.01313400e-01
-4.39041466e-01 6.26084447e-01 4.20365557e-02 -4.54168379e-01
1.91435456e-01 9.88855779e-01 -6.74029231e-01 -7.69839659e-02
8.34041297e-01 3.93690854e-01 -5.12945592e-01 8.10518801e-01
3.47858459e-01 7.57556200e-01 -6.14304185e-01 -6.73790336e-01
4.56714064e-01 -4.24964011e-01 -9.10507858e-01 6.54455498e-02
5.41115284e-01 -1.21862188e-01 -1.13565361e+00 1.06874871e+00
2.28234544e-01 -1.44344360e-01 7.48053133e-01 -1.40633428e+00
-7.05435336e-01 7.47836947e-01 -3.25853258e-01 4.28925753e-02
5.59691131e-01 2.16868863e-01 1.33316267e+00 -4.24558371e-01
9.99997556e-01 1.02436697e+00 9.81812775e-01 2.76011199e-01
-1.39509630e+00 -5.61063468e-01 -8.43191892e-02 -5.17847855e-03
-1.35864496e+00 -1.76919326e-01 1.46726215e+00 -3.87281120e-01
4.42613006e-01 5.98532736e-01 1.09608328e+00 1.03416085e+00
4.56161827e-01 2.83340156e-01 1.04007041e+00 -9.51546058e-02
-1.41632359e-03 -6.40388668e-01 -2.40513757e-01 1.12785065e+00
3.53319734e-01 1.93522051e-01 -2.23017663e-01 2.78970320e-02
1.06949604e+00 -1.11941881e-01 -4.03301030e-01 -4.69256341e-01
-1.16953444e+00 8.73773813e-01 1.06257081e+00 3.78852069e-01
-2.67227054e-01 5.91555774e-01 1.78435892e-01 6.30904675e-01
3.12863559e-01 8.16496789e-01 7.14793131e-02 -1.51968161e-02
-6.25227213e-01 -1.82549998e-01 7.99349368e-01 9.78609800e-01
7.45282710e-01 2.48916879e-01 -1.29454270e-01 1.23982646e-01
8.65251869e-02 2.98433781e-01 -8.62332508e-02 -9.97909725e-01
1.95263118e-01 9.19756174e-01 -5.23693442e-01 -1.51196766e+00
-3.89690697e-01 -4.92009968e-01 -1.34755659e+00 -4.79131527e-02
2.82994151e-01 2.62374550e-01 -9.72592354e-01 1.99650693e+00
1.19504005e-01 1.55078933e-01 -3.53762895e-01 5.66926301e-01
8.09072435e-01 2.04486072e-01 -3.40173841e-01 7.83693641e-02
8.90664458e-01 -6.77534044e-01 -3.04906815e-01 1.21950604e-01
3.49962860e-01 -4.10606146e-01 8.61825407e-01 1.30856767e-01
-1.22671628e+00 -3.29627544e-01 -1.13421810e+00 1.63901233e-04
-4.56053287e-01 -4.26344335e-01 9.95867193e-01 6.37734294e-01
-1.68076515e+00 8.53667080e-01 -6.81832016e-01 -3.51692528e-01
6.67168558e-01 2.60286063e-01 -5.77096641e-01 -1.09413803e-01
-9.53738809e-01 4.73753333e-01 2.53457338e-01 -7.50478869e-03
-8.95859897e-01 -5.30387521e-01 -7.92606652e-01 1.01455957e-01
2.52758980e-01 -1.11419678e+00 3.76939684e-01 -5.91940045e-01
-1.41463745e+00 7.12209940e-01 1.30786330e-01 -4.18759376e-01
6.19830608e-01 7.48530746e-01 -5.37206173e-01 6.63912237e-01
-9.18885842e-02 5.23934960e-01 9.50633824e-01 -1.26825750e+00
9.23091620e-02 -3.43035966e-01 4.69602674e-01 -2.71570999e-02
-1.96933359e-01 -5.20918190e-01 -2.81453609e-01 -8.83032620e-01
4.70065057e-01 -1.03574300e+00 -3.68862122e-01 -1.36162072e-01
-1.13543522e+00 2.77895391e-01 8.07316780e-01 -7.06917524e-01
1.10612082e+00 -1.73297942e+00 5.29946744e-01 8.33843291e-01
1.09202433e+00 -4.61389795e-02 -3.15148681e-01 7.05314159e-01
1.80728268e-02 6.55272961e-01 -2.04458445e-01 3.89967929e-03
6.70471638e-02 1.06337905e-01 -3.60076934e-01 5.84699214e-01
8.38876516e-02 1.69359040e+00 -1.08076000e+00 -3.99158567e-01
3.21776509e-01 3.61886829e-01 -4.98987257e-01 -1.53929234e-01
2.47732773e-02 7.27634072e-01 -4.50969577e-01 7.12425828e-01
5.83598912e-01 -3.59890610e-01 5.81795216e-01 -2.67873287e-01
5.37252545e-01 1.21295355e-01 -5.91267228e-01 1.45513093e+00
-3.54196936e-01 6.81389272e-01 1.07704468e-01 -1.20959783e+00
7.59454310e-01 -6.02304898e-02 7.13775635e-01 -4.89046007e-01
5.76135330e-02 -1.43061966e-01 2.20437855e-01 2.41753459e-01
4.18316185e-01 6.29806966e-02 -3.13572437e-01 4.69443113e-01
1.33097759e-02 -4.16128099e-01 1.80255339e-01 6.53123736e-01
1.85101247e+00 -3.86668533e-01 4.72934544e-02 -2.75543511e-01
2.67335832e-01 -1.52769119e-01 1.01286061e-01 7.11479545e-01
-1.34786610e-02 6.00441575e-01 7.37684906e-01 -5.26826322e-01
-1.25187302e+00 -1.42597258e+00 -1.98603552e-02 5.14962733e-01
2.22503006e-01 -6.36965275e-01 -6.20827675e-01 -9.28758860e-01
1.09982841e-01 2.03445986e-01 -9.97491181e-01 -6.45262301e-01
-6.89838290e-01 -4.68413651e-01 8.61936510e-01 5.75766504e-01
2.68685371e-01 -8.90925229e-01 -5.65946549e-02 8.34212303e-02
8.40641484e-02 -1.07991362e+00 -6.86945856e-01 -1.10752970e-01
-9.93834436e-01 -1.59058547e+00 -3.23035687e-01 -7.76775539e-01
1.30413699e+00 2.79400468e-01 1.56199300e+00 6.31231308e-01
-1.75635874e-01 7.13100493e-01 -2.91353852e-01 3.58963966e-01
-8.07122529e-01 3.35175961e-01 -2.65403837e-01 -2.25850880e-01
-6.24106050e-01 -1.32982934e+00 -5.29433608e-01 3.48960519e-01
-9.00784731e-01 1.71895325e-01 5.03509521e-01 6.95861220e-01
5.66058099e-01 5.39344728e-01 2.55455136e-01 -1.08711493e+00
7.79221416e-01 -3.82993400e-01 -3.45754564e-01 2.54618526e-01
-6.08435452e-01 -7.93962032e-02 8.93930852e-01 -1.37954429e-01
-2.88665205e-01 -2.94732630e-01 -3.90090235e-02 -4.17052239e-01
2.95581847e-01 5.56071937e-01 -1.82969332e-01 -7.23979414e-01
3.10644209e-01 1.78196564e-01 2.95950975e-02 -5.25436625e-02
5.35737574e-01 -2.80472845e-01 6.51161492e-01 -3.63593221e-01
1.49899900e+00 5.83858013e-01 7.78496504e-01 -5.03828883e-01
-2.44668320e-01 1.26990229e-01 -8.62640023e-01 -2.50913769e-01
6.54366732e-01 -4.04543877e-01 -9.00530159e-01 3.37298840e-01
-7.36853242e-01 -2.89843142e-01 -4.87969458e-01 -7.80237839e-02
-5.11115968e-01 5.81217885e-01 -8.20646882e-01 -1.83638915e-01
-2.89481282e-01 -7.57530212e-01 8.83601964e-01 -1.20101951e-01
1.80879533e-02 -1.46749544e+00 -5.31892888e-02 1.95207577e-02
4.18074608e-01 1.01042163e+00 1.08195853e+00 -3.26176316e-01
-9.18422878e-01 -4.72942770e-01 -2.03376919e-01 -1.67438388e-01
2.72747278e-01 1.95221484e-01 -4.47996169e-01 -5.32011807e-01
-3.17540824e-01 1.69543107e-03 8.26345623e-01 3.48810762e-01
1.07936847e+00 -6.76215053e-01 -3.70687425e-01 1.04789984e+00
1.44525898e+00 -2.33873457e-01 1.01232481e+00 -1.05125971e-01
1.32054341e+00 7.21356198e-02 -1.75267085e-01 4.30016294e-02
2.70685464e-01 5.11657119e-01 6.42010093e-01 -4.20254916e-01
-6.25605047e-01 -8.18103313e-01 9.82815251e-02 1.04830265e+00
-3.53881866e-01 -3.94796610e-01 -8.00791085e-01 2.05610275e-01
-1.49618566e+00 -1.02902198e+00 -1.42486662e-01 1.97831678e+00
4.22984213e-01 2.94061750e-01 1.61818862e-01 2.35130742e-01
9.68597591e-01 5.02045095e-01 -5.76172888e-01 -2.21002564e-01
-3.42225939e-01 5.39336681e-01 7.19160497e-01 5.03399074e-01
-7.59654939e-01 9.75105107e-01 7.96479368e+00 5.88169277e-01
-8.91008019e-01 -1.62981480e-01 4.74711835e-01 4.59406525e-01
-9.88541126e-01 2.59688109e-01 1.80767596e-01 3.71289492e-01
4.53167319e-01 -4.08667862e-01 1.10353673e+00 4.37070698e-01
-4.09518540e-01 4.21080142e-01 -9.89972353e-01 4.71271962e-01
-1.68904841e-01 -1.56283236e+00 4.43834692e-01 5.89963794e-01
8.43506217e-01 -1.70283020e-01 1.33214980e-01 -1.50274504e-02
7.42607713e-01 -1.49304664e+00 4.20537323e-01 5.53294837e-01
1.13677466e+00 -9.34575319e-01 5.42440772e-01 -3.13654169e-02
-1.52131724e+00 1.89110383e-01 -2.07893789e-01 1.91479102e-01
6.58315718e-02 6.25375211e-01 -9.80862141e-01 9.82514322e-01
1.77060112e-01 7.97507823e-01 -8.51680219e-01 8.34753573e-01
-5.37091255e-01 6.66461408e-01 -1.51818320e-01 4.11564887e-01
-4.95005772e-02 -4.83584911e-01 9.13411200e-01 6.56469703e-01
1.75570413e-01 -1.10265844e-01 3.05650473e-01 1.14991689e+00
-6.42157733e-01 -2.63579756e-01 -1.01442814e+00 -4.42404687e-01
6.16154313e-01 1.18239975e+00 -1.19399977e+00 -8.21294487e-02
9.23961177e-02 9.90960896e-01 4.18437004e-01 2.50735104e-01
-8.56362700e-01 -3.69294703e-01 3.88415933e-01 2.80238032e-01
2.07818508e-01 -5.11988044e-01 -4.64044809e-01 -1.15277874e+00
8.34545679e-03 -7.91817129e-01 2.08156571e-01 -6.73551440e-01
-1.13157725e+00 6.04112864e-01 -4.03776854e-01 -1.05312502e+00
7.18445182e-02 -1.80461735e-01 -1.07327211e+00 4.96800244e-01
-9.87014174e-01 -1.50753093e+00 -5.24375200e-01 6.37453377e-01
-1.65711775e-01 2.78227385e-02 8.34441245e-01 -6.20065853e-02
-4.12022948e-01 6.75725043e-01 -4.79421988e-02 4.08771694e-01
-6.54136622e-03 -1.50682247e+00 9.43264544e-01 9.85765100e-01
5.37163854e-01 6.78594530e-01 4.40179318e-01 -9.03135061e-01
-1.97219467e+00 -1.04292488e+00 1.77116394e-01 -5.12024641e-01
8.19035530e-01 -4.97920990e-01 -5.70229292e-01 9.13411438e-01
-5.76571189e-02 1.95870057e-01 2.37374589e-01 1.35201216e-01
-4.65498030e-01 1.50967747e-01 -1.49683499e+00 6.07326686e-01
1.78670645e+00 -8.12797546e-01 -1.70324281e-01 3.38926226e-01
8.67610455e-01 -4.70959574e-01 -1.27997661e+00 6.35305524e-01
3.50759894e-01 -9.83044267e-01 1.19968677e+00 -7.16787577e-01
4.62924331e-01 -1.59222037e-01 1.11958116e-01 -1.51791120e+00
-7.33484387e-01 -8.94320428e-01 -2.83092529e-01 1.13304925e+00
2.41780236e-01 -8.40055168e-01 1.00984442e+00 1.29897371e-01
-1.70166597e-01 -4.82562810e-01 -9.91109073e-01 -7.80977905e-01
7.28291199e-02 -7.70316944e-02 7.40548730e-01 1.08548403e+00
-1.58906877e-01 3.59860241e-01 -2.14272320e-01 7.28736147e-02
6.36895359e-01 1.47075936e-01 8.90398920e-01 -1.50965047e+00
-8.42253864e-02 -8.87622237e-01 -8.77356470e-01 -6.79072201e-01
3.42804849e-01 -1.34151649e+00 -4.97667402e-01 -1.66433191e+00
4.41425480e-02 -5.27219772e-01 -1.33669868e-01 3.44317228e-01
9.66099128e-02 9.56991792e-01 5.59546240e-02 2.99537539e-01
-4.29303437e-01 5.56820989e-01 1.63747835e+00 -4.15125787e-01
3.03078862e-03 -1.08899161e-01 -8.67230594e-01 3.76309335e-01
6.58381939e-01 -1.40403435e-01 -4.42030132e-01 -1.66090772e-01
4.24627215e-01 1.97012544e-01 5.85573316e-01 -9.22800720e-01
-8.43621865e-02 -2.17826106e-02 5.10026217e-01 -1.65266782e-01
1.50531396e-01 -8.18015456e-01 5.93517542e-01 6.37962162e-01
-1.90966204e-01 2.24485189e-01 6.02224916e-02 7.80085742e-01
-8.43474865e-02 3.07062775e-01 7.20615447e-01 -1.27695724e-01
-3.59523535e-01 6.42907917e-01 4.13507931e-02 3.13373178e-01
1.02475572e+00 -4.73709434e-01 -6.09734595e-01 -6.81369185e-01
-7.50728667e-01 -9.59796011e-02 1.28639579e+00 1.19548574e-01
6.14356756e-01 -1.53126264e+00 -5.44715524e-01 2.03750819e-01
-2.01983333e-01 -2.56413445e-02 5.22577427e-02 8.15133512e-01
-7.41520882e-01 5.24516515e-02 -4.67565507e-01 -3.42185974e-01
-8.97048950e-01 6.88776314e-01 1.82280555e-01 -5.91558456e-01
-9.20507550e-01 5.82018316e-01 6.65178373e-02 -3.41177285e-01
-1.20178431e-01 -8.09240937e-02 2.02541798e-01 -3.49444598e-01
-1.76231593e-01 3.94846886e-01 -2.23179329e-02 -6.88476384e-01
-3.19615364e-01 6.40116513e-01 3.46072137e-01 1.55688494e-01
1.29522216e+00 1.12686180e-01 -3.31159890e-01 -1.30938843e-01
1.12566757e+00 6.71884596e-01 -9.96820688e-01 1.40598342e-01
-2.20994845e-01 -4.04795974e-01 -5.13622649e-02 -6.09253645e-01
-1.65817821e+00 4.19114172e-01 -1.79067180e-01 9.20689881e-01
1.18530154e+00 1.03138052e-01 8.23573589e-01 5.90218529e-02
6.17176652e-01 -5.45364201e-01 2.46367708e-01 3.65921199e-01
9.37484682e-01 -6.99658692e-01 1.74211890e-01 -9.68890131e-01
-2.27889955e-01 9.87202823e-01 2.72564858e-01 -5.97281754e-01
6.14646733e-01 1.53085470e-01 -5.82419038e-01 -5.21431983e-01
-4.51110184e-01 -1.79652587e-01 5.78896880e-01 9.44332480e-01
-2.74442956e-02 3.08593184e-01 -4.62097861e-02 -9.89420563e-02
-7.36951411e-01 -5.65468311e-01 6.92076564e-01 5.06955147e-01
-1.24674752e-01 -1.11353362e+00 3.65557671e-02 6.06647313e-01
-8.72183740e-02 -1.23852760e-01 -1.07464421e+00 9.75982428e-01
-4.33944285e-01 6.81950808e-01 -2.10231561e-02 -9.90462601e-01
1.11243747e-01 -4.65758592e-01 7.58312106e-01 -4.15891081e-01
-3.50476712e-01 -6.72430813e-01 1.77728921e-01 -6.89857721e-01
-4.54066284e-02 -2.42024735e-01 -9.75716352e-01 -8.84469628e-01
-1.22477502e-01 1.73452646e-01 3.41406584e-01 4.24236089e-01
5.91246426e-01 6.56192601e-01 1.08633626e+00 -6.35685682e-01
-6.49583042e-02 -8.53801608e-01 -6.48152173e-01 7.27440476e-01
1.48360625e-01 -5.63421130e-01 -6.57976210e-01 -3.86004955e-01] | [6.991308212280273, 6.213389873504639] |
e191a2fa-af09-496c-a816-634207234d66 | longform-optimizing-instruction-tuning-for | 2304.08460 | null | https://arxiv.org/abs/2304.08460v1 | https://arxiv.org/pdf/2304.08460v1.pdf | LongForm: Optimizing Instruction Tuning for Long Text Generation with Corpus Extraction | Instruction tuning enables language models to generalize more effectively and better follow user intent. However, obtaining instruction data can be costly and challenging. Prior works employ methods such as expensive human annotation, crowd-sourced datasets with alignment issues, or generating noisy examples via LLMs. We introduce the LongForm dataset, which is created by leveraging English corpus examples with augmented instructions. We select a diverse set of human-written documents from existing corpora such as C4 and Wikipedia and generate instructions for the given documents via LLMs. This approach provides a cheaper and cleaner instruction-tuning dataset and one suitable for long text generation. We finetune T5, OPT, and LLaMA models on our dataset and show that even smaller LongForm models have good generalization capabilities for text generation. Our models outperform 10x larger language models without instruction tuning on various tasks such as story/recipe generation and long-form question answering. Moreover, LongForm models outperform prior instruction-tuned models such as FLAN-T5 and Alpaca by a large margin. Finally, our models can effectively follow and answer multilingual instructions; we demonstrate this for news generation. We publicly release our data and models: https://github.com/akoksal/LongForm. | ['Hinrich Schütze', 'Anna Korhonen', 'Timo Schick', 'Abdullatif Köksal'] | 2023-04-17 | null | null | null | null | ['recipe-generation', 'long-form-question-answering', 'news-generation'] | ['miscellaneous', 'natural-language-processing', 'natural-language-processing'] | [ 9.21450183e-02 1.01771191e-01 -2.52197951e-01 -3.94888699e-01
-1.37870169e+00 -9.08115387e-01 8.13239694e-01 -5.10729142e-02
-3.77301335e-01 9.88738894e-01 6.78610384e-01 -6.37558758e-01
3.60981047e-01 -7.79982805e-01 -1.12501836e+00 -6.71634674e-02
4.91895616e-01 7.03051805e-01 -6.99257851e-02 -7.28692234e-01
2.92365193e-01 -4.14865434e-01 -1.54541671e+00 5.87725401e-01
1.65843010e+00 3.77297878e-01 5.04048407e-01 9.89670634e-01
-4.86772120e-01 7.65227199e-01 -9.58326042e-01 -6.43288732e-01
3.53338756e-02 -5.42458475e-01 -8.64214540e-01 -2.88669229e-01
7.81285286e-01 -5.70819199e-01 -8.30474496e-02 6.07376337e-01
5.45620203e-01 2.72702694e-01 6.92240357e-01 -1.08487356e+00
-1.29878736e+00 1.27463293e+00 2.62686405e-02 -1.36382207e-01
8.78273845e-01 4.80572283e-01 1.05564523e+00 -9.74996448e-01
6.90491080e-01 1.21888959e+00 5.22643089e-01 9.22941089e-01
-1.14801788e+00 -5.91806948e-01 7.66707882e-02 -7.97861740e-02
-1.02938879e+00 -4.94570822e-01 2.37436846e-01 -3.01678777e-01
1.20794320e+00 5.40326715e-01 8.50240588e-02 1.69451046e+00
-7.89510161e-02 1.25844240e+00 9.87834811e-01 -6.32815838e-01
-1.06281780e-01 4.01509590e-02 3.46324176e-01 7.50067353e-01
1.45484179e-01 3.00087966e-02 -5.33986866e-01 5.53770810e-02
3.17184269e-01 -2.54342496e-01 -6.19845152e-01 3.53303492e-01
-1.58539355e+00 9.43450511e-01 1.29889145e-01 1.01179883e-01
5.41549921e-03 1.57862946e-01 4.30518270e-01 4.60309893e-01
2.73149997e-01 1.08576798e+00 -8.30707014e-01 -4.04481113e-01
-5.67536831e-01 6.90946519e-01 1.12146926e+00 1.47154939e+00
7.40861654e-01 4.48792651e-02 -6.75731480e-01 8.00050020e-01
-3.71001773e-02 7.81674266e-01 1.02817416e+00 -1.06306994e+00
1.03321493e+00 3.57865661e-01 2.03614473e-01 -4.46852356e-01
-1.70955405e-01 -2.09348351e-01 -4.76201385e-01 -5.07843852e-01
6.77902758e-01 -3.72892529e-01 -8.19600582e-01 1.88369656e+00
6.37107715e-02 -1.75457954e-01 2.56065249e-01 4.48895752e-01
1.24826050e+00 8.35771918e-01 1.30308801e-02 2.36943096e-01
1.24364090e+00 -1.51268113e+00 -6.59309745e-01 -5.92148662e-01
1.26453221e+00 -9.57969189e-01 1.98998702e+00 3.54872972e-01
-9.32898641e-01 -7.67917156e-01 -8.53093445e-01 -5.34341276e-01
-5.59690595e-01 4.97627437e-01 6.96656346e-01 4.59890664e-01
-9.07139301e-01 3.67572129e-01 -4.91016120e-01 -3.17116082e-01
-7.71755800e-02 -1.55308604e-01 -6.61881715e-02 -4.06965494e-01
-1.21486509e+00 7.23007202e-01 4.96024489e-01 -3.09230149e-01
-8.32645535e-01 -8.93082976e-01 -1.23506975e+00 -2.46364594e-01
4.97529715e-01 -8.58674526e-01 1.93387401e+00 -4.62130189e-01
-1.59144008e+00 6.28299117e-01 -2.57205635e-01 -3.83070022e-01
3.66752535e-01 -5.92199087e-01 -2.56698072e-01 -4.14529622e-01
3.94220322e-01 8.92989576e-01 5.05250931e-01 -9.79994059e-01
-3.93783510e-01 2.89111696e-02 2.96559155e-01 1.89268470e-01
-1.57366544e-01 -2.52814472e-01 -2.10442305e-01 -8.51862371e-01
-5.13720751e-01 -9.51018214e-01 -1.36721969e-01 -8.08110476e-01
-7.29760528e-01 -4.85802948e-01 1.66889280e-01 -9.39224303e-01
1.51838720e+00 -1.69684780e+00 3.11390441e-02 -4.14483249e-01
3.90763767e-03 2.33946979e-01 -6.88924611e-01 5.72443187e-01
3.13874662e-01 4.60664153e-01 -5.72866276e-02 -3.72142673e-01
5.54756880e-01 3.08552623e-01 -4.32716131e-01 -4.87177908e-01
8.75682980e-02 1.37029243e+00 -1.07015836e+00 -3.13476861e-01
-1.45039529e-01 6.31511435e-02 -1.06720340e+00 5.21775126e-01
-9.56219792e-01 3.59939754e-01 -4.49863493e-01 4.88252997e-01
-3.61245088e-02 -2.96270549e-01 -2.45751277e-01 1.17524758e-01
-2.67011933e-02 1.03433299e+00 -6.77034855e-01 2.14495230e+00
-1.06411290e+00 4.38819826e-01 -3.52394879e-01 -3.33845556e-01
8.58249903e-01 1.25443354e-01 -3.96526486e-01 -6.67987227e-01
-8.60861987e-02 5.13340831e-01 -1.40890479e-01 -9.44284320e-01
1.04912376e+00 3.38319153e-01 -5.90336204e-01 7.20246792e-01
2.12947354e-01 -6.30307138e-01 8.51780534e-01 5.07944524e-01
9.98865604e-01 4.22752738e-01 1.86231554e-01 -7.34921843e-02
4.07046199e-01 1.43801406e-01 1.08042568e-01 8.69245172e-01
1.34866714e-01 4.76169169e-01 6.27427921e-02 -1.65804207e-01
-6.92329168e-01 -7.05545068e-01 2.63614565e-01 1.71540546e+00
-4.14293885e-01 -8.50318193e-01 -9.54429984e-01 -9.74441826e-01
-2.28505787e-02 1.54298210e+00 -4.58730698e-01 -5.04276901e-02
-9.02275264e-01 -5.35258174e-01 5.93588114e-01 5.00096321e-01
3.78375888e-01 -1.09640455e+00 -2.25608237e-02 2.41621107e-01
-8.39261293e-01 -1.09881306e+00 -1.15091681e+00 -3.44618745e-02
-5.30699074e-01 -9.12816286e-01 -3.69221419e-01 -8.13206673e-01
5.54723084e-01 2.92133614e-02 1.92225087e+00 2.20248654e-01
-4.74252775e-02 2.36103013e-01 -6.03357613e-01 -4.92929608e-01
-9.22375321e-01 7.03756690e-01 -1.54723093e-01 -6.63436890e-01
4.74745929e-01 -2.83726871e-01 -4.12444293e-01 1.39903814e-01
-9.98676956e-01 2.50511706e-01 5.61399519e-01 9.38289702e-01
4.05576438e-01 -7.78265595e-01 8.83737385e-01 -1.27741647e+00
1.05940998e+00 -5.86476982e-01 -3.74499261e-01 4.34529752e-01
-5.48416436e-01 4.58616257e-01 9.11404371e-01 -4.17575806e-01
-1.18343210e+00 -3.18202078e-01 -3.62048566e-01 1.81889892e-01
-3.03512782e-01 5.38540900e-01 -1.66767180e-01 5.19858897e-01
1.23765469e+00 2.34258622e-01 -3.11558604e-01 -5.38397968e-01
9.56426740e-01 7.61834621e-01 6.78032875e-01 -1.11736846e+00
6.68921590e-01 -2.96514511e-01 -7.40264118e-01 -4.70239401e-01
-1.16347539e+00 -1.00459993e-01 -3.35210800e-01 1.42821774e-01
7.88286150e-01 -7.76294529e-01 -2.91216880e-01 2.24309057e-01
-1.35943806e+00 -8.69619846e-01 -2.54764378e-01 3.45694184e-01
-4.98187602e-01 1.19278066e-01 -9.39596057e-01 -2.02348903e-01
-6.52840018e-01 -1.35116982e+00 1.35335994e+00 1.73248664e-01
-7.34035850e-01 -1.09287071e+00 2.90281475e-01 8.22267175e-01
5.76456547e-01 6.03926703e-02 1.08581698e+00 -8.93465042e-01
-7.30048299e-01 -5.81836291e-02 2.75068045e-01 2.37315655e-01
1.48917586e-01 6.41316473e-02 -7.13425934e-01 -1.78903416e-02
-2.80364275e-01 -8.79273415e-01 6.66093469e-01 -9.13727283e-02
1.11032319e+00 -6.98488712e-01 -5.05959913e-02 5.99130750e-01
1.03918421e+00 -1.06458306e-01 3.85482192e-01 4.06722397e-01
8.55562806e-01 6.18876696e-01 6.53391242e-01 1.08988658e-01
1.01095796e+00 4.14209217e-01 1.53815866e-01 5.02718687e-01
-3.86632681e-01 -8.12502384e-01 7.68246353e-01 1.61710978e+00
3.49602461e-01 -4.55643922e-01 -8.16892982e-01 7.00234115e-01
-1.52906477e+00 -6.32869482e-01 -3.05507720e-01 2.01483870e+00
1.50829637e+00 -1.40490204e-01 -9.17440057e-02 -4.06929553e-01
2.15479225e-01 -4.93450388e-02 -3.99356067e-01 -5.51064074e-01
-9.66692194e-02 4.16990578e-01 1.18525758e-01 8.14050078e-01
-7.63955712e-01 1.17341399e+00 5.94264746e+00 9.59036112e-01
-6.13352835e-01 1.01392642e-01 5.70905805e-01 -2.43301615e-01
-8.63873124e-01 -1.54910192e-01 -1.29685760e+00 4.94030833e-01
1.15111184e+00 -4.24768716e-01 6.28486872e-01 7.81964898e-01
-5.46238273e-02 1.54761702e-01 -1.46697855e+00 7.68034935e-01
3.38231921e-01 -1.28643811e+00 2.86774069e-01 -3.46147060e-01
1.21976018e+00 1.06840372e-01 -8.41986015e-02 1.27470648e+00
8.89371693e-01 -1.11910224e+00 6.99163079e-01 3.16555977e-01
7.34950900e-01 -4.35534239e-01 5.08951962e-01 7.36516774e-01
-7.89215922e-01 1.53734446e-01 -2.15452909e-01 -8.51871595e-02
1.80194765e-01 4.27068233e-01 -9.52783346e-01 4.95056540e-01
3.25942099e-01 3.57849181e-01 -1.03343654e+00 5.31014025e-01
-6.95824683e-01 6.32645249e-01 -1.23110130e-01 -4.19018328e-01
2.40907848e-01 -1.42355740e-01 2.34935477e-01 1.13651395e+00
6.85713351e-01 -9.38875079e-02 4.99611914e-01 9.65139985e-01
-3.98509860e-01 3.28350663e-01 -6.36396527e-01 -2.24028438e-01
6.72407746e-01 1.25433576e+00 4.29832302e-02 -6.55919731e-01
-5.07126570e-01 9.33604538e-01 4.72618222e-01 4.69869614e-01
-7.92489052e-01 -5.34932613e-01 5.02629995e-01 1.67431459e-02
-9.79568362e-02 -1.70682922e-01 -1.34200394e-01 -1.43901682e+00
1.16510848e-02 -1.69005895e+00 3.26364398e-01 -9.10548031e-01
-1.28184319e+00 8.51104677e-01 -6.19798079e-02 -9.79216516e-01
-1.03017879e+00 -7.03355908e-01 -6.01605356e-01 8.01670969e-01
-1.48086405e+00 -1.02276278e+00 -3.69211107e-01 4.51652467e-01
1.10218120e+00 -1.88640833e-01 8.36312771e-01 2.83218384e-01
-7.08880901e-01 9.49362099e-01 -2.99927723e-02 1.12871654e-01
1.18663216e+00 -1.62614942e+00 8.55492830e-01 8.13251734e-01
1.93548992e-01 9.20967937e-01 7.29771852e-01 -7.41743088e-01
-1.49253774e+00 -1.39467025e+00 1.17930984e+00 -1.08000231e+00
8.96631241e-01 -5.29166579e-01 -7.90394068e-01 1.07801151e+00
6.71475291e-01 -5.70522308e-01 7.41418064e-01 -4.04201150e-02
-4.59915757e-01 3.29327166e-01 -7.02237487e-01 1.00525820e+00
1.13750505e+00 -5.07386684e-01 -7.71781325e-01 7.74048805e-01
1.39073241e+00 -6.95203304e-01 -8.78301799e-01 1.03441402e-01
1.67398900e-01 -7.33310521e-01 5.63859582e-01 -7.02621222e-01
8.41895044e-01 -1.31361783e-01 -1.81457967e-01 -1.89307237e+00
9.99826491e-02 -9.23953891e-01 -2.18562439e-01 1.52489650e+00
9.70356226e-01 -6.40418768e-01 4.66019392e-01 5.69089413e-01
-7.08717823e-01 -6.81114256e-01 -1.22589573e-01 -8.58225107e-01
5.10522187e-01 -3.54705334e-01 1.15530944e+00 8.05309832e-01
-3.84822488e-04 7.22936332e-01 1.20284762e-02 -3.58734310e-01
3.14752787e-01 2.26035178e-01 1.22334385e+00 -5.69761992e-01
-6.89695239e-01 -4.63517815e-01 6.58937931e-01 -1.54416752e+00
3.68178368e-01 -1.31847048e+00 3.41701806e-01 -1.48206604e+00
-4.89320867e-02 -4.31147456e-01 5.48783422e-01 4.61291492e-01
-8.63276303e-01 -8.34554620e-03 3.35638300e-02 -1.11850955e-01
-5.95218778e-01 6.39852524e-01 1.42297471e+00 -7.57629275e-02
-1.14795238e-01 -2.30766073e-01 -9.78497505e-01 5.09748340e-01
9.98566568e-01 -6.03232384e-02 -5.58231592e-01 -1.09321618e+00
4.62638676e-01 -7.36498758e-02 -1.20362788e-01 -6.01868927e-01
-7.59177804e-02 -5.23513332e-02 5.53623699e-02 -3.85806620e-01
-1.35366291e-01 3.38663757e-02 -2.23391145e-01 1.94040984e-01
-6.77519619e-01 4.38757062e-01 1.98674142e-01 1.39137179e-01
-2.24854231e-01 -4.58152592e-01 6.73969835e-02 -4.01153743e-01
-6.12585008e-01 1.48481160e-01 -2.14643180e-01 6.66444540e-01
5.85128784e-01 2.63645560e-01 -1.11125946e+00 -6.00353301e-01
-2.56856084e-01 6.11868382e-01 3.60440344e-01 8.19496512e-01
2.76461601e-01 -1.45629609e+00 -1.07662821e+00 1.16646416e-01
4.33845460e-01 1.07933566e-01 2.86938623e-02 6.02778018e-01
-6.90349698e-01 7.42874146e-01 2.85738200e-01 -3.59317124e-01
-8.11474025e-01 4.12695348e-01 4.90421057e-02 -5.80944359e-01
-7.36780539e-02 1.08886957e+00 1.57965899e-01 -1.34648943e+00
1.14699192e-02 -8.51791084e-01 -5.54751530e-02 -1.18588239e-01
7.42071509e-01 3.31664115e-01 1.49682313e-01 -2.25875974e-01
2.58023500e-01 1.65133268e-01 -2.60004103e-01 -6.21571019e-02
9.37875152e-01 -7.80552924e-02 -4.53981012e-02 2.94579685e-01
1.13007963e+00 3.13203663e-01 -9.52851474e-01 -2.17978120e-01
1.26207560e-01 -1.50288567e-01 -5.22053361e-01 -9.87656236e-01
-6.06558442e-01 7.12032497e-01 -1.88158154e-01 7.51201436e-02
7.76540697e-01 3.41240503e-02 1.26335001e+00 7.74403334e-01
4.17077363e-01 -9.10342216e-01 3.70048642e-01 9.28055525e-01
1.17271101e+00 -1.46010041e+00 -5.52149236e-01 -2.50466943e-01
-5.52313626e-01 7.58614659e-01 1.12682498e+00 1.60286739e-01
-1.04559958e-01 2.02927157e-01 1.92362458e-01 1.82375580e-01
-1.18588519e+00 -7.53817558e-02 3.65254462e-01 5.74297190e-01
9.92019415e-01 3.15566570e-01 -2.69637287e-01 9.40941036e-01
-1.19842303e+00 -1.09204218e-01 7.05466509e-01 7.46504068e-01
-3.04623991e-01 -1.41049707e+00 -3.53889346e-01 6.93182588e-01
-3.70547652e-01 -6.12737060e-01 -3.21304262e-01 8.38946819e-01
-1.17175147e-01 1.05163729e+00 -3.67099941e-01 -2.90374249e-01
3.64014477e-01 4.67713267e-01 5.44152975e-01 -1.00265992e+00
-7.68034101e-01 -4.11500454e-01 5.74234784e-01 -6.17865205e-01
3.01290661e-01 -5.62185943e-01 -1.16973376e+00 -3.39418769e-01
-2.29603902e-01 2.19730675e-01 5.11719465e-01 9.11081731e-01
3.97238284e-01 6.68094397e-01 2.41873175e-01 -6.18507862e-01
-9.07334447e-01 -1.33855963e+00 1.45647988e-01 5.77613056e-01
1.15232915e-01 -1.31350458e-01 -3.77814174e-01 2.36450791e-01] | [11.1998291015625, 8.649285316467285] |
6312fa04-2aa0-4fab-86ba-5a59ddaa3fc4 | hierarchic-temporal-convolutional-network | null | null | https://ieeexplore.ieee.org/document/9812509 | https://ieeexplore.ieee.org/document/9812509 | Hierarchic Temporal Convolutional Network With Cross-Domain Encoder for Music Source Separation | Recently, the time-domain-based methods (i.e., the method of modeling the raw waveform directly) for audio source separation have shown tremendous potential. In this paper, we propose a model which combines the complexed spectrogram domain feature and time-domain feature by a cross-domain encoder (CDE) and adopts the hierarchic temporal convolutional network (HTCN) for multiple music sources separation. The CDE is designed to enable the network to code the interactive information of the time-domain and complexed spectrogram domain features. HTCN enables it to learn the long-time series dependence effectively. We also designed a feature calibration unit (FCU) to be applied in the HTCN and adopted the multi-stage training strategy during the training stage. The ablation study demonstrates the effectiveness of each designed component in the model. We conducted the experiments on the MUSDB18 dataset. The experimental results indicate that our proposed CDE-HTCN model outperforms the top-of-the-line methods and, compared with the state-of-the-art method, DEMUCS, achieves the improvement of the average SDR score of 0.61 dB. Significantly, the improvement of the SDR score for the bass source has a sizable margin of 0.91 dB. | ['Hao Huang', 'Liang He', 'Wenzhong Yang', 'Yadong Chen', 'Ying Hu'] | 2022-06-30 | null | null | null | ieee-signal-processing-letters-2022-6 | ['audio-source-separation', 'music-source-separation'] | ['audio', 'music'] | [ 1.76407874e-01 -4.09529686e-01 1.92821771e-01 -5.86587451e-02
-1.10452116e+00 -5.03881872e-01 1.07091144e-01 -4.04736310e-01
-1.52859181e-01 3.91302586e-01 1.48069665e-01 -8.90033320e-02
-3.24234456e-01 -3.72016460e-01 -4.69179958e-01 -7.01614082e-01
-3.80338192e-01 -4.34131891e-01 1.59038991e-01 -1.28683895e-01
1.05066672e-02 1.80396542e-01 -1.53440535e+00 3.65377307e-01
9.84128535e-01 1.57145262e+00 2.25951225e-01 7.43562877e-01
1.21025145e-01 8.08232963e-01 -8.64630044e-01 -9.46432799e-02
3.67855757e-01 -7.23458290e-01 -2.11855292e-01 -2.07292452e-01
1.17350876e-01 -2.68129557e-01 -4.46624696e-01 8.40082943e-01
9.47669625e-01 8.64423513e-02 3.70718300e-01 -1.13144374e+00
-3.85213017e-01 8.15039098e-01 -6.41717017e-01 2.43650198e-01
1.39489338e-01 -2.04902142e-01 9.98407960e-01 -9.90792274e-01
1.37069136e-01 9.63577569e-01 8.36323202e-01 2.48048767e-01
-9.42939460e-01 -1.16392028e+00 -3.04416955e-01 3.39567631e-01
-1.53205574e+00 -5.70468366e-01 1.13890839e+00 -3.88562828e-01
7.43142962e-01 1.64083108e-01 4.58742082e-01 9.42843616e-01
-6.21199235e-02 7.93122470e-01 8.46650660e-01 -5.87275028e-01
2.95321457e-02 -2.56354690e-01 7.19505921e-02 3.51527989e-01
-3.30638349e-01 5.05443037e-01 -8.29438627e-01 -1.05933569e-01
9.00902092e-01 -2.09150165e-01 -4.80916023e-01 6.94329515e-02
-9.89135742e-01 5.58916688e-01 4.07275468e-01 4.38927621e-01
-1.57521263e-01 2.64339954e-01 5.75094104e-01 4.11823809e-01
3.06655228e-01 4.18899953e-01 -5.30562282e-01 -3.69843930e-01
-1.16225398e+00 5.92882782e-02 6.00535929e-01 8.67523789e-01
1.62996680e-01 6.73952878e-01 -2.83596069e-01 1.03424144e+00
9.01056379e-02 4.30497855e-01 7.95744836e-01 -9.12875473e-01
4.47577059e-01 1.92802608e-01 -5.62651977e-02 -7.98652709e-01
-2.47677296e-01 -9.07565773e-01 -9.28597093e-01 -2.93373159e-04
2.14322656e-01 -4.63310689e-01 -7.41392314e-01 1.92759573e+00
7.79461935e-02 6.92341805e-01 2.51018465e-01 1.00435603e+00
7.60023117e-01 8.94460499e-01 -3.66728604e-01 -3.48120332e-01
1.10141742e+00 -1.06118917e+00 -9.90580857e-01 1.11413568e-01
3.20587829e-02 -9.87090230e-01 8.52598310e-01 7.06542373e-01
-1.00852680e+00 -1.04930639e+00 -1.38512611e+00 2.11269170e-01
5.56905381e-03 7.37495720e-01 2.45442063e-01 4.76261258e-01
-6.91129088e-01 8.47210109e-01 -7.31229484e-01 4.69338149e-02
3.21762040e-02 3.03122371e-01 -1.49822250e-01 4.97821659e-01
-1.44034410e+00 3.59667808e-01 4.19481814e-01 3.50210555e-02
-1.02041745e+00 -7.99065113e-01 -6.34704053e-01 4.29077625e-01
2.14406058e-01 -3.02472591e-01 1.39311755e+00 -1.03505099e+00
-1.85205913e+00 1.12817310e-01 1.19736241e-02 -5.89615047e-01
1.56821907e-01 -4.37989503e-01 -9.87218380e-01 3.25869471e-01
-6.54707626e-02 2.50242174e-01 1.25090861e+00 -9.31563497e-01
-8.48679781e-01 2.84793731e-02 -1.13430522e-01 -2.44935486e-03
-4.81075674e-01 7.64094070e-02 -3.15003872e-01 -1.16493511e+00
1.06497169e-01 -7.76695073e-01 2.36557350e-01 -2.04248294e-01
-2.87782729e-01 1.06895708e-01 8.10327888e-01 -8.07957053e-01
1.75925279e+00 -2.83016515e+00 7.55878836e-02 -3.61855440e-02
2.73828469e-02 4.37948495e-01 -1.40813470e-01 4.79346544e-01
-3.74540269e-01 -1.11692786e-01 -2.12673292e-01 -2.61105955e-01
-1.75098958e-03 -3.23173851e-01 -5.47586739e-01 2.69827545e-01
2.60875314e-01 4.46557522e-01 -6.58256292e-01 -2.21585676e-01
-3.09454952e-03 5.59251785e-01 -5.44621050e-01 5.29311001e-01
2.28112325e-01 4.40173239e-01 -6.96458146e-02 3.56482059e-01
6.78857505e-01 -1.11456485e-02 4.13209386e-02 -4.48723048e-01
-3.08802992e-01 4.70944762e-01 -1.37414920e+00 1.94997931e+00
-5.03005087e-01 6.89577222e-01 1.01931214e-01 -5.20268798e-01
1.07166624e+00 7.89143622e-01 5.22416055e-01 -5.78399301e-01
2.41106048e-01 5.46209335e-01 3.62844050e-01 -3.50764126e-01
2.10792184e-01 -1.78644121e-01 -2.76774168e-02 2.29467988e-01
4.77950275e-01 1.71121821e-01 -8.67903680e-02 -1.41379178e-01
1.01874006e+00 9.79490876e-02 2.22902715e-01 1.60341710e-03
6.64079905e-01 -4.84659344e-01 7.57359266e-01 3.35983843e-01
-2.23718248e-02 7.51251996e-01 2.42489979e-01 5.88516109e-02
-8.67455304e-01 -1.02655649e+00 -9.16474089e-02 1.03794694e+00
-8.13989416e-02 -7.20446706e-01 -7.67519176e-01 -2.16161460e-01
-2.11808205e-01 5.63653111e-01 -3.38181943e-01 -3.08948010e-01
-5.46130896e-01 -3.82960051e-01 1.01598823e+00 6.42734051e-01
7.83074498e-01 -7.38418400e-01 -5.24997711e-01 4.51053739e-01
-3.26986462e-01 -1.11807263e+00 -7.03606784e-01 4.97227788e-01
-6.47906184e-01 -6.73147500e-01 -6.94941401e-01 -7.00216889e-01
-1.10471942e-01 2.81996518e-01 4.95187849e-01 -5.12067556e-01
7.52802119e-02 1.18350890e-02 -7.65538573e-01 -5.38428605e-01
-1.04631700e-01 9.65447947e-02 1.74075544e-01 4.98481840e-01
8.39909464e-02 -1.11047626e+00 -5.78786075e-01 2.67872572e-01
-8.57101381e-01 -1.15126386e-01 5.55698037e-01 9.00029719e-01
3.46907169e-01 4.27073777e-01 1.05541325e+00 -3.47876966e-01
7.20051050e-01 -2.83779919e-01 -4.73458201e-01 -1.20069042e-01
-4.81512100e-01 -1.16173238e-01 9.61921155e-01 -6.99550629e-01
-9.87314641e-01 5.80724590e-02 -2.91252494e-01 -8.80397141e-01
1.32477239e-01 6.14673913e-01 -1.37047559e-01 1.91641316e-01
5.06481647e-01 3.51828516e-01 -2.76942134e-01 -8.97268414e-01
1.73914760e-01 1.15409124e+00 8.17115426e-01 -4.65945423e-01
8.55138183e-01 8.39883834e-02 -3.42427820e-01 -6.23680353e-01
-8.30957592e-01 -5.63729346e-01 -3.90947044e-01 -1.03908710e-01
8.06335926e-01 -1.25064230e+00 -5.59307933e-01 6.45233512e-01
-1.25296259e+00 -8.07757080e-02 -1.78518221e-01 8.39364290e-01
-5.16051710e-01 4.61944155e-02 -6.59789920e-01 -1.04943991e+00
-4.94577140e-01 -8.84395957e-01 9.36974227e-01 2.23613963e-01
-7.70049319e-02 -4.53509152e-01 1.97964720e-02 -1.18735753e-01
4.91727531e-01 2.32815534e-01 7.64258683e-01 -7.82736242e-01
-2.31717676e-01 -1.89569611e-02 8.41776058e-02 6.76482141e-01
1.16174459e-01 -1.26709804e-01 -1.62403417e+00 -2.40907729e-01
4.95901138e-01 -1.39858752e-01 7.70589828e-01 4.16283190e-01
1.14749849e+00 -1.83429480e-01 1.05470963e-01 8.06217432e-01
1.40611589e+00 6.65331841e-01 6.42006934e-01 5.51039800e-02
4.99618053e-01 -1.62333604e-02 5.50534010e-01 6.87428534e-01
3.97342928e-02 8.20920646e-01 1.72423884e-01 -1.72146961e-01
-3.94482017e-01 -4.17085379e-01 5.67106545e-01 1.38362503e+00
1.65458061e-02 -1.57203123e-01 -4.33611721e-01 4.45817828e-01
-1.76464581e+00 -9.69465613e-01 9.97675583e-03 2.06747103e+00
1.03722799e+00 2.02364519e-01 1.33125871e-01 8.19687545e-01
7.78339565e-01 1.42821431e-01 -4.78491843e-01 -2.63038278e-01
1.57936979e-02 4.68700856e-01 5.42312823e-02 2.01934084e-01
-1.16164482e+00 5.33634067e-01 5.74471712e+00 1.32244742e+00
-1.40354216e+00 2.24440500e-01 1.74452048e-02 -6.92873597e-02
2.87813306e-01 -2.46703029e-01 -5.18159628e-01 4.97820139e-01
1.29260838e+00 -2.77485520e-01 7.34616399e-01 6.34486139e-01
2.62557119e-01 2.82563329e-01 -1.10796607e+00 1.20764744e+00
-1.04863346e-01 -7.88167834e-01 -3.12580615e-01 -2.91861504e-01
4.97265130e-01 -2.99919337e-01 1.51610687e-01 4.85780030e-01
-2.22209662e-01 -8.15497100e-01 1.06392002e+00 3.75486642e-01
1.18670011e+00 -9.31861341e-01 5.49743295e-01 3.56108695e-01
-1.69274592e+00 -5.36962926e-01 -1.15808986e-01 -1.12978049e-01
4.39687353e-03 6.69793248e-01 -6.49448097e-01 9.10254359e-01
6.62095726e-01 8.67434561e-01 -2.42473841e-01 1.16196430e+00
-1.27254948e-01 1.07934475e+00 -1.49474144e-01 4.88118172e-01
9.28700939e-02 -4.41549756e-02 6.52382195e-01 1.21575952e+00
6.18246257e-01 7.27803335e-02 -1.14794575e-01 6.57720506e-01
1.61243510e-02 -3.29921022e-02 -1.90156221e-01 -1.14993103e-01
7.34207690e-01 1.18863225e+00 -9.98757109e-02 -7.68586248e-02
-3.42209280e-01 7.08205521e-01 4.82520312e-02 3.01567733e-01
-1.14713824e+00 -9.70558643e-01 5.03071547e-01 -1.32653847e-01
7.48450398e-01 -1.56149253e-01 -1.33193225e-01 -9.24867213e-01
5.61036095e-02 -1.09698558e+00 2.10064188e-01 -8.57050717e-01
-1.29519868e+00 1.04113626e+00 -2.98655629e-01 -1.98077607e+00
-3.79306644e-01 -4.12247598e-01 -6.58945203e-01 1.06702077e+00
-1.52207589e+00 -8.76820028e-01 -5.63167129e-03 8.35588515e-01
4.38207865e-01 -4.29096580e-01 8.74209821e-01 7.27904499e-01
-5.63376188e-01 7.36701071e-01 2.59016603e-01 3.38843286e-01
8.47215772e-01 -1.04907095e+00 3.84354368e-02 8.53795052e-01
1.50992066e-01 5.55194557e-01 4.33987290e-01 -1.93462640e-01
-1.12267983e+00 -1.15564322e+00 7.36084878e-01 1.74480796e-01
6.85764670e-01 -4.77501601e-01 -6.64596558e-01 3.15610468e-01
3.66002351e-01 -1.07863404e-01 8.67698371e-01 -1.00455917e-01
-5.09275973e-01 -6.33525193e-01 -7.53027856e-01 3.03339094e-01
7.50086665e-01 -8.35777283e-01 -7.57999778e-01 -3.76803607e-01
1.09500873e+00 -4.63925183e-01 -9.19953108e-01 4.42852169e-01
7.44630516e-01 -1.02790856e+00 9.01041925e-01 -2.05361724e-01
5.64655781e-01 -6.17909908e-01 -4.44549501e-01 -1.47495532e+00
-4.44222003e-01 -9.37466204e-01 -3.84948850e-01 1.60722256e+00
3.22150618e-01 -2.67137378e-01 3.55903059e-02 -1.89348295e-01
-4.41150844e-01 -6.04732811e-01 -9.46798682e-01 -1.11167943e+00
-2.34502152e-01 -5.37779331e-01 6.19223475e-01 6.31604373e-01
2.17103183e-01 5.62005699e-01 -5.96824408e-01 2.55208641e-01
2.59397984e-01 2.81955391e-01 4.04448986e-01 -1.21413326e+00
-6.01773679e-01 -2.37531051e-01 -2.16428652e-01 -1.10970855e+00
-8.11236426e-02 -7.15766013e-01 1.10208981e-01 -9.75694180e-01
-1.46104813e-01 -1.35699958e-01 -8.20266128e-01 2.90162057e-01
-1.36833429e-01 -2.29776036e-02 5.45317888e-01 1.32490201e-02
-3.58592391e-01 6.26473963e-01 1.17232084e+00 -7.89166614e-02
-4.21493948e-01 1.47326156e-01 -6.75063908e-01 6.89850271e-01
8.51958871e-01 -5.93236327e-01 -6.33613050e-01 -2.90957659e-01
-2.36939460e-01 5.24563611e-01 1.86119452e-01 -1.64272392e+00
3.03557605e-01 2.84457773e-01 3.25635105e-01 -6.20626450e-01
6.39948487e-01 -9.87914443e-01 1.78091601e-01 2.80259520e-01
-4.90748078e-01 -2.24082246e-01 3.66541296e-01 5.22342980e-01
-7.40717232e-01 5.30813225e-02 8.40344071e-01 2.96420306e-01
-2.98513204e-01 4.61586937e-02 -1.83689088e-01 9.37492773e-02
5.75977266e-01 1.08253695e-01 -1.11938462e-01 -4.58397686e-01
-5.79236746e-01 -1.80271253e-01 -3.98122579e-01 2.66327590e-01
5.68049490e-01 -1.69780159e+00 -6.74783826e-01 3.80491644e-01
-4.89709377e-02 -3.56476843e-01 2.16071650e-01 7.86828279e-01
1.00300036e-01 4.09294277e-01 -2.07200646e-01 -4.55073476e-01
-1.01889026e+00 4.03005540e-01 4.43751454e-01 -2.69519567e-01
-3.33555013e-01 8.71859968e-01 2.68215060e-01 -6.85580969e-02
5.37747800e-01 -4.25494283e-01 -2.24000752e-01 8.90863463e-02
5.73356509e-01 5.37363768e-01 1.05193622e-01 -5.01288116e-01
-3.36219937e-01 6.50813401e-01 3.31981033e-01 -4.26058531e-01
1.46509862e+00 -3.48038692e-03 1.18406497e-01 6.71890497e-01
1.39575422e+00 3.41631711e-01 -1.21295357e+00 -2.94319689e-01
-2.21663013e-01 -3.27894628e-01 2.06795141e-01 -1.07869792e+00
-1.27027726e+00 1.13622487e+00 7.99974263e-01 3.18840683e-01
1.81534493e+00 -5.41103363e-01 1.00158095e+00 1.17504308e-02
1.88794747e-01 -8.86582375e-01 2.04133034e-01 6.47373915e-01
9.64103699e-01 -7.01495051e-01 -2.72858143e-01 -2.73682564e-01
-6.41809344e-01 1.32801664e+00 3.87005419e-01 -2.11623847e-01
8.80173266e-01 6.17282391e-01 1.94660395e-01 1.83835790e-01
-7.93456852e-01 -2.16339543e-01 5.46771467e-01 4.68292385e-01
5.18664896e-01 -2.72067916e-02 -2.73135275e-01 1.44577420e+00
-5.19026041e-01 1.74414530e-01 2.76029050e-01 5.81489265e-01
-3.53302479e-01 -1.08098257e+00 -4.74203229e-01 1.73263505e-01
-7.09618986e-01 -2.40555346e-01 -2.53140807e-01 4.77283508e-01
3.66690338e-01 1.33111918e+00 -1.25464559e-01 -1.00043750e+00
4.54995364e-01 2.18006313e-01 1.82064876e-01 -2.76349992e-01
-8.28777552e-01 8.55672717e-01 8.27575196e-03 -4.21183228e-01
-4.30843949e-01 -3.78030092e-01 -1.35363889e+00 4.78533916e-02
-5.37034988e-01 2.21930146e-01 3.74780446e-01 6.40175939e-01
4.17473882e-01 1.09755123e+00 1.18506646e+00 -7.89357662e-01
-6.30379915e-01 -1.13816178e+00 -9.66484070e-01 6.11051396e-02
7.43855655e-01 -5.16504407e-01 -3.12024653e-01 2.44589865e-01] | [15.396627426147461, 5.473220348358154] |
fbb55fb5-5625-4069-847a-f6a7cc497872 | deep-video-matting-via-spatio-temporal | 2104.11208 | null | https://arxiv.org/abs/2104.11208v1 | https://arxiv.org/pdf/2104.11208v1.pdf | Deep Video Matting via Spatio-Temporal Alignment and Aggregation | Despite the significant progress made by deep learning in natural image matting, there has been so far no representative work on deep learning for video matting due to the inherent technical challenges in reasoning temporal domain and lack of large-scale video matting datasets. In this paper, we propose a deep learning-based video matting framework which employs a novel and effective spatio-temporal feature aggregation module (ST-FAM). As optical flow estimation can be very unreliable within matting regions, ST-FAM is designed to effectively align and aggregate information across different spatial scales and temporal frames within the network decoder. To eliminate frame-by-frame trimap annotations, a lightweight interactive trimap propagation network is also introduced. The other contribution consists of a large-scale video matting dataset with groundtruth alpha mattes for quantitative evaluation and real-world high-resolution videos with trimaps for qualitative evaluation. Quantitative and qualitative experimental results show that our framework significantly outperforms conventional video matting and deep image matting methods applied to video in presence of multi-frame temporal information. | ['Yu-Wing Tai', 'Chi-Keung Tang', 'Qiao Gu', 'Guanzhi Wang', 'Yanan sun'] | 2021-04-22 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Sun_Deep_Video_Matting_via_Spatio-Temporal_Alignment_and_Aggregation_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Sun_Deep_Video_Matting_via_Spatio-Temporal_Alignment_and_Aggregation_CVPR_2021_paper.pdf | cvpr-2021-1 | ['video-matting'] | ['computer-vision'] | [ 6.41630888e-02 -9.66454446e-02 -7.06366971e-02 -2.14758977e-01
-7.79890597e-01 -2.23435804e-01 3.65121514e-01 -5.19731045e-01
-2.14845121e-01 5.42402387e-01 3.69636178e-01 -1.89258322e-01
1.61786675e-01 -6.05210185e-01 -1.22282624e+00 -3.78638715e-01
-7.39916265e-02 1.73813522e-01 2.62861371e-01 -4.31429856e-02
-1.23519383e-01 1.89437404e-01 -1.20855916e+00 8.45125377e-01
6.95583403e-01 9.93508160e-01 1.81226298e-01 9.35603619e-01
-5.61771356e-02 1.36373782e+00 -4.11334038e-01 -4.35672790e-01
6.19793773e-01 -6.19036496e-01 -7.07372248e-01 5.61033249e-01
1.19448435e+00 -9.07680035e-01 -8.99092793e-01 7.22648382e-01
2.60721892e-01 6.77898824e-02 1.34657979e-01 -1.63815141e+00
-5.05959332e-01 7.91765273e-01 -5.35248280e-01 2.93886244e-01
4.95798856e-01 6.44407630e-01 8.16683471e-01 -8.91355217e-01
7.82083333e-01 1.36378098e+00 7.12898195e-01 3.76981646e-01
-1.08720016e+00 -5.03274322e-01 2.89551288e-01 4.82193649e-01
-9.24992085e-01 -4.01130021e-01 7.85162687e-01 -5.00462294e-01
8.16497087e-01 1.49929479e-01 1.15350413e+00 1.22126245e+00
4.03095782e-01 9.85481143e-01 9.64523733e-01 -1.98105499e-02
1.60328690e-02 -5.14915526e-01 -5.74436486e-01 9.52120066e-01
-2.34591052e-01 6.52567446e-02 -9.69044507e-01 4.19875383e-01
1.15580332e+00 1.57076120e-01 -8.89086351e-02 -3.00928265e-01
-1.71232820e+00 5.71161926e-01 5.10153890e-01 1.74567372e-01
-4.80687946e-01 7.05718696e-01 4.69034523e-01 6.03426635e-01
6.90763116e-01 6.15749545e-02 -5.63994907e-02 -3.94082993e-01
-1.75984609e+00 1.78834036e-01 4.80421185e-01 1.02903068e+00
7.71300793e-01 6.86288774e-01 -2.97035962e-01 3.71563435e-01
1.75570235e-01 5.86109757e-01 1.63727134e-01 -1.47064233e+00
6.86307669e-01 4.23198223e-01 1.59740508e-01 -1.27545106e+00
-1.08953029e-01 -2.36732289e-02 -1.01411319e+00 4.06076372e-01
4.53461409e-01 -3.26811075e-02 -1.17404437e+00 1.36066449e+00
1.32472336e-01 7.12284684e-01 -2.08791852e-01 1.23725033e+00
9.48686540e-01 7.93104351e-01 -2.04296261e-01 -1.98609784e-01
8.53075325e-01 -1.52654433e+00 -1.03637516e+00 -1.12709261e-01
3.63832265e-01 -8.36335421e-01 9.64928329e-01 3.31138104e-01
-1.65752578e+00 -5.21994770e-01 -1.06247318e+00 -3.37396055e-01
3.31292860e-03 -2.09433794e-01 6.31144881e-01 3.48200858e-01
-1.43982494e+00 4.43276167e-01 -1.03804255e+00 -1.90501809e-01
6.53814018e-01 2.46748492e-01 -5.63979447e-01 -3.01498920e-01
-9.75565374e-01 7.61950552e-01 3.08390319e-01 4.38451082e-01
-1.54903829e+00 -8.17966342e-01 -1.09043825e+00 -2.33376369e-01
4.22349811e-01 -9.75268781e-01 1.10130954e+00 -1.51723504e+00
-1.40701497e+00 5.73871136e-01 -2.12562770e-01 -7.95539856e-01
1.00420558e+00 -4.79492754e-01 -1.36568949e-01 5.27773857e-01
7.65288696e-02 1.00080335e+00 1.21924114e+00 -1.18613327e+00
-3.90710264e-01 1.41837463e-01 3.05452585e-01 3.69437307e-01
-2.25359842e-01 -6.73040450e-02 -4.96528983e-01 -9.84410524e-01
-6.56495690e-02 -7.13808417e-01 -1.69431448e-01 1.76915839e-01
-3.81630361e-01 6.67719424e-01 1.23189569e+00 -1.11570764e+00
1.11741292e+00 -1.82790363e+00 6.07076466e-01 -1.56219229e-01
6.53194189e-01 3.21358651e-01 -3.79169673e-01 3.08094054e-01
-1.89645216e-03 -1.91715360e-01 -1.24748126e-01 -6.21632755e-01
-2.92343289e-01 4.00266409e-01 -2.97096223e-01 4.97072905e-01
2.73980517e-02 1.37568736e+00 -1.18187726e+00 -6.05318725e-01
8.84518683e-01 4.69317555e-01 -5.54092467e-01 2.82811791e-01
-5.01677692e-01 4.43738282e-01 -3.04262829e-03 8.09677422e-01
6.88354194e-01 -5.05771069e-03 -2.06496298e-01 -6.78103745e-01
-6.84701502e-02 -4.75855693e-02 -8.55080724e-01 2.17472148e+00
-3.37107867e-01 9.83442485e-01 2.97973514e-01 -9.77873623e-01
3.88065577e-01 5.01132548e-01 1.07845199e+00 -6.20007098e-01
7.39362538e-02 -5.22454605e-02 -2.49581933e-01 -6.15204453e-01
7.45766878e-01 1.76500157e-01 3.45167637e-01 3.54703695e-01
2.04035923e-01 -9.76125076e-02 2.01811925e-01 6.42802179e-01
1.36092126e+00 3.84671569e-01 -2.95294613e-01 1.25711948e-01
1.82018429e-01 -9.82790347e-03 4.76962507e-01 5.04239142e-01
-4.20773208e-01 1.06120789e+00 2.79211521e-01 -8.99216294e-01
-1.42768192e+00 -1.24744737e+00 5.70911646e-01 8.95104766e-01
3.58035088e-01 -6.50852680e-01 -9.15883303e-01 -5.87069929e-01
-2.18421683e-01 2.35459551e-01 -7.50942349e-01 6.05775155e-02
-7.88301766e-01 -3.07703406e-01 4.92643833e-01 4.76264626e-01
9.21535015e-01 -1.12173367e+00 -4.16620046e-01 3.09832484e-01
-6.43791139e-01 -1.53566480e+00 -7.11595476e-01 -4.00552094e-01
-9.93626952e-01 -9.03584540e-01 -7.80760944e-01 -6.03891253e-01
3.41805398e-01 6.11375332e-01 1.28523719e+00 4.16860096e-02
-1.88249975e-01 4.14583862e-01 -4.13799882e-01 4.16875780e-02
-5.95951378e-01 -8.48666653e-02 -2.13028163e-01 6.15136288e-02
-2.08140891e-02 -5.63994765e-01 -7.21447825e-01 3.62973779e-01
-1.16227674e+00 8.14491630e-01 5.99526405e-01 8.15260291e-01
2.74117619e-01 -2.83748657e-01 3.07608336e-01 -4.69323546e-01
8.18111226e-02 -4.68233883e-01 -3.21532071e-01 8.33968297e-02
-9.68827903e-02 -2.79981136e-01 3.24486285e-01 -3.35106462e-01
-8.11383843e-01 -2.23341119e-02 3.99977453e-02 -9.67128217e-01
8.52452368e-02 4.18436706e-01 3.83042656e-02 -2.04886243e-01
3.85592282e-01 2.22559139e-01 3.34725410e-01 -8.75385329e-02
4.84390140e-01 1.40326023e-01 7.11386621e-01 -2.85761088e-01
1.08333647e+00 7.57732928e-01 -2.49006972e-02 -7.15866387e-01
-1.05226731e+00 -2.75964409e-01 -8.27758014e-01 -7.51080453e-01
1.27771819e+00 -1.23825490e+00 -3.93693179e-01 7.60662913e-01
-1.20890200e+00 -7.57380188e-01 -2.02064142e-01 3.14847350e-01
-8.61533344e-01 6.49905503e-01 -1.01629293e+00 -3.04386288e-01
-4.25930053e-01 -1.37231612e+00 1.19691002e+00 -1.79969519e-01
3.76555659e-02 -1.06953347e+00 -2.15469331e-01 7.94951320e-01
5.13912678e-01 5.61337113e-01 1.59158885e-01 4.38665420e-01
-1.29334557e+00 1.73277184e-01 -2.90836483e-01 4.18009847e-01
2.26820052e-01 1.78064078e-01 -8.10889244e-01 -3.78420711e-01
-2.44488403e-01 -2.32842833e-01 1.09423876e+00 6.84354603e-01
1.01004374e+00 -3.90713274e-01 9.78107899e-02 1.00781441e+00
1.10417366e+00 -9.83821824e-02 1.00712776e+00 5.59671104e-01
1.49054873e+00 6.06904291e-02 6.94273055e-01 2.98831403e-01
4.04830188e-01 7.98139811e-01 8.26325059e-01 -3.98065239e-01
-5.27889311e-01 -1.44097775e-01 8.57235432e-01 8.98985088e-01
-3.22745413e-01 -4.24847424e-01 -5.21334827e-01 5.89218378e-01
-2.26265693e+00 -1.43395925e+00 -8.81805271e-02 1.80184484e+00
6.54988229e-01 -6.97042234e-03 3.06137383e-01 5.01399487e-02
5.38674057e-01 4.52588588e-01 -3.39167714e-01 -2.34811455e-01
-4.05663252e-01 -2.01056466e-01 3.99693131e-01 7.14911580e-01
-1.20685565e+00 1.11579525e+00 6.17702818e+00 7.49168813e-01
-1.01677895e+00 2.93368638e-01 7.39278495e-01 -3.14764708e-01
-2.57661045e-01 -1.38437554e-01 -1.29508346e-01 5.07606804e-01
7.68484294e-01 8.52731466e-02 5.95753908e-01 3.07046741e-01
6.55279219e-01 -1.62951976e-01 -1.12993324e+00 1.15017819e+00
1.01709038e-01 -1.79093599e+00 2.78413892e-01 -1.17786393e-01
9.14857388e-01 2.99007967e-02 1.68834701e-01 -3.60406041e-02
1.38071805e-01 -1.19120550e+00 1.02647257e+00 4.82174039e-01
8.08486342e-01 -5.73291659e-01 6.41742885e-01 -8.43047798e-02
-1.39424229e+00 1.86916143e-01 -2.16722220e-01 -7.78225213e-02
6.34816945e-01 6.44941270e-01 -7.13769913e-01 6.78294897e-01
6.59358203e-01 1.28980219e+00 -5.23364067e-01 1.02683902e+00
6.26898259e-02 6.24117434e-01 -2.08365470e-01 6.52381122e-01
6.31063104e-01 -2.46184751e-01 7.09334552e-01 1.26037407e+00
4.33229953e-01 -4.13066387e-01 2.18022197e-01 7.71199465e-01
-6.48790449e-02 -3.23207170e-01 -6.50998294e-01 -1.70142323e-01
1.24124706e-01 1.34803498e+00 -7.05688477e-01 -4.90560472e-01
-5.33140659e-01 1.32610655e+00 1.50705408e-02 5.65451145e-01
-1.22364104e+00 4.49354410e-01 6.87498212e-01 3.82738322e-01
1.39304608e-01 -5.58382094e-01 -3.14017415e-01 -1.44404137e+00
1.33047298e-01 -8.96779776e-01 2.81389058e-01 -1.06465554e+00
-1.01036406e+00 4.70515311e-01 8.23948458e-02 -1.40392923e+00
-2.19878048e-01 -3.11057299e-01 -6.15014791e-01 4.31788832e-01
-1.08173001e+00 -1.69476151e+00 -8.34541321e-01 9.00869429e-01
9.33963001e-01 -2.83620059e-01 1.75254807e-01 5.98002791e-01
-3.12823385e-01 5.07554591e-01 -7.79736042e-02 3.33174586e-01
8.78088415e-01 -1.24312127e+00 5.24006784e-01 1.28542864e+00
1.95498884e-01 2.32209891e-01 7.48186886e-01 -7.57018328e-01
-1.68558109e+00 -1.52853632e+00 3.12360615e-01 -4.32837844e-01
6.28611922e-01 -5.14519513e-01 -7.67050982e-01 9.70328331e-01
8.31669986e-01 1.73547223e-01 6.47099987e-02 -5.13379872e-01
-3.42482239e-01 -2.15538561e-01 -1.00407255e+00 7.69536555e-01
1.07583058e+00 -4.03582364e-01 -1.70224160e-01 4.46105897e-01
9.62844729e-01 -6.98705494e-01 -8.12188625e-01 3.28111619e-01
5.15646935e-01 -1.21957660e+00 1.11392355e+00 -1.78649276e-01
9.33387816e-01 -5.70380211e-01 3.72919850e-02 -1.31694007e+00
-1.03691772e-01 -9.82828677e-01 -4.87020761e-01 1.01310003e+00
-1.36646023e-02 6.73112497e-02 1.07302547e+00 3.35427999e-01
-5.16147435e-01 -5.27545452e-01 -9.11200166e-01 -5.38726568e-01
-2.65466273e-01 -5.95589578e-01 1.50676623e-01 1.05551171e+00
-2.75663376e-01 7.34491274e-02 -1.18660116e+00 -9.28027406e-02
8.26387405e-01 2.69104037e-02 9.62424159e-01 -3.48716229e-01
-3.51916909e-01 -1.75420955e-01 -8.14407647e-01 -9.99901593e-01
4.51665111e-02 -5.82138658e-01 1.04023486e-01 -1.73299682e+00
1.86449260e-01 2.00785603e-02 5.03984243e-02 3.52155089e-01
-8.85751694e-02 6.98769271e-01 3.43199551e-01 7.31950924e-02
-9.30702269e-01 6.36104405e-01 1.62854350e+00 -3.78536791e-01
8.59303549e-02 -4.80008304e-01 1.11641534e-01 5.21076441e-01
5.74218750e-01 -1.16285488e-01 -4.76956755e-01 -7.65111685e-01
9.32663903e-02 2.60236323e-01 6.58615828e-01 -1.11337471e+00
8.37376118e-02 -3.75376493e-01 5.47734797e-01 -7.40204871e-01
6.10362113e-01 -7.85634100e-01 4.30879027e-01 3.66899550e-01
-2.35739812e-01 4.73570347e-01 2.69185364e-01 6.09713376e-01
-3.78747374e-01 4.15772706e-01 8.03192437e-01 -2.55995601e-01
-9.89280045e-01 6.63760900e-01 -5.39794266e-01 3.91779421e-03
1.14823210e+00 -3.94197196e-01 -3.77138644e-01 -7.73329973e-01
-7.49030292e-01 1.37645841e-01 6.35919929e-01 5.65415084e-01
9.83191073e-01 -1.43953276e+00 -7.52889454e-01 9.34468061e-02
-2.49372244e-01 1.01165555e-01 4.91936803e-01 1.08379233e+00
-1.15362120e+00 -4.36464213e-02 -6.07693851e-01 -8.39803100e-01
-1.28359056e+00 5.93801141e-01 3.71228397e-01 -1.69218723e-02
-9.60153461e-01 8.30747426e-01 3.19501638e-01 1.49206519e-01
3.16681951e-01 -4.84566957e-01 4.10974205e-01 -1.80076480e-01
5.43223858e-01 3.71109426e-01 4.24088798e-02 -9.11364257e-01
5.53518459e-02 2.14381456e-01 -8.34081396e-02 -2.82286376e-01
1.25740242e+00 -4.84022409e-01 -2.72930682e-01 4.17710841e-01
9.53438878e-01 -5.13121724e-01 -1.70395887e+00 -1.04140311e-01
-3.99056971e-01 -8.24521363e-01 6.94644004e-02 -4.37941074e-01
-1.55789554e+00 8.25631857e-01 4.32779193e-01 -6.81507513e-02
1.08968151e+00 -4.21813309e-01 1.26403356e+00 1.51445299e-01
3.01670045e-01 -1.04259324e+00 6.60144746e-01 4.27116513e-01
8.65357935e-01 -1.29355228e+00 -6.71622455e-02 -4.34624434e-01
-6.07131600e-01 1.10798585e+00 8.55453253e-01 -2.86437333e-01
2.13292092e-01 5.68199396e-01 2.60651618e-01 -6.91328421e-02
-7.92418838e-01 2.71132559e-01 3.33907932e-01 4.88389641e-01
3.65876943e-01 -2.79180586e-01 1.38749838e-01 -3.72207463e-01
-1.70147903e-02 1.49274170e-01 7.91509449e-01 8.68313372e-01
-2.42108658e-01 -8.15939486e-01 -3.02313983e-01 4.28400844e-01
-4.92651790e-01 -1.42239600e-01 -2.18905613e-01 6.72354698e-01
-4.27275561e-02 8.63269031e-01 3.15911858e-03 -6.09775722e-01
-2.67382443e-01 -3.60054314e-01 7.88678885e-01 -2.19582811e-01
-6.53280973e-01 2.05893204e-01 9.25606862e-02 -1.23223877e+00
-9.79916573e-01 -4.51137781e-01 -9.86168206e-01 -7.75689125e-01
2.11550876e-01 -2.72809684e-01 2.44862095e-01 1.06521463e+00
2.45668232e-01 7.15889335e-01 2.93742925e-01 -1.41692007e+00
2.39024475e-01 -8.75106335e-01 -3.82114947e-01 6.43219411e-01
6.21781945e-01 -5.13679266e-01 -2.12712958e-01 6.79000854e-01] | [10.664950370788574, -0.8955824971199036] |
27f2b62a-d9db-489a-bf8c-48805c0a3709 | detecting-arguments-in-cjeu-decisions-on | null | null | https://aclanthology.org/2022.argmining-1.14 | https://aclanthology.org/2022.argmining-1.14.pdf | Detecting Arguments in CJEU Decisions on Fiscal State Aid | The successful application of argument mining in the legal domain can dramatically impact many disciplines related to law. For this purpose, we present Demosthenes, a novel corpus for argument mining in legal documents, composed of 40 decisions of the Court of Justice of the European Union on matters of fiscal state aid. The annotation specifies three hierarchical levels of information: the argumentative elements, their types, and their argument schemes. In our experimental evaluation, we address 4 different classification tasks, combining advanced language models and traditional classifiers. | ['Paolo Torroni', 'Giovanni Sartor', 'Federico Ruggeri', 'Elena Palmieri', 'Francesca Lagioia', 'Francesco Godano', 'Federico Galli', 'Andrea Galassi', 'Piera Santin', 'Giulia Grundler'] | null | null | null | null | argmining-acl-2022-10 | ['argument-mining'] | ['natural-language-processing'] | [ 4.30434421e-02 4.68846023e-01 -9.61469352e-01 -2.92670548e-01
-7.20656455e-01 -9.08307433e-01 9.77731049e-01 6.60866857e-01
-6.54998481e-01 1.24487746e+00 7.51802206e-01 -1.42728281e+00
-4.14622813e-01 -6.87914789e-01 -2.46569559e-01 -1.48213565e-01
1.15604050e-01 5.23269832e-01 2.45696574e-01 -5.14061093e-01
8.29551697e-01 3.64799052e-01 -1.07489824e+00 1.02868032e+00
1.03060651e+00 7.80897737e-01 -6.37108147e-01 1.51094511e-01
-4.95871097e-01 1.26326489e+00 -9.44534421e-01 -1.16191995e+00
2.44805753e-01 -2.31827050e-01 -1.29954135e+00 -8.11088264e-01
4.02416378e-01 -2.22191259e-01 -1.88235611e-01 9.54328239e-01
2.99595892e-01 -2.06901655e-01 7.96313822e-01 -9.03696477e-01
-3.49945813e-01 1.35265017e+00 -4.89601910e-01 6.81693971e-01
6.72589540e-01 -5.12350023e-01 1.49707961e+00 -5.46588838e-01
1.10047436e+00 1.42629158e+00 3.82778019e-01 3.50564182e-01
-9.11150694e-01 -5.33551574e-01 1.85982779e-01 4.58166182e-01
-5.23362458e-01 -3.61087531e-01 8.16881895e-01 -9.18050647e-01
9.80666816e-01 1.65041924e-01 5.54464638e-01 1.08506298e+00
2.98755467e-01 8.16169858e-01 1.16300380e+00 -7.06211150e-01
4.41860348e-01 -2.24101469e-01 7.18220651e-01 1.58808157e-01
8.74723732e-01 -1.79533899e-01 -1.80484265e-01 -1.09142256e+00
-6.44997926e-04 -6.78306282e-01 3.17522109e-01 3.91798886e-03
-5.63523650e-01 1.26504636e+00 -2.01042831e-01 7.40144014e-01
-1.91509783e-01 -1.72438212e-02 8.63306761e-01 2.45893151e-01
5.67589462e-01 3.44589442e-01 -5.64178824e-01 -2.69491315e-01
-5.07711351e-01 7.64355123e-01 1.00894451e+00 4.17031586e-01
-2.26392038e-02 -6.57294631e-01 -2.50494063e-01 8.12175870e-01
3.64243865e-01 1.41539440e-01 9.95885208e-02 -9.72560465e-01
1.26937282e+00 8.65762770e-01 7.48211071e-02 -6.18140280e-01
-2.69253284e-01 -2.06401646e-01 -1.77167624e-01 3.16186965e-01
5.48229456e-01 -2.64741480e-01 -3.35377365e-01 1.11906672e+00
2.82447219e-01 -8.23247313e-01 3.19528222e-01 3.54780555e-01
9.32957947e-01 6.08407483e-02 5.62013924e-01 -2.39642620e-01
1.48530495e+00 -3.27343941e-01 -9.85923469e-01 2.68138852e-02
8.81999671e-01 -9.35007453e-01 6.98694110e-01 3.24349910e-01
-1.20221841e+00 1.44607157e-01 -7.63217092e-01 -4.10157531e-01
-5.37971139e-01 2.73766667e-01 1.04853463e+00 5.05970240e-01
-8.79110489e-03 4.93242264e-01 1.93092730e-02 2.20073804e-01
1.04163802e+00 -3.61102611e-01 -1.37236863e-01 1.94594160e-01
-1.40397668e+00 1.07538378e+00 6.20189786e-01 -2.73091257e-01
3.06066066e-01 -4.19073761e-01 -8.27900052e-01 -4.00390066e-02
6.92731977e-01 -1.75292268e-01 1.14517140e+00 -1.65966749e-01
-7.24347055e-01 1.48496985e+00 1.76840101e-03 -7.30232596e-01
8.09966743e-01 -3.92792642e-01 -6.05438471e-01 -6.62611872e-02
7.82323539e-01 -2.91914254e-01 3.57222497e-01 -6.48229897e-01
-9.17322576e-01 -4.71884280e-01 6.81418717e-01 -3.11524659e-01
-5.81772588e-02 7.54885375e-01 3.22293013e-01 -8.74315560e-01
-1.79645360e-01 -5.86871982e-01 -1.08980544e-01 -3.92704308e-01
-5.48539877e-01 -1.05788398e+00 6.46028519e-01 -6.46439493e-01
1.59058523e+00 -1.96901906e+00 -3.21534455e-01 3.64309311e-01
1.69349834e-01 3.52871895e-01 4.96903270e-01 5.85911512e-01
-1.34597540e-01 5.78056455e-01 -1.64366171e-01 3.67909402e-01
2.87562370e-01 3.92449439e-01 -7.99856186e-01 1.43813550e-01
-2.39081889e-01 9.53163147e-01 -7.96484590e-01 -9.60611343e-01
-1.55861229e-01 -2.81872183e-01 -5.16069353e-01 -4.39993918e-01
-3.14923942e-01 -4.64397520e-02 -1.11621451e+00 9.48271990e-01
3.79383832e-01 -1.18604873e-03 5.63455224e-01 -1.42262653e-01
-2.65185386e-01 1.30522752e+00 -7.18655229e-01 1.31000769e+00
3.20567340e-02 6.04303598e-01 1.39970243e-01 -1.04914224e+00
5.64989030e-01 3.35279405e-01 4.16493744e-01 -9.00678337e-01
3.41411918e-01 6.60125613e-01 3.73321682e-01 -7.11050153e-01
2.56428093e-01 -3.77996117e-02 -4.56473202e-01 7.87874639e-01
-5.98323762e-01 2.95857713e-02 1.18038714e+00 2.99314320e-01
1.05810356e+00 1.89124838e-01 9.34815049e-01 -4.31325912e-01
8.05076361e-01 3.22178781e-01 4.95105863e-01 6.51643872e-01
-2.40830090e-02 -2.64099330e-01 9.71326530e-01 -8.14763367e-01
-8.34761024e-01 -6.35225892e-01 -8.02107871e-01 1.01071000e+00
-4.51040804e-01 -4.91934478e-01 -5.21734178e-01 -1.32693398e+00
5.03081322e-01 8.05526137e-01 -6.99420333e-01 5.00670075e-01
-9.42406416e-01 -6.57866716e-01 7.17914462e-01 3.57264936e-01
4.97652411e-01 -1.23478401e+00 -1.10094082e+00 2.68966734e-01
-3.30787301e-01 -1.31661272e+00 1.54378161e-01 4.38610576e-02
-6.37866616e-01 -1.90564334e+00 7.36503527e-02 -2.22627461e-01
1.81031376e-01 -4.93065625e-01 1.18710828e+00 5.32180548e-01
-3.21291476e-01 -2.63193429e-01 -5.03846407e-01 -7.82340288e-01
-7.70737410e-01 1.03113450e-01 -5.67854285e-01 -5.92889071e-01
5.89517415e-01 -4.24738437e-01 7.86185563e-02 -1.98963940e-01
-8.36618841e-01 -4.56069142e-01 4.04791951e-01 5.97007632e-01
6.33822531e-02 -2.52128452e-01 6.49070859e-01 -1.49089408e+00
1.51936018e+00 -5.52471936e-01 -6.42001092e-01 3.96858066e-01
-5.84734380e-01 9.62265357e-02 2.50692278e-01 9.06990394e-02
-1.15776789e+00 -9.52971160e-01 -4.48555976e-01 7.73933530e-01
-2.00776801e-01 8.15291107e-01 -2.78848670e-02 2.78598249e-01
8.99293900e-01 -6.18835330e-01 -4.29237574e-01 -6.09252334e-01
3.39234948e-01 9.44391847e-01 4.72222865e-01 -1.13677597e+00
6.05351210e-01 4.68170643e-01 1.19730197e-01 -4.03110206e-01
-1.32796168e+00 -2.16102600e-01 -5.92177927e-01 -9.75123942e-02
5.09155810e-01 -3.12255263e-01 -7.67273784e-01 -2.62917042e-01
-1.48805106e+00 1.09237865e-01 -2.82164395e-01 5.58066964e-01
-1.72213301e-01 5.81019402e-01 -6.75496399e-01 -9.70441163e-01
-3.86100918e-01 -6.18886828e-01 6.11380041e-01 -2.97065526e-01
-6.91440284e-01 -9.49660301e-01 1.14423789e-01 9.90116537e-01
-2.24421937e-02 5.45451105e-01 1.60609674e+00 -1.01778781e+00
3.44059020e-01 -2.22429931e-01 -2.04261601e-01 2.18325585e-01
-3.57813276e-02 -3.81135903e-02 -6.58826172e-01 2.68592238e-01
-6.98606521e-02 -3.69849950e-01 8.85393143e-01 5.98876029e-02
7.88519621e-01 -5.55980742e-01 -4.04530078e-01 -3.00812304e-01
1.01204002e+00 6.35803342e-01 6.62725568e-01 9.91275787e-01
1.04379930e-01 7.70427287e-01 8.87320936e-01 5.55609107e-01
5.93430437e-02 6.10165358e-01 3.21803913e-02 2.19764054e-01
-8.68570060e-02 -1.88523397e-01 -1.04030356e-01 7.43030831e-02
-5.70372343e-01 -1.65152401e-01 -1.11104023e+00 4.05629933e-01
-1.94727027e+00 -1.38826799e+00 -4.32087243e-01 1.51809764e+00
7.79130936e-01 5.15311122e-01 1.38369456e-01 7.10427165e-01
4.48856682e-01 1.58696979e-01 -3.42169285e-01 -8.69349182e-01
-3.96101534e-01 3.73041511e-01 1.20175526e-01 4.19178069e-01
-1.17246866e+00 8.13581467e-01 6.66630650e+00 8.64230394e-01
-3.22480857e-01 5.46810217e-02 4.71855104e-01 -1.18648577e-02
-6.45504236e-01 3.25157672e-01 -7.02360153e-01 5.27398944e-01
3.97473335e-01 -4.50542510e-01 -2.38215119e-01 8.20796967e-01
7.90349543e-02 -3.43324065e-01 -8.43288004e-01 3.24357241e-01
-1.07308000e-01 -1.87995553e+00 2.28444576e-01 5.83875418e-01
4.80652303e-01 -3.18377942e-01 -2.33692661e-01 2.53341705e-01
5.43700755e-01 -8.57981741e-01 1.09315836e+00 4.21247557e-02
5.40353715e-01 -3.99383724e-01 9.76336837e-01 3.99705112e-01
-5.66812456e-01 -6.50587916e-01 -1.43017232e-01 -5.44533312e-01
4.33174610e-01 7.41144896e-01 -3.39533061e-01 6.91402674e-01
6.18069589e-01 6.52022302e-01 -6.55558854e-02 5.34625888e-01
-7.42224753e-01 6.96833551e-01 -1.59729552e-02 -2.61363328e-01
3.96486491e-01 -3.18713188e-01 5.83440483e-01 1.19204521e+00
-8.35290253e-02 5.62258780e-01 1.96566544e-02 5.47517478e-01
-2.04494923e-01 5.44443905e-01 -6.97789133e-01 1.10460229e-01
5.77934802e-01 7.70355761e-01 -5.63822627e-01 -5.24670482e-01
-5.16246974e-01 -2.41871491e-01 3.25072795e-01 7.16508478e-02
-6.66058302e-01 -2.13575829e-03 4.40114975e-01 4.64254081e-01
1.39083359e-02 7.32737482e-02 -4.03678596e-01 -1.00330222e+00
5.81881464e-01 -9.79806662e-01 9.89298284e-01 -3.91709954e-01
-1.26725066e+00 4.36155260e-01 5.26784480e-01 -9.02500749e-01
-4.96694535e-01 -7.84315825e-01 -6.24295533e-01 5.48149228e-01
-1.54520726e+00 -1.01848006e+00 6.05343759e-01 2.39714295e-01
2.28913277e-01 -6.05553567e-01 5.97043395e-01 4.39701974e-01
-5.07366806e-02 3.18888724e-02 -2.10105166e-01 6.06200218e-01
4.69965965e-01 -9.63923573e-01 4.27939415e-01 5.92252612e-01
2.98814923e-01 6.83946550e-01 7.22074509e-01 -8.81382287e-01
-6.73066437e-01 -2.51305103e-01 1.59574866e+00 -3.84052426e-01
1.07958937e+00 5.11666667e-03 -6.67788386e-01 4.75881219e-01
2.42429256e-01 -5.19837320e-01 8.86815429e-01 5.48453689e-01
-7.19992101e-01 4.22132701e-01 -1.23750818e+00 4.70441759e-01
1.29299164e+00 -5.69798112e-01 -1.45121682e+00 4.66845065e-01
3.34300958e-02 -1.27317712e-01 -6.78333640e-01 4.77282494e-01
7.93496251e-01 -8.85028899e-01 8.12262654e-01 -1.19966054e+00
6.62184119e-01 9.34580863e-02 -1.09948881e-01 -5.60880899e-01
1.24580905e-01 -3.34919125e-01 -1.07937098e-01 9.67698634e-01
8.62445414e-01 -9.00403917e-01 5.73663414e-01 6.73920751e-01
-9.02475044e-02 -1.01394284e+00 -1.38023007e+00 -5.25011659e-01
6.12617075e-01 -7.42335379e-01 5.81417322e-01 1.03495860e+00
6.50074303e-01 6.12742662e-01 1.26291096e-01 -6.01621568e-01
6.34422898e-01 7.04282165e-01 6.07929289e-01 -1.83295321e+00
1.01321544e-02 -9.27238762e-01 -1.41108781e-01 -5.77883899e-01
6.70229912e-01 -8.79817605e-01 -8.90354216e-01 -1.75857210e+00
1.66782275e-01 -3.32721412e-01 1.36367574e-01 7.00374007e-01
1.75362885e-01 -4.68957156e-01 4.35939096e-02 4.69895899e-01
-2.97093183e-01 3.65829729e-02 9.68592346e-01 -4.48315680e-01
1.63026035e-01 1.59175128e-01 -9.17580962e-01 1.14558673e+00
8.45196128e-01 -6.22743726e-01 -8.76492411e-02 -3.94164622e-01
7.53954530e-01 -1.32763296e-01 8.32452923e-02 -4.10341293e-01
1.59707546e-01 -2.85009891e-01 -9.13389251e-02 -7.40886629e-01
-6.93221837e-02 -4.49775964e-01 -3.74667615e-01 5.31114936e-01
-7.44110405e-01 -3.53053911e-03 1.55150160e-01 2.48202726e-01
-6.11813724e-01 -7.00334847e-01 2.76555091e-01 -1.08468048e-02
-1.44085094e-01 -2.14320838e-01 -4.79960889e-01 5.42854846e-01
9.15861428e-01 -5.55751957e-02 -8.56478035e-01 1.54456496e-02
-6.04873538e-01 4.81920429e-02 -3.22419889e-02 4.29091662e-01
3.35806668e-01 -1.09327483e+00 -1.26370108e+00 -2.83901721e-01
-3.40039544e-02 -5.15323699e-01 -4.25815135e-01 6.90924048e-01
-3.25675845e-01 6.20350540e-01 5.95186539e-02 2.84160882e-01
-1.63110292e+00 2.71759093e-01 -1.49242496e-02 -7.71418810e-01
-6.23399675e-01 5.81461973e-02 -3.71833920e-01 -1.22071251e-01
-2.91455891e-02 -3.83509606e-01 -9.67562616e-01 5.07035017e-01
4.00815547e-01 4.86784577e-01 5.34681901e-02 -5.43213308e-01
-3.59784484e-01 3.45678627e-01 -2.32471466e-01 -2.89802819e-01
1.35156393e+00 4.74794388e-01 -7.48656571e-01 2.80217409e-01
7.01702535e-01 6.87317610e-01 -1.78757459e-01 -1.56682491e-01
8.42471957e-01 -4.48883802e-01 -3.76486927e-01 -9.24153984e-01
-5.57015300e-01 4.58066046e-01 -2.83785313e-01 7.75570452e-01
3.62837285e-01 4.16076660e-01 5.27324140e-01 6.95217192e-01
4.79800522e-01 -1.48450804e+00 -4.90320802e-01 7.52855599e-01
1.15819848e+00 -7.46815920e-01 5.39212108e-01 -7.20910966e-01
-3.40879142e-01 1.16585815e+00 -5.95840812e-02 8.49527940e-02
7.07745135e-01 4.92233574e-01 3.33772004e-01 -6.65103614e-01
-7.00768709e-01 -1.06209092e-01 2.37007037e-01 5.84695973e-02
9.39231217e-01 -4.44144383e-02 -1.81502748e+00 6.15581095e-01
-4.03070092e-01 -1.04616530e-01 3.24878991e-01 1.20684028e+00
-3.58921349e-01 -1.56123710e+00 -5.21186888e-01 6.23690546e-01
-1.15702641e+00 -2.96406448e-01 -1.10802209e+00 1.23656702e+00
4.91880715e-01 1.05112565e+00 -3.06422114e-01 4.17101711e-01
5.56030214e-01 -8.25211927e-02 2.23536640e-01 -5.33895433e-01
-7.31788278e-01 -1.52411804e-01 1.17832410e+00 -2.62335032e-01
-7.72415161e-01 -9.92321372e-01 -1.39547265e+00 -7.69481808e-02
-1.32832304e-01 9.44220304e-01 6.03857040e-01 1.46429944e+00
-1.23354495e-02 1.94128379e-01 -2.94235582e-03 8.81983191e-02
-6.59412384e-01 -9.38640654e-01 -4.64364082e-01 5.66326559e-01
-1.16354577e-01 -7.36180544e-01 -3.21338505e-01 -2.08624467e-01] | [9.566206932067871, 9.549662590026855] |
80e21cb9-4643-440e-afd1-58126697b6c1 | asset-pricing-and-deep-learning | 2209.12014 | null | https://arxiv.org/abs/2209.12014v1 | https://arxiv.org/pdf/2209.12014v1.pdf | Asset Pricing and Deep Learning | Traditional machine learning methods have been widely studied in financial innovation. My study focuses on the application of deep learning methods on asset pricing. I investigate various deep learning methods for asset pricing, especially for risk premia measurement. All models take the same set of predictive signals (firm characteristics, systematic risks and macroeconomics). I demonstrate high performance of all kinds of state-of-the-art (SOTA) deep learning methods, and figure out that RNNs with memory mechanism and attention have the best performance in terms of predictivity. Furthermore, I demonstrate large economic gains to investors using deep learning forecasts. The results of my comparative experiments highlight the importance of domain knowledge and financial theory when designing deep learning models. I also show return prediction tasks bring new challenges to deep learning. The time varying distribution causes distribution shift problem, which is essential for financial time series prediction. I demonstrate that deep learning methods can improve asset risk premium measurement. Due to the booming deep learning studies, they can constantly promote the study of underlying financial mechanisms behind asset pricing. I also propose a promising research method that learning from data and figuring out the underlying economic mechanisms through explainable artificial intelligence (AI) methods. My findings not only justify the value of deep learning in blooming fintech development, but also highlight their prospects and advantages over traditional machine learning methods. | ['Chen Zhang'] | 2022-09-24 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [-7.40906477e-01 -2.42572755e-01 -5.59942842e-01 -2.00803369e-01
-2.10893184e-01 -4.12277490e-01 5.88297963e-01 -6.37955248e-01
-1.04480304e-01 4.84052122e-01 2.79073298e-01 -1.09216702e+00
-5.29484510e-01 -1.06620419e+00 -6.73950136e-01 -6.48677349e-01
-1.81859836e-01 3.40340823e-01 -5.58913767e-01 -3.69227499e-01
5.66003621e-01 1.79455861e-01 -8.43112767e-01 1.83734387e-01
5.50406575e-01 1.57497311e+00 -1.85025692e-01 3.65157098e-01
-4.52152520e-01 1.54686916e+00 -5.18172801e-01 -9.81198430e-01
5.73127687e-01 -1.39223874e-01 -3.92626673e-01 -6.63426399e-01
-2.57249832e-01 -6.15465164e-01 -3.60850871e-01 1.00112891e+00
3.56205821e-01 -2.49986261e-01 9.28477287e-01 -1.06853163e+00
-1.44761121e+00 1.06892359e+00 -7.26603389e-01 1.00624311e+00
-6.01505399e-01 7.18801990e-02 1.43929398e+00 -8.24235022e-01
-1.19006529e-01 1.06436729e+00 1.05806351e+00 2.41949350e-01
-6.14837289e-01 -9.26478624e-01 3.01136613e-01 4.47666585e-01
-3.01585436e-01 2.11057454e-01 9.70373094e-01 -7.79344022e-01
1.01834607e+00 1.85135808e-02 6.93900526e-01 1.19698858e+00
8.92526448e-01 8.97507906e-01 8.53692710e-01 -3.08583647e-01
2.11002817e-03 2.60748118e-02 2.63742059e-01 2.19946593e-01
4.80772913e-01 7.60500133e-01 -6.47679642e-02 9.05935764e-02
1.16243851e+00 6.67773724e-01 3.78945082e-01 3.60232949e-01
-9.00080264e-01 1.37579644e+00 3.92970860e-01 4.08123404e-01
-6.39945090e-01 4.63738799e-01 4.54662442e-01 8.15122783e-01
7.04044819e-01 5.03352344e-01 -9.40530300e-01 -1.65064573e-01
-6.13645911e-01 4.66580093e-01 5.83761573e-01 2.35024661e-01
5.28190844e-02 1.07976460e+00 6.12149611e-02 6.10337079e-01
3.25196892e-01 6.79564357e-01 1.01642597e+00 -9.96312916e-01
5.50883412e-01 3.40651453e-01 3.31481025e-02 -1.09585238e+00
-4.76465195e-01 -1.12258434e+00 -8.87964666e-01 4.38653171e-01
2.86203623e-01 -5.25444865e-01 -3.70837659e-01 1.23469913e+00
-5.71684718e-01 2.69647896e-01 2.56098181e-01 4.50344265e-01
3.74013543e-01 9.46348488e-01 5.84168453e-03 -1.51246384e-01
1.20223796e+00 -9.31901813e-01 -6.55827820e-01 -3.36786732e-02
6.56937182e-01 -3.47289801e-01 5.89067817e-01 5.55785477e-01
-1.11673391e+00 -7.19400465e-01 -7.30882645e-01 4.35335666e-01
-3.55323255e-01 -2.33242989e-01 1.25885117e+00 6.94462299e-01
-6.23793900e-01 7.51287937e-01 -5.05697131e-01 7.90746272e-01
6.65383339e-01 1.50688961e-01 4.93301839e-01 6.22064292e-01
-1.69464159e+00 8.02039504e-01 1.71711907e-01 7.13935345e-02
-9.15052533e-01 -1.03981972e+00 -2.25543410e-01 4.67354327e-01
2.41071526e-02 -6.70881033e-01 1.55949676e+00 -1.42668176e+00
-1.29214656e+00 3.92259330e-01 7.13882208e-01 -1.34727716e+00
4.11434203e-01 -5.21918058e-01 -6.85675621e-01 -3.51926386e-01
-1.69130117e-01 -2.41850372e-02 7.90439188e-01 -4.77314204e-01
-9.17832136e-01 -1.08758755e-01 -1.67785928e-01 -4.23399508e-01
-6.33465350e-01 4.86383028e-02 6.35821044e-01 -1.55392289e+00
-3.99329752e-01 -4.97648001e-01 -1.04692928e-01 -7.72816598e-01
1.45413488e-01 -5.76008677e-01 3.98603439e-01 -1.05381548e+00
1.39443088e+00 -1.76882911e+00 -4.90207583e-01 1.33881569e-01
6.12176694e-02 2.76965737e-01 5.89895584e-02 1.48875639e-01
-6.72004342e-01 4.12489951e-01 1.49324000e-01 1.23009168e-01
4.05345947e-01 -1.16380222e-01 -1.11806583e+00 -2.99778301e-02
3.15931201e-01 1.53539765e+00 -3.99669379e-01 3.06328893e-01
-6.29323628e-03 1.96907952e-01 -2.90919930e-01 6.33340925e-02
-1.03218012e-01 -2.38007717e-02 -5.30369341e-01 6.87827528e-01
7.03815818e-01 -2.21517876e-01 -2.25075930e-01 3.20864141e-01
-1.70104846e-01 5.11264741e-01 -6.74255192e-01 6.27602041e-01
-4.26727682e-01 8.32493484e-01 -7.69828618e-01 -1.86750400e+00
1.08226502e+00 2.71021038e-01 3.64098668e-01 -1.30593252e+00
1.14008300e-01 4.96022314e-01 5.09282053e-01 -3.51598650e-01
7.90121332e-02 -5.69141984e-01 8.00814480e-02 8.50925565e-01
-3.71877491e-01 5.27254283e-01 -4.77720708e-01 -3.35347176e-01
6.03611708e-01 1.74399524e-03 -3.32744792e-02 -4.28705603e-01
9.68305916e-02 -5.09192884e-01 7.66839921e-01 8.39981377e-01
1.21088680e-02 3.03758830e-02 9.75222468e-01 -1.26535547e+00
-1.13448775e+00 -8.78912032e-01 -3.28175694e-01 1.32056415e+00
-8.51172745e-01 6.97135627e-01 -4.58617508e-01 -4.39631045e-01
6.34362757e-01 7.77384400e-01 -1.05317175e+00 -1.02732636e-01
-6.79563999e-01 -1.35975564e+00 3.57782334e-01 1.16823721e+00
5.99150956e-01 -1.34855878e+00 -5.06940067e-01 3.92664015e-01
3.99614811e-01 -3.75835568e-01 1.29827559e-01 2.70983428e-01
-1.24254441e+00 -9.04401302e-01 -1.07050824e+00 -6.16631389e-01
-1.19118102e-01 -9.18487161e-02 1.31752944e+00 -2.38802642e-01
7.66693950e-02 9.16325599e-02 -1.25593707e-01 -1.26460588e+00
-2.79800147e-01 -7.94189349e-02 1.06291629e-01 -1.88110515e-01
7.22002923e-01 -4.48780566e-01 -6.05254292e-01 5.53675294e-02
-6.10835671e-01 -4.37969714e-01 6.18236423e-01 9.43006635e-01
2.78180778e-01 6.18134081e-01 1.55226648e+00 -8.40682983e-01
8.53893459e-01 -1.00136566e+00 -9.69163239e-01 2.12271750e-01
-1.32211256e+00 -6.40393049e-02 4.11055923e-01 -2.52920359e-01
-1.34984338e+00 -1.13039649e+00 5.75583987e-02 -3.07676822e-01
4.93373156e-01 9.28050041e-01 3.61332864e-01 3.46415460e-01
1.62566185e-01 1.28584281e-01 2.10805628e-02 -6.11871421e-01
-1.29557520e-01 2.89101213e-01 3.92771244e-01 -3.29518408e-01
6.06000423e-01 2.73710519e-01 8.00631661e-03 -7.79679269e-02
-1.06709838e+00 4.39935803e-01 -2.60062903e-01 9.79904532e-02
6.61029637e-01 -1.19334877e+00 -6.65640235e-01 8.72959137e-01
-8.23784292e-01 -2.85683572e-01 -2.64359772e-01 7.66532362e-01
-4.71202672e-01 -1.65728599e-01 -1.21102405e+00 -1.15156209e+00
-4.97692704e-01 -7.18390346e-01 1.17360279e-01 3.83147836e-01
1.58052638e-01 -1.89575839e+00 2.39517838e-01 3.73213381e-01
7.51370490e-01 2.71439731e-01 1.15488195e+00 -1.02228487e+00
-4.78399545e-01 -3.66270751e-01 -2.63444901e-01 7.29654014e-01
2.88152061e-02 -5.23624420e-02 -9.83746767e-01 1.04399547e-01
4.75649416e-01 4.64268997e-02 1.37230110e+00 1.17731273e+00
1.46309793e+00 -5.53048551e-01 1.91656619e-01 8.24099422e-01
1.20640397e+00 9.00406182e-01 6.46110058e-01 1.16515887e+00
5.24882555e-01 7.37725377e-01 3.63559127e-01 5.73809505e-01
3.24686319e-01 -2.34249849e-02 5.26589036e-01 -1.30468294e-01
6.27235532e-01 -9.90353748e-02 5.87847769e-01 8.18966687e-01
-6.42923534e-01 9.06301588e-02 -1.03602540e+00 2.88572699e-01
-1.86956537e+00 -1.51418138e+00 5.30947139e-03 1.85101211e+00
4.43310678e-01 6.39748573e-01 5.42495549e-01 1.79235071e-01
3.55319619e-01 1.32481575e-01 -8.18423688e-01 -6.70212209e-01
-5.47781825e-01 3.24369431e-01 5.47362924e-01 1.49601102e-01
-1.33861387e+00 5.56721091e-01 6.76831722e+00 5.44128537e-01
-1.02007353e+00 -1.98352989e-02 1.51483476e+00 -8.52530971e-02
-7.79176056e-01 -4.89345759e-01 -9.34017479e-01 7.86047518e-01
1.39358544e+00 -5.16633570e-01 9.32950303e-02 1.19811773e+00
2.11611375e-01 5.40781856e-01 -8.86797726e-01 9.23122883e-01
-3.46608400e-01 -1.85631216e+00 7.47947991e-02 5.59660375e-01
9.89223540e-01 3.21181938e-02 9.74380612e-01 7.30006874e-01
4.62955117e-01 -1.39395607e+00 6.54399157e-01 1.10098815e+00
9.99399833e-03 -1.30328894e+00 1.45014179e+00 -2.68223770e-02
-7.74923265e-01 -9.60875213e-01 -8.86751771e-01 -6.94815636e-01
-5.38443513e-02 7.68562853e-01 -1.12434015e-01 3.26898068e-01
9.06204343e-01 1.22160268e+00 -3.08985412e-01 6.35786355e-01
3.45504284e-02 9.73915815e-01 2.67212182e-01 1.19335994e-01
5.05042672e-01 -4.68081027e-01 -5.52570224e-02 1.00223529e+00
5.94035029e-01 -5.97264944e-03 -5.04082024e-01 1.05507720e+00
-5.47902286e-02 -1.44456714e-01 -7.06782162e-01 -3.42025369e-01
1.18821092e-01 5.59167147e-01 -2.39465982e-01 -2.76151687e-01
-8.09807837e-01 2.15172708e-01 -1.44083515e-01 2.81748563e-01
-9.40355003e-01 -3.70127082e-01 7.99738765e-01 8.37164819e-02
4.35725480e-01 1.25654936e-01 -8.28349113e-01 -1.14524019e+00
-5.63415289e-02 -7.71359682e-01 7.01116562e-01 -5.40025949e-01
-1.80436826e+00 1.02497183e-01 -3.65976512e-01 -1.16769731e+00
-4.93386954e-01 -1.22403657e+00 -1.14114738e+00 9.59290683e-01
-2.02900124e+00 -6.24343395e-01 5.65023601e-01 3.77720714e-01
5.95921516e-01 -1.19497132e+00 4.28647697e-01 2.05549568e-01
-7.63371408e-01 5.69600463e-01 8.75977099e-01 6.57084048e-01
1.56626076e-01 -1.32071888e+00 1.05848432e+00 4.73360687e-01
2.22841278e-01 4.71966326e-01 7.40651563e-02 -5.83465576e-01
-1.02136660e+00 -7.93428063e-01 6.71109915e-01 -6.01637304e-01
1.28452098e+00 3.40891272e-01 -1.02372122e+00 9.47534502e-01
2.76441187e-01 -4.49384153e-01 9.46625173e-01 1.94376156e-01
-3.49805802e-01 -4.46329594e-01 -5.88451445e-01 5.12113683e-02
4.80166227e-01 -4.16738570e-01 -8.25919688e-01 1.05581693e-01
8.46570909e-01 3.05755347e-01 -9.33392644e-01 2.38498718e-01
7.42426872e-01 -1.29970729e+00 1.29545105e+00 -1.24008775e+00
9.62676346e-01 6.08173966e-01 5.71129769e-02 -1.11811697e+00
-8.92831504e-01 -5.52326322e-01 -4.97707635e-01 1.17939210e+00
4.60892826e-01 -1.19452584e+00 7.79656529e-01 5.93050718e-01
3.33634131e-02 -8.59507442e-01 -8.23827982e-01 -1.15667570e+00
1.07482731e+00 -6.33130491e-01 1.10899293e+00 1.35888135e+00
-2.15732992e-01 -1.15476951e-01 -5.39841950e-01 -3.15841168e-01
7.24429011e-01 4.05448288e-01 3.08001876e-01 -1.80730247e+00
-4.21136260e-01 -1.18330622e+00 -7.00322464e-02 -5.41204453e-01
5.48767328e-01 -6.77768409e-01 -1.08725858e+00 -9.29472327e-01
1.21809334e-01 -3.47809941e-01 -1.28831315e+00 2.08445087e-01
-1.09690912e-01 -2.97662944e-01 8.85942131e-02 5.17992020e-01
9.59337130e-02 4.55950290e-01 9.25731540e-01 -2.23551750e-01
8.36414769e-02 4.77604061e-01 -1.11632085e+00 9.34369504e-01
1.25493872e+00 -1.60366938e-01 -2.75157660e-01 -6.66940391e-01
7.77192175e-01 6.20727874e-02 4.84391898e-01 -5.69104433e-01
-4.35864925e-01 -6.57317698e-01 8.56359541e-01 -4.86576170e-01
-3.37511510e-01 -5.77749133e-01 -1.15007743e-01 8.86046112e-01
-3.93238574e-01 6.43805146e-01 2.06593826e-01 5.20255327e-01
-3.62652898e-01 -4.12485152e-01 6.52605176e-01 -4.44464684e-01
-6.02804184e-01 3.84782881e-01 -3.07109267e-01 1.45197317e-01
8.34902048e-01 -5.13878278e-02 -3.12809497e-01 -5.41255832e-01
-6.27469599e-01 2.79073715e-02 -2.86581337e-01 6.19211853e-01
4.28983539e-01 -1.73470879e+00 -8.55869174e-01 1.94606274e-01
-5.07527590e-01 -4.65021282e-01 1.64753780e-01 4.70649689e-01
-5.11534572e-01 1.09390163e+00 -4.72831488e-01 1.21137291e-01
-2.26270780e-01 6.67063951e-01 7.13370979e-01 -4.52149332e-01
-5.44960737e-01 1.15463746e+00 7.85922647e-01 1.45272657e-01
2.09502980e-01 -5.34896314e-01 -5.77410519e-01 5.54120958e-01
8.31265807e-01 6.85729504e-01 -2.03950852e-01 -5.60423322e-02
8.23094025e-02 6.04230583e-01 -2.33023375e-01 2.10778669e-01
2.08489633e+00 1.82503313e-01 6.03890754e-02 6.32587850e-01
9.19236600e-01 -4.50136244e-01 -1.33194911e+00 -2.22563311e-01
7.12877929e-01 -3.66485089e-01 1.78998008e-01 -7.67827451e-01
-1.71410787e+00 1.34613645e+00 5.49069464e-01 7.11939514e-01
9.24011528e-01 -4.15701687e-01 1.05175149e+00 4.60073471e-01
7.61865675e-02 -1.45809078e+00 4.01800454e-01 5.10961294e-01
7.82087445e-01 -1.48826456e+00 -1.32314697e-01 7.72386611e-01
-8.50120842e-01 1.43200707e+00 3.04906815e-01 -4.90435004e-01
1.31210625e+00 1.71122938e-01 2.63601333e-01 -2.38838002e-01
-9.36551213e-01 3.30867052e-01 1.22653112e-01 4.52178568e-01
5.73801041e-01 2.68974513e-01 4.53854017e-02 1.49426472e+00
-5.77796638e-01 -1.31503329e-01 5.37095904e-01 3.47038478e-01
-4.85255480e-01 -8.95339251e-01 -4.29913491e-01 7.12303579e-01
-1.36931288e+00 -3.11192900e-01 -6.86974972e-02 7.46732593e-01
-2.98704028e-01 4.14074719e-01 5.08188069e-01 -1.82946563e-01
8.50982368e-02 3.46953154e-01 -2.63141453e-01 -9.99387354e-02
-7.25819349e-01 1.90722525e-01 -3.51060152e-01 -7.55355731e-02
-1.02234557e-01 -9.24830914e-01 -8.94888639e-01 -5.95312953e-01
-1.35188237e-01 2.53867311e-03 4.03844714e-01 8.55409980e-01
2.04269916e-01 7.26194263e-01 1.22382879e+00 -4.65715706e-01
-9.73633707e-01 -8.68562162e-01 -1.01716578e+00 -1.00851245e-02
3.91841829e-01 -7.44113803e-01 -5.97588181e-01 -1.03127630e-02] | [4.471303939819336, 4.176861763000488] |
20be2036-f68f-4f04-9235-85f624c18745 | small-language-models-for-tabular-data | 2211.02941 | null | https://arxiv.org/abs/2211.02941v3 | https://arxiv.org/pdf/2211.02941v3.pdf | Small Language Models for Tabular Data | Supervised deep learning is most commonly applied to difficult problems defined on large and often extensively curated datasets. Here we demonstrate the ability of deep representation learning to address problems of classification and regression from small and poorly formed tabular datasets by encoding input information as abstracted sequences composed of a fixed number of characters per input field. We find that small models have sufficient capacity for approximation of various functions and achieve record classification benchmark accuracy. Such models are shown to form useful embeddings of various input features in their hidden layers, even if the learned task does not explicitly require knowledge of those features. These models are also amenable to input attribution, allowing for an estimation of the importance of each input element to the model output as well as of which inputs features are effectively embedded in the model. We present a proof-of-concept for the application of small language models to mixed tabular data without explicit feature engineering, cleaning, or preprocessing, relying on the model to perform these tasks as part of the representation learning process. | ['Benjamin L. Badger'] | 2022-11-05 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [ 2.76914269e-01 3.14170420e-01 -3.35264772e-01 -5.68244874e-01
-8.24893117e-01 -7.87541330e-01 4.78641629e-01 6.97110295e-01
-3.36228877e-01 6.31630242e-01 2.74327844e-01 -5.24540961e-01
-2.93853015e-01 -1.19167876e+00 -1.28087556e+00 -4.50894922e-01
-2.17929184e-01 7.88145304e-01 -4.54346836e-01 -1.78472877e-01
1.39989600e-01 5.53521395e-01 -1.65087605e+00 6.77280247e-01
3.86538148e-01 1.10248029e+00 -1.77149251e-01 6.50670171e-01
-4.86144751e-01 9.76568460e-01 -8.38936925e-01 -3.21434468e-01
2.42165491e-01 1.63633004e-01 -7.76344001e-01 -1.94738239e-01
7.33076453e-01 -1.91706330e-01 -3.79269481e-01 5.97901940e-01
9.39886793e-02 7.14088138e-03 9.85413790e-01 -1.09233952e+00
-9.30956483e-01 7.54916430e-01 -2.13626791e-02 -4.15857211e-02
2.65270144e-01 1.98117048e-02 1.54342258e+00 -7.66851366e-01
6.41696751e-01 1.10609818e+00 7.54479587e-01 2.61956573e-01
-1.45650196e+00 -5.01972675e-01 -1.08066045e-01 1.30003691e-03
-1.00550699e+00 -4.18846548e-01 3.06526214e-01 -6.87198281e-01
1.10812163e+00 2.80902416e-01 2.36386910e-01 8.11104178e-01
2.00330436e-01 7.04664826e-01 4.60629642e-01 -3.79799157e-01
1.64791107e-01 5.21341026e-01 6.31922483e-01 8.57235551e-01
6.18804634e-01 -1.46575838e-01 -3.66498411e-01 -2.96954274e-01
4.20039713e-01 1.94988251e-01 1.69593897e-02 -5.94835758e-01
-1.11831105e+00 1.01905537e+00 4.77312207e-01 1.29721835e-01
-1.55353799e-01 4.10200924e-01 7.06061125e-01 5.98237932e-01
3.15917850e-01 9.03103590e-01 -9.82639611e-01 -1.03691770e-02
-6.49814844e-01 3.22960705e-01 9.39944744e-01 1.09032130e+00
9.77997899e-01 -8.93858522e-02 -2.59395093e-01 7.51684666e-01
-1.54524729e-01 4.18461293e-01 4.42261577e-01 -6.44454598e-01
6.56671464e-01 1.07596624e+00 -7.67993033e-02 -8.40917051e-01
-4.24105078e-01 -3.80568951e-01 -7.39085913e-01 -7.00133890e-02
4.71038312e-01 2.56816298e-02 -9.37725663e-01 1.60308516e+00
-6.60471767e-02 -6.71476424e-01 7.57148564e-02 3.36276621e-01
5.83980501e-01 8.76948833e-01 -1.99223310e-02 2.36220792e-01
1.16542518e+00 -6.17086470e-01 -3.92445534e-01 -2.85649121e-01
1.17514598e+00 -2.23520815e-01 1.14378750e+00 3.36087048e-01
-8.33065927e-01 -6.41904771e-01 -1.04657006e+00 -5.67694485e-01
-1.07466459e+00 1.87254176e-02 8.36739898e-01 6.08338714e-01
-8.10809612e-01 7.55642653e-01 -3.30855101e-01 -9.67799574e-02
6.39284849e-01 7.05473185e-01 -7.47362912e-01 -2.31773838e-01
-1.17222202e+00 9.26757991e-01 4.08293992e-01 -2.43440848e-02
-6.45023346e-01 -7.48491764e-01 -1.38252866e+00 3.97561491e-01
1.44441307e-01 -4.68578190e-01 1.02179098e+00 -1.01477385e+00
-6.98648632e-01 6.20548844e-01 -1.92903519e-01 -6.11116171e-01
1.49862438e-01 -2.07190439e-01 -2.51676917e-01 -1.21930271e-01
-4.75204438e-02 4.29723859e-01 9.37185526e-01 -8.57640028e-01
-3.01019311e-01 -3.94379973e-01 2.59120971e-01 -2.18823716e-01
-6.97794199e-01 -1.84081540e-01 -1.46869004e-01 -4.85422283e-01
-2.48628199e-01 -6.38355076e-01 -1.73856512e-01 2.38594860e-01
-3.48506689e-01 -1.43719316e-01 3.85888070e-01 -6.81273580e-01
1.17459226e+00 -1.88651001e+00 2.61302859e-01 3.48654211e-01
4.05491263e-01 2.61548236e-02 -2.85246760e-01 5.63020229e-01
-2.65870720e-01 4.93872792e-01 -3.56811672e-01 -9.35952961e-02
3.28061074e-01 2.77443737e-01 -5.57842493e-01 4.71470326e-01
6.26198828e-01 9.47968304e-01 -6.17041230e-01 -2.42393371e-03
1.26733705e-01 2.73987710e-01 -7.67802954e-01 3.53636742e-01
-4.99750316e-01 -3.23501140e-01 -2.84363925e-01 5.47967076e-01
2.77674913e-01 -2.56891161e-01 2.06891879e-01 -7.18038082e-02
2.01722875e-01 7.03795195e-01 -1.07745922e+00 1.25774896e+00
-6.38678133e-01 7.63197482e-01 -1.99937120e-01 -1.12932086e+00
9.93890405e-01 -8.47922787e-02 3.39236975e-01 -6.52669430e-01
2.83231167e-03 2.29220137e-01 1.50451064e-02 -3.04390371e-01
7.67372489e-01 3.18802297e-02 -5.12143373e-01 7.26647139e-01
1.89254478e-01 2.11075976e-01 3.14457029e-01 2.48548791e-01
1.16918910e+00 -7.79957399e-02 2.63376057e-01 -1.94380715e-01
4.40358043e-01 1.38186201e-01 1.00957669e-01 8.81829321e-01
5.04461467e-01 4.65943754e-01 9.61978853e-01 -8.46903980e-01
-1.34885406e+00 -5.57008743e-01 -3.37181240e-01 1.38258946e+00
-5.25525272e-01 -7.10527062e-01 -3.81132275e-01 -6.69913471e-01
6.80484831e-01 6.08283937e-01 -1.31829381e+00 -3.50174308e-01
-5.06326556e-01 -6.28087401e-01 6.35793626e-01 8.79627705e-01
-3.48623127e-01 -1.12132764e+00 -4.13476288e-01 2.18557745e-01
4.58714992e-01 -7.12072730e-01 -7.72890896e-02 1.03768539e+00
-7.51805484e-01 -1.20421243e+00 -2.61911601e-01 -6.88482463e-01
8.72898698e-01 -3.13540965e-01 1.10923684e+00 1.35742113e-01
-4.27239835e-01 3.63963276e-01 -1.25034288e-01 -3.28646779e-01
-6.62705004e-01 4.66711700e-01 -4.37592715e-02 -1.47145182e-01
5.93627095e-01 -1.90997049e-01 -5.67521192e-02 3.80257145e-02
-1.12513554e+00 -2.71701235e-02 5.35493910e-01 1.10906208e+00
2.88458794e-01 -3.79728168e-01 3.60070348e-01 -1.28495359e+00
6.12911463e-01 -7.59787977e-01 -5.59615254e-01 2.95879960e-01
-5.02679050e-01 7.34678864e-01 1.08379138e+00 -2.67877668e-01
-2.43361756e-01 -2.17547119e-01 2.68814503e-03 -2.96936482e-01
-1.05922505e-01 8.04774344e-01 -1.72518536e-01 3.39294195e-01
6.50887311e-01 1.15925826e-01 1.64685190e-01 -5.98869979e-01
4.36263114e-01 6.87023938e-01 2.44290426e-01 -8.84507656e-01
7.49148548e-01 8.09635893e-02 -2.64144549e-03 -4.93347853e-01
-5.18017173e-01 -1.86838433e-01 -8.57921243e-01 5.92026830e-01
4.28984791e-01 -7.22276330e-01 -6.35908008e-01 -3.20705655e-03
-9.72792149e-01 -3.72125030e-01 -4.61655349e-01 -1.61829934e-01
-4.23071682e-01 -1.41574636e-01 -5.66355109e-01 -4.69167680e-01
-3.38251114e-01 -9.40055668e-01 9.73054290e-01 -2.87282974e-01
-4.12715882e-01 -1.09319723e+00 -1.92485005e-01 1.89708799e-01
4.02372509e-01 7.70686194e-02 1.86058438e+00 -1.12864864e+00
-5.29542029e-01 -6.87211514e-01 -2.21757755e-01 5.25756359e-01
1.38352185e-01 1.03154488e-01 -1.09065223e+00 -2.87087560e-01
-4.75597918e-01 -5.75377643e-01 9.25885439e-01 7.34870415e-03
1.69300687e+00 -5.84505558e-01 -9.71768573e-02 7.53822386e-01
1.31077027e+00 -1.83068231e-01 6.08197272e-01 4.76823121e-01
7.56566465e-01 6.79480433e-01 2.03129888e-01 3.16104084e-01
1.22593306e-01 3.42755139e-01 2.95317709e-01 3.07562258e-02
2.54399419e-01 -3.51830631e-01 3.77325296e-01 5.43898046e-01
6.25638723e-01 -2.56261289e-01 -1.05515242e+00 5.90529561e-01
-1.51382506e+00 -7.25417793e-01 9.67481136e-02 2.24950743e+00
8.29290152e-01 3.70004296e-01 -1.30621135e-01 3.03899467e-01
2.40687281e-01 6.69468120e-02 -7.15837419e-01 -8.64698648e-01
7.77667295e-03 5.13941705e-01 8.71045232e-01 2.67827362e-01
-9.98680651e-01 5.90965629e-01 6.80461693e+00 4.88356233e-01
-9.40641046e-01 -4.04666811e-01 7.58805096e-01 -5.57102337e-02
-6.03314281e-01 -2.00552166e-01 -7.97564566e-01 1.57626674e-01
1.43535757e+00 -1.24297544e-01 5.64989448e-01 8.76329064e-01
-3.43387485e-01 8.93259048e-02 -1.72392094e+00 6.10588133e-01
-6.37220778e-03 -1.60837495e+00 5.44631124e-01 1.58840477e-01
2.82566041e-01 -3.18759054e-01 1.63795084e-01 6.63188934e-01
2.03158990e-01 -1.72380948e+00 6.37328088e-01 3.94961119e-01
1.17643726e+00 -7.83493519e-01 6.60760164e-01 1.69529915e-01
-7.32279539e-01 -4.66350794e-01 -7.07738638e-01 -3.08729202e-01
-6.18541956e-01 1.55944899e-01 -9.52481389e-01 2.84415126e-01
3.63961041e-01 9.22546983e-01 -9.08434272e-01 5.81291378e-01
1.50812671e-01 6.21567905e-01 -1.52120396e-01 -1.93109110e-01
3.05944264e-01 1.65757388e-01 -2.62511056e-02 1.34203386e+00
4.69679125e-02 -3.44285309e-01 -4.37597558e-02 9.27401960e-01
-4.49552357e-01 1.19582690e-01 -8.81249845e-01 -5.92050612e-01
3.67518067e-01 1.00993955e+00 -1.51224211e-01 -4.92693514e-01
-3.75425100e-01 4.34394687e-01 6.55862331e-01 2.71549046e-01
-4.18499827e-01 -6.35683298e-01 1.00479746e+00 3.38199854e-01
6.19607747e-01 -8.41440409e-02 -6.87040508e-01 -1.12686694e+00
6.70386553e-02 -1.19231761e+00 4.59211200e-01 -6.78130805e-01
-1.09219134e+00 3.19960594e-01 -5.62663041e-02 -8.24062049e-01
-5.34361422e-01 -1.15919828e+00 -1.64483398e-01 1.13236785e+00
-1.20673764e+00 -1.08243656e+00 5.84854037e-02 3.89018089e-01
2.94903874e-01 -4.15230155e-01 1.23326159e+00 1.24594860e-01
-5.31687260e-01 7.86552131e-01 5.24084151e-01 5.52187443e-01
5.25298774e-01 -1.33807981e+00 6.27140582e-01 4.13171947e-01
1.45797789e-01 1.14903927e+00 5.39712429e-01 -3.12965482e-01
-2.06320667e+00 -1.07915676e+00 8.65527391e-01 -9.04000223e-01
7.81936347e-01 -1.01119661e+00 -1.03135478e+00 9.94645834e-01
-1.24252319e-01 2.09323540e-01 8.79895568e-01 4.13459629e-01
-8.45578313e-01 -2.67306238e-01 -9.94309843e-01 1.38797805e-01
7.93728709e-01 -8.00918579e-01 -7.56812453e-01 9.32259634e-02
7.75383294e-01 -2.30044991e-01 -9.05103087e-01 1.11086540e-01
6.99295938e-01 -3.28726053e-01 8.51942003e-01 -1.43187535e+00
8.26355875e-01 2.77029388e-02 -4.86621767e-01 -1.13441622e+00
-3.69502485e-01 -2.75865465e-01 -3.40739965e-01 1.04199171e+00
7.46127188e-01 -4.85540777e-01 5.66815555e-01 8.02834928e-01
1.88214723e-02 -6.68532431e-01 -5.67078173e-01 -3.52872461e-01
4.80265766e-01 -4.92080450e-01 7.47048438e-01 9.08080995e-01
-4.02796976e-02 3.19863021e-01 -1.43998533e-01 -5.00145666e-02
2.71356761e-01 3.04801106e-01 1.03681028e+00 -1.37177551e+00
-1.43863693e-01 -3.37257564e-01 -5.53563356e-01 -6.66979074e-01
2.76185840e-01 -1.21811259e+00 -1.96772039e-01 -1.54179907e+00
-5.51159377e-04 -7.27339864e-01 -5.33874214e-01 8.04935753e-01
1.42676741e-01 -1.10402584e-01 -4.14567105e-02 -1.61394384e-02
-3.43476176e-01 2.46854037e-01 6.94259346e-01 -5.84840596e-01
2.33071178e-01 -3.21173787e-01 -1.07111347e+00 2.32770085e-01
5.73353589e-01 -6.85968041e-01 -2.56424010e-01 -6.58936977e-01
6.60091758e-01 -1.95083544e-01 2.61917919e-01 -7.09235549e-01
2.62274239e-02 -1.07871972e-01 8.63625944e-01 -2.84836650e-01
2.12634966e-01 -1.00626767e+00 -8.97792354e-02 2.74671525e-01
-1.10394287e+00 2.48494491e-01 4.93853599e-01 4.33940053e-01
-3.57325934e-02 -2.74667770e-01 3.52856904e-01 -1.49362072e-01
-6.24676943e-01 1.50806889e-01 -2.31641039e-01 5.46149462e-02
6.57000363e-01 -1.62600446e-02 -4.63226169e-01 -1.57602936e-01
-4.49728996e-01 1.15316108e-01 5.44318855e-01 5.96388340e-01
4.70002264e-01 -1.09865725e+00 -5.16182125e-01 6.81994200e-01
3.99736106e-01 3.67256850e-02 -4.09072161e-01 2.04459488e-01
-6.34526193e-01 6.92476034e-01 -3.81962955e-01 -2.12925255e-01
-9.19699073e-01 9.08447504e-01 2.76873738e-01 -2.44284436e-01
-5.24144769e-01 5.54073513e-01 5.96017167e-02 -7.81438470e-01
5.35437346e-01 -8.81636798e-01 6.57861456e-02 3.65122229e-01
6.01445258e-01 -6.63003996e-02 3.90158832e-01 -2.44499534e-01
-3.00200760e-01 2.08033577e-01 -3.80175740e-01 3.70961010e-01
1.67189014e+00 4.64078486e-01 -2.24111557e-01 6.35017514e-01
1.59609330e+00 -8.36290941e-02 -8.89337182e-01 -2.65948117e-01
1.69977054e-01 -2.33694375e-01 -1.51060045e-01 -8.56328905e-01
-8.01485598e-01 1.21537817e+00 2.25544363e-01 7.17343092e-02
6.02185726e-01 -1.64879054e-01 5.50885379e-01 1.03589094e+00
3.03162247e-01 -8.15707862e-01 -2.34061778e-01 7.01465249e-01
7.84407198e-01 -1.08375669e+00 1.09215796e-01 1.32557690e-01
-2.67750412e-01 1.52002263e+00 3.82023245e-01 -1.54641345e-01
3.25570703e-01 5.39450526e-01 -1.42202437e-01 -1.40457749e-01
-1.22214925e+00 1.15440570e-01 4.58548188e-01 5.33490360e-01
5.40179908e-01 -9.56210401e-03 2.24193767e-01 7.00808167e-01
-2.00856283e-01 -1.97262809e-01 4.40845668e-01 9.85840142e-01
-4.26544040e-01 -1.00185812e+00 -3.03707033e-01 1.09422374e+00
-2.29614988e-01 -3.01851392e-01 -4.40573514e-01 9.08802629e-01
-5.76133169e-02 2.62442678e-01 3.02522808e-01 -4.54448313e-01
2.69048482e-01 4.99489069e-01 2.87849128e-01 -9.00980830e-01
-9.67097759e-01 -6.55469120e-01 1.95608348e-01 -3.42020035e-01
4.11164820e-01 -6.44383669e-01 -1.07052338e+00 -3.61748725e-01
1.34217724e-01 1.16614491e-01 7.57398069e-01 7.44776607e-01
3.50336850e-01 4.54124033e-01 3.80698472e-01 -5.72247982e-01
-9.30995345e-01 -9.57316279e-01 -4.21587110e-01 5.29454887e-01
8.45806301e-01 -4.07816321e-01 -2.49501035e-01 -2.69328360e-03] | [9.652084350585938, 7.434591770172119] |
4fe7c3f5-c0bc-4403-a825-e8855eff4ab0 | scoregrad-multivariate-probabilistic-time | 2106.10121 | null | https://arxiv.org/abs/2106.10121v1 | https://arxiv.org/pdf/2106.10121v1.pdf | ScoreGrad: Multivariate Probabilistic Time Series Forecasting with Continuous Energy-based Generative Models | Multivariate time series prediction has attracted a lot of attention because of its wide applications such as intelligence transportation, AIOps. Generative models have achieved impressive results in time series modeling because they can model data distribution and take noise into consideration. However, many existing works can not be widely used because of the constraints of functional form of generative models or the sensitivity to hyperparameters. In this paper, we propose ScoreGrad, a multivariate probabilistic time series forecasting framework based on continuous energy-based generative models. ScoreGrad is composed of time series feature extraction module and conditional stochastic differential equation based score matching module. The prediction can be achieved by iteratively solving reverse-time SDE. To the best of our knowledge, ScoreGrad is the first continuous energy based generative model used for time series forecasting. Furthermore, ScoreGrad achieves state-of-the-art results on six real-world datasets. The impact of hyperparameters and sampler types on the performance are also explored. Code is available at https://github.com/yantijin/ScoreGradPred. | ['Yuanqing Xia', 'Yufeng Zhan', 'Tong Zhou', 'Hongwei Zhang', 'Tijin Yan'] | 2021-06-18 | null | null | null | null | ['probabilistic-time-series-forecasting'] | ['time-series'] | [-4.35802668e-01 -7.03677773e-01 1.08544737e-01 -3.63338053e-01
-8.32880855e-01 -3.06237102e-01 6.67997956e-01 -3.50204080e-01
9.84080508e-02 5.67016840e-01 -1.70388520e-02 -1.08985886e-01
-2.70814002e-01 -9.78549004e-01 -4.53529835e-01 -8.96058261e-01
-1.82272717e-01 5.22114336e-01 2.76429534e-01 -6.78660125e-02
1.20557040e-01 2.61048645e-01 -1.62009287e+00 -2.32051313e-01
1.13762641e+00 1.04821503e+00 2.01954231e-01 4.66179371e-01
-1.15657538e-01 5.40098727e-01 -3.86987001e-01 -1.50315911e-01
1.62185505e-02 -6.52778506e-01 -7.64141157e-02 -3.45483929e-01
-3.54694933e-01 -1.47685129e-02 -4.37725902e-01 8.60923827e-01
5.44580817e-01 5.37494719e-01 7.81376362e-01 -1.71423280e+00
-5.07979095e-01 4.69014913e-01 -4.23581392e-01 3.00412059e-01
2.83463243e-02 2.36047595e-03 6.60403728e-01 -7.97650099e-01
1.86713621e-01 9.46289361e-01 6.98685050e-01 2.32341498e-01
-1.01154137e+00 -8.92136812e-01 5.11349440e-02 6.23505771e-01
-1.44688511e+00 -7.13881552e-02 1.25481534e+00 -5.55817425e-01
9.45162654e-01 3.28217089e-01 7.63927042e-01 1.01424360e+00
5.76401889e-01 7.49401331e-01 1.07768631e+00 9.60190780e-03
5.03831387e-01 -2.53343761e-01 4.89739664e-02 2.73209929e-01
-1.99696034e-01 2.07068861e-01 -3.38826507e-01 -4.38370049e-01
3.22740227e-01 4.15943056e-01 -4.24563624e-02 -2.80579068e-02
-7.95812547e-01 8.83299768e-01 9.98472199e-02 3.51387560e-01
-5.49389005e-01 2.50745416e-01 2.13098660e-01 6.45900285e-03
9.00587261e-01 -1.69035703e-01 -3.64862800e-01 -6.51276290e-01
-1.09457195e+00 3.24477792e-01 7.13568628e-01 7.54597187e-01
4.43299919e-01 4.30129081e-01 6.85933083e-02 7.86628425e-01
5.28735161e-01 9.27734852e-01 6.02325439e-01 -6.17092669e-01
2.59649873e-01 4.20648485e-01 -2.28998244e-01 -1.03958333e+00
-2.87032366e-01 -4.84323919e-01 -1.03745294e+00 -8.85150731e-02
5.80860898e-02 -3.85592222e-01 -7.28137314e-01 1.61351073e+00
3.00613970e-01 7.69139707e-01 -1.04358438e-02 9.04471815e-01
6.76977873e-01 1.34076524e+00 9.58753228e-02 -3.92541647e-01
9.05706823e-01 -5.61667860e-01 -6.78249359e-01 1.49418071e-01
1.39048576e-01 -9.02315080e-01 6.12631381e-01 3.24813187e-01
-8.66186559e-01 -4.09748763e-01 -7.02662945e-01 3.53159666e-01
-2.25852266e-01 2.02785451e-02 5.24173498e-01 4.72506851e-01
-7.19353855e-01 6.47855043e-01 -1.53479195e+00 -1.88199222e-01
1.49014592e-01 1.15597351e-02 2.65789539e-01 2.88097948e-01
-1.31311738e+00 6.61474049e-01 8.65297467e-02 1.78637072e-01
-6.88383996e-01 -8.13107789e-01 -6.70408607e-01 9.59116817e-02
4.57579978e-02 -5.20473897e-01 1.25893056e+00 -5.65111637e-01
-1.58933139e+00 1.33076310e-01 -4.05707002e-01 -3.41582835e-01
5.31252444e-01 -5.12788966e-02 -8.15461516e-01 -2.74463713e-01
-1.65676177e-01 6.57029375e-02 7.40748525e-01 -6.90940201e-01
-2.06281647e-01 -4.34447318e-01 -6.03284001e-01 -1.29349917e-01
-1.58722341e-01 1.18576866e-02 -2.06041664e-01 -9.60417688e-01
1.72729105e-01 -1.14082205e+00 -2.47370407e-01 -4.97443259e-01
-1.85895130e-01 -4.60876793e-01 9.96141791e-01 -8.49871695e-01
1.44848347e+00 -2.03101110e+00 5.99696226e-02 2.89228827e-01
-2.80361861e-01 -5.44596314e-02 2.86650062e-01 8.46538126e-01
5.75091951e-02 -5.41329496e-02 -4.17306036e-01 -3.87796164e-01
2.28506163e-01 1.25093181e-02 -5.97407639e-01 3.42953861e-01
1.24361798e-01 7.09144413e-01 -5.86694539e-01 -3.47916663e-01
3.67878705e-01 9.48423624e-01 -2.80027032e-01 1.98969960e-01
-2.23492756e-01 3.47399205e-01 -6.35169506e-01 5.45092106e-01
8.29080641e-01 -1.89335749e-01 -3.67879905e-02 -1.09670632e-01
-2.63905138e-01 3.00773904e-02 -1.06833887e+00 1.47230554e+00
-2.70745754e-01 6.09271288e-01 -6.04376853e-01 -8.98852706e-01
1.27362823e+00 2.99419254e-01 8.88531208e-01 -6.17009461e-01
1.25976026e-01 3.40243697e-01 -7.78015777e-02 -3.95276040e-01
4.97163594e-01 -2.37469777e-01 -3.82429436e-02 3.80714446e-01
-1.24614410e-01 -3.31379682e-01 1.47321373e-01 -8.52179714e-03
7.03147769e-01 3.44317377e-01 -9.95927751e-02 -1.19103901e-01
2.40537852e-01 -5.54400682e-02 8.49579871e-01 1.57857500e-02
-1.28506375e-02 6.91798091e-01 2.22452536e-01 -2.25534961e-01
-9.25681055e-01 -1.09444320e+00 -1.37186021e-01 6.98350310e-01
-2.17715930e-02 -4.74785179e-01 -4.90383178e-01 -8.57556239e-02
-4.33528163e-02 1.03120005e+00 -4.62736040e-01 -3.14655274e-01
-4.46443290e-01 -1.18994784e+00 2.75863975e-01 6.83150649e-01
3.90356213e-01 -8.52223933e-01 -4.54052389e-01 4.38347816e-01
-2.83422172e-01 -6.95784152e-01 -4.57226932e-01 -9.02235806e-02
-9.87653136e-01 -7.88217485e-01 -7.48472691e-01 -3.61686230e-01
3.47580373e-01 7.12774247e-02 8.67351413e-01 -4.07235861e-01
-1.44854575e-01 3.35701138e-01 -3.13061595e-01 -6.31212413e-01
-2.67528117e-01 7.23550543e-02 -8.63204598e-02 5.17966263e-02
4.83849406e-01 -1.01130903e+00 -6.48034155e-01 4.85860765e-01
-8.09895396e-01 -1.14023192e-02 1.20665260e-01 4.52206403e-01
8.70599091e-01 2.49000520e-01 5.87033033e-01 -3.44793141e-01
6.45881355e-01 -8.59231591e-01 -9.65355396e-01 2.03224704e-01
-7.11493492e-01 3.69889438e-02 6.38286889e-01 -5.28870404e-01
-1.11096835e+00 -1.33685425e-01 -2.42793158e-01 -6.48422003e-01
9.37075019e-02 7.55324721e-01 7.46910125e-02 3.86301100e-01
1.14127308e-01 6.29194498e-01 -8.48703682e-02 -5.26916385e-01
-1.63786843e-01 4.09622550e-01 1.31559491e-01 -5.14441490e-01
6.87622190e-01 2.73493797e-01 1.77369490e-01 -5.75819016e-01
-4.10405040e-01 -3.26642603e-01 -8.75401273e-02 -4.99033898e-01
6.83344901e-01 -8.04271817e-01 -5.53804755e-01 8.44674408e-01
-9.74333286e-01 -1.94886684e-01 -4.25434336e-02 8.28919590e-01
-5.87607205e-01 1.04908362e-01 -4.73492473e-01 -1.15415502e+00
-4.11832124e-01 -1.01149297e+00 8.48249435e-01 4.46001410e-01
6.38481462e-03 -1.13068497e+00 5.60635149e-01 -1.11896172e-01
7.80425668e-01 5.66082954e-01 4.68841940e-01 -4.82904613e-01
-5.07511497e-01 -2.05492854e-01 3.21378112e-01 2.78348267e-01
-7.44282752e-02 5.04786491e-01 -8.64768565e-01 -6.33075610e-02
1.16860926e-01 1.77737445e-01 7.98542380e-01 6.96565151e-01
1.16597641e+00 -1.40937895e-01 -3.50856811e-01 6.49033546e-01
1.39650416e+00 7.23375142e-01 7.04552174e-01 7.16106892e-02
4.65793908e-01 2.85878241e-01 4.92219955e-01 8.40639234e-01
5.78401864e-01 6.43621445e-01 2.93456286e-01 3.63434374e-01
3.24072391e-01 -4.09905076e-01 6.10161483e-01 1.31403637e+00
-2.45689571e-01 -3.36166561e-01 -1.15075254e+00 4.05948967e-01
-1.99049330e+00 -1.43334544e+00 -4.63173866e-01 2.06487703e+00
4.92113471e-01 2.43722256e-02 3.01521599e-01 1.53594106e-01
5.83854377e-01 1.61180839e-01 -7.34090030e-01 -8.10094550e-02
-1.11161709e-01 6.25147447e-02 1.66532367e-01 1.49695426e-01
-1.03515184e+00 4.14949745e-01 5.29166031e+00 1.00750983e+00
-1.28574598e+00 2.82339692e-01 6.43477619e-01 -7.57673681e-02
-4.68695849e-01 3.43646854e-02 -6.88460469e-01 1.04919994e+00
1.09272122e+00 -5.56277931e-01 3.31514180e-01 8.28931093e-01
3.89637977e-01 5.82508221e-02 -4.87286657e-01 1.12884927e+00
-3.75040695e-02 -1.01000130e+00 -4.13681090e-01 3.16238143e-02
8.02879989e-01 3.38373750e-01 1.31884813e-01 3.17248970e-01
1.64147392e-01 -8.27772379e-01 6.79194510e-01 1.13807404e+00
1.85005873e-01 -9.97935593e-01 7.26188183e-01 3.91564816e-01
-1.51006627e+00 2.06345513e-01 -2.79592484e-01 9.72563326e-02
6.09455645e-01 1.06274009e+00 -3.88778716e-01 6.40852928e-01
6.32440209e-01 9.23804283e-01 -1.23267069e-01 1.35180473e+00
-9.53198224e-02 1.22753131e+00 -7.02050030e-01 -2.33100012e-01
-7.73765966e-02 -5.05407512e-01 7.06481159e-01 9.17920113e-01
1.08423221e+00 2.35867754e-01 2.52544165e-01 9.73988593e-01
4.00487393e-01 -2.60638837e-02 -2.93335736e-01 -1.68220416e-01
5.78444660e-01 1.07170868e+00 -5.43778300e-01 -1.03263132e-01
-1.72633484e-01 5.19972026e-01 -2.61858940e-01 4.52022702e-01
-1.22618282e+00 -2.56630719e-01 5.70195854e-01 1.48774728e-01
2.75397122e-01 -6.57847583e-01 -7.53848106e-02 -1.41007721e+00
1.38691500e-01 -3.30472052e-01 4.82738525e-01 -8.94006908e-01
-1.40470350e+00 7.32387006e-01 2.13327155e-01 -1.55865204e+00
-5.53771019e-01 -1.27783537e-01 -9.99369860e-01 8.59067261e-01
-1.22767723e+00 -8.31249058e-01 -3.53598297e-01 4.85182673e-01
5.58262169e-01 -1.64832711e-01 7.59990811e-01 2.68537253e-01
-7.65902221e-01 2.34474272e-01 6.00334346e-01 -2.59699225e-01
3.36603582e-01 -8.82428110e-01 4.66911316e-01 9.91106391e-01
-9.44739878e-02 2.28141174e-01 8.59246075e-01 -6.66990995e-01
-1.51679611e+00 -1.12773752e+00 8.07850301e-01 -1.35011926e-01
7.18523026e-01 -1.69305146e-01 -1.07241416e+00 3.99076730e-01
2.27216616e-01 -2.13229835e-01 6.64572299e-01 -1.80567011e-01
6.83790958e-03 -3.42649251e-01 -1.01264286e+00 1.92490444e-01
7.42409229e-01 -1.99692577e-01 -2.05709979e-01 1.43292159e-01
3.73491228e-01 -3.41668785e-01 -1.05101776e+00 4.28309292e-01
5.50190985e-01 -8.28123271e-01 7.79857337e-01 -2.25762904e-01
3.83980125e-01 -3.27506006e-01 -1.96710721e-01 -1.51334810e+00
-2.77092725e-01 -7.42849886e-01 -4.54173356e-01 1.45175517e+00
2.47583225e-01 -1.07752156e+00 3.76564592e-01 6.76262736e-01
-1.55057415e-01 -8.10464859e-01 -1.03976750e+00 -1.05612969e+00
-1.31815985e-01 -7.97814608e-01 1.00150681e+00 7.85082400e-01
-1.23176835e-01 -3.84927504e-02 -4.39383715e-01 1.12143002e-01
6.67532504e-01 4.97988850e-01 6.14752829e-01 -1.29559755e+00
-3.60052466e-01 -4.98304814e-01 -4.02365893e-01 -6.64870024e-01
1.33757383e-01 -7.81662643e-01 -3.73410210e-02 -1.39685690e+00
5.59049509e-02 -5.78188121e-01 -3.44453901e-01 3.26001585e-01
-9.95956212e-02 -3.92848700e-02 1.71793535e-01 3.45010161e-01
-2.82764196e-01 1.11832988e+00 8.72609735e-01 1.14106923e-01
-1.86143622e-01 3.29717815e-01 -2.12134778e-01 4.82733369e-01
1.48401380e+00 -6.05836749e-01 -6.85890377e-01 -1.87556848e-01
1.74366012e-01 3.60464334e-01 3.93527001e-01 -1.02916014e+00
2.98858613e-01 -4.67247009e-01 2.41066068e-01 -9.27357972e-01
3.87857586e-01 -7.03152895e-01 1.02121365e+00 3.68906200e-01
1.30519226e-01 6.21638179e-01 3.35780501e-01 4.57766116e-01
-4.52717572e-01 6.72441944e-02 4.76957649e-01 2.94035971e-01
-5.91296494e-01 6.01975560e-01 -4.93795455e-01 4.47324291e-02
1.06377208e+00 -2.62145922e-02 -2.39571050e-01 -4.90988135e-01
-5.75151205e-01 1.32653370e-01 2.11345598e-01 5.11523366e-01
6.15218997e-01 -1.60338509e+00 -8.49411368e-01 1.76923946e-01
-2.48402804e-01 -2.34991148e-01 5.43845356e-01 1.07076383e+00
-2.36533090e-01 2.27642953e-01 1.09798715e-01 -6.29773438e-01
-9.45269883e-01 3.10379088e-01 2.77979672e-01 -2.33628288e-01
-4.75125164e-01 4.13530082e-01 -1.34821415e-01 -2.98128307e-01
-8.72457996e-02 -4.23790812e-01 8.57579410e-02 -2.72734393e-03
3.79241526e-01 6.86017096e-01 3.77887152e-02 -5.77296019e-01
-5.85385323e-01 6.87744141e-01 3.28415841e-01 -1.77246705e-01
1.60121536e+00 -7.74553139e-03 -1.43067986e-02 7.35815108e-01
1.19792962e+00 -2.73112565e-01 -1.26016855e+00 -2.60307100e-02
-3.12878758e-01 -2.97362268e-01 1.13633044e-01 -6.97763205e-01
-1.26185167e+00 8.37970853e-01 7.39480078e-01 3.59693587e-01
1.39693582e+00 -2.00049669e-01 1.14433026e+00 -1.80551693e-01
4.97369319e-01 -8.97050500e-01 -3.41022372e-01 5.45420408e-01
8.70444775e-01 -1.09128821e+00 -2.47760028e-01 -1.38665482e-01
-5.53091586e-01 1.09320688e+00 3.46507043e-01 -3.47990006e-01
1.22440684e+00 3.86754006e-01 -1.61343828e-01 7.94352144e-02
-9.33498383e-01 1.22121554e-02 6.67409003e-01 2.70542175e-01
5.15803993e-01 3.71287465e-01 -4.45733935e-01 8.69379342e-01
-3.89420927e-01 1.19857259e-01 7.82604665e-02 7.76265681e-01
-1.71554103e-01 -1.06124127e+00 -3.19703341e-01 4.06658649e-01
-2.56566495e-01 4.71991003e-02 7.50969648e-02 4.48446125e-01
-2.56072968e-01 9.51635301e-01 1.17644817e-01 -4.60890859e-01
2.34885633e-01 2.05407158e-01 1.89748645e-01 7.36420155e-02
-3.77002418e-01 2.93063670e-01 -2.70043105e-01 -3.29250485e-01
-5.42804837e-01 -1.16103482e+00 -1.31144321e+00 -5.34124374e-01
-2.76427388e-01 4.09624279e-01 8.43090534e-01 6.58709943e-01
7.17910886e-01 4.75306690e-01 7.73166120e-01 -6.64759099e-01
-4.26490784e-01 -9.46365714e-01 -4.72946942e-01 4.28128950e-02
-3.78721431e-02 -7.63131738e-01 -3.27964604e-01 -3.17667536e-02] | [6.915626049041748, 3.091158151626587] |
33568d25-795d-41cd-97ae-86f325eeebc1 | investigating-failures-to-generalize-for | 2303.09092 | null | https://arxiv.org/abs/2303.09092v1 | https://arxiv.org/pdf/2303.09092v1.pdf | Investigating Failures to Generalize for Coreference Resolution Models | Coreference resolution models are often evaluated on multiple datasets. Datasets vary, however, in how coreference is realized -- i.e., how the theoretical concept of coreference is operationalized in the dataset -- due to factors such as the choice of corpora and annotation guidelines. We investigate the extent to which errors of current coreference resolution models are associated with existing differences in operationalization across datasets (OntoNotes, PreCo, and Winogrande). Specifically, we distinguish between and break down model performance into categories corresponding to several types of coreference, including coreferring generic mentions, compound modifiers, and copula predicates, among others. This break down helps us investigate how state-of-the-art models might vary in their ability to generalize across different coreference types. In our experiments, for example, models trained on OntoNotes perform poorly on generic mentions and copula predicates in PreCo. Our findings help calibrate expectations of current coreference resolution models; and, future work can explicitly account for those types of coreference that are empirically associated with poor generalization when developing models. | ['Jackie Chi Kit Cheung', 'Adam Trischler', 'Kaheer Suleman', 'Alexandra Olteanu', 'Ian Porada'] | 2023-03-16 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [ 4.19973768e-02 2.13249654e-01 -5.12805283e-01 -3.98716986e-01
-8.35873544e-01 -1.10284007e+00 7.62456775e-01 4.62816805e-01
-5.43753028e-01 5.75084984e-01 1.13829052e+00 -2.34318569e-01
-5.55406690e-01 -5.32322586e-01 -5.73451042e-01 -2.09864914e-01
2.23022938e-01 1.00394177e+00 1.77237839e-01 -4.16161507e-01
3.56065989e-01 3.73563528e-01 -1.49494398e+00 4.90518272e-01
6.82711720e-01 2.14371905e-01 5.93038499e-02 1.39457822e-01
1.19947009e-02 4.65958327e-01 -6.43113732e-01 -6.59063101e-01
-8.79200920e-02 -1.71175331e-01 -1.23284197e+00 -6.78091228e-01
6.34304106e-01 1.63243920e-01 -3.11200291e-01 1.07185638e+00
4.56566900e-01 1.78789254e-02 6.10962927e-01 -1.13946688e+00
-4.45272595e-01 1.36679912e+00 -2.02378929e-01 4.90489602e-01
6.63611293e-01 -7.22918957e-02 1.46682429e+00 -3.25519413e-01
1.10809743e+00 1.69343269e+00 8.99119437e-01 5.70636272e-01
-1.41529202e+00 -9.04340386e-01 -1.84444822e-02 4.12431508e-01
-1.15687156e+00 -8.10098588e-01 4.82719719e-01 -3.89076680e-01
9.05128181e-01 3.37206274e-01 9.25016627e-02 1.28854167e+00
-1.18287563e-01 5.20124137e-01 9.53196049e-01 -3.71437579e-01
-2.08745241e-01 -5.88642098e-02 6.21503353e-01 -2.26980150e-01
7.36389160e-01 4.01816010e-01 -5.94667017e-01 -2.73514479e-01
2.63208479e-01 -6.70295000e-01 -8.70000839e-01 -3.19412827e-01
-1.03447402e+00 7.45507717e-01 3.24997425e-01 6.42441571e-01
-1.63226292e-01 -5.55582047e-01 5.82240880e-01 1.43713549e-01
-1.79111362e-01 1.07220984e+00 -5.79877138e-01 -3.04635465e-01
-6.67473197e-01 5.48023701e-01 1.04469621e+00 8.54510605e-01
4.51747268e-01 -4.99064296e-01 8.76062587e-02 9.28380847e-01
4.65746410e-02 1.39106899e-01 5.14675438e-01 -1.30313921e+00
8.43299568e-01 4.50550824e-01 3.93077880e-01 -7.93352544e-01
-8.78089190e-01 -1.62532896e-01 1.94118619e-01 -2.25751013e-01
6.10285759e-01 -1.08164474e-02 -2.53045440e-01 2.22921205e+00
-1.59670017e-03 -2.63430834e-01 3.78588080e-01 1.04136598e+00
9.75062549e-01 -2.55875587e-02 5.70455432e-01 -3.60450655e-01
1.71893954e+00 -4.77030516e-01 -6.44590259e-01 -5.94919801e-01
9.15851533e-01 -9.31650937e-01 9.58150268e-01 -3.77539955e-02
-1.08742607e+00 -2.97154337e-01 -8.33610177e-01 -1.10317595e-01
-3.48966271e-02 -5.49041271e-01 6.18717194e-01 4.23375934e-01
-4.33730543e-01 7.62014091e-01 -6.66773975e-01 -7.66557515e-01
-1.54783025e-01 2.72993267e-01 -5.71046591e-01 1.15543567e-01
-1.50594175e+00 1.41261256e+00 8.70252907e-01 -3.98664236e-01
-2.26686925e-01 -9.42333639e-01 -8.62079859e-01 2.38568053e-01
2.09970906e-01 -5.62055826e-01 1.48701501e+00 -9.61613595e-01
-7.36717880e-01 1.22414780e+00 -1.18782688e-02 -3.62946332e-01
-5.53021580e-02 -3.13455701e-01 -7.38316357e-01 -6.50556907e-02
4.14890140e-01 4.89727437e-01 -1.99320227e-01 -1.33835173e+00
-8.58617067e-01 -5.45075834e-01 3.65117848e-01 5.12772501e-01
1.71355247e-01 4.15788352e-01 -1.35744065e-01 -2.55653828e-01
2.88400203e-01 -1.04486620e+00 4.46693867e-01 -6.42208993e-01
-1.90363616e-01 -5.29452324e-01 6.14122152e-01 -2.22936600e-01
1.34075916e+00 -2.25884128e+00 1.08621322e-01 -1.96234494e-01
-2.40652859e-01 1.37076125e-01 -7.85215199e-02 6.92676663e-01
-5.97823143e-01 2.82727301e-01 1.13792025e-01 -1.30155087e-01
2.53013551e-01 3.24612468e-01 -5.01226366e-01 4.63169307e-01
-2.21437335e-01 7.11538911e-01 -9.56072092e-01 -5.85107505e-01
-5.97847365e-02 2.81660795e-01 -6.39383972e-01 -1.26677006e-01
-5.91790825e-02 2.28307396e-01 -1.98037893e-01 3.34716529e-01
5.46227574e-01 1.72055200e-01 7.93495655e-01 -5.39287329e-01
-4.92850095e-01 1.36322367e+00 -1.05219960e+00 1.50714326e+00
-1.15680762e-01 5.63762963e-01 2.46268287e-01 -7.21155941e-01
5.54267704e-01 5.47890186e-01 1.80400252e-01 -6.14428520e-01
3.27588208e-02 2.71534950e-01 6.05137587e-01 -3.64574373e-01
8.77738059e-01 -3.84759903e-01 -1.94630861e-01 4.63851482e-01
-6.21858751e-03 -5.32198623e-02 3.07567090e-01 1.75477073e-01
7.42549837e-01 4.34788689e-02 3.41427654e-01 -6.69030726e-01
1.25259906e-01 5.09653032e-01 1.16102624e+00 6.03076756e-01
-3.27063262e-01 5.13669789e-01 6.76943660e-01 5.24704047e-02
-7.55258501e-01 -1.05549157e+00 -8.48436832e-01 1.11701214e+00
2.70633847e-01 -5.80787361e-01 -5.23782313e-01 -4.65734690e-01
-5.70913963e-02 1.34188938e+00 -5.56509614e-01 -2.24573150e-01
-8.45112503e-01 -6.73692882e-01 9.72634852e-01 6.74849093e-01
-6.81604296e-02 -1.16660440e+00 -8.99936080e-01 2.36524418e-01
-7.70254493e-01 -1.11170650e+00 -3.11834097e-01 2.33089924e-01
-8.22896242e-01 -1.62088048e+00 1.54034540e-01 -6.29951596e-01
-3.40954848e-02 2.94372529e-01 1.59030032e+00 2.12393418e-01
4.04894292e-01 5.31084418e-01 -4.54534531e-01 -4.22226608e-01
-5.02207577e-01 2.14107037e-01 -1.63948424e-02 -7.46012986e-01
1.03993547e+00 -4.04141665e-01 -2.67490983e-01 2.89863765e-01
-6.77060187e-01 -4.30156380e-01 1.59447417e-01 6.28522158e-01
7.77447820e-02 -4.04069722e-01 3.00275058e-01 -1.29828286e+00
7.95772910e-01 -6.23572350e-01 -2.09499925e-01 3.21751356e-01
-5.51687717e-01 1.12840645e-01 -5.39310575e-02 -3.59096557e-01
-1.37384164e+00 -7.37806439e-01 8.82664621e-02 -1.20582068e-02
-2.55036652e-01 5.74536860e-01 -2.73019105e-01 3.49592894e-01
9.15918648e-01 -4.73740757e-01 -3.07607859e-01 -5.12680113e-01
1.64570093e-01 6.25234306e-01 9.67158318e-01 -1.31248462e+00
4.75953817e-01 1.25026777e-01 -4.65123028e-01 -5.30235946e-01
-1.11829722e+00 -4.30440307e-01 -6.93551481e-01 3.73078853e-01
7.22072423e-01 -9.76336598e-01 -7.77909696e-01 -2.72839610e-02
-1.12009478e+00 -3.74718964e-01 -7.48215392e-02 8.26124012e-01
-4.94515955e-01 4.60605651e-01 -7.89611042e-01 -2.02064887e-01
-3.99944127e-01 -1.21901762e+00 6.93950534e-01 3.92208964e-01
-1.27795732e+00 -1.10474110e+00 4.10682976e-01 4.11535949e-01
7.74393305e-02 1.32990077e-01 1.20653284e+00 -1.00429118e+00
8.18170086e-02 2.24541038e-01 -1.63325846e-01 -3.50791425e-01
-7.08428072e-03 9.11479443e-02 -7.93308437e-01 -3.07180911e-01
2.63547152e-02 -4.25607294e-01 4.92599547e-01 2.43965089e-01
3.22112978e-01 -9.10316873e-03 -9.10918117e-01 5.26189089e-01
1.22851026e+00 1.58563003e-01 5.22282243e-01 9.17038023e-01
1.22334063e-01 8.85073960e-01 9.14928317e-01 -1.20986104e-01
6.65908396e-01 9.28880513e-01 1.90126017e-01 6.14885449e-01
-9.99442711e-02 -1.99449658e-01 9.51325893e-02 4.76308018e-01
7.65264556e-02 -5.76473549e-02 -1.08689606e+00 8.77665937e-01
-1.69068658e+00 -1.25904131e+00 -3.36762995e-01 2.27163410e+00
1.07967508e+00 2.72464037e-01 -1.81793887e-02 -2.23814044e-02
9.10176396e-01 2.65249133e-01 -5.06656095e-02 -3.68515939e-01
-3.30863476e-01 2.93004870e-01 2.46381462e-01 7.60470867e-01
-8.58787119e-01 9.62284446e-01 6.58317566e+00 -7.03348443e-02
-9.50086176e-01 -8.97910073e-03 -3.57021272e-01 -3.56482640e-02
-2.90218413e-01 3.83531332e-01 -8.99338007e-01 2.80795813e-01
9.34497595e-01 -1.60966277e-01 2.04992607e-01 4.85720545e-01
-2.79598713e-01 9.92453471e-02 -1.55559444e+00 3.99420589e-01
3.67116816e-02 -9.69742715e-01 -1.85495108e-01 -2.48590156e-01
3.15384924e-01 3.03594232e-01 -2.92554557e-01 5.02067626e-01
5.60546935e-01 -7.63883770e-01 9.12852168e-01 -7.47029111e-02
3.91802847e-01 -4.47856575e-01 7.05129802e-01 7.42898583e-02
-9.61490095e-01 -1.87989950e-01 -2.86946386e-01 -4.52672951e-02
3.56523395e-01 -3.10395151e-01 -4.14460152e-01 5.23709595e-01
7.93065488e-01 1.65201813e-01 -3.14761490e-01 9.12131310e-01
-4.46233481e-01 5.21431804e-01 -3.56873572e-01 4.20004845e-01
1.03661835e-01 1.67110890e-01 7.58234024e-01 1.35206854e+00
-1.03218809e-01 4.53886360e-01 1.55029222e-02 6.98268592e-01
2.14313984e-01 -2.32296839e-01 -1.59501061e-01 2.30421275e-01
1.48529255e+00 1.03457856e+00 -3.44123393e-01 -1.43729839e-02
-6.15082443e-01 2.26862818e-01 5.83032846e-01 2.26962864e-01
-7.37515628e-01 -1.42479539e-01 1.12723815e+00 1.65340513e-01
8.40125084e-02 1.80006742e-01 -1.95686966e-01 -1.15285230e+00
-2.99759239e-01 -1.20736396e+00 1.18476677e+00 -7.73431122e-01
-1.13087881e+00 3.56937259e-01 5.06455481e-01 -6.03892505e-01
-2.57759303e-01 -4.21274334e-01 -7.01793134e-01 8.04159880e-01
-1.21347320e+00 -1.07343698e+00 -1.34100482e-01 5.77166557e-01
1.70644134e-01 3.02270859e-01 1.08072233e+00 2.32915118e-01
-3.72492969e-01 7.62881577e-01 -3.71682465e-01 4.61646199e-01
1.38106406e+00 -1.09650564e+00 2.70879269e-01 5.67943573e-01
1.95138112e-01 1.17405283e+00 1.23597789e+00 -5.65204620e-01
-8.27331662e-01 -4.46902424e-01 1.04715991e+00 -7.13515222e-01
7.40898371e-01 3.17161769e-01 -1.25230384e+00 1.40972888e+00
3.62611473e-01 -5.42428970e-01 9.24769402e-01 1.16916358e+00
-9.39670384e-01 2.31706113e-01 -1.18047738e+00 5.92905223e-01
1.37251246e+00 -5.85088313e-01 -1.67708409e+00 -1.89298868e-01
6.89851046e-01 -7.37938941e-01 -1.19719243e+00 7.08520055e-01
3.38290274e-01 -9.99247432e-01 8.56853902e-01 -1.07493842e+00
1.66656718e-01 8.16781167e-03 -3.65146995e-01 -1.32362354e+00
-7.51193583e-01 -3.25099796e-01 2.35841945e-01 1.65510607e+00
4.92297322e-01 -5.53211153e-01 3.61386687e-01 9.84110892e-01
-4.18559432e-01 -8.35484415e-02 -8.27970266e-01 -4.12721664e-01
6.35202885e-01 -3.53402942e-01 7.66823173e-01 1.45718741e+00
7.29020894e-01 6.90456212e-01 4.78959143e-01 3.27338219e-01
6.15738511e-01 3.37376058e-01 5.40022790e-01 -1.73352540e+00
-1.76696241e-01 -6.04796588e-01 -5.11159077e-02 -6.25931561e-01
6.37058437e-01 -7.69822180e-01 -2.28491753e-01 -1.06368911e+00
2.52112180e-01 -8.40565681e-01 -1.51686579e-01 4.23342168e-01
-2.52806574e-01 -1.01064421e-01 5.85966706e-01 5.23441076e-01
-2.64344156e-01 -9.33694765e-02 7.24192321e-01 2.25736216e-01
-3.43857259e-01 -3.36014867e-01 -1.33311522e+00 9.34928775e-01
5.92544377e-01 -5.97264647e-01 -1.47668183e-01 -7.02472925e-01
2.71563921e-02 2.70182520e-01 8.09467733e-02 -8.09417844e-01
4.14460480e-01 -3.40552330e-01 -1.18796445e-01 -2.06902653e-01
2.96718419e-01 -5.34355342e-01 3.73795360e-01 2.57464677e-01
-4.73468721e-01 2.08559901e-01 5.37473738e-01 2.07397323e-02
-1.52566880e-01 -5.39089978e-01 6.38432682e-01 -2.17714995e-01
-6.56218708e-01 -4.23566848e-01 -6.16760878e-03 8.48419189e-01
5.25107324e-01 -6.12653680e-02 -8.56435657e-01 -3.76499109e-02
-5.51499307e-01 2.50591725e-01 7.73238122e-01 8.68452847e-01
-2.46385649e-01 -1.21720970e+00 -7.67778933e-01 -3.74043614e-01
2.64845908e-01 -2.31097341e-01 7.32083246e-02 7.46379197e-01
-4.67683040e-02 7.81066477e-01 -2.90994674e-01 -3.04706126e-01
-1.38374949e+00 5.86805999e-01 5.63749492e-01 -2.74445653e-01
-5.06456017e-01 4.88277406e-01 3.17782015e-01 -6.19439244e-01
1.25412956e-01 -6.03910051e-02 -3.35056961e-01 4.06951368e-01
3.63592118e-01 1.10090613e-01 -1.87579811e-01 -1.03164935e+00
-6.93022668e-01 5.09731948e-01 -3.41567934e-01 -9.37907621e-02
1.16707706e+00 -4.88845371e-02 -2.24826466e-02 4.02786583e-01
6.45329893e-01 1.82743981e-01 -8.36871743e-01 -2.11881012e-01
3.75426590e-01 -1.39827341e-01 -2.78885156e-01 -8.64448726e-01
-5.43532908e-01 3.79914016e-01 1.32751107e-01 -5.94052635e-02
6.42835855e-01 3.12734008e-01 3.88528913e-01 1.14884824e-01
5.98735094e-01 -9.60540175e-01 -5.85727990e-01 5.67586958e-01
8.17449868e-01 -7.96888590e-01 1.23189770e-01 -2.52489924e-01
-6.72407925e-01 1.02939224e+00 8.29285443e-01 8.31829980e-02
2.69182503e-01 3.52079839e-01 2.24627465e-01 -4.47861910e-01
-8.17009985e-01 -1.30806148e-01 -2.72317021e-03 6.24718547e-01
1.04552162e+00 2.67690301e-01 -9.32946861e-01 8.59902263e-01
-8.36688697e-01 -3.64949822e-01 6.02229178e-01 4.95899290e-01
-3.05322409e-01 -1.19139183e+00 -6.21741474e-01 3.66554521e-02
-7.61309385e-01 -1.68200344e-01 -6.36353731e-01 1.36593354e+00
2.20584702e-02 1.00965607e+00 5.66530168e-01 -1.44774735e-01
6.10923290e-01 2.84131706e-01 5.80545485e-01 -8.86666417e-01
-7.54248798e-01 -3.72858524e-01 6.90039754e-01 -3.93240064e-01
-6.15423620e-01 -1.24763668e+00 -1.33296013e+00 -4.06985164e-01
-6.32147133e-01 6.29895627e-01 8.60878825e-03 1.24046946e+00
1.33101627e-01 5.08969054e-02 -4.00703430e-01 -4.86232370e-01
-6.62986100e-01 -1.23370361e+00 -2.48888344e-01 1.03257823e+00
-3.77425291e-02 -1.01744843e+00 -4.93074208e-01 -4.29300845e-01] | [9.346334457397461, 9.512125968933105] |
0d8ce309-c674-44c1-ac88-a52250ea846d | osis-efficient-one-stage-network-for-3d | 2303.07011 | null | https://arxiv.org/abs/2303.07011v1 | https://arxiv.org/pdf/2303.07011v1.pdf | OSIS: Efficient One-stage Network for 3D Instance Segmentation | Current 3D instance segmentation models generally use multi-stage methods to extract instance objects, including clustering, feature extraction, and post-processing processes. However, these multi-stage approaches rely on hyperparameter settings and hand-crafted processes, which restrict the inference speed of the model. In this paper, we propose a new 3D point cloud instance segmentation network, named OSIS. OSIS is a one-stage network, which directly segments instances from 3D point cloud data using neural network. To segment instances directly from the network, we propose an instance decoder, which decodes instance features from the network into instance segments. Our proposed OSIS realizes the end-to-end training by bipartite matching, therefore, our network does not require computationally expensive post-processing steps such as non maximum suppression (NMS) and clustering during inference. The results show that our network finally achieves excellent performance in the commonly used indoor scene instance segmentation dataset, and the inference speed of our network is only an average of 138ms per scene, which substantially exceeds the previous fastest method. | ['Xi Yang', 'Chuan Tang'] | 2023-03-13 | null | null | null | null | ['3d-instance-segmentation-1'] | ['computer-vision'] | [ 5.07471621e-01 1.20247953e-01 -2.93302417e-01 -6.69355810e-01
-7.15496361e-01 -3.54563832e-01 2.15597421e-01 -3.75096276e-02
-6.26339674e-01 1.78040072e-01 -5.73850870e-01 -4.67209935e-01
-4.20635641e-02 -9.57282245e-01 -1.02763343e+00 -5.21046162e-01
2.75795639e-01 8.80307734e-01 5.36620259e-01 4.14044082e-01
3.99030417e-01 6.41012371e-01 -1.49807119e+00 -4.57123779e-02
8.74712884e-01 1.16198111e+00 4.67280388e-01 7.67757177e-01
-7.00865149e-01 2.42479444e-01 -7.18608499e-01 -1.96137875e-01
3.50510895e-01 -8.94730687e-02 -6.63967073e-01 2.48675391e-01
3.71571809e-01 -2.89511085e-01 -3.70344192e-01 1.13468182e+00
5.25986612e-01 7.65040889e-02 5.33699811e-01 -1.31990373e+00
-1.20931730e-01 5.99166036e-01 -6.86310112e-01 -1.25994518e-01
-8.87747258e-02 6.34892210e-02 6.61730289e-01 -8.07634532e-01
2.33287483e-01 1.15500808e+00 7.84184575e-01 2.94852018e-01
-9.45836246e-01 -9.14480925e-01 3.09923589e-01 3.35829966e-02
-1.73708487e+00 -3.34977746e-01 7.35991299e-01 -2.09776357e-01
1.07707608e+00 2.70545632e-01 8.67919147e-01 3.95842463e-01
-3.03753018e-01 1.10715437e+00 7.42777467e-01 7.40563672e-04
3.88863176e-01 -1.45562142e-01 2.43085459e-01 6.12891078e-01
9.31148231e-02 -1.33625120e-01 -7.23306015e-02 8.43448937e-02
1.04373300e+00 1.63622439e-01 5.86299412e-03 -5.88830300e-02
-1.17045820e+00 3.52877617e-01 5.19231796e-01 -1.42973781e-01
-1.87129512e-01 3.49127531e-01 2.49018118e-01 1.84589643e-02
4.77476895e-01 -6.14701994e-02 -7.06076622e-01 -4.21810001e-02
-1.26537144e+00 8.34376365e-02 8.43794644e-01 1.61046910e+00
1.01447761e+00 -6.01388514e-01 -1.50370911e-01 8.00574481e-01
5.44341922e-01 8.02012801e-01 -1.43054292e-01 -9.26120520e-01
6.89678252e-01 8.28377604e-01 -2.45042995e-01 -8.65209043e-01
-4.95849818e-01 -3.50591332e-01 -9.38506246e-01 -2.09647864e-01
9.50615332e-02 -1.62170410e-01 -1.23033631e+00 1.29617536e+00
8.09479177e-01 7.44759083e-01 -1.80489525e-01 1.13746536e+00
1.02309442e+00 8.58614385e-01 -3.23410146e-02 1.97189860e-02
1.10682499e+00 -1.11430025e+00 -2.55608737e-01 -3.93038243e-01
3.15100461e-01 -6.47968352e-01 8.92399013e-01 2.18199670e-01
-1.23860681e+00 -7.18415082e-01 -1.04222286e+00 -2.84574360e-01
-2.16825813e-01 2.29965821e-01 6.11361027e-01 4.66089576e-01
-7.32350111e-01 4.71467793e-01 -1.00395024e+00 -2.88959034e-02
6.33044899e-01 9.31402445e-01 -1.51898069e-02 -3.68635729e-02
-6.80211067e-01 2.85052299e-01 7.33000338e-01 5.26995540e-01
-6.79919600e-01 -6.00790799e-01 -7.64005005e-01 1.71065867e-01
5.96604764e-01 -7.63337255e-01 1.19136500e+00 -4.80982602e-01
-1.81182301e+00 7.61122108e-01 -5.01608133e-01 -1.33144915e-01
2.09474325e-01 -7.09954724e-02 9.68316197e-02 7.49154314e-02
1.79098882e-02 8.71249139e-01 7.97822416e-01 -1.16706312e+00
-9.08132851e-01 -4.85954851e-01 9.44168195e-02 2.61353523e-01
1.54266998e-01 -2.72836357e-01 -1.52777660e+00 -1.03027761e-01
7.27805614e-01 -1.13144386e+00 -5.94162524e-01 -5.45697995e-02
-8.22145998e-01 -2.75485873e-01 7.60215282e-01 -3.32093328e-01
9.74266827e-01 -2.32152843e+00 -8.13254118e-02 7.11543918e-01
2.38332659e-01 1.14557356e-01 2.72017494e-02 -5.77879213e-02
3.37359965e-01 9.77608934e-02 -7.28601158e-01 -5.76514244e-01
3.44022810e-01 4.21107918e-01 3.88828069e-02 3.57599586e-01
1.79044798e-01 8.56959939e-01 -6.99795067e-01 -8.58244181e-01
4.03726280e-01 3.08071882e-01 -7.04462469e-01 2.35121995e-01
-4.69811529e-01 4.01337087e-01 -5.63699901e-01 5.15152037e-01
1.19378579e+00 -5.32939374e-01 -1.87838953e-02 -1.18130378e-01
-5.50440289e-02 3.94462168e-01 -1.34747684e+00 2.16045809e+00
-3.45956981e-01 3.43664467e-01 2.69829817e-02 -1.03389752e+00
8.32707822e-01 -5.52631961e-03 3.18947405e-01 -6.32680237e-01
1.62217483e-01 2.48388067e-01 -3.92580152e-01 -5.40366530e-01
3.71720284e-01 3.46413195e-01 -1.89413905e-01 2.00095400e-01
-1.50338992e-01 -4.60150748e-01 7.74030611e-02 4.42083878e-03
9.65781510e-01 1.89116910e-01 -2.01294228e-01 6.95141777e-02
6.63739979e-01 2.63282418e-01 8.24781418e-01 8.17823589e-01
5.97505122e-02 6.09660804e-01 3.81855249e-01 -1.21734671e-01
-9.59770679e-01 -1.01530623e+00 -2.26199538e-01 6.44760430e-01
8.17000926e-01 -2.71075577e-01 -1.00757062e+00 -6.76597714e-01
-4.38818857e-02 4.31037545e-01 3.06932684e-02 2.97476966e-02
-5.40216088e-01 -7.34593928e-01 5.78287482e-01 5.40942848e-01
9.76869643e-01 -7.68245757e-01 -2.88573295e-01 2.78866500e-01
-7.83843100e-02 -1.47406960e+00 -3.70400399e-01 1.97032437e-01
-1.29168224e+00 -9.39906538e-01 -4.55653757e-01 -9.10582662e-01
1.07078850e+00 2.13727325e-01 1.02296102e+00 2.81957626e-01
-2.19123811e-01 -9.08286795e-02 2.78628543e-02 -2.78941691e-01
1.43738464e-01 5.60946047e-01 -2.39264712e-01 -3.95925611e-01
6.97654784e-01 -6.43588424e-01 -6.93942726e-01 3.82395446e-01
-7.71715283e-01 3.74498427e-01 9.36411083e-01 4.69536453e-01
1.16258478e+00 1.80215761e-01 1.78079814e-01 -9.44583833e-01
1.08236678e-01 -2.26075634e-01 -1.06135964e+00 6.36486039e-02
-3.30070168e-01 3.44532565e-03 4.92144316e-01 -1.89918622e-01
-1.00555825e+00 4.85496789e-01 -5.03608644e-01 -5.32362759e-01
-4.88197744e-01 4.30262744e-01 -3.80904794e-01 -1.55578613e-01
1.54601768e-01 3.02701533e-01 -2.20553711e-01 -4.36698914e-01
2.89457500e-01 9.63574171e-01 6.33135021e-01 -5.95996976e-01
9.39629912e-01 3.35189432e-01 4.33741063e-02 -7.89444208e-01
-8.85424614e-01 -5.75143814e-01 -8.04962814e-01 -4.55398969e-02
1.02270544e+00 -9.38402772e-01 -1.24451697e+00 5.72776675e-01
-1.38528311e+00 -4.97710258e-01 4.83977273e-02 5.89565873e-01
-5.25850713e-01 1.36680111e-01 -7.68861711e-01 -6.32474422e-01
-3.46915424e-01 -1.28700757e+00 1.45942926e+00 4.01531190e-01
3.29266876e-01 -5.69129288e-01 -3.32572311e-01 5.66130102e-01
3.16190012e-02 9.74089578e-02 8.40167820e-01 -4.88348544e-01
-1.30884850e+00 -1.36036351e-01 -6.26871943e-01 1.73078164e-01
-2.30388969e-01 7.74082318e-02 -1.00199604e+00 -3.11299227e-02
-2.04320624e-01 1.11921065e-01 6.49515569e-01 4.75198030e-01
1.85075569e+00 -6.82518333e-02 -6.69820964e-01 1.09522378e+00
1.42007279e+00 3.15403134e-01 6.07482135e-01 7.91201144e-02
8.40400636e-01 1.70808703e-01 6.72391832e-01 2.96667010e-01
6.06642962e-01 2.99625784e-01 5.75521529e-01 -2.71894872e-01
2.57261097e-01 -2.20156744e-01 -1.30725190e-01 1.17147672e+00
2.91631799e-02 -1.79702267e-01 -9.23524201e-01 3.77525657e-01
-1.96746123e+00 -6.02896690e-01 -2.03621402e-01 2.04558563e+00
6.87106490e-01 4.66327608e-01 -2.20294356e-01 2.30967235e-02
8.81188631e-01 4.15144823e-02 -9.08221483e-01 2.69083492e-02
5.72742462e-01 3.70916605e-01 7.73274541e-01 4.22892720e-01
-1.12974668e+00 1.21678913e+00 5.59248352e+00 9.35635626e-01
-8.58642936e-01 -1.84059232e-01 5.54677308e-01 -2.48115212e-01
3.87644581e-02 -1.79198440e-02 -1.02037752e+00 6.20573997e-01
6.88925147e-01 3.34739417e-01 4.39498842e-01 1.05165493e+00
1.53555661e-01 -2.77547445e-02 -1.15216660e+00 1.22286928e+00
-1.79063261e-01 -1.27001154e+00 -2.74190843e-01 1.22738874e-03
4.39245880e-01 2.57566720e-01 -3.09362710e-01 3.95638973e-01
2.60903165e-02 -7.38429785e-01 5.21296084e-01 4.13682669e-01
6.22170985e-01 -1.09913862e+00 6.04421616e-01 6.91017210e-01
-1.22729242e+00 2.56168842e-01 -7.45753586e-01 1.37528688e-01
2.88079649e-01 9.99255121e-01 -8.01907122e-01 4.03345406e-01
7.08757877e-01 4.69289452e-01 -1.97079614e-01 1.36309898e+00
-1.78095266e-01 5.94358087e-01 -8.97532761e-01 -1.64962307e-01
2.95619816e-01 -4.21475828e-01 2.98031032e-01 1.19609058e+00
3.46601367e-01 4.76409882e-01 4.75657374e-01 1.03449702e+00
-2.61307925e-01 -3.74812216e-01 -2.15191528e-01 8.79945308e-02
8.04700673e-01 1.24705493e+00 -1.03010988e+00 -5.35440028e-01
-2.06641659e-01 1.12078965e+00 1.51142508e-01 2.43487671e-01
-1.19781327e+00 -9.33368862e-01 6.49185598e-01 -2.65284389e-01
3.59316707e-01 -4.83943105e-01 -5.44958472e-01 -8.51116300e-01
1.03157535e-01 -4.53105927e-01 -1.10726796e-01 -5.92488825e-01
-9.72663820e-01 2.56954610e-01 -7.10573494e-02 -9.92610693e-01
7.05111995e-02 -5.07664859e-01 -4.50438410e-01 6.39242947e-01
-1.52811790e+00 -8.35093856e-01 -6.78360283e-01 6.46751642e-01
4.85798329e-01 4.34849530e-01 1.96895882e-01 6.63834333e-01
-8.40705335e-01 4.57660466e-01 -2.46503219e-01 3.26895207e-01
2.03954905e-01 -1.11645591e+00 9.01542306e-01 5.95677674e-01
2.99800113e-02 5.76674700e-01 1.72378272e-01 -6.43369019e-01
-1.47040176e+00 -1.31239510e+00 7.05027938e-01 -8.91204253e-02
1.64780971e-02 -6.10661209e-01 -6.85800791e-01 6.08129561e-01
-1.50411770e-01 -1.14869112e-02 6.08890891e-01 1.31993309e-01
9.56071764e-02 -1.68823183e-01 -1.04918861e+00 6.11525178e-01
1.61892688e+00 -3.90805304e-01 -3.58946294e-01 5.14943123e-01
1.04738760e+00 -1.01924276e+00 -8.52900326e-01 6.04533792e-01
1.88378349e-01 -6.40028238e-01 1.13935208e+00 -2.08200827e-01
3.70520145e-01 -6.72908425e-01 -3.74035574e-02 -7.12923467e-01
-3.52860153e-01 -3.61546814e-01 -1.91576734e-01 1.19676721e+00
7.39110351e-01 -4.43377584e-01 1.19365990e+00 7.23997176e-01
-4.59428817e-01 -8.56075764e-01 -7.60744154e-01 -5.03972650e-01
-5.31478643e-01 -8.42466116e-01 1.16507828e+00 6.43855810e-01
-6.15793228e-01 4.01715606e-01 1.99614346e-01 7.32022226e-01
9.02357161e-01 3.90281290e-01 1.15673399e+00 -1.21476340e+00
-2.80920208e-01 -2.31993720e-01 -2.76647121e-01 -1.99636281e+00
1.95304304e-01 -8.89381409e-01 4.38116789e-01 -1.75244534e+00
1.87190548e-02 -1.06464005e+00 9.24845343e-04 4.08375949e-01
-1.92843497e-01 1.79635987e-01 -3.29288864e-03 2.07095623e-01
-8.39039028e-01 4.34040099e-01 1.39527237e+00 -3.13172460e-01
-4.60414082e-01 2.84276485e-01 -2.87259370e-01 8.75259519e-01
6.19868875e-01 -6.22224569e-01 -5.37415862e-01 -8.14891696e-01
1.35269344e-01 1.53528273e-01 2.91647404e-01 -1.29054832e+00
6.65867984e-01 -1.70297921e-01 6.94023669e-01 -1.30762219e+00
5.52620649e-01 -1.14563417e+00 1.66643322e-01 2.66150445e-01
2.56553981e-02 -1.74961060e-01 2.72488087e-01 5.53466082e-01
-5.40295690e-02 -4.39506799e-01 4.05409157e-01 -1.91659510e-01
-7.23784268e-01 8.31779957e-01 -3.80492955e-02 -1.22182993e-02
9.88549352e-01 -5.65911710e-01 -4.56281938e-02 2.88128525e-01
-6.72566354e-01 5.40661395e-01 2.91172862e-01 1.85267732e-01
6.05747461e-01 -1.10228026e+00 -2.85289019e-01 2.79287815e-01
-2.29846567e-01 1.21314871e+00 3.70997727e-01 7.29416609e-01
-8.19938421e-01 1.50768220e-01 4.21470374e-01 -1.28034747e+00
-1.00829005e+00 3.53196770e-01 1.04894325e-01 1.11075789e-01
-7.17859924e-01 9.14751589e-01 1.87901575e-02 -9.93894219e-01
3.07925403e-01 -4.81068522e-01 2.51063287e-01 -2.94517994e-01
4.35692556e-02 3.50310594e-01 -5.23501672e-02 -1.87426701e-01
-4.32304382e-01 8.17789197e-01 -4.57116924e-02 -8.21319222e-02
1.21979594e+00 4.19262052e-02 -3.32719505e-01 2.66489744e-01
1.26619732e+00 -4.84521925e-01 -1.40932000e+00 -1.05132371e-01
-8.82496983e-02 -3.93587619e-01 1.09233066e-01 -4.49356705e-01
-1.30214095e+00 9.15249467e-01 3.57811898e-01 -7.11445287e-02
1.18252110e+00 -8.68462697e-02 1.35199523e+00 7.89028823e-01
5.22682965e-01 -1.31408799e+00 -4.26447093e-01 7.85871565e-01
6.26037940e-02 -1.03796053e+00 -6.66820183e-02 -7.58296609e-01
-7.66328573e-02 8.10782313e-01 8.48419905e-01 -2.59895205e-01
8.05389106e-01 3.21561277e-01 -9.97670665e-02 -1.74939767e-01
-4.40865785e-01 -2.22709969e-01 1.16289094e-01 1.95682555e-01
-8.02382529e-02 -9.27202925e-02 -3.02459970e-02 5.47084093e-01
-2.99413592e-01 1.28937930e-01 -1.61294878e-01 8.15226436e-01
-5.61740339e-01 -8.45768809e-01 -2.23284751e-01 5.76563895e-01
-2.65355229e-01 -1.18125811e-01 -2.12577507e-01 7.76934981e-01
2.72816598e-01 8.29816222e-01 5.15678644e-01 -5.25904655e-01
4.04176086e-01 -1.85763031e-01 3.53390694e-01 -5.47625363e-01
-6.16791010e-01 -5.11204861e-02 -2.10522369e-01 -6.14558458e-01
-4.60457027e-01 -2.90409654e-01 -1.72197139e+00 -3.29995811e-01
-7.28415370e-01 8.37927312e-02 1.07830739e+00 1.06522071e+00
7.41772830e-01 5.40539920e-01 6.19264185e-01 -1.06043506e+00
3.17074396e-02 -6.94252253e-01 -2.01332301e-01 -1.83218115e-04
1.18343860e-01 -3.88070285e-01 -1.35916665e-01 -9.70732123e-02] | [8.037376403808594, -3.1371748447418213] |
a4cdb29b-a297-4d97-8ab4-681a3d659196 | utilizing-chatgpt-generated-data-to-retrieve | 2307.02313 | null | https://arxiv.org/abs/2307.02313v2 | https://arxiv.org/pdf/2307.02313v2.pdf | Utilizing ChatGPT Generated Data to Retrieve Depression Symptoms from Social Media | In this work, we present the contribution of the BLUE team in the eRisk Lab task on searching for symptoms of depression. The task consists of retrieving and ranking Reddit social media sentences that convey symptoms of depression from the BDI-II questionnaire. Given that synthetic data provided by LLMs have been proven to be a reliable method for augmenting data and fine-tuning downstream models, we chose to generate synthetic data using ChatGPT for each of the symptoms of the BDI-II questionnaire. We designed a prompt such that the generated data contains more richness and semantic diversity than the BDI-II responses for each question and, at the same time, contains emotional and anecdotal experiences that are specific to the more intimate way of sharing experiences on Reddit. We perform semantic search and rank the sentences' relevance to the BDI-II symptoms by cosine similarity. We used two state-of-the-art transformer-based models (MentalRoBERTa and a variant of MPNet) for embedding the social media posts, the original and generated responses of the BDI-II. Our results show that using sentence embeddings from a model designed for semantic search outperforms the approach using embeddings from a model pre-trained on mental health data. Furthermore, the generated synthetic data were proved too specific for this task, the approach simply relying on the BDI-II responses had the best performance. | ['Ana-Maria Bucur'] | 2023-07-05 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [-2.60929912e-01 6.38583958e-01 1.05912022e-01 -4.58345979e-01
-8.13603759e-01 -2.68020809e-01 6.28907681e-01 7.99290597e-01
-5.80765188e-01 6.34140790e-01 9.40426052e-01 1.94867566e-01
-4.98834312e-01 -8.14030945e-01 1.53200477e-01 -1.38830200e-01
1.09911062e-01 8.38271916e-01 -2.01336578e-01 -8.93237174e-01
2.93093771e-01 1.95665702e-01 -1.40998435e+00 8.06654513e-01
5.30486524e-01 1.06596863e+00 2.09643736e-01 5.19251943e-01
-3.31524223e-01 7.77938664e-01 -1.00418019e+00 -6.23048604e-01
-3.46628994e-01 -4.24655557e-01 -1.17858922e+00 -2.71417767e-01
-4.29447414e-03 -2.82708168e-01 -4.42592859e-01 6.73915505e-01
8.75200808e-01 6.82927296e-02 5.41194081e-01 -1.23595583e+00
-1.01260686e+00 5.77966094e-01 2.19754115e-01 2.57155299e-01
9.84569728e-01 -1.27122939e-01 1.00742793e+00 -9.20415103e-01
1.07215965e+00 1.67198527e+00 7.68619716e-01 9.01122332e-01
-1.33923471e+00 -4.69747514e-01 -3.55494171e-01 4.74899501e-01
-1.14792299e+00 -2.98705131e-01 5.78030348e-01 -2.15679631e-01
1.46855319e+00 2.37973332e-01 7.80720294e-01 1.78029859e+00
-6.76675215e-02 2.69752920e-01 9.32507813e-01 -2.31465384e-01
4.46579814e-01 8.03984880e-01 1.37542635e-01 3.27474862e-01
-2.20924616e-01 -3.26794416e-01 -6.54189944e-01 -7.67586529e-01
1.05052948e-01 -5.58075234e-02 -9.42449793e-02 3.05521458e-01
-9.12033081e-01 1.32000601e+00 3.90126020e-01 5.99307179e-01
-6.44296825e-01 -2.45630458e-01 8.93387437e-01 5.95458508e-01
9.29535329e-01 7.48850286e-01 -6.28371596e-01 -3.51559013e-01
-8.15550625e-01 5.75822651e-01 1.08102715e+00 4.39073563e-01
4.10780311e-01 -5.52828908e-01 -4.45091605e-01 1.44736362e+00
1.58546433e-01 2.44698972e-01 8.75793457e-01 -1.00472891e+00
4.58381116e-01 7.93025196e-01 9.96847302e-02 -1.41998720e+00
-6.54032409e-01 -1.62663683e-01 -4.93149012e-01 -3.99967402e-01
1.21761188e-01 -3.36627722e-01 -4.62199897e-01 1.86619401e+00
1.19669080e-01 -3.61118376e-01 2.52863050e-01 7.79547811e-01
1.37203979e+00 4.93895948e-01 2.66956747e-01 1.16457425e-01
1.55621672e+00 -6.11646652e-01 -1.03205848e+00 -4.01033700e-01
1.15608597e+00 -6.74737334e-01 1.09390771e+00 2.09941939e-01
-1.19735289e+00 -4.67658848e-01 -7.60858536e-01 -1.69524342e-01
-8.08393240e-01 -1.82546005e-01 3.89856964e-01 3.00578326e-01
-1.48941374e+00 7.68799007e-01 -9.57807600e-02 -1.17038655e+00
1.02525093e-01 6.70700446e-02 -6.24992251e-01 -7.00673014e-02
-1.72217846e+00 1.32778895e+00 1.95880324e-01 -4.83616799e-01
-3.36245000e-01 -7.86517203e-01 -7.44440854e-01 8.25811625e-02
-2.02304721e-01 -6.63168132e-01 1.09023011e+00 -8.37449312e-01
-1.12654459e+00 1.39352715e+00 6.79481104e-02 -3.80172879e-01
-1.34041980e-02 3.00349053e-02 -8.11393201e-01 7.87863851e-01
3.64142448e-01 9.66872096e-01 4.52468663e-01 -5.31019092e-01
-3.95587832e-02 -5.74327230e-01 2.03511223e-01 2.00774185e-02
-1.06161165e+00 4.50267196e-01 1.57404527e-01 -5.34782290e-01
-3.16234171e-01 -6.28614068e-01 -1.22471958e-01 5.55200726e-02
-2.09274232e-01 -6.09097719e-01 6.36363626e-01 -8.59735608e-01
1.30793226e+00 -2.14504242e+00 -2.14436054e-02 4.67039011e-02
2.58910209e-01 2.91599959e-01 -4.35988307e-01 1.20625758e+00
-2.86817193e-01 4.11360204e-01 2.67136127e-01 -6.19629741e-01
4.55884077e-02 2.73883224e-01 -1.78702667e-01 4.13408577e-02
5.24603844e-01 6.94234371e-01 -1.21861637e+00 -5.22576153e-01
-1.23038121e-01 4.44767714e-01 -7.49235332e-01 4.10354674e-01
-2.75701601e-02 -5.98957986e-02 -3.19202572e-01 5.27827628e-02
4.06509817e-01 -1.88705519e-01 4.49026795e-03 -1.03274219e-01
2.46772096e-01 6.39357328e-01 -5.14163256e-01 1.72228146e+00
-6.71906888e-01 4.13080484e-01 6.63473383e-02 -1.08793998e+00
1.16456735e+00 6.47119343e-01 6.16517782e-01 -8.04191470e-01
-7.40007609e-02 1.34590894e-01 -3.12435955e-01 -1.05841279e+00
4.16305959e-01 -3.85971099e-01 -2.69561112e-01 5.07880330e-01
4.60290819e-01 -2.59284824e-01 7.37171844e-02 7.23342538e-01
1.64716649e+00 -4.41289932e-01 5.73708825e-02 -2.50875384e-01
2.65356481e-01 5.34742735e-02 3.26328799e-02 5.41004896e-01
-9.04364139e-02 7.65292227e-01 6.69674814e-01 -8.55488852e-02
-8.84740651e-01 -8.64664555e-01 -1.71807408e-01 8.91040742e-01
-4.52824503e-01 -8.35205138e-01 -6.91883206e-01 -4.57347214e-01
-8.05795416e-02 1.12272906e+00 -9.12760317e-01 -5.44201195e-01
1.43243268e-01 -5.59188545e-01 8.10596526e-01 2.59891480e-01
2.38635808e-01 -1.17121220e+00 -3.56057972e-01 4.25689161e-01
-7.66173899e-01 -9.22535658e-01 -1.11027390e-01 1.99056134e-01
-5.98296285e-01 -8.62436116e-01 -8.09538007e-01 -7.74576008e-01
2.80377358e-01 -1.94373399e-01 1.32045043e+00 6.65238500e-02
-4.69029605e-01 7.03372598e-01 -6.72096610e-01 -2.12839723e-01
-5.30208647e-01 -5.34365773e-02 -1.49773285e-01 -1.55577213e-01
8.85518193e-01 -6.49459183e-01 -7.19098926e-01 -1.22315958e-01
-1.07444966e+00 -2.79358029e-01 3.96247432e-02 6.65324867e-01
-3.04680556e-01 -3.79762203e-01 1.00518394e+00 -6.40474319e-01
1.42295647e+00 -1.29815221e+00 6.24197841e-01 -2.19720185e-01
-5.30969501e-01 -2.51902282e-01 5.54635942e-01 -3.54226023e-01
-7.95572221e-01 -5.41035533e-01 -6.75867736e-01 -1.30998001e-01
-3.03383082e-01 6.50127351e-01 2.85622030e-01 4.81203914e-01
1.14545429e+00 -2.26405457e-01 2.19879568e-01 -7.38227844e-01
3.15894663e-01 1.13698852e+00 1.16673633e-01 -1.11842588e-01
1.21886805e-01 3.56569976e-01 -3.72515261e-01 -1.01080561e+00
-8.48846793e-01 -7.77129471e-01 1.28278479e-01 -4.77957120e-03
1.03566623e+00 -7.42453516e-01 -6.33126140e-01 1.47687212e-01
-1.40845823e+00 -7.07057640e-02 -3.69319588e-01 2.13009208e-01
-5.36441624e-01 1.88821495e-01 -9.11792934e-01 -6.40734434e-01
-5.16994596e-01 -5.24797559e-01 9.81746376e-01 -2.37325191e-01
-1.20194530e+00 -1.25059819e+00 4.39075947e-01 2.19436020e-01
7.23017871e-01 1.07508548e-01 1.28116953e+00 -1.27379990e+00
7.87888050e-01 -4.20806676e-01 -4.52818543e-01 4.50784177e-01
1.98917255e-01 -3.33194762e-01 -9.58447218e-01 1.04503721e-01
3.30315471e-01 -1.10663104e+00 5.55979133e-01 -1.74235031e-01
1.01828170e+00 -7.38762617e-01 -1.90714076e-01 -1.01759613e-01
1.23933399e+00 -1.86748490e-01 7.38303363e-01 4.75784510e-01
-3.15518789e-02 1.13241637e+00 5.05999386e-01 9.58746135e-01
5.92116892e-01 7.84268141e-01 2.59650379e-01 -2.71887053e-02
6.53041899e-03 -2.70432055e-01 4.60538447e-01 5.53439617e-01
5.14816940e-01 -1.53234392e-01 -6.97503030e-01 8.41483533e-01
-1.62466109e+00 -9.79261041e-01 -2.29405954e-01 1.64522803e+00
1.03698981e+00 -1.96991116e-01 2.57350266e-01 1.81506366e-01
2.51964301e-01 1.54189421e-02 -9.96703804e-02 -1.09512031e+00
-1.34216502e-01 7.37018466e-01 -1.65899679e-01 3.47097963e-01
-4.34193999e-01 4.61629719e-01 6.08979368e+00 6.64261162e-01
-8.26285243e-01 3.58199209e-01 5.32592535e-01 -3.55573237e-01
-5.65432549e-01 -3.24302256e-01 -4.55963135e-01 6.74880385e-01
1.56782675e+00 -4.93481681e-02 3.13689351e-01 5.60094297e-01
6.24051929e-01 4.06702720e-02 -1.20060813e+00 8.29248488e-01
1.21318735e-01 -1.14951396e+00 -1.48262993e-01 -3.89649779e-01
2.36713946e-01 4.65965346e-02 -2.08532661e-02 4.52967912e-01
7.82452971e-02 -9.41906869e-01 3.86977404e-01 4.82132226e-01
6.88565969e-01 -3.62052113e-01 9.10474658e-01 1.96337268e-01
-4.45888668e-01 -1.40746534e-01 -3.54633212e-01 -1.25169203e-01
4.40428331e-02 6.44338071e-01 -1.13713753e+00 2.55472749e-01
8.83260548e-01 8.01030278e-01 -7.16656983e-01 5.98309934e-01
-4.43978496e-02 4.85305101e-01 -3.30579758e-01 -3.27958822e-01
4.18405831e-01 8.07081163e-02 2.88540602e-01 1.51646352e+00
5.61400056e-01 2.81719826e-02 -3.91899765e-01 1.08675587e+00
6.21520914e-02 3.25590909e-01 -8.32944751e-01 -5.26115656e-01
4.05149579e-01 1.31492996e+00 -2.09920019e-01 -4.61902469e-01
-2.80526519e-01 1.17643225e+00 4.23291534e-01 2.58321643e-01
-5.92845857e-01 -5.12998998e-01 6.15268230e-01 3.96001250e-01
-6.18415624e-02 5.31574786e-01 -5.09950593e-02 -8.16861510e-01
-1.64747626e-01 -8.03134978e-01 5.96547544e-01 -1.31620038e+00
-1.71422410e+00 6.77487254e-01 -3.21876444e-02 -7.31085360e-01
-5.30703604e-01 -3.95288557e-01 -5.97155690e-01 9.97580528e-01
-9.69686806e-01 -7.29657233e-01 -2.42682114e-01 6.28973067e-01
3.99187118e-01 9.84109938e-02 1.52799594e+00 4.55867290e-01
-2.02005148e-01 3.10332268e-01 -6.44499958e-02 -2.23258495e-01
1.05831325e+00 -1.10101891e+00 1.19501948e-01 -3.30893725e-01
-5.94317675e-01 6.44252181e-01 8.20064723e-01 -7.06780493e-01
-7.94764102e-01 -9.60145772e-01 1.69845200e+00 -2.97091782e-01
1.09031630e+00 -2.92066157e-01 -8.43562365e-01 2.91184336e-01
5.21067977e-01 -7.06002653e-01 9.97113943e-01 1.03347778e-01
-1.38884753e-01 1.03812955e-01 -1.66922832e+00 5.60848713e-01
9.78840768e-01 -7.59378433e-01 -1.07296085e+00 9.16359901e-01
7.90745914e-01 3.75071377e-01 -1.08914566e+00 -2.54280895e-01
9.37945396e-02 -1.01719940e+00 1.08536279e+00 -9.82086182e-01
1.04679787e+00 7.13367760e-01 -1.59460276e-01 -1.75071442e+00
-1.51700780e-01 -4.16184932e-01 3.92085046e-01 1.38581097e+00
4.13171411e-01 -6.97186589e-01 5.33697188e-01 8.54852676e-01
-6.76831231e-02 -7.55304039e-01 -8.50858748e-01 -4.56882358e-01
2.07767189e-02 -3.33382785e-01 1.74093843e-01 1.02345920e+00
6.50114954e-01 5.49527466e-01 7.20214695e-02 -4.63350475e-01
1.03501812e-01 -5.47871530e-01 2.47760564e-01 -1.28712928e+00
-2.64711261e-01 -2.91235000e-01 -3.42251182e-01 -3.05785060e-01
2.21373439e-01 -1.11523652e+00 -3.19599956e-01 -1.86532116e+00
2.08066776e-01 -3.03425491e-01 -1.94083229e-01 5.28740764e-01
5.03066964e-02 2.72035509e-01 3.42872627e-02 -2.70874828e-01
-2.56067514e-01 5.16621888e-01 7.59125948e-01 -1.13494068e-01
-1.33033782e-01 -4.72424746e-01 -1.15063262e+00 6.16486251e-01
9.33386147e-01 -7.33951271e-01 -4.60616529e-01 -1.80449694e-01
7.13756263e-01 2.04177842e-01 4.88364905e-01 -5.99571109e-01
-1.40929118e-01 2.38677710e-01 8.41750950e-02 -1.69848934e-01
1.03625000e+00 -4.69656616e-01 -2.62604773e-01 4.96932656e-01
-8.81796956e-01 9.93645862e-02 2.60703981e-01 2.57801265e-01
-2.64627039e-01 -7.27311552e-01 5.38143873e-01 -4.27537024e-01
-2.29625165e-01 -1.13799818e-01 -8.10491383e-01 4.07287151e-01
5.20446658e-01 5.52070178e-02 -2.47476175e-01 -9.35270727e-01
-1.03676927e+00 4.81401682e-02 1.95662439e-01 5.97471535e-01
1.00309741e+00 -1.23012340e+00 -8.58736575e-01 -1.25140592e-01
3.33223522e-01 -7.84527659e-01 3.03214163e-01 9.32132363e-01
-9.33183730e-02 4.00395602e-01 -2.39746839e-01 -2.38200631e-02
-1.18023849e+00 5.95708072e-01 2.22853459e-02 -4.29385424e-01
-4.89773393e-01 1.00371408e+00 -8.99416059e-02 -5.41576266e-01
1.83859915e-01 -1.25747755e-01 -4.54223514e-01 6.52656853e-01
6.62642241e-01 4.63671148e-01 3.61840218e-01 -3.29838574e-01
-3.80911589e-01 -5.06715700e-02 3.42243984e-02 -4.30339128e-01
1.66729724e+00 -1.91633359e-01 -4.60351229e-01 4.07441586e-01
1.84894526e+00 -3.50039005e-01 7.15598091e-02 -3.20562087e-02
2.06816122e-01 -1.99392423e-01 4.97075878e-02 -1.05368423e+00
-6.41484022e-01 9.11355913e-01 5.42775571e-01 6.74667478e-01
9.43074405e-01 2.22163320e-01 9.65415716e-01 5.30892253e-01
-3.25278044e-02 -1.38536036e+00 5.95188320e-01 3.43696177e-01
1.36438453e+00 -8.92533720e-01 -6.23929024e-01 -8.14421754e-03
-8.27033758e-01 1.18162096e+00 3.30224782e-01 -7.96191171e-02
6.49363041e-01 -1.40480533e-01 1.15519270e-01 -5.58311582e-01
-9.81540501e-01 8.12582523e-02 -1.70886591e-01 7.38785088e-01
4.39093202e-01 -1.83822319e-01 -6.69414759e-01 9.04588938e-01
-4.24613088e-01 8.39528069e-03 4.88427609e-01 5.65117717e-01
-2.30297521e-01 -1.12767088e+00 -1.55710101e-01 7.16264606e-01
-6.41849339e-01 -2.14949921e-01 -1.03437805e+00 4.94316965e-01
2.47963686e-02 1.46515107e+00 -1.30468458e-02 -5.55408418e-01
3.97290558e-01 4.59304273e-01 1.13564037e-01 -9.85594213e-01
-1.08487236e+00 -5.43707728e-01 8.38152945e-01 -8.25861335e-01
-2.53440589e-01 -4.39591020e-01 -1.10584533e+00 -2.59386957e-01
2.24262580e-01 4.18106079e-01 8.16755474e-01 9.00812864e-01
6.72750413e-01 2.90188968e-01 6.47624731e-01 -5.57016730e-01
-7.70121336e-01 -1.37085474e+00 -6.25111878e-01 9.51968312e-01
5.57404757e-02 -4.26937371e-01 -4.50159252e-01 -5.07908881e-01] | [8.928421020507812, 9.624839782714844] |
893b32a1-6b25-4e4f-806a-aa0a938855e8 | syntactically-robust-training-on-partially | 2301.06841 | null | https://arxiv.org/abs/2301.06841v1 | https://arxiv.org/pdf/2301.06841v1.pdf | Syntactically Robust Training on Partially-Observed Data for Open Information Extraction | Open Information Extraction models have shown promising results with sufficient supervision. However, these models face a fundamental challenge that the syntactic distribution of training data is partially observable in comparison to the real world. In this paper, we propose a syntactically robust training framework that enables models to be trained on a syntactic-abundant distribution based on diverse paraphrase generation. To tackle the intrinsic problem of knowledge deformation of paraphrasing, two algorithms based on semantic similarity matching and syntactic tree walking are used to restore the expressionally transformed knowledge. The training framework can be generally applied to other syntactic partial observable domains. Based on the proposed framework, we build a new evaluation set called CaRB-AutoPara, a syntactically diverse dataset consistent with the real-world setting for validating the robustness of the models. Experiments including a thorough analysis show that the performance of the model degrades with the increase of the difference in syntactic distribution, while our framework gives a robust boundary. The source code is publicly available at https://github.com/qijimrc/RobustOIE. | ['Bin Xu', 'Juanzi Li', 'Lei Hou', 'Yuxiang Chen', 'Ji Qi'] | 2023-01-17 | null | null | null | null | ['paraphrase-generation', 'open-information-extraction', 'semantic-textual-similarity', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 1.65091679e-01 3.52277160e-01 -2.97784656e-01 -5.63511848e-01
-8.85067523e-01 -6.70974612e-01 5.97776115e-01 -1.13639407e-01
-1.49100691e-01 7.88249373e-01 3.24878514e-01 -1.07887648e-02
-1.74648896e-01 -6.53814852e-01 -8.51517856e-01 -4.88676727e-01
6.31416559e-01 4.62433070e-01 1.58974588e-01 -2.51996100e-01
8.21574926e-02 1.23690017e-01 -1.52844524e+00 7.54867554e-01
1.08292186e+00 7.50290930e-01 1.99106947e-01 8.60378593e-02
-3.34836513e-01 6.74866438e-01 -4.99215782e-01 -6.31597519e-01
4.52735901e-01 -4.16948408e-01 -1.13626909e+00 -1.26900792e-01
3.86320680e-01 -2.05710270e-02 -1.34760052e-01 1.28516734e+00
3.67095888e-01 -2.31471136e-01 5.84293127e-01 -1.15226471e+00
-1.00981951e+00 7.83375382e-01 -1.04860768e-01 5.72761297e-02
6.62166119e-01 -1.40094519e-01 1.25452411e+00 -9.61857080e-01
1.07212925e+00 1.07870829e+00 4.90332782e-01 4.84322280e-01
-1.19430792e+00 -4.26986724e-01 3.95740047e-02 5.71501493e-01
-1.23237562e+00 -2.07761690e-01 9.52894270e-01 -3.14113468e-01
6.68087423e-01 1.30182639e-01 2.52774209e-01 1.65717983e+00
1.09159329e-03 7.89285958e-01 1.32838345e+00 -6.30621910e-01
2.41897374e-01 4.66381103e-01 4.99541521e-01 4.83444244e-01
3.31831604e-01 -2.26406213e-02 -5.20057201e-01 -1.62402585e-01
3.38964723e-02 -9.93369296e-02 -5.25338233e-01 -6.59617484e-01
-8.93802345e-01 7.33782411e-01 3.78822386e-01 4.61856812e-01
-1.12199247e-01 -5.19024014e-01 3.60223681e-01 7.07442641e-01
5.46198547e-01 5.21629453e-01 -7.25947797e-01 -7.29361996e-02
-7.84282267e-01 3.87268215e-01 1.11066210e+00 9.47945595e-01
8.04024160e-01 -3.93825740e-01 -1.19251227e-02 1.05563056e+00
1.19481133e-02 4.97097194e-01 8.13704312e-01 -9.50197816e-01
7.76936173e-01 9.32092309e-01 6.57537058e-02 -8.48174036e-01
-1.54295772e-01 -2.62445390e-01 -4.97768730e-01 -2.59618908e-01
6.72260165e-01 7.12916348e-03 -5.27107477e-01 1.86538255e+00
2.99979240e-01 -1.18977137e-01 4.24619257e-01 6.80385113e-01
8.63438189e-01 3.53527665e-01 -2.66939729e-01 4.33728322e-02
1.28994405e+00 -8.79671335e-01 -5.95378578e-01 -1.82256609e-01
6.74334705e-01 -5.74507892e-01 1.35847259e+00 1.05886944e-01
-6.85615480e-01 -4.63924706e-01 -9.37497437e-01 -1.81622908e-01
-6.06971502e-01 9.73878056e-02 2.22546518e-01 4.75592613e-01
-6.66182101e-01 6.30121112e-01 -5.07241488e-01 -5.76107085e-01
2.74180382e-01 -7.80499205e-02 -5.62610924e-01 -2.74088889e-01
-1.32103682e+00 1.05163920e+00 7.61157691e-01 -6.61584437e-02
-3.86107981e-01 -7.56440222e-01 -9.69186783e-01 1.39529437e-01
2.72390425e-01 -7.25915074e-01 1.12960482e+00 -9.99931574e-01
-1.41692400e+00 1.08867741e+00 -1.58417299e-01 -5.00640929e-01
6.79143608e-01 -3.89292210e-01 -1.49140328e-01 -1.63880736e-02
3.75431389e-01 3.48339200e-01 6.53005719e-01 -1.18485022e+00
-2.65519798e-01 -5.98111868e-01 9.12578702e-02 4.69621941e-02
-4.78119552e-01 9.42039639e-02 -4.22346182e-02 -6.22451782e-01
-4.06443700e-02 -8.67252886e-01 5.47771901e-02 -5.28340578e-01
-4.28321689e-01 -1.33910552e-01 7.33261704e-01 -8.22769463e-01
1.13499141e+00 -2.09659815e+00 3.83328438e-01 -8.00088346e-02
-2.11383149e-01 2.03194574e-01 -2.33227059e-01 5.49489975e-01
-3.16354901e-01 2.91009843e-02 -4.66323316e-01 -2.90232331e-01
1.87383629e-02 3.35002542e-01 -6.94781721e-01 6.46455958e-02
2.62166589e-01 8.76803160e-01 -9.43626285e-01 -3.15369397e-01
-1.54736102e-01 -5.85528612e-02 -4.07265574e-01 3.38577837e-01
-4.04327601e-01 4.55612391e-01 -5.13048232e-01 6.41103983e-01
7.56390870e-01 -1.68357700e-01 2.74654210e-01 -4.31652963e-02
2.04735816e-01 3.43544871e-01 -9.41134095e-01 1.94880295e+00
-4.86967057e-01 2.99590647e-01 -2.27056414e-01 -1.10983634e+00
1.07462740e+00 1.83769405e-01 2.69443333e-01 -6.76309466e-01
4.94308956e-02 3.12752008e-01 -1.32751852e-01 -6.35673404e-01
4.66109097e-01 -2.47280039e-02 -2.34615073e-01 4.50653672e-01
3.71614844e-01 -1.69095755e-01 3.62367064e-01 1.04715586e-01
1.21657896e+00 3.76398861e-01 3.56306642e-01 -3.29001665e-01
6.89649940e-01 1.26109153e-01 7.88456440e-01 6.25789940e-01
-1.59863263e-01 7.34184206e-01 5.04571497e-01 -3.86048585e-01
-1.17604256e+00 -1.08470881e+00 -4.26098913e-01 8.61669004e-01
4.64655571e-02 -6.08217120e-01 -1.05470765e+00 -9.66905475e-01
-5.00251651e-02 8.24092090e-01 -5.58023036e-01 -1.75835222e-01
-4.30868924e-01 -6.78374887e-01 6.23850107e-01 3.19547206e-01
6.73804402e-01 -1.18137228e+00 -3.31334561e-01 -3.26002724e-02
-5.97651422e-01 -1.45941365e+00 1.28323078e-01 1.22568533e-01
-6.43776000e-01 -1.14513242e+00 -5.10782897e-01 -7.73800433e-01
4.77389038e-01 8.38347971e-02 1.14834750e+00 -2.05825388e-01
-9.27988365e-02 3.34114850e-01 -7.78853357e-01 -3.48684549e-01
-9.18798029e-01 3.29573065e-01 -1.59946650e-01 5.21391677e-03
5.82529366e-01 -6.66233838e-01 -2.39409730e-01 3.77843976e-01
-8.84496331e-01 5.19638248e-02 4.57909554e-01 9.56312358e-01
3.86057764e-01 -2.72330344e-01 8.25291336e-01 -1.11750245e+00
7.54861772e-01 -8.06335449e-01 -3.76233757e-01 4.74067390e-01
-4.81640339e-01 4.21889305e-01 8.61827791e-01 -1.52416259e-01
-1.44656670e+00 3.16063426e-02 -3.42399403e-02 -3.14040571e-01
-4.93908256e-01 5.62651396e-01 -4.29125488e-01 2.82775372e-01
8.30374718e-01 2.91985333e-01 -1.10953785e-01 -8.19704294e-01
5.88158786e-01 9.68976617e-01 5.35909057e-01 -8.15246463e-01
8.30013573e-01 4.59573835e-01 -3.22198033e-01 -6.10024810e-01
-1.22962022e+00 -3.55394393e-01 -9.34985161e-01 2.67572433e-01
5.10244727e-01 -8.35378647e-01 2.24759117e-01 4.08466697e-01
-1.34166813e+00 -1.69645652e-01 -4.57959831e-01 1.15863316e-01
-6.65448844e-01 7.31541455e-01 -5.01574099e-01 -7.78228790e-02
-3.84982377e-01 -8.40417862e-01 9.62895453e-01 1.22308144e-02
-3.36040735e-01 -9.44361746e-01 4.34864879e-01 7.72068441e-01
1.47953361e-01 1.45997167e-01 1.06124544e+00 -1.14358413e+00
-3.62160027e-01 -1.22569799e-02 -1.33223876e-01 6.38921440e-01
2.25311890e-01 -1.32443488e-01 -1.05138314e+00 -5.33455648e-02
2.42799684e-01 -5.40739477e-01 7.13805020e-01 -2.65031159e-01
8.60372961e-01 -2.55312830e-01 -4.28624749e-02 4.41977233e-01
1.16212964e+00 -3.86519551e-01 5.72671950e-01 6.79501414e-01
4.07788426e-01 7.80651212e-01 6.79445863e-01 3.41894060e-01
3.96437824e-01 6.63351059e-01 1.21582381e-01 3.11591953e-01
-3.48773986e-01 -5.22678852e-01 5.84921658e-01 8.80085886e-01
1.94948360e-01 2.11241003e-02 -8.67369175e-01 6.90195978e-01
-2.11419058e+00 -1.06127656e+00 1.02006733e-01 2.04528213e+00
1.07701564e+00 -1.84497908e-02 -2.49030858e-01 -4.73182090e-02
5.86769581e-01 6.04851842e-02 -3.71952087e-01 -5.19157469e-01
-3.40098262e-01 1.46118626e-01 1.79500226e-02 3.29686731e-01
-8.52766275e-01 9.43317652e-01 5.62156725e+00 8.48832548e-01
-7.88112581e-01 2.55544782e-01 1.74894810e-01 1.66244030e-01
-4.39374775e-01 2.15937376e-01 -7.88247824e-01 7.24270821e-01
9.99827385e-01 -3.76261711e-01 1.89490512e-01 1.02026463e+00
5.85804880e-02 6.09535910e-02 -1.33181155e+00 7.21049428e-01
1.39096096e-01 -1.00593448e+00 2.12915793e-01 -1.48279771e-01
7.37063169e-01 1.80076599e-01 -2.15516314e-01 4.81428117e-01
3.45258087e-01 -6.69146121e-01 5.44156432e-01 3.84806722e-01
4.18046296e-01 -2.77429163e-01 9.60991561e-01 7.22843647e-01
-7.57048726e-01 -1.15669735e-01 -4.83816743e-01 -6.38010874e-02
-5.98420687e-02 5.34282684e-01 -8.23224783e-01 1.00328267e+00
8.39695215e-01 8.02222848e-01 -1.03567314e+00 6.57004356e-01
-7.84527957e-01 5.24022400e-01 -2.40491509e-01 1.77809224e-01
-2.35078394e-01 -3.87902588e-01 5.99521220e-01 1.20474589e+00
2.26950958e-01 -2.62672424e-01 -4.14215494e-03 1.18328142e+00
-4.50710282e-02 3.20648491e-01 -9.50878263e-01 1.44395918e-01
4.78790492e-01 1.08469951e+00 -1.58855796e-01 -2.27599770e-01
-7.26700246e-01 1.03390539e+00 8.68469119e-01 3.33748698e-01
-7.59687185e-01 -2.23844409e-01 3.80430251e-01 -1.05945587e-01
2.07753852e-01 1.81144357e-01 -2.45461687e-01 -1.68589687e+00
5.61398208e-01 -9.61927414e-01 6.18519366e-01 -8.22945118e-01
-1.55727327e+00 6.05803311e-01 2.41248593e-01 -1.20508313e+00
-4.10444319e-01 -6.19321048e-01 -5.90802193e-01 6.23732209e-01
-1.50387061e+00 -1.25755596e+00 -4.85323727e-01 5.53342223e-01
6.42137885e-01 -2.58937538e-01 9.90397215e-01 8.93000364e-02
-6.44675434e-01 6.30440950e-01 3.12401712e-01 1.75805286e-01
8.47916365e-01 -1.20566761e+00 2.51985192e-01 9.69543099e-01
3.18521649e-01 6.79678440e-01 7.83432305e-01 -5.34394324e-01
-9.79207754e-01 -1.01387656e+00 1.07990646e+00 -7.63891459e-01
9.37872112e-01 -4.54908073e-01 -1.27924740e+00 8.30740213e-01
3.32522601e-01 -8.91029611e-02 6.33281171e-01 2.21915171e-01
-7.82290995e-01 -1.67745221e-02 -1.13557208e+00 4.69412595e-01
1.21436739e+00 -5.27340531e-01 -1.37741745e+00 3.63699019e-01
6.56179786e-01 -2.49959663e-01 -9.01554406e-01 5.93568981e-01
2.79281586e-01 -1.09430575e+00 6.41807258e-01 -8.50905776e-01
6.92033768e-01 -5.54187708e-02 -3.27698678e-01 -1.36702907e+00
-1.79522187e-02 -3.31613332e-01 -2.61973683e-02 1.42941940e+00
5.81225336e-01 -6.27014935e-01 6.39822125e-01 5.65768540e-01
1.17127402e-02 -4.96869415e-01 -1.12124217e+00 -1.03881967e+00
3.84655952e-01 -1.68767169e-01 3.48017603e-01 1.06181931e+00
4.36543822e-01 4.87440169e-01 -4.49277051e-02 4.00048383e-02
4.82029170e-01 5.62287331e-01 9.36942816e-01 -1.08726168e+00
-3.95885855e-01 -2.48664338e-02 -3.35815638e-01 -7.51238763e-01
6.68899953e-01 -1.27458060e+00 -2.49264404e-01 -1.16240656e+00
6.52547538e-01 -1.90472752e-01 -1.94164157e-01 5.80986321e-01
-1.32768512e-01 -2.70698547e-01 1.85441330e-01 3.25525522e-01
-5.82084715e-01 8.45123529e-01 8.85776281e-01 -9.27469805e-02
-4.42606770e-02 -9.11907479e-02 -6.21905446e-01 8.46865237e-01
1.09125829e+00 -5.87228000e-01 -3.26765001e-01 -3.70267272e-01
2.09135935e-01 -2.82205403e-01 4.77905035e-01 -7.64414251e-01
-8.68029818e-02 -7.36817392e-03 -1.15343943e-01 -2.27533832e-01
1.24741264e-01 -9.03560579e-01 9.84448642e-02 2.51077354e-01
-3.28410625e-01 -3.95818502e-02 9.69256461e-02 4.66754258e-01
-5.13351679e-01 -6.40316665e-01 6.28710926e-01 -1.51422203e-01
-7.25408375e-01 -1.48720995e-01 7.09276497e-02 4.78110701e-01
9.13511872e-01 -7.93313012e-02 -6.49170637e-01 -1.37586758e-01
-6.72673166e-01 7.77213350e-02 6.92696393e-01 7.56000280e-01
2.69490361e-01 -1.28518617e+00 -7.74590492e-01 7.91787356e-02
4.91830349e-01 -2.11903334e-01 2.08730176e-01 4.84514624e-01
-1.49058804e-01 4.95927334e-01 -2.85121918e-01 -3.96202147e-01
-1.12650216e+00 6.44286275e-01 2.89337456e-01 -4.80388761e-01
-6.62164152e-01 2.97810823e-01 1.09669693e-01 -9.30093229e-01
-3.68148200e-02 -3.61808062e-01 -7.60994554e-02 -4.35358025e-02
2.85296053e-01 1.85436040e-01 1.82154074e-01 -5.70723534e-01
-1.71893954e-01 4.62819248e-01 -2.46684760e-01 1.73442766e-01
1.43216097e+00 -1.94162205e-01 -1.92754939e-01 2.35898420e-01
1.19906878e+00 9.21814442e-02 -9.21704173e-01 -6.17191911e-01
4.05271083e-01 -4.63170737e-01 -5.22846460e-01 -7.34613299e-01
-6.26889229e-01 6.14594519e-01 2.64784694e-01 8.43540877e-02
7.91831255e-01 2.16499865e-01 6.06961846e-01 5.20237267e-01
5.61184883e-01 -1.07987082e+00 5.54506183e-02 6.87572718e-01
1.11393750e+00 -1.18291581e+00 -2.47415811e-01 -6.44714832e-01
-6.54121041e-01 9.91622508e-01 6.32307887e-01 -6.79135323e-03
4.60996419e-01 1.30165547e-01 1.14525765e-01 -1.12562075e-01
-7.40015030e-01 -1.74193591e-01 2.83790044e-02 5.36538780e-01
1.49548665e-01 -1.75369591e-01 -4.79840159e-01 1.00869644e+00
-5.23252964e-01 3.42366844e-02 5.71060836e-01 7.64856637e-01
-3.17819089e-01 -1.37678862e+00 -1.20517358e-01 -3.28237540e-03
-3.64235699e-01 -7.64359981e-02 -7.83105075e-01 7.15000033e-01
-2.86971517e-02 8.64073873e-01 -3.69880468e-01 -1.08271405e-01
5.48479974e-01 5.52651405e-01 4.85380411e-01 -8.29689682e-01
-2.79841542e-01 -5.88561356e-01 2.68630773e-01 -6.00486279e-01
-4.45497572e-01 -7.33003020e-01 -1.03303313e+00 -1.32944817e-02
-1.75211608e-01 2.03442052e-01 3.88737112e-01 9.93802845e-01
6.84279025e-01 1.10434897e-01 5.24674177e-01 -3.24066460e-01
-1.02412903e+00 -1.10206282e+00 -4.14226562e-01 1.23410714e+00
-5.81937730e-02 -6.21874750e-01 -4.94032681e-01 8.69544894e-02] | [11.461881637573242, 9.376086235046387] |
b1eb10b3-99f1-422e-b6a0-341c134a9a58 | dam-al-dilated-attention-mechanism-with | 2112.13559 | null | https://arxiv.org/abs/2112.13559v1 | https://arxiv.org/pdf/2112.13559v1.pdf | DAM-AL: Dilated Attention Mechanism with Attention Loss for 3D Infant Brain Image Segmentation | While Magnetic Resonance Imaging (MRI) has played an essential role in infant brain analysis, segmenting MRI into a number of tissues such as gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) is crucial and complex due to the extremely low intensity contrast between tissues at around 6-9 months of age as well as amplified noise, myelination, and incomplete volume. In this paper, we tackle those limitations by developing a new deep learning model, named DAM-AL, which contains two main contributions, i.e., dilated attention mechanism and hard-case attention loss. Our DAM-AL network is designed with skip block layers and atrous block convolution. It contains both channel-wise attention at high-level context features and spatial attention at low-level spatial structural features. Our attention loss consists of two terms corresponding to region information and hard samples attention. Our proposed DAM-AL has been evaluated on the infant brain iSeg 2017 dataset and the experiments have been conducted on both validation and testing sets. We have benchmarked DAM-AL on Dice coefficient and ASD metrics and compared it with state-of-the-art methods. | ['Ngan T. H Le', 'Minh-Triet Tran', 'Gia-Han Diep', 'Dinh-Hieu Hoang'] | 2021-12-27 | null | null | null | null | ['brain-image-segmentation'] | ['medical'] | [ 7.99462348e-02 2.16122314e-01 3.87420267e-01 -3.56405109e-01
-1.84782714e-01 1.39314219e-01 3.45782340e-01 1.68617994e-01
-5.44353306e-01 4.92676675e-01 4.15041119e-01 -6.42664805e-02
-7.84448385e-02 -5.04720747e-01 -6.99625969e-01 -6.30543768e-01
-5.15671015e-01 2.99189776e-01 5.16981602e-01 2.32537523e-01
3.37702870e-01 4.42382812e-01 -1.22037292e+00 3.48797441e-01
1.21149170e+00 1.06569731e+00 3.89888823e-01 3.20854098e-01
-3.27358782e-01 1.07506287e+00 -8.46036673e-02 -3.45555812e-01
2.45722253e-02 -2.36735418e-01 -9.64677155e-01 -3.54789227e-01
3.53892922e-01 -5.94655573e-01 -3.56074840e-01 1.18896306e+00
9.13709402e-01 -7.00188056e-02 8.74860108e-01 -9.36620533e-01
-1.07589841e+00 6.99379146e-01 -1.04720771e+00 8.63543987e-01
-2.73721009e-01 -3.11395992e-02 2.94844329e-01 -1.13614261e+00
3.48618060e-01 1.14560521e+00 6.72464073e-01 7.91278362e-01
-8.11248183e-01 -7.94237614e-01 2.33049199e-01 6.11180186e-01
-9.33368504e-01 -2.24515721e-01 5.58341861e-01 -8.90482903e-01
9.53349769e-01 -2.57267356e-01 5.00271022e-01 6.26627088e-01
5.97184837e-01 7.38179624e-01 1.20666397e+00 -1.73784912e-01
1.95260011e-02 -2.83180147e-01 1.45646349e-01 6.53531611e-01
1.22621767e-01 -9.20538604e-02 -3.95606346e-02 3.22487921e-01
6.88980937e-01 -4.60962728e-02 -2.71135271e-01 -2.91826665e-01
-1.09419167e+00 5.17200351e-01 6.52149618e-01 4.73493427e-01
-5.48330128e-01 2.04588994e-02 5.92284262e-01 -1.72635779e-01
7.63726950e-01 2.09476389e-02 -3.27949643e-01 1.62991300e-01
-1.02367008e+00 -6.48069456e-02 2.89261729e-01 7.21943378e-01
2.56727517e-01 -3.92194837e-02 -3.54221612e-01 1.24736524e+00
3.42345297e-01 2.35040411e-01 9.29489911e-01 -6.55261040e-01
6.50624931e-01 3.87354761e-01 -6.29970074e-01 -9.14482772e-01
-7.97946036e-01 -6.13171995e-01 -1.13885403e+00 2.38710731e-01
-1.09745584e-01 -4.21599120e-01 -1.11689115e+00 1.98541403e+00
3.45828414e-01 2.44192064e-01 -5.53191483e-01 1.04678595e+00
1.25544918e+00 2.61225104e-01 2.20181033e-01 -6.29256964e-02
1.13106477e+00 -1.30125642e+00 -7.27832615e-01 -1.56782523e-01
4.35098231e-01 -4.65932310e-01 5.24087906e-01 5.10370061e-02
-1.62802744e+00 -5.67733347e-01 -9.40114021e-01 -2.38207996e-01
-3.69434685e-01 -1.02971479e-01 5.22796035e-01 3.21353674e-01
-1.34400058e+00 5.72758496e-01 -8.63459826e-01 -4.54093143e-02
8.38524878e-01 3.57061744e-01 -5.97768903e-01 -4.10095155e-02
-1.13192260e+00 1.08622873e+00 1.71327844e-01 1.95824698e-01
-9.26133454e-01 -1.09623134e+00 -6.09531224e-01 1.32173285e-01
-1.83085769e-01 -4.75062877e-01 7.78186500e-01 -5.98983228e-01
-9.47239995e-01 9.93305922e-01 1.92680940e-01 -3.77458364e-01
3.36583227e-01 -3.39076400e-01 -3.70955765e-01 5.20118833e-01
1.42979309e-01 7.81305373e-01 7.20890760e-01 -1.02622235e+00
-2.37633660e-01 -6.96157694e-01 -2.51245499e-01 1.36531545e-02
-2.33729929e-01 6.14222288e-01 -1.12103447e-01 -8.80411565e-01
3.83056849e-01 -5.42538166e-01 1.00945623e-03 4.58372049e-02
-9.34394822e-02 1.80321127e-01 5.21118879e-01 -1.47763968e+00
1.26526368e+00 -1.92671716e+00 5.82198985e-03 -1.23009942e-01
7.89453566e-01 4.51639712e-01 -3.20015013e-01 -9.50940922e-02
-5.80660880e-01 7.86771476e-02 -3.96163940e-01 -2.94741422e-01
-2.63956368e-01 -3.90576124e-01 2.05616295e-01 6.39453590e-01
2.46749625e-01 7.83925116e-01 -8.77745926e-01 -6.83683336e-01
7.49638537e-03 6.31351769e-01 -7.02038765e-01 2.37077355e-01
4.40711349e-01 7.02416539e-01 -1.12067349e-01 6.52845621e-01
1.07001817e+00 1.76555607e-02 -4.80183780e-01 -1.54463917e-01
-1.82178378e-01 4.77822907e-02 -5.50143659e-01 1.63350618e+00
-3.28850180e-01 4.54480201e-01 4.66283649e-01 -1.14213872e+00
5.36217511e-01 3.64385128e-01 6.78400576e-01 -9.42700267e-01
2.78325647e-01 1.83897793e-01 6.91076517e-01 -8.44931722e-01
-3.06463540e-01 9.21147093e-02 7.97978282e-01 5.64366400e-01
3.44425589e-01 1.69159189e-01 5.66245690e-02 6.62631467e-02
1.16658378e+00 1.28758522e-02 -1.47500277e-01 -6.07096612e-01
6.43291175e-01 -8.10102344e-01 6.77654445e-01 2.09410489e-01
-5.98246872e-01 9.98297632e-01 4.31527287e-01 -2.35865220e-01
-1.06623220e+00 -1.02240026e+00 -3.29716623e-01 9.15440023e-01
-2.82614619e-01 2.57444799e-01 -1.31309462e+00 -5.11962771e-01
-2.03161880e-01 2.59972364e-01 -9.87836123e-01 -2.28914157e-01
-7.42315412e-01 -1.07441556e+00 3.89098704e-01 7.72805810e-01
6.88011110e-01 -1.26338291e+00 -5.50674558e-01 1.90424159e-01
9.30896550e-02 -9.72714126e-01 -8.23329926e-01 6.30559549e-02
-7.09605277e-01 -1.00466883e+00 -1.26513422e+00 -1.05832398e+00
9.09484565e-01 6.14880696e-02 1.02990246e+00 1.67363331e-01
-5.94582379e-01 -2.67338604e-02 -4.71211255e-01 -5.82654536e-01
3.15017588e-02 -9.89180524e-04 -1.01399742e-01 7.27654025e-02
1.33896649e-01 -1.01903057e+00 -1.23921001e+00 6.87433630e-02
-8.93380225e-01 1.34290710e-01 8.56389940e-01 7.84115672e-01
3.35494787e-01 -3.56187671e-01 8.04041207e-01 -6.89247906e-01
4.28564489e-01 -9.71714556e-01 -1.91361040e-01 1.91873610e-01
-4.31303740e-01 -1.54859900e-01 2.86820859e-01 -3.84721816e-01
-1.00770366e+00 -5.75839996e-01 -3.75337452e-01 -2.79131144e-01
1.93325952e-02 2.99188256e-01 -1.49065331e-01 -2.89522976e-01
6.17357530e-02 1.48550078e-01 7.82918409e-02 -7.62844682e-01
-3.27162445e-02 6.36066139e-01 6.69778466e-01 -5.60804844e-01
2.90567696e-01 2.09922925e-01 -2.53333785e-02 -4.12588447e-01
-6.33583248e-01 -1.56858698e-01 -8.56995642e-01 -1.30194440e-01
1.27511179e+00 -6.36300147e-01 -1.90291837e-01 8.47871840e-01
-1.25518548e+00 -2.96680003e-01 1.72108665e-01 7.14761317e-01
-3.46366674e-01 3.76148671e-01 -9.49932218e-01 -5.01987875e-01
-1.01140213e+00 -1.37526512e+00 5.03244758e-01 1.16515271e-01
1.82520464e-01 -8.88628364e-01 2.02329271e-03 4.87784892e-01
8.14968705e-01 3.61576468e-01 1.27853596e+00 -7.51318574e-01
-1.26327842e-01 1.48536101e-01 -7.08334029e-01 7.63338149e-01
-5.87575845e-02 -2.09740683e-01 -9.43437815e-01 -3.80805522e-01
2.58538038e-01 -1.63922712e-01 1.02409267e+00 6.07619405e-01
1.68677688e+00 -5.79897910e-02 -4.28787768e-02 9.02519107e-01
1.16683209e+00 4.06338066e-01 5.49459159e-01 7.92476237e-02
6.77680969e-01 8.57935131e-01 1.61022782e-01 2.28057131e-01
4.97739524e-01 2.48853356e-01 6.59556031e-01 -3.60891312e-01
-5.79242885e-01 3.04334730e-01 -1.11855313e-01 1.34975028e+00
-1.47323743e-01 3.26485068e-01 -1.16814172e+00 1.01532316e+00
-1.37825811e+00 -7.45909870e-01 3.30814645e-02 1.89463449e+00
1.02184081e+00 -3.60286608e-02 -7.72894695e-02 -5.60372602e-03
8.07864726e-01 -1.15505762e-01 -6.93857312e-01 -6.73845172e-01
-9.20603424e-02 4.86247122e-01 2.93501258e-01 1.42652392e-01
-1.07576489e+00 3.57766718e-01 5.85372400e+00 6.68605804e-01
-1.17597604e+00 6.72826707e-01 9.54646349e-01 -1.90678999e-01
-1.77760608e-02 -6.17253840e-01 -3.35439622e-01 9.97047842e-01
6.08565509e-01 1.72300026e-01 4.84580100e-01 4.95638907e-01
5.03461249e-03 4.01782133e-02 -8.85627747e-01 7.42929757e-01
2.08775371e-01 -8.37176502e-01 -2.24093780e-01 -2.63539344e-01
8.14429343e-01 5.49192607e-01 8.39831233e-02 2.00503528e-01
-2.62239903e-01 -1.33370042e+00 7.56614149e-01 6.55271351e-01
8.94116163e-01 -6.46661460e-01 9.89720702e-01 2.05199853e-01
-9.98182654e-01 -1.47878870e-01 -2.84996510e-01 9.17821527e-02
-1.90261945e-01 6.83101356e-01 -3.01027328e-01 2.70573467e-01
9.16925728e-01 2.50006020e-01 -5.03621042e-01 1.50955868e+00
6.53281957e-02 4.47306126e-01 6.31899759e-02 3.56801271e-01
2.61659533e-01 -7.53563046e-02 2.73616701e-01 1.37679768e+00
3.18375617e-01 3.10274154e-01 -3.07171613e-01 1.05735195e+00
-9.87435579e-02 2.55165517e-01 -7.49588832e-02 3.55723500e-01
2.88527578e-01 1.21792912e+00 -6.45350456e-01 -1.73680812e-01
-7.61555552e-01 5.58755636e-01 4.78099525e-01 2.10296303e-01
-7.07476079e-01 -5.79854548e-01 4.68820930e-01 4.38255996e-01
1.88863307e-01 3.10961343e-02 -4.52154785e-01 -8.89253020e-01
-4.32594009e-02 -6.34077787e-01 -6.96239248e-02 -6.89172626e-01
-1.26451063e+00 7.97120929e-01 -2.85692662e-02 -6.02057219e-01
2.75888711e-01 -4.82684493e-01 -9.17002559e-01 1.27203536e+00
-1.66227639e+00 -1.01041842e+00 -3.25483561e-01 3.31033945e-01
2.70442873e-01 -1.66220009e-01 3.47542137e-01 9.83077705e-01
-7.48700678e-01 8.02945197e-01 -5.65577932e-02 4.51392591e-01
3.78394216e-01 -1.24153054e+00 3.18762571e-01 7.98724532e-01
-6.89710081e-01 7.98419654e-01 3.36934030e-01 -5.82746446e-01
-6.93273485e-01 -1.12552011e+00 6.95542812e-01 1.96752667e-01
7.12079167e-01 -2.16583893e-01 -1.18865538e+00 5.37975490e-01
2.40195736e-01 4.37826842e-01 5.10921359e-01 -4.72775191e-01
-8.71822387e-02 2.57934071e-02 -1.52823913e+00 2.58790046e-01
1.10557532e+00 -1.96375921e-01 -6.87459588e-01 1.04107879e-01
9.28333521e-01 -4.34199959e-01 -1.13933587e+00 8.82149279e-01
4.65309024e-01 -1.04080319e+00 8.74448359e-01 -4.05324847e-01
7.34943151e-01 6.36954419e-03 1.20565094e-01 -1.28891671e+00
-5.88157117e-01 -1.15849659e-01 -3.48569870e-01 1.37418067e+00
2.17357993e-01 -6.61123216e-01 4.29746211e-01 5.58193088e-01
-5.59759259e-01 -1.41735363e+00 -9.23351347e-01 -4.66409802e-01
7.05938935e-01 -1.01015165e-01 9.60497141e-01 8.41835499e-01
-2.61831045e-01 6.30377606e-02 3.45157534e-02 -1.03711858e-01
7.22893536e-01 -2.71869928e-01 -1.82122111e-01 -9.84142900e-01
1.66585028e-01 -7.56811857e-01 -4.17372614e-01 -5.25728106e-01
4.48034406e-02 -9.69176471e-01 -7.84922540e-02 -1.68434691e+00
7.15407789e-01 -4.12979037e-01 -8.03165257e-01 3.21985155e-01
-2.42783979e-01 1.26014844e-01 6.14021532e-03 -2.61785239e-01
-2.84471989e-01 5.17558873e-01 1.65033829e+00 -1.14139043e-01
3.01625907e-01 -3.05422992e-01 -4.27084714e-01 8.73238802e-01
8.17647338e-01 -1.72525048e-01 -3.50773066e-01 -9.40055609e-01
-1.15416676e-01 -1.20078318e-01 1.77154720e-01 -1.21081841e+00
4.83066179e-02 1.62794948e-01 5.82415938e-01 -6.75088644e-01
-4.42650355e-02 -6.23786390e-01 -3.67982596e-01 4.77326870e-01
-3.75531495e-01 1.94575429e-01 1.46481544e-02 -1.65392339e-01
-2.27474749e-01 -1.44457966e-01 1.21760595e+00 -2.38236278e-01
-4.29261714e-01 8.67447078e-01 2.60755811e-02 3.62819046e-01
8.66767108e-01 -1.02355361e-01 -3.20749938e-01 1.35949582e-01
-7.40640163e-01 3.80771637e-01 1.66475758e-01 4.04829830e-01
8.21248710e-01 -1.25926185e+00 -9.70736980e-01 3.56906831e-01
-2.74182379e-01 -1.11738378e-02 5.76983690e-01 1.42463374e+00
-7.51325369e-01 4.89054590e-01 -6.91451848e-01 -3.10061514e-01
-1.06292284e+00 5.81501186e-01 3.77542853e-01 -2.04422608e-01
-6.69832110e-01 1.29042315e+00 6.72891736e-01 -4.14003372e-01
5.47202945e-01 -6.31964028e-01 -6.53426468e-01 -2.94763036e-02
8.20376337e-01 6.68838382e-01 1.46563351e-01 -8.25436294e-01
-4.84816849e-01 8.42741668e-01 -2.42097691e-01 3.08567882e-01
1.51025879e+00 -2.79095978e-01 -7.82411456e-01 1.40536157e-02
1.35539544e+00 -3.54506850e-01 -1.04769886e+00 -1.90972984e-01
-6.14231974e-02 -1.98994309e-01 2.90334731e-01 -1.00193977e+00
-1.66134226e+00 1.52773917e+00 9.76813674e-01 -6.04699664e-02
1.22482586e+00 -1.89801425e-01 1.20400739e+00 -4.90807444e-01
2.64085054e-01 -1.01399803e+00 -2.27388293e-02 5.57467818e-01
1.17538106e+00 -1.27875233e+00 -1.51037410e-01 -8.15015882e-02
-2.33124167e-01 7.46657431e-01 8.82105529e-01 -1.75437704e-01
1.00904858e+00 4.76457477e-01 -1.48557305e-01 -1.59722507e-01
-6.88368082e-01 -1.66928361e-03 6.29214764e-01 7.64738023e-01
7.59055436e-01 -6.61960766e-02 -6.31933570e-01 1.08304644e+00
-2.79169455e-02 -7.94418454e-02 1.24468870e-01 6.53361976e-01
-5.47722638e-01 -4.25430626e-01 -1.56284332e-01 8.38134229e-01
-1.24044931e+00 -5.15515327e-01 1.85887933e-01 4.51504827e-01
7.70997405e-01 6.76657200e-01 7.64641687e-02 -2.15068653e-01
-1.37760460e-01 -1.40386745e-01 5.72245777e-01 -7.17015862e-01
-6.53646886e-01 -2.74019837e-01 -2.68008262e-01 -5.26848733e-01
-1.98524848e-01 -6.69462323e-01 -1.60315800e+00 -1.26414075e-01
-1.89491678e-02 -1.27142757e-01 8.06188941e-01 9.68914092e-01
2.53263831e-01 9.13937211e-01 5.43343961e-01 -8.04378450e-01
-3.01421672e-01 -1.35818672e+00 -5.76459587e-01 2.72353590e-01
5.92157125e-01 -8.05472553e-01 -1.70508936e-01 -2.05228031e-01] | [14.240853309631348, -2.3076980113983154] |
672cbd5b-a35b-4b8d-8b0d-2f32222d76ff | field-based-plot-extraction-using-uav-rgb | 2109.00632 | null | https://arxiv.org/abs/2109.00632v1 | https://arxiv.org/pdf/2109.00632v1.pdf | Field-Based Plot Extraction Using UAV RGB Images | Unmanned Aerial Vehicles (UAVs) have become popular for use in plant phenotyping of field based crops, such as maize and sorghum, due to their ability to acquire high resolution data over field trials. Field experiments, which may comprise thousands of plants, are planted according to experimental designs to evaluate varieties or management practices. For many types of phenotyping analysis, we examine smaller groups of plants known as "plots." In this paper, we propose a new plot extraction method that will segment a UAV image into plots. We will demonstrate that our method achieves higher plot extraction accuracy than existing approaches. | ['Edward J. Delp', 'Melba Crawford', 'Enyu Cai', 'Sriram Baireddy', 'Changye Yang'] | 2021-09-01 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [ 3.42756540e-01 -2.32518837e-01 -4.07001942e-01 2.84585189e-02
1.19739212e-01 -1.21098769e+00 1.23452824e-02 6.64390564e-01
2.20267817e-01 8.16134453e-01 -4.34063405e-01 -1.04107583e+00
-4.84735891e-02 -1.27375472e+00 -3.15196395e-01 -5.74681342e-01
-1.63733482e-01 -2.66866475e-01 4.40766186e-01 -3.29513371e-01
-2.73376763e-01 9.57379222e-01 -1.43203950e+00 -2.95596011e-02
9.01165783e-01 6.80064857e-01 7.03843653e-01 8.03731084e-01
3.35669637e-01 -3.19462977e-02 -5.06737232e-01 1.18054047e-01
1.76540256e-01 -3.56402099e-02 -5.88785768e-01 6.15653157e-01
1.81552649e-01 -6.68169856e-01 -3.64632048e-02 1.37372470e+00
2.33224891e-02 -3.65867168e-01 3.74473125e-01 -1.44235134e+00
-5.53356230e-01 8.80813479e-01 -1.17654121e+00 -2.76353747e-01
-1.11938734e-02 -5.96759655e-02 8.74384165e-01 -3.10224503e-01
7.82683849e-01 8.65130305e-01 5.95637321e-01 -3.29371661e-01
-1.50351000e+00 -4.46149111e-01 7.57142082e-02 6.44946694e-02
-1.40545106e+00 -4.26647812e-02 2.55332440e-01 -5.71112990e-01
3.82100791e-01 3.04696232e-01 1.10494673e+00 1.99655712e-01
5.86567521e-01 8.20185840e-01 6.65513933e-01 -2.86830962e-01
2.08763048e-01 -2.44965732e-01 -3.01729161e-02 7.50013292e-01
8.39527547e-01 7.80830011e-02 1.48172572e-01 -2.17631102e-01
1.10223293e+00 8.80703777e-02 -4.34587747e-01 -8.16983163e-01
-1.02315867e+00 9.58993733e-01 9.45949376e-01 1.53467404e-02
-8.56134832e-01 -2.73109823e-01 2.89366037e-01 -1.53260440e-01
1.01232678e-01 7.65293837e-01 -5.42265952e-01 3.45655411e-01
-9.57937121e-01 4.15894330e-01 4.23226774e-01 1.29221773e+00
6.98675931e-01 1.60300970e-01 -3.39895636e-02 6.74904287e-01
7.59412423e-02 9.00173247e-01 -2.92408615e-02 -8.08084965e-01
-2.02267140e-01 9.79292154e-01 4.80655611e-01 -1.15828884e+00
-3.32007796e-01 6.59414679e-02 -9.96477902e-01 2.27923244e-01
-1.70382455e-01 -1.98951945e-01 -1.01663780e+00 1.20414364e+00
3.17440599e-01 -2.71131098e-01 1.20803736e-01 6.13303244e-01
6.36644125e-01 5.04076958e-01 1.48365304e-01 -2.22666502e-01
1.63340354e+00 -3.60425651e-01 -6.22206867e-01 4.92609292e-02
4.87030298e-01 -6.50889933e-01 6.23857975e-01 3.69638711e-01
-5.01557231e-01 -4.27461565e-01 -1.19889975e+00 6.21189415e-01
-7.25456715e-01 7.86453605e-01 1.13192201e+00 6.42678499e-01
-8.67141366e-01 6.12391889e-01 -8.73780668e-01 -8.78111303e-01
7.31563151e-01 1.72076017e-01 -5.61733961e-01 1.65688515e-01
-5.16597331e-01 7.55567789e-01 5.27805150e-01 4.72753018e-01
-8.81287456e-01 -4.75598603e-01 -1.00771022e+00 1.20149501e-01
4.18556184e-01 1.31305665e-01 9.90036130e-01 -2.03288823e-01
-1.25165844e+00 8.41658711e-01 2.28912476e-02 -3.25760454e-01
-3.66311222e-01 -1.16256759e-01 -4.24943954e-01 1.78668767e-01
3.32095981e-01 6.83450282e-01 5.95855415e-01 -1.21477771e+00
-7.53343701e-01 -5.13855219e-01 2.33154520e-01 -1.39156118e-01
1.01552494e-02 1.03571795e-01 5.38951695e-01 -4.26714778e-01
5.98876655e-01 -1.35918307e+00 -5.32147646e-01 1.47887364e-01
-5.94213068e-01 5.96623242e-01 1.30562651e+00 -5.17642081e-01
7.54183650e-01 -2.03988481e+00 6.14773557e-02 -8.68562683e-02
5.15830159e-01 7.86641419e-01 -1.90929666e-01 3.17457676e-01
1.51843995e-01 2.99230993e-01 -1.63585320e-01 7.90172040e-01
-4.75213170e-01 2.82179862e-01 -2.71463633e-01 3.24076593e-01
3.81616622e-01 5.59793770e-01 -9.42552269e-01 -2.20027477e-01
6.27282917e-01 -8.04243162e-02 1.38163209e-01 1.40839502e-01
-5.90560287e-02 4.26124670e-02 -6.13139570e-01 1.34746516e+00
1.48072505e+00 -6.96802139e-02 5.76649308e-01 -2.01762214e-01
-8.14040244e-01 -5.45221806e-01 -7.84839928e-01 1.03009427e+00
6.58746660e-02 7.34564841e-01 5.05764425e-01 -7.81134367e-01
1.01201093e+00 2.64997274e-01 4.86549467e-01 5.10857999e-01
1.58452794e-01 -7.36087263e-02 8.41121525e-02 -7.25377649e-02
9.26606953e-01 5.20390391e-01 1.76102415e-01 -4.52337898e-02
9.14662778e-02 -4.73089904e-01 4.39372480e-01 9.60271433e-02
1.03517902e+00 1.58306554e-01 1.01313460e+00 -4.50451016e-01
1.06164813e-01 8.65219474e-01 5.36038399e-01 3.85612428e-01
-7.10161626e-01 9.75180194e-02 4.34592009e-01 -2.77655929e-01
-9.10080969e-01 -1.02179408e+00 -4.75585550e-01 6.58695161e-01
4.38142568e-01 -4.10536706e-01 -3.70793700e-01 -4.85730112e-01
4.91920710e-01 3.85798901e-01 -3.45357865e-01 -9.26482864e-03
3.45099598e-01 -9.59292293e-01 6.26026273e-01 7.60773242e-01
7.98913419e-01 -1.08258665e+00 -1.07054305e+00 3.13693434e-01
9.59131047e-02 -1.17073822e+00 3.85360211e-01 5.66070914e-01
-6.81443036e-01 -1.20785475e+00 -6.90969646e-01 -5.27304471e-01
5.92754304e-01 1.19011354e+00 7.04995394e-01 -1.31312301e-02
-7.43704498e-01 -4.28358465e-02 -8.52355421e-01 -9.34841454e-01
-1.93916887e-01 2.34820113e-01 4.06661034e-02 -4.30846691e-01
4.84993547e-01 -6.83234215e-01 -3.17239493e-01 3.80741805e-01
-6.37815773e-01 -7.06716776e-02 7.66116977e-01 7.80332446e-01
8.76673579e-01 4.78481472e-01 1.08954266e-01 -7.61359811e-01
3.51915628e-01 -4.19111043e-01 -1.30416262e+00 8.33132923e-01
-8.29368159e-02 -3.82648528e-01 2.89237350e-01 -2.50772119e-01
-5.45805156e-01 5.29187799e-01 6.29325092e-01 1.34994753e-03
-8.82039785e-01 8.41594160e-01 -4.00828689e-01 -5.25565863e-01
4.22863305e-01 -2.96814412e-01 -7.48527423e-02 -1.56095475e-01
3.53854120e-01 7.31362641e-01 6.41347110e-01 -1.96150765e-01
1.09384859e+00 7.91057348e-02 2.86158144e-01 -1.48662543e+00
-5.30931234e-01 -5.54634690e-01 -1.14201128e+00 -3.40619206e-01
8.10664117e-01 -7.87009716e-01 -8.93349528e-01 4.81039822e-01
-9.67736304e-01 -3.26568365e-01 3.32887769e-02 1.01606846e+00
-2.58236825e-01 -2.59397309e-02 -3.64419460e-01 -5.94577849e-01
-1.79839864e-01 -1.23760438e+00 1.26597905e+00 8.64762366e-01
-1.20163120e-01 -5.83556294e-01 -6.46390840e-02 -4.73751009e-01
1.61733568e-01 6.88140690e-01 7.67400920e-01 -1.51099572e-02
-3.96729529e-01 -3.91901910e-01 -8.91377330e-01 -8.21586773e-02
7.90382504e-01 9.47356284e-01 -7.07599938e-01 -3.19787800e-01
-6.69599950e-01 -2.19560504e-01 3.33413720e-01 8.48645210e-01
8.22607994e-01 4.08380091e-01 -6.91510499e-01 7.58118153e-01
1.58666945e+00 5.42872429e-01 4.15333033e-01 2.65142381e-01
6.88427031e-01 5.04757822e-01 1.21872187e+00 5.36190093e-01
-2.90985852e-01 4.15761411e-01 6.64954185e-01 -3.00855160e-01
5.09628415e-01 -2.61469722e-01 -1.80314079e-01 6.65882081e-02
-1.77598298e-01 -4.72379476e-01 -9.33369696e-01 5.12999475e-01
-1.82301354e+00 -9.15287971e-01 -3.90377611e-01 1.88866186e+00
2.26333365e-01 -3.04857135e-01 -7.95474574e-02 1.13390140e-01
8.92943382e-01 3.30466539e-01 -6.58608973e-01 -3.76140654e-01
-4.17169154e-01 1.28526345e-01 1.34445715e+00 -3.89989726e-02
-1.48947620e+00 1.43523419e+00 7.30030107e+00 2.25197613e-01
-1.07445812e+00 -6.77778542e-01 1.51597098e-01 8.68573666e-01
2.67549992e-01 5.45914590e-01 -8.42821419e-01 -4.37868267e-01
3.57870162e-01 -1.25297844e-01 1.62352785e-01 9.68879998e-01
2.13452756e-01 -6.03633583e-01 -3.48125398e-01 3.79524350e-01
-5.29516876e-01 -1.04112613e+00 -2.61690654e-02 3.63667190e-01
6.44858539e-01 -5.41987300e-01 -2.63959825e-01 -1.24534249e-01
1.22298694e+00 -7.46841550e-01 1.32354647e-01 -2.15649784e-01
9.44861352e-01 -4.48272169e-01 6.78541124e-01 -1.35151073e-02
-1.91186643e+00 1.51712880e-01 -1.16432881e+00 -3.13862078e-02
1.47737533e-01 5.98131537e-01 -9.78788435e-01 9.45463836e-01
5.59333086e-01 7.66884029e-01 -9.97017026e-01 1.11548924e+00
-3.47600788e-01 7.45080769e-01 -2.72223175e-01 -1.86582301e-02
2.93036044e-01 -3.43306810e-01 3.11117709e-01 4.66574818e-01
6.14840031e-01 3.29157293e-01 6.35293841e-01 7.57876575e-01
1.90459907e-01 6.86312020e-02 -1.14890528e+00 -4.50629264e-01
6.69828534e-01 1.52812982e+00 -1.27647817e+00 1.07998308e-02
-7.02603906e-02 8.34980309e-01 -1.35416001e-01 1.34112284e-01
-5.72931230e-01 -7.38499701e-01 6.60717309e-01 -1.62525579e-01
6.03517711e-01 -5.71500242e-01 1.09950444e-02 -9.20478046e-01
-3.35457683e-01 -8.61997008e-01 -2.42923275e-01 -1.09570491e+00
-5.55649579e-01 5.14056265e-01 1.45304233e-01 -1.23061061e+00
2.58422066e-02 -8.74318242e-01 -3.79282981e-01 7.98158288e-01
-8.73597443e-01 -1.49698019e+00 -1.00151503e+00 3.79525796e-02
2.44042590e-01 -3.83489817e-01 1.38838625e+00 -3.58844489e-01
-7.68170178e-01 2.97817923e-02 1.87500715e-01 1.34074792e-01
4.19278860e-01 -1.19302022e+00 4.11499918e-01 1.24101615e+00
1.33819446e-01 1.80331960e-01 4.98920798e-01 -1.08709979e+00
-1.34748685e+00 -1.26972485e+00 2.17993155e-01 2.41165221e-01
7.30449617e-01 7.80482441e-02 -5.01160085e-01 1.01512897e+00
4.23203595e-02 1.22354329e-01 5.77428579e-01 1.54596463e-01
1.87037110e-01 -1.48234755e-01 -1.29241872e+00 5.58660150e-01
5.95994473e-01 -1.10615909e-01 3.01729918e-01 3.48230898e-01
6.30188167e-01 -2.48414993e-01 -1.09608305e+00 5.46680868e-01
8.33651423e-01 -2.85171121e-01 6.92458034e-01 -5.72499692e-01
1.76263869e-01 -5.44356406e-01 -7.57464945e-01 -1.87936354e+00
-9.74646926e-01 -4.30204690e-01 5.91818511e-01 1.38593757e+00
1.65843934e-01 -3.60975653e-01 5.62940121e-01 4.75244708e-02
4.53670442e-01 1.14721499e-01 1.77396998e-01 -8.63407135e-01
-5.28100431e-01 3.24911386e-01 1.13368773e+00 6.92811549e-01
-7.86973760e-02 1.49595663e-01 -1.09162830e-01 9.83985782e-01
5.89050770e-01 3.22940856e-01 8.96131098e-01 -1.64789557e+00
2.44617581e-01 -3.16695660e-01 -1.00649524e+00 -4.59297627e-01
5.12721278e-02 -2.62539893e-01 1.60494760e-01 -1.31982446e+00
-1.71956047e-02 -3.76255423e-01 3.17298323e-01 6.44923210e-01
-4.72948372e-01 -1.50849074e-01 -2.26640943e-02 -1.96219996e-01
2.23191857e-01 9.86239165e-02 8.28777015e-01 -2.51689285e-01
-4.06749636e-01 2.83051997e-01 -4.75215733e-01 7.44217753e-01
1.09464180e+00 -2.81477123e-01 -6.05609953e-01 -4.10714507e-01
-4.29810882e-01 3.64066094e-01 4.96377975e-01 -8.98911655e-01
-5.86681902e-01 -8.17183971e-01 4.64257091e-01 -1.19382560e+00
4.10334906e-03 -8.18996370e-01 3.72381210e-01 5.40851414e-01
-1.70926273e-01 4.41616356e-01 4.93322521e-01 4.34148848e-01
-8.56985822e-02 -4.51727748e-01 6.91713035e-01 -7.24993050e-02
-9.30071950e-01 4.46717799e-01 -8.52925181e-01 -7.63094783e-01
1.45218587e+00 2.25019172e-01 -5.66773415e-01 -2.20857233e-01
-5.37490904e-01 1.91398680e-01 6.26143157e-01 2.16173023e-01
4.81325746e-01 -1.34463704e+00 -7.19764590e-01 2.68473566e-01
3.76165032e-01 -1.76909640e-01 -4.88972850e-02 5.28729498e-01
-1.45002174e+00 5.44189930e-01 -1.00852013e+00 -8.17306399e-01
-1.60822833e+00 7.74682820e-01 -1.66783065e-01 -2.53072202e-01
-4.64509308e-01 5.00191212e-01 8.77366140e-02 -3.05851221e-01
-2.91416913e-01 -5.94184756e-01 -4.29774970e-01 4.23708186e-02
4.70524758e-01 1.58030301e-01 -8.02706555e-02 -6.48278117e-01
-3.24822336e-01 1.42075211e-01 1.64066181e-01 1.01882562e-01
1.33131707e+00 1.78516328e-01 -1.51639313e-01 4.11266834e-01
3.84371430e-01 -2.31662080e-01 -1.03689086e+00 2.39438772e-01
6.48905933e-02 -7.00830698e-01 1.18751153e-01 -3.04433852e-01
-1.15392065e+00 6.05045259e-01 7.11073101e-01 8.05707097e-01
1.17312777e+00 -5.19935727e-01 3.17109555e-01 5.55739224e-01
7.07646608e-01 -5.03707767e-01 -1.05859900e+00 5.75998724e-02
5.85879326e-01 -1.31206846e+00 3.53598624e-01 -8.15954626e-01
-4.04051632e-01 1.06716442e+00 5.63383341e-01 -6.39077201e-02
7.71716177e-01 5.37709653e-01 -8.32126886e-02 -8.75711590e-02
-5.28754652e-01 -6.13320529e-01 -4.05578107e-01 1.37319732e+00
3.88283640e-01 1.06063366e+00 -1.73360735e-01 1.44044891e-01
-3.27019542e-01 -5.77323101e-02 7.99796045e-01 1.35916471e+00
-8.49717736e-01 -1.07234347e+00 -6.79943979e-01 6.94781303e-01
1.35667861e-01 9.93937701e-02 -7.62859821e-01 1.02312803e+00
-1.70100287e-01 9.40350771e-01 -1.90047637e-01 -5.48209250e-01
3.83339077e-01 -5.24507344e-01 4.41786319e-01 -6.92264736e-01
1.88262742e-02 -9.35292628e-04 -6.32737353e-02 -1.45247564e-01
-4.56508338e-01 -8.39904964e-01 -5.83724141e-01 -7.30944753e-01
-8.78480375e-01 -5.19598508e-03 6.95048332e-01 4.85760003e-01
9.35746357e-02 7.16263175e-01 8.82066309e-01 -8.22954834e-01
-5.69612645e-02 -9.57164466e-01 -1.45218265e+00 -2.95894116e-01
7.15729743e-02 -1.10379112e+00 3.66824865e-01 1.73583940e-01] | [9.082182884216309, -1.6110081672668457] |
51d77a6f-5496-463e-b2af-0fa7b96479cb | enhancing-sequential-recommendation-with | 2205.14837 | null | https://arxiv.org/abs/2205.14837v2 | https://arxiv.org/pdf/2205.14837v2.pdf | Enhancing Sequential Recommendation with Graph Contrastive Learning | The sequential recommendation systems capture users' dynamic behavior patterns to predict their next interaction behaviors. Most existing sequential recommendation methods only exploit the local context information of an individual interaction sequence and learn model parameters solely based on the item prediction loss. Thus, they usually fail to learn appropriate sequence representations. This paper proposes a novel recommendation framework, namely Graph Contrastive Learning for Sequential Recommendation (GCL4SR). Specifically, GCL4SR employs a Weighted Item Transition Graph (WITG), built based on interaction sequences of all users, to provide global context information for each interaction and weaken the noise information in the sequence data. Moreover, GCL4SR uses subgraphs of WITG to augment the representation of each interaction sequence. Two auxiliary learning objectives have also been proposed to maximize the consistency between augmented representations induced by the same interaction sequence on WITG, and minimize the difference between the representations augmented by the global context on WITG and the local representation of the original sequence. Extensive experiments on real-world datasets demonstrate that GCL4SR consistently outperforms state-of-the-art sequential recommendation methods. | ['Chunyan Miao', 'Lizhen Cui', 'wei he', 'Chenyi Lei', 'Hao Xiong', 'Yonghui Xu', 'Yong liu', 'Yixin Zhang'] | 2022-05-30 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [ 3.41944426e-01 -2.30269805e-01 -7.02334642e-01 -3.30603689e-01
4.85387519e-02 -4.32110399e-01 3.24614853e-01 2.39110813e-01
-5.21703474e-02 3.40847999e-01 5.41622341e-01 -2.94996679e-01
-3.81024659e-01 -7.97293186e-01 -5.97563207e-01 -5.36867142e-01
-1.70082822e-01 2.15894848e-01 9.83692035e-02 -4.04100120e-01
2.97487527e-01 8.69315714e-02 -1.30258691e+00 6.69702888e-01
9.04765606e-01 7.66392887e-01 5.56077600e-01 3.14031750e-01
-2.60709733e-01 8.54905903e-01 -1.63198654e-02 5.06641679e-02
2.68877238e-01 -7.44357586e-01 -4.71899778e-01 1.11715928e-01
1.98463783e-01 -2.94653267e-01 -1.04523706e+00 8.66045654e-01
1.77133068e-01 9.78206456e-01 5.51433742e-01 -9.79898691e-01
-1.08166337e+00 1.01056743e+00 -6.47128701e-01 5.43854952e-01
5.56094468e-01 -7.97186494e-02 1.38626778e+00 -7.50750661e-01
5.38357377e-01 9.63876247e-01 3.94112349e-01 4.75074172e-01
-1.20371091e+00 -5.71018219e-01 1.09763861e+00 4.25948530e-01
-1.12628102e+00 1.45180851e-01 8.18464756e-01 -2.53994882e-01
1.01191676e+00 2.77105033e-01 6.70930982e-01 1.15765011e+00
-5.48017733e-02 1.01018345e+00 4.66202378e-01 -2.67083079e-01
-7.46743679e-02 -1.65844113e-01 9.64553833e-01 8.42624843e-01
2.94940233e-01 1.91743877e-02 -5.87991297e-01 -3.53067517e-01
8.66841257e-01 7.47952700e-01 -4.33803320e-01 -3.43924344e-01
-8.81466627e-01 7.15840936e-01 5.30963898e-01 4.83601213e-01
-3.92970562e-01 -1.43708810e-01 2.37048045e-01 5.96114337e-01
3.83363545e-01 2.08689794e-01 -5.57299078e-01 3.01948398e-01
-3.51046264e-01 6.01221472e-02 6.21101737e-01 9.86889720e-01
8.68700802e-01 -5.72192222e-02 -4.35971737e-01 8.45667660e-01
4.68631417e-01 8.48039761e-02 7.75538802e-01 -2.44380251e-01
7.18645811e-01 8.89505267e-01 1.33609816e-01 -1.31152403e+00
-2.41045803e-01 -7.38472760e-01 -7.89388657e-01 -7.73470938e-01
3.22660834e-01 -2.51667976e-01 -6.46521509e-01 1.61689425e+00
1.91645160e-01 8.51461232e-01 -3.72797757e-01 7.46036112e-01
8.45601022e-01 9.20738578e-01 -6.26503453e-02 -3.54411900e-01
6.33340955e-01 -1.18151140e+00 -3.68170470e-01 -3.01232249e-01
9.81263340e-01 -2.15852052e-01 1.07305574e+00 3.09974372e-01
-6.03596270e-01 -7.76760578e-01 -1.01964545e+00 3.03030640e-01
8.06151032e-02 1.19547173e-01 6.27619147e-01 3.09115469e-01
-7.55895019e-01 7.94122338e-01 -5.89690983e-01 -2.66764075e-01
-6.65461719e-02 4.45118308e-01 -1.00813441e-01 -4.83054459e-01
-1.15409398e+00 3.72507572e-01 3.78440112e-01 1.21981762e-01
-5.28237879e-01 -5.38498998e-01 -7.10607171e-01 3.36789519e-01
6.95065022e-01 -4.47663933e-01 9.35694098e-01 -1.13318026e+00
-1.29254127e+00 -2.24865624e-03 -1.94650576e-01 -2.45372459e-01
-8.58491566e-03 -4.10442591e-01 -7.27559924e-01 -3.28814834e-01
-3.67009103e-01 -2.44945839e-01 7.74445295e-01 -1.10314345e+00
-7.06941485e-01 -3.66298616e-01 6.34877160e-02 4.62958157e-01
-4.55944210e-01 -4.37779635e-01 -7.69018233e-01 -9.89583492e-01
8.68780445e-03 -9.76653516e-01 -5.83833635e-01 -6.42954409e-01
-1.68658152e-01 -4.52090234e-01 6.98947966e-01 -7.06354082e-01
1.75282693e+00 -2.15321016e+00 4.05578136e-01 7.07187593e-01
7.10565373e-02 2.92215168e-01 -8.50730598e-01 6.65390968e-01
9.86543000e-02 -1.52567387e-01 2.06167340e-01 3.77850630e-03
-3.67313862e-01 3.01881254e-01 -3.45200509e-01 4.05219316e-01
-6.23027146e-01 7.96720505e-01 -1.18776083e+00 7.09543154e-02
2.25878507e-01 2.99795777e-01 -7.22279072e-01 4.50805098e-01
-3.24132919e-01 5.37709713e-01 -8.29313695e-01 -1.85658764e-02
3.36502075e-01 -5.44334531e-01 9.58657563e-01 -1.47169843e-01
2.37529710e-01 5.04333436e-01 -1.20062780e+00 1.66240561e+00
-4.40188318e-01 -1.63147718e-01 -4.49888110e-01 -1.11977744e+00
9.62932944e-01 3.34100910e-02 6.43827200e-01 -8.24659824e-01
-1.79857373e-01 -2.05044955e-01 1.66352224e-02 -2.46965200e-01
3.71880502e-01 4.48923528e-01 3.15347575e-02 7.45657921e-01
1.83060423e-01 8.14117849e-01 5.46804443e-02 4.87904191e-01
1.01850617e+00 1.22656137e-01 5.95300198e-01 -6.77584112e-02
7.04772413e-01 -6.65311515e-01 7.71538377e-01 1.17013800e+00
3.47004503e-01 2.71597564e-01 1.67517647e-01 -4.89707947e-01
-6.27512753e-01 -6.36714935e-01 6.82793021e-01 1.52231181e+00
5.29298365e-01 -7.70476162e-01 -2.78173029e-01 -1.36224830e+00
4.75540720e-02 7.92258024e-01 -7.30029285e-01 -5.87555349e-01
-7.35824883e-01 -4.20017868e-01 -2.14084953e-01 5.64765096e-01
1.04898579e-01 -9.93917227e-01 4.55264896e-01 4.55953479e-01
-1.59898356e-01 -6.58339620e-01 -1.28312087e+00 -2.86550581e-01
-1.05788314e+00 -1.07256103e+00 -3.21012825e-01 -7.77923048e-01
7.90838301e-01 1.06619883e+00 8.25750828e-01 6.34009182e-01
1.48985162e-01 3.94990414e-01 -8.92819345e-01 6.09842837e-01
4.54274043e-02 -1.10058356e-02 1.15495645e-01 3.98810059e-01
3.87598008e-01 -6.48559391e-01 -7.83911526e-01 6.33006930e-01
-7.74601698e-01 9.16241184e-02 4.00750965e-01 8.42188060e-01
6.57101095e-01 -4.96081933e-02 5.91729760e-01 -1.44655728e+00
5.97428679e-01 -1.05353904e+00 -1.53699532e-01 4.96506900e-01
-9.61418211e-01 8.54539275e-02 1.04897583e+00 -7.33784676e-01
-1.12631285e+00 -1.93269387e-01 -1.54903410e-02 -3.27129930e-01
9.58964303e-02 9.33005214e-01 -1.68592051e-01 2.27490693e-01
4.96793985e-01 5.16104221e-01 -4.12556380e-01 -9.11908686e-01
5.08328557e-01 3.94088179e-01 3.65862399e-02 -3.71375799e-01
3.23718995e-01 5.61892577e-02 -3.75558227e-01 -7.39278257e-01
-1.08820641e+00 -8.91141474e-01 -4.36504871e-01 -1.04908027e-01
8.08503404e-02 -6.72707438e-01 -4.85195875e-01 2.37376422e-01
-4.76090431e-01 -4.01997685e-01 -1.84481531e-01 6.63002968e-01
-2.57057190e-01 8.83026302e-01 -6.97143078e-01 -6.37858808e-01
-3.00492018e-01 -6.56562209e-01 3.86286199e-01 1.79202139e-01
-1.08635202e-01 -9.60742593e-01 2.38285184e-01 2.35583931e-01
-3.83437015e-02 -2.56542772e-01 1.15347016e+00 -9.88973916e-01
-3.35980207e-01 -3.35058510e-01 -4.31460254e-02 2.42663532e-01
4.95058388e-01 -3.07447612e-01 -1.83437154e-01 -7.31100380e-01
-2.81497359e-01 1.64558381e-01 9.07768548e-01 3.33501965e-01
1.40073383e+00 -7.65110254e-01 -6.15155518e-01 3.80218476e-01
1.35600841e+00 5.47825098e-01 5.08286357e-01 -1.21202953e-01
1.29878831e+00 2.59966373e-01 6.52021587e-01 5.36196113e-01
1.96208835e-01 7.91008234e-01 5.35144471e-02 5.07434428e-01
-1.29268259e-01 -9.06475008e-01 5.90375721e-01 1.09759927e+00
-3.22763711e-01 -7.55413115e-01 -2.52937138e-01 2.15743333e-01
-2.47806501e+00 -8.92503202e-01 -1.47140145e-01 2.45123649e+00
1.09322146e-01 -1.03472836e-01 2.97839969e-01 -1.98468670e-01
8.88206244e-01 2.97990829e-01 -7.21795678e-01 -1.09258853e-01
1.71752840e-01 1.00438997e-01 3.21014255e-01 5.07489622e-01
-8.05112422e-01 7.80380368e-01 5.57828045e+00 8.76756787e-01
-6.49308741e-01 -6.66338950e-02 3.42407912e-01 -3.00981402e-02
-6.53584063e-01 -1.47108197e-01 -6.74161911e-01 7.43161321e-01
8.48018527e-01 -1.28316939e-01 7.27810562e-01 7.53810823e-01
2.69890040e-01 4.32577997e-01 -1.01459885e+00 8.32085431e-01
1.51747018e-01 -1.18214715e+00 6.00760221e-01 1.76000576e-02
9.70358849e-01 -6.22138195e-02 1.10706799e-01 6.03341639e-01
6.38240755e-01 -6.49850309e-01 8.59868750e-02 8.09322298e-01
4.39809263e-01 -8.81528020e-01 6.59366369e-01 5.23384690e-01
-1.52168036e+00 -3.94072235e-01 -3.63182366e-01 -1.23608224e-01
-3.53706382e-05 3.34160566e-01 -5.37489235e-01 8.32081318e-01
4.33542997e-01 1.42719495e+00 -3.92095774e-01 8.79752040e-01
-3.11755389e-01 1.05257106e+00 1.63551811e-02 -2.03001723e-01
2.03236327e-01 -7.34143019e-01 6.04116321e-01 1.12692451e+00
3.88366282e-01 4.63243574e-01 7.23926246e-01 -7.38140242e-03
-2.57741511e-01 6.15406156e-01 -4.77006733e-01 -2.08905280e-01
3.02944094e-01 9.92666006e-01 -4.32482898e-01 -2.19758928e-01
-8.72583747e-01 1.02601337e+00 7.69169033e-01 4.98635203e-01
-4.60066944e-01 8.22557285e-02 7.84419835e-01 2.05281392e-01
6.00336015e-01 -1.70236826e-01 -5.05092889e-02 -1.40208602e+00
-2.71754622e-01 -8.67530644e-01 8.81321430e-01 -2.68494815e-01
-1.67776382e+00 2.98410386e-01 -2.72446394e-01 -1.28234506e+00
-1.75788805e-01 -1.55193940e-01 -6.57436788e-01 6.02563500e-01
-8.34039330e-01 -9.26645696e-01 -7.63950795e-02 7.97320485e-01
7.82017350e-01 -2.63103634e-01 6.50908053e-01 2.16113478e-01
-7.94109762e-01 9.87227976e-01 5.13888896e-01 -6.62255287e-02
2.22586289e-01 -8.48252535e-01 4.40183401e-01 7.75099277e-01
4.71563488e-01 9.44467127e-01 4.68233615e-01 -1.01699650e+00
-1.54225743e+00 -1.25732064e+00 4.94378418e-01 -1.62505105e-01
5.24470568e-01 -1.27773598e-01 -1.18183088e+00 1.01921010e+00
-2.80410707e-01 3.42453234e-02 9.03372347e-01 5.47724903e-01
-6.24758005e-01 6.86887186e-03 -8.57951880e-01 6.94204390e-01
1.60829592e+00 -4.41691130e-01 -6.10893428e-01 2.04004645e-01
7.72062302e-01 3.80897969e-02 -5.57611406e-01 2.76251733e-01
6.36463165e-01 -4.88603473e-01 9.13077176e-01 -1.16366434e+00
1.44447669e-01 -2.87068844e-01 -6.13715388e-02 -1.37995899e+00
-1.07515514e+00 -4.57180947e-01 -8.70942652e-01 8.60668898e-01
4.15427119e-01 -4.85736370e-01 7.95095026e-01 2.73428917e-01
-1.22799844e-01 -7.81674147e-01 -3.12843770e-01 -6.74216509e-01
-4.89078701e-01 -8.32325667e-02 7.18562722e-01 1.05349505e+00
3.34562689e-01 6.42022789e-01 -9.65900838e-01 9.26529318e-02
4.63420540e-01 4.98650312e-01 6.24557197e-01 -1.17577147e+00
-8.36443484e-01 -9.16712806e-02 -1.78530186e-01 -1.64111304e+00
3.53934877e-02 -1.19411528e+00 -8.36724862e-02 -1.67787671e+00
4.75061119e-01 -3.87178689e-01 -1.05252564e+00 3.32421005e-01
-5.96509695e-01 -1.68117195e-01 -2.01841276e-02 3.96569192e-01
-7.70566642e-01 4.06845003e-01 1.38304424e+00 -1.01665653e-01
-6.12808585e-01 3.33513021e-01 -9.43591714e-01 5.14145851e-01
6.69771016e-01 -3.30263853e-01 -9.95901406e-01 -2.31402516e-01
1.88028708e-01 1.78140566e-01 -3.48876357e-01 -5.20467162e-01
1.89951465e-01 -4.79196250e-01 2.45284274e-01 -5.13570130e-01
-2.67043913e-04 -7.13218868e-01 3.33452702e-01 3.13614488e-01
-7.49492586e-01 -2.97507554e-01 -2.96082854e-01 1.42817044e+00
2.85514385e-01 -1.38880819e-01 2.93765545e-01 -8.88330266e-02
-9.52351153e-01 8.68281424e-01 -3.60356271e-01 -2.43158400e-01
7.56805956e-01 -2.82760710e-02 -8.64004940e-02 -4.46308434e-01
-9.36775148e-01 2.12546840e-01 2.23491251e-01 6.62235081e-01
7.72084534e-01 -1.29784214e+00 -4.75731999e-01 1.31908923e-01
2.44089052e-01 -5.16779959e-01 7.82558918e-01 2.82836765e-01
1.13548175e-01 2.56353706e-01 -6.66180439e-03 5.86931817e-02
-1.37292051e+00 1.09141958e+00 1.25467340e-02 -6.55295253e-01
-9.90567565e-01 9.24975336e-01 4.33225632e-01 -3.74368161e-01
1.79836258e-01 -1.92803770e-01 -8.75361264e-01 -1.25728697e-01
7.73322582e-01 5.28372049e-01 -8.75987783e-02 -5.65911233e-01
-1.08144686e-01 1.88725814e-01 -8.16505373e-01 3.46722484e-01
1.33778274e+00 -3.51210415e-01 2.39961416e-01 4.35163200e-01
1.02428091e+00 5.16330972e-02 -1.24661100e+00 -8.05635870e-01
-4.69608828e-02 -8.46662700e-01 1.23844996e-01 -5.88496029e-01
-1.28506899e+00 1.63472831e-01 3.30818743e-01 7.42057711e-02
8.16702604e-01 -2.72378027e-01 9.96679366e-01 5.82950950e-01
6.76671326e-01 -8.47770452e-01 2.55850941e-01 5.12273610e-01
6.87867463e-01 -7.48216093e-01 1.82843313e-03 -5.02081394e-01
-7.22865880e-01 7.39866376e-01 7.60048449e-01 -5.66354990e-01
8.77960265e-01 -4.49455559e-01 -5.90037823e-01 9.25125033e-02
-7.50785112e-01 -1.18649304e-01 7.44013667e-01 5.08127213e-01
5.52938104e-01 2.42883295e-01 -5.45246065e-01 1.08184254e+00
5.22861421e-01 -2.80052936e-03 1.99970543e-01 7.79395759e-01
-4.25735503e-01 -1.38842392e+00 3.22119653e-01 1.12400877e+00
-1.44801348e-01 -7.65140951e-02 -4.57090706e-01 1.73657730e-01
-6.54851049e-02 1.11532879e+00 -1.79384112e-01 -1.00354517e+00
3.68016183e-01 -2.21168414e-01 5.29329419e-01 -9.89275992e-01
-6.50356770e-01 -5.69092147e-02 3.31681930e-02 -5.99255204e-01
1.24111259e-02 -6.30469024e-01 -1.06828105e+00 -3.53187531e-01
-4.95037377e-01 5.25537670e-01 -5.74028306e-02 1.12053299e+00
4.69735295e-01 4.35082704e-01 1.01796067e+00 -6.85541689e-01
-4.27972257e-01 -9.04031694e-01 -9.30477977e-01 5.50180376e-01
1.04241431e-01 -6.30374670e-01 -1.42477155e-01 -3.18338394e-01] | [10.199026107788086, 5.605485439300537] |
09348113-0d7c-44c5-a1d0-0270ccd70066 | activity-detection-in-long-surgical-videos | 2205.02805 | null | https://arxiv.org/abs/2205.02805v3 | https://arxiv.org/pdf/2205.02805v3.pdf | An Empirical Study on Activity Recognition in Long Surgical Videos | Activity recognition in surgical videos is a key research area for developing next-generation devices and workflow monitoring systems. Since surgeries are long processes with highly-variable lengths, deep learning models used for surgical videos often consist of a two-stage setup using a backbone and temporal sequence model. In this paper, we investigate many state-of-the-art backbones and temporal models to find architectures that yield the strongest performance for surgical activity recognition. We first benchmark the models performance on a large-scale activity recognition dataset containing over 800 surgery videos captured in multiple clinical operating rooms. We further evaluate the models on the two smaller public datasets, the Cholec80 and Cataract-101 datasets, containing only 80 and 101 videos respectively. We empirically found that Swin-Transformer+BiGRU temporal model yielded strong performance on both datasets. Finally, we investigate the adaptability of the model to new domains by fine-tuning models to a new hospital and experimenting with a recent unsupervised domain adaptation approach. | ['Muhammad Abdullah Jamal', 'Aidean Sharghi', 'Ali Mottaghi', 'Zhuohong He', 'Omid Mohareri'] | 2022-05-05 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [ 4.47716027e-01 -7.87323490e-02 -6.51533961e-01 -9.68018770e-02
-5.97256780e-01 -5.45249701e-01 5.86888790e-01 -4.10782499e-03
-7.36004710e-01 5.61532497e-01 6.72155261e-01 -3.24478716e-01
-3.34761679e-01 -2.28420109e-01 -6.21169984e-01 -8.11092973e-01
-4.84833479e-01 2.95624554e-01 2.11810678e-01 2.90636301e-01
1.42468035e-01 3.65823030e-01 -1.21496296e+00 7.09605694e-01
3.50235611e-01 9.29835260e-01 -3.39203179e-02 1.02393436e+00
1.81375802e-01 1.35688627e+00 -5.64370632e-01 2.24494766e-02
3.85601193e-01 -4.11047399e-01 -8.34868312e-01 2.10351348e-01
2.84068495e-01 -2.50553608e-01 -6.48676515e-01 5.55675685e-01
5.67513704e-01 -2.22395182e-01 3.93199533e-01 -8.67616832e-01
-2.20271740e-02 4.69972968e-01 -3.12790498e-02 7.47422516e-01
4.55940902e-01 3.94996554e-01 3.73559475e-01 -4.59033042e-01
1.10749650e+00 5.33963084e-01 5.85055113e-01 6.64063990e-01
-7.55944729e-01 -5.57239175e-01 -3.83623317e-02 2.22683698e-01
-8.77572536e-01 -4.64533001e-01 5.70107818e-01 -6.29146278e-01
1.12379670e+00 1.28714852e-02 1.11386108e+00 1.95755875e+00
7.62525201e-01 9.43793833e-01 9.26547766e-01 -8.84886980e-02
1.34332567e-01 -3.13053906e-01 -4.07808758e-02 9.66181517e-01
2.90071905e-01 2.24165782e-01 -6.93052411e-01 -5.54041974e-02
7.84774303e-01 3.48367661e-01 -3.77371490e-01 -5.83581090e-01
-2.05898976e+00 4.60889727e-01 1.00137487e-01 6.37807369e-01
-3.99397016e-01 1.53513700e-01 8.29554796e-01 4.90735114e-01
-2.14421339e-02 7.27279127e-01 -6.82097077e-01 -8.65885496e-01
-8.60598087e-01 -2.65219390e-01 1.20148027e+00 9.93392944e-01
-5.81736080e-02 -3.16689402e-01 -5.27431667e-01 5.27164996e-01
1.97584778e-02 -2.44417086e-01 9.84783769e-01 -9.22729313e-01
3.37748319e-01 5.71611404e-01 -8.04091990e-02 -2.55178362e-01
-6.45835638e-01 -7.08944917e-01 -8.43484521e-01 -2.72616178e-01
5.23006558e-01 -2.24472001e-01 -1.27370918e+00 1.22834635e+00
-2.33178623e-02 7.64746308e-01 1.98376477e-01 5.13694823e-01
8.06757927e-01 2.23584116e-01 2.02474594e-01 -4.45592135e-01
1.28316939e+00 -1.44679427e+00 -7.97828972e-01 -1.96221054e-01
1.33871138e+00 -5.04625380e-01 8.45964074e-01 6.57800496e-01
-1.00760531e+00 -2.48604760e-01 -1.03798568e+00 6.89734817e-02
-1.48958728e-01 3.73952597e-01 6.50305867e-01 3.06134969e-01
-8.52501929e-01 6.87960744e-01 -1.50409019e+00 -6.53507769e-01
6.80785060e-01 4.93801087e-01 -7.43302464e-01 -3.60596806e-01
-5.66897571e-01 7.69512355e-01 4.06586468e-01 4.48493753e-03
-1.53159034e+00 -7.90388942e-01 -8.03534269e-01 -8.38607848e-02
3.74590278e-01 -8.15032363e-01 1.38727176e+00 -9.30240691e-01
-1.45752633e+00 1.06366491e+00 -4.47521415e-06 -5.95640004e-01
6.33256257e-01 -2.85492033e-01 -6.41551554e-01 3.02970950e-02
-2.64509112e-01 2.85955966e-01 6.09846056e-01 -5.52277088e-01
-5.61340392e-01 -6.22349568e-02 -5.31864613e-02 -2.49988250e-02
-5.11787117e-01 -8.61937255e-02 -5.93301356e-01 -8.00192177e-01
-2.00169548e-01 -1.31709754e+00 -3.63105893e-01 -5.60241267e-02
-1.05940096e-01 1.55277818e-01 4.85084146e-01 -4.45159644e-01
1.39568806e+00 -2.29143906e+00 2.90057063e-01 -1.11070417e-01
1.73945978e-01 2.82139242e-01 -3.10723335e-01 4.76989776e-01
-2.97917545e-01 -2.81504601e-01 -1.27552059e-02 -1.14279166e-01
-5.77437878e-01 6.41327202e-01 3.28289837e-01 5.89068890e-01
-2.40671821e-02 8.03435445e-01 -1.09299326e+00 -7.46150196e-01
1.04377277e-01 1.27482951e-01 -7.54065633e-01 3.87283623e-01
7.80749395e-02 6.32198334e-01 -2.28962079e-01 1.00258851e+00
-9.71662402e-02 -5.84156990e-01 3.99037808e-01 -2.40114108e-01
1.20105416e-01 1.19163387e-01 -7.17471063e-01 2.78306818e+00
-2.94885665e-01 6.15513325e-01 -1.72761545e-01 -1.08407998e+00
2.75743902e-01 5.53202868e-01 1.41448069e+00 -5.75504780e-01
4.08667922e-01 7.23116919e-02 4.74349201e-01 -9.34214115e-01
8.41973871e-02 3.87398563e-02 -1.02453001e-01 1.56045705e-01
6.03489339e-01 5.70922136e-01 5.32446027e-01 -4.51915078e-02
2.08206153e+00 4.96083274e-02 5.10397136e-01 -2.13795155e-01
4.36467469e-01 3.08678776e-01 5.60815394e-01 7.50514448e-01
-7.53312230e-01 4.84708518e-01 6.92834795e-01 -7.43600428e-01
-6.66950941e-01 -9.97495949e-01 2.71335449e-02 9.02972341e-01
-1.81120783e-02 -6.12979174e-01 -4.73987579e-01 -1.21893907e+00
-3.01079154e-01 1.92297131e-01 -9.75295067e-01 -3.56304675e-01
-8.13063383e-01 -8.75567675e-01 4.73471463e-01 8.26380610e-01
1.38812616e-01 -1.17487669e+00 -8.32206666e-01 4.16062891e-01
-2.05040082e-01 -1.40909588e+00 -4.90356445e-01 3.57003510e-01
-1.10282600e+00 -1.51320279e+00 -6.97780132e-01 -9.57824230e-01
5.50025403e-01 3.27888541e-02 1.12038565e+00 -1.08234122e-01
-6.08975172e-01 5.18542051e-01 -4.12556589e-01 -5.89131057e-01
-5.27393401e-01 3.93310636e-01 6.78303763e-02 -1.00175008e-01
3.30336273e-01 -3.32737863e-01 -9.72012877e-01 3.06184083e-01
-9.66695368e-01 5.96263744e-02 1.28273404e+00 9.12799835e-01
4.86244440e-01 -5.14852762e-01 -4.96831909e-02 -9.66620445e-01
3.90127987e-01 -7.03915954e-01 -3.19815874e-01 1.23754211e-01
-5.39756119e-01 2.50987709e-01 2.96475530e-01 -7.72402227e-01
-7.21396863e-01 4.13916945e-01 2.98495201e-04 -7.07541823e-01
-2.91529428e-02 6.47328615e-01 3.70938510e-01 -6.51627779e-02
7.91376472e-01 3.87613714e-01 1.70818910e-01 -3.55023980e-01
-1.71804622e-01 1.64034396e-01 5.79816639e-01 -2.23573416e-01
4.03475583e-01 5.64611733e-01 9.03852470e-03 -5.09661794e-01
-8.34303856e-01 -9.13475335e-01 -5.74271560e-01 -1.41182572e-01
9.40198302e-01 -1.12204921e+00 -6.53967381e-01 5.44147491e-01
-7.40472138e-01 -5.55340290e-01 -3.34281951e-01 8.66690099e-01
-6.88716829e-01 1.98838621e-01 -7.68446922e-01 -1.52386986e-02
-2.29350671e-01 -1.28577054e+00 1.04502249e+00 -9.70599428e-02
-2.80138910e-01 -9.01633739e-01 7.31689632e-01 3.33207101e-01
3.83076012e-01 4.88433748e-01 5.71957111e-01 -7.27371812e-01
-5.94631255e-01 -3.32883239e-01 1.55375555e-01 2.47170597e-01
3.51525277e-01 -1.57420278e-01 -5.69157720e-01 -5.55529833e-01
-2.05254450e-01 -1.50663778e-01 8.22553158e-01 3.61910909e-01
1.61920536e+00 -2.05072835e-01 -7.49611974e-01 1.01418841e+00
1.27838230e+00 3.22124183e-01 9.11211073e-01 5.53314030e-01
4.47329551e-01 4.92508225e-02 8.07818353e-01 4.29517478e-01
-4.92818207e-02 3.02301615e-01 5.79313874e-01 2.88511254e-03
-2.68652029e-02 1.25191119e-02 5.02609193e-01 1.06133974e+00
-4.29295450e-01 -2.41525009e-01 -1.15257132e+00 8.20906639e-01
-1.85562122e+00 -1.07746065e+00 3.13491106e-01 1.90645278e+00
7.17009485e-01 1.66024104e-01 5.82864657e-02 -5.82550578e-02
-1.84973031e-01 1.80794731e-01 -4.18630183e-01 1.66567117e-02
3.29952091e-01 4.06497806e-01 8.36500764e-01 -2.39872694e-01
-1.34949636e+00 6.11005485e-01 7.06328917e+00 3.95173043e-01
-1.18156266e+00 2.07014516e-01 3.72469306e-01 -8.00895989e-01
6.23679817e-01 -3.03034872e-01 -4.13565695e-01 6.00457311e-01
1.34084177e+00 4.43264470e-02 4.11576480e-02 1.02228773e+00
2.05415994e-01 -1.69230718e-02 -1.44831944e+00 1.16888714e+00
6.21226951e-02 -1.79924130e+00 -1.60193771e-01 3.07977080e-01
8.18027318e-01 5.22453070e-01 -3.41010511e-01 3.74804020e-01
2.20338508e-01 -1.16914129e+00 1.85179532e-01 5.57827592e-01
7.96125770e-01 -9.89961699e-02 9.88344848e-01 1.22913115e-01
-1.06820524e+00 -5.19901812e-01 2.10112169e-01 2.94375092e-01
-6.73306659e-02 -4.46992868e-04 -1.07632732e+00 3.75489831e-01
8.67601037e-01 1.29134238e+00 -5.14467895e-01 1.38061976e+00
1.81267485e-01 6.10022068e-01 4.74240817e-02 2.03982756e-01
3.59334201e-01 1.45074904e-01 3.37304652e-01 1.55296981e+00
4.66235489e-01 -2.20271260e-01 1.34984612e-01 -1.28407106e-01
-2.23091722e-01 -2.70358741e-01 -6.69466376e-01 -3.93705726e-01
-1.03247955e-01 1.24674022e+00 -4.70144540e-01 -3.57199699e-01
-7.92051971e-01 9.64023650e-01 1.75833493e-01 8.21851343e-02
-1.16370678e+00 -5.18407226e-02 9.28849518e-01 2.19900399e-01
6.92206621e-02 -3.86037409e-01 1.70765117e-01 -1.41070771e+00
-6.53219596e-02 -1.19492531e+00 8.48819554e-01 -3.93372566e-01
-8.23234320e-01 4.44313526e-01 -1.60643443e-01 -1.90858901e+00
-2.30952665e-01 -9.60010290e-01 -3.15240771e-01 5.75235784e-02
-1.53303373e+00 -1.10045934e+00 -9.56324041e-01 1.00656593e+00
8.31221282e-01 -2.07925528e-01 8.96887660e-01 4.72659171e-01
-6.69382691e-01 3.98899466e-01 4.39297184e-02 5.14427364e-01
1.04440451e+00 -1.09819603e+00 -1.04678601e-01 8.49894941e-01
1.01001002e-01 4.71199453e-01 4.02656347e-01 -2.78116852e-01
-1.73423648e+00 -1.20782936e+00 3.29543144e-01 -7.24613488e-01
7.95368910e-01 -2.23540634e-01 -5.50993979e-01 1.08659446e+00
3.47703725e-01 3.51060957e-01 1.05269217e+00 -2.06118509e-01
1.73957080e-01 -3.31685990e-02 -8.61433923e-01 3.52066129e-01
1.68036854e+00 -1.76133782e-01 -5.14804363e-01 4.99318719e-01
4.99588370e-01 -7.42550313e-01 -1.36137295e+00 6.46359324e-01
7.73933887e-01 -7.04947174e-01 7.95193136e-01 -9.99757826e-01
6.44793630e-01 -3.68395485e-02 1.75685674e-01 -1.21147752e+00
-2.74380714e-01 -7.17116714e-01 -5.65473974e-01 2.72990376e-01
3.40572894e-01 -3.37500542e-01 1.16166544e+00 2.42462605e-02
-4.93009835e-01 -8.73266280e-01 -8.83820653e-01 -7.65774965e-01
-4.00177300e-01 -1.53057918e-01 9.94865671e-02 1.06360340e+00
1.82318822e-01 -7.92792961e-02 -3.15532982e-01 -1.12284854e-01
8.96121860e-02 -1.95424795e-01 7.77992845e-01 -1.02502143e+00
-4.33520168e-01 -3.33815008e-01 -6.64636672e-01 -8.55040908e-01
-3.13667715e-01 -6.84832335e-01 -1.00519255e-01 -1.41291845e+00
3.93151045e-01 -1.97863281e-02 -9.27661836e-01 5.38359344e-01
2.63221264e-01 -7.68422261e-02 -2.70538330e-01 2.79494196e-01
-7.65915513e-01 2.38687590e-01 1.31810820e+00 -3.43183428e-01
-2.88659662e-01 4.77253273e-02 -3.32668543e-01 4.94981378e-01
4.78488714e-01 -5.96317708e-01 -5.17678380e-01 -4.74761039e-01
-8.42169859e-04 2.47561976e-01 4.52301241e-02 -1.49259508e+00
3.23380828e-01 -1.75830483e-01 2.96408325e-01 -1.92568883e-01
2.21048921e-01 -9.21215892e-01 2.44675487e-01 1.01172781e+00
-2.16351107e-01 2.13206232e-01 2.08736375e-01 8.04160476e-01
-4.02653545e-01 3.38181138e-01 7.23180592e-01 -3.73263389e-01
-8.84061992e-01 6.70208216e-01 -5.51797748e-01 1.13013322e-02
1.47795093e+00 -3.73750061e-01 -3.75871778e-01 1.72501892e-01
-1.03306067e+00 6.80373088e-02 3.86597931e-01 9.06424463e-01
4.60591078e-01 -1.12583995e+00 -5.13055980e-01 2.38554239e-01
6.63723588e-01 -7.55465254e-02 1.86424136e-01 1.47643590e+00
-1.01504755e+00 7.00624585e-01 -4.55662161e-01 -8.72802675e-01
-1.46096241e+00 7.65694439e-01 5.30345321e-01 -8.73998225e-01
-6.40230596e-01 6.01017356e-01 2.63938922e-02 -8.98450911e-02
4.56328094e-01 -9.27862048e-01 -1.42026827e-01 -1.27627969e-01
4.03383672e-01 9.38625559e-02 2.71183133e-01 -8.06919336e-02
-6.10759616e-01 2.53129572e-01 -1.35325670e-01 3.26555759e-01
1.37733865e+00 4.99917865e-01 3.09771478e-01 2.43923008e-01
1.26372337e+00 -4.26887512e-01 -1.15776813e+00 -1.32920861e-01
2.36917228e-01 -2.92059422e-01 5.32612614e-02 -8.38381231e-01
-1.22284853e+00 5.37241399e-01 8.52371752e-01 -4.02499527e-01
1.32422447e+00 -3.24876793e-02 6.31265700e-01 4.87988383e-01
4.43607330e-01 -9.21393275e-01 5.55281103e-01 3.39813828e-01
6.05089605e-01 -1.32228863e+00 -5.00816405e-02 -1.11244105e-01
-6.00097477e-01 1.06980336e+00 6.12941265e-01 -2.53577605e-02
7.81394362e-01 3.44754905e-01 7.07062930e-02 -4.30231839e-01
-1.04491282e+00 1.05329484e-01 2.46701062e-01 2.95593768e-01
5.56500733e-01 -1.17553860e-01 -2.52196103e-01 3.33063364e-01
2.06972867e-01 8.21188867e-01 7.24011660e-01 1.54131305e+00
1.07194513e-01 -1.04083323e+00 1.94772825e-01 6.94627702e-01
-7.32039392e-01 4.91736196e-02 -1.20307609e-01 1.01744044e+00
-5.04056960e-02 4.79727924e-01 -7.29293823e-02 -3.17196101e-01
4.44269985e-01 1.27300441e-01 6.66447401e-01 -6.06921017e-01
-7.45390415e-01 -1.29376352e-01 3.03997278e-01 -1.35594928e+00
-7.40614116e-01 -7.28848815e-01 -8.60845625e-01 4.05651182e-02
2.63323873e-01 -2.00695157e-01 5.93131065e-01 7.88911223e-01
6.12765431e-01 1.09382391e+00 1.56693950e-01 -4.46754992e-01
-3.28911364e-01 -1.19384933e+00 -3.37277949e-01 4.93438512e-01
4.34168220e-01 -5.93038678e-01 -8.42686594e-02 2.83599555e-01] | [14.10906982421875, -3.363959789276123] |
60361f12-0062-468e-829a-d659a2352599 | point-cloud-completion-with-pretrained-text | 2306.10533 | null | https://arxiv.org/abs/2306.10533v1 | https://arxiv.org/pdf/2306.10533v1.pdf | Point-Cloud Completion with Pretrained Text-to-image Diffusion Models | Point-cloud data collected in real-world applications are often incomplete. Data is typically missing due to objects being observed from partial viewpoints, which only capture a specific perspective or angle. Additionally, data can be incomplete due to occlusion and low-resolution sampling. Existing completion approaches rely on datasets of predefined objects to guide the completion of noisy and incomplete, point clouds. However, these approaches perform poorly when tested on Out-Of-Distribution (OOD) objects, that are poorly represented in the training dataset. Here we leverage recent advances in text-guided image generation, which lead to major breakthroughs in text-guided shape generation. We describe an approach called SDS-Complete that uses a pre-trained text-to-image diffusion model and leverages the text semantics of a given incomplete point cloud of an object, to obtain a complete surface representation. SDS-Complete can complete a variety of objects using test-time optimization without expensive collection of 3D information. We evaluate SDS Complete on incomplete scanned objects, captured by real-world depth sensors and LiDAR scanners. We find that it effectively reconstructs objects that are absent from common datasets, reducing Chamfer loss by 50% on average compared with current methods. Project page: https://sds-complete.github.io/ | ['Gal Chechik', 'Ohad Rahamim', 'Yoni Kasten'] | 2023-06-18 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [ 4.77981806e-01 3.80418226e-02 2.13108018e-01 -4.65790242e-01
-1.20140171e+00 -7.73388386e-01 4.73758221e-01 2.25681961e-02
1.77251741e-01 2.79140025e-01 1.66180227e-02 1.67667761e-01
1.31303249e-02 -8.12012672e-01 -1.11373842e+00 -1.74268350e-01
3.32588583e-01 1.42455709e+00 3.72597128e-01 -3.57186794e-02
3.43695045e-01 8.14215899e-01 -1.93328810e+00 3.33245277e-01
8.42902243e-01 8.63569975e-01 5.60883522e-01 5.18606722e-01
-4.09870356e-01 2.93443818e-02 -3.25030893e-01 -2.27509841e-01
5.87878823e-01 1.63382083e-01 -5.19711852e-01 4.14666682e-01
8.89280617e-01 -5.41171312e-01 -5.50822914e-02 7.65756786e-01
2.75468856e-01 -2.83871680e-01 6.49172544e-01 -1.17786050e+00
-2.51184136e-01 -7.64195248e-02 -6.42908931e-01 -6.52266562e-01
5.19225836e-01 1.70093641e-01 8.03278983e-01 -1.42017889e+00
1.00590098e+00 1.17900193e+00 8.06215823e-01 3.98577124e-01
-1.54374254e+00 -5.53511679e-01 -1.44611776e-01 -2.12526828e-01
-1.51886261e+00 -4.44864571e-01 1.04568565e+00 -5.75950861e-01
6.91902816e-01 3.03061247e-01 7.20944107e-01 7.66324461e-01
-1.64486349e-01 6.72025859e-01 6.95148468e-01 -1.51247784e-01
3.60942930e-01 -2.02487051e-01 -2.93547809e-01 3.27488840e-01
4.65291679e-01 8.81767645e-02 -6.56749964e-01 -5.16229391e-01
8.73325408e-01 2.42600247e-01 -1.92447692e-01 -1.06361997e+00
-1.22296143e+00 5.72962224e-01 2.98241347e-01 -4.24114197e-01
-5.28769553e-01 7.77328312e-02 -1.78989068e-01 1.00798376e-01
7.74953663e-01 2.10297510e-01 -5.54003239e-01 -1.12814032e-01
-1.08309162e+00 6.65109694e-01 8.26459408e-01 1.53916311e+00
1.15969622e+00 -2.21866801e-01 4.09334511e-01 8.01336288e-01
2.85618514e-01 1.02422988e+00 -3.04464579e-01 -1.40108502e+00
6.67776823e-01 7.86157966e-01 3.70158762e-01 -6.51600599e-01
-2.02149808e-01 -7.25508947e-03 -4.66963083e-01 6.76184058e-01
1.70003369e-01 3.31468314e-01 -1.10282719e+00 1.06676936e+00
7.52152622e-01 -6.34717941e-02 -2.85268098e-01 8.47620487e-01
5.75241983e-01 3.74789149e-01 -6.41001582e-01 1.80923268e-01
9.40144598e-01 -5.28867424e-01 -2.42114916e-01 -3.82279098e-01
3.45492661e-01 -1.08114588e+00 1.18125856e+00 8.01435113e-01
-1.26673067e+00 -1.61831230e-01 -9.75860476e-01 -2.76395708e-01
5.03969453e-02 -1.38476551e-01 2.86016881e-01 3.10139090e-01
-9.26113963e-01 7.19555378e-01 -9.64413285e-01 -6.01542257e-02
9.17174280e-01 3.89165998e-01 -3.59744549e-01 -8.00443470e-01
-5.06001264e-02 5.26106000e-01 -1.85095310e-01 -3.10591370e-01
-9.94510829e-01 -1.25584435e+00 -5.61222851e-01 -4.13212389e-01
4.89115804e-01 -8.85219693e-01 1.35879934e+00 -5.00042379e-01
-9.85184371e-01 8.44109237e-01 -2.76175499e-01 -3.31809744e-02
6.71905041e-01 -5.01594067e-01 3.51270258e-01 2.30765224e-01
2.31779709e-01 8.52035284e-01 9.22258258e-01 -1.81315243e+00
-3.62054616e-01 -7.71904767e-01 -3.16061735e-01 4.18220729e-01
2.92622238e-01 -4.60732222e-01 -6.37228668e-01 -4.18455809e-01
8.28664184e-01 -9.52156067e-01 -2.89671421e-01 6.95066810e-01
-4.29528981e-01 1.40184686e-01 1.30688763e+00 -4.60767567e-01
3.33576053e-01 -2.00337958e+00 -9.49241892e-02 1.58839032e-01
1.48024172e-01 -1.98675945e-01 -2.10643679e-01 7.15322912e-01
2.72148252e-01 1.56820081e-02 -6.05209768e-01 -9.05605555e-01
-7.15690851e-02 5.62789142e-01 -4.79766130e-01 5.38556337e-01
1.93377480e-01 5.26022732e-01 -6.26115441e-01 -4.11505461e-01
5.76171637e-01 6.75842702e-01 -7.89705992e-01 1.54996514e-01
-8.76262486e-01 6.26246214e-01 -4.62184787e-01 1.13005221e+00
1.30235279e+00 -1.50951520e-01 -2.59522259e-01 -3.38895321e-01
-4.60173003e-02 2.86372632e-01 -1.37601888e+00 2.33551645e+00
-3.12792331e-01 3.21151197e-01 3.16778600e-01 -4.69589978e-01
9.27002549e-01 5.18469289e-02 9.25376713e-01 -2.87096411e-01
-2.08286047e-01 4.35236186e-01 -5.95479727e-01 -1.71939164e-01
6.69460595e-01 -1.30553812e-01 3.83224726e-01 4.43243891e-01
-3.56062025e-01 -1.19140100e+00 -2.07096055e-01 2.63424486e-01
1.29314268e+00 4.85162079e-01 -1.56827867e-01 7.93332681e-02
-1.96488827e-01 6.49193466e-01 3.11243296e-01 5.07567525e-01
5.30093014e-01 1.56661248e+00 -4.09278870e-02 -3.38052690e-01
-1.53635609e+00 -1.21182847e+00 -4.27693963e-01 2.83621937e-01
3.82871091e-01 -3.32653642e-01 -5.37268519e-01 -3.67246270e-01
2.71965474e-01 7.45038450e-01 -2.59637237e-01 2.46516168e-01
-5.66842437e-01 -2.80531764e-01 7.82677531e-02 4.35945004e-01
-4.22150688e-03 -8.22643161e-01 -7.19584823e-01 1.93472967e-01
-8.91200826e-02 -1.24075615e+00 -3.13553631e-01 -9.24684405e-02
-1.56536961e+00 -1.24077904e+00 -5.11305273e-01 -3.71866047e-01
1.12136710e+00 6.09355330e-01 1.46113241e+00 3.07468385e-01
-5.30183971e-01 7.50468433e-01 -3.45503122e-01 -5.27519882e-01
-1.98018208e-01 -1.41611800e-01 -1.37333661e-01 -2.33263448e-01
2.04975292e-01 -8.18755865e-01 -6.03065729e-01 4.46813196e-01
-1.10649729e+00 2.36503363e-01 6.27466321e-01 6.01034582e-01
1.06210470e+00 -1.22767307e-01 -1.13999367e-01 -6.86067104e-01
-3.71954553e-02 -4.32794631e-01 -7.34819651e-01 -2.70523846e-01
-4.65905100e-01 -3.26833986e-02 1.55985821e-02 -3.52414429e-01
-8.80041003e-01 4.79191452e-01 -4.11569737e-02 -1.08334851e+00
-2.13950172e-01 1.64089486e-01 -1.80199459e-01 -1.24331214e-01
6.55800641e-01 1.70517266e-01 2.07424700e-01 -8.68775606e-01
2.24758938e-01 3.82948786e-01 4.24809813e-01 -7.31426835e-01
1.05349886e+00 1.20593476e+00 3.45803574e-02 -9.68176365e-01
-6.43004179e-01 -5.08868098e-01 -7.66665220e-01 -6.54072985e-02
2.85005391e-01 -8.62301528e-01 -3.65363032e-01 3.48268569e-01
-1.36980045e+00 -3.92705292e-01 -7.60589540e-01 3.47431302e-01
-6.89843535e-01 3.13704789e-01 -1.18180864e-01 -7.58599699e-01
-2.47207761e-01 -9.81684983e-01 1.87075663e+00 -3.61061126e-01
1.57185849e-02 -4.69737530e-01 1.62965190e-02 5.68368077e-01
-2.09458992e-02 2.92792946e-01 4.85833466e-01 -9.93713439e-02
-1.36447501e+00 -4.29611206e-01 -1.01896368e-01 3.93605113e-01
3.14151607e-02 1.81004435e-01 -8.19477439e-01 -2.85518080e-01
7.39549622e-02 -3.87977690e-01 3.53403777e-01 4.52442437e-01
1.05876672e+00 -1.33963183e-01 -6.07403755e-01 7.55443692e-01
1.59183264e+00 -2.79474020e-01 6.60013437e-01 3.73046398e-02
7.19415188e-01 7.47084439e-01 9.58902419e-01 6.49033725e-01
4.93747741e-01 7.31161118e-01 9.87212896e-01 1.40155926e-01
-3.07164222e-01 -4.27953899e-01 3.76494527e-02 5.62937438e-01
-3.51085179e-02 -7.82292262e-02 -1.15160573e+00 6.60198808e-01
-1.39650238e+00 -6.07176602e-01 -7.63320744e-01 2.52194881e+00
7.26488948e-01 -3.71627063e-02 -1.81216538e-01 1.82142556e-02
4.21332121e-01 -1.49782315e-01 -7.49593437e-01 3.87490720e-01
-2.12144360e-01 3.85169923e-01 5.89317322e-01 5.86293817e-01
-5.47823608e-01 6.92608714e-01 5.91586447e+00 6.16631866e-01
-8.68694425e-01 2.04696223e-01 8.89880136e-02 -4.64875311e-01
-7.42690146e-01 3.74296159e-01 -7.25411057e-01 1.48397312e-01
4.12889689e-01 -1.10382801e-02 3.17752063e-01 1.06719828e+00
1.52378768e-01 -4.28157151e-01 -1.20509148e+00 1.23363435e+00
2.48366907e-01 -1.37835455e+00 -1.45893702e-02 4.84158933e-01
9.47174132e-01 6.17827535e-01 -1.93473458e-01 -2.92899787e-01
4.57708597e-01 -7.97041059e-01 9.31158781e-01 6.36812270e-01
1.01607132e+00 -4.43673372e-01 2.67657191e-01 6.95978045e-01
-9.82747853e-01 3.28294337e-01 -6.69533193e-01 1.42409250e-01
3.95557851e-01 1.18630528e+00 -1.14100885e+00 5.85590124e-01
7.49998271e-01 6.22529209e-01 -4.52973932e-01 1.12394595e+00
8.10494348e-02 3.85722309e-01 -1.02612185e+00 4.01015818e-01
-2.50396609e-01 -3.18117231e-01 8.06961060e-01 4.42936271e-01
6.82953835e-01 2.11452588e-01 2.66697466e-01 1.14899218e+00
-1.12194486e-01 -1.70942143e-01 -8.86110723e-01 4.10847396e-01
6.19708419e-01 1.06680894e+00 -5.51006317e-01 -1.70565233e-01
-2.95131534e-01 9.07993376e-01 5.06870225e-02 1.41179368e-01
-4.09273326e-01 1.85182244e-01 5.54309070e-01 7.77267396e-01
2.92253524e-01 -5.20733774e-01 -8.35275114e-01 -1.01709056e+00
5.10287702e-01 -6.23377800e-01 -1.48687050e-01 -1.28381646e+00
-1.27402425e+00 3.57641637e-01 2.02126697e-01 -1.71002209e+00
-8.40060487e-02 -3.43900830e-01 -1.81200027e-01 8.54052782e-01
-1.28246403e+00 -1.34031665e+00 -7.43816674e-01 4.38890427e-01
8.38171959e-01 2.32490987e-01 5.70279181e-01 1.97033197e-01
2.75426477e-01 -2.37527922e-01 1.69206738e-01 -4.07773376e-01
4.65408355e-01 -9.68533993e-01 6.04199171e-01 4.65091825e-01
2.58512586e-01 2.44913235e-01 6.38274968e-01 -1.09611976e+00
-1.72593355e+00 -8.98984134e-01 4.06629443e-01 -9.19199646e-01
-5.88160893e-03 -4.63213176e-01 -9.44118559e-01 6.53910100e-01
-4.21217084e-01 2.92618006e-01 6.29310608e-02 -2.49011740e-01
-2.37297654e-01 2.58241910e-02 -1.32431304e+00 4.26561505e-01
1.35230422e+00 -2.99480110e-01 -5.81185400e-01 4.75961745e-01
6.03674054e-01 -8.04430842e-01 -6.72800362e-01 6.17445230e-01
5.08294284e-01 -1.08073056e+00 1.29514778e+00 -4.25968692e-02
5.57649910e-01 -3.79736334e-01 -4.81132716e-01 -1.20550966e+00
1.52924210e-01 -4.65620995e-01 -8.40108022e-02 9.11043763e-01
8.48047808e-02 -3.87022525e-01 1.35193992e+00 7.68539250e-01
-5.38035095e-01 -5.34354091e-01 -1.04292798e+00 -7.99837232e-01
-1.52345374e-01 -8.80798101e-01 8.84875715e-01 7.80326426e-01
-6.96567953e-01 4.47073877e-02 -4.36648503e-02 3.65558237e-01
1.17489541e+00 2.65977293e-01 1.38454092e+00 -1.60702205e+00
1.38588890e-01 4.37987484e-02 -3.15400273e-01 -1.33387554e+00
-3.88725370e-01 -7.29487896e-01 2.41401464e-01 -1.73319387e+00
-1.21253684e-01 -1.02667308e+00 7.94754207e-01 2.96574801e-01
2.57112771e-01 3.56840283e-01 8.86809547e-03 6.74529910e-01
-3.42085771e-02 7.30987608e-01 1.26233494e+00 -8.77490491e-02
-1.34222031e-01 6.40468970e-02 -3.40503454e-01 8.78744900e-01
5.58479846e-01 -7.96603858e-01 -4.14572716e-01 -9.52186346e-01
2.75451779e-01 8.34733099e-02 6.89007223e-01 -1.15322089e+00
8.19499344e-02 -3.72357011e-01 3.30078989e-01 -1.52653718e+00
1.04244363e+00 -1.15825152e+00 7.03316092e-01 1.00981623e-01
2.03999788e-01 -8.30692276e-02 -3.69902290e-02 6.02797687e-01
1.83004469e-01 -3.49443585e-01 5.54963827e-01 -2.94534355e-01
-9.79759246e-02 7.10960090e-01 4.67635244e-01 6.96097165e-02
8.65147471e-01 -5.69287419e-01 -8.01499635e-02 -2.46902525e-01
-4.87654060e-01 5.53132407e-02 1.24607778e+00 3.81696314e-01
1.03393054e+00 -1.21870768e+00 -9.37631845e-01 4.11302954e-01
2.85412908e-01 1.18957722e+00 2.53861517e-01 5.34097850e-01
-8.78374219e-01 8.76578391e-02 2.56088018e-01 -1.28031206e+00
-1.11500478e+00 1.22338533e-01 2.62622852e-02 2.57547081e-01
-1.16481924e+00 6.66275442e-01 3.04414272e-01 -8.01855445e-01
-1.35120109e-01 -4.78082240e-01 6.64576948e-01 -3.09827954e-01
1.85208097e-01 3.55721354e-01 4.70322222e-01 -4.86995071e-01
-8.72700140e-02 9.45800900e-01 1.98243648e-01 -4.73354340e-01
1.55962825e+00 -9.64372512e-03 6.75317869e-02 4.18380320e-01
9.56196189e-01 2.94555247e-01 -1.57191467e+00 -2.87611276e-01
-2.71608889e-01 -1.01869178e+00 2.91619892e-03 -5.86591601e-01
-9.18392003e-01 8.96098614e-01 2.88191527e-01 -1.74932793e-01
7.08214760e-01 2.93781579e-01 8.20572019e-01 2.50439376e-01
9.85635817e-01 -8.47511053e-01 6.61364868e-02 1.52874708e-01
1.42997515e+00 -1.17859948e+00 4.57876921e-01 -8.43290687e-01
-4.03133422e-01 1.01630890e+00 4.38527614e-01 -1.89819053e-01
7.09754348e-01 4.74485815e-01 -1.36168107e-01 -6.84533238e-01
-7.22463548e-01 8.35987926e-02 1.60046726e-01 9.03530896e-01
-3.20525348e-01 -1.36769051e-02 2.46853560e-01 7.43951797e-02
-4.22919661e-01 1.21999711e-01 5.81428051e-01 1.31905019e+00
-4.06378061e-01 -1.13486755e+00 -9.54617262e-01 7.77993023e-01
7.64111951e-02 3.82498875e-02 -3.06414187e-01 5.58156848e-01
-1.10720143e-01 6.05943322e-01 3.07611674e-01 -1.56770468e-01
5.46747744e-01 -1.66853979e-01 5.83466113e-01 -9.95173633e-01
8.82346854e-02 1.32491350e-01 -7.00663775e-03 -7.55708575e-01
-2.98196912e-01 -1.18584013e+00 -1.29613447e+00 -2.43665144e-01
-3.74936491e-01 -2.52150387e-01 1.11671686e+00 5.54872394e-01
6.01994991e-01 2.69767493e-02 5.73313057e-01 -1.62323391e+00
-6.07159793e-01 -8.03220332e-01 -4.35174465e-01 5.19668162e-01
1.76740065e-01 -8.33228946e-01 -3.40104520e-01 3.02477658e-01] | [8.40218734741211, -3.145479679107666] |
1a810792-8b77-4278-8241-6488ccf29c10 | provable-and-practical-efficient-exploration | 2305.18246 | null | https://arxiv.org/abs/2305.18246v1 | https://arxiv.org/pdf/2305.18246v1.pdf | Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo | We present a scalable and effective exploration strategy based on Thompson sampling for reinforcement learning (RL). One of the key shortcomings of existing Thompson sampling algorithms is the need to perform a Gaussian approximation of the posterior distribution, which is not a good surrogate in most practical settings. We instead directly sample the Q function from its posterior distribution, by using Langevin Monte Carlo, an efficient type of Markov Chain Monte Carlo (MCMC) method. Our method only needs to perform noisy gradient descent updates to learn the exact posterior distribution of the Q function, which makes our approach easy to deploy in deep RL. We provide a rigorous theoretical analysis for the proposed method and demonstrate that, in the linear Markov decision process (linear MDP) setting, it has a regret bound of $\tilde{O}(d^{3/2}H^{5/2}\sqrt{T})$, where $d$ is the dimension of the feature mapping, $H$ is the planning horizon, and $T$ is the total number of steps. We apply this approach to deep RL, by using Adam optimizer to perform gradient updates. Our approach achieves better or similar results compared with state-of-the-art deep RL algorithms on several challenging exploration tasks from the Atari57 suite. | ['Kamyar Azizzadenesheli', 'Anima Anandkumar', 'Doina Precup', 'A. Rupam Mahmood', 'Pan Xu', 'Qingfeng Lan', 'Haque Ishfaq'] | 2023-05-29 | null | null | null | null | ['thompson-sampling', 'efficient-exploration'] | ['methodology', 'methodology'] | [-2.38054037e-01 5.05208261e-02 -1.80071592e-01 -7.60016497e-03
-1.25118709e+00 -4.32737827e-01 3.55051130e-01 -1.91788375e-02
-9.17139471e-01 1.08966255e+00 -1.09256022e-01 -5.48233986e-01
-1.23432867e-01 -8.64731371e-01 -9.65478420e-01 -9.19274271e-01
-2.74624258e-01 8.33055735e-01 -1.15478732e-01 -5.69550619e-02
2.61203885e-01 1.96720779e-01 -1.25873566e+00 -4.94287908e-01
9.09097254e-01 1.22560370e+00 1.86220348e-01 5.33732593e-01
6.04563989e-02 6.64222121e-01 -5.88618755e-01 -2.62721986e-01
3.62237364e-01 -7.47637331e-01 -7.22056389e-01 -1.06246509e-01
-3.30912203e-01 -6.92658305e-01 -1.63600072e-01 1.12117743e+00
6.68436587e-01 5.20579338e-01 5.91803551e-01 -7.87161052e-01
1.63331032e-01 5.49908042e-01 -7.34781802e-01 3.77081782e-02
3.08806747e-02 4.19610798e-01 1.04116595e+00 -4.78813350e-01
5.92617154e-01 1.22427106e+00 4.34448659e-01 2.20451176e-01
-1.20850801e+00 -7.31595159e-01 2.56764829e-01 4.60509881e-02
-1.18599820e+00 -9.34471563e-02 4.39903736e-01 -2.05079392e-01
9.74565446e-01 -1.65686041e-01 1.05541539e+00 9.44432735e-01
4.07647610e-01 1.08563566e+00 1.31577098e+00 -3.03238034e-01
1.02107716e+00 -2.94922352e-01 -5.14594495e-01 8.69699359e-01
7.92542547e-02 5.62435389e-01 -5.47815204e-01 -2.89229602e-01
8.63332391e-01 -9.60866287e-02 5.44935800e-02 -5.09223700e-01
-9.37979758e-01 1.31689239e+00 1.93571523e-01 -3.89388621e-01
-5.21650910e-01 8.24923873e-01 3.42328310e-01 3.06192279e-01
3.65259141e-01 4.53838319e-01 -3.92730862e-01 -7.93728828e-01
-9.52370942e-01 6.83256686e-01 8.36430073e-01 6.83202207e-01
7.99925029e-01 1.85460508e-01 -2.75861770e-01 3.72311562e-01
3.09969813e-01 8.55924368e-01 3.46482009e-01 -1.31400800e+00
5.28369427e-01 5.96186444e-02 8.31662178e-01 -3.81535143e-01
-2.02279881e-01 -6.64721131e-01 -4.95703042e-01 4.51316893e-01
5.88117778e-01 -7.39814341e-01 -7.77352333e-01 1.63104475e+00
4.37883735e-01 -2.10167423e-01 -2.94223018e-02 7.60690093e-01
-8.57411548e-02 5.38640022e-01 -2.46184319e-01 -3.90163779e-01
9.12575245e-01 -9.51940298e-01 -4.72413480e-01 -3.89196962e-01
5.46117723e-01 -3.53413314e-01 1.19261861e+00 5.28525054e-01
-1.15670264e+00 -1.02120996e-01 -1.06546843e+00 4.66448814e-01
1.53222978e-01 9.10532251e-02 8.69343162e-01 6.37169898e-01
-6.51993871e-01 9.84758794e-01 -1.26621807e+00 3.24021369e-01
5.70094466e-01 2.86080956e-01 3.02167475e-01 -5.09189218e-02
-9.65262353e-01 8.07229459e-01 4.44361240e-01 1.08693853e-01
-1.65965986e+00 -1.93333805e-01 -4.83761311e-01 9.91513282e-02
8.52868259e-01 -3.93608451e-01 1.58220315e+00 -4.23716694e-01
-2.05342650e+00 1.77258611e-01 -9.86496210e-02 -8.29756320e-01
8.71389568e-01 -3.74858916e-01 3.53344828e-01 4.56263544e-03
-6.51993454e-02 5.38276076e-01 9.51252878e-01 -6.71208560e-01
-4.94145185e-01 -2.99745619e-01 4.87126783e-02 4.29595143e-01
1.38788044e-01 -3.80081505e-01 -4.21058714e-01 -3.72315884e-01
-9.49093997e-02 -1.22257400e+00 -6.60418749e-01 -2.15988681e-01
-2.91347444e-01 -5.81326596e-02 6.25623390e-02 -4.33318794e-01
1.13644791e+00 -1.81098425e+00 2.75523931e-01 3.42957884e-01
-9.99976918e-02 -4.65916877e-04 2.17663080e-01 6.18701100e-01
6.36242867e-01 -1.55244201e-01 -4.42231268e-01 -5.17914593e-01
3.66493434e-01 3.62358093e-01 -4.53559399e-01 5.43393910e-01
-3.87162685e-01 9.79036033e-01 -1.08116376e+00 -2.10901201e-01
-2.30805110e-02 1.76803395e-01 -5.99051356e-01 4.94119618e-03
-5.98024309e-01 4.82547522e-01 -7.78163314e-01 3.08128834e-01
3.40516299e-01 -3.66068363e-01 3.47733468e-01 4.28170025e-01
-6.66745529e-02 3.16208303e-01 -1.42675459e+00 1.87178147e+00
-5.74600458e-01 3.24274093e-01 -1.35815546e-01 -8.44763219e-01
8.15630257e-01 -3.17977890e-02 3.12025487e-01 -5.92572153e-01
2.92464554e-01 1.65773630e-01 -2.27811798e-01 4.20530811e-02
2.09463552e-01 -2.05026567e-01 -1.93526223e-01 9.36905026e-01
-1.43402845e-01 -5.15620708e-01 2.64438927e-01 -2.09124118e-01
1.24113667e+00 5.76006889e-01 3.89420182e-01 -1.10536836e-01
-7.85211772e-02 1.22391611e-01 7.10236609e-01 1.13387740e+00
-1.04336306e-01 1.70382708e-01 9.47125733e-01 -5.33137262e-01
-9.79827166e-01 -9.98828888e-01 6.17891960e-02 8.49265099e-01
-1.24810804e-02 -3.96197170e-01 -8.19620013e-01 -7.94332445e-01
3.68737819e-04 1.00623274e+00 -7.57600725e-01 -1.42023087e-01
-4.37062293e-01 -1.03724766e+00 3.00609648e-01 5.20717263e-01
6.40677452e-01 -1.14172530e+00 -1.29055715e+00 4.69645172e-01
-2.45475248e-02 -5.17900765e-01 -2.19017223e-01 4.97433066e-01
-1.13248992e+00 -7.34585762e-01 -6.51681185e-01 -4.48943786e-02
5.52732408e-01 -2.80186057e-01 9.56625462e-01 -3.38376045e-01
-1.71299368e-01 3.09320074e-02 -1.88587621e-01 -4.40351963e-01
-2.35224977e-01 1.26953796e-02 -3.18704583e-02 -5.26789248e-01
1.71782216e-03 -3.47268611e-01 -9.13625896e-01 1.81202218e-01
-5.73658347e-01 -1.61415413e-02 6.96810067e-01 1.10078871e+00
1.05710900e+00 2.33454496e-01 1.93270266e-01 -6.59397364e-01
6.62529349e-01 -1.77245274e-01 -1.27721477e+00 3.98904830e-02
-7.26454616e-01 6.51314199e-01 5.59880316e-01 -3.72714490e-01
-8.42737019e-01 9.51135233e-02 -6.64870292e-02 -3.38418901e-01
3.57147217e-01 5.55306792e-01 3.34235638e-01 9.03645456e-02
4.17388797e-01 4.06748384e-01 1.68217734e-01 -5.00187218e-01
3.32146853e-01 1.80905938e-01 1.16943792e-01 -7.96778679e-01
5.51717937e-01 6.19551003e-01 1.97364658e-01 -3.26468945e-01
-1.03553581e+00 1.86824724e-01 -1.54495224e-01 4.51604389e-02
5.74990928e-01 -8.51025999e-01 -1.14201021e+00 1.99522510e-01
-5.56331813e-01 -9.48795080e-01 -6.15685344e-01 6.98472977e-01
-1.17898417e+00 1.68173701e-01 -4.25362498e-01 -1.18363941e+00
-5.01023650e-01 -1.41137850e+00 8.45788240e-01 1.43160701e-01
1.08886268e-02 -6.38628960e-01 1.83562413e-01 1.75275877e-01
4.37539369e-01 3.88776690e-01 7.86988437e-01 -1.46383241e-01
-6.46090627e-01 -2.38819897e-01 1.63355097e-01 3.39041263e-01
-2.26230860e-01 -3.89802873e-01 -4.44792479e-01 -6.65018260e-01
9.67963412e-02 -6.59304321e-01 8.47531438e-01 4.71556693e-01
1.08355367e+00 -4.30085033e-01 -1.92936227e-01 5.45101702e-01
1.33304179e+00 3.34246248e-01 5.22802770e-01 4.41588193e-01
1.52011901e-01 -3.72000933e-02 1.15275598e+00 1.18838453e+00
2.53395438e-01 5.99511743e-01 6.64352059e-01 3.20761770e-01
4.92737502e-01 -5.00993371e-01 4.69204485e-01 1.21092297e-01
2.44381074e-02 1.38904331e-02 -9.49714959e-01 3.67106229e-01
-1.88510716e+00 -5.56526184e-01 5.59999883e-01 2.53540635e+00
1.09700274e+00 4.60534722e-01 2.12572813e-01 -1.76914603e-01
1.80213004e-01 6.11012913e-02 -1.01678026e+00 -4.07159299e-01
3.27467591e-01 5.62405467e-01 7.65219808e-01 5.32944977e-01
-8.54415894e-01 1.09602618e+00 6.10336065e+00 1.00975621e+00
-8.15943837e-01 1.13198899e-01 6.17844462e-01 -6.44396722e-01
-8.76008868e-02 3.22800249e-01 -9.28039789e-01 6.42808497e-01
1.01765132e+00 -1.25772841e-02 9.77010071e-01 9.51045275e-01
1.41646519e-01 -6.91980541e-01 -9.77060497e-01 7.78060496e-01
-5.00683308e-01 -1.25566661e+00 -4.15253907e-01 3.41115385e-01
8.82289648e-01 3.40324908e-01 1.95914164e-01 5.04566193e-01
1.03264666e+00 -1.19751465e+00 7.97340631e-01 3.19523156e-01
5.58498263e-01 -1.29199076e+00 6.55482411e-01 6.48486078e-01
-7.92392612e-01 -3.14865261e-01 -5.31357765e-01 -6.30188808e-02
2.14620322e-01 6.50918245e-01 -8.74190152e-01 2.55239397e-01
8.75759780e-01 2.02855915e-02 7.54200853e-03 8.45851302e-01
-5.81813872e-01 5.39375067e-01 -7.03848064e-01 -4.96569306e-01
6.71976745e-01 -3.94367576e-01 5.39484680e-01 6.20837927e-01
3.29438657e-01 -1.89113900e-01 2.24004343e-01 8.23363304e-01
7.56701007e-02 -1.56970859e-01 -1.65777579e-01 -2.35727534e-01
5.86577892e-01 7.98771679e-01 -8.46527219e-01 -1.41032964e-01
1.10964783e-01 8.50326300e-01 5.59920907e-01 2.40584180e-01
-9.19757009e-01 -5.14873743e-01 4.99482691e-01 -3.52217615e-01
8.93862665e-01 -5.55111051e-01 -9.29144118e-03 -8.68406296e-01
3.90104167e-02 -9.75255311e-01 2.38979861e-01 -2.40619689e-01
-6.94134772e-01 2.83078969e-01 1.31935641e-01 -1.02126849e+00
-8.56612504e-01 -2.77049899e-01 -3.16820145e-01 8.50669920e-01
-1.26752472e+00 -4.30059671e-01 2.13494882e-01 2.70008415e-01
3.22650164e-01 -1.83143184e-01 7.24871695e-01 -4.04965669e-01
-6.05637312e-01 4.91354167e-01 6.64955735e-01 -1.70883819e-01
2.08025917e-01 -1.28452027e+00 5.46879947e-01 5.52202225e-01
-8.98700953e-02 3.22916210e-01 9.45443332e-01 -6.41782463e-01
-1.69929874e+00 -6.83612883e-01 1.33556202e-01 -8.85788873e-02
6.06599033e-01 -3.86521995e-01 -3.35579485e-01 6.09467268e-01
-1.60911918e-01 -4.91542481e-02 4.54973698e-01 -2.16945503e-02
1.54787317e-01 -1.48307160e-01 -1.12336504e+00 6.99252844e-01
7.17580140e-01 -1.20998211e-01 -1.58527136e-01 3.84656250e-01
3.86453122e-01 -7.81508684e-01 -8.78792405e-01 2.19384432e-01
5.35902798e-01 -9.81813967e-01 7.14551568e-01 -3.33559841e-01
1.81665659e-01 -1.74259439e-01 -1.92360297e-01 -1.46765482e+00
1.80834800e-01 -1.09933448e+00 -5.42004585e-01 4.97451663e-01
4.11121845e-01 -6.95625365e-01 9.59338188e-01 3.15842152e-01
2.65493006e-01 -1.31050324e+00 -1.34482503e+00 -8.36932778e-01
3.80679309e-01 -3.66146773e-01 6.88934505e-01 1.48734346e-01
-2.93788791e-01 -1.46411061e-01 -3.96225959e-01 -1.81648806e-01
8.99492085e-01 4.24384147e-01 8.15386355e-01 -7.32296109e-01
-1.10380936e+00 -2.46058375e-01 2.92860031e-01 -1.22950947e+00
3.26403268e-02 -3.67120177e-01 1.65937364e-01 -1.40277696e+00
3.72414961e-02 -4.89058018e-01 -1.59622654e-01 3.51429403e-01
4.99423184e-02 -2.69746423e-01 1.04453303e-01 7.90882558e-02
-7.43092299e-01 1.18279779e+00 1.36814415e+00 1.66612387e-01
-2.41771698e-01 1.99489132e-01 -4.18040723e-01 7.18486965e-01
7.27315784e-01 -5.74834704e-01 -4.26332235e-01 -4.18314129e-01
6.54960334e-01 5.95071614e-01 6.92940652e-02 -8.58265877e-01
5.70720248e-03 -3.77413779e-01 3.96534443e-01 -7.16676831e-01
5.40438354e-01 -2.83294737e-01 -9.42143798e-02 8.50317776e-01
-3.40251595e-01 5.00708334e-02 7.73880407e-02 8.87458563e-01
8.31922293e-02 -3.43646884e-01 8.17056239e-01 -4.05521482e-01
-2.28232369e-01 2.45490849e-01 -4.60091501e-01 2.17775881e-01
9.04163778e-01 1.74290866e-01 7.46337995e-02 -6.13022208e-01
-5.06963789e-01 5.63983381e-01 4.55978990e-01 -1.26277670e-01
4.76013303e-01 -1.07152021e+00 -3.94328952e-01 -2.44160686e-02
-3.80326003e-01 2.51070321e-01 6.57514781e-02 8.02111387e-01
-4.74295586e-01 2.92044908e-01 6.11630492e-02 -4.25388932e-01
-4.33412194e-01 2.21353129e-01 3.84224683e-01 -8.69847238e-01
-7.54362762e-01 8.96936893e-01 -2.49766663e-01 -2.80554861e-01
4.13893968e-01 -2.10240543e-01 3.14552218e-01 -7.87716880e-02
4.13601041e-01 6.83385074e-01 -1.68096453e-01 1.98246181e-01
-1.38854235e-01 2.02319399e-01 -1.18923858e-01 -7.18023062e-01
1.25752437e+00 -4.57880460e-02 2.45977551e-01 4.81147796e-01
9.16171432e-01 -4.41547096e-01 -2.03969049e+00 -1.25243902e-01
-2.12603971e-01 -3.21274668e-01 3.66546482e-01 -8.31081510e-01
-8.58124018e-01 8.55741441e-01 6.49626732e-01 -2.58423418e-01
7.14138389e-01 -2.71945477e-01 7.64577389e-01 7.90994942e-01
7.57233024e-01 -1.55266500e+00 3.35206650e-02 6.56164110e-01
6.68266892e-01 -1.08694232e+00 3.13625395e-01 4.04139668e-01
-8.96867454e-01 8.25090408e-01 2.72475272e-01 -3.23721856e-01
4.89102095e-01 1.15141138e-01 -3.88544440e-01 -4.11184058e-02
-8.54826808e-01 -1.11711822e-01 -2.28721887e-01 1.68921240e-02
-9.85026956e-02 2.19388664e-01 -2.61492044e-01 2.63489813e-01
-4.29609299e-01 7.13374764e-02 2.93085575e-01 1.32191050e+00
-5.22377133e-01 -1.10905337e+00 2.52470355e-02 4.47771549e-01
-5.79041541e-01 3.54447146e-03 2.07477450e-01 8.02317083e-01
-4.51831251e-01 7.09924042e-01 3.44657265e-02 2.02661529e-02
-8.55932161e-02 -5.11453897e-02 7.46478796e-01 -2.46326163e-01
-1.70594990e-01 1.78188577e-01 4.04926091e-02 -9.32250500e-01
2.11642563e-01 -9.16722178e-01 -1.32814717e+00 -2.46015400e-01
-6.74925894e-02 3.91064614e-01 9.44951057e-01 1.11038268e+00
2.89936721e-01 1.95104942e-01 6.71954274e-01 -8.74332368e-01
-1.30681968e+00 -8.29592645e-01 -6.46729946e-01 -1.29418477e-01
1.77325457e-01 -9.98973966e-01 -3.61513674e-01 -8.11513007e-01] | [4.1612348556518555, 2.255988836288452] |
dec4bf4a-90b0-48e6-805e-48c66ff384cc | bevers-a-general-simple-and-performant | 2303.16974 | null | https://arxiv.org/abs/2303.16974v1 | https://arxiv.org/pdf/2303.16974v1.pdf | BEVERS: A General, Simple, and Performant Framework for Automatic Fact Verification | Automatic fact verification has become an increasingly popular topic in recent years and among datasets the Fact Extraction and VERification (FEVER) dataset is one of the most popular. In this work we present BEVERS, a tuned baseline system for the FEVER dataset. Our pipeline uses standard approaches for document retrieval, sentence selection, and final claim classification, however, we spend considerable effort ensuring optimal performance for each component. The results are that BEVERS achieves the highest FEVER score and label accuracy among all systems, published or unpublished. We also apply this pipeline to another fact verification dataset, Scifact, and achieve the highest label accuracy among all systems on that dataset as well. We also make our full code available. | ['Stephen Scott', 'Mitchell DeHaven'] | 2023-03-29 | null | null | null | null | ['fact-verification'] | ['natural-language-processing'] | [-5.82749285e-02 -7.24978969e-02 -6.11994386e-01 -4.36353870e-02
-1.72163928e+00 -8.37451935e-01 1.03802693e+00 5.79380453e-01
-3.60497743e-01 8.39078367e-01 4.26607758e-01 -2.67115265e-01
-6.13121614e-02 -5.43271959e-01 -4.86591905e-01 -5.82316285e-03
4.18372691e-01 4.41937298e-01 5.69608331e-01 6.26371289e-03
7.47478187e-01 2.46931717e-01 -1.20218492e+00 7.12124348e-01
5.83403111e-01 1.06295955e+00 -5.18829703e-01 6.40717745e-01
1.44992545e-01 1.17216611e+00 -8.48120272e-01 -1.30648172e+00
1.53308585e-01 -1.28774464e-01 -1.33630300e+00 -2.11498916e-01
8.15163612e-01 -1.24304734e-01 -3.37192744e-01 7.92367399e-01
6.42109513e-01 -3.94485593e-01 6.58823609e-01 -1.00680673e+00
-9.82001781e-01 1.11207068e+00 -6.96337938e-01 4.64726835e-01
6.89954937e-01 -2.54060090e-01 1.17587483e+00 -9.71339047e-01
1.13482285e+00 8.82619619e-01 7.99300373e-01 3.68752815e-02
-1.00520933e+00 -6.21398568e-01 -2.78913498e-01 5.38084209e-01
-1.50902593e+00 -7.12635517e-01 6.00697875e-01 -5.83907962e-01
9.31931973e-01 2.34164760e-01 2.00023159e-01 1.10650456e+00
3.55163068e-01 1.15887010e+00 1.32736564e+00 -6.00079000e-01
1.90233871e-01 2.11099833e-01 8.10955346e-01 6.09231532e-01
4.98829514e-01 -3.37150007e-01 -6.91965342e-01 -6.53014362e-01
-1.10125151e-02 -1.49516448e-01 -2.79557019e-01 7.95217007e-02
-1.10686064e+00 7.83266246e-01 -1.70585408e-03 3.28004003e-01
-6.27236009e-01 -1.64206252e-01 7.83385158e-01 3.36020440e-01
4.88621622e-01 5.23464262e-01 -6.45782888e-01 -2.48345762e-01
-1.42928863e+00 6.60146892e-01 9.16069925e-01 7.26712227e-01
6.90033212e-02 -6.54611647e-01 -8.34512949e-01 6.93963826e-01
-1.85709402e-01 3.05907667e-01 3.03862095e-01 -1.00878656e+00
6.74671888e-01 7.85495341e-01 1.80216864e-01 -1.08943665e+00
-3.83173436e-01 -6.42454743e-01 -6.92523301e-01 -3.65929753e-02
1.51102692e-01 1.19419180e-01 -7.63712049e-01 1.10990787e+00
1.79520562e-01 -2.21267462e-01 1.88040242e-01 3.65262866e-01
1.15275061e+00 3.32109667e-02 1.47852615e-01 -2.84140766e-01
1.78437841e+00 -7.51046717e-01 -8.81837010e-01 1.15322225e-01
5.88798165e-01 -1.18782580e+00 7.32689619e-01 4.44228649e-01
-9.24444854e-01 -1.25765845e-01 -9.39548433e-01 -2.42929742e-01
-3.76796275e-01 4.94352698e-01 7.31646836e-01 8.25195134e-01
-9.11218107e-01 3.83511215e-01 -6.03051603e-01 -4.99439418e-01
7.84906924e-01 -1.31596953e-01 -4.14955974e-01 -1.36587054e-01
-1.29409027e+00 9.43179548e-01 3.17693859e-01 -4.37519997e-01
-3.99840415e-01 -6.63942873e-01 -5.23340166e-01 -5.90060800e-02
5.36137640e-01 -5.67413747e-01 1.36167169e+00 1.20281447e-02
-1.01883769e+00 1.18912005e+00 -1.97886452e-01 -9.59890664e-01
4.19427544e-01 -1.72920525e-01 -7.96027660e-01 3.96203965e-01
4.88414854e-01 1.26807138e-01 4.48296756e-01 -8.82231593e-01
-5.01945674e-01 -2.02378243e-01 -2.17815535e-03 -4.20088798e-01
-1.57946557e-01 6.20894372e-01 -2.82575816e-01 -7.43583083e-01
2.76761100e-04 -9.55499530e-01 3.01009297e-01 -5.37870347e-01
-7.56757975e-01 -5.92709780e-01 6.24518633e-01 -1.08131313e+00
1.65368104e+00 -1.90252602e+00 -5.28018653e-01 -4.87530380e-02
1.03927135e-01 1.71904311e-01 2.37960756e-01 7.17468441e-01
1.02207810e-03 4.69881356e-01 -2.47285560e-01 -3.07684183e-01
-1.16838962e-02 -3.17711204e-01 -4.90346014e-01 2.87584275e-01
1.63957998e-01 9.93734896e-01 -5.21834433e-01 -7.69909441e-01
-4.00252998e-01 1.20653056e-01 -4.49214548e-01 -3.58124226e-01
-1.49128333e-01 2.45642420e-02 -4.22421396e-01 8.50954533e-01
7.44702756e-01 -4.72215384e-01 1.64895788e-01 -1.99013054e-01
1.66037828e-01 6.42633677e-01 -1.12265921e+00 1.58281147e+00
4.35971515e-03 4.55255389e-01 -1.95051134e-01 -3.35702807e-01
7.72639632e-01 5.83493710e-01 4.30196583e-01 -3.78572285e-01
-2.10155677e-02 3.27135652e-01 -3.60663444e-01 -7.01294422e-01
1.01736665e+00 5.78564219e-02 -3.89884830e-01 7.67896116e-01
-1.66173846e-01 -1.38073089e-02 6.04593158e-01 9.91629601e-01
1.50013447e+00 -2.61954874e-01 5.52620709e-01 -1.34737976e-03
5.20837784e-01 5.16329527e-01 2.52867818e-01 8.47336173e-01
-2.20931813e-01 8.57369363e-01 7.33048081e-01 -2.57091433e-01
-7.85793304e-01 -7.59154141e-01 -4.07001913e-01 5.17146289e-01
-3.22902918e-01 -8.39603126e-01 -4.51470941e-01 -1.16497493e+00
1.47518024e-01 6.80441141e-01 -6.67871833e-01 3.44990969e-01
-2.54729956e-01 -6.04647279e-01 8.68279219e-01 2.53270954e-01
7.28993356e-01 -1.25017655e+00 -3.08467358e-01 2.94141974e-02
-5.71648479e-01 -1.19630635e+00 -2.12012157e-01 -2.17630103e-01
-3.12652975e-01 -1.50059271e+00 -5.57732224e-01 -5.39671779e-01
3.29595730e-02 -1.24558127e-02 1.29802835e+00 3.25714678e-01
-1.50408745e-01 1.28435940e-01 -5.10954261e-01 -3.54942143e-01
-4.93589044e-01 5.92584431e-01 -2.50206202e-01 -2.54088759e-01
4.23772693e-01 2.11876407e-02 -3.69570494e-01 -1.88216552e-01
-9.41991925e-01 -3.67250204e-01 3.96369427e-01 6.23331070e-01
4.59648758e-01 3.30611408e-01 6.75151706e-01 -1.37073302e+00
9.35937107e-01 -4.32448417e-01 -2.05829993e-01 4.02767658e-01
-7.26473153e-01 -1.91481620e-01 1.96229666e-01 1.47229701e-01
-9.24826324e-01 -2.08277181e-01 -3.21379185e-01 6.70958608e-02
-8.32786411e-02 9.12996411e-01 2.12232813e-01 3.68329823e-01
8.93767893e-01 -2.00692520e-01 -3.98465604e-01 -6.44110680e-01
2.67871946e-01 1.13429463e+00 5.96071541e-01 -2.38691732e-01
5.57486832e-01 4.50507611e-01 -2.74606019e-01 -1.81157634e-01
-1.50420129e+00 -5.42227566e-01 -4.58821476e-01 5.06583937e-02
4.52420950e-01 -9.19076145e-01 -7.82343686e-01 4.05920863e-01
-1.21050584e+00 3.65230083e-01 -2.25263849e-01 3.64375323e-01
-4.02796268e-02 3.75341624e-01 -9.35222268e-01 -9.53391850e-01
-5.73845923e-01 -8.91301692e-01 1.20061707e+00 -1.47061720e-01
-5.74511409e-01 -5.42010307e-01 1.72365725e-01 8.08512926e-01
2.02023849e-01 4.47401851e-01 7.35384285e-01 -9.18959975e-01
-2.87173718e-01 -7.17263281e-01 -3.78496408e-01 -1.09186433e-01
-7.32505694e-02 9.40430090e-02 -8.73712361e-01 -7.26149753e-02
-7.74662271e-02 -6.36453748e-01 1.43548381e+00 2.99580157e-01
9.21880364e-01 -2.66639948e-01 -4.32722569e-01 -1.34168997e-01
1.39243615e+00 -2.60199279e-01 6.15084410e-01 7.81750441e-01
2.76966244e-01 4.14142817e-01 6.87571406e-01 2.84282893e-01
7.37350166e-01 6.43660545e-01 -1.04911149e-01 2.66003311e-01
-4.67911482e-01 -2.37369791e-01 2.83493966e-01 6.69276834e-01
2.54107654e-01 -2.49793097e-01 -1.08874011e+00 6.15516663e-01
-1.80952549e+00 -1.43001235e+00 -5.95924854e-01 1.83074224e+00
9.72365797e-01 3.95382434e-01 2.06287697e-01 5.08452713e-01
5.93485236e-01 2.57433444e-01 -3.65658738e-02 -3.03430289e-01
-5.85950911e-01 3.84937048e-01 3.49821836e-01 3.83537799e-01
-1.44086993e+00 8.79327595e-01 7.40245438e+00 9.52852070e-01
-6.59718990e-01 5.24005353e-01 5.63835740e-01 -1.42818421e-01
-1.31284967e-01 1.40225753e-01 -9.17486072e-01 4.52109039e-01
1.01499772e+00 -1.27465846e-02 -1.49559319e-01 6.60913348e-01
-1.32087156e-01 -4.56392050e-01 -6.99590206e-01 7.12818146e-01
3.34985912e-01 -1.73420775e+00 -3.42181772e-02 -1.01334296e-01
8.40069771e-01 7.92813301e-02 -1.84465304e-01 4.83474731e-01
4.65896100e-01 -5.67733765e-01 9.49620426e-01 4.72992271e-01
7.85023749e-01 -5.55138469e-01 1.18141603e+00 1.09893665e-01
-7.96330392e-01 -9.78109799e-03 3.33053613e-04 1.56560764e-01
3.91682178e-01 1.18552935e+00 -6.79993570e-01 8.91247630e-01
6.75711274e-01 7.01474607e-01 -1.10467589e+00 1.18128049e+00
-3.91202599e-01 9.21636105e-01 -7.75875449e-02 7.97735304e-02
-3.22938651e-01 4.48852032e-01 4.88194793e-01 1.44065583e+00
3.23596299e-02 4.56798598e-02 4.30332929e-01 2.76537836e-01
-5.80470324e-01 1.08143196e-01 -2.25253060e-01 7.91279972e-02
5.39969802e-01 1.32406473e+00 -8.97245347e-01 -8.22987974e-01
-8.59093666e-02 7.86997378e-01 4.97480869e-01 3.43600288e-02
-8.07426631e-01 -3.24204445e-01 1.29079834e-01 1.53361401e-02
4.19023782e-01 4.01996933e-02 -5.90014517e-01 -1.26895881e+00
3.11998725e-01 -8.65348458e-01 1.07039237e+00 -7.77340889e-01
-1.40163529e+00 6.32782519e-01 -2.20935822e-01 -9.66958702e-01
-2.37330869e-02 -2.59946316e-01 -1.87843680e-01 6.09287024e-01
-1.63785863e+00 -1.10072923e+00 8.52723569e-02 4.57608938e-01
2.40859494e-01 -1.99978039e-01 7.71549046e-01 5.67710519e-01
-6.48159206e-01 6.07176840e-01 -1.70161292e-01 3.69679362e-01
9.97172832e-01 -1.08336627e+00 4.51992810e-01 9.65720296e-01
4.06532615e-01 8.36456180e-01 5.96841514e-01 -1.12082255e+00
-9.44590807e-01 -9.50628698e-01 1.58773220e+00 -9.60743666e-01
8.82021844e-01 -1.32017300e-01 -7.54917622e-01 6.08927786e-01
5.25011480e-01 -4.14897621e-01 6.08629704e-01 4.20131475e-01
-7.40653038e-01 1.99498385e-01 -1.25519133e+00 2.45351553e-01
9.27668810e-01 -7.32387722e-01 -7.63059139e-01 8.17540586e-01
5.02487242e-01 -5.25228500e-01 -1.05941486e+00 2.79136777e-01
5.16923428e-01 -8.83136094e-01 5.77419996e-01 -4.27278697e-01
7.37186611e-01 -4.14853603e-01 5.99623956e-02 -7.30625927e-01
-5.22568643e-01 -4.37484473e-01 -3.38287741e-01 1.87568545e+00
8.79368305e-01 -6.86809719e-01 7.39531636e-01 5.24239719e-01
3.10206801e-01 -7.26698995e-01 -8.79078567e-01 -7.56270170e-01
-4.26672436e-02 -5.52562356e-01 5.65079868e-01 1.17843747e+00
1.27324671e-01 5.44952273e-01 -2.04835415e-01 5.15527911e-02
4.32392687e-01 7.07617998e-01 7.17668235e-01 -1.15769756e+00
-1.77020818e-01 -3.86963248e-01 -2.29041636e-01 -3.44158590e-01
6.90544173e-02 -1.37675679e+00 -3.55489194e-01 -2.02692842e+00
8.76993716e-01 -3.43780249e-01 -1.25672594e-01 7.16361821e-01
-4.55226570e-01 6.27125621e-01 1.22805111e-01 6.94917440e-01
-8.32784474e-01 1.54469252e-01 7.46374726e-01 -2.13703811e-01
2.21095055e-01 -3.90749276e-02 -1.02243280e+00 3.44522715e-01
8.33530843e-01 -7.79666662e-01 3.07975531e-01 -1.83390617e-01
2.80802608e-01 -2.68689126e-01 4.23409104e-01 -9.21260893e-01
2.90641010e-01 2.35597864e-01 4.55355763e-01 -9.91265476e-01
-3.29481363e-02 -3.99761707e-01 6.86940327e-02 3.67031664e-01
-4.67917234e-01 1.35976195e-01 -5.24995662e-02 3.73954624e-01
-2.90871620e-01 -2.74839789e-01 5.02886891e-01 2.64339987e-02
-2.76366204e-01 1.75608054e-01 -2.68509746e-01 2.61169821e-01
7.07486689e-01 2.11775914e-01 -9.76325154e-01 -2.85692304e-01
-4.00552869e-01 -2.52741836e-02 4.30646926e-01 4.52552021e-01
2.58780181e-01 -1.18400872e+00 -1.20923769e+00 -2.51570880e-01
2.58149952e-01 -7.38774240e-01 1.51748911e-01 1.16629136e+00
-2.76074350e-01 6.13592148e-01 2.65364498e-01 -1.90885782e-01
-1.43109035e+00 7.64106750e-01 -3.54160756e-01 -8.97648990e-01
-6.23427033e-01 5.80283284e-01 -7.67976165e-01 -3.06654036e-01
-9.12880674e-02 -1.06962159e-01 -5.53139329e-01 1.65309608e-01
7.51701713e-01 4.68024820e-01 5.74316382e-01 -6.20407104e-01
-5.79486907e-01 2.17739195e-01 -4.54705507e-01 -1.95535973e-01
1.29255223e+00 6.00851178e-02 -2.75151581e-01 2.06164792e-01
9.93450880e-01 6.38209403e-01 -2.95229405e-01 -2.61979789e-01
5.49096763e-01 -4.30615246e-01 1.14253752e-01 -1.22501683e+00
-9.04949725e-01 1.72399372e-01 2.77158082e-01 8.35063934e-01
8.69675100e-01 3.38758796e-01 8.99654269e-01 -1.89900827e-02
2.99737692e-01 -9.57493842e-01 -4.61519957e-01 6.42357349e-01
9.42514122e-01 -1.18941998e+00 6.83802545e-01 -6.38120592e-01
-7.30962515e-01 7.05048740e-01 -4.99463752e-02 -1.02459267e-01
6.09383881e-01 3.40153128e-01 1.59063235e-01 -6.90356255e-01
-7.97625542e-01 -9.84800607e-02 3.66463780e-01 2.08918706e-01
7.43829131e-01 -7.71349743e-02 -8.80920410e-01 6.55735672e-01
-5.16335368e-01 2.12124005e-01 5.74136138e-01 1.01335812e+00
-7.78311193e-02 -1.30214179e+00 -4.33770537e-01 9.84304726e-01
-1.20537806e+00 -2.79949516e-01 -8.64001691e-01 7.42556572e-01
9.26793832e-03 1.19606709e+00 -4.60363626e-01 -3.38371247e-01
5.30879855e-01 2.55116075e-01 3.06369364e-01 -6.82654500e-01
-1.26938045e+00 -3.97215873e-01 6.30807996e-01 -3.57546300e-01
-6.67020798e-01 -1.06490958e+00 -1.07667017e+00 -5.95721662e-01
-5.75538695e-01 3.63674194e-01 5.15146077e-01 8.80123258e-01
7.07658410e-01 1.62227064e-01 3.00813735e-01 -5.54682724e-02
-2.66865462e-01 -1.13794446e+00 -5.96599579e-01 6.02702320e-01
9.91660208e-02 -5.92952251e-01 -2.02556297e-01 1.64384604e-01] | [8.9137544631958, 9.558188438415527] |
e0237984-416c-40ed-90a8-82288d2ca88c | on-the-complementary-nature-of-knowledge | 2010.05732 | null | https://arxiv.org/abs/2010.05732v1 | https://arxiv.org/pdf/2010.05732v1.pdf | On the Complementary Nature of Knowledge Graph Embedding, Fine Grain Entity Types, and Language Modeling | We demonstrate the complementary natures of neural knowledge graph embedding, fine-grain entity type prediction, and neural language modeling. We show that a language model-inspired knowledge graph embedding approach yields both improved knowledge graph embeddings and fine-grain entity type representations. Our work also shows that jointly modeling both structured knowledge tuples and language improves both. | ['Francis Ferraro', 'Rajat Patel'] | 2020-10-12 | null | https://aclanthology.org/2020.deelio-1.11 | https://aclanthology.org/2020.deelio-1.11.pdf | emnlp-deelio-2020-11 | ['type-prediction'] | ['computer-code'] | [-7.11832941e-01 7.95873523e-01 -1.04025948e+00 -1.33350760e-01
-1.10221356e-01 -6.87534869e-01 5.43698132e-01 7.41356432e-01
-4.19332266e-01 7.64116108e-01 6.68075025e-01 -4.70786035e-01
-3.35531503e-01 -1.55391276e+00 -8.53588164e-01 1.60072356e-01
-4.12883818e-01 6.25521362e-01 1.14072196e-01 -1.92286715e-01
-5.69681898e-02 5.60414314e-01 -1.16414165e+00 2.84960032e-01
4.45590347e-01 9.71471012e-01 -6.15805209e-01 7.61354208e-01
-8.93235326e-01 1.46647167e+00 -3.79243135e-01 -9.64358926e-01
-2.46625066e-01 5.98183334e-01 -9.62154925e-01 -6.92621768e-01
4.77857709e-01 7.28158876e-02 -8.85177851e-01 6.93158329e-01
2.19433114e-01 5.08954048e-01 8.38448703e-01 -1.25469720e+00
-1.93045986e+00 8.44109356e-01 -1.88411534e-01 1.42677456e-01
3.94394696e-01 -3.31795335e-01 1.45520997e+00 -7.97999501e-01
9.14126456e-01 1.43074048e+00 1.16798830e+00 4.80921239e-01
-1.04395401e+00 -2.14929745e-01 1.84363723e-02 4.81876373e-01
-1.54453337e+00 1.09394286e-02 7.86284089e-01 -6.21998310e-01
1.98787665e+00 -3.91008615e-01 6.00277185e-01 8.00831318e-01
1.05197176e-01 6.70392096e-01 4.30722862e-01 -3.56252223e-01
9.11838338e-02 4.57278758e-01 9.22759831e-01 1.35904562e+00
8.17215800e-01 3.31243545e-01 -4.10466611e-01 -5.93467414e-01
9.21041012e-01 -7.30126351e-02 3.29104573e-01 -5.78615308e-01
-7.73206413e-01 1.08714879e+00 6.54879987e-01 3.79565924e-01
-6.14046574e-01 8.04909885e-01 6.98427320e-01 3.75637591e-01
4.08138514e-01 8.66530240e-01 -8.13523531e-01 -8.00056458e-02
-3.70766371e-01 -2.04086844e-02 1.39474154e+00 1.31729662e+00
1.17159045e+00 6.53867245e-01 -2.37130001e-01 7.90860593e-01
4.08525944e-01 4.55998182e-01 4.96363699e-01 -6.37876987e-01
-1.77363784e-03 1.07550561e+00 -1.64326385e-01 -1.03732610e+00
-3.67310017e-01 -1.07671767e-01 -3.13833356e-01 -4.45505917e-01
-4.79933992e-03 -1.37814879e-01 -8.46899569e-01 1.42408371e+00
9.02454108e-02 3.51427674e-01 5.99902630e-01 1.84540808e-01
1.28235531e+00 5.11917889e-01 6.61674619e-01 2.97576815e-01
1.54056942e+00 -8.52422237e-01 -7.17112780e-01 -4.68611941e-02
9.48542893e-01 4.81325090e-02 6.84286296e-01 -5.55588424e-01
-1.00154531e+00 -3.71621907e-01 -8.46047461e-01 -3.45243186e-01
-1.46800327e+00 -3.94417316e-01 1.25691354e+00 7.04945624e-01
-1.32189560e+00 3.44090015e-01 -6.04455829e-01 -3.56430292e-01
5.78317821e-01 3.76815975e-01 -5.64848125e-01 -1.08306915e-01
-1.54098511e+00 9.72807467e-01 1.02171981e+00 -4.66683358e-01
-2.32624099e-01 -1.04794204e+00 -1.45653117e+00 4.32401389e-01
3.22025180e-01 -1.37734413e+00 8.70033205e-01 -1.84648171e-01
-8.97773862e-01 6.02670193e-01 -3.47587079e-01 -6.71657562e-01
-6.36744916e-01 2.05869347e-01 -5.73520601e-01 -7.86818042e-02
-3.53927851e-01 4.20134574e-01 3.55382890e-01 -1.13360655e+00
-5.06886899e-01 -5.61619341e-01 3.09490353e-01 2.00224649e-02
-6.56898618e-01 -2.35138282e-01 -3.65982801e-01 -6.65949523e-01
-7.09196150e-01 -3.03203523e-01 -1.32537305e-01 -4.54875559e-01
-2.82035887e-01 -8.38716030e-01 5.54726005e-01 -6.95926309e-01
1.54883683e+00 -1.81837499e+00 7.00660273e-02 4.27520752e-01
8.16167176e-01 6.99618906e-02 -2.97802478e-01 5.53318918e-01
8.96500126e-02 7.04933941e-01 4.62067336e-01 5.45446910e-02
6.75969601e-01 7.46862173e-01 -2.29070127e-01 -2.96705633e-01
2.08787888e-01 2.14488125e+00 -9.66100812e-01 -4.65902746e-01
2.76602954e-02 5.91323555e-01 -6.55293703e-01 1.81818739e-01
-4.67124432e-01 -8.64218950e-01 -6.33941114e-01 8.30240369e-01
1.51154935e-01 -6.06026232e-01 4.04530466e-01 -5.39460361e-01
3.29137981e-01 -5.01723588e-02 -7.66609430e-01 1.09311855e+00
-4.89967585e-01 3.92407149e-01 -2.60647148e-01 -8.19761276e-01
7.84310520e-01 4.27172601e-01 2.51951218e-01 -4.13558036e-01
-1.18142746e-01 -9.42950547e-02 -4.60816413e-01 -4.16976273e-01
9.49610114e-01 -1.97131589e-01 -2.43401140e-01 3.47368360e-01
5.46573281e-01 2.05428794e-01 3.00389971e-03 4.36622292e-01
1.14785802e+00 -2.74345011e-01 7.28582740e-01 -4.37448584e-02
-1.27726078e-01 2.33337149e-01 2.68375665e-01 9.80046690e-01
-6.18018135e-02 -3.78123790e-01 5.04935682e-01 -5.92140079e-01
-9.58230972e-01 -1.34852171e+00 2.44617775e-01 1.46187186e+00
-1.34128556e-02 -8.10371578e-01 -1.94241866e-01 -9.98126864e-01
1.00210595e+00 8.32552850e-01 -9.24130082e-01 -5.04081249e-01
-4.61473078e-01 -3.63792509e-01 1.08141780e+00 1.31759751e+00
-2.13467315e-01 -1.30792618e+00 3.33582133e-01 2.33983606e-01
4.31638300e-01 -1.06491339e+00 -1.65455490e-02 2.51364768e-01
-7.36039877e-01 -1.06223273e+00 -3.43098104e-01 -1.19538879e+00
1.27431333e-01 -2.39964873e-01 1.31795144e+00 3.00053321e-02
-7.49393478e-02 1.06269372e+00 -4.58885998e-01 -2.17060030e-01
-3.02320480e-01 1.45602599e-01 1.87482327e-01 -4.97175723e-01
1.01641440e+00 -4.96437848e-01 1.37829423e-01 -4.34747368e-01
-8.89525831e-01 -7.45515347e-01 5.87990165e-01 9.68093216e-01
5.93338788e-01 3.53475660e-02 8.85667622e-01 -1.20463109e+00
9.88701522e-01 -7.89499104e-01 -2.48410001e-01 7.82080472e-01
-9.91567731e-01 5.37641168e-01 4.07825351e-01 -3.31502289e-01
-8.53378117e-01 -5.13537169e-01 -1.59318268e-01 -7.41289079e-01
-3.72524232e-01 1.08266580e+00 1.20739430e-01 -4.12325650e-01
6.82949662e-01 2.08619043e-01 -2.79544622e-01 -3.19701403e-01
1.35775018e+00 3.49900991e-01 4.24037457e-01 -6.89808786e-01
9.13304090e-01 -3.32998373e-02 -3.45757067e-01 -1.09905803e+00
-5.26610553e-01 -4.04616624e-01 -7.47674942e-01 5.71422696e-01
1.19054699e+00 -9.10093129e-01 -7.66797245e-01 -1.49046851e-03
-1.23728216e+00 -2.94087410e-01 -8.25660944e-01 2.12164745e-01
-5.75979590e-01 4.12774801e-01 -7.89714336e-01 -3.82543564e-01
-3.91555399e-01 -3.71968031e-01 8.94858241e-01 4.06617969e-02
-1.93849504e-01 -1.98624170e+00 3.00987482e-01 5.73256239e-02
5.35187483e-01 4.04494489e-03 1.79214537e+00 -1.08549535e+00
-5.61927676e-01 -3.81052405e-01 -6.32623613e-01 -1.58319995e-01
-1.58314258e-02 -4.03141469e-01 -7.46319175e-01 2.09333584e-01
-9.25324857e-01 -4.93678540e-01 9.44702804e-01 1.89330891e-01
7.33243287e-01 -6.88319921e-01 -5.34930289e-01 7.33528912e-01
1.55337262e+00 -3.70743237e-02 3.23379517e-01 8.18475857e-02
1.47166848e+00 2.62002766e-01 -3.51188146e-02 3.70902300e-01
1.01653016e+00 3.76055449e-01 -1.40526667e-01 1.43923640e-01
-1.78561896e-01 -7.97219753e-01 -8.53808746e-02 7.24008024e-01
-7.62236714e-02 -2.77780473e-01 -1.08135653e+00 1.02900898e+00
-1.85087359e+00 -1.17307711e+00 2.36305013e-01 1.28664839e+00
9.31186736e-01 -2.93925881e-01 1.72723040e-01 -5.40321827e-01
4.11008954e-01 2.56581247e-01 -4.21033591e-01 -9.41001296e-01
-2.71689802e-01 5.01497805e-01 1.06084979e+00 7.48235822e-01
-9.58230495e-01 1.64828074e+00 7.34965563e+00 7.46298134e-01
-3.33458900e-01 2.48026401e-01 -2.61578619e-01 2.40106419e-01
-1.18545437e+00 -1.78274766e-01 -7.69633234e-01 -1.87977448e-01
1.05231571e+00 -6.64859414e-01 5.70628226e-01 9.92224753e-01
-7.28889942e-01 6.58643067e-01 -1.22438860e+00 9.26317036e-01
8.49147514e-02 -2.13052440e+00 6.24932706e-01 1.72474772e-01
5.74418902e-01 1.11198552e-01 -4.28283483e-01 8.92784774e-01
1.11781490e+00 -1.19292545e+00 1.64526656e-01 7.00809956e-01
7.15870321e-01 -7.23276138e-01 6.38933957e-01 -2.04181924e-01
-1.55654395e+00 -2.80856460e-01 -5.99079907e-01 1.39620647e-01
1.22199617e-01 4.05771196e-01 -1.04019141e+00 8.94423068e-01
2.84567565e-01 8.80807877e-01 -7.29341626e-01 4.74571615e-01
-4.37418967e-01 4.99601126e-01 1.79691035e-02 -2.83201039e-01
-3.77930258e-03 1.94096535e-01 4.77949470e-01 1.56790662e+00
-2.32198328e-01 1.11728713e-01 2.71659434e-01 1.17274177e+00
-4.49587762e-01 -1.26008932e-02 -1.31025171e+00 -9.00245786e-01
8.71022403e-01 9.14328158e-01 8.59041661e-02 -8.05207193e-01
-7.73993313e-01 7.20253050e-01 1.06076562e+00 8.20536315e-01
-1.91137522e-01 -6.93511605e-01 1.09085488e+00 -2.60284156e-01
7.55720794e-01 -3.43804926e-01 -2.96772122e-01 -1.38249433e+00
-2.96056747e-01 -1.75338104e-01 9.18964505e-01 -5.92517376e-01
-1.72976720e+00 2.88966686e-01 -1.07749492e-01 1.40996933e-01
-6.63358390e-01 -8.95510733e-01 -3.70969057e-01 7.56081641e-01
-1.70924735e+00 -1.55372906e+00 2.95532972e-01 6.79094195e-01
-1.91914544e-01 -4.13454562e-01 1.31251431e+00 1.38422534e-01
-5.04259884e-01 1.25786376e+00 1.12303428e-01 5.67977548e-01
8.04516952e-03 -1.83904862e+00 6.67720437e-01 1.53400362e-01
3.15082937e-01 9.10559177e-01 8.73210281e-02 -1.02516305e+00
-2.11606336e+00 -1.37996829e+00 1.19754386e+00 -8.86404395e-01
1.26655042e+00 -1.33698031e-01 -1.15764236e+00 1.21195698e+00
1.82628989e-01 5.81307337e-02 9.56533968e-01 9.20954347e-01
-1.12227249e+00 3.86795759e-01 -1.00476038e+00 2.75622278e-01
9.48655546e-01 -1.14220917e+00 -1.29437661e+00 9.03140754e-02
1.34260237e+00 2.69508630e-01 -1.93574572e+00 3.29815358e-01
6.42216623e-01 -2.92101614e-02 1.34155917e+00 -1.48137736e+00
1.00649387e-01 1.99425787e-01 -5.05152762e-01 -1.41332877e+00
-8.04193377e-01 -9.75934509e-03 -1.30764234e+00 9.63500857e-01
6.45953476e-01 -8.64304364e-01 1.00756824e+00 8.04402888e-01
-2.65871137e-02 -7.23738492e-01 -5.03957391e-01 -1.10774565e+00
4.06251460e-01 -3.97541881e-01 6.74618661e-01 1.34893620e+00
6.19918525e-01 3.66668671e-01 9.06839818e-02 4.18674767e-01
4.05990839e-01 2.65634924e-01 5.00787616e-01 -1.29689956e+00
-2.52568036e-01 -6.96584463e-01 -1.12322438e+00 -4.97834325e-01
8.90673459e-01 -1.31852055e+00 -6.64343536e-01 -2.07248783e+00
-2.89884824e-02 -1.98028818e-01 -6.85263038e-01 9.04491305e-01
-1.41446695e-01 -4.27297987e-02 -1.37302563e-01 -4.12705988e-01
-6.65435076e-01 4.75025296e-01 6.43238068e-01 -3.16380858e-01
-1.55104622e-01 -7.48906016e-01 -9.79649246e-01 4.50918615e-01
5.71194232e-01 -1.82144701e-01 -4.04262304e-01 -5.60840368e-01
6.80341959e-01 -6.42266646e-02 5.60781896e-01 -3.93200099e-01
2.59136975e-01 -1.36512905e-01 3.22604179e-01 -1.83956534e-01
4.13425267e-01 -3.98877174e-01 -2.92686701e-01 1.11012921e-01
-2.96735257e-01 -2.95543876e-02 5.89359581e-01 9.01726723e-01
-3.79629523e-01 1.30701615e-02 2.68375456e-01 -2.12313056e-01
-1.63358748e+00 5.47365546e-01 -2.13890418e-01 5.35315692e-01
6.72948956e-01 -4.69403327e-01 -5.99189222e-01 -1.94370359e-01
-1.06267095e+00 3.18861365e-01 5.07148504e-01 6.97993934e-01
7.92041302e-01 -1.68376827e+00 -2.89760202e-01 1.94719434e-01
5.28529465e-01 -6.59695804e-01 1.57806836e-02 3.16533089e-01
-3.59624326e-02 6.16655350e-01 -3.71462181e-02 3.01462620e-01
-7.63197124e-01 1.07999933e+00 1.88628346e-01 -3.14051300e-01
-3.10133308e-01 1.08784688e+00 1.32797956e-01 -9.64048505e-01
3.43734324e-01 -3.64456654e-01 -4.34350044e-01 2.72774279e-01
3.84392023e-01 5.34499347e-01 -2.02908859e-01 -3.26062828e-01
-4.52623039e-01 5.63444912e-01 8.48877069e-04 2.62083232e-01
1.24181736e+00 1.87667623e-01 -2.46959075e-01 5.61465800e-01
1.47982264e+00 3.59054804e-02 -3.04734856e-01 -7.00989366e-01
3.82184267e-01 -1.37558160e-02 5.60530461e-02 -8.72103393e-01
-7.21370220e-01 7.50903428e-01 1.37731969e-01 3.17015380e-01
4.69346285e-01 6.60446823e-01 8.73627722e-01 1.06983399e+00
4.48562801e-01 -8.07326496e-01 -3.54882956e-01 1.11301768e+00
4.00421739e-01 -9.51828241e-01 -2.23970860e-01 -3.47919762e-01
-5.94731748e-01 9.18786347e-01 5.37241280e-01 -2.35678330e-01
1.04389524e+00 2.54927248e-01 -5.64970337e-02 -9.72727299e-01
-9.27788377e-01 -5.12099266e-01 7.35149860e-01 1.08856678e+00
2.95574248e-01 4.65480953e-01 4.63365078e-01 1.15574825e+00
-2.76521772e-01 2.37648040e-01 2.00037837e-01 7.91215837e-01
-5.55790722e-01 -9.98699069e-01 1.50304869e-01 9.58880842e-01
-1.78250954e-01 -6.11174047e-01 -6.71285391e-01 9.25321043e-01
-2.36052066e-01 6.26536727e-01 1.06296942e-01 -7.06699193e-01
4.00789678e-01 6.69319689e-01 4.85948235e-01 -9.68695462e-01
-4.82264787e-01 -8.71243894e-01 5.44161499e-01 -6.82545185e-01
1.34888396e-01 2.88068652e-02 -1.48546517e+00 -5.35178006e-01
-2.46914804e-01 3.78520250e-01 3.23138505e-01 7.04304159e-01
8.77490163e-01 4.75463629e-01 -7.65174553e-02 -1.66180491e-01
-3.25344324e-01 -7.01010883e-01 -9.21958268e-01 2.61146694e-01
2.63053954e-01 -7.13755786e-01 -5.35475351e-02 -9.47111174e-02] | [8.886982917785645, 7.955042362213135] |
f12eb0e8-17a6-4e5c-a58c-4db8b6584b91 | progressive-multi-view-human-mesh-recovery | 2212.05223 | null | https://arxiv.org/abs/2212.05223v1 | https://arxiv.org/pdf/2212.05223v1.pdf | Progressive Multi-view Human Mesh Recovery with Self-Supervision | To date, little attention has been given to multi-view 3D human mesh estimation, despite real-life applicability (e.g., motion capture, sport analysis) and robustness to single-view ambiguities. Existing solutions typically suffer from poor generalization performance to new settings, largely due to the limited diversity of image-mesh pairs in multi-view training data. To address this shortcoming, people have explored the use of synthetic images. But besides the usual impact of visual gap between rendered and target data, synthetic-data-driven multi-view estimators also suffer from overfitting to the camera viewpoint distribution sampled during training which usually differs from real-world distributions. Tackling both challenges, we propose a novel simulation-based training pipeline for multi-view human mesh recovery, which (a) relies on intermediate 2D representations which are more robust to synthetic-to-real domain gap; (b) leverages learnable calibration and triangulation to adapt to more diversified camera setups; and (c) progressively aggregates multi-view information in a canonical 3D space to remove ambiguities in 2D representations. Through extensive benchmarking, we demonstrate the superiority of the proposed solution especially for unseen in-the-wild scenarios. | ['Ziyan Wu', 'David Doermann', 'Junsong Yuan', 'Terrence Chen', 'Benjamin Planche', 'Meng Zheng', 'Liangchen Song', 'Xuan Gong'] | 2022-12-10 | null | null | null | null | ['human-mesh-recovery'] | ['computer-vision'] | [ 2.67198205e-01 -2.64920533e-01 -6.67471020e-03 -1.96136013e-01
-1.09475136e+00 -5.99339545e-01 4.69683290e-01 -2.63249815e-01
-9.03155953e-02 6.31575227e-01 2.04441488e-01 3.38336259e-01
-8.90000388e-02 -6.05327189e-01 -9.75205243e-01 -4.24263328e-01
2.43611559e-01 9.15388465e-01 3.57447684e-01 -2.04518259e-01
-1.84548330e-02 5.26947498e-01 -1.88633049e+00 2.25705504e-01
5.58970630e-01 7.61469960e-01 1.46046862e-01 6.88243449e-01
1.94330275e-01 1.29469931e-01 -4.12171513e-01 -2.45772868e-01
6.06067955e-01 -2.54622847e-01 -4.00873393e-01 5.40571570e-01
8.84235442e-01 -4.58902240e-01 -2.01804459e-01 8.17427754e-01
6.89742267e-01 -6.80541694e-02 5.26597261e-01 -1.12013388e+00
-3.49510998e-01 -3.10798138e-01 -8.19054902e-01 -5.59313968e-02
8.21945369e-01 3.23886126e-01 6.51596963e-01 -9.88376915e-01
1.03231955e+00 1.32425821e+00 1.00860834e+00 5.19998014e-01
-1.41117775e+00 -4.98005360e-01 1.15990467e-01 -1.80467635e-01
-1.20590019e+00 -3.93826455e-01 8.87991905e-01 -4.80284482e-01
5.57496250e-01 5.82425594e-02 9.01967227e-01 1.57369423e+00
2.45160475e-01 7.35499442e-01 1.19261837e+00 -1.85745850e-01
9.06880200e-02 1.23551302e-01 -5.01544952e-01 4.85112637e-01
4.28973496e-01 1.26597553e-01 -6.15799546e-01 -9.47888941e-02
1.05528128e+00 3.13786305e-02 -3.55215639e-01 -1.11537707e+00
-1.42432880e+00 5.12207568e-01 1.33649930e-01 -1.96490228e-01
-5.10766029e-01 3.45584601e-02 3.92019451e-01 1.94475442e-01
5.70710123e-01 2.47618854e-01 -5.03368318e-01 -1.72471315e-01
-9.66098189e-01 6.05265796e-01 5.70855379e-01 1.01672494e+00
7.60717332e-01 1.28753990e-01 2.23405138e-01 9.22962844e-01
1.82163343e-01 8.17630112e-01 2.94526368e-01 -1.02473271e+00
7.37215519e-01 5.68454504e-01 1.51590109e-01 -1.07911086e+00
-3.61647576e-01 -5.65863371e-01 -6.74924612e-01 1.82689562e-01
7.07630515e-01 4.83943783e-02 -9.35964465e-01 1.48203504e+00
7.65322030e-01 1.59071777e-02 -1.13636799e-01 1.10088491e+00
6.98802352e-01 1.49152175e-01 -2.90170103e-01 7.66761228e-02
1.13576949e+00 -6.64253950e-01 -2.02696279e-01 -6.27371609e-01
2.43574828e-01 -8.57761323e-01 1.06614411e+00 5.33452094e-01
-1.17108750e+00 -5.66083670e-01 -8.73054028e-01 9.16192960e-03
-1.44825473e-01 -7.42850825e-02 3.26706231e-01 6.37948930e-01
-7.21285284e-01 4.51605827e-01 -7.41859555e-01 -6.02563024e-01
3.68888408e-01 2.41357759e-01 -7.25675166e-01 -4.78623003e-01
-8.87372315e-01 7.16544449e-01 1.74857855e-01 1.29038364e-01
-8.05053055e-01 -9.75411296e-01 -1.01854527e+00 -3.56292367e-01
6.54706895e-01 -1.15524423e+00 8.55002165e-01 -1.01410401e+00
-1.36125314e+00 9.68048096e-01 -5.16814599e-03 -5.14926314e-02
8.65146637e-01 -4.36443508e-01 -1.52347952e-01 2.91558921e-01
1.43101275e-01 5.26451647e-01 1.04120266e+00 -1.86956203e+00
-3.51557285e-01 -6.83669627e-01 -5.51184453e-02 3.55778515e-01
9.83742718e-03 -4.97146547e-01 -5.08117497e-01 -8.63330603e-01
4.03402090e-01 -1.20069802e+00 -5.39715923e-02 2.60330260e-01
-2.77210951e-01 4.35562611e-01 7.97957242e-01 -6.18791342e-01
6.85708284e-01 -1.79399860e+00 3.14012527e-01 8.85395240e-03
-2.07420029e-02 6.26609698e-02 -9.33474116e-03 5.09707570e-01
-8.12172890e-02 -4.52338815e-01 2.82886121e-02 -6.42933309e-01
-1.72763139e-01 4.15234357e-01 -9.67959240e-02 8.43645930e-01
1.03731506e-01 6.92301810e-01 -9.20072258e-01 -5.87916434e-01
6.30739212e-01 5.93718350e-01 -7.43289053e-01 2.91037023e-01
-1.82790279e-01 9.24400747e-01 -3.34251434e-01 8.62841487e-01
8.41229796e-01 -4.01449889e-01 1.13391280e-02 -3.91887486e-01
1.67770788e-01 -3.06474566e-01 -1.58485675e+00 2.14764071e+00
-7.07620084e-01 2.67090470e-01 2.67063111e-01 -8.09233665e-01
6.09992504e-01 2.30433106e-01 7.79611647e-01 -4.58982855e-01
-7.63170980e-03 2.97717988e-01 -4.43030298e-01 -6.02271616e-01
5.86574674e-01 -3.24409634e-01 6.98435530e-02 1.10498823e-01
-5.42967673e-03 -3.65894735e-01 -2.76627630e-01 -2.31512766e-02
6.81842327e-01 7.14626074e-01 3.91475201e-01 1.11125372e-01
4.01971519e-01 3.01941484e-03 5.79888523e-01 3.41810733e-01
-5.28264418e-02 1.35086691e+00 1.86014131e-01 -5.82145333e-01
-1.16876948e+00 -1.22124600e+00 -7.47216642e-02 5.85987926e-01
2.66315639e-01 -1.49593458e-01 -5.55597126e-01 -5.86340129e-01
2.50221789e-01 3.87757689e-01 -6.67698383e-01 6.34195507e-02
-7.49811411e-01 -4.92262542e-01 2.60729164e-01 4.93328780e-01
3.71608764e-01 -6.11547232e-01 -9.65384007e-01 7.62462690e-02
-1.48471639e-01 -1.42009807e+00 -5.05068541e-01 -1.54688478e-01
-9.73153234e-01 -1.24909449e+00 -9.60403144e-01 -3.06188464e-01
4.64836210e-01 5.16610205e-01 1.27154577e+00 -1.58511952e-01
-3.99672061e-01 1.01067793e+00 -3.28397542e-01 -4.58505936e-02
-4.87788618e-01 -8.25079456e-02 1.86581790e-01 1.80195272e-01
-1.71881154e-01 -7.10870922e-01 -9.78427768e-01 6.25791848e-01
-7.00090110e-01 3.93901989e-02 5.65235555e-01 9.75800812e-01
7.68071294e-01 -3.64261299e-01 3.04381222e-01 -8.93637896e-01
-6.49151281e-02 -5.23733377e-01 -3.93307358e-01 1.58821568e-01
-4.78209704e-01 -1.30074739e-01 5.08747935e-01 -5.89644670e-01
-9.38278735e-01 3.23020726e-01 4.29421738e-02 -1.04013419e+00
-2.61035889e-01 1.00627519e-01 -2.10636064e-01 -1.31148160e-01
5.77734053e-01 1.32563099e-01 3.12076330e-01 -3.21161270e-01
2.09968999e-01 3.56906593e-01 5.15283525e-01 -7.52487421e-01
8.80952835e-01 8.55400205e-01 2.28382394e-01 -1.00530195e+00
-7.81906068e-01 -5.61670661e-01 -8.45657527e-01 -4.98703629e-01
8.72383177e-01 -1.25598621e+00 -3.62885505e-01 4.54740763e-01
-9.21202421e-01 -1.35941595e-01 -2.62806773e-01 5.82689703e-01
-7.88379073e-01 7.19946444e-01 -2.43325233e-01 -8.26153100e-01
-5.23352958e-02 -1.35996354e+00 1.82136333e+00 -5.08085126e-03
-2.96276957e-01 -1.09225762e+00 1.91637855e-02 7.00070024e-01
6.26531914e-02 6.50687397e-01 5.67820966e-01 -1.86247364e-01
-5.84785044e-01 -3.78267199e-01 1.58738382e-02 3.65524352e-01
1.25500068e-01 -1.94239929e-01 -1.04635322e+00 -6.40153766e-01
-9.93155539e-02 -5.55773139e-01 4.15206909e-01 4.36574191e-01
8.15176010e-01 1.10462785e-01 -3.57117981e-01 7.89495587e-01
1.36417055e+00 -5.52636147e-01 4.85462099e-01 4.18430299e-01
8.89466703e-01 7.35385716e-01 7.94384420e-01 5.36066771e-01
4.97549653e-01 1.14579189e+00 6.35425150e-01 -9.55372006e-02
-3.38537842e-01 -5.10612130e-01 1.21173397e-01 6.73900545e-01
-3.56508531e-02 -8.96330327e-02 -7.23109126e-01 6.41444921e-01
-1.56027842e+00 -9.09707725e-01 -8.27703550e-02 2.49721360e+00
3.79968137e-01 1.52710259e-01 3.63224924e-01 -4.07700948e-02
4.65037167e-01 2.29581103e-01 -7.03574598e-01 2.58676857e-01
-1.13459416e-01 8.63678902e-02 6.46952450e-01 1.33769765e-01
-8.91436458e-01 6.63567483e-01 5.58953190e+00 7.54438937e-01
-1.02888668e+00 5.42592742e-02 3.03937197e-01 -9.02955979e-02
-3.28507394e-01 6.98841643e-03 -5.65994084e-01 2.92002976e-01
4.23460931e-01 4.36292648e-01 2.58916467e-01 8.37744057e-01
2.47105565e-02 -1.84325293e-01 -1.07550061e+00 1.44877660e+00
3.66305560e-01 -1.02080524e+00 1.88096404e-01 2.67303228e-01
8.09939981e-01 -7.53362179e-02 -3.78221013e-02 1.19800568e-01
4.88673374e-02 -8.20071816e-01 9.73237932e-01 4.84284520e-01
9.02828574e-01 -5.55221736e-01 4.91967976e-01 5.11920154e-01
-1.21446943e+00 8.64100382e-02 -2.41533786e-01 1.73641190e-01
4.03794855e-01 5.92925370e-01 -6.97834492e-01 9.66607630e-01
6.34368658e-01 8.37123811e-01 -5.95077097e-01 7.19482839e-01
2.71154821e-01 1.61236793e-01 -3.75349492e-01 5.31625867e-01
-1.39263570e-01 -1.67044729e-01 7.38647878e-01 8.54761600e-01
4.42116559e-01 -1.86818287e-01 3.83361995e-01 6.11447632e-01
3.11236948e-01 -1.92617476e-01 -8.10880244e-01 3.77452940e-01
3.90213877e-01 1.07853866e+00 -8.22615206e-01 -2.15106029e-02
-4.67585802e-01 1.15588212e+00 1.55373693e-01 4.09586459e-01
-7.45310068e-01 3.62318277e-01 6.43723965e-01 7.26362169e-01
5.03447533e-01 -3.50988597e-01 -1.86818019e-01 -1.28140712e+00
3.34162265e-01 -1.06590772e+00 3.45115095e-01 -8.89168859e-01
-1.33344042e+00 4.97499675e-01 3.65060955e-01 -1.72213781e+00
-3.87218565e-01 -5.56078255e-01 -1.58032119e-01 7.03710139e-01
-1.20039701e+00 -1.52864873e+00 -4.92803276e-01 4.86340284e-01
8.95916700e-01 -1.38680339e-01 7.11353660e-01 4.36363548e-01
-1.08655423e-01 5.56308746e-01 -2.13866290e-02 -3.15190285e-01
1.03329647e+00 -1.12328339e+00 2.94081032e-01 5.13921142e-01
3.70388478e-02 4.35135990e-01 9.14370418e-01 -7.12101936e-01
-1.79155803e+00 -9.64880228e-01 1.21324778e-01 -8.47813010e-01
2.20379964e-01 -4.33779180e-01 -8.38549018e-01 6.32234573e-01
-2.47378007e-01 3.59938502e-01 2.87835240e-01 -5.20442687e-02
-2.72984415e-01 -1.41913295e-01 -1.16943264e+00 4.37174052e-01
1.13657820e+00 -3.59410465e-01 -3.16134781e-01 1.84654474e-01
2.14407623e-01 -1.04900086e+00 -1.04078019e+00 5.58073759e-01
9.25849736e-01 -1.27875304e+00 1.43062878e+00 -4.15036023e-01
4.82537270e-01 -2.84639537e-01 -4.58543390e-01 -1.41967332e+00
1.62329420e-01 -4.13164914e-01 -2.40334775e-02 9.75644410e-01
-8.71611908e-02 -4.56156284e-01 9.82756317e-01 2.51896232e-01
-5.63967489e-02 -9.10898626e-01 -1.13675654e+00 -7.78394341e-01
3.98414806e-02 -4.76618469e-01 4.65914607e-01 8.24883878e-01
-6.47523820e-01 1.98999107e-01 -6.52059197e-01 4.01242048e-01
8.14140379e-01 2.66842753e-01 1.50275207e+00 -1.27763414e+00
-6.37978256e-01 3.63760963e-02 -5.57265818e-01 -1.26864183e+00
-2.96717193e-02 -5.47825277e-01 1.37425233e-02 -1.42251945e+00
9.10543501e-02 -4.02487785e-01 4.41053152e-01 -2.54588693e-01
-2.24098295e-01 4.60686445e-01 1.39291346e-01 2.81588823e-01
-4.31046396e-01 5.55283785e-01 1.42795372e+00 2.28201181e-01
-4.77387710e-03 1.23737723e-01 -3.23259830e-01 8.10971916e-01
2.39634976e-01 -3.86246592e-01 -4.95490581e-01 -5.46343982e-01
3.87378991e-01 3.84946525e-01 7.64022708e-01 -1.18368232e+00
-1.25426039e-01 -1.20106414e-01 5.14377952e-01 -6.12454832e-01
7.00012445e-01 -1.00401366e+00 6.12748623e-01 1.06076129e-01
1.09223761e-01 3.42390031e-01 2.48224828e-02 1.03509176e+00
5.64714000e-02 5.56671247e-03 8.05242956e-01 -4.10676956e-01
-4.51590657e-01 3.23236108e-01 1.41500726e-01 4.40406412e-01
8.53140831e-01 -8.44382286e-01 1.65286660e-01 -4.08184379e-01
-5.57880044e-01 2.02213973e-01 1.07577252e+00 4.94804502e-01
5.97248256e-01 -1.25033629e+00 -6.72466338e-01 4.47347403e-01
3.26322556e-01 4.67574239e-01 5.04097462e-01 9.45078015e-01
-5.72159827e-01 -8.55256617e-02 -8.65115300e-02 -1.08908582e+00
-1.25261855e+00 3.79001766e-01 3.58220607e-01 -2.32103854e-01
-9.56299424e-01 4.76501942e-01 2.58644819e-01 -5.95155418e-01
6.68372363e-02 -2.19048023e-01 1.98138312e-01 -8.46680775e-02
8.12866762e-02 5.56921422e-01 2.17943236e-01 -9.94787514e-01
-2.39306927e-01 1.10568607e+00 1.10460870e-01 4.15767048e-04
1.32890272e+00 -2.66499341e-01 4.35929805e-01 6.51493311e-01
1.16955650e+00 1.88840345e-01 -1.62120402e+00 -8.16973373e-02
-3.53610486e-01 -8.08188677e-01 -2.28459254e-01 -4.23986793e-01
-1.08477914e+00 8.34929645e-01 6.92846417e-01 -2.28746846e-01
7.83755779e-01 -6.02925681e-02 9.77338374e-01 -3.76942451e-03
6.23446226e-01 -1.07377017e+00 2.68206120e-01 1.29390225e-01
8.37171614e-01 -1.36489737e+00 5.29547751e-01 -4.85087752e-01
-7.74264038e-01 1.17249095e+00 4.76865113e-01 -2.28920698e-01
5.04350245e-01 -1.05270175e-02 8.83951709e-02 -4.79645729e-01
-4.21136409e-01 1.12015858e-01 2.53522605e-01 9.12029088e-01
1.30708978e-01 -2.94267505e-01 3.04413944e-01 8.88344496e-02
-8.75563920e-02 1.16405012e-02 3.91013652e-01 8.75524342e-01
4.91338819e-02 -9.67823923e-01 -6.84279978e-01 3.13443720e-01
-1.31954864e-01 4.35286194e-01 -8.46238509e-02 1.16217136e+00
2.52368718e-01 5.72635055e-01 -1.57633156e-01 -1.84550658e-01
8.81467283e-01 -1.87269911e-01 8.02226484e-01 -5.99701047e-01
-4.52973038e-01 1.75314233e-01 -2.71765050e-02 -7.32975364e-01
-6.80733562e-01 -1.03391838e+00 -7.53702283e-01 -1.21327937e-01
-3.10248315e-01 -3.54158312e-01 4.31634426e-01 8.85918438e-01
4.41157937e-01 3.81553441e-01 4.17998791e-01 -1.46482491e+00
-6.75488710e-01 -7.55966961e-01 -5.59973896e-01 8.64970207e-01
4.43976432e-01 -1.29261160e+00 -4.39008445e-01 -3.91106270e-02] | [7.971161842346191, -2.4196279048919678] |
1fe048c8-6cd2-4004-ac89-b15edc90f364 | in-situ-process-quality-monitoring-and-defect | 2112.01921 | null | https://arxiv.org/abs/2112.01921v1 | https://arxiv.org/pdf/2112.01921v1.pdf | In situ process quality monitoring and defect detection for direct metal laser melting | Quality control and quality assurance are challenges in Direct Metal Laser Melting (DMLM). Intermittent machine diagnostics and downstream part inspections catch problems after undue cost has been incurred processing defective parts. In this paper we demonstrate two methodologies for in-process fault detection and part quality prediction that can be readily deployed on existing commercial DMLM systems with minimal hardware modification. Novel features were derived from the time series of common photodiode sensors along with standard machine control signals. A Bayesian approach attributes measurements to one of multiple process states and a least squares regression model predicts severity of certain material defects. | ['Thomas Spears', 'Subhrajit Roychowdhury', 'Xiaohu Ping', 'Gabriel Lipsa', 'Michael Lexa', 'H. Kirk Mathews', 'Saikat Ray Majumder', 'Sarah Felix'] | 2021-12-03 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 3.31324935e-01 6.76644742e-02 -4.29442115e-02 -3.49836111e-01
-6.96300447e-01 -1.24861792e-01 1.08817600e-01 2.06023961e-01
4.40309078e-01 4.25267875e-01 -7.24971175e-01 -2.11395975e-02
-5.65009356e-01 -6.57604039e-01 -4.31771517e-01 -7.96331227e-01
1.82691708e-01 9.48146999e-01 3.16988409e-01 9.22316685e-02
4.86438811e-01 9.43539619e-01 -1.31208706e+00 3.36050838e-01
3.97296876e-01 1.26492596e+00 1.45356074e-01 8.08516264e-01
2.71627188e-01 6.98680937e-01 -8.52999270e-01 1.85097888e-01
1.57075003e-01 -1.06054761e-01 -7.05696523e-01 7.44197905e-01
-3.09037030e-01 -1.16143107e-01 -4.94412243e-01 5.79701781e-01
2.15349197e-01 -2.12295398e-01 8.48735750e-01 -1.51222718e+00
-3.00262868e-01 1.74315915e-01 -3.82252634e-01 -9.20805186e-02
5.40494621e-01 5.99796534e-01 1.50705308e-01 -5.90218425e-01
1.83620483e-01 1.22182655e+00 7.39891708e-01 3.64969552e-01
-1.61553979e+00 -3.03146869e-01 -6.27835870e-01 -1.29999872e-02
-8.65866184e-01 -5.40014505e-01 6.77252293e-01 -5.53803504e-01
1.35821033e+00 7.38045499e-02 2.72786677e-01 6.52767479e-01
1.31865907e+00 4.87168252e-01 1.15909851e+00 -3.73191029e-01
3.55351478e-01 -2.10634395e-01 3.24218541e-01 8.86792183e-01
5.00553370e-01 4.87201422e-01 -6.87969804e-01 -2.33253926e-01
6.60810232e-01 1.24656238e-01 3.78015032e-03 -2.24651888e-01
-5.33344328e-01 2.81426162e-01 -4.45072979e-01 1.37196228e-01
-5.45417190e-01 4.70957190e-01 3.11266184e-01 7.34801054e-01
3.39807600e-01 5.52160740e-01 -5.72914898e-01 -2.60125905e-01
-8.71689498e-01 -4.48657870e-02 8.74815643e-01 1.27790844e+00
7.93078184e-01 8.36521834e-02 -2.81674683e-01 5.41988969e-01
6.81522310e-01 7.94365942e-01 2.29332857e-02 -1.06289256e+00
-4.91285585e-02 6.33831739e-01 2.81319439e-01 -1.61360785e-01
-3.94218564e-01 3.38736445e-01 -4.44826543e-01 3.77553850e-01
-2.33128369e-01 -1.73645079e-01 -1.21225095e+00 3.46050888e-01
4.53363033e-03 1.22602843e-02 7.12782666e-02 1.70222536e-01
1.30252898e-01 4.36186492e-01 -2.90542990e-01 -5.06845891e-01
1.10965776e+00 -2.64391541e-01 -1.26525056e+00 -4.02483851e-01
3.22627872e-01 -1.03834403e+00 2.94609606e-01 9.92555678e-01
-1.23094285e+00 -5.04056215e-01 -1.25545180e+00 3.51389229e-01
1.85517490e-01 3.20139557e-01 5.91457367e-01 7.73584604e-01
-8.26039314e-01 1.37240255e+00 -1.44526422e+00 1.16676003e-01
2.84493119e-01 6.55090392e-01 -2.42372960e-01 -3.87158573e-01
-4.33391511e-01 1.08821726e+00 -2.68343031e-01 7.39796400e-01
-1.29166579e+00 -5.51296175e-01 -7.53330529e-01 -3.81719321e-01
1.34556264e-01 -3.37734878e-01 1.80019546e+00 2.33248159e-01
-1.85547745e+00 4.98539448e-01 -1.68649182e-01 -5.38824685e-02
7.09662512e-02 -5.35991549e-01 -7.60286748e-01 4.70565736e-01
4.20549549e-02 -3.99084985e-01 1.20348632e+00 -1.20109797e+00
-4.19595867e-01 -4.61129218e-01 -9.81845319e-01 -6.43138289e-01
4.56827521e-01 2.07028240e-01 1.29963130e-01 1.35830551e-01
7.71715820e-01 -6.56800210e-01 -2.60188967e-01 -3.13936293e-01
-4.83389765e-01 -3.32717836e-01 8.41279328e-01 -4.44507241e-01
7.22362041e-01 -1.83525169e+00 -2.02500090e-01 3.77111912e-01
-3.63835096e-01 -2.16052949e-01 2.88333952e-01 1.08416557e+00
5.05208634e-02 -3.97716999e-01 -2.70260572e-01 -2.03164995e-01
1.20929532e-01 4.89101678e-01 1.08043008e-01 9.94925261e-01
8.06616426e-01 5.00027120e-01 -6.95080101e-01 -8.85399953e-02
3.89112890e-01 -1.50132746e-01 2.33630329e-01 3.16914886e-01
-9.99146998e-02 -2.59763580e-02 -5.18610358e-01 1.38388157e+00
7.11837649e-01 3.21368515e-01 -1.73522338e-01 -3.38615209e-01
-1.45235257e-02 8.60680863e-02 -1.01476836e+00 1.44928575e+00
-4.06656712e-01 5.07882237e-01 3.35293144e-01 -7.27747262e-01
1.39126039e+00 8.85516346e-01 9.03123260e-01 -3.36152226e-01
3.10094237e-01 2.10628867e-01 -4.08321172e-01 -1.10014749e+00
3.17303300e-01 -6.88016772e-01 -5.48060574e-02 2.04424739e-01
1.69280857e-01 -1.09943485e+00 -2.08222456e-02 -2.96217173e-01
1.73593938e+00 7.76023045e-02 -4.36287522e-01 -3.40954810e-01
1.43128902e-01 2.73203313e-01 7.70055532e-01 4.55775708e-01
-4.46419269e-02 3.72257292e-01 5.57288468e-01 1.73840210e-01
-9.40314949e-01 -1.13892615e+00 -3.17978799e-01 -7.74601847e-02
8.44150186e-02 -1.68518797e-01 -2.85062045e-01 -1.24411955e-01
6.22911811e-01 6.57813728e-01 -1.51943266e-01 -8.63744617e-01
-2.37632200e-01 -5.12911975e-01 4.63001132e-01 7.09949195e-01
-8.66451189e-02 -8.80005419e-01 -3.60088021e-01 5.37035346e-01
6.99803710e-01 -8.41809392e-01 3.03435177e-01 7.74121940e-01
-1.45240474e+00 -1.45823252e+00 2.62336195e-01 -5.28564215e-01
5.91706097e-01 9.19137441e-04 1.08596957e+00 8.09047148e-02
-1.06764114e+00 7.25594878e-01 -1.20200045e-01 -5.20939708e-01
-9.67935443e-01 -7.40432262e-01 3.56277138e-01 -3.66447896e-01
6.82946563e-01 -9.60521922e-02 -2.52342433e-01 4.66936797e-01
-7.20573723e-01 -8.64127040e-01 9.08425748e-01 7.49304950e-01
5.04091144e-01 7.57467508e-01 4.35082316e-01 -8.00366700e-01
6.46790206e-01 -2.73758471e-01 -6.98288977e-01 2.32927084e-01
-1.18235815e+00 8.05166364e-02 -5.55350818e-02 3.07596382e-02
-1.38184917e+00 2.10895807e-01 2.45906767e-02 -5.15012085e-01
-4.29402173e-01 2.47673854e-01 -2.33982384e-01 1.32441625e-01
4.45114255e-01 -3.23193073e-01 5.19804180e-01 -6.70927405e-01
-4.47864532e-01 8.14359367e-01 7.10813046e-01 -7.95033872e-01
7.08006442e-01 3.12737703e-01 6.50329888e-01 -8.44684124e-01
-2.86063552e-01 -8.07742119e-01 -6.70459569e-01 -5.13433099e-01
6.61405385e-01 -7.07266867e-01 -1.06785142e+00 8.94473433e-01
-9.31172788e-01 -3.22910905e-01 -4.46262956e-01 5.36687016e-01
-7.71634698e-01 4.92277056e-01 -1.29254806e+00 -1.33294857e+00
-3.94265950e-02 -9.68084037e-01 1.51375639e+00 -8.36480856e-02
-1.88833192e-01 -5.93344629e-01 -4.26949114e-02 2.17199937e-01
-7.48965666e-02 3.31961215e-01 7.21435070e-01 7.20292702e-02
-6.24855280e-01 -9.87785876e-01 5.01770973e-01 8.44466984e-01
6.98238969e-01 5.44585407e-01 -1.02621794e+00 -4.20942187e-01
8.75828087e-01 -5.97529858e-02 6.17678821e-01 5.33356249e-01
6.77461386e-01 3.41726393e-01 -7.39405572e-01 -1.56886846e-01
1.45208561e+00 4.54996914e-01 4.99085754e-01 2.76417732e-02
1.09929681e-01 6.80172086e-01 1.28805625e+00 5.37502229e-01
-6.23785317e-01 1.10315926e-01 2.89559573e-01 1.50969133e-01
3.67016345e-01 8.82421434e-02 7.42962360e-01 3.71979833e-01
1.32552730e-02 -6.07270859e-02 -1.00380301e+00 3.89900893e-01
-1.48662996e+00 -8.27094376e-01 -9.74123061e-01 2.01878667e+00
5.28864384e-01 5.11049688e-01 -5.33067107e-01 5.80415010e-01
6.37760699e-01 -7.10775793e-01 -4.47876573e-01 -7.33452916e-01
4.30634171e-01 5.08075714e-01 9.78305519e-01 2.10041732e-01
-5.52463830e-01 1.91477165e-01 8.19004822e+00 3.38844568e-01
-2.54241139e-01 -5.85348941e-02 8.97562951e-02 -1.78397801e-02
1.19564861e-01 3.25509608e-01 -7.61365831e-01 1.61036491e-01
1.49285448e+00 3.38054121e-01 -1.72384918e-01 6.08054519e-01
4.74767596e-01 -7.98072517e-01 -1.65396380e+00 5.67685425e-01
-4.40501273e-01 -7.88461864e-01 -7.22086847e-01 8.79777819e-02
5.17979503e-01 -2.75276601e-01 -2.27159992e-01 -1.27753794e-01
2.81632572e-01 -5.58340371e-01 4.65982199e-01 1.20406342e+00
4.27992225e-01 -8.72150540e-01 9.75524247e-01 7.84655362e-02
-6.85184300e-01 -3.70289952e-01 -6.08593702e-01 -6.07546903e-02
6.22760832e-01 1.30640244e+00 -9.84258592e-01 8.16515625e-01
6.48546815e-01 4.46189880e-01 -6.06009103e-02 9.79441702e-01
-2.21579567e-01 7.05319822e-01 -2.54635096e-01 3.61382127e-01
-1.90097377e-01 -2.21607238e-01 3.26660603e-01 7.65167356e-01
4.85677749e-01 -1.10809878e-01 7.81452060e-02 8.77410769e-01
5.55348098e-01 -9.62355614e-01 -6.34866655e-01 -1.15443066e-01
2.85953522e-01 9.01796758e-01 -6.57638609e-01 -7.50411972e-02
-2.14960620e-01 9.26887393e-01 -7.13843405e-01 9.37011018e-02
-3.78899515e-01 -3.34792763e-01 9.28714812e-01 3.68664652e-01
7.62254819e-02 -4.38818395e-01 -3.71426612e-01 -3.06750357e-01
-1.37712464e-01 -3.41521472e-01 4.27960865e-02 -8.09578717e-01
-1.75605345e+00 -5.13259098e-02 1.78421184e-01 -1.07697105e+00
-2.98689336e-01 -1.05486214e+00 -7.14697838e-01 7.77101457e-01
-1.22627699e+00 -6.52583361e-01 -9.33222938e-03 2.67513007e-01
6.05667233e-01 -1.91785917e-01 5.19966960e-01 -4.02379557e-02
-1.02266860e+00 -1.39185727e-01 3.32152814e-01 -4.33220685e-01
6.93268597e-01 -1.17177272e+00 2.87455201e-01 6.93428874e-01
-6.16169870e-01 1.16715305e-01 1.20850265e+00 -1.30341256e+00
-2.13327241e+00 -8.38496387e-01 5.22495866e-01 -5.23701727e-01
6.76368475e-01 -5.93014210e-02 -8.64603341e-01 6.73257411e-01
1.59574181e-01 -2.24433929e-01 3.96554887e-01 -2.00042445e-02
6.22032821e-01 -2.47721553e-01 -1.54570401e+00 -2.46083826e-01
4.46645498e-01 -6.68646872e-01 -6.67509139e-01 7.13215828e-01
1.37950733e-01 -3.67895275e-01 -1.40093279e+00 5.33644319e-01
-1.22787831e-02 -5.92537522e-01 4.95814830e-01 -3.64849806e-01
1.91765144e-01 -3.08058083e-01 2.05363348e-01 -1.16846097e+00
-3.64438266e-01 -8.40515971e-01 -2.13547349e-01 1.44250154e+00
1.54306933e-01 -5.47293782e-01 7.56165147e-01 1.04000735e+00
-7.49749005e-01 -4.44075078e-01 -1.01699913e+00 -1.08546817e+00
-2.99432516e-01 -5.35856605e-01 3.53227198e-01 5.87814264e-02
3.40999931e-01 -6.10288233e-02 -6.68251738e-02 8.23945343e-01
7.42460191e-01 -5.89218251e-02 7.58705486e-04 -1.49742794e+00
-3.59214395e-01 1.98729143e-01 -7.93839395e-01 -4.39535826e-01
-1.16878279e-01 -2.62566060e-01 9.09636438e-01 -1.66523170e+00
-2.57502824e-01 -1.47865877e-01 -2.02325523e-01 2.75524169e-01
2.88836718e-01 -1.87678650e-01 -6.55549645e-01 5.75401001e-02
1.33369640e-01 4.72893804e-01 9.25772071e-01 -3.46959829e-01
1.29826367e-01 5.14318526e-01 3.09893847e-01 3.11737716e-01
7.73983777e-01 -8.01963389e-01 -1.85013071e-01 -1.63689733e-01
2.23677740e-01 6.82767808e-01 4.21081036e-01 -1.29244447e+00
4.72840071e-01 1.53944846e-02 4.42775935e-01 -8.87705803e-01
3.55012476e-01 -1.17320728e+00 4.76385206e-01 7.79305935e-01
2.54623502e-01 2.26518929e-01 1.84049338e-01 9.49182391e-01
-3.66405904e-01 -8.46821904e-01 7.43960381e-01 1.86618306e-02
-2.80495733e-01 -5.69501743e-02 -1.17531466e+00 -9.77592707e-01
1.17234313e+00 -3.36721957e-01 -1.93950906e-01 5.02937615e-01
-9.02478755e-01 3.60407263e-01 3.76052737e-01 1.61546960e-01
8.86544704e-01 -9.70056951e-01 -4.26577359e-01 4.08239901e-01
-1.75618708e-01 1.08227387e-01 4.12127614e-01 9.72979248e-01
-5.09659171e-01 3.16897184e-01 -1.18638434e-01 -1.01768231e+00
-1.12268519e+00 6.27895415e-01 3.66993636e-01 1.09118514e-01
-5.69303274e-01 6.80288672e-01 -9.57414389e-01 2.72070408e-01
-3.60393971e-01 -7.71826684e-01 5.97751558e-01 -1.71221241e-01
2.90033650e-02 8.97333622e-01 8.09620380e-01 2.48468723e-02
-1.06946938e-01 2.08038002e-01 1.08338647e-01 7.74012655e-02
1.46787763e+00 -8.92160237e-02 -3.57799053e-01 1.01321912e+00
6.73271954e-01 -6.17705762e-01 -1.49207652e+00 1.97464824e-01
5.98774970e-01 -4.52441931e-01 2.20548332e-01 -6.06256664e-01
-8.66985917e-01 5.96826613e-01 8.74156117e-01 4.55324471e-01
1.07638955e+00 2.13775039e-01 5.85623860e-01 5.79982817e-01
5.38217723e-01 -1.65964258e+00 1.32206887e-01 -4.05750945e-02
6.50156856e-01 -9.67548907e-01 2.81191707e-01 -6.43221557e-01
-2.33409196e-01 1.33492482e+00 4.28658843e-01 -2.98133731e-01
8.95468414e-01 9.52353656e-01 -7.03250319e-02 -8.68723094e-01
-1.22032523e+00 1.40944690e-01 -2.33116642e-01 7.23002851e-01
3.52388531e-01 -1.29408881e-01 3.92374163e-03 2.70392597e-01
4.98351395e-01 1.19268157e-01 6.68154418e-01 1.74083340e+00
-7.33113706e-01 -1.25476074e+00 -7.57360935e-01 8.21101189e-01
-3.03666145e-01 6.03782952e-01 -6.51962608e-02 6.04135692e-01
-1.25201598e-01 1.65171087e+00 1.92474782e-01 -1.52752519e-01
1.01168704e+00 3.85772586e-01 6.85248017e-01 -9.12862241e-01
-6.56081080e-01 5.87115772e-02 2.74623036e-01 -9.29738462e-01
-2.92812824e-01 -1.16463816e+00 -1.41884172e+00 -6.03576340e-02
-8.67799282e-01 -5.57609685e-02 8.86162221e-01 9.03333485e-01
3.91308330e-02 9.85186577e-01 8.45554590e-01 -7.31103420e-01
-9.19410586e-01 -1.22728074e+00 -1.45858514e+00 -2.00004637e-01
2.15416700e-01 -1.01962757e+00 -3.05054188e-01 6.82452917e-02] | [6.8296380043029785, 2.3790061473846436] |
38ba3c24-6406-4bfb-81c1-228f7e50d5fc | skoltechnlp-at-semeval-2021-task-5-leveraging | null | null | https://aclanthology.org/2021.semeval-1.126 | https://aclanthology.org/2021.semeval-1.126.pdf | SkoltechNLP at SemEval-2021 Task 5: Leveraging Sentence-level Pre-training for Toxic Span Detection | This work describes the participation of the Skoltech NLP group team (Sk) in the Toxic Spans Detection task at SemEval-2021. The goal of the task is to identify the most toxic fragments of a given sentence, which is a binary sequence tagging problem. We show that fine-tuning a RoBERTa model for this problem is a strong baseline. This baseline can be further improved by pre-training the RoBERTa model on a large dataset labeled for toxicity at the sentence level. While our solution scored among the top 20{\%} participating models, it is only 2 points below the best result. This suggests the viability of our approach. | ['Alexander Panchenko', 'Nikita Semenov', 'Olga Kozlova', 'Varvara Logacheva', 'Igor Markov', 'David Dale'] | 2021-08-01 | null | null | null | semeval-2021 | ['toxic-spans-detection'] | ['natural-language-processing'] | [ 2.58431882e-01 1.47748902e-01 -1.35310248e-01 -7.84756094e-02
-1.50136781e+00 -1.03392303e+00 6.49349451e-01 5.00678480e-01
-7.20745146e-01 1.08097899e+00 4.03168797e-01 -2.72261381e-01
8.36450383e-02 -3.69431913e-01 -7.85997510e-01 -5.61872780e-01
2.63519045e-02 4.39137548e-01 4.13692325e-01 -1.13214418e-01
3.69179010e-01 5.53039610e-01 -6.42580152e-01 8.98730755e-01
7.02072263e-01 5.68037808e-01 1.75315924e-02 8.98161054e-01
3.06201965e-01 8.30430210e-01 -7.83636868e-01 -6.25567079e-01
1.50919542e-01 -4.18837905e-01 -1.28952324e+00 -3.16761822e-01
6.17179930e-01 2.42248759e-01 -3.31851542e-01 9.56153572e-01
6.13438129e-01 -1.83538371e-03 8.93092275e-01 -1.06112444e+00
-2.17805505e-01 7.86921263e-01 -3.39625686e-01 7.21912861e-01
3.74011964e-01 4.40605551e-01 1.22062111e+00 -5.86746216e-01
8.23360920e-01 1.05961382e+00 8.44676793e-01 6.80348694e-01
-1.14263213e+00 -6.33635461e-01 7.33074695e-02 3.41531038e-01
-1.20629787e+00 -5.09920716e-01 3.62526089e-01 -6.11756027e-01
1.63030779e+00 3.29793483e-01 2.50506938e-01 1.41411591e+00
3.13833028e-01 7.90834665e-01 1.18626940e+00 -2.45240211e-01
3.34508628e-01 -1.01859592e-01 4.15630311e-01 5.48876166e-01
1.30025372e-01 -1.54560372e-01 -7.86605835e-01 -3.90148461e-01
-1.97639450e-01 -6.82131946e-01 -2.95362294e-01 3.73330534e-01
-8.23042214e-01 7.77921915e-01 1.82455868e-01 4.48870867e-01
-2.97610641e-01 2.59530127e-01 5.96622407e-01 1.12928398e-01
7.46627390e-01 1.04470754e+00 -8.77656162e-01 -1.86989680e-01
-1.16223705e+00 4.89710331e-01 6.81893826e-01 4.98936564e-01
-3.14945471e-03 -3.40712428e-01 -6.60870671e-01 6.27910674e-01
6.09742552e-02 7.29809180e-02 2.16546401e-01 -1.04892087e+00
7.82408893e-01 2.85467744e-01 2.51308650e-01 -2.30451554e-01
-7.74185658e-01 -3.57471496e-01 -1.38244972e-01 1.70189157e-01
8.01609397e-01 -5.32879412e-01 -9.46512043e-01 1.79166818e+00
-1.19441986e-01 1.01985157e-01 5.62817380e-02 4.92105275e-01
5.99318981e-01 7.32833326e-01 8.15940559e-01 -3.97395909e-01
1.27791250e+00 -9.24699306e-01 -3.52499783e-01 -3.70663017e-01
1.19732594e+00 -5.79720497e-01 7.16474295e-01 7.99534202e-01
-1.11033213e+00 2.10824162e-02 -1.02840400e+00 -9.00497213e-02
-3.98430407e-01 -2.01398998e-01 3.91138762e-01 7.97897100e-01
-9.72739339e-01 8.27293456e-01 -4.99368399e-01 -5.81471026e-01
4.22106147e-01 3.11417937e-01 -4.85754013e-01 2.97982320e-02
-1.41954362e+00 1.36202133e+00 4.46491718e-01 -4.52865601e-01
-9.77077246e-01 -8.68282199e-01 -5.17105639e-01 4.53443527e-02
1.34568274e-01 -5.14472544e-01 1.38209391e+00 -1.44429550e-01
-9.06525254e-01 9.02157903e-01 2.37798430e-02 -9.33086336e-01
5.81523538e-01 -2.51426548e-01 -1.82064310e-01 2.36056834e-01
2.54947376e-02 5.50041795e-01 -6.96811825e-02 -6.89939737e-01
-6.50253057e-01 -3.29837501e-01 7.40644634e-02 1.74083948e-01
-3.26918870e-01 7.61972249e-01 -1.36153013e-01 -4.28382069e-01
-4.90505993e-01 -8.48209023e-01 -5.24159551e-01 -6.11173511e-01
-5.74188590e-01 -6.67660534e-01 1.78011626e-01 -1.08633351e+00
1.09866929e+00 -1.69768631e+00 -1.26873419e-01 -1.80666298e-01
-1.69666056e-02 1.82567403e-01 -4.04453367e-01 5.81042290e-01
-3.87665242e-01 5.16492128e-01 -1.77538335e-01 -3.31421971e-01
8.88193771e-03 -5.36261320e-01 -3.02699804e-01 5.95004439e-01
3.12390059e-01 8.74708414e-01 -1.03988051e+00 -5.92434347e-01
-3.53711724e-01 1.27298042e-01 -2.64809549e-01 -1.24438480e-01
-5.42798519e-01 1.56384170e-01 -2.81576961e-01 3.14024717e-01
4.02480215e-01 1.14939012e-01 1.11627311e-01 1.10762436e-02
-1.20772682e-01 7.93528795e-01 -4.60151374e-01 1.70552683e+00
-1.27636120e-01 5.38370550e-01 -2.77124763e-01 -8.38047504e-01
5.83689928e-01 4.28560913e-01 5.83978713e-01 -6.41260684e-01
-7.56255258e-03 -4.42118198e-03 2.12634340e-01 -6.57675147e-01
2.39900038e-01 -5.20813525e-01 -3.26841056e-01 3.33654583e-01
1.69405982e-01 -8.15590918e-02 7.01416671e-01 3.72221887e-01
1.92817271e+00 1.65271848e-01 6.83361143e-02 -4.59983766e-01
3.03598046e-01 2.33621508e-01 7.24935412e-01 6.77140713e-01
-2.87012577e-01 7.61577547e-01 8.42472792e-01 -1.86035022e-01
-1.02242160e+00 -8.71375620e-01 -6.12193272e-02 1.01344967e+00
-4.63267148e-01 -7.49965131e-01 -1.03704202e+00 -1.38831806e+00
-3.58315140e-01 1.13059521e+00 -5.39162457e-01 -2.00048268e-01
-3.72783750e-01 -1.24602389e+00 1.16095579e+00 5.76261401e-01
4.07768860e-02 -1.08530796e+00 -3.08382154e-01 1.33571014e-01
-4.73079383e-01 -1.07995856e+00 -4.59037900e-01 7.23275959e-01
-5.16003668e-01 -1.05778182e+00 -5.16951263e-01 -4.92093563e-01
3.39239910e-02 -3.25015366e-01 1.18032515e+00 -3.03264976e-01
-2.28119180e-01 -2.55005121e-01 -2.72439122e-01 -6.73129022e-01
-6.10606670e-01 9.94158536e-02 -3.52273956e-02 -6.76211298e-01
4.58751321e-01 -2.15915158e-01 -4.69746619e-01 -2.09522605e-01
-4.79591548e-01 -2.19378784e-01 4.18562472e-01 4.24324960e-01
4.33115244e-01 1.07742539e-02 8.40052783e-01 -1.20311892e+00
6.29296660e-01 -4.65662479e-01 -3.23430359e-01 3.78762662e-01
-4.22801673e-01 7.76727451e-03 6.92210615e-01 -2.05665141e-01
-7.40648508e-01 5.40404737e-01 -5.51771700e-01 9.42802653e-02
-3.14985663e-01 4.92802888e-01 -4.56233889e-01 2.70659596e-01
1.06326199e+00 2.88842581e-02 -6.92043960e-01 -6.04014933e-01
2.08356053e-01 4.84945059e-01 4.19011921e-01 -3.61447543e-01
5.02095401e-01 -2.03368440e-01 1.70352235e-01 -5.90086222e-01
-1.23366058e+00 -5.57194114e-01 -3.86673152e-01 1.82369892e-02
9.36634243e-01 -9.46600020e-01 -5.12434006e-01 4.35495138e-01
-1.55389905e+00 -5.87450385e-01 -2.26302147e-01 2.33754829e-01
-5.79592764e-01 6.48953438e-01 -9.90121901e-01 -9.12815273e-01
-6.00960076e-01 -7.00802684e-01 8.89604330e-01 -1.14598788e-01
-6.25305295e-01 -7.44629443e-01 5.93017459e-01 7.85286486e-01
-1.38875410e-01 3.57682645e-01 9.61317480e-01 -1.23918819e+00
1.85270801e-01 -4.02740449e-01 5.79984672e-02 2.18410671e-01
-3.02136064e-01 -7.75342286e-02 -1.06588185e+00 -1.66838661e-01
-3.47966440e-02 -7.95484483e-01 1.34168077e+00 2.28415683e-01
1.08356726e+00 -1.55786261e-01 -3.97398680e-01 -1.08080721e-02
1.26016688e+00 2.14572400e-01 7.37851858e-01 3.95024568e-01
2.88710326e-01 4.72213805e-01 5.35945654e-01 1.85971722e-01
2.53898352e-01 6.20323002e-01 2.71375597e-01 1.89293385e-01
-4.08319324e-01 -2.91209966e-01 7.15370715e-01 2.63734430e-01
2.15970755e-01 -8.08057308e-01 -1.02589941e+00 5.90081215e-01
-1.58333111e+00 -1.20045888e+00 -5.43843806e-01 1.87298107e+00
1.08692372e+00 5.28744698e-01 4.36999053e-01 1.96312204e-01
5.60598969e-01 -4.33728956e-02 -3.60189319e-01 -8.61485183e-01
-2.02575967e-01 4.69361722e-01 6.99763775e-01 4.76678520e-01
-1.21342111e+00 1.02501333e+00 7.81641722e+00 1.11163020e+00
-5.84516168e-01 3.63989949e-01 8.28921080e-01 -3.13079208e-01
-5.92770614e-02 -1.42861560e-01 -1.06502831e+00 7.66349375e-01
1.83352172e+00 -2.78201699e-01 1.92892790e-01 4.33710873e-01
5.74255705e-01 -2.89141834e-01 -1.12577689e+00 4.61198449e-01
2.65512943e-01 -1.29902017e+00 -3.58655930e-01 1.52897298e-01
6.11376703e-01 5.14130652e-01 -1.64211765e-01 4.53729481e-01
7.19899297e-01 -1.32993710e+00 6.58335149e-01 3.95720303e-01
3.72862935e-01 -8.86812568e-01 6.17028892e-01 6.83088601e-01
-5.59552312e-01 -1.31611437e-01 -3.50565404e-01 5.08298576e-02
6.01363555e-02 7.70088553e-01 -9.32832122e-01 3.80126566e-01
5.32592118e-01 3.54619682e-01 -9.21108484e-01 1.52627265e+00
-4.44915026e-01 1.19841504e+00 -3.07196885e-01 -1.48556396e-01
2.21668214e-01 3.27990562e-01 3.61291349e-01 1.68810356e+00
8.79860818e-02 1.39450431e-01 8.75303224e-02 4.88867015e-01
-6.85647070e-01 1.42207056e-01 -5.16573489e-01 -3.00593019e-01
1.44898728e-01 1.28112411e+00 -6.61583483e-01 -2.59092450e-01
-2.00780734e-01 9.38781202e-01 5.97169101e-01 -9.73303542e-02
-8.19072187e-01 -3.64010453e-01 1.76284790e-01 -3.40538807e-02
1.76082119e-01 1.61103740e-01 -7.49996841e-01 -7.47252285e-01
-2.54757702e-01 -7.31684804e-01 7.65507340e-01 -7.96162605e-01
-1.31232870e+00 4.41212684e-01 -3.74985069e-01 -9.20790017e-01
-8.71614739e-02 -6.69104874e-01 -7.71398902e-01 1.08142388e+00
-1.11621952e+00 -9.63870823e-01 4.19183463e-01 9.33726132e-02
6.89264655e-01 1.45709425e-01 7.98675418e-01 3.81104350e-02
-5.39715827e-01 6.49386287e-01 1.26158416e-01 -1.22984909e-01
8.41822743e-01 -1.50350213e+00 6.77928448e-01 1.05972743e+00
2.46451259e-01 1.81829169e-01 1.08835649e+00 -8.20511639e-01
-8.18196476e-01 -1.35060537e+00 1.62950146e+00 -1.04009247e+00
8.00981939e-01 -4.36251998e-01 -6.52976453e-01 4.09700841e-01
4.13229704e-01 -3.75412077e-01 8.65366340e-01 2.31450289e-01
-5.66341877e-01 4.62415427e-01 -1.27781534e+00 2.35196799e-01
9.74372268e-01 -4.26587313e-01 -6.29891336e-01 1.02989995e+00
6.81195974e-01 -1.15834892e-01 -8.87432814e-01 1.19280525e-01
9.35623236e-03 -5.93460739e-01 6.59468293e-01 -1.16113436e+00
8.44512761e-01 -1.37166619e-01 4.29368988e-02 -1.49762917e+00
-5.42984486e-01 -4.75253522e-01 2.45961230e-02 1.32422233e+00
8.32796991e-01 2.11879656e-01 1.06373632e+00 7.79189289e-01
-1.60219997e-01 -6.44852340e-01 -9.97730374e-01 -1.09711897e+00
8.97697687e-01 -4.87542510e-01 -1.00331552e-01 6.58233881e-01
4.86685067e-01 8.89231205e-01 -2.42675945e-01 1.30253240e-01
4.83629912e-01 -4.36241716e-01 1.34935051e-01 -1.08435607e+00
-3.87800127e-01 -4.33688015e-01 -3.71598274e-01 -3.43030185e-01
3.89507324e-01 -1.22604799e+00 3.64647985e-01 -1.71502221e+00
8.33127260e-01 1.69944003e-01 -6.34701014e-01 9.02676523e-01
-3.21099728e-01 4.84738380e-01 3.49155426e-01 -2.51518607e-01
-8.87920558e-01 9.14520025e-02 6.03125572e-01 -4.07746553e-01
2.22912028e-01 -1.28137127e-01 -1.06291306e+00 5.60926735e-01
9.95846868e-01 -8.67076159e-01 -1.47711039e-01 -1.09758914e-01
3.47204864e-01 -9.57105309e-02 1.18285477e-01 -1.05760491e+00
1.51108742e-01 -2.71335661e-01 3.89167011e-01 -6.69164956e-01
2.87764996e-01 -1.94227755e-01 1.71783622e-02 6.90411270e-01
-8.24541688e-01 -2.66551897e-02 4.00896996e-01 4.54962879e-01
3.25142562e-01 -7.20087290e-01 9.48144734e-01 -3.24539661e-01
-2.60039568e-01 4.44703959e-02 -6.66091681e-01 3.42720926e-01
9.24266815e-01 2.58606344e-01 -7.18956530e-01 9.77399293e-03
-6.70875967e-01 2.42819354e-01 3.62066776e-01 1.15580842e-01
1.05843633e-01 -7.66313136e-01 -9.92704511e-01 -8.59971344e-01
8.81905332e-02 -5.20276606e-01 1.04315415e-01 6.98372006e-01
-2.79390782e-01 6.05239093e-01 9.46970880e-02 2.05611780e-01
-1.42426670e+00 6.86152041e-01 3.12448591e-01 -7.92177379e-01
-3.47220004e-01 1.04424047e+00 1.25961974e-01 -1.00465536e-01
1.30146354e-01 1.53111488e-01 -3.83689225e-01 2.30422756e-03
6.91735864e-01 4.45482671e-01 5.90262830e-01 -3.98028761e-01
-5.04784465e-01 -5.99170523e-03 -2.33850881e-01 -2.83947349e-01
1.47217357e+00 3.87181818e-01 5.29509485e-02 1.64295539e-01
1.19698668e+00 1.61720246e-01 -9.13764298e-01 3.10508937e-01
4.70717609e-01 4.07894850e-02 9.88667309e-02 -1.67946327e+00
-6.60427392e-01 9.06632841e-01 4.19768661e-01 -1.26141375e-02
6.71646237e-01 3.49976160e-02 7.93909609e-01 5.88828087e-01
1.35963619e-01 -1.12666762e+00 -6.60917461e-02 5.96870363e-01
8.80765915e-01 -6.42955065e-01 8.86584818e-02 -3.55206013e-01
-7.69430757e-01 8.24264288e-01 5.84795475e-01 -1.06177464e-01
6.77160323e-02 3.29788744e-01 -1.68661207e-01 -8.69119987e-02
-1.29891872e+00 -2.78435707e-01 2.18729791e-03 6.45147502e-01
5.91097772e-01 -1.00825541e-01 -9.58983898e-01 9.54413652e-01
-2.11312369e-01 -2.48790774e-02 8.02027047e-01 7.04006195e-01
-6.06252372e-01 -1.20476723e+00 -1.93851680e-01 5.02342284e-01
-9.94087934e-01 -4.18438822e-01 -1.01152039e+00 2.77772874e-01
1.91830203e-01 1.25611055e+00 -5.99204421e-01 -5.49754381e-01
4.96490151e-01 5.34009099e-01 6.51448071e-01 -7.61737168e-01
-1.07885265e+00 -7.04201981e-02 7.18762994e-01 -2.79586524e-01
-1.42679708e-02 -9.84329760e-01 -1.25612879e+00 -3.20720933e-02
-1.09834164e-01 4.58395451e-01 5.96839786e-01 1.00023866e+00
1.00155123e-01 2.90979892e-01 3.99918705e-01 -3.47877622e-01
-6.15933239e-01 -1.14220536e+00 -4.82556611e-01 3.30667317e-01
-1.84937328e-01 -2.33983427e-01 -2.76033401e-01 2.09132463e-01] | [8.944388389587402, 10.6254243850708] |
a5d09585-a9e0-4fdb-b40f-f701bbaae96f | psnet-parallel-symmetric-network-for-video | 2210.05912 | null | https://arxiv.org/abs/2210.05912v1 | https://arxiv.org/pdf/2210.05912v1.pdf | PSNet: Parallel Symmetric Network for Video Salient Object Detection | For the video salient object detection (VSOD) task, how to excavate the information from the appearance modality and the motion modality has always been a topic of great concern. The two-stream structure, including an RGB appearance stream and an optical flow motion stream, has been widely used as a typical pipeline for VSOD tasks, but the existing methods usually only use motion features to unidirectionally guide appearance features or adaptively but blindly fuse two modality features. However, these methods underperform in diverse scenarios due to the uncomprehensive and unspecific learning schemes. In this paper, following a more secure modeling philosophy, we deeply investigate the importance of appearance modality and motion modality in a more comprehensive way and propose a VSOD network with up and down parallel symmetry, named PSNet. Two parallel branches with different dominant modalities are set to achieve complete video saliency decoding with the cooperation of the Gather Diffusion Reinforcement (GDR) module and Cross-modality Refinement and Complement (CRC) module. Finally, we use the Importance Perception Fusion (IPF) module to fuse the features from two parallel branches according to their different importance in different scenarios. Experiments on four dataset benchmarks demonstrate that our method achieves desirable and competitive performance. | ['Sam Kwong', 'Yao Zhao', 'Guanghui Yue', 'Jianjun Lei', 'Weiyu Song', 'Runmin Cong'] | 2022-10-12 | null | null | null | null | ['video-salient-object-detection'] | ['computer-vision'] | [ 3.65577519e-01 -3.44336092e-01 -1.01139113e-01 -2.30552673e-01
-1.89961985e-01 -2.08213050e-02 7.01065779e-01 -2.11683378e-01
-4.38833594e-01 4.43923324e-01 3.22448641e-01 1.33398905e-01
4.88753691e-02 -5.06294727e-01 -6.28622115e-01 -9.95965362e-01
3.50508660e-01 -2.29531378e-01 9.49194729e-01 -4.42922115e-01
4.70946074e-01 4.01757993e-02 -1.93619442e+00 2.39656687e-01
8.86363685e-01 1.20953250e+00 6.64963901e-01 3.86915088e-01
-2.19995931e-01 1.00073969e+00 -7.73289502e-02 -1.73120350e-01
2.14884534e-01 -5.29032588e-01 -7.07573712e-01 1.04031175e-01
2.00734243e-01 -5.36175191e-01 -3.40349674e-01 1.19476199e+00
5.71938574e-01 8.86765495e-02 3.48670304e-01 -1.46816087e+00
-3.92777145e-01 5.55355012e-01 -8.93892527e-01 4.47371185e-01
4.33639795e-01 2.50801504e-01 8.57161045e-01 -9.09957886e-01
5.03874242e-01 1.34440613e+00 2.39253461e-01 4.83534992e-01
-8.35440516e-01 -5.50168157e-01 7.24466503e-01 8.30630720e-01
-1.00513041e+00 -3.97747815e-01 1.13531399e+00 -2.54698068e-01
4.08770740e-01 1.68544769e-01 8.17235351e-01 1.10512435e+00
4.64037620e-03 1.22246897e+00 1.00643539e+00 -7.05442950e-03
6.45187050e-02 9.53477696e-02 8.60019848e-02 5.53130627e-01
8.84934515e-02 6.72009438e-02 -8.05545330e-01 2.59148806e-01
7.20057666e-01 2.68553436e-01 -6.18732274e-01 -6.09785557e-01
-1.37185216e+00 3.93417537e-01 7.33261645e-01 2.51368761e-01
-4.82024342e-01 6.44105896e-02 3.04851085e-01 1.96891814e-01
7.42717683e-02 -5.43600731e-02 -2.93484539e-01 4.09114026e-02
-7.16890872e-01 2.11755663e-01 3.43353957e-01 9.02351618e-01
8.48039329e-01 8.10304135e-02 -4.06410605e-01 7.42571831e-01
5.86580634e-01 3.99691343e-01 5.58664978e-01 -9.00131524e-01
3.70548308e-01 7.03738928e-01 2.62363195e-01 -1.30272460e+00
-2.71319598e-01 -2.83761382e-01 -9.35608447e-01 1.81708694e-01
2.17640743e-01 6.35419041e-02 -7.34205186e-01 1.88020372e+00
6.64174497e-01 6.65524364e-01 8.81567318e-03 1.56365740e+00
1.00434506e+00 7.58539796e-01 2.52268553e-01 -3.77293378e-01
1.35227370e+00 -1.25607252e+00 -6.81436956e-01 -8.23387057e-02
1.41223133e-01 -7.98195481e-01 8.22025120e-01 3.17063600e-01
-1.19269300e+00 -8.36088836e-01 -1.12621737e+00 -3.46349359e-01
-1.09952740e-01 -7.77592659e-02 6.24821305e-01 2.29622945e-01
-9.87005413e-01 4.44452137e-01 -7.14498401e-01 -1.86000213e-01
4.33241189e-01 2.92810649e-01 -1.28170758e-01 -2.29253426e-01
-1.32963610e+00 5.37339389e-01 3.72035265e-01 4.82519001e-01
-1.12811875e+00 -5.10863304e-01 -7.94780672e-01 4.78924215e-02
4.35160577e-01 -9.34910119e-01 9.79779720e-01 -1.40515959e+00
-1.46791184e+00 3.18877101e-01 -2.35178053e-01 -1.24483697e-01
4.97313678e-01 -1.66801557e-01 -4.83578235e-01 4.37777549e-01
-1.13331906e-01 8.74278486e-01 1.34649730e+00 -1.30393505e+00
-1.23661351e+00 -2.21150920e-01 3.00777346e-01 7.36867249e-01
-4.68821853e-01 1.46469802e-01 -5.95981300e-01 -5.71218610e-01
2.90001422e-01 -5.19677699e-01 -9.01427492e-02 2.03181252e-01
-1.09561019e-01 -1.78684726e-01 1.09152579e+00 -6.05295777e-01
1.29329967e+00 -2.14178705e+00 7.62897849e-01 -2.24810869e-01
3.66173714e-01 3.02693278e-01 -3.06362696e-02 -1.19246440e-02
6.91299587e-02 -4.19152051e-01 -4.25242484e-01 -3.83807391e-01
-1.53104663e-01 4.36097123e-02 -2.54537374e-01 3.27551663e-01
4.41692352e-01 7.40129828e-01 -1.29433882e+00 -6.90122008e-01
3.38923067e-01 4.86392200e-01 -5.97971916e-01 3.47357154e-01
-1.13533840e-01 4.20059800e-01 -6.49119616e-01 6.70059443e-01
9.19035852e-01 -2.73689091e-01 -9.14532840e-02 -4.62369204e-01
-2.72120237e-01 -1.03449322e-01 -1.52754581e+00 1.94408071e+00
1.33052026e-03 2.44237512e-01 3.14302981e-01 -9.59198475e-01
7.63962805e-01 4.51810397e-02 4.92155731e-01 -7.29580641e-01
1.32805556e-01 2.24898264e-01 3.21396105e-02 -6.57389224e-01
5.89734197e-01 2.22513024e-02 2.32775271e-01 1.41046733e-01
8.21781084e-02 2.57313937e-01 6.44121692e-03 2.72953153e-01
8.67347300e-01 4.76486295e-01 1.68208793e-01 -2.69606173e-01
1.16322052e+00 -1.20049961e-01 8.77482653e-01 3.19084913e-01
-6.08398914e-01 8.05871844e-01 4.22765464e-01 -3.31119478e-01
-6.97085619e-01 -8.84095967e-01 1.20310798e-01 1.16339135e+00
1.12647915e+00 -2.70385295e-01 -4.25746143e-01 -7.29639173e-01
-2.82386720e-01 2.65556574e-01 -6.61277890e-01 -4.39840108e-01
-6.01608455e-01 -6.38486505e-01 -9.15490538e-02 4.48446900e-01
8.32366645e-01 -1.27803957e+00 -7.24396467e-01 1.52350441e-01
-2.95535743e-01 -9.72077787e-01 -5.33909917e-01 -9.31571424e-02
-4.17709053e-01 -9.78245616e-01 -9.84370291e-01 -9.47409511e-01
5.44064522e-01 9.65151668e-01 4.83363867e-01 2.59845257e-01
3.82605456e-02 2.71037042e-01 -5.46962798e-01 -6.02999739e-02
2.27318760e-02 -1.23077124e-01 -3.50909233e-02 6.28458619e-01
2.25693792e-01 -6.90346062e-01 -1.05233526e+00 4.33551729e-01
-1.16728711e+00 4.57359940e-01 6.36473715e-01 8.59350085e-01
2.97136337e-01 -2.46819004e-01 3.63649964e-01 -3.94268602e-01
1.67817667e-01 -6.43617868e-01 -4.24155623e-01 2.11310014e-01
-4.52284724e-01 -2.30534505e-02 5.44005275e-01 -2.43091673e-01
-1.40973353e+00 8.62114727e-02 7.31205335e-03 -7.56448865e-01
-1.16729520e-01 1.41061127e-01 -3.93902123e-01 -4.61469293e-02
1.35348037e-01 6.49259984e-01 1.86301246e-02 -2.45161980e-01
3.18834782e-01 5.42684436e-01 4.54108119e-01 -3.70531648e-01
8.04360688e-01 6.23207688e-01 -1.51037335e-01 -4.32320178e-01
-5.68500042e-01 -5.93412220e-01 -5.97540915e-01 -4.50746536e-01
9.87309992e-01 -1.08065701e+00 -6.09851301e-01 8.75155032e-01
-1.23095381e+00 7.63153136e-02 -5.94201088e-02 4.16385114e-01
-3.61534953e-01 7.24246562e-01 -4.80036914e-01 -5.35206378e-01
-3.23354363e-01 -1.51639390e+00 1.21447432e+00 6.60639048e-01
3.79086792e-01 -5.87256253e-01 -3.55045587e-01 2.02666953e-01
4.98023689e-01 -2.73334626e-02 4.73666430e-01 -1.28752381e-01
-9.83434081e-01 5.24280787e-01 -6.07559741e-01 2.06159011e-01
1.82997331e-01 4.75846836e-03 -9.98568654e-01 -2.16148406e-01
4.43216376e-02 -2.68750936e-01 1.13066578e+00 2.92705089e-01
1.07885194e+00 8.61374438e-02 -2.19653934e-01 7.41647184e-01
1.41175568e+00 5.63091300e-02 5.93452632e-01 4.57864612e-01
9.38238561e-01 8.36656809e-01 8.26527357e-01 5.38368225e-01
6.55852199e-01 5.60919583e-01 7.37659752e-01 -3.20941705e-04
-3.40018481e-01 -2.34474033e-01 7.18298614e-01 9.07794058e-01
-1.96578786e-01 -3.16039175e-02 -3.69235128e-01 3.85501206e-01
-2.20847106e+00 -1.14520013e+00 -2.55798817e-01 1.99038386e+00
7.14156210e-01 7.45066553e-02 2.28462994e-01 1.42962784e-01
8.87950301e-01 5.65049589e-01 -5.69510400e-01 -8.31313338e-03
-3.07367802e-01 -4.89964277e-01 1.35999322e-01 2.43352592e-01
-1.03416240e+00 7.71197557e-01 5.00627756e+00 1.01624000e+00
-1.26440346e+00 1.17208794e-01 5.01323342e-01 6.45171255e-02
-5.17423272e-01 2.20252648e-01 -7.44515479e-01 8.18618000e-01
9.54701677e-02 5.99617586e-02 4.73512620e-01 5.58154643e-01
1.94789067e-01 -2.28568375e-01 -7.02678442e-01 1.21939206e+00
9.49859247e-02 -1.20989287e+00 2.37224326e-01 -3.80665213e-01
6.05306566e-01 -8.78367424e-02 4.80029061e-02 1.99820533e-01
-1.23392649e-01 -5.03622651e-01 1.05932546e+00 6.82583749e-01
3.78098339e-01 -4.51982319e-01 5.48469126e-01 2.62443423e-01
-1.56254506e+00 -4.01315510e-01 -3.92822385e-01 -4.17596400e-02
5.07735252e-01 4.05720115e-01 -1.34844571e-01 9.84116554e-01
9.62036014e-01 1.33788884e+00 -5.09189725e-01 1.12781692e+00
-2.12260857e-01 3.17643017e-01 -2.33616859e-01 5.28180413e-02
3.07063311e-01 -2.44449049e-01 8.14077139e-01 8.69050205e-01
1.29179418e-01 1.62016049e-01 3.58119570e-02 6.78353608e-01
3.14848930e-01 -6.53013587e-02 -1.16181828e-01 4.44675505e-01
3.93656135e-01 1.59007251e+00 -6.39812768e-01 -4.29760963e-01
-6.44456565e-01 1.07583117e+00 1.77122205e-01 3.45060229e-01
-9.91535246e-01 -1.29320517e-01 5.23083925e-01 6.21513799e-02
5.76927245e-01 -1.11724045e-02 4.65566628e-02 -1.41911674e+00
1.30910829e-01 -7.49833286e-01 4.23811764e-01 -9.51571941e-01
-1.14821291e+00 4.65961546e-01 -8.59766006e-02 -1.61696935e+00
3.24725538e-01 -4.23448473e-01 -6.07094526e-01 8.14215600e-01
-2.13753748e+00 -1.21578026e+00 -6.91686034e-01 1.00249016e+00
7.31525004e-01 -2.16823798e-02 2.21841224e-02 4.15234625e-01
-8.89838815e-01 2.39179686e-01 -2.59901136e-01 -2.41154179e-01
7.01914489e-01 -1.07893395e+00 -2.03550830e-01 1.02059901e+00
-2.87977606e-01 3.55178267e-01 4.69734490e-01 -4.81808871e-01
-1.54845774e+00 -9.88752961e-01 4.69699472e-01 6.72004819e-02
4.78944629e-01 -6.22660518e-02 -9.81621027e-01 2.60047197e-01
3.94774765e-01 9.37007144e-02 2.34289709e-02 -3.76603723e-01
-8.12490210e-02 -4.07360762e-01 -7.50331700e-01 6.91848516e-01
1.36194754e+00 -1.36608213e-01 -6.78573489e-01 -1.24617718e-01
1.00878477e+00 -2.71500200e-01 -5.32820523e-01 6.19420230e-01
3.70326161e-01 -1.25350356e+00 9.36180234e-01 -3.56567472e-01
7.92326391e-01 -9.61101353e-01 -1.61358684e-01 -8.99749398e-01
-4.84988540e-01 -6.12158954e-01 -2.89031535e-01 1.43133402e+00
-1.22208044e-01 -4.18917179e-01 3.75279129e-01 2.75871187e-01
-2.55298465e-01 -8.17689776e-01 -7.64571607e-01 -2.01752424e-01
-6.32006168e-01 -2.51455903e-01 6.87096536e-01 8.90341163e-01
-9.66858789e-02 4.85606551e-01 -6.19839668e-01 1.92063615e-01
5.39268970e-01 5.14098227e-01 6.15599990e-01 -1.02201629e+00
-3.37085485e-01 -7.90013790e-01 -3.66616488e-01 -1.49366713e+00
-1.80504069e-01 -6.61323428e-01 1.22202754e-01 -1.32269013e+00
4.15106356e-01 -3.61998498e-01 -7.39905059e-01 2.18128487e-01
-7.59107471e-01 -1.16125681e-02 3.53173226e-01 3.20302933e-01
-1.05412591e+00 1.04408860e+00 1.52468824e+00 -8.43358338e-02
-5.00687100e-02 -1.57125533e-01 -8.23363066e-01 6.76678538e-01
4.30581778e-01 -6.69274032e-02 -6.95853174e-01 -6.72186077e-01
2.18877327e-02 7.21808523e-02 5.49297214e-01 -8.89325142e-01
5.28354228e-01 -2.52920121e-01 4.40532297e-01 -6.93312466e-01
3.26512128e-01 -8.99171948e-01 -1.95084244e-01 3.39193821e-01
-1.45580158e-01 3.34018804e-02 -1.03577487e-01 8.74082625e-01
-4.32536840e-01 7.79347168e-03 6.74293041e-01 -2.74121575e-02
-1.52011907e+00 5.80922782e-01 -3.31401452e-02 -1.65550783e-01
1.17774665e+00 -3.88102621e-01 -2.50165999e-01 -1.11752883e-01
-4.39287543e-01 6.07483149e-01 4.29163188e-01 7.31283367e-01
9.32452917e-01 -1.39494169e+00 -6.47815287e-01 3.94188672e-01
2.18319856e-02 2.04391137e-01 7.26840556e-01 1.29864144e+00
-2.55750358e-01 -9.37398896e-02 -4.91622418e-01 -8.81026626e-01
-9.16274011e-01 7.47954547e-01 2.80504078e-01 2.09450200e-02
-5.08393884e-01 9.07033205e-01 5.79553545e-01 2.18214118e-03
1.62561908e-01 -3.76644433e-02 -6.73082769e-01 2.21840993e-01
7.41602659e-01 3.92416418e-01 -2.71737784e-01 -8.67192686e-01
-3.54684651e-01 7.19589114e-01 -8.45846683e-02 8.46925452e-02
1.21424329e+00 -6.58244193e-01 -1.60145670e-01 3.27711880e-01
9.63702321e-01 -3.63606304e-01 -1.73750949e+00 -3.97948921e-01
-3.10841739e-01 -6.04202390e-01 6.14987276e-02 -3.74516428e-01
-1.31222558e+00 9.44774687e-01 5.27481318e-01 1.10151716e-01
1.64504862e+00 -2.23846138e-01 8.96991432e-01 -3.24485660e-01
3.14788938e-01 -9.53411043e-01 1.36464685e-01 2.30962008e-01
7.74163663e-01 -1.29185855e+00 -8.55802447e-02 -5.68573475e-01
-8.16360712e-01 1.08550572e+00 1.00491834e+00 -8.96200538e-02
6.82988584e-01 -7.43711516e-02 -2.22435310e-01 9.52373892e-02
-7.58351862e-01 -4.10579652e-01 3.46056044e-01 4.79682148e-01
1.74035560e-02 -4.20835078e-01 -5.41090131e-01 7.38314867e-01
4.21270192e-01 3.51974070e-02 3.46366405e-01 7.71224976e-01
-5.21700025e-01 -8.99827302e-01 -2.32626021e-01 1.83500350e-01
-4.67990749e-02 -1.19123034e-01 9.79668349e-02 4.37710375e-01
3.83135319e-01 8.39183629e-01 -1.34277299e-01 -5.50691426e-01
1.01250805e-01 -4.33836222e-01 2.51897216e-01 -1.97971120e-01
-4.45085347e-01 2.13934824e-01 -3.21642578e-01 -8.27609181e-01
-1.03077471e+00 -7.83484161e-01 -1.27966666e+00 -1.48841009e-01
-1.53908208e-01 -1.01086177e-01 1.69721723e-01 9.36666965e-01
3.38261336e-01 6.24461830e-01 8.93986821e-01 -1.10652328e+00
-4.36424434e-01 -6.72020733e-01 -5.31275272e-01 5.07856250e-01
4.72835749e-01 -9.05528605e-01 -2.61583954e-01 7.37638175e-02] | [9.449562072753906, -0.38089337944984436] |
992fe2d8-4a74-448d-80bf-3f293f41c3fb | ar-diffusion-auto-regressive-diffusion-model | 2305.09515 | null | https://arxiv.org/abs/2305.09515v2 | https://arxiv.org/pdf/2305.09515v2.pdf | AR-Diffusion: Auto-Regressive Diffusion Model for Text Generation | Diffusion models have gained significant attention in the realm of image generation due to their exceptional performance. Their success has been recently expanded to text generation via generating all tokens within a sequence concurrently. However, natural language exhibits a far more pronounced sequential dependency in comparison to images, and the majority of existing language models are trained with a left-to-right auto-regressive approach. To account for the inherent sequential characteristic of natural language, we introduce Auto-Regressive Diffusion (AR-Diffusion). AR-Diffusion ensures that the generation of tokens on the right depends on the generated ones on the left, a mechanism achieved through employing a dynamic number of denoising steps that vary based on token position. This results in tokens on the left undergoing fewer denoising steps than those on the right, thereby enabling them to generate earlier and subsequently influence the generation of tokens on the right. In a series of experiments on various text generation tasks, including text summarization, machine translation, and common sense generation, AR-Diffusion clearly demonstrated its superiority over existing diffusion language models and that it can be $100\times\sim600\times$ faster when achieving comparable results. Our code is available at https://github.com/microsoft/ProphetNet/tree/master/AR-diffusion. | ['Weizhu Chen', 'Nan Duan', 'Jian Guo', 'Zhongyu Wei', 'Juntao Li', 'Hai-Tao Zheng', 'Jian Jiao', 'Yelong Shen', 'Yeyun Gong', 'Xiao Liu', 'Zhihao Fan', 'Tong Wu'] | 2023-05-16 | null | null | null | null | ['text-summarization', 'common-sense-reasoning'] | ['natural-language-processing', 'reasoning'] | [ 5.15794337e-01 9.16261598e-02 1.44010028e-02 2.03325655e-02
-7.05900848e-01 -4.25199211e-01 1.12430477e+00 1.95839375e-01
-3.99153590e-01 8.17659020e-01 6.56586468e-01 -3.26048881e-01
3.14116329e-01 -9.36481714e-01 -4.94452387e-01 -8.97137642e-01
1.87707275e-01 2.08026052e-01 -2.57869195e-02 -4.23016936e-01
3.84968519e-01 1.45512611e-01 -1.18632829e+00 4.14633781e-01
8.07351947e-01 3.52724522e-01 4.00812924e-01 9.54876661e-01
-2.90718645e-01 1.07402623e+00 -9.18084204e-01 -4.27165210e-01
8.01933631e-02 -9.25007343e-01 -6.36382937e-01 8.16354752e-02
1.85529083e-01 -3.42478752e-01 -3.72839600e-01 8.60017478e-01
7.68445134e-01 1.02482297e-01 8.68691266e-01 -8.69266629e-01
-1.11629891e+00 7.35362530e-01 -7.59096801e-01 2.81248689e-01
4.14277494e-01 1.94715410e-01 9.58864987e-01 -9.06606197e-01
8.82095397e-01 1.26609802e+00 2.19741523e-01 7.40531683e-01
-1.29330182e+00 -4.86254215e-01 -5.14638759e-02 -8.75970349e-02
-1.32379282e+00 -5.61820030e-01 6.70463622e-01 -3.18771958e-01
1.17970514e+00 1.73881426e-01 4.72498566e-01 1.14776659e+00
6.59326851e-01 8.46524000e-01 1.03354740e+00 -7.31798053e-01
1.30215436e-01 -9.95600075e-02 -4.90257055e-01 5.00080884e-01
-6.64481297e-02 -5.00762910e-02 -7.51425028e-01 -3.84256802e-02
7.76121736e-01 -3.04709166e-01 -2.06693277e-01 1.41007677e-01
-1.45866787e+00 9.22062337e-01 3.77417177e-01 5.25896430e-01
-7.48032868e-01 3.00881803e-01 3.62672269e-01 3.96860451e-01
7.41693139e-01 2.88009286e-01 -6.63611218e-02 -2.58530200e-01
-1.16680348e+00 4.19606090e-01 6.44298196e-01 8.17828178e-01
5.70683658e-01 2.24920824e-01 -4.73859936e-01 8.40891838e-01
9.90144461e-02 5.06139815e-01 8.99900138e-01 -8.31806064e-01
5.76567054e-01 4.49365377e-01 -3.66502330e-02 -9.54933167e-01
4.17772830e-02 -3.64919931e-01 -1.12712777e+00 2.66995192e-01
2.53823936e-01 -4.36789215e-01 -1.13841891e+00 1.63949037e+00
1.34136766e-01 -1.00177199e-01 1.37608364e-01 4.84550148e-01
5.14489472e-01 1.14725804e+00 5.25029600e-02 -3.27059448e-01
1.13927984e+00 -8.69749606e-01 -6.77598774e-01 -1.77828789e-01
7.04116046e-01 -1.14588499e+00 7.76424885e-01 3.11495304e-01
-1.31806278e+00 -4.70947325e-01 -8.21732521e-01 -1.76564887e-01
-2.55878359e-01 -8.51370245e-02 3.11015308e-01 3.56149256e-01
-1.48468435e+00 5.10304093e-01 -5.62737763e-01 -1.08347014e-01
2.72739142e-01 4.41679507e-02 -9.64347497e-02 -1.30017310e-01
-1.21188927e+00 8.72893929e-01 1.56006813e-01 9.99481231e-02
-5.83183289e-01 -4.41660970e-01 -7.53953278e-01 -1.13864183e-01
1.29308268e-01 -1.06229591e+00 1.28157079e+00 -1.06407249e+00
-1.60591400e+00 6.41243577e-01 -4.38801110e-01 -7.41933942e-01
8.48984718e-01 -1.31573513e-01 -1.59272507e-01 1.69626325e-01
2.89654315e-01 1.10074151e+00 1.17454457e+00 -1.04731405e+00
-5.39483249e-01 -9.24408212e-02 -2.35949829e-01 4.70642716e-01
-1.76759779e-01 -1.10555127e-01 -2.39705577e-01 -1.06571448e+00
-2.02662274e-01 -9.01417673e-01 -3.91219437e-01 -2.50112623e-01
-3.00388873e-01 -3.59017342e-01 6.10957086e-01 -7.27237284e-01
1.34190428e+00 -1.92491448e+00 3.97001743e-01 -1.01490140e-01
2.26656795e-01 3.01818162e-01 -3.29313934e-01 1.00089347e+00
-1.37485996e-01 2.50642300e-01 -3.04205388e-01 -2.81355739e-01
-2.23663568e-01 1.16145983e-01 -5.20708084e-01 1.26920983e-01
3.02470654e-01 9.55231845e-01 -9.76523876e-01 -5.69242001e-01
7.15472475e-02 5.36090553e-01 -4.27994460e-01 -6.12018965e-02
-2.59293795e-01 3.58045995e-01 -3.93410325e-01 1.40701115e-01
4.14058894e-01 -2.03641564e-01 -1.28317267e-01 2.72384077e-01
-3.41116488e-01 1.96624354e-01 -8.81121218e-01 1.56301498e+00
-5.53323030e-01 8.33970726e-01 -1.68159336e-01 -6.97070837e-01
9.09228563e-01 5.50911903e-01 4.26915646e-01 -7.95451283e-01
1.52690988e-03 1.40905201e-01 1.45470262e-01 -1.72350660e-01
1.06104982e+00 -1.83996022e-01 1.32136345e-01 7.34067321e-01
-2.57547289e-01 -5.00510454e-01 7.19692409e-01 7.55811334e-01
1.04856181e+00 1.96098238e-02 2.16606855e-01 8.10004547e-02
4.68251675e-01 -1.18256907e-03 1.72772512e-01 9.30128276e-01
1.49004549e-01 6.86558723e-01 4.31237608e-01 -1.44364178e-01
-1.30228162e+00 -9.38720107e-01 2.16144994e-01 9.28170085e-01
-1.74152300e-01 -4.55316246e-01 -8.79640579e-01 -3.39419872e-01
-2.38562360e-01 1.03608751e+00 -5.68972707e-01 -1.36656687e-01
-6.62858546e-01 -7.60654867e-01 5.68648756e-01 3.54451716e-01
4.30443615e-01 -1.49901044e+00 -5.71096122e-01 4.47018176e-01
-4.12635416e-01 -7.32349992e-01 -6.14630580e-01 -1.55825943e-01
-8.74635160e-01 -4.19806987e-01 -1.22939742e+00 -6.67427778e-01
8.51930201e-01 2.37781048e-01 1.03205121e+00 7.33451024e-02
-2.81550676e-01 3.34010124e-01 -4.64455515e-01 -6.08238161e-01
-9.08782005e-01 2.11427465e-01 -2.43306428e-01 -6.07905723e-02
-6.45908490e-02 -3.59293520e-01 -6.88589334e-01 -1.29093021e-01
-1.37248719e+00 2.81696230e-01 8.01906765e-01 8.16279709e-01
4.52462107e-01 3.89806591e-02 6.91480994e-01 -7.85919547e-01
1.23116481e+00 -3.95319164e-01 -2.24191949e-01 -1.61476303e-02
-6.07224405e-01 1.98011473e-01 4.95187521e-01 -4.71070856e-01
-1.16074157e+00 -3.40427577e-01 -2.03161031e-01 -2.20176000e-02
1.15030207e-01 6.31799221e-01 5.18956006e-01 5.64522922e-01
7.05110192e-01 6.58609509e-01 1.60237819e-01 -8.34383592e-02
6.49665833e-01 5.86646259e-01 2.18914583e-01 -2.04343081e-01
7.96461523e-01 4.60500926e-01 -1.84426904e-01 -1.06291974e+00
-2.96090782e-01 -1.96404591e-01 -4.10254866e-01 -2.80716538e-01
8.43403578e-01 -9.69106972e-01 -7.16603249e-02 7.70273983e-01
-1.34536946e+00 -5.38415849e-01 -4.90586877e-01 1.38483047e-01
-3.88705701e-01 3.40739578e-01 -7.54275084e-01 -7.13843822e-01
-7.64215291e-01 -8.34874570e-01 9.40520167e-01 2.39562482e-01
-5.71430326e-01 -1.06235957e+00 9.35793296e-02 1.36463270e-01
5.85602164e-01 4.03072275e-02 8.90309572e-01 -2.68634230e-01
-4.91023064e-01 -2.37212226e-01 8.83563422e-03 4.27685797e-01
2.41868705e-01 1.18739977e-01 -4.64530677e-01 -2.04920441e-01
-3.52061540e-02 -2.66886242e-02 1.04842079e+00 5.35411954e-01
5.75968146e-01 -4.41454142e-01 -1.25548109e-01 1.20225176e-02
1.17761433e+00 1.19230911e-01 9.85941529e-01 1.81299835e-01
4.58912104e-01 4.23560113e-01 2.79349416e-01 4.88601744e-01
3.98330629e-01 3.30056548e-01 5.52024469e-02 -2.77315527e-01
-4.36631799e-01 -2.86452770e-01 6.33935153e-01 1.04785955e+00
-6.23415224e-02 -7.56739557e-01 -7.46401072e-01 7.33124673e-01
-1.61361837e+00 -1.15287566e+00 -2.77733952e-01 2.01132107e+00
1.08261621e+00 1.12589546e-01 -6.68180808e-02 9.93588567e-02
6.82129025e-01 5.04794598e-01 -3.88352633e-01 -6.94875062e-01
-2.43411064e-01 2.53653228e-01 3.94311875e-01 5.08232653e-01
-5.79214931e-01 1.05531037e+00 6.19468689e+00 9.77456689e-01
-1.20422769e+00 -7.01002181e-02 7.86717951e-01 -1.98418170e-01
-4.67162281e-01 -1.09115988e-01 -5.28502464e-01 4.55160409e-01
8.12066078e-01 -5.31287313e-01 3.11143845e-01 3.87170941e-01
5.94403148e-01 -4.54373986e-01 -7.49365747e-01 6.49351954e-01
1.30818456e-01 -1.45663679e+00 5.48813939e-01 1.79383308e-01
1.02042544e+00 3.06143239e-02 1.89302161e-01 5.57810925e-02
4.82619464e-01 -8.66096616e-01 7.96633005e-01 3.94460112e-01
7.12605000e-01 -5.92701137e-01 4.72793907e-01 5.37420094e-01
-9.06291783e-01 9.17522311e-02 -3.75479102e-01 -2.24092841e-01
4.43840295e-01 8.53031695e-01 -1.00194561e+00 5.83458126e-01
2.52528012e-01 4.96083796e-01 -3.69654357e-01 4.54749703e-01
-4.47474509e-01 5.99921525e-01 8.35103095e-02 -1.37990296e-01
3.17937434e-01 -3.49265069e-01 6.71578050e-01 1.26778746e+00
4.87265676e-01 1.25747025e-01 -2.14566946e-01 8.76396835e-01
-2.15419367e-01 3.04009795e-01 -7.44583309e-01 -1.85167030e-01
1.68632179e-01 1.08557320e+00 -7.45914042e-01 -6.68054819e-01
-2.50536293e-01 1.27133214e+00 -2.85697635e-03 4.72704709e-01
-7.43860066e-01 -4.73019511e-01 2.55658627e-01 1.82608530e-01
3.97316903e-01 -4.73565072e-01 -2.44647205e-01 -8.93416524e-01
-7.59880338e-03 -9.30248022e-01 1.32296547e-01 -8.55839431e-01
-1.11354899e+00 7.11834252e-01 -1.48090795e-01 -1.02061760e+00
-7.11823404e-01 -1.61691695e-01 -6.23993814e-01 1.04562545e+00
-1.36757839e+00 -8.70518267e-01 1.42765015e-01 4.40012783e-01
9.40278053e-01 -1.83676034e-02 6.94277167e-01 -2.98741572e-02
-2.78582722e-01 3.67352992e-01 1.20652974e-01 3.50406170e-02
7.40201354e-01 -1.14045322e+00 7.08504915e-01 9.92482305e-01
3.33720505e-01 5.84179521e-01 7.07733512e-01 -8.74508798e-01
-1.05057216e+00 -9.15358067e-01 1.29111040e+00 -1.94896773e-01
6.56153679e-01 -1.01505019e-01 -7.30137765e-01 4.47528958e-01
7.84766853e-01 -6.23883128e-01 3.89342904e-01 -5.19224286e-01
-6.33632615e-02 1.11154668e-01 -7.29344964e-01 1.01597595e+00
7.13077486e-01 -3.28265607e-01 -3.09029371e-01 2.96180725e-01
5.40886283e-01 -3.32588762e-01 -6.35574222e-01 1.57578252e-02
3.12768668e-01 -9.29473579e-01 6.93351507e-01 2.85110250e-02
1.10490239e+00 -2.34356299e-01 3.04072797e-01 -1.45016730e+00
-2.57534415e-01 -9.39347863e-01 3.24481651e-02 1.25534070e+00
5.88325441e-01 -6.89742386e-01 4.90912646e-01 3.32594514e-01
1.25132754e-01 -6.65997028e-01 -7.83428669e-01 -5.71292818e-01
3.60587031e-01 -1.66647628e-01 2.71521181e-01 6.10254586e-01
-1.97371170e-01 5.58641136e-01 -5.02224267e-01 -3.09987664e-01
3.08478862e-01 -1.37810290e-01 7.19702244e-01 -6.82095230e-01
-1.13038629e-01 -6.45217359e-01 1.49580454e-02 -1.33949566e+00
-1.76221639e-01 -8.72166932e-01 2.04514980e-01 -1.85417366e+00
1.26508445e-01 -1.33870870e-01 8.33575502e-02 3.49331349e-01
-3.67561817e-01 3.69492650e-01 4.32274491e-01 4.74119574e-01
-1.56073108e-01 6.28190279e-01 1.52693367e+00 -1.89271614e-01
-3.18888545e-01 -9.41836741e-03 -7.95762479e-01 4.93913502e-01
8.68661582e-01 -4.36008185e-01 -3.84022772e-01 -6.85657859e-01
2.70995110e-01 1.45937160e-01 1.15514368e-01 -7.79210329e-01
2.54832387e-01 8.93731266e-02 4.14361537e-01 -4.20828432e-01
1.88643768e-01 -1.72621414e-01 3.10914759e-02 5.71478426e-01
-5.81876278e-01 5.80714643e-01 -6.24013250e-04 5.49896300e-01
-1.87184423e-01 -2.79536605e-01 4.21802640e-01 -3.99680763e-01
-4.46934402e-01 4.09320369e-02 -9.81394768e-01 -1.76232029e-02
8.98656428e-01 -2.23825276e-01 -1.01769708e-01 -7.85900116e-01
-4.69633251e-01 -9.47807878e-02 3.66325945e-01 5.82300961e-01
7.10120499e-01 -1.04027581e+00 -1.18807852e+00 7.32233450e-02
-2.88280129e-01 4.32620607e-02 2.95587629e-01 7.30495274e-01
-5.42041481e-01 4.04312074e-01 1.39695063e-01 -4.23506379e-01
-1.25057459e+00 3.12387586e-01 1.98893547e-02 -6.63518846e-01
-6.22373521e-01 7.48729944e-01 1.84147775e-01 1.16788164e-01
-1.95043966e-01 -9.82739180e-02 2.79942434e-03 1.71219140e-01
5.74012339e-01 3.80529761e-01 -2.21567638e-02 -6.45906687e-01
5.98107353e-02 2.41804227e-01 -3.82696509e-01 -6.40777946e-01
1.24012244e+00 -2.92310685e-01 -2.95159876e-01 3.31194609e-01
8.56158555e-01 1.46409556e-01 -1.12805831e+00 -1.92526981e-01
-2.25812197e-01 -1.61887392e-01 -7.52734765e-02 -6.95549965e-01
-9.58712280e-01 7.50379145e-01 1.36554614e-01 3.26294869e-01
1.04949105e+00 -1.18307695e-01 8.83909285e-01 -1.29721528e-02
3.93404476e-02 -1.19208467e+00 4.31604981e-01 5.59196889e-01
1.08275521e+00 -7.69561589e-01 1.97669908e-01 -8.32150057e-02
-8.71900022e-01 9.85844254e-01 1.69987097e-01 -9.93571952e-02
1.64702371e-01 2.44477600e-01 3.36839825e-01 6.25129193e-02
-9.71880257e-01 7.86970481e-02 -9.07771476e-03 4.48652059e-01
7.56976843e-01 -5.23075052e-02 -5.73018909e-01 -2.77660131e-01
-3.36681932e-01 1.47617664e-02 8.57178807e-01 9.92649257e-01
-2.73828417e-01 -1.46092260e+00 -3.83538216e-01 4.29688394e-01
-5.83717704e-01 -5.11396945e-01 -4.41843748e-01 5.47596991e-01
-5.78916557e-02 1.17589438e+00 6.53881207e-02 6.46618083e-02
7.49818683e-02 7.56343901e-02 4.08649117e-01 -6.16304278e-01
-6.85483277e-01 2.21003026e-01 -1.06451340e-01 -1.50917232e-01
-3.96670818e-01 -8.74896824e-01 -1.26200020e+00 -3.95731449e-01
-2.36400142e-01 1.01160191e-01 7.75676370e-01 7.35654533e-01
5.51588118e-01 7.13500142e-01 5.59645116e-01 -8.62177074e-01
-6.39207304e-01 -1.18451011e+00 -2.77387410e-01 3.77299309e-01
1.70249939e-01 2.17649639e-02 -2.04877347e-01 5.22228003e-01] | [12.06943416595459, 9.101019859313965] |
a653be3f-cea4-4d89-8c62-dc5b0e2596ca | unified-visual-relationship-detection-with | 2303.08998 | null | https://arxiv.org/abs/2303.08998v1 | https://arxiv.org/pdf/2303.08998v1.pdf | Unified Visual Relationship Detection with Vision and Language Models | This work focuses on training a single visual relationship detector predicting over the union of label spaces from multiple datasets. Merging labels spanning different datasets could be challenging due to inconsistent taxonomies. The issue is exacerbated in visual relationship detection when second-order visual semantics are introduced between pairs of objects. To address this challenge, we propose UniVRD, a novel bottom-up method for Unified Visual Relationship Detection by leveraging vision and language models (VLMs). VLMs provide well-aligned image and text embeddings, where similar relationships are optimized to be close to each other for semantic unification. Our bottom-up design enables the model to enjoy the benefit of training with both object detection and visual relationship datasets. Empirical results on both human-object interaction detection and scene-graph generation demonstrate the competitive performance of our model. UniVRD achieves 38.07 mAP on HICO-DET, outperforming the current best bottom-up HOI detector by 60% relatively. More importantly, we show that our unified detector performs as well as dataset-specific models in mAP, and achieves further improvements when we scale up the model. | ['Ting Liu', 'Hartwig Adam', 'Ming-Hsuan Yang', 'Florian Schroff', 'Yin Cui', 'Boqing Gong', 'Liangzhe Yuan', 'Long Zhao'] | 2023-03-16 | null | null | null | null | ['human-object-interaction-detection', 'visual-relationship-detection', 'scene-graph-generation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.53678283e-01 -3.58243063e-02 -4.10279810e-01 -3.35462511e-01
-7.41871953e-01 -5.83564222e-01 7.30956852e-01 3.93705755e-01
-2.58269638e-01 1.33147240e-01 3.84981692e-01 -1.85246304e-01
1.29348278e-01 -4.36371624e-01 -7.10255206e-01 -1.14548825e-01
-1.01390176e-01 6.12270355e-01 3.74271244e-01 4.16010618e-04
-9.08074602e-02 4.61918205e-01 -1.55420613e+00 3.47009927e-01
3.70418966e-01 8.60106468e-01 3.45335692e-01 7.48138309e-01
1.89680457e-01 7.62997091e-01 -3.91693920e-01 -4.58253920e-01
3.61146778e-01 -7.51815960e-02 -7.10725307e-01 2.78047144e-01
1.14910066e+00 -3.14105064e-01 -6.43690944e-01 8.07973027e-01
2.66647071e-01 1.80527605e-02 8.48706543e-01 -1.90176582e+00
-1.12261212e+00 5.36780000e-01 -9.39871490e-01 2.58307040e-01
2.47812077e-01 2.32174527e-02 1.63145721e+00 -1.26187813e+00
9.73635972e-01 1.45160007e+00 4.53119934e-01 2.89357483e-01
-1.44451642e+00 -6.50438011e-01 3.96574914e-01 2.69973427e-01
-1.67669451e+00 -2.62852401e-01 3.43107909e-01 -5.74929178e-01
1.09701622e+00 1.40373304e-01 4.07525957e-01 7.77875125e-01
-3.29779893e-01 7.51345813e-01 6.43360972e-01 -4.89836156e-01
-2.47644857e-01 4.87800747e-01 3.61449242e-01 8.27949107e-01
7.06701159e-01 -2.47313827e-01 -8.46407473e-01 -2.28614267e-02
7.08257973e-01 -2.92144087e-03 -1.14166677e-01 -1.00365603e+00
-1.24916327e+00 6.88756645e-01 9.56359744e-01 1.34650335e-01
6.28278404e-02 4.06806976e-01 2.28155807e-01 -2.35123187e-02
1.57009944e-01 6.78287148e-01 -2.56372858e-02 4.67305273e-01
-6.38848424e-01 2.47451350e-01 4.73873138e-01 1.62776017e+00
7.31835067e-01 -3.65746170e-01 -4.17302817e-01 7.30505586e-01
3.80095899e-01 3.14580441e-01 -8.13517570e-02 -8.82326424e-01
8.03015709e-01 7.18409538e-01 5.28619215e-02 -1.21488547e+00
-4.67663258e-01 -5.28838098e-01 -4.88354653e-01 9.33117718e-02
2.83790976e-01 2.41531804e-01 -7.94030488e-01 1.74878800e+00
2.43767768e-01 1.28190845e-01 2.91912705e-02 1.04572177e+00
1.27150953e+00 3.92972142e-01 2.69074500e-01 1.97444677e-01
1.70183980e+00 -1.30484307e+00 -4.44401026e-01 -6.40162647e-01
8.94136369e-01 -4.84911114e-01 1.02077544e+00 -3.45452391e-02
-8.32037389e-01 -6.18295133e-01 -1.33381927e+00 -5.86567760e-01
-3.74040186e-01 2.68271357e-01 8.20364475e-01 2.02224538e-01
-1.02611864e+00 3.08273379e-02 -3.66979808e-01 -7.58004665e-01
5.79769254e-01 2.48867437e-01 -6.60364509e-01 -8.22943822e-02
-8.19660366e-01 8.02556098e-01 5.07728040e-01 -2.42451578e-01
-7.43919253e-01 -5.56505919e-01 -9.24352646e-01 1.43527016e-01
5.54894507e-01 -8.29459131e-01 9.73168433e-01 -3.27800035e-01
-4.50955570e-01 1.30907571e+00 -2.61333287e-01 -5.75812340e-01
2.85652429e-01 -2.05158547e-01 -4.10862774e-01 2.83176005e-01
3.74580175e-01 1.04930329e+00 4.60561961e-01 -1.77800369e+00
-9.36325371e-01 -2.48349160e-01 1.62368461e-01 6.84107780e-01
-5.13587177e-01 1.38577089e-01 -1.09227288e+00 -2.69272953e-01
2.27923229e-01 -1.00113273e+00 4.17743400e-02 2.45820895e-01
-6.03145063e-01 -4.06423986e-01 9.74883735e-01 -4.47098047e-01
1.19063294e+00 -2.23156285e+00 1.08107544e-01 8.70746523e-02
8.04648399e-01 -3.73476073e-02 -1.94262624e-01 2.92935103e-01
-1.31180316e-01 1.91321895e-01 2.10360065e-01 -6.12038493e-01
1.19936325e-01 2.38918275e-01 -2.08483115e-01 4.86273736e-01
6.74606711e-02 1.17185915e+00 -9.30052340e-01 -7.95851648e-01
1.75654024e-01 7.93398246e-02 -5.39373219e-01 3.68808985e-01
5.19873947e-03 1.63742378e-01 -2.80317990e-03 6.58794940e-01
5.58741987e-01 -7.70231128e-01 5.53919852e-01 -5.71518123e-01
1.45617619e-01 1.40730679e-01 -1.13249016e+00 1.59188664e+00
-2.93078452e-01 1.04146218e+00 -2.15493545e-01 -8.19419861e-01
8.47142518e-01 2.10211221e-02 2.79404879e-01 -6.91618502e-01
-5.59242666e-02 -1.19342737e-01 -1.44848272e-01 -4.32436138e-01
6.99273109e-01 2.19232365e-01 -2.68985927e-01 2.01792389e-01
1.57581419e-01 -9.16201621e-02 2.47496814e-01 8.03460300e-01
9.35870051e-01 -6.87037706e-02 4.56041962e-01 -3.18177827e-02
1.39815971e-01 9.58374739e-02 3.34335744e-01 1.04935658e+00
-3.87772799e-01 7.29392350e-01 4.99413818e-01 -1.11691579e-01
-1.12398517e+00 -1.26071000e+00 -1.38076052e-01 1.41644692e+00
7.99465299e-01 -7.38241851e-01 -1.71806410e-01 -1.01178145e+00
2.96683103e-01 6.47625029e-01 -5.51371574e-01 -4.83101606e-02
-5.08862078e-01 -4.86709565e-01 3.23524892e-01 9.06724095e-01
2.13027865e-01 -6.57203257e-01 -3.01197678e-01 -1.86347857e-01
-2.13928431e-01 -1.81267273e+00 -4.90866661e-01 2.32397884e-01
-3.73605043e-01 -1.03343391e+00 -2.87596464e-01 -1.14565408e+00
5.61943471e-01 9.30556715e-01 1.30143654e+00 7.25072548e-02
-4.80234653e-01 4.89761382e-01 -1.67880908e-01 -2.15589672e-01
-2.28188857e-01 -2.93239783e-02 8.21972713e-02 -1.36250705e-01
4.17942852e-01 -2.09462330e-01 -4.54426080e-01 3.72612894e-01
-4.94778126e-01 3.06411713e-01 2.97979981e-01 7.15126872e-01
5.43982148e-01 -2.63070822e-01 2.56752282e-01 -7.25673556e-01
3.00310820e-01 -5.32085896e-01 -4.77733940e-01 5.52294552e-01
-5.53514898e-01 9.80754122e-02 2.24010780e-01 -3.34643811e-01
-8.65796924e-01 6.20868094e-02 6.75049901e-01 -6.64968431e-01
-1.18025996e-01 6.93131164e-02 -1.04360878e-01 2.95809601e-02
7.32117355e-01 -2.76991218e-01 -3.63695562e-01 -3.32970291e-01
1.01494515e+00 7.00360835e-01 9.92338181e-01 -2.45475873e-01
9.99201238e-01 6.89666629e-01 6.44531474e-02 -5.41740119e-01
-1.20388162e+00 -1.09320843e+00 -8.19113612e-01 -2.02830821e-01
1.11538625e+00 -1.34811938e+00 -5.68459570e-01 -2.08801538e-01
-1.17181015e+00 3.10444795e-02 8.65761191e-02 4.09401953e-01
-3.69936734e-01 4.70726699e-01 -4.20629770e-01 -4.78300095e-01
-1.06968097e-01 -9.20724332e-01 1.27766037e+00 2.66446263e-01
-2.56345987e-01 -7.61759341e-01 -2.03011483e-01 4.95434344e-01
-2.38266036e-01 -9.33697540e-03 8.96607280e-01 -7.75330961e-01
-7.41249621e-01 -1.88897401e-01 -1.02859426e+00 3.91033515e-02
-5.09251691e-02 -8.76760706e-02 -9.47015882e-01 -3.54048580e-01
-6.68258011e-01 -6.18492961e-01 9.44795012e-01 9.03154686e-02
9.29431796e-01 1.32038996e-01 -8.87206197e-01 6.19133532e-01
1.46017516e+00 -1.66952848e-01 1.43419057e-01 4.28760320e-01
1.36815131e+00 5.72602093e-01 6.86167717e-01 4.31431532e-01
8.35197628e-01 1.18975532e+00 6.14636481e-01 -4.36353236e-01
-7.19093442e-01 -4.05101985e-01 -3.03285811e-02 4.14401472e-01
2.98254222e-01 -4.61150020e-01 -1.11178315e+00 6.40541196e-01
-2.12099600e+00 -8.05313468e-01 -6.41096711e-01 2.21243334e+00
3.61819565e-01 1.48868039e-01 2.85833687e-01 -4.00407523e-01
8.74502182e-01 7.28170946e-02 -2.38063261e-01 -8.40498358e-02
-2.21769378e-01 -3.97590160e-01 7.64979243e-01 3.71790737e-01
-1.41539443e+00 1.24035227e+00 6.59310484e+00 5.36349952e-01
-6.26291931e-01 2.08328351e-01 2.64629424e-01 -1.83351502e-01
-1.43439963e-01 2.05229282e-01 -1.22107315e+00 -8.93069059e-02
4.03058946e-01 -1.69918835e-02 2.71171391e-01 8.83968771e-01
-2.27672473e-01 -8.90721530e-02 -1.42802441e+00 1.47178102e+00
5.07812202e-01 -1.44313157e+00 6.93920255e-02 1.46839291e-01
7.15826571e-01 7.84736723e-02 8.60791132e-02 3.88899416e-01
4.89619195e-01 -1.01670384e+00 9.10283387e-01 1.56084254e-01
8.23054194e-01 -5.40185928e-01 5.19641757e-01 3.60833071e-02
-1.72089195e+00 -7.45391697e-02 -4.14413661e-01 2.91823950e-02
1.89093381e-01 6.70728758e-02 -1.24236298e+00 6.45396829e-01
6.42998576e-01 9.48630631e-01 -1.15375984e+00 1.03956020e+00
-1.20329984e-01 1.48331240e-01 -3.08056414e-01 1.90798923e-01
1.04390435e-01 1.73691154e-01 5.24358988e-01 1.31980622e+00
6.15389226e-03 -3.29748690e-01 6.94875836e-01 9.01763082e-01
-4.14478213e-01 1.75228827e-02 -9.40328181e-01 3.96088995e-02
7.86268115e-01 1.46216524e+00 -7.19615936e-01 -6.22377813e-01
-7.65381992e-01 1.05286431e+00 9.21573520e-01 4.15641636e-01
-1.19303119e+00 -1.09001711e-01 5.56097746e-01 2.04432812e-02
2.87731677e-01 -4.52814192e-01 -4.82865065e-01 -1.16757524e+00
-6.50670901e-02 -3.93833846e-01 7.46770203e-01 -9.74695623e-01
-1.27247775e+00 3.88546884e-01 2.61153609e-01 -1.33273721e+00
1.98367998e-01 -7.09233284e-01 -1.34426266e-01 5.54065287e-01
-1.27382433e+00 -1.49875283e+00 -6.12264931e-01 2.82512337e-01
4.40629333e-01 -2.49222908e-02 5.70067108e-01 3.08624119e-01
-6.57718658e-01 7.95408547e-01 -2.66998470e-01 2.02434450e-01
9.33251262e-01 -1.38137829e+00 4.87995356e-01 9.64697540e-01
8.56918275e-01 4.23127890e-01 5.95049202e-01 -6.21002793e-01
-1.21715820e+00 -1.21525085e+00 1.04549325e+00 -9.63981748e-01
8.25238824e-01 -8.56461227e-01 -8.25172603e-01 9.87851083e-01
2.13461190e-01 4.69903439e-01 4.98849452e-01 4.66445863e-01
-1.01384175e+00 4.18285839e-02 -5.94082415e-01 8.84082496e-01
1.56061113e+00 -8.56161535e-01 -5.97653925e-01 6.13166273e-01
8.32728565e-01 -3.49641591e-01 -7.13392913e-01 4.24399048e-01
3.50225955e-01 -7.99852371e-01 1.30533624e+00 -6.26940429e-01
1.31138250e-01 -5.13854980e-01 -4.10700649e-01 -8.00065339e-01
-7.12908268e-01 -2.04040840e-01 -2.71171540e-01 1.29635262e+00
4.30779308e-01 -1.55471936e-01 6.24998689e-01 4.10683692e-01
-4.35615666e-02 -3.34343970e-01 -5.26828647e-01 -1.09659612e+00
-3.56650025e-01 -5.30918300e-01 2.84997076e-01 9.87000585e-01
9.41014290e-02 1.01174092e+00 -4.90376949e-01 6.40424371e-01
7.04244912e-01 2.93116838e-01 1.11816287e+00 -1.12418151e+00
-4.51021969e-01 -4.40718681e-01 -5.87496877e-01 -1.30913043e+00
7.21923783e-02 -1.27686858e+00 -1.78031682e-03 -1.89682686e+00
7.64412940e-01 -5.67547500e-01 -3.14761192e-01 6.33397162e-01
-3.19768906e-01 5.87655067e-01 5.91459632e-01 4.45046484e-01
-1.17019808e+00 2.90489823e-01 9.83843803e-01 -2.43690625e-01
-1.02534369e-01 -6.69588506e-01 -7.90315926e-01 5.87515712e-01
5.58450699e-01 -3.91380101e-01 -4.49333161e-01 -4.57799137e-01
1.43137142e-01 -2.44986311e-01 6.54420197e-01 -1.03113008e+00
2.83708572e-01 -3.56587805e-02 2.86282033e-01 -6.79186165e-01
3.82479936e-01 -8.04530799e-01 -3.02525125e-02 1.67722050e-02
-4.38544899e-01 5.39664030e-02 3.51999328e-02 8.24529707e-01
-3.80760804e-02 -1.83751173e-02 6.65227234e-01 1.62615642e-01
-1.21179140e+00 1.70151606e-01 3.57358530e-02 2.18173623e-01
1.13646233e+00 -1.56355590e-01 -7.03004062e-01 -3.41767102e-01
-6.61226273e-01 4.84413087e-01 5.39055467e-01 7.64238894e-01
5.42967737e-01 -1.54658592e+00 -5.74150503e-01 -1.31160334e-01
1.01335430e+00 -1.51541069e-01 6.63072318e-02 7.19161034e-01
-3.30818325e-01 4.33961123e-01 2.74987351e-02 -8.99345934e-01
-1.66966522e+00 9.53258216e-01 9.55035016e-02 -1.41140223e-01
-8.59345257e-01 1.11743212e+00 8.07615161e-01 -2.17557177e-01
4.23107475e-01 -1.95235938e-01 -3.05303156e-01 9.41717103e-02
3.83929998e-01 1.29197583e-01 -2.53523260e-01 -8.98013949e-01
-5.74596822e-01 4.94307488e-01 -2.17468396e-01 -5.41425608e-02
8.52757752e-01 -3.76287878e-01 1.25906840e-01 4.74928141e-01
1.31345069e+00 -4.38554585e-03 -1.16054702e+00 -5.02137482e-01
1.19410150e-01 -4.93720949e-01 -1.56063423e-03 -6.25010014e-01
-6.68654263e-01 7.46998668e-01 4.84476238e-01 1.90317839e-01
6.24759078e-01 7.16718256e-01 4.53082651e-01 2.64562279e-01
3.06393921e-01 -9.14827466e-01 5.83892465e-01 3.62976760e-01
8.24061275e-01 -1.49320245e+00 1.99554518e-01 -8.83849323e-01
-1.00798821e+00 7.99007118e-01 1.06598496e+00 7.46679083e-02
2.22618774e-01 -1.09891836e-02 -8.91804472e-02 -5.96994042e-01
-8.17670226e-01 -7.68049777e-01 7.95265555e-01 7.40498424e-01
3.07657748e-01 2.64481843e-01 5.60977198e-02 1.66393310e-01
1.08951770e-01 -5.69927275e-01 3.35120380e-01 6.88502669e-01
-3.82688671e-01 -8.25619280e-01 -2.60816813e-01 2.81693786e-01
1.11634545e-01 -2.06746668e-01 -3.45449924e-01 1.08844852e+00
1.49449483e-01 1.09024012e+00 3.21588784e-01 -4.71755981e-01
3.09681475e-01 -1.86361581e-01 4.88458872e-01 -1.02162790e+00
-2.14968041e-01 8.26223642e-02 5.14426589e-01 -5.03597081e-01
-2.05799267e-01 -3.32690716e-01 -1.50382912e+00 7.62360543e-02
-4.03494209e-01 -3.22851390e-01 3.14140141e-01 7.37466276e-01
4.69008595e-01 4.22504574e-01 4.40118581e-01 -7.24843025e-01
-1.81701824e-01 -5.63038647e-01 -6.01387143e-01 9.71146703e-01
1.74412772e-01 -9.45749402e-01 -2.05080777e-01 1.07625620e-02] | [10.274496078491211, 1.6285871267318726] |
03ea1fb6-96fd-482d-921b-06d140acaf0a | a-machine-learning-framework-for-sleeping | 1910.01092 | null | https://arxiv.org/abs/1910.01092v2 | https://arxiv.org/pdf/1910.01092v2.pdf | A Machine Learning framework for Sleeping Cell Detection in a Smart-city IoT Telecommunications Infrastructure | The smooth operation of largely deployed Internet of Things (IoT) applications will depend on, among other things, effective infrastructure failure detection. Access failures in wireless network Base Stations (BSs) produce a phenomenon called "sleeping cells", which can render a cell catatonic without triggering any alarms or provoking immediate effects on cell performance, making them difficult to discover. To detect this kind of failure, we propose a Machine Learning (ML) framework based on the use of Key Performance Indicator (KPI) statistics from the BS under study, as well as those of the neighboring BSs with propensity to have their performance affected by the failure. A simple way to define neighbors is to use adjacency in Voronoi diagrams. In this paper, we propose a much more realistic approach based on the nature of radio-propagation and the way devices choose the BS to which they send access requests. We gather data from large-scale simulators that use real location data for BSs and IoT devices and pose the detection problem as a supervised binary classification problem. We measure the effects on the detection performance by the size of time aggregations of the data, the level of traffic and the parameters of the neighborhood definition. The Extra Trees and Naive Bayes classifiers achieve Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) scores of 0.996 and 0.993, respectively, with False Positive Rate (FPR) under 5 %. The proposed framework holds potential for other pattern recognition tasks in smart-city wireless infrastructures, that would enable the monitoring, prediction and improvement of the Quality of Service (QoS) experienced by IoT applications. | ['Filippo Malandra', 'Orestes Manzanilla-Salazar', 'Constant Wette', 'Brunilde Sanso', 'Hakim Mellah'] | 2019-10-02 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [-1.27771080e-01 5.95596135e-02 -1.42019644e-01 -6.19273931e-02
-2.35136375e-01 -3.79726201e-01 4.78501230e-01 6.13288045e-01
-1.93089366e-01 1.03921056e+00 -3.34443480e-01 -7.00803816e-01
-5.01386106e-01 -1.11580670e+00 -4.28155661e-01 -1.12582648e+00
-5.10269761e-01 6.68625891e-01 5.59403718e-01 1.81763902e-01
4.77540586e-03 8.13965440e-01 -1.27380943e+00 -9.77819785e-02
6.27525032e-01 1.44293296e+00 2.68686544e-02 4.85033125e-01
1.42235592e-01 4.08995241e-01 -1.02747571e+00 1.61592171e-01
2.01723538e-02 1.33989200e-01 -3.33463818e-01 -2.98266262e-01
-6.55818403e-01 8.00075755e-02 -4.85106520e-02 6.59727573e-01
6.12692535e-01 -2.71251053e-01 7.32145488e-01 -1.57857096e+00
1.50343850e-01 6.69768691e-01 -4.32201028e-01 6.24250710e-01
3.02502155e-01 -8.43630508e-02 4.11390036e-01 -2.84034789e-01
-9.79078468e-05 7.55476952e-01 6.06147408e-01 -2.42992993e-02
-1.05272210e+00 -6.89982176e-01 -3.25571626e-01 2.54609913e-01
-1.73078430e+00 -6.03462100e-01 3.97814393e-01 -3.30773115e-01
5.70118189e-01 5.41513562e-01 6.21104956e-01 7.26108074e-01
4.33993995e-01 4.05862043e-03 7.99012542e-01 -3.83821100e-01
8.15593839e-01 2.45985374e-01 -1.71928972e-01 2.75372058e-01
6.69217110e-01 -1.88440204e-01 1.13599502e-01 -4.34854060e-01
4.67342913e-01 5.89916855e-02 -9.16558132e-02 -2.05442816e-01
-1.24583423e+00 5.19357026e-01 3.89025629e-01 6.98777080e-01
-5.63656628e-01 2.00910017e-01 -1.03288025e-01 2.40146890e-01
2.21221402e-01 4.16444950e-02 -7.25579798e-01 1.14820667e-01
-7.19256759e-01 -5.70434511e-01 9.28001463e-01 7.46262848e-01
5.55711091e-01 -1.79434657e-01 -3.28527212e-01 2.39842877e-01
3.87762755e-01 7.40531862e-01 2.82249421e-01 -5.40516615e-01
7.94581100e-02 5.02989888e-01 3.85874003e-01 -8.32167327e-01
-9.23372149e-01 -8.79745185e-01 -9.74296331e-01 -2.09098294e-01
2.92805135e-01 -3.93633366e-01 -4.67739522e-01 1.46174538e+00
3.87788057e-01 5.31103730e-01 9.23780799e-02 2.26406828e-01
3.41459274e-01 6.84840262e-01 1.40568512e-02 -7.12239861e-01
1.08902574e+00 -9.33681130e-02 -4.51568604e-01 2.23974288e-01
7.06563473e-01 -3.99567842e-01 4.39719170e-01 2.75880605e-01
-7.54672408e-01 -2.81456202e-01 -1.00270927e+00 1.05765879e+00
-4.40004766e-01 -3.43752792e-03 4.10731196e-01 1.00393748e+00
-9.95668411e-01 5.84302992e-02 -7.51742244e-01 -5.56263268e-01
4.69174415e-01 7.92980492e-01 1.64922729e-01 1.04278661e-01
-8.61111820e-01 5.50078690e-01 1.02968402e-01 -2.19265953e-01
-7.32905567e-01 -5.83846927e-01 -1.47974521e-01 2.39861056e-01
8.69957656e-02 -6.48927152e-01 6.25304043e-01 -5.10273039e-01
-9.31195915e-01 4.49515194e-01 1.62564628e-02 -6.34377837e-01
3.27766329e-01 5.39577782e-01 -9.39334571e-01 -1.23949252e-01
2.37418085e-01 1.74141705e-01 5.75963080e-01 -1.14421320e+00
-8.16745162e-01 -3.68123025e-01 6.86126128e-02 -5.50171018e-01
-2.00225681e-01 -2.68359333e-01 -3.35061923e-02 -2.74672300e-01
1.46356896e-01 -8.40173006e-01 -2.97056764e-01 -5.03607035e-01
-3.24043334e-01 -3.16767901e-01 8.20212066e-01 -8.81354734e-02
1.22279382e+00 -2.27833414e+00 -5.20697773e-01 7.57389903e-01
6.16617687e-02 6.72703013e-02 2.36978471e-01 3.45459193e-01
3.43105316e-01 1.50353506e-01 1.40485108e-01 1.73892900e-01
-3.05034578e-01 1.72189578e-01 9.53294039e-02 6.95923805e-01
-4.23146367e-01 3.56431991e-01 -7.98471093e-01 -2.18012109e-01
3.79213363e-01 3.54659170e-01 -1.67314112e-01 -4.91678268e-02
1.53930917e-01 7.98543036e-01 -8.42845380e-01 5.03946662e-01
5.87078094e-01 -3.30138952e-01 1.29549801e-01 -3.44042331e-01
-9.39424634e-02 2.36078158e-01 -1.22412026e+00 7.16430306e-01
-7.01791406e-01 2.61336565e-01 -7.28996024e-02 -1.22104490e+00
8.61060202e-01 4.66329694e-01 9.59286690e-01 -7.72091508e-01
2.73175538e-01 1.99786305e-01 -2.57422607e-02 -3.97953212e-01
-5.73868811e-01 1.84450433e-01 -1.38069645e-01 2.46901914e-01
-3.74371260e-01 2.52893656e-01 1.59390137e-01 -2.10726950e-02
1.67089903e+00 -9.47560549e-01 6.40552521e-01 -4.32416916e-01
7.10594058e-01 -3.96942943e-01 3.55225831e-01 9.49339688e-01
-8.69179145e-02 -1.08385690e-01 2.54803002e-01 -4.33696866e-01
-5.14315605e-01 -1.19266653e+00 -4.62215066e-01 6.33615553e-01
4.11958396e-01 1.01202734e-01 -5.00762463e-01 -4.03694302e-01
9.51246172e-02 7.67942846e-01 -2.63252735e-01 -2.17830047e-01
-4.38639112e-02 -1.11467052e+00 3.58284950e-01 -1.13151252e-01
4.85724688e-01 -6.86835885e-01 -5.82763016e-01 3.83132428e-01
-9.55061615e-02 -1.38226378e+00 4.05739576e-01 3.85718733e-01
-7.79661238e-01 -9.56612527e-01 -1.84397578e-01 -3.98808509e-01
5.78118742e-01 3.48105967e-01 1.00525045e+00 2.78431386e-01
-1.69830751e-02 4.69302267e-01 -4.30468023e-01 -4.24862832e-01
-4.21446323e-01 1.16552189e-01 3.88274848e-01 3.62015992e-01
1.09617412e-01 -9.10039306e-01 -6.58531189e-01 8.55679691e-01
-5.03952801e-01 -4.81683850e-01 5.84988415e-01 1.20491952e-01
2.93638200e-01 7.95876086e-01 1.07843637e+00 -3.61736268e-01
2.71132797e-01 -9.80555832e-01 -7.37010956e-01 2.11214736e-01
-5.86276114e-01 -1.96503237e-01 5.02228081e-01 -2.52102494e-01
-4.93062705e-01 -9.25446153e-02 9.62322280e-02 2.49370009e-01
-3.46106917e-01 1.70220330e-01 -4.63304311e-01 -1.73009619e-01
4.88796860e-01 -1.36635423e-01 -5.80330133e-01 -2.01170504e-01
-1.18033037e-01 9.67459917e-01 9.88378674e-02 -1.41658112e-01
8.36854339e-01 7.79678226e-01 3.18849593e-01 -1.22505713e+00
-3.88643056e-01 -6.18616164e-01 -1.61175773e-01 -3.48113030e-01
5.14593065e-01 -9.04504716e-01 -8.46220970e-01 8.94717723e-02
-8.61074626e-01 -5.09600081e-02 -3.73075828e-02 4.88799483e-01
-7.02088997e-02 -6.71032490e-03 -1.37003213e-01 -1.08786798e+00
-5.48549034e-02 -8.48654449e-01 7.96009541e-01 2.63235837e-01
-7.24612400e-02 -8.97886276e-01 -3.00990552e-01 2.73792803e-01
6.39626980e-01 1.23224795e-01 8.84184659e-01 -7.73817956e-01
-7.11873293e-01 -4.29654241e-01 -1.42176166e-01 -1.28470689e-01
3.29550594e-01 -1.32657617e-01 -9.36037540e-01 -3.66733044e-01
5.05371466e-02 5.69619417e-01 2.80840635e-01 7.37021327e-01
1.09641075e+00 -4.78190631e-01 -1.05857873e+00 2.87817627e-01
1.25562334e+00 5.11234939e-01 7.03684032e-01 2.02597558e-01
1.70102566e-01 3.72738570e-01 4.54151571e-01 7.57144749e-01
2.60290712e-01 7.68837392e-01 8.88168693e-01 -1.26389012e-01
2.74586588e-01 2.39233628e-01 2.98636436e-01 3.59338880e-01
6.49245456e-02 -6.69602931e-01 -9.56500292e-01 4.37603414e-01
-1.51625013e+00 -8.27227652e-01 -3.15111101e-01 2.49122834e+00
1.27211019e-01 5.12363791e-01 1.93695620e-01 7.54510820e-01
8.38172495e-01 -3.33238155e-01 -3.46877724e-01 -4.08575609e-02
3.78956310e-02 1.26256673e-02 9.29460704e-01 3.12566280e-01
-9.13513303e-01 2.11848244e-01 4.81948233e+00 8.61786485e-01
-1.11745214e+00 3.48873407e-01 8.39366078e-01 1.74889684e-01
-4.45313416e-02 -1.57280266e-01 -5.18215299e-01 8.76264095e-01
1.19366717e+00 1.34123474e-01 3.39769810e-01 5.67370236e-01
8.99682581e-01 -4.74888146e-01 -7.90565550e-01 1.04089057e+00
-3.96505266e-01 -1.05165744e+00 -3.54563683e-01 3.42823237e-01
5.53651512e-01 3.42797428e-01 -1.94360793e-01 -8.67832005e-02
1.39079571e-01 -7.11093009e-01 5.72589040e-01 5.98868430e-01
5.50730884e-01 -5.80336154e-01 1.07477617e+00 5.45625031e-01
-1.19625020e+00 -6.76715374e-01 -5.80669206e-04 -1.94567308e-01
2.31315330e-01 1.42833042e+00 -1.07494581e+00 4.04597819e-01
7.52435327e-01 1.38286397e-01 -4.66970414e-01 1.54578698e+00
1.74567625e-01 8.15475106e-01 -8.43089402e-01 -2.71448165e-01
-1.60501510e-01 5.90779446e-02 5.85505486e-01 7.92457461e-01
7.63968349e-01 8.36024210e-02 -5.91917112e-02 3.77477646e-01
1.17175817e-01 -3.92619185e-02 -5.47629774e-01 5.52868485e-01
1.02560675e+00 1.14194262e+00 -1.21412408e+00 -8.03176537e-02
-3.49720299e-01 3.99944752e-01 -5.81673682e-01 4.66351569e-01
-8.60884964e-01 -1.43396705e-02 4.07698393e-01 5.62571108e-01
3.78959835e-01 -6.20315149e-02 -5.00764906e-01 -6.41742885e-01
-1.28628865e-01 -2.38877907e-01 1.75636545e-01 -5.21227419e-01
-1.16533589e+00 1.96169972e-01 -1.60925850e-01 -1.27038383e+00
1.55225426e-01 -2.47655362e-01 -6.63248658e-01 2.65404850e-01
-1.17510724e+00 -5.84600866e-01 -3.21476847e-01 4.69954699e-01
3.80709991e-02 -9.35317352e-02 5.85059762e-01 6.00474596e-01
-4.78317916e-01 2.72724062e-01 3.66416931e-01 -1.51297569e-01
4.30669338e-02 -6.94136202e-01 -1.38561279e-01 3.38028342e-01
2.11588040e-01 4.30739298e-02 8.68712425e-01 -3.37096602e-01
-9.69912231e-01 -9.99379814e-01 7.84865499e-01 -3.06443125e-01
5.26471257e-01 -2.08194807e-01 -1.30775928e-01 2.48556972e-01
-2.81086266e-01 2.62259543e-01 5.95898807e-01 -1.51293293e-01
4.06764299e-01 -8.52719069e-01 -1.58365428e+00 3.60899270e-01
7.84799993e-01 -2.29485314e-02 -2.45668576e-03 3.88688713e-01
3.62105101e-01 4.57689643e-01 -7.30013430e-01 7.06920624e-01
5.51066518e-01 -1.04285300e+00 1.10543060e+00 3.20506692e-02
-9.19544578e-01 -5.87764084e-01 -3.34349722e-01 -9.99731600e-01
-2.40108743e-01 -4.76629794e-01 -9.33304653e-02 1.31107807e+00
7.13028967e-01 -8.50225210e-01 4.95053589e-01 5.15797287e-02
3.30498040e-01 -6.19184673e-01 -1.46200526e+00 -7.23964155e-01
-5.52785933e-01 -5.34982979e-01 8.81071687e-01 7.03638613e-01
-1.41715646e-01 4.66197908e-01 9.15612131e-02 7.39142656e-01
5.16687036e-01 -4.09614503e-01 6.88995600e-01 -1.77689266e+00
6.90723807e-02 -2.68929899e-01 -7.12089121e-01 -7.31860876e-01
-4.76463735e-01 -4.21702236e-01 -2.47834221e-01 -1.49007046e+00
-2.20335960e-01 -1.23979437e+00 -6.59878314e-01 2.76731968e-01
6.86721683e-01 2.43142873e-01 -3.62925261e-01 2.78234214e-01
-8.11474741e-01 6.19599037e-03 4.16882873e-01 -2.33896390e-01
-3.66612852e-01 7.90270329e-01 -1.93511799e-01 7.31801331e-01
1.08589458e+00 -6.41978800e-01 -4.46859986e-01 -4.06918153e-02
3.88848871e-01 3.93675774e-01 4.53054667e-01 -1.71506286e+00
1.70547530e-01 6.63015479e-03 4.08503383e-01 -4.72359031e-01
1.55446306e-01 -1.43529701e+00 6.04795754e-01 8.23598146e-01
3.21099818e-01 -1.75825190e-02 -1.50955528e-01 7.42085159e-01
4.09584761e-01 -1.62953394e-03 6.88601673e-01 2.88128883e-01
-1.45162284e-01 1.68277368e-01 -8.08554530e-01 -4.70506191e-01
1.47373986e+00 -2.90417641e-01 -1.53624475e-01 -6.27850950e-01
-6.71661675e-01 1.23356804e-01 2.31228203e-01 3.46518569e-02
7.66982809e-02 -9.08876896e-01 -2.27718592e-01 1.58338234e-01
2.06360534e-01 -5.25655746e-01 -1.51297286e-01 1.24387074e+00
-2.13611603e-01 5.45196176e-01 1.55270532e-01 -7.63693929e-01
-8.99376273e-01 4.45815742e-01 5.23459673e-01 -1.41773537e-01
-5.33285439e-02 3.91173899e-01 -1.07295588e-01 5.15222959e-02
3.72665226e-01 -4.65924889e-01 -3.54369670e-01 4.40908298e-02
2.27468923e-01 7.32548118e-01 4.50326055e-01 -5.79163730e-01
-7.61511683e-01 3.87127966e-01 6.61560178e-01 2.01254562e-01
1.04067707e+00 -5.75183868e-01 -1.03021115e-01 3.65314484e-01
6.86613560e-01 2.77762234e-01 -5.34121335e-01 1.39502242e-01
2.47443020e-01 6.07580207e-02 1.68021783e-01 -9.05860364e-01
-9.84537065e-01 3.39930862e-01 1.02022278e+00 1.10591018e+00
1.11532354e+00 4.74703431e-01 5.80779850e-01 3.48961860e-01
1.07338417e+00 -7.43377805e-01 -3.13871741e-01 -2.99569294e-02
2.10320324e-01 -9.59816098e-01 -2.17269465e-01 -4.84364718e-01
2.76060790e-01 7.19458103e-01 6.61105439e-02 3.67822260e-01
1.20324445e+00 3.51474881e-01 -1.47127047e-01 -2.34874368e-01
-4.80926991e-01 -4.14043248e-01 -4.44942176e-01 7.70430923e-01
-2.08391562e-01 4.80830044e-01 -3.94279540e-01 7.23007083e-01
-4.03899625e-02 3.95383276e-02 3.35285932e-01 5.94722509e-01
-8.03432941e-01 -9.79778051e-01 -5.65958560e-01 7.45549083e-01
-5.02181709e-01 2.31690988e-01 1.54342443e-01 5.39702892e-01
5.51951408e-01 1.62910223e+00 2.79104590e-01 -4.08393800e-01
1.33942246e-01 -4.79023248e-01 1.48992866e-01 -3.10596108e-01
1.04119293e-01 -2.74489850e-01 1.58188373e-01 -2.83298522e-01
-3.76970887e-01 -5.61644077e-01 -1.33226776e+00 -2.94872105e-01
-4.92275655e-01 5.25005102e-01 7.26498604e-01 1.28574574e+00
4.02398586e-01 5.18401563e-01 1.08885944e+00 -6.05316341e-01
-2.58612186e-02 -8.17997515e-01 -6.87934279e-01 -1.33731321e-01
4.47594881e-01 -9.91333365e-01 -8.08447421e-01 -3.82287741e-01] | [6.144965648651123, 1.4303029775619507] |
48c2a39e-a49c-46e3-95a1-cf143f988fab | think-global-and-act-local-bayesian | 2102.07188 | null | https://arxiv.org/abs/2102.07188v2 | https://arxiv.org/pdf/2102.07188v2.pdf | Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces | High-dimensional black-box optimisation remains an important yet notoriously challenging problem. Despite the success of Bayesian optimisation methods on continuous domains, domains that are categorical, or that mix continuous and categorical variables, remain challenging. We propose a novel solution -- we combine local optimisation with a tailored kernel design, effectively handling high-dimensional categorical and mixed search spaces, whilst retaining sample efficiency. We further derive convergence guarantee for the proposed approach. Finally, we demonstrate empirically that our method outperforms the current baselines on a variety of synthetic and real-world tasks in terms of performance, computational costs, or both. | ['Michael A. Osborne', 'Cong Lu', 'Binxin Ru', 'Huong Ha', 'Vu Nguyen', 'Xingchen Wan'] | 2021-02-14 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 1.97822317e-01 -1.31985396e-01 -2.50143409e-01 -4.95240241e-01
-1.26780868e+00 -5.22421181e-01 8.26299012e-01 1.33227840e-01
-8.09519112e-01 9.32995796e-01 2.08063930e-01 -3.56773376e-01
-8.20331216e-01 -2.99150914e-01 -4.20093060e-01 -9.35634136e-01
-3.29053134e-01 7.99973726e-01 -5.70116118e-02 2.20376834e-01
3.59985799e-01 1.15147606e-01 -1.37162948e+00 -3.85755390e-01
9.19957459e-01 1.04731870e+00 -3.52737069e-01 7.26474047e-01
4.75701064e-01 -2.93183383e-02 -4.55413729e-01 -5.84933639e-01
5.49104214e-01 -6.95923865e-02 -4.85260218e-01 -2.78808940e-02
3.23357910e-01 1.42602608e-01 8.92389491e-02 9.38037157e-01
9.69699740e-01 4.23062593e-01 1.00251341e+00 -1.14901912e+00
-4.86242741e-01 1.83239982e-01 -6.68898940e-01 5.49474657e-02
2.61933319e-02 4.60642189e-01 1.02409720e+00 -7.06779897e-01
2.27290377e-01 1.62940550e+00 8.32626402e-01 2.19487205e-01
-1.81860125e+00 -4.78211969e-01 3.85832369e-01 -2.04773676e-02
-1.23537791e+00 -5.90996206e-01 3.15831006e-01 -4.32560682e-01
1.00677133e+00 3.29399616e-01 1.00739188e-01 1.18127418e+00
2.04985052e-01 6.27149761e-01 1.28978026e+00 -1.85633168e-01
6.51332319e-01 -1.94677025e-01 -3.98409553e-02 2.33774558e-01
1.12935849e-01 3.24426383e-01 -2.46224657e-01 -6.63671732e-01
5.22087693e-01 -3.29056591e-01 6.08331673e-02 -7.12360919e-01
-1.35670042e+00 1.25977218e+00 -1.38295546e-01 -3.19556713e-01
-4.72659588e-01 2.72999078e-01 5.07148743e-01 3.29288781e-01
7.30156481e-01 6.07416570e-01 -5.96213996e-01 -6.68109417e-01
-8.46047878e-01 6.08747423e-01 9.78507817e-01 7.23234832e-01
1.00599378e-01 -1.28941104e-01 -3.05548161e-01 1.07176340e+00
5.09763956e-01 3.51633102e-01 2.52275854e-01 -1.07248425e+00
5.84612489e-01 1.53328776e-01 3.84570330e-01 -9.09289598e-01
-5.78866541e-01 -5.85094154e-01 -1.15871239e+00 8.59860927e-02
5.16813099e-01 -3.27091813e-01 -8.44724953e-01 1.84935844e+00
7.68437624e-01 1.01709321e-01 -7.69106089e-04 1.03015816e+00
2.25157946e-01 4.94920313e-01 2.75357753e-01 -2.94667989e-01
1.27287757e+00 -9.06301796e-01 -8.24884653e-01 -6.56729639e-01
3.29131544e-01 -4.98272657e-01 1.02579665e+00 5.40132761e-01
-1.24453735e+00 -1.52239084e-01 -8.09441566e-01 2.09284462e-02
-2.33081300e-02 3.10128070e-02 8.58044386e-01 9.42693830e-01
-8.69093657e-01 3.55051666e-01 -8.43355000e-01 -5.93080707e-02
4.26847011e-01 6.49741173e-01 -8.57024267e-02 7.45691136e-02
-1.01853967e+00 7.83933043e-01 4.34862852e-01 1.71239495e-01
-7.29428411e-01 -6.71369553e-01 -9.00115371e-01 3.00155431e-02
7.61542976e-01 -8.63540769e-01 1.29412270e+00 -1.73774451e-01
-1.62862730e+00 3.21773946e-01 -1.74629867e-01 -5.09468734e-01
7.41010845e-01 -4.81134057e-01 -1.37871623e-01 -2.67842859e-01
-6.85979670e-04 4.08720821e-01 9.67042029e-01 -8.18719089e-01
-6.91502154e-01 -4.22666341e-01 -2.93041766e-01 5.23179352e-01
-2.47361273e-01 1.23166218e-01 -4.00654942e-01 -9.26127553e-01
-4.97305840e-02 -8.75293493e-01 -7.51451910e-01 -1.73367858e-01
-2.13469937e-01 -4.36994582e-01 4.10649687e-01 -4.68261361e-01
1.35893762e+00 -2.27238894e+00 5.77411056e-01 3.38605225e-01
7.38826841e-02 1.31786570e-01 -1.58519521e-01 1.71264470e-01
3.60947065e-02 1.53087646e-01 -7.40650117e-01 -6.14756227e-01
6.33216918e-01 8.21271464e-02 -2.85549946e-02 7.65137434e-01
3.02888125e-01 1.07524121e+00 -7.80208886e-01 -3.84195536e-01
2.15814486e-01 2.83713818e-01 -6.98760271e-01 3.31958085e-02
-3.30231071e-01 4.04085100e-01 -3.43453854e-01 5.26966453e-01
5.17400086e-01 -2.11854264e-01 2.31819078e-02 3.49895507e-01
2.95192689e-01 2.49053091e-01 -1.71939707e+00 1.63250589e+00
-3.67806047e-01 5.37615299e-01 4.18477863e-01 -1.25687790e+00
7.97341347e-01 7.26182982e-02 2.68518150e-01 -6.36980236e-01
1.84629131e-02 1.12375744e-01 6.86160550e-02 -3.50885600e-01
4.08667535e-01 -1.53946206e-01 -3.90181869e-01 2.74752110e-01
-1.59093827e-01 -2.39928856e-01 1.12442791e-01 -2.91395605e-01
1.29304790e+00 1.50079444e-01 4.09526497e-01 -2.97716945e-01
3.11879158e-01 -2.27823734e-01 6.00942314e-01 1.12252927e+00
-2.88758516e-01 4.54353780e-01 7.89058864e-01 -7.55079975e-03
-9.00855899e-01 -1.08577716e+00 -5.73000908e-01 1.27363205e+00
-4.57859300e-02 -1.38540134e-01 -3.31409991e-01 -5.23172140e-01
3.29627037e-01 6.79863453e-01 -4.39109802e-01 -1.77238554e-01
-3.96734089e-01 -1.34578872e+00 4.66680765e-01 2.87808299e-01
2.05263481e-01 -6.64753556e-01 -4.95665073e-01 3.98674309e-01
1.31297141e-01 -1.01608050e+00 -2.58661449e-01 3.35162014e-01
-9.94482756e-01 -6.66697919e-01 -7.06492841e-01 -5.63450277e-01
1.37629226e-01 -3.09704155e-01 1.13273227e+00 -6.38280213e-01
-3.58354270e-01 3.22464466e-01 -3.27227339e-02 -2.23824427e-01
-4.18069251e-02 2.92798549e-01 -3.62259410e-02 -4.89777550e-02
3.88198614e-01 -3.63424033e-01 -6.93478763e-01 5.70115685e-01
-7.33186424e-01 -4.99758005e-01 6.67640269e-01 1.01361454e+00
4.40967411e-01 2.22874582e-01 7.83924818e-01 -6.11555636e-01
1.10017383e+00 -8.20300817e-01 -7.87446678e-01 7.62558654e-02
-6.04441881e-01 2.92180508e-01 3.38682920e-01 -6.87443197e-01
-9.17112827e-01 -1.70831963e-01 -2.71994714e-02 -1.27509087e-01
-2.69881397e-01 6.12691760e-01 -1.62565380e-01 2.08322033e-01
6.99046195e-01 -5.31427711e-02 1.04691513e-01 -5.73649704e-01
4.05076325e-01 7.89719343e-01 4.74693984e-01 -9.19790089e-01
6.58376932e-01 3.71030182e-01 1.49160221e-01 -7.28222251e-01
-4.61905867e-01 -5.68607986e-01 -3.87049764e-01 3.65967214e-01
6.50563061e-01 -9.69057083e-01 -8.05496454e-01 3.40191096e-01
-7.90341735e-01 -5.21796227e-01 -1.34783402e-01 5.48791945e-01
-6.44213378e-01 4.39909041e-01 -1.82400376e-01 -1.16971946e+00
-7.57948533e-02 -1.28316653e+00 1.35102260e+00 -9.61848721e-03
-3.49709362e-01 -1.26505136e+00 2.45895609e-01 1.08431160e-01
4.47375685e-01 3.07941139e-01 7.37949550e-01 -7.56520987e-01
-2.50382900e-01 -2.38267660e-01 -2.66595513e-01 2.12612927e-01
-1.85497940e-01 -2.03245997e-01 -6.01381600e-01 -4.46237475e-01
1.61396042e-01 -4.52471763e-01 8.92818928e-01 6.92814946e-01
1.33376336e+00 -3.50825727e-01 -1.95048168e-01 7.28491008e-01
1.14512241e+00 7.26291165e-02 3.51088971e-01 5.91532469e-01
2.39361718e-01 8.10571551e-01 9.77970183e-01 7.61388659e-01
4.59850490e-01 8.79455268e-01 3.70630413e-01 -5.99499568e-02
4.49058592e-01 2.61377424e-01 1.39505818e-01 3.68925035e-01
2.34573483e-01 -1.92915410e-01 -1.04048526e+00 8.68747354e-01
-2.41938472e+00 -6.98653817e-01 -1.11408290e-02 2.31967425e+00
1.10561669e+00 2.26076558e-01 4.57644373e-01 -1.34152742e-02
5.13870001e-01 6.60389662e-02 -7.50329137e-01 -6.73028827e-01
-7.40303621e-02 3.44598740e-01 6.16935611e-01 4.79524076e-01
-1.60892570e+00 5.37437499e-01 7.20088148e+00 1.15233314e+00
-5.11027157e-01 1.20444097e-01 5.58140874e-01 -4.52759147e-01
3.57971489e-02 -1.35364532e-01 -8.14458847e-01 5.74069560e-01
1.02666724e+00 1.03276737e-01 4.92945552e-01 8.55050206e-01
1.59230724e-01 -2.87483573e-01 -1.17607725e+00 9.24890518e-01
-1.68893576e-01 -7.73999572e-01 -6.72672927e-01 2.83142388e-01
6.88801467e-01 -1.83477640e-01 4.55893368e-01 5.46416104e-01
7.25292504e-01 -1.37191153e+00 3.97694707e-01 3.27798963e-01
5.86121202e-01 -8.24078023e-01 7.37916172e-01 3.57592374e-01
-7.78972387e-01 -2.46107057e-01 -2.99559742e-01 -5.27868383e-02
2.34708011e-01 7.88995743e-01 -3.60065579e-01 3.92915428e-01
6.69899464e-01 4.89633858e-01 -6.32464767e-01 1.51991355e+00
-3.37617919e-02 7.73258507e-01 -7.32710242e-01 -1.62936166e-01
4.44457561e-01 -3.70608419e-01 7.24532187e-01 1.35059142e+00
1.77354574e-01 -6.74970672e-02 1.13684639e-01 7.11823642e-01
3.55154335e-01 -1.64256081e-01 -4.34756339e-01 5.75977303e-02
5.75609148e-01 9.91167545e-01 -3.77884984e-01 5.32467552e-02
-2.41228715e-01 5.65619946e-01 2.06070408e-01 5.92758417e-01
-8.86092782e-01 -5.83343506e-01 8.73639166e-01 -5.13000309e-01
4.59573358e-01 -4.70520765e-01 -6.94547832e-01 -9.60150301e-01
1.53609619e-01 -1.23091257e+00 6.28999352e-01 -6.53265715e-02
-1.68092155e+00 -3.51407751e-02 3.18132550e-01 -7.36964345e-01
-5.89435399e-01 -5.65722704e-01 -1.48896739e-01 9.94266093e-01
-1.32806790e+00 -7.30995655e-01 6.07000515e-02 3.92020196e-01
4.55973178e-01 -1.75799996e-01 5.48734367e-01 2.95513242e-01
-6.62572205e-01 7.21286058e-01 7.06239700e-01 -3.01553130e-01
6.75973952e-01 -1.48785102e+00 5.95926404e-01 5.81318915e-01
-2.07002431e-01 6.56887889e-01 7.58286238e-01 -5.62351108e-01
-1.42718685e+00 -8.00655842e-01 4.45195556e-01 -5.73439002e-01
7.91780531e-01 -6.89039886e-01 -9.36521709e-01 3.29793990e-01
-1.46219045e-01 -3.00410897e-01 6.07011855e-01 6.04344308e-01
-6.42291233e-02 2.00455949e-01 -1.24779189e+00 8.30337822e-01
1.02439547e+00 -2.36515298e-01 -7.18492806e-01 4.08537716e-01
6.00263655e-01 -4.51891154e-01 -1.08850527e+00 7.73360789e-01
5.22755861e-01 -6.14124954e-01 1.35387564e+00 -6.77658200e-01
2.09377140e-01 9.03652981e-03 -1.42672062e-01 -1.29178059e+00
-2.87041485e-01 -9.63590086e-01 -4.11856711e-01 1.12480080e+00
1.74525887e-01 -8.93296957e-01 6.66957438e-01 9.31942284e-01
2.35780835e-01 -7.99748838e-01 -1.48260975e+00 -9.99508262e-01
2.57974207e-01 -6.49033725e-01 3.87766510e-01 6.81961656e-01
-2.53879786e-01 4.74835753e-01 -5.03314078e-01 3.22118551e-02
9.97059047e-01 -1.79826736e-01 9.32783484e-01 -1.05636692e+00
-5.95269918e-01 -7.55775869e-01 -4.79498416e-01 -1.03526771e+00
2.06947386e-01 -4.71050054e-01 9.30043533e-02 -1.29659534e+00
-8.81800950e-02 -5.59902787e-01 -6.34174421e-02 3.66024017e-01
-4.65372354e-01 1.14754578e-02 -2.58358598e-01 -7.90172163e-03
-7.17500269e-01 9.31685686e-01 7.92189062e-01 1.02761187e-01
-3.25168401e-01 1.67558804e-01 -6.96978450e-01 4.42302257e-01
6.98393285e-01 -4.38292593e-01 -3.70568365e-01 -2.69595772e-01
3.51516634e-01 -9.04182345e-02 4.54101354e-01 -6.71679795e-01
2.79405527e-02 -4.19217318e-01 1.88657939e-01 -4.61852431e-01
4.85180974e-01 -7.11655319e-01 -4.94847074e-02 2.32392460e-01
-4.83357996e-01 -2.00144909e-02 4.26748365e-01 9.28608000e-01
4.38375724e-03 -1.61727756e-01 8.30567122e-01 4.03196990e-01
-3.58763635e-01 7.45817497e-02 -4.00883257e-02 4.16741103e-01
1.14197671e+00 -1.46123990e-01 -2.12178484e-01 -2.59105116e-01
-7.60258615e-01 8.01441073e-01 2.66680449e-01 3.75015020e-01
5.60140193e-01 -1.25064468e+00 -9.51559544e-01 8.13210532e-02
-7.65123144e-02 3.22029799e-01 -1.01106457e-01 1.11466682e+00
-1.18438318e-01 3.92802179e-01 3.15808594e-01 -8.85453403e-01
-1.06032968e+00 4.17360693e-01 7.82776698e-02 -3.14822882e-01
-4.53079253e-01 7.09435761e-01 5.35160787e-02 -9.08810019e-01
6.80233240e-01 -9.91427526e-02 1.12798005e-01 3.24886650e-01
1.64128132e-02 6.83509231e-01 -8.49450976e-02 -1.65204361e-01
-3.48388970e-01 4.99383360e-01 1.57776535e-01 -5.47709286e-01
1.44749987e+00 -2.30135620e-01 3.23838927e-02 5.68423748e-01
1.14774883e+00 -4.99000251e-01 -1.36966002e+00 -4.15286005e-01
4.06094134e-01 -8.45413208e-01 1.67244956e-01 -8.86715293e-01
-3.27908665e-01 7.33804286e-01 6.75645947e-01 1.53172866e-01
9.70976174e-01 -9.39187631e-02 2.88903534e-01 4.82470393e-01
7.46791810e-02 -1.15582204e+00 -1.45604849e-01 5.75368226e-01
7.70157874e-01 -1.48055959e+00 1.76717713e-01 -7.99168497e-02
-7.21161544e-01 5.11189640e-01 2.50660092e-01 -1.33100301e-01
5.57265937e-01 -6.28361925e-02 -3.28146338e-01 -3.28385681e-02
-9.79782522e-01 -1.79318190e-01 5.94615996e-01 7.44256556e-01
1.19469844e-01 -1.59480527e-01 -5.26935101e-01 4.94595528e-01
-6.41757250e-02 -2.85630018e-01 -1.90150049e-02 9.42206502e-01
-1.23070501e-01 -9.87920702e-01 -6.70970976e-01 7.67937660e-01
-5.87461531e-01 -5.32489419e-02 -2.29304612e-01 7.02185929e-01
-3.77869636e-01 1.06372440e+00 4.27983738e-02 7.22952485e-02
1.72616094e-01 9.96013656e-02 2.62805253e-01 -5.15276551e-01
-6.22770250e-01 4.53858972e-01 2.71096975e-01 -4.93995488e-01
-3.14827174e-01 -9.26489055e-01 -5.15646458e-01 -2.21680209e-01
-3.26779693e-01 4.41145487e-02 9.42087352e-01 8.59413326e-01
4.45051879e-01 3.55732679e-01 4.97358114e-01 -8.86534393e-01
-1.36185467e+00 -9.87770796e-01 -4.66087222e-01 2.17774063e-01
5.55847704e-01 -1.01700020e+00 -6.75530672e-01 -4.74036813e-01] | [6.445954322814941, 3.9055633544921875] |
d651951c-d9c6-46ae-856e-612ef546968f | easydgl-encode-train-and-interpret-for | 2303.12341 | null | https://arxiv.org/abs/2303.12341v1 | https://arxiv.org/pdf/2303.12341v1.pdf | EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph Learning | Dynamic graphs arise in various real-world applications, and it is often welcomed to model the dynamics directly in continuous time domain for its flexibility. This paper aims to design an easy-to-use pipeline (termed as EasyDGL which is also due to its implementation by DGL toolkit) composed of three key modules with both strong fitting ability and interpretability. Specifically the proposed pipeline which involves encoding, training and interpreting: i) a temporal point process (TPP) modulated attention architecture to endow the continuous-time resolution with the coupled spatiotemporal dynamics of the observed graph with edge-addition events; ii) a principled loss composed of task-agnostic TPP posterior maximization based on observed events on the graph, and a task-aware loss with a masking strategy over dynamic graph, where the covered tasks include dynamic link prediction, dynamic node classification and node traffic forecasting; iii) interpretation of the model outputs (e.g., representations and predictions) with scalable perturbation-based quantitative analysis in the graph Fourier domain, which could more comprehensively reflect the behavior of the learned model. Extensive experimental results on public benchmarks show the superior performance of our EasyDGL for time-conditioned predictive tasks, and in particular demonstrate that EasyDGL can effectively quantify the predictive power of frequency content that a model learn from the evolving graph data. | ['Junchi Yan', 'Xiaokang Yang', 'Nianzu Yang', 'Haoyu Geng', 'Chao Chen'] | 2023-03-22 | null | null | null | null | ['dynamic-link-prediction', 'fraud-detection', 'sequential-recommendation'] | ['graphs', 'miscellaneous', 'miscellaneous'] | [ 2.32134193e-01 2.60027587e-01 -1.59002453e-01 -9.35759768e-02
-3.39263469e-01 -2.02546015e-01 6.81283593e-01 9.85827819e-02
3.65055233e-01 4.89310205e-01 1.73224345e-01 -3.73567700e-01
-5.10696828e-01 -1.03601861e+00 -8.96076024e-01 -9.38480794e-01
-9.15940225e-01 6.41882241e-01 2.23426402e-01 -2.81189561e-01
-2.72707850e-01 4.75323021e-01 -1.16152954e+00 -1.07022665e-01
1.00226831e+00 1.05896223e+00 1.03631124e-01 7.05181658e-01
1.29517108e-01 8.68253827e-01 -8.58527273e-02 -5.00596583e-01
1.10707745e-01 -6.49131611e-02 -6.56672180e-01 9.93850082e-02
-1.05988748e-01 8.76386985e-02 -1.07449675e+00 8.67385268e-01
4.47560579e-01 1.82067350e-01 6.56245112e-01 -1.54517281e+00
-8.00377190e-01 5.38688242e-01 -7.27501333e-01 6.10963523e-01
2.50809014e-01 5.78648448e-01 1.27787089e+00 -4.37629968e-01
6.64496243e-01 1.37107229e+00 8.74847412e-01 4.82869335e-02
-1.51848459e+00 -5.20288885e-01 5.91918409e-01 4.68908191e-01
-1.34417200e+00 -2.07319617e-01 1.19850469e+00 -4.48147178e-01
1.01457250e+00 1.69204593e-01 7.53683805e-01 1.68350756e+00
5.10703087e-01 5.42013764e-01 4.19848591e-01 2.04250053e-01
-2.50559479e-01 -3.54801029e-01 -4.75052372e-02 8.86033237e-01
-3.78888324e-02 3.33540559e-01 -4.84769255e-01 -2.73756742e-01
5.09807229e-01 -3.23032402e-02 -4.19688076e-01 -1.59264818e-01
-1.12963283e+00 4.48629200e-01 6.63744450e-01 -1.99516177e-01
-6.32569611e-01 5.05405366e-01 8.02952111e-01 4.06192660e-01
9.92047369e-01 -1.90387189e-01 -4.37524259e-01 7.74879456e-02
-5.47531664e-01 1.34812102e-01 7.80518234e-01 9.14289773e-01
5.05889237e-01 1.46834448e-01 -3.33556294e-01 4.93393123e-01
4.01569754e-01 4.81898457e-01 1.28429323e-01 -4.98634607e-01
4.98419404e-01 5.36355138e-01 -2.77807385e-01 -1.59236634e+00
-5.38629651e-01 -7.44148374e-01 -1.42068589e+00 -4.27069694e-01
4.55587059e-02 3.08084600e-02 -5.40791273e-01 2.16118169e+00
4.30747181e-01 1.02098668e+00 -4.03300464e-01 7.00239062e-01
7.90635049e-01 9.88301456e-01 3.29844505e-01 -4.81816769e-01
9.36809838e-01 -8.36499035e-01 -7.37559676e-01 -8.07188451e-02
5.98980606e-01 -7.79433772e-02 9.67839539e-01 -4.79242429e-02
-7.82733381e-01 -5.69110453e-01 -8.36694956e-01 3.15228365e-02
-2.09317774e-01 -2.38033891e-01 7.88898766e-01 7.00013191e-02
-1.11788487e+00 8.09327245e-01 -8.77593994e-01 -3.53458613e-01
3.28614652e-01 1.82876244e-01 -2.89707452e-01 6.24497160e-02
-1.53094459e+00 4.12605554e-01 2.98156142e-01 3.92451882e-01
-1.01363826e+00 -1.00564623e+00 -7.82560825e-01 3.67598623e-01
5.73220730e-01 -8.96312296e-01 4.85838145e-01 -7.31933296e-01
-1.20820808e+00 8.20923805e-01 2.93959514e-03 -6.51112676e-01
6.08387470e-01 2.18686029e-01 -7.50883758e-01 5.60974069e-02
-1.02661317e-03 2.34653871e-03 1.04854691e+00 -7.49155343e-01
-2.63628215e-01 -1.35760933e-01 -1.63122505e-01 3.96528803e-02
-2.17504486e-01 -4.01447833e-01 -6.05588257e-01 -8.55322838e-01
-5.30365594e-02 -8.25390637e-01 -6.88474849e-02 -1.18621495e-02
-6.25783086e-01 -4.73590553e-01 8.70271385e-01 -1.06129003e+00
1.51460612e+00 -2.08974433e+00 3.77650738e-01 2.52788931e-01
6.90900564e-01 -2.48956829e-02 -3.09457272e-01 8.94655108e-01
-2.58710682e-01 6.97419792e-02 -1.90581873e-01 -4.69826341e-01
6.44897744e-02 7.11524859e-02 -6.76164329e-01 6.58927977e-01
5.38743675e-01 1.11039948e+00 -1.20626700e+00 -4.11312073e-01
1.19240656e-01 5.10793984e-01 -2.68238127e-01 2.11153418e-01
-4.24131036e-01 5.23316205e-01 -5.85735381e-01 6.69078588e-01
6.19571269e-01 -7.33008623e-01 2.14795604e-01 -3.89519572e-01
3.82381707e-01 1.96450993e-01 -9.11510527e-01 1.38820887e+00
-1.74095064e-01 5.21095812e-01 1.41026936e-02 -1.30566311e+00
8.05722058e-01 2.74463356e-01 7.75180936e-01 -4.86903876e-01
-2.73203582e-01 -1.48955733e-01 -2.06322018e-02 -5.98300695e-01
2.50538886e-02 2.34892920e-01 -2.22965479e-02 1.42604917e-01
2.87760079e-01 4.55166310e-01 3.44102494e-02 5.46765089e-01
1.66532648e+00 1.49701297e-01 7.53578469e-02 -3.79147053e-01
5.53290248e-01 -3.06684554e-01 6.19100392e-01 5.51074445e-01
-3.35524589e-01 1.22679230e-02 8.84986162e-01 -4.92674977e-01
-8.10508430e-01 -1.18271565e+00 5.27408672e-03 9.53975201e-01
2.80267715e-01 -4.79324222e-01 -1.14874884e-01 -6.72619343e-01
2.73815185e-01 4.57956791e-01 -7.76984572e-01 -7.32555866e-01
-6.05565190e-01 -1.13231421e+00 3.50726515e-01 3.41941506e-01
3.97491992e-01 -9.79645252e-01 4.71168131e-01 4.16960299e-01
-2.15838388e-01 -1.14015186e+00 -6.35046959e-01 1.94957256e-02
-6.13794744e-01 -1.18451512e+00 -6.34884089e-02 -6.87268853e-01
4.38212872e-01 1.39784977e-01 1.32869875e+00 1.03821225e-01
-4.15333092e-01 6.71369433e-01 -1.35489315e-01 9.70436633e-02
-2.56156206e-01 1.61480933e-01 -8.56928080e-02 5.27639329e-01
-7.74913728e-02 -1.15228903e+00 -6.63790762e-01 1.94944426e-01
-4.68377233e-01 2.33652607e-01 6.29637659e-01 8.35917294e-01
6.81626260e-01 2.31775656e-01 7.83425093e-01 -9.00284529e-01
6.81001365e-01 -1.15087998e+00 -4.45650905e-01 3.12741190e-01
-7.84222901e-01 -1.39373326e-04 8.62787545e-01 -5.66180944e-01
-8.53876054e-01 -3.81346017e-01 1.98127896e-01 -6.87860250e-01
3.33465129e-01 8.17524254e-01 -2.20639884e-01 4.86334460e-03
4.50710714e-01 5.49880087e-01 1.29817724e-01 -1.53064311e-01
3.48918647e-01 -1.12301940e-02 5.43809831e-01 -7.84156978e-01
1.17428255e+00 4.52753276e-01 6.13233864e-01 -7.99882829e-01
-6.50733113e-01 -2.62648106e-01 -1.79233521e-01 -6.72778189e-01
4.59219813e-01 -1.01931179e+00 -9.28032577e-01 5.40469468e-01
-8.73471081e-01 -5.14374435e-01 -3.03338170e-01 1.49674460e-01
-6.07472301e-01 5.93903124e-01 -8.89489949e-01 -7.68092036e-01
-5.27446032e-01 -6.37860417e-01 1.21361828e+00 -9.59664881e-02
2.59016186e-01 -1.63064396e+00 6.31275773e-02 -8.94299820e-02
3.26463044e-01 7.72260010e-01 1.09516442e+00 -5.56922972e-01
-8.35591316e-01 -3.89382213e-01 -4.31563556e-01 -2.01949049e-02
-1.52226865e-01 3.29619914e-01 -7.68326700e-01 -4.56376016e-01
-2.57790089e-01 8.45008250e-03 9.53620970e-01 6.26316905e-01
1.50766218e+00 -4.47285563e-01 -7.62329280e-01 9.48580801e-01
1.36099124e+00 -4.89887387e-01 4.74825829e-01 -3.63491654e-01
9.28145766e-01 4.98373628e-01 5.16022325e-01 3.96160483e-01
5.76513231e-01 6.86273098e-01 7.58655310e-01 8.55162367e-02
-2.94551641e-01 -7.05134928e-01 5.22273660e-01 1.18620718e+00
-1.27842382e-01 -7.77757943e-01 -7.82098114e-01 4.32957500e-01
-2.20106125e+00 -1.19008100e+00 -3.48823667e-01 1.89658248e+00
4.28164065e-01 2.58736402e-01 1.92957580e-01 -7.77990520e-02
9.27918494e-01 7.66402960e-01 -8.59760702e-01 7.85216466e-02
-1.10624485e-01 -2.89611310e-01 4.49075013e-01 4.60404485e-01
-1.16504455e+00 6.37965679e-01 5.60334492e+00 1.21618104e+00
-1.07082736e+00 5.83393238e-02 6.73734963e-01 1.69438630e-01
-2.22774804e-01 -4.73508127e-02 -5.62760472e-01 8.55720222e-01
1.16582668e+00 -6.13782108e-01 7.17645228e-01 5.69179296e-01
4.94651169e-01 6.70855582e-01 -9.87408698e-01 6.86671972e-01
-4.27899212e-01 -1.31318629e+00 7.86760822e-02 3.68197113e-01
4.39146131e-01 2.29997978e-01 8.61290246e-02 5.73759496e-01
3.67233038e-01 -9.39979613e-01 3.66369754e-01 1.11998177e+00
7.56409228e-01 -3.84088755e-01 3.96605134e-01 4.92522120e-01
-1.84889185e+00 -5.53958267e-02 -2.86689490e-01 1.42003655e-01
3.48384947e-01 9.66255724e-01 -7.42195785e-01 9.35208261e-01
5.86115777e-01 1.58112526e+00 -3.63845259e-01 8.28718901e-01
-4.31301780e-02 1.17515135e+00 -4.30139035e-01 1.18451610e-01
6.77677169e-02 -3.75138074e-01 1.14113402e+00 1.10385787e+00
2.19218448e-01 -2.99204171e-01 4.56588417e-01 9.21090245e-01
-1.56197593e-01 -1.10972539e-01 -6.51882529e-01 -2.31399581e-01
6.48394585e-01 1.29831028e+00 -4.22580421e-01 4.24916595e-02
-3.29744756e-01 8.26600611e-01 6.47422194e-01 6.51744127e-01
-1.22318304e+00 -1.12007633e-01 7.00760245e-01 5.09844482e-01
2.07685322e-01 -1.47050366e-01 1.71225622e-01 -1.08716214e+00
2.29364067e-01 -2.64177203e-01 7.41558135e-01 -6.85044348e-01
-1.87873411e+00 3.88077170e-01 -2.82633826e-02 -1.19022584e+00
-2.24713206e-01 -4.29648668e-01 -1.18576872e+00 9.97985601e-01
-1.50497019e+00 -1.50197780e+00 -4.82484996e-01 5.62697470e-01
2.60757178e-01 -6.05302211e-03 5.05245626e-01 5.57833731e-01
-1.06220615e+00 4.67562020e-01 -3.45322080e-02 -4.21189889e-03
4.96857762e-01 -1.37085021e+00 5.51970840e-01 8.41582239e-01
-1.87490642e-01 1.71691611e-01 7.10331738e-01 -8.68328989e-01
-1.63978314e+00 -1.60660958e+00 6.26700640e-01 -2.42459729e-01
1.28669548e+00 -3.26241434e-01 -1.01574433e+00 8.39451075e-01
-4.13324744e-01 5.17460525e-01 2.92687476e-01 4.52407748e-02
-1.57266691e-01 -4.78915185e-01 -9.21732605e-01 4.72803921e-01
1.77061546e+00 -6.28062844e-01 1.34128019e-01 8.95831287e-01
1.15143144e+00 -2.75534332e-01 -1.01680410e+00 5.31272352e-01
4.59350944e-02 -4.26805347e-01 1.15081918e+00 -9.34951425e-01
2.64067024e-01 -2.53105432e-01 1.09038673e-01 -1.23829091e+00
-8.18462133e-01 -9.47910964e-01 -1.05444062e+00 1.39662957e+00
1.03595532e-01 -9.61267114e-01 4.04009551e-01 1.54353872e-01
-3.17986310e-01 -1.01811457e+00 -1.09749603e+00 -6.43285453e-01
-4.36223805e-01 -4.01405543e-01 5.65415204e-01 9.17239487e-01
-2.60283172e-01 3.41044426e-01 -5.99366069e-01 6.20834708e-01
7.56920934e-01 7.12846071e-02 5.88481188e-01 -1.47767138e+00
-5.12415886e-01 -4.71678525e-01 -8.08340549e-01 -1.04039085e+00
4.15022790e-01 -1.25475776e+00 -3.56881440e-01 -1.24418569e+00
3.44079994e-02 -3.35513264e-01 -3.51383358e-01 2.78112795e-02
-4.18714285e-01 -3.89439136e-01 -3.33690882e-01 4.32414383e-01
-7.57871151e-01 9.47831571e-01 1.29053056e+00 -2.01948628e-01
1.24183923e-01 2.03128055e-01 -4.75757062e-01 3.48337322e-01
3.64054978e-01 -2.98920810e-01 -6.26563311e-01 1.04897492e-01
4.45504516e-01 3.06003720e-01 7.71912158e-01 -8.31703663e-01
2.06719518e-01 -1.32855430e-01 5.35937212e-02 -4.98267323e-01
2.23410368e-01 -7.32727945e-01 5.22410989e-01 4.83963907e-01
-1.91757813e-01 -1.79059684e-01 -1.03278913e-01 1.56365335e+00
-1.48334913e-02 4.15087372e-01 3.77549917e-01 2.69913524e-01
-6.84299111e-01 1.22581255e+00 7.67165050e-02 2.59899288e-01
1.09852421e+00 1.90248877e-01 -4.67170447e-01 -5.77715099e-01
-9.50621665e-01 6.68170273e-01 1.17420271e-01 2.96950042e-01
1.88822821e-01 -1.49892247e+00 -8.64877343e-01 4.23850529e-02
9.58632156e-02 -1.23908065e-01 5.87391973e-01 1.03313076e+00
-2.34386325e-01 6.25849962e-02 3.37295085e-01 -6.61265254e-01
-5.86271226e-01 8.04323554e-01 5.98674774e-01 -9.75083888e-01
-1.09740412e+00 6.54939353e-01 1.40229106e-01 -4.19790775e-01
2.44305342e-01 -1.50498182e-01 -5.49216419e-02 -8.01507607e-02
1.13039494e-01 4.29462731e-01 -7.73483589e-02 -4.97439682e-01
-2.59103179e-01 3.30931425e-01 3.22323114e-01 5.39420307e-01
1.39403164e+00 -3.26317161e-01 -6.46628663e-02 5.60139656e-01
1.14204836e+00 -2.81789333e-01 -1.69302917e+00 -5.96717417e-01
-1.46719113e-01 -8.71118605e-02 8.81478842e-03 -4.78398085e-01
-1.20496774e+00 7.31638789e-01 2.45659947e-01 8.49647343e-01
1.29839110e+00 1.14284486e-01 8.38406682e-01 1.30184114e-01
3.23087931e-01 -7.19942093e-01 -5.58697246e-02 3.30000639e-01
1.11997783e+00 -1.01745415e+00 -8.10205862e-02 -9.09335256e-01
-2.04308659e-01 9.76988494e-01 4.77924317e-01 -4.34398711e-01
1.24411476e+00 -1.44960761e-01 -8.54883611e-01 -4.35012192e-01
-1.32519424e+00 -7.90216550e-02 5.28738320e-01 7.77151465e-01
-1.21121131e-01 1.55762672e-01 -1.11476943e-01 8.73995483e-01
1.46530434e-01 -3.43335629e-01 5.40611781e-02 1.06114954e-01
-3.94918658e-02 -5.35270095e-01 3.00134987e-01 8.97502065e-01
-6.39405325e-02 9.60000232e-02 -5.09027913e-02 5.79018474e-01
-1.82688534e-01 6.25811338e-01 1.40849808e-02 -5.19445240e-01
2.20932603e-01 2.42961925e-02 -3.72578427e-02 -3.69498998e-01
-3.85796398e-01 -3.33166569e-01 2.10991532e-01 -8.22670817e-01
-6.93024769e-02 -6.05474412e-01 -8.52908492e-01 -6.96035624e-01
-1.75638765e-01 -1.07031859e-01 1.48238152e-01 7.67736137e-01
7.42141962e-01 1.02335382e+00 9.46629643e-01 -8.44563365e-01
-5.53721070e-01 -1.00362682e+00 -7.32242346e-01 4.60017681e-01
3.14955920e-01 -7.46439517e-01 -8.19023430e-01 -1.63743392e-01] | [7.192079067230225, 5.845260143280029] |
89691f08-8702-4a46-9770-ed38ce0471e7 | a-review-and-comparative-study-of-close-range | 2306.09014 | null | https://arxiv.org/abs/2306.09014v1 | https://arxiv.org/pdf/2306.09014v1.pdf | A Review and Comparative Study of Close-Range Geometric Camera Calibration Tools | In many camera-based applications, it is necessary to find the geometric relationship between incoming rays and image pixels, i.e., the projection model, through the geometric camera calibration (GCC). Aiming to provide practical calibration guidelines, this work surveys and evaluates the existing GCC tools. The survey covers camera models, calibration targets, and algorithms used in these tools, highlighting their properties and the trends in GCC development. The evaluation compares six target-based GCC tools, namely, BabelCalib, Basalt, Camodocal, Kalibr, the MATLAB calibrator, and the OpenCV-based ROS calibrator, with simulated and real data for cameras of wide-angle and fisheye lenses described by three traditional projection models. These tests reveal the strengths and weaknesses of these camera models, as well as the repeatability of these GCC tools. In view of the survey and evaluation, future research directions of GCC are also discussed. | ['Alper Yilmaz', 'Yijia He', 'Junhui Liu', 'Binliang Wang', 'Grzegorz Jozkow', 'Yuxin Shao', 'Yuan Zhuang', 'Jianzhu Huai'] | 2023-06-15 | null | null | null | null | ['camera-calibration'] | ['computer-vision'] | [-2.78051734e-01 -6.29518390e-01 4.54709023e-01 -3.02175611e-01
9.47858393e-02 -9.09822643e-01 4.44389492e-01 -5.33294797e-01
-1.82495683e-01 2.33796999e-01 -5.35514764e-02 -5.68620384e-01
4.60581407e-02 -6.13823235e-01 -5.52556694e-01 -5.32016158e-01
4.48425204e-01 -1.30218849e-01 5.46046078e-01 -5.84809035e-02
4.26935732e-01 6.12756193e-01 -1.48646367e+00 -1.18407309e-01
5.46769679e-01 7.10896909e-01 2.28063866e-01 1.09740257e+00
1.06761515e-01 6.01179898e-01 -5.10618508e-01 -6.47801936e-01
4.68139231e-01 -4.97066826e-01 -2.26453930e-01 1.05905026e-01
7.27604806e-01 -3.94054949e-01 -2.72785444e-02 7.96220183e-01
3.11922759e-01 -3.18196654e-01 2.72132397e-01 -1.10314763e+00
-3.84001672e-01 4.48451042e-02 -4.80470210e-01 -1.12583190e-01
7.80905843e-01 2.33410493e-01 1.63295627e-01 -8.36282313e-01
4.48703438e-01 9.52349126e-01 1.12852240e+00 1.25039592e-01
-7.02186882e-01 -5.70308983e-01 -2.31888667e-01 -1.91670313e-01
-1.33565116e+00 6.72386214e-02 3.91817212e-01 -5.57139218e-01
5.53441465e-01 3.82259279e-01 1.11068988e+00 8.64331841e-01
4.49693680e-01 -1.36894003e-01 1.45630956e+00 -8.91727388e-01
1.27163544e-01 6.26819193e-01 2.47083426e-01 4.97384906e-01
3.97538036e-01 4.63034213e-01 -3.86896938e-01 9.30333394e-04
1.24309957e+00 -2.89032191e-01 -5.63179433e-01 -6.06803358e-01
-1.08831811e+00 5.56978047e-01 3.91133964e-01 -9.10490900e-02
3.77898753e-01 1.71278715e-01 -1.52565718e-01 1.28120184e-02
-3.74407582e-02 2.47368634e-01 -2.36586019e-01 -1.10712171e-01
-6.36496007e-01 -6.59447312e-02 7.51972139e-01 1.69647801e+00
6.97104454e-01 1.70472369e-01 6.25904739e-01 4.95488882e-01
8.19072902e-01 8.32675695e-01 1.68987542e-01 -1.06147170e+00
1.91985399e-01 3.67398202e-01 -6.00032844e-02 -9.31426048e-01
-3.09330076e-01 -9.76386070e-02 -1.17336079e-01 6.73406243e-01
2.80745089e-01 -5.08268833e-01 -5.01477122e-01 6.30101979e-01
2.92180806e-01 1.93357870e-01 1.56230435e-01 9.48523998e-01
8.89144421e-01 6.34774327e-01 -5.71345985e-01 9.74675454e-03
1.06868064e+00 -1.03347123e+00 -5.52108169e-01 -1.88985303e-01
3.83403033e-01 -1.41490364e+00 1.12308848e+00 6.57382727e-01
-8.87766659e-01 -6.68821514e-01 -1.23588967e+00 -3.51577885e-02
-3.01857680e-01 4.96196717e-01 4.18882251e-01 1.28946531e+00
-1.11826646e+00 4.83344533e-02 -5.88785231e-01 -7.45993435e-01
-3.59521002e-01 3.36809903e-02 -2.54117660e-02 -1.00898914e-01
-4.15637225e-01 1.04050636e+00 2.74331644e-02 4.26085815e-02
-5.70500493e-01 -7.75207520e-01 -5.25587499e-01 -2.99042493e-01
-2.17791293e-02 -5.49042284e-01 1.35022569e+00 -9.63290334e-01
-1.99690592e+00 6.03090227e-01 3.59897941e-01 -9.67388153e-02
5.73314369e-01 -4.93523657e-01 -6.93526745e-01 2.18804464e-01
-2.45493010e-01 4.28548932e-01 3.34288687e-01 -1.43732727e+00
-7.10481226e-01 -7.00329393e-02 1.98932931e-01 3.04211825e-01
6.28835559e-02 3.85493338e-01 -8.65879774e-01 -1.09725900e-01
-8.38945135e-02 -1.01507699e+00 -7.84335509e-02 2.40131497e-01
-2.13879928e-01 7.19594598e-01 1.14295137e+00 -2.78057456e-01
1.15530217e+00 -2.31329536e+00 -4.58054483e-01 3.48810434e-01
-3.26634407e-01 2.89237499e-01 3.14474434e-01 7.54989147e-01
-1.66957587e-01 -2.79820174e-01 1.31991282e-01 2.13667721e-01
-5.19980133e-01 -9.76913124e-02 -2.58893996e-01 5.98179400e-01
-4.86900240e-01 2.48229533e-01 -5.56905925e-01 -3.09669971e-01
1.02509153e+00 6.69652879e-01 -1.47711098e-01 3.18153083e-01
2.01955691e-01 3.72446716e-01 5.54471016e-02 7.70220160e-01
1.09739459e+00 3.04119408e-01 9.15839896e-02 -3.38049054e-01
-1.03955257e+00 -3.31185907e-01 -1.62886643e+00 1.15695834e+00
-4.94278938e-01 1.06093192e+00 6.14838488e-02 1.18597053e-01
1.18015790e+00 7.05633387e-02 3.65406238e-02 -4.17629331e-01
2.72250026e-01 2.50640064e-01 -3.98919165e-01 -5.93246162e-01
5.08472204e-01 4.38510537e-01 3.73042643e-01 1.91551596e-01
-6.20729616e-03 -8.02813649e-01 1.27494156e-01 1.50291696e-01
4.60355729e-01 6.52768016e-01 5.86440802e-01 -4.01196569e-01
7.13083625e-01 4.22990799e-01 -1.18633891e-02 2.82582670e-01
1.97159275e-01 1.14559484e+00 3.88311058e-01 -4.63712007e-01
-1.19857562e+00 -1.03681684e+00 -4.97296274e-01 2.40225971e-01
6.36610091e-01 -6.75243735e-01 -1.12677276e+00 2.42945421e-02
-3.88874412e-01 5.92367232e-01 -2.21199527e-01 4.00496304e-01
-3.44295472e-01 -5.68268895e-01 4.01144892e-01 3.94051820e-01
6.27037823e-01 -2.97142953e-01 -1.29644239e+00 -3.03620815e-01
4.87749219e-01 -1.32040417e+00 -1.91606134e-01 -1.56429395e-01
-9.92799819e-01 -1.55277705e+00 -3.29267085e-01 -3.00934821e-01
7.95036912e-01 1.00373840e+00 1.01057553e+00 1.63516790e-01
-2.64687896e-01 1.07719326e+00 -6.70364201e-01 -6.04885519e-01
-3.92600633e-02 -5.85418105e-01 -4.51474786e-01 -3.43382001e-01
3.93849105e-01 -8.09150264e-02 -5.48903108e-01 1.01926386e+00
-8.20827901e-01 1.34148747e-01 3.09881866e-01 2.75718600e-01
4.66042221e-01 -1.73641771e-01 -7.45410681e-01 -7.25377202e-01
4.21355724e-01 -8.15120637e-02 -1.57732666e+00 2.28219703e-01
-8.44200611e-01 -6.12101316e-01 3.88643622e-01 -2.36151516e-02
-1.37087464e+00 3.77192080e-01 2.45663345e-01 -2.48151943e-01
-1.79177612e-01 1.41977355e-01 -7.43384883e-02 -6.62506640e-01
9.09101665e-01 -2.56497622e-01 -4.30654455e-03 -2.56610006e-01
3.57927769e-01 7.16599643e-01 8.31067204e-01 -3.52059275e-01
7.95377553e-01 6.51468873e-01 -1.75855517e-01 -1.15804815e+00
-3.60884488e-01 -5.53962827e-01 -9.00448740e-01 -9.75660264e-01
8.97859752e-01 -1.03288007e+00 -2.30888426e-01 6.59926116e-01
-1.24237406e+00 -2.24738181e-01 4.98939082e-02 9.52229738e-01
-2.75419027e-01 3.32860678e-01 -2.70259023e-01 -5.86574972e-01
1.26539469e-01 -1.53218567e+00 8.42549026e-01 7.50081837e-01
1.09297112e-01 -1.14304030e+00 5.71545124e-01 3.72933775e-01
2.84924328e-01 2.91583478e-01 2.16587901e-01 4.33759332e-01
-9.23893392e-01 -2.57823795e-01 -1.83280870e-01 5.57214975e-01
-3.48051369e-01 1.28793204e+00 -1.02591610e+00 1.71122011e-02
-1.56198312e-02 2.80007154e-01 -3.12870294e-02 4.45418745e-01
6.61477447e-01 2.70049423e-01 -3.17106366e-01 1.24577856e+00
2.19139981e+00 5.33148587e-01 1.07246411e+00 5.92022777e-01
4.36987847e-01 2.84216970e-01 7.77064085e-01 3.69949013e-01
1.96175426e-01 7.24556804e-01 7.76500404e-01 -6.66146353e-02
-2.04355419e-01 -1.11706570e-01 5.52312672e-01 9.57632840e-01
-5.94731867e-01 -1.61413938e-01 -9.87519801e-01 -2.26318419e-01
-1.28458309e+00 -6.62231863e-01 -1.46431267e+00 2.33774233e+00
1.35238498e-01 -1.93204671e-01 -7.90610090e-02 -1.22524448e-01
6.90354764e-01 -2.80217379e-01 2.38842741e-01 -7.62357891e-01
-1.44845009e-01 1.35586187e-01 9.51405764e-01 5.14512897e-01
-9.29209113e-01 7.55419791e-01 7.90480995e+00 -2.62347721e-02
-1.29118526e+00 5.79331890e-02 -1.10896621e-02 1.64968938e-01
6.51234314e-02 6.90727949e-01 -1.15838027e+00 4.91194457e-01
9.76911724e-01 3.63871008e-02 5.68273425e-01 1.13032770e+00
3.39024365e-01 -7.75607765e-01 -7.26076722e-01 9.30659533e-01
5.20154238e-01 -1.16197658e+00 -4.32008535e-01 7.07203010e-03
1.00142610e+00 1.01489343e-01 -2.17133373e-01 -3.73860717e-01
2.81549901e-01 -5.21664739e-01 8.84997189e-01 6.30842805e-01
6.50491118e-01 -5.25347769e-01 1.08202672e+00 -2.12169662e-01
-1.08236015e+00 -4.33652028e-02 -7.47769117e-01 -1.69135824e-01
1.67480797e-01 4.02252614e-01 -5.98042965e-01 7.50775278e-01
1.03217494e+00 5.82008183e-01 -1.05659580e+00 1.62812889e+00
-5.38914382e-01 4.23863322e-01 -3.07474613e-01 -6.76189596e-03
2.01710299e-01 -9.75723326e-01 1.01483241e-01 1.26168418e+00
8.45176995e-01 -2.21341904e-02 -3.94776195e-01 7.46109307e-01
6.93457425e-01 8.03225487e-02 -7.07725465e-01 5.40637791e-01
5.29988885e-01 1.44792402e+00 -6.44066095e-01 1.87541217e-01
-9.76779521e-01 5.70711613e-01 -4.02370870e-01 2.72912234e-01
-1.22676444e+00 -3.10541928e-01 3.17131490e-01 3.43828976e-01
1.29031921e-02 -5.46845317e-01 -4.13281053e-01 -7.87500978e-01
-1.42805383e-01 -7.84789205e-01 6.57432601e-02 -1.71175218e+00
-5.98776221e-01 4.19711411e-01 5.74753523e-01 -1.89881706e+00
1.31931350e-01 -1.09198403e+00 -9.30479348e-01 6.68893337e-01
-1.05122614e+00 -1.37827873e+00 -1.23971212e+00 6.56328499e-01
2.58622348e-01 -2.51505882e-01 4.91908997e-01 2.19886288e-01
-5.27252972e-01 3.51702049e-02 5.39543629e-01 -1.02396987e-01
8.38386238e-01 -1.05257642e+00 -1.46509204e-02 1.12750566e+00
-6.62806481e-02 6.81447446e-01 6.80318654e-01 -3.72835755e-01
-1.43536770e+00 -6.42369390e-01 6.63054734e-02 -6.56383693e-01
5.08679330e-01 -2.38775074e-01 -3.42620045e-01 9.48561788e-01
6.58190846e-01 -5.13911694e-02 4.39950645e-01 -5.26245713e-01
-1.48744345e-01 -5.40727913e-01 -7.29913175e-01 5.87975383e-01
5.10861933e-01 -2.15597540e-01 -2.67645180e-01 2.01138884e-01
2.92217553e-01 -8.61758471e-01 -6.57946050e-01 -9.15906578e-02
9.15130556e-01 -1.85661304e+00 9.38858509e-01 4.00556892e-01
3.23371500e-01 -6.18348122e-01 -1.04716405e-01 -1.29560912e+00
8.61604363e-02 -4.38494503e-01 4.87230539e-01 1.29579628e+00
2.58015454e-01 -6.55685842e-01 3.71419698e-01 4.28115934e-01
-4.72057670e-01 -1.74131840e-01 -5.57704747e-01 -6.63213551e-01
-1.81526423e-01 -4.86865401e-01 5.59435606e-01 6.11849129e-01
-1.85368463e-01 1.97713554e-01 -2.18817651e-01 4.65254486e-01
4.52909827e-01 -3.43009204e-01 1.53969073e+00 -1.03223610e+00
-1.19327143e-01 -1.56718776e-01 -6.62934542e-01 -8.12097847e-01
-7.01849163e-01 8.33651125e-02 -3.78478318e-01 -1.34733081e+00
-3.82685699e-02 -2.56480843e-01 7.40419447e-01 -4.51824993e-01
4.07180876e-01 4.66280356e-02 2.74527848e-01 2.19850212e-01
6.19776100e-02 -2.23628148e-01 9.92085099e-01 4.07290846e-01
-3.37065198e-02 1.64573565e-01 -3.19308817e-01 1.09193444e+00
6.22116387e-01 -2.92764664e-01 -5.38792849e-01 -7.83731878e-01
5.85844040e-01 -2.02478737e-01 5.87185621e-01 -1.67156208e+00
4.92978185e-01 -1.35963544e-01 3.83873403e-01 -9.13684905e-01
2.26228639e-01 -1.44803858e+00 8.36152613e-01 3.14477772e-01
2.16417611e-01 7.13152289e-01 5.64767458e-02 2.54072756e-01
-1.25810117e-01 -8.64605546e-01 1.23777843e+00 -2.48654217e-01
-7.86951602e-01 -3.12038958e-01 -4.06037599e-01 -3.31460327e-01
1.64233661e+00 -9.43957329e-01 -6.79593742e-01 -3.15814614e-01
-1.41726092e-01 -6.37123957e-02 9.54891443e-01 2.75375228e-02
7.23668337e-01 -1.11303830e+00 -1.39533922e-01 4.64222759e-01
1.50648907e-01 7.62927299e-03 -5.88213652e-02 7.98076689e-01
-1.85447752e+00 5.87101161e-01 -5.44569433e-01 -8.35975587e-01
-1.61341822e+00 5.04728019e-01 6.41235292e-01 5.84298909e-01
-4.16021466e-01 4.42939222e-01 -1.48394993e-02 -4.06207889e-01
6.90844953e-02 -4.73227412e-01 -1.61855400e-01 -5.40380538e-01
4.13855910e-01 7.89968133e-01 2.11878717e-01 -5.15542090e-01
-1.60858601e-01 1.26553535e+00 6.12023294e-01 -2.34721079e-01
8.66953611e-01 -4.10892159e-01 -3.04030538e-01 2.27031022e-01
7.22692668e-01 5.38457692e-01 -1.16933680e+00 4.36110407e-01
-4.39477950e-01 -9.26454663e-01 9.96015295e-02 -7.33293355e-01
-1.07290685e+00 9.78088081e-01 9.56064284e-01 5.79164326e-02
1.04644334e+00 -3.34431946e-01 -6.34386167e-02 1.04473867e-01
5.05923331e-01 -1.13307047e+00 -1.62832931e-01 2.74189919e-01
7.03021109e-01 -7.85151780e-01 5.08892477e-01 -8.15325320e-01
-6.16713822e-01 1.87643492e+00 9.99141455e-01 -3.42448540e-02
8.34139228e-01 7.68478572e-01 5.09445846e-01 -1.87220812e-01
-2.45537415e-01 1.86042666e-01 -1.41056553e-01 1.02089274e+00
4.83238459e-01 -1.33900061e-01 -2.04896346e-01 -1.06767073e-01
-3.91278535e-01 -1.61312278e-02 1.26758170e+00 9.49639320e-01
-4.18895304e-01 -9.88968790e-01 -1.15925992e+00 -1.44809872e-01
2.86819171e-02 7.44345263e-02 -6.81811154e-01 1.37253201e+00
2.29081944e-01 9.74740863e-01 5.67881539e-02 -3.97830814e-01
5.69681942e-01 -6.38579726e-01 2.93439955e-01 -2.94570148e-01
-7.58256614e-01 1.92085311e-01 3.33347358e-02 -5.40258467e-01
-6.49552882e-01 -6.40073061e-01 -6.17928147e-01 -4.72795278e-01
-8.09840024e-01 -4.60354351e-02 1.17008781e+00 3.00524443e-01
-4.94793989e-03 2.06742793e-01 6.44791007e-01 -6.55256867e-01
-1.33868620e-01 -6.65490568e-01 -3.29736412e-01 -3.45042616e-01
-8.10817033e-02 -4.34558868e-01 -5.38710356e-01 4.49424744e-01] | [8.190723419189453, -2.2565338611602783] |
26ea26c4-fd5a-446d-a979-265c8826e3a1 | attention-based-ingredient-phrase-parser | 2210.02535 | null | https://arxiv.org/abs/2210.02535v1 | https://arxiv.org/pdf/2210.02535v1.pdf | Attention-based Ingredient Phrase Parser | As virtual personal assistants have now penetrated the consumer market, with products such as Siri and Alexa, the research community has produced several works on task-oriented dialogue tasks such as hotel booking, restaurant booking, and movie recommendation. Assisting users to cook is one of these tasks that are expected to be solved by intelligent assistants, where ingredients and their corresponding attributes, such as name, unit, and quantity, should be provided to users precisely and promptly. However, existing ingredient information scraped from the cooking website is in the unstructured form with huge variation in the lexical structure, for example, '1 garlic clove, crushed', and '1 (8 ounce) package cream cheese, softened', making it difficult to extract information exactly. To provide an engaged and successful conversational service to users for cooking tasks, we propose a new ingredient parsing model that can parse an ingredient phrase of recipes into the structure form with its corresponding attributes with over 0.93 F1-score. Experimental results show that our model achieves state-of-the-art performance on AllRecipes and Food.com datasets. | ['Aldo Lipani', 'To Eun Kim', 'MeiHui Wang', 'Pin Ni', 'Zhengxiang Shi'] | 2022-10-05 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [-1.36700468e-02 2.35932797e-01 -3.47168207e-01 -8.06830883e-01
-4.92884934e-01 -9.19073343e-01 8.79583601e-03 7.30312347e-01
-2.87461758e-01 5.35549462e-01 5.87774098e-01 -3.61717075e-01
2.01225340e-01 -8.52672458e-01 -4.93171066e-01 -3.89776945e-01
1.47790134e-01 6.25662684e-01 -4.05664779e-02 -7.00880826e-01
-8.31511170e-02 -5.44624209e-01 -1.40813017e+00 6.69905782e-01
9.64314461e-01 8.84881377e-01 6.10140800e-01 5.62109768e-01
-7.60515332e-01 6.37911916e-01 -2.76518166e-01 -7.31348038e-01
3.92735191e-02 -2.33541086e-01 -9.43246245e-01 7.77796954e-02
5.79824559e-02 -7.21983671e-01 1.38875693e-01 1.13165152e+00
1.63072601e-01 9.57154483e-02 3.76620740e-01 -9.58047211e-01
-8.65388155e-01 1.42354274e+00 -2.31048211e-01 -3.60071719e-01
9.37233150e-01 -1.19639739e-01 1.42652881e+00 -3.01439852e-01
2.78342068e-01 1.12000108e+00 4.29204077e-01 5.60377300e-01
-7.25073278e-01 -3.90262663e-01 3.63595665e-01 7.62368366e-02
-7.27569520e-01 -2.19081149e-01 6.23269081e-01 -1.35247722e-01
8.26603770e-01 5.52881241e-01 5.79028428e-01 9.80701804e-01
-4.11500484e-01 1.23468947e+00 8.03824604e-01 -3.34651798e-01
1.15273483e-01 5.61801195e-01 5.88986754e-01 6.18557096e-01
1.28337264e-01 -5.86526871e-01 -3.37722659e-01 -1.95718314e-02
4.55188900e-01 9.71454382e-02 -1.72163710e-01 -1.61994904e-01
-1.27335024e+00 1.24501491e+00 7.17366189e-02 6.66633546e-02
-5.72472394e-01 -5.13003647e-01 5.85131586e-01 1.97208017e-01
4.42693859e-01 4.68049318e-01 -1.02030671e+00 -2.74202526e-01
-1.73727889e-02 4.81592208e-01 1.42208612e+00 1.42330241e+00
4.83104497e-01 -7.81104445e-01 6.18504509e-02 1.17509198e+00
5.11622369e-01 4.95776355e-01 2.12031394e-01 -9.04179990e-01
6.56802833e-01 7.41121292e-01 6.29498482e-01 -8.42455626e-01
-6.25717163e-01 2.75039196e-01 -8.14803839e-01 -6.49721265e-01
7.22024858e-01 -5.50327837e-01 -2.94742405e-01 1.50682747e+00
7.53681302e-01 -5.31220078e-01 1.76397488e-01 1.07059455e+00
1.55427349e+00 7.43813455e-01 5.21643758e-01 -1.75682783e-01
2.19324040e+00 -1.24727464e+00 -1.27899599e+00 -2.81645030e-01
5.25173545e-01 -1.11895323e+00 1.21106112e+00 3.74605924e-01
-8.72718394e-01 -5.42319596e-01 -8.67147863e-01 -3.53535891e-01
-5.64736068e-01 4.14966345e-02 1.23084199e+00 6.48815334e-01
-2.94608951e-01 6.74601018e-01 -5.04362583e-01 -4.80044931e-01
-1.52242035e-01 1.21692210e-01 -3.08293819e-01 -2.83793986e-01
-1.45435417e+00 5.71032047e-01 3.38191390e-01 1.04511872e-01
-2.31903970e-01 -7.23966837e-01 -1.24532402e+00 -6.36948496e-02
7.13144839e-01 -5.99905550e-01 1.68655252e+00 -6.41895652e-01
-1.67403746e+00 7.69480586e-01 4.72105183e-02 1.07516916e-02
1.47418268e-02 -5.08119881e-01 -6.58232987e-01 -1.16312779e-01
3.36876541e-01 4.30799007e-01 4.29099172e-01 -7.87545860e-01
-1.11486626e+00 -4.66715336e-01 6.70703113e-01 4.60377932e-01
-1.63176835e-01 3.18511128e-01 -2.84190536e-01 -2.48821825e-01
5.79550043e-02 -5.98701656e-01 -2.36837372e-01 -2.65501142e-01
-3.71593237e-01 -7.34913170e-01 2.73927987e-01 -1.13618314e+00
1.28698111e+00 -2.08596063e+00 -8.26443210e-02 -2.32323110e-01
2.52408922e-01 1.69390887e-01 -5.83881699e-02 5.49462438e-01
4.68589127e-01 3.16697061e-02 9.37658176e-02 -1.41260639e-01
6.01052642e-01 2.65296966e-01 2.06127316e-01 5.43742739e-02
-8.33578408e-02 6.26918375e-01 -1.28441620e+00 -3.37245405e-01
1.86552688e-01 4.60452795e-01 -5.20098925e-01 5.76078892e-01
-7.44735122e-01 1.76544949e-01 -8.60596478e-01 6.79485738e-01
7.09939122e-01 -2.90203601e-01 3.91372651e-01 -4.97408211e-01
-1.97503403e-01 7.94728100e-01 -1.24821496e+00 1.96363556e+00
-4.36675668e-01 -1.22338161e-01 6.22487962e-01 -5.50210416e-01
7.88018584e-01 4.94506478e-01 4.08218324e-01 -6.66600108e-01
1.63221583e-01 -5.98883703e-02 -2.17961997e-01 -9.85476732e-01
7.86200225e-01 1.79434136e-01 -5.08105278e-01 5.80031335e-01
-2.56894052e-01 1.07902244e-01 5.28437316e-01 1.69636786e-01
7.58461356e-01 2.64271885e-01 3.71544629e-01 -2.71735609e-01
5.48064053e-01 1.71982169e-01 3.46599072e-01 3.69761556e-01
-2.05392092e-01 4.49625365e-02 1.13381229e-01 -5.68913460e-01
-1.08738792e+00 -6.82529330e-01 -2.44127456e-02 1.69421637e+00
1.59057289e-01 -7.08720207e-01 -8.81188989e-01 -7.29972959e-01
-1.12982698e-01 7.32497752e-01 -2.31888324e-01 3.41455162e-01
-6.35687232e-01 -5.27917922e-01 -1.74514204e-01 4.26798463e-01
8.63468528e-01 -1.22614956e+00 -5.26933372e-01 7.96020746e-01
-1.00436687e+00 -1.13740230e+00 -9.05153990e-01 2.01578736e-01
-3.16334456e-01 -1.07740104e+00 -4.00312781e-01 -1.16169047e+00
2.02985555e-01 5.18337548e-01 1.55621731e+00 2.62921184e-01
-3.39366160e-02 7.66600817e-02 -8.90797198e-01 -4.01748776e-01
-3.37053627e-01 3.93288322e-02 -2.75911123e-01 -7.32940361e-02
1.08369064e+00 -2.82715529e-01 -8.76772881e-01 4.05805230e-01
-6.45816088e-01 4.67256129e-01 2.07117572e-01 5.61172664e-01
2.79647499e-01 3.51425074e-02 5.43863177e-01 -1.18296254e+00
7.82804072e-01 -6.42851472e-01 -1.46760851e-01 3.02482277e-01
-4.93525773e-01 1.35486042e-02 8.53801191e-01 -4.72771078e-01
-1.03367722e+00 1.63994089e-01 -5.02470255e-01 7.91303039e-01
-5.52425683e-01 5.15575826e-01 -5.53553700e-01 7.34353900e-01
2.86014467e-01 -9.20307860e-02 -2.36457353e-03 -8.36366713e-01
6.46183550e-01 1.19048393e+00 5.71410358e-01 -7.22189665e-01
2.48825878e-01 -9.74790975e-02 -8.40072691e-01 -7.91662216e-01
-1.25993264e+00 -9.47976470e-01 -3.76254529e-01 4.84654866e-02
1.06383061e+00 -8.25559020e-01 -1.36231303e+00 2.83092380e-01
-1.16259205e+00 -2.41915703e-01 1.69457138e-01 4.68266845e-01
-2.08558977e-01 5.18448412e-01 -9.73029792e-01 -7.76212752e-01
-7.78146625e-01 -9.10217047e-01 8.12199771e-01 4.21419889e-01
-5.23045719e-01 -8.74328792e-01 -1.63145006e-01 9.16637778e-01
5.91551006e-01 2.99452674e-02 1.22993422e+00 -9.69651639e-01
-1.58729747e-01 -4.09203880e-02 -2.44678423e-01 6.02352358e-02
3.55073273e-01 -4.29247409e-01 -6.33788526e-01 5.29082753e-02
-8.34580138e-02 -1.86541066e-01 2.15178728e-01 2.19239056e-01
6.32223964e-01 -5.96441269e-01 -8.32364634e-02 8.91683847e-02
1.01607656e+00 3.55911493e-01 3.13832223e-01 2.92499721e-01
5.50287783e-01 8.71632278e-01 9.47654188e-01 6.31419718e-01
1.02304447e+00 5.08360147e-01 3.87061656e-01 8.70956667e-03
1.57556757e-01 -4.33423489e-01 2.37474129e-01 9.13784385e-01
3.16847079e-02 -8.62817094e-02 -8.01102519e-02 5.08116364e-01
-1.95294595e+00 -8.38873804e-01 -4.43198413e-01 2.06723785e+00
1.25839269e+00 -8.22603628e-02 3.65238994e-01 -1.90485582e-01
6.21285677e-01 5.30520976e-02 -4.23353165e-01 -4.27624702e-01
3.52231055e-01 -5.50728701e-02 2.80634224e-01 4.64821428e-01
-1.44244277e+00 1.00937462e+00 5.49722099e+00 3.48512888e-01
-2.89851367e-01 -3.57381850e-02 4.51780051e-01 3.86402428e-01
-2.93726236e-01 -3.41389388e-01 -1.13177824e+00 6.31282926e-01
7.86379457e-01 1.57653064e-01 1.05991352e+00 8.99781406e-01
2.16314927e-01 6.03288040e-02 -1.41019833e+00 9.46430981e-01
-1.72616065e-01 -8.66648078e-01 -3.29227775e-01 -3.23480159e-01
2.31750354e-01 -4.38358992e-01 -3.08849454e-01 8.09084296e-01
6.10660374e-01 -7.31398821e-01 5.35470724e-01 -6.12147264e-02
2.66768068e-01 -7.38551497e-01 7.99767911e-01 5.57773530e-01
-1.45788443e+00 1.30710065e-01 -4.96828318e-01 -1.79155231e-01
2.70878434e-01 3.33405584e-01 -6.10314548e-01 3.37002963e-01
8.04226756e-01 6.09312594e-01 -6.67445660e-02 7.01469779e-01
-2.83021867e-01 3.38673085e-01 -3.38445067e-01 -7.52087951e-01
1.94969982e-01 -7.05836833e-01 2.85781007e-02 1.13204503e+00
6.20713197e-02 6.14199877e-01 4.96207625e-01 5.81542909e-01
-1.07271366e-01 7.52061546e-01 -2.62540162e-01 -2.32423946e-01
3.09160084e-01 1.58560622e+00 -6.86306953e-01 -3.07605237e-01
-7.35703349e-01 1.03752637e+00 1.56916037e-01 -8.62520412e-02
-7.99007475e-01 -3.57216179e-01 9.28779781e-01 -1.43380156e-02
3.56226236e-01 6.81179762e-02 1.31409615e-01 -9.36375856e-01
4.28296663e-02 -1.28924406e+00 4.61717308e-01 -4.72407430e-01
-1.53922224e+00 4.30862337e-01 -2.45893359e-01 -7.35421836e-01
-1.78417832e-01 -6.06049001e-01 -2.61029243e-01 7.15659618e-01
-1.43007851e+00 -1.15556800e+00 -3.03664178e-01 5.22052109e-01
9.69963729e-01 1.67742491e-01 1.32640374e+00 6.70763254e-01
-3.07093322e-01 4.57029343e-01 -9.06369686e-02 3.84412348e-01
7.47704744e-01 -1.40760720e+00 4.45166558e-01 1.35390386e-01
-6.14362769e-02 7.38503814e-01 9.35312152e-01 -5.89171708e-01
-1.84475982e+00 -8.05105209e-01 1.36086857e+00 -4.86260533e-01
5.37844837e-01 -4.23595995e-01 -7.17349589e-01 5.37945449e-01
4.65846479e-01 -6.71974361e-01 1.06015134e+00 5.31906068e-01
-3.22387338e-01 1.15091749e-01 -1.24982393e+00 4.33588028e-01
9.03966784e-01 -2.85856754e-01 -5.76919556e-01 8.68097782e-01
1.09995377e+00 -5.85861504e-01 -1.11088538e+00 -8.29873607e-02
6.49957299e-01 -5.96430361e-01 1.01247239e+00 -8.53213489e-01
4.65091467e-01 -7.20964419e-03 -3.06122303e-01 -1.25084519e+00
-3.41652900e-01 -7.26494968e-01 2.58476636e-03 1.37891686e+00
6.75952137e-01 -2.57290393e-01 5.48706174e-01 1.28105628e+00
-1.45679682e-01 -3.38087380e-01 -1.57443017e-01 -6.31528944e-02
-4.43869084e-01 -2.25722551e-01 1.14291978e+00 9.62039649e-01
4.80501026e-01 9.72967803e-01 -6.27190650e-01 9.69966948e-02
4.52343643e-01 3.82650882e-01 6.66397691e-01 -1.34625244e+00
-5.77132285e-01 -1.00410670e-01 4.71224666e-01 -1.60650516e+00
-2.36660317e-01 -7.63538003e-01 2.43754566e-01 -1.84822893e+00
2.39035547e-01 -5.61662018e-01 8.25178772e-02 5.13805509e-01
-3.32458109e-01 -3.19153994e-01 -3.02875806e-02 -4.21124846e-01
-9.13779676e-01 -1.30752802e-01 1.36544108e+00 -3.68259102e-01
-4.27973002e-01 3.42313766e-01 -1.26131439e+00 6.01090491e-01
7.18189836e-01 -2.56455362e-01 -3.11232328e-01 -4.10333484e-01
5.39640665e-01 2.65180081e-01 -2.61625350e-01 -1.78043470e-01
5.98876476e-02 -2.92885244e-01 -5.41964136e-02 -5.37273288e-01
2.42706656e-01 -1.00944424e+00 5.75324111e-02 1.16642065e-01
-5.31143963e-01 -1.19971529e-01 -1.56610593e-01 3.91376346e-01
2.04311490e-01 -5.62769830e-01 1.31421834e-01 -4.87518579e-01
-8.28377724e-01 2.76071906e-01 -3.52515817e-01 -7.44045665e-03
6.68589294e-01 4.52018142e-01 -5.15870512e-01 -3.94645423e-01
-6.61894858e-01 5.90180218e-01 -4.61590737e-02 6.69803441e-01
2.68904597e-01 -1.21173847e+00 -9.13952351e-01 1.49271056e-01
1.76318437e-01 1.05296187e-01 4.32274163e-01 3.94245565e-01
-4.80355859e-01 3.51150274e-01 -5.37240207e-02 -1.97566509e-01
-1.37433851e+00 8.91711593e-01 -2.11831048e-01 -4.43098843e-01
-7.42240608e-01 6.39802992e-01 -5.33301085e-02 -7.67667234e-01
6.44787431e-01 -6.38809800e-01 -6.66323423e-01 1.03629693e-01
1.00573337e+00 5.72529994e-02 -5.98877296e-02 -2.04367161e-01
-7.46650388e-03 2.43176043e-01 -2.60089934e-01 5.01044750e-01
1.36929870e+00 -4.25194204e-01 -2.09264308e-01 2.91302741e-01
1.06015992e+00 -1.32024512e-01 -8.98619235e-01 -4.24374521e-01
-1.09651387e-01 -2.35577360e-01 -1.37668192e-01 -1.04277575e+00
-9.26064372e-01 4.47040707e-01 3.19801033e-01 7.61153281e-01
7.28035808e-01 1.10387973e-01 1.48427999e+00 5.29163599e-01
4.83548224e-01 -1.23274934e+00 -3.73009235e-01 3.19429755e-01
5.72961807e-01 -1.59561276e+00 -2.59707749e-01 -6.07163489e-01
-7.55075336e-01 1.00082028e+00 3.23297054e-01 3.86446834e-01
7.22299993e-01 2.44120792e-01 2.39753425e-01 -2.45394975e-01
-5.41653633e-01 -1.69655010e-01 9.53721851e-02 6.55850530e-01
8.24897945e-01 7.17723906e-01 -4.68284369e-01 1.34431672e+00
-3.40291321e-01 -3.79398614e-01 1.02649771e-01 8.55552554e-01
-6.54508650e-01 -1.33479941e+00 -2.77172513e-02 3.50259006e-01
-5.59417963e-01 -3.24788570e-01 -1.21402159e-01 5.20085037e-01
-7.80230090e-02 1.58262789e+00 -1.43371478e-01 -1.96124360e-01
6.13897681e-01 1.45384341e-01 1.66421503e-01 -7.16709495e-01
-1.05647266e+00 2.69756734e-01 7.31826067e-01 -3.54652256e-01
-4.32412237e-01 -4.12470818e-01 -1.41085958e+00 -6.13716185e-01
-2.63771176e-01 4.92375553e-01 9.10653055e-01 9.66959417e-01
-1.36781260e-01 3.10285658e-01 6.55513287e-01 -4.05593723e-01
-6.05277836e-01 -1.10654306e+00 -7.16585100e-01 8.08219492e-01
6.62917048e-02 -3.20202351e-01 1.98722146e-02 1.83847472e-01] | [11.517009735107422, 4.695313453674316] |
219af7eb-b938-45df-8763-4007caf5b9f3 | unbiased-monte-carlo-cluster-updates-with | 2105.05650 | null | https://arxiv.org/abs/2105.05650v3 | https://arxiv.org/pdf/2105.05650v3.pdf | Unbiased Monte Carlo Cluster Updates with Autoregressive Neural Networks | Efficient sampling of complex high-dimensional probability distributions is a central task in computational science. Machine learning methods like autoregressive neural networks, used with Markov chain Monte Carlo sampling, provide good approximations to such distributions, but suffer from either intrinsic bias or high variance. In this Letter, we propose a way to make this approximation unbiased and with low variance. Our method uses physical symmetries and variable-size cluster updates which utilize the structure of autoregressive factorization. We test our method for first- and second-order phase transitions of classical spin systems, showing its viability for critical systems and in the presence of metastable states. | ['Giuseppe Carleo', 'Riccardo Rossi', 'Dian Wu'] | 2021-05-12 | null | null | null | null | ['variational-monte-carlo'] | ['miscellaneous'] | [-1.10018052e-01 -2.25429818e-01 -1.74255863e-01 -1.69242740e-01
-6.38135910e-01 -1.25577703e-01 8.31434309e-01 -1.80181593e-01
-3.97723109e-01 1.21735418e+00 6.88056573e-02 -3.60710531e-01
-1.62839711e-01 -8.15470815e-01 -4.65224326e-01 -1.02322817e+00
-2.51543880e-01 1.08949137e+00 2.23412722e-01 -1.82879344e-01
4.27066207e-01 6.42632544e-01 -1.44292438e+00 -5.80247194e-02
8.85896683e-01 8.43828082e-01 -1.02031104e-01 6.51381850e-01
8.39063618e-03 9.00804579e-01 -2.33671352e-01 -1.40860481e-02
-6.13326691e-02 -4.40104276e-01 -7.31343687e-01 -3.38910788e-01
-1.31233603e-01 -3.43535282e-02 -3.81360888e-01 1.21194458e+00
3.69047672e-01 4.87401515e-01 1.32394874e+00 -8.49080265e-01
-5.96630156e-01 7.96337128e-01 -2.38212153e-01 1.36864617e-01
-7.45891733e-03 3.30987304e-01 8.29503715e-01 -7.71451294e-01
4.70334411e-01 1.18046212e+00 6.19294703e-01 5.27749836e-01
-1.60094810e+00 -5.06100118e-01 -3.50928158e-01 3.47522616e-01
-1.55284095e+00 -4.98781860e-01 8.55343401e-01 -5.83337426e-01
1.17688310e+00 3.69164981e-02 6.12143815e-01 1.23865438e+00
1.01026988e+00 3.11099589e-01 1.58460116e+00 -5.49631357e-01
9.31538045e-01 -1.00283854e-01 5.21767795e-01 3.72976094e-01
3.19786161e-01 3.82105112e-01 -2.85731554e-01 -6.29056990e-01
5.83584189e-01 8.10344052e-03 -1.13434028e-02 -6.43705606e-01
-8.77882779e-01 1.08828270e+00 9.55936238e-02 3.06619793e-01
-7.04805315e-01 4.94225770e-01 2.32660443e-01 -4.22939286e-02
4.15662974e-01 5.40940702e-01 -1.00501046e-01 -4.75060612e-01
-1.11962748e+00 3.44181418e-01 9.52908576e-01 3.69410276e-01
7.68598676e-01 2.46522039e-01 7.30352327e-02 3.60517383e-01
1.76342323e-01 9.12728369e-01 5.41118622e-01 -8.88436556e-01
-2.72760332e-01 -1.06630959e-01 1.97233200e-01 -7.19785929e-01
-5.20654619e-01 -1.07105859e-01 -1.43081713e+00 2.16412261e-01
2.95809895e-01 -4.77630086e-02 -8.51371169e-01 1.48289096e+00
2.60894328e-01 -4.87595014e-02 -5.83973452e-02 6.38742208e-01
1.27196193e-01 8.15996706e-01 -4.64824624e-02 -6.60446584e-01
9.21321154e-01 -5.13841450e-01 -7.38229930e-01 1.97086364e-01
1.71541288e-01 -4.70173448e-01 8.18874478e-01 6.19380832e-01
-1.11106336e+00 -2.48144895e-01 -7.99021840e-01 3.44889194e-01
-7.38954917e-02 -1.94560573e-01 8.40281308e-01 7.65756667e-01
-9.33986127e-01 1.15779674e+00 -1.31813908e+00 -2.47926135e-02
1.01983018e-01 3.49097043e-01 -3.31594497e-02 1.26700968e-01
-1.11191475e+00 8.35109234e-01 2.60132730e-01 1.22544780e-01
-9.19232666e-01 -3.03791672e-01 -4.54463333e-01 7.03367144e-02
3.29986781e-01 -4.64449108e-01 1.12082219e+00 -4.01528448e-01
-1.90032578e+00 1.74561813e-01 -4.07164574e-01 -5.65181792e-01
1.50117561e-01 -9.06088110e-03 -2.55961120e-01 7.85197169e-02
-1.89622730e-01 -1.93233676e-02 1.14511430e+00 -6.67861581e-01
3.95809829e-01 -2.52993971e-01 -4.58534092e-01 -3.85724872e-01
6.01207279e-02 -1.60041749e-01 3.03210855e-01 -1.03666060e-01
3.60768706e-01 -1.18813229e+00 -8.17735076e-01 -8.71042252e-01
-4.11195368e-01 -1.52195379e-01 1.75857365e-01 -3.61950278e-01
1.03829539e+00 -1.60072362e+00 4.39890951e-01 6.48821950e-01
2.81668067e-01 -3.00526265e-02 3.60311061e-01 6.80805147e-01
1.89654008e-02 -1.04125626e-01 -2.37755269e-01 -5.33573180e-02
1.33560136e-01 1.72305480e-01 -3.86222035e-01 5.89548409e-01
1.63563505e-01 6.27134740e-01 -6.40011191e-01 -3.10312539e-01
1.59580275e-01 4.82143641e-01 -8.02920640e-01 -9.69139561e-02
-2.42566526e-01 4.50377375e-01 -4.45281833e-01 2.99815863e-01
6.07157409e-01 -4.91641819e-01 3.05504829e-01 -1.10527582e-01
-1.88388824e-01 3.20525885e-01 -1.21935761e+00 1.01904190e+00
-3.15359235e-01 3.73097718e-01 -1.80177748e-01 -1.08421099e+00
8.90188992e-01 1.03767747e-02 4.01310593e-01 -4.51789439e-01
3.04756463e-01 2.30525404e-01 3.13515991e-01 -1.43135220e-01
8.70322645e-01 -6.11299872e-01 -1.85297593e-01 7.02921748e-01
1.92059055e-01 -3.92701983e-01 1.79311275e-01 2.48520941e-01
1.04031515e+00 -1.69563264e-01 5.29854655e-01 -7.46539712e-01
3.52335930e-01 -1.99992210e-01 2.79321045e-01 1.15778935e+00
-8.24510306e-02 3.08811188e-01 8.35250616e-01 -5.38719535e-01
-1.37498438e+00 -1.04122210e+00 -3.84996116e-01 5.53675354e-01
-2.91762382e-01 -5.65707445e-01 -7.91959703e-01 -6.71251938e-02
-1.34406298e-01 7.12838709e-01 -6.07906818e-01 -4.97052729e-01
-5.50362110e-01 -1.35917127e+00 9.46168303e-02 2.36962914e-01
-2.64383908e-02 -1.10718703e+00 -3.26953560e-01 3.94501030e-01
2.49650761e-01 -8.24056447e-01 1.26285106e-01 5.20782650e-01
-9.02418494e-01 -8.78377378e-01 -4.96807843e-01 1.27142057e-01
4.68800753e-01 -3.36251378e-01 1.17630410e+00 -3.93403828e-01
-3.41983229e-01 -1.79924164e-03 7.50344014e-03 2.14332521e-01
-7.49592364e-01 1.88097909e-01 8.08357000e-01 -1.85313895e-01
3.12039644e-01 -8.25816512e-01 -2.39510626e-01 1.51592851e-01
-8.05996180e-01 -4.26313937e-01 6.00203216e-01 1.06851339e+00
6.14767909e-01 1.25592843e-01 2.00666606e-01 -8.21874201e-01
8.16434801e-01 -4.66086179e-01 -9.88471866e-01 -1.52584121e-01
-6.77501082e-01 6.90997005e-01 8.68001401e-01 -4.72527653e-01
-8.47967923e-01 1.36573967e-02 -3.30680281e-01 -3.27200443e-01
-5.08531816e-02 4.34291661e-01 9.02677849e-02 -1.11178152e-01
8.41018140e-01 3.22314054e-01 -1.22645693e-02 -4.87729430e-01
3.34902436e-01 4.63357806e-01 2.94501841e-01 -9.76945519e-01
6.26500607e-01 5.21066964e-01 5.82003057e-01 -9.67611074e-01
-5.90977490e-01 -1.58155933e-02 -6.98227644e-01 -1.70401469e-01
6.62502289e-01 -6.45318449e-01 -9.60038602e-01 6.38685226e-01
-9.20551658e-01 -4.08995599e-01 -4.51438457e-01 8.19232047e-01
-8.17344964e-01 3.82795751e-01 -7.31052995e-01 -1.21280849e+00
-1.99899554e-01 -1.24591136e+00 7.72327483e-01 1.11897714e-01
-1.19233303e-01 -7.32251704e-01 4.74703878e-01 -2.27853656e-01
6.86559975e-01 -4.81040291e-02 9.97317791e-01 -5.40513158e-01
-5.69382071e-01 -1.52368575e-01 2.27641001e-01 2.01146677e-01
-1.96553126e-01 4.64756340e-01 -6.35776281e-01 -2.99405992e-01
1.42659217e-01 -2.47640416e-01 9.39283192e-01 8.58613908e-01
9.83204663e-01 -1.26084208e-01 -3.36136252e-01 2.20623806e-01
1.25794196e+00 -8.62571448e-02 6.19481504e-01 -1.86022162e-01
5.51001608e-01 4.05970663e-01 1.81963772e-01 5.77530742e-01
-1.33483350e-01 5.91180503e-01 -8.72994773e-03 5.80125630e-01
3.24833155e-01 -8.84378031e-02 3.25562447e-01 1.27716970e+00
-3.00886095e-01 1.41498744e-01 -1.26168013e+00 2.82508224e-01
-1.66770148e+00 -1.21885347e+00 -4.05758023e-01 2.27384496e+00
7.73940802e-01 3.74404162e-01 1.20856173e-01 3.02380286e-02
6.33159339e-01 7.42952377e-02 -4.61100221e-01 -5.30579209e-01
6.22443259e-02 5.32027483e-01 4.88276094e-01 6.78641498e-01
-7.46313691e-01 9.23546433e-01 7.87363672e+00 1.03848684e+00
-1.07561719e+00 2.36196697e-01 5.88216603e-01 -1.22806408e-01
-3.08053941e-01 2.74951011e-01 -9.36165512e-01 6.99960291e-01
1.45859039e+00 9.18248743e-02 5.97478628e-01 8.85580063e-01
8.90386924e-02 -3.97598237e-01 -6.27259791e-01 8.17949593e-01
-2.44046673e-01 -1.42300844e+00 -1.52605072e-01 1.74625114e-01
9.83306885e-01 1.56154200e-01 2.70568039e-02 4.29726064e-01
7.08596647e-01 -1.11088741e+00 4.68450546e-01 8.92897666e-01
5.89586198e-01 -1.01823008e+00 6.41270280e-01 3.03011805e-01
-6.79763913e-01 1.13662347e-01 -6.67318583e-01 -3.72653782e-01
4.00201052e-01 1.28506720e+00 -4.77213800e-01 -2.13423502e-02
4.21566308e-01 3.27790290e-01 -2.25405112e-01 7.42036283e-01
-6.02963828e-02 9.28486884e-01 -7.10915089e-01 -5.63441634e-01
2.51230776e-01 -6.72442496e-01 5.81760883e-01 5.84034204e-01
2.41270214e-01 -3.85193117e-02 -1.42104840e-02 1.08293092e+00
2.86912978e-01 -9.50843915e-02 -5.30491352e-01 -2.63081253e-01
4.06459242e-01 9.81622040e-01 -9.32349920e-01 -4.25131470e-01
1.20487899e-01 5.64669311e-01 3.61166745e-01 3.55812401e-01
-6.69595718e-01 -1.89724594e-01 6.15180135e-01 5.39522432e-02
5.51781356e-01 -7.89096594e-01 9.95653197e-02 -1.40831244e+00
-2.70975798e-01 -7.29738951e-01 -2.72180021e-01 -4.41721141e-01
-1.29508352e+00 6.28474355e-01 9.51227248e-02 -6.72670841e-01
-7.07986116e-01 -6.25303388e-01 -5.66590667e-01 6.90193653e-01
-8.81390929e-01 -6.57801092e-01 2.91126758e-01 4.30432051e-01
-6.10212646e-02 -2.43756756e-01 7.96408415e-01 -2.39254013e-02
-5.42102396e-01 7.66508877e-02 9.25363123e-01 -3.33010375e-01
3.86495084e-01 -1.18074095e+00 4.11924630e-01 6.34257138e-01
1.33486418e-02 7.91906178e-01 1.21142209e+00 -7.57891297e-01
-1.52562344e+00 -6.56639218e-01 5.97165823e-01 -2.25287333e-01
9.97726202e-01 -5.50704360e-01 -9.01538253e-01 3.85460764e-01
-1.95526347e-01 1.29203707e-01 5.38937330e-01 4.94668841e-01
-4.70686592e-02 2.42297202e-01 -1.04358244e+00 5.67823589e-01
5.10335207e-01 -7.17103720e-01 -4.33605939e-01 4.46298182e-01
3.26997638e-01 3.17245610e-02 -9.39812541e-01 1.14350796e-01
5.32929480e-01 -1.07298791e+00 8.41139376e-01 -7.86429346e-01
2.97513425e-01 -4.62507829e-02 -1.74304634e-01 -1.41482604e+00
-4.57554311e-01 -9.35330808e-01 -3.04358602e-01 8.00493717e-01
2.57086933e-01 -7.16882050e-01 7.92337656e-01 5.29782593e-01
2.42051855e-01 -5.00079751e-01 -1.14787602e+00 -8.78119588e-01
5.40012896e-01 -5.09282887e-01 4.44660813e-01 4.95057136e-01
3.09195090e-02 3.61223429e-01 -7.24859774e-01 -1.22880777e-02
8.91580403e-01 1.81570828e-01 5.57494760e-01 -1.32654798e+00
-4.74969327e-01 -2.83959717e-01 -4.11958814e-01 -7.55982459e-01
5.27509332e-01 -5.50575256e-01 2.43264765e-01 -7.14349747e-01
4.11211193e-01 -4.10323232e-01 -4.83719595e-02 -2.00713903e-01
-4.38737608e-02 1.80541009e-01 -2.67381251e-01 4.63632584e-01
-7.05325544e-01 1.04683733e+00 6.10295951e-01 2.88349837e-01
-1.29516842e-02 -7.70486742e-02 -5.24217300e-02 7.61240363e-01
8.69417548e-01 -7.74018049e-01 1.28884181e-01 4.54167217e-01
5.97428083e-01 4.50655557e-02 2.76166052e-01 -1.35512543e+00
2.24439263e-01 -2.10110873e-01 1.93677872e-01 -6.12060785e-01
5.60388386e-01 -3.28257799e-01 3.48060668e-01 5.85886419e-01
-2.36659914e-01 -4.19846661e-02 -1.94346860e-01 6.65910363e-01
-9.51861888e-02 -4.88287151e-01 1.02139342e+00 -2.04242855e-01
-2.64995486e-01 1.36744216e-01 -1.14550948e+00 -4.29902785e-02
7.04883456e-01 4.21140552e-01 -7.91155845e-02 -4.66365516e-01
-8.68290544e-01 -2.66803622e-01 5.79278827e-01 -2.84596205e-01
3.84366244e-01 -1.27975464e+00 -4.85105008e-01 1.52916640e-01
-3.05573106e-01 -4.54590619e-01 4.38858598e-01 1.04537070e+00
-4.78165507e-01 5.03742993e-01 -4.08116162e-01 -6.99241757e-01
-7.03901827e-01 7.89363265e-01 3.07072788e-01 -5.52505970e-01
-3.22607756e-01 3.33010465e-01 -3.00883859e-01 -5.23814797e-01
-5.01976848e-01 -3.32228750e-01 9.65651795e-02 -4.47516069e-02
5.62988818e-01 4.82376337e-01 1.29916975e-02 -5.64611018e-01
-1.66132733e-01 4.51418519e-01 -1.66228935e-01 -1.81394801e-01
1.30324018e+00 5.21138031e-03 -4.25281107e-01 7.84669936e-01
1.00786901e+00 2.47366037e-02 -1.00981975e+00 -1.13792337e-01
-2.50678472e-02 -1.52158931e-01 2.71978557e-01 -5.83466701e-02
-6.49338841e-01 1.04717135e+00 3.93014044e-01 5.01539111e-01
4.91054356e-01 6.97722733e-02 5.31318247e-01 7.44328678e-01
6.04008138e-01 -1.09153104e+00 -1.17307998e-01 7.17478037e-01
3.04443121e-01 -1.09639490e+00 2.20831618e-01 -4.87678573e-02
-4.25632596e-01 1.16825771e+00 9.86505449e-02 -6.13745034e-01
8.90351236e-01 2.89346993e-01 -5.46629548e-01 -3.04270089e-01
-9.94222403e-01 -8.12247247e-02 -2.65063881e-03 4.84914958e-01
2.54065871e-01 3.36030275e-01 -3.10367227e-01 3.06063771e-01
-3.05487037e-01 -1.65107891e-01 7.88613617e-01 6.75985038e-01
-5.18775165e-01 -1.06052768e+00 -4.01607901e-01 6.76091731e-01
-4.27911788e-01 -4.21677858e-01 6.15635999e-02 5.52553535e-01
-5.29911339e-01 6.53746367e-01 1.40126586e-01 -8.71942416e-02
-2.77137429e-01 2.52297491e-01 5.68976879e-01 -1.40486568e-01
-2.47635558e-01 -7.92714208e-02 -4.04738970e-02 -6.90927565e-01
-2.89631993e-01 -9.60608602e-01 -8.67327094e-01 -6.63127899e-01
-4.48920161e-01 6.06958866e-01 7.73295879e-01 1.00413799e+00
2.52375335e-01 3.56128603e-01 5.01479387e-01 -1.07490528e+00
-1.08556831e+00 -1.07644796e+00 -9.95837927e-01 5.78069910e-02
2.75719434e-01 -7.88613021e-01 -6.31980062e-01 -4.68113303e-01] | [5.688281059265137, 4.82820987701416] |
a2b340ef-84ea-46f2-9dcd-663f54f077da | learning-the-right-layers-a-data-driven-layer | 2306.00152 | null | https://arxiv.org/abs/2306.00152v1 | https://arxiv.org/pdf/2306.00152v1.pdf | Learning the Right Layers: a Data-Driven Layer-Aggregation Strategy for Semi-Supervised Learning on Multilayer Graphs | Clustering (or community detection) on multilayer graphs poses several additional complications with respect to standard graphs as different layers may be characterized by different structures and types of information. One of the major challenges is to establish the extent to which each layer contributes to the cluster assignment in order to effectively take advantage of the multilayer structure and improve upon the classification obtained using the individual layers or their union. However, making an informed a-priori assessment about the clustering information content of the layers can be very complicated. In this work, we assume a semi-supervised learning setting, where the class of a small percentage of nodes is initially provided, and we propose a parameter-free Laplacian-regularized model that learns an optimal nonlinear combination of the different layers from the available input labels. The learning algorithm is based on a Frank-Wolfe optimization scheme with inexact gradient, combined with a modified Label Propagation iteration. We provide a detailed convergence analysis of the algorithm and extensive experiments on synthetic and real-world datasets, showing that the proposed method compares favourably with a variety of baselines and outperforms each individual layer when used in isolation. | ['Francesco Tudisco', 'Francesco Rinaldi', 'Andrea Cristofari', 'Sara Venturini'] | 2023-05-31 | null | null | null | null | ['community-detection'] | ['graphs'] | [ 4.05960977e-01 1.21174991e-01 -2.06658542e-01 -2.22190544e-01
-5.22554994e-01 -5.54453909e-01 5.28202951e-01 5.21064818e-01
-4.49523926e-01 5.18095613e-01 -2.86657717e-02 -8.37083831e-02
-3.81805569e-01 -4.82889950e-01 -5.84874094e-01 -1.11035645e+00
-5.13526618e-01 7.07529306e-01 2.23040119e-01 3.21268797e-01
1.05925724e-01 3.81531209e-01 -1.25898600e+00 2.69495368e-01
9.58102465e-01 8.56590092e-01 1.79774940e-01 5.36671638e-01
-1.70420185e-01 8.18518162e-01 -2.45979160e-01 -2.66083717e-01
1.39547512e-01 -1.65400311e-01 -9.47902262e-01 7.44255304e-01
3.89889181e-01 3.22609574e-01 1.42840609e-01 1.27626932e+00
2.32174188e-01 -1.68650642e-01 1.08546340e+00 -1.05354786e+00
1.57221090e-02 8.87621284e-01 -9.21552718e-01 -1.32260367e-01
-3.27351019e-02 -4.12234455e-01 1.14831090e+00 -7.13063478e-01
6.49132431e-01 1.12915552e+00 8.17472041e-01 1.16651252e-01
-1.77440858e+00 -4.94284123e-01 5.64574897e-01 3.76638249e-02
-1.63323677e+00 -2.52250403e-01 9.77694273e-01 -7.49719441e-01
2.73319304e-01 -9.63829551e-03 2.91380614e-01 6.12913549e-01
-3.60556096e-01 7.75842309e-01 1.34221458e+00 -4.92159724e-01
2.59193569e-01 6.02850199e-01 4.19523001e-01 9.38569069e-01
4.53932941e-01 -5.91766894e-01 -2.82652378e-01 -3.25384498e-01
3.52670133e-01 -2.10640192e-01 -2.03822792e-01 -1.07722211e+00
-1.02335024e+00 8.22027206e-01 8.11835229e-01 3.84372354e-01
-3.45615685e-01 -4.36829552e-02 6.14761822e-02 1.51485220e-01
6.69264317e-01 1.31142676e-01 -5.43877631e-02 6.18445218e-01
-1.10983503e+00 -1.83226064e-01 9.66920257e-01 5.16385078e-01
1.26305711e+00 -4.17165309e-01 1.73484325e-01 8.80607545e-01
5.60797513e-01 -1.40011311e-02 -8.98744073e-03 -9.42362905e-01
4.82225209e-01 9.76383686e-01 -1.47911668e-01 -1.13330412e+00
-5.70048630e-01 -7.95571506e-01 -1.19790637e+00 4.06404287e-01
6.90742075e-01 -3.96584421e-01 -7.78704107e-01 1.71567941e+00
4.62794632e-01 3.06476623e-01 -1.40780866e-01 6.28006995e-01
3.68238926e-01 3.95299882e-01 -9.08496678e-02 -3.68764520e-01
9.58809018e-01 -8.02451253e-01 -5.25061846e-01 -2.66730309e-01
7.16508985e-01 -6.08499527e-01 5.07482827e-01 2.90738285e-01
-9.09708858e-01 -1.95973098e-01 -1.15354240e+00 3.81682456e-01
-4.48107570e-01 3.37880969e-01 5.19109547e-01 6.97909534e-01
-1.30853796e+00 7.41634607e-01 -7.98338056e-01 -3.21968049e-01
4.23331112e-01 7.73199439e-01 -3.64833891e-01 -1.18989833e-01
-6.70184851e-01 4.80947942e-01 5.96820891e-01 1.80884719e-01
-4.99889284e-01 -3.21926057e-01 -6.82652354e-01 -1.50391549e-01
5.34251809e-01 -5.06655931e-01 6.32132351e-01 -1.07624710e+00
-9.83756602e-01 9.57254589e-01 -1.86559856e-01 -4.04150814e-01
5.81737816e-01 3.67176831e-01 2.37762570e-01 3.84975225e-01
8.73509720e-02 6.18273675e-01 7.31014371e-01 -1.83663976e+00
-6.45881951e-01 -3.93616498e-01 4.54147570e-02 3.90269250e-01
-5.29250443e-01 -1.97586611e-01 -5.83580732e-01 -2.63703704e-01
3.13909918e-01 -1.13948047e+00 -4.13470179e-01 -1.42263472e-02
-6.81868315e-01 7.00863078e-03 5.35300195e-01 -4.51946437e-01
1.23352945e+00 -1.97550416e+00 4.90503401e-01 8.13932598e-01
5.76500833e-01 -3.98030728e-02 1.37836680e-01 4.23312575e-01
-2.27899365e-02 1.88373685e-01 -6.08910799e-01 -5.67780554e-01
-1.47815436e-01 6.26276955e-02 3.85158330e-01 7.81857491e-01
-1.02250434e-01 3.92178535e-01 -1.03808212e+00 -8.27493072e-01
1.37472779e-01 5.09853840e-01 -3.96475881e-01 -4.19149101e-02
7.77391195e-02 3.54350954e-01 -3.47355753e-01 4.70977455e-01
5.85405290e-01 -8.12346399e-01 7.49381602e-01 -1.54089496e-01
3.85888778e-02 3.46905440e-02 -1.68347156e+00 1.26186121e+00
-1.62658662e-01 4.90005434e-01 6.87396288e-01 -1.24380589e+00
5.81844985e-01 3.21432561e-01 6.49472177e-01 1.84268564e-01
-3.00894934e-03 2.09310204e-01 1.30635068e-01 -6.26025423e-02
-2.56016967e-03 1.63640995e-02 1.84801862e-01 6.84531271e-01
3.67197953e-02 3.46716851e-01 4.36855435e-01 4.36018378e-01
1.14562905e+00 -3.64851654e-01 2.26083890e-01 -4.08296317e-01
6.14802480e-01 -2.02714592e-01 3.00876915e-01 7.06363976e-01
-4.03545313e-02 4.69168305e-01 7.90933132e-01 2.15001255e-02
-7.26053953e-01 -8.46300602e-01 -1.43518150e-01 8.52889538e-01
1.07988566e-01 -2.97108918e-01 -9.74268913e-01 -8.87592018e-01
-1.33709282e-01 1.52600139e-01 -8.32707584e-01 6.99063540e-02
-3.29088658e-01 -1.30439925e+00 2.30373710e-01 1.63708776e-01
3.51523161e-01 -7.87699282e-01 8.81698634e-03 1.06755599e-01
-2.88476735e-01 -1.08902872e+00 -1.85746536e-01 5.89067698e-01
-9.81334329e-01 -1.25020385e+00 -5.57963669e-01 -1.15270460e+00
1.25727212e+00 4.24204022e-01 1.01318347e+00 2.55799174e-01
6.73499405e-02 2.54984170e-01 -1.38420954e-01 1.73344225e-01
-3.62328172e-01 5.07198274e-01 -2.18084514e-01 5.38354039e-01
3.39071117e-02 -6.61989450e-01 -4.33889955e-01 1.79434389e-01
-6.62538350e-01 7.28078932e-02 7.46052563e-01 6.46198511e-01
4.60810363e-01 4.71193939e-01 3.66443664e-01 -1.46807528e+00
5.80706596e-01 -6.17023230e-01 -4.39466268e-01 4.26626027e-01
-8.02389562e-01 2.26603642e-01 4.04773533e-01 -2.82345474e-01
-8.49979162e-01 5.68959177e-01 4.09414560e-01 -2.21288115e-01
-8.23776349e-02 7.40421176e-01 -2.00498328e-01 -3.31555992e-01
4.11900282e-01 -1.26415670e-01 -4.79252897e-02 -6.69538856e-01
3.67864013e-01 6.82444274e-01 3.36760134e-01 -5.78315318e-01
9.26917672e-01 6.56772435e-01 2.40359716e-02 -8.52857649e-01
-8.35693896e-01 -7.18106687e-01 -1.25224447e+00 -5.14664173e-01
7.78837621e-01 -7.10740209e-01 -5.64529061e-01 4.56289858e-01
-8.67403626e-01 -4.05244023e-01 1.38655350e-01 2.92485803e-01
-1.12349875e-01 6.32460892e-01 -6.74670160e-01 -7.93658912e-01
4.81969379e-02 -1.08822870e+00 7.32065022e-01 -5.35313748e-02
2.56991014e-02 -1.47684932e+00 -2.55234465e-02 4.72369075e-01
-5.87462746e-02 4.07658368e-01 1.01007330e+00 -6.12371862e-01
-5.00995696e-01 -3.51352245e-01 -3.35979670e-01 3.92331719e-01
2.51233727e-01 7.26149380e-02 -9.51982796e-01 -5.68383515e-01
-2.60206521e-01 -2.33571902e-01 1.12868118e+00 3.70519400e-01
9.35315788e-01 -3.73043090e-01 -5.54941297e-01 4.37130809e-01
1.50511742e+00 -3.96670014e-01 3.67378555e-02 1.94429144e-01
1.04145086e+00 8.87496054e-01 2.65977299e-03 1.93018407e-01
5.66959202e-01 4.51553524e-01 5.21318316e-01 -2.37928689e-01
-8.54109898e-02 -3.75492475e-03 1.84594691e-01 9.94072855e-01
-8.36216956e-02 -6.52500093e-02 -9.53328788e-01 5.02153218e-01
-1.95929945e+00 -6.19526386e-01 -3.26215476e-01 2.49327016e+00
8.67193639e-01 2.80540407e-01 2.88286299e-01 4.14741129e-01
1.09026206e+00 8.35961848e-02 -3.71892393e-01 2.19563827e-01
-1.23019792e-01 -2.89944112e-01 5.70917785e-01 8.02931964e-01
-1.42700601e+00 6.58150792e-01 6.38300705e+00 5.56964636e-01
-9.94030058e-01 -1.56659186e-01 6.91436887e-01 1.53790772e-01
-4.61560823e-02 6.83833435e-02 -5.98251641e-01 2.72104353e-01
6.99357808e-01 1.26436818e-02 5.68971455e-01 4.72219259e-01
-2.79966854e-02 5.05376561e-03 -1.15121377e+00 5.04361272e-01
-6.24706373e-02 -1.01998103e+00 -2.22804442e-01 3.73652637e-01
1.04933333e+00 1.09622218e-01 -5.50238490e-02 -1.47294670e-01
5.71004212e-01 -7.38017976e-01 4.81126785e-01 2.70330548e-01
4.54144508e-01 -7.34767199e-01 6.57227337e-01 5.78203499e-01
-1.33122575e+00 -6.53300509e-02 -2.74070114e-01 -3.92596573e-02
-2.25339741e-01 7.36088336e-01 -1.03841853e+00 5.08533299e-01
5.06857097e-01 8.62798035e-01 -7.41411030e-01 1.12912607e+00
-1.81026563e-01 8.23265851e-01 -4.45192963e-01 1.16739094e-01
3.86080623e-01 -3.79318237e-01 4.43598330e-01 1.49784410e+00
-1.43612400e-01 -2.93438286e-01 5.55912554e-01 5.87585270e-01
-2.52628744e-01 3.47936451e-01 -3.56145889e-01 3.41091268e-02
4.48297828e-01 1.72442925e+00 -1.36570072e+00 -2.79570967e-01
-4.14228082e-01 6.64928436e-01 9.64856625e-01 5.88272989e-01
-2.95796186e-01 -7.92573988e-02 1.32920250e-01 3.62999976e-01
4.20590550e-01 -1.18763022e-01 -3.59058529e-01 -1.03990757e+00
-1.69155508e-01 -5.24843752e-01 5.63916564e-01 -1.64229915e-01
-1.45240331e+00 3.84081572e-01 -2.65545212e-03 -1.06083620e+00
-2.09553272e-01 -4.92550164e-01 -5.33934772e-01 6.86567485e-01
-1.51730037e+00 -1.10169399e+00 -1.92751065e-01 3.75087649e-01
-9.26595926e-02 6.97999820e-02 5.70953965e-01 4.05055195e-01
-8.09139669e-01 5.06266654e-01 3.70710820e-01 3.05460066e-01
5.43342650e-01 -1.66235650e+00 -2.84986049e-01 8.72510552e-01
1.76475197e-01 5.20750701e-01 6.32224441e-01 -6.20111346e-01
-8.34787130e-01 -1.27279127e+00 8.47583055e-01 -9.66842100e-02
7.56426454e-01 -6.15536392e-01 -1.09280896e+00 6.18620336e-01
1.16212696e-01 1.27625959e-02 6.99236393e-01 3.16237450e-01
-1.95765868e-01 -1.59737393e-01 -9.49599206e-01 4.23655152e-01
7.43238330e-01 -3.73181820e-01 -9.67959389e-02 5.07094383e-01
1.76015019e-01 1.59018427e-01 -8.43993723e-01 3.81594509e-01
1.31089687e-01 -8.78540456e-01 8.92527580e-01 -1.58490196e-01
8.78992900e-02 -5.09696364e-01 3.88287678e-02 -1.23570418e+00
-4.50249195e-01 -4.53586251e-01 8.50122124e-02 1.32497644e+00
6.46754265e-01 -4.92021173e-01 9.22322989e-01 3.66767377e-01
1.73575118e-01 -7.57819235e-01 -6.45529687e-01 -4.53494281e-01
-4.22855429e-02 -1.26285702e-01 -9.65879485e-03 1.13997555e+00
1.04629993e-01 7.05821157e-01 -3.27345163e-01 4.36909735e-01
1.30329490e+00 1.74391240e-01 4.99901891e-01 -1.70553303e+00
-1.84903249e-01 -4.86476600e-01 -3.42017055e-01 -8.75833392e-01
3.21972847e-01 -1.25869751e+00 5.76773584e-02 -1.68510091e+00
6.34662628e-01 -6.47831500e-01 -4.91492808e-01 5.04890323e-01
-3.22077215e-01 4.70668435e-01 3.12785991e-02 4.60658848e-01
-9.67541456e-01 9.02046859e-02 8.97081733e-01 -2.66251951e-01
-1.46160513e-01 3.51671696e-01 -6.66742325e-01 1.05128872e+00
5.76265156e-01 -5.58961630e-01 -4.54129130e-01 -2.20964834e-01
3.78134161e-01 -1.30993336e-01 1.85559466e-01 -9.86224413e-01
6.55876279e-01 2.40883261e-01 3.49496484e-01 -4.10113215e-01
2.16671497e-01 -8.72739792e-01 7.06139728e-02 3.86786163e-01
-6.38282061e-01 -4.08132106e-01 -3.45840156e-01 8.03573430e-01
-4.19337451e-02 -3.60898048e-01 7.98188090e-01 -1.94270968e-01
-2.72908241e-01 1.30690113e-01 -3.70208591e-01 -1.13827251e-02
8.63158643e-01 -2.53829420e-01 2.74234023e-02 -3.99324089e-01
-9.47073758e-01 4.71134275e-01 7.26401269e-01 -1.22771412e-01
2.27112636e-01 -1.16913879e+00 -7.91312277e-01 -1.15903266e-01
-8.51879716e-02 -6.08825497e-02 -5.09739555e-02 9.83923793e-01
-2.96678156e-01 4.13007066e-02 1.35332361e-01 -7.25813866e-01
-1.43741238e+00 5.81857502e-01 4.25389230e-01 -6.84893668e-01
-4.14968371e-01 6.70345545e-01 3.38477850e-01 -5.30262530e-01
6.31331503e-01 2.38895454e-02 -5.53237557e-01 4.18952316e-01
1.22322097e-01 5.10453761e-01 -3.60263139e-02 -8.19743633e-01
-3.57311219e-01 5.79458952e-01 -2.51815915e-01 -1.24051757e-01
1.22849834e+00 -4.06671286e-01 -4.66923535e-01 7.22475588e-01
1.21416414e+00 -1.00985616e-01 -1.38315058e+00 -6.45558655e-01
3.73486608e-01 -3.99006978e-02 1.74537420e-01 -6.47864759e-01
-1.25101519e+00 8.29667151e-01 3.88408720e-01 4.71871614e-01
9.19644177e-01 9.83939692e-02 8.20008293e-02 4.19183820e-01
1.42160282e-01 -1.02096415e+00 8.13284367e-02 1.72420800e-01
3.17396522e-01 -1.24545729e+00 2.28322700e-01 -7.48213768e-01
-4.39094275e-01 9.45943534e-01 3.16933542e-01 -1.38455451e-01
9.11139369e-01 2.40388036e-01 1.97518393e-01 -2.85198838e-01
-6.90210640e-01 -3.90065342e-01 4.48175341e-01 4.15394783e-01
5.62798202e-01 1.08291760e-01 -1.33339062e-01 7.30242506e-02
1.31556764e-01 -5.20038843e-01 4.59693402e-01 7.85792530e-01
-4.72616762e-01 -1.17580342e+00 -3.47646117e-01 5.70531547e-01
-5.85699260e-01 1.41823575e-01 -8.09932053e-01 5.11094570e-01
1.97093382e-01 1.09530997e+00 -2.08076730e-01 -3.21735322e-01
-1.70778707e-01 4.82899994e-02 3.99960220e-01 -7.09155798e-01
-4.60780472e-01 4.92193729e-01 -9.81204957e-03 -1.30315289e-01
-7.88934529e-01 -8.72487247e-01 -1.20061803e+00 -1.56713501e-01
-5.49200058e-01 3.14120352e-01 7.18305111e-01 9.45459247e-01
-7.85607845e-02 4.57275003e-01 7.91089952e-01 -1.06233120e+00
-4.45973814e-01 -8.00649047e-01 -7.60222852e-01 4.29160625e-01
2.80514121e-01 -5.72726250e-01 -7.34533966e-01 6.71842694e-02] | [7.2025909423828125, 5.344334602355957] |
951c30c3-96df-4c0b-9380-f7c462b1ed21 | a-simple-parametric-classification-baseline | 2211.11727 | null | https://arxiv.org/abs/2211.11727v2 | https://arxiv.org/pdf/2211.11727v2.pdf | Parametric Classification for Generalized Category Discovery: A Baseline Study | Generalized Category Discovery (GCD) aims to discover novel categories in unlabelled datasets using knowledge learned from labelled samples. Previous studies argued that parametric classifiers are prone to overfitting to seen categories, and endorsed using a non-parametric classifier formed with semi-supervised k-means. However, in this study, we investigate the failure of parametric classifiers, verify the effectiveness of previous design choices when high-quality supervision is available, and identify unreliable pseudo-labels as a key problem. We demonstrate that two prediction biases exist: the classifier tends to predict seen classes more often, and produces an imbalanced distribution across seen and novel categories. Based on these findings, we propose a simple yet effective parametric classification method that benefits from entropy regularisation, achieves state-of-the-art performance on multiple GCD benchmarks and shows strong robustness to unknown class numbers. We hope the investigation and proposed simple framework can serve as a strong baseline to facilitate future studies in this field. Our code is available at: https://github.com/CVMI-Lab/SimGCD. | ['Xiaojuan Qi', 'Bingchen Zhao', 'Xin Wen'] | 2022-11-21 | null | null | null | null | ['novel-class-discovery', 'novel-class-discovery'] | ['computer-vision', 'methodology'] | [ 2.27064833e-01 3.05103809e-01 -6.08350694e-01 -8.06548655e-01
-7.94265032e-01 -5.42353094e-01 6.28064752e-01 1.65220454e-01
-3.42341252e-02 1.04636407e+00 -8.39537457e-02 -2.81451464e-01
-3.07855099e-01 -6.14219129e-01 -6.04447842e-01 -8.01665008e-01
-1.77315921e-01 7.43636429e-01 7.84594193e-02 4.58221644e-01
5.13159037e-01 2.82882154e-01 -1.94612145e+00 3.43668729e-01
8.49035501e-01 9.75039423e-01 -3.33156377e-01 3.68158281e-01
1.71341345e-01 6.46758676e-01 -4.10832345e-01 -4.48222339e-01
2.68769026e-01 -2.27779776e-01 -8.23005736e-01 -1.11589245e-01
3.39371145e-01 -1.94992095e-01 2.64413714e-01 7.78999031e-01
4.71807271e-01 -1.51842702e-02 1.10359502e+00 -1.75578833e+00
-6.97130144e-01 7.75203824e-01 -5.19695222e-01 5.18411100e-02
1.18031465e-01 2.55283624e-01 9.81835246e-01 -8.52570295e-01
4.37927395e-01 1.04440522e+00 9.23764110e-01 6.18825555e-01
-1.33565307e+00 -9.19534743e-01 -3.08455583e-02 2.49181852e-01
-1.48229754e+00 -5.39981723e-01 6.45557165e-01 -5.03798783e-01
6.01319253e-01 2.73021549e-01 2.42607400e-01 1.41043520e+00
-2.29931753e-02 6.67276621e-01 1.60284507e+00 -4.43209678e-01
5.78485131e-01 6.93382204e-01 4.19872433e-01 2.77982742e-01
5.56001544e-01 3.81774634e-01 -3.55772942e-01 -5.06880343e-01
1.38601616e-01 -1.19427830e-01 -1.68173432e-01 -6.21951342e-01
-1.15923548e+00 9.21965182e-01 8.18418115e-02 2.23467797e-01
-1.28429040e-01 -1.67185754e-01 4.63367462e-01 1.59262314e-01
6.04762614e-01 5.00674129e-01 -8.33637238e-01 -3.77984434e-01
-1.16891110e+00 2.15984851e-01 1.01178074e+00 9.10455823e-01
6.30378366e-01 -2.62752295e-01 -2.06918299e-01 8.42793584e-01
1.63693815e-01 3.05816621e-01 8.11300755e-01 -8.48076284e-01
-8.62916410e-02 5.41343391e-01 -2.05538142e-02 -6.58525109e-01
-4.31610435e-01 -6.00921929e-01 -8.09962809e-01 -3.03940382e-02
5.32047629e-01 8.29933286e-02 -8.73305619e-01 1.51463377e+00
3.46708447e-01 2.32485026e-01 -1.09349973e-02 6.92484260e-01
7.23221958e-01 1.62587985e-01 2.10890278e-01 -3.04984808e-01
1.04649985e+00 -7.05535471e-01 -5.30423582e-01 2.42654439e-02
6.67035997e-01 -4.70864475e-01 1.09678841e+00 5.94474673e-01
-6.16539717e-01 -5.54227293e-01 -8.67787957e-01 1.46420017e-01
-4.32420164e-01 3.49816650e-01 8.25056851e-01 9.41010356e-01
-7.92754352e-01 6.45935059e-01 -7.34458923e-01 -3.84412229e-01
7.55537689e-01 2.82783240e-01 -1.74335554e-01 -7.59385526e-02
-9.52907324e-01 7.01016545e-01 6.83838308e-01 -2.13097204e-02
-8.04975390e-01 -6.43940508e-01 -5.42380989e-01 -2.09176064e-01
1.23010650e-01 -4.44230616e-01 1.23303735e+00 -9.16710675e-01
-1.25700212e+00 9.96067345e-01 -1.87012285e-01 -5.10335565e-01
7.51844645e-01 5.10411710e-02 -3.08044463e-01 -1.71612203e-01
4.41688625e-03 7.50684977e-01 6.55211687e-01 -1.47638810e+00
-5.25945365e-01 -4.54963893e-01 -5.00815690e-01 -7.82172382e-02
-2.83911675e-01 -2.54217684e-01 4.01261777e-01 -4.74276215e-01
2.36156657e-01 -8.94423008e-01 1.25901327e-01 -3.04331601e-01
-5.45104325e-01 -4.61855471e-01 7.87366331e-01 -4.38706547e-01
1.10157263e+00 -2.02523136e+00 -3.95044863e-01 2.89555430e-01
1.01840355e-01 9.72913429e-02 3.90195668e-01 1.31287992e-01
-2.97014028e-01 4.05041337e-01 -3.91382217e-01 -2.14403763e-01
1.67905077e-01 1.56254098e-01 -4.33682948e-01 5.52260339e-01
1.59080103e-01 8.00734699e-01 -8.61615241e-01 -4.46663469e-01
-3.88178527e-02 3.27520758e-01 -2.16239750e-01 9.57260579e-02
-3.60464193e-02 3.12267423e-01 -5.15184328e-02 9.05508697e-01
8.78617764e-01 -3.02213490e-01 1.10922143e-01 -3.16224992e-02
1.48659647e-01 3.21567655e-01 -1.11639225e+00 1.21041000e+00
-1.72862783e-02 2.95157850e-01 -4.93359357e-01 -1.39008582e+00
1.22877955e+00 1.19294219e-01 7.72216320e-02 -1.85814187e-01
1.20081469e-01 4.45686221e-01 -5.44470511e-02 -3.61481309e-01
2.47671485e-01 -3.27395946e-01 -5.37199453e-02 3.47631305e-01
1.86827973e-01 -4.91016954e-02 -9.01565701e-02 -1.36547208e-01
9.28133368e-01 2.47370362e-01 5.31211495e-01 -2.95665890e-01
1.94269955e-01 5.12729585e-02 6.02219164e-01 9.48051572e-01
-4.17640686e-01 5.85629642e-01 5.51517725e-01 -3.64351213e-01
-9.51746762e-01 -1.10495651e+00 -7.25153446e-01 1.01789033e+00
-2.19087675e-01 9.51328948e-02 -5.07794440e-01 -8.92415762e-01
2.64192224e-01 1.10496724e+00 -7.32247472e-01 -2.45185241e-01
5.39231934e-02 -1.18061590e+00 6.85590982e-01 4.76725340e-01
3.44748557e-01 -8.78177345e-01 -3.56750101e-01 -4.81125340e-02
-3.27803828e-02 -7.89146960e-01 2.03416422e-01 5.88837683e-01
-1.10855997e+00 -1.26011610e+00 -4.25984710e-01 -7.90261209e-01
6.84677124e-01 -1.46743149e-01 1.09426129e+00 -1.16228350e-01
-1.42959818e-01 6.58815131e-02 -5.31980813e-01 -5.30863762e-01
-5.19366384e-01 1.54329911e-01 2.50381768e-01 -1.27007395e-01
8.86739671e-01 -6.12023413e-01 -4.69358087e-01 5.59076369e-01
-6.01938009e-01 -1.49505869e-01 6.30059659e-01 9.95366812e-01
5.25813222e-01 2.68176585e-01 1.07711482e+00 -1.22128856e+00
4.58949536e-01 -8.67623508e-01 -3.14198256e-01 7.28481263e-02
-1.09444451e+00 1.99298542e-02 5.86937487e-01 -5.19952476e-01
-9.90085304e-01 2.01736167e-02 7.73702040e-02 -2.51365066e-01
-6.50718391e-01 2.00959310e-01 -8.20971187e-03 1.82525292e-01
1.02877605e+00 1.86651796e-01 7.65962005e-02 -4.56291974e-01
2.62705952e-01 1.30085909e+00 3.37972283e-01 -6.38516724e-01
7.90505290e-01 3.50704759e-01 -1.92326829e-01 -4.41089064e-01
-7.67162681e-01 -6.11959040e-01 -8.27019513e-01 6.34829476e-02
2.65031964e-01 -1.01534617e+00 -3.72617871e-01 4.41552401e-01
-6.00371778e-01 -3.41642946e-01 -3.13797802e-01 4.22683656e-01
-5.52920759e-01 3.09344739e-01 -2.52701908e-01 -1.03673065e+00
-4.49614108e-01 -7.70751119e-01 8.77774775e-01 1.84544921e-01
-5.32420039e-01 -9.49265599e-01 -6.65214211e-02 5.57762206e-01
2.82114714e-01 3.11945319e-01 7.59419024e-01 -1.33544374e+00
-1.47317126e-01 -3.80208135e-01 -1.45131201e-01 5.47501445e-01
4.98682074e-02 2.58458048e-01 -1.40982461e+00 -3.19507480e-01
-1.36691183e-01 -5.48175335e-01 9.16111231e-01 2.20686629e-01
1.58442068e+00 -4.16167885e-01 -4.70415384e-01 3.84128720e-01
1.29303825e+00 1.15386667e-02 5.27757227e-01 3.19937110e-01
3.87937486e-01 6.27917945e-01 6.16454840e-01 4.62771088e-01
4.97643471e-01 4.31914091e-01 1.32801667e-01 2.70270109e-01
2.14425661e-02 -2.60433435e-01 9.04658139e-02 5.85066259e-01
1.35513321e-01 -3.59329656e-02 -1.12324965e+00 7.29673326e-01
-1.71139216e+00 -8.90137851e-01 -3.06681007e-01 2.52155447e+00
1.11255252e+00 3.07136893e-01 2.99294084e-01 5.00751972e-01
7.03431308e-01 -5.05476117e-01 -5.91712117e-01 -4.51512218e-01
-5.91061264e-02 1.69850528e-01 4.62715656e-01 1.23935519e-02
-1.19491959e+00 5.01004517e-01 6.34665728e+00 9.70764518e-01
-9.51265872e-01 1.58894822e-01 1.08018768e+00 -4.79372777e-03
-1.77313238e-01 6.89042583e-02 -7.95731843e-01 8.63905609e-01
1.18255949e+00 -1.09286465e-01 1.51862681e-01 1.10674119e+00
-7.22575840e-03 -2.04666600e-01 -1.27033329e+00 7.79691339e-01
-2.25099232e-02 -7.76890337e-01 -3.16923320e-01 7.41743892e-02
8.24756920e-01 -6.09910935e-02 8.74732584e-02 6.23297930e-01
3.59057248e-01 -1.13590145e+00 4.92083132e-01 4.58870918e-01
7.72940218e-01 -6.38109326e-01 1.07253098e+00 5.47072947e-01
-6.40511930e-01 -2.99502939e-01 -4.65967149e-01 -2.83067107e-01
-5.08084595e-01 1.10112822e+00 -1.32513762e+00 4.13846701e-01
7.74158239e-01 6.45962477e-01 -9.07020986e-01 1.31887484e+00
-1.24108016e-01 1.22125685e+00 -4.51719701e-01 -1.38337880e-01
-2.38083377e-01 2.51138121e-01 1.51589423e-01 1.25739586e+00
3.05944979e-01 -1.28892511e-01 -5.53574180e-03 8.46764445e-01
5.03194518e-02 1.48092881e-01 -6.86158895e-01 1.25511304e-01
7.16128886e-01 1.20370150e+00 -8.93550038e-01 -3.20767432e-01
-1.50278762e-01 5.55217028e-01 2.92730808e-01 2.28763700e-01
-6.83419108e-01 -1.44144505e-01 1.49209842e-01 1.63739383e-01
2.03187928e-01 2.47433782e-01 -8.30160916e-01 -1.06212890e+00
-3.04119079e-03 -7.44759858e-01 5.94477057e-01 -6.58332527e-01
-1.64993608e+00 2.83883184e-01 2.81586796e-01 -1.32402742e+00
-2.71990955e-01 -4.04918820e-01 -4.23680693e-01 5.23033977e-01
-1.51177442e+00 -1.04079139e+00 -3.91526639e-01 1.08691007e-01
1.82300687e-01 -1.32717490e-01 9.55069065e-01 5.98315634e-02
-5.12405157e-01 1.00699878e+00 4.70194280e-01 -8.72782618e-02
8.89309466e-01 -1.33050311e+00 1.72468964e-02 3.68743271e-01
-9.38475057e-02 4.67729628e-01 6.87961876e-01 -5.75780988e-01
-8.26653063e-01 -1.22394478e+00 8.34901929e-01 -7.46090651e-01
5.02628446e-01 -3.38600427e-01 -1.09677875e+00 6.20642483e-01
-2.59682208e-01 1.31791458e-01 1.10991907e+00 2.07160816e-01
-4.77856964e-01 -1.70519650e-01 -1.57182312e+00 -1.17145382e-01
9.52089369e-01 -2.51426280e-01 -7.23632932e-01 4.10142124e-01
3.18331033e-01 -1.67979494e-01 -9.45267200e-01 6.02391720e-01
6.03279173e-01 -1.08255017e+00 6.87451959e-01 -3.29135746e-01
3.80677491e-01 -1.92273498e-01 -1.16453364e-01 -1.32467020e+00
-1.96160108e-01 -2.21825138e-01 -2.30019793e-01 1.52996814e+00
5.44667661e-01 -8.76447260e-01 8.26868892e-01 6.17369473e-01
2.05944732e-01 -8.53882968e-01 -9.22084808e-01 -9.79676545e-01
3.35748136e-01 -4.09591794e-01 5.44104636e-01 1.25379038e+00
1.55154511e-01 7.70556629e-02 -2.37452567e-01 6.39265776e-02
8.99918973e-01 2.85199024e-02 6.35343909e-01 -1.59393966e+00
-4.92036156e-02 -3.22258711e-01 -6.06882155e-01 -4.70873117e-01
2.36549556e-01 -1.03326499e+00 1.01978585e-01 -9.65806425e-01
4.77515817e-01 -8.12828898e-01 -2.84239382e-01 8.15201104e-01
-1.84335500e-01 4.98873651e-01 -1.97159410e-01 3.86510313e-01
-6.07386529e-01 4.42480445e-01 8.04089308e-01 6.73644468e-02
-1.56321883e-01 1.12714715e-01 -1.09167254e+00 5.05747199e-01
1.33311152e+00 -5.91606438e-01 -3.71746212e-01 2.97782391e-01
-2.51394678e-02 -6.20863020e-01 4.68253553e-01 -1.09567750e+00
-2.92755198e-02 -1.31319150e-01 6.07271254e-01 -4.23935711e-01
-2.47220090e-03 -7.31020451e-01 2.72934556e-01 1.81920514e-01
-4.31390435e-01 -5.68330646e-01 1.33018583e-01 5.36777198e-01
-3.76442373e-02 -3.13754052e-01 6.68792605e-01 -4.46697623e-02
-5.61783254e-01 -7.48217553e-02 3.54750417e-02 1.21174850e-01
1.22565746e+00 -4.36759770e-01 -4.93274152e-01 -4.99022976e-02
-5.34847796e-01 2.35644519e-01 6.19922757e-01 3.27244341e-01
3.71945769e-01 -1.22911894e+00 -7.90718019e-01 2.06640422e-01
3.94437104e-01 1.51014015e-01 -5.66033162e-02 8.21198642e-01
-2.81076938e-01 4.95032161e-01 3.48022282e-02 -7.92047024e-01
-1.04204082e+00 4.89477724e-01 1.98642269e-01 2.21471265e-02
-2.14899629e-01 8.51946652e-01 -1.79965943e-01 -9.43560719e-01
2.81845510e-01 -3.52183342e-01 -8.46874192e-02 1.73836291e-01
3.65517944e-01 5.66749990e-01 2.13248506e-01 -4.99637246e-01
-4.61824268e-01 2.62308270e-01 -1.39682889e-01 4.48633254e-01
1.25873423e+00 -5.18693291e-02 2.61095148e-02 8.11005652e-01
1.17718458e+00 -4.84982550e-01 -1.18709922e+00 -9.82774124e-02
1.64062098e-01 -4.05035287e-01 1.82714555e-02 -1.18980408e+00
-5.91464520e-01 6.49954736e-01 8.00019205e-01 2.58916408e-01
1.00068748e+00 4.89870161e-02 2.21991569e-01 1.97888166e-01
4.22295690e-01 -1.05757916e+00 -1.50697514e-01 1.27462745e-01
6.75193012e-01 -1.56993389e+00 2.14115918e-01 -4.19714391e-01
-8.41524661e-01 1.05888653e+00 5.78466535e-01 2.47716561e-01
7.03639925e-01 3.74440849e-02 1.06814913e-01 -3.53473909e-02
-8.62610400e-01 4.28182296e-02 2.68585443e-01 9.40098047e-01
4.35265511e-01 3.63233835e-01 -3.30385983e-01 7.96290100e-01
-4.89844292e-01 3.13743234e-01 5.71040452e-01 8.02557766e-01
-3.20307910e-01 -9.11187708e-01 -4.52064306e-01 1.04850721e+00
-4.35049981e-01 -8.75466224e-03 -2.79092789e-01 6.53200507e-01
2.98504770e-01 9.69570279e-01 -2.52622887e-02 -3.86275262e-01
2.33997609e-02 6.91875458e-01 1.50405586e-01 -5.62863171e-01
-2.04604506e-01 -3.31458330e-01 2.96055339e-02 -2.23074630e-01
-5.94119310e-01 -9.90334988e-01 -9.42410350e-01 -2.52023101e-01
-5.00037074e-01 2.33050734e-01 5.98568678e-01 6.53257668e-01
4.92721349e-01 1.37164682e-01 6.85764849e-01 -5.68614900e-01
-9.70338166e-01 -1.28084850e+00 -6.31613851e-01 4.61079150e-01
1.80174142e-01 -9.72665548e-01 -9.82841194e-01 1.76021859e-01] | [8.815450668334961, 4.166370868682861] |
66ffc61e-5fdd-43c9-97bf-835913119aa2 | acoustic-word-embeddings-for-untranscribed | 2306.02153 | null | https://arxiv.org/abs/2306.02153v1 | https://arxiv.org/pdf/2306.02153v1.pdf | Acoustic Word Embeddings for Untranscribed Target Languages with Continued Pretraining and Learned Pooling | Acoustic word embeddings are typically created by training a pooling function using pairs of word-like units. For unsupervised systems, these are mined using k-nearest neighbor (KNN) search, which is slow. Recently, mean-pooled representations from a pre-trained self-supervised English model were suggested as a promising alternative, but their performance on target languages was not fully competitive. Here, we explore improvements to both approaches: we use continued pre-training to adapt the self-supervised model to the target language, and we use a multilingual phone recognizer (MPR) to mine phone n-gram pairs for training the pooling function. Evaluating on four languages, we show that both methods outperform a recent approach on word discrimination. Moreover, the MPR method is orders of magnitude faster than KNN, and is highly data efficient. We also show a small improvement from performing learned pooling on top of the continued pre-trained representations. | ['Sharon Goldwater', 'Hao Tang', 'Ondrej Klejch', 'Ramon Sanabria'] | 2023-06-03 | null | null | null | null | ['word-embeddings'] | ['methodology'] | [ 1.89202845e-01 1.21349785e-02 -2.65077382e-01 -5.78869939e-01
-1.58930886e+00 -6.72724783e-01 6.21367335e-01 2.87334710e-01
-1.14336336e+00 5.20279229e-01 4.31217164e-01 -3.64460766e-01
2.80831873e-01 -7.11664557e-01 -7.21416295e-01 -5.45879781e-01
1.07438685e-02 4.59483624e-01 4.12622839e-01 -4.28597853e-02
2.57488400e-01 4.26531166e-01 -1.42555988e+00 2.61620611e-01
6.30347490e-01 7.99586594e-01 5.16246736e-01 8.62773716e-01
-3.22531849e-01 1.35682911e-01 -3.21488202e-01 -2.17471078e-01
1.15872532e-01 -2.28939742e-01 -6.55661404e-01 -3.14909965e-01
3.56333613e-01 -7.72938058e-02 -1.29713804e-01 9.58368242e-01
7.22695827e-01 3.53597045e-01 6.97432816e-01 -5.76831281e-01
-8.48018527e-01 8.97202849e-01 -1.54376745e-01 9.82709676e-02
2.73960501e-01 -2.93702126e-01 1.44010818e+00 -1.60075462e+00
3.43358278e-01 1.13201761e+00 8.09757531e-01 5.96111596e-01
-1.34625328e+00 -6.19752347e-01 8.40273220e-03 1.10245205e-01
-1.58868253e+00 -7.55515158e-01 4.74993289e-01 -1.00065857e-01
1.69162858e+00 1.29492328e-01 1.78343907e-01 9.89294767e-01
-3.05190325e-01 6.75413430e-01 1.04525495e+00 -9.80359018e-01
3.47192854e-01 7.42369294e-01 1.39523417e-01 5.60554326e-01
-3.05654574e-02 -7.81773999e-02 -7.03134477e-01 -3.49565923e-01
3.46323162e-01 -3.55441839e-01 -8.90016630e-02 -2.57058293e-01
-1.17408741e+00 9.52476442e-01 3.06082159e-01 6.50421441e-01
-2.22352341e-01 4.96171154e-02 3.66087258e-01 2.52530843e-01
6.41554236e-01 5.83547413e-01 -6.14967823e-01 -1.53287008e-01
-1.09795034e+00 -1.14612460e-01 8.78197908e-01 6.66843116e-01
1.16678143e+00 -3.53747681e-02 9.96329784e-02 1.35273159e+00
3.34504068e-01 4.78359938e-01 1.13155758e+00 -4.49909240e-01
3.60249400e-01 1.42851815e-01 -1.97206557e-01 -5.21744430e-01
-2.58313268e-01 -1.01679377e-03 -4.66231346e-01 3.62004898e-02
4.13586378e-01 -1.51220813e-01 -1.07357049e+00 1.58119571e+00
-9.02025700e-02 1.79561973e-01 3.33379298e-01 5.13810873e-01
4.67638731e-01 9.68937278e-01 1.94357708e-01 2.37894654e-02
1.26329517e+00 -1.05898297e+00 -3.40270430e-01 -5.19993365e-01
9.37982798e-01 -8.30029190e-01 1.12952578e+00 3.45738739e-01
-9.57301915e-01 -7.43893564e-01 -1.11370385e+00 -1.47863314e-01
-9.39133346e-01 2.37575009e-01 2.75140703e-01 1.00976253e+00
-1.17767394e+00 6.69086635e-01 -6.83285177e-01 -4.49388772e-01
7.42668137e-02 4.68106806e-01 -7.28971004e-01 -9.46711376e-02
-1.35318232e+00 1.01157224e+00 5.44179618e-01 -1.23344101e-01
-6.93300605e-01 -5.74453712e-01 -1.12361753e+00 -7.89210026e-04
-4.34547514e-02 2.04587337e-02 1.04118383e+00 -7.34854579e-01
-1.85780966e+00 1.05874205e+00 -2.63622046e-01 -6.08891070e-01
-1.36175111e-01 -2.33995840e-01 -5.24032354e-01 -1.33610561e-01
-2.14688525e-01 8.26995134e-01 7.09475040e-01 -1.03468680e+00
-6.13687634e-01 -1.40458554e-01 -2.59670168e-01 2.47151271e-01
-8.29424381e-01 2.86488831e-01 -4.38767046e-01 -6.74094439e-01
7.92590380e-02 -7.55145133e-01 -3.81943315e-01 -4.23368067e-01
-2.65236020e-01 -5.55624366e-01 2.69870669e-01 -7.58759320e-01
1.37821686e+00 -2.35302734e+00 -5.97813614e-02 3.68483186e-01
-3.20754617e-01 4.34802085e-01 -5.11007428e-01 4.08126920e-01
-5.73659614e-02 1.86333969e-01 -4.71927881e-01 -7.02756524e-01
2.69742936e-01 3.61993194e-01 -3.49090040e-01 3.24540377e-01
4.90374237e-01 7.21871138e-01 -8.67089629e-01 -3.80205542e-01
3.46415453e-02 6.31416261e-01 -7.03900456e-01 3.26265723e-01
9.55195352e-02 -2.61656493e-01 4.20184899e-03 4.26135659e-01
5.32086968e-01 3.35620135e-01 3.74338061e-01 1.02540655e-02
-1.34459376e-01 8.17891240e-01 -1.24974847e+00 1.80444658e+00
-1.01666009e+00 5.59653342e-01 -2.44392589e-01 -1.05025351e+00
1.32663023e+00 2.92540103e-01 -1.40237242e-01 -4.21339452e-01
-2.90345252e-01 8.29548776e-01 -2.40936279e-02 -2.26627111e-01
6.91374600e-01 -3.06843460e-01 -2.14860752e-01 3.56014401e-01
6.44167900e-01 -2.20127046e-01 -5.13829440e-02 -1.99633747e-01
1.03081250e+00 1.13976128e-01 4.09381032e-01 -3.47319156e-01
5.54215014e-01 -2.67181993e-01 3.99144590e-01 9.11084473e-01
-8.10291693e-02 9.10164595e-01 7.29813650e-02 -1.42377228e-01
-8.68259609e-01 -1.24210739e+00 -2.41313934e-01 1.61759448e+00
-3.73694569e-01 -5.29239058e-01 -7.31683671e-01 -8.73411298e-01
-7.93652013e-02 7.24066615e-01 -4.02886480e-01 4.65346128e-02
-8.42545092e-01 -7.81414807e-01 8.07869136e-01 7.84420907e-01
-1.27476513e-01 -1.29364538e+00 -2.12873116e-01 5.23959041e-01
1.29035085e-01 -1.03359663e+00 -4.83921587e-01 9.32069719e-01
-5.90471685e-01 -5.05231738e-01 -1.01305556e+00 -1.21092486e+00
4.64444846e-01 -1.04921490e-01 1.04680324e+00 -3.27363759e-01
-7.39090741e-02 2.01238796e-01 -4.80265111e-01 -2.49322027e-01
-4.79202032e-01 5.74012697e-01 5.80754101e-01 1.32220611e-03
9.70990956e-01 -5.20186007e-01 -1.97635368e-01 1.25892043e-01
-7.35035002e-01 -6.75777614e-01 6.88974977e-01 8.14611793e-01
7.42475152e-01 -2.38916814e-01 7.21729934e-01 -8.94779086e-01
6.15936756e-01 -3.19916427e-01 -3.04972053e-01 1.42723843e-01
-5.15360892e-01 2.54302382e-01 6.51753128e-01 -5.98858654e-01
-8.27544034e-01 3.96946222e-01 -6.50714397e-01 -3.30734439e-02
-2.65871346e-01 3.53724748e-01 -2.49324635e-01 7.52806142e-02
7.15024471e-01 2.38704279e-01 -2.50784993e-01 -8.60319138e-01
6.13375247e-01 1.24062824e+00 4.83784258e-01 -4.86794680e-01
7.52470136e-01 -2.50189323e-02 -8.25717866e-01 -1.00367665e+00
-5.41782081e-01 -6.69281483e-01 -7.80731618e-01 4.35126573e-01
1.01364291e+00 -8.78996134e-01 6.40961751e-02 3.06184828e-01
-1.29666746e+00 -4.18558151e-01 -4.68198031e-01 8.62696111e-01
-4.36914474e-01 2.93610513e-01 -6.19007528e-01 -8.39373648e-01
-1.65728956e-01 -9.89588022e-01 9.71292019e-01 4.68867868e-02
-2.53831446e-01 -1.11423409e+00 5.32222092e-01 -6.67504445e-02
6.58126473e-01 -6.74125195e-01 8.68521094e-01 -1.48950672e+00
6.06064126e-02 -3.40868503e-01 -1.61541685e-01 9.42970097e-01
1.91704243e-01 -2.65898973e-01 -1.44421124e+00 -2.17136145e-01
-2.50767589e-01 -5.70040107e-01 1.14654315e+00 -1.29109370e-02
9.89741266e-01 -1.04076462e-02 -3.24788719e-01 4.68380362e-01
1.39472997e+00 -1.28976107e-02 5.59414566e-01 2.44381696e-01
5.73114991e-01 7.00708628e-01 2.64578134e-01 1.23503683e-02
3.85471463e-01 6.32088423e-01 -2.07331330e-01 9.56340227e-03
-1.11790776e-01 -3.95766646e-01 8.61132503e-01 1.53027523e+00
9.86984819e-02 -4.75365482e-02 -9.70858991e-01 8.24464202e-01
-1.42769420e+00 -5.32658637e-01 4.40511197e-01 2.37561750e+00
1.05587482e+00 4.04582292e-01 -4.06394526e-02 1.59835055e-01
5.13736010e-01 2.13862807e-01 -1.42783716e-01 -8.58999610e-01
-1.76583886e-01 8.45400214e-01 6.62003338e-01 8.08422208e-01
-1.13295531e+00 1.26332951e+00 7.02073717e+00 1.17493939e+00
-8.92151058e-01 3.20480019e-01 4.43871796e-01 3.43381256e-01
-3.50619107e-01 5.98871075e-02 -1.13616097e+00 1.45544425e-01
1.43842328e+00 1.29517257e-01 3.28845829e-01 9.21898603e-01
-3.46673012e-01 -3.51348668e-02 -1.13824153e+00 8.58002484e-01
3.39025885e-01 -9.31075096e-01 -1.16761573e-01 -5.83937429e-02
5.13061225e-01 4.15377975e-01 -1.48164466e-01 6.35851264e-01
3.35983515e-01 -1.05823922e+00 2.62908489e-01 2.59187877e-01
8.20679784e-01 -8.20664763e-01 6.73387706e-01 2.11611778e-01
-1.21021843e+00 1.44232035e-01 -8.85640204e-01 1.14362106e-01
1.31973788e-01 5.41204035e-01 -8.52337003e-01 2.29950860e-01
6.86395645e-01 3.14276963e-01 -6.07306957e-01 7.05856621e-01
-2.07776144e-01 8.18689108e-01 -5.66122353e-01 -3.03492814e-01
3.62284452e-01 2.03625001e-02 3.16207796e-01 1.84667122e+00
4.20442641e-01 -3.99774015e-01 9.44085941e-02 4.21826571e-01
-1.22885391e-01 5.59492469e-01 -6.62094355e-01 -2.25527897e-01
4.37387019e-01 1.32713592e+00 -4.58859086e-01 -4.36414689e-01
-5.43747365e-01 1.11391294e+00 7.76197731e-01 1.65231735e-01
-3.75620753e-01 -9.93225873e-01 7.01174617e-01 -1.44317105e-01
6.59577310e-01 -3.32390606e-01 1.32246658e-01 -9.02199209e-01
-1.64712399e-01 -6.38241410e-01 2.68944323e-01 -4.88597274e-01
-1.48185098e+00 9.69022274e-01 -2.23706901e-01 -9.90726352e-01
-5.04857540e-01 -1.13200331e+00 -4.57538992e-01 1.13750863e+00
-1.74034131e+00 -7.79648483e-01 4.92206544e-01 2.49091700e-01
5.48960090e-01 -4.70172763e-01 1.49220181e+00 3.51534754e-01
-2.65960991e-01 9.56283867e-01 2.48741686e-01 2.69002974e-01
9.72561181e-01 -1.57757282e+00 5.38545966e-01 6.42985702e-01
8.63579869e-01 5.93264937e-01 3.10896486e-01 -3.19246441e-01
-1.14331782e+00 -9.21509504e-01 1.53817141e+00 -4.90611464e-01
9.02330577e-01 -8.31792176e-01 -1.16280532e+00 5.34020007e-01
4.03406233e-01 2.58285850e-01 1.08932579e+00 3.41043413e-01
-7.34484732e-01 -1.11165904e-01 -8.62458408e-01 3.74991834e-01
8.14349234e-01 -9.65602815e-01 -9.28709328e-01 3.05568308e-01
8.01787794e-01 3.15073505e-02 -6.76515698e-01 1.78827330e-01
5.94632685e-01 -7.77785301e-01 8.71656239e-01 -5.84673285e-01
-4.02000025e-02 -1.61445081e-01 -7.13180840e-01 -1.54648888e+00
-1.82198152e-01 -4.36195523e-01 1.85135365e-01 1.45492446e+00
9.47978318e-01 -8.21884215e-01 6.86269045e-01 3.61586623e-02
-5.23841865e-02 -5.74002802e-01 -1.06569576e+00 -9.69299793e-01
4.50704753e-01 -8.17453325e-01 3.38226795e-01 7.17827737e-01
2.37716109e-01 3.28285247e-01 -2.77549654e-01 2.08000585e-01
1.86960742e-01 -3.92968029e-01 4.90441084e-01 -8.58073354e-01
-6.75811470e-01 -2.72617996e-01 -5.60836852e-01 -1.16454065e+00
4.68187481e-01 -1.31678045e+00 5.83712518e-01 -1.14665973e+00
-9.43677127e-02 -4.66682047e-01 -9.76796210e-01 5.14305770e-01
-1.85787335e-01 7.46668100e-01 3.54312621e-02 -6.33299723e-02
-3.80753607e-01 2.88403600e-01 3.22164834e-01 -1.29307821e-01
-2.74881065e-01 -7.38025084e-02 -5.31719267e-01 7.70966887e-01
8.28394055e-01 -6.86804116e-01 1.63411386e-02 -5.12677252e-01
1.09975554e-01 -5.97294271e-01 -1.12146474e-01 -9.74679112e-01
3.34318131e-01 3.62885982e-01 2.67978519e-01 -3.42534691e-01
4.52558696e-01 -5.31272590e-01 -6.22752309e-01 9.36808735e-02
-3.47510219e-01 1.12517163e-01 1.69013634e-01 4.45147783e-01
-4.74132270e-01 -7.46443093e-01 7.13263571e-01 -3.04850899e-02
-7.16304421e-01 -7.06848204e-02 -5.88883340e-01 -2.53045224e-02
4.32494789e-01 -1.51246473e-01 3.15424472e-01 -1.28451928e-01
-6.63039029e-01 -1.23319343e-01 2.48970427e-02 4.99496609e-01
5.02057910e-01 -1.43629038e+00 -6.72334015e-01 4.94363934e-01
3.56332719e-01 -3.36526841e-01 -1.04708016e-01 4.32341486e-01
-2.80270070e-01 2.78736025e-01 1.76012427e-01 -3.11968446e-01
-9.52333629e-01 4.03353453e-01 2.19312206e-01 -3.76016706e-01
-1.84354797e-01 1.17889082e+00 -4.68482487e-02 -1.14985061e+00
3.68148893e-01 -3.42438132e-01 -2.24793464e-01 3.17288160e-01
7.20220447e-01 1.58775732e-01 3.16012770e-01 -7.27282584e-01
-7.28681862e-01 7.34820783e-01 -1.27280504e-01 -5.78251421e-01
1.42068803e+00 -1.95833929e-02 4.92192432e-02 7.60788262e-01
1.51846254e+00 5.28004348e-01 -8.27110350e-01 -5.56355894e-01
4.05784994e-01 -9.39446874e-03 -3.31352949e-02 -4.17373866e-01
-6.66164577e-01 1.12779474e+00 5.37422836e-01 2.17042729e-01
9.12872493e-01 1.12142451e-01 7.59159625e-01 7.17010200e-01
1.85904816e-01 -1.43368399e+00 -1.42180800e-01 6.34573102e-01
5.53417265e-01 -1.27562356e+00 -3.05739671e-01 -1.92307487e-01
-4.61176097e-01 1.28967035e+00 2.83953488e-01 -5.35501420e-01
1.02745426e+00 4.87485260e-01 3.33254009e-01 3.79482806e-01
-4.70898002e-01 -4.49535042e-01 4.71579850e-01 7.67091155e-01
6.68014348e-01 1.29366368e-01 -1.73219740e-01 8.15144122e-01
-2.71603584e-01 -3.40634555e-01 1.67832077e-01 6.85563922e-01
-6.47450387e-01 -1.58078265e+00 -1.92205936e-01 3.38549852e-01
-3.69385093e-01 -5.97380102e-01 -1.58955574e-01 5.47825634e-01
8.31997916e-02 8.53051841e-01 2.35401317e-01 -3.68989736e-01
3.08199942e-01 6.93499863e-01 3.58165622e-01 -1.01292372e+00
-5.84306359e-01 2.04422092e-03 1.15468450e-01 -3.91858429e-01
-2.72727549e-01 -4.42420006e-01 -1.11064875e+00 3.65419894e-01
-4.65128124e-01 1.81696042e-01 8.99217963e-01 9.33769524e-01
1.33005992e-01 2.07001809e-02 7.22384632e-01 -1.14348722e+00
-7.86360562e-01 -1.04412365e+00 -6.71240211e-01 1.47028819e-01
2.01712415e-01 -2.54903048e-01 -5.47552824e-01 -1.02824524e-01] | [14.231948852539062, 6.7878193855285645] |
f6af1e79-e184-4a72-8b5a-344880dc881c | sea-net-squeeze-and-excitation-attention-net | 2010.15344 | null | https://arxiv.org/abs/2010.15344v1 | https://arxiv.org/pdf/2010.15344v1.pdf | Sea-Net: Squeeze-And-Excitation Attention Net For Diabetic Retinopathy Grading | Diabetes is one of the most common disease in individuals. \textit{Diabetic retinopathy} (DR) is a complication of diabetes, which could lead to blindness. Automatic DR grading based on retinal images provides a great diagnostic and prognostic value for treatment planning. However, the subtle differences among severity levels make it difficult to capture important features using conventional methods. To alleviate the problems, a new deep learning architecture for robust DR grading is proposed, referred to as SEA-Net, in which, spatial attention and channel attention are alternatively carried out and boosted with each other, improving the classification performance. In addition, a hybrid loss function is proposed to further maximize the inter-class distance and reduce the intra-class variability. Experimental results have shown the effectiveness of the proposed architecture. | ['XiaoLi Li', 'Zeng Zeng', 'Kartik Chopra', 'Ziyuan Zhao'] | 2020-10-29 | null | null | null | null | ['diabetic-retinopathy-grading'] | ['medical'] | [-4.96606007e-02 -1.95540860e-01 -3.59327672e-03 -6.52972996e-01
-4.25847769e-01 6.76502436e-02 1.09778628e-01 -1.25049829e-01
-3.14467221e-01 8.35946918e-01 3.54078084e-01 -9.03791040e-02
-2.72487164e-01 -6.80235326e-01 -5.56175783e-02 -1.02210581e+00
3.89770418e-01 -8.54516178e-02 8.28522146e-02 6.45995140e-02
3.97543311e-01 6.40172303e-01 -1.47531807e+00 2.04369441e-01
1.56346774e+00 1.12642467e+00 2.36781880e-01 1.65859446e-01
-3.69942635e-01 7.59512067e-01 -5.79582334e-01 -3.87574017e-01
1.76292479e-01 -4.36816782e-01 -2.95069963e-01 6.53823018e-02
6.67865455e-01 -6.60689354e-01 -5.28082311e-01 1.28749847e+00
1.07371306e+00 -1.94834843e-02 5.26398897e-01 -5.88390768e-01
-9.28416789e-01 6.31895885e-02 -9.05583322e-01 4.23146993e-01
-1.40476987e-01 3.58164072e-01 4.52706546e-01 -4.78346705e-01
1.34656550e-02 1.31088829e+00 3.67420137e-01 7.72821665e-01
-1.12478113e+00 -5.48848569e-01 7.70605505e-02 4.94594723e-01
-1.20608580e+00 -3.07793170e-01 6.51379228e-01 -5.33870876e-01
3.10417652e-01 8.03459808e-02 7.63987899e-01 6.27349555e-01
8.93123224e-02 6.59038901e-01 1.33031273e+00 -1.68892696e-01
-7.64658973e-02 -1.52186841e-01 2.81199664e-01 5.58727860e-01
4.15664673e-01 7.66920000e-02 2.29140922e-01 2.21663252e-01
9.92131948e-01 1.90759167e-01 -5.59179723e-01 -2.65567690e-01
-8.81626010e-01 6.94966137e-01 9.47954059e-01 -1.29823580e-01
-3.82377326e-01 -9.34388489e-02 3.41599464e-01 -9.65345055e-02
3.20290864e-01 1.44772366e-01 -4.16160636e-02 1.29648343e-01
-5.00124931e-01 5.04491925e-02 -1.33240893e-01 4.11243767e-01
3.29557329e-01 6.00590706e-02 -6.92391574e-01 1.13660753e+00
3.86309296e-01 3.75824869e-01 3.86521071e-01 -9.47009385e-01
3.82270098e-01 1.06842446e+00 -5.92823001e-03 -8.14181387e-01
-4.72773433e-01 -7.01852441e-01 -1.34109330e+00 5.55510104e-01
4.09875095e-01 -2.54566431e-01 -1.37816286e+00 1.28528571e+00
8.23151544e-02 8.85237828e-02 -1.23237215e-01 1.35750198e+00
1.06735039e+00 2.55015075e-01 1.91199884e-01 -1.65109307e-01
1.21952534e+00 -7.73036480e-01 -7.53305316e-01 -7.05044419e-02
2.37040207e-01 -5.30643046e-01 9.94466722e-01 2.51976818e-01
-9.82177913e-01 -4.68456060e-01 -7.26711690e-01 -3.73669654e-01
6.71969261e-03 4.93581444e-01 7.67752409e-01 4.76989836e-01
-9.60245192e-01 2.18554363e-02 -5.49193442e-01 -3.37502271e-01
9.65539217e-01 3.50883663e-01 -6.34904653e-02 -4.43159044e-01
-8.72674584e-01 8.02947342e-01 -4.15799133e-02 6.84432983e-01
-4.15536731e-01 -4.51635420e-01 -4.89601821e-01 -5.11619076e-02
-8.14222693e-02 -8.76487553e-01 7.92110920e-01 -8.02935362e-01
-1.41376734e+00 7.79131830e-01 -3.73119026e-01 -1.26722440e-01
5.85392952e-01 -3.04813832e-01 -5.30886889e-01 9.90586728e-02
-1.07031263e-01 5.17209530e-01 3.69397968e-01 -9.17828679e-01
-6.63171411e-01 -8.29587102e-01 6.28026649e-02 3.15450549e-01
-4.22888920e-02 9.96633396e-02 -3.82479757e-01 -5.57750285e-01
1.24222048e-01 -3.85449678e-01 -4.53947067e-01 3.39593142e-01
-5.61568737e-01 -1.85301840e-01 5.41107833e-01 -7.77171493e-01
1.17347324e+00 -2.08260870e+00 5.04438132e-02 2.05864072e-01
5.06424546e-01 6.54956818e-01 -3.99247967e-02 -3.28484178e-01
9.39117149e-02 1.22345194e-01 -2.02956200e-02 2.78074473e-01
-4.00860518e-01 -1.62494015e-02 8.04802477e-02 4.23549265e-01
2.17894524e-01 7.35944331e-01 -5.69129646e-01 -2.30490297e-01
2.76800215e-01 9.02561665e-01 -2.13400960e-01 1.70423701e-01
1.74521096e-02 6.98000312e-01 -6.54340148e-01 6.86768174e-01
8.79876137e-01 -3.92002732e-01 -2.66350269e-01 -3.04123163e-01
-1.62112489e-01 1.00372687e-01 -8.69765341e-01 1.15167809e+00
-2.50320211e-02 5.24372697e-01 -1.12261258e-01 -9.55640137e-01
1.04827106e+00 -7.24685267e-02 2.51054138e-01 -1.05124760e+00
2.84967870e-01 1.35729730e-01 3.33495140e-01 -7.91136920e-01
-1.02927819e-01 1.54061586e-01 5.62318921e-01 -3.28653395e-01
-6.61322296e-01 6.27524257e-01 4.59920131e-02 -2.66182423e-01
7.07443357e-01 -1.46118239e-01 2.43268996e-01 4.83183116e-02
6.20581985e-01 -3.63195628e-01 9.63920414e-01 2.58987784e-01
-3.68181169e-01 8.28104675e-01 6.48198843e-01 -6.44591212e-01
-7.02246130e-01 -9.06568468e-01 -4.81712729e-01 1.44781455e-01
4.99716699e-01 2.71979362e-01 -4.57786262e-01 -5.93498111e-01
2.31903270e-01 2.78404295e-01 -6.60107136e-01 -1.43871695e-01
-2.72315234e-01 -1.16300166e+00 2.68122137e-01 7.02748358e-01
9.60237086e-01 -5.84948957e-01 -1.12576790e-01 -1.80403627e-02
-5.81926033e-02 -5.06515563e-01 -3.49180251e-01 -5.48752129e-01
-9.11314368e-01 -1.16900504e+00 -1.30223870e+00 -7.91172326e-01
8.51211131e-01 5.06729662e-01 6.84370816e-01 1.39922976e-01
-6.69231594e-01 -2.26334944e-01 -8.72823894e-02 -2.59178460e-01
2.29472876e-01 -3.63003641e-01 -2.48896062e-01 4.18458611e-01
6.85499609e-01 -4.49031740e-01 -1.32194924e+00 2.12517962e-01
-3.76459897e-01 -2.50854790e-01 1.12430930e+00 8.17519248e-01
8.88705313e-01 3.10951807e-02 6.06187403e-01 -5.86991668e-01
4.05326098e-01 -1.50033474e-01 -6.95754409e-01 2.29905933e-01
-6.38596296e-01 -2.52379745e-01 1.61540329e-01 -1.66467622e-01
-1.08906591e+00 -2.89773345e-01 9.47554260e-02 -2.43793994e-01
-2.66877800e-01 3.57584476e-01 -5.85382700e-01 -3.08781415e-01
2.66446054e-01 1.68761283e-01 2.03457698e-01 -7.08878815e-01
9.50748771e-02 9.51921582e-01 3.87049139e-01 5.78644872e-02
2.64362067e-01 2.23034784e-01 1.03150882e-01 -8.29239547e-01
-9.80691493e-01 -2.76484519e-01 -2.75640815e-01 1.74722157e-03
1.15625024e+00 -1.08739483e+00 -8.28621686e-01 7.96214163e-01
-1.00484085e+00 -1.42297253e-01 1.90742806e-01 9.42025185e-01
-6.49203509e-02 3.38491917e-01 -4.16889280e-01 -6.06103480e-01
-3.83524686e-01 -1.16843712e+00 5.43179154e-01 9.61224496e-01
4.50906128e-01 -7.58480966e-01 -1.50391683e-01 6.90290868e-01
5.74188054e-01 1.92714065e-01 1.24651134e+00 -1.53428391e-01
-7.38933444e-01 -1.26125365e-01 -1.20955300e+00 5.66509426e-01
2.91009158e-01 1.85087383e-01 -7.33537614e-01 -1.67516932e-01
-6.40543818e-01 -8.30221176e-02 1.07383096e+00 8.10811579e-01
1.57999015e+00 -6.73090965e-02 -3.17887664e-01 7.80866861e-01
1.43872476e+00 4.44020987e-01 1.24004817e+00 3.27275693e-01
8.26794565e-01 3.59824836e-01 4.11816567e-01 2.58260280e-01
5.64823806e-01 7.23634183e-01 4.80906814e-01 -5.36842346e-01
-6.08082473e-01 2.84303039e-01 -9.21072811e-02 2.15381771e-01
-4.76525635e-01 -1.45194888e-01 -8.17011118e-01 5.20098150e-01
-1.58365810e+00 -9.70013142e-01 -5.88276863e-01 2.39551377e+00
7.24786043e-01 -5.94273955e-02 -3.28755490e-02 -3.58464628e-01
9.36597288e-01 -9.13713649e-02 -6.25703692e-01 -1.95169985e-01
-3.52916032e-01 8.26046169e-02 4.54364479e-01 1.79546818e-01
-1.00458777e+00 3.85002851e-01 6.03137398e+00 4.48436201e-01
-1.44694328e+00 -2.87177593e-01 7.31942832e-01 -1.33148491e-01
-1.54423609e-01 -2.96313733e-01 -8.26122701e-01 8.54745328e-01
1.53663114e-01 4.89775464e-02 3.11652482e-01 4.37184960e-01
7.54023492e-01 -8.93046055e-03 -4.89231706e-01 9.28249955e-01
1.32207796e-02 -9.29074705e-01 2.90092915e-01 8.96439031e-02
5.39368331e-01 -9.18240026e-02 2.30074570e-01 1.00831203e-01
9.33235586e-02 -1.19288993e+00 -1.71882421e-01 1.04322755e+00
1.01325822e+00 -9.92825449e-01 1.10172307e+00 -2.52363265e-01
-8.63461912e-01 -2.39458665e-01 -5.80936551e-01 1.87736288e-01
4.88267876e-02 7.99669385e-01 -2.99903661e-01 3.65617692e-01
7.04332471e-01 9.10660744e-01 -6.81910098e-01 1.92830014e+00
-3.94049942e-01 4.28842068e-01 1.86997026e-01 1.81924120e-01
-5.19826673e-02 -5.35572827e-01 7.07594872e-01 6.80674851e-01
4.71440256e-01 2.72742361e-01 1.54274881e-01 8.40792418e-01
-9.56932921e-03 1.32486582e-01 -2.79019237e-01 1.96991369e-01
2.61145532e-01 1.05640972e+00 -7.54686445e-02 5.15445545e-02
-6.42921090e-01 7.43562758e-01 1.25709772e-01 7.09651828e-01
-5.99682033e-01 -8.39056730e-01 7.89023697e-01 2.49149486e-01
2.91098297e-01 2.19183758e-01 -4.87980664e-01 -1.17574012e+00
1.89097688e-01 -5.82489133e-01 3.07153761e-01 -8.83720577e-01
-1.46184623e+00 3.72837186e-01 -7.26560295e-01 -1.34734213e+00
5.03065526e-01 -4.43003714e-01 -7.59947419e-01 1.35454011e+00
-1.92603552e+00 -1.16437924e+00 -8.72925103e-01 6.04043245e-01
1.62860021e-01 -3.53711009e-01 5.55691540e-01 5.78870773e-01
-1.14199376e+00 6.95482373e-01 2.66543418e-01 1.68274909e-01
9.83663619e-01 -1.35954201e+00 -3.84601831e-01 7.68963337e-01
-8.23923349e-01 5.74596703e-01 1.89256981e-01 -6.01928890e-01
-7.96567261e-01 -1.39748812e+00 7.48350263e-01 2.29276687e-01
2.44642213e-01 3.38807374e-01 -1.03141785e+00 2.98476666e-01
-1.27459951e-02 2.18598828e-01 6.62736833e-01 1.69274192e-02
-3.63871127e-01 -7.43094623e-01 -1.16805172e+00 6.06192827e-01
8.21269095e-01 -1.26216546e-01 -3.96660566e-01 1.52306393e-01
4.13026065e-01 -2.99843997e-01 -8.35804284e-01 6.03292823e-01
5.68177521e-01 -9.76247907e-01 7.79019475e-01 -5.58530688e-01
5.48776865e-01 -5.51968038e-01 6.30269051e-02 -1.26659727e+00
-3.93863052e-01 -2.44910076e-01 1.06904265e-02 1.34211874e+00
1.60058841e-01 -8.62438679e-01 6.70514941e-01 6.63111746e-01
-2.61614054e-01 -7.66986966e-01 -6.18810713e-01 -5.29458225e-01
7.90289138e-03 4.33233023e-01 3.61082226e-01 8.19256544e-01
-5.25033295e-01 2.87149370e-01 -1.17875636e-01 4.57985938e-01
8.54696035e-01 5.75886555e-02 4.78405148e-01 -1.45896339e+00
7.65238553e-02 -8.11929286e-01 -8.34647655e-01 -1.04247618e+00
-2.88863927e-01 -6.98873281e-01 -2.13985026e-01 -2.17058778e+00
3.91695082e-01 -5.53971231e-01 -6.39661670e-01 4.28746849e-01
-5.15178025e-01 2.96553999e-01 -4.18673456e-01 1.04856990e-01
-3.50082129e-01 6.41951203e-01 1.67073691e+00 -1.78008094e-01
-3.90329570e-01 2.47955322e-01 -1.01657939e+00 5.94526827e-01
8.03685009e-01 1.26420125e-01 -4.41796124e-01 -7.52895296e-01
-1.51092365e-01 -5.04712686e-02 6.21014059e-01 -9.17162657e-01
-1.17704822e-02 -2.56275237e-01 7.06157565e-01 -4.74842370e-01
1.07474804e-01 -5.29195964e-01 -3.98762673e-01 2.38928184e-01
-3.64870071e-01 -5.12709618e-01 2.60206312e-02 6.05375886e-01
-4.00817841e-01 4.16213214e-01 1.13877797e+00 2.20244527e-01
-6.20456398e-01 6.02417111e-01 -6.22558594e-02 -1.29494667e-01
1.02371299e+00 -3.28743398e-01 -7.32465446e-01 -8.44735373e-03
-6.77029133e-01 5.30946553e-01 3.29913765e-01 1.55836031e-01
8.37594330e-01 -1.36600578e+00 -1.04768836e+00 5.82678132e-02
3.60245973e-01 2.04344541e-01 7.28999019e-01 1.28328609e+00
-5.33889771e-01 3.58233184e-01 -3.99008155e-01 -4.39598858e-01
-1.39956713e+00 2.75233060e-01 6.97447777e-01 9.10049230e-02
-7.97660708e-01 8.01149249e-01 3.60331565e-01 1.25409707e-01
4.49732661e-01 -5.12175001e-02 -8.38690162e-01 -7.66874924e-02
1.02236748e+00 5.84615290e-01 6.74262317e-03 -3.05308998e-01
-2.02133417e-01 9.54290450e-01 -5.04179418e-01 6.36625528e-01
1.24450111e+00 -5.48279881e-01 -3.60923529e-01 -9.83173996e-02
7.03298926e-01 -7.98899233e-02 -1.32155848e+00 -2.74629265e-01
-2.91346729e-01 -7.22513735e-01 6.16234422e-01 -1.30437553e+00
-1.57568586e+00 1.29558456e+00 1.19466412e+00 -5.62657341e-02
1.30906951e+00 -3.55819315e-01 8.64357412e-01 2.36041043e-02
5.26930876e-02 -4.93163764e-01 -2.42163286e-01 1.32144898e-01
7.43609905e-01 -1.32344449e+00 -8.00067559e-02 -4.10520494e-01
-6.35530531e-01 1.14514732e+00 8.38642716e-01 -7.95678273e-02
3.45075399e-01 -3.26113641e-01 4.68657970e-01 -3.82932350e-02
-2.90570438e-01 -4.21501696e-01 5.77816904e-01 8.70501220e-01
4.09632802e-01 -6.17594048e-02 -7.10395694e-01 6.47861481e-01
5.56654632e-01 3.40957969e-01 5.41421235e-01 3.80057454e-01
-6.34160280e-01 -9.49047983e-01 2.34665666e-02 8.87038589e-01
-5.98920465e-01 -9.91098732e-02 -3.82687896e-01 4.99544770e-01
2.69171417e-01 7.31286287e-01 5.47323637e-02 -2.09440961e-01
4.61260676e-01 -3.13197792e-01 3.38844597e-01 -5.17406583e-01
9.04105604e-03 1.49428144e-01 6.56298921e-02 -5.27918398e-01
-4.21654642e-01 -3.71128768e-01 -1.19286048e+00 -1.29884973e-01
-5.13139814e-02 -3.42780620e-01 3.10842991e-01 7.43953049e-01
5.99730492e-01 6.28294468e-01 7.75832057e-01 -1.89151466e-01
-4.62815702e-01 -8.94440949e-01 -7.52646744e-01 7.89109543e-02
5.88627636e-01 -8.09389770e-01 -2.62084752e-01 -1.12208344e-01] | [15.812490463256836, -3.9801650047302246] |
4940b11d-40bd-46e2-a549-436d0c07b6c0 | videopipe-2022-challenge-real-world-video | 2210.11158 | null | https://arxiv.org/abs/2210.11158v1 | https://arxiv.org/pdf/2210.11158v1.pdf | VideoPipe 2022 Challenge: Real-World Video Understanding for Urban Pipe Inspection | Video understanding is an important problem in computer vision. Currently, the well-studied task in this research is human action recognition, where the clips are manually trimmed from the long videos, and a single class of human action is assumed for each clip. However, we may face more complicated scenarios in the industrial applications. For example, in the real-world urban pipe system, anomaly defects are fine-grained, multi-labeled, domain-relevant. To recognize them correctly, we need to understand the detailed video content. For this reason, we propose to advance research areas of video understanding, with a shift from traditional action recognition to industrial anomaly analysis. In particular, we introduce two high-quality video benchmarks, namely QV-Pipe and CCTV-Pipe, for anomaly inspection in the real-world urban pipe systems. Based on these new datasets, we will host two competitions including (1) Video Defect Classification on QV-Pipe and (2) Temporal Defect Localization on CCTV-Pipe. In this report, we describe the details of these benchmarks, the problem definitions of competition tracks, the evaluation metric, and the result summary. We expect that, this competition would bring new opportunities and challenges for video understanding in smart city and beyond. The details of our VideoPipe challenge can be found in https://videopipe.github.io. | ['Yali Wang', 'Yu Qiao', 'Yi Dai', 'Wei Yao', 'Fei Xie', 'Haiping Tang', 'Lixia Qiu', 'Yabing Jiang', 'Guixin Liang', 'Ying Li', 'Xuan Zhang', 'Yi Liu'] | 2022-10-20 | null | null | null | null | ['video-defect-classification', 'temporal-defect-localization'] | ['computer-code', 'computer-vision'] | [ 1.54187694e-01 -2.68210173e-01 -1.59605756e-01 -1.51439086e-01
-6.00942671e-01 -4.34069991e-01 -1.53343938e-03 -1.04628816e-01
1.48367047e-01 2.63459265e-01 6.97018728e-02 -2.51027733e-01
8.88374224e-02 -5.06239116e-01 -7.81471550e-01 -8.40497375e-01
-6.50154706e-03 7.31601417e-02 5.04754722e-01 1.20266769e-02
3.24717283e-01 1.39595106e-01 -1.59722722e+00 6.80849552e-01
7.64559269e-01 1.38522482e+00 1.32335171e-01 8.85462105e-01
1.56640902e-01 1.31764114e+00 -4.61715370e-01 -3.46637279e-01
1.85281992e-01 -3.50650907e-01 -1.07346058e+00 8.96523297e-01
5.33775270e-01 -6.10165000e-01 -5.55278420e-01 1.19378960e+00
1.07368194e-01 3.78146097e-02 5.01941562e-01 -1.78196692e+00
-3.06163490e-01 2.10151941e-01 -6.28793120e-01 7.17800915e-01
5.23096621e-01 5.05804896e-01 9.85440612e-01 -7.43092954e-01
5.31046331e-01 1.09559882e+00 4.44500983e-01 2.73397356e-01
-4.87020969e-01 -4.56416279e-01 5.33701122e-01 1.05335259e+00
-1.10530663e+00 -2.94817477e-01 6.96421444e-01 -8.37515831e-01
7.69803703e-01 1.47293389e-01 7.38683403e-01 1.25380003e+00
2.45975658e-01 1.37514389e+00 3.42684031e-01 1.70768015e-02
2.71139592e-01 -3.96974385e-01 2.19595611e-01 6.77521050e-01
5.89497164e-02 -1.70163333e-01 -4.23173904e-01 1.88138649e-01
7.03020096e-01 3.93764555e-01 -4.52011913e-01 -3.46054345e-01
-1.15715754e+00 6.39236033e-01 -4.40321118e-02 2.38581195e-01
-2.70844758e-01 3.48654054e-02 9.83564317e-01 4.61763054e-01
4.30925518e-01 5.77360429e-02 -5.63817024e-01 -6.16279483e-01
-5.79780877e-01 1.09898023e-01 6.76266372e-01 1.20100498e+00
5.23748100e-01 2.05924556e-01 -8.20415094e-02 6.84619486e-01
2.83058643e-01 4.01666254e-01 3.88152272e-01 -1.34234250e+00
7.57929444e-01 4.08661783e-01 3.23335975e-02 -1.10387838e+00
-1.91351339e-01 1.61205186e-03 -9.06240284e-01 8.13652277e-02
4.44964916e-01 1.00907803e-01 -1.03056526e+00 9.30977821e-01
2.55974203e-01 7.06906140e-01 -9.93777290e-02 9.15102959e-01
6.87383354e-01 7.22430050e-01 -1.01701736e-01 -2.97846138e-01
1.09096074e+00 -1.47551501e+00 -7.71140754e-01 -1.47027105e-01
1.02435613e+00 -7.55300343e-01 1.07071817e+00 9.03579712e-01
-8.64121795e-01 -8.37958097e-01 -8.55831563e-01 2.34930471e-01
-1.36282533e-01 6.28087446e-02 2.96535432e-01 5.08037806e-01
-5.25339305e-01 4.80978727e-01 -1.06089938e+00 -5.00363529e-01
6.04301810e-01 -8.93511772e-02 -4.73022342e-01 -6.40705407e-01
-7.08104074e-01 2.32590601e-01 2.53958941e-01 2.70278901e-01
-1.30274260e+00 -5.32260478e-01 -9.40434933e-01 -1.58893809e-01
8.66621733e-01 -2.04631582e-01 1.55271065e+00 -1.15851784e+00
-9.99514878e-01 7.97143579e-01 -4.47353423e-02 -2.49088839e-01
3.95143569e-01 -5.33557594e-01 -5.98613083e-01 3.65199834e-01
3.54083955e-01 2.80222856e-02 8.46710980e-01 -1.05449104e+00
-1.20057631e+00 -2.58503050e-01 2.62349784e-01 -1.17359243e-01
1.88055076e-02 5.45932390e-02 -7.73495078e-01 -6.52941704e-01
2.37576127e-01 -8.33095014e-01 -9.58208889e-02 4.32634540e-03
-4.93761271e-01 -2.71498650e-01 1.25939226e+00 -1.02119732e+00
1.21836066e+00 -2.49212360e+00 5.30632921e-02 -8.01944211e-02
2.17330873e-01 3.18702102e-01 -2.02305749e-01 2.04496875e-01
-1.84018761e-01 1.06141925e-01 -1.48916736e-01 -8.83432999e-02
-2.26951808e-01 3.88554811e-01 -1.85239956e-01 5.74803829e-01
-2.69974340e-02 5.31395376e-01 -9.59283471e-01 -4.57553655e-01
4.51786250e-01 -3.49793941e-01 -6.94169343e-01 3.09585243e-01
-1.19656451e-01 8.05690467e-01 -7.56758988e-01 1.32438636e+00
7.85720289e-01 -1.39023975e-01 -3.15715820e-01 -3.87714267e-01
1.02814607e-01 -2.54018515e-01 -1.31002295e+00 1.74821663e+00
-1.63782477e-01 6.62922442e-01 1.65843576e-01 -1.64703357e+00
4.20390368e-01 5.14613986e-01 1.08829963e+00 -6.76016927e-01
-7.81422481e-02 1.28820479e-01 -2.33377859e-01 -1.33010542e+00
2.04320416e-01 4.45118338e-01 1.10565946e-02 1.45629689e-01
-5.95478043e-02 -5.58864735e-02 6.15444064e-01 1.48947284e-01
1.58979273e+00 -2.11085286e-02 1.26656517e-01 1.38877541e-01
7.69563735e-01 1.01637043e-01 9.47875559e-01 5.63891768e-01
-7.95231044e-01 9.28710043e-01 6.27804995e-01 -7.42630541e-01
-1.04932070e+00 -1.22103202e+00 2.19960004e-01 7.69479930e-01
4.03996944e-01 -5.56721330e-01 -6.42540216e-01 -1.21182609e+00
-7.87981600e-02 3.58375400e-01 -2.19221070e-01 -1.71201557e-01
-7.84389257e-01 -5.05212069e-01 3.35020065e-01 6.97271168e-01
7.70144343e-01 -1.19323003e+00 -2.81568408e-01 2.04052657e-01
-6.78579450e-01 -1.56803918e+00 -5.42268693e-01 -2.81647235e-01
-8.40238333e-01 -1.72284698e+00 -3.31015110e-01 -8.28293383e-01
4.17447835e-01 5.75351179e-01 1.33164859e+00 3.32147747e-01
-4.69963849e-01 9.79590297e-01 -9.06202853e-01 -2.69796312e-01
-1.41269788e-01 -3.66429180e-01 -4.33711745e-02 3.37741911e-01
4.47275311e-01 -3.18912446e-01 -6.38320148e-01 7.94260681e-01
-1.02427042e+00 -2.17823952e-01 4.68878984e-01 6.48269951e-01
6.37492657e-01 3.30234528e-01 2.40630746e-01 -6.55020595e-01
-9.74829942e-02 -6.56464577e-01 -3.61595958e-01 1.90507233e-01
-1.88472271e-02 -5.58970690e-01 4.73131955e-01 -2.52690107e-01
-1.03083956e+00 1.88032672e-01 -2.86671758e-01 -7.34409034e-01
-6.27642274e-01 2.86652654e-01 -4.59533632e-01 1.68329567e-01
3.01099062e-01 2.30608210e-01 -3.39220911e-01 -5.68483651e-01
-2.61469986e-02 7.57531941e-01 4.55767214e-01 -5.44377327e-01
6.46592498e-01 6.82063103e-01 -4.57384169e-01 -1.16703010e+00
-9.42982256e-01 -1.05911875e+00 -7.25251317e-01 -5.60100973e-01
1.30708444e+00 -9.99503195e-01 -5.72039485e-01 9.81340051e-01
-1.20528913e+00 -3.46017778e-01 -3.30935597e-01 6.05224967e-01
-8.30606103e-01 8.45696092e-01 -8.18905652e-01 -5.77424407e-01
1.73509181e-01 -1.36901820e+00 1.31050014e+00 1.62978340e-02
1.41689658e-01 -7.52540767e-01 -7.87395760e-02 9.19471085e-01
-8.67543593e-02 2.00950131e-01 6.15419507e-01 -3.77052337e-01
-1.00854552e+00 -1.12185124e-02 -2.49522924e-01 8.42300713e-01
1.70871139e-01 1.81165233e-01 -8.42079818e-01 -2.08959356e-01
1.45779729e-01 -3.93234521e-01 8.94116223e-01 5.96633971e-01
1.74149740e+00 -5.59762381e-02 -3.24075758e-01 5.28046906e-01
1.06014204e+00 4.90761340e-01 8.68274987e-01 2.93110877e-01
9.98338342e-01 4.74952251e-01 1.21501327e+00 6.40797198e-01
2.82148540e-01 6.36786103e-01 8.69048953e-01 7.66440928e-02
5.97971454e-02 1.43882141e-01 7.31744170e-01 1.01269424e+00
-4.37910885e-01 -4.40750182e-01 -9.22666728e-01 6.23094738e-01
-2.11540341e+00 -1.02019715e+00 -5.06633103e-01 1.78103900e+00
7.13308295e-03 7.48671368e-02 4.22163419e-02 5.90119243e-01
6.42842293e-01 2.31603131e-01 -4.51155424e-01 4.43033874e-02
1.33695394e-01 -3.73545110e-01 2.77627736e-01 2.08881442e-02
-1.61463654e+00 6.95829034e-01 5.36619711e+00 8.36017549e-01
-7.61055887e-01 2.86011726e-01 7.64737129e-01 2.18941458e-02
4.21810627e-01 -2.59006292e-01 -6.55282497e-01 6.17489457e-01
5.92130959e-01 2.07583070e-01 7.71070868e-02 1.06109679e+00
5.28384447e-01 -2.81695604e-01 -1.26246035e+00 1.30536342e+00
1.89364985e-01 -1.02725720e+00 -7.53498822e-02 -2.46050451e-02
7.42833734e-01 5.06868772e-02 -2.35417172e-01 4.70708042e-01
-7.72209018e-02 -7.85744190e-01 6.86582863e-01 2.78513342e-01
2.34226063e-01 -4.76635963e-01 9.34457958e-01 1.85926989e-01
-1.55494297e+00 -5.56757808e-01 -4.00916338e-01 2.37236917e-02
5.15502155e-01 6.92762196e-01 -7.71843046e-02 7.30687618e-01
1.26841831e+00 1.52104509e+00 -4.84137625e-01 1.40118384e+00
-1.87697951e-02 7.09579706e-01 1.21015035e-01 6.27165318e-01
2.20522135e-01 -2.29088590e-01 3.84139925e-01 9.57764089e-01
5.73171675e-01 1.64794579e-01 7.50312567e-01 3.06741863e-01
2.23625705e-01 2.53612944e-03 -7.66666710e-01 9.73555520e-02
-1.48837551e-01 9.86699045e-01 -5.91840446e-01 -3.52713078e-01
-1.02875912e+00 1.28708959e+00 -2.63915122e-01 4.72260118e-01
-1.17740381e+00 8.47466290e-03 1.08441317e+00 4.85397913e-02
5.43684304e-01 -2.18667954e-01 9.55098122e-02 -1.42048109e+00
3.26361001e-01 -1.12104058e+00 7.26040423e-01 -8.29046011e-01
-1.28515971e+00 2.23985881e-01 -4.69082408e-03 -1.66620624e+00
2.28016451e-02 -9.57777202e-01 -7.15195894e-01 -8.80872831e-02
-1.34317589e+00 -1.07108891e+00 -7.63040304e-01 8.01814079e-01
1.46481192e+00 -1.54226661e-01 2.39475772e-01 8.31328750e-01
-8.24169040e-01 1.66377798e-01 8.47572684e-02 4.35212106e-01
5.44530451e-01 -9.80101883e-01 3.36904138e-01 1.07343554e+00
2.68496066e-01 -4.98585790e-01 6.30748689e-01 -6.41944528e-01
-1.20211232e+00 -1.35931623e+00 3.79487574e-01 -6.34360969e-01
7.22733140e-01 4.98494925e-03 -9.63209510e-01 9.27161515e-01
6.58613443e-02 3.31896424e-01 2.89948761e-01 -3.22044313e-01
-1.76921822e-02 -1.37717739e-01 -8.63777041e-01 1.16863072e-01
1.34691358e+00 -3.80215406e-01 -2.48112708e-01 7.04852164e-01
6.13432229e-01 -3.92213941e-01 -8.76250327e-01 4.35985923e-01
1.81495965e-01 -1.14956927e+00 9.87193763e-01 -6.20800316e-01
6.34244204e-01 -4.59738880e-01 -3.25511664e-01 -1.15348840e+00
-1.33731917e-01 -1.58493578e-01 -2.80010670e-01 1.10863781e+00
4.59851623e-02 -1.10256977e-01 1.05973732e+00 2.26780981e-01
-7.36284614e-01 -5.19534767e-01 -9.56540763e-01 -9.92419004e-01
-3.21321011e-01 -8.99144828e-01 9.86654833e-02 6.87065244e-01
-1.54259548e-01 -1.70856327e-01 -6.36375129e-01 5.10185719e-01
5.22189558e-01 -2.28040650e-01 8.04809690e-01 -1.11088753e+00
-3.31412166e-01 -8.61930773e-02 -8.73513579e-01 -1.38956237e+00
-9.09716338e-02 -2.94121593e-01 1.63642049e-01 -1.40072346e+00
2.01000080e-01 -9.07377689e-04 -2.28959486e-01 1.71527728e-01
1.25996649e-01 2.77461916e-01 1.64668441e-01 1.48733914e-01
-1.05501080e+00 5.24331033e-01 1.31567371e+00 -6.55515194e-01
9.70707536e-02 3.40089530e-01 -1.62779272e-01 1.01939213e+00
9.49933171e-01 -2.17447668e-01 -4.91638392e-01 -5.34009993e-01
-8.72430131e-02 -1.50402356e-02 4.92345035e-01 -1.32470644e+00
9.38877091e-02 -3.89551520e-01 -7.74973407e-02 -6.73006356e-01
2.68344641e-01 -1.07115948e+00 -2.26375878e-01 4.92821068e-01
2.91177303e-01 2.23358557e-01 -8.84224176e-02 8.20607483e-01
-7.69258797e-01 -2.89545268e-01 6.39947474e-01 -4.04630631e-01
-1.48987687e+00 6.60008550e-01 -6.91323638e-01 2.48654991e-01
1.45542514e+00 -3.09965461e-01 -3.12819898e-01 -4.84992921e-01
-9.18624878e-01 3.42877626e-01 2.25339383e-01 6.56919062e-01
7.68104017e-01 -1.37929785e+00 -6.69457197e-01 3.15958351e-01
4.78097320e-01 1.57556638e-01 7.80997932e-01 1.21721125e+00
-7.30314553e-01 2.92379498e-01 -2.59625763e-01 -1.02290869e+00
-1.44561160e+00 6.57871664e-01 3.37203622e-01 -2.07370073e-01
-7.88303852e-01 5.32645643e-01 5.87649226e-01 -1.17405993e-03
5.15040338e-01 -7.74703085e-01 -3.34791631e-01 -1.67364970e-01
6.70751929e-01 7.52783179e-01 1.15917683e-01 -6.75928295e-01
-2.14383528e-01 7.38400280e-01 1.49560586e-01 5.62758207e-01
1.14523900e+00 -4.16501731e-01 9.58033502e-02 3.29075933e-01
1.04002643e+00 -3.72692227e-01 -1.39239788e+00 -4.52419333e-02
1.15430996e-01 -7.55506873e-01 -1.63755402e-01 -1.87781170e-01
-1.68229759e+00 8.95115554e-01 7.71066189e-01 3.89663011e-01
1.24251473e+00 1.73253417e-01 1.05255604e+00 3.89438987e-01
3.26466739e-01 -1.42433143e+00 7.03243077e-01 5.91761887e-01
8.23450148e-01 -1.60948026e+00 -3.50496769e-01 -9.40492690e-01
-7.52077162e-01 1.04787254e+00 1.09101975e+00 1.25685334e-01
8.37447405e-01 -1.03907136e-03 1.79706007e-01 -3.60662460e-01
-4.47702795e-01 -1.67092875e-01 -9.86680202e-03 8.18139374e-01
1.52938247e-01 -2.04464719e-01 2.38813952e-01 5.08312643e-01
4.09346879e-01 -4.07538237e-03 7.75914013e-01 9.77378190e-01
-6.23268008e-01 -9.15714920e-01 -3.79576057e-01 5.09174347e-01
-4.43677038e-01 3.14474612e-01 1.27092004e-01 6.34711683e-01
4.00782496e-01 1.28854370e+00 2.05447108e-01 -4.05387193e-01
7.03434587e-01 -1.63291216e-01 1.97048247e-01 -5.31019688e-01
1.02349010e-03 -1.73285291e-01 1.92016527e-01 -1.23205912e+00
-5.49655139e-01 -8.66769552e-01 -9.79445755e-01 -3.31894696e-01
-1.26900122e-01 -4.47515436e-02 1.65850669e-01 1.06591976e+00
1.81789950e-01 6.71361625e-01 6.16023302e-01 -8.56000423e-01
-1.74208358e-01 -8.47264290e-01 -9.51306164e-01 8.71507287e-01
3.50766957e-01 -7.86658347e-01 -5.03413618e-01 5.60762763e-01] | [7.9050469398498535, 1.5161805152893066] |
c1950e98-0d02-4896-8b4f-2b5d104a0934 | graph-regularized-probabilistic-matrix | 2210.10784 | null | https://arxiv.org/abs/2210.10784v1 | https://arxiv.org/pdf/2210.10784v1.pdf | Graph Regularized Probabilistic Matrix Factorization for Drug-Drug Interactions Prediction | Co-administration of two or more drugs simultaneously can result in adverse drug reactions. Identifying drug-drug interactions (DDIs) is necessary, especially for drug development and for repurposing old drugs. DDI prediction can be viewed as a matrix completion task, for which matrix factorization (MF) appears as a suitable solution. This paper presents a novel Graph Regularized Probabilistic Matrix Factorization (GRPMF) method, which incorporates expert knowledge through a novel graph-based regularization strategy within an MF framework. An efficient and sounded optimization algorithm is proposed to solve the resulting non-convex problem in an alternating fashion. The performance of the proposed method is evaluated through the DrugBank dataset, and comparisons are provided against state-of-the-art techniques. The results demonstrate the superior performance of GRPMF when compared to its counterparts. | ['Angshul Majumdar', 'Kriti Kumar', 'Emilie Chouzenoux', 'Stuti Jain'] | 2022-10-19 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 1.03116617e-01 -5.52577116e-02 -6.44629359e-01 -1.74015537e-01
-6.10691369e-01 -4.41738397e-01 3.39059502e-01 4.81980145e-01
-7.26753771e-02 8.26929390e-01 1.48891702e-01 -8.16274762e-01
-4.68444824e-01 -3.06943357e-01 -5.22017896e-01 -6.77814603e-01
-2.67298847e-01 5.69280982e-01 -4.38865036e-01 1.85379729e-01
-3.52167785e-02 6.27518237e-01 -6.45191908e-01 1.26959875e-01
1.37889373e+00 3.60909969e-01 1.41549855e-01 2.40514889e-01
-1.12596340e-02 7.74480700e-01 -1.51963890e-01 -5.11543572e-01
7.91898221e-02 -2.46165141e-01 -6.83255851e-01 3.78460407e-01
1.22989178e-01 9.00573358e-02 -3.96046042e-01 1.03277910e+00
3.58561099e-01 3.08925390e-01 8.05360138e-01 -9.94076431e-01
-6.23485625e-01 1.77635953e-01 -8.63515198e-01 5.03861122e-02
6.20894969e-01 -1.57816336e-01 1.16536093e+00 -1.20707428e+00
6.63977623e-01 1.27383912e+00 3.38363707e-01 4.73578513e-01
-1.42432642e+00 -3.78341138e-01 3.69524449e-01 1.98139280e-01
-1.77211785e+00 4.54122052e-02 4.99887377e-01 -7.12820709e-01
1.09302211e+00 2.87721127e-01 4.16518897e-01 6.41286016e-01
5.30349672e-01 9.11764026e-01 9.04339015e-01 -1.15836188e-01
3.59048903e-01 -2.54651308e-02 2.39213333e-01 4.94010031e-01
3.74958664e-01 -4.55557518e-02 -3.48857701e-01 -8.93091559e-01
3.70966554e-01 3.43702406e-01 -5.01118422e-01 -2.92517483e-01
-7.44587541e-01 1.02684093e+00 2.17484772e-01 1.88165709e-01
-8.35749865e-01 -2.93291688e-01 5.40366620e-02 -4.02189195e-02
6.99953377e-01 1.12404391e-01 -4.14641887e-01 1.41039640e-01
-7.48997033e-01 1.78677946e-01 8.19729507e-01 5.07272899e-01
3.82105052e-01 -1.16740853e-01 -1.34982139e-01 8.56367588e-01
9.40653503e-01 3.44455779e-01 1.11345695e-02 -6.78430796e-02
4.07965153e-01 6.75144613e-01 9.46248174e-02 -1.09428263e+00
-5.17722189e-01 -4.53074127e-01 -1.05286849e+00 -1.49898306e-01
9.32527930e-02 -1.05500132e-01 -8.17998052e-01 1.53308690e+00
7.18589485e-01 6.18427157e-01 3.00751235e-02 8.26257527e-01
7.95374572e-01 7.06704199e-01 5.96590996e-01 -8.11116874e-01
1.28674138e+00 -6.34876549e-01 -1.04106307e+00 4.51977476e-02
6.51137292e-01 -8.81603360e-01 4.57369298e-01 8.08561802e-01
-6.61872387e-01 -3.57629433e-02 -9.20330346e-01 3.13129187e-01
-1.92149486e-02 1.01027206e-01 1.04549885e+00 7.77240217e-01
-5.95882118e-01 7.18307972e-01 -8.89937162e-01 2.96122469e-02
5.61103940e-01 7.37410784e-01 -5.04422069e-01 -3.88737768e-01
-1.17131591e+00 9.39894736e-01 4.40818556e-02 2.94435889e-01
-7.20172703e-01 -9.74509180e-01 -8.25450182e-01 -3.49860728e-01
2.16111571e-01 -8.69930744e-01 6.83215797e-01 -3.93417984e-01
-1.50828731e+00 5.30915082e-01 -2.91903347e-01 -3.81555468e-01
3.70219052e-01 -2.81646311e-01 -4.93475735e-01 2.39945166e-02
-9.48020741e-02 -7.40128607e-02 8.52297902e-01 -8.18347394e-01
-1.89140677e-01 -5.85421324e-01 -1.53464571e-01 1.35055035e-01
-9.28773880e-02 7.29548484e-02 -4.03973490e-01 -8.32910836e-01
-1.55998752e-01 -1.07683122e+00 -8.09146583e-01 -4.60815489e-01
-7.42177427e-01 -6.50077701e-01 5.77178061e-01 -7.56695449e-01
1.78650081e+00 -1.77797878e+00 5.87927759e-01 7.48646498e-01
6.14005864e-01 4.55390662e-01 -7.62989968e-02 6.93573534e-01
-7.13160038e-01 -3.51112671e-02 -3.48063231e-01 -2.19256803e-01
-2.73713112e-01 1.47019416e-01 1.22132555e-01 1.00714183e+00
1.45002007e-01 6.32919669e-01 -1.01445198e+00 -8.74392241e-02
2.56700993e-01 8.83103848e-01 -5.01367748e-01 2.21414834e-01
-2.61908025e-01 7.15746582e-01 -7.46597767e-01 5.39202511e-01
1.05146086e+00 -7.01974690e-01 9.07839537e-01 -2.80910730e-01
2.15002924e-01 -3.51577136e-03 -1.31156552e+00 1.69934011e+00
-1.66032240e-01 -1.55308470e-01 -1.68899745e-01 -9.76711750e-01
5.78995407e-01 5.59551358e-01 8.64053845e-01 -1.85177192e-01
7.59331360e-02 1.19883254e-01 1.34271830e-01 -4.67839092e-01
-1.15740970e-01 -1.83398768e-01 4.86598432e-01 4.14815456e-01
-3.12249422e-01 3.99922609e-01 3.11850131e-01 5.41087627e-01
1.17507660e+00 4.55971137e-02 6.15319729e-01 -4.02825505e-01
9.07328665e-01 -1.99679017e-01 7.64168322e-01 2.89755642e-01
2.74574459e-01 3.96319805e-03 3.16123545e-01 -5.41458488e-01
-4.60567653e-01 -8.21731031e-01 -5.05285919e-01 3.98573190e-01
-2.62566030e-01 -8.34745288e-01 -5.38050532e-01 -9.49312568e-01
2.74496019e-01 3.65107805e-01 -4.92137551e-01 1.38436094e-01
-2.16616035e-01 -1.45752907e+00 -1.27582207e-01 1.18574142e-01
-1.07390970e-01 -1.61057159e-01 4.12984401e-01 5.98291039e-01
1.00712389e-01 -8.88597846e-01 -6.04718089e-01 -5.87529019e-02
-8.82613242e-01 -1.46331763e+00 -8.25363874e-01 -4.75242943e-01
9.39117074e-01 3.20668012e-01 9.25551713e-01 6.68639317e-02
-3.86910588e-01 2.04201654e-01 -1.44022167e-01 -2.73482531e-01
-4.06406045e-01 -3.37937504e-01 4.32167768e-01 4.49047267e-01
5.10207117e-01 -5.16737819e-01 -1.00942445e+00 2.84603536e-01
-1.02364409e+00 -2.76161432e-01 5.05694628e-01 8.34536612e-01
9.06382620e-01 6.31675497e-02 6.99799776e-01 -1.69723105e+00
9.72934902e-01 -8.31801653e-01 -7.29049563e-01 4.27580059e-01
-1.03270888e+00 1.28249288e-01 4.97514278e-01 -3.08236808e-01
-9.18845773e-01 5.14732718e-01 -2.42144853e-01 -4.49064344e-01
2.00373724e-01 1.08987820e+00 -3.46882999e-01 -3.70409071e-01
6.35928631e-01 -9.17088240e-02 4.31007892e-02 -6.95035934e-01
4.60692853e-01 7.49471843e-01 -2.41216108e-01 -3.01914543e-01
4.97129261e-01 2.53174305e-01 2.57932782e-01 -8.17497432e-01
-6.96747005e-01 -7.32370079e-01 -1.99850217e-01 8.52235928e-02
6.16623998e-01 -1.16644895e+00 -1.02632010e+00 2.22895399e-01
-1.09216726e+00 1.45524234e-01 4.30616021e-01 9.12445784e-01
-1.43435538e-01 9.67620492e-01 -6.93435192e-01 -7.25127339e-01
-5.06284058e-01 -1.29935420e+00 4.37581301e-01 -1.50349542e-01
-3.23514998e-01 -1.25130749e+00 6.73158824e-01 5.12900054e-01
-1.13586605e-01 2.89622456e-01 1.12778449e+00 -8.32018077e-01
-2.83387959e-01 -3.02968979e-01 -1.34329587e-01 2.53003567e-01
5.10128021e-01 4.30935528e-03 -5.96839249e-01 -5.63092113e-01
-3.45345512e-02 2.00392455e-01 6.05858803e-01 6.51743650e-01
8.64541531e-01 -4.73476559e-01 -6.53498471e-01 3.44186425e-01
1.49728072e+00 3.67015898e-01 6.34252906e-01 -1.86976939e-01
1.01791561e+00 2.52620280e-01 6.51714206e-01 8.55986893e-01
3.46814305e-01 7.42004871e-01 1.63361192e-01 -2.27253824e-01
1.32934913e-01 -1.50350947e-02 8.52743164e-02 7.28137732e-01
-1.51871741e-01 -3.16181630e-01 -8.80793452e-01 1.12774156e-01
-2.26481986e+00 -4.00518239e-01 -5.89679062e-01 2.25685453e+00
8.58826220e-01 -5.28364837e-01 2.75540709e-01 -9.51218456e-02
7.64270902e-01 -1.34560794e-01 -6.70727849e-01 -3.46269339e-01
-1.16056548e-02 5.30653477e-01 4.27354902e-01 6.23089552e-01
-1.17253196e+00 6.25364780e-01 6.59603596e+00 1.15095961e+00
-7.27650940e-01 -1.63408425e-02 5.94065964e-01 3.83730829e-01
-4.85861272e-01 -1.59441158e-01 -5.93187273e-01 4.45191234e-01
9.61298108e-01 -1.39314950e-01 3.78247201e-01 6.69251621e-01
7.16063380e-01 1.62689053e-02 -1.03758144e+00 1.32638454e+00
-1.92023981e-02 -1.44382501e+00 2.39780948e-01 3.86590689e-01
8.34781766e-01 -1.08010635e-01 1.59602031e-01 -3.88155222e-01
3.05855811e-01 -1.09242058e+00 -2.07096696e-01 3.25499594e-01
5.72119474e-01 -9.61468637e-01 4.54851866e-01 -8.84718727e-03
-1.17350197e+00 1.79089203e-01 -2.61874795e-01 1.63157687e-01
1.28868267e-01 1.09375608e+00 -9.07621920e-01 1.05436075e+00
-1.74382161e-02 1.39743745e+00 -1.40391603e-01 1.17003608e+00
-5.14683247e-01 5.48924983e-01 -1.40645802e-01 2.19426364e-01
6.86203092e-02 -7.56435752e-01 5.56655526e-01 1.10693157e+00
5.19861504e-02 3.50246340e-01 4.60532427e-01 4.47990328e-01
-2.48797297e-01 7.20610678e-01 -6.07941389e-01 -4.83761609e-01
9.56873223e-02 1.20527744e+00 -4.52595651e-01 -1.17592655e-01
-9.02666390e-01 9.09664690e-01 2.33413875e-01 4.98692364e-01
-7.11810350e-01 -4.61701192e-02 8.06151450e-01 -2.45991740e-02
2.35252716e-02 6.02558553e-02 2.47935802e-02 -1.34539545e+00
-1.44251227e-01 -1.12648487e+00 8.80249381e-01 -6.97155818e-02
-1.64853084e+00 6.22330964e-01 -3.76607120e-01 -1.49551356e+00
-8.55599716e-02 -6.05610251e-01 -2.59326041e-01 9.43451047e-01
-1.68379796e+00 -9.46773350e-01 4.37330186e-01 9.40729260e-01
1.76765606e-01 -2.20370770e-01 1.06493306e+00 8.36960912e-01
-1.03337169e+00 5.39085031e-01 3.22559953e-01 -3.08492720e-01
4.78752047e-01 -1.18299282e+00 -5.56810685e-02 7.51103163e-01
1.54877782e-01 8.96898806e-01 7.89447904e-01 -9.87735391e-01
-1.81969965e+00 -1.17586863e+00 8.65838885e-01 -7.01043680e-02
7.81888127e-01 5.48586212e-02 -9.29619730e-01 3.67341071e-01
1.55562520e-01 -5.80632016e-02 1.57975149e+00 3.01817089e-01
-4.83542770e-01 2.49829024e-01 -1.10334611e+00 4.37126070e-01
6.82006717e-01 -4.06568080e-01 -3.18441242e-01 1.15277040e+00
2.49725431e-01 -2.58637577e-01 -1.27766895e+00 2.87527502e-01
2.63925552e-01 -2.41817981e-01 1.16749477e+00 -1.08360422e+00
1.37823686e-01 -6.72659934e-01 4.23167162e-02 -1.29866481e+00
-5.56237698e-01 -1.06460297e+00 -4.92214739e-01 6.24345005e-01
7.05646276e-01 -6.09449923e-01 9.20639396e-01 9.29368496e-01
1.73284590e-01 -1.06760728e+00 -8.38476837e-01 -7.29248881e-01
-2.14842498e-01 -4.15253699e-01 2.18143940e-01 1.06399083e+00
5.24526894e-01 7.25668430e-01 -8.13041329e-01 3.13677281e-01
7.13569403e-01 9.10367295e-02 5.28080404e-01 -1.21625102e+00
-6.18692994e-01 4.17321138e-02 -5.54709494e-01 -8.74595106e-01
3.25198859e-01 -1.23010039e+00 -4.56636071e-01 -1.59899414e+00
4.35795546e-01 -2.37045556e-01 -5.39556503e-01 3.78422081e-01
-5.57023942e-01 -4.20763381e-02 -2.81597339e-02 2.40500003e-01
-4.24167693e-01 3.57351452e-01 1.09967983e+00 -3.58073860e-01
-5.01594901e-01 2.61658460e-01 -6.55529916e-01 5.69870472e-01
5.16535938e-01 -5.16951561e-01 -6.32924080e-01 8.33079144e-02
3.96706253e-01 2.94070810e-01 -2.33246714e-01 -1.17091767e-01
1.25120088e-01 -3.02915573e-01 2.30596155e-01 -4.90673751e-01
6.57474846e-02 -7.75852323e-01 7.22519100e-01 6.75771594e-01
1.34334248e-02 1.54621853e-02 2.07073033e-01 1.05885804e+00
-2.80035436e-01 -2.41330639e-02 5.65229118e-01 2.00797152e-02
-2.86738127e-01 8.29393268e-01 -4.82612103e-01 -2.73110271e-01
9.86578286e-01 1.26603290e-01 1.61047861e-01 -1.32692000e-03
-1.16470230e+00 2.76579767e-01 -1.70650825e-01 2.03622520e-01
9.18003619e-01 -1.28541768e+00 -8.91233325e-01 -1.97936714e-01
1.38068885e-01 -4.95364696e-01 6.12689078e-01 1.10385728e+00
-3.42536777e-01 5.36223292e-01 4.47005421e-01 -3.93984646e-01
-1.47469985e+00 8.43221486e-01 6.94644675e-02 -8.16644013e-01
-5.57596087e-01 7.82240570e-01 3.79397303e-01 2.21990328e-02
1.66641444e-01 2.25532800e-01 -2.86600471e-01 5.88194914e-02
7.95439422e-01 2.37919167e-01 3.45120192e-01 -4.76542234e-01
-7.34464288e-01 4.95428354e-01 -5.10890603e-01 4.34532106e-01
1.59380209e+00 -2.49940809e-02 -4.15589184e-01 -1.86854899e-01
1.21602213e+00 -2.64602993e-02 -7.97504544e-01 -3.23904574e-01
1.98769391e-01 -6.44648969e-01 2.69818544e-01 -7.64971435e-01
-1.06774998e+00 4.51080739e-01 5.99350274e-01 -2.31298491e-01
9.27652359e-01 -2.15326220e-01 7.13941753e-01 1.81535289e-01
2.42035866e-01 -8.09238911e-01 -3.04335445e-01 1.21028265e-02
7.14885592e-01 -1.09908426e+00 4.73081917e-01 -9.97792006e-01
-6.40305996e-01 1.06236064e+00 -1.01081051e-01 -9.97344628e-02
1.09266424e+00 -2.43699759e-01 -1.83653519e-01 -4.50368702e-01
-6.29746139e-01 3.71448360e-02 7.35858619e-01 3.56698722e-01
7.05214441e-01 3.45001936e-01 -9.72994328e-01 5.59460223e-01
7.66740441e-01 1.86001509e-01 1.62129179e-01 8.97489071e-01
2.16387019e-01 -1.86810338e+00 -1.37089416e-01 5.67043185e-01
-8.49030197e-01 -3.53499085e-01 -3.01860750e-01 2.52287954e-01
-2.55546421e-01 1.25340366e+00 -7.92673230e-01 -3.71809453e-01
3.66189718e-01 -1.30027235e-01 4.81292814e-01 -7.64927924e-01
-6.12311244e-01 6.31159544e-01 2.68889889e-02 -7.27423728e-01
-8.02393496e-01 -5.26022971e-01 -1.25833642e+00 -1.91900596e-01
-4.59869027e-01 5.87251365e-01 5.51915944e-01 9.14330542e-01
6.95824385e-01 4.52466965e-01 9.81253505e-01 -1.99198857e-01
-3.47675294e-01 -4.38574463e-01 -9.10000026e-01 3.82842064e-01
1.32746682e-01 -6.95351541e-01 -2.01620147e-01 -8.85457769e-02] | [5.525824546813965, 5.855812072753906] |
64f3aebb-b403-4670-8f6d-9fee614495ba | evaluation-of-online-dialogue-policy-learning | null | null | https://aclanthology.org/L12-1126 | https://aclanthology.org/L12-1126.pdf | Evaluation of Online Dialogue Policy Learning Techniques | The number of applied Dialogue Systems is ever increasing in several service providing and other applications as a way to efficiently and inexpensively serve large numbers of customers. A DS that employs some form of adaptation to the environment and its users is called an Adaptive Dialogue System (ADS). A significant part of the research community has lately focused on ADS and many existing or novel techniques are being applied to this problem. One of the most promising techniques is Reinforcement Learning (RL) and especially online RL. This paper focuses on online RL techniques used to achieve adaptation in Dialogue Management and provides an evaluation of various such methods in an effort to aid the designers of ADS in deciding which method to use. To the best of our knowledge there is no other work to compare online RL techniques on the dialogue management problem. | ['ros', 'Alex Papangelis', 'Vangelis Karkaletsis', 'Fillia Makedon'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['dialogue-management'] | ['natural-language-processing'] | [-3.17894369e-01 3.99481595e-01 3.04303132e-02 -5.95642090e-01
-2.58890241e-01 -5.46823859e-01 8.16620648e-01 3.13208640e-01
-4.88806516e-01 1.12404776e+00 2.16448635e-01 -1.13480061e-01
7.69931674e-02 -7.00603545e-01 3.12559128e-01 -3.72933567e-01
-1.85627155e-02 9.89249945e-01 5.65879226e-01 -1.19844139e+00
8.25499892e-01 7.10232258e-01 -1.42394102e+00 9.21168029e-02
6.77338243e-01 5.22251606e-01 4.14205015e-01 1.04692161e+00
-8.22778106e-01 8.94111514e-01 -7.68095970e-01 -1.87087759e-01
5.11609428e-02 -9.73252118e-01 -1.37768972e+00 2.78467298e-01
-4.91207391e-01 -5.61578691e-01 -4.35063280e-02 4.90071028e-01
9.51094747e-01 6.15070224e-01 4.33406889e-01 -1.02802324e+00
-1.74898803e-01 6.01435363e-01 -5.87625913e-02 4.53242421e-01
1.16780376e+00 -1.28964838e-02 6.72327697e-01 -4.52755719e-01
3.68366867e-01 1.36947250e+00 2.03273937e-01 7.38346219e-01
-9.19468045e-01 -9.61933956e-02 -3.18328775e-02 4.26414818e-01
-7.46073604e-01 -5.76603353e-01 6.31620467e-01 3.47976834e-02
1.22294748e+00 3.52054268e-01 7.30168164e-01 6.50093734e-01
-1.01251170e-01 9.87250388e-01 1.29251790e+00 -9.89162028e-01
5.23412466e-01 6.72191501e-01 9.68154594e-02 3.49041164e-01
-5.34731030e-01 -3.76390815e-01 -3.96772176e-01 -4.40809608e-01
7.74282277e-01 -6.30130053e-01 1.81076288e-01 -2.29521453e-01
-4.54895824e-01 1.26772177e+00 -1.49839506e-01 6.09286010e-01
-4.68955129e-01 -5.66973746e-01 7.54772425e-01 6.60645008e-01
1.73889294e-01 8.40897262e-01 -5.48062623e-01 -9.30096149e-01
1.51218586e-02 4.36788976e-01 1.60030925e+00 6.98025465e-01
5.13912261e-01 1.69048384e-01 1.04916193e-01 1.50605166e+00
3.84896338e-01 -1.05089545e-02 7.64514863e-01 -1.19413519e+00
5.82038462e-02 7.06936598e-01 3.70411813e-01 -4.76593107e-01
-5.67518294e-01 2.54842877e-01 -1.98555499e-01 2.11084858e-01
4.12965775e-01 -2.66272813e-01 6.21927008e-02 1.09916711e+00
7.55059481e-01 -6.15536213e-01 2.90964246e-01 5.74634671e-01
5.81314862e-01 8.58296633e-01 -1.85729742e-01 -4.94349986e-01
9.83392119e-01 -8.69309306e-01 -1.08473539e+00 -6.38529472e-03
6.55051589e-01 -1.08564138e+00 1.06535029e+00 6.61722124e-01
-1.29058075e+00 -1.83683917e-01 -8.27735066e-01 2.95021147e-01
-4.88735765e-01 -5.48076391e-01 6.14446461e-01 9.47492182e-01
-1.15457499e+00 4.83681828e-01 -3.12198371e-01 -9.44091499e-01
-4.18919444e-01 6.59344971e-01 1.10653907e-01 2.14773938e-01
-1.50258100e+00 1.47221220e+00 1.49952456e-01 -1.03363782e-01
-1.48929328e-01 3.34916621e-01 -5.20934463e-01 -3.50781024e-01
4.10345972e-01 -3.20486844e-01 2.14713669e+00 -1.13233924e+00
-2.59330106e+00 6.11525536e-01 2.05144241e-01 -3.90737295e-01
4.32700574e-01 -1.74230590e-01 -4.65826392e-01 5.98333403e-02
-1.93229586e-01 2.06500769e-01 4.51938659e-01 -1.08335161e+00
-9.33914661e-01 -3.70735943e-01 4.55591828e-01 8.28286409e-01
-2.80755609e-01 3.86973023e-01 -4.21059364e-03 3.28339525e-02
-2.82626748e-01 -7.76314437e-01 -2.96851248e-01 -5.34293294e-01
-4.45674732e-02 -8.21612477e-01 7.92766333e-01 -5.15401602e-01
1.29135752e+00 -1.55351627e+00 9.89277884e-02 1.40464261e-01
-2.13942528e-01 6.03796899e-01 2.20687389e-01 1.44767487e+00
5.05357206e-01 -1.72906354e-01 7.38890981e-03 3.13590728e-02
1.65935665e-01 6.25516415e-01 6.70703575e-02 9.06256586e-02
-2.73722291e-01 2.49559104e-01 -1.01388621e+00 -4.56010252e-01
3.71579498e-01 5.23274615e-02 -4.00597334e-01 8.81676912e-01
-2.17770875e-01 5.25567591e-01 -7.01391816e-01 2.79496461e-01
1.32548690e-01 1.58022538e-01 4.29315001e-01 4.94699061e-01
-4.76905972e-01 4.30031151e-01 -1.10683978e+00 1.18264985e+00
-6.85328186e-01 4.00513440e-01 1.99073389e-01 -9.54988122e-01
1.12758100e+00 6.29390478e-01 6.31394386e-01 -8.88395131e-01
1.22267090e-01 2.73247719e-01 2.61772871e-01 -1.02120221e+00
8.38077962e-01 7.79134221e-03 1.47310058e-02 6.53749168e-01
-9.69411060e-02 -6.59453198e-02 2.50189006e-01 2.90139258e-01
8.77339661e-01 8.25659186e-02 9.67713356e-01 2.07489058e-02
1.02301848e+00 1.50742933e-01 1.49448127e-01 7.57359445e-01
-5.41781127e-01 -2.39482909e-01 4.45299506e-01 -3.98490697e-01
-9.62698519e-01 -3.90442878e-01 1.20184459e-01 1.45959687e+00
-1.21807702e-01 -2.35472918e-01 -8.13468516e-01 -4.50455874e-01
-1.77294791e-01 9.21924531e-01 -6.73857033e-02 1.06546834e-01
-5.60231268e-01 -2.33392388e-01 4.17627543e-01 1.68133855e-01
9.23680544e-01 -1.43355441e+00 -5.59187114e-01 8.31911206e-01
-4.71479893e-02 -9.05421019e-01 -2.99316704e-01 -1.39254391e-01
-8.81860673e-01 -7.30097890e-01 -4.69354749e-01 -7.70745039e-01
1.46590129e-01 1.66266188e-01 8.71918499e-01 -2.70764641e-02
6.05520755e-02 8.99306774e-01 -9.08448696e-01 -4.46236759e-01
-1.07979488e+00 2.49893785e-01 4.71296757e-02 -1.51767686e-01
5.24848402e-01 -2.19470426e-01 -4.75282490e-01 5.14261186e-01
-6.33170426e-01 -2.65261084e-01 2.93024540e-01 9.12372112e-01
-2.96465158e-01 -9.42489281e-02 1.19002450e+00 -1.33777034e+00
1.58976400e+00 -5.24524808e-01 -3.43770534e-01 1.93026066e-01
-7.29442000e-01 2.64459793e-02 6.81839824e-01 -1.51504427e-01
-1.32178426e+00 -5.08781336e-02 -6.16412580e-01 6.46980643e-01
-3.75380754e-01 3.07223976e-01 1.62157696e-02 -2.75447369e-01
6.75776362e-01 1.49220511e-01 4.40277338e-01 -4.27798659e-01
2.41994292e-01 1.40026855e+00 -1.81912661e-01 -4.56027806e-01
1.06689192e-01 -2.67613173e-01 -3.37549031e-01 -1.17785430e+00
-2.23254800e-01 -8.16306949e-01 -5.52525818e-01 -6.25553250e-01
4.52739120e-01 -3.54533494e-02 -1.02653837e+00 4.68262017e-01
-8.01425695e-01 -5.98889887e-01 -1.00986972e-01 1.13958277e-01
-9.55220163e-01 5.72628260e-01 -6.54804766e-01 -1.49803627e+00
-3.42545897e-01 -9.06336248e-01 2.87038147e-01 7.75726199e-01
-4.70036447e-01 -1.22794497e+00 3.68065178e-01 4.72619206e-01
8.44665349e-01 -3.06762695e-01 6.53894663e-01 -1.03550100e+00
-5.46298102e-02 -3.12490821e-01 3.33410501e-01 2.61056215e-01
3.10574859e-01 -9.07173902e-02 -8.23986411e-01 -2.27212124e-02
3.09950650e-01 -5.39163947e-01 -2.81236678e-01 5.33902645e-02
4.14628327e-01 -5.65677166e-01 1.45738274e-01 -6.46488011e-01
1.06857622e+00 1.09735966e+00 7.82441437e-01 6.96743071e-01
-4.51373458e-02 9.11001742e-01 1.23236358e+00 9.71982956e-01
4.10797656e-01 9.74710166e-01 -4.14605178e-02 1.60085112e-01
4.85787600e-01 -4.43759933e-02 3.88411134e-01 9.43481326e-01
-3.40360440e-02 -1.87714502e-01 -9.32981372e-01 1.46678656e-01
-2.12732816e+00 -1.03025353e+00 2.24765465e-01 2.10821772e+00
9.06359971e-01 -1.35155758e-02 6.66288733e-01 2.23157778e-01
6.79885387e-01 -2.10824355e-01 -5.06534100e-01 -1.42240441e+00
1.42510295e-01 1.32243991e-01 1.35165304e-01 6.53850555e-01
-5.50270021e-01 9.84815776e-01 6.41555119e+00 3.96604478e-01
-6.95757270e-01 -5.87384924e-02 4.04028744e-01 5.27319789e-01
2.97885835e-01 1.59968771e-02 -6.59772575e-01 7.84218162e-02
1.09415984e+00 -4.41272378e-01 7.98453510e-01 8.91833782e-01
6.73675835e-01 -6.07005537e-01 -6.92081571e-01 7.10720539e-01
-7.61864856e-02 -8.71096075e-01 -2.31926322e-01 4.47715959e-03
3.07780564e-01 -4.94947702e-01 -3.74529123e-01 4.80453014e-01
3.55185211e-01 -5.96661091e-01 -1.43161848e-01 4.15998071e-01
1.28540993e-01 -1.00908542e+00 8.86656940e-01 5.66769540e-01
-7.66678154e-01 -3.29949677e-01 -1.57455117e-01 -2.48745278e-01
3.42330307e-01 -3.77714962e-01 -1.57947695e+00 1.80009559e-01
3.28695118e-01 5.50428778e-02 -5.68045117e-02 1.19269562e+00
1.70706436e-01 3.96002144e-01 -1.30460083e-01 -7.02694714e-01
3.72097552e-01 -4.31471735e-01 4.93319720e-01 1.06396651e+00
-1.90476537e-01 4.56888169e-01 3.85800064e-01 7.95016736e-02
4.47620273e-01 8.93162787e-01 -8.95751953e-01 -2.08099023e-01
5.60073912e-01 1.16547835e+00 -4.55030948e-01 -2.80888647e-01
-6.16432250e-01 1.03197992e+00 8.45161676e-02 -2.86690090e-02
-4.12325352e-01 -5.06882548e-01 1.99526832e-01 1.51418716e-01
-1.95289701e-01 -5.00729144e-01 7.28361979e-02 -3.98745924e-01
-5.56182444e-01 -1.23827958e+00 4.20310825e-01 -5.91788828e-01
-1.24014509e+00 6.66642010e-01 6.01248816e-02 -9.31455493e-01
-7.68818319e-01 -3.60565960e-01 -5.04097164e-01 6.95958972e-01
-1.14381635e+00 -5.97891450e-01 -8.20063204e-02 4.82274264e-01
1.24933672e+00 -7.44187355e-01 1.23523259e+00 2.79960013e-03
-3.88157070e-01 3.09336454e-01 2.56957650e-01 -3.57115775e-01
9.64591622e-01 -1.47672522e+00 -8.48989710e-02 -2.15464029e-02
-4.10159230e-01 4.60809916e-01 9.52605605e-01 -2.77229488e-01
-1.48408341e+00 -1.60644069e-01 8.76489043e-01 4.02813070e-02
5.47735810e-01 7.92969204e-03 -9.03955698e-01 3.10943782e-01
8.02813113e-01 -1.00806582e+00 8.54624629e-01 1.11223616e-01
5.50564289e-01 -1.82506636e-01 -1.40554118e+00 6.79569721e-01
4.01277900e-01 -3.13935280e-01 -5.02170384e-01 5.10869861e-01
2.71924853e-01 -5.09733737e-01 -1.05831194e+00 -1.75954238e-01
3.14224422e-01 -1.22775388e+00 5.10362446e-01 -4.70458895e-01
-1.80249766e-01 1.83285937e-01 2.28885859e-02 -1.44194043e+00
-1.03316925e-01 -1.13437867e+00 4.21919907e-03 1.29790330e+00
2.55093038e-01 -1.11447656e+00 6.52502954e-01 1.14608371e+00
-6.47502765e-02 -8.03269565e-01 -6.60030842e-01 -3.79185647e-01
-1.64844051e-01 2.00022981e-01 3.48234683e-01 7.66056359e-01
6.31671190e-01 7.66597390e-01 -4.74912435e-01 -4.32722747e-01
-6.58664014e-03 -1.99748382e-01 9.85380352e-01 -8.80202413e-01
-4.90326792e-01 -3.17510009e-01 -2.13706970e-01 -1.11993396e+00
-1.93318859e-01 -1.12792246e-01 -5.87172657e-02 -1.62388062e+00
-5.53176284e-01 -4.55914348e-01 2.58534282e-01 9.13291052e-02
1.91236466e-01 -5.18425822e-01 7.19555393e-02 1.41701579e-01
-5.74906111e-01 6.71466053e-01 1.10155582e+00 3.32745999e-01
-8.42416823e-01 6.82863355e-01 -5.67215621e-01 5.17150402e-01
1.65453136e+00 -1.51351988e-01 -5.52301764e-01 3.07704568e-01
5.47554754e-02 5.15381813e-01 -5.01375556e-01 -4.77464050e-01
4.71974015e-01 -4.42272782e-01 -7.99500123e-02 -2.79185504e-01
4.10817266e-01 -5.56934476e-01 -2.13790178e-01 3.79354656e-01
-5.04687548e-01 4.12320793e-01 8.41894671e-02 3.45171213e-01
-3.02881241e-01 -6.89224601e-01 8.14041495e-01 -5.69681406e-01
-1.05355084e+00 -3.25971514e-01 -1.20588541e+00 -1.40241012e-01
1.18353105e+00 -3.99812996e-01 -4.43069413e-02 -1.14438093e+00
-6.16804302e-01 5.56918442e-01 2.26332247e-01 3.71286929e-01
5.21613479e-01 -8.05165052e-01 -3.44383776e-01 -2.00551584e-01
-1.02153443e-01 -4.88795161e-01 9.07260925e-02 4.79279608e-01
-8.50293219e-01 5.00596225e-01 -5.06014407e-01 -6.67492524e-02
-1.47133553e+00 1.46958068e-01 5.91644228e-01 -2.83691704e-01
-5.92520952e-01 4.63389844e-01 -5.12613535e-01 -3.87689710e-01
4.28999215e-01 6.01477444e-01 -9.46780622e-01 5.72001189e-03
4.63556349e-01 6.95036709e-01 -2.19289213e-01 -3.82152319e-01
-5.13939885e-03 1.77709926e-02 -3.72269958e-01 -7.47500658e-01
1.13845146e+00 -6.47103846e-01 -6.41122460e-02 9.71338034e-01
6.46657765e-01 -1.84845179e-01 -8.01343858e-01 -1.49800047e-01
3.83076280e-01 -4.91591841e-01 -3.59032154e-01 -9.35011983e-01
-3.96711588e-01 5.23906410e-01 7.77423263e-01 1.16475892e+00
1.00890589e+00 -3.78021538e-01 6.95664704e-01 8.65893662e-01
5.88987470e-01 -2.05655646e+00 2.91782677e-01 7.33918369e-01
1.07474029e+00 -1.22977030e+00 -1.31179288e-01 -4.79581468e-02
-1.37591052e+00 1.43215978e+00 7.27486253e-01 -4.49567996e-02
4.58146483e-01 8.72962549e-02 3.30607444e-01 -3.54553536e-02
-8.78993213e-01 -1.87982082e-01 -4.52896804e-01 7.40920663e-01
8.59653890e-01 -1.10213079e-01 -8.50205123e-01 7.13843107e-02
5.38001861e-03 -8.99534374e-02 8.56935978e-01 1.39444244e+00
-8.43971670e-01 -2.04457283e+00 -2.92154431e-01 4.42868590e-01
-1.61065608e-01 5.10942817e-01 -9.78248477e-01 7.98107147e-01
-5.16313016e-01 1.33570123e+00 -4.43482846e-01 -1.15471005e-01
6.61756575e-01 3.15292507e-01 4.31873679e-01 -6.67314649e-01
-1.12721419e+00 9.88274068e-03 8.43293488e-01 -2.55304515e-01
-4.69173014e-01 -8.46329391e-01 -1.51893497e+00 -4.87447113e-01
-4.90191430e-01 5.57336569e-01 8.88383210e-01 7.60059357e-01
2.36559641e-02 4.88654003e-02 1.16104138e+00 -6.07920349e-01
-9.14429188e-01 -1.09829485e+00 -8.30103457e-01 1.58498988e-01
-2.00713038e-01 -5.97806334e-01 1.88070163e-02 -4.84816939e-01] | [13.065800666809082, 7.962672233581543] |
e3a1b463-02dd-4f84-8945-678a234305ce | revisiting-image-reconstruction-for-semi | 2303.09794 | null | https://arxiv.org/abs/2303.09794v1 | https://arxiv.org/pdf/2303.09794v1.pdf | Revisiting Image Reconstruction for Semi-supervised Semantic Segmentation | Autoencoding, which aims to reconstruct the input images through a bottleneck latent representation, is one of the classic feature representation learning strategies. It has been shown effective as an auxiliary task for semi-supervised learning but has become less popular as more sophisticated methods have been proposed in recent years. In this paper, we revisit the idea of using image reconstruction as the auxiliary task and incorporate it with a modern semi-supervised semantic segmentation framework. Surprisingly, we discover that such an old idea in semi-supervised learning can produce results competitive with state-of-the-art semantic segmentation algorithms. By visualizing the intermediate layer activations of the image reconstruction module, we show that the feature map channel could correlate well with the semantic concept, which explains why joint training with the reconstruction task is helpful for the segmentation task. Motivated by our observation, we further proposed a modification to the image reconstruction task, aiming to further disentangle the object clue from the background patterns. From experiment evaluation on various datasets, we show that using reconstruction as auxiliary loss can lead to consistent improvements in various datasets and methods. The proposed method can further lead to significant improvement in object-centric segmentation tasks. | ['Javen Qinfeng Shi', 'Jinan Zou', 'Lingqiao Liu', 'HaiMing Xu', 'YuHao Lin'] | 2023-03-17 | null | null | null | null | ['semi-supervised-semantic-segmentation'] | ['computer-vision'] | [ 4.94899571e-01 3.84856254e-01 -1.82296410e-01 -4.78127152e-01
-6.45668864e-01 -4.50775266e-01 6.88274622e-01 2.35596523e-02
-4.28233117e-01 4.68646914e-01 1.01473488e-01 -2.50330591e-03
3.56146693e-02 -6.51091754e-01 -9.53344584e-01 -9.40730572e-01
4.73980635e-01 5.55406213e-01 5.17008364e-01 -6.89840817e-04
2.58617133e-01 3.16997319e-01 -1.68564761e+00 3.87344807e-01
8.02160800e-01 8.22121203e-01 6.03841603e-01 2.20250994e-01
-4.44211453e-01 8.33289862e-01 -3.59417051e-01 -1.58071682e-01
2.46406570e-01 -7.07429349e-01 -1.17908776e+00 4.53200072e-01
2.73425907e-01 7.47735873e-02 -5.17306700e-02 1.12062573e+00
1.35788858e-01 -2.31205393e-02 6.15744591e-01 -1.06935406e+00
-2.80998141e-01 6.41408801e-01 -5.94863594e-01 -4.29993123e-02
-2.88162958e-02 -8.46006870e-02 1.07444108e+00 -7.60822117e-01
7.87490427e-01 1.18258333e+00 5.56030512e-01 4.81465161e-01
-1.57863665e+00 -3.35397065e-01 2.49004200e-01 2.49315977e-01
-1.08153272e+00 -3.07593077e-01 1.10366476e+00 -2.97206134e-01
4.89684969e-01 7.40022212e-02 5.91101050e-01 9.88763809e-01
-1.70178995e-01 1.45325172e+00 1.38775373e+00 -5.59604406e-01
7.90562928e-02 5.70020854e-01 2.68970072e-01 8.73111129e-01
1.07225366e-01 -5.39383973e-06 -3.41424704e-01 3.10365647e-01
7.84992337e-01 9.83939469e-02 -4.26538616e-01 -8.95800114e-01
-1.06290317e+00 9.92062569e-01 6.57437086e-01 4.80001539e-01
-9.64958668e-02 2.05750018e-01 3.52912813e-01 2.03037545e-01
7.00522423e-01 4.20834243e-01 -3.30073923e-01 1.32375449e-01
-1.18958795e+00 1.00010969e-02 6.88049614e-01 5.71601510e-01
1.03041792e+00 5.49270958e-02 -8.82054046e-02 8.11425149e-01
2.73773551e-01 1.20870210e-02 4.48667675e-01 -1.03423691e+00
1.76229760e-01 7.82516241e-01 8.77923798e-03 -6.67248487e-01
-3.73095244e-01 -7.37450898e-01 -7.31651425e-01 1.34903580e-01
6.94753706e-01 2.99611419e-01 -1.01611185e+00 1.78760052e+00
2.23082453e-01 3.13798487e-01 -1.61632616e-02 8.98028672e-01
4.67726886e-01 5.30343413e-01 4.61428612e-03 -2.19446093e-01
1.03660059e+00 -1.36104345e+00 -6.51797593e-01 -2.13847250e-01
5.17468154e-01 -6.79571033e-01 1.13621950e+00 4.51745808e-01
-9.86982226e-01 -8.00708413e-01 -1.05748737e+00 -7.00550973e-02
-3.76251638e-01 1.56902641e-01 9.17900681e-01 5.38902760e-01
-1.05287302e+00 8.90698612e-01 -1.12719643e+00 -5.86169541e-01
8.14612210e-01 1.94558159e-01 -3.24643224e-01 -1.93054937e-02
-7.27558017e-01 7.56169021e-01 4.70628530e-01 -3.73979025e-02
-8.85834873e-01 -4.08016354e-01 -8.09980214e-01 1.16785262e-02
5.76526761e-01 -7.12781489e-01 8.26782405e-01 -1.39083326e+00
-1.54822397e+00 1.17887485e+00 -2.26383135e-01 -5.12619078e-01
5.76309919e-01 -2.02083826e-01 7.48780966e-02 4.41986799e-01
1.77411512e-01 8.18007946e-01 1.18999267e+00 -1.54223299e+00
-5.06353378e-01 -4.62261647e-01 -1.41393960e-01 3.02750692e-02
-8.63537043e-02 -3.47720712e-01 -4.86373633e-01 -7.64326632e-01
4.84359533e-01 -9.47866380e-01 -3.05881262e-01 4.16982286e-02
-4.15395558e-01 -1.66134834e-01 7.10054517e-01 -5.01505494e-01
6.98537648e-01 -2.19884491e+00 5.87180138e-01 4.90436144e-02
6.85843900e-02 3.08937162e-01 -1.12452604e-01 1.87053859e-01
-2.09808141e-01 -5.82531057e-02 -6.98578715e-01 -7.82264531e-01
-1.81245640e-01 4.04163152e-01 -4.32555169e-01 6.61873102e-01
2.33135164e-01 1.20536864e+00 -7.93223679e-01 -3.79676104e-01
3.65299284e-01 4.13317144e-01 -4.95082527e-01 2.95825809e-01
-3.72283608e-01 6.84353828e-01 -3.24406654e-01 3.24178934e-01
6.03487074e-01 -5.45731306e-01 2.64636166e-02 -1.10122263e-01
8.10689554e-02 1.35347694e-01 -9.74392116e-01 2.18635726e+00
-4.01096314e-01 7.10054040e-01 2.49971315e-01 -1.78545499e+00
6.20582700e-01 1.07711531e-01 4.79967982e-01 -6.40882850e-01
5.04780114e-02 1.80888265e-01 -2.03564420e-01 -2.78142929e-01
1.90359741e-01 -3.12508047e-01 1.61144763e-01 5.56098819e-01
4.12560523e-01 -2.41988212e-01 1.38528615e-01 2.31627613e-01
5.99311054e-01 5.61133862e-01 2.44533196e-01 -3.61823887e-01
5.57702422e-01 1.08773880e-01 2.30318755e-01 7.39355206e-01
-4.00590803e-03 7.83013642e-01 5.54720640e-01 -4.17613328e-01
-7.45555222e-01 -1.14877403e+00 -2.21632898e-01 9.11490083e-01
3.05351347e-01 -2.42414936e-01 -9.84299839e-01 -9.15774584e-01
-2.23942101e-01 5.94941318e-01 -7.10178614e-01 -1.40439555e-01
-5.11666179e-01 -7.83545434e-01 2.54823983e-01 6.00741148e-01
7.21157253e-01 -1.15619648e+00 -6.88053191e-01 2.10040644e-01
-1.85186610e-01 -1.17508650e+00 -3.66530977e-02 6.56119645e-01
-1.06147349e+00 -1.12051570e+00 -9.07290459e-01 -8.11725914e-01
8.56391728e-01 4.40422773e-01 1.02344203e+00 1.93002060e-01
-3.41706932e-01 4.27428424e-01 -3.94313216e-01 -4.32084836e-02
-4.54940438e-01 1.77168369e-01 -3.46041709e-01 2.84075141e-01
-4.44700345e-02 -6.86147332e-01 -5.25418520e-01 2.74459690e-01
-1.13912678e+00 5.23713887e-01 6.05311215e-01 8.84266257e-01
7.03976512e-01 -2.24555116e-02 4.99502361e-01 -1.42413223e+00
7.01861307e-02 -1.85667127e-01 -5.57928205e-01 1.36771798e-01
-7.05886066e-01 5.83312750e-01 5.93170464e-01 -2.07126960e-01
-1.08455276e+00 4.18845981e-01 -3.03468287e-01 -4.10402864e-01
-3.33926976e-01 2.70293862e-01 -2.50400245e-01 1.44810183e-02
3.43415469e-01 4.91006911e-01 8.07297975e-02 -6.84713900e-01
5.68095744e-01 3.97157282e-01 3.51655602e-01 -4.04407650e-01
7.98132181e-01 9.65192795e-01 -2.56248098e-02 -8.14883173e-01
-1.24779940e+00 -7.33918369e-01 -8.75607491e-01 1.43609941e-01
1.05925953e+00 -8.39283586e-01 -3.15052122e-01 4.60743606e-01
-1.14261532e+00 -5.80200970e-01 -6.25387788e-01 1.36199445e-01
-8.56404841e-01 4.82408404e-01 -3.35810184e-01 -5.79888523e-01
1.19953968e-01 -1.26847613e+00 1.16656804e+00 1.03171244e-01
1.67152479e-01 -1.14583528e+00 -8.41529518e-02 4.90447402e-01
4.04881835e-01 4.13444266e-02 8.89125347e-01 -6.50472760e-01
-6.96219504e-01 1.74160600e-01 -4.56447512e-01 5.82520247e-01
1.27522975e-01 -6.04688406e-01 -1.22563350e+00 -2.42947295e-01
4.04522032e-01 -3.56274813e-01 1.62554443e+00 3.26577216e-01
1.36865723e+00 -8.02513137e-02 -3.87151837e-01 8.33932698e-01
1.55571187e+00 -3.84370714e-01 6.87211454e-01 1.25538364e-01
8.51704121e-01 8.62923741e-01 4.77266937e-01 1.86824892e-02
2.67322868e-01 8.35900724e-01 3.83637637e-01 -3.04886967e-01
-5.13786376e-01 -3.81232858e-01 3.26510012e-01 6.93210840e-01
1.19336076e-01 7.46374205e-02 -5.56958795e-01 4.21141952e-01
-2.03896689e+00 -6.77359700e-01 1.79031007e-02 1.99576926e+00
8.35948527e-01 2.69555211e-01 1.31031707e-01 2.57572979e-01
4.56532598e-01 3.93961877e-01 -4.69048947e-01 -8.75337347e-02
-1.53656870e-01 4.48451757e-01 4.67672527e-01 4.71339405e-01
-1.11096454e+00 1.38445425e+00 5.76377869e+00 8.74349654e-01
-1.25313902e+00 3.14569086e-01 6.23212516e-01 3.21636766e-01
-3.43380123e-01 1.92620531e-01 -6.05619907e-01 2.62117356e-01
5.85294306e-01 3.11751664e-01 3.91825616e-01 7.28471041e-01
3.39449160e-02 -3.09195429e-01 -1.28050017e+00 9.74990010e-01
3.09932172e-01 -1.23060250e+00 2.41951123e-01 -1.24263801e-01
6.67235434e-01 -7.94139057e-02 4.69863825e-02 1.92787796e-01
5.44822142e-02 -1.05220437e+00 7.84742177e-01 3.04854900e-01
4.93097693e-01 -4.75668728e-01 6.59159958e-01 5.07125795e-01
-1.11722028e+00 7.79175460e-02 -2.76261538e-01 -1.20151248e-02
2.10426390e-01 4.09588426e-01 -5.72873652e-01 5.11415184e-01
4.51921821e-01 1.05968988e+00 -8.16202283e-01 8.54751706e-01
-5.38488209e-01 7.47467935e-01 -1.26715735e-01 3.57957661e-01
3.83040220e-01 -3.19797665e-01 5.46163559e-01 1.06196296e+00
-1.62081718e-01 -3.00912082e-01 2.00083300e-01 1.20778000e+00
9.38398913e-02 5.54219857e-02 -5.06650746e-01 -1.94507882e-01
-2.75455356e-01 1.17896676e+00 -1.40685797e+00 -3.54487777e-01
-3.26506585e-01 1.44724071e+00 5.15064895e-01 5.55857956e-01
-6.91129208e-01 -7.12901652e-02 1.49533853e-01 2.61243045e-01
5.78868449e-01 -1.36950254e-01 -2.69681931e-01 -1.21469796e+00
-1.57073647e-01 -4.92310554e-01 1.17728710e-01 -5.51196158e-01
-9.72689092e-01 3.89802635e-01 -8.01908299e-02 -1.08089483e+00
-2.29741335e-01 -5.85432291e-01 -4.52454776e-01 4.94478017e-01
-1.85892844e+00 -1.30607033e+00 -1.67840570e-01 6.79076254e-01
8.77836704e-01 8.51449147e-02 8.33664775e-01 5.35040013e-02
-4.62907255e-01 3.14340800e-01 -6.96878657e-02 3.71392667e-02
4.79431719e-01 -1.44906139e+00 1.74617842e-02 8.79908502e-01
7.95656502e-01 5.44652462e-01 6.62782907e-01 -3.08737457e-01
-1.12273026e+00 -1.02231681e+00 4.08026934e-01 -2.39151254e-01
4.35452163e-01 -5.59657514e-01 -9.55133080e-01 6.13092542e-01
2.19545931e-01 2.47813612e-01 4.24873918e-01 -1.28586143e-01
-2.29412422e-01 3.88101162e-03 -8.68785203e-01 4.67209071e-01
1.01316166e+00 -5.58568537e-01 -6.91074193e-01 2.03681692e-01
8.34789395e-01 -1.19225875e-01 -2.35776767e-01 2.66566932e-01
2.25720599e-01 -1.09035361e+00 9.84267771e-01 -4.65592742e-01
4.44723725e-01 -2.05832392e-01 -8.19073543e-02 -1.28254104e+00
-2.09631417e-02 -5.18906593e-01 -2.33074166e-02 1.15699613e+00
2.34012648e-01 -5.28461277e-01 9.29927945e-01 7.86925927e-02
-1.43033385e-01 -7.44309247e-01 -8.43386948e-01 -6.85215235e-01
5.65460436e-02 -3.85404378e-01 -4.03698124e-02 6.52260065e-01
-2.95554101e-01 4.42200303e-01 -2.86928475e-01 -7.37579688e-02
7.20876813e-01 5.21870732e-01 7.16186643e-01 -1.37224483e+00
-5.47822058e-01 -4.30834591e-01 -2.80494630e-01 -1.35904670e+00
5.22987545e-01 -1.26780939e+00 9.69328731e-02 -1.67842257e+00
3.57431829e-01 -4.07321543e-01 -4.12539542e-01 4.49348539e-01
-1.46054432e-01 4.50025767e-01 1.73377663e-01 3.08780611e-01
-8.55552435e-01 5.65548778e-01 1.32936811e+00 -1.00165442e-01
-2.35044092e-01 2.61233270e-01 -8.68723869e-01 8.70256543e-01
5.82825661e-01 -5.95745265e-01 -4.46640670e-01 -3.09365034e-01
1.26977429e-01 -1.15374312e-01 5.00816047e-01 -7.94402421e-01
1.22107580e-01 2.08795860e-01 2.42831841e-01 -3.77491146e-01
3.58424008e-01 -9.81363237e-01 -2.15672404e-01 5.14630377e-01
-4.46531653e-01 -6.29079819e-01 5.44788539e-02 7.85508871e-01
-4.75138634e-01 -3.59776735e-01 9.45438385e-01 -3.68839204e-01
-7.67320871e-01 -4.45547625e-02 -9.85706747e-02 4.35489640e-02
8.71640444e-01 -4.31120157e-01 1.37479320e-01 -1.78495631e-01
-8.77570629e-01 -4.99253999e-03 5.51555634e-01 3.87449473e-01
5.45003772e-01 -8.84053230e-01 -4.34490770e-01 3.36807698e-01
5.53550385e-02 6.78180307e-02 -3.42668071e-02 1.04689622e+00
-3.54293048e-01 3.95250112e-01 -1.87322363e-01 -9.59662616e-01
-9.53585863e-01 5.71981370e-01 1.05605677e-01 -4.08800691e-01
-7.76923239e-01 7.66713381e-01 5.26457071e-01 -2.99629718e-01
4.16522413e-01 -3.26836258e-01 -2.18678832e-01 1.34773403e-01
2.73341268e-01 2.07336441e-01 -1.15626894e-01 -6.02436185e-01
-1.77938566e-01 7.40210354e-01 -6.28116950e-02 -1.76651523e-01
1.60483837e+00 -4.00386274e-01 -8.13941285e-02 6.75652146e-01
1.33549035e+00 -2.11369768e-01 -1.47771823e+00 -4.24801707e-01
1.68421760e-01 -2.48698846e-01 1.49406999e-01 -5.85185707e-01
-1.36026859e+00 1.23640096e+00 6.28479540e-01 1.31141946e-01
1.09739184e+00 3.33051801e-01 7.23116040e-01 1.05201073e-01
5.10851145e-01 -9.43045080e-01 3.63804132e-01 2.66182095e-01
6.35785937e-01 -1.64460540e+00 -1.09038902e-02 -6.11630261e-01
-5.85591555e-01 9.74933028e-01 2.14239046e-01 -3.34368289e-01
6.55125380e-01 5.82346171e-02 -1.01184554e-01 -2.10203275e-01
-2.74734616e-01 -4.95103300e-01 3.26957852e-01 3.38543117e-01
2.65723318e-01 -5.37362508e-02 -1.96042269e-01 6.59631312e-01
1.18790798e-01 -1.07202843e-01 2.80361980e-01 7.18807638e-01
-5.13580918e-01 -1.36708200e+00 -1.45776030e-02 3.03461015e-01
-3.81608248e-01 -6.72286674e-02 -3.59929711e-01 7.93803632e-01
8.20512474e-02 5.76545298e-01 -1.63511615e-02 2.96070445e-02
-7.91799501e-02 2.10874870e-01 7.22210526e-01 -8.42094541e-01
-4.39099640e-01 2.27991790e-01 -3.60440642e-01 -9.45991814e-01
-7.36375749e-01 -4.92077678e-01 -1.39109242e+00 5.03067911e-01
-2.98771143e-01 1.70989245e-01 7.12929189e-01 1.18728924e+00
7.51353949e-02 5.08520365e-01 4.83780295e-01 -9.85188127e-01
-4.00044620e-01 -7.06443131e-01 -6.66338742e-01 6.35670781e-01
3.14053088e-01 -7.67651737e-01 -4.71810251e-01 2.76942730e-01] | [9.562963485717773, 0.9703558087348938] |
e1578e39-1ea1-4cd1-a459-be0193d021ed | rnnids-enhancing-network-intrusion-detection | null | null | https://doi.org/10.1016/j.cose.2020.102151 | https://arxiv.org/pdf/1807.03212.pdf | RNNIDS: Enhancing Network Intrusion Detection Systems through Deep Learning | Security of information passing through the Internet is threatened by today’s most advanced malware
ranging from orchestrated botnets to simpler polymorphic worms. These threats, as examples of zero-day
attacks, are able to change their behavior several times in the early phases of their existence to bypass the
network intrusion detection systems (NIDS). In fact, even well-designed, and frequently-updated signaturebased NIDS cannot detect the zero-day treats due to the lack of an adequate signature database, adaptive
to intelligent attacks on the Internet. More importantly, having an NIDS, it should be tested on malicious
traffic dataset that not only represents known attacks, but also can to some extent reflect the characteristics of
unknown, zero-day attacks. Generating such traffic is identified in the literature as one of the main obstacles
for evaluating the effectiveness of NIDS. To address these issues, we introduce RNNIDS that applies Recurrent
Neural Networks (RNNs) to find complex patterns in attacks and generate similar ones. In this regard, for
the first time, we demonstrate that RNNs are helpful to generate new, unseen mutants of attacks as well as
synthetic signatures from the most advanced malware to improve the intrusion detection rate. Besides, to
further enhance the design of an NIDS, RNNs can be employed to generate malicious datasets containing, e.g.,
unseen mutants of a malware. To evaluate the feasibility of our approaches, we conduct extensive experiments
by incorporating publicly available datasets, where we show a considerable improvement in the detection rate
of an off-the-shelf NIDS (up to 16.67%). | ['Fatemeh Ganji', 'Jean-Pierre Seifertac', 'Soroush M. Sohia'] | 2020-12-21 | null | null | null | scienceredirect-2020-12 | ['network-intrusion-detection'] | ['miscellaneous'] | [ 2.05076158e-01 -4.61919129e-01 3.84039320e-02 1.11911789e-01
1.41254112e-01 -1.01592731e+00 6.92881703e-01 -3.77840549e-01
-2.58978784e-01 6.71706855e-01 -5.02075016e-01 -7.50891626e-01
1.50327489e-03 -1.01190484e+00 -3.36629331e-01 -4.20632422e-01
-3.25072438e-01 4.69630897e-01 8.37905824e-01 -6.42722487e-01
5.00989139e-01 8.96043897e-01 -1.46586335e+00 2.05623314e-01
6.05301917e-01 4.85608846e-01 -3.68948355e-02 6.11400127e-01
-3.05459887e-01 3.34978193e-01 -1.23514915e+00 -4.34385598e-01
4.32657450e-01 -3.44856322e-01 -4.22493279e-01 -2.66917467e-01
-1.53133243e-01 -5.48958480e-01 -4.54243839e-01 1.24272788e+00
3.27936023e-01 -2.61356652e-01 2.40780950e-01 -1.46359324e+00
-3.96582007e-01 7.89163709e-01 -3.55849802e-01 5.04961789e-01
3.75778437e-01 8.01726639e-01 4.28593695e-01 -3.91911566e-01
7.37225771e-01 1.31058860e+00 4.87192780e-01 1.06423354e+00
-1.02553070e+00 -9.09910202e-01 -2.63133682e-02 1.27771199e-01
-9.22132850e-01 -4.21815902e-01 9.52468753e-01 -1.37023002e-01
6.52063787e-01 2.64418423e-01 4.70759720e-01 2.08624077e+00
-4.33018319e-02 2.67859906e-01 9.26512182e-01 -7.72929564e-02
2.14774996e-01 2.14202151e-01 2.16157898e-01 5.57886958e-01
8.83587599e-01 4.64668810e-01 2.37473652e-01 -4.53912228e-01
4.99342799e-01 2.19163731e-01 -3.66382837e-01 3.92182544e-02
-8.72463286e-01 8.93233836e-01 3.21680725e-01 7.05451488e-01
-2.88605541e-01 -1.61456078e-01 6.73837781e-01 3.66704762e-01
-2.06448026e-02 8.57692480e-01 -5.54358423e-01 -4.27659273e-01
-3.79928082e-01 -8.56465697e-02 1.10284364e+00 3.36503536e-01
6.94037318e-01 7.45214760e-01 2.01664925e-01 4.84339803e-01
-1.63027272e-02 5.72271407e-01 7.28503466e-01 -4.08059835e-01
2.98365980e-01 8.03505361e-01 -1.75104260e-01 -1.19911325e+00
-4.39092159e-01 -4.60790634e-01 -7.79222190e-01 1.59835294e-01
5.11209011e-01 -3.35717559e-01 -8.53265226e-01 1.69207823e+00
3.10917974e-01 5.26475012e-01 4.98420298e-02 3.74687880e-01
3.27672184e-01 5.53726792e-01 -3.11631620e-01 -1.92494020e-01
1.11562932e+00 -3.70951414e-01 -3.07762086e-01 -1.45574100e-02
5.25413513e-01 -3.05892199e-01 1.02507651e+00 2.90806353e-01
-3.66856426e-01 -2.93838263e-01 -9.73111451e-01 1.17762327e+00
-8.51701319e-01 -5.26040733e-01 5.81857145e-01 1.32369828e+00
-8.48056853e-01 5.54498613e-01 -7.68656075e-01 -4.68973368e-01
2.23939598e-01 2.14351594e-01 -1.50425985e-01 1.31575644e-01
-1.46355844e+00 4.98082459e-01 5.18293679e-01 -1.72270507e-01
-1.21811879e+00 -6.68759286e-01 -4.21144187e-01 6.56266063e-02
6.31989181e-01 -1.71251640e-01 8.18615258e-01 -6.62312746e-01
-1.61844862e+00 3.45780730e-01 2.71262884e-01 -6.20908797e-01
3.50092024e-01 5.01132309e-01 -8.55389357e-01 1.28863111e-01
-5.10358177e-02 5.38058579e-02 1.10618246e+00 -1.24975610e+00
-3.62611949e-01 -3.49016041e-01 2.62609303e-01 -7.71579504e-01
-5.99458575e-01 3.24071944e-01 7.80884922e-02 -6.16726160e-01
-3.80605966e-01 -1.01044035e+00 -3.10423404e-01 -5.53381503e-01
-7.20508039e-01 1.07796922e-01 1.62596488e+00 -3.74321908e-01
1.16551173e+00 -2.06293082e+00 -2.88822293e-01 4.64552373e-01
2.04380915e-01 1.24197745e+00 -5.43361962e-01 4.19100732e-01
-1.36082798e-01 7.95846879e-01 -2.23781720e-01 2.93729186e-01
-4.86687422e-02 2.13995919e-01 -6.66621983e-01 3.45409811e-02
3.80563825e-01 7.97574759e-01 -7.71448851e-01 2.30231747e-01
3.83142568e-02 4.47752297e-01 -6.25689924e-01 2.29454532e-01
-3.93119872e-01 7.22479105e-01 -6.98690414e-01 7.07324266e-01
4.82774705e-01 -9.77771133e-02 1.33462444e-01 6.52436614e-02
-1.81251597e-02 2.26699993e-01 -8.77254128e-01 6.80719018e-01
-3.29925716e-01 2.91182548e-01 -8.03436190e-02 -1.07137084e+00
1.00340164e+00 1.97347417e-01 4.34531897e-01 -7.09745705e-01
2.09483996e-01 4.04950351e-01 5.37704527e-01 -3.88023764e-01
8.11020471e-03 4.21575129e-01 7.42655843e-02 9.17710602e-01
-2.90748239e-01 3.52518827e-01 6.73959434e-01 3.48737538e-02
1.76344621e+00 -6.24748409e-01 1.78601995e-01 3.54137599e-01
8.49520743e-01 -1.07311264e-01 5.86835206e-01 1.05143154e+00
-3.79868358e-01 -4.74835746e-02 7.20465004e-01 -5.42179525e-01
-8.59656930e-01 -9.88284290e-01 4.24151719e-02 7.15678036e-01
-1.57365054e-01 -2.46471494e-01 -7.59379506e-01 -1.15271604e+00
-1.98360622e-01 6.51844680e-01 -2.77993351e-01 -4.34028745e-01
-8.81915987e-01 -1.06162608e+00 1.31151807e+00 -4.90991510e-02
8.26679349e-01 -1.32313192e+00 -4.91145223e-01 3.62034738e-01
1.46269932e-01 -1.27655029e+00 2.48578545e-02 -4.73756306e-02
-5.59150219e-01 -1.46338630e+00 -3.37331772e-01 -2.93881238e-01
5.59807837e-01 3.29850793e-01 6.89952254e-01 4.28431273e-01
-4.69663113e-01 1.46518335e-01 -6.47814572e-01 -7.00641647e-02
-1.17697930e+00 2.64506429e-01 3.19064885e-01 4.82556373e-02
2.37564743e-01 -9.13137734e-01 -9.51030925e-02 5.83773375e-01
-1.21949923e+00 -8.01210225e-01 7.24610269e-01 6.62865222e-01
-1.94102421e-01 5.26346326e-01 9.38867271e-01 -1.06653178e+00
9.48129117e-01 -6.49023712e-01 -8.22747648e-01 1.73224628e-01
-4.78464127e-01 6.28246889e-02 1.39386117e+00 -1.06915700e+00
-7.61354506e-01 -4.88883376e-01 -3.88691276e-01 -3.36594284e-01
-4.56685960e-01 1.93278924e-01 -2.23892257e-01 -4.64658380e-01
9.97043848e-01 5.20304203e-01 7.22080469e-02 -3.18347067e-01
1.76218197e-01 6.38026893e-01 1.93260103e-01 -6.42618120e-01
1.48461890e+00 4.10292506e-01 8.09789822e-03 -9.42153990e-01
-3.10057729e-01 -3.27364057e-02 -1.97946563e-01 3.59971859e-02
2.14668155e-01 2.02393215e-02 -8.74571741e-01 6.51352644e-01
-1.30686271e+00 -1.65208653e-01 8.14029668e-03 8.73855129e-02
1.44058885e-02 6.76377714e-01 -8.17442238e-01 -6.09905064e-01
-3.85653198e-01 -1.38163435e+00 3.33574384e-01 1.27885237e-01
6.34272099e-02 -7.97803402e-01 2.89827913e-01 1.81351025e-02
9.86577094e-01 1.95713103e-01 1.10361183e+00 -1.46954501e+00
-6.27838492e-01 -4.29559290e-01 -1.83835819e-01 3.46736640e-01
5.53665161e-01 4.67870951e-01 -5.65800905e-01 -3.56625527e-01
2.15576932e-01 3.20627950e-02 5.57835042e-01 -2.61191815e-01
9.37154412e-01 -6.83652699e-01 -3.12333047e-01 5.73841691e-01
1.18997502e+00 9.08920050e-01 7.13770270e-01 4.38047886e-01
5.84968865e-01 4.59399849e-01 1.03531161e-03 5.26249588e-01
-2.84644008e-01 6.96656108e-01 5.63193381e-01 3.25461388e-01
2.20323503e-02 -1.51199386e-01 6.20812118e-01 5.97068012e-01
1.30711243e-01 -2.66596258e-01 -8.88541877e-01 -6.05334230e-02
-1.11784053e+00 -1.31400096e+00 6.71893824e-03 2.12460470e+00
5.17462552e-01 4.88018483e-01 3.02262306e-01 2.97619671e-01
9.90728259e-01 2.12170586e-01 -6.98841870e-01 -5.64531744e-01
-5.00358380e-02 4.19400156e-01 1.67007193e-01 1.20929152e-01
-9.04961467e-01 1.15295506e+00 5.86257410e+00 8.33780885e-01
-1.51549637e+00 -4.92517166e-02 2.94695318e-01 4.39737886e-01
-5.68760335e-02 1.06176451e-01 -8.62941086e-01 9.00846779e-01
1.29794192e+00 -1.53183341e-01 8.69197309e-01 9.21706438e-01
2.10698724e-01 6.36591196e-01 -6.20329857e-01 5.76344609e-01
-4.58120834e-03 -1.25949311e+00 4.47975129e-01 3.13420266e-01
4.27307934e-01 1.45373251e-02 2.26264179e-01 6.42920315e-01
3.93758565e-01 -6.72380865e-01 -1.44493297e-01 -1.13398554e-02
5.07825136e-01 -7.90279567e-01 7.27644205e-01 4.26595479e-01
-9.93520081e-01 -4.09334868e-01 -3.10189843e-01 1.63326845e-01
-5.13481535e-02 6.10430837e-01 -1.24465799e+00 3.75251770e-01
2.97475517e-01 4.30206209e-01 -8.60800922e-01 8.57681215e-01
-2.19053119e-01 8.20486605e-01 -3.25115323e-01 -3.20635825e-01
2.85793602e-01 -1.21170692e-01 9.84971821e-01 9.40057814e-01
2.92120278e-01 -2.56685525e-01 1.71708375e-01 1.05582762e+00
-9.35070291e-02 -2.24235967e-01 -1.11347461e+00 -5.78233957e-01
6.66267812e-01 1.19858134e+00 -7.02291369e-01 -8.23647678e-02
-4.38419543e-02 6.71441615e-01 5.80148846e-02 4.81999815e-01
-8.73673499e-01 -6.01785183e-01 8.85426223e-01 1.56410292e-01
1.04024127e-01 -1.63357362e-01 2.52255172e-01 -1.30943215e+00
-1.30260825e-01 -1.46117175e+00 1.78164199e-01 -1.47393897e-01
-1.40075135e+00 9.60236847e-01 -2.35286891e-01 -1.15249681e+00
-5.37053704e-01 -7.72725224e-01 -9.90045607e-01 3.40699911e-01
-1.16741681e+00 -8.03403497e-01 1.01788621e-02 6.66846573e-01
3.28996360e-01 -6.89512253e-01 7.31373310e-01 3.52067053e-01
-1.07095063e+00 6.25316679e-01 -1.97565392e-01 4.90568280e-01
2.38103196e-01 -5.78460395e-01 7.94323325e-01 1.10918069e+00
1.47496223e-01 8.61298859e-01 5.85973144e-01 -8.39618981e-01
-1.36105871e+00 -1.19916141e+00 1.08810328e-01 -2.38609761e-01
1.18769228e+00 -4.08938795e-01 -1.08554685e+00 5.28874755e-01
-4.68628347e-01 -2.33748347e-01 3.55196089e-01 -3.49675506e-01
-6.73680902e-01 -8.96586701e-02 -1.42260098e+00 1.04633820e+00
1.01282358e+00 -3.59343141e-01 -2.59648621e-01 1.90616980e-01
9.68432724e-01 2.29715064e-01 -2.67857552e-01 6.13058031e-01
1.90024525e-01 -1.23781002e+00 1.01935267e+00 -8.80163789e-01
-7.97202438e-02 -2.53102541e-01 -2.44389940e-02 -1.21755803e+00
2.85126232e-02 -8.07802737e-01 -3.01520616e-01 1.34439719e+00
2.98442334e-01 -1.36554468e+00 9.58985627e-01 2.17894893e-02
1.89747289e-01 -5.32116771e-01 -9.74785328e-01 -1.07989526e+00
-2.23311782e-01 -3.60525161e-01 8.52139890e-01 1.03268933e+00
-3.26604217e-01 9.11364704e-02 -2.75671363e-01 2.38243148e-01
5.46817899e-01 -3.81812781e-01 9.85473812e-01 -1.45509672e+00
-3.07923585e-01 -7.64057517e-01 -7.59180188e-01 -5.91270924e-01
5.25756240e-01 -6.91195548e-01 -5.04242539e-01 -6.04529142e-01
-1.78536862e-01 -5.29496193e-01 -1.73835069e-01 4.21505213e-01
1.08325191e-01 8.17752033e-02 8.91826898e-02 1.67162254e-01
-1.37609765e-01 2.81333089e-01 8.65332246e-01 -2.73733288e-01
-3.17306578e-01 3.31427038e-01 -5.72954953e-01 7.39148200e-01
1.19291341e+00 -6.53046727e-01 -4.86261368e-01 1.63226530e-01
-5.82389832e-02 -1.80854708e-01 2.74674088e-01 -1.04199135e+00
-8.02493170e-02 -1.83891475e-01 -1.11203350e-01 -2.37218574e-01
2.19400063e-01 -6.79381013e-01 7.02619851e-02 1.01927090e+00
1.90559477e-01 3.10838848e-01 1.76304951e-02 5.51095605e-01
8.11380744e-02 -4.09504503e-01 8.24801326e-01 -3.23284030e-01
-5.95920742e-01 5.16678095e-01 -6.70992434e-01 1.28614530e-01
1.08515871e+00 -3.09215784e-01 -7.71238089e-01 -1.75472960e-01
-1.02950156e-01 -2.71497279e-01 5.41436136e-01 4.63593096e-01
7.60330558e-01 -8.17075729e-01 -5.14563262e-01 5.99174082e-01
-1.75286755e-01 -6.98502839e-01 -4.41603176e-02 4.49187666e-01
-5.72819412e-01 5.63592672e-01 -4.69385386e-01 -3.59272987e-01
-9.51226115e-01 9.21722054e-01 3.77386034e-01 -6.03129387e-01
-4.68416661e-01 1.48941204e-01 -2.07882270e-01 -7.45750785e-01
1.18240029e-01 1.78606480e-01 -2.56750762e-01 -3.01034361e-01
7.78822362e-01 4.04393762e-01 -9.22934562e-02 -3.78027320e-01
-3.81496876e-01 2.14801595e-01 -2.51518756e-01 1.35089412e-01
1.07004678e+00 4.81698513e-01 -4.27412480e-01 -2.52745859e-02
1.04661572e+00 1.48240998e-01 -5.77334166e-01 7.07425922e-02
3.37585837e-01 -5.94389558e-01 -7.59385645e-01 -6.99676037e-01
-1.31109190e+00 6.66185021e-01 4.22569335e-01 6.92621827e-01
9.09267068e-01 -4.69523042e-01 1.08629274e+00 9.49041307e-01
7.21074820e-01 -2.64947414e-01 5.12119651e-01 6.98801041e-01
3.37765157e-01 -9.18167353e-01 -6.05197430e-01 -3.26078087e-01
-1.87472224e-01 1.28900576e+00 7.27695942e-01 -1.03111789e-01
4.92056131e-01 3.23043764e-01 -7.56080002e-02 -1.43372089e-01
-7.45394409e-01 -1.93022098e-02 -3.89497519e-01 9.89061773e-01
-2.40477905e-01 -7.45717362e-02 -1.31060675e-01 2.05283061e-01
-7.30097517e-02 -5.03375351e-01 9.37971652e-01 6.77415133e-01
-5.14665723e-01 -1.46821845e+00 -4.94985104e-01 3.79151106e-01
-4.12617505e-01 -4.00506612e-03 -7.67154098e-01 8.02029729e-01
-1.81834236e-01 1.19579864e+00 -3.55783880e-01 -8.43899488e-01
2.64681458e-01 8.91929567e-02 -1.35035932e-01 -5.42802632e-01
-7.30420828e-01 -6.36903822e-01 1.23522580e-02 -5.21223009e-01
1.75412416e-01 -1.52566567e-01 -9.93153691e-01 -7.28145242e-01
-1.97662398e-01 2.05694124e-01 7.29722977e-01 7.51746237e-01
3.93951654e-01 4.96103823e-01 1.20803630e+00 -7.03845203e-01
-8.92437696e-01 -8.99549246e-01 -2.49208197e-01 5.94955087e-01
-2.07994971e-02 -6.82773054e-01 -9.52916563e-01 -5.96923769e-01] | [5.384665012359619, 7.3871002197265625] |
9ff2e827-bed0-4649-a9bc-2c1c3af30c1c | more-interpretable-graph-similarity | 2208.04580 | null | https://arxiv.org/abs/2208.04580v3 | https://arxiv.org/pdf/2208.04580v3.pdf | More Interpretable Graph Similarity Computation via Maximum Common Subgraph Inference | Graph similarity measurement, which computes the distance/similarity between two graphs, arises in various graph-related tasks. Recent learning-based methods lack interpretability, as they directly transform interaction information between two graphs into one hidden vector and then map it to similarity. To cope with this problem, this study proposes a more interpretable end-to-end paradigm for graph similarity learning, named Similarity Computation via Maximum Common Subgraph Inference (INFMCS). Our critical insight into INFMCS is the strong correlation between similarity score and Maximum Common Subgraph (MCS). We implicitly infer MCS to obtain the normalized MCS size, with the supervision information being only the similarity score during training. To capture more global information, we also stack some vanilla transformer encoder layers with graph convolution layers and propose a novel permutation-invariant node Positional Encoding. The entire model is quite simple yet effective. Comprehensive experiments demonstrate that INFMCS consistently outperforms state-of-the-art baselines for graph-graph classification and regression tasks. Ablation experiments verify the effectiveness of the proposed computation paradigm and other components. Also, visualization and statistics of results reveal the interpretability of INFMCS. | ['Fei Ma', 'Ye Ma', 'Binjie Hong', 'Zixun Lan'] | 2022-08-09 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [ 4.61699694e-01 3.19918305e-01 -3.82948011e-01 -5.79031169e-01
-3.03599179e-01 -4.51082766e-01 7.59514391e-01 5.70919693e-01
-4.57975380e-02 2.75543511e-01 2.49311581e-01 -4.43158299e-01
-2.62464702e-01 -1.03489816e+00 -6.70659900e-01 -5.04101038e-01
-2.80374289e-01 2.99119174e-01 9.09715146e-02 -2.35500425e-01
2.35904664e-01 2.29508594e-01 -1.19832945e+00 1.56725347e-01
1.03521085e+00 7.62982309e-01 1.59663841e-01 6.12335384e-01
-1.46746275e-03 8.28587353e-01 -2.74233043e-01 -7.07515001e-01
2.48098269e-01 -4.98616934e-01 -9.65160549e-01 -4.01237570e-02
6.19471490e-01 -8.17974582e-02 -6.12488985e-01 1.14472103e+00
2.93148428e-01 1.07611388e-01 4.89946276e-01 -1.62476575e+00
-9.73808527e-01 8.94206285e-01 -5.12383938e-01 2.28698432e-01
4.84592766e-01 1.59956977e-01 1.71005332e+00 -4.76562440e-01
5.35343707e-01 1.26003110e+00 6.90876245e-01 -1.23513073e-01
-1.18677890e+00 -4.20201421e-01 3.98694068e-01 4.26371604e-01
-1.36662769e+00 2.54699532e-02 9.98005450e-01 -2.85341918e-01
1.05171168e+00 5.66253662e-01 7.58758426e-01 8.29754770e-01
1.18389115e-01 6.31350219e-01 6.89881563e-01 -2.95268238e-01
-2.41633989e-02 -3.06013167e-01 3.67202669e-01 1.18038595e+00
4.62438315e-01 -1.32461384e-01 -3.05880904e-01 5.06998040e-02
5.77300310e-01 3.43889177e-01 -2.67502278e-01 -4.26982075e-01
-1.23247111e+00 7.98898280e-01 1.09373009e+00 4.62958008e-01
-1.30678788e-02 3.61969769e-01 5.52779794e-01 5.26861966e-01
4.65438038e-01 5.15708506e-01 -6.05582073e-02 2.33493000e-01
-5.00650287e-01 -9.31165367e-02 7.55023301e-01 1.11852705e+00
9.08718824e-01 -1.41884580e-01 -4.53816921e-01 4.84339476e-01
3.27146858e-01 7.68759847e-02 3.02694917e-01 -4.17786717e-01
5.66028178e-01 1.21272123e+00 -7.44405150e-01 -1.43500614e+00
-4.14246738e-01 -7.67211199e-01 -1.16764462e+00 -3.15007567e-01
9.73847434e-02 3.63679320e-01 -7.80498564e-01 1.84865963e+00
2.87096679e-01 6.73265576e-01 -3.37622583e-01 8.50086033e-01
1.05838037e+00 2.40204424e-01 -4.51781116e-02 1.70280188e-01
1.18357122e+00 -1.18066609e+00 -4.86956686e-01 -3.61048043e-01
9.58315730e-01 -3.50314558e-01 1.32422972e+00 -2.21210301e-01
-7.65619218e-01 -4.96673912e-01 -1.32220364e+00 -3.93614948e-01
-4.54940319e-01 -2.06532069e-02 1.12831700e+00 3.74810904e-01
-1.07860136e+00 1.05874789e+00 -7.89496720e-01 -6.25012040e-01
2.94762850e-01 3.36620390e-01 -5.39930284e-01 -4.60420698e-02
-1.20431674e+00 6.49573267e-01 4.05274004e-01 7.69883022e-02
-3.88437629e-01 -5.32404721e-01 -1.24047375e+00 4.43890005e-01
4.30885375e-01 -9.49989200e-01 7.58834302e-01 -8.50428283e-01
-1.24580836e+00 8.62331033e-01 1.14719467e-02 -4.58827853e-01
1.08367860e-01 2.39305392e-01 -3.56877178e-01 1.88100506e-02
1.99946418e-01 2.49255478e-01 7.44388103e-01 -8.89960825e-01
-2.04651609e-01 -4.03310716e-01 2.34000459e-01 1.58813506e-01
-5.21382511e-01 -2.26945907e-01 -6.58226550e-01 -7.41634965e-01
2.61234403e-01 -9.13298786e-01 -2.46549562e-01 -1.77605793e-01
-7.16387033e-01 -2.87737399e-01 4.43294853e-01 -5.74608028e-01
1.67811477e+00 -1.80860138e+00 2.81645864e-01 4.93409336e-01
8.94371450e-01 9.47989821e-02 -4.32858855e-01 6.64970577e-01
-3.96223426e-01 1.52378187e-01 -5.08574009e-01 -2.26466000e-01
2.29852319e-01 2.08702952e-01 -1.52980745e-01 4.82037872e-01
4.31765497e-01 1.42207444e+00 -1.07848477e+00 -4.07869697e-01
2.58323878e-01 2.43861735e-01 -6.31192863e-01 3.21640283e-01
7.28602568e-03 2.60659009e-01 -4.71500278e-01 4.23138916e-01
7.11643279e-01 -8.30496967e-01 6.25905156e-01 -4.93309379e-01
3.60083461e-01 4.85325634e-01 -9.94478941e-01 1.96849382e+00
-3.56366336e-01 4.08550650e-01 -3.09246182e-01 -1.42994642e+00
8.52387607e-01 -2.19705984e-01 2.46697649e-01 -6.11461401e-01
5.26432395e-02 -1.14238955e-01 1.85617954e-01 -2.96418607e-01
2.97579110e-01 3.30573827e-01 -1.08825132e-01 4.84646231e-01
1.67566761e-01 1.79224983e-01 3.25763583e-01 6.41949058e-01
1.62125719e+00 -1.45529225e-01 7.75958300e-01 -3.03337902e-01
6.57816648e-01 -4.99961376e-01 2.71531284e-01 6.01578772e-01
-2.43367944e-02 4.91632819e-01 8.84064615e-01 -4.71334070e-01
-6.22269213e-01 -1.24774480e+00 3.65255445e-01 1.15887606e+00
2.92713642e-01 -1.05804837e+00 -6.16578639e-01 -9.81499791e-01
1.57700777e-01 2.76358902e-01 -8.35071623e-01 -6.84688151e-01
-5.87214231e-01 -4.78827775e-01 3.27867061e-01 5.46499610e-01
3.66780400e-01 -6.10979021e-01 1.59945771e-01 -1.08048925e-02
-5.13847843e-02 -1.18772459e+00 -8.99486184e-01 -1.72786772e-01
-8.41061413e-01 -1.26915300e+00 -6.85729906e-02 -9.28096294e-01
9.01292622e-01 5.89248657e-01 1.46585774e+00 5.87558806e-01
-2.46297464e-01 1.43070579e-01 -3.24448496e-01 1.67391747e-01
-3.03213209e-01 3.52257967e-01 -4.29103315e-01 8.43831003e-02
2.40743145e-01 -1.01000977e+00 -5.74545741e-01 9.57460608e-03
-7.44113505e-01 2.45270044e-01 8.02457988e-01 8.49410892e-01
4.59037811e-01 -2.04907849e-01 3.20867866e-01 -1.17487013e+00
9.36130702e-01 -4.34179217e-01 -4.38696921e-01 5.00124097e-01
-7.97670186e-01 5.44986129e-01 8.22792828e-01 2.39668861e-02
-4.57909465e-01 -9.12111029e-02 1.08411841e-01 -4.53489810e-01
1.11314490e-01 7.58791983e-01 -3.64865780e-01 -1.92587167e-01
3.08599949e-01 1.79623440e-01 1.25548551e-02 -2.67245710e-01
6.62348270e-01 3.26375723e-01 5.40769637e-01 -2.48109251e-01
1.01368678e+00 3.38465303e-01 3.79669964e-01 -5.22343338e-01
-7.06216156e-01 -5.30986547e-01 -7.80855536e-01 5.93647361e-02
5.75133085e-01 -8.25596929e-01 -1.02300298e+00 7.47396201e-02
-1.10389400e+00 2.39721197e-03 -3.90051790e-02 3.52621436e-01
-2.85213977e-01 7.31517732e-01 -5.90246797e-01 -2.55352885e-01
-5.89176655e-01 -9.57752943e-01 1.32207882e+00 -2.14815676e-01
-2.56202370e-01 -1.32106876e+00 3.52580957e-02 2.86851525e-01
2.42015868e-01 4.54655558e-01 1.16267610e+00 -7.93404639e-01
-7.94666111e-01 -3.15696388e-01 -5.25941491e-01 6.52335659e-02
1.90428987e-01 -6.84037805e-02 -7.08573341e-01 -6.27194345e-01
-4.40797091e-01 2.94066519e-02 1.07834506e+00 1.61464423e-01
1.48016095e+00 -4.71606404e-01 -5.14961600e-01 9.08597708e-01
1.32329166e+00 -4.11778688e-01 3.80192906e-01 -1.65800363e-01
1.24937069e+00 3.11441928e-01 3.74130487e-01 2.34988481e-01
7.05385029e-01 6.21689618e-01 6.30279005e-01 -2.56820172e-01
-1.95933074e-01 -5.99914312e-01 2.38625303e-01 1.33302653e+00
-2.63415389e-02 -4.01830107e-01 -6.36914194e-01 1.50809392e-01
-1.95936823e+00 -8.92113328e-01 -2.30457887e-01 2.18594313e+00
4.64542359e-01 1.81082770e-01 -2.19869819e-02 1.91235945e-01
8.17097306e-01 4.12684202e-01 -3.85549992e-01 -2.97679961e-01
2.16784790e-01 3.16756546e-01 4.09327805e-01 6.31231189e-01
-1.03437841e+00 9.55636919e-01 5.43029785e+00 7.38573313e-01
-1.07377434e+00 -6.36623278e-02 4.94189620e-01 3.25239778e-01
-6.90757394e-01 1.71001613e-01 -5.43315113e-02 4.54845309e-01
6.94759905e-01 -4.53107119e-01 6.01960540e-01 7.89293945e-01
-1.72494590e-01 3.82676095e-01 -1.61493051e+00 9.83056068e-01
1.96492583e-01 -1.35711026e+00 3.22789043e-01 -1.03093535e-01
4.26294684e-01 7.54480809e-02 -1.06755860e-01 4.33511376e-01
2.27919325e-01 -1.12094688e+00 3.10391545e-01 4.16120559e-01
7.67770469e-01 -5.06944597e-01 6.88586295e-01 -7.83657003e-03
-1.87281036e+00 1.35698810e-01 -3.92302930e-01 -3.66797924e-01
-1.87549651e-01 5.85397363e-01 -1.18410802e+00 9.88629818e-01
3.05980325e-01 1.28016257e+00 -1.19299841e+00 6.79280519e-01
-4.95298207e-01 4.87150759e-01 -1.28458276e-01 -1.50903061e-01
1.61277264e-01 -4.52489227e-01 4.31970030e-01 1.17520726e+00
1.79155186e-01 -1.55233368e-01 3.01445603e-01 1.17137587e+00
-4.00576085e-01 4.83993664e-02 -8.38330865e-01 -3.61923486e-01
5.62300980e-01 1.53286457e+00 -7.84126282e-01 -2.83517122e-01
-4.74978000e-01 1.24337363e+00 8.27061892e-01 1.61555707e-01
-9.44588900e-01 -6.88050508e-01 6.17637396e-01 3.25729027e-02
5.14899008e-02 -1.75210133e-01 -2.51970261e-01 -1.26451612e+00
2.61268348e-01 -6.32661045e-01 6.48021340e-01 -4.83807921e-01
-1.44287992e+00 5.75534582e-01 -1.36416554e-01 -1.19654930e+00
-1.17146067e-01 -5.56702912e-01 -1.07433736e+00 5.60927153e-01
-1.29926825e+00 -1.43652463e+00 -7.74341404e-01 5.44500828e-01
7.06597865e-02 -4.31481488e-02 7.50045538e-01 2.73749828e-01
-7.26683915e-01 9.84570503e-01 -1.68785065e-01 2.63557434e-01
4.83931631e-01 -1.51323080e+00 1.12596583e+00 9.54664946e-01
4.17669803e-01 7.95034468e-01 3.71646136e-01 -5.94227731e-01
-1.80885530e+00 -1.28618944e+00 8.75770450e-01 -4.08074766e-01
8.58076811e-01 -6.35541558e-01 -9.58689630e-01 8.10362279e-01
1.77787244e-01 2.77780026e-01 5.87885499e-01 2.91085362e-01
-5.38993657e-01 -2.17067987e-01 -7.55934179e-01 7.69861460e-01
1.68824196e+00 -8.24534178e-01 -3.98608804e-01 3.59041989e-01
1.11661625e+00 -1.05800040e-01 -1.02882779e+00 4.77824777e-01
2.80946702e-01 -9.37630653e-01 9.64365244e-01 -8.36743116e-01
4.07656312e-01 -2.02758729e-01 6.43723831e-02 -1.48638189e+00
-6.99569941e-01 -7.47188926e-01 -2.15339258e-01 1.08594191e+00
2.70160735e-01 -7.27570713e-01 7.95122087e-01 1.36335641e-01
-2.84354419e-01 -8.32102060e-01 -5.85898817e-01 -7.51070142e-01
-2.46757194e-01 -1.46995708e-01 9.09349263e-01 1.36296940e+00
4.13261414e-01 8.61016452e-01 -1.51112035e-01 2.25282535e-01
5.57795882e-01 5.77369153e-01 9.13482189e-01 -1.32908297e+00
-4.34064806e-01 -5.05029082e-01 -8.77501130e-01 -8.73894334e-01
4.09049481e-01 -1.76384449e+00 -3.73657227e-01 -1.71252775e+00
3.54958177e-01 -3.59583385e-02 -3.98916692e-01 4.79558885e-01
-5.94171941e-01 -1.12720206e-01 1.26916438e-01 -9.80537161e-02
-7.66226470e-01 7.18402803e-01 1.39077437e+00 -3.13397199e-01
5.14593758e-02 -1.60523459e-01 -8.14138830e-01 4.84732777e-01
7.48068154e-01 -3.10041934e-01 -8.02351058e-01 -4.10161346e-01
2.30506927e-01 -1.73224330e-01 4.17298645e-01 -8.54715586e-01
3.60225677e-01 -6.01452813e-02 -2.78360397e-02 -2.47545809e-01
-9.11820754e-02 -6.74900830e-01 8.09139386e-02 6.33238614e-01
-4.72027034e-01 2.17344418e-01 -2.85542637e-01 8.10719728e-01
-2.47410581e-01 2.50013649e-01 4.82820451e-01 9.29697081e-02
-6.51679575e-01 6.72732055e-01 2.43366882e-01 2.08279610e-01
8.93192172e-01 -1.59236476e-01 -3.34599882e-01 -4.24560100e-01
-4.94503021e-01 2.32946157e-01 2.95940876e-01 5.19515812e-01
6.67996883e-01 -1.60106099e+00 -7.16921151e-01 4.48889166e-01
4.34765756e-01 -1.78614363e-01 2.71707252e-02 9.28258657e-01
-4.65787798e-01 4.19273406e-01 -8.62310827e-02 -5.11500895e-01
-1.32087457e+00 6.54670060e-01 1.87440336e-01 -5.66650152e-01
-5.93217492e-01 7.95060098e-01 3.42714667e-01 -7.16684818e-01
2.19668038e-02 -6.99646950e-01 1.40422415e-02 -3.06081742e-01
1.31728053e-01 3.99192601e-01 1.67138264e-01 -3.46420169e-01
-4.39386278e-01 5.11157393e-01 -8.15533251e-02 6.34732604e-01
1.18402839e+00 6.94598630e-02 -4.09072429e-01 1.12493327e-02
1.60266101e+00 -1.68723464e-01 -8.11291099e-01 -3.60564619e-01
2.68419474e-01 -5.14512539e-01 -1.25790730e-01 -3.21276963e-01
-1.22844517e+00 8.65781069e-01 1.81765601e-01 2.95187920e-01
1.07246602e+00 2.74046343e-02 8.18079650e-01 6.35236621e-01
4.48145606e-02 -6.32261872e-01 2.59045213e-01 4.11929876e-01
9.53926623e-01 -1.47181368e+00 2.29616061e-01 -9.52860177e-01
-3.98145646e-01 9.92681384e-01 7.75324643e-01 -2.22569779e-01
6.16408944e-01 2.02242490e-02 -6.02206707e-01 -4.12368178e-01
-8.24002981e-01 -3.96349251e-01 6.77881420e-01 4.44935590e-01
6.36017501e-01 1.32532790e-01 -4.35799569e-01 5.99278629e-01
-2.73529917e-01 -4.08487231e-01 1.45107642e-01 6.29894137e-01
-1.04742818e-01 -1.09504795e+00 4.02315646e-01 6.03071570e-01
9.84727684e-03 -3.51236910e-01 -7.73941696e-01 7.43794262e-01
-1.72479436e-01 6.94164097e-01 8.33471343e-02 -8.33350599e-01
1.90254897e-01 -3.60519975e-01 4.14243072e-01 -6.68558896e-01
-6.36996269e-01 -4.21237558e-01 1.12873897e-01 -8.56485188e-01
-7.11180195e-02 -1.85149357e-01 -1.33057368e+00 -5.26453137e-01
-4.37676132e-01 1.08579785e-01 2.19666287e-01 7.46854186e-01
6.29376709e-01 7.98305869e-01 7.97967970e-01 -5.65020919e-01
-6.29509211e-01 -8.56823683e-01 -5.43662548e-01 8.48081946e-01
1.19058974e-01 -3.40700537e-01 -3.44621688e-01 -3.36300761e-01] | [7.159734725952148, 6.281630039215088] |
cbf6231a-133d-413a-9bf8-2ca36f6a7df5 | cur-transformer-a-convolutional-unbiased | null | null | https://dl.acm.org/doi/full/10.1145/3566125 | https://dl.acm.org/doi/pdf/10.1145/3566125 | CUR Transformer: A Convolutional Unbiased Regional Transformer for Image Denoising | Image denoising is a fundamental problem in computer vision and multimedia computation. Non-local filters are effective for image denoising. But existing deep learning methods that use non-local computation structures are mostly designed for high-level tasks, and global self-attention is usually adopted. For the task of image denoising, they have high computational complexity and have a lot of redundant computation of uncorrelated pixels. To solve this problem and combine the marvelous advantages of non-local filter and deep learning, we propose a Convolutional Unbiased Regional (CUR) transformer. Based on the prior that, for each pixel, its similar pixels are usually spatially close, our insights are that (1) we partition the image into non-overlapped windows and perform regional self-attention to reduce the search range of each pixel, and (2) we encourage pixels across different windows to communicate with each other. Based on our insights, the CUR transformer is cascaded by a series of convolutional regional self-attention (CRSA) blocks with U-style short connections. In each CRSA block, we use convolutional layers to extract the query, key, and value features, namely Q, K, and V, of the input feature. Then, we partition the Q, K, and V features into local non-overlapped windows and perform regional self-attention within each window to obtain the output feature of this CRSA block. Among different CRSA blocks, we perform the unbiased window partition by changing the partition positions of the windows. Experimental results show that the CUR transformer outperforms the state-of-the-art methods significantly on four low-level vision tasks, including real and synthetic image denoising, JPEG compression artifact reduction, and low-light image enhancement. | ['Xuan Dong', 'Xiaojie Wang', 'Ke Yan', 'Xiaoyan Hu', 'Xia Wang', 'Weixin Li', 'Kang Xu'] | 2023-02-25 | null | null | null | journal-2023-2 | ['jpeg-compression-artifact-reduction', 'image-enhancement', 'low-light-image-enhancement'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.00977415e-01 -6.53867364e-01 5.86042143e-02 -3.26615989e-01
-7.99380183e-01 -6.42172024e-02 4.76751290e-02 3.50589156e-02
-5.51633894e-01 2.44533271e-01 1.86254784e-01 -7.08276033e-02
9.15668011e-02 -1.00368834e+00 -8.98666382e-01 -1.07151186e+00
3.06880087e-01 -6.67413652e-01 5.63874066e-01 -2.64510810e-01
1.96170717e-01 3.40846151e-01 -1.25054133e+00 5.36003888e-01
9.78343725e-01 1.41463649e+00 4.27872419e-01 3.42021734e-01
1.04316864e-02 7.59436965e-01 -4.60334331e-01 -1.49970636e-01
2.87932783e-01 -5.50133348e-01 -3.62777203e-01 1.15133837e-01
2.53119946e-01 -6.62476242e-01 -5.17073095e-01 1.44244826e+00
5.57223678e-01 1.90788805e-01 2.99690545e-01 -7.03960180e-01
-9.52037871e-01 4.02582258e-01 -9.49679554e-01 3.23346615e-01
-7.89087340e-02 2.39367634e-01 8.28628600e-01 -8.55147421e-01
2.73681045e-01 1.28385079e+00 8.19614172e-01 2.76471436e-01
-1.09940457e+00 -8.06876779e-01 4.03934211e-01 2.79249758e-01
-1.45385432e+00 -1.67874277e-01 8.83586943e-01 -6.48024008e-02
5.54578125e-01 1.94950461e-01 6.47155702e-01 6.16447270e-01
2.41721690e-01 7.73687303e-01 9.40788686e-01 -1.54943794e-01
1.76856637e-01 -2.74672568e-01 1.75729692e-02 6.20811522e-01
-1.70454904e-02 -1.30983531e-01 -5.06489933e-01 1.59762338e-01
1.08524621e+00 3.82196963e-01 -5.01600564e-01 -5.03733158e-02
-1.07401502e+00 7.60492802e-01 9.03912544e-01 3.12184244e-01
-4.91756558e-01 3.66961330e-01 3.95456314e-01 3.90829653e-01
5.91206670e-01 -2.13999283e-02 -4.98106837e-01 3.76253605e-01
-8.24316621e-01 2.13351231e-02 2.33093441e-01 5.69572330e-01
1.08572233e+00 -1.01415321e-01 -5.12540936e-01 8.48446131e-01
1.20631211e-01 2.96153903e-01 5.82053006e-01 -9.45374668e-01
4.78039682e-01 5.02199233e-01 4.17755656e-02 -1.24146032e+00
-2.64776032e-02 -3.84658813e-01 -1.44911873e+00 3.07856351e-01
9.10736769e-02 7.40142539e-02 -9.89705563e-01 1.50881553e+00
1.52634338e-01 2.93183088e-01 -1.41340926e-01 1.12494946e+00
8.22189271e-01 9.62593257e-01 3.53789590e-02 -4.67654794e-01
1.27273381e+00 -1.03340697e+00 -6.36162460e-01 -1.12441532e-01
1.39723063e-01 -7.86776006e-01 1.06961632e+00 2.62282670e-01
-1.03594875e+00 -1.03130925e+00 -1.01918924e+00 -5.42684317e-01
-2.29874462e-01 1.75835460e-01 3.86012137e-01 2.90586770e-01
-8.99242818e-01 7.60023236e-01 -7.67830849e-01 1.32190943e-01
6.90272212e-01 5.12014329e-02 -8.55534673e-02 -3.09751183e-01
-1.18037200e+00 3.33338767e-01 -9.58271474e-02 4.77921695e-01
-8.42535853e-01 -4.29286689e-01 -9.97516036e-01 3.80989283e-01
3.28821719e-01 -5.12312889e-01 7.90232301e-01 -1.16517639e+00
-1.23607337e+00 6.51096225e-01 -3.58926684e-01 -3.72531623e-01
4.94982064e-01 -3.31100702e-01 -1.94534034e-01 1.72390640e-01
2.52024919e-01 5.11944234e-01 1.28429854e+00 -1.11994708e+00
-5.12251675e-01 -3.60232681e-01 -9.65144113e-02 1.38106301e-01
-5.74501097e-01 -9.34170038e-02 -8.71383309e-01 -1.12262702e+00
5.67093611e-01 -2.54063368e-01 -4.03652191e-01 1.69387504e-01
-2.30045870e-01 -1.69049859e-01 8.77092779e-01 -8.05867493e-01
1.40239346e+00 -2.62790918e+00 -1.71823557e-02 2.50390738e-01
3.00216973e-01 2.71076232e-01 -3.97446334e-01 -5.92587665e-02
-2.04536259e-01 2.09039360e-01 -3.57808530e-01 -1.99353531e-01
-4.41471666e-01 4.91185971e-02 -3.70790482e-01 3.84941816e-01
2.08218023e-01 7.81777501e-01 -9.09781158e-01 -3.68267357e-01
4.08150882e-01 5.66448390e-01 -5.56031883e-01 2.00298637e-01
-3.17622498e-02 3.26807678e-01 -6.15342796e-01 3.83887947e-01
1.02478516e+00 -7.21882805e-02 -1.74154863e-01 -7.76150167e-01
-2.54093379e-01 6.39837235e-02 -1.24022031e+00 1.51406682e+00
-4.74900454e-01 4.97070760e-01 2.70279408e-01 -1.12738979e+00
9.98353899e-01 1.38894292e-02 4.52797115e-01 -1.03732061e+00
1.85339496e-01 1.41869158e-01 -2.36017942e-01 -5.90399742e-01
6.04554787e-02 1.81798205e-01 2.93516964e-01 1.43335834e-01
-1.37511194e-01 2.12562634e-04 1.22507505e-01 1.00716069e-01
9.95140612e-01 2.56259255e-02 7.73658007e-02 -1.58119440e-01
8.26407313e-01 -5.73317528e-01 8.55015397e-01 8.51796508e-01
-2.47222900e-01 9.85734105e-01 4.17433172e-01 -7.58438647e-01
-8.75618875e-01 -8.97906721e-01 8.56410712e-02 1.08263767e+00
7.13197649e-01 -4.15439814e-01 -8.40466142e-01 -4.67650801e-01
-1.29773691e-01 1.92162275e-01 -6.18718326e-01 -4.02512133e-01
-7.49370396e-01 -7.25243449e-01 1.55631534e-03 5.83568096e-01
1.08626223e+00 -1.24597120e+00 -4.80026960e-01 3.87677968e-01
-2.52763927e-01 -7.90057540e-01 -1.00708127e+00 2.15389431e-01
-8.57026398e-01 -9.77851868e-01 -9.46979761e-01 -1.02781022e+00
7.47467637e-01 7.45315909e-01 9.20294762e-01 3.81899178e-01
-1.41379192e-01 -1.50183231e-01 -3.49895120e-01 8.51262212e-02
2.15882629e-01 -2.41508350e-01 -4.52723831e-01 4.22727019e-01
2.04726994e-01 -6.27460241e-01 -1.09732819e+00 3.36762846e-01
-1.19769609e+00 -1.21971704e-01 7.70408809e-01 1.13927495e+00
9.20864880e-01 4.14254218e-01 4.06088173e-01 -6.80306911e-01
6.84681475e-01 -1.61251366e-01 -5.40726900e-01 1.37350738e-01
-2.37432122e-01 -9.77809727e-02 7.12382734e-01 -4.45079535e-01
-9.40301299e-01 -1.76410694e-02 -2.93904006e-01 -5.73201001e-01
7.55961761e-02 4.35599476e-01 -4.34450865e-01 -6.66273832e-02
4.10622269e-01 4.75615740e-01 -8.68175104e-02 -5.45384943e-01
2.36386836e-01 4.98944163e-01 5.13338506e-01 -2.17038348e-01
7.17200518e-01 5.60369611e-01 -2.44889453e-01 -6.84365094e-01
-9.17766154e-01 -3.67838174e-01 -2.28526667e-01 2.42256001e-02
1.06663013e+00 -1.14006734e+00 -7.42738247e-01 9.37139452e-01
-1.13474953e+00 -3.27223837e-01 -2.46927276e-01 1.44869491e-01
-1.18535705e-01 5.29927313e-01 -7.59453714e-01 -5.89860499e-01
-3.80131394e-01 -1.46029615e+00 1.04738903e+00 5.22216737e-01
2.39513353e-01 -4.05896783e-01 -5.89150012e-01 1.73905388e-01
5.12988806e-01 -1.29082233e-01 8.92251551e-01 3.98779362e-02
-7.93205500e-01 -1.77233964e-01 -6.46973789e-01 8.06111693e-01
2.31039912e-01 -2.15198845e-01 -7.49748051e-01 -3.63845736e-01
2.05522314e-01 -2.42950395e-01 1.44935596e+00 7.26863623e-01
1.88898695e+00 -2.50317484e-01 -7.43774027e-02 9.34179246e-01
1.42233849e+00 8.26047063e-02 8.52788806e-01 3.10215831e-01
6.98844135e-01 2.38965228e-01 3.50060761e-01 2.79862881e-01
2.25998521e-01 3.34776998e-01 6.24199927e-01 -5.39803803e-01
-2.17634052e-01 -8.87084156e-02 2.90277481e-01 7.46497929e-01
-9.10691544e-02 2.20659971e-02 -3.54653478e-01 6.79837048e-01
-1.83759463e+00 -8.41416121e-01 1.99369136e-02 2.24791813e+00
9.76325810e-01 2.05476865e-01 -3.78967166e-01 3.50627378e-02
8.38826537e-01 5.12348413e-01 -6.98970199e-01 -7.08046369e-03
-3.95918339e-01 3.11169565e-01 4.95179206e-01 4.13106173e-01
-1.27288413e+00 6.39746308e-01 4.68567133e+00 1.20624614e+00
-1.05972517e+00 1.02255791e-01 1.25028384e+00 7.17294291e-02
-2.67975867e-01 -1.78116217e-01 -4.51417893e-01 6.97287321e-01
9.63850021e-02 4.47610736e-01 5.88549018e-01 5.98479331e-01
3.04328173e-01 -2.07556680e-01 -8.03714931e-01 1.11182761e+00
-3.56488936e-02 -1.28614926e+00 1.00313120e-01 -3.21352065e-01
1.00497389e+00 -1.00760348e-01 2.26415828e-01 1.90371886e-01
-5.50512932e-02 -9.30779397e-01 6.31256402e-01 4.92530942e-01
5.52699804e-01 -8.77083540e-01 9.12546754e-01 2.33038962e-01
-1.38519228e+00 -3.20449948e-01 -5.92451036e-01 1.32516026e-01
-1.66344285e-01 8.77536356e-01 6.60463691e-01 3.95433635e-01
1.35805738e+00 8.36488843e-01 -4.14121896e-01 9.21213806e-01
-3.55819285e-01 4.92979437e-01 -1.63098440e-01 2.55286783e-01
2.57134050e-01 -6.22209132e-01 2.19947815e-01 8.93813729e-01
3.63798350e-01 2.66055822e-01 -5.68120833e-03 8.43310714e-01
-4.19961303e-01 -9.62088257e-02 -1.36071816e-01 4.75754946e-01
3.11585337e-01 1.17078066e+00 -5.51839232e-01 -4.26293939e-01
-6.28388226e-01 1.29496121e+00 1.35491565e-01 6.26578212e-01
-6.01858079e-01 -8.05871487e-01 8.21716666e-01 4.02998589e-02
7.37751245e-01 4.26841341e-02 -5.23661673e-01 -1.05173969e+00
3.23962897e-01 -9.39017713e-01 3.20326567e-01 -8.70224357e-01
-1.34070921e+00 4.86609519e-01 -6.17559075e-01 -1.26881874e+00
4.49673891e-01 -3.50936532e-01 -9.12651062e-01 1.13402736e+00
-1.75156212e+00 -1.11451197e+00 -5.66625357e-01 7.44403481e-01
5.94170153e-01 1.81497246e-01 3.73425245e-01 3.60650033e-01
-6.47516191e-01 4.89822567e-01 3.55944455e-01 3.92133415e-01
7.76221156e-01 -7.94206142e-01 2.72289544e-01 1.06165814e+00
-1.47622824e-01 6.71875894e-01 2.50960469e-01 -5.86169541e-01
-1.15306079e+00 -1.25661349e+00 6.23963594e-01 2.58235395e-01
3.68702382e-01 -2.53894359e-01 -1.24146032e+00 2.90102124e-01
1.94939673e-01 5.79685926e-01 3.28673646e-02 -3.00779551e-01
-4.54263657e-01 -7.79600799e-01 -9.87829447e-01 5.71274817e-01
8.17493200e-01 -5.45908391e-01 -2.04924569e-01 1.64400965e-01
8.00811589e-01 -3.09435397e-01 -5.02710044e-01 4.58734512e-01
3.30496043e-01 -1.27078354e+00 1.28317440e+00 -5.04581109e-02
6.68081522e-01 -5.72133958e-01 -2.05494575e-02 -1.09623790e+00
-4.55782115e-01 -5.21008849e-01 1.48554072e-01 1.19424140e+00
1.53043773e-02 -4.37513053e-01 5.10623336e-01 1.94843963e-01
-1.56958252e-01 -9.48928893e-01 -8.09818268e-01 -3.02063048e-01
-1.08041607e-01 -2.40926564e-01 4.73200589e-01 6.98413789e-01
-6.43355906e-01 2.19214633e-01 -4.77498472e-01 4.10972685e-01
6.07678890e-01 3.20156723e-01 3.80183280e-01 -8.30143929e-01
3.69998254e-02 -5.36770463e-01 -9.25645605e-02 -1.52692008e+00
-2.43908420e-01 -2.69284755e-01 2.50133723e-01 -1.40646756e+00
2.76397079e-01 -1.31300643e-01 -6.24831080e-01 4.30316836e-01
-5.99346876e-01 6.60036504e-01 2.00387686e-02 2.32828274e-01
-5.44819653e-01 7.46860862e-01 1.39917231e+00 -3.65277797e-01
-2.29388654e-01 -8.21760949e-03 -6.22058928e-01 9.02267098e-01
5.57931244e-01 -2.55625427e-01 -5.15521392e-02 -8.42411935e-01
1.53285250e-01 -3.08917642e-01 5.74825883e-01 -1.00706875e+00
4.11570162e-01 -6.39097206e-03 8.60744417e-01 -6.28962576e-01
2.23480642e-01 -7.68541932e-01 -4.50074345e-01 4.88805652e-01
-1.87476754e-01 -2.06231102e-01 -8.74946043e-02 5.98234951e-01
-6.23790503e-01 -6.73349574e-02 1.02936399e+00 -2.53256947e-01
-7.52527654e-01 5.50255477e-01 -3.07659268e-01 -1.61462367e-01
6.94534898e-01 -2.40916982e-01 -1.05755761e-01 -4.25667554e-01
-5.22575319e-01 1.97032213e-01 2.27035344e-01 1.21959709e-01
8.65381181e-01 -1.20047569e+00 -6.27690971e-01 5.70885122e-01
-1.66731775e-01 4.26408201e-01 7.08034456e-01 6.82469726e-01
-4.77531135e-01 -1.70381427e-01 -1.42520845e-01 -4.83988732e-01
-9.62601602e-01 4.85690266e-01 3.54288459e-01 -1.14111356e-01
-6.40086293e-01 1.04303968e+00 5.46615601e-01 6.18311623e-03
2.38225222e-01 -4.80108142e-01 -1.64474338e-01 4.30308096e-03
6.62445307e-01 2.42418289e-01 -2.08159927e-02 -3.82504582e-01
-1.75140157e-01 8.70285749e-01 -1.24222226e-01 2.93834597e-01
1.49026918e+00 -3.74720544e-01 -4.73838001e-01 5.84833650e-03
1.24899209e+00 9.41237956e-02 -1.58198154e+00 -4.46842819e-01
-5.60534477e-01 -5.80671787e-01 3.98937345e-01 -3.79238158e-01
-1.70460880e+00 9.84593689e-01 8.20255458e-01 2.11444452e-01
1.83479095e+00 -1.69375330e-01 1.02714503e+00 1.50719583e-01
-1.79747865e-01 -1.05025995e+00 3.34234059e-01 4.88514483e-01
9.07113016e-01 -1.29889882e+00 5.37973717e-02 -4.20540214e-01
-3.34417880e-01 9.98654008e-01 8.16426098e-01 -2.73762852e-01
7.05614030e-01 1.08521581e-01 2.30696350e-01 7.67851621e-02
-2.86639690e-01 -2.01025277e-01 6.69562221e-02 4.33012128e-01
2.81062365e-01 -3.62526596e-01 -3.19167644e-01 8.03573072e-01
4.11084116e-01 -1.18226409e-01 7.83191249e-02 6.76442444e-01
-5.11364579e-01 -7.60495126e-01 -3.09896857e-01 4.01683182e-01
-4.98413563e-01 -4.30982560e-01 1.90724790e-01 3.03145796e-01
5.16944647e-01 8.42366695e-01 1.54155672e-01 -2.94115424e-01
2.70993888e-01 -5.50016165e-01 5.06267697e-02 -1.41442314e-01
-6.43704355e-01 4.36428607e-01 -6.61598086e-01 -6.95680916e-01
-3.43502432e-01 -3.34042251e-01 -9.70646858e-01 -1.56034827e-01
-2.43400514e-01 -6.78964853e-02 2.97663838e-01 8.32078636e-01
2.40110621e-01 8.61249506e-01 8.71025026e-01 -8.89583170e-01
-5.23671329e-01 -1.03135562e+00 -5.70857048e-01 6.29800200e-01
6.44259989e-01 -1.46047786e-01 -2.67698884e-01 2.05059960e-01] | [11.109371185302734, -2.086163282394409] |
bed872e7-d87e-467c-baaa-d4b45bdf2602 | tsgcnext-dynamic-static-multi-graph | 2304.11631 | null | https://arxiv.org/abs/2304.11631v1 | https://arxiv.org/pdf/2304.11631v1.pdf | TSGCNeXt: Dynamic-Static Multi-Graph Convolution for Efficient Skeleton-Based Action Recognition with Long-term Learning Potential | Skeleton-based action recognition has achieved remarkable results in human action recognition with the development of graph convolutional networks (GCNs). However, the recent works tend to construct complex learning mechanisms with redundant training and exist a bottleneck for long time-series. To solve these problems, we propose the Temporal-Spatio Graph ConvNeXt (TSGCNeXt) to explore efficient learning mechanism of long temporal skeleton sequences. Firstly, a new graph learning mechanism with simple structure, Dynamic-Static Separate Multi-graph Convolution (DS-SMG) is proposed to aggregate features of multiple independent topological graphs and avoid the node information being ignored during dynamic convolution. Next, we construct a graph convolution training acceleration mechanism to optimize the back-propagation computing of dynamic graph learning with 55.08\% speed-up. Finally, the TSGCNeXt restructure the overall structure of GCN with three Spatio-temporal learning modules,efficiently modeling long temporal features. In comparison with existing previous methods on large-scale datasets NTU RGB+D 60 and 120, TSGCNeXt outperforms on single-stream networks. In addition, with the ema model introduced into the multi-stream fusion, TSGCNeXt achieves SOTA levels. On the cross-subject and cross-set of the NTU 120, accuracies reach 90.22% and 91.74%. | ['Yuxin Tian', 'Zijie Cai', 'Yijing Lu', 'Miao Yao', 'Pengpeng Chen', 'Dongjingdin Liu'] | 2023-04-23 | null | null | null | null | ['skeleton-based-action-recognition', 'action-recognition-in-videos', 'action-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-2.86022536e-02 -2.65832543e-01 -6.73475266e-02 -1.35057613e-01
-1.55905649e-01 1.70521587e-01 3.17911178e-01 -9.64433104e-02
-6.17273152e-01 3.42655450e-01 2.62517065e-01 -2.98829786e-02
-1.30169481e-01 -9.78194714e-01 -6.39045715e-01 -6.33294940e-01
-4.74151194e-01 3.08837742e-02 7.73944080e-01 -1.78274959e-01
-2.60542403e-03 5.66419601e-01 -1.38250041e+00 5.18949151e-01
6.64134026e-01 1.22010517e+00 1.61813170e-01 7.80393004e-01
-3.26903105e-01 1.13965106e+00 -3.95977765e-01 -3.46615583e-01
2.67888635e-01 -3.31540674e-01 -5.16409814e-01 4.40786406e-02
5.61141849e-01 -3.32584381e-01 -1.01195538e+00 7.83130586e-01
7.82813907e-01 2.69489795e-01 9.28958282e-02 -1.45146310e+00
-3.79044592e-01 6.31499887e-01 -7.38309622e-01 4.93446678e-01
1.98954970e-01 4.30874944e-01 6.93601489e-01 -4.52928931e-01
6.05491996e-01 1.43376958e+00 8.42881382e-01 3.38102877e-01
-7.90906787e-01 -7.59115279e-01 5.31107068e-01 6.67430043e-01
-1.19832790e+00 6.42063282e-03 7.26975739e-01 -1.82103902e-01
1.39442301e+00 -5.83490953e-02 1.18249798e+00 1.05856574e+00
5.06871104e-01 1.05266702e+00 6.67309582e-01 1.55733272e-01
-9.84094217e-02 -1.07434714e+00 3.91212963e-02 1.11861026e+00
-9.90518704e-02 4.08686185e-03 -6.68713808e-01 2.81945288e-01
1.07826567e+00 3.41003180e-01 -1.02577165e-01 -1.51536539e-01
-1.20986342e+00 4.84239846e-01 8.13248694e-01 2.39902899e-01
-2.60511816e-01 7.15554893e-01 9.05729353e-01 2.26705611e-01
3.56782377e-01 -9.73917842e-02 -4.23832238e-01 -3.23507607e-01
-6.41424537e-01 6.28537163e-02 3.52943093e-01 9.98521745e-01
5.81788361e-01 3.39521140e-01 -2.57588059e-01 7.06131399e-01
2.47223973e-02 5.35029948e-01 7.17468441e-01 -7.17001259e-01
8.39455009e-01 1.12731183e+00 -8.05551648e-01 -1.20529950e+00
-7.69471943e-01 -4.95880812e-01 -1.21822691e+00 -1.35575479e-03
3.37060869e-01 -7.17106601e-03 -1.08978820e+00 1.43457258e+00
2.88147390e-01 6.46892190e-01 -1.03342265e-01 8.35468411e-01
9.60208833e-01 5.75434744e-01 3.03497612e-01 -1.28447786e-01
9.84684706e-01 -1.25641644e+00 -5.65184951e-01 -6.13614991e-02
9.78254616e-01 -3.40605646e-01 1.09285331e+00 4.31244522e-01
-8.42076659e-01 -8.83269370e-01 -8.81211817e-01 -1.11196622e-01
-3.65064293e-01 2.60905325e-01 1.05481207e+00 8.86285827e-02
-9.89103317e-01 8.84015977e-01 -1.09108007e+00 -3.95954072e-01
7.64306426e-01 3.44673216e-01 -4.21726048e-01 -1.66612104e-01
-1.01643801e+00 3.73722583e-01 7.45838463e-01 2.77718395e-01
-8.57493937e-01 -4.09539133e-01 -8.38521242e-01 -9.15855542e-02
5.73095858e-01 -6.02188468e-01 8.38637948e-01 -9.41073775e-01
-1.44829702e+00 3.31056505e-01 1.91717103e-01 -5.74778676e-01
5.88100612e-01 -3.07036340e-01 -6.43963814e-01 3.93144876e-01
3.05497181e-02 6.34140670e-01 6.27385616e-01 -3.66085052e-01
-5.55064440e-01 -4.57087964e-01 -2.62719486e-03 2.54145503e-01
-3.51356179e-01 -8.24830437e-04 -7.63967693e-01 -9.37275767e-01
2.99899817e-01 -7.91423261e-01 -3.90833080e-01 9.83792618e-02
-1.24624535e-01 -3.05370033e-01 1.04982316e+00 -6.13163948e-01
1.54048026e+00 -2.14489722e+00 3.27464730e-01 -7.66531974e-02
2.91174769e-01 5.15796840e-01 -3.03162366e-01 2.62030065e-01
-2.89493948e-01 -3.83084327e-01 -1.03635222e-01 -2.72077531e-01
-3.10163170e-01 4.96891201e-01 1.26929805e-01 3.30303669e-01
5.06178811e-02 1.39424884e+00 -7.73428440e-01 -8.00795078e-01
3.71621996e-01 1.76208556e-01 -4.01978225e-01 -1.16362378e-01
-2.56384641e-01 9.81777087e-02 -6.33570373e-01 7.31013596e-01
5.15117586e-01 -5.01022696e-01 -2.49626976e-03 -3.29233646e-01
-4.78545576e-03 -2.02585444e-01 -1.20531988e+00 2.28591275e+00
-6.22552703e-04 3.12115520e-01 -2.56058306e-01 -1.24840498e+00
7.95757473e-01 -2.86811367e-02 9.33515310e-01 -1.08937192e+00
2.21192420e-01 -4.82335091e-02 -2.81077847e-02 -7.73829162e-01
2.15720534e-01 6.57928586e-02 8.22500363e-02 2.84776330e-01
2.48563573e-01 3.96434069e-01 3.57933372e-01 3.04973662e-01
1.62357187e+00 4.29033548e-01 -1.08772025e-01 -8.60490929e-03
6.01891518e-01 -1.66526243e-01 6.45083547e-01 3.48624498e-01
-3.05605799e-01 5.01736104e-01 4.79236305e-01 -7.82644570e-01
-5.52569389e-01 -8.59193921e-01 4.44218874e-01 9.73625958e-01
1.54341742e-01 -8.39874744e-01 -5.35406411e-01 -9.28295672e-01
6.39571846e-02 1.41247928e-01 -5.31487882e-01 -3.04707378e-01
-9.53295648e-01 -6.97755754e-01 7.89031565e-01 9.75291252e-01
1.23502564e+00 -1.24336684e+00 -5.53028524e-01 4.14846063e-01
-1.38053879e-01 -1.26246870e+00 -5.70274651e-01 -1.52027577e-01
-1.26122582e+00 -1.35744929e+00 -6.19323969e-01 -5.35796821e-01
3.48667890e-01 2.60722160e-01 8.21470797e-01 2.27103397e-01
-4.67840225e-01 3.73106480e-01 -5.30216157e-01 1.56778693e-02
2.21009865e-01 -2.18952410e-02 -2.29250371e-01 7.42615834e-02
2.41710573e-01 -9.02031183e-01 -6.23779476e-01 3.00397664e-01
-8.34691525e-01 4.00263757e-01 6.96896851e-01 5.16070604e-01
6.81209147e-01 1.35268390e-01 2.85601318e-01 -2.16278031e-01
3.34970802e-01 -2.58118391e-01 -3.61639380e-01 3.32272321e-01
-4.61956054e-01 -5.68153672e-02 8.44591796e-01 -6.58222139e-01
-7.76131034e-01 9.76060033e-02 -1.09286450e-01 -8.47996950e-01
-3.14394124e-02 5.00968277e-01 -2.82225609e-01 -1.38907000e-01
4.72300529e-01 5.25101840e-01 1.75237089e-01 -3.48419935e-01
2.16838121e-01 5.55548221e-02 4.98528093e-01 -3.71510893e-01
4.04434204e-01 5.33004105e-01 3.09646577e-01 -8.97063553e-01
-5.86648285e-01 -4.52887148e-01 -7.39717484e-01 -5.67097783e-01
1.09351313e+00 -8.98684800e-01 -7.94390142e-01 1.22793436e+00
-9.15354073e-01 -9.21862006e-01 -2.26416022e-01 6.90815806e-01
-6.18593216e-01 7.58578837e-01 -7.14092433e-01 -3.97419244e-01
-3.43455195e-01 -8.41086447e-01 1.16368401e+00 1.85368314e-01
2.93249667e-01 -9.67415631e-01 -8.58428478e-02 2.86364764e-01
1.64099708e-01 6.05153918e-01 4.73977208e-01 -2.76513040e-01
-7.24261582e-01 -3.38037312e-02 -4.32581991e-01 4.89628911e-01
-3.40734720e-02 -6.76299408e-02 -4.98868674e-01 -1.85618892e-01
-3.69226396e-01 -3.42130005e-01 1.04854751e+00 4.71603572e-01
1.40618873e+00 -2.44165380e-02 -4.09528524e-01 1.07750618e+00
1.29774010e+00 2.00177327e-01 8.88540685e-01 2.58235246e-01
1.25365090e+00 1.36943504e-01 5.96408308e-01 4.88503426e-01
4.04336989e-01 4.47983265e-01 4.18457001e-01 -6.94663972e-02
-4.73364770e-01 -2.62291580e-01 5.02364099e-01 8.35891485e-01
-5.29752970e-01 -1.09476402e-01 -7.38971412e-01 1.89334124e-01
-2.19203520e+00 -1.07159472e+00 -4.44019645e-01 1.65262294e+00
2.66586423e-01 3.95393640e-01 2.40789607e-01 1.18493803e-01
5.43628633e-01 5.47582090e-01 -6.36410952e-01 6.31530136e-02
-3.71919274e-01 3.17355454e-01 6.87252581e-01 4.06659506e-02
-1.28560460e+00 1.06997681e+00 5.55169296e+00 1.13136542e+00
-1.16778278e+00 -5.02968021e-03 3.97519559e-01 -1.75238788e-01
2.67009705e-01 -1.66341782e-01 -6.33960545e-01 4.91143644e-01
7.08624125e-01 8.14283192e-02 2.75981009e-01 7.67711282e-01
3.03153157e-01 -1.64503321e-01 -6.55316353e-01 1.27997541e+00
7.58156255e-02 -1.27919257e+00 1.85063079e-01 -5.21433949e-02
4.10790712e-01 3.02410930e-01 -4.29259121e-01 4.03999060e-01
1.94659159e-01 -9.02577519e-01 4.71866339e-01 7.02871680e-01
6.87591255e-01 -6.21164918e-01 5.49055099e-01 3.23736787e-01
-1.86710775e+00 -4.17082429e-01 -3.31454784e-01 -3.33034992e-01
3.04471523e-01 4.14637715e-01 -1.92120343e-01 1.00075078e+00
8.11652660e-01 1.43616736e+00 -8.88021886e-01 1.00058103e+00
-2.83263236e-01 4.29397523e-01 -4.03594047e-01 -1.02118589e-01
5.21972179e-01 -1.87329307e-01 3.07269722e-01 1.15533113e+00
2.62343794e-01 3.65518600e-01 4.56962258e-01 4.06823128e-01
2.18978494e-01 6.81964904e-02 -3.99428934e-01 -1.30556092e-01
-3.03594112e-01 1.20569646e+00 -8.60211968e-01 -4.80227053e-01
-4.75453347e-01 1.14705944e+00 4.86827910e-01 3.70738745e-01
-1.15542400e+00 -2.46105820e-01 3.91152412e-01 6.00690842e-02
3.27270031e-01 -7.79284716e-01 3.39768343e-02 -1.25336099e+00
1.62633523e-01 -6.98229074e-01 9.22075093e-01 -7.72204280e-01
-9.53322470e-01 4.41138208e-01 1.19341679e-01 -1.21619129e+00
2.55901486e-01 -7.86968350e-01 -7.50553310e-01 4.28591192e-01
-1.30720937e+00 -1.45959222e+00 -7.20765650e-01 1.17376614e+00
5.84028661e-01 -2.48184428e-01 4.49539185e-01 6.10445619e-01
-7.76653886e-01 4.43887800e-01 -4.36257362e-01 4.99176323e-01
3.56577128e-01 -1.04389906e+00 5.83173037e-01 1.07820487e+00
4.61210907e-02 1.35655105e-01 -6.50037900e-02 -1.05428553e+00
-1.51700878e+00 -1.36142957e+00 4.49363589e-01 2.43927562e-03
7.99091756e-01 -1.24502555e-01 -8.97287309e-01 5.86607516e-01
-1.61082178e-01 4.27477956e-01 2.52209753e-01 -1.44592851e-01
-3.35549474e-01 -2.72981554e-01 -7.49733210e-01 5.11971772e-01
1.88080513e+00 -2.73703516e-01 -2.77688205e-01 3.96089822e-01
7.43743300e-01 -5.46700835e-01 -9.24118340e-01 4.57226753e-01
5.52058518e-01 -1.06176782e+00 8.63066435e-01 -6.88096821e-01
3.83736432e-01 -4.75956470e-01 -3.80978063e-02 -8.57128978e-01
-4.18516189e-01 -5.77427745e-01 -4.11695451e-01 6.10860646e-01
-1.24322623e-01 -6.23153806e-01 1.03285205e+00 1.60841122e-02
-5.74882150e-01 -9.45410967e-01 -9.77684140e-01 -1.00751758e+00
-3.18215013e-01 -7.68639803e-01 4.97385919e-01 6.09334230e-01
-1.71871632e-01 2.21937150e-01 -2.76877850e-01 -6.24535680e-02
6.41889274e-01 2.74142642e-02 9.92455304e-01 -9.25107718e-01
-3.58426839e-01 -4.57764119e-01 -8.65221322e-01 -1.28710568e+00
8.73780623e-03 -1.00680912e+00 -4.15896475e-01 -1.71590102e+00
-9.43639204e-02 -1.97333246e-01 -3.67128640e-01 7.29032338e-01
-1.32030740e-01 -4.50969115e-03 1.86833650e-01 -1.53069831e-02
-9.79627788e-01 8.47707212e-01 1.78214371e+00 -2.28733718e-01
-6.17621616e-02 -8.59540179e-02 -2.25961320e-02 7.05548763e-01
7.24827886e-01 -1.16398253e-01 -7.62234628e-01 -3.92280847e-01
9.99372802e-04 4.80363937e-03 6.92736328e-01 -1.63785040e+00
4.47254926e-01 -2.09196910e-01 4.18020725e-01 -8.97390425e-01
2.42045671e-01 -7.00586319e-01 3.09414119e-01 7.24713564e-01
1.06176332e-01 2.21576124e-01 2.90338159e-01 7.46922255e-01
-2.06709996e-01 3.48482490e-01 6.21430635e-01 -3.08005154e-01
-1.16346920e+00 9.99769747e-01 1.11901164e-02 7.02869520e-02
1.09316635e+00 -5.80046356e-01 -2.17509255e-01 6.16193302e-02
-8.77890408e-01 4.11724836e-01 5.15938513e-02 5.91049016e-01
8.58537495e-01 -1.60431242e+00 -3.94268572e-01 1.85000330e-01
-6.13254644e-02 2.28213906e-01 7.09501743e-01 1.09373844e+00
-7.57351816e-01 1.30754441e-01 -4.77695167e-01 -6.18312180e-01
-1.30167878e+00 5.28974533e-01 5.01028538e-01 -5.10282815e-01
-1.19713748e+00 9.24831331e-01 -5.44437207e-02 -1.56881765e-01
2.44428948e-01 -6.59392297e-01 -1.28348202e-01 8.21205825e-02
3.58915359e-01 6.60085678e-01 -8.04515481e-02 -5.58609366e-01
-3.51257652e-01 6.79207623e-01 1.97777495e-01 2.40680665e-01
1.41412258e+00 2.19004601e-01 -1.35608479e-01 2.54271209e-01
1.31647432e+00 -5.88301182e-01 -1.50905514e+00 -9.87360477e-02
-2.12280646e-01 -3.95423949e-01 -1.90805778e-01 -4.30137396e-01
-1.67500937e+00 8.60411108e-01 6.71109557e-01 -7.32323378e-02
1.26729524e+00 -2.37278417e-01 1.18095231e+00 4.58082259e-01
3.46454293e-01 -1.31923103e+00 6.25952423e-01 7.04738140e-01
9.45310593e-01 -9.54891264e-01 1.97490260e-01 -2.52566040e-01
-5.13292551e-01 1.31916308e+00 1.00235629e+00 -3.17196757e-01
7.11423993e-01 1.14484623e-01 -2.39473656e-01 -6.60084724e-01
-5.35039246e-01 -3.13045740e-01 2.50142843e-01 4.81335133e-01
6.05593137e-02 3.19708697e-02 -2.67992288e-01 4.15503144e-01
3.46921012e-02 2.36103430e-01 7.18828887e-02 1.00000238e+00
-3.32501829e-01 -8.86760056e-01 1.82646155e-01 5.67894995e-01
-1.34605974e-01 1.29223568e-02 -7.69319534e-02 9.82280791e-01
3.95811915e-01 5.36457002e-01 -1.07766185e-02 -8.33688736e-01
5.35688519e-01 -4.03763391e-02 5.06608367e-01 -3.33790779e-01
-4.91537869e-01 1.91639170e-01 9.73190442e-02 -1.31098938e+00
-7.04449117e-01 -6.84317648e-01 -1.60148752e+00 -1.88201666e-01
-2.14030415e-01 -2.71531105e-01 1.61818668e-01 9.69639480e-01
4.80548918e-01 9.64974046e-01 1.78169459e-01 -7.08319724e-01
-1.37261182e-01 -9.39648628e-01 -7.90991127e-01 3.89083594e-01
-1.62791327e-01 -7.90036201e-01 -1.16386294e-01 -7.29887784e-02] | [7.853048801422119, 0.35736146569252014] |
f4e7a72e-4102-4ac0-bb20-62a47d6faf3a | guided-depth-map-super-resolution-a-survey | 2302.09598 | null | https://arxiv.org/abs/2302.09598v2 | https://arxiv.org/pdf/2302.09598v2.pdf | Guided Depth Map Super-resolution: A Survey | Guided depth map super-resolution (GDSR), which aims to reconstruct a high-resolution (HR) depth map from a low-resolution (LR) observation with the help of a paired HR color image, is a longstanding and fundamental problem, it has attracted considerable attention from computer vision and image processing communities. A myriad of novel and effective approaches have been proposed recently, especially with powerful deep learning techniques. This survey is an effort to present a comprehensive survey of recent progress in GDSR. We start by summarizing the problem of GDSR and explaining why it is challenging. Next, we introduce some commonly used datasets and image quality assessment methods. In addition, we roughly classify existing GDSR methods into three categories, i.e., filtering-based methods, prior-based methods, and learning-based methods. In each category, we introduce the general description of the published algorithms and design principles, summarize the representative methods, and discuss their highlights and limitations. Moreover, the depth related applications are introduced. Furthermore, we conduct experiments to evaluate the performance of some representative methods based on unified experimental configurations, so as to offer a systematic and fair performance evaluation to readers. Finally, we conclude this survey with possible directions and open problems for further research. All the related materials can be found at \url{https://github.com/zhwzhong/Guided-Depth-Map-Super-resolution-A-Survey}. | ['Xiangyang Ji', 'Debin Zhao', 'Junjun Jiang', 'Xianming Liu', 'Zhiwei Zhong'] | 2023-02-19 | null | null | null | null | ['depth-image-upsampling', 'depth-map-super-resolution'] | ['computer-vision', 'computer-vision'] | [ 5.30139029e-01 -1.39959231e-01 -1.47586018e-01 -3.40116888e-01
-1.18953359e+00 -2.11678110e-02 1.45985857e-01 -5.52575648e-01
-1.05602778e-01 8.58512998e-01 1.01747885e-01 3.51100713e-01
-2.88490444e-01 -8.67146730e-01 -4.13823277e-01 -8.85495245e-01
-1.34952739e-01 -1.35954648e-01 3.08923215e-01 -1.32754132e-01
4.39861745e-01 5.91437638e-01 -1.82863069e+00 3.08963418e-01
9.93739843e-01 1.18182576e+00 7.24680722e-01 3.84245545e-01
1.78395435e-01 6.99899137e-01 -1.92279696e-01 5.67594171e-02
2.98970044e-01 -5.52640438e-01 -8.72932255e-01 1.25607513e-02
6.11833513e-01 -7.51989126e-01 -5.88639319e-01 1.24371064e+00
7.55063951e-01 2.40869626e-01 3.27084094e-01 -7.01792955e-01
-8.29574943e-01 8.63181576e-02 -8.68928373e-01 6.80736899e-01
4.33937311e-01 -2.00914577e-01 7.57471740e-01 -1.18327415e+00
6.46353483e-01 1.10881722e+00 3.31690729e-01 6.09400332e-01
-1.03120863e+00 -7.84213901e-01 1.78893685e-01 5.68649471e-01
-1.33128047e+00 -4.43898708e-01 9.02459502e-01 -2.10071728e-01
5.87212741e-01 2.02759638e-01 3.66236866e-01 8.21052253e-01
7.41811618e-02 5.92464387e-01 1.51308119e+00 -4.57682043e-01
7.68313035e-02 -1.23562261e-01 -4.84734960e-02 7.15661407e-01
5.08508161e-02 4.35115606e-01 -7.95221031e-01 1.23700470e-01
1.30347502e+00 -1.28192067e-01 -4.70693409e-01 -2.56407410e-01
-1.04648447e+00 6.57526255e-01 5.49191415e-01 3.17087144e-01
-1.71107918e-01 -2.36697361e-01 -1.18866600e-01 7.16889575e-02
7.82979369e-01 1.16767794e-01 -1.98973775e-01 3.50264221e-01
-8.16917896e-01 2.51402676e-01 -5.85888997e-02 8.02869499e-01
8.87035906e-01 9.31093767e-02 3.65274586e-02 1.22230566e+00
-4.11588326e-03 4.74372327e-01 1.31135464e-01 -1.35845912e+00
7.33084045e-03 2.60765143e-02 1.80060208e-01 -1.05666769e+00
-3.81010711e-01 -2.65725076e-01 -1.14330244e+00 5.16738296e-01
-4.91102487e-02 1.42630205e-01 -6.58276975e-01 1.23490012e+00
2.93150425e-01 2.24076346e-01 -1.05544537e-01 1.19591069e+00
1.55481672e+00 5.95080376e-01 -3.59129131e-01 -5.97785771e-01
1.24621904e+00 -9.61251676e-01 -8.63963306e-01 -1.15614645e-01
-3.59811962e-01 -7.15078294e-01 1.01504445e+00 5.95753312e-01
-1.34517264e+00 -7.71984160e-01 -9.77390707e-01 -5.03876567e-01
-6.32891208e-02 2.71725625e-01 5.41474342e-01 1.55125350e-01
-1.15484655e+00 8.30136120e-01 -8.60502303e-01 -2.97455311e-01
5.30159593e-01 -1.02943189e-01 -1.90427154e-01 -5.47499597e-01
-1.13589013e+00 6.63053036e-01 -4.44348194e-02 6.86301440e-02
-8.48621368e-01 -7.38872647e-01 -8.02184761e-01 -4.83770639e-01
1.47108018e-01 -6.66988373e-01 1.16632020e+00 -3.34190667e-01
-1.52826822e+00 1.16889822e+00 -5.74609399e-01 -1.04806103e-01
2.77815938e-01 -2.13603348e-01 -5.66069484e-01 6.35394394e-01
9.50299483e-03 4.65574920e-01 6.37494087e-01 -1.47435200e+00
-1.17689013e+00 -5.75938284e-01 5.03538921e-02 3.46534401e-01
-1.28163239e-02 3.78660262e-01 -5.99898338e-01 -4.84642714e-01
6.42298996e-01 -3.55641603e-01 -3.82384807e-01 1.40884921e-01
-1.81855977e-01 -4.87535819e-03 4.75124955e-01 -6.64248407e-01
1.32126915e+00 -2.05617595e+00 1.07174635e-01 -3.77355605e-01
5.11601985e-01 -5.28669916e-02 6.26461860e-03 3.61183345e-01
-1.11002028e-01 -7.76410326e-02 -3.29794943e-01 -3.40983868e-01
-5.37936330e-01 -2.04135731e-01 -3.74081910e-01 7.99806893e-01
-1.82423115e-01 7.24081159e-01 -8.31874430e-01 -4.77676541e-01
6.21325910e-01 8.76651466e-01 6.08413815e-02 3.09300631e-01
2.49649465e-01 9.76962447e-01 -3.98882806e-01 8.94046485e-01
1.04684460e+00 -3.06177109e-01 -1.46700487e-01 -5.57125509e-01
-5.48857510e-01 1.48163155e-01 -1.17090130e+00 1.49971712e+00
-4.49788451e-01 5.73307335e-01 1.47593230e-01 -8.48520994e-01
1.06438923e+00 -4.06337194e-02 7.25201428e-01 -1.18302512e+00
-2.80785054e-01 3.29401523e-01 -5.97753525e-01 -3.84366632e-01
4.97215390e-01 -2.72984087e-01 2.99820960e-01 1.41275078e-01
-3.59869510e-01 2.25788597e-02 -4.11298424e-02 -5.90263382e-02
7.06684709e-01 4.12544236e-02 4.58491325e-01 -2.03776322e-02
8.41547370e-01 -1.90543756e-01 6.66828454e-01 5.44963360e-01
-3.36681128e-01 9.74486709e-01 -1.05664402e-01 -6.22237206e-01
-9.33088720e-01 -1.23795545e+00 -6.58371270e-01 7.14198530e-01
7.48702228e-01 -1.38661176e-01 -3.18446666e-01 -1.36047706e-01
-4.79897350e-01 1.01300746e-01 -7.00632393e-01 3.16591024e-01
-5.63980281e-01 -1.06817913e+00 -1.19230680e-01 4.42635536e-01
1.12677491e+00 -1.03126991e+00 -4.84261930e-01 -2.33457148e-01
-7.39774883e-01 -1.41851187e+00 1.38387516e-01 -4.55858558e-01
-1.22248757e+00 -9.76270974e-01 -1.08917975e+00 -8.59612048e-01
5.22234321e-01 8.19155991e-01 1.31429923e+00 -1.12051135e-02
-4.41637874e-01 3.86818081e-01 -4.79121059e-01 1.15932293e-01
2.95398474e-01 -2.53266931e-01 -6.31199107e-02 -1.79114103e-01
2.74958372e-01 -7.21263111e-01 -1.04070508e+00 4.54007417e-01
-7.44315147e-01 2.72277564e-01 7.04097092e-01 5.98619163e-01
1.48766482e+00 4.87534761e-01 4.07040834e-01 -7.23505497e-01
7.93022141e-02 -1.67130843e-01 -8.23824763e-01 -7.61918426e-02
-6.54219389e-01 -3.81877691e-01 2.60777682e-01 1.38347566e-01
-1.31921840e+00 2.39086188e-02 -3.47113222e-01 -2.96332628e-01
-2.88753122e-01 7.34248981e-02 -3.18456888e-01 -4.61778343e-01
5.29192626e-01 6.29432261e-01 -1.54756829e-01 -9.68429923e-01
4.07325268e-01 5.25784373e-01 7.40957677e-01 -3.81824046e-01
8.39957416e-01 1.18738472e+00 4.91695702e-02 -1.07704306e+00
-1.31809616e+00 -6.97896898e-01 -6.24095082e-01 -3.62191916e-01
8.84969711e-01 -1.09388113e+00 -2.32939124e-01 6.26014054e-01
-7.89820790e-01 -4.86154884e-01 -5.72006218e-02 4.30427730e-01
-9.35422719e-01 5.07149994e-01 -8.61479759e-01 -7.89464653e-01
-4.26709861e-01 -1.02991819e+00 1.11482930e+00 6.60458446e-01
5.54963171e-01 -9.19406891e-01 1.57155305e-01 6.54314697e-01
5.28930843e-01 4.76831585e-01 4.78966922e-01 4.31976020e-01
-1.08582759e+00 2.73500770e-01 -6.14297152e-01 3.44563931e-01
2.01928169e-01 -2.42861882e-01 -9.78495121e-01 -4.76310074e-01
1.49905100e-01 -2.72640586e-01 1.02712297e+00 7.94168532e-01
1.47173393e+00 1.31253943e-01 -3.81465822e-01 1.14968264e+00
2.06083226e+00 1.24390654e-01 1.08610404e+00 5.36776960e-01
5.41066885e-01 6.11599445e-01 1.04757082e+00 2.73676485e-01
3.84117037e-01 8.87533903e-01 5.01628995e-01 -3.57634425e-01
-4.98250514e-01 3.12380609e-03 1.05568394e-01 4.28090721e-01
-5.37052035e-01 9.47616324e-02 -3.77205998e-01 3.12386066e-01
-1.49013114e+00 -1.00525713e+00 -2.98363626e-01 2.25639701e+00
5.32934546e-01 -3.52982372e-01 8.82842317e-02 2.37504914e-01
8.03505361e-01 4.64417189e-01 -6.30631626e-01 3.01230401e-01
-4.91360873e-01 3.84923697e-01 3.58792216e-01 7.02104509e-01
-1.07491612e+00 9.53829706e-01 6.24480486e+00 9.06893432e-01
-1.01406765e+00 2.92274296e-01 7.55001962e-01 -1.28770936e-02
-4.42259833e-02 -3.54465008e-01 -9.80443537e-01 1.96359217e-01
4.08764094e-01 -1.26556844e-01 5.45626581e-01 6.68247461e-01
4.57246780e-01 -4.32761043e-01 -6.09906435e-01 1.49428880e+00
2.16721565e-01 -1.43718827e+00 -1.19882800e-01 -5.58892339e-02
8.55203032e-01 3.01642478e-01 3.79253596e-01 -1.27118021e-01
-1.02321111e-01 -1.05902433e+00 2.24896342e-01 6.90272987e-01
1.44243968e+00 -8.32467675e-01 4.83397305e-01 3.02745681e-02
-1.50815177e+00 -8.00606310e-02 -8.02936494e-01 -1.30850691e-02
2.56209582e-01 8.72520328e-01 4.91328239e-01 9.94504452e-01
1.44925678e+00 1.17549300e+00 -4.05161232e-01 1.20726156e+00
-3.93699318e-01 9.79879573e-02 2.11265251e-01 7.36402631e-01
-3.33124816e-01 -2.43263990e-01 5.50833285e-01 8.79207313e-01
2.74430603e-01 6.33168459e-01 3.42767537e-02 9.97932136e-01
2.79062659e-01 -5.39532304e-02 -3.41052026e-01 4.92946833e-01
3.91443789e-01 1.51029074e+00 -5.53769827e-01 -1.48884058e-01
-6.07194185e-01 9.43488777e-01 2.67101884e-01 5.23200810e-01
-6.02007389e-01 -4.44092363e-01 6.88058376e-01 4.58748192e-01
2.71309875e-02 -3.41683179e-01 -3.73885512e-01 -1.14702797e+00
3.49569134e-02 -7.23592103e-01 5.42165339e-01 -1.12037575e+00
-1.31262243e+00 8.00461888e-01 1.77432224e-01 -1.54147553e+00
1.35050505e-01 -3.95333111e-01 -2.47502208e-01 1.07482016e+00
-2.32158399e+00 -7.11552322e-01 -9.88895118e-01 6.69775307e-01
7.57179797e-01 1.35211006e-01 4.47697580e-01 4.71197724e-01
-6.53608680e-01 2.77874768e-01 2.74940103e-01 3.10866851e-02
6.52877927e-01 -9.62582469e-01 1.07857309e-01 1.03982949e+00
-3.14227283e-01 3.20595026e-01 5.79915166e-01 -4.43264306e-01
-1.09863889e+00 -1.07002807e+00 5.97434998e-01 -2.56960571e-01
1.44730538e-01 -2.75026746e-02 -1.11258101e+00 4.17853445e-01
-1.49604445e-02 3.71208996e-01 3.65618229e-01 -3.03146839e-01
-9.20537040e-02 -3.95710647e-01 -1.34878981e+00 2.37020671e-01
1.38461185e+00 -3.50573629e-01 -2.28478402e-01 2.69010425e-01
5.73667705e-01 -6.70089781e-01 -9.68191147e-01 6.22900248e-01
5.32868862e-01 -1.69910681e+00 1.34292293e+00 1.81107089e-01
7.16550350e-01 -5.75404286e-01 -2.84308910e-01 -9.10547614e-01
-7.01400101e-01 -2.30426624e-01 -2.65140951e-01 8.27650785e-01
-2.44515315e-01 -6.07025504e-01 7.99326241e-01 1.88543886e-01
-1.95872724e-01 -9.99568641e-01 -7.74447322e-01 -7.97283232e-01
7.39268064e-02 -2.20144033e-01 3.13659281e-01 6.70070767e-01
-3.09117138e-01 1.82798907e-01 -3.74159247e-01 5.20883620e-01
1.43418074e+00 3.90335888e-01 5.52739084e-01 -1.08207369e+00
4.19886671e-02 -3.38132650e-01 -8.08339491e-02 -1.39208984e+00
-1.93387747e-01 -5.04852414e-01 -7.43713900e-02 -2.17836809e+00
3.90255839e-01 -3.61790389e-01 -2.12943986e-01 -1.67617351e-02
-9.28265750e-02 7.83359528e-01 -2.48248294e-01 4.49414223e-01
-5.77867925e-01 5.29241741e-01 1.59862244e+00 3.32005382e-01
-1.21556714e-01 9.17062238e-02 -8.15424740e-01 7.19051600e-01
7.69284010e-01 -2.05652826e-02 -4.81772780e-01 -4.03079927e-01
1.80083930e-01 1.77488133e-01 4.51656938e-01 -1.00723827e+00
4.32659201e-02 -3.20330650e-01 6.15426362e-01 -9.84703362e-01
5.62485337e-01 -4.90206093e-01 7.56451413e-02 1.52569190e-01
-1.01981662e-01 -3.10552746e-01 -9.83070061e-02 5.32773495e-01
-4.29243147e-01 2.81331509e-01 1.54680324e+00 -4.68453139e-01
-1.23245502e+00 7.18298733e-01 2.82499380e-02 -1.43334523e-01
8.00049543e-01 -3.95130336e-01 -4.06759620e-01 -4.03431416e-01
-7.39806592e-01 1.74901053e-01 5.10243356e-01 2.44068295e-01
1.17557919e+00 -1.29118657e+00 -9.65172708e-01 9.63746160e-02
2.13292941e-01 2.53346592e-01 1.11976457e+00 7.95585692e-01
-3.58481139e-01 3.66428167e-01 -3.90209943e-01 -4.57468778e-01
-1.26821315e+00 6.15210235e-01 6.71517253e-01 1.85158837e-03
-1.19729316e+00 8.50357771e-01 6.02920175e-01 4.23874222e-02
2.17331111e-01 -5.11041023e-02 -6.46967053e-01 -4.46147949e-01
1.20580888e+00 6.77755892e-01 2.89891334e-03 -7.31100976e-01
-3.29100728e-01 1.20058775e+00 1.30484894e-01 1.92157049e-02
1.46983433e+00 -9.23924148e-01 -2.62255341e-01 3.55623007e-01
9.30619180e-01 -8.00062120e-02 -1.23969734e+00 -5.32262146e-01
-5.27902484e-01 -9.65464890e-01 3.89431477e-01 -7.11554348e-01
-1.45838141e+00 9.23528433e-01 8.69438171e-01 -1.60714075e-01
1.80437672e+00 2.78781503e-01 8.03913474e-01 -2.33314231e-01
8.35498989e-01 -7.16261089e-01 2.24257961e-01 2.21770853e-01
1.07299578e+00 -1.42827511e+00 4.29261714e-01 -7.52926290e-01
-2.78510243e-01 1.04221010e+00 7.64470696e-01 -2.31802702e-01
7.46396244e-01 8.80786926e-02 3.90988737e-02 -1.74420655e-01
-4.52134013e-01 -4.36378300e-01 2.58705139e-01 1.06682515e+00
4.28185582e-01 -3.12387198e-01 -3.71980935e-01 5.19025743e-01
-1.67715579e-01 1.86774194e-01 6.59469485e-01 3.68196130e-01
-6.86712980e-01 -8.91761303e-01 -4.25874770e-01 1.90576687e-01
-3.67390901e-01 -9.30527449e-02 7.31256679e-02 4.84806061e-01
1.44725293e-01 1.03435922e+00 7.74982572e-02 -4.02321786e-01
3.26630205e-01 -7.59613633e-01 6.51212037e-01 -5.09489000e-01
4.62545723e-01 9.39037011e-04 -2.64449239e-01 -9.53465998e-01
-8.47983479e-01 -5.79406619e-01 -1.09283018e+00 -3.77065569e-01
9.50553268e-02 -2.27312967e-01 3.94048661e-01 5.17938256e-01
1.59738958e-01 3.67029041e-01 7.35659122e-01 -9.91131067e-01
1.22991838e-01 -6.89113021e-01 -9.38398361e-01 -2.42192030e-01
5.96570075e-01 -8.51753831e-01 -5.71231365e-01 -7.18902126e-02] | [10.151205062866211, -2.3769478797912598] |
e40b5fb7-d67c-437c-8380-5929b8e73a36 | mathprompter-mathematical-reasoning-using | 2303.05398 | null | https://arxiv.org/abs/2303.05398v1 | https://arxiv.org/pdf/2303.05398v1.pdf | MathPrompter: Mathematical Reasoning using Large Language Models | Large Language Models (LLMs) have limited performance when solving arithmetic reasoning tasks and often provide incorrect answers. Unlike natural language understanding, math problems typically have a single correct answer, making the task of generating accurate solutions more challenging for LLMs. To the best of our knowledge, we are not aware of any LLMs that indicate their level of confidence in their responses which fuels a trust deficit in these models impeding their adoption. To address this deficiency, we propose `MathPrompter', a technique that improves performance of LLMs on arithmetic problems along with increased reliance in the predictions. MathPrompter uses the Zero-shot chain-of-thought prompting technique to generate multiple Algebraic expressions or Python functions to solve the same math problem in different ways and thereby raise the confidence level in the output results. This is in contrast to other prompt based CoT methods, where there is no check on the validity of the intermediate steps followed. Our technique improves over state-of-the-art on the MultiArith dataset ($78.7\%\rightarrow92.5\%$) evaluated using 175B parameter GPT-based LLM. | ['Harsh Shrivastava', 'Liang Du', 'Shima Imani'] | 2023-03-04 | null | null | null | null | ['mathematical-reasoning', 'arithmetic-reasoning'] | ['natural-language-processing', 'reasoning'] | [ 3.57108451e-02 5.68225384e-01 2.78578997e-01 -5.22424698e-01
-8.82929504e-01 -5.74439108e-01 4.87568319e-01 4.92578268e-01
-1.01189375e-01 7.80169427e-01 1.36998996e-01 -7.95030892e-01
-2.00552925e-01 -1.18970740e+00 -8.03337693e-01 -2.43544881e-03
4.87563312e-01 7.28227437e-01 1.22953683e-01 -4.62777376e-01
7.97972739e-01 -1.81556225e-03 -1.26624131e+00 9.04887497e-01
1.11137176e+00 5.16270638e-01 -2.06924230e-02 7.90952623e-01
-5.39595306e-01 1.80213630e+00 -8.76119435e-01 -7.34941304e-01
8.42754319e-02 -3.07816625e-01 -1.18935370e+00 -8.28656435e-01
5.30186713e-01 -3.70720327e-01 9.33249518e-02 8.57091010e-01
4.34124768e-01 1.79423526e-01 4.63207901e-01 -1.25456226e+00
-7.11526275e-01 9.31801498e-01 -2.32970297e-01 2.92196758e-02
9.92438316e-01 3.80986452e-01 9.03722167e-01 -8.77535164e-01
3.19039047e-01 1.45107186e+00 8.80023003e-01 4.30585206e-01
-1.30596828e+00 -6.06386721e-01 -1.02712914e-01 2.19170198e-01
-1.30721211e+00 -2.87327886e-01 1.63389072e-01 -5.05075514e-01
1.64745116e+00 3.34527075e-01 3.92153114e-01 9.04054821e-01
3.18419069e-01 5.20569026e-01 1.45327592e+00 -6.73794270e-01
2.41016567e-01 2.02179074e-01 4.19217497e-01 7.47894168e-01
3.89854759e-02 -1.06462777e-01 -7.09196985e-01 -3.00112903e-01
6.01335585e-01 -4.99848425e-01 4.85737575e-03 3.61533433e-01
-1.05020893e+00 8.78103435e-01 1.61737129e-01 9.15476233e-02
-2.90993035e-01 2.47591257e-01 9.94492322e-02 5.70618689e-01
2.11296871e-01 9.29321289e-01 -6.46033883e-01 -5.08578241e-01
-9.51071084e-01 9.63346422e-01 1.31852841e+00 8.04682851e-01
4.57922637e-01 1.39200971e-01 -3.17952275e-01 4.67875838e-01
2.82426834e-01 2.03974336e-01 3.99924546e-01 -1.28360868e+00
6.39525831e-01 8.59647155e-01 1.54684946e-01 -1.19191825e+00
-3.52701217e-01 -2.53617138e-01 -2.89678961e-01 5.01643836e-01
8.07025075e-01 -2.70568933e-02 -4.70294654e-01 1.57009196e+00
-4.49931063e-03 6.60933778e-02 2.73603499e-01 6.44821644e-01
9.37460899e-01 9.15755749e-01 4.42205429e-01 2.58843631e-01
8.77984583e-01 -1.06199229e+00 -3.59068573e-01 -3.99675339e-01
1.10094845e+00 -9.77758348e-01 1.25853789e+00 7.64729202e-01
-1.45050085e+00 -5.91806769e-01 -7.77401924e-01 -3.13128352e-01
-2.02083632e-01 -2.42922977e-01 9.68589365e-01 6.22864485e-01
-1.20001996e+00 8.29525650e-01 -5.80366910e-01 4.77441996e-02
4.35287841e-02 2.32073143e-01 -7.20633417e-02 -3.85354161e-01
-1.11624086e+00 1.37675428e+00 4.24605668e-01 -2.13130325e-01
-3.22923064e-01 -1.25105524e+00 -9.13927138e-01 1.28301159e-01
3.86116028e-01 -6.08130932e-01 1.74731708e+00 -6.46033823e-01
-1.53226817e+00 6.01857007e-01 -1.13325112e-01 -4.91487354e-01
6.51917219e-01 -3.46424043e-01 -1.07207142e-01 -1.61166832e-01
1.20684780e-01 9.19944346e-01 2.58373439e-01 -6.39865875e-01
-1.89870432e-01 1.57776430e-01 4.66936737e-01 1.04756102e-01
4.13766742e-01 1.60394415e-01 2.39575654e-01 -4.06748205e-01
1.48058906e-01 -4.80175525e-01 -3.76431495e-01 -2.21094102e-01
1.20860199e-02 -5.04379988e-01 6.73406944e-02 -7.71425545e-01
1.37224042e+00 -1.61748374e+00 -8.37839469e-02 7.19614550e-02
1.43127054e-01 3.27096701e-01 -2.01740086e-01 7.42923081e-01
-2.02091888e-01 5.15138984e-01 8.18605945e-02 -4.99359779e-02
4.32413280e-01 5.60661852e-02 -6.83775187e-01 -1.57402650e-01
3.38437140e-01 1.10999739e+00 -9.89510119e-01 -4.21900630e-01
3.01899582e-01 1.44171178e-01 -9.97697175e-01 3.25932920e-01
-6.84847057e-01 3.56153697e-02 -2.24273965e-01 4.78394985e-01
5.65014720e-01 -2.55085617e-01 -5.22728935e-02 3.13624293e-01
-1.55793399e-01 9.07733500e-01 -1.20434570e+00 1.51060212e+00
-5.16570628e-01 3.52651715e-01 -4.76586491e-01 -6.86517179e-01
1.02501070e+00 3.77672940e-01 -2.36057982e-01 -7.87809849e-01
-2.29212016e-01 4.91661906e-01 2.47601807e-01 -4.81139988e-01
5.69948971e-01 -1.37180448e-01 -1.40689090e-01 7.14875102e-01
-1.16323940e-01 -9.12877917e-01 3.33898604e-01 4.58493799e-01
1.24976134e+00 5.22235990e-01 4.20734346e-01 -3.18361908e-01
5.75865388e-01 1.74493745e-01 2.25391209e-01 1.23595178e+00
1.63090989e-01 4.86986339e-01 6.78470910e-01 -6.09931588e-01
-9.05001283e-01 -7.78245211e-01 2.53316641e-01 1.07131064e+00
-5.27660489e-01 -9.18719292e-01 -7.56886125e-01 -1.41962633e-01
-1.66790053e-01 1.53238368e+00 -1.32773951e-01 -4.97967415e-02
-5.20694554e-01 -5.01428187e-01 7.38449216e-01 5.15854836e-01
3.66119236e-01 -1.31725168e+00 -8.04586709e-01 4.11461353e-01
-3.58749121e-01 -9.20387745e-01 2.91124582e-01 -3.48073766e-02
-7.94676006e-01 -9.77149189e-01 -4.25455607e-02 -3.35325599e-01
5.82016826e-01 -2.58365422e-01 1.71783209e+00 5.30568540e-01
3.42838727e-02 2.32936472e-01 -3.47687423e-01 -4.98924285e-01
-8.51182640e-01 -2.67271958e-02 -3.26801777e-01 -1.03257835e+00
6.19809389e-01 -5.50864995e-01 -2.11285517e-01 -6.12786077e-02
-5.92026711e-01 5.99959731e-01 4.19509172e-01 4.86985803e-01
-7.29322359e-02 -1.08155988e-01 4.58472252e-01 -8.80462170e-01
9.25955534e-01 -4.73032594e-01 -5.24415016e-01 3.17055672e-01
-6.34327054e-01 1.57972053e-01 7.06571400e-01 -2.75530368e-01
-8.74921799e-01 -4.46647972e-01 -2.78861701e-01 2.53838956e-01
-1.24592915e-01 7.38632083e-01 4.54306930e-01 -1.77924067e-01
1.01016581e+00 6.64407853e-03 -3.54531020e-01 -2.12470904e-01
2.86168635e-01 2.66780406e-01 5.85489452e-01 -1.31024659e+00
4.88600582e-01 -4.42873716e-01 -8.16330910e-02 -4.02130783e-01
-1.00821674e+00 1.17581598e-01 -9.70302820e-02 -8.17034543e-02
3.10535848e-01 -7.93271065e-01 -9.97689664e-01 5.13311327e-01
-1.49088812e+00 -7.51973212e-01 -1.57295942e-01 2.39656389e-01
-6.54354751e-01 3.15927565e-01 -9.41220820e-01 -1.07277513e+00
-4.80854958e-01 -1.06316519e+00 7.18379915e-01 3.15582067e-01
-1.17044926e+00 -8.16219449e-01 -1.83490381e-01 5.89316785e-01
7.37740576e-01 5.74789979e-02 1.46101332e+00 -7.26974964e-01
-6.49297178e-01 -1.90252155e-01 -2.37963215e-01 1.47688136e-01
-5.43460011e-01 -1.95852537e-02 -8.58957112e-01 2.63733655e-01
7.55551979e-02 -8.03363442e-01 1.69568241e-01 5.95588312e-02
1.05705428e+00 -5.08238733e-01 1.52018964e-01 9.52712223e-02
1.24781156e+00 -5.80036901e-02 9.17234659e-01 3.11194509e-01
3.19214851e-01 6.62142873e-01 6.15527630e-01 3.82561862e-01
5.83039224e-01 2.87586868e-01 1.78319439e-02 3.42561007e-01
2.44343221e-01 -5.74168086e-01 5.51793814e-01 6.54867351e-01
1.29731968e-01 -1.12052135e-01 -1.35358918e+00 2.93902397e-01
-1.72680330e+00 -7.26975799e-01 -6.56164527e-01 2.02118707e+00
1.25932097e+00 3.77116174e-01 -5.85766852e-01 2.32630730e-01
1.00216426e-01 -6.22409917e-02 4.85162809e-02 -9.49615359e-01
3.00715387e-01 1.10292697e+00 -2.10280884e-02 9.69834626e-01
-2.84514815e-01 1.21473634e+00 6.72857809e+00 8.48681688e-01
-7.35064089e-01 -1.37806833e-01 6.33982241e-01 5.61977066e-02
-4.41292465e-01 3.32412243e-01 -6.50556624e-01 3.22777033e-01
1.37620306e+00 -2.26220638e-01 6.57510519e-01 7.73182690e-01
1.63124874e-01 -6.46640718e-01 -1.27389133e+00 7.30216682e-01
-5.79319149e-02 -1.29352486e+00 -7.16637354e-03 -6.68570817e-01
5.12525082e-01 -4.03730035e-01 -1.53348133e-01 8.70814145e-01
8.15014064e-01 -1.56295609e+00 9.60073292e-01 7.42590666e-01
2.32861772e-01 -7.04993069e-01 6.92241728e-01 8.57172906e-01
-6.61227167e-01 7.60840774e-02 -2.79529601e-01 -1.07437122e+00
8.61111879e-02 3.51281017e-01 -1.15939271e+00 2.93788254e-01
5.53953767e-01 4.37366664e-02 -8.24114859e-01 6.79260015e-01
-7.63105690e-01 7.82776117e-01 -3.41055781e-01 -1.30273119e-01
1.30119979e-01 2.71709785e-02 2.10295264e-02 1.02396381e+00
3.26598138e-01 5.35640061e-01 2.71378964e-01 1.37921476e+00
4.53734100e-01 1.30152315e-01 -2.51165390e-01 -3.74009609e-01
4.74192500e-01 9.25556839e-01 -4.86978292e-01 -5.98419726e-01
-2.03843132e-01 6.33592784e-01 5.00425696e-01 1.21309511e-01
-7.09844649e-01 -5.26160039e-02 2.35269651e-01 1.04241446e-01
-1.93625107e-01 -3.01951706e-01 -7.39171326e-01 -9.03938532e-01
-1.19150043e-01 -1.42070317e+00 2.58377820e-01 -1.49335146e+00
-1.13257897e+00 3.29604506e-01 1.97018266e-01 -3.14094931e-01
-6.39275610e-01 -7.44093180e-01 -5.96672177e-01 1.44178283e+00
-1.03765488e+00 -9.19294298e-01 -3.64433765e-01 6.71034232e-02
5.08773506e-01 1.89285278e-01 1.28733850e+00 -1.17955528e-01
-3.79091613e-02 2.60027230e-01 -7.42681921e-01 -1.87545389e-01
7.65007377e-01 -1.26831436e+00 7.45201349e-01 7.20526695e-01
-2.82728583e-01 1.05484211e+00 9.49560404e-01 -8.44092488e-01
-1.08531976e+00 -5.94008863e-01 1.55002105e+00 -9.49093282e-01
6.84580326e-01 -7.97989890e-02 -1.15943193e+00 6.92384124e-01
1.88019738e-01 -4.79243636e-01 6.61372483e-01 -6.39536604e-02
-3.77081305e-01 4.95761335e-01 -1.21131945e+00 7.22679496e-01
6.95021749e-01 -4.70413566e-01 -1.01702070e+00 4.35713142e-01
7.16209769e-01 -9.72493887e-01 -7.71264195e-01 2.89301902e-01
3.00262064e-01 -1.05635023e+00 9.48492706e-01 -8.11354220e-01
1.11312973e+00 -1.41912311e-01 -2.62771755e-01 -1.14912212e+00
-1.61358118e-01 -6.28625274e-01 -1.67052001e-01 1.09821570e+00
4.47965711e-01 -6.96904302e-01 5.58182240e-01 1.33536339e+00
-4.50996794e-02 -7.66775489e-01 -3.50309968e-01 -1.25196621e-01
4.04824793e-01 -9.68808174e-01 7.47993231e-01 9.03870881e-01
2.73150623e-01 1.90796375e-01 -2.65307724e-02 -2.36991569e-02
1.92786783e-01 7.61549771e-02 8.96963537e-01 -1.03020287e+00
-6.44477725e-01 -3.51778209e-01 -4.73806188e-02 -7.54805684e-01
1.76727802e-01 -9.54604864e-01 -1.88566521e-01 -1.48556948e+00
4.82003763e-02 -5.25041521e-01 2.99351901e-01 7.12565720e-01
-3.01469713e-01 4.40503955e-02 2.68705100e-01 -1.83923513e-01
-4.59327251e-01 -6.98108897e-02 7.64675617e-01 2.05501318e-01
6.83949292e-02 -1.82909042e-01 -9.95706797e-01 8.08078051e-01
8.47545624e-01 -5.04516304e-01 -4.44726437e-01 -5.32999873e-01
8.66166294e-01 2.62844533e-01 5.96609175e-01 -1.09428906e+00
3.61435890e-01 -4.22100663e-01 3.51124197e-01 -4.67218786e-01
1.77313879e-01 -2.57895768e-01 2.81979144e-01 4.90476787e-01
-5.73438287e-01 4.64964569e-01 5.41104376e-01 -1.76549077e-01
4.35902104e-02 -7.90171981e-01 3.53758246e-01 -7.63431609e-01
-6.78351104e-01 -4.47200447e-01 -5.75344205e-01 1.40337825e-01
6.05690777e-01 -5.94077818e-02 -5.77880621e-01 -5.05759001e-01
-2.54403621e-01 2.74753392e-01 3.29771847e-01 2.66655892e-01
4.42368805e-01 -9.88361835e-01 -7.62677133e-01 1.15256287e-01
-1.85341790e-01 3.14308494e-01 2.14762449e-01 8.25317144e-01
-9.77341533e-01 6.14485860e-01 -1.49080560e-01 -3.08515698e-01
-9.88430738e-01 3.83358389e-01 3.07540298e-01 -4.99950916e-01
-4.04364586e-01 1.03487861e+00 -1.92516282e-01 -8.36774588e-01
1.64448470e-02 -5.82009733e-01 5.73939122e-02 -3.49932492e-01
6.61815822e-01 3.41204315e-01 2.47206271e-01 -9.32440348e-03
-1.09127713e-02 3.99570949e-02 -5.44701815e-02 -2.87090182e-01
1.30016851e+00 4.57149893e-01 -5.22581995e-01 4.51301873e-01
4.13455725e-01 7.53801838e-02 -7.02923834e-01 -7.48270378e-02
7.90269673e-02 -5.88842511e-01 -3.09913248e-01 -1.38790786e+00
-2.58216292e-01 8.40950310e-01 -6.00749142e-02 -6.35002628e-02
6.51758730e-01 -4.17255104e-01 5.08064151e-01 6.62641287e-01
6.58413410e-01 -8.53697538e-01 1.90045744e-01 8.11012030e-01
9.97777700e-01 -1.17890465e+00 1.03511728e-01 -5.49459696e-01
-5.07793725e-01 1.29935193e+00 1.01803029e+00 3.61935496e-02
-1.92241475e-01 5.21318197e-01 8.78811106e-02 -1.36250690e-01
-1.28536713e+00 4.92933571e-01 -5.59802130e-02 4.68916237e-01
9.95114803e-01 7.26351589e-02 -4.64855880e-01 9.18640554e-01
-9.37344015e-01 3.68466258e-01 7.57601857e-01 1.19436812e+00
-3.72713298e-01 -1.24305260e+00 -6.58356369e-01 2.98711509e-01
-5.37547648e-01 -6.16444170e-01 -5.00669837e-01 6.96399808e-01
-9.08589363e-02 1.10873806e+00 -1.88471705e-01 -1.58856004e-01
7.79838040e-02 5.47836185e-01 8.26376379e-01 -8.63756597e-01
-9.82474744e-01 -7.41731584e-01 2.94818401e-01 -5.63435256e-01
1.15244210e-01 -3.98121923e-01 -1.34181738e+00 -1.04347491e+00
9.31898952e-02 1.10907227e-01 3.24308753e-01 1.15752888e+00
1.20494805e-01 3.77136260e-01 -2.79650480e-01 -6.64025903e-01
-1.04160690e+00 -1.04049885e+00 2.64828920e-01 2.70202249e-01
-2.37034470e-01 -3.15867126e-01 -1.60444267e-02 -1.41513392e-01] | [9.647418975830078, 7.350124835968018] |
615c7a06-943d-40c4-925b-d0f4d13cd4ad | multi-scale-fusion-methodologies-for-head-and | 2210.16704 | null | https://arxiv.org/abs/2210.16704v1 | https://arxiv.org/pdf/2210.16704v1.pdf | Multi-Scale Fusion Methodologies for Head and Neck Tumor Segmentation | Head and Neck (H\&N) organ-at-risk (OAR) and tumor segmentations are essential components of radiation therapy planning. The varying anatomic locations and dimensions of H\&N nodal Gross Tumor Volumes (GTVn) and H\&N primary gross tumor volume (GTVp) are difficult to obtain due to lack of accurate and reliable delineation methods. The downstream effect of incorrect segmentation can result in unnecessary irradiation of normal organs. Towards a fully automated radiation therapy planning algorithm, we explore the efficacy of multi-scale fusion based deep learning architectures for accurately segmenting H\&N tumors from medical scans. | ['Ulas Bagci', 'Mohamed E. Abazeed', 'Bulent Aydogan', 'Debesh Jha', 'Abhishek Srivastava'] | 2022-10-29 | null | null | null | null | ['tumor-segmentation'] | ['computer-vision'] | [ 7.50696361e-02 5.18780947e-01 -5.28195143e-01 -9.20959562e-02
-1.39077234e+00 -6.49550080e-01 2.68124074e-01 4.52938527e-01
-2.36731097e-01 6.32088482e-01 5.87410271e-01 -9.15568590e-01
-1.72978923e-01 -6.96334481e-01 -1.18126139e-01 -8.69421840e-01
2.57218838e-01 7.27652192e-01 -1.26410872e-01 -1.97009653e-01
-1.87021434e-01 1.04130280e+00 -4.70105648e-01 4.91365679e-02
4.50891256e-01 1.16597497e+00 4.42890733e-01 8.29062879e-01
-4.06873316e-01 4.68852729e-01 -3.73913020e-01 5.82131483e-02
2.24335015e-01 -4.99357581e-01 -1.08112156e+00 -4.07409668e-02
4.80856836e-01 -2.92557597e-01 -5.25500596e-01 8.83890331e-01
4.57007170e-01 -2.84371644e-01 9.67076242e-01 -9.10725415e-01
-2.03580856e-01 3.08451325e-01 -3.00821990e-01 3.40908438e-01
-2.61815637e-01 2.78578997e-01 6.24340653e-01 -5.37149489e-01
5.23514152e-01 3.15409631e-01 1.05519783e+00 6.59088731e-01
-8.64390075e-01 -3.37098807e-01 -2.17435598e-01 -7.34142661e-01
-1.36170828e+00 -8.98850933e-02 1.14306785e-01 -3.59497726e-01
7.98734903e-01 5.38757205e-01 7.74318039e-01 6.16983354e-01
1.21152020e+00 4.23208147e-01 5.14093161e-01 -2.13145182e-01
2.67130882e-01 -3.74596208e-01 2.20391736e-03 6.85775280e-01
1.64286569e-01 1.24686427e-01 2.85250098e-01 -9.22029987e-02
9.91693139e-01 3.58543754e-01 -4.21740443e-01 6.62817284e-02
-8.29157293e-01 7.88012743e-01 1.41145813e+00 6.67431653e-01
-4.01119441e-01 4.19656217e-01 4.59146887e-01 -1.70670912e-01
3.37042630e-01 1.46629456e-02 -9.00162235e-02 3.04924965e-01
-9.53948677e-01 2.60433070e-02 2.84284383e-01 8.21199834e-01
-1.05001867e-01 -8.95627216e-02 -4.16215062e-01 5.30158460e-01
4.97734308e-01 3.09536070e-01 9.50133085e-01 -5.42870581e-01
1.73183560e-01 6.53950810e-01 -1.40634179e-01 8.13072845e-02
-1.38303947e+00 -7.75223136e-01 -1.16670525e+00 5.17738909e-02
7.04482496e-01 4.67441604e-02 -1.85111296e+00 1.37095296e+00
4.25606728e-01 -6.74363136e-01 -1.74344763e-01 7.69908726e-01
1.14138162e+00 -3.34890676e-03 6.97536647e-01 -2.22707197e-01
1.50782204e+00 -8.08413625e-01 -4.78886336e-01 -4.95895028e-01
1.53865778e+00 -7.30099380e-01 4.03009504e-01 -4.65170622e-01
-1.28712070e+00 1.14418432e-01 -3.57284606e-01 -2.07378000e-01
-1.78360805e-01 4.23569381e-02 7.98206806e-01 9.14346814e-01
-1.36441743e+00 3.71295542e-01 -9.54658568e-01 -3.25330555e-01
9.75399852e-01 8.86497080e-01 -5.78722477e-01 4.37001735e-02
-6.82862997e-01 1.29097474e+00 3.63140494e-01 2.17002153e-01
-1.17465544e+00 -1.23447037e+00 -7.12614834e-01 -3.76743414e-02
1.19441822e-01 -8.68991137e-01 1.67347491e+00 -3.66439521e-01
-9.53901231e-01 9.19380009e-01 -1.61419809e-01 -1.72028422e-01
3.32455426e-01 5.87642789e-01 -6.84731454e-02 4.58240211e-02
9.54998378e-03 6.06680930e-01 3.09096605e-01 -1.04053855e+00
-5.60369909e-01 -9.40406978e-01 -7.72589386e-01 5.53675711e-01
2.14936599e-01 -2.08072960e-01 -7.40207285e-02 -5.52922487e-01
6.40848935e-01 -1.02023220e+00 -8.16063464e-01 3.07422698e-01
-4.47306305e-01 9.50698555e-02 8.24019790e-01 -8.63869965e-01
1.00283790e+00 -1.61662984e+00 -4.97180298e-02 5.28226197e-01
8.50221395e-01 1.61640704e-01 2.21797347e-01 -9.50550735e-02
-1.21239915e-01 3.90179247e-01 -4.32392843e-02 -1.45639807e-01
-4.24658000e-01 3.86851847e-01 4.26189929e-01 7.32734740e-01
-6.84916794e-01 1.52108836e+00 -5.27761400e-01 -7.21744418e-01
4.76435035e-01 3.13300967e-01 -1.75394014e-01 -6.51681796e-02
-1.73383713e-01 6.89445078e-01 -4.64792818e-01 9.77639735e-01
3.48026067e-01 -2.60704517e-01 5.66622056e-02 -1.25641078e-01
6.04166649e-02 1.59147859e-01 -1.73138723e-01 1.55722046e+00
-4.75111514e-01 4.49558556e-01 5.63380659e-01 -2.76949078e-01
3.79565209e-01 5.65503895e-01 1.19279253e+00 -8.86635303e-01
8.43185484e-01 4.89826292e-01 4.05061364e-01 -1.77855359e-03
3.05684626e-01 -7.60752976e-01 -1.84966251e-02 3.79133016e-01
-6.78209886e-02 -7.04687476e-01 -4.89793271e-01 -6.80534616e-02
1.35152650e+00 -5.68403244e-01 4.96051550e-01 -3.85109186e-01
1.12505965e-01 2.17467472e-01 3.51683557e-01 5.02910078e-01
-6.28466010e-01 8.25899839e-01 4.45288926e-01 -3.57299894e-01
-1.03553426e+00 -1.13427186e+00 -7.49242723e-01 7.45161653e-01
-2.51124799e-01 2.06606910e-01 -6.79640174e-01 -6.57979548e-01
7.80850416e-03 6.17904663e-01 -1.02666831e+00 1.18508078e-01
-6.94361150e-01 -8.53794396e-01 6.65422440e-01 8.99960041e-01
-9.60822403e-02 -8.63919139e-01 -4.51194286e-01 4.47962433e-01
-1.63059771e-01 -8.92783463e-01 -5.04364491e-01 7.03280926e-01
-1.19180179e+00 -1.25821960e+00 -1.35708845e+00 -7.11077690e-01
1.06099987e+00 6.45330772e-02 1.03126514e+00 2.87417382e-01
-5.91616392e-01 2.36386940e-01 1.26112059e-01 -7.17044950e-01
-1.06462300e+00 6.28456771e-02 -3.96093369e-01 -7.97552943e-01
8.43448192e-03 -3.16916853e-02 -8.06582928e-01 6.44907534e-01
-7.79329896e-01 2.30670482e-01 7.56033957e-01 5.90805173e-01
8.37332606e-01 -8.60215127e-02 8.95071849e-02 -7.46636093e-01
4.62041974e-01 -3.11857462e-01 -3.70559603e-01 3.13827693e-01
-2.82102376e-01 -4.15980667e-01 5.49991012e-01 1.39729097e-01
-6.79331481e-01 -1.42120957e-01 -4.39480871e-01 -1.50130972e-01
-1.70738891e-01 3.83789897e-01 3.89937639e-01 -6.24452233e-01
8.60743105e-01 -1.18138157e-01 7.16598034e-02 2.58287311e-01
-1.24604605e-01 4.62609559e-01 4.08357441e-01 6.02291711e-03
5.50692081e-01 5.49933076e-01 6.73129201e-01 -6.95855677e-01
-8.30274463e-01 -4.87108082e-01 -7.46669173e-01 -2.24927768e-01
9.74171638e-01 -4.19428974e-01 -6.46595895e-01 2.33476415e-01
-6.67298675e-01 -6.72755241e-01 -6.97689235e-01 4.82190698e-01
-5.73083341e-01 -1.94892697e-02 -7.10417509e-01 -3.84580463e-01
-7.95215607e-01 -1.54816413e+00 1.40221250e+00 4.58005279e-01
-5.80874562e-01 -1.20694029e+00 -9.19405147e-02 2.32547730e-01
8.42551529e-01 5.22430956e-01 9.89726484e-01 -6.71002090e-01
-2.87079722e-01 -6.38075471e-01 -2.07175642e-01 -2.71093726e-01
4.12793964e-01 2.29961099e-03 -6.66192234e-01 -1.80497214e-01
-8.09460506e-02 -1.08900569e-01 4.43127036e-01 1.26535559e+00
1.11717761e+00 2.37973705e-01 -1.00390649e+00 7.30135322e-01
1.47342718e+00 5.98971009e-01 5.09151220e-01 2.11603902e-02
8.49358022e-01 3.05859774e-01 -2.04253606e-02 8.40337723e-02
2.93191195e-01 4.00849462e-01 7.58539498e-01 -2.71304041e-01
-4.89351273e-01 -5.38753085e-02 -4.94165659e-01 1.69707775e-01
1.02899559e-01 -4.79381651e-01 -1.53613353e+00 7.35848367e-01
-9.18727458e-01 -2.83678800e-01 -3.10041219e-01 1.88519073e+00
4.63008732e-01 1.52790830e-01 -3.30651104e-01 2.35270895e-02
6.19913816e-01 7.78502822e-02 -7.14854240e-01 -3.16949397e-01
3.15542489e-01 2.63539642e-01 1.06563091e+00 5.33187568e-01
-8.57030332e-01 3.85169715e-01 7.03464603e+00 7.71050811e-01
-9.45888460e-01 2.49655381e-01 9.21704173e-01 -1.67759389e-01
-3.56141835e-01 -1.40630290e-01 -9.79201794e-01 4.78811422e-03
1.07471848e+00 1.21134363e-01 -2.36452296e-01 5.46347916e-01
3.81086394e-02 -4.42840934e-01 -1.00352561e+00 7.46024430e-01
-3.21931571e-01 -1.66707098e+00 -4.82476592e-01 5.97018123e-01
6.54350102e-01 4.53029066e-01 -3.97255179e-03 2.95109034e-01
4.82649446e-01 -1.42562461e+00 2.00175822e-01 1.60073653e-01
1.02967358e+00 -4.17236120e-01 9.50081289e-01 2.16420174e-01
-1.19688070e+00 -6.43953010e-02 -1.14780620e-01 7.44421780e-01
4.13959950e-01 -2.11528484e-02 -1.26415777e+00 4.73779708e-01
2.37058714e-01 -3.23640853e-02 -3.89406711e-01 9.53181684e-01
2.55280077e-01 1.50107190e-01 -7.19092131e-01 5.40170595e-02
4.94897366e-01 4.40418810e-01 1.61829144e-01 3.99889946e-01
3.89813334e-01 7.19969630e-01 -1.05092429e-01 5.20248890e-01
-3.98399949e-01 -4.50266600e-02 -1.29774302e-01 -3.75480130e-02
1.07940719e-01 1.38606381e+00 -1.31337750e+00 3.44697796e-02
-2.44692191e-01 3.67648631e-01 9.40701216e-02 -1.29823536e-02
-6.90108538e-01 5.28537929e-01 1.81064129e-01 5.05946577e-01
-2.59255022e-01 6.00968003e-02 -8.31623614e-01 -6.23904347e-01
-5.36165237e-01 -3.06282550e-01 7.93024063e-01 -5.97912073e-01
-9.39529061e-01 6.92697108e-01 -2.64168024e-01 -9.28231180e-01
5.91815729e-03 -6.08784735e-01 -9.19545233e-01 8.66431415e-01
-1.19901323e+00 -1.32692719e+00 -6.94309771e-01 3.81471604e-01
4.47679728e-01 1.50351569e-01 1.09855974e+00 -1.12959273e-01
-3.74285430e-01 8.94452333e-01 -3.73717770e-02 2.10483626e-01
3.28268185e-02 -1.30913365e+00 8.89762715e-02 1.77344158e-01
-6.38321042e-01 1.68925282e-02 3.51685077e-01 -8.25746536e-01
-1.38044119e+00 -1.20270181e+00 3.25360477e-01 -5.79237461e-01
4.43431139e-01 9.24162343e-02 -6.45345747e-01 1.05589771e+00
-2.54158154e-02 3.00715297e-01 1.06962073e+00 -4.07271475e-01
4.04336214e-01 3.10289741e-01 -1.66958952e+00 6.67578399e-01
5.56616724e-01 -2.05609292e-01 -4.45109084e-02 7.29284704e-01
3.74016762e-01 -1.14382410e+00 -1.58732808e+00 6.81888521e-01
3.02873552e-01 -6.18353188e-01 9.99986768e-01 -1.86529681e-01
5.30352741e-02 1.22704163e-01 -4.66383919e-02 -9.83745933e-01
-3.33836198e-01 -1.65480733e-01 4.08968806e-01 4.26137835e-01
5.02584994e-01 -6.79156303e-01 1.37728596e+00 1.52277327e+00
-7.72905827e-01 -9.71460521e-01 -1.31277943e+00 -3.05904955e-01
6.64546311e-01 -4.00638789e-01 4.69406456e-01 6.05907202e-01
-3.09496731e-01 -4.38041985e-01 6.04929507e-01 1.34240255e-01
5.41107297e-01 -2.93070734e-01 8.77187848e-02 -8.62764180e-01
2.09241942e-01 -1.18140709e+00 -4.33181226e-01 -3.48142922e-01
-3.49364936e-01 -1.11461926e+00 -2.18467534e-01 -2.15033746e+00
8.86796936e-02 -5.93097687e-01 -3.67332697e-01 6.46860063e-01
1.26860708e-01 2.11663261e-01 -7.76491016e-02 3.48703653e-01
4.17768881e-02 6.09328508e-01 1.81846845e+00 1.20622896e-01
-2.00125441e-01 2.38110572e-01 -5.20779371e-01 9.38317657e-01
6.46756828e-01 -4.83599514e-01 -5.37942946e-02 -1.66040257e-01
-3.99253935e-01 9.26416159e-01 3.20983171e-01 -6.96761906e-01
2.57459372e-01 -2.62720376e-01 9.00806308e-01 -9.89636064e-01
2.65800297e-01 -1.12324715e+00 1.65204555e-01 7.92183757e-01
-1.56336486e-01 -2.28917971e-03 4.96448368e-01 1.41839385e-01
-1.14830561e-01 -2.25212395e-01 1.10412812e+00 -5.06222367e-01
3.08839399e-02 8.25893998e-01 -5.18210709e-01 -3.10452223e-01
1.49440658e+00 -6.39131606e-01 -4.27298486e-01 -2.73918033e-01
-1.07534158e+00 2.97914028e-01 3.68201673e-01 1.62669048e-01
5.82394719e-01 -1.29574776e+00 -5.93543828e-01 -1.05345987e-01
-3.86675708e-02 6.24382317e-01 7.40984917e-01 1.20345867e+00
-1.02634144e+00 8.25112879e-01 -1.79335196e-02 -5.29802859e-01
-1.04213905e+00 2.01775149e-01 1.22032440e+00 -7.79608011e-01
-7.35356450e-01 1.25953686e+00 5.75918317e-01 -4.80890781e-01
1.48761153e-01 -5.80807328e-01 -3.55294496e-02 -3.46206337e-01
2.07986087e-01 3.08788475e-03 2.68649668e-01 -8.52413774e-01
-2.06078723e-01 3.32063437e-01 -4.30739760e-01 1.56263649e-01
8.90752971e-01 -5.97691424e-02 6.35379553e-02 -3.34629864e-01
1.31758106e+00 -4.64675367e-01 -9.54104960e-01 -1.63826793e-01
-2.93192059e-01 -1.94240510e-01 6.65381968e-01 -8.03294241e-01
-1.16279888e+00 4.65285093e-01 9.15497780e-01 -8.25144649e-02
1.25535643e+00 3.38954926e-01 1.16540432e+00 -4.74368513e-01
1.44062594e-01 -6.74134851e-01 -4.03284222e-01 8.30186605e-02
8.01837742e-01 -1.40820110e+00 1.05372787e-01 -4.88538802e-01
-3.68666232e-01 1.34593892e+00 6.35984540e-01 1.93385497e-01
7.57689893e-01 4.36182171e-01 3.90628546e-01 -6.84956968e-01
-2.60345399e-01 -1.78450003e-01 2.76867092e-01 1.91609964e-01
6.44250691e-01 3.32894266e-01 7.32670724e-02 2.87054032e-01
-4.82434213e-01 -8.36738944e-02 2.97328204e-01 1.16961932e+00
-5.33265829e-01 -9.98813808e-01 -4.97032464e-01 9.08298135e-01
-6.19351506e-01 1.05531983e-01 -3.26472491e-01 1.23720539e+00
-2.62804031e-01 3.23379487e-01 -2.13909209e-01 2.06905216e-01
2.73007303e-01 -1.80329368e-01 5.50220668e-01 -5.90010762e-01
-1.16553354e+00 3.43125641e-01 -1.57825083e-01 -3.62083852e-01
2.22177431e-01 -5.07007897e-01 -1.70005548e+00 -2.22201258e-01
-4.09496158e-01 -1.99446946e-01 1.15475249e+00 1.23246062e+00
-3.34081948e-01 9.15417075e-01 4.78680909e-01 -6.88768446e-01
-2.75404811e-01 -8.33453596e-01 -6.57140076e-01 -7.77796209e-02
2.37871245e-01 -3.61857712e-01 -2.49302775e-01 -6.51238263e-01] | [14.709993362426758, -2.5164854526519775] |
5d00fbb1-6bb1-4500-822a-9f7ea32a337c | ensemble-modeling-with-contrastive-knowledge | 2304.14668 | null | https://arxiv.org/abs/2304.14668v3 | https://arxiv.org/pdf/2304.14668v3.pdf | Ensemble Modeling with Contrastive Knowledge Distillation for Sequential Recommendation | Sequential recommendation aims to capture users' dynamic interest and predicts the next item of users' preference. Most sequential recommendation methods use a deep neural network as sequence encoder to generate user and item representations. Existing works mainly center upon designing a stronger sequence encoder. However, few attempts have been made with training an ensemble of networks as sequence encoders, which is more powerful than a single network because an ensemble of parallel networks can yield diverse prediction results and hence better accuracy. In this paper, we present Ensemble Modeling with contrastive Knowledge Distillation for sequential recommendation (EMKD). Our framework adopts multiple parallel networks as an ensemble of sequence encoders and recommends items based on the output distributions of all these networks. To facilitate knowledge transfer between parallel networks, we propose a novel contrastive knowledge distillation approach, which performs knowledge transfer from the representation level via Intra-network Contrastive Learning (ICL) and Cross-network Contrastive Learning (CCL), as well as Knowledge Distillation (KD) from the logits level via minimizing the Kullback-Leibler divergence between the output distributions of the teacher network and the student network. To leverage contextual information, we train the primary masked item prediction task alongside the auxiliary attribute prediction task as a multi-task learning scheme. Extensive experiments on public benchmark datasets show that EMKD achieves a significant improvement compared with the state-of-the-art methods. Besides, we demonstrate that our ensemble method is a generalized approach that can also improve the performance of other sequential recommenders. Our code is available at this link: https://github.com/hw-du/EMKD. | ['Victor S. Sheng', 'Lei Zhao', 'Guanfeng Liu', 'Fuzhen Zhuang', 'Pengpeng Zhao', 'Huanhuan Yuan', 'Hanwen Du'] | 2023-04-28 | null | null | null | null | ['sequential-recommendation'] | ['miscellaneous'] | [ 2.71439433e-01 -3.13579470e-01 -4.72202629e-01 -4.19576317e-01
-3.91058892e-01 -7.21013188e-01 4.86769706e-01 -4.36006933e-01
-3.01540166e-01 7.91891336e-01 3.53805542e-01 -5.49038589e-01
-5.34927845e-02 -8.10002327e-01 -9.72214043e-01 -6.03661120e-01
1.06034525e-01 3.15533936e-01 -7.14828521e-02 -2.41869926e-01
3.45439352e-02 9.76067409e-02 -1.41127670e+00 7.14900732e-01
9.45270658e-01 1.13006365e+00 2.88902313e-01 4.98164922e-01
-5.31347282e-02 9.05890405e-01 -2.16454357e-01 -6.61541343e-01
3.72371167e-01 -5.58679402e-01 -7.18312085e-01 -5.26462078e-01
2.09432036e-01 -5.75184047e-01 -4.11287755e-01 7.37512112e-01
7.39048958e-01 5.46625018e-01 7.19711542e-01 -1.10649741e+00
-1.36148190e+00 1.19615686e+00 -2.35767126e-01 3.44908386e-02
1.51993722e-01 3.76766287e-02 1.27959692e+00 -1.03657925e+00
9.53642353e-02 9.61595774e-01 7.03043938e-01 6.26030445e-01
-1.28010798e+00 -1.10527527e+00 5.36693394e-01 4.00937647e-01
-1.24244928e+00 -2.67641723e-01 6.58892393e-01 -2.97416389e-01
1.09169412e+00 -4.43069683e-03 4.03334379e-01 1.59015870e+00
-1.05442956e-01 1.19433594e+00 7.55145133e-01 -1.99110255e-01
-1.06827609e-01 3.76848578e-01 1.08170733e-01 5.44133365e-01
-3.47849801e-02 4.74542439e-01 -4.70248044e-01 -1.87086493e-01
8.14968050e-01 5.85270405e-01 -3.14079165e-01 -1.31605238e-01
-1.12871504e+00 8.65196645e-01 5.17346144e-01 1.98144868e-01
-4.98640448e-01 1.55149242e-02 3.14822912e-01 6.25855267e-01
4.85929459e-01 3.07748556e-01 -9.23760176e-01 1.00935228e-01
-7.29530275e-01 2.01773241e-01 9.40822423e-01 8.42634022e-01
5.03616214e-01 2.37548694e-01 -4.88596797e-01 9.93245304e-01
3.20950568e-01 4.03805643e-01 7.52007723e-01 -5.38960993e-01
3.66431832e-01 2.47588232e-01 2.63551380e-02 -6.66001737e-01
-4.47926745e-02 -8.15238357e-01 -1.00645161e+00 -2.45500922e-01
2.06392869e-01 -5.67863345e-01 -6.80637300e-01 1.75018179e+00
5.63447773e-02 8.57625842e-01 1.42150894e-01 8.33747268e-01
7.09860265e-01 7.23300755e-01 1.03941187e-01 -4.90935706e-02
1.00014222e+00 -1.30152225e+00 -4.12068993e-01 1.18993439e-01
6.14291966e-01 -5.16339123e-01 9.89292502e-01 4.98688340e-01
-1.01912749e+00 -9.64095771e-01 -9.65932250e-01 1.34065107e-01
-2.75677681e-01 3.03154349e-01 5.34488201e-01 3.81895244e-01
-9.97764289e-01 7.88545012e-01 -5.25078237e-01 1.69269428e-01
4.05882686e-01 5.74212551e-01 2.89695375e-02 6.13483824e-02
-1.59464693e+00 6.85363650e-01 5.18888056e-01 -2.70061959e-02
-6.10562801e-01 -1.08456266e+00 -5.75715661e-01 2.71535605e-01
1.96228445e-01 -1.04440200e+00 1.39873958e+00 -1.47208405e+00
-2.03093648e+00 9.69262049e-02 9.46711078e-02 -5.85039556e-01
2.57915586e-01 -4.02190506e-01 -6.70994341e-01 -4.75788921e-01
-3.84304643e-01 5.09010613e-01 6.97564065e-01 -9.97935236e-01
-8.55805814e-01 -7.61578009e-02 2.35505868e-02 3.46121669e-01
-6.09508038e-01 -9.66978818e-02 -2.41420433e-01 -1.01618457e+00
-6.51497304e-01 -9.43141699e-01 -2.54508317e-01 -4.03725803e-01
-2.71918595e-01 -4.33045894e-01 5.13667822e-01 -7.17326760e-01
1.57315445e+00 -2.20603371e+00 2.09269956e-01 3.18325281e-01
-3.79184224e-02 6.22535706e-01 -5.32433927e-01 3.77917320e-01
-1.36737213e-01 -3.82431149e-02 5.67806847e-02 -2.85486609e-01
1.03557527e-01 1.06359221e-01 -4.49245960e-01 2.77907662e-02
-3.67034450e-02 1.14184391e+00 -9.45415020e-01 1.57723576e-01
1.46337925e-02 8.44582915e-01 -7.99939096e-01 3.76176119e-01
-3.52926433e-01 4.19347495e-01 -3.32957923e-01 2.01062620e-01
4.19712305e-01 -6.44234359e-01 4.55706835e-01 -2.65240788e-01
1.61641285e-01 7.30328798e-01 -1.16623962e+00 1.68455303e+00
-6.13374352e-01 -7.11996853e-02 -5.65588355e-01 -1.03198588e+00
9.77153122e-01 4.95364964e-01 3.78520846e-01 -7.65956879e-01
-7.97167197e-02 1.46936491e-01 5.96795790e-02 -1.89021528e-01
4.57691848e-01 -1.33349672e-01 1.52019784e-01 7.98802853e-01
2.91616797e-01 7.49300838e-01 -2.14269444e-01 7.97798187e-02
7.86667943e-01 4.11363214e-01 2.04722524e-01 1.26123399e-01
5.53497195e-01 -5.93900442e-01 5.70248842e-01 9.30093527e-01
3.05256546e-01 2.28238553e-01 -1.37179177e-02 -4.83463794e-01
-9.50089455e-01 -9.64366257e-01 1.82505026e-01 1.57805860e+00
-1.29003063e-01 -2.80092537e-01 -2.61376381e-01 -1.12685204e+00
3.36880028e-01 1.00717986e+00 -5.40295184e-01 -3.92854244e-01
-4.00050312e-01 -6.32410347e-01 4.78767872e-01 9.20801878e-01
3.75009030e-01 -1.25185704e+00 2.47848734e-01 3.81487578e-01
-7.63613805e-02 -6.68805003e-01 -8.33149731e-01 2.88211048e-01
-7.13032067e-01 -5.74531138e-01 -6.40980005e-01 -7.52231002e-01
3.65506023e-01 2.80765265e-01 1.13076758e+00 -2.87803053e-03
3.70975435e-01 1.68964460e-01 -5.82376182e-01 -1.72406450e-01
-1.95621416e-01 3.41780752e-01 3.55106086e-01 2.58308470e-01
6.56988144e-01 -8.47517014e-01 -8.19857776e-01 3.71705681e-01
-7.84734547e-01 1.47789031e-01 7.42255807e-01 9.71317053e-01
5.39229572e-01 -1.81469589e-01 8.93639147e-01 -1.26341438e+00
7.10886598e-01 -1.00841451e+00 -3.24131101e-01 3.02525997e-01
-9.47661877e-01 2.00421378e-01 1.13532591e+00 -6.87611997e-01
-1.16785300e+00 -8.76029208e-02 -3.19462478e-01 -4.50145990e-01
-2.18272775e-01 6.40710175e-01 1.23125523e-01 2.85950989e-01
5.54489434e-01 5.15577912e-01 -7.01906830e-02 -7.96448946e-01
6.58206642e-01 8.83911669e-01 1.76254302e-01 -4.97873634e-01
5.05408704e-01 -1.07919373e-01 -6.10636294e-01 -1.23260543e-01
-1.18021142e+00 -3.76107574e-01 -5.49313843e-01 9.57366601e-02
3.07319194e-01 -1.08050644e+00 -7.98258066e-01 3.28574657e-01
-8.19190979e-01 -6.63035512e-01 -1.84797689e-01 6.29407823e-01
-3.60691279e-01 4.25657332e-02 -7.51796782e-01 -4.55367029e-01
-7.59289742e-01 -9.60244894e-01 5.07969916e-01 1.11451223e-01
-1.74601778e-01 -1.18296587e+00 1.86324805e-01 2.22406417e-01
6.96232378e-01 -4.97284681e-01 8.12873960e-01 -1.16755331e+00
-2.67086476e-01 5.73094487e-02 -1.93876982e-01 7.59612024e-01
1.94842145e-01 -3.59940857e-01 -7.51932025e-01 -4.29510295e-01
-4.62083012e-01 -3.64812851e-01 9.25757110e-01 3.37284416e-01
1.58603799e+00 -7.62088537e-01 -2.83524722e-01 7.48349130e-01
1.40564036e+00 3.62123609e-01 5.50954878e-01 2.22963288e-01
8.82658780e-01 1.17520034e-01 1.65554523e-01 5.29319763e-01
5.55160642e-01 7.18220651e-01 6.16189875e-02 2.63045490e-01
-1.57948315e-01 -5.42942226e-01 7.07849205e-01 1.00706446e+00
-2.65584767e-01 -5.22762418e-01 -4.59233850e-01 2.56474167e-01
-2.14240265e+00 -1.28199506e+00 3.52153838e-01 2.19989228e+00
1.09028888e+00 -2.67963618e-01 3.89321417e-01 -2.82172292e-01
4.86344814e-01 -1.72933131e-01 -7.69478142e-01 -3.08371037e-01
9.38134491e-02 2.66301632e-01 3.68441343e-01 3.81950915e-01
-1.01873469e+00 8.21888506e-01 5.50154781e+00 8.91535103e-01
-1.23006678e+00 1.89996257e-01 5.53339362e-01 -3.60343665e-01
-5.50553918e-01 -3.75810802e-01 -1.11258483e+00 9.67668533e-01
1.26790953e+00 1.12545222e-01 6.69656098e-01 7.62496889e-01
2.59439033e-02 6.33978546e-01 -1.13986051e+00 8.16079021e-01
5.47636859e-02 -1.35740924e+00 3.20639133e-01 2.54179165e-02
1.02384579e+00 2.51800209e-01 4.53943342e-01 7.54406333e-01
8.59590769e-01 -9.69592512e-01 3.60574603e-01 8.58702898e-01
6.66396558e-01 -8.51507246e-01 7.88546562e-01 4.01583523e-01
-1.09252644e+00 -3.91766548e-01 -3.28592718e-01 -7.36539662e-02
1.22195527e-01 4.90081370e-01 -7.27438450e-01 7.02383161e-01
6.27730668e-01 1.20140433e+00 -1.15467951e-01 8.72834802e-01
-2.67373651e-01 1.01947975e+00 3.18738371e-02 -1.94196284e-01
1.06478743e-01 -2.88792729e-01 1.23965949e-01 1.41851902e+00
4.23154056e-01 1.87843949e-01 2.99863786e-01 5.21659970e-01
-4.40230817e-01 1.96661770e-01 -3.42927843e-01 1.44969160e-03
5.00263393e-01 1.10661280e+00 6.32688776e-03 -4.35500860e-01
-7.92897403e-01 1.25080168e+00 7.10129082e-01 5.35386443e-01
-7.90808260e-01 -2.12267548e-01 7.70099938e-01 -1.64592877e-01
9.16600168e-01 7.84580484e-02 -9.79099572e-02 -1.27188814e+00
-2.46627256e-01 -1.13608003e+00 5.39250553e-01 -4.36633438e-01
-1.87080014e+00 4.65606272e-01 -3.25502932e-01 -1.29236722e+00
-3.53258252e-01 -5.86996138e-01 -4.71049875e-01 1.03380513e+00
-1.63894606e+00 -1.11599517e+00 1.06308214e-01 7.75753081e-01
4.35650587e-01 -4.77265775e-01 9.10575092e-01 5.98181427e-01
-5.69168210e-01 1.17849350e+00 5.16260684e-01 2.26092547e-01
7.79410303e-01 -1.17569649e+00 2.07832560e-01 4.41152811e-01
2.14003369e-01 7.79511213e-01 1.11197010e-01 -4.66894776e-01
-1.23994803e+00 -1.46896052e+00 7.49345303e-01 -4.56123710e-01
6.50117278e-01 -1.07492648e-01 -9.77446973e-01 1.03105891e+00
2.16623411e-01 -7.16895238e-02 1.26621950e+00 4.13092166e-01
-6.09019697e-01 -1.99758112e-01 -6.18304133e-01 4.59604234e-01
1.12411511e+00 -5.07682085e-01 -3.89328659e-01 1.02270961e-01
8.89067769e-01 -8.03753808e-02 -1.13180447e+00 4.19350803e-01
9.85812843e-01 -7.42696822e-01 1.08977091e+00 -1.01505303e+00
6.16508067e-01 -1.46339282e-01 -1.80301398e-01 -1.65066612e+00
-9.63826239e-01 -3.53100449e-01 -7.74365008e-01 1.17614186e+00
7.75238693e-01 -6.69009507e-01 6.44122839e-01 2.33198002e-01
-1.17093034e-01 -1.01628029e+00 -1.80003837e-01 -7.42697120e-01
1.29583359e-01 -3.33815128e-01 9.16401923e-01 1.04993808e+00
-1.69613864e-02 6.89769804e-01 -8.41598749e-01 1.34224037e-03
2.93990016e-01 3.14556628e-01 5.07018209e-01 -1.15182805e+00
-8.31698060e-01 -5.36708295e-01 1.26597464e-01 -1.53266728e+00
3.60910296e-01 -1.33792436e+00 -1.95169359e-01 -1.32962060e+00
2.76241153e-01 -4.91813451e-01 -1.11411476e+00 7.79753268e-01
-3.29332620e-01 1.57065168e-01 4.68001254e-02 1.06950901e-01
-6.96504831e-01 5.98817110e-01 1.22299993e+00 1.10247105e-01
-3.06987852e-01 3.75780970e-01 -1.07615745e+00 3.17054361e-01
9.85992312e-01 -3.50279540e-01 -6.12709045e-01 -4.67806339e-01
3.50455225e-01 -1.09940477e-01 1.20632887e-01 -5.95957518e-01
2.92538881e-01 1.22499261e-02 6.36864781e-01 -4.12822753e-01
8.05797204e-02 -7.47079253e-01 2.31980890e-01 3.40989232e-01
-7.31158793e-01 -1.02823563e-02 3.67416963e-02 6.82802200e-01
6.73040003e-03 -1.31294476e-02 4.30826187e-01 -1.53709948e-01
-6.92360580e-01 8.13208640e-01 -1.10271439e-01 -2.46558294e-01
6.19767189e-01 6.58876672e-02 -1.88428417e-01 -3.06532949e-01
-7.81758904e-01 2.48466462e-01 -3.99380252e-02 7.09349692e-01
7.01570272e-01 -1.51463354e+00 -8.71035457e-01 3.21280479e-01
-3.38140801e-02 -4.63297576e-01 3.99909616e-01 7.32433915e-01
2.09197313e-01 4.54025596e-01 -1.36213884e-01 -7.64373317e-02
-9.19473410e-01 7.09102273e-01 3.27394634e-01 -4.44875300e-01
-3.85515690e-01 1.12900710e+00 1.16326183e-01 -8.81827116e-01
3.44287992e-01 8.47685188e-02 -4.78638470e-01 6.65488467e-02
7.68079102e-01 3.00714999e-01 -7.26139471e-02 -2.92737931e-01
-5.33007942e-02 1.11476704e-01 -6.62160516e-01 2.81776935e-01
1.46361625e+00 -8.12925622e-02 1.59760922e-01 2.45858133e-01
1.28936970e+00 -3.81863445e-01 -1.36162615e+00 -9.02379692e-01
-3.27888966e-01 -1.99993864e-01 1.41722202e-01 -1.21949160e+00
-1.16468394e+00 5.84262133e-01 4.31681067e-01 5.83682433e-02
1.17253804e+00 -3.00474077e-01 9.84746397e-01 3.70350182e-01
1.43252596e-01 -8.17312956e-01 1.05701365e-01 7.16081500e-01
5.97183645e-01 -1.18518651e+00 -3.29546571e-01 1.09136298e-01
-8.30492079e-01 9.34006989e-01 7.92289734e-01 -2.51672417e-01
1.08993542e+00 1.87444523e-01 -1.19514607e-01 2.71315664e-01
-1.11025822e+00 -1.16043575e-01 6.54159963e-01 2.97426194e-01
8.04914773e-01 2.17684656e-01 -7.73787573e-02 1.29194534e+00
-8.07017535e-02 3.76946419e-01 -3.13648432e-02 5.06281257e-01
-2.38226667e-01 -1.36467910e+00 2.60679305e-01 8.96879733e-01
-4.93657410e-01 -6.06588542e-01 1.83521714e-02 1.52860954e-01
1.97509587e-01 7.28951812e-01 3.75326239e-02 -9.11089301e-01
2.22502619e-01 2.74610221e-01 3.47002417e-01 -7.25698769e-01
-9.10236180e-01 -2.70952910e-01 1.84026100e-02 -4.27125186e-01
-3.47624362e-01 -5.85159540e-01 -8.65523517e-01 -3.89008224e-01
-3.03499490e-01 3.15753222e-01 2.92247087e-01 1.06070471e+00
7.64566123e-01 6.27931595e-01 8.24727297e-01 -7.25180447e-01
-9.44210827e-01 -9.87563133e-01 -5.48265874e-01 2.96679676e-01
3.27958882e-01 -4.82361764e-01 -6.88291416e-02 4.78033014e-02] | [10.143059730529785, 5.616687297821045] |
0f6893e7-fef0-4405-9159-99e2a239b6ab | smart-metro-deep-learning-approaches-to | 2304.07303 | null | https://arxiv.org/abs/2304.07303v1 | https://arxiv.org/pdf/2304.07303v1.pdf | Smart Metro: Deep Learning Approaches to Forecasting the MRT Line 3 Ridership | Since its establishment in 1999, the Metro Rail Transit Line 3 (MRT3) has served as a transportation option for numerous passengers in Metro Manila, Philippines. The Philippine government's transportation department records more than a thousand people using the MRT3 daily and forecasting the daily passenger count may be rather challenging. The MRT3's daily ridership fluctuates owing to variables such as holidays, working days, and other unexpected issues. Commuters do not know how many other commuters are on their route on a given day, which may hinder their ability to plan an efficient itinerary. Currently, the DOTr depends on spreadsheets containing historical data, which might be challenging to examine. This study presents a time series prediction of daily traffic to anticipate future attendance at a particular station on specific days. | ['Gabriel Avelino Sampedro', 'Shekinah Lor Huyo-a', 'Mideth Abisado', 'Mary Grace Verzon', 'Jean Allyson Junsay', 'Jayrald Empino'] | 2023-04-14 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [-8.12340558e-01 -5.58595181e-01 -5.36139786e-01 -3.62670600e-01
-4.04724509e-01 -4.05864209e-01 2.40275830e-01 4.09146786e-01
-4.70675796e-01 1.12243462e+00 1.70991391e-01 -9.17470217e-01
-2.17090517e-01 -1.38232410e+00 -2.63874680e-01 -5.08755624e-01
2.03827806e-02 5.92379034e-01 1.10938564e-01 -5.48744261e-01
2.93806672e-01 8.73937249e-01 -1.20035517e+00 -4.08913761e-01
8.85343671e-01 6.31510913e-01 2.07663745e-01 2.93358654e-01
-1.58546254e-01 2.33346507e-01 -3.87079805e-01 -2.52578080e-01
2.63009489e-01 4.08589914e-02 -3.86655897e-01 8.46213400e-02
-2.69098967e-01 -4.88834530e-01 -6.72922492e-01 3.24408770e-01
-7.41611794e-02 7.07448423e-01 5.85620940e-01 -1.70794606e+00
1.02704220e-01 2.92246282e-01 -4.24295694e-01 5.87721825e-01
3.91235888e-01 3.37994516e-01 8.53529394e-01 -5.53197980e-01
1.17267326e-01 6.36469424e-01 5.86049318e-01 -1.41732544e-01
-1.14924502e+00 -7.15676963e-01 3.81804407e-02 1.41460463e-01
-1.83027601e+00 -1.17148519e-01 3.95770222e-01 -3.18548918e-01
9.46729183e-01 3.92091304e-01 7.04989851e-01 4.00396347e-01
3.98108512e-01 4.16969329e-01 6.77792847e-01 -8.97633955e-02
-9.00355577e-02 6.87692314e-02 1.04260601e-01 1.66270763e-01
3.20929378e-01 -9.08859223e-02 -9.70343649e-02 -3.23530361e-02
5.29892147e-01 5.23677230e-01 1.12273663e-01 5.40622771e-01
-1.12077117e+00 8.65471125e-01 5.09326041e-01 4.68285680e-01
-8.01601708e-01 -2.43414313e-01 1.88425541e-01 3.55305582e-01
2.44007707e-01 -3.20520438e-02 -5.01849711e-01 -7.64121890e-01
-8.30953956e-01 3.99970889e-01 7.39424229e-01 1.02233839e+00
9.92171705e-01 5.97828953e-03 3.53587568e-01 6.45401955e-01
-5.54088876e-02 6.99036956e-01 -1.23617798e-01 -6.46380603e-01
8.68225813e-01 4.45654064e-01 5.36012530e-01 -1.21597886e+00
-8.47190022e-01 -1.07246578e-01 -7.65005291e-01 -4.58730012e-01
1.15807807e+00 -6.44681990e-01 -3.98716569e-01 9.24155712e-01
-1.24354579e-01 -4.88281846e-02 -4.05745625e-01 7.87379265e-01
1.96161836e-01 9.86743212e-01 5.47775626e-02 -1.05917364e-01
8.59584212e-01 -5.46516180e-01 -7.54139483e-01 1.60511881e-01
8.96323144e-01 -7.11726427e-01 7.60789573e-01 1.47294953e-01
-1.11653090e+00 -3.32984149e-01 -1.59234464e-01 4.84730095e-01
-6.35549903e-01 -6.53687492e-02 8.62820566e-01 8.02938819e-01
-4.79842603e-01 3.94379050e-01 -1.15279937e+00 -4.94842798e-01
2.86084525e-02 3.92391831e-01 -2.26746261e-01 -1.09032281e-01
-1.17827487e+00 1.02651179e+00 9.21798870e-02 2.62449861e-01
-1.97070286e-01 -4.87413347e-01 -8.81319404e-01 2.44773462e-01
2.19272852e-01 -1.28469244e-01 9.15890098e-01 7.02328756e-02
-8.95455658e-01 7.10321441e-02 -5.04686892e-01 -1.53895631e-01
1.73405886e-01 6.40991271e-01 -1.27650070e+00 -4.70360875e-01
3.40569645e-01 3.67651209e-02 -1.58053577e-01 -4.75502461e-01
-1.36192584e+00 -2.62783766e-01 1.19562864e-01 9.74568725e-02
1.97593287e-01 -1.27014339e-01 -2.18185112e-01 -5.35420060e-01
1.53008342e-01 -1.14390755e+00 -2.88181514e-01 -8.71989250e-01
-2.67489582e-01 -5.86267710e-01 4.14881051e-01 -5.16603529e-01
1.85168946e+00 -1.82993984e+00 -8.65750253e-01 7.30093718e-01
-5.23271441e-01 -1.22105055e-01 4.52299118e-01 1.16698790e+00
2.37462550e-01 3.19721587e-02 1.91509798e-01 1.77277535e-01
1.39801532e-01 5.59741318e-01 -1.34830579e-01 5.63836038e-01
-1.17817253e-01 5.73474109e-01 -9.17443395e-01 -4.47115116e-02
5.80989897e-01 -3.77544668e-03 -2.04160020e-01 -3.23334366e-01
4.19469237e-01 5.61919212e-01 -2.97843307e-01 6.73916280e-01
9.05663967e-01 -4.15688232e-02 -5.42278960e-02 4.75786507e-01
-8.95120263e-01 8.36910367e-01 -1.08022881e+00 1.06425059e+00
-5.36845803e-01 9.98323023e-01 -4.52370524e-01 -7.97750592e-01
9.97834563e-01 4.42010432e-01 6.05563581e-01 -9.01512563e-01
-1.88810587e-01 3.91454279e-01 -3.41114141e-02 -5.48853099e-01
9.73519027e-01 -5.50562851e-02 -2.69771039e-01 7.04901218e-01
-7.92520702e-01 2.92608768e-01 6.97704077e-01 -9.06554610e-02
9.84356463e-01 -5.30182481e-01 -8.75411741e-03 -2.67957151e-01
2.32226133e-01 3.56565475e-01 6.47542417e-01 2.54202694e-01
-8.21599811e-02 1.87070414e-01 4.73118603e-01 -5.35803676e-01
-9.33346152e-01 -1.13020277e+00 -4.40650553e-01 8.57706964e-01
-2.73515992e-02 1.39268890e-01 -1.81350023e-01 -2.39241272e-01
2.60399640e-01 1.35572529e+00 -1.35250151e-01 5.76140761e-01
-5.53850591e-01 -6.66694462e-01 7.51897693e-02 2.44462341e-01
4.20484453e-01 -5.01684546e-01 -7.84071535e-02 6.19974673e-01
-6.51386261e-01 -7.71811068e-01 -9.00309324e-01 -2.10941136e-01
-5.77970147e-01 -7.53418744e-01 -6.90659285e-01 -4.91305530e-01
1.00011504e+00 9.49041903e-01 8.81792605e-01 2.94815034e-01
3.13714981e-01 1.26031131e-01 5.31312563e-02 -2.75653720e-01
-1.02445945e-01 4.91930783e-01 -3.15097012e-02 1.69223860e-01
1.04336560e+00 -5.38241804e-01 -6.82041347e-01 9.81747866e-01
-5.21320879e-01 -1.94768429e-01 -2.19114363e-01 3.61092746e-01
7.15178326e-02 7.84019530e-01 9.98849511e-01 -6.13022327e-01
3.50156963e-01 -1.18997550e+00 -7.51148760e-01 -7.97255486e-02
-5.42043746e-01 -6.07235491e-01 3.72932464e-01 6.84487373e-02
-1.00370157e+00 -3.74948621e-01 -3.49874318e-01 6.89295828e-01
-5.07703543e-01 7.96481252e-01 1.41677603e-01 3.84312719e-01
1.69180080e-01 1.70519918e-01 -1.88447326e-01 -2.97551900e-01
-5.43250978e-01 9.57019985e-01 1.36811927e-01 -2.05800101e-01
8.95025730e-01 4.39448446e-01 -7.77840912e-02 -1.03826344e+00
-3.95396411e-01 -9.45922673e-01 -5.97010016e-01 -4.87445593e-01
4.47313696e-01 -9.73576546e-01 -1.57590377e+00 2.77933031e-01
-8.43598008e-01 -2.85856307e-01 3.94626111e-01 1.11672401e+00
-1.33816674e-01 -2.11664811e-01 -5.34278929e-01 -9.34673667e-01
6.36033595e-01 -9.51832831e-01 9.45929363e-02 4.10509437e-01
-5.69872618e-01 -1.59033978e+00 -1.23721428e-01 3.46930981e-01
4.90556568e-01 2.56016076e-01 6.49050653e-01 -3.66275638e-01
-6.60833716e-01 -6.34364426e-01 -1.80838853e-01 -1.01864167e-01
6.78892553e-01 1.31649613e-01 -2.20500842e-01 -3.78184378e-01
-6.82310224e-01 5.21995127e-01 1.07343763e-01 6.37565911e-01
8.02621305e-01 -3.42846334e-01 -4.70498413e-01 -1.22840047e-01
1.39642215e+00 6.60078704e-01 7.21689999e-01 4.87546921e-01
1.31520241e-01 6.91924572e-01 8.77414763e-01 6.61964238e-01
1.19798040e+00 5.67870915e-01 1.90934867e-01 -3.72474156e-02
6.41360760e-01 -4.16856587e-01 1.88800190e-02 6.41355574e-01
-3.58565152e-01 -2.88785934e-01 -1.06197822e+00 1.02996480e+00
-1.44985271e+00 -1.23679507e+00 -7.18754172e-01 2.50378013e+00
2.85903871e-01 3.91330779e-01 6.75431430e-01 2.04888985e-01
6.54797018e-01 -3.40818077e-01 -1.91359520e-01 -5.37449837e-01
3.11825454e-01 -4.20615524e-01 1.17058146e+00 5.84715724e-01
-1.59553766e-01 2.54645407e-01 6.34668589e+00 5.54804444e-01
-9.24345970e-01 -1.11541204e-01 6.37481391e-01 1.17496096e-01
-5.39076209e-01 1.42909393e-01 -1.24186420e+00 9.19053078e-01
1.34566700e+00 -4.42308813e-01 5.91023862e-01 2.87805855e-01
1.26931214e+00 -7.02249765e-01 -5.34483552e-01 5.85126519e-01
-4.86491561e-01 -1.42369854e+00 -4.52202559e-01 6.29125059e-01
9.42310691e-01 -4.98834960e-02 -2.99181137e-02 4.92521703e-01
6.29799291e-02 -8.49417865e-01 9.45010632e-02 7.44567871e-01
2.55953103e-01 -1.56545186e+00 6.92627728e-01 7.16781020e-01
-1.25548148e+00 -1.76325459e-02 -8.18104520e-02 -4.86529231e-01
8.01232159e-01 5.38527846e-01 -1.07828116e+00 3.90996754e-01
4.83200908e-01 5.61073065e-01 -3.36311720e-02 1.09705043e+00
2.32852951e-01 8.74322891e-01 -6.23367071e-01 -1.40435055e-01
7.38636136e-01 -6.09939575e-01 3.55976582e-01 7.97571540e-01
7.57095516e-01 1.59546539e-01 -2.00334061e-02 5.40591717e-01
1.28606990e-01 -1.46839678e-01 -7.35976398e-01 2.63929129e-01
6.91115618e-01 8.14543068e-01 -8.26816261e-01 7.66427591e-02
-1.01240754e+00 1.77959949e-01 -2.04422966e-01 6.67849123e-01
-8.49897265e-01 -6.37710512e-01 7.94776142e-01 8.34104836e-01
-1.74373854e-02 -7.91354835e-01 -2.95168012e-01 -7.11037457e-01
-2.11608291e-01 -1.17449708e-01 2.79124349e-01 -5.19786596e-01
-1.02859139e+00 1.79244598e-04 1.16671622e-01 -1.26327074e+00
-4.23868448e-01 -1.91672996e-01 -8.68824422e-01 1.05740309e+00
-1.65449202e+00 -6.71620250e-01 2.01364756e-01 7.60364711e-01
4.76034403e-01 -2.14058459e-02 5.10915220e-01 7.72221446e-01
-6.31448507e-01 5.30168712e-01 3.16679090e-01 2.58258224e-01
2.91614860e-01 -1.02602363e+00 5.83215237e-01 3.63693058e-01
-3.36774349e-01 8.41416478e-01 7.38344908e-01 -6.24172032e-01
-1.06511617e+00 -8.85926366e-01 1.86331189e+00 -3.02194536e-01
9.04268980e-01 1.40606329e-01 -7.88006186e-01 1.11858237e+00
-6.49261400e-02 -6.45867646e-01 9.46698129e-01 4.41990614e-01
7.25659430e-01 -3.71126205e-01 -1.09024167e+00 7.69282520e-01
3.76971215e-01 -2.70310432e-01 -1.71162322e-01 5.35834491e-01
3.07462830e-02 -2.87028193e-01 -1.15498471e+00 -3.89994621e-01
4.38008487e-01 -7.90257633e-01 5.03812551e-01 -1.77405611e-01
-3.73707712e-01 -2.11948767e-01 5.57487644e-02 -1.44099915e+00
-8.20454583e-02 -9.84673023e-01 7.84461141e-01 1.19687545e+00
8.97044182e-01 -1.24128222e+00 8.05174649e-01 1.60554147e+00
-2.54072964e-01 -3.82718086e-01 -1.10780978e+00 -8.16602826e-01
-2.22635880e-01 -8.00202549e-01 1.40429270e+00 9.66968536e-01
9.97056365e-02 -1.28937647e-01 -4.93566662e-01 3.43701363e-01
3.49880368e-01 -5.89446202e-02 1.03169334e+00 -9.74438429e-01
3.93336028e-01 -3.66017818e-01 -1.25317350e-01 -1.46069288e+00
-1.55257538e-01 -7.38492846e-01 -4.47429359e-01 -1.58752728e+00
-3.01080197e-01 -9.22341049e-01 1.48366811e-02 2.54059672e-01
4.97706503e-01 -1.11217804e-01 -2.06054464e-01 -2.89734006e-02
2.65446976e-02 2.18784317e-01 1.31524658e+00 1.05708256e-01
-6.05727971e-01 1.10322762e+00 -3.36199462e-01 5.11440754e-01
9.69166756e-01 -4.34216022e-01 -4.60856482e-02 -1.90785617e-01
5.01580656e-01 7.75419474e-01 9.44518149e-02 -6.69821084e-01
5.75075984e-01 -7.97416389e-01 3.41825187e-02 -1.47622716e+00
3.32635105e-01 -1.38230288e+00 6.31122649e-01 2.98979521e-01
5.68184704e-02 6.08613551e-01 2.54822403e-01 3.27424496e-01
-2.17524096e-01 -6.24520145e-02 2.96314210e-01 1.21359475e-01
-3.04860830e-01 5.34268677e-01 -1.31594396e+00 -4.28778499e-01
9.64874804e-01 -6.31378531e-01 -1.05073608e-01 -6.71739280e-01
-7.01149821e-01 8.34789395e-01 4.66285795e-01 1.63020045e-01
3.12754810e-01 -1.59933424e+00 -5.50551295e-01 2.21468046e-01
-1.13221370e-01 -5.03642440e-01 6.19068563e-01 1.19591856e+00
-7.12559462e-01 9.76883173e-01 -3.04081261e-01 -2.46410608e-01
-6.44780219e-01 1.33992195e-01 -1.88479349e-02 -2.44121000e-01
-5.87548375e-01 -1.72006205e-01 -6.33789003e-01 -3.91583711e-01
-1.43232793e-01 -3.31548482e-01 -2.51015067e-01 2.64796913e-01
4.56814796e-01 1.13521779e+00 -4.30260040e-02 -8.87641609e-01
-1.25811100e-01 2.00066209e-01 1.91479057e-01 -1.46122361e-02
1.20920992e+00 -1.03393841e+00 8.14318582e-02 9.08313632e-01
1.03927362e+00 -1.89958781e-01 -1.26042938e+00 -7.84235820e-02
1.36171266e-01 -9.24068272e-01 -1.77482739e-02 -2.23311141e-01
-1.05367482e+00 4.45602417e-01 -1.62332043e-01 7.28614628e-01
8.81064117e-01 -4.66631681e-01 1.65974450e+00 3.11328918e-01
4.97352898e-01 -1.32790780e+00 -8.10981870e-01 5.87749958e-01
2.86426157e-01 -1.41302001e+00 -3.15929204e-01 4.82730977e-02
-5.26489854e-01 1.13501346e+00 -3.12556736e-02 2.86399573e-01
1.18407679e+00 -2.88180888e-01 -2.19149709e-01 1.31396696e-01
-6.21955097e-01 -3.18413764e-01 3.11367586e-02 4.21775520e-01
3.63173217e-01 5.51764965e-01 -5.39211810e-01 -1.61293317e-02
-4.29266959e-01 -5.19837998e-02 1.02357626e+00 7.26786137e-01
-4.74445283e-01 -9.08871055e-01 -3.92525464e-01 6.95387363e-01
-3.49523544e-01 2.11863473e-01 9.55368400e-01 1.17756927e+00
-7.94964805e-02 1.50958991e+00 7.03183413e-01 -7.52176717e-02
5.50489187e-01 -1.92815214e-01 -2.10460991e-01 -4.26388413e-01
-4.25695479e-01 -1.39175057e-01 3.87221187e-01 -8.80143940e-02
-2.75353521e-01 -1.23280954e+00 -1.51222837e+00 -1.67893624e+00
-2.64577657e-01 5.29134572e-01 8.01389992e-01 1.09806907e+00
-1.74543679e-01 -1.55133575e-01 1.36988223e+00 -6.81796193e-01
3.43703002e-01 -7.30221152e-01 -1.24140251e+00 2.18753237e-02
4.54686433e-01 -6.21122658e-01 -4.62444425e-01 -2.96656668e-01] | [6.20851469039917, 1.8472944498062134] |
92dc3913-f659-49de-af1f-5703a1fb564b | compressing-cross-lingual-multi-task-models | 2211.15927 | null | https://arxiv.org/abs/2211.15927v1 | https://arxiv.org/pdf/2211.15927v1.pdf | Compressing Cross-Lingual Multi-Task Models at Qualtrics | Experience management is an emerging business area where organizations focus on understanding the feedback of customers and employees in order to improve their end-to-end experiences. This results in a unique set of machine learning problems to help understand how people feel, discover issues they care about, and find which actions need to be taken on data that are different in content and distribution from traditional NLP domains. In this paper, we present a case study of building text analysis applications that perform multiple classification tasks efficiently in 12 languages in the nascent business area of experience management. In order to scale up modern ML methods on experience data, we leverage cross lingual and multi-task modeling techniques to consolidate our models into a single deployment to avoid overhead. We also make use of model compression and model distillation to reduce overall inference latency and hardware cost to the level acceptable for business needs while maintaining model prediction quality. Our findings show that multi-task modeling improves task performance for a subset of experience management tasks in both XLM-R and mBert architectures. Among the compressed architectures we explored, we found that MiniLM achieved the best compression/performance tradeoff. Our case study demonstrates a speedup of up to 15.61x with 2.60% average task degradation (or 3.29x speedup with 1.71% degradation) and estimated savings of 44% over using the original full-size model. These results demonstrate a successful scaling up of text classification for the challenging new area of ML for experience management. | ['Aaron Colak', 'Zhengzheng Xing', 'Wei Du', 'Yashmeet Gambhir', 'Samir Joshi', 'Daniel Perry', 'Daniel Campos'] | 2022-11-29 | null | null | null | null | ['xlm-r'] | ['natural-language-processing'] | [ 2.73635775e-01 -1.07165113e-01 -5.53874910e-01 -7.16133833e-01
-1.18235779e+00 -2.72515893e-01 5.50802462e-02 6.10953808e-01
-4.14968491e-01 3.62313777e-01 2.24145174e-01 -5.29554307e-01
-2.02215277e-02 -3.32733959e-01 -4.34372663e-01 -1.64141327e-01
2.00862184e-01 6.47181869e-01 -4.29256171e-01 -3.11825536e-02
2.77889848e-01 -1.72290076e-02 -1.47760916e+00 1.12379956e+00
5.15993536e-01 1.23114693e+00 5.57591736e-01 8.49039674e-01
-3.92666668e-01 1.22103655e+00 -4.35240000e-01 -4.13883895e-01
1.51428357e-01 2.46492833e-01 -8.45212281e-01 1.86901435e-01
5.71429729e-01 -3.54661167e-01 1.04989827e-01 4.20731932e-01
5.56101918e-01 -2.61156783e-02 2.48216256e-01 -1.21178341e+00
-3.81540835e-01 7.36894488e-01 -8.71034145e-01 1.68036431e-01
1.54071301e-01 -2.77258009e-01 1.25843859e+00 -7.15167284e-01
4.99095142e-01 1.09137428e+00 7.62433112e-01 2.06660986e-01
-1.34806108e+00 -7.00076222e-01 1.23672105e-01 1.15143396e-01
-1.25044179e+00 -6.91259503e-01 5.93611151e-02 -4.18269485e-01
1.80061305e+00 4.98000741e-01 2.58015245e-01 9.03632104e-01
6.32512093e-01 8.69729936e-01 1.04220748e+00 -5.32649636e-01
5.28457910e-02 5.81061244e-01 4.08636749e-01 6.34248435e-01
3.07226568e-01 -8.64027321e-01 -8.92742932e-01 -2.28077054e-01
6.63706139e-02 1.04588822e-01 2.97779977e-01 1.13231726e-01
-9.44363892e-01 6.28966033e-01 -5.54962158e-02 6.40370026e-02
-3.59428674e-01 2.70978272e-01 6.19861424e-01 3.89761537e-01
6.46832705e-01 5.45368075e-01 -9.76285696e-01 -7.68130839e-01
-1.11361396e+00 4.28941309e-01 1.11607575e+00 1.13670182e+00
4.17715102e-01 -7.55859986e-02 -1.81153074e-01 1.08560514e+00
2.20672384e-01 5.54620266e-01 5.92370868e-01 -1.14416873e+00
8.92804503e-01 3.81443769e-01 8.17278698e-02 -7.97799408e-01
-6.03384972e-01 -8.85653496e-01 -3.59506816e-01 -2.14575171e-01
3.02942991e-01 -1.98362201e-01 -7.18290687e-01 1.28964186e+00
-1.08673349e-01 -4.55117077e-01 -6.27243146e-02 3.50266308e-01
3.36318761e-01 7.00459838e-01 2.62959510e-01 -2.58962959e-01
1.64778674e+00 -1.14791811e+00 -9.45660770e-01 -8.40566874e-01
1.45843625e+00 -1.10017765e+00 1.23935580e+00 8.89817655e-01
-1.28560829e+00 -5.82320631e-01 -1.10617745e+00 -4.85774010e-01
-2.11282656e-01 3.25941920e-01 5.95108271e-01 5.66897631e-01
-6.49890602e-01 4.55556750e-01 -7.26083219e-01 -4.13529724e-01
4.30734694e-01 3.26876700e-01 -3.43599170e-02 -2.39352405e-01
-6.10069931e-01 9.73067999e-01 1.40886545e-01 -2.04252258e-01
-1.02778569e-01 -9.49557006e-01 -5.33506691e-01 3.40785921e-01
5.00014067e-01 -4.66095120e-01 1.69127953e+00 -7.49500275e-01
-8.14401686e-01 7.67338753e-01 -5.05416095e-01 -6.79824650e-01
1.28478393e-01 -6.58956885e-01 -5.27571797e-01 -2.93449312e-01
-5.74688939e-03 2.89888173e-01 5.86133957e-01 -1.02376390e+00
-1.09110510e+00 -4.62312222e-01 -3.93511921e-01 3.86658430e-01
-8.67939532e-01 1.73537463e-01 -4.42832708e-01 -3.32617432e-01
4.31688391e-02 -9.81911778e-01 -3.16019170e-02 -4.51603144e-01
-1.16246186e-01 2.02179968e-01 7.17222869e-01 -1.07206500e+00
1.64095795e+00 -2.05234003e+00 -1.08202778e-01 1.48256034e-01
5.12694180e-01 4.31415103e-02 1.21824592e-01 6.03880703e-01
4.01247054e-01 4.10078317e-01 5.10680616e-01 -9.61183608e-01
7.75783136e-02 1.75384581e-01 -4.25108641e-01 -1.04916744e-01
-2.50692397e-01 9.47537303e-01 -3.01938057e-01 -6.29633248e-01
-8.60402808e-02 1.48427770e-01 -6.11192763e-01 1.22031525e-01
-2.58828253e-01 -1.54831707e-01 -4.02212739e-01 7.57927299e-01
4.03536797e-01 -7.03273654e-01 5.63007653e-01 -1.60514072e-01
8.39159563e-02 4.47932661e-01 -1.05591857e+00 1.76708865e+00
-1.07761359e+00 7.27425218e-01 3.19532722e-01 -7.56936908e-01
7.94580340e-01 1.55355692e-01 5.77847779e-01 -8.94581437e-01
-8.97492189e-03 1.71239614e-01 1.17775481e-02 -6.93387687e-01
8.80945325e-01 -1.80312797e-01 -3.02911639e-01 5.04238904e-01
-3.09383154e-01 1.53566360e-01 3.28179412e-02 6.02212287e-02
1.19156635e+00 -9.85376313e-02 3.53433073e-01 -7.63631985e-02
-2.43158251e-01 2.29289740e-01 4.21999246e-01 8.49868119e-01
-5.17979730e-03 2.38384545e-01 3.01487565e-01 -4.45361644e-01
-1.31482530e+00 -7.05052793e-01 -2.14510411e-01 1.74037445e+00
-4.32021737e-01 -9.52954352e-01 -4.89630967e-01 -2.06082240e-01
2.02142894e-01 1.01873183e+00 -1.10186331e-01 3.01748998e-02
-3.71392518e-01 -7.89780974e-01 4.16019440e-01 7.11900711e-01
5.20423472e-01 -6.70508146e-01 -8.35395813e-01 3.48936826e-01
-3.86277229e-01 -1.57658267e+00 -1.81816027e-01 5.00919700e-01
-9.06880558e-01 -3.42709482e-01 1.44881690e-02 -5.60842574e-01
3.98334377e-02 2.84110188e-01 1.13370967e+00 -2.44255468e-01
-4.91925299e-01 2.75016755e-01 -2.95567423e-01 -7.10778117e-01
-2.91010708e-01 4.83818442e-01 2.63077764e-05 -3.96173269e-01
8.06719959e-01 -3.52381825e-01 -2.54515111e-01 6.86535314e-02
-5.29293597e-01 3.69283706e-01 9.57207441e-01 6.69499934e-01
4.85774428e-01 1.83878914e-01 4.70687419e-01 -1.16830325e+00
7.44674385e-01 -5.97837329e-01 5.95161654e-02 4.40380245e-01
-1.22750032e+00 5.60229644e-02 3.56478989e-01 -4.46266800e-01
-1.18995082e+00 -1.42358050e-01 7.27535486e-02 -1.39614463e-01
4.59773652e-02 8.42592657e-01 2.10604683e-01 5.10078430e-01
5.58449030e-01 -1.68874502e-01 8.11813474e-02 -5.69625437e-01
1.25781432e-01 1.23910999e+00 7.41669163e-02 -5.18638968e-01
4.63019125e-02 3.09557527e-01 -2.74335206e-01 -7.85357177e-01
-1.20990312e+00 -7.36417830e-01 -3.54192466e-01 8.40421021e-02
8.03319812e-01 -1.20009768e+00 -8.34362090e-01 6.69872835e-02
-7.71526277e-01 -5.96526444e-01 -1.28615558e-01 3.63313854e-01
-5.18140316e-01 -2.11591702e-02 -8.58568490e-01 -9.55604494e-01
-6.09019816e-01 -8.52809846e-01 1.19162691e+00 -2.42345989e-01
-7.39002764e-01 -7.73840547e-01 -4.76487786e-01 1.20972729e+00
5.94193637e-01 -4.02127892e-01 1.09832704e+00 -9.06280398e-01
-2.28983089e-01 -3.40846688e-01 -2.79852480e-01 3.61614734e-01
-7.37381503e-02 -4.79107440e-01 -1.03813207e+00 -5.84984720e-01
2.26344898e-01 -5.60894072e-01 4.96006131e-01 3.35992008e-01
1.36516595e+00 -2.04498842e-01 -4.82277244e-01 4.35084730e-01
1.28743172e+00 1.88193843e-01 4.12223995e-01 3.78228784e-01
7.69111097e-01 7.60266006e-01 9.24717724e-01 8.18711638e-01
5.40682971e-01 8.07894588e-01 -7.16262981e-02 -4.29138988e-02
1.32305458e-01 -2.11006984e-01 4.41535860e-01 1.06363928e+00
1.73578396e-01 -3.37809265e-01 -9.82041478e-01 2.46341899e-01
-2.08035445e+00 -6.72146022e-01 -1.20917097e-01 2.17696476e+00
6.85380995e-01 6.75578773e-01 -1.10704094e-01 -8.96291956e-02
1.28971010e-01 -8.44168738e-02 -5.74445009e-01 -9.57635164e-01
3.07888985e-01 1.88152507e-01 9.44827676e-01 2.68512309e-01
-9.02850211e-01 7.36560047e-01 6.09532499e+00 9.10514474e-01
-9.46381509e-01 3.03278774e-01 9.29453373e-01 -8.44752431e-01
4.56076041e-02 -7.16770366e-02 -1.36552036e+00 2.64694780e-01
1.61906874e+00 -3.00953776e-01 4.26779002e-01 1.20955777e+00
3.96536618e-01 -1.62828013e-01 -1.16394043e+00 1.26892126e+00
2.22470388e-01 -1.20270991e+00 -2.37868935e-01 4.64326471e-01
5.52421749e-01 1.99793801e-02 2.01453596e-01 5.75416565e-01
1.54042959e-01 -1.18141770e+00 7.01309264e-01 4.46125239e-01
5.50313473e-01 -7.37474859e-01 7.71952689e-01 4.55672354e-01
-1.20529711e+00 -5.64755261e-01 -2.76990950e-01 -4.32079703e-01
3.77603203e-01 5.36748588e-01 -1.28327477e+00 1.28222480e-01
8.42278957e-01 5.36674380e-01 -5.59235096e-01 3.78853828e-01
6.78293169e-01 5.24484456e-01 -1.28825694e-01 1.51586218e-03
-3.39515246e-02 2.30440766e-01 3.19698416e-02 1.56849575e+00
5.06177545e-01 -2.36616462e-01 4.01614130e-01 5.30648351e-01
-9.90319178e-02 2.96665519e-01 -5.51564097e-01 -1.34157181e-01
5.80326557e-01 1.48584521e+00 -4.73673016e-01 -5.15009284e-01
-6.14995122e-01 8.22688818e-01 2.53523052e-01 1.65499017e-01
-7.17635870e-01 -1.45039126e-01 8.04201961e-01 5.36832869e-01
3.69352728e-01 -4.73192245e-01 -7.73215890e-01 -9.04057860e-01
2.46944115e-01 -1.21886396e+00 1.92423046e-01 -6.95427597e-01
-1.04875827e+00 2.99137443e-01 9.82532129e-02 -7.44326472e-01
-3.91625136e-01 -5.74526668e-01 4.49604318e-02 8.27980459e-01
-1.18599904e+00 -1.29249609e+00 -2.89098889e-01 -7.63152540e-02
9.44458723e-01 -1.29627109e-01 8.51496696e-01 5.37378013e-01
-4.20081258e-01 6.53964043e-01 3.23317140e-01 -4.75494117e-01
8.85532260e-01 -1.21933627e+00 3.68611872e-01 4.16594923e-01
4.58367057e-02 7.78358161e-01 6.70092762e-01 -7.41741061e-01
-1.72547126e+00 -9.63913143e-01 1.24472606e+00 -4.91436601e-01
6.98092341e-01 -7.81655610e-01 -6.68284893e-01 1.06223154e+00
8.63916054e-02 -5.66690564e-01 1.04301715e+00 7.61676311e-01
-3.06034774e-01 -4.43721861e-01 -8.97122502e-01 5.11069179e-01
8.64608526e-01 -6.87545598e-01 -1.50492281e-01 4.80039150e-01
7.98378289e-01 -2.82834172e-01 -1.26429820e+00 1.13930106e-01
8.32796037e-01 -5.70204020e-01 7.90629625e-01 -8.21426749e-01
5.13557553e-01 4.05909419e-01 -6.63907766e-01 -8.16713631e-01
-1.49781734e-01 -5.26935399e-01 -2.50461370e-01 1.24281025e+00
6.11313462e-01 -3.62740457e-01 8.28582585e-01 1.09685183e+00
-1.82569295e-01 -1.01349688e+00 -3.54347378e-01 -4.12586033e-01
-1.34217456e-01 -9.51711774e-01 1.83139861e-01 7.61607111e-01
1.98555604e-01 6.18626177e-01 -6.82915628e-01 -2.65792131e-01
4.96744215e-01 1.59963250e-01 7.25569904e-01 -9.51352954e-01
-7.00751007e-01 -8.19722563e-02 9.86994728e-02 -1.25631344e+00
-8.60253051e-02 -9.89981234e-01 -1.61779746e-01 -1.64767134e+00
5.26855588e-01 -5.17246962e-01 -2.26061761e-01 6.49017870e-01
2.04993129e-01 -4.98538688e-02 3.18008661e-01 1.84077457e-01
-7.15002358e-01 -1.38216347e-01 8.23875189e-01 9.93952453e-02
-2.99489498e-01 -3.53939049e-02 -1.01873446e+00 6.36291325e-01
8.40656817e-01 -3.35993826e-01 -6.50321603e-01 -6.60562456e-01
6.07267320e-01 6.58271462e-02 9.06521827e-03 -9.11499619e-01
2.52422243e-01 -4.28403988e-02 4.22014236e-01 -5.10678172e-01
7.47852027e-01 -7.53803968e-01 3.72690484e-02 2.45814607e-01
-6.79609299e-01 3.57497007e-01 4.05465066e-01 3.75828415e-01
9.22722220e-02 -2.11437508e-01 3.42055768e-01 -5.72665036e-02
-7.33742833e-01 -1.13812625e-01 -4.91582870e-01 -3.76157947e-02
9.35595453e-01 6.18505068e-02 -5.32526493e-01 -4.76018131e-01
-9.14851904e-01 2.91795850e-01 8.29549655e-02 4.70920056e-01
5.35114825e-01 -9.50978160e-01 -6.01207554e-01 -3.50867547e-02
1.53287575e-01 -3.95755798e-01 3.54447931e-01 8.81092668e-01
-4.94300395e-01 6.99579954e-01 -1.32478714e-01 -4.30895746e-01
-1.78958952e+00 4.40030754e-01 -3.54314633e-02 -6.63913786e-01
-4.05074418e-01 6.51189268e-01 -2.33320668e-01 -1.23069607e-01
3.81368011e-01 -7.13555276e-01 1.65464923e-01 1.13282464e-01
5.35500646e-01 5.30519843e-01 4.80002820e-01 -1.06997117e-02
-1.43235072e-01 4.42643501e-02 -5.15686452e-01 -8.69200677e-02
1.29699099e+00 -3.94782066e-01 -1.73862427e-02 9.04957652e-01
1.39596689e+00 2.65012379e-03 -8.17945242e-01 -3.26940477e-01
3.32037240e-01 -4.24193412e-01 4.30445731e-01 -9.60309565e-01
-5.42383254e-01 8.42533290e-01 6.66553974e-01 3.19736451e-01
1.10436070e+00 5.57188988e-02 1.13841510e+00 7.24686384e-01
2.40062848e-01 -1.52240479e+00 9.29308757e-02 1.40219405e-01
5.33715606e-01 -1.23685706e+00 3.67664039e-01 -2.48097599e-01
-1.18555367e+00 9.01445806e-01 5.03752887e-01 5.01647532e-01
6.81889057e-01 7.62386382e-01 -5.47615625e-02 -2.25485206e-01
-1.32544982e+00 9.59289819e-02 8.48898441e-02 1.58218175e-01
7.62037396e-01 2.35953212e-01 -2.54764464e-02 7.50889182e-01
-3.63347411e-01 6.99890405e-02 2.67939389e-01 1.28142476e+00
-7.11292744e-01 -1.30705845e+00 4.99910414e-02 1.15465224e+00
-7.29486704e-01 -3.63692820e-01 1.70859441e-01 6.27963781e-01
3.17944437e-02 1.17107236e+00 3.18227112e-01 -7.17294097e-01
2.77813315e-01 4.48664427e-01 3.26442748e-01 -8.96624207e-01
-6.88981414e-01 2.93836802e-01 7.80807853e-01 -5.85257351e-01
-5.73781843e-04 -8.45523894e-01 -1.23596513e+00 -5.59724391e-01
9.69712157e-03 -1.68711200e-01 1.04926717e+00 9.66650426e-01
8.02911341e-01 7.39596188e-01 2.53517181e-01 -2.46035933e-01
-9.31446373e-01 -1.15601158e+00 -6.65165126e-01 2.45294780e-01
-7.96737522e-02 -2.92198867e-01 9.98526067e-02 3.91302496e-01] | [9.938096046447754, 6.177868366241455] |
fe10e85e-9c86-43cc-b713-24b77732440a | from-two-to-one-a-new-scene-text-recognizer | 2108.09661 | null | https://arxiv.org/abs/2108.09661v1 | https://arxiv.org/pdf/2108.09661v1.pdf | From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network | In this paper, we abandon the dominant complex language model and rethink the linguistic learning process in the scene text recognition. Different from previous methods considering the visual and linguistic information in two separate structures, we propose a Visual Language Modeling Network (VisionLAN), which views the visual and linguistic information as a union by directly enduing the vision model with language capability. Specially, we introduce the text recognition of character-wise occluded feature maps in the training stage. Such operation guides the vision model to use not only the visual texture of characters, but also the linguistic information in visual context for recognition when the visual cues are confused (e.g. occlusion, noise, etc.). As the linguistic information is acquired along with visual features without the need of extra language model, VisionLAN significantly improves the speed by 39% and adaptively considers the linguistic information to enhance the visual features for accurate recognition. Furthermore, an Occlusion Scene Text (OST) dataset is proposed to evaluate the performance on the case of missing character-wise visual cues. The state of-the-art results on several benchmarks prove our effectiveness. Code and dataset are available at https://github.com/wangyuxin87/VisionLAN. | ['Yongdong Zhang', 'Shenggao Zhu', 'Jing Wang', 'Shancheng Fang', 'Hongtao Xie', 'Yuxin Wang'] | 2021-08-22 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Wang_From_Two_to_One_A_New_Scene_Text_Recognizer_With_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Wang_From_Two_to_One_A_New_Scene_Text_Recognizer_With_ICCV_2021_paper.pdf | iccv-2021-1 | ['scene-text-recognition'] | ['computer-vision'] | [-5.51476656e-03 -5.54656863e-01 -1.77331686e-01 -4.25347477e-01
-2.77683318e-01 -3.87998521e-01 7.72984505e-01 9.23447311e-03
-6.61556959e-01 9.22106728e-02 5.35670146e-02 -3.28888506e-01
5.34974456e-01 -5.39667308e-01 -5.44657290e-01 -7.05301166e-01
6.10896051e-01 1.84267417e-01 2.57881790e-01 -3.55205685e-02
4.25253123e-01 3.37139726e-01 -1.47251689e+00 7.79005587e-01
8.12542021e-01 9.58518207e-01 6.62973285e-01 6.32977247e-01
-8.41758788e-01 9.64741945e-01 -5.01884401e-01 -2.48540804e-01
5.90684600e-02 -1.47935912e-01 -3.45563263e-01 6.45271063e-01
5.28333902e-01 -2.94886291e-01 -4.60419804e-01 1.20550835e+00
4.22753900e-01 6.16482496e-02 5.92179596e-01 -1.06364679e+00
-1.07792890e+00 5.00145555e-01 -9.27773893e-01 1.26385733e-01
2.77610987e-01 1.95664763e-01 6.64305925e-01 -1.67075050e+00
4.43063319e-01 1.58732724e+00 2.33483166e-01 2.90690511e-01
-8.32111537e-01 -4.04482365e-01 7.52290070e-01 6.28813207e-01
-1.40500736e+00 -5.44736743e-01 9.26295459e-01 -5.82686424e-01
7.43956923e-01 2.84958005e-01 3.91206741e-01 1.04517591e+00
9.16297883e-02 1.17859232e+00 8.64686310e-01 -7.54234791e-01
-2.17614770e-01 4.36501205e-01 5.17520785e-01 8.64105582e-01
2.22238507e-02 5.44144548e-02 -5.42319000e-01 3.10477436e-01
4.43533212e-01 3.03851277e-01 -2.24383518e-01 -2.59027272e-01
-1.09534383e+00 5.00676811e-01 4.15580601e-01 4.12435889e-01
-8.66290405e-02 -8.23574513e-02 4.21108723e-01 2.00123131e-01
2.78427064e-01 -3.50028872e-01 3.60933840e-02 3.54196966e-01
-8.27155173e-01 -6.01911426e-01 4.67828542e-01 1.03352726e+00
8.59886885e-01 4.62969571e-01 -4.01473314e-01 1.08003068e+00
7.34439850e-01 8.60202014e-01 5.35381198e-01 -2.99701095e-01
8.70726585e-01 9.42800164e-01 -2.52069056e-01 -1.24779892e+00
-2.17762828e-01 -2.41682783e-01 -9.73739684e-01 1.12760536e-01
2.45430753e-01 1.50739610e-01 -1.15521359e+00 1.06978118e+00
7.20994547e-02 -1.08981960e-01 1.18679553e-01 9.52855170e-01
1.25663435e+00 8.75808120e-01 1.62125021e-01 -9.08708945e-02
1.50167906e+00 -1.34572208e+00 -7.82862842e-01 -5.59561551e-01
3.53968024e-01 -1.16574228e+00 1.36418569e+00 2.97676563e-01
-7.78080225e-01 -8.93405616e-01 -1.01845813e+00 -4.41539943e-01
-7.24101841e-01 8.34903002e-01 2.97017634e-01 4.41242397e-01
-9.98343408e-01 -2.33344674e-01 -5.96837819e-01 -3.85352254e-01
1.69188902e-01 9.94741917e-02 -1.72876328e-01 -4.08818603e-01
-9.77254391e-01 6.63037300e-01 4.69043314e-01 5.32957137e-01
-7.57546961e-01 -7.21158311e-02 -9.72135365e-01 -1.28875049e-02
4.66476828e-01 -2.00731874e-01 6.63552046e-01 -1.33090389e+00
-1.12138224e+00 7.76562870e-01 -5.08655548e-01 6.23978078e-02
8.56306791e-01 -2.95120180e-02 -5.52715600e-01 2.06106439e-01
3.59302014e-03 6.08692050e-01 1.00636804e+00 -1.55564153e+00
-5.47747135e-01 -1.78304762e-01 -6.15123622e-02 4.30168897e-01
-5.09611428e-01 1.65020317e-01 -1.25537515e+00 -7.43105233e-01
1.21904172e-01 -5.54725111e-01 2.09980637e-01 4.32402730e-01
-5.72978497e-01 -2.16296881e-01 1.01567435e+00 -7.62306392e-01
1.12956262e+00 -2.40539408e+00 -1.79026723e-02 9.18955132e-02
1.93530336e-01 3.75973970e-01 -3.70865554e-01 3.44075143e-01
6.53825849e-02 9.90619585e-02 -1.10203050e-01 -7.32690752e-01
-6.32392541e-02 1.78737283e-01 -2.68507749e-01 3.59460682e-01
9.49405283e-02 1.01400983e+00 -4.70657289e-01 -9.46474254e-01
7.07429588e-01 5.59698880e-01 -1.27716318e-01 1.03714876e-01
-1.14135049e-01 1.15758561e-01 -4.74135369e-01 8.81691277e-01
8.06444883e-01 -3.49904209e-01 2.22332980e-02 -3.77846241e-01
-2.09189534e-01 -3.05095404e-01 -1.22813976e+00 1.45231473e+00
-3.56514871e-01 7.93477654e-01 7.15814680e-02 -9.34862137e-01
1.08838165e+00 -1.24043906e-02 2.09033955e-02 -9.58832443e-01
2.14820489e-01 -1.13304168e-01 -1.66051790e-01 -8.25768769e-01
4.29587871e-01 4.36157852e-01 1.10510029e-01 1.47964805e-01
-2.04640687e-01 3.28035742e-01 3.07630271e-01 2.09990710e-01
2.80079037e-01 2.27442697e-01 7.82358125e-02 -3.99418436e-02
1.09004641e+00 2.73016311e-04 3.12167346e-01 7.42698431e-01
-1.89347491e-01 5.98015130e-01 1.04550056e-01 -4.43803549e-01
-8.60393941e-01 -8.52694869e-01 -1.11409888e-01 1.38674641e+00
4.53290612e-01 -4.34904039e-01 -3.45327348e-01 -5.68306327e-01
-1.46610918e-03 6.54801965e-01 -5.41874170e-01 -3.74821648e-02
-4.44575697e-01 -6.57670140e-01 3.92985970e-01 5.17790318e-01
8.22540879e-01 -1.14744401e+00 -2.37912595e-01 -4.83168028e-02
-1.27534568e-01 -1.28040171e+00 -7.55492806e-01 5.74468111e-04
-4.10376787e-01 -9.16141152e-01 -6.30627692e-01 -1.19464338e+00
9.73970592e-01 5.87635040e-01 6.60801232e-01 4.11798328e-01
-3.36409539e-01 5.14721394e-01 -3.00650090e-01 -8.35562497e-02
-2.95920998e-01 -3.82129937e-01 -2.20240697e-01 3.70376080e-01
5.52396059e-01 -6.11285828e-02 -3.05671781e-01 3.02555412e-01
-7.90106654e-01 4.55728889e-01 5.45875072e-01 8.89852285e-01
7.04860568e-01 -2.31953859e-01 5.11137173e-02 -5.91828525e-01
4.38879430e-01 -2.02789888e-01 -5.07966161e-01 7.45696783e-01
-3.94337326e-01 -1.86591148e-01 8.64853799e-01 -6.84185445e-01
-1.11103368e+00 8.37598816e-02 2.04882294e-01 -5.60062349e-01
-3.38552117e-01 5.38098335e-01 -3.11908156e-01 -1.73544005e-01
2.99219847e-01 8.33367646e-01 -2.08763584e-01 -5.95729172e-01
3.15901846e-01 9.51007307e-01 3.57663929e-01 -4.81814981e-01
6.57692909e-01 4.67920899e-01 -2.74827957e-01 -1.11209583e+00
-4.92171466e-01 -4.36183125e-01 -8.44829857e-01 -3.17595303e-01
7.40451336e-01 -1.02602887e+00 -6.49174333e-01 5.48999488e-01
-1.28739989e+00 -4.84036095e-02 2.27268696e-01 3.49608332e-01
-1.28069475e-01 9.49655652e-01 -5.72796226e-01 -9.08260643e-01
-3.17071170e-01 -1.29120767e+00 1.05421400e+00 2.20411032e-01
5.75438201e-01 -1.01725161e+00 -5.89643955e-01 2.95796812e-01
1.82476491e-01 -1.91434637e-01 8.57612431e-01 -5.39932489e-01
-6.36780262e-01 -1.30801164e-02 -6.85973108e-01 3.22833091e-01
7.96246082e-02 2.78619409e-01 -1.03860331e+00 -2.12534726e-01
-4.34667943e-03 -2.18509778e-01 1.10887754e+00 8.65066126e-02
1.07570338e+00 -1.22760579e-01 -1.98949248e-01 7.86375701e-01
1.49588144e+00 4.37904030e-01 4.09821063e-01 3.26440126e-01
1.27449393e+00 7.11060464e-01 5.11100531e-01 4.07291502e-01
5.79966307e-01 5.40536165e-01 2.48722002e-01 -4.12665963e-01
-4.66726869e-01 -2.51146615e-01 5.28015375e-01 1.02181602e+00
3.77494901e-01 -4.97694790e-01 -1.21006203e+00 2.08688855e-01
-1.95313823e+00 -6.52681887e-01 -2.76185185e-01 1.72461569e+00
6.41728818e-01 3.13952491e-02 -3.79943162e-01 -6.51601478e-02
1.03627634e+00 2.71329761e-01 -5.81087589e-01 -2.10063428e-01
-5.56895256e-01 -4.82908249e-01 2.26088494e-01 7.44627833e-01
-1.13009262e+00 1.29015338e+00 5.45359945e+00 9.87899899e-01
-1.37776601e+00 -9.14551690e-02 6.52677238e-01 6.01894595e-02
-1.97228372e-01 -1.36438280e-01 -9.65258777e-01 5.46276808e-01
1.49921909e-01 6.20553866e-02 5.26037157e-01 7.33700871e-01
2.89075315e-01 -1.72345832e-01 -9.05660033e-01 1.35306406e+00
6.14859939e-01 -1.09608197e+00 6.18181825e-01 -3.33454549e-01
2.89405733e-01 -3.95165235e-02 1.84119374e-01 1.86001062e-01
-4.06756848e-02 -1.03415310e+00 9.03680444e-01 8.15400541e-01
8.76183808e-01 -4.88356292e-01 6.09756172e-01 4.29698169e-01
-1.51086915e+00 2.69587059e-02 -4.73767519e-01 1.75407812e-01
-7.17247874e-02 2.81572282e-01 -4.48643923e-01 5.79896152e-01
5.40996850e-01 1.14307272e+00 -1.15654159e+00 8.53635490e-01
-2.67076433e-01 3.41675788e-01 -2.66337901e-01 -4.31291088e-02
3.97380024e-01 -1.92298636e-01 3.94190282e-01 1.44001114e+00
-1.24251813e-01 -3.03066224e-01 6.53189898e-01 9.05671537e-01
2.17762366e-01 4.66803551e-01 -4.63606894e-01 6.25460893e-02
3.08148801e-01 1.14121723e+00 -6.52939200e-01 -5.33462346e-01
-7.36618102e-01 9.80278075e-01 3.59343231e-01 7.81806588e-01
-6.67761445e-01 -2.57979810e-01 4.20631245e-02 -3.83238941e-01
2.93990344e-01 -4.80969429e-01 -3.75238001e-01 -1.44033349e+00
3.31041008e-01 -6.95625365e-01 2.90503532e-01 -1.02743125e+00
-1.17498875e+00 6.97384417e-01 -2.47058123e-01 -1.26506734e+00
1.91343740e-01 -9.12883043e-01 -5.11452854e-01 9.84879315e-01
-1.79520428e+00 -1.42964840e+00 -5.33871770e-01 8.56168449e-01
1.02107847e+00 -4.04056847e-01 3.13512087e-01 3.05485100e-01
-8.04662466e-01 6.87489390e-01 1.67867094e-01 4.68265414e-01
7.44590700e-01 -8.87112379e-01 2.27469221e-01 1.06721818e+00
3.21238160e-01 3.89638990e-01 2.46027097e-01 -7.11283147e-01
-1.55387938e+00 -1.00367296e+00 8.51232469e-01 -1.76321983e-01
5.92226028e-01 -6.35567605e-01 -9.83865738e-01 4.19600457e-01
2.99046904e-01 2.15233192e-02 3.64546180e-01 -4.63644922e-01
-4.13633913e-01 -3.33767720e-02 -7.67097116e-01 8.35081697e-01
8.84968817e-01 -6.37340069e-01 -5.79548597e-01 2.01410189e-01
6.06590569e-01 -2.86374658e-01 -2.59832084e-01 7.24511817e-02
4.41424161e-01 -7.49180198e-01 9.96611953e-01 -3.63765180e-01
1.61706477e-01 -6.02674067e-01 -4.44028169e-01 -5.88317752e-01
-2.13458180e-01 7.18053281e-02 2.27980286e-01 1.35079420e+00
2.59408474e-01 -5.33332288e-01 3.22346300e-01 1.62376463e-01
4.59540673e-02 -4.23623711e-01 -7.40167797e-01 -5.37642241e-01
-9.24291536e-02 -4.44978803e-01 1.37350753e-01 9.42833543e-01
-1.93430781e-01 4.09528047e-01 -5.74495077e-01 1.33235589e-01
4.99372929e-01 3.09274167e-01 4.86866027e-01 -8.99863899e-01
-5.03076836e-02 -4.70215589e-01 -1.99733064e-01 -1.47224605e+00
2.87934273e-01 -1.07261753e+00 -3.93476300e-02 -1.65954065e+00
3.62720132e-01 -1.58190444e-01 -2.07799137e-01 6.67693377e-01
-2.62909412e-01 8.90363529e-02 7.09935248e-01 3.97258997e-01
-8.00470769e-01 6.97079360e-01 1.36597824e+00 -6.14155531e-01
4.34308592e-03 -2.70911634e-01 -4.61862594e-01 8.08235526e-01
7.37222612e-01 -9.60032716e-02 -2.64844835e-01 -9.48212266e-01
-1.93474770e-01 -8.85999724e-02 4.57977235e-01 -7.46153057e-01
6.84996307e-01 -1.76937208e-01 7.48143375e-01 -9.20445859e-01
3.25976968e-01 -1.09119689e+00 -3.73153687e-01 3.79997581e-01
-3.69354576e-01 1.61301374e-01 4.17180926e-01 5.53571582e-01
-2.96825856e-01 -2.73460358e-01 6.21766865e-01 1.47067355e-02
-1.20628762e+00 1.71981320e-01 -4.78569478e-01 -1.20265514e-01
7.88972199e-01 -3.96850944e-01 -6.46401227e-01 -1.26754284e-01
-8.21326971e-01 5.99963486e-01 4.38390583e-01 6.85709894e-01
9.20972228e-01 -1.23248088e+00 -6.46513700e-01 4.27617490e-01
3.65207464e-01 -3.32886368e-01 3.53712589e-01 7.54494011e-01
-5.32154322e-01 3.73905748e-01 -2.90609393e-02 -9.11501944e-01
-1.43384600e+00 9.74643469e-01 4.92706239e-01 5.62158860e-02
-5.79064608e-01 4.68610317e-01 6.66549623e-01 -1.87199429e-01
6.82023883e-01 -3.18228543e-01 -6.06226385e-01 4.62336689e-02
6.76738143e-01 2.32117865e-02 -2.54314572e-01 -9.31447268e-01
-5.22036135e-01 1.04181743e+00 -2.14208454e-01 3.37311141e-02
7.70331264e-01 -5.84266543e-01 -1.78245723e-01 7.71865189e-01
1.01722205e+00 1.29343927e-01 -1.19443476e+00 -7.41296947e-01
1.16423366e-03 -4.23723161e-01 3.38391997e-02 -9.34147418e-01
-1.16734159e+00 1.27978361e+00 7.25307584e-01 -6.87883049e-02
1.06660295e+00 -2.43110582e-01 1.97849616e-01 4.78237331e-01
5.14986552e-02 -1.08589900e+00 2.09739730e-01 7.42864549e-01
1.01799321e+00 -1.46162820e+00 3.46158631e-02 -4.74363238e-01
-9.30754244e-01 1.37958825e+00 7.66906619e-01 -3.52603905e-02
6.56532466e-01 3.11864078e-01 4.23811644e-01 1.24010645e-01
-7.37445772e-01 -3.84478897e-01 5.42508364e-01 5.86290836e-01
3.41356218e-01 -3.79570574e-02 -1.47563353e-01 4.91612434e-01
2.96544433e-01 -4.43238765e-01 2.79224753e-01 7.23939300e-01
-5.81008852e-01 -9.40860033e-01 -4.82303202e-01 1.11655958e-01
-3.08609586e-02 -5.14505923e-01 -5.64819872e-01 6.81709111e-01
1.60893574e-01 1.10351121e+00 1.37761071e-01 -2.77554333e-01
2.22265735e-01 1.05301090e-01 7.01616257e-02 -5.27959943e-01
-5.06735563e-01 4.07429188e-01 -1.79780588e-01 -2.84889787e-01
-3.22626829e-01 -3.48479986e-01 -1.31875694e+00 -2.55268514e-01
-9.96936485e-02 -2.37209350e-01 6.15328252e-01 9.12791371e-01
2.09109634e-01 5.33504128e-01 5.51458180e-01 -6.37038887e-01
-1.97641253e-01 -8.33492279e-01 -3.54400039e-01 2.98763990e-01
4.04411197e-01 -5.48610032e-01 -3.99748713e-01 2.62284547e-01] | [11.757828712463379, 2.0848379135131836] |
5ea792b0-6aa9-4df0-937e-213262cdeba3 | tghop-an-explainable-efficient-and | 2107.04020 | null | https://arxiv.org/abs/2107.04020v1 | https://arxiv.org/pdf/2107.04020v1.pdf | TGHop: An Explainable, Efficient and Lightweight Method for Texture Generation | An explainable, efficient and lightweight method for texture generation, called TGHop (an acronym of Texture Generation PixelHop), is proposed in this work. Although synthesis of visually pleasant texture can be achieved by deep neural networks, the associated models are large in size, difficult to explain in theory, and computationally expensive in training. In contrast, TGHop is small in its model size, mathematically transparent, efficient in training and inference, and able to generate high quality texture. Given an exemplary texture, TGHop first crops many sample patches out of it to form a collection of sample patches called the source. Then, it analyzes pixel statistics of samples from the source and obtains a sequence of fine-to-coarse subspaces for these patches by using the PixelHop++ framework. To generate texture patches with TGHop, we begin with the coarsest subspace, which is called the core, and attempt to generate samples in each subspace by following the distribution of real samples. Finally, texture patches are stitched to form texture images of a large size. It is demonstrated by experimental results that TGHop can generate texture images of superior quality with a small model size and at a fast speed. | ['C. -C. Jay Kuo', 'Kaitai Zhang', 'Ganning Zhao', 'Xuejing Lei'] | 2021-07-08 | null | null | null | null | ['texture-synthesis'] | ['computer-vision'] | [ 4.64159459e-01 3.80026996e-01 1.69676736e-01 2.33513694e-02
-6.64222360e-01 -7.55269751e-02 2.54038274e-01 -3.98302823e-01
5.04630327e-01 8.28023374e-01 -2.07847565e-01 1.55182824e-01
1.79639701e-02 -1.18624198e+00 -9.62840497e-01 -1.14946783e+00
2.62596011e-01 5.07968724e-01 -9.27428529e-03 -4.21205945e-02
2.27453426e-01 5.05560458e-01 -1.77107894e+00 4.67854440e-01
9.33474004e-01 1.36302054e+00 4.49561030e-01 5.07273793e-01
-5.75844757e-02 5.47744751e-01 -3.82557929e-01 -2.78178126e-01
2.48263136e-01 -6.09139860e-01 -6.44664884e-01 6.80243313e-01
6.07841849e-01 -3.20373476e-01 -8.84244815e-02 1.10946703e+00
3.35651755e-01 -6.23067208e-02 5.99518001e-01 -1.01413989e+00
-9.84370828e-01 5.84504783e-01 -8.44839871e-01 -6.70153439e-01
-2.82491576e-02 1.09517016e-02 5.68200886e-01 -1.12988985e+00
9.09079373e-01 1.44695580e+00 6.48984015e-01 5.88103712e-01
-1.77451348e+00 -4.80469346e-01 -2.67478913e-01 -2.09836572e-01
-1.41210973e+00 -5.31976640e-01 1.01515794e+00 -4.56115417e-02
3.46877992e-01 4.55145210e-01 1.00779235e+00 1.17451835e+00
5.94634473e-01 8.74940991e-01 1.39955389e+00 -5.80944300e-01
4.59051132e-01 -8.22360814e-02 -2.84285903e-01 8.63540769e-01
4.97624092e-02 2.56775051e-01 -5.00667393e-01 -9.44337919e-02
1.50909352e+00 -6.57259151e-02 -2.17792854e-01 -5.28385758e-01
-1.30947196e+00 6.82337582e-01 5.34042239e-01 -9.24017951e-02
-6.66741908e-01 2.87168562e-01 8.34617484e-03 2.19573081e-01
7.05778897e-01 3.33597362e-01 -1.18205570e-01 2.49893755e-01
-9.92451072e-01 5.07601559e-01 7.74705112e-01 9.40213501e-01
1.18046999e+00 4.04642195e-01 -3.85171212e-02 9.70867515e-01
8.12007710e-02 1.07602882e+00 8.65075737e-02 -1.17654192e+00
1.56890482e-01 2.81632543e-01 8.73184428e-02 -1.46791232e+00
5.06838709e-02 -1.38928235e-01 -1.44577205e+00 5.65457165e-01
2.41696820e-01 1.97854396e-02 -1.02791202e+00 1.23368526e+00
4.60447818e-01 -1.15343332e-01 2.91860756e-02 9.98071313e-01
6.06381714e-01 9.39532638e-01 -2.55428970e-01 -1.18988208e-01
1.19536376e+00 -1.05663323e+00 -6.08445048e-01 -1.41268522e-01
-1.37751000e-02 -1.01696467e+00 1.23058426e+00 7.33513176e-01
-1.17735946e+00 -7.21197307e-01 -8.77177358e-01 1.88644007e-02
3.58939804e-02 2.43696660e-01 6.97072625e-01 2.89377600e-01
-1.04498339e+00 7.87342846e-01 -7.83846438e-01 -7.13744462e-02
7.22851694e-01 -3.83290462e-02 -3.00215453e-01 -1.59773782e-01
-6.82331741e-01 2.81554788e-01 2.42830917e-01 2.25465968e-01
-1.05432534e+00 -5.69122970e-01 -8.77967954e-01 6.66860268e-02
2.90643513e-01 -7.49431491e-01 8.91988158e-01 -1.48924506e+00
-1.75202119e+00 5.62981308e-01 -4.08597469e-01 -2.53603369e-01
3.69852632e-01 3.92415049e-03 -1.96124658e-01 1.96560428e-01
1.83155313e-01 1.04300833e+00 1.57895792e+00 -1.77178466e+00
-5.01558661e-01 5.93358688e-02 -4.22484726e-01 -5.96335791e-02
1.94513792e-04 -5.88573694e-01 -5.26179373e-01 -9.02344763e-01
4.12314624e-01 -8.79097044e-01 -4.08683777e-01 1.69470847e-01
-7.58755922e-01 3.47805291e-01 9.77561057e-01 -6.78166211e-01
8.98782969e-01 -2.22875619e+00 7.40633309e-02 4.66111362e-01
4.17239755e-01 -2.57051289e-01 -2.81416386e-01 3.25363308e-01
7.00134188e-02 -2.80435979e-02 -2.66391218e-01 -2.67666399e-01
-4.88227084e-02 2.75107712e-01 -4.85953063e-01 1.47369839e-02
2.91636676e-01 7.82484889e-01 -8.08870614e-01 -5.61765730e-01
3.36431623e-01 5.24244010e-01 -8.17468524e-01 -8.82393345e-02
-5.36907434e-01 3.53240192e-01 -6.18839204e-01 8.94265592e-01
1.00362289e+00 -4.49870884e-01 2.07030233e-02 -4.71323133e-01
5.58678322e-02 -5.81762671e-01 -1.14620996e+00 1.28883672e+00
-5.44264853e-01 7.90666699e-01 -5.93190342e-02 -6.76919580e-01
1.39601743e+00 1.85380414e-01 2.83757895e-01 -5.78839600e-01
4.17983048e-02 3.88283432e-01 -2.36926541e-01 -4.30344880e-01
7.80815899e-01 -5.47346286e-02 3.67103405e-02 4.45076168e-01
-2.64698505e-01 -4.46391732e-01 1.76572904e-01 -2.41014093e-01
6.31844103e-01 2.85612226e-01 8.69440213e-02 -3.60725582e-01
3.71219337e-01 2.02559471e-01 4.04754758e-01 6.71287715e-01
3.82074982e-01 1.13848090e+00 4.18568432e-01 -9.97418404e-01
-1.67352903e+00 -1.18385875e+00 -6.26137704e-02 5.59508741e-01
4.47314829e-01 -1.66514307e-01 -1.21694410e+00 -2.65999228e-01
-1.56700462e-01 3.25195134e-01 -8.40860009e-01 1.67395338e-01
-4.66990560e-01 -6.07913554e-01 -1.09078372e-02 1.73810795e-01
9.34364080e-01 -1.68680894e+00 -3.13152403e-01 3.26008230e-01
-3.46908122e-01 -7.37533510e-01 -3.47825259e-01 -1.47921845e-01
-8.63486350e-01 -9.49782789e-01 -9.05615389e-01 -9.62371945e-01
1.21949899e+00 2.72971243e-01 1.26512969e+00 1.60165012e-01
-4.21819955e-01 -1.06078386e-01 -2.70216376e-01 -3.29621017e-01
-5.34687221e-01 -2.71649987e-01 -3.32520574e-01 4.67250496e-01
-1.65060684e-01 -4.49133188e-01 -6.00565314e-01 3.24307770e-01
-1.11338854e+00 8.18751752e-01 8.61634910e-01 1.35713089e+00
1.45373619e+00 6.32123649e-01 1.49976954e-01 -8.05519700e-01
4.38970894e-01 -1.20681889e-01 -5.69477975e-01 4.19056006e-02
-2.27138013e-01 -3.86821828e-03 8.43082190e-01 -5.86493731e-01
-1.22028494e+00 6.61620423e-02 9.81686115e-02 -5.72425187e-01
-3.14477086e-01 3.53169769e-01 -1.44734412e-01 -1.64007917e-01
7.18918800e-01 6.06094658e-01 3.52538377e-01 -4.20036435e-01
3.19270492e-01 2.25653082e-01 4.64010924e-01 -6.44728363e-01
8.38373542e-01 8.25573623e-01 -1.07799552e-01 -1.08318031e+00
-8.27336907e-01 6.41884208e-01 -4.19305235e-01 -2.98968732e-01
7.21832156e-01 -6.31265521e-01 -2.89631903e-01 8.15465271e-01
-9.10459459e-01 -8.14330339e-01 -5.32275856e-01 -4.20650980e-03
-9.03651536e-01 7.63153285e-02 -6.60651803e-01 -5.29490352e-01
-5.33193409e-01 -1.27061331e+00 1.39285636e+00 2.75689125e-01
-2.30370685e-01 -9.77312982e-01 -4.09358859e-01 1.94707215e-01
4.95499402e-01 4.48994815e-01 1.04437459e+00 4.77108300e-01
-1.03751099e+00 -8.59259740e-02 -2.57776976e-01 3.82071137e-01
2.68830031e-01 2.12943807e-01 -7.55713999e-01 -1.68324277e-01
-8.85827690e-02 -2.93124765e-01 5.77854276e-01 7.43419766e-01
1.69956720e+00 -4.78116304e-01 -3.97518277e-01 7.42417634e-01
1.62499595e+00 -4.92380653e-03 9.48832810e-01 4.79557246e-01
7.20752597e-01 5.14740348e-01 6.05021775e-01 2.99699277e-01
8.48874897e-02 4.85731691e-01 1.46828145e-01 -5.99281907e-01
-4.49103922e-01 -2.53440410e-01 8.65940824e-02 7.41588831e-01
-1.34836048e-01 -1.10894598e-01 -5.13356209e-01 3.54488164e-01
-1.72203195e+00 -9.47467685e-01 -8.93947929e-02 1.95822597e+00
7.67894506e-01 1.96910705e-02 -2.42329434e-01 2.73405641e-01
8.47957909e-01 -4.62236255e-02 -4.07850146e-01 -4.07614022e-01
-4.18522656e-01 3.59412879e-01 2.96893746e-01 4.69909310e-01
-9.48219299e-01 1.33843160e+00 6.44419909e+00 1.38059354e+00
-1.28614700e+00 -4.41181183e-01 1.21735203e+00 2.02836230e-01
-4.50773358e-01 6.67254701e-02 -4.49488282e-01 5.58999598e-01
3.21684152e-01 3.13866995e-02 7.42145836e-01 8.96363199e-01
3.77731055e-01 -1.85370967e-01 -7.39444971e-01 9.59118128e-01
-3.49090882e-02 -1.66123474e+00 5.40857375e-01 4.32836562e-02
1.14956152e+00 -5.38532555e-01 2.73267180e-01 -2.31970847e-01
1.11736760e-01 -1.08512890e+00 9.30875480e-01 7.73785532e-01
9.33157206e-01 -9.44564939e-01 4.91517127e-01 9.95605141e-02
-1.01952362e+00 1.76771164e-01 -8.82582903e-01 2.31188059e-01
1.96206309e-02 1.01361799e+00 -3.94164652e-01 4.37711775e-01
8.43103826e-01 7.39613235e-01 -3.94713223e-01 8.46804857e-01
-9.57772285e-02 4.42764789e-01 -2.65683364e-02 6.08257577e-02
1.54817134e-01 -6.22512460e-01 3.33894283e-01 5.77848196e-01
8.46579611e-01 -8.00774470e-02 2.30429009e-01 1.38325524e+00
1.30205616e-01 -4.40031290e-02 -5.88461161e-01 1.61049172e-01
6.14459634e-01 1.47585142e+00 -1.03703380e+00 -6.91483915e-01
6.91946074e-02 1.02932262e+00 1.07698120e-01 5.53886771e-01
-6.33620977e-01 -4.55852777e-01 2.72586018e-01 1.07845083e-01
4.81044769e-01 2.01842815e-01 -5.14603198e-01 -8.30322266e-01
7.32189044e-02 -1.33433211e+00 -3.73897910e-01 -1.18980086e+00
-1.34717762e+00 9.49467361e-01 -3.79664123e-01 -1.56408310e+00
-4.05122079e-02 -4.35019940e-01 -4.46144998e-01 1.03662241e+00
-1.00919712e+00 -1.47725880e+00 -7.01559424e-01 5.78832626e-01
6.04656041e-01 -1.85486421e-01 8.78232419e-01 -1.97922245e-01
-4.28606838e-01 3.63898575e-01 3.38800013e-01 -2.56840974e-01
4.02500004e-01 -1.07113135e+00 4.67814624e-01 4.34688121e-01
-1.26332581e-01 2.56540090e-01 7.60248184e-01 -7.37927139e-01
-1.48604000e+00 -1.13818681e+00 6.27583563e-01 -5.70599698e-02
3.85474235e-01 -3.36804211e-01 -9.30388153e-01 2.45975122e-01
1.29916921e-01 -8.92771408e-02 1.61055312e-01 -2.92913318e-01
-1.99468303e-02 -2.21312448e-01 -1.11313498e+00 1.07728684e+00
7.33100772e-01 -1.27563640e-01 -2.41823509e-01 2.32987687e-01
3.98260593e-01 -5.24415910e-01 -9.51020002e-01 7.62204304e-02
7.14535534e-01 -1.20986342e+00 9.10323322e-01 1.22194313e-01
1.08316100e+00 -2.53377318e-01 6.18598871e-02 -1.49276483e+00
-5.46654761e-01 -7.17503190e-01 1.37379691e-01 1.04620874e+00
3.62165868e-01 -5.72939873e-01 1.18573356e+00 1.26412109e-01
-1.04675181e-01 -9.24399555e-01 -4.89201158e-01 -7.79528201e-01
-2.96771731e-02 -9.03049037e-02 9.63702738e-01 7.20108688e-01
-4.43919033e-01 1.69689469e-02 -7.53197014e-01 1.28190115e-01
1.08168972e+00 7.75021493e-01 1.02410913e+00 -1.14124095e+00
-1.37325794e-01 -4.02674168e-01 -4.08928189e-03 -9.72307980e-01
-1.16763487e-01 -5.06370127e-01 3.35461795e-01 -1.46861267e+00
2.04629391e-01 -7.93882787e-01 2.73557544e-01 3.65117580e-01
-9.52014029e-02 5.48701465e-01 -5.37738577e-02 4.13891464e-01
-3.55153754e-02 6.83955550e-01 2.04138184e+00 -2.74296701e-01
-1.64007723e-01 -3.26963872e-01 -6.97768152e-01 6.53331518e-01
5.98370194e-01 -2.29953945e-01 -3.00413132e-01 -4.54836518e-01
-8.80433321e-02 2.28163168e-01 4.97496277e-01 -1.06167305e+00
-3.65482807e-01 -2.74755150e-01 9.51349795e-01 -6.68056846e-01
3.33521724e-01 -6.58189535e-01 6.35038018e-01 2.75968373e-01
-1.59534022e-01 -3.43873858e-01 1.12690777e-01 3.06202471e-01
-4.91236359e-01 1.41607732e-01 8.33956480e-01 -1.18866920e-01
-5.54820478e-01 5.79370260e-01 -4.44145620e-01 -3.81839126e-01
8.48507524e-01 -5.93166351e-01 -1.49913654e-01 -3.08458209e-01
-7.77371585e-01 -2.03527540e-01 8.74025106e-01 2.71968514e-01
8.71014655e-01 -1.87046635e+00 -5.79579532e-01 5.80040991e-01
-7.91222081e-02 2.11438552e-01 6.66693926e-01 5.04477859e-01
-1.06059909e+00 6.09510019e-02 -4.64455813e-01 -8.04346144e-01
-9.54202414e-01 5.61653793e-01 2.42922112e-01 2.58503761e-03
-1.11984587e+00 5.70713341e-01 8.04937065e-01 -3.84515494e-01
-1.60033539e-01 -1.20035693e-01 1.20679840e-01 -4.16350186e-01
5.96555710e-01 2.97084562e-02 -2.20495626e-01 -4.21844661e-01
2.77007043e-01 8.67864966e-01 1.59442991e-01 -3.73761430e-02
1.41143668e+00 -1.16980396e-01 -3.74762684e-01 2.15039626e-02
9.95957077e-01 -4.03237715e-02 -1.70337093e+00 -4.46944050e-02
-5.21972418e-01 -7.30644941e-01 3.43152434e-02 -4.96086538e-01
-1.48581779e+00 7.71769941e-01 4.59475309e-01 3.55198830e-01
1.34415019e+00 -2.59047896e-01 1.04336917e+00 2.34526947e-01
5.31975925e-01 -1.08208966e+00 2.92931229e-01 4.04695243e-01
1.22859061e+00 -9.56820905e-01 -2.11487263e-01 -6.05548859e-01
-6.05402231e-01 1.23109210e+00 5.59733272e-01 -4.79364216e-01
5.32861114e-01 3.02211702e-01 2.71267980e-01 -3.13171387e-01
-7.22085416e-01 2.56624699e-01 2.47922599e-01 7.00113535e-01
1.06888406e-01 1.49418116e-01 1.73694059e-01 2.27102771e-01
-5.53006470e-01 6.89060381e-03 4.14583206e-01 7.07331657e-01
-4.72904474e-01 -1.02817094e+00 -8.86295557e-01 5.57852447e-01
-1.04359359e-01 -2.56020695e-01 -1.56712964e-01 7.61778772e-01
1.69570446e-01 5.87199032e-01 3.69608879e-01 -4.25238550e-01
1.04760654e-01 -4.02753443e-01 5.29398918e-01 -1.62450299e-01
3.20457295e-02 5.15466750e-01 -7.51579925e-02 -7.15170979e-01
-1.36031374e-01 -3.99666697e-01 -7.42006063e-01 -6.00547910e-01
-7.67371356e-02 -2.41014324e-02 3.35306317e-01 6.53995693e-01
3.76726955e-01 4.59299535e-01 8.15058887e-01 -1.37758493e+00
3.08821443e-02 -8.19474578e-01 -9.14666414e-01 4.98114467e-01
1.95246220e-01 -5.65557182e-01 -2.96702832e-01 5.36852121e-01] | [11.398276329040527, -1.0992320775985718] |
961f937d-a03f-4c85-a709-31a6f338939f | figureqa-an-annotated-figure-dataset-for | 1710.07300 | null | http://arxiv.org/abs/1710.07300v2 | http://arxiv.org/pdf/1710.07300v2.pdf | FigureQA: An Annotated Figure Dataset for Visual Reasoning | We introduce FigureQA, a visual reasoning corpus of over one million
question-answer pairs grounded in over 100,000 images. The images are
synthetic, scientific-style figures from five classes: line plots, dot-line
plots, vertical and horizontal bar graphs, and pie charts. We formulate our
reasoning task by generating questions from 15 templates; questions concern
various relationships between plot elements and examine characteristics like
the maximum, the minimum, area-under-the-curve, smoothness, and intersection.
To resolve, such questions often require reference to multiple plot elements
and synthesis of information distributed spatially throughout a figure. To
facilitate the training of machine learning systems, the corpus also includes
side data that can be used to formulate auxiliary objectives. In particular, we
provide the numerical data used to generate each figure as well as bounding-box
annotations for all plot elements. We study the proposed visual reasoning task
by training several models, including the recently proposed Relation Network as
a strong baseline. Preliminary results indicate that the task poses a
significant machine learning challenge. We envision FigureQA as a first step
towards developing models that can intuitively recognize patterns from visual
representations of data. | ['Vincent Michalski', 'Yoshua Bengio', 'Samira Ebrahimi Kahou', 'Akos Kadar', 'Adam Trischler', 'Adam Atkinson'] | 2017-10-19 | figureqa-an-annotated-figure-dataset-for-1 | https://openreview.net/forum?id=SyunbfbAb | https://openreview.net/pdf?id=SyunbfbAb | iclr-2018-1 | ['chart-question-answering', 'chart-question-answering'] | ['computer-code', 'computer-vision'] | [ 9.67098624e-02 4.69870716e-01 2.38222882e-01 -3.94099861e-01
-8.25382948e-01 -1.02646184e+00 7.28788257e-01 5.78233719e-01
7.52415061e-02 5.20774186e-01 1.80115059e-01 -7.46917963e-01
4.58964258e-02 -7.71839142e-01 -9.50691164e-01 -1.40400320e-01
5.72164208e-02 5.18338442e-01 3.61477762e-01 -3.18175197e-01
5.29528737e-01 7.87992477e-01 -1.33554673e+00 8.22860003e-01
8.78399312e-01 8.82785439e-01 -1.40683770e-01 7.00541854e-01
-4.94632870e-01 9.08522546e-01 -1.23309445e+00 -6.59189463e-01
-1.26043305e-01 -5.47304928e-01 -6.32235706e-01 1.99682891e-01
6.79251909e-01 -1.27508506e-01 3.94968577e-02 8.51986170e-01
1.04388930e-01 3.11900564e-02 1.15315318e+00 -1.72778106e+00
-1.04986989e+00 2.99062192e-01 -7.76018560e-01 1.69712499e-01
7.06114471e-01 3.05187583e-01 1.15946376e+00 -9.61910725e-01
9.97674406e-01 1.24672222e+00 4.04014289e-01 2.78142899e-01
-1.23899186e+00 -1.57400846e-01 1.99467316e-01 1.83295056e-01
-1.07139182e+00 -2.18080282e-02 8.87305796e-01 -5.69128633e-01
8.55552018e-01 5.80184460e-01 7.95069218e-01 8.73385489e-01
-3.68489921e-02 8.06388378e-01 9.26973760e-01 -4.42228585e-01
2.98426211e-01 1.90350145e-01 -2.82881316e-02 9.77769732e-01
1.38634853e-02 -4.30142403e-01 -2.52582252e-01 -1.25957161e-01
1.10138917e+00 -2.84271568e-01 -2.48029888e-01 -6.93018436e-01
-1.24411511e+00 6.64693117e-01 1.01445472e+00 1.54439405e-01
-2.71671154e-02 2.08050996e-01 -1.32074421e-02 2.77733263e-02
5.08812666e-02 7.80432284e-01 2.30764925e-01 2.33499721e-01
-7.66103268e-01 4.75625336e-01 6.48491979e-01 1.12607121e+00
7.98476517e-01 -8.79339874e-02 -3.83824646e-01 5.92364728e-01
2.38332972e-01 4.05504256e-01 -1.80749267e-01 -1.02574980e+00
9.98523235e-01 1.02829063e+00 2.94635355e-01 -1.34347439e+00
-4.90334004e-01 -1.51788384e-01 -6.59502447e-01 4.55962926e-01
8.59957516e-01 6.37991400e-03 -8.54978740e-01 1.33563244e+00
2.89810240e-01 -3.97226214e-01 -1.24034949e-01 1.01723969e+00
1.23035884e+00 9.55703378e-01 6.36523440e-02 2.66196460e-01
1.68801606e+00 -9.96452987e-01 -6.21811330e-01 -2.24307761e-01
4.70504314e-01 -6.98531330e-01 1.54544318e+00 1.51566520e-01
-1.22707534e+00 -5.11286616e-01 -1.04727495e+00 -5.49643397e-01
-8.85366857e-01 3.74492198e-01 1.94376647e-01 2.40049437e-01
-8.98627639e-01 1.67785689e-01 -2.24960729e-01 -2.30981186e-01
5.49822271e-01 -3.01628351e-01 -1.88736930e-01 1.10338777e-01
-7.97209442e-01 8.85560215e-01 -5.57217784e-02 1.17599711e-01
-3.11553448e-01 -8.71322036e-01 -9.63012815e-01 2.98456281e-01
3.70668143e-01 -7.25632727e-01 1.12526739e+00 -7.23281682e-01
-9.39529240e-01 1.22607005e+00 2.58424878e-02 -1.54573768e-01
6.25414073e-01 1.17256433e-01 -3.70483011e-01 4.52874273e-01
9.57980901e-02 9.19402421e-01 5.89467764e-01 -1.68011057e+00
-3.19204807e-01 -2.52631783e-01 4.07591403e-01 -9.62737128e-02
3.66353184e-01 -1.99548006e-01 -5.79576731e-01 -7.04587042e-01
-7.69603848e-02 -4.79768991e-01 -2.24575512e-02 6.00182950e-01
-7.63146520e-01 -2.56572187e-01 8.25753450e-01 -7.87644982e-01
1.10616422e+00 -1.87210214e+00 -1.13346830e-01 3.87545049e-01
2.94407129e-01 -1.88685521e-01 -1.61117390e-01 5.64963937e-01
-1.80881351e-01 3.06003064e-01 -2.10416242e-01 2.95050740e-01
1.86288625e-01 -1.94512948e-01 -4.96856034e-01 7.61570185e-02
7.19639003e-01 1.23368454e+00 -8.01221550e-01 -6.29381478e-01
1.37145996e-01 2.58465707e-01 -2.21703842e-01 4.35101092e-01
-7.55596042e-01 1.03046194e-01 -2.34172806e-01 4.97854501e-01
4.49483246e-01 -8.10914338e-01 6.36521131e-02 -4.78386819e-01
3.80378552e-02 1.13009900e-01 -9.18967724e-01 1.35292196e+00
-2.35728905e-01 9.68391776e-01 -3.04751843e-01 -7.37676144e-01
1.10730624e+00 -1.62899628e-01 -9.15328115e-02 -9.77438509e-01
-3.95158771e-03 -2.11883545e-01 -1.26930252e-01 -7.01235175e-01
4.18642938e-01 1.68629736e-01 -2.13492781e-01 5.28143048e-01
-3.35766196e-01 -6.75415277e-01 6.13150537e-01 5.20485222e-01
7.81531394e-01 2.47598335e-01 2.92230338e-01 -5.77141531e-02
3.04426432e-01 4.11683142e-01 -1.22201465e-01 5.79323530e-01
2.57168233e-01 8.54225636e-01 1.26349747e+00 -6.63851619e-01
-1.34494460e+00 -1.39958143e+00 2.15781301e-01 7.12746143e-01
2.31692582e-01 -4.20981437e-01 -6.73605859e-01 -5.76740623e-01
-6.25164015e-03 1.01800060e+00 -9.39390719e-01 3.37398291e-01
-6.48098588e-01 -8.65368396e-02 2.03694344e-01 7.73013294e-01
2.42720887e-01 -1.17399096e+00 -1.03652382e+00 -2.92028546e-01
-1.29155740e-01 -8.50078762e-01 -4.87271786e-01 3.22429463e-02
-4.69867796e-01 -1.40873921e+00 -9.06891406e-01 -9.32268322e-01
1.05372143e+00 2.54258532e-02 1.57505846e+00 2.39368826e-01
-3.65894794e-01 3.86641204e-01 -2.21987367e-02 -4.75319922e-01
-2.80047625e-01 -2.61496246e-01 -9.83682513e-01 -4.26880091e-01
-2.62413383e-01 -1.06645271e-01 -6.80192053e-01 3.74010384e-01
-8.60322535e-01 5.74834287e-01 3.29116762e-01 5.52221656e-01
6.34569466e-01 -5.95440865e-01 2.00583875e-01 -8.96194935e-01
8.87156785e-01 -4.23551440e-01 -5.76409400e-01 9.01424170e-01
1.06144302e-01 2.86218554e-01 5.89418352e-01 -2.17825890e-01
-9.17678773e-01 -3.63324165e-01 1.73844188e-01 -3.27372015e-01
-2.20524356e-01 4.01292950e-01 -2.27057412e-01 5.06063938e-01
9.19094563e-01 -1.26277894e-01 -1.09281145e-01 -5.13481572e-02
1.11154592e+00 1.15007773e-01 8.21484029e-01 -6.89217389e-01
8.81043255e-01 3.47165376e-01 7.47278184e-02 -7.07820415e-01
-7.13219523e-01 -1.00789115e-01 -5.29232204e-01 -4.76240098e-01
8.88890684e-01 -3.97589058e-01 -9.20079410e-01 -3.14376652e-01
-1.51465344e+00 -4.95062292e-01 -2.42753208e-01 -4.42272067e-01
-7.05470741e-01 -5.40472195e-02 -2.52974510e-01 -7.11919010e-01
-1.83594555e-01 -9.02251422e-01 1.18859422e+00 4.68805671e-01
-6.01873875e-01 -9.43430543e-01 -2.59536356e-01 2.82826364e-01
4.25043255e-02 8.52398455e-01 1.62888730e+00 -3.24453682e-01
-8.83382559e-01 3.86354625e-02 -7.58454084e-01 -4.10368800e-01
-4.86157984e-02 5.20069361e-01 -5.67438781e-01 2.13585749e-01
-7.48407364e-01 -5.82336307e-01 5.93238652e-01 8.07081163e-02
1.44303858e+00 -3.92964959e-01 -3.66886169e-01 4.08897281e-01
1.21217227e+00 4.90925223e-01 6.14745140e-01 1.40762523e-01
6.83869541e-01 9.78161335e-01 3.99104834e-01 2.05244035e-01
5.34845471e-01 5.05238593e-01 5.31243145e-01 -4.83134300e-01
-1.54109001e-01 -5.32698095e-01 -3.70921344e-01 2.54935980e-01
2.74078131e-01 -6.34990156e-01 -1.21086848e+00 5.58689713e-01
-1.66203284e+00 -9.86277878e-01 -4.66279596e-01 1.65294075e+00
4.26113218e-01 1.42808855e-01 2.97165543e-01 4.93206158e-02
5.27350783e-01 2.22461119e-01 -5.94540000e-01 -5.97583771e-01
-1.06059164e-01 -2.81457976e-02 -1.80691183e-01 2.55222231e-01
-7.39367127e-01 6.00966334e-01 6.43416834e+00 4.69465643e-01
-9.66553867e-01 -6.62749708e-01 1.10264111e+00 2.52447307e-01
-7.99853921e-01 -8.22599679e-02 -1.69706881e-01 1.07157513e-01
4.27092493e-01 -3.85341384e-02 2.15375140e-01 6.87360764e-01
1.54075786e-01 -2.81183541e-01 -1.22018206e+00 1.10330105e+00
2.12372541e-01 -1.74383771e+00 4.91239250e-01 -4.36284900e-01
5.37489176e-01 -8.44719529e-01 1.88120916e-01 -2.05156542e-02
3.74985933e-01 -1.30277252e+00 1.02206123e+00 6.39770508e-01
1.02275443e+00 -5.54808259e-01 1.73064351e-01 -3.93185765e-02
-1.14679909e+00 2.43591040e-01 -1.60863280e-01 1.64522842e-01
1.04118973e-01 9.30830017e-02 -1.00038958e+00 5.40918767e-01
6.15141869e-01 3.99971873e-01 -1.05240333e+00 9.92526770e-01
-5.69248497e-01 1.30632997e-01 -1.37375206e-01 -5.10298073e-01
2.24883944e-01 -3.06836993e-01 -7.31001273e-02 1.09627187e+00
2.76949644e-01 1.21415339e-01 -3.07012767e-01 1.39972758e+00
-1.48145407e-01 1.24539651e-01 -6.03588939e-01 -2.36673579e-01
3.44612151e-01 1.14327133e+00 -1.09023046e+00 -3.70413512e-01
-4.34626162e-01 7.00640261e-01 6.92150056e-01 6.54829562e-01
-9.69498754e-01 -7.36702144e-01 2.27625355e-01 4.08698201e-01
2.52022117e-01 -9.88580734e-02 -7.48592973e-01 -7.28423953e-01
1.73411340e-01 -8.71885478e-01 5.60572505e-01 -1.84401822e+00
-1.14126706e+00 6.87071145e-01 7.63264671e-02 -1.14225507e+00
-3.08126777e-01 -8.53295624e-01 -9.01125729e-01 8.91468346e-01
-9.64450240e-01 -1.13296354e+00 -6.66751802e-01 2.41455302e-01
3.42102945e-01 1.85399890e-01 4.90232229e-01 -2.77519554e-01
-2.49260619e-01 2.16719970e-01 -2.25688413e-01 4.18480217e-01
4.60401714e-01 -1.48268795e+00 6.65591419e-01 4.54973578e-01
6.09061360e-01 4.66652751e-01 6.71433747e-01 -4.87359881e-01
-1.01548016e+00 -7.53358424e-01 6.11620903e-01 -5.95702589e-01
7.45034277e-01 -5.73433578e-01 -1.08917677e+00 5.16928375e-01
4.45267528e-01 -1.25386328e-01 4.16584820e-01 -4.73865271e-02
-7.13797271e-01 1.53511167e-01 -8.65412295e-01 9.71250892e-01
8.81486773e-01 -4.36882466e-01 -6.91128731e-01 1.82068527e-01
4.12898242e-01 -6.20217443e-01 -4.64613914e-01 -3.55816893e-02
5.15421331e-01 -1.07375014e+00 1.02885020e+00 -8.81977975e-01
1.00501764e+00 -3.97013903e-01 2.67946124e-01 -1.36135340e+00
-1.00584023e-01 -3.51705581e-01 -1.01337619e-02 1.07035589e+00
8.34023952e-01 3.46765220e-02 8.20836365e-01 6.49914443e-01
1.15871474e-01 -8.82386625e-01 -5.03904104e-01 -2.93017000e-01
1.04927994e-01 -1.26777485e-01 6.55412555e-01 7.08274603e-01
2.64199585e-01 4.72093344e-01 7.02162087e-02 -6.50583804e-02
3.12614679e-01 6.29570425e-01 1.00586665e+00 -9.53732610e-01
1.43951541e-02 -6.95501626e-01 -1.47252500e-01 -1.15654171e+00
-2.27652401e-01 -6.88138545e-01 -1.83228552e-01 -2.24772263e+00
-7.74853304e-03 -1.55551732e-01 4.42799598e-01 3.24193299e-01
-3.23388159e-01 -5.47441915e-02 5.06585360e-01 -1.23509675e-01
-5.98800898e-01 2.82662123e-01 1.78409088e+00 -3.82715881e-01
6.63593635e-02 -3.54501188e-01 -5.91472864e-01 6.64031446e-01
6.33303285e-01 1.02959285e-02 -5.10535419e-01 -5.40994406e-01
5.23098111e-01 4.35058028e-01 7.43736148e-01 -7.16828406e-01
2.21309572e-01 -3.82078618e-01 9.33085442e-01 -1.01961672e+00
3.36460024e-01 -6.93584085e-01 1.02915250e-01 1.63265958e-01
-5.83453655e-01 6.57518685e-01 2.88262337e-01 4.27050233e-01
-4.83277068e-02 3.55343036e-02 6.73097074e-01 -2.06695110e-01
-3.66052210e-01 -2.37111166e-01 -2.05797151e-01 3.56930673e-01
1.02402580e+00 -2.11390495e-01 -1.04521132e+00 -7.06336677e-01
-7.76015580e-01 4.46203440e-01 4.40697610e-01 4.58377153e-01
8.23962808e-01 -1.35146201e+00 -3.93553942e-01 -8.84968117e-02
4.26089168e-01 1.21451385e-01 2.08683237e-01 1.08701684e-01
-9.42459166e-01 3.06957990e-01 -4.24747974e-01 -4.67619717e-01
-1.10384655e+00 7.88263202e-01 1.64477438e-01 -1.23126291e-01
-5.48853576e-01 6.29340172e-01 4.67169881e-01 -1.38084903e-01
2.33858407e-01 -8.20488155e-01 -2.63445318e-01 9.08366069e-02
5.01821458e-01 2.19192281e-01 -3.16607207e-01 -3.14293027e-01
-1.56445846e-01 5.54838538e-01 4.17600065e-01 -1.42405376e-01
1.02420247e+00 1.47545397e-01 1.82745427e-01 6.70742333e-01
9.44057703e-01 -6.83035329e-02 -1.31619644e+00 3.88157517e-02
1.39349878e-01 -2.07232341e-01 -7.53501236e-01 -1.08149087e+00
-6.77846134e-01 1.20519114e+00 6.62955716e-02 6.62953258e-01
6.77779257e-01 2.97437519e-01 1.27436981e-01 2.89195061e-01
-6.76724166e-02 -7.81763315e-01 8.05799603e-01 9.76468697e-02
1.75240755e+00 -1.13466144e+00 8.11256915e-02 -6.45641327e-01
-7.14165807e-01 1.40037405e+00 9.99804318e-01 2.61543114e-02
3.47295642e-01 2.33808085e-01 3.47467124e-01 -6.60593987e-01
-7.37947166e-01 -9.98304784e-02 7.86996543e-01 6.63770139e-01
4.95965302e-01 -1.67488188e-01 1.07349627e-01 2.03370512e-01
-4.49823469e-01 -5.54384410e-01 4.08177406e-01 7.30105758e-01
-3.58063281e-01 -5.69389105e-01 -5.18894255e-01 5.61223984e-01
6.31351471e-02 8.78569260e-02 -8.21238875e-01 1.13110805e+00
-1.22446172e-01 7.16787696e-01 5.67431211e-01 1.89806357e-01
6.12815261e-01 -3.12038828e-02 5.23583531e-01 -3.45000654e-01
-3.64163607e-01 -2.55042344e-01 3.05387944e-01 -2.81895697e-01
-9.94921923e-02 -3.25867087e-01 -1.43434834e+00 -1.16943449e-01
2.66785771e-01 7.23480061e-02 4.99322981e-01 6.03681564e-01
2.69767970e-01 6.79894567e-01 1.55424923e-02 -5.34595668e-01
-1.63463075e-02 -8.15506041e-01 -1.57369554e-01 8.75556231e-01
2.55854279e-01 -5.15559793e-01 -1.01256892e-01 3.80501896e-01] | [11.163628578186035, 1.9907842874526978] |
97011aa6-2526-4a98-96b4-c35a0cc589b2 | crowd-sourced-data-analysis-mapping-of | 1903.12495 | null | http://arxiv.org/abs/1903.12495v1 | http://arxiv.org/pdf/1903.12495v1.pdf | Crowd Sourced Data Analysis: Mapping of Programming Concepts to Syntactical Patterns | Since programming concepts do not match their syntactic representations, code
search is a very tedious task. For instance in Java or C, array doesn't match
[], so using "array" as a query, one cannot find what they are looking for.
Often developers have to search code whether to understand any code, or to
reuse some part of that code, or just to read it, without natural language
searching, developers have to often scroll back and forth or use variable names
as their queries. In our work, we have used Stackoverflow (SO) question and
answers to make a mapping of programming concepts with their respective natural
language keywords, and then tag these natural language terms to every line of
code, which can further we used in searching using natural language keywords. | ['Deepak Thukral', 'Darvesh Punia'] | 2019-03-28 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [-1.08708106e-01 -2.63840109e-01 -2.14334324e-01 -2.43328527e-01
-2.90257245e-01 -1.23736274e+00 3.58064860e-01 8.46979260e-01
-7.90985078e-02 1.56592384e-01 -8.57710689e-02 -1.02057660e+00
-5.22144977e-03 -1.10635090e+00 -3.99344355e-01 7.94236660e-02
6.21016473e-02 -1.08196281e-01 8.43573093e-01 -2.99785584e-01
8.16441774e-01 -8.99796262e-02 -1.80691254e+00 2.99629599e-01
8.81600857e-01 4.14618760e-01 7.16485143e-01 6.16157532e-01
-1.48769963e+00 8.89420211e-01 -6.58613861e-01 -7.36755729e-01
-1.64204270e-01 -2.80541867e-01 -1.23647022e+00 -6.62261963e-01
3.21389109e-01 8.54520947e-02 5.35229504e-01 1.70082617e+00
-2.12241769e-01 -3.04461956e-01 1.10916406e-01 -1.15796530e+00
-5.43926895e-01 9.10924494e-01 -6.05115294e-01 9.65141430e-02
1.03909993e+00 -2.99315631e-01 1.24273872e+00 -8.31717551e-01
8.31329226e-01 9.37839746e-01 6.42131925e-01 6.70882225e-01
-1.03154910e+00 -5.03375888e-01 8.47175531e-03 2.42737327e-02
-1.24259758e+00 6.49858043e-02 4.43617672e-01 -8.55586112e-01
1.12443197e+00 6.96425438e-01 6.54049039e-01 2.33394474e-01
3.78853709e-01 3.42106074e-02 6.23931289e-01 -8.48796189e-01
3.41878012e-02 4.57437336e-01 7.35016882e-01 1.01467896e+00
3.20816517e-01 -3.74256194e-01 -6.60769548e-03 -6.04667962e-01
2.03913465e-01 3.29251111e-01 -2.39890426e-01 -2.37545501e-02
-9.41713095e-01 8.47872019e-01 7.66695365e-02 7.71287799e-01
3.25765491e-01 5.12737453e-01 6.08106017e-01 6.02642715e-01
-3.22796375e-01 7.03612566e-01 -6.06804729e-01 -4.00499195e-01
-6.64269924e-01 3.74362051e-01 1.26013672e+00 1.48699260e+00
1.52492535e+00 -6.46122634e-01 2.90911794e-01 7.37014592e-01
4.59767014e-01 1.87769026e-01 6.65053427e-01 -7.16013014e-01
3.28699887e-01 1.34565055e+00 1.27353212e-02 -1.05460083e+00
-7.22370297e-02 -6.51254505e-02 -9.50358436e-03 3.53074580e-01
2.93214113e-01 2.86127776e-01 -2.97551990e-01 1.28756714e+00
-2.50909757e-02 -6.22715175e-01 -4.37674150e-02 4.34714794e-01
7.70493686e-01 4.14079100e-01 1.84089825e-01 -2.38011777e-02
1.61898315e+00 -8.38355720e-01 -4.71839219e-01 -5.47061086e-01
1.03672159e+00 -1.12117493e+00 1.13591850e+00 1.14123248e-01
-8.20813715e-01 -3.35926920e-01 -8.34406435e-01 -2.47230440e-01
-8.19720566e-01 -2.85520047e-01 5.86449325e-01 6.65692568e-01
-1.03563619e+00 4.52887803e-01 -5.48535228e-01 -6.53139532e-01
-2.06727713e-01 -1.43488199e-01 -1.85542881e-01 2.85995454e-02
-9.60559905e-01 6.16412461e-01 5.78886271e-01 -4.40383375e-01
-5.69244742e-01 -6.99228287e-01 -9.37977731e-01 3.44715804e-01
6.56152070e-01 -6.04512453e-01 1.36106253e+00 -1.20310509e+00
-7.11410463e-01 1.13720822e+00 -4.08135921e-01 -1.79931492e-01
-1.09000161e-01 -2.04337493e-01 -5.83759427e-01 -3.59628439e-01
6.37267411e-01 -1.16244458e-01 5.10027111e-01 -1.06100893e+00
-8.59655917e-01 -3.06239396e-01 9.43123996e-01 -5.78424633e-01
-2.46468261e-01 9.13546145e-01 -5.45892656e-01 -4.89112020e-01
3.08508426e-01 -6.31545365e-01 -1.56180039e-01 1.29959464e-01
-8.23877901e-02 -4.75387752e-01 5.24532557e-01 -5.69857836e-01
2.15992093e+00 -2.25533509e+00 -1.24211177e-01 5.62205136e-01
6.48677945e-01 -8.92309994e-02 2.42592558e-01 7.60080755e-01
-3.28270137e-01 8.76837015e-01 -4.00508434e-01 6.55909717e-01
2.53985286e-01 1.59574419e-01 -2.90822536e-01 -2.34035566e-01
-7.78429583e-02 4.99000251e-01 -1.12735665e+00 -4.81751949e-01
-3.15910399e-01 -7.96803385e-02 -8.22167695e-01 1.37427106e-01
-7.44413376e-01 -3.27702790e-01 -7.19629407e-01 5.73270380e-01
4.49384481e-01 -3.59457076e-01 2.94303447e-01 2.27264985e-01
-6.56120300e-01 3.73574674e-01 -1.24354124e+00 1.55273402e+00
-1.01100469e+00 5.64217508e-01 -1.27907783e-01 -7.19717801e-01
1.05610991e+00 1.78237215e-01 4.67700278e-03 -5.99937320e-01
-4.58277941e-01 6.86324477e-01 -2.40523860e-01 -8.87455642e-01
2.16378585e-01 1.38323441e-01 -3.65129262e-01 6.07906580e-01
-3.71214539e-01 -2.51077205e-01 4.80699748e-01 2.57030547e-01
1.44377983e+00 2.74229944e-01 6.13892376e-01 -5.33093810e-01
1.15268064e+00 3.01110476e-01 3.69954556e-01 7.49614179e-01
6.96595550e-01 2.23091975e-01 1.09602785e+00 -6.13765538e-01
-6.71771884e-01 -8.78935635e-01 -4.55958955e-02 1.45242774e+00
5.21329269e-02 -1.49988449e+00 -8.35692227e-01 -5.96011221e-01
-2.86598146e-01 6.83414638e-01 -4.80651170e-01 2.28186548e-01
-7.83763051e-01 1.93348154e-01 2.88445532e-01 3.23358119e-01
1.98065266e-01 -8.27228606e-01 -1.19672751e+00 4.62336153e-01
1.80232689e-01 -4.90585417e-01 -6.30714655e-01 5.85756116e-02
-6.14622653e-01 -1.35182846e+00 -4.25833255e-01 -8.71423244e-01
6.53075218e-01 1.29834384e-01 1.64542472e+00 1.19876075e+00
-3.75334710e-01 2.87735194e-01 -5.72502792e-01 -2.31413737e-01
-7.43478715e-01 -3.19964737e-02 -9.05454218e-01 -7.43170738e-01
5.98418593e-01 -5.71572244e-01 -3.08006853e-01 1.46659121e-01
-1.18530643e+00 -3.19185078e-01 -8.13171864e-02 5.80738544e-01
1.03877202e-01 -2.13362183e-02 -2.37166226e-01 -1.07089329e+00
6.29958212e-01 -5.62482297e-01 -1.12016714e+00 5.37322998e-01
-5.26591539e-01 4.79512483e-01 7.67348111e-01 -3.18497419e-02
-6.78220332e-01 -1.87635943e-01 -3.12147826e-01 -5.28815836e-02
2.27462612e-02 1.29835284e+00 3.78229469e-02 -4.02513519e-02
7.48219848e-01 1.63608074e-01 -3.15575600e-01 -6.86695635e-01
2.21243799e-01 5.40414751e-01 1.77911848e-01 -8.98272574e-01
8.39423358e-01 -2.31976118e-02 -3.06322128e-01 -4.84748542e-01
-1.77082881e-01 -6.32892907e-01 -3.78724158e-01 1.99374065e-01
9.50361729e-01 -2.70402849e-01 -5.22857368e-01 6.86364993e-02
-1.58251488e+00 1.92693740e-01 2.05442999e-02 -2.73616344e-01
-1.07707232e-01 7.03871787e-01 -2.13025380e-02 -6.27847135e-01
-9.46367234e-02 -1.23140979e+00 7.27709711e-01 2.43036121e-01
-5.36491930e-01 -1.02638841e+00 2.55389303e-01 -3.15866590e-01
7.82960713e-01 -2.69042570e-02 1.83987916e+00 -6.37417495e-01
-6.87150598e-01 -2.48289809e-01 -2.27213025e-01 -5.26766106e-02
2.15620786e-01 4.01526183e-01 -2.76206285e-01 1.31199822e-01
-1.43894747e-01 4.61297721e-01 -5.75878210e-02 -5.15277743e-01
1.26185584e+00 -5.58243394e-01 -6.16762757e-01 4.01827276e-01
2.07292533e+00 3.64139616e-01 7.79098630e-01 8.29423130e-01
5.34183085e-01 7.90865064e-01 2.59542644e-01 2.99794704e-01
4.46727514e-01 5.42802632e-01 3.17770332e-01 5.60925245e-01
2.47838542e-01 -2.53701925e-01 1.34092107e-01 8.75903070e-01
2.43029773e-01 2.99453259e-01 -1.47966969e+00 5.45247793e-01
-1.50048137e+00 -8.24167848e-01 -6.75803244e-01 2.39475489e+00
1.01901674e+00 -1.29974252e-02 -2.09807530e-01 -1.88551590e-01
4.90156978e-01 -1.81288943e-01 9.47509632e-02 -6.06435061e-01
4.76515025e-01 4.80195642e-01 2.53086179e-01 5.12155533e-01
-4.02140856e-01 5.91062546e-01 5.79850721e+00 6.20694399e-01
-1.20638168e+00 2.53799498e-01 -3.08240712e-01 7.99763381e-01
-1.09999609e+00 8.86731505e-01 -7.47342050e-01 6.18369102e-01
8.34654987e-01 -8.46709907e-01 6.76901817e-01 1.15575504e+00
-3.05697650e-01 -3.31639022e-01 -1.50822914e+00 8.96951199e-01
-1.39039144e-01 -1.30130601e+00 -1.35987559e-02 -4.16929126e-01
2.52686381e-01 -3.11591268e-01 -7.99993098e-01 3.31338227e-01
2.84897923e-01 -1.02520096e+00 9.39905882e-01 7.03223288e-01
3.96237165e-01 -1.95843920e-01 4.68509197e-01 2.47204810e-01
-1.59521639e+00 -2.53346890e-01 -3.46046537e-01 7.46849924e-02
-4.44844693e-01 2.71217108e-01 -2.42919222e-01 5.38102090e-01
1.03555369e+00 2.09578693e-01 -9.06503618e-01 1.32441401e+00
-1.45352706e-01 2.25207359e-01 -2.00870395e-01 -5.53708971e-01
1.50751146e-02 -1.89826071e-01 3.64704043e-01 1.54129553e+00
6.80316210e-01 -1.84906676e-01 1.92672774e-01 1.26661551e+00
1.31548345e-01 5.02140343e-01 -5.14480829e-01 -1.27429426e-01
4.62774009e-01 9.47959781e-01 -6.28403366e-01 -5.10764778e-01
-1.05826843e+00 6.06040835e-01 -3.23582105e-02 4.34446901e-01
-6.15254343e-01 -1.31109715e+00 6.02943301e-01 4.49499100e-01
-2.13300274e-03 -1.37985423e-01 1.12900496e-01 -1.09726977e+00
6.06307387e-01 -1.01934636e+00 3.07198405e-01 -9.53767240e-01
-9.57800686e-01 6.42215967e-01 2.09968224e-01 -1.18893588e+00
-3.98840934e-01 -4.56239909e-01 -9.25558329e-01 1.16109192e+00
-1.19878614e+00 -5.22105157e-01 -3.23638797e-01 3.99264604e-01
3.11698675e-01 -1.71006396e-02 9.54844117e-01 6.84913158e-01
6.89241439e-02 3.28211010e-01 -1.98058054e-01 2.03684509e-01
2.88002282e-01 -1.23507869e+00 3.65410626e-01 7.65833914e-01
7.02461898e-02 1.64834630e+00 8.59119833e-01 -3.75635743e-01
-1.63428521e+00 -5.64009607e-01 1.25419307e+00 -3.05810988e-01
1.16254508e+00 -2.82802135e-01 -1.42043889e+00 7.13082373e-01
3.51550728e-01 -5.69462068e-02 5.70603848e-01 -1.57210514e-01
-7.28102207e-01 -1.11395694e-01 -8.02781284e-01 7.15456605e-01
8.05816770e-01 -8.66380036e-01 -8.03609133e-01 4.03794497e-01
8.85201156e-01 -1.44659579e-01 -7.57744670e-01 -2.92102993e-01
5.32909036e-01 -9.83315945e-01 6.02089703e-01 -5.29309332e-01
3.70400280e-01 -8.26964736e-01 -2.50745505e-01 -8.60716820e-01
5.74942604e-02 -7.65407026e-01 4.73967344e-01 1.59571528e+00
7.40530670e-01 -8.74902248e-01 2.76799202e-01 8.11686635e-01
9.17648897e-02 -2.14540020e-01 -5.19344926e-01 -6.39992714e-01
1.74512446e-01 -6.12348020e-01 1.15799737e+00 1.06063843e+00
4.24438566e-01 -2.69622589e-03 5.20200729e-01 2.44749039e-02
1.95464402e-01 4.40652013e-01 7.50551462e-01 -1.33660269e+00
-5.00100434e-01 -7.64591455e-01 -4.30960983e-01 -1.00323713e+00
-1.51475845e-02 -1.12931573e+00 -2.87408615e-03 -1.41440225e+00
2.85956133e-02 -6.15921676e-01 3.16035330e-01 4.96387035e-01
4.04636040e-02 -4.05508101e-01 1.15302101e-01 2.01403081e-01
-2.85627484e-01 -4.00999635e-01 4.16184336e-01 -3.34133089e-01
1.55322611e-01 8.31281068e-04 -8.18446994e-01 7.89563358e-01
3.71323973e-01 -9.25515473e-01 -1.37163281e-01 -6.82524502e-01
1.33765984e+00 4.81905580e-01 4.63147849e-01 -9.89975154e-01
4.30995792e-01 -4.51444060e-01 -4.34312552e-01 -5.71312197e-02
-6.43945992e-01 -1.38162446e+00 4.83563870e-01 4.91431415e-01
-2.34007195e-01 6.84487522e-01 2.27668911e-01 1.47983097e-02
-3.84005725e-01 -1.72987306e+00 2.67767638e-01 -8.40993166e-01
-9.43161190e-01 4.13638838e-02 -5.30194223e-01 3.35504740e-01
8.60348880e-01 -2.70704597e-01 -3.57614160e-01 2.24348292e-01
-5.11618912e-01 2.65026391e-01 1.06090772e+00 5.44468343e-01
3.60651940e-01 -9.94047821e-01 -1.80407032e-01 5.48709743e-02
5.37000537e-01 -3.34103465e-01 -3.01286131e-01 4.07324791e-01
-1.21632612e+00 1.23854525e-01 5.67277484e-02 -4.52340275e-01
-1.35714090e+00 8.96318853e-01 3.08496654e-01 -2.59202998e-02
-4.49885398e-01 6.25210524e-01 9.53464434e-02 -5.52445114e-01
-3.42477523e-02 -6.81970060e-01 -3.24112803e-01 -8.39940608e-02
8.77371967e-01 2.30257809e-02 9.66747180e-02 -1.90639377e-01
-7.43721664e-01 1.21945632e+00 2.22835630e-01 2.82270517e-02
1.00062442e+00 7.36303208e-03 -1.16156316e+00 4.64323610e-01
1.42357051e+00 7.01703787e-01 -2.00951278e-01 -7.39473179e-02
8.85941148e-01 -8.91408026e-01 -6.85480893e-01 -3.63605201e-01
-8.01434994e-01 9.49566245e-01 5.11243582e-01 1.02842259e+00
8.55486691e-01 4.67929214e-01 3.30773026e-01 7.39538372e-01
8.96602750e-01 -5.94484270e-01 -2.60672063e-01 7.53330290e-01
9.10440385e-01 -7.31657624e-01 -1.50295675e-01 -6.54488504e-01
1.14441440e-01 1.90335119e+00 4.18862790e-01 1.30778449e-02
7.50662625e-01 5.14729202e-01 1.83654428e-01 -5.24071634e-01
-6.67456925e-01 -6.18271232e-02 -1.05542690e-01 5.59535623e-01
9.15637434e-01 -4.69890654e-01 -8.48687172e-01 4.51238900e-01
-2.54372030e-01 1.18882768e-01 7.18971848e-01 1.34955323e+00
-6.78416252e-01 -1.75898111e+00 -4.76038009e-01 4.98654425e-01
-8.50011826e-01 -5.94589114e-01 -1.66104604e-02 5.49420774e-01
2.59848326e-01 6.52641773e-01 9.77525115e-02 -1.87054470e-01
4.10648972e-01 1.38607755e-01 1.72304571e-01 -1.21227252e+00
-1.02851570e+00 -5.95606148e-01 -7.68960491e-02 -5.69741964e-01
-1.28135011e-01 -3.47184926e-01 -1.60540986e+00 -1.15903296e-01
-3.45952302e-01 5.40203035e-01 6.92167759e-01 9.96080697e-01
3.70852090e-02 2.70177335e-01 2.68066585e-01 1.27966747e-01
-2.74620622e-01 -3.22980523e-01 7.14232447e-03 3.24037820e-01
3.30291748e-01 -4.47954506e-01 -3.68597358e-01 3.90439272e-01] | [7.552624702453613, 8.041665077209473] |
b5286c28-bc54-4e1e-8762-8d8bb5736cd7 | tivgan-text-to-image-to-video-generation-with | 2009.02018 | null | https://arxiv.org/abs/2009.02018v2 | https://arxiv.org/pdf/2009.02018v2.pdf | TiVGAN: Text to Image to Video Generation with Step-by-Step Evolutionary Generator | Advances in technology have led to the development of methods that can create desired visual multimedia. In particular, image generation using deep learning has been extensively studied across diverse fields. In comparison, video generation, especially on conditional inputs, remains a challenging and less explored area. To narrow this gap, we aim to train our model to produce a video corresponding to a given text description. We propose a novel training framework, Text-to-Image-to-Video Generative Adversarial Network (TiVGAN), which evolves frame-by-frame and finally produces a full-length video. In the first phase, we focus on creating a high-quality single video frame while learning the relationship between the text and an image. As the steps proceed, our model is trained gradually on more number of consecutive frames.This step-by-step learning process helps stabilize the training and enables the creation of high-resolution video based on conditional text descriptions. Qualitative and quantitative experimental results on various datasets demonstrate the effectiveness of the proposed method. | ['Do-Yeon Kim', 'Junmo Kim', 'Donggyu Joo'] | 2020-09-04 | null | null | null | null | ['image-to-video'] | ['computer-vision'] | [ 7.02393949e-01 -2.23609973e-02 1.43315107e-01 -5.83584718e-02
-8.24153006e-01 -3.71588767e-01 7.99711645e-01 -4.11934048e-01
-7.24463165e-02 1.09181738e+00 1.57294080e-01 -5.13366759e-02
4.55134898e-01 -8.70725095e-01 -1.08460200e+00 -7.90798724e-01
4.11911249e-01 6.36284724e-02 1.65828496e-01 1.87867999e-01
2.38479882e-01 2.38050729e-01 -1.43575156e+00 4.88844842e-01
8.22675049e-01 6.45597875e-01 3.94195259e-01 7.37353027e-01
-1.21942526e-02 1.11252475e+00 -6.55750573e-01 -5.63497484e-01
9.79657993e-02 -9.84689534e-01 -5.94349921e-01 4.84572083e-01
2.49026567e-01 -4.88361508e-01 -4.57225651e-01 9.19874012e-01
4.17424768e-01 1.12444937e-01 6.32875919e-01 -1.28400636e+00
-7.45872319e-01 5.35247505e-01 -4.93967354e-01 -2.32820228e-01
5.94243467e-01 3.45149249e-01 5.15980244e-01 -9.58519161e-01
8.51995587e-01 1.20127332e+00 2.87873983e-01 8.98266971e-01
-1.10732639e+00 -7.30059922e-01 1.84377283e-02 -2.88774520e-02
-1.27927232e+00 -3.62094462e-01 9.19402838e-01 -4.78728056e-01
3.72562259e-01 4.50448948e-04 7.79453456e-01 1.43838549e+00
2.51711190e-01 8.51606607e-01 9.36936677e-01 -5.11820197e-01
7.07184076e-02 -6.87555298e-02 -9.31243122e-01 6.25106990e-01
1.56411991e-01 1.16971947e-01 -5.01881540e-01 3.11038554e-01
9.52974439e-01 8.41112509e-02 -4.56173569e-01 -9.99121368e-02
-1.08128190e+00 7.39507496e-01 9.31111351e-02 2.37415656e-01
-4.67239469e-01 2.37772495e-01 2.30814517e-01 1.07564315e-01
3.20947081e-01 1.19853541e-01 2.28755951e-01 -5.99195398e-02
-1.33313680e+00 3.84627968e-01 5.56439340e-01 8.90883207e-01
6.15361333e-01 5.74646592e-01 -2.87772447e-01 5.71039140e-01
1.46334410e-01 5.88559687e-01 3.38712782e-01 -7.59235322e-01
4.60482419e-01 3.61089170e-01 2.02614844e-01 -8.12612176e-01
2.34257206e-01 -1.09902151e-01 -1.08857608e+00 3.40766907e-01
1.62132844e-01 -5.37504017e-01 -1.18852675e+00 1.47644794e+00
3.13578933e-01 4.14944440e-01 1.96767613e-01 8.24094415e-01
8.19204569e-01 1.09091556e+00 2.05656797e-01 -5.45458496e-01
8.48084688e-01 -1.04770720e+00 -8.50310087e-01 -4.48486954e-02
8.09200946e-03 -9.24231052e-01 7.28085995e-01 3.07357699e-01
-1.45114851e+00 -8.48696113e-01 -1.01931357e+00 4.78700995e-02
-1.10565074e-01 -2.77101193e-02 8.40678588e-02 3.30222249e-01
-1.06206465e+00 2.88905352e-01 -6.87892854e-01 -5.20775095e-02
4.23678964e-01 1.09839998e-01 -4.78855997e-01 -2.78612435e-01
-1.26709175e+00 4.94705021e-01 6.87378526e-01 -5.70143433e-03
-1.30586207e+00 -4.03694838e-01 -9.12242830e-01 -3.91858481e-02
4.59594727e-01 -9.36568618e-01 1.01381135e+00 -1.42020988e+00
-1.61005116e+00 6.03862226e-01 -1.34478524e-01 -5.50540924e-01
7.79527307e-01 -8.93658847e-02 -4.08347040e-01 3.57898593e-01
9.75574031e-02 1.04987383e+00 1.43058491e+00 -1.64081740e+00
-7.86339402e-01 1.86816961e-01 5.95110245e-02 1.01413183e-01
-1.02426060e-01 -1.05147265e-01 -6.48744047e-01 -8.61251533e-01
-2.36398175e-01 -8.25616598e-01 4.81404923e-02 -2.30275378e-01
-4.80262518e-01 8.47901702e-02 9.31749523e-01 -7.28526413e-01
1.17541575e+00 -1.95797861e+00 3.50958467e-01 -2.78181016e-01
2.31739096e-02 4.17292416e-01 -3.84986587e-02 5.66420138e-01
-1.18891656e-01 3.41074854e-01 -3.17471981e-01 -4.01709527e-01
-3.08636934e-01 -7.86533430e-02 -3.13518941e-01 8.90598595e-02
5.52589476e-01 1.01395285e+00 -8.19037914e-01 -8.47716808e-01
4.63668674e-01 6.99234009e-01 -5.85347652e-01 5.50174952e-01
-5.33019483e-01 8.79728377e-01 -3.91041338e-01 5.95904529e-01
6.97217166e-01 -1.57153443e-01 -3.87593359e-02 -1.98944747e-01
-5.87773211e-02 -4.38866198e-01 -7.41693556e-01 1.55860507e+00
-3.38077873e-01 7.08410084e-01 -2.24627584e-01 -9.45889890e-01
8.22066665e-01 5.38155377e-01 4.67599899e-01 -5.94072104e-01
2.08937347e-01 4.54239473e-02 -1.73412979e-01 -7.79388666e-01
6.02153957e-01 -3.59197795e-01 -3.79824382e-03 1.79403350e-01
5.67584764e-03 -2.62220651e-01 4.71467346e-01 1.10969767e-01
7.50379920e-01 6.79086566e-01 1.71072319e-01 5.20613015e-01
6.56535268e-01 -7.57444352e-02 2.96715617e-01 4.81292874e-01
4.76843156e-02 9.51426506e-01 3.92242253e-01 -2.55161107e-01
-1.56542718e+00 -8.93435061e-01 1.81854516e-01 4.56403017e-01
1.08961791e-01 -1.38905153e-01 -9.91404951e-01 -5.85278809e-01
-4.51314449e-01 6.29391670e-01 -6.52225733e-01 -1.21597312e-01
-7.08962083e-01 -3.81447375e-01 3.72837365e-01 3.28137636e-01
7.56642640e-01 -1.62738156e+00 -4.69162077e-01 3.14483792e-01
-4.03909862e-01 -1.39422643e+00 -4.78045970e-01 -3.93890232e-01
-6.80971801e-01 -8.06923389e-01 -1.16960335e+00 -1.15861320e+00
8.40686858e-01 1.07223764e-01 9.08357382e-01 -3.06300651e-02
-1.23886429e-01 1.62979245e-01 -5.95766902e-01 -1.37642622e-01
-8.59843075e-01 -8.69479254e-02 -3.16355914e-01 3.39514703e-01
-2.17129722e-01 -4.46736276e-01 -6.26703203e-01 8.02028775e-02
-1.52300656e+00 5.87202013e-01 8.73278439e-01 9.48513389e-01
6.44455850e-01 2.84555733e-01 6.13931179e-01 -7.78538585e-01
4.90169078e-01 -4.74240988e-01 -5.76528966e-01 2.24781603e-01
-6.82704151e-02 3.89499515e-02 1.02384174e+00 -5.90019763e-01
-1.34183836e+00 2.58817077e-01 -1.79549754e-01 -7.43240297e-01
-1.95722863e-01 5.34374475e-01 -2.30606452e-01 1.83172688e-01
3.31698298e-01 7.33386993e-01 -1.06148638e-01 1.11319371e-01
3.72374207e-01 5.67037404e-01 7.09680319e-01 -4.22780216e-01
1.13867259e+00 4.95480031e-01 -2.46844385e-02 -7.19092429e-01
-5.45859933e-01 1.76769331e-01 -4.51290667e-01 -5.17397881e-01
1.26654935e+00 -9.84834492e-01 -4.53120500e-01 5.81061721e-01
-1.18030643e+00 -3.60138625e-01 5.78195341e-02 2.58751035e-01
-7.13247061e-01 1.90150857e-01 -3.89427811e-01 -7.93347538e-01
-4.08296466e-01 -1.14391005e+00 1.11898911e+00 3.88879895e-01
9.91904885e-02 -9.38878000e-01 -9.74579342e-03 3.54092658e-01
1.70833722e-01 6.13609850e-01 5.87079704e-01 -1.13367267e-01
-1.04133940e+00 -1.98466986e-01 -5.18497005e-02 5.36799848e-01
1.82579830e-01 3.13938379e-01 -4.75649059e-01 -3.46893609e-01
-9.09436941e-02 -4.97248769e-01 7.03051448e-01 3.56219560e-01
1.13021612e+00 -3.18693340e-01 -1.91571891e-01 6.22224152e-01
1.60167706e+00 5.27896047e-01 9.81958151e-01 2.47585177e-01
8.28712761e-01 2.54426152e-01 7.33117819e-01 3.70483696e-01
4.09818403e-02 4.30358768e-01 4.63594556e-01 -2.41005078e-01
-3.10370982e-01 -7.06614614e-01 4.17446554e-01 5.66197395e-01
-1.47374377e-01 -6.23915374e-01 -5.31275153e-01 5.20618200e-01
-1.63733017e+00 -1.35512030e+00 1.82890952e-01 2.00616026e+00
7.49003768e-01 2.37448528e-01 -4.40828092e-02 1.40811607e-01
1.03495955e+00 1.83106706e-01 -4.97335106e-01 -1.68921752e-03
7.31616374e-03 1.27404913e-01 1.41699597e-01 3.56051117e-01
-9.31983113e-01 1.26720238e+00 5.89226151e+00 8.48084748e-01
-1.55394375e+00 -1.74494788e-01 7.75948048e-01 7.02206790e-02
-2.73016334e-01 -3.05615775e-02 -6.29615605e-01 7.18812466e-01
7.93881893e-01 -2.41518214e-01 3.61307621e-01 5.29232681e-01
5.64463079e-01 -6.63325191e-02 -7.90923715e-01 9.29363847e-01
3.38950217e-01 -1.43587625e+00 6.82825923e-01 -2.43016824e-01
1.22965753e+00 -6.82709217e-01 6.27862290e-02 2.05145970e-01
1.54583275e-01 -1.09050488e+00 8.78791273e-01 6.07877612e-01
1.19486904e+00 -9.82697666e-01 6.11233711e-01 3.18896711e-01
-1.19527912e+00 5.70265129e-02 -1.85399711e-01 3.41317058e-01
5.44073641e-01 2.14922622e-01 -7.17639029e-01 7.36734271e-01
5.23705184e-01 6.75936401e-01 -2.67007351e-01 8.36169660e-01
-3.54912698e-01 5.43215275e-01 3.87044370e-01 8.76908749e-02
2.11106002e-01 -2.40855604e-01 4.20365363e-01 9.81638730e-01
5.78248024e-01 1.67277575e-01 2.16161415e-01 9.87841725e-01
-3.20636898e-01 1.82105564e-02 -8.01477849e-01 -3.26250643e-01
3.11437130e-01 1.14838398e+00 -6.10391676e-01 -5.33000290e-01
-4.22751099e-01 1.15827370e+00 4.23473455e-02 5.13771355e-01
-1.24796891e+00 -3.67041856e-01 7.55247707e-03 3.09907049e-01
5.54258823e-01 -4.12668437e-02 1.90514326e-01 -1.17417836e+00
8.01823065e-02 -1.17355394e+00 -1.06641687e-01 -1.15418899e+00
-8.38238299e-01 8.49874854e-01 -1.31926062e-02 -1.44232213e+00
-5.53377032e-01 -8.45481902e-02 -5.14479041e-01 7.75867999e-01
-1.43675578e+00 -1.32971132e+00 -6.26409769e-01 7.42723227e-01
9.80762124e-01 -2.41654769e-01 4.70476002e-01 2.77416676e-01
-4.49909955e-01 4.40192401e-01 2.95557603e-02 3.34618300e-01
7.62754917e-01 -7.60151148e-01 3.40696663e-01 1.23047709e+00
-1.61446214e-01 3.17550391e-01 8.63737881e-01 -8.62594962e-01
-1.11525381e+00 -1.32746792e+00 4.73732680e-01 -2.49895286e-02
3.70530069e-01 -2.11299941e-01 -7.21744299e-01 6.46673143e-01
5.67695975e-01 -8.77541155e-02 2.04138845e-01 -9.66269970e-01
1.88131452e-01 -4.77825478e-02 -1.04490876e+00 8.37568700e-01
8.18602324e-01 -3.56185466e-01 -2.71019757e-01 1.54881626e-02
8.40585947e-01 -5.62630773e-01 -6.90648198e-01 3.43695134e-01
4.44111109e-01 -9.93044972e-01 7.73553371e-01 -3.10422599e-01
1.13284135e+00 -4.69041049e-01 9.77244005e-02 -1.25153625e+00
-4.94176634e-02 -9.37366545e-01 9.17292461e-02 1.37834013e+00
1.38277948e-01 -9.98699889e-02 7.95165837e-01 1.99205980e-01
-1.54374063e-01 -5.38005531e-01 -4.61074293e-01 -4.13176030e-01
1.72280557e-02 -8.92196447e-02 3.47318679e-01 7.21525669e-01
-3.11101764e-01 3.83161813e-01 -1.01846182e+00 -1.85298398e-02
5.84769785e-01 4.53671999e-02 9.78212476e-01 -6.84387267e-01
-1.41529426e-01 -7.18143433e-02 -2.73515701e-01 -1.02442777e+00
1.83591276e-01 -5.25217116e-01 2.18792602e-01 -1.64496791e+00
3.46062928e-01 4.31279577e-02 2.06635520e-01 1.68697402e-01
-4.76350456e-01 4.27733779e-01 2.72377640e-01 1.57605007e-01
-5.26158571e-01 6.02649927e-01 1.77828884e+00 -1.03679679e-01
-8.64493027e-02 -1.38459131e-01 -4.58098680e-01 4.18250293e-01
7.39424467e-01 -3.44993025e-01 -5.62288165e-01 -1.78335428e-01
-2.69300379e-02 5.44603169e-01 3.13326895e-01 -1.15819776e+00
4.78149951e-02 -3.06745410e-01 6.62366390e-01 -6.49044514e-01
3.66952449e-01 -6.84919178e-01 4.94673848e-01 4.80906546e-01
-3.48642021e-01 2.15185490e-02 3.67920250e-02 4.79866624e-01
-5.20620763e-01 -2.26387396e-01 9.20586169e-01 -2.92261750e-01
-5.97762227e-01 4.84252244e-01 -3.70396048e-01 1.02094389e-01
1.25265491e+00 -2.75588512e-01 -1.11333951e-01 -7.58894145e-01
-6.21184945e-01 -5.07998616e-02 5.48689365e-01 5.06340086e-01
8.37438524e-01 -1.37704635e+00 -9.57389891e-01 1.22477718e-01
-1.25084549e-01 1.04358464e-01 3.51157665e-01 3.23050112e-01
-8.21912289e-01 4.35705334e-01 -3.89546722e-01 -5.89084923e-01
-1.24528456e+00 8.01355422e-01 1.46102712e-01 -2.48302341e-01
-4.69904512e-01 4.38478738e-01 3.64284813e-01 1.81120411e-01
1.88964866e-02 1.25919715e-01 -1.83720306e-01 -1.87174559e-01
4.58000094e-01 2.64762528e-02 -4.80817258e-01 -7.30445623e-01
1.99325413e-01 4.90691453e-01 -1.95816774e-02 -3.55560124e-01
1.22386003e+00 -1.59844235e-01 1.52711779e-01 1.72045901e-01
1.09613252e+00 -1.19601488e-02 -1.72154593e+00 1.40709758e-01
-5.52342832e-01 -5.31824529e-01 -2.57676601e-01 -4.93112683e-01
-1.22198462e+00 7.98226595e-01 4.36358690e-01 2.36202046e-01
1.24079323e+00 -3.47162127e-01 9.53087449e-01 -2.34608516e-01
1.45630240e-01 -8.36605728e-01 6.22709215e-01 9.46219414e-02
8.41899216e-01 -1.26674783e+00 -1.21213019e-01 -3.21427435e-01
-7.11894810e-01 1.11867476e+00 6.92130983e-01 -1.03327870e-01
7.51362890e-02 1.86115265e-01 2.08247416e-02 3.17758232e-01
-7.36627758e-01 -1.15273573e-01 1.75739959e-01 6.24775171e-01
4.88165647e-01 -2.56992251e-01 -3.88707250e-01 -3.00399121e-02
-7.08400309e-02 4.33228403e-01 6.89187765e-01 8.29401791e-01
-3.85603815e-01 -1.26655889e+00 -4.22638625e-01 1.09570622e-01
-7.08119214e-01 -2.66168594e-01 -1.10239662e-01 8.26467931e-01
1.78848550e-01 9.41301286e-01 -1.96908917e-02 -2.56949782e-01
-4.74817343e-02 -2.75164656e-02 5.03416121e-01 -3.59041542e-01
-1.77610695e-01 3.24248672e-01 -1.60250366e-01 -1.50203973e-01
-6.52053773e-01 -6.50407255e-01 -1.19156432e+00 -2.44926333e-01
-1.01912163e-01 4.39806208e-02 5.32518864e-01 8.70855749e-01
1.38733402e-01 8.63199472e-01 7.85578609e-01 -1.07133889e+00
-6.52414411e-02 -9.16441739e-01 -1.42773584e-01 6.84167743e-01
3.08431983e-01 -3.73422891e-01 -3.00380886e-01 8.11752379e-01] | [10.920517921447754, -0.26702454686164856] |
e4264818-ee57-4fbb-9b52-fbfec76339a3 | deep-learning-for-musical-form-recognition | null | null | http://dx.doi.org/10.13140/RG.2.2.33554.12481 | https://raw.githubusercontent.com/danielathome19/Form-NN/master/PaperAndPresentation/Master%20Thesis.pdf | Deep Learning for Musical Form: Recognition and Analysis | Musical form analysis is a rigorous task that frequently challenges the expertise of human analysts and signal processing algorithms alike. While numerous systems have been proposed to perform the tasks of musical segmentation, genre classification, and single-label segment classification in popular music, none have specifically focused on the analytical process used by classical musicians. Classical music form analysis facilitates a combination of these tasks, including form classification, structural segmentation, and multilabel large- and small-segment classification – tasks that lack feasible algorithms, machine learning models, and extensive research. Form analysis has many applications in the world of music, and a viable analytical system would greatly benefit performing musicians and academic researchers, both in musicology and signal processing. As well, current datasets used for related research tasks lack standardized analytical conventions, including form classification, and suffer from erroneous annotations and extensibility due to the data sources used for the music. In this thesis, we propose a new system to perform the task of automatic musical form analysis using deep learning models, as well as a new standardized dataset. | ['Daniel Szelogowski'] | 2022-04-29 | null | null | null | master-thesis-2022-4 | ['genre-classification'] | ['computer-vision'] | [ 3.49097043e-01 -3.96957785e-01 1.24761790e-01 -1.34338945e-01
-8.06332529e-01 -1.11948562e+00 6.42545894e-02 1.52210489e-01
-3.37375760e-01 3.69899035e-01 -1.04390539e-01 -4.79205437e-02
-3.93066794e-01 -4.65790153e-01 -9.75618884e-02 -5.74358582e-01
2.10733995e-01 7.57954597e-01 -8.65993872e-02 -1.90164819e-01
4.14477944e-01 4.64317858e-01 -1.49552751e+00 3.79431248e-01
5.82259476e-01 1.21021867e+00 -9.96426642e-02 6.97716653e-01
-4.69285965e-01 6.03580475e-01 -7.37679660e-01 -3.48796338e-01
3.51648360e-01 -6.68929935e-01 -7.94625700e-01 -8.35599303e-02
5.41795015e-01 2.20199153e-01 1.44982278e-01 9.88963068e-01
7.97443092e-01 1.96261287e-01 7.03751385e-01 -9.50059950e-01
-3.24290723e-01 1.10932922e+00 -6.53203666e-01 -4.45146114e-01
5.50104260e-01 -1.12550177e-01 1.42661095e+00 -6.21557415e-01
4.01460320e-01 9.73939419e-01 1.06129110e+00 3.28132838e-01
-1.24904299e+00 -9.77744818e-01 -4.43199158e-01 1.11052923e-01
-1.46346200e+00 -2.67698407e-01 1.02954555e+00 -9.12747383e-01
4.23660129e-01 5.58167040e-01 1.10346293e+00 7.94454634e-01
-1.87686816e-01 9.32500243e-01 9.81555045e-01 -5.55229664e-01
1.29951462e-01 -3.22992802e-01 2.65299261e-01 3.91335189e-01
-1.58436090e-01 -2.96683967e-01 -6.19438231e-01 -7.34373331e-02
7.81424105e-01 -2.78984189e-01 1.80035718e-02 1.59611106e-02
-1.26528370e+00 6.58489466e-01 -8.59282389e-02 7.35104918e-01
-1.07062615e-01 -8.39151908e-03 7.71974564e-01 5.18540442e-01
1.81155682e-01 1.30056906e+00 -3.48359406e-01 -3.52406114e-01
-1.76246655e+00 4.96049434e-01 8.17594230e-01 6.92957759e-01
6.96423352e-01 3.00911337e-01 -1.15964428e-01 1.09693217e+00
1.67691603e-01 2.74420500e-01 6.97108507e-01 -1.20932460e+00
5.01146801e-02 6.97015941e-01 -3.17446023e-01 -9.26920533e-01
-7.26139784e-01 -6.67856336e-01 -8.17354202e-01 2.85250753e-01
8.38978469e-01 2.48384282e-01 -6.34951234e-01 1.52591789e+00
-5.80158420e-02 1.97837912e-02 -5.19919157e-01 1.04160988e+00
9.09803748e-01 4.37674403e-01 -1.20966479e-01 -2.62887150e-01
1.36941195e+00 -8.05707633e-01 -6.62137568e-01 2.25683674e-02
4.31084752e-01 -1.29458046e+00 1.40463209e+00 1.21761858e+00
-1.16007912e+00 -7.84023821e-01 -1.14742434e+00 -3.20321769e-01
-1.29765034e-01 3.11909288e-01 6.53503299e-01 6.81965232e-01
-6.48951173e-01 7.88932860e-01 -3.67905140e-01 -1.36589766e-01
4.64199662e-01 4.49636370e-01 -1.50315493e-01 4.94078070e-01
-1.01143229e+00 4.28302467e-01 2.65371650e-01 3.70997004e-02
-6.70202672e-01 -5.47464848e-01 -4.36699897e-01 -2.22115546e-01
1.67674020e-01 -5.05982757e-01 1.40354538e+00 -1.40870106e+00
-1.65928829e+00 1.41753554e+00 3.91456068e-01 -3.04555058e-01
2.71841317e-01 -3.10635448e-01 -3.06214273e-01 -3.86363566e-02
1.95089430e-01 3.05530757e-01 1.00549817e+00 -8.34260881e-01
-5.32851398e-01 -5.98408759e-01 -2.08107740e-01 9.34555382e-02
-2.99038023e-01 1.75964385e-01 -3.06255162e-01 -1.21517444e+00
5.11063397e-01 -1.17296398e+00 -2.20334344e-02 -1.14747666e-01
-3.99649739e-01 -2.81269908e-01 3.83909613e-01 -7.52738118e-01
1.64704943e+00 -2.64692068e+00 3.02418053e-01 3.93208146e-01
2.36722723e-01 1.42312586e-01 4.55397964e-02 -2.20709424e-02
-5.12363277e-02 9.66621414e-02 -3.55093420e-01 -2.39270627e-01
3.78085881e-01 3.82963382e-02 -3.68425906e-01 3.38141561e-01
-4.20736432e-01 5.21895766e-01 -6.27136230e-01 -6.60546124e-01
-1.04766682e-01 -2.75661163e-02 -4.22586977e-01 9.10352319e-02
-3.35467339e-01 4.48169827e-01 -2.74371147e-01 7.69684434e-01
3.66937369e-01 8.67813304e-02 1.47368208e-01 -3.10375392e-01
-2.04947621e-01 4.19317424e-01 -1.50002265e+00 2.18717527e+00
-3.05421501e-01 6.96589887e-01 4.53018785e-01 -9.76003230e-01
1.02244163e+00 3.58517259e-01 9.28703725e-01 -3.10602695e-01
2.84998775e-01 5.81838608e-01 2.80101508e-01 -4.68478650e-01
9.24494565e-01 -4.67844754e-01 -3.94412875e-01 8.32646728e-01
1.42577097e-01 -4.91042882e-01 5.40590167e-01 -2.31873319e-01
7.39858866e-01 4.19166297e-01 2.42043287e-01 -1.12865373e-01
4.29143161e-01 3.28280255e-02 7.62680650e-01 3.94604474e-01
-1.27335981e-01 7.67673910e-01 3.56567889e-01 -5.13380587e-01
-8.96606565e-01 -9.88214076e-01 -1.06681049e-01 1.74082899e+00
-8.82937238e-02 -6.40888095e-01 -7.55982041e-01 -2.67811548e-02
-8.23424011e-02 1.40611902e-01 -1.74122125e-01 1.98687762e-01
-7.18734682e-01 -4.79530454e-01 1.20397532e+00 2.69421250e-01
8.64154771e-02 -1.60453498e+00 -4.88355577e-01 4.96154457e-01
-2.82262623e-01 -8.86790454e-01 -4.34394300e-01 3.12928587e-01
-6.35038078e-01 -1.17092121e+00 -7.11454272e-01 -1.09723878e+00
-2.00908750e-01 -3.05052161e-01 1.26966572e+00 -2.88810790e-01
-5.55844784e-01 1.52376652e-01 -3.59079570e-01 -6.03688240e-01
-3.46893102e-01 5.51117182e-01 1.29217058e-01 1.92092746e-01
2.33750746e-01 -7.81253278e-01 -3.35030466e-01 3.16744506e-01
-7.35337377e-01 -1.18845522e-01 6.53664887e-01 4.42255914e-01
9.24551904e-01 -6.24575354e-02 6.14670515e-01 -6.90834761e-01
8.06882739e-01 2.12582275e-01 -2.51866937e-01 -2.14412548e-02
-5.09577811e-01 -2.80916423e-01 5.47419727e-01 -4.25801903e-01
-6.27249956e-01 1.09285034e-01 -3.18272710e-01 -3.10368001e-01
-2.42699400e-01 6.75591946e-01 -3.35266769e-01 -2.25158185e-02
9.56480622e-01 -5.77410050e-02 -1.61793470e-01 -7.32209206e-01
3.67761970e-01 7.88786769e-01 8.13173592e-01 -8.08710635e-01
4.72386926e-01 1.34530634e-01 1.40228271e-01 -9.58399117e-01
-9.92401004e-01 -7.83308983e-01 -8.34779143e-01 -3.70440602e-01
1.10469520e+00 -6.13758862e-01 -5.27350366e-01 6.50110364e-01
-8.81264448e-01 -6.12405278e-02 -6.69580042e-01 4.83486861e-01
-8.34582746e-01 6.09525084e-01 -8.18705678e-01 -6.70702279e-01
-6.33683503e-01 -9.43041086e-01 9.58471715e-01 1.19303852e-01
-9.19731200e-01 -6.30163193e-01 2.33440533e-01 6.17203355e-01
-9.81013626e-02 2.71543890e-01 9.12903547e-01 -3.35236669e-01
-2.33569354e-01 -2.06392720e-01 2.03638017e-01 3.46114635e-01
6.85969964e-02 8.10343921e-02 -1.27455866e+00 3.98860499e-03
-2.11086258e-01 -5.95090926e-01 7.66258776e-01 5.33389091e-01
1.28105056e+00 1.27859145e-01 1.56790003e-01 8.02282035e-01
7.83385217e-01 1.88021824e-01 3.70155215e-01 3.56249213e-01
8.64973962e-01 6.64643586e-01 4.52259988e-01 4.31971788e-01
-2.55716801e-01 7.76879847e-01 1.73149388e-02 -6.50594309e-02
-1.70195878e-01 -4.24094312e-02 3.73843551e-01 1.24345553e+00
-3.09787780e-01 1.62371993e-01 -7.14013934e-01 4.23971236e-01
-1.75633156e+00 -1.11342287e+00 -4.82449412e-01 2.07876563e+00
1.10852039e+00 -2.78619286e-02 5.69635391e-01 1.12029803e+00
4.65380758e-01 -7.36114904e-02 -3.60394567e-01 -4.38678294e-01
-3.63602281e-01 5.76583683e-01 -7.96798691e-02 7.18257530e-03
-1.51872349e+00 8.75498235e-01 6.61069632e+00 1.12348080e+00
-1.21254313e+00 -1.71387687e-01 1.75381806e-02 -2.63221055e-01
-4.16438915e-02 -2.24053338e-01 -3.50572199e-01 2.31915385e-01
4.03823793e-01 1.60081238e-01 6.21607304e-01 7.61826336e-01
2.17274949e-01 3.49104732e-01 -1.17054117e+00 1.65525520e+00
6.91548362e-02 -1.01023948e+00 9.19846632e-03 -7.65293464e-02
6.49828553e-01 -2.61074901e-01 1.30976707e-01 7.85059407e-02
-3.17430556e-01 -1.07009101e+00 1.16695511e+00 5.42874455e-01
9.00861263e-01 -4.50545013e-01 4.00738448e-01 3.31691653e-02
-1.32530808e+00 -8.65600631e-02 -1.39186543e-03 -3.34845215e-01
1.66829035e-01 6.49125814e-01 -3.86239886e-01 4.33413774e-01
6.56733692e-01 8.40620100e-01 -4.89008188e-01 1.21026099e+00
-1.22749887e-01 9.23724174e-01 -2.72504658e-01 1.65585101e-01
-1.83643904e-02 -6.18746579e-01 6.66570425e-01 1.28956258e+00
4.33600694e-01 -3.97431225e-01 6.34476244e-01 8.64586651e-01
1.01992287e-01 6.62265003e-01 -1.24077290e-01 -4.93519545e-01
1.48249760e-01 1.45122695e+00 -1.03017712e+00 -1.71028569e-01
-3.48308831e-02 6.32813692e-01 -1.40345559e-01 2.96888649e-02
-4.95527506e-01 -4.86017853e-01 5.39966702e-01 5.04983544e-01
-2.02388167e-01 -4.73644882e-01 -9.36868966e-01 -8.38349104e-01
-1.03617683e-01 -1.22950041e+00 6.00538254e-01 -5.52919626e-01
-1.51092589e+00 4.23799247e-01 -5.38397074e-01 -1.58174419e+00
-2.84294158e-01 -7.30282009e-01 -4.16747898e-01 5.93485057e-01
-7.35538185e-01 -1.41342962e+00 -1.78105399e-01 6.27592564e-01
4.35048580e-01 -5.37926137e-01 1.05961645e+00 8.21136773e-01
-2.36321405e-01 6.24462903e-01 -2.38249764e-01 4.27385598e-01
1.13594735e+00 -1.40222812e+00 -2.34452322e-01 3.15740526e-01
8.53335440e-01 3.93634498e-01 5.23397207e-01 -2.24105433e-01
-1.09079182e+00 -8.48586679e-01 6.50175095e-01 -2.13233516e-01
7.68550158e-01 -2.27066368e-01 -6.21380687e-01 2.59504706e-01
-6.46871924e-02 -6.77820683e-01 1.35733950e+00 5.58337688e-01
-3.39197338e-01 -1.92406729e-01 -5.71206510e-01 3.14582050e-01
1.04408872e+00 -8.30823421e-01 -6.36873543e-01 9.74468887e-02
1.09906994e-01 -2.58544862e-01 -9.06462908e-01 3.01873852e-02
1.18999672e+00 -8.34646642e-01 6.98895156e-01 -3.79362553e-01
2.17790335e-01 -6.15464211e-01 -9.32842568e-02 -8.65818799e-01
-5.87973833e-01 -8.51186931e-01 8.38837251e-02 1.24805653e+00
4.75170523e-01 5.81634231e-02 7.57301092e-01 -1.13565892e-01
-5.90610087e-01 -1.93855181e-01 -7.50276923e-01 -6.59253716e-01
-3.70064192e-02 -9.60633636e-01 4.47340846e-01 1.13896203e+00
2.35011503e-02 7.71664560e-01 -3.68772149e-01 -5.21786988e-01
5.17992496e-01 8.35263193e-01 9.41262305e-01 -1.90344608e+00
-6.50256515e-01 -1.22085261e+00 -5.48790097e-01 -8.08173239e-01
2.87627399e-01 -1.28561056e+00 -1.38901621e-01 -1.41995955e+00
-1.19017869e-01 -4.88526255e-01 -3.23754787e-01 5.57255507e-01
1.53408796e-01 8.59530151e-01 3.13304305e-01 4.84320641e-01
-6.56141639e-01 2.10126013e-01 1.35184813e+00 -5.99363208e-01
-4.98121530e-01 2.91389346e-01 -6.91031396e-01 1.05746257e+00
6.51734471e-01 -3.14279288e-01 -2.03653440e-01 -2.88378268e-01
1.01584733e+00 -3.70394409e-01 1.05151102e-01 -1.26949036e+00
7.24654123e-02 1.06765397e-01 2.98661202e-01 -7.13887632e-01
2.65778571e-01 -5.65486729e-01 2.78264970e-01 1.12547129e-01
-3.84829521e-01 -1.28964916e-01 4.89186272e-02 -3.82014066e-02
-5.25800884e-01 -4.42814440e-01 8.23426247e-01 -2.36729234e-01
-6.97999001e-01 2.28072122e-01 -4.97630298e-01 2.40402251e-01
6.81158543e-01 -3.54138523e-01 3.96837473e-01 -3.58687699e-01
-1.26909614e+00 -1.50693685e-01 3.02188933e-01 3.01107138e-01
1.95220187e-01 -1.40292180e+00 -7.58341372e-01 2.53577232e-01
1.77395508e-01 -8.01980868e-03 3.80747318e-02 7.81908393e-01
-8.19022000e-01 -1.88001052e-01 -4.50724065e-01 -5.60549676e-01
-1.34629512e+00 3.68603349e-01 2.28186041e-01 -2.78775185e-01
-4.68979180e-01 8.14193130e-01 1.73050433e-01 -4.60147798e-01
3.30015361e-01 -4.93447661e-01 -4.48864937e-01 7.46985972e-01
3.44718069e-01 5.07085025e-01 1.84739649e-01 -6.14713430e-01
2.03236751e-02 9.21460867e-01 4.49335992e-01 -2.59154350e-01
1.16368604e+00 1.20264702e-01 -5.05614519e-01 1.11155820e+00
5.72407186e-01 2.12980375e-01 -3.76389623e-01 3.03822681e-02
2.49332994e-01 -4.19826135e-02 -6.36442602e-02 -7.52387822e-01
-7.44260788e-01 1.04698348e+00 4.94545609e-01 6.53947532e-01
1.21023309e+00 -2.67163068e-01 9.22685683e-01 3.52837741e-01
2.93236434e-01 -1.63424504e+00 3.39297876e-02 7.76149690e-01
9.34306324e-01 -6.19967937e-01 6.53342009e-02 -2.09600925e-01
-2.78894752e-01 1.14494979e+00 2.53438473e-01 2.86862887e-02
6.25656009e-01 4.61249024e-01 4.69750375e-01 -1.93746507e-01
-5.31010143e-02 -2.44770214e-01 6.07220173e-01 4.02757257e-01
1.02192354e+00 3.60292673e-01 -4.83000278e-01 1.30777717e+00
-9.92828608e-01 -1.31745348e-02 8.28021094e-02 5.07005811e-01
-4.66860443e-01 -1.24870658e+00 -6.64354920e-01 5.49708903e-01
-8.31857324e-01 -3.37758511e-02 -9.67527270e-01 2.27014452e-01
7.48390138e-01 8.29044044e-01 -3.71150076e-02 -6.41527832e-01
1.66848987e-01 3.27613533e-01 7.97872424e-01 -5.63728094e-01
-9.88567591e-01 7.84344316e-01 7.32551739e-02 -2.71139473e-01
-5.92684269e-01 -6.45262241e-01 -1.30639887e+00 -3.61435600e-02
-2.30624154e-01 2.52757758e-01 5.20494759e-01 8.94766510e-01
-1.42805010e-01 6.81581974e-01 3.41826737e-01 -1.09434164e+00
-3.03283870e-01 -1.34275222e+00 -1.35239089e+00 6.74169064e-01
-8.28966200e-02 -4.80399489e-01 -3.80866937e-02 4.38388228e-01] | [15.94118881225586, 5.213170051574707] |
094037e5-bc63-4859-b981-9c34b2453d30 | parallax-tolerant-image-stitching | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Zhang_Parallax-tolerant_Image_Stitching_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Zhang_Parallax-tolerant_Image_Stitching_2014_CVPR_paper.pdf | Parallax-tolerant Image Stitching | Parallax handling is a challenging task for image stitching. This paper presents a local stitching method to handle parallax based on the observation that input images do not need to be perfectly aligned over the whole overlapping region for stitching. Instead, they only need to be aligned in a way that there exists a local region where they can be seamlessly blended together. We adopt a hybrid alignment model that combines homography and content-preserving warping to provide flexibility for handling parallax and avoiding objectionable local distortion. We then develop an efficient randomized algorithm to search for a homography, which, combined with content-preserving warping, allows for optimal stitching. We predict how well a homography enables plausible stitching by finding a plausible seam and using the seam cost as the quality metric. We develop a seam finding method that estimates a plausible seam from only roughly aligned images by considering both geometric alignment and image content. We then pre-align input images using the optimal homography and further use content-preserving warping to locally refine the alignment. We finally compose aligned images together using a standard seam-cutting algorithm and a multi-band blending algorithm. Our experiments show that our method can effectively stitch images with large parallax that are difficult for existing methods. | ['Feng Liu', 'Fan Zhang'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['image-stitching'] | ['computer-vision'] | [ 6.79722667e-01 -1.78980842e-01 -2.33461156e-01 8.34446177e-02
-5.83119035e-01 -9.96809602e-01 5.28755426e-01 -1.29070446e-01
-6.25055097e-03 2.11290017e-01 1.76791117e-01 -7.51048401e-02
-8.13472643e-02 -6.34179235e-01 -9.32120383e-01 -7.41914988e-01
2.38493025e-01 3.16273510e-01 4.44764197e-01 -3.20183784e-01
5.36692560e-01 4.98834968e-01 -1.25992715e+00 2.99998857e-02
1.15767694e+00 5.59848189e-01 1.64219961e-01 7.99606144e-01
1.85612246e-01 1.14717707e-02 -4.46650118e-01 -4.03750539e-01
8.22985709e-01 -6.86362267e-01 -7.28632867e-01 6.96188152e-01
9.44454074e-01 -5.41292310e-01 -1.03855245e-01 1.07455182e+00
1.44641668e-01 8.47468078e-02 4.01832253e-01 -1.20393443e+00
-9.54611450e-02 1.42234772e-01 -1.24184322e+00 -2.06807241e-01
4.35135186e-01 3.18564288e-02 8.61233234e-01 -4.27182645e-01
8.93555582e-01 1.09774578e+00 6.68070078e-01 7.42676333e-02
-1.39909267e+00 -5.72830915e-01 -3.48296255e-01 -2.11399660e-01
-1.19353127e+00 -4.57167596e-01 1.22682858e+00 -2.64996886e-01
4.63286817e-01 5.53138077e-01 8.44168425e-01 4.39657778e-01
6.29285932e-01 3.14069897e-01 9.92911458e-01 -8.15060616e-01
-1.12229966e-01 -3.88837725e-01 -2.38946021e-01 6.91292644e-01
2.15727016e-01 3.63707364e-01 -4.11308140e-01 -2.38224849e-01
1.18791950e+00 1.45958692e-01 -4.49675411e-01 -6.12792969e-01
-1.67752934e+00 5.99217653e-01 4.78290290e-01 5.04960060e-01
-4.64133583e-02 3.57912719e-01 4.62325290e-02 2.44706884e-01
4.11295086e-01 6.34031117e-01 1.89745113e-01 2.08873019e-01
-1.32535398e+00 2.15038106e-01 4.30561751e-01 8.89347017e-01
9.88794684e-01 -1.11306466e-01 3.71261835e-01 7.91022480e-01
-1.40684158e-01 4.67970878e-01 1.97015524e-01 -1.34660590e+00
4.48561877e-01 2.08785251e-01 1.16248794e-01 -1.39722061e+00
2.02167869e-01 -5.12619466e-02 -8.61954033e-01 5.10174036e-01
3.28533858e-01 1.74995393e-01 -7.62704074e-01 1.33996975e+00
3.75576705e-01 4.34927434e-01 -8.66179466e-02 7.53591120e-01
9.24862921e-02 8.12119722e-01 -8.97041202e-01 -3.82876575e-01
1.32690978e+00 -1.19615078e+00 -8.08540344e-01 -2.89646566e-01
4.04484212e-01 -1.32075524e+00 7.79534221e-01 3.67808431e-01
-1.42025638e+00 -4.32047248e-01 -1.42702091e+00 -1.03273839e-01
2.89150506e-01 -2.27940023e-01 1.28378958e-01 4.68686789e-01
-1.02972138e+00 6.43930674e-01 -6.53360844e-01 -2.20894348e-02
-2.49427766e-01 2.14597523e-01 -4.93134469e-01 -1.67827770e-01
-5.92574179e-01 8.84866357e-01 4.16356415e-01 -2.39097685e-01
-2.98108727e-01 -4.36779976e-01 -9.28509653e-01 -6.81197345e-02
4.74434048e-01 -8.45448017e-01 7.96088398e-01 -1.24688792e+00
-1.35335886e+00 7.48224139e-01 -3.44362766e-01 -3.26261252e-01
5.30218005e-01 -2.19445005e-02 -3.41488346e-02 4.24700111e-01
-1.72501355e-02 7.48596072e-01 1.24580860e+00 -1.72096705e+00
-4.85373616e-01 -5.80803631e-03 -2.97751665e-01 4.21035826e-01
-2.15775594e-01 -6.91774338e-02 -6.09725118e-01 -1.09370816e+00
5.20153940e-01 -1.05406344e+00 -3.66172761e-01 2.46948246e-02
-4.67172086e-01 3.96223009e-01 1.14031816e+00 -7.50972688e-01
1.27817166e+00 -2.15884399e+00 4.02441919e-01 5.16590714e-01
2.81736523e-01 -7.55603984e-02 -2.91853279e-01 6.72195017e-01
-5.80947809e-02 -2.26437375e-02 -5.20846486e-01 -5.18394768e-01
-6.46691084e-01 1.84709847e-01 -6.18714929e-01 6.26320899e-01
-2.20434383e-01 6.79386079e-01 -8.51193666e-01 -5.57540596e-01
4.80542421e-01 2.36500055e-01 -6.19415700e-01 1.38135031e-01
-4.30589393e-02 3.36625963e-01 1.30927056e-01 4.82113779e-01
1.07893491e+00 1.87882483e-01 -1.83256418e-02 -5.85015476e-01
-5.87105155e-01 -3.38856280e-01 -1.23322463e+00 1.85098433e+00
-4.37682241e-01 8.34731877e-01 2.38811880e-01 -6.38486028e-01
9.69793558e-01 1.32219836e-01 7.94650912e-01 -2.19261482e-01
1.89979032e-01 5.33856213e-01 -3.27111781e-01 -1.39015079e-01
7.94937015e-01 -2.26621643e-01 8.97320509e-02 5.84361494e-01
-3.57764482e-01 -7.26303816e-01 2.52038613e-02 7.01090619e-02
7.35797942e-01 -1.38194486e-01 2.84316540e-01 -2.98084766e-01
3.19216818e-01 1.17284425e-01 4.17382658e-01 3.46566081e-01
2.32927904e-01 1.26652932e+00 8.34367350e-02 -4.74319220e-01
-1.55899322e+00 -8.78102601e-01 -1.77040268e-02 2.98611253e-01
9.07935679e-01 -3.95583302e-01 -9.14918005e-01 -2.79699236e-01
-3.70893270e-01 3.59678119e-01 -3.99696141e-01 -6.91265315e-02
-8.80658686e-01 -1.86585784e-01 2.23408043e-01 9.57323536e-02
8.23400080e-01 -4.95343089e-01 -7.72005796e-01 1.52622402e-01
-4.30643320e-01 -9.45226669e-01 -1.36216891e+00 -1.57025144e-01
-1.16769958e+00 -9.72246170e-01 -8.83179069e-01 -1.06116700e+00
1.02626157e+00 1.18130934e+00 7.29313672e-01 5.54039299e-01
-7.60536864e-02 1.47001788e-01 -1.90247685e-01 3.86271894e-01
-6.31333470e-01 -4.44560081e-01 -1.82230160e-01 9.37325358e-02
-7.36497223e-01 -6.34928524e-01 -8.02498460e-01 8.41330111e-01
-1.35350454e+00 6.48344040e-01 4.09000576e-01 9.81043875e-01
6.31178975e-01 4.41434205e-01 -2.96447515e-01 -2.91530609e-01
2.85446435e-01 1.44769713e-01 -6.12886488e-01 3.58556032e-01
-3.66385370e-01 7.23009855e-02 3.95168334e-01 -7.03204453e-01
-8.47025692e-01 2.31993094e-01 6.24278337e-02 -8.10095012e-01
2.81739175e-01 1.35252923e-01 8.30587465e-03 -5.50116479e-01
4.54648465e-01 2.59484380e-01 3.88172001e-01 -2.01924577e-01
6.24119103e-01 3.73291582e-01 9.58476841e-01 -4.82271582e-01
1.33406115e+00 8.63096774e-01 1.28236920e-01 -8.97744715e-01
-1.56872600e-01 -4.38055992e-01 -7.62048066e-01 -3.64649177e-01
8.11144054e-01 -4.43917841e-01 -3.95279795e-01 6.01783693e-01
-1.29913259e+00 -1.34388208e-01 -2.03769609e-01 3.29650909e-01
-7.25585639e-01 1.09375072e+00 -3.70685577e-01 -4.23261642e-01
-3.14675063e-01 -1.26211607e+00 1.16415262e+00 6.09113229e-03
-2.16661617e-01 -8.75641108e-01 3.93309355e-01 4.10219103e-01
2.70892918e-01 6.07305646e-01 6.59744740e-01 2.31304348e-01
-8.33094358e-01 2.19282089e-03 1.30877979e-02 2.01727021e-02
5.24686456e-01 2.90949851e-01 -4.74361002e-01 -5.63646913e-01
1.87776789e-01 7.92756006e-02 7.28235662e-01 5.75316370e-01
5.55850208e-01 -6.89418137e-01 -4.36355948e-01 9.40886140e-01
1.59755540e+00 3.04927111e-01 8.48377943e-01 5.11291862e-01
8.69481802e-01 7.51183212e-01 5.50615609e-01 3.43965553e-02
-1.86490510e-02 1.12387013e+00 4.47215229e-01 -4.33870137e-01
-2.65488446e-01 -4.28064376e-01 2.65593112e-01 7.84804225e-01
2.50927708e-03 -2.40709081e-01 -6.47229850e-01 5.93195200e-01
-1.80920553e+00 -9.36265826e-01 -8.75313580e-02 2.51954508e+00
8.65238845e-01 -1.26625553e-01 1.20640785e-01 3.41365516e-01
9.75430965e-01 2.89390385e-01 -1.55984804e-01 -4.62474555e-01
-2.30020478e-01 -4.75442223e-02 7.93430030e-01 1.11479187e+00
-8.36471021e-01 8.78810465e-01 5.99268627e+00 1.17185783e+00
-1.01679242e+00 -1.90849155e-01 4.45433438e-01 3.28698039e-01
-8.79478395e-01 4.98798907e-01 -2.08772928e-01 3.82209629e-01
2.09013775e-01 -1.20314293e-01 5.64838707e-01 2.95599848e-01
1.48709044e-01 -4.52756733e-01 -6.62913680e-01 9.72833633e-01
3.01346868e-01 -1.64382219e+00 3.06885112e-02 2.34029904e-01
1.12498140e+00 -6.04399025e-01 6.34011328e-02 -8.46596301e-01
2.95306630e-02 -7.55114734e-01 8.87431681e-01 4.05842625e-02
8.38534117e-01 -8.58412623e-01 3.11960697e-01 3.68324518e-01
-1.44527960e+00 2.10990742e-01 -1.32552415e-01 4.07327324e-01
5.18177867e-01 5.62379062e-01 -5.27789414e-01 7.00601339e-01
3.35138857e-01 5.99178255e-01 -1.39045477e-01 1.15348256e+00
1.00303978e-01 -2.78570950e-02 -4.11288321e-01 6.44738793e-01
3.80648524e-02 -7.83364058e-01 8.23473513e-01 8.51991117e-01
7.92345464e-01 1.35613546e-01 1.22236356e-01 5.71551144e-01
1.71502873e-01 4.39576665e-03 -8.91019344e-01 3.28646153e-01
5.64944685e-01 9.40799117e-01 -1.18593454e+00 -2.38904774e-01
-2.55211711e-01 1.50186694e+00 -3.56016159e-01 2.65324563e-01
-6.81085825e-01 -3.49726707e-01 4.96011555e-01 1.57520160e-01
1.26387805e-01 -6.10860527e-01 -5.51096320e-01 -1.26097023e+00
-1.49079440e-02 -1.00179267e+00 1.22441463e-01 -8.62513602e-01
-8.29827785e-01 6.41774237e-01 1.87115446e-01 -1.82461464e+00
-2.21813336e-01 6.75949529e-02 -8.63865495e-01 5.43681502e-01
-1.23894370e+00 -1.36428523e+00 -3.43607128e-01 4.96373445e-01
7.87830889e-01 4.04330820e-01 2.20233694e-01 -1.73993662e-01
-1.31105453e-01 4.32457954e-01 4.29090485e-02 -3.34794283e-01
1.01448023e+00 -8.49299848e-01 4.23287183e-01 1.23932898e+00
1.51392862e-01 6.36893034e-01 8.98705900e-01 -8.65396440e-01
-1.37433994e+00 -6.65914953e-01 7.45599627e-01 -4.10331693e-03
3.75634670e-01 -8.03805739e-02 -9.48771954e-01 4.92605835e-01
7.35164583e-01 -3.93574178e-01 1.29446641e-01 -6.83153033e-01
-4.67094690e-01 -1.11218497e-01 -1.16423965e+00 8.57847750e-01
1.00645399e+00 -2.84965992e-01 -5.74091077e-01 -1.00630082e-01
8.08248460e-01 -6.26770794e-01 -6.12625539e-01 3.84303212e-01
6.47601426e-01 -1.20667207e+00 1.23553240e+00 4.10846144e-01
5.74592531e-01 -7.86508262e-01 2.11532399e-01 -1.39715505e+00
-2.08120197e-01 -1.31396639e+00 2.97000378e-01 1.11985636e+00
-1.02091901e-01 -5.86758733e-01 6.02656245e-01 2.09038958e-01
-1.90102994e-01 -3.74205649e-01 -9.03458357e-01 -9.48238611e-01
-2.01300591e-01 6.09664135e-02 4.51420218e-01 1.17932224e+00
7.36137480e-02 -1.04638368e-01 -8.11555922e-01 1.82833269e-01
8.37326467e-01 4.95806336e-01 8.73251677e-01 -7.60777414e-01
-2.91990310e-01 -5.58174491e-01 -2.19332710e-01 -1.29983103e+00
-1.61606267e-01 -4.88150954e-01 1.90714747e-01 -1.19125557e+00
1.98242307e-01 -4.78687882e-01 6.29531801e-01 2.61276186e-01
-8.81235078e-02 5.70145786e-01 2.38246620e-01 6.34226561e-01
1.71356797e-01 3.31787616e-01 1.50594461e+00 -3.38930939e-03
-4.97833192e-01 -2.50611722e-01 -3.41847450e-01 4.82298076e-01
6.31529272e-01 -1.84726819e-01 -4.52614933e-01 -5.46792448e-01
1.52073830e-01 4.94241446e-01 1.80151924e-01 -9.77240622e-01
2.76586592e-01 -4.45993155e-01 8.65157396e-02 -5.84401786e-01
3.65516990e-01 -1.17480016e+00 6.99654698e-01 6.50158465e-01
-1.80836275e-01 3.54694545e-01 1.29708678e-01 3.17693889e-01
-2.86286503e-01 -4.25464123e-01 9.55071867e-01 3.67803365e-01
-1.97316006e-01 1.48215413e-01 -1.43226191e-01 -5.42178810e-01
1.21856046e+00 -7.86351323e-01 -3.34958553e-01 -5.20690560e-01
-1.38532355e-01 6.12388849e-02 1.39099550e+00 2.09610745e-01
8.37667406e-01 -1.50784111e+00 -4.91485149e-01 4.72589791e-01
-1.25228256e-01 -4.23812568e-02 2.00190395e-01 6.67022824e-01
-1.06981993e+00 -1.79366603e-01 -3.79130453e-01 -7.71064699e-01
-1.67560983e+00 5.84164143e-01 2.63359785e-01 -8.92431065e-02
-7.29187787e-01 4.89435434e-01 3.24545324e-01 1.28512412e-01
-4.91706580e-02 -4.04729962e-01 3.55484158e-01 -3.00938994e-01
4.38599825e-01 3.07855397e-01 -2.20583342e-02 -7.65463948e-01
-1.23678241e-02 1.53654599e+00 1.90559506e-01 -7.02082336e-01
1.02785635e+00 -3.68617326e-01 -2.17962518e-01 -2.19855353e-01
1.35803854e+00 6.24685168e-01 -1.50242889e+00 1.12509942e-02
-4.51361120e-01 -1.19797480e+00 -3.13356593e-02 -1.02455385e-01
-1.23813283e+00 6.43255651e-01 2.32574463e-01 1.88557833e-01
1.44403601e+00 -9.20521766e-02 1.26702094e+00 -1.83214188e-01
2.09715381e-01 -6.90504253e-01 5.75194061e-01 1.55314982e-01
1.02926362e+00 -7.76200771e-01 1.46716058e-01 -7.58388460e-01
-3.85138065e-01 1.48732233e+00 3.21968228e-01 -3.04043442e-01
1.55276120e-01 4.51879561e-01 -9.22516063e-02 1.18013835e-02
-2.99782395e-01 1.17718711e-01 4.42523509e-01 3.89581412e-01
5.63342795e-02 -1.98006511e-01 -4.27984118e-01 -4.06895250e-01
-3.30064833e-01 -4.06270057e-01 7.50539422e-01 9.76963222e-01
-7.27833867e-01 -1.50632393e+00 -1.00929880e+00 -1.68894991e-01
6.26059100e-02 4.21165563e-02 -3.61652970e-01 4.45264608e-01
-3.14004952e-03 9.67069685e-01 9.04624164e-02 -6.80741429e-01
7.72325024e-02 -5.51495731e-01 8.11433136e-01 -2.08444685e-01
-4.22556996e-01 8.19172382e-01 -1.07340612e-01 -4.91297454e-01
-4.11987990e-01 -3.44275266e-01 -1.00778234e+00 -6.00176513e-01
-4.79793638e-01 -5.21770157e-02 4.38044339e-01 7.57209837e-01
8.98307636e-02 -1.25072479e-01 1.13180137e+00 -1.21934617e+00
1.35478765e-01 -2.34541029e-01 -1.89488932e-01 3.77253503e-01
6.52326167e-01 -3.84064049e-01 -5.14016807e-01 4.44336504e-01] | [9.410402297973633, -2.3608312606811523] |
0fa78808-ac8d-410c-93ec-c88d90922139 | latent-preserving-generative-adversarial | 2209.01555 | null | https://arxiv.org/abs/2209.01555v1 | https://arxiv.org/pdf/2209.01555v1.pdf | Latent Preserving Generative Adversarial Network for Imbalance classification | Many real-world classification problems have imbalanced frequency of class labels; a well-known issue known as the "class imbalance" problem. Classic classification algorithms tend to be biased towards the majority class, leaving the classifier vulnerable to misclassification of the minority class. While the literature is rich with methods to fix this problem, as the dimensionality of the problem increases, many of these methods do not scale-up and the cost of running them become prohibitive. In this paper, we present an end-to-end deep generative classifier. We propose a domain-constraint autoencoder to preserve the latent-space as prior for a generator, which is then used to play an adversarial game with two other deep networks, a discriminator and a classifier. Extensive experiments are carried out on three different multi-class imbalanced problems and a comparison with state-of-the-art methods. Experimental results confirmed the superiority of our method over popular algorithms in handling high-dimensional imbalanced classification problems. Our code is available on https://github.com/TanmDL/SLPPL-GAN. | ['Hussein A. Abbass', 'Senthilnath Jayavelu', 'Sreenatha G. Anavatti', 'Mahardhika Pratama', 'Md Meftahul Ferdaus', 'Tanmoy Dam'] | 2022-09-04 | null | null | null | null | ['imbalanced-classification'] | ['miscellaneous'] | [ 1.20603599e-01 5.30086756e-02 -2.67254204e-01 -5.66281259e-01
-8.15328002e-01 -3.75375450e-01 2.50156432e-01 -5.80766313e-02
-1.62359089e-01 8.78258646e-01 -1.69478104e-01 -2.26848498e-01
2.19746888e-01 -1.11128879e+00 -5.85831344e-01 -8.49047005e-01
3.28819275e-01 7.96691597e-01 -8.20545778e-02 -9.42597613e-02
1.62139922e-01 1.96467772e-01 -1.53488708e+00 5.05945742e-01
9.03541028e-01 1.01178646e+00 -5.19322217e-01 3.52830917e-01
1.69017538e-01 7.75736988e-01 -1.11104286e+00 -7.19526350e-01
4.43924308e-01 -6.42115176e-01 -7.48148024e-01 6.76182061e-02
2.69907534e-01 -2.39379004e-01 -3.07625920e-01 1.08689916e+00
9.18345511e-01 4.42127651e-03 6.68056905e-01 -1.88701367e+00
-7.25617826e-01 3.31593841e-01 -6.02108121e-01 5.93830049e-02
-3.81684117e-02 1.32846490e-01 8.18622291e-01 -6.38998270e-01
4.35969085e-01 1.18564260e+00 8.40789199e-01 7.29628563e-01
-1.11091101e+00 -1.17325532e+00 -6.87945634e-02 2.23317698e-01
-1.19960368e+00 -1.92713842e-01 1.09419131e+00 -5.62985659e-01
6.43621683e-01 1.63579538e-01 7.06822634e-01 1.38099098e+00
3.60219568e-01 7.49534786e-01 1.12376738e+00 -3.41069847e-01
3.97706300e-01 2.49290764e-02 -2.91844346e-02 3.91590685e-01
4.58369374e-01 1.75931409e-01 -3.12491834e-01 -2.10461304e-01
2.93017626e-01 1.13321416e-01 1.81671493e-02 -4.56028283e-01
-7.04751909e-01 1.16163456e+00 4.97141540e-01 2.16191739e-01
-2.18556508e-01 -1.00505561e-01 5.12030244e-01 4.56888586e-01
1.13909626e+00 3.27310503e-01 -2.07130283e-01 -3.14455442e-02
-9.36328173e-01 4.39061671e-01 7.69926071e-01 6.11410737e-01
4.87073302e-01 7.42777288e-02 -2.67793518e-02 9.71817374e-01
3.78311519e-03 2.11862713e-01 8.37499380e-01 -6.33014798e-01
5.51250517e-01 7.61500955e-01 -2.04734698e-01 -1.19430566e+00
-4.10138756e-01 -7.91894853e-01 -1.38274002e+00 3.60210866e-01
3.39423686e-01 -1.01867162e-01 -9.22249854e-01 1.67395186e+00
5.30731440e-01 6.81088492e-02 1.72138661e-02 8.82291973e-01
4.56992149e-01 6.97148442e-01 -1.71455801e-01 4.26879227e-02
1.02155185e+00 -1.02289879e+00 -6.45172954e-01 -4.33063686e-01
5.39649010e-01 -6.40212834e-01 1.02666044e+00 4.85218853e-01
-9.15903687e-01 -5.14123082e-01 -1.06881177e+00 -2.31032143e-03
-4.12074566e-01 2.41390485e-02 6.13971293e-01 8.79841208e-01
-5.98722517e-01 5.54440022e-01 -8.60451639e-01 1.82603747e-01
8.93097937e-01 2.89767176e-01 -2.83306152e-01 -1.33599907e-01
-1.30853653e+00 8.15979302e-01 4.66124326e-01 8.93365964e-02
-9.12050784e-01 -7.37847984e-01 -7.29156494e-01 -1.14112899e-01
-4.70339768e-02 -7.72108138e-01 1.14082217e+00 -1.24298799e+00
-1.43518364e+00 1.10154152e+00 1.78698689e-01 -3.65404099e-01
8.00710082e-01 -1.32189766e-01 -3.58360767e-01 -3.68172705e-01
1.93921492e-01 4.20152634e-01 8.46298158e-01 -1.10462403e+00
-4.57500815e-01 -4.94234055e-01 -5.94350472e-02 2.44240418e-01
-3.38543713e-01 -2.87254393e-01 9.00076404e-02 -8.62983763e-01
6.29244298e-02 -9.67878222e-01 -2.00939327e-02 -1.72878981e-01
-6.89403415e-01 -2.33177677e-01 8.91209483e-01 -4.58911538e-01
1.24833715e+00 -2.04623938e+00 1.40820190e-01 1.61440310e-03
3.08229029e-01 4.04964536e-01 -6.99146651e-04 3.48644912e-01
-5.18135548e-01 1.36390597e-01 -5.07999718e-01 -3.90879244e-01
8.45508575e-02 1.51744500e-01 -3.80776405e-01 7.78116167e-01
1.99482068e-01 6.72441125e-01 -8.19202542e-01 1.78194866e-02
-1.23469099e-01 4.89346385e-01 -7.64951289e-01 4.43622589e-01
-8.14809725e-02 3.55749965e-01 -5.52456826e-02 6.34546041e-01
9.22743320e-01 -2.83337742e-01 1.06481336e-01 2.93969512e-02
4.89318758e-01 4.62579131e-01 -1.18630338e+00 1.37884045e+00
-3.48816603e-01 4.57600504e-01 -3.87628734e-01 -1.40153062e+00
9.75919902e-01 2.05242664e-01 1.95079729e-01 -5.82699537e-01
2.65281677e-01 3.29349607e-01 6.73383549e-02 -3.77217412e-01
1.93967640e-01 -6.09862447e-01 -2.67772973e-01 5.27160108e-01
4.78641614e-02 -1.97892353e-01 1.18766494e-01 -1.92325220e-01
9.06298459e-01 -1.09888025e-01 2.06400141e-01 -4.54179160e-02
2.21802369e-01 -1.61471859e-01 8.93955290e-01 4.11714345e-01
-1.06326431e-01 7.45634317e-01 8.25645387e-01 -6.86686814e-01
-1.14469945e+00 -7.82719433e-01 -1.61227107e-01 7.09178150e-01
-6.23848140e-02 -7.67456964e-02 -1.03763521e+00 -9.29367423e-01
6.29154220e-02 6.88982546e-01 -6.90869629e-01 -6.33064926e-01
-3.09329629e-01 -1.39166474e+00 7.43794143e-01 3.97202969e-01
6.12242758e-01 -9.43879664e-01 -4.70633715e-01 1.42292544e-01
-2.76155174e-01 -7.01528966e-01 -1.50805682e-01 1.72696769e-01
-8.67927253e-01 -1.11696196e+00 -5.42906165e-01 -6.61300361e-01
8.06025982e-01 -1.86029524e-01 1.29692173e+00 1.09488666e-01
-3.47859472e-01 -3.85606736e-01 -2.02766851e-01 -6.43502712e-01
-6.34670973e-01 4.53538537e-01 3.36961783e-02 2.83409283e-02
5.61180055e-01 -5.77091515e-01 -7.20846772e-01 3.69809449e-01
-1.18807709e+00 9.47373137e-02 2.75396347e-01 1.20509779e+00
5.38008213e-01 4.37911242e-01 8.84323657e-01 -1.26154971e+00
6.28546655e-01 -7.14168608e-01 -5.19171655e-01 -2.29607731e-01
-6.36586308e-01 -2.92665929e-01 7.25111306e-01 -5.00707567e-01
-6.87102854e-01 -1.38799116e-01 -4.58853245e-01 -3.87990862e-01
-2.13256404e-01 2.45361865e-01 -5.16653419e-01 3.17071497e-01
6.52080297e-01 7.00403645e-04 1.40930668e-01 -4.18251991e-01
-5.76764636e-04 9.02918041e-01 2.16796845e-01 -2.85120606e-01
7.51117647e-01 4.11124408e-01 -1.41052157e-01 -2.55046278e-01
-1.22731948e+00 -6.65981770e-02 -4.86089021e-01 5.06147519e-02
4.83880311e-01 -9.55790818e-01 -3.51745397e-01 1.15934932e+00
-9.67103541e-01 -3.86456549e-01 -4.13245976e-01 9.58257690e-02
-6.41272783e-01 -8.24473724e-02 -6.16787910e-01 -4.40880567e-01
-3.82836461e-01 -1.14335346e+00 9.53078389e-01 1.73817590e-01
-1.46785080e-01 -1.04291034e+00 3.24193388e-01 7.21068919e-01
5.11260390e-01 6.23057544e-01 1.06230283e+00 -8.37999344e-01
-1.27095863e-01 -6.24448776e-01 1.76913470e-01 7.30638027e-01
1.04642212e-01 -2.47351572e-01 -1.16968513e+00 -5.32228053e-01
2.22784892e-01 -5.02962828e-01 8.11967015e-01 1.00463934e-01
1.64041007e+00 -5.16136646e-01 -2.19164908e-01 7.48421311e-01
1.33353078e+00 1.38752341e-01 7.92940199e-01 2.45242715e-01
7.15215385e-01 4.81579870e-01 4.56129283e-01 2.95298606e-01
3.29695433e-01 7.34454930e-01 5.70924461e-01 -3.97491276e-01
-8.15207586e-02 -2.88280278e-01 8.76250342e-02 9.50535059e-01
2.66332209e-01 -6.51542544e-01 -1.12705636e+00 4.41566736e-01
-1.54062271e+00 -9.23913479e-01 2.33813703e-01 2.08432126e+00
1.16592538e+00 1.34151235e-01 1.45836130e-01 7.40683854e-01
8.78785312e-01 1.20994106e-01 -6.94633961e-01 -3.15357178e-01
-3.00439727e-02 2.38429427e-01 1.20256953e-01 3.92783910e-01
-1.31624889e+00 7.57650971e-01 5.67502975e+00 9.98878837e-01
-1.33857739e+00 3.63279760e-01 1.25683677e+00 -1.72113270e-01
-1.29697040e-01 -2.85504490e-01 -7.36191869e-01 8.60098839e-01
7.85483301e-01 -5.69516234e-02 1.38403550e-01 8.95679533e-01
-3.14944237e-01 1.11244947e-01 -1.02701426e+00 8.49579990e-01
1.98942333e-01 -1.05326211e+00 -1.03616834e-01 -2.40050163e-02
8.50949287e-01 -1.75640643e-01 1.73586488e-01 4.63407189e-01
1.63354635e-01 -1.18137586e+00 5.89856863e-01 2.39117399e-01
8.92977715e-01 -1.03354859e+00 9.01735723e-01 4.86375719e-01
-4.10448402e-01 -8.41988847e-02 -4.95166123e-01 -2.44226873e-01
-1.91534519e-01 1.17033422e+00 -6.15751684e-01 3.13160509e-01
5.91715038e-01 6.30934179e-01 -4.77731645e-01 8.60106587e-01
-2.85126001e-01 6.92783833e-01 -1.78894222e-01 2.30842322e-01
-5.78380302e-02 2.89651845e-02 4.70752716e-01 9.53572750e-01
2.08569303e-01 -8.96441042e-02 3.82023677e-02 6.98412597e-01
-5.24195492e-01 -1.42672494e-01 -6.53265834e-01 6.33933172e-02
3.48315954e-01 1.05105281e+00 -7.10776687e-01 -2.55331099e-01
-2.35578697e-02 1.04989207e+00 3.45132619e-01 9.56090912e-02
-9.66269791e-01 -5.55166602e-01 6.81885839e-01 2.05694482e-01
8.52348879e-02 2.22907379e-01 -4.17798102e-01 -1.37716532e+00
2.57369190e-01 -1.42429125e+00 5.41474223e-01 -4.42521185e-01
-1.68456388e+00 6.73327267e-01 -2.81781822e-01 -1.50906265e+00
-1.75683931e-01 -4.67810392e-01 -3.86849940e-01 7.45437205e-01
-1.42316186e+00 -1.01157618e+00 -3.58252376e-01 2.99779654e-01
4.02696937e-01 -3.83362293e-01 1.00700855e+00 6.73210204e-01
-7.09984720e-01 8.79804075e-01 2.01136768e-01 3.18114638e-01
7.23681450e-01 -1.08623493e+00 3.29802364e-01 7.55804360e-01
-1.06988542e-01 1.55199647e-01 4.60569948e-01 -5.59745848e-01
-1.03802669e+00 -1.23941052e+00 8.46482396e-01 -4.19692755e-01
3.89217138e-01 -7.06207216e-01 -1.01211739e+00 6.28807425e-01
-8.69594864e-04 2.54224181e-01 1.04187238e+00 -7.83333853e-02
-4.75890666e-01 -4.00122583e-01 -1.39219475e+00 1.15985498e-01
9.19845641e-01 -2.93677211e-01 -4.01802897e-01 4.71278161e-01
4.44601625e-01 -8.92811894e-01 -6.78579330e-01 4.72922355e-01
4.90569800e-01 -1.10564137e+00 8.09033811e-01 -7.02555716e-01
9.54079032e-01 -1.62164941e-01 2.41866093e-02 -1.78451812e+00
-2.15584308e-01 -1.93480045e-01 -1.51339740e-01 1.16399884e+00
1.98750690e-01 -8.68035555e-01 8.58478904e-01 2.39278451e-01
4.80346121e-02 -1.13175225e+00 -1.15474987e+00 -6.97524607e-01
5.21447062e-01 -2.87850171e-01 8.19222450e-01 1.17020082e+00
-3.92687470e-01 3.16593617e-01 -4.55943286e-01 1.04495376e-01
5.90902805e-01 3.49853933e-01 7.45010972e-01 -1.18184125e+00
-3.30132455e-01 -4.62395400e-01 -7.13916361e-01 -5.33584714e-01
3.84494185e-01 -1.11314583e+00 -2.14501157e-01 -1.13945186e+00
7.99904093e-02 -6.38333738e-01 -2.02127248e-01 6.75551295e-01
-2.79198349e-01 6.72747612e-01 6.71472214e-03 1.44298702e-01
-3.63910764e-01 6.86828077e-01 1.10055125e+00 -2.58906454e-01
8.16750899e-02 2.65296191e-01 -8.84528518e-01 6.86022878e-01
1.16527641e+00 -1.04271781e+00 -3.98212165e-01 -4.38371629e-01
4.25305784e-01 -1.90799847e-01 2.93229699e-01 -1.14607501e+00
-3.19330804e-02 3.37050855e-02 7.00762093e-01 -3.21343422e-01
1.69849902e-01 -7.20568657e-01 2.11550087e-01 6.87481105e-01
-2.89631933e-01 6.98727518e-02 1.89312086e-01 3.24220806e-01
-4.23649162e-01 -1.40645549e-01 1.15129209e+00 1.88416332e-01
-2.93372255e-02 4.23505068e-01 -1.82221472e-01 4.51601267e-01
1.19946623e+00 1.23963691e-01 -4.79497731e-01 -3.73756140e-01
-3.98652881e-01 1.02404825e-01 5.35516441e-01 6.19458079e-01
4.16809946e-01 -1.46986997e+00 -8.40407610e-01 5.19065917e-01
-3.04168258e-02 4.07416344e-01 2.29522660e-01 4.04100329e-01
-5.62276244e-01 -1.01950519e-01 -3.90700430e-01 -5.22394478e-01
-1.04566336e+00 4.83394891e-01 6.96526468e-01 -5.37379146e-01
-4.53371078e-01 9.12114799e-01 7.75000378e-02 -8.01506400e-01
2.36527607e-01 6.11964725e-02 1.83087453e-01 3.43116015e-01
3.79314601e-01 3.78715932e-01 4.34748501e-01 -3.23628932e-01
-3.57644349e-01 2.77601063e-01 -6.51067346e-02 3.41026872e-01
1.52848423e+00 2.59737581e-01 -2.65296459e-01 5.59890032e-01
1.25305200e+00 -2.89370835e-01 -1.04705691e+00 -2.17071865e-02
-3.84616494e-01 -6.06743753e-01 -6.15370832e-02 -8.51542652e-01
-1.51098108e+00 1.10816681e+00 8.29789817e-01 3.56831521e-01
1.41782916e+00 -4.22944695e-01 8.72490644e-01 2.92300917e-02
2.31120154e-01 -9.37348247e-01 1.38391718e-01 5.02680779e-01
8.94678056e-01 -1.34504902e+00 -5.64489700e-02 -2.79949993e-01
-5.49194455e-01 8.97191823e-01 7.79960692e-01 -3.82980198e-01
7.17955470e-01 4.43477064e-01 3.18745226e-01 -4.79918607e-02
-6.84577942e-01 4.49273169e-01 4.89935167e-02 4.84773010e-01
4.36241567e-01 1.13677688e-01 -1.93293259e-01 6.67660356e-01
-5.44525683e-01 5.37116118e-02 4.37234402e-01 8.47216785e-01
1.27655938e-01 -1.33560491e+00 -2.97520995e-01 4.83598024e-01
-8.36727321e-01 1.07842363e-01 -1.11472808e-01 5.62035620e-01
5.01902580e-01 6.17048025e-01 4.42446411e-01 -3.23831856e-01
3.10352147e-01 3.00597817e-01 2.56719261e-01 -6.22069359e-01
-7.07166672e-01 -3.75281870e-01 -2.07599223e-01 -4.74461436e-01
-2.68022209e-01 -4.30064172e-01 -7.65589774e-01 -4.33483183e-01
-2.53206462e-01 9.82508995e-03 5.56173801e-01 6.01328194e-01
4.57613856e-01 6.15725219e-01 8.52575779e-01 -6.98148310e-01
-7.75428593e-01 -9.62663651e-01 -4.22294766e-01 7.52600193e-01
3.99135679e-01 -7.51753390e-01 -5.37950695e-01 1.48144178e-02] | [9.025907516479492, 3.989884376525879] |
e31035ab-94d7-4263-8c53-4b38cc3e64f7 | kari-kanari-qcri-s-end-to-end-systems-for-the | 2106.05885 | null | https://arxiv.org/abs/2106.05885v2 | https://arxiv.org/pdf/2106.05885v2.pdf | Balanced End-to-End Monolingual pre-training for Low-Resourced Indic Languages Code-Switching Speech Recognition | The success in designing Code-Switching (CS) ASR often depends on the availability of the transcribed CS resources. Such dependency harms the development of ASR in low-resourced languages such as Bengali and Hindi. In this paper, we exploit the transfer learning approach to design End-to-End (E2E) CS ASR systems for the two low-resourced language pairs using different monolingual speech data and a small set of noisy CS data. We trained the CS-ASR, following two steps: (i) building a robust bilingual ASR system using a convolution-augmented transformer (Conformer) based acoustic model and n-gram language model, and (ii) fine-tuned the entire E2E ASR with limited noisy CS data. We tested our method on MUCS 2021 challenge and achieved 3rd place in the CS track. We then tested the proposed method using noisy CS data released for Hindi-English and Bengali-English pairs in Multilingual and Code-Switching ASR Challenges for Low Resource Indian Languages (MUCS 2021) and achieved 3rd place in the CS track. Unlike, the leading two systems that benefited from crawling YouTube and learning transliteration pairs, our proposed transfer learning approach focused on using only the limited CS data with no data-cleaning or data re-segmentation. Our approach achieved 14.1% relative gain in word error rate (WER) in Hindi-English and 27.1% in Bengali-English. We provide detailed guidelines on the steps to finetune the self-attention based model for limited data for ASR. Moreover, we release the code and recipe used in this paper. | ['Ahmed Ali', 'Najim Dehak', 'Shammur Chowdhury', 'Amir Hussein'] | 2021-06-10 | null | null | null | null | ['transliteration'] | ['natural-language-processing'] | [ 5.83339157e-03 -2.19659790e-01 3.15359533e-01 -4.30089623e-01
-1.73745418e+00 -8.38419318e-01 2.61081070e-01 -2.93805122e-01
-8.03552389e-01 3.72880518e-01 2.62140393e-01 -9.42906022e-01
3.36153418e-01 -2.93108761e-01 -8.02202761e-01 -3.51577550e-01
2.61882246e-01 5.18589854e-01 -8.49830806e-02 -7.02723444e-01
-1.97259081e-03 1.62478596e-01 -9.15003538e-01 4.50511187e-01
1.11315417e+00 5.53717017e-01 8.51320446e-01 9.49156284e-01
-2.65275757e-03 8.09814274e-01 -6.93844736e-01 -4.89664525e-01
3.93796623e-01 -5.14765918e-01 -9.39424992e-01 -3.71167928e-01
2.20108405e-01 1.06566422e-01 -5.16826570e-01 9.14379239e-01
8.98665309e-01 1.61909297e-01 2.05740243e-01 -7.29462087e-01
-1.07849300e+00 1.17634618e+00 -1.69170484e-01 2.73791492e-01
4.08861965e-01 1.34483144e-01 8.89799953e-01 -1.30441499e+00
4.94864106e-01 1.06304646e+00 5.75933695e-01 9.29566562e-01
-8.67002547e-01 -9.84092653e-01 -1.16810217e-01 1.50479466e-01
-1.69117999e+00 -1.03873360e+00 4.56875503e-01 -1.87653109e-01
1.45943832e+00 2.79747933e-01 3.42313290e-01 1.15863049e+00
-3.03006887e-01 8.91109228e-01 1.22249198e+00 -8.00752580e-01
-8.84935912e-03 2.46546865e-01 -1.60728678e-01 3.03569496e-01
-5.24177492e-01 -3.69897410e-02 -6.19467616e-01 2.35703364e-01
4.33435321e-01 -4.28537071e-01 -1.72505856e-01 5.67541361e-01
-1.26101577e+00 7.75572836e-01 1.25131920e-01 6.51132762e-01
-3.33024981e-03 9.16937180e-03 6.02034092e-01 8.57477546e-01
5.21320581e-01 4.08551723e-01 -9.36856866e-01 -7.15102077e-01
-9.48865414e-01 -3.46151054e-01 5.53514004e-01 1.48762071e+00
4.33236837e-01 4.17039007e-01 -1.10903904e-01 1.60748518e+00
2.36666873e-01 9.58952308e-01 8.76380861e-01 -5.79693139e-01
9.59429264e-01 9.16971415e-02 -3.34396154e-01 -1.54951870e-01
9.42345187e-02 -4.74748194e-01 -6.15609109e-01 -4.23133075e-01
7.00367913e-02 -2.84019589e-01 -1.26342487e+00 1.74602234e+00
-2.47169271e-01 -2.61032462e-01 4.80940551e-01 5.38649261e-01
7.97232091e-01 1.04290354e+00 -1.90420300e-01 7.21845925e-02
1.09321606e+00 -1.14553571e+00 -7.50477731e-01 -2.28593081e-01
9.60578680e-01 -1.20331991e+00 1.64067066e+00 1.79128140e-01
-1.17209208e+00 -7.59851575e-01 -8.30860019e-01 -2.07293540e-01
-3.09413195e-01 4.55135375e-01 -8.13612789e-02 8.29113662e-01
-1.40997827e+00 3.18307251e-01 -9.03335512e-01 -5.81578553e-01
4.85224873e-02 3.39717418e-01 -3.47257823e-01 -3.80249679e-01
-1.33890915e+00 9.06365037e-01 1.94873869e-01 1.20865516e-01
-1.09630275e+00 -6.75421238e-01 -8.93070638e-01 -1.90681830e-01
5.92094027e-02 5.80373630e-02 1.38792169e+00 -1.05587029e+00
-2.01081681e+00 1.00258219e+00 -9.34155583e-02 -2.40658119e-01
4.44109738e-01 -4.62904841e-01 -8.70642543e-01 -3.90477985e-01
3.48180942e-02 3.32951427e-01 3.89075518e-01 -9.64413583e-01
-7.14241266e-01 -1.83693290e-01 -2.13950351e-01 3.57821465e-01
-2.59716213e-01 6.06400073e-01 -6.87399983e-01 -8.46897721e-01
-8.56280252e-02 -1.13018978e+00 -5.41421734e-02 -1.01294470e+00
-2.72721857e-01 -7.23347515e-02 5.49191833e-01 -1.27136481e+00
1.35808289e+00 -2.38825870e+00 2.21556127e-02 1.39449045e-01
-4.63701278e-01 6.50206089e-01 -5.61617434e-01 5.81816018e-01
-6.51670620e-02 2.30472699e-01 -3.42794716e-01 -5.38296759e-01
-1.66189715e-01 1.74663976e-01 -1.62440315e-01 3.35613608e-01
2.54713178e-01 6.93927824e-01 -9.53829527e-01 -8.24142992e-02
9.89527628e-02 5.98515987e-01 -5.51421106e-01 6.64460957e-01
3.88298124e-01 7.09234834e-01 6.55066296e-02 6.84459805e-01
4.18360382e-01 5.52167952e-01 1.08402580e-01 8.30977708e-02
-3.70502472e-01 9.59154785e-01 -9.53287601e-01 2.01060390e+00
-1.34045339e+00 6.69511020e-01 2.13471562e-01 -9.43175197e-01
1.20794201e+00 6.64651513e-01 1.22944318e-01 -1.10504520e+00
-4.75665070e-02 9.66188252e-01 2.60774225e-01 -4.00588274e-01
4.33657974e-01 -8.01354554e-03 -2.41590634e-01 1.27579466e-01
4.09340024e-01 -4.61513251e-01 2.19911356e-02 1.01717390e-01
1.27938426e+00 9.50217173e-02 1.27140209e-01 -4.37041521e-01
6.77240670e-01 -1.14979073e-01 4.29804474e-01 6.71520352e-01
-5.15132286e-02 1.00370669e+00 -2.16221273e-01 1.71460230e-02
-1.18765342e+00 -1.09372771e+00 -5.95167764e-02 1.34082735e+00
-5.05032361e-01 -4.78905112e-01 -7.89694846e-01 -5.92091680e-01
-6.50035203e-01 9.31796372e-01 -2.11415648e-01 5.17744804e-03
-1.04791248e+00 -4.37156409e-01 1.06481671e+00 3.09254527e-01
4.30042267e-01 -1.10142016e+00 1.94946766e-01 3.89570922e-01
-5.58531404e-01 -1.22430503e+00 -9.25498009e-01 5.33551037e-01
-3.73083740e-01 -6.42756402e-01 -7.79798150e-01 -1.11611032e+00
2.73210496e-01 1.09918416e-01 1.11519766e+00 4.65429807e-03
6.28495142e-02 2.21088871e-01 -8.72568965e-01 -2.10065112e-01
-1.07178879e+00 5.45496345e-01 1.41975760e-01 -1.24020293e-01
4.49030459e-01 -3.05468947e-01 -1.09440044e-01 2.59119570e-01
-5.97152710e-01 -2.53606498e-01 5.41628063e-01 7.53641486e-01
4.18559849e-01 -4.77563173e-01 7.53627717e-01 -9.55354869e-01
4.68668878e-01 -5.85505426e-01 -5.43667674e-01 3.43276769e-01
-4.13437515e-01 7.37998821e-03 9.90028441e-01 -3.70761842e-01
-1.11203635e+00 1.84791297e-01 -8.94942462e-01 -1.65391728e-01
1.57175250e-02 5.40897608e-01 -4.67001289e-01 3.04267526e-01
6.80189192e-01 3.87559503e-01 -5.54133058e-01 -7.02347219e-01
4.85453516e-01 1.64600420e+00 5.51508009e-01 -7.15355992e-01
7.77841866e-01 -2.52052575e-01 -1.07948315e+00 -9.14898038e-01
-4.83680725e-01 -7.00259447e-01 -6.69534147e-01 8.95963684e-02
8.49251330e-01 -1.34190178e+00 -1.11211009e-01 6.03953242e-01
-1.25735366e+00 -6.70315146e-01 -1.63604468e-01 5.80980122e-01
-5.47641814e-01 7.69804290e-04 -9.21272099e-01 -6.93935335e-01
-5.07464111e-01 -1.49241567e+00 9.09206271e-01 -3.56573254e-01
-1.09554425e-01 -7.29523718e-01 4.59395438e-01 6.17514908e-01
8.33979726e-01 -6.27062261e-01 6.76724672e-01 -9.17637289e-01
-8.06891471e-02 -1.76753867e-02 1.85028184e-02 8.96138012e-01
2.80346125e-01 -1.94542372e-04 -1.09034848e+00 -5.40164649e-01
-7.91885331e-02 -4.38183844e-01 3.83900642e-01 7.71674141e-02
8.42072785e-01 -2.46445313e-01 2.22665772e-01 7.62922943e-01
1.31573439e+00 5.86591303e-01 6.96661770e-01 1.73478112e-01
9.05571580e-01 3.94265354e-01 5.47436774e-01 -9.85193532e-03
4.55862552e-01 7.70514667e-01 -2.00698107e-01 -2.18751222e-01
-3.97590280e-01 -2.98291177e-01 1.02432537e+00 2.12297034e+00
1.06659211e-01 -2.29698509e-01 -1.20261061e+00 1.01134109e+00
-1.44610858e+00 -3.92788857e-01 -1.77378088e-01 2.31613612e+00
1.35756469e+00 -1.91562042e-01 1.36171677e-03 -1.08946830e-01
7.85408080e-01 -2.31491894e-01 -1.52434424e-01 -7.96790659e-01
-2.56175905e-01 5.72303712e-01 6.12198591e-01 8.27813864e-01
-7.50251234e-01 1.49272144e+00 5.17491961e+00 1.21232116e+00
-1.09985340e+00 7.19835758e-01 4.73536789e-01 1.63903832e-02
-4.05578434e-01 1.25373900e-01 -8.41924012e-01 4.45681423e-01
1.69298828e+00 1.78910360e-01 8.75976026e-01 6.85784101e-01
1.40009299e-01 3.11659276e-01 -1.03622520e+00 1.12398565e+00
-6.31605163e-02 -8.83046210e-01 -3.44151258e-01 -3.72017652e-01
8.31375659e-01 8.48565698e-01 -6.91018552e-02 7.54390061e-01
8.85386527e-01 -1.17513740e+00 9.90713775e-01 -2.92390376e-01
1.41331589e+00 -7.88101733e-01 7.79680908e-01 1.60673380e-01
-1.32253385e+00 5.08590192e-02 -2.63153046e-01 2.83276498e-01
1.52863329e-02 2.86139160e-01 -7.50671566e-01 4.77563232e-01
8.99947226e-01 5.86844325e-01 -4.69564050e-01 5.98676145e-01
-2.33374402e-01 1.27656257e+00 -3.77877682e-01 1.82625949e-01
2.91935056e-01 -1.66039154e-01 4.23546284e-01 1.86420417e+00
5.94102979e-01 -1.36038467e-01 -9.50633511e-02 4.05775934e-01
-3.07633013e-01 4.62941527e-01 -4.63217229e-01 -3.83710638e-02
7.48979151e-01 9.48914886e-01 -4.00386453e-01 -2.02628821e-01
-5.67452908e-01 1.26767051e+00 4.46961641e-01 4.18846250e-01
-6.78365469e-01 -6.37905419e-01 5.08343697e-01 1.11366836e-02
2.00388595e-01 -4.18414891e-01 -3.17205153e-02 -1.23102105e+00
-7.64939412e-02 -1.49009967e+00 1.44323334e-01 -4.74501163e-01
-1.23222733e+00 1.32101727e+00 -3.59656096e-01 -1.12049937e+00
-1.06749699e-01 -4.60430622e-01 -2.72452414e-01 1.08552325e+00
-1.53944683e+00 -1.40181649e+00 1.50299668e-01 7.72281528e-01
1.14174867e+00 -5.94386160e-01 9.15052056e-01 9.45073426e-01
-4.55047339e-01 9.86747146e-01 5.36330521e-01 5.42031050e-01
1.00902641e+00 -1.28484082e+00 9.06996131e-01 1.09213674e+00
4.20618176e-01 5.96673608e-01 3.96371335e-01 -4.98273075e-01
-1.30900860e+00 -1.20786798e+00 1.09161472e+00 -5.64189970e-01
8.15920770e-01 -1.02905989e+00 -7.53959835e-01 8.57225418e-01
3.72213334e-01 6.16336912e-02 6.86595142e-01 1.10690400e-01
-3.59994173e-01 -1.33542687e-01 -8.79979551e-01 5.47820330e-01
1.12341440e+00 -1.02999198e+00 -4.06457007e-01 2.26189449e-01
1.11250985e+00 -4.49471056e-01 -9.12767053e-01 1.74819618e-01
2.26808816e-01 -3.83975267e-01 5.41350901e-01 -5.48349500e-01
2.23973528e-01 -2.21919313e-01 -8.60754430e-01 -1.85108864e+00
-1.51675150e-01 -8.92054677e-01 5.70724905e-01 1.59218323e+00
8.28175664e-01 -3.70025605e-01 8.93469378e-02 1.31396160e-01
-7.34338403e-01 -2.28371918e-01 -1.08423293e+00 -1.01858795e+00
4.45412815e-01 -8.02897513e-01 3.44661266e-01 1.05877316e+00
-2.21216932e-01 4.01039660e-01 -3.04918438e-01 8.48423541e-02
8.88987333e-02 -5.31497538e-01 5.78894079e-01 -5.42602718e-01
-4.28123385e-01 7.59740826e-03 7.23304302e-02 -8.92022848e-01
2.34407142e-01 -1.27000737e+00 5.12457728e-01 -1.05091298e+00
-2.46647634e-02 -9.00720000e-01 -4.15589660e-01 5.67695677e-01
-2.30706409e-01 2.61362970e-01 3.39675605e-01 1.19998939e-01
-3.67279768e-01 5.28064311e-01 6.17035508e-01 -3.34974788e-02
-3.20584953e-01 -4.97242659e-02 -4.35002685e-01 1.73994586e-01
8.09739709e-01 -9.06274974e-01 -2.35367626e-01 -9.02385473e-01
1.92994088e-01 1.21984081e-02 -3.74555707e-01 -9.43597376e-01
7.99495801e-02 1.35066837e-01 -1.77958727e-01 -2.72719711e-01
-4.84041013e-02 -7.57341027e-01 -3.93697023e-02 2.88648605e-01
-4.09952492e-01 3.09287071e-01 3.40152830e-01 -1.11594997e-01
-3.81209105e-01 -2.60664433e-01 1.02932739e+00 -2.16063142e-01
-4.59108889e-01 7.06985220e-02 -6.81457460e-01 4.92591083e-01
5.20418465e-01 1.01882719e-01 -6.42500119e-03 -3.63198161e-01
-5.03535926e-01 2.89395787e-02 3.23911190e-01 8.15751433e-01
4.15913552e-01 -1.20714986e+00 -1.34128559e+00 4.81769592e-01
2.76414335e-01 -1.56154960e-01 8.64652991e-02 5.46005785e-01
-5.63509703e-01 4.00808483e-01 -1.12852575e-02 -3.91610414e-01
-1.08201480e+00 1.90831199e-01 3.57125461e-01 -1.73123300e-01
-1.34827137e-01 1.06286716e+00 -1.49888098e-01 -1.23259187e+00
1.37811750e-01 -1.48053154e-01 6.56959787e-02 -2.73583084e-01
2.58322358e-01 3.00944477e-01 6.44965053e-01 -9.52778459e-01
-5.58327675e-01 4.48790014e-01 -1.52148351e-01 -5.27920425e-01
1.51695514e+00 -4.49959338e-01 -1.61036104e-02 6.52049184e-01
1.55538166e+00 5.66067755e-01 -7.96486497e-01 -4.05717790e-01
1.12283587e-01 -1.71279564e-01 8.54764506e-02 -9.39521968e-01
-1.12011278e+00 9.68843460e-01 6.77669764e-01 -1.37467742e-01
1.08722842e+00 4.33688648e-02 1.08609402e+00 3.07554960e-01
3.06357563e-01 -1.50379825e+00 -1.58977881e-01 1.02686107e+00
8.53507280e-01 -1.31741118e+00 -7.73095667e-01 -1.05335247e-02
-8.88576448e-01 1.00258160e+00 4.86144930e-01 1.17822569e-02
5.95922887e-01 6.20226562e-01 6.51726007e-01 1.37393996e-01
-5.04141808e-01 -1.69457689e-01 2.39797220e-01 7.74656117e-01
9.46032047e-01 1.96235895e-01 -1.38269052e-01 6.78020537e-01
-6.12799346e-01 -3.25228542e-01 6.00811243e-01 8.22784483e-01
-9.78012830e-02 -1.32576799e+00 -2.18841493e-01 3.89530733e-02
-8.01416039e-01 -7.59401858e-01 -1.56701013e-01 5.59651792e-01
-7.53190741e-02 1.32868993e+00 -6.31579533e-02 -5.72527468e-01
4.72777933e-01 7.15977997e-02 1.36196837e-01 -1.00045538e+00
-9.43765461e-01 1.87848806e-01 2.00700030e-01 -5.63941061e-01
-7.77078494e-02 -5.16068280e-01 -1.18703330e+00 -9.55286026e-02
-3.29389393e-01 2.47788966e-01 1.03906453e+00 8.89495254e-01
2.22005665e-01 4.46062386e-01 8.85583043e-01 -3.54604840e-01
-5.44824302e-01 -1.25253999e+00 -3.62627983e-01 3.26826632e-01
3.81818831e-01 1.37132078e-01 -2.84251779e-01 1.97455063e-01] | [14.394051551818848, 7.0228986740112305] |
3aa8901a-4275-40f6-9cd0-7168140b9ab6 | affective-eeg-based-person-identification | 1807.03147 | null | http://arxiv.org/abs/1807.03147v3 | http://arxiv.org/pdf/1807.03147v3.pdf | Affective EEG-Based Person Identification Using the Deep Learning Approach | Electroencephalography (EEG) is another mode for performing Person
Identification (PI). Due to the nature of the EEG signals, EEG-based PI is
typically done while the person is performing some kind of mental task, such as
motor control. However, few works have considered EEG-based PI while the person
is in different mental states (affective EEG). The aim of this paper is to
improve the performance of affective EEG-based PI using a deep learning
approach. \textcolor{red}{We proposed a cascade of deep learning using a
combination of Convolutional Neural Networks (CNNs) and Recurrent Neural
Networks (RNNs)}. CNNs are used to handle the spatial information from the EEG
while RNNs extract the temporal information. \textcolor{red}{We evaluated two
types of RNNs, namely, Long Short-Term Memory (CNN-LSTM) and Gated Recurrent
Unit (CNN-GRU). } The proposed method is evaluated on the state-of-the-art
affective dataset DEAP. The results indicate that CNN-GRU and CNN-LSTM can
perform PI from different affective states and reach up to 99.90--100\% mean
Correct Recognition Rate (CRR), significantly outperforming a support vector
machine (SVM) baseline system that uses power spectral density (PSD) features.
Notably, the 100\% mean \emph{CRR} comes from only 40 subjects in DEAP dataset.
To reduce the number of EEG electrodes from thirty-two to five for more
practical applications, the frontal region gives the best results reaching up
to 99.17\% CRR (from CNN-GRU). Amongst the two deep learning models, we find
CNN-GRU to slightly outperform CNN-LSTM, while having faster training time.
\textcolor{red}{Furthermore, CNN-GRU overcomes the influence of affective
states in EEG-Based PI reported in the previous works. | ['Ekapol Chuangsuwanich', 'Nannapas Banluesombatkul', 'Karis Matchaparn', 'Apiwat Ditthapron', 'Theerawit Wilaiprasitporn', 'Tanaboon Tongbuasirilai'] | 2018-07-05 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [ 8.83451942e-03 -2.01030493e-01 3.70346189e-01 -1.43048614e-01
-4.78016973e-01 -1.58308581e-01 2.71781087e-01 -2.01346382e-01
-5.48166394e-01 1.06342638e+00 1.82453915e-02 1.33673251e-01
-8.53809789e-02 -6.68949187e-01 -4.94424224e-01 -8.61440480e-01
-2.05710009e-01 -1.11875005e-01 -4.29337591e-01 -2.42172882e-01
1.45896852e-01 4.24871415e-01 -1.58959079e+00 4.62403774e-01
5.26201367e-01 1.49431205e+00 3.21274973e-03 4.32453811e-01
3.31377357e-01 8.27076793e-01 -9.56182003e-01 -2.15996191e-01
-1.66567445e-01 -3.77175301e-01 -6.33130610e-01 -4.82351780e-01
-1.88738242e-01 7.90500194e-02 -4.81343269e-01 8.71002197e-01
9.13498223e-01 4.02985454e-01 8.28117847e-01 -1.24583912e+00
-8.02505970e-01 2.74919331e-01 -4.05034989e-01 4.61464107e-01
6.35734677e-01 -4.55901325e-02 4.95985329e-01 -8.19264889e-01
1.65792760e-02 5.34241498e-01 7.40449309e-01 6.90985262e-01
-7.97287703e-01 -1.14414549e+00 8.71993660e-04 4.54274625e-01
-1.66536939e+00 2.33464558e-02 7.63887286e-01 -4.21333790e-01
1.53241670e+00 1.81273907e-01 9.90242064e-01 1.63204026e+00
5.94460547e-01 6.22491777e-01 1.52499080e+00 -1.61449891e-02
3.20966214e-01 1.84508309e-01 4.39270496e-01 2.16008589e-01
-1.37027100e-01 -3.90514508e-02 -7.58080900e-01 -1.07157575e-02
8.16722214e-01 7.91830420e-02 -4.63128746e-01 6.52159691e-01
-1.02532804e+00 3.44831645e-01 6.53091908e-01 7.36848831e-01
-7.90603936e-01 4.66311015e-02 6.70441985e-01 3.01326782e-01
4.07891154e-01 5.95555902e-01 -3.78511280e-01 -4.89211410e-01
-8.00648093e-01 -1.46937713e-01 6.62094951e-01 6.96315348e-01
4.19759721e-01 4.79492188e-01 -4.04050499e-01 9.61875439e-01
-2.67342180e-01 4.16608483e-01 8.37913156e-01 -3.73333752e-01
1.82391122e-01 5.73466599e-01 1.19147049e-02 -9.75817978e-01
-6.52049839e-01 -5.98695934e-01 -1.35802448e+00 3.82740311e-02
1.65241826e-02 -4.71138835e-01 -7.86595762e-01 1.62947977e+00
-6.17651761e-01 2.83107221e-01 1.91916585e-01 8.54276478e-01
1.14480782e+00 8.61157417e-01 2.75350422e-01 -3.75155061e-01
1.48376429e+00 -5.46677887e-01 -7.69455552e-01 -2.40473196e-01
2.15043649e-01 -2.06140339e-01 9.19959486e-01 6.95856631e-01
-9.31255221e-01 -7.01289773e-01 -1.14931190e+00 4.13120449e-01
-7.15335250e-01 3.78255218e-01 4.48021859e-01 6.25083089e-01
-1.26877022e+00 5.82667947e-01 -5.20543754e-01 -3.64233166e-01
3.39433640e-01 9.28595483e-01 -4.59065944e-01 5.08119881e-01
-1.63314021e+00 9.79265153e-01 4.29426223e-01 4.46091443e-01
-5.15801609e-01 -3.47277403e-01 -4.99294251e-01 1.49030864e-01
-2.04303086e-01 -4.10515130e-01 5.47598302e-01 -1.23764181e+00
-1.72830498e+00 5.72648048e-01 -9.07664075e-02 -3.11667264e-01
-3.03404257e-02 -2.82298356e-01 -6.32493198e-01 1.68939188e-01
-3.33471358e-01 7.01680243e-01 6.93197846e-01 -9.18313444e-01
-1.52043954e-01 -3.75134557e-01 -2.62258202e-01 1.02566056e-01
-6.98317528e-01 2.34588116e-01 -1.75692439e-01 -6.84269786e-01
-2.17222914e-01 -1.16723669e+00 2.95820296e-01 -9.89234447e-01
-4.18596506e-01 -5.11525691e-01 6.45986497e-01 -8.53296578e-01
1.33144617e+00 -2.08282089e+00 1.50261551e-01 2.77192056e-01
4.06003967e-02 4.83739227e-01 1.33403301e-01 2.15904549e-01
-6.20961964e-01 -8.39450657e-02 -5.05324714e-02 -2.08987534e-01
7.62371421e-02 2.00530551e-02 -1.02897338e-03 2.68779933e-01
1.94363251e-01 9.82006192e-01 -5.47245324e-01 7.54580945e-02
3.34898293e-01 8.26896131e-01 1.32087663e-01 1.65904760e-01
5.53716362e-01 6.65683329e-01 -9.48186964e-02 5.87740421e-01
3.68779719e-01 -2.00682814e-04 -2.28107333e-01 -3.24167192e-01
-5.40292822e-02 4.18606102e-02 -8.55680704e-01 1.47703183e+00
-5.53930581e-01 8.64927053e-01 -4.58206803e-01 -1.04410744e+00
1.21906531e+00 8.47807467e-01 5.83418310e-01 -9.84599411e-01
5.65143108e-01 1.04743227e-01 7.73012266e-02 -4.50618774e-01
9.86165926e-02 -8.03486109e-02 -8.32478181e-02 3.50906849e-01
2.39981934e-01 4.29937273e-01 -2.26882592e-01 -3.38010550e-01
1.10431314e+00 6.10917632e-04 1.86647952e-01 -3.88476819e-01
5.45552254e-01 -6.10380888e-01 4.44795936e-01 4.93831456e-01
-2.90029943e-01 5.30145943e-01 4.20553416e-01 -4.80935842e-01
-5.85596323e-01 -8.03410053e-01 -1.98469833e-01 9.44418371e-01
-9.03872699e-02 -1.95034295e-01 -8.66772711e-01 -1.08630061e-01
-4.97523427e-01 6.95795059e-01 -9.02485728e-01 -4.39039975e-01
-3.03479165e-01 -1.09470296e+00 7.79843748e-01 9.20735061e-01
8.75116229e-01 -1.64851153e+00 -8.45716298e-01 2.13255674e-01
-3.73548806e-01 -1.05219388e+00 1.87445790e-01 5.69736779e-01
-4.59826767e-01 -7.14419723e-01 -9.67740476e-01 -6.85705304e-01
3.05717140e-01 -3.78546983e-01 9.29310203e-01 -4.76756155e-01
-1.59073710e-01 3.22030395e-01 -4.33863938e-01 -5.01393318e-01
3.68533939e-01 8.67252722e-02 4.24095631e-01 3.79580371e-02
8.48226547e-01 -9.44894373e-01 -8.17679107e-01 1.28037527e-01
-4.77598161e-01 -5.90271913e-02 6.91901982e-01 7.34318376e-01
4.65322465e-01 9.85345170e-02 9.66959476e-01 -2.33030856e-01
8.67123842e-01 -4.18350756e-01 2.71661013e-01 1.16193533e-01
-1.89929873e-01 -3.28856468e-01 7.41336226e-01 -6.53483987e-01
-9.42716658e-01 -1.96648106e-01 -4.60719228e-01 -5.17049253e-01
-3.71169031e-01 3.94099653e-01 7.30771422e-02 -8.12998489e-02
4.44853157e-01 3.71093214e-01 -5.97711265e-01 6.95713684e-02
-5.05319595e-01 9.11816061e-01 5.53970277e-01 -3.20050120e-01
-1.28785288e-02 -6.69345409e-02 -2.77794451e-01 -9.27844822e-01
-5.74376643e-01 -2.36594111e-01 -4.48613256e-01 -4.29336697e-01
1.15851188e+00 -1.04764819e+00 -1.16537738e+00 6.72583699e-01
-1.02888858e+00 -3.85303915e-01 1.69071123e-01 7.91655898e-01
-5.18290758e-01 -1.39125615e-01 -9.56775308e-01 -1.08602083e+00
-1.02736497e+00 -1.04286742e+00 7.44040966e-01 3.37269098e-01
-6.40606105e-01 -7.23400235e-01 -9.97189581e-02 1.70652077e-01
3.98590326e-01 4.60843593e-01 8.39985609e-01 -7.11035371e-01
3.11228305e-01 -4.87189323e-01 -2.02034056e-01 4.06777501e-01
4.27917503e-02 -3.75484437e-01 -1.66237235e+00 -2.20252126e-01
1.62597120e-01 -2.77380973e-01 6.27630353e-01 5.04987299e-01
1.34103775e+00 -1.89690098e-01 -4.86682579e-02 4.73943859e-01
1.31515718e+00 7.68636823e-01 1.25227606e+00 3.28883499e-01
6.18504703e-01 3.26763004e-01 5.78167848e-02 5.48979104e-01
2.07357466e-01 4.98792440e-01 2.12646067e-01 -1.82987124e-01
2.88906336e-01 3.17179471e-01 6.83647990e-01 8.75561178e-01
-7.01769292e-01 -1.02024280e-01 -9.13462460e-01 2.74083883e-01
-1.74366570e+00 -9.37356889e-01 -2.82714386e-02 2.19101834e+00
5.33463120e-01 6.49605468e-02 1.12935960e-01 5.43675184e-01
5.03273308e-01 -2.92671770e-01 -4.90851045e-01 -7.58203685e-01
-1.41226366e-01 7.83242404e-01 1.85221359e-01 -2.05360159e-01
-1.10716939e+00 5.93481064e-01 5.33307695e+00 5.89184761e-01
-1.56602263e+00 1.66552901e-01 7.13057697e-01 -2.61994630e-01
4.83294278e-01 -9.15354848e-01 -4.68366206e-01 6.92949057e-01
1.40399969e+00 1.47175714e-01 4.56774980e-01 4.52571243e-01
1.37437612e-01 -3.83474410e-01 -1.07064843e+00 1.60397124e+00
2.07129627e-01 -6.66854322e-01 -1.83195204e-01 -6.40591159e-02
3.71059299e-01 -2.29971670e-02 2.61377007e-01 6.33819878e-01
-2.25976661e-01 -1.45773530e+00 3.27625632e-01 8.23553145e-01
9.97581542e-01 -1.12346148e+00 1.26190174e+00 1.29870862e-01
-1.24726164e+00 -2.96869427e-01 -2.75122553e-01 -3.93853188e-01
-2.74538457e-01 4.37443435e-01 -2.21911639e-01 5.42371213e-01
1.26138055e+00 7.43481636e-01 -4.99015957e-01 6.64257467e-01
-6.56035319e-02 6.19336843e-01 -7.96521083e-02 -3.96424264e-01
1.79352790e-01 -1.65925965e-01 2.09722653e-01 1.53844166e+00
5.66413164e-01 4.43886369e-01 -2.14686811e-01 7.72550464e-01
3.09217963e-02 1.02042016e-02 -6.34167731e-01 -1.57062209e-03
1.58148646e-01 1.31444895e+00 -7.22658873e-01 -2.21772119e-01
-2.01861545e-01 1.42337871e+00 6.07038550e-02 5.59555829e-01
-1.01958501e+00 -6.69172287e-01 4.47705477e-01 -4.04837400e-01
-2.35097408e-01 8.60214457e-02 -4.85843271e-01 -9.92509484e-01
-2.32353210e-02 -5.68890631e-01 1.11320920e-01 -1.15300608e+00
-1.20851469e+00 1.21234858e+00 -9.21248421e-02 -1.15193427e+00
-1.33940652e-01 -5.95954359e-01 -7.15678155e-01 1.06847155e+00
-1.06031895e+00 -9.31442261e-01 -5.56685269e-01 9.24236119e-01
3.88695657e-01 -1.11139029e-01 1.24673450e+00 4.33806956e-01
-9.13109601e-01 5.76283157e-01 -2.02912703e-01 3.33863854e-01
6.62529051e-01 -1.20173025e+00 -3.69611718e-02 4.12892729e-01
-3.16317946e-01 7.84648240e-01 3.57936382e-01 -3.39478940e-01
-1.13418102e+00 -1.05071056e+00 6.57397270e-01 -1.01545990e-01
3.38376641e-01 -3.84205550e-01 -8.28087389e-01 5.78035176e-01
6.84885263e-01 -1.91501275e-01 1.06494701e+00 1.28254143e-03
1.72711581e-01 -2.20493570e-01 -1.07004833e+00 4.83227044e-01
5.10717809e-01 -8.24031234e-01 -5.82228482e-01 6.18327074e-02
2.52023824e-02 -1.36992902e-01 -1.15818429e+00 4.42650616e-01
7.12093472e-01 -1.16369557e+00 8.07728291e-01 -4.82649170e-02
3.83567810e-01 1.20333594e-03 -4.22930606e-02 -1.57569349e+00
-4.72444624e-01 -2.93064684e-01 -8.60512257e-03 1.11060858e+00
2.49271154e-01 -8.07461083e-01 3.94346744e-01 6.98891759e-01
-2.16221571e-01 -9.44852054e-01 -7.89762855e-01 -5.27256489e-01
-5.53591363e-02 -4.64326024e-01 1.89301312e-01 7.76648760e-01
4.42600280e-01 4.81461346e-01 -5.01337945e-01 -4.73913178e-02
1.52040571e-01 -4.31844085e-01 8.47796202e-02 -1.16804123e+00
7.26820901e-02 -3.47748190e-01 -6.21789515e-01 -2.91824579e-01
3.11674476e-01 -6.24304295e-01 -1.45229921e-01 -1.67027688e+00
2.55093068e-01 -4.62977178e-02 -1.00768149e+00 8.30418229e-01
1.59553345e-02 6.76043868e-01 -1.76952947e-02 -6.79562464e-02
-2.51665026e-01 5.04762530e-01 7.78227210e-01 -1.49529085e-01
-4.15358424e-01 -1.15673160e-02 -4.66537476e-01 7.24111021e-01
1.06396365e+00 -4.90398370e-02 -3.27998638e-01 6.56662881e-02
3.63576680e-01 1.14878759e-01 3.98293376e-01 -1.59337258e+00
3.07504982e-01 4.28836465e-01 1.21746576e+00 -4.80342180e-01
7.22265124e-01 -6.13481283e-01 3.21152419e-01 3.50993067e-01
-6.08215854e-02 3.39918971e-01 4.03965145e-01 1.11968972e-01
-2.19926491e-01 1.16870068e-01 6.80568516e-01 -2.87587047e-01
-7.34589994e-01 2.13762939e-01 -8.60778809e-01 -4.31971997e-01
1.12243986e+00 -5.80167651e-01 -2.20994681e-01 -4.01796758e-01
-9.85892177e-01 -1.48723826e-01 -2.50996023e-01 2.59068221e-01
8.02610934e-01 -1.26860249e+00 -3.08552742e-01 2.51079142e-01
-2.16740500e-02 -5.37891090e-01 4.81145442e-01 1.27825880e+00
-7.94726759e-02 6.10773504e-01 -7.90642917e-01 -3.70339662e-01
-1.12091315e+00 1.97522834e-01 5.80100715e-01 5.87727576e-02
-6.35136366e-01 9.46798146e-01 2.93968227e-02 -7.85492286e-02
3.76943946e-01 -1.78583741e-01 -8.11270595e-01 4.74117130e-01
5.66897333e-01 5.07702708e-01 4.60704386e-01 -6.38959348e-01
-5.87198019e-01 4.22918946e-01 1.94587395e-01 -3.03898919e-02
1.52378845e+00 1.60588682e-01 -2.65880734e-01 8.33528757e-01
1.22089946e+00 -5.53695798e-01 -9.28421617e-01 3.93041819e-01
-2.74726808e-01 2.64948845e-01 5.84331490e-02 -1.17587972e+00
-1.10997343e+00 1.13235176e+00 1.02447522e+00 -5.47123179e-02
1.47485626e+00 -5.51302135e-01 8.66128325e-01 5.38420260e-01
5.51500499e-01 -1.41217577e+00 2.96513140e-01 6.00922883e-01
8.36519420e-01 -8.47147405e-01 -2.79284269e-01 2.37213254e-01
-9.91471708e-01 1.23776281e+00 7.55495012e-01 -3.29884350e-01
6.14569545e-01 3.02726716e-01 -1.58030003e-01 -2.66979575e-01
-5.44789493e-01 -1.19451312e-02 3.70008916e-01 4.51973587e-01
5.51930130e-01 2.29484484e-01 6.22579362e-03 1.21128106e+00
-4.33790356e-01 2.49023154e-01 3.08243752e-01 7.24926412e-01
-8.69258568e-02 -4.54092532e-01 -2.43938252e-01 7.37967312e-01
-7.44671762e-01 -1.35734975e-01 -4.09846753e-01 5.78868151e-01
2.77851671e-01 1.09177387e+00 1.21193856e-01 -8.62454474e-01
2.88916588e-01 3.53845000e-01 4.43985909e-01 -3.73397589e-01
-1.24412751e+00 4.09248198e-04 -1.17978662e-01 -4.38731194e-01
-5.45357049e-01 -4.44489300e-01 -1.34493923e+00 -2.06061080e-01
-1.03590071e-01 1.43204495e-01 3.93166989e-01 1.14241803e+00
2.02791154e-01 1.03652382e+00 4.54390794e-01 -1.04768836e+00
3.07201296e-01 -1.34334075e+00 -8.00783873e-01 2.13991284e-01
9.46615487e-02 -7.46206403e-01 -2.19221100e-01 -1.84935600e-01] | [13.175280570983887, 3.452174186706543] |
d23ecee6-bcc0-4a90-a848-132dc58bc455 | human-intracranial-eeg-quantitative-analysis | 1904.03603 | null | http://arxiv.org/abs/1904.03603v1 | http://arxiv.org/pdf/1904.03603v1.pdf | Human Intracranial EEG Quantitative Analysis and Automatic Feature Learning for Epileptic Seizure Prediction | Objective: The aim of this study is to develop an efficient and reliable
epileptic seizure prediction system using intracranial EEG (iEEG) data,
especially for people with drug-resistant epilepsy. The prediction procedure
should yield accurate results in a fast enough fashion to alert patients of
impending seizures. Methods: We quantitatively analyze the human iEEG data to
obtain insights into how the human brain behaves before and between epileptic
seizures. We then introduce an efficient pre-processing method for reducing the
data size and converting the time-series iEEG data into an image-like format
that can be used as inputs to convolutional neural networks (CNNs). Further, we
propose a seizure prediction algorithm that uses cooperative multi-scale CNNs
for automatic feature learning of iEEG data. Results: 1) iEEG channels contain
complementary information and excluding individual channels is not advisable to
retain the spatial information needed for accurate prediction of epileptic
seizures. 2) The traditional PCA is not a reliable method for iEEG data
reduction in seizure prediction. 3) Hand-crafted iEEG features may not be
suitable for reliable seizure prediction performance as the iEEG data varies
between patients and over time for the same patient. 4) Seizure prediction
results show that our algorithm outperforms existing methods by achieving an
average sensitivity of 87.85% and AUC score of 0.84. Conclusion: Understanding
how the human brain behaves before seizure attacks and far from them
facilitates better designs of epileptic seizure predictors. Significance:
Accurate seizure prediction algorithms can warn patients about the next seizure
attack so they could avoid dangerous activities. Medications could then be
administered to abort the impending seizure and minimize the risk of injury. | ['Ramy Hussein', 'Rabab Ward', 'Yi Guo', 'Mohamed Osama Ahmed', 'Levin Kuhlmann', 'Z. Jane Wang'] | 2019-04-07 | null | null | null | null | ['seizure-prediction'] | ['medical'] | [ 1.84506886e-02 -3.91065150e-01 4.04110640e-01 -2.15027153e-01
-6.01818979e-01 -3.29447269e-01 9.37232971e-02 1.29581600e-01
-2.45661184e-01 6.67992055e-01 2.77232528e-01 -3.51210088e-01
-4.05108780e-01 -4.83386070e-01 -2.72852540e-01 -8.10582936e-01
-7.70107448e-01 1.36422455e-01 -2.01486781e-01 -8.78064856e-02
3.33200932e-01 9.97798145e-01 -1.21917009e+00 3.77198309e-01
7.84697473e-01 1.17942989e+00 4.68990624e-01 4.73418742e-01
2.49337465e-01 5.58799803e-01 -9.65837836e-01 4.48736936e-01
3.29956204e-01 -5.84975958e-01 -4.50735688e-01 -5.46450987e-02
-4.69548076e-01 -1.04952462e-01 -4.17837381e-01 9.41019654e-01
7.84153521e-01 -2.86218643e-01 7.16644943e-01 -8.88156772e-01
-2.26502985e-01 3.44660789e-01 -2.73733348e-01 6.12592459e-01
2.14320049e-01 -3.64314467e-02 1.88005522e-01 -6.60462439e-01
1.63402632e-01 3.27792943e-01 6.32451952e-01 5.30978680e-01
-1.01230013e+00 -1.08668745e+00 -2.72253692e-01 5.59282541e-01
-1.63580930e+00 -2.30341628e-01 8.35381567e-01 -5.75090289e-01
1.18732846e+00 4.74161297e-01 1.40254295e+00 1.02982712e+00
1.11848748e+00 3.25087905e-01 1.18802023e+00 -1.46905571e-01
3.07714075e-01 -3.08395118e-01 4.32606190e-02 8.08215514e-02
9.13042575e-02 2.51422167e-01 -7.94371843e-01 -2.54641384e-01
6.74093843e-01 2.86498249e-01 -6.98333859e-01 2.73534600e-02
-1.20920157e+00 6.44695818e-01 4.37570065e-01 7.60917425e-01
-9.21608150e-01 -2.30368301e-01 2.25603729e-01 5.03459215e-01
3.90214175e-01 8.25939715e-01 -6.53233111e-01 -3.30739081e-01
-1.26424980e+00 -7.47279301e-02 5.48315763e-01 4.87538815e-01
3.32207263e-01 1.51025638e-01 1.11949831e-01 6.23467743e-01
-3.15762997e-01 1.53868452e-01 1.02732384e+00 -1.71973258e-01
1.52880967e-01 5.93247235e-01 -3.24025214e-01 -9.14481640e-01
-9.65820432e-01 -7.67831504e-01 -1.05191624e+00 8.97352472e-02
1.81016713e-01 -3.89261574e-01 -8.25538814e-01 1.17458653e+00
-6.04656458e-01 3.59039903e-01 4.28781956e-02 6.36769712e-01
5.63326120e-01 4.20038491e-01 1.28865257e-01 -4.35058653e-01
1.37143290e+00 -2.13497847e-01 -6.43154621e-01 -3.01917732e-01
9.10254717e-01 -5.38216829e-01 4.76687253e-01 7.87415087e-01
-6.91740811e-01 -3.80069427e-02 -9.90975261e-01 6.50446534e-01
-3.65872741e-01 1.99207962e-01 7.05290198e-01 3.17624241e-01
-1.19382298e+00 4.78776187e-01 -1.20812261e+00 -3.07835340e-01
5.86280465e-01 1.03555346e+00 -6.28293157e-01 3.53062063e-01
-1.00966370e+00 1.17530906e+00 4.30115163e-01 1.15706474e-01
-6.63603604e-01 -7.07443774e-01 -4.82246339e-01 2.17566818e-01
-3.24351072e-01 -1.64327279e-01 7.63020217e-01 -9.17975008e-01
-1.03329980e+00 3.15986842e-01 -1.77942231e-01 -6.81703985e-01
4.93799411e-02 1.04880042e-01 -6.11233652e-01 1.02905504e-01
-8.33840594e-02 5.39966762e-01 7.15371490e-01 -5.44730008e-01
-7.08442688e-01 -7.85793364e-01 -7.34523952e-01 1.21891193e-01
-3.63998622e-01 1.88639343e-01 2.38933526e-02 -8.58012795e-01
3.35466355e-01 -8.28418493e-01 -2.57483244e-01 -7.09136426e-01
-2.87396669e-01 -7.96153769e-02 9.15305138e-01 -9.87715900e-01
1.17301905e+00 -1.90836227e+00 -1.99294358e-01 3.56567085e-01
5.39423704e-01 7.87578374e-02 -4.41251956e-02 2.75298208e-01
-6.48759007e-01 -2.17406139e-01 -1.71769232e-01 3.56451333e-01
-6.50722802e-01 -4.79550928e-01 -2.06533104e-01 5.35395801e-01
3.35731804e-02 9.88993406e-01 -4.36004996e-01 1.68731108e-01
3.07540536e-01 8.07095468e-01 -1.91935852e-01 2.40535557e-01
5.76075315e-01 8.80670905e-01 -5.10936081e-01 7.14224458e-01
3.59806657e-01 -6.00617863e-02 -1.17557362e-01 -1.56516775e-01
-1.09408446e-01 1.59074783e-01 -5.55807531e-01 1.35437131e+00
-3.80108774e-01 1.06498027e+00 -3.92837346e-01 -1.17334998e+00
1.20740473e+00 5.55769980e-01 8.27327490e-01 -9.40501034e-01
4.35345232e-01 3.53746802e-01 5.00739694e-01 -4.47240174e-01
-2.94719785e-01 2.80943122e-02 2.80289408e-02 3.87212753e-01
-7.77253136e-02 -1.13035597e-01 -1.68410361e-01 -3.37735057e-01
1.48318231e+00 -4.54038024e-01 6.00120068e-01 -6.00323915e-01
2.23073587e-01 -8.27171728e-02 6.33461475e-01 5.72243392e-01
-4.35218327e-02 4.84248132e-01 3.31367344e-01 -9.42738712e-01
-5.42943716e-01 -5.46590209e-01 -4.30581599e-01 3.71407449e-01
-1.69869974e-01 -1.26625493e-01 -8.82984936e-01 -2.90472120e-01
-3.19878012e-01 4.73356038e-01 -6.75951123e-01 -2.32283071e-01
-4.00303692e-01 -1.18864036e+00 1.48155540e-01 6.61899984e-01
4.48483974e-01 -1.29453182e+00 -9.45419848e-01 6.54270113e-01
-1.02971263e-01 -6.15119517e-01 -1.26087710e-01 7.65542030e-01
-9.05457675e-01 -1.11417997e+00 -8.10840428e-01 -9.29066837e-01
7.58171856e-01 -1.33651346e-01 5.52690566e-01 2.92611471e-03
-4.81288612e-01 1.40918163e-03 -4.85007584e-01 -7.75776029e-01
2.38108814e-01 -7.87492841e-03 3.49713385e-01 -1.00115843e-01
1.14916098e+00 -1.02884936e+00 -8.66241992e-01 1.70413032e-02
-5.85871637e-01 2.10972466e-02 7.92717874e-01 7.53054500e-01
7.08611786e-01 2.66054034e-01 7.43521631e-01 -2.44015187e-01
1.06270492e+00 -4.50399846e-01 -5.67345917e-01 1.52133197e-01
-5.44056892e-01 -3.11079562e-01 9.01090503e-01 -5.61769247e-01
-3.30314279e-01 3.38130206e-01 -4.46904339e-02 -1.65981829e-01
-4.00561780e-01 3.48043531e-01 -2.50744149e-02 -4.93698627e-01
5.30289948e-01 8.38786423e-01 -2.39320830e-01 -4.77854103e-01
-6.80353880e-01 8.98611188e-01 4.17225122e-01 2.58435577e-01
3.25646281e-01 2.53108740e-01 1.84909720e-02 -9.00635123e-01
-4.55236100e-02 -3.47997993e-01 -7.17772961e-01 2.43373327e-02
7.63887048e-01 -8.27256858e-01 -6.23201787e-01 5.73339999e-01
-9.52162266e-01 -1.68866634e-01 1.84774294e-01 1.06946146e+00
-5.59631050e-01 -1.77437570e-02 -5.38746238e-01 -4.30336654e-01
-6.91266298e-01 -1.48558414e+00 5.85216701e-01 9.50601846e-02
-4.30834174e-01 -7.91543365e-01 -1.34406267e-02 -1.43954441e-01
5.93365669e-01 1.91278309e-01 7.89000571e-01 -1.26401353e+00
-3.40948015e-01 -5.56582451e-01 1.37319583e-02 1.08629175e-01
3.89887840e-01 -6.19711399e-01 -8.30229521e-01 -3.70128542e-01
3.92619580e-01 1.78193361e-01 5.91131926e-01 9.02628899e-01
1.54273987e+00 -1.39856204e-01 -5.34815371e-01 1.00568569e+00
1.24449968e+00 1.19531429e+00 8.67675066e-01 5.97325563e-01
3.72904509e-01 3.83770257e-01 5.83774522e-02 3.30420583e-01
7.30106384e-02 4.46629465e-01 6.42669797e-02 -5.63488752e-02
1.34602427e-01 2.90896565e-01 1.57300383e-01 9.54642773e-01
-2.11118922e-01 8.14769417e-03 -1.16859567e+00 6.44321918e-01
-1.34256244e+00 -5.70937455e-01 8.18727762e-02 2.12743998e+00
4.90798265e-01 -2.62880951e-01 -2.01949313e-01 3.11279029e-01
3.90777439e-01 -4.96046126e-01 -4.36200559e-01 -1.81051821e-01
-1.79290131e-01 6.47825599e-01 5.31736076e-01 7.09472522e-02
-9.46750998e-01 6.69999063e-01 6.37882710e+00 5.65149486e-01
-1.73010361e+00 1.27082607e-02 7.47596204e-01 -1.85853153e-01
2.81026155e-01 -2.27419257e-01 -4.46782500e-01 6.59143806e-01
1.18194246e+00 -3.85914773e-01 5.00634551e-01 4.96501446e-01
5.01711726e-01 -1.76618751e-02 -9.45169210e-01 1.52582502e+00
6.82018101e-02 -1.35975206e+00 -1.03708677e-01 2.31826231e-01
4.17843491e-01 1.92357406e-01 -2.16372952e-01 -1.15822203e-01
-3.23455781e-01 -1.33024716e+00 2.36347720e-01 7.87646234e-01
7.19770074e-01 -1.03379416e+00 1.05903387e+00 2.87833363e-01
-1.12902880e+00 -2.30586141e-01 -2.12448165e-01 -1.97876319e-01
-1.04178853e-01 3.34277570e-01 -1.00214505e+00 1.25451133e-01
8.99201095e-01 7.85616934e-01 -7.83157885e-01 1.49539030e+00
-2.90858615e-02 5.86028755e-01 -2.45366931e-01 -1.31430775e-01
1.39732495e-01 -1.91613603e-02 5.79617858e-01 1.04184115e+00
9.64658082e-01 6.08419716e-01 -2.63337255e-01 5.02114654e-01
2.84846693e-01 3.59440982e-01 -7.12528169e-01 -1.34666055e-01
4.26738650e-01 9.93866086e-01 -9.83708322e-01 1.39156416e-01
-5.54427683e-01 8.53477657e-01 -5.12530729e-02 4.69905883e-01
-7.29373693e-02 -7.91640341e-01 4.04768229e-01 1.58270776e-01
3.96592058e-02 1.54975384e-01 -7.13270485e-01 -1.14117897e+00
8.21847394e-02 -7.76265740e-01 6.39496520e-02 -6.60958529e-01
-9.25049305e-01 1.26980770e+00 -3.30418944e-01 -1.58817494e+00
-4.97317314e-01 -6.38923645e-01 -9.38702106e-01 9.12849009e-01
-1.17341363e+00 -8.22236598e-01 -1.97058082e-01 9.75732923e-01
4.46322352e-01 -6.39989018e-01 1.28329027e+00 -1.82748679e-02
-3.19059998e-01 2.92744040e-01 5.97163886e-02 2.21830577e-01
2.67609596e-01 -8.94666851e-01 -1.29077805e-03 7.90882587e-01
3.79406661e-02 5.04260540e-01 5.40104449e-01 -7.79929936e-01
-1.14417851e+00 -9.52493787e-01 9.14543569e-01 -1.95808500e-01
6.98150337e-01 -2.30261907e-01 -9.72272336e-01 6.11978114e-01
3.72483991e-02 -2.33329728e-01 9.63077962e-01 -1.76299959e-01
3.79834890e-01 -2.86933601e-01 -9.11057949e-01 4.42938447e-01
4.04430836e-01 -3.41806889e-01 -9.19510782e-01 3.21246684e-01
-9.16074514e-02 -7.94881880e-02 -8.97203326e-01 4.68215287e-01
4.78478551e-01 -7.67962337e-01 6.02257609e-01 -2.03177869e-01
-2.11280156e-02 3.31234373e-02 1.14905529e-01 -1.75842965e+00
-3.54482502e-01 -5.84120989e-01 2.12239340e-01 1.39968723e-01
4.91816968e-01 -8.84007573e-01 7.99768150e-01 7.42728233e-01
-1.67119101e-01 -1.06712663e+00 -9.60346699e-01 -6.08720958e-01
4.77846377e-02 -7.64632463e-01 8.98478806e-01 6.81924760e-01
5.55380046e-01 -2.93871373e-01 -2.25888237e-01 2.24697039e-01
3.93393517e-01 -5.04442789e-02 8.25200304e-02 -1.37162089e+00
2.89357543e-01 -4.10690367e-01 -1.04754376e+00 -2.56969124e-01
-1.39312353e-02 -7.75649309e-01 -1.73909932e-01 -1.42701113e+00
2.72752404e-01 -1.76980957e-01 -6.56546891e-01 7.84137249e-01
2.90344238e-01 4.59332883e-01 -1.22065164e-01 3.22080046e-01
1.95033289e-02 1.91205725e-01 1.00028586e+00 -3.72480415e-02
-7.15275228e-01 2.66908795e-01 -6.97446167e-01 8.34845304e-01
1.11000669e+00 -6.07598186e-01 -3.98379415e-01 -1.34921655e-01
-1.77861840e-01 3.78811598e-01 2.34154373e-01 -1.32126725e+00
6.21290386e-01 1.20520279e-01 1.00548363e+00 -6.72093809e-01
2.02663690e-01 -9.19971287e-01 4.09891278e-01 5.54920018e-01
1.36217698e-01 8.81854817e-02 4.00879651e-01 1.63232803e-01
-5.45996726e-01 1.50153935e-01 5.26851475e-01 -6.45628422e-02
-7.03379095e-01 4.59909499e-01 -9.96054113e-01 -4.09278244e-01
1.34149885e+00 -6.03639066e-01 2.88018078e-01 -5.49366355e-01
-1.15150917e+00 -2.49239221e-01 1.11503303e-01 4.32884932e-01
1.04376805e+00 -1.13904727e+00 -6.33242488e-01 7.69804358e-01
1.15232624e-01 -6.26217484e-01 2.47387320e-01 1.16504252e+00
-6.77395701e-01 8.76165926e-01 -7.47382283e-01 -4.33082432e-01
-1.29947114e+00 5.81531413e-02 5.71343541e-01 4.53060344e-02
-1.00812781e+00 9.16374445e-01 2.34657317e-01 1.81750655e-01
2.66718805e-01 -3.07418823e-01 -6.07305348e-01 1.88055392e-02
1.06188869e+00 -9.73137766e-02 7.07329214e-01 -6.08890772e-01
-6.33102536e-01 4.72352028e-01 -2.09429085e-01 4.12508130e-01
1.90134823e+00 1.92743450e-01 -2.90386289e-01 -7.27967098e-02
1.21419382e+00 -3.48270297e-01 -1.02468002e+00 3.86951357e-01
-1.03464536e-01 -3.26581657e-01 5.17911077e-01 -1.01248419e+00
-1.40107787e+00 8.89389157e-01 1.02284074e+00 1.82246312e-01
1.75856650e+00 -1.26324981e-01 6.55411661e-01 5.07061899e-01
7.91610777e-01 -9.00344729e-01 -5.24672925e-01 2.95980692e-01
9.94466245e-01 -7.25482821e-01 -2.78071523e-01 9.45237577e-02
-5.96632063e-01 1.51159370e+00 2.26017952e-01 -3.25949967e-01
1.01534832e+00 5.43012977e-01 1.71350479e-01 -5.15116513e-01
-5.62737525e-01 1.95210919e-01 5.17603934e-01 7.52732575e-01
2.87463427e-01 3.63390297e-01 -3.83673459e-01 1.09852493e+00
-6.39563680e-01 1.82221651e-01 4.28555548e-01 7.25825548e-01
-3.58972847e-01 -9.64808166e-01 -4.39731181e-01 1.19071269e+00
-6.06857419e-01 -3.62052053e-01 -4.25136983e-01 7.04725206e-01
1.97997659e-01 8.65147710e-01 1.45282343e-01 -6.92438304e-01
1.40040442e-01 1.44071847e-01 2.94226706e-01 -4.99063551e-01
-6.12164736e-01 3.93065751e-01 -4.26839501e-01 -6.58706963e-01
-7.55690485e-02 -5.85397601e-01 -1.17548609e+00 1.69550955e-01
-2.78709203e-01 3.14883918e-01 6.68108225e-01 1.09238505e+00
6.38836980e-01 5.65715253e-01 5.77408016e-01 -9.02314425e-01
2.07569227e-01 -1.17090666e+00 -1.23850751e+00 -2.81812027e-02
4.04239535e-01 -5.33403695e-01 -6.00585520e-01 2.69072670e-02] | [13.222505569458008, 3.5171661376953125] |
53ec37bb-bcbb-4053-b3ef-ec47d2df70f3 | emotion-detection-from-eeg-using-transfer | 2306.05680 | null | https://arxiv.org/abs/2306.05680v1 | https://arxiv.org/pdf/2306.05680v1.pdf | Emotion Detection from EEG using Transfer Learning | The detection of emotions using an Electroencephalogram (EEG) is a crucial area in brain-computer interfaces and has valuable applications in fields such as rehabilitation and medicine. In this study, we employed transfer learning to overcome the challenge of limited data availability in EEG-based emotion detection. The base model used in this study was Resnet50. Additionally, we employed a novel feature combination in EEG-based emotion detection. The input to the model was in the form of an image matrix, which comprised Mean Phase Coherence (MPC) and Magnitude Squared Coherence (MSC) in the upper-triangular and lower-triangular matrices, respectively. We further improved the technique by incorporating features obtained from the Differential Entropy (DE) into the diagonal, which previously held little to no useful information for classifying emotions. The dataset used in this study, SEED EEG (62 channel EEG), comprises three classes (Positive, Neutral, and Negative). We calculated both subject-independent and subject-dependent accuracy. The subject-dependent accuracy was obtained using a 10-fold cross-validation method and was 93.1%, while the subject-independent classification was performed by employing the leave-one-subject-out (LOSO) strategy. The accuracy obtained in subject-independent classification was 71.6%. Both of these accuracies are at least twice better than the chance accuracy of classifying 3 classes. The study found the use of MSC and MPC in EEG-based emotion detection promising for emotion classification. The future scope of this work includes the use of data augmentation techniques, enhanced classifiers, and better features for emotion classification. | ['Sana Parveen K', 'Jerrin Thomas Panachakel', 'Ranjana H', 'Ashish Abraham Samuel', 'Sidharth Sidharth'] | 2023-06-09 | null | null | null | null | ['emotion-classification', 'eeg', 'emotion-classification', 'eeg'] | ['computer-vision', 'methodology', 'natural-language-processing', 'time-series'] | [ 2.01180980e-01 -1.52889282e-01 3.62133741e-01 -4.05678838e-01
-4.47063357e-01 -2.31151670e-01 2.44689614e-01 3.36792529e-01
-7.48381019e-01 1.07079256e+00 7.52599817e-03 -1.19615719e-02
-4.29107845e-01 -6.04344964e-01 -3.31407905e-01 -8.64586353e-01
-5.80807328e-01 -2.56185949e-01 -4.74048257e-02 -2.23110288e-01
4.01735961e-01 5.27407706e-01 -1.68196321e+00 4.77941990e-01
9.74396110e-01 1.40276861e+00 1.20992467e-01 2.56825954e-01
2.61702925e-01 4.19536680e-01 -8.02938282e-01 -2.08931603e-02
-2.99411407e-03 -6.57608151e-01 -5.37340164e-01 -1.71781734e-01
-2.80737162e-01 2.12703377e-01 1.66461349e-01 7.90613770e-01
9.09149349e-01 2.31729388e-01 6.39620304e-01 -1.34282684e+00
-2.76424646e-01 1.68690324e-01 -3.67599070e-01 4.42563474e-01
6.14713192e-01 -1.64822787e-01 6.00050330e-01 -9.42251027e-01
3.75018060e-01 5.10556459e-01 5.39278924e-01 2.14919686e-01
-1.09826589e+00 -1.12064350e+00 -2.29644686e-01 6.78720951e-01
-1.54475784e+00 -1.71578035e-01 8.94868553e-01 -6.72985017e-01
1.17420149e+00 3.90787274e-01 1.37105405e+00 8.71931851e-01
6.65556729e-01 3.00258577e-01 1.94679224e+00 -4.82645184e-01
6.52051330e-01 6.40820384e-01 4.41261753e-02 9.43366289e-02
-3.90384980e-02 1.17224909e-01 -6.04948699e-01 -2.43067071e-01
5.64432442e-01 -4.31164503e-01 -5.14545083e-01 -3.48012634e-02
-1.08883822e+00 7.72957921e-01 4.31399763e-01 8.94968629e-01
-7.20170677e-01 -5.64736247e-01 5.18651187e-01 5.08092225e-01
7.91466832e-01 7.61354089e-01 -6.39199615e-01 -4.73896980e-01
-1.00148487e+00 -2.71983206e-01 8.29111576e-01 1.89449936e-01
4.67647374e-01 1.37434274e-01 1.03460923e-01 8.44539523e-01
-1.92213804e-01 2.08950117e-01 8.76510024e-01 -3.74998212e-01
3.07665952e-02 6.20029032e-01 -1.95368662e-01 -1.08812606e+00
-8.65801215e-01 -6.81935728e-01 -9.64227200e-01 2.36878008e-01
-3.04732323e-02 -3.92949998e-01 -6.23951316e-01 1.69643557e+00
-8.84649828e-02 8.50576758e-02 -2.16278601e-02 9.77416813e-01
6.37013972e-01 4.31549937e-01 1.05751134e-01 -5.89820504e-01
1.42791688e+00 -2.93269664e-01 -8.21353555e-01 1.24546811e-01
5.51968753e-01 -5.38293421e-01 8.13088417e-01 7.02896714e-01
-7.84688234e-01 -3.96315992e-01 -1.19431615e+00 7.41275012e-01
-6.65367901e-01 2.87283510e-01 5.68719327e-01 9.59137559e-01
-1.17254043e+00 4.84780699e-01 -6.34926856e-01 -4.94902909e-01
3.53296548e-01 8.22477818e-01 -7.17681289e-01 5.43286443e-01
-1.44927049e+00 1.25644922e+00 3.15652281e-01 1.61391012e-02
-1.77144796e-01 -4.36790645e-01 -6.71650469e-01 9.30598751e-02
-1.75184891e-01 -2.67623514e-01 4.05598253e-01 -1.26340115e+00
-1.54084074e+00 7.84013391e-01 -8.71735588e-02 -2.29886532e-01
1.28360629e-01 1.87462226e-01 -6.75227940e-01 3.94628286e-01
-1.56046480e-01 4.73162353e-01 6.56919837e-01 -9.10464287e-01
-2.00021058e-01 -6.18063867e-01 -3.92581165e-01 8.22355002e-02
-5.99198520e-01 1.71251863e-01 2.92822719e-01 -5.42250633e-01
1.55210420e-01 -7.87741542e-01 2.19275564e-01 -6.54871821e-01
2.20510125e-01 -7.40599483e-02 4.30048764e-01 -9.09310520e-01
1.18581522e+00 -2.08218336e+00 9.94705111e-02 6.68024719e-01
1.07535958e-01 1.66202396e-01 2.88062692e-02 3.81433815e-01
-7.19931185e-01 -2.12146062e-02 -1.99081510e-01 2.61374384e-01
-2.73268402e-01 -2.99686074e-01 1.42714337e-01 5.70046008e-01
3.46166372e-01 6.32819295e-01 -6.42703652e-01 -2.63972282e-01
3.79101783e-01 5.94610333e-01 -3.40403855e-01 4.56627794e-02
6.93114936e-01 6.15250528e-01 -7.57799000e-02 4.21146244e-01
5.44604957e-01 2.52887398e-01 -4.87874374e-02 -3.76149595e-01
-2.16737226e-01 3.40906158e-02 -1.05997729e+00 1.26409996e+00
-4.69125748e-01 8.96533966e-01 -1.16202630e-01 -1.03479862e+00
1.08966768e+00 6.72533274e-01 7.48310268e-01 -8.22731972e-01
5.29980779e-01 2.24101573e-01 4.62786287e-01 -5.95313549e-01
5.61017282e-02 -2.67786473e-01 2.48817489e-01 2.41687208e-01
2.21248671e-01 -7.04678893e-02 5.23576252e-02 -1.69097170e-01
8.63955498e-01 -7.57957324e-02 5.58393538e-01 -5.67178726e-01
6.15908742e-01 -3.50201547e-01 3.75452995e-01 2.56874919e-01
-2.96007216e-01 2.99881279e-01 5.48017919e-01 -1.88935846e-01
-4.27095324e-01 -5.96289277e-01 -5.92159688e-01 6.33848667e-01
-1.53878182e-01 -3.91779602e-01 -8.05479527e-01 -1.73209459e-01
-3.02254200e-01 6.77817643e-01 -7.30304718e-01 -5.17444730e-01
4.93936315e-02 -1.14804125e+00 2.91712463e-01 4.70011085e-01
3.94119442e-01 -1.32375860e+00 -8.20025444e-01 2.27986529e-01
-2.50991255e-01 -8.01943421e-01 2.26004079e-01 6.43066764e-01
-9.06211078e-01 -9.88571167e-01 -7.23048210e-01 -6.23992383e-01
5.73759437e-01 -3.28614295e-01 5.93441367e-01 -2.07037255e-01
-3.66099656e-01 3.99450064e-01 -5.30607700e-01 -6.37519479e-01
1.23519801e-01 -1.38926715e-01 3.44716311e-01 1.90353215e-01
5.16582429e-01 -8.49948287e-01 -6.25710309e-01 2.39357427e-01
-7.21730113e-01 -3.24256122e-01 7.62381256e-01 9.65224385e-01
1.60790443e-01 1.94202796e-01 1.11692548e+00 -1.71612248e-01
1.04576409e+00 -3.33711177e-01 -7.28581175e-02 -3.12038399e-02
-6.33218884e-01 -4.22701120e-01 5.11691868e-01 -6.03741825e-01
-7.40913510e-01 -7.08784536e-02 -2.55381823e-01 -1.01714276e-01
-2.81956047e-01 6.80160522e-01 -2.41509005e-02 -5.20392478e-01
6.21821046e-01 2.27969453e-01 3.38008590e-02 3.69130336e-02
-3.65788311e-01 9.34030175e-01 3.96916792e-02 -3.05540413e-01
5.78279793e-02 -3.74607407e-02 -5.43290265e-02 -1.10121024e+00
-9.28194523e-02 -5.41639984e-01 -7.16941357e-01 -4.47963417e-01
7.53253102e-01 -8.69666576e-01 -8.17177176e-01 6.05185032e-01
-8.08908165e-01 -1.32009745e-01 -1.13330103e-01 1.11278152e+00
-4.55484480e-01 8.16637203e-02 -3.95430386e-01 -1.03852367e+00
-6.43523037e-01 -1.07358515e+00 6.12383127e-01 7.41017982e-02
-5.95276713e-01 -7.89751351e-01 2.81340368e-02 1.37998164e-02
5.04516482e-01 3.50112289e-01 9.67111588e-01 -9.04035747e-01
2.83171803e-01 -4.89956796e-01 6.36080280e-02 6.04894400e-01
2.58968890e-01 -3.51048350e-01 -1.17571568e+00 -2.76917398e-01
4.03989881e-01 -3.12268138e-01 4.66587007e-01 3.31191123e-01
1.02169347e+00 2.69214720e-01 8.67720414e-03 1.29842564e-01
1.20705569e+00 7.57011950e-01 8.85914028e-01 4.23143297e-01
1.00045420e-01 6.10458195e-01 3.87626052e-01 5.33758521e-01
1.54634327e-01 5.58430791e-01 4.78685424e-02 -2.42765740e-01
3.58403176e-01 4.43057358e-01 2.17693716e-01 8.61802220e-01
-3.66768509e-01 6.10546544e-02 -8.16515744e-01 3.18329573e-01
-1.45802808e+00 -8.28847289e-01 -9.53629017e-02 2.27157927e+00
6.43864393e-01 1.23532131e-01 1.21588781e-01 7.82132566e-01
4.26331371e-01 -3.94717187e-01 -3.21124941e-01 -6.94301963e-01
-1.54080868e-01 6.37635827e-01 9.33705717e-02 8.80096778e-02
-9.84223008e-01 4.12513644e-01 6.19212770e+00 5.49902141e-01
-1.49451005e+00 1.73782017e-02 5.18883407e-01 -4.83324938e-02
3.66487324e-01 -3.86377126e-01 -1.32537082e-01 5.27463913e-01
1.21876180e+00 -9.47011709e-02 5.23303688e-01 5.09675503e-01
3.98643911e-01 -7.50243545e-01 -7.85905719e-01 1.23970342e+00
3.95696312e-01 -6.22682691e-01 -4.95549828e-01 -8.13621748e-03
4.65129942e-01 -2.31648013e-01 -1.67213872e-01 3.27551872e-01
-8.50213230e-01 -7.88099945e-01 4.14258659e-01 5.63709199e-01
9.04271126e-01 -8.73318851e-01 1.22654605e+00 2.28536800e-01
-8.81994069e-01 -1.60324767e-01 -2.05607153e-02 -4.80707020e-01
-2.17341617e-01 6.04321539e-01 -6.88627362e-01 4.76425260e-01
8.99370551e-01 7.11901188e-01 -4.59699452e-01 1.11634851e+00
-1.13857370e-02 5.68057120e-01 -2.11136460e-01 -3.19567442e-01
-1.79855913e-01 -4.67959553e-01 4.24861133e-01 1.16095579e+00
3.67903858e-01 3.06816965e-01 -3.44441235e-01 6.08245850e-01
3.13988298e-01 4.64014053e-01 -4.30311978e-01 2.79399157e-02
1.95667520e-01 1.43233263e+00 -1.05417252e+00 -2.06482813e-01
-2.71504462e-01 9.46051419e-01 6.27690693e-03 2.73746192e-01
-6.18430614e-01 -8.90082479e-01 3.35836798e-01 -2.12753624e-01
-2.10608970e-02 8.92812163e-02 -5.13676584e-01 -1.08796382e+00
1.55987546e-01 -7.65241504e-01 6.15022294e-02 -9.27625477e-01
-1.12955701e+00 8.43374610e-01 2.39543945e-01 -1.15144598e+00
-1.63233012e-01 -6.59527183e-01 -5.71951985e-01 1.14501834e+00
-1.07592988e+00 -6.26211345e-01 -3.41684610e-01 6.96999550e-01
9.04920883e-03 -5.17504700e-02 1.27307045e+00 3.92859519e-01
-4.98960584e-01 4.59908813e-01 1.83345620e-02 -1.12607345e-01
7.41947770e-01 -1.09263349e+00 -6.02492690e-01 4.07516956e-01
-2.62823671e-01 6.12798512e-01 5.76872706e-01 -4.72714901e-01
-9.58035111e-01 -5.60156763e-01 7.98790276e-01 9.35487002e-02
6.03870273e-01 -4.28121418e-01 -7.47868419e-01 2.03658864e-01
3.12026799e-01 -3.71632397e-01 1.17636287e+00 8.60399753e-02
2.15089798e-01 -1.66213393e-01 -1.45048487e+00 3.02763134e-01
4.07147974e-01 -5.64736962e-01 -6.66164458e-01 6.38548881e-02
-3.82823907e-02 -1.78588837e-01 -1.25924969e+00 4.80749071e-01
9.03928459e-01 -1.03569531e+00 5.88699698e-01 -3.15552473e-01
1.69818699e-01 9.14063454e-02 -1.77031755e-03 -1.77130723e+00
-4.59309429e-01 -5.06647117e-02 3.40154886e-01 9.47978020e-01
3.74976069e-01 -9.59251702e-01 4.47850883e-01 7.01334596e-01
-7.94226006e-02 -1.09297502e+00 -9.49317634e-01 -5.95718741e-01
-7.70060271e-02 -5.16160369e-01 3.08440596e-01 8.65981221e-01
7.35890627e-01 2.98091829e-01 -5.54409400e-02 -3.02295029e-01
-6.83966056e-02 -1.22311614e-01 8.36616606e-02 -1.29460359e+00
2.30073184e-01 -4.53920275e-01 -1.00972986e+00 1.23898432e-01
2.06383556e-01 -1.02453649e+00 -1.66222781e-01 -1.45122778e+00
1.81583971e-01 -7.60624409e-02 -7.30570316e-01 5.46419919e-01
-1.92908570e-02 4.34779406e-01 8.98437127e-02 -1.36960223e-01
-5.09328842e-02 6.38858020e-01 8.09140027e-01 1.46392390e-01
-6.41531646e-01 1.06727527e-02 -6.18010342e-01 5.98663867e-01
1.21180570e+00 -4.05352741e-01 -3.70496601e-01 3.78681332e-01
8.00595153e-03 2.45802458e-02 1.91902906e-01 -1.26542187e+00
1.18587673e-01 3.60906631e-01 9.23210919e-01 -3.63920212e-01
6.17766798e-01 -9.88527954e-01 2.07259417e-01 5.82053840e-01
-7.91376606e-02 1.63033053e-01 6.01188064e-01 1.30179405e-01
-2.85581172e-01 -5.71999662e-02 6.13486528e-01 1.41702443e-01
-6.19897962e-01 -2.55490243e-01 -8.22781384e-01 -3.70839417e-01
1.29883099e+00 -4.43921238e-01 -8.60128179e-03 -4.84509647e-01
-1.02051699e+00 -2.32724428e-01 4.81369272e-02 1.71375796e-01
7.79344916e-01 -1.14272475e+00 -4.85136181e-01 5.37693858e-01
1.01303458e-01 -9.53405201e-01 3.04650366e-01 1.55023360e+00
-6.25906512e-02 4.60023969e-01 -8.52160096e-01 -4.82516199e-01
-1.39203429e+00 2.32092857e-01 3.94629419e-01 1.02101639e-01
-2.11997658e-01 6.01072431e-01 -1.72054946e-01 -1.33818701e-01
1.56059921e-01 -1.80545360e-01 -6.64485574e-01 6.35384202e-01
4.70073700e-01 5.64848781e-01 6.31352782e-01 -7.52247691e-01
-6.16334021e-01 4.62985903e-01 3.27693999e-01 -2.70044029e-01
1.53503406e+00 9.61257592e-02 -5.02375305e-01 7.80476749e-01
1.28301287e+00 -1.70775980e-01 -4.99475539e-01 4.48716074e-01
-1.70530573e-01 -2.61413783e-01 2.47943431e-01 -1.24721885e+00
-9.51612830e-01 9.00035501e-01 1.11893523e+00 2.36720279e-01
1.64913213e+00 -4.08230364e-01 2.13118508e-01 2.16015622e-01
6.07463717e-01 -1.14855313e+00 -2.56357819e-01 2.47286841e-01
1.10297191e+00 -1.06006527e+00 1.18685467e-02 -2.11017281e-01
-7.21450925e-01 1.12202549e+00 3.94127190e-01 -2.50402605e-03
8.83944869e-01 1.45009086e-01 -7.14521483e-02 -2.47622728e-01
-5.97327530e-01 -6.54177517e-02 5.78922808e-01 5.58883190e-01
7.67063260e-01 1.89326376e-01 -8.85644734e-01 1.04519939e+00
-4.03883070e-01 3.85602891e-01 3.05608273e-01 9.70504940e-01
-1.40770048e-01 -8.55074406e-01 -4.79749203e-01 9.03288841e-01
-5.01684487e-01 -9.12550539e-02 -4.12667692e-01 8.64912868e-01
3.35700989e-01 1.27359009e+00 -4.38117012e-02 -8.94736946e-01
3.55606854e-01 3.59422743e-01 5.65773368e-01 -2.33886033e-01
-9.36586559e-01 4.40201461e-02 -1.94486678e-02 -4.66634929e-01
-6.43103004e-01 -5.50974607e-01 -1.06470847e+00 1.06950618e-01
-5.16413331e-01 4.21232700e-01 9.58134353e-01 8.57133865e-01
4.44320291e-01 6.57060385e-01 7.01016545e-01 -9.67935741e-01
2.57471167e-02 -1.37705302e+00 -9.41539645e-01 2.07413286e-01
4.67284396e-02 -7.83219755e-01 -5.89986205e-01 -9.04596075e-02] | [13.254937171936035, 3.351513147354126] |
3c1faceb-99a1-4ffa-9e43-44020c7a6114 | the-use-of-the-word-gamma-u-psion-nai-k-appa | 2210.11837 | null | https://arxiv.org/abs/2210.11837v1 | https://arxiv.org/pdf/2210.11837v1.pdf | The use of the word "\{gamma}\u{psion}ναι\k{appa}ο\k{appa}τονια" (femicide) in Greek-speaking Twitter | Between 2019 and 2022, Greek media attention has been attracted by a rather unusually high number of femicide cases which have been trending for several weeks up to months in the public debate and one of the contributing factors is the feedback loop between traditional media and social media. In this paper we are investigating the use of the term "\{gamma}\u{psion}{\nu}{\alpha}{\iota}\k{appa}{\omicron}\k{appa}{\tau}{\omicron}{\nu}{\iota}{\alpha}" (femicide) in Greek speaking twitter. More specifically, we approach the problem from a stance detection perspective, aiming to automatically identify user position with regards to the feministic semantics of the word. We also discuss findings from an identity analysis perspective and intercorrelations with hate speech that have been identified in the collected corpus of tweets. | ['Konstantinos Perifanos', 'Ioanna Tsounidi', 'Athina Kontostavlaki', 'Nikoleta Gkatzoli', 'Efstathia Bambili', 'Aglaia Aggistrioti'] | 2022-10-21 | null | null | null | null | ['stance-detection'] | ['natural-language-processing'] | [ 1.22431852e-01 3.66447270e-01 4.62048091e-02 2.40845650e-01
-3.92846644e-01 -7.09833980e-01 9.90651131e-01 7.03413308e-01
-5.78303874e-01 8.86314809e-01 5.39882898e-01 -6.72524452e-01
-3.32281440e-01 -6.34698927e-01 -1.34803370e-01 -5.73206902e-01
3.18974555e-01 2.82156289e-01 4.30671219e-03 -5.16224504e-01
5.60734332e-01 1.39041439e-01 -1.59744549e+00 2.83817947e-01
6.22393429e-01 8.64604056e-01 -2.83532023e-01 2.39090502e-01
-1.26187783e-03 8.26106727e-01 -9.19139326e-01 -8.23431849e-01
-1.90308303e-01 -5.51239252e-01 -8.33172262e-01 -3.02365690e-01
1.01049222e-01 -8.86049047e-02 3.09018660e-02 1.35675561e+00
4.93770838e-01 -1.58090536e-02 7.89253354e-01 -8.17393005e-01
-2.40370274e-01 8.65183890e-01 -8.66590679e-01 6.29773676e-01
5.37767351e-01 -8.48772004e-02 7.59457707e-01 -7.09556937e-01
9.37273920e-01 1.15939653e+00 6.79078758e-01 1.60661653e-01
-7.79130042e-01 -8.56020510e-01 -1.85766786e-01 -1.85312495e-01
-1.49717128e+00 -2.40473434e-01 6.15797579e-01 -8.45129490e-01
5.31324029e-01 6.49672031e-01 7.74007440e-01 1.42355299e+00
7.95538425e-02 3.18114191e-01 1.15619242e+00 -5.03592014e-01
-2.40988526e-02 2.97617674e-01 2.85367191e-01 3.82595718e-01
5.81259370e-01 -5.26902676e-01 -4.62345570e-01 -5.09101689e-01
2.88961649e-01 -4.14496928e-01 2.09421262e-01 9.00276959e-01
-9.84361649e-01 1.29773223e+00 -1.98420510e-01 8.85696173e-01
-3.02006990e-01 -1.87865451e-01 6.08048558e-01 -4.99533080e-02
8.09896469e-01 3.88916314e-01 7.87566751e-02 -8.41315150e-01
-7.76377439e-01 4.69761342e-01 6.37227595e-01 5.35009265e-01
4.35107291e-01 -1.29101247e-01 1.88678533e-01 7.04117060e-01
1.04296645e-02 4.07192677e-01 2.69596457e-01 -8.99216056e-01
5.50329089e-01 8.03922594e-01 2.45800212e-01 -1.30591607e+00
-4.63801563e-01 -2.69510955e-01 -4.53356385e-01 -1.63049027e-02
8.02337348e-01 -5.39243281e-01 -1.01703983e-02 1.45774388e+00
4.05224383e-01 -5.38011253e-01 -4.35869962e-01 4.80727464e-01
7.19203591e-01 5.88360786e-01 3.75568300e-01 -6.96421623e-01
1.55968964e+00 5.44274189e-02 -6.56489193e-01 1.18782386e-01
7.15655744e-01 -9.58828449e-01 1.15095341e+00 3.85106027e-01
-1.06898236e+00 5.52799441e-02 -7.57053673e-01 -1.49931209e-02
-5.78867495e-01 -4.95063998e-02 1.75765932e-01 1.15310073e+00
-6.52347326e-01 5.81267416e-01 -2.64311939e-01 -7.25802004e-01
2.51271337e-01 7.21310750e-02 -1.02234036e-01 5.50172210e-01
-9.75777686e-01 7.63107538e-01 5.01763403e-01 -3.95822898e-02
-4.94443215e-02 -3.71857285e-01 -4.46399361e-01 -5.01020253e-01
5.72440565e-01 9.25614312e-02 8.40043306e-01 -1.03065693e+00
-1.09892941e+00 1.12379253e+00 1.10503413e-01 -1.56912968e-01
7.04141259e-01 -3.17207038e-01 -8.08815360e-01 2.75570869e-01
4.53349590e-01 3.02893091e-02 4.92760509e-01 -1.02364087e+00
-6.96157277e-01 -7.20174789e-01 2.12355316e-01 -2.26754904e-01
-7.01032579e-01 1.15581822e+00 5.78181386e-01 -9.14968550e-01
7.51025230e-02 -1.00017786e+00 4.56683934e-01 -7.90919721e-01
-6.52066290e-01 -4.47384506e-01 9.78387833e-01 -8.42456579e-01
2.16792679e+00 -1.97470939e+00 -1.07997827e-01 6.00164592e-01
5.26662409e-01 4.89130884e-01 1.07457471e+00 1.02550113e+00
1.22791668e-02 5.60010672e-01 -1.05424009e-01 2.56364465e-01
-7.51097575e-02 9.98115540e-02 -2.43655950e-01 7.21510410e-01
-3.92802984e-01 3.55424732e-01 -1.22286308e+00 -4.44533318e-01
-1.76749840e-01 2.80655056e-01 -2.35206902e-01 -4.90153730e-01
-7.53144547e-02 6.33564234e-01 -5.01358926e-01 6.41933739e-01
4.30031985e-01 -2.13323355e-01 2.73746848e-01 4.61596288e-02
-1.02900183e+00 2.08421454e-01 -7.67395854e-01 8.57661307e-01
2.81825751e-01 7.45745838e-01 -5.67679154e-03 -4.70897377e-01
8.32062960e-01 2.99657166e-01 6.81086004e-01 -5.86602867e-01
9.08552170e-01 4.08360660e-01 3.08473200e-01 -3.71886909e-01
8.25161934e-01 -1.39077142e-01 -3.74027997e-01 7.72408187e-01
-4.62578624e-01 2.82636106e-01 3.73304904e-01 2.25426946e-02
8.39874208e-01 4.29196917e-02 5.74521184e-01 -7.34315634e-01
7.00273931e-01 -1.27968147e-01 3.44988763e-01 3.13205540e-01
-2.37357110e-01 2.80813575e-01 1.12732267e+00 -3.87351960e-01
-1.18403339e+00 -5.81052244e-01 -3.68631303e-01 1.17566371e+00
4.03568298e-02 -6.54903650e-01 -1.13418710e+00 -3.27139944e-01
-3.97840619e-01 8.88566375e-01 -7.61505902e-01 1.33034930e-01
-5.81970572e-01 -8.18708360e-01 9.89664257e-01 1.52881995e-01
4.88881201e-01 -1.12147582e+00 -1.21885836e+00 7.09574670e-02
-4.95037735e-01 -9.79731321e-01 6.89064935e-02 -1.54718325e-01
-1.70979425e-01 -9.56474960e-01 -5.47306061e-01 7.68984258e-02
4.38233554e-01 -2.84994453e-01 7.00672209e-01 1.58686772e-01
-2.37961605e-01 -2.13235125e-01 -7.39734352e-01 -9.56783175e-01
-5.67716300e-01 8.41731653e-02 1.63185120e-01 1.68445751e-01
4.67388034e-01 -6.40708208e-01 -4.45088089e-01 -3.30711305e-02
-9.79450107e-01 -1.75502345e-01 -1.77905068e-01 1.64308280e-01
-1.68003365e-01 -3.28636587e-01 4.26506788e-01 -1.19810903e+00
5.72851658e-01 -9.75260735e-01 -2.00152189e-01 -3.84226292e-01
-2.96831340e-01 -5.71183085e-01 3.76198083e-01 -2.93398231e-01
-8.29892159e-01 -8.44439626e-01 -3.17462891e-01 9.97382328e-02
-1.61236778e-01 5.57714760e-01 9.27537829e-02 4.45427507e-01
9.49606240e-01 -2.58630186e-01 -1.68104529e-01 -5.33202112e-01
-5.00861779e-02 7.55824924e-01 2.48803496e-01 -4.27577734e-01
7.18793094e-01 7.30972230e-01 -1.66276291e-01 -1.41366470e+00
-1.00210905e+00 -2.75592208e-01 -4.44473714e-01 -5.82764328e-01
1.19525635e+00 -5.79457998e-01 -1.15904200e+00 4.71754640e-01
-1.05925620e+00 -6.79241586e-03 -1.30771458e-01 3.66223454e-01
-2.16306865e-01 5.81130564e-01 -5.02212167e-01 -1.20162463e+00
-3.65102649e-01 -5.67256510e-01 5.29529452e-01 3.84512573e-01
-1.32787323e+00 -8.81683052e-01 4.82551791e-02 5.54451764e-01
5.65900952e-02 9.39268053e-01 1.01195121e+00 -9.33893621e-01
4.74599272e-01 -9.49798524e-02 -3.24846029e-01 2.94237554e-01
2.26653650e-01 2.58341819e-01 -7.29656935e-01 -3.00338375e-03
1.91224456e-01 -1.22086763e-01 -1.08248107e-01 -2.74887323e-01
6.36433601e-01 -8.10658336e-01 -1.11121602e-01 -2.00730771e-01
1.18445110e+00 2.06204727e-01 6.89853370e-01 5.67542553e-01
4.95050311e-01 7.20792711e-01 4.28648919e-01 9.80184853e-01
2.62967944e-01 3.89595598e-01 4.78991985e-01 5.60809433e-01
2.05787823e-01 2.96399705e-02 3.30538481e-01 6.31534398e-01
-6.43013239e-01 -1.62926957e-01 -1.21385765e+00 7.27131724e-01
-1.59190857e+00 -1.25432444e+00 -6.60453439e-01 1.89690590e+00
6.07931197e-01 1.73732460e-01 5.24890721e-01 4.21604782e-01
8.94446850e-01 2.87243277e-01 3.26805145e-01 -7.01124787e-01
-1.47885308e-01 4.88113225e-01 3.19230676e-01 3.29268813e-01
-7.53932059e-01 6.78456604e-01 4.66568422e+00 9.00274932e-01
-1.11793649e+00 2.66195267e-01 4.39998955e-01 3.53383310e-02
-3.44467998e-01 1.24946594e-01 -8.01509559e-01 1.08948624e+00
8.43155265e-01 -1.66895837e-01 -3.02315354e-01 4.99889165e-01
3.47403407e-01 -8.04508328e-01 -5.11784434e-01 7.29305923e-01
2.23776191e-01 -1.09153092e+00 -1.19008362e-01 5.54799080e-01
9.08187509e-01 -3.16004992e-01 1.40485957e-01 -2.18179822e-01
1.08716212e-01 -8.34402561e-01 1.16475153e+00 3.38264495e-01
7.91655898e-01 -6.82448685e-01 4.39420015e-01 5.00402570e-01
-7.71283031e-01 -2.06107900e-01 1.64664105e-01 -5.35056412e-01
1.47451892e-01 6.13719821e-01 -4.67519432e-01 6.37052953e-01
8.38586628e-01 1.62542909e-01 -3.23266476e-01 3.00889194e-01
-2.88076401e-01 7.01664686e-01 -3.48040253e-01 -4.29960042e-01
4.97218996e-01 -6.64350808e-01 1.00991845e+00 1.26269221e+00
3.93773586e-01 2.91052878e-01 -1.83669746e-01 6.03468716e-01
1.82363704e-01 3.00394237e-01 -6.04685307e-01 -3.43886733e-01
3.94195586e-01 1.11251867e+00 -1.39422846e+00 -6.72211051e-02
-2.21520737e-01 3.26181561e-01 8.59488454e-03 -2.40645912e-02
-1.08664978e+00 -3.56721878e-01 5.32300830e-01 1.04894125e+00
2.67785918e-02 -1.74106762e-01 -3.92694265e-01 -6.52500153e-01
-6.48922846e-02 -7.09213495e-01 2.92725325e-01 -3.67519587e-01
-1.01821864e+00 3.51874471e-01 3.33244652e-01 -9.50796723e-01
-2.38764256e-01 -3.48360658e-01 -3.72073799e-01 7.10057378e-01
-4.99445438e-01 -9.31631148e-01 2.01149695e-02 4.51959252e-01
-1.32716998e-01 -1.18235141e-01 7.91362643e-01 4.81081367e-01
-3.76376510e-01 3.17100555e-01 -1.15772737e-02 3.20773751e-01
2.16333956e-01 -6.68289244e-01 -4.24035490e-02 7.27283597e-01
-4.06477869e-01 5.48722804e-01 1.10284317e+00 -1.00663388e+00
-5.87636292e-01 -5.65870166e-01 1.47129917e+00 -5.76138496e-01
1.27497613e+00 -2.56988287e-01 -5.24578035e-01 6.31094515e-01
4.07148212e-01 -6.54220521e-01 1.09828317e+00 -2.02846676e-01
-3.37645411e-01 4.57848042e-01 -1.20067632e+00 8.40282083e-01
1.02181721e+00 -4.82631236e-01 -5.34574687e-01 2.97454029e-01
1.44042358e-01 -2.03839928e-01 -1.08150566e+00 3.13266404e-02
5.65193415e-01 -1.25274980e+00 3.53526264e-01 -3.61200631e-01
5.98435462e-01 3.86180216e-03 -2.20422223e-02 -5.77098012e-01
1.21997222e-02 -1.06380379e+00 2.79757112e-01 1.53084373e+00
3.98878336e-01 -7.19408929e-01 5.69173157e-01 4.93100405e-01
-1.99280120e-02 -3.21595907e-01 -1.30282724e+00 -2.98285574e-01
3.87345344e-01 -3.30180824e-01 1.31801441e-01 1.39493799e+00
3.14028561e-01 -5.99629665e-03 -4.58809465e-01 -4.40195709e-01
4.25907761e-01 -3.66072774e-01 4.27071869e-01 -1.45306742e+00
3.88802104e-02 -5.20802975e-01 -2.42308691e-01 -4.45799194e-02
-3.59539807e-01 -5.45715570e-01 -7.40891635e-01 -1.11219215e+00
2.42741749e-01 -2.38300443e-01 2.82636970e-01 2.17778295e-01
3.03762257e-01 6.07332289e-01 2.33947605e-01 1.83393002e-01
-3.77147347e-01 -9.51296687e-02 8.05830598e-01 5.34835398e-01
3.79955135e-02 -2.94050872e-01 -9.66552794e-01 1.20592916e+00
6.81479633e-01 -4.06971425e-01 -8.53207111e-02 -1.61715150e-01
1.35668910e+00 -2.32271150e-01 3.51328224e-01 -8.43040943e-01
-9.18206200e-02 -1.66062772e-01 1.58413069e-03 -5.29724419e-01
3.14340830e-01 -4.88857239e-01 3.88785064e-01 4.86217886e-01
1.58413216e-01 4.29082870e-01 3.12861316e-02 -9.27416012e-02
-7.17536593e-03 -2.96052843e-01 7.12101936e-01 -2.05609426e-01
4.42682505e-02 -1.25855923e-01 -9.94030714e-01 4.15621847e-01
1.33759999e+00 -4.90972787e-01 -4.75697219e-01 -4.56620604e-01
-7.76411057e-01 -2.41964698e-01 4.26478595e-01 3.37321490e-01
-7.32055232e-02 -1.03062034e+00 -7.24260271e-01 -4.47280467e-01
4.92592603e-02 -5.46536505e-01 1.10124618e-01 1.09158731e+00
-7.57776618e-01 3.56363542e-02 -3.56106132e-01 -4.41957079e-02
-1.21926546e+00 1.91253856e-01 -2.19804183e-01 -1.92820340e-01
-3.10186088e-01 6.31374359e-01 -3.45275789e-01 2.86248885e-02
-3.07236403e-01 1.77794725e-01 -3.10995430e-01 9.53356624e-01
4.45305169e-01 1.28228772e+00 -9.50488374e-02 -1.44782615e+00
-3.31398129e-01 1.07470535e-01 1.43419653e-01 -3.35424304e-01
1.13523316e+00 -2.33176395e-01 -7.21702695e-01 9.23396230e-01
1.19079268e+00 4.26304609e-01 -3.02181572e-01 2.88198411e-01
4.53671783e-01 -5.84918678e-01 -7.76006043e-01 -6.61682427e-01
-2.51860887e-01 4.94409591e-01 1.53588474e-01 8.06077063e-01
3.43445331e-01 1.76102996e-01 7.92263806e-01 -2.08784297e-01
2.85356849e-01 -1.81689751e+00 1.03371575e-01 7.23404169e-01
7.47348607e-01 -5.68428993e-01 3.24306309e-01 -6.05672181e-01
-4.51097220e-01 1.13444114e+00 -9.67759639e-03 -6.96669444e-02
7.18119919e-01 1.11457177e-01 1.60903022e-01 -4.96394992e-01
2.40371168e-01 -1.70535609e-01 -1.94748133e-01 1.27497464e-01
7.61350572e-01 1.50119096e-01 -1.53473723e+00 6.70988381e-01
-8.30927610e-01 -3.65759641e-01 8.14433157e-01 9.31332588e-01
-4.41211373e-01 -1.15455365e+00 -4.35114235e-01 3.62110615e-01
-1.21774578e+00 3.64121906e-02 -7.62573063e-01 1.04583251e+00
6.01672709e-01 1.07186377e+00 6.64026439e-02 -3.51477772e-01
1.25302458e-02 2.55599618e-01 3.02002102e-01 -3.63815576e-01
-1.37428439e+00 1.82717070e-01 7.10021675e-01 -5.01580127e-02
-5.50278246e-01 -1.29271674e+00 -1.39127934e+00 -7.02511370e-01
-8.09170119e-03 1.85867056e-01 4.95358884e-01 1.21432090e+00
-6.62449896e-02 1.72252521e-01 2.26099044e-01 -4.42854583e-01
-3.08748007e-01 -1.05066133e+00 -6.48896694e-01 3.69842827e-01
-2.72474825e-01 -6.02618694e-01 -2.39700899e-01 -2.74794012e-01] | [8.769037246704102, 10.394815444946289] |
7336505b-0228-45b7-8af9-de456131e5ac | kuleuven-liir-at-semeval-2017-task-12-cross | null | null | https://aclanthology.org/S17-2181 | https://aclanthology.org/S17-2181.pdf | KULeuven-LIIR at SemEval-2017 Task 12: Cross-Domain Temporal Information Extraction from Clinical Records | In this paper, we describe the system of the KULeuven-LIIR submission for Clinical TempEval 2017. We participated in all six subtasks, using a combination of Support Vector Machines (SVM) for event and temporal expression detection, and a structured perceptron for extracting temporal relations. Moreover, we present and analyze the results from our submissions, and verify the effectiveness of several system components. Our system performed above average for all subtasks in both phases. | ['Marie-Francine Moens', 'Artuur Leeuwenberg'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['temporal-information-extraction'] | ['natural-language-processing'] | [ 2.67553896e-01 1.76438734e-01 -5.56973279e-01 -3.87957245e-01
-5.88419497e-01 -5.45613825e-01 6.93614006e-01 9.50215042e-01
-7.65145838e-01 9.40713108e-01 3.25009078e-01 -2.13252440e-01
-3.55827332e-01 -2.99597621e-01 -1.90807953e-01 -5.19923627e-01
-5.73377848e-01 5.75574636e-01 4.27713960e-01 -3.94509077e-01
-1.93189323e-01 3.48185539e-01 -1.18601990e+00 1.16245747e+00
2.78637081e-01 9.98775005e-01 -4.10636038e-01 9.02216434e-01
3.28217983e-01 1.18030465e+00 -5.21924913e-01 -2.62427002e-01
-2.58614093e-01 -2.59240180e-01 -1.05983841e+00 -5.40680468e-01
-4.61924165e-01 2.54338115e-01 -5.28900802e-01 4.27771121e-01
5.49432933e-01 1.14825159e-01 4.29572612e-01 -1.23145235e+00
2.02694952e-01 6.61726892e-01 -1.61079690e-01 8.28680515e-01
7.89775193e-01 -3.93169113e-02 1.00120401e+00 -6.80347204e-01
1.10128248e+00 4.67679381e-01 7.20708847e-01 5.34560919e-01
-1.06614602e+00 -4.51028734e-01 -1.74110219e-01 6.78964913e-01
-1.10720038e+00 -6.57810032e-01 3.29649597e-01 -5.48774183e-01
1.83544457e+00 6.60909891e-01 8.98172915e-01 1.19655645e+00
5.23798704e-01 9.23559368e-01 1.12983930e+00 -4.63890076e-01
5.41395128e-01 2.41274238e-01 6.07129276e-01 5.24975240e-01
-3.65484148e-01 2.84651697e-01 -7.01434314e-01 -7.52444983e-01
-3.41086239e-02 -1.95246577e-01 -6.04158156e-02 4.01119947e-01
-1.22734916e+00 5.69278061e-01 -1.53058723e-01 6.84999168e-01
-6.81643784e-01 -2.12392524e-01 1.09128237e+00 2.69549042e-01
6.03499353e-01 2.76045322e-01 -7.59603918e-01 -3.04207236e-01
-1.18793619e+00 -6.27287244e-03 9.84526694e-01 5.80105603e-01
-4.98808533e-01 -6.19376957e-01 -6.38956726e-01 6.85413182e-01
-5.45601249e-02 -1.12315856e-01 6.49278045e-01 -6.97255313e-01
-2.51602624e-02 4.88559604e-01 -1.92706391e-01 -4.97113198e-01
-1.07320845e+00 -5.56617714e-02 -8.58830273e-01 -3.65316719e-01
9.09342393e-02 -1.37244657e-01 -7.29192734e-01 1.29800928e+00
2.29284286e-01 3.69777888e-01 3.23082656e-01 5.71334660e-01
1.26724803e+00 3.48857075e-01 5.77135503e-01 -8.18821132e-01
1.72580040e+00 -9.85155761e-01 -1.42362785e+00 -1.39928712e-02
1.11851084e+00 -6.22321546e-01 1.35236764e-02 6.73860252e-01
-1.13109601e+00 6.25225157e-02 -9.72269237e-01 1.57889605e-01
-5.31047285e-01 2.38474131e-01 8.06527793e-01 1.61273956e-01
-1.19547594e+00 6.95449352e-01 -1.04073763e+00 -6.35452509e-01
6.50575757e-02 4.97840881e-01 -5.51836550e-01 6.45829141e-01
-1.89987469e+00 1.36944354e+00 6.42460048e-01 1.12479843e-01
-3.17645162e-01 -6.23647273e-01 -6.96942031e-01 -2.24557593e-01
-5.69366775e-02 -5.37503660e-01 1.41177785e+00 -4.51988429e-01
-1.21860564e+00 1.27589977e+00 -4.60317522e-01 -8.97122681e-01
4.55752999e-01 1.66860268e-01 -8.55916202e-01 1.61257550e-01
-1.26422137e-01 1.32323802e-01 -9.80493948e-02 -1.00500368e-01
-6.73081398e-01 -3.65257591e-01 -3.19391608e-01 -3.46530303e-02
7.71647021e-02 8.62555802e-01 -3.45490813e-01 -5.45845985e-01
-3.48470300e-01 -6.69182599e-01 -6.59003675e-01 -5.39110363e-01
-2.50296682e-01 -6.73334837e-01 3.12619716e-01 -1.09775877e+00
1.63675344e+00 -2.31729865e+00 6.58143908e-02 1.39953256e-01
2.81547815e-01 6.71527088e-02 2.56498992e-01 5.90368748e-01
-9.12235081e-01 -2.17351317e-01 3.27451341e-02 -4.50208813e-01
-3.41959685e-01 2.57828474e-01 -3.90051752e-01 5.55545151e-01
2.52912074e-01 1.16186726e+00 -9.68820155e-01 -7.88708925e-01
2.40528107e-01 1.96278498e-01 5.74067272e-02 -4.61849310e-02
-3.02249968e-01 2.07588509e-01 -3.81189317e-01 4.84909147e-01
9.36551094e-02 -2.12557361e-01 5.62240303e-01 3.10452580e-02
-8.23226646e-02 4.89464313e-01 -6.29624546e-01 1.47722101e+00
-1.01059891e-01 5.00273645e-01 -2.71885067e-01 -1.07287717e+00
5.20466208e-01 9.60030496e-01 9.79109287e-01 -6.99170113e-01
1.04211651e-01 -2.44740695e-02 -2.20678329e-01 -6.16450727e-01
1.67326972e-01 -1.03227675e-01 -1.11168116e-01 2.26146266e-01
1.25469089e-01 3.26793402e-01 5.07897854e-01 2.87225634e-01
1.42481101e+00 -1.24709278e-01 1.02157032e+00 2.22442043e-03
5.02994895e-01 2.47522473e-01 6.65172875e-01 4.14768845e-01
-6.47085905e-01 3.86954933e-01 1.00325346e+00 -7.15905190e-01
-5.02671540e-01 -6.79605484e-01 -3.77843499e-01 9.26521122e-01
-5.34117639e-01 -1.01215756e+00 -3.31276447e-01 -8.42703164e-01
-4.35728937e-01 9.06713784e-01 -1.02667487e+00 -5.99318147e-02
-4.86500502e-01 -1.09941828e+00 9.16213334e-01 5.48934877e-01
-3.52376074e-01 -1.17173970e+00 -6.86168671e-01 3.72474670e-01
-2.89806187e-01 -1.34336281e+00 -1.73958376e-01 6.57521188e-01
-7.26333439e-01 -1.04611123e+00 -3.00576776e-01 -4.10150886e-01
1.48268482e-02 -6.59352779e-01 8.65747750e-01 -3.73155832e-01
-6.46010995e-01 -1.24602340e-01 -2.66254842e-01 -8.44996154e-01
-3.66444349e-01 -1.06305093e-01 -4.84813564e-02 -3.58049303e-01
5.65213263e-01 -3.12964082e-01 -3.98306221e-01 -6.95946589e-02
-4.77348596e-01 2.13170305e-01 3.52853537e-01 6.23575926e-01
5.46538591e-01 -1.60533160e-01 3.42780709e-01 -8.46460819e-01
5.48424959e-01 -7.52650499e-01 -4.05928254e-01 3.78452033e-01
-8.22978079e-01 -2.09836975e-01 3.81449491e-01 -1.01534307e-01
-6.90718174e-01 3.29930753e-01 -3.01260650e-01 -1.00839950e-01
-2.33364642e-01 7.79299080e-01 6.13158584e-01 4.93393749e-01
9.05677378e-01 4.12209541e-01 1.96075696e-03 -1.21274076e-01
1.85451388e-01 7.04082787e-01 3.13587248e-01 -3.12049668e-02
-5.08329123e-02 5.73381186e-01 1.33544579e-01 -6.36954844e-01
-7.11299896e-01 -9.40124214e-01 -3.17350626e-01 -8.16604570e-02
9.26145732e-01 -8.95185947e-01 -9.81215715e-01 3.66931677e-01
-1.43828237e+00 -1.28308401e-01 -4.85261828e-01 6.61187589e-01
-4.35127944e-01 -7.97846541e-02 -9.96958435e-01 -6.93459749e-01
-6.68975353e-01 -9.03059483e-01 1.09838879e+00 -1.23837918e-01
-1.07690012e+00 -1.01871884e+00 7.68027782e-01 1.42883912e-01
1.46598935e-01 3.89838219e-01 5.23941219e-01 -1.21033144e+00
3.86967808e-01 -5.03049076e-01 -4.10592817e-02 -3.48099858e-01
-6.78870380e-02 2.05102667e-01 -9.77800310e-01 5.50030060e-02
-2.02297807e-01 -1.10779166e-01 9.65816498e-01 4.17504072e-01
9.50306475e-01 -1.48491666e-01 -8.72244835e-01 2.00383142e-01
7.87326276e-01 4.79461789e-01 3.41899753e-01 2.36185551e-01
-1.59513071e-01 6.19339049e-01 7.50659764e-01 7.03591168e-01
2.34094605e-01 8.96549046e-01 -1.34366930e-01 -2.16616407e-01
3.12453032e-01 3.06851804e-01 2.04601422e-01 3.98206145e-01
-3.57254952e-01 3.79204601e-02 -1.16362989e+00 6.43586814e-01
-2.33299470e+00 -7.91173518e-01 -4.40162420e-01 1.51819420e+00
1.21641636e+00 2.78376136e-02 2.91956395e-01 1.79134563e-01
1.78535625e-01 6.46090582e-02 -1.02168620e-01 -7.95524597e-01
-1.80084571e-01 6.15707397e-01 4.24041182e-01 2.82655388e-01
-1.23169160e+00 8.13107431e-01 7.84501219e+00 8.99084091e-01
-1.09027064e+00 5.56840837e-01 4.54113156e-01 -5.58428586e-01
2.14835525e-01 -2.18709752e-01 -4.30372626e-01 3.02364737e-01
1.67241812e+00 -5.06412446e-01 5.75473793e-02 6.51616931e-01
5.25602341e-01 -2.51172543e-01 -1.19737136e+00 6.69092476e-01
-1.81073979e-01 -1.80749071e+00 -7.69656003e-01 -3.10357392e-01
2.02201605e-01 3.56530994e-01 -3.40678304e-01 3.16010356e-01
4.24911380e-01 -1.11197364e+00 4.04987574e-01 5.77693343e-01
4.43958372e-01 -3.18870962e-01 1.14795268e+00 1.58997938e-01
-8.67798805e-01 2.63068646e-01 4.42877024e-01 -7.11388066e-02
1.93321332e-01 9.96694267e-01 -1.25664604e+00 1.03579390e+00
7.29511678e-01 9.59176481e-01 -1.58534721e-01 7.95950890e-01
-4.88628834e-01 8.34173203e-01 -3.35615724e-01 -2.59814411e-01
-8.08998421e-02 3.82433742e-01 7.03268945e-01 1.76610041e+00
-4.77018118e-01 4.49081808e-01 -7.17650121e-03 1.11455731e-01
3.14134449e-01 4.58455443e-01 -3.43630552e-01 -2.08161280e-01
3.39696072e-02 1.30835724e+00 -6.76206648e-01 -6.07377350e-01
-2.52041250e-01 8.55502367e-01 1.29846498e-01 1.30535677e-01
-1.00638449e+00 -3.32477331e-01 3.94253254e-01 -2.23997962e-02
-1.82544231e-01 2.00730145e-01 -5.01618147e-01 -9.51582372e-01
7.09850788e-02 -6.65695012e-01 1.52347279e+00 -8.85122359e-01
-9.19712842e-01 7.55401850e-01 -4.31893170e-02 -8.37679863e-01
-4.42091376e-01 -3.61312866e-01 -4.10252571e-01 8.49944353e-01
-8.75176370e-01 -8.36840630e-01 5.29246628e-01 5.22832155e-01
2.06118956e-01 1.96579397e-01 1.30367446e+00 4.13139254e-01
-7.56199837e-01 3.80693913e-01 -1.39653847e-01 1.21725865e-01
1.08496225e+00 -1.11565602e+00 1.30415961e-01 4.79056686e-01
-1.20106600e-01 6.69117451e-01 8.05675745e-01 -5.96194744e-01
-1.11527336e+00 -1.12500679e+00 1.84770298e+00 -5.03309906e-01
1.05780065e+00 -1.89998686e-01 -4.52252001e-01 1.02489209e+00
4.03376818e-01 -3.63162123e-02 9.58327591e-01 4.93238688e-01
-1.62145212e-01 1.62188187e-01 -1.19078934e+00 2.71750182e-01
6.49772942e-01 -4.21400398e-01 -8.79824460e-01 8.47082138e-01
7.72454083e-01 -6.84956014e-01 -1.25399268e+00 6.19368374e-01
5.84197998e-01 -5.20708799e-01 8.29559863e-01 -1.21720874e+00
6.77441597e-01 -1.33519158e-01 1.13372900e-01 -1.04236591e+00
-1.59263596e-01 -6.23974860e-01 -4.38770980e-01 5.73821723e-01
7.36313760e-01 -7.42469788e-01 4.64538008e-01 4.49413538e-01
-3.40783820e-02 -1.04064989e+00 -1.28356087e+00 -4.19905663e-01
-4.00804043e-01 -7.82165051e-01 -1.58417240e-01 1.35054362e+00
1.11019564e+00 4.69610929e-01 -2.35622432e-02 6.43284852e-03
1.21527992e-01 1.00046717e-01 -1.45663857e-01 -7.07075775e-01
-5.43557167e-01 -4.26356703e-01 -5.28308570e-01 1.78830251e-01
2.30119988e-01 -1.30622661e+00 -3.78170103e-01 -1.53300858e+00
5.53441346e-01 1.34978563e-01 -8.63042176e-01 1.25807202e+00
5.62392473e-02 -4.19056378e-02 -3.41270357e-01 3.45502049e-02
-7.71275461e-01 -5.83292060e-02 3.24778825e-01 -1.83669850e-01
-3.68593365e-01 -6.58905432e-02 -4.61080998e-01 5.92944860e-01
6.47994399e-01 -6.37967050e-01 8.31152275e-02 3.07087868e-01
4.03702646e-01 4.48257208e-01 1.65935710e-01 -3.28581482e-01
5.47426879e-01 -2.41422296e-01 2.89190263e-01 -8.73324096e-01
1.31692827e-01 -3.41739565e-01 4.55037445e-01 9.46162045e-01
-4.55803245e-01 -1.19013011e-01 8.33479404e-01 1.81595609e-01
-5.20665407e-01 2.38970220e-01 6.23550951e-01 2.55778909e-01
-5.32451808e-01 -2.89965421e-02 -7.96279430e-01 -2.46043637e-01
1.46265113e+00 2.04768658e-01 -2.55427748e-01 9.58109498e-02
-1.54229891e+00 5.56292593e-01 -2.85517216e-01 3.42287540e-01
3.42235923e-01 -9.00116146e-01 -9.24866915e-01 -2.42580459e-01
4.57074970e-01 -6.70922697e-01 4.90746647e-01 1.68310702e+00
-2.44480014e-01 8.43598127e-01 -2.99642812e-02 -5.42731345e-01
-1.88688624e+00 8.64467561e-01 1.62676618e-01 -9.59425151e-01
-4.90108043e-01 5.42234123e-01 -3.76485586e-01 -2.76483536e-01
1.83646277e-01 -1.49702653e-01 -5.04965127e-01 5.62037766e-01
8.02603722e-01 1.98576614e-01 6.54897332e-01 -1.02971792e-01
-1.09540474e+00 -5.44358827e-02 -1.10040665e-01 -3.07171196e-01
1.38579142e+00 4.85146314e-01 -5.13899624e-01 5.07479906e-01
9.17408407e-01 -4.15212870e-01 1.96804598e-01 -1.85115054e-01
3.47904056e-01 4.99339819e-01 1.74561560e-01 -1.17836010e+00
-4.30154443e-01 2.97686815e-01 5.98833978e-01 1.98805675e-01
1.20176256e+00 2.84209639e-01 5.31643569e-01 2.47002885e-01
2.48676941e-01 -9.46962535e-01 -8.16680968e-01 5.58453321e-01
6.77961528e-01 -8.84052992e-01 8.52853656e-02 -6.48383439e-01
-6.24509156e-01 1.12569118e+00 -6.36198148e-02 3.53467226e-01
7.78342068e-01 7.98941135e-01 -8.87718275e-02 -3.94931078e-01
-1.69539988e+00 -9.88177955e-02 2.96672553e-01 9.47875381e-02
7.80376136e-01 3.45763355e-01 -1.10407495e+00 1.04561710e+00
1.15821816e-01 7.28748500e-01 1.98317304e-01 1.10446417e+00
1.65763214e-01 -1.13402987e+00 5.92193604e-02 3.76227170e-01
-8.17481756e-01 -3.38316053e-01 -6.32081509e-01 6.29154623e-01
-3.23262624e-02 8.67517710e-01 -2.61250380e-02 -3.95052791e-01
6.80584490e-01 3.22322875e-01 4.02260691e-01 -3.48528862e-01
-1.11490822e+00 -7.08430633e-02 8.77076566e-01 -1.13194883e+00
-2.44494334e-01 -1.11175013e+00 -1.62055111e+00 1.03168748e-01
9.61309671e-02 3.12291950e-01 5.34629166e-01 9.90171909e-01
3.33325535e-01 8.73283207e-01 3.86847883e-01 2.04105452e-01
-1.25499308e-01 -8.96626949e-01 -4.56084579e-01 1.05070295e-02
7.31724873e-02 -2.32889518e-01 -1.84504494e-01 1.57616347e-01] | [8.51677131652832, 9.021197319030762] |
9dad9782-6e2f-4f6b-8f9d-1ec11a06a1bd | sanskritshala-a-neural-sanskrit-nlp-toolkit | 2302.09527 | null | https://arxiv.org/abs/2302.09527v2 | https://arxiv.org/pdf/2302.09527v2.pdf | SanskritShala: A Neural Sanskrit NLP Toolkit with Web-Based Interface for Pedagogical and Annotation Purposes | We present a neural Sanskrit Natural Language Processing (NLP) toolkit named SanskritShala (a school of Sanskrit) to facilitate computational linguistic analyses for several tasks such as word segmentation, morphological tagging, dependency parsing, and compound type identification. Our systems currently report state-of-the-art performance on available benchmark datasets for all tasks. SanskritShala is deployed as a web-based application, which allows a user to get real-time analysis for the given input. It is built with easy-to-use interactive data annotation features that allow annotators to correct the system predictions when it makes mistakes. We publicly release the source codes of the 4 modules included in the toolkit, 7 word embedding models that have been trained on publicly available Sanskrit corpora and multiple annotated datasets such as word similarity, relatedness, categorization, analogy prediction to assess intrinsic properties of word embeddings. So far as we know, this is the first neural-based Sanskrit NLP toolkit that has a web-based interface and a number of NLP modules. We are sure that the people who are willing to work with Sanskrit will find it useful for pedagogical and annotative purposes. SanskritShala is available at: https://cnerg.iitkgp.ac.in/sanskritshala. The demo video of our platform can be accessed at: https://youtu.be/x0X31Y9k0mw4. | ['Pawan Goyal', 'Tushar Sandhan', 'Laxmidhar Behera', 'Anshul Agarwal', 'Jivnesh Sandhan'] | 2023-02-19 | null | null | null | null | ['word-similarity', 'dependency-parsing', 'morphological-tagging'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-2.63578236e-01 -2.45662361e-01 -2.46626407e-01 -3.11409980e-01
-5.86219132e-01 -1.09158564e+00 3.03260267e-01 4.98199254e-01
-7.77457356e-01 4.33715969e-01 3.11319232e-01 -9.18950915e-01
-4.51941043e-03 -5.54611087e-01 -2.91175723e-01 -4.76434857e-01
3.09085637e-01 8.02691579e-01 2.05757141e-01 -3.09976161e-01
5.04398942e-01 4.47056681e-01 -8.41182530e-01 3.05056810e-01
9.57518518e-01 4.72166777e-01 4.41562533e-01 1.03210807e+00
-2.96967417e-01 4.69791442e-01 -2.81493753e-01 -8.53751481e-01
1.43090546e-01 -2.65755691e-04 -1.09128463e+00 -8.99182677e-01
4.49063331e-01 -2.92976171e-01 -2.36820489e-01 9.68436360e-01
7.08364129e-01 1.74825341e-01 4.57443237e-01 -7.31320024e-01
-1.18904603e+00 8.80223572e-01 -3.20427790e-02 4.76439446e-01
2.84362674e-01 2.61047602e-01 1.25552714e+00 -9.28214848e-01
6.73959196e-01 8.28701794e-01 7.25711048e-01 8.42730582e-01
-7.93311298e-01 -7.58382857e-01 -2.14793816e-01 3.77692163e-01
-9.67726767e-01 -4.04203951e-01 5.85845768e-01 -4.08196181e-01
1.40301847e+00 3.38894188e-01 5.45704246e-01 1.09409988e+00
-9.53517780e-02 1.16779506e+00 1.01074874e+00 -5.27827322e-01
1.71332568e-01 -2.54696812e-02 9.41883862e-01 8.20162535e-01
-2.29297012e-01 -2.42320746e-01 -3.51498336e-01 -2.57241242e-02
7.65366793e-01 -2.49059290e-01 5.04230708e-02 2.01602876e-01
-1.12447023e+00 1.04863250e+00 2.59922296e-01 6.32449806e-01
-1.85874879e-01 2.74803340e-02 5.98412812e-01 4.11736220e-01
5.03837526e-01 6.30004406e-01 -9.32889223e-01 -7.28619576e-01
-5.40291905e-01 1.28686041e-01 6.18852913e-01 6.19818866e-01
1.47670090e-01 4.87734284e-03 2.10543558e-01 1.32117581e+00
3.49817872e-01 3.91466618e-01 8.27454627e-01 -5.89407563e-01
2.94247806e-01 6.51398182e-01 -3.94638568e-01 -6.63438916e-01
-2.46592909e-01 1.35430872e-01 -2.75338262e-01 -1.65935367e-01
7.06154644e-01 -2.24126726e-01 -9.19854939e-01 1.22973561e+00
4.92346764e-01 2.57578611e-01 1.87113151e-01 8.53672922e-01
1.40751922e+00 7.58421063e-01 3.45214248e-01 1.88813314e-01
1.61464083e+00 -9.02203441e-01 -6.09740436e-01 7.18138143e-02
1.00609040e+00 -1.02502465e+00 1.30886137e+00 3.90987128e-01
-9.90485668e-01 -3.20432633e-01 -5.85427642e-01 -3.53336006e-01
-9.34530199e-01 1.28887802e-01 7.14211285e-01 9.18602765e-01
-7.92752028e-01 4.23006237e-01 -1.09731162e+00 -8.87611926e-01
1.10412546e-01 3.35353702e-01 -4.49186325e-01 7.18671903e-02
-1.25046718e+00 9.96299386e-01 7.00631499e-01 1.33311197e-01
-5.84872603e-01 -7.74138749e-01 -1.01075518e+00 -4.76503164e-01
1.33042112e-01 3.84477852e-03 1.32434678e+00 -7.21467972e-01
-1.40570045e+00 1.09881556e+00 -1.70239694e-02 -2.32481465e-01
2.22332142e-02 -4.87023056e-01 -4.97610748e-01 3.62891965e-02
-1.64936587e-01 4.74589527e-01 2.72780180e-01 -5.76163709e-01
-1.84783235e-01 -4.38272655e-01 -2.87871305e-02 2.46354640e-01
-4.21937734e-01 8.90055358e-01 -4.01313126e-01 -8.66677999e-01
-2.13675037e-01 -9.60793078e-01 -8.73440206e-02 -5.16606748e-01
-4.07615393e-01 -5.88050485e-01 7.26144433e-01 -1.22935188e+00
1.23862243e+00 -2.03137159e+00 -5.02551720e-02 -2.22837910e-01
-8.45531896e-02 9.85770822e-01 -4.67639089e-01 7.74860680e-01
-2.15644658e-01 3.87215614e-01 -1.07076548e-01 -1.26559988e-01
4.01227847e-02 3.39978635e-01 -1.28978550e-01 3.08174193e-01
9.48787481e-02 1.11248422e+00 -1.10180807e+00 -2.24159122e-01
4.65281725e-01 5.16037464e-01 -2.29221076e-01 2.97602892e-01
-1.30101547e-01 4.68776017e-01 -4.59696442e-01 6.85919821e-01
5.55901229e-01 3.05030137e-01 1.46304341e-02 1.49740815e-01
-1.76807523e-01 6.69294119e-01 -9.49299514e-01 1.68506062e+00
-4.20781165e-01 6.42315745e-01 -1.01145916e-02 -7.77625084e-01
8.76973569e-01 3.81590188e-01 4.32706662e-02 -3.12129319e-01
2.14130521e-01 3.60456944e-01 -1.10593466e-02 -6.39233649e-01
7.53997445e-01 1.46677658e-01 -2.00860634e-01 6.49006724e-01
3.21471840e-01 -1.19157329e-01 1.72036633e-01 4.83831674e-01
1.30016482e+00 1.09890334e-01 3.98829430e-01 -1.17350109e-01
5.04242420e-01 4.63361070e-02 3.50032777e-01 4.06305075e-01
-2.96684176e-01 3.02112401e-01 2.49079958e-01 -6.87357306e-01
-1.06962860e+00 -9.87107038e-01 -3.81655753e-01 1.59322846e+00
-4.94258612e-01 -6.18674695e-01 -7.03296065e-01 -1.06866908e+00
-2.38434166e-01 9.85510230e-01 -3.70274901e-01 5.01713097e-01
-6.61956310e-01 -5.19210517e-01 9.53829110e-01 6.00859940e-01
2.07512140e-01 -1.67569768e+00 -9.21066105e-02 4.06054407e-02
1.81948662e-01 -9.00573552e-01 -4.77616847e-01 2.58675456e-01
-6.36047065e-01 -1.02595997e+00 -4.73278165e-01 -1.17645216e+00
4.99815196e-01 -3.14517319e-01 8.01321566e-01 2.53127843e-01
-2.06892803e-01 4.03438985e-01 -7.91626453e-01 -2.34226570e-01
-4.01507795e-01 1.63056985e-01 1.30909309e-01 -4.79721963e-01
9.13342655e-01 -3.55335921e-01 -2.63731152e-01 -1.52269285e-02
-8.86398196e-01 -7.28253499e-02 2.72662580e-01 5.53820491e-01
3.30845296e-01 -1.86421961e-01 5.04907608e-01 -1.05679941e+00
8.53667200e-01 -4.60674614e-01 -3.49957138e-01 9.10434425e-02
-2.72469908e-01 -3.05377871e-01 6.74310625e-01 -3.61144483e-01
-7.99536169e-01 2.58164138e-01 -9.48122025e-01 6.83785453e-02
-8.34316671e-01 5.79033434e-01 -1.70893937e-01 -3.49325757e-03
4.05971050e-01 3.57122809e-01 -4.25431609e-01 -7.88216174e-01
7.89141774e-01 1.24218631e+00 3.06071937e-01 -4.84591722e-01
5.03842294e-01 -3.36065859e-01 -6.51346624e-01 -1.26526737e+00
-6.39909506e-01 -5.71030140e-01 -1.08961058e+00 -1.09079152e-01
1.10704589e+00 -5.16038239e-01 -6.88446403e-01 6.38110816e-01
-1.15224361e+00 -4.64905590e-01 -5.84444776e-02 4.10126626e-01
-1.19470760e-01 4.60778743e-01 -1.24721003e+00 -7.17640042e-01
-6.17614448e-01 -8.93513918e-01 7.00741112e-01 3.10586691e-01
-4.94634002e-01 -1.33007562e+00 4.16102886e-01 8.12906802e-01
2.67720491e-01 -1.40045956e-01 1.02330923e+00 -1.73874378e+00
2.60461830e-02 -1.52166009e-01 -1.33142862e-02 5.86893082e-01
-1.42111078e-01 1.60734206e-01 -7.07525313e-01 4.98996563e-02
-3.59014928e-01 -5.99647164e-01 6.60825968e-01 1.91259131e-01
8.26077998e-01 -5.69506764e-01 1.11966088e-01 3.68155926e-01
1.13478422e+00 3.26782823e-01 3.93806279e-01 4.06384438e-01
1.04616606e+00 5.81737220e-01 4.67769802e-01 -1.11923233e-01
4.98119980e-01 5.05931258e-01 1.34041294e-01 4.13725317e-01
5.50287142e-02 -3.31696540e-01 5.77437341e-01 1.46679819e+00
3.44214998e-02 -1.54040262e-01 -1.41497457e+00 8.47249866e-01
-1.87835038e+00 -5.96802533e-01 -5.69009423e-01 1.87350941e+00
1.06944835e+00 -2.25057334e-01 1.01251848e-01 -1.30236849e-01
5.09842753e-01 2.28618875e-01 -1.97433338e-01 -1.20820093e+00
3.24921608e-01 4.54149395e-01 2.21742317e-01 8.67165565e-01
-1.09863186e+00 1.63905048e+00 5.02773380e+00 1.10112882e+00
-9.77758050e-01 3.42819095e-01 4.88163710e-01 -3.74821573e-02
-2.50568777e-01 -1.89214349e-01 -9.65929329e-01 4.98739392e-01
1.40585041e+00 2.36781746e-01 5.00012577e-01 6.74455523e-01
2.34979361e-01 1.99019402e-01 -8.62557530e-01 7.39394248e-01
7.51886964e-02 -1.28442597e+00 -6.27630875e-02 -1.23255365e-01
1.61964566e-01 6.20782495e-01 -9.56896618e-02 4.07673568e-01
7.34526396e-01 -1.14531767e+00 2.09868431e-01 1.65012091e-01
7.16985881e-01 -8.74777913e-01 8.32120180e-01 3.73993367e-01
-1.04852617e+00 2.09053516e-01 -2.43191436e-01 -9.65678394e-02
2.18550563e-01 3.56413305e-01 -9.64954674e-01 2.20909774e-01
4.72736239e-01 7.82319605e-01 -7.07657456e-01 9.10759926e-01
-7.72343040e-01 1.06523478e+00 -2.34895393e-01 -5.69978178e-01
3.43629122e-01 -2.06626847e-01 6.05764210e-01 1.57113552e+00
4.88415062e-02 3.34110439e-01 1.14408508e-01 2.90574580e-01
5.93053550e-02 7.46369779e-01 -4.92749184e-01 -5.50348699e-01
5.28485417e-01 1.51107752e+00 -8.91096532e-01 -1.20236367e-01
-2.61152864e-01 1.04132056e+00 4.89682019e-01 1.29928282e-02
-5.76571226e-01 -5.45239031e-01 6.38958335e-01 1.26870237e-02
2.93220967e-01 -6.68092668e-01 -2.73285151e-01 -1.19979203e+00
-4.08369571e-01 -9.57378924e-01 6.33218527e-01 -8.99317503e-01
-1.51695669e+00 7.46884227e-01 -2.33109176e-01 -4.90538567e-01
-9.17422324e-02 -1.10843956e+00 -6.58029735e-01 7.77535856e-01
-9.51451898e-01 -1.56189394e+00 4.08370137e-01 5.63258946e-01
7.55066216e-01 -4.05945987e-01 1.22684085e+00 1.99178934e-01
-6.86981499e-01 5.53709626e-01 1.62695065e-01 8.33610415e-01
5.74831605e-01 -1.56562459e+00 6.76967204e-01 9.01423216e-01
5.21596372e-01 8.39263022e-01 3.81470978e-01 -1.01468611e+00
-1.19347155e+00 -8.78341496e-01 1.44825304e+00 -9.54912663e-01
1.30904639e+00 -4.92103130e-01 -9.11423147e-01 8.88202131e-01
5.09724021e-01 -1.46292329e-01 1.30414522e+00 2.99267352e-01
-2.85773128e-01 2.60050297e-01 -1.12219930e+00 6.99675798e-01
7.51837909e-01 -6.45466030e-01 -1.01596701e+00 5.55205166e-01
7.31196404e-01 -3.66584241e-01 -1.01256478e+00 -5.47702005e-03
5.74188292e-01 -4.61544067e-01 5.79358757e-01 -8.94729614e-01
4.16102946e-01 -1.30288512e-01 -4.31839600e-02 -1.30074096e+00
-2.55630940e-01 -5.31822979e-01 1.03174318e-02 1.53469145e+00
6.93110228e-01 -6.50619388e-01 7.63618112e-01 6.20206177e-01
-1.66526288e-01 -7.25833297e-01 -1.00548923e+00 -5.74706495e-01
5.05868375e-01 -1.19830966e+00 2.08256751e-01 1.40172815e+00
3.55267137e-01 5.73213696e-01 -1.26181781e-01 1.45853922e-01
1.19546555e-01 -2.65961796e-01 4.23583537e-01 -9.60600019e-01
-3.51250082e-01 -3.65366578e-01 -3.81579816e-01 -8.81583035e-01
2.66445339e-01 -1.19465232e+00 -3.13881576e-01 -1.60305023e+00
-2.92533766e-02 -5.96858799e-01 -3.43780935e-01 1.04842257e+00
6.07828051e-03 5.33954859e-01 2.00939804e-01 1.50366485e-01
-6.15603507e-01 9.01038945e-02 8.78782570e-01 1.93076566e-01
-3.32960129e-01 -2.42178574e-01 -5.89121163e-01 8.77584815e-01
1.21680510e+00 -5.50002456e-01 -6.58562593e-03 -7.36805856e-01
3.84962082e-01 -3.66248250e-01 -2.31371615e-02 -5.17471910e-01
2.17691302e-01 -1.36204049e-01 2.50970393e-01 -6.40831232e-01
2.54792273e-01 -6.07257307e-01 -2.35694736e-01 3.34325969e-01
-4.06602949e-01 3.59946162e-01 3.18508923e-01 -9.12509188e-02
1.09410778e-01 -7.19740927e-01 4.74931300e-01 -2.22514838e-01
-8.94376993e-01 7.07416758e-02 -5.97011268e-01 1.09702442e-02
1.09161222e+00 -1.33722737e-01 -4.66648221e-01 -1.59218550e-01
-7.94757307e-01 2.17465520e-01 3.55250925e-01 6.00731671e-01
6.53838515e-01 -1.15658629e+00 -7.40507007e-01 3.96241426e-01
1.35441695e-03 -3.24964195e-01 1.91013947e-01 5.99160373e-01
-9.57375348e-01 4.19049442e-01 -2.08408058e-01 4.92426306e-02
-1.71497476e+00 4.29631948e-01 -2.11615026e-01 -3.20144147e-01
-5.12773156e-01 1.19827771e+00 -4.31229144e-01 -1.23578477e+00
1.86507162e-02 -1.81919411e-01 -7.09686160e-01 1.41354561e-01
8.21774840e-01 2.34909981e-01 1.37705401e-01 -9.91190195e-01
-5.37460685e-01 4.33373898e-01 -4.12495375e-01 -1.48332953e-01
1.68790257e+00 2.00287327e-01 -2.69992143e-01 4.57494706e-01
8.18743527e-01 5.27264833e-01 -5.07404685e-01 -6.79491237e-02
4.65609059e-02 -3.65342289e-01 -5.55274859e-02 -1.22410631e+00
-8.06604505e-01 9.45409656e-01 4.04128194e-01 3.30408901e-01
9.08496141e-01 2.01684132e-01 1.14681625e+00 5.08517623e-01
-9.54080969e-02 -1.09641528e+00 -3.28921586e-01 1.19715297e+00
6.71996176e-01 -1.05334234e+00 -2.84806341e-01 -1.66875303e-01
-8.51570904e-01 1.18373036e+00 4.45200562e-01 -2.58583397e-01
7.76063323e-01 2.52286345e-01 4.43964034e-01 -9.14157778e-02
-5.54151177e-01 -2.47407705e-01 3.68825674e-01 7.80009449e-01
8.37787390e-01 2.94496477e-01 -8.62991214e-01 8.06912482e-01
-6.80156767e-01 -4.79101628e-01 5.54558575e-01 8.06522667e-01
-2.49604911e-01 -1.58304596e+00 -1.14701912e-01 3.54296654e-01
-8.96701217e-01 -6.94442272e-01 -7.68533170e-01 4.96327817e-01
4.08582017e-02 6.99618042e-01 -2.23558154e-02 -3.36672992e-01
9.85760689e-02 3.84892911e-01 1.71518445e-01 -1.03449309e+00
-1.08255339e+00 -1.56582147e-01 5.27947307e-01 -2.95940727e-01
-1.03494644e-01 -6.17211461e-01 -1.42981291e+00 -3.95322263e-01
-6.54641166e-02 3.44469666e-01 8.56189728e-01 7.50601470e-01
1.14169039e-01 -5.88646196e-02 -1.44362107e-01 -6.19288743e-01
1.08693736e-02 -1.14292216e+00 -4.73610759e-01 1.97869882e-01
-7.30336756e-02 8.06023628e-02 -5.94683439e-02 -9.16759446e-02] | [10.487292289733887, 10.012885093688965] |
d2e8639f-b73b-453e-a52a-db2fcd2b6da8 | nested-named-entity-recognition-with-span | null | null | https://aclanthology.org/2022.acl-long.63 | https://aclanthology.org/2022.acl-long.63.pdf | Nested Named Entity Recognition with Span-level Graphs | Span-based methods with the neural networks backbone have great potential for the nested named entity recognition (NER) problem. However, they face problems such as degenerating when positive instances and negative instances largely overlap. Besides, the generalization ability matters a lot in nested NER, as a large proportion of entities in the test set hardly appear in the training set. In this work, we try to improve the span representation by utilizing retrieval-based span-level graphs, connecting spans and entities in the training data based on n-gram features. Specifically, we build the entity-entity graph and span-entity graph globally based on n-gram similarity to integrate the information of similar neighbor entities into the span representation. To evaluate our method, we conduct experiments on three common nested NER datasets, ACE2004, ACE2005, and GENIA datasets. Experimental results show that our method achieves general improvements on all three benchmarks (+0.30 \sim 0.85 micro-F1), and obtains special superiority on low frequency entities (+0.56 \sim 2.08 recall). | ['Yong Yu', 'Weinan Zhang', 'Dongyu Ru', 'Juncheng Wan'] | null | null | null | null | acl-2022-5 | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [-5.50326586e-01 -1.56622991e-01 -2.15763301e-01 -3.17841977e-01
-6.38243318e-01 -7.54393816e-01 1.12549216e-01 4.65207577e-01
-7.16351926e-01 8.17485332e-01 2.18118072e-01 -6.92690164e-02
-2.09389642e-01 -1.24548411e+00 -4.73174572e-01 -3.69062513e-01
-4.93341714e-01 3.52906704e-01 2.41398409e-01 -3.11609894e-01
-1.62519868e-02 4.78271663e-01 -9.83532548e-01 1.25458345e-01
1.31063139e+00 9.53973413e-01 -1.47613630e-01 1.61614455e-02
-5.66336453e-01 7.04834402e-01 -8.09222281e-01 -6.88593030e-01
3.52065265e-02 -5.19626625e-02 -8.62011135e-01 -5.25012195e-01
3.57128114e-01 3.33583131e-02 -5.75943172e-01 1.02245700e+00
5.77762306e-01 4.44678366e-01 5.15382946e-01 -1.05493951e+00
-9.02244747e-01 8.49102318e-01 -5.59131920e-01 1.95126042e-01
2.21679613e-01 -3.66121113e-01 1.39645648e+00 -1.09618545e+00
6.91060007e-01 1.00614047e+00 1.05776238e+00 2.91532248e-01
-8.41118693e-01 -7.46000171e-01 3.74137461e-02 2.17477262e-01
-1.78638887e+00 5.13303056e-02 3.95341575e-01 1.16303563e-02
1.11979759e+00 3.04068118e-01 1.44420922e-01 6.12770498e-01
-1.48660243e-01 7.16309011e-01 6.10875547e-01 -2.06977248e-01
-5.09578548e-02 -1.03324927e-01 5.38901091e-01 5.66590667e-01
5.45892835e-01 -2.46918142e-01 -6.38954863e-02 -3.56794059e-01
5.38882732e-01 -3.17567885e-02 -3.10443282e-01 -3.80721525e-03
-1.00627184e+00 6.60348296e-01 1.00018823e+00 7.85970807e-01
-4.36359793e-01 -2.54051268e-01 5.24521291e-01 1.45853698e-01
3.07972372e-01 5.99949181e-01 -6.73112214e-01 3.01824182e-01
-6.87072217e-01 4.81402241e-02 9.27135110e-01 1.02594185e+00
7.93665946e-01 -1.05829984e-01 -4.51137185e-01 1.07754254e+00
1.72934309e-02 3.88486922e-01 6.35760367e-01 -3.67893398e-01
8.58153760e-01 9.79660153e-01 -5.43112755e-02 -1.34389842e+00
-6.68136954e-01 -5.30803084e-01 -1.25278652e+00 -8.45344543e-01
9.42145661e-02 -3.76673222e-01 -7.99699247e-01 1.89560902e+00
4.09920365e-01 1.41683683e-01 1.31485730e-01 5.18778801e-01
1.35669649e+00 7.36725569e-01 4.01205122e-01 -1.54009566e-01
1.37189734e+00 -9.33361948e-01 -6.88477099e-01 -5.77134565e-02
1.08773994e+00 -3.94968897e-01 7.68431187e-01 -2.01562658e-01
-6.87186360e-01 -4.15109843e-01 -6.80613577e-01 -8.12302828e-02
-8.65460217e-01 4.18936998e-01 4.88380700e-01 4.96165335e-01
-7.07455158e-01 7.30655253e-01 -3.97320569e-01 -2.09143102e-01
1.56939700e-01 3.52513164e-01 -6.20487928e-01 -8.64868537e-02
-1.89065409e+00 6.84966505e-01 1.09021366e+00 1.91804186e-01
-1.66344136e-01 -6.77435577e-01 -1.06762421e+00 4.31500077e-01
3.25970381e-01 -4.35898453e-01 7.32081890e-01 -2.61379391e-01
-5.20820439e-01 6.58239722e-01 -6.60593659e-02 -2.96400845e-01
1.22597121e-01 -2.69615531e-01 -1.10216665e+00 -1.61346957e-01
2.77406245e-01 4.58305806e-01 -2.72136718e-01 -9.50367808e-01
-6.33247256e-01 -4.22512203e-01 1.45327955e-01 1.36939138e-01
-8.62406790e-01 -1.59589857e-01 -5.63967049e-01 -7.18885601e-01
-8.81111063e-03 -7.62520969e-01 -3.43841344e-01 -7.00061202e-01
-6.66380048e-01 -8.80195498e-01 4.93727803e-01 -7.17968106e-01
1.98031723e+00 -2.14207911e+00 -3.80841643e-01 3.52308273e-01
3.24896038e-01 3.65624785e-01 -3.23035508e-01 7.48713970e-01
-1.92067996e-01 5.57052255e-01 -2.52400488e-02 1.63779274e-01
1.34616634e-02 2.48264242e-02 -2.71583319e-01 2.53315642e-02
1.76717475e-01 8.86708200e-01 -8.32228720e-01 -6.62521720e-01
-4.08812791e-01 2.05765635e-01 -2.49970630e-01 1.33623565e-02
2.38543928e-01 -2.81833261e-01 -5.44593811e-01 6.72055304e-01
7.45385826e-01 -4.49092597e-01 3.98324549e-01 -4.39483285e-01
2.10876204e-02 3.52987111e-01 -1.26860952e+00 1.38936031e+00
-3.27239305e-01 2.70164311e-01 -5.12265801e-01 -7.87476838e-01
1.05203319e+00 3.40010196e-01 3.14709306e-01 -6.84711456e-01
-1.24535829e-01 2.18898624e-01 -2.11526215e-01 -2.80608058e-01
7.94160187e-01 1.31708160e-01 -4.61296737e-01 2.15255655e-02
1.72784984e-01 6.59575641e-01 8.11144054e-01 2.48639524e-01
1.19740379e+00 -4.42959726e-01 4.90472883e-01 -1.14525683e-01
5.72506011e-01 6.17516860e-02 9.95898008e-01 6.47740602e-01
-1.40902456e-02 2.87892938e-01 4.87083644e-01 -5.49537420e-01
-7.81848431e-01 -1.00205469e+00 -2.59036779e-01 1.10820961e+00
3.62690866e-01 -7.30610192e-01 -5.96981823e-01 -1.08980072e+00
-1.17270015e-01 6.88946366e-01 -6.21905148e-01 -1.61560446e-01
-7.60887921e-01 -8.46070707e-01 1.05084574e+00 8.84611368e-01
7.57259309e-01 -1.10959387e+00 2.63933182e-01 2.82918215e-01
-4.48188514e-01 -1.11932588e+00 -6.99742973e-01 3.82194147e-02
-8.63262534e-01 -9.90964174e-01 -7.65286922e-01 -1.13258910e+00
5.69606483e-01 1.69592544e-01 1.42643178e+00 9.77713466e-02
-1.26468837e-01 -1.28534794e-01 -3.54574412e-01 3.22305933e-02
2.58088976e-01 4.76774454e-01 1.46220205e-02 -5.38059831e-01
6.74777925e-01 -4.61558372e-01 -4.44782108e-01 5.64861476e-01
-8.15239429e-01 -5.75311720e-01 6.76668465e-01 1.07872128e+00
5.45611560e-01 2.76759803e-01 8.34967256e-01 -9.26459432e-01
6.22736692e-01 -7.25414455e-01 -3.71324718e-01 6.88016593e-01
-7.76259422e-01 -6.68381453e-02 9.09657121e-01 -4.31026757e-01
-1.00502503e+00 -2.42852882e-01 -9.74178761e-02 -2.19316483e-01
-9.07172635e-02 8.87722909e-01 -4.64783132e-01 3.31797868e-01
7.17431247e-01 1.37063831e-01 -9.44556475e-01 -5.13595045e-01
4.80292082e-01 6.50808871e-01 5.21218956e-01 -5.20319521e-01
6.67916715e-01 3.96372527e-02 -2.23760113e-01 -5.77012360e-01
-9.20079470e-01 -7.59702563e-01 -4.78532702e-01 2.96305150e-01
5.29433191e-01 -1.11019921e+00 -5.48094988e-01 1.36338696e-01
-1.19555914e+00 3.16968054e-01 -1.56393230e-01 4.78272259e-01
2.63942689e-01 5.83487511e-01 -9.24907088e-01 -3.92798901e-01
-6.63993120e-01 -5.36568999e-01 7.66220510e-01 7.40712941e-01
-1.34019151e-01 -9.17177856e-01 3.39875102e-01 5.13348095e-02
8.46871436e-02 1.40068457e-01 9.70898151e-01 -1.20979643e+00
-1.51258290e-01 -4.51870650e-01 -4.06417400e-01 1.73928380e-01
1.58220440e-01 -2.63913959e-01 -6.34252012e-01 -1.93200678e-01
-5.50762475e-01 -2.05367282e-01 9.61126626e-01 -1.14085831e-01
1.22292387e+00 -3.25119227e-01 -6.31470680e-01 4.27132934e-01
1.49265480e+00 1.61972642e-01 6.44991875e-01 3.02612334e-01
9.38822091e-01 5.62286258e-01 6.25210583e-01 2.90055722e-01
4.94423807e-01 2.54039258e-01 1.19761184e-01 5.18761985e-02
1.84176952e-01 -5.39575338e-01 5.58975488e-02 1.05070746e+00
-6.54648170e-02 -5.00974119e-01 -1.11096549e+00 8.34068298e-01
-1.59688640e+00 -1.00261748e+00 -3.06352526e-01 2.08190441e+00
9.56559062e-01 4.63229194e-02 -3.02038062e-02 -2.32759297e-01
1.17229629e+00 1.26182690e-01 -4.13668543e-01 1.69460282e-01
-3.91668081e-01 1.28313810e-01 4.29792225e-01 -1.81966484e-01
-1.39629877e+00 8.79396319e-01 5.32624292e+00 1.32169998e+00
-7.82222271e-01 -1.47366822e-01 6.39427364e-01 4.28041101e-01
-2.97827274e-01 -1.89419851e-01 -1.17298973e+00 5.13801873e-01
8.75623167e-01 -4.58162397e-01 -2.26732306e-02 1.00691366e+00
-3.91666263e-01 3.55093569e-01 -8.92347157e-01 9.03838336e-01
-8.68144557e-02 -1.08425617e+00 6.95826411e-02 -1.06302731e-01
8.72360408e-01 1.78355098e-01 -2.66493469e-01 9.12888288e-01
3.58013451e-01 -9.69541252e-01 2.12869197e-02 2.98337430e-01
8.85812998e-01 -1.01797247e+00 1.10812342e+00 3.52408379e-01
-1.81967044e+00 3.18266526e-02 -6.04647040e-01 4.80439991e-01
-1.71478558e-03 8.19090009e-01 -6.38177752e-01 1.05661404e+00
9.34422851e-01 4.65181053e-01 -4.58229005e-01 1.21708870e+00
-1.53745264e-01 5.11451840e-01 -5.03654659e-01 -2.98479050e-01
3.05791765e-01 -6.95200413e-02 3.15998137e-01 1.46718717e+00
3.23669493e-01 2.31105044e-01 7.09171891e-02 6.76658094e-01
-7.64124513e-01 6.26340151e-01 -5.73584497e-01 -1.83224797e-01
8.64622653e-01 1.50286829e+00 -6.13004088e-01 -3.55209738e-01
-4.30153459e-01 8.03184092e-01 8.72840762e-01 2.66805470e-01
-8.05139184e-01 -1.15229857e+00 2.51779407e-01 -4.07827556e-01
4.74613249e-01 -2.22953185e-02 -5.12395129e-02 -1.28433657e+00
1.71182707e-01 -6.41326606e-01 9.51992810e-01 -5.08319855e-01
-1.80298460e+00 8.46533716e-01 -3.64047229e-01 -1.23032618e+00
-7.61666819e-02 -5.34429491e-01 -6.85939968e-01 7.36253500e-01
-1.14987254e+00 -9.74547923e-01 -3.36530626e-01 3.73039216e-01
3.19577903e-02 -2.02947040e-03 9.66671169e-01 7.02970207e-01
-7.94659555e-01 9.90580916e-01 3.73353869e-01 1.00676298e+00
8.32821667e-01 -1.37744057e+00 5.28316557e-01 5.57884276e-01
4.35798645e-01 1.08826959e+00 1.22581914e-01 -8.02226663e-01
-8.99366319e-01 -1.38845217e+00 1.43236971e+00 -3.05984080e-01
6.39171600e-01 1.14996649e-01 -1.04427266e+00 6.59232974e-01
1.53008372e-01 2.65496433e-01 9.68506694e-01 5.92312872e-01
-6.26791656e-01 -8.72470513e-02 -1.07457399e+00 6.74726963e-01
1.19126368e+00 -5.54514110e-01 -8.97377133e-01 2.15314373e-01
9.28954661e-01 -3.13681364e-01 -1.32316315e+00 6.88091397e-01
2.75229514e-01 -5.72525978e-01 9.09502149e-01 -9.26134169e-01
2.07357019e-01 -4.16025966e-01 -8.03772807e-02 -1.33188224e+00
-4.29460227e-01 -1.21023938e-01 -2.21357495e-01 1.77395439e+00
6.42604113e-01 -5.22923708e-01 6.26624227e-01 3.21457118e-01
-1.11488895e-02 -9.04160082e-01 -7.35283077e-01 -1.08358455e+00
1.87461987e-01 2.38237344e-02 8.73711884e-01 1.39495826e+00
7.47671276e-02 6.21049523e-01 -7.16202632e-02 3.94561917e-01
3.27264845e-01 3.02060813e-01 3.48986685e-01 -1.15356112e+00
1.12451203e-01 -3.36324275e-01 -3.45262259e-01 -1.08904505e+00
1.62217423e-01 -9.62970257e-01 -1.25742048e-01 -1.45976651e+00
2.91915327e-01 -5.60332119e-01 -8.22138429e-01 7.47523427e-01
-5.95885277e-01 -2.95771267e-02 2.06649657e-02 3.22996438e-01
-1.00001633e+00 5.94713509e-01 8.31659198e-01 -1.51679918e-01
-1.29608557e-01 -3.80970985e-01 -5.50817609e-01 6.24445438e-01
8.02751184e-01 -5.88068962e-01 1.26332780e-02 -4.75523859e-01
3.71844202e-01 4.07273322e-02 -5.82698360e-03 -9.36375022e-01
5.20790100e-01 5.56108840e-02 5.62858641e-01 -8.88072491e-01
-1.20852821e-01 -6.96258545e-01 5.42331971e-02 2.10679665e-01
-3.11077267e-01 3.24111104e-01 1.38024449e-01 6.13278627e-01
-5.97978890e-01 -2.81324029e-01 3.07447493e-01 -1.76705774e-02
-8.58557284e-01 5.32991230e-01 2.27050781e-01 6.04441166e-01
8.13876510e-01 2.02900797e-01 -5.53896248e-01 8.42338726e-02
-6.16514325e-01 6.67962372e-01 6.71351925e-02 3.96052748e-01
3.35155487e-01 -1.81641150e+00 -6.36524379e-01 -2.60850787e-01
3.17658097e-01 5.51371649e-02 6.65544033e-01 5.20339906e-01
-3.89894098e-01 5.20569384e-01 1.53511882e-01 -1.80241838e-01
-1.08430564e+00 5.79214871e-01 3.08437198e-01 -8.73328149e-01
-2.53857851e-01 7.58594096e-01 2.38132238e-01 -9.86296773e-01
1.55921444e-01 -8.30664188e-02 -5.93089700e-01 1.75522551e-01
5.80124557e-01 6.02478743e-01 6.79574162e-02 -5.19781232e-01
-4.50332701e-01 3.56940955e-01 -3.10182095e-01 5.09453118e-01
1.28286135e+00 6.20874204e-02 -3.67940009e-01 2.39836574e-01
1.25715673e+00 3.98701340e-01 -1.91585988e-01 -4.59580570e-01
4.15941685e-01 3.00559942e-02 -3.37871909e-01 -6.16133273e-01
-1.12185681e+00 5.25556743e-01 3.58843088e-01 5.52109063e-01
9.90103841e-01 -2.20841286e-03 1.16571796e+00 9.64310229e-01
2.55148143e-01 -1.05813742e+00 -3.14058900e-01 8.83386672e-01
5.44627190e-01 -9.47816193e-01 -1.97937638e-01 -4.87200290e-01
-4.30086076e-01 1.04020548e+00 9.53352630e-01 -9.38027799e-02
5.30676842e-01 -8.10616091e-02 -1.42313048e-01 -1.96080506e-01
-4.91846114e-01 -2.21363306e-01 5.69782734e-01 1.83346704e-01
5.92915893e-01 6.28207177e-02 -7.64340580e-01 1.01489377e+00
-4.46314625e-02 -2.24146456e-01 -5.10561839e-02 6.68186665e-01
-3.90306711e-01 -9.75025356e-01 -8.01995918e-02 6.14699125e-01
-6.30791008e-01 -4.86322582e-01 -3.30497444e-01 9.05073822e-01
2.70197928e-01 7.79585004e-01 2.66484451e-03 -4.75921243e-01
5.92017710e-01 1.31649941e-01 -2.13591140e-02 -4.23451751e-01
-6.92646921e-01 -3.78180891e-01 3.35856259e-01 -3.61807764e-01
-1.24039188e-01 -2.48198003e-01 -1.58903670e+00 -3.53813320e-01
-8.07043731e-01 6.61939681e-01 1.84970751e-01 7.54959524e-01
6.03160560e-01 3.73162776e-01 7.19183207e-01 7.61002824e-02
-6.45178080e-01 -1.13461542e+00 -7.45927095e-01 5.34124732e-01
-2.81036139e-01 -4.33536291e-01 -4.18033451e-01 -3.82376850e-01] | [9.56633472442627, 9.405906677246094] |
a31a51b6-c777-4482-9005-b1fa8c7107b8 | rockafellian-relaxation-in-optimization-under | 2204.04762 | null | https://arxiv.org/abs/2204.04762v3 | https://arxiv.org/pdf/2204.04762v3.pdf | Rockafellian Relaxation in Optimization under Uncertainty: Asymptotically Exact Formulations | In practice, optimization models are often prone to unavoidable inaccuracies due to dubious assumptions and corrupted data. Traditionally, this placed special emphasis on risk-based and robust formulations, and their focus on ``conservative" decisions. We develop, in contrast, an ``optimistic" framework based on Rockafellian relaxations in which optimization is conducted not only over the original decision space but also jointly with a choice of model perturbation. The framework enables us to address challenging problems with ambiguous probability distributions from the areas of two-stage stochastic optimization without relatively complete recourse, probability functions lacking continuity properties, expectation constraints, and outlier analysis. We are also able to circumvent the fundamental difficulty in stochastic optimization that convergence of distributions fails to guarantee convergence of expectations. The framework centers on the novel concepts of exact and asymptotically exact Rockafellians, with interpretations of ``negative'' regularization emerging in certain settings. We illustrate the role of Phi-divergence, examine rates of convergence under changing distributions, and explore extensions to first-order optimality conditions. The main development is free of assumptions about convexity, smoothness, and even continuity of objective functions. Numerical results in the setting of computer vision with label noise illustrate the framework. | ['Eric Eckstrand', 'Louis L. Chen', 'Johannes O. Royset'] | 2022-04-10 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 3.16911280e-01 1.92976400e-01 2.61960067e-02 -3.45905095e-01
-1.10372043e+00 -5.92289209e-01 2.70324260e-01 2.46186286e-01
-6.30235970e-01 9.24230933e-01 1.24337450e-02 -2.02473015e-01
-6.90547347e-01 -3.99797738e-01 -6.93862736e-01 -1.11914992e+00
9.98624340e-02 2.41156742e-01 -3.18043053e-01 -2.12676618e-02
3.26633751e-01 3.58247161e-01 -1.22403407e+00 -4.68808293e-01
1.13730121e+00 1.12476087e+00 -1.14565618e-01 4.36935246e-01
2.32897297e-01 3.40261102e-01 -3.41600120e-01 -4.94549543e-01
4.66331482e-01 -7.16411695e-02 -3.57556283e-01 5.45966089e-01
3.42270762e-01 -1.89644396e-02 2.90658385e-01 1.62424099e+00
4.54330146e-01 4.50854361e-01 7.18545318e-01 -1.32537568e+00
-6.58192754e-01 3.12571526e-01 -8.12490165e-01 8.57449919e-02
5.97109310e-02 1.23661920e-01 1.03516555e+00 -1.00382471e+00
3.38503599e-01 9.34115648e-01 9.56517577e-01 3.90687525e-01
-1.49536061e+00 3.19158360e-02 4.19076562e-01 -2.57281661e-01
-1.43701649e+00 -3.83532345e-01 4.41159815e-01 -7.67248929e-01
2.44964719e-01 5.43303192e-01 2.57018823e-02 9.01287019e-01
1.69387728e-01 6.64536178e-01 9.64498818e-01 -3.11838537e-01
3.71752053e-01 3.62895668e-01 3.31926376e-01 4.92724925e-01
5.44954240e-01 2.40938857e-01 -1.44624427e-01 -3.57189327e-01
4.60274339e-01 -1.98036879e-01 -6.67304516e-01 -4.70053703e-01
-9.80443716e-01 9.82522547e-01 -6.24700598e-02 8.52029771e-02
-5.37359357e-01 -1.72206908e-01 2.04350337e-01 2.05030829e-01
7.04175711e-01 4.52233553e-01 -2.86521524e-01 9.62063521e-02
-7.87634909e-01 4.20952797e-01 8.25997889e-01 9.06773508e-01
3.10802400e-01 1.71581551e-01 -2.24616587e-01 5.84786177e-01
3.42102438e-01 4.94424552e-01 1.15409955e-01 -1.11774659e+00
5.60100853e-01 -1.77495658e-01 5.30018628e-01 -1.15352786e+00
-3.53096962e-01 -8.77834976e-01 -8.03796411e-01 3.00601035e-01
8.84366751e-01 -3.17059755e-01 -6.35578275e-01 1.99159086e+00
3.17149997e-01 -1.39048412e-01 -5.31087294e-02 9.60237324e-01
-1.99223071e-01 4.25901085e-01 -2.11936727e-01 -7.28093803e-01
9.28205967e-01 -4.16995227e-01 -7.91578889e-01 6.18514419e-02
3.28797191e-01 -5.41318238e-01 1.21847427e+00 6.39572561e-01
-1.29574800e+00 -5.07588685e-02 -8.47726107e-01 1.84375525e-01
5.17948195e-02 -2.21437097e-01 2.82361656e-01 8.64292204e-01
-9.65433538e-01 7.48961389e-01 -7.09399223e-01 -1.69524215e-02
3.59000325e-01 2.75256306e-01 -1.95199475e-01 1.94130510e-01
-7.76718080e-01 8.84004951e-01 7.35379383e-02 6.03640437e-01
-5.63775599e-01 -9.33648765e-01 -8.08545947e-01 1.20009094e-01
7.04554439e-01 -7.32095003e-01 9.70250309e-01 -1.05625510e+00
-1.21605551e+00 6.07191920e-01 4.87236343e-02 -4.33715254e-01
1.11208498e+00 -3.28306735e-01 1.20631978e-01 -1.42340645e-01
2.51234174e-01 -2.18702540e-01 8.63735974e-01 -1.13199186e+00
-4.17264014e-01 -5.86557209e-01 -1.06708594e-01 2.69748837e-01
-4.96991794e-04 3.93656902e-02 -7.43436590e-02 -7.62259126e-01
1.05315350e-01 -8.13687563e-01 -7.02525735e-01 1.26908138e-01
-4.94342417e-01 1.73014760e-01 1.25989825e-01 -6.36174381e-01
1.09016573e+00 -2.20028830e+00 2.29058534e-01 4.74085778e-01
2.42722966e-02 -3.06737214e-01 1.35319993e-01 1.56910881e-01
-1.51145533e-01 2.19939470e-01 -8.40810239e-01 -4.72071677e-01
3.75546098e-01 1.44187972e-01 -4.05888259e-01 1.12957442e+00
1.09546445e-01 2.97566742e-01 -8.98248374e-01 -1.95237607e-01
1.20909370e-01 2.81210899e-01 -7.81569242e-01 -2.11289510e-01
-8.85051265e-02 5.24604917e-01 -5.40583253e-01 6.28797770e-01
7.15691745e-01 -3.02735895e-01 2.63080210e-03 1.88433915e-01
-3.44288915e-01 -2.58788556e-01 -1.60328770e+00 1.35634506e+00
-3.17637414e-01 3.20909828e-01 6.92132294e-01 -1.33705151e+00
4.67767805e-01 1.62532732e-01 6.12377703e-01 5.42425402e-02
2.30604649e-01 2.89885283e-01 -2.43817925e-01 -3.98053050e-01
3.20041567e-01 -6.41747594e-01 -3.49782892e-02 1.22369833e-01
-2.09871322e-01 -1.36699170e-01 6.96329549e-02 -2.30293944e-01
7.19297051e-01 -1.20070986e-02 1.77177027e-01 -7.85028100e-01
4.21699941e-01 -1.98358759e-01 8.62550139e-01 8.20374787e-01
-2.29198262e-01 8.20532501e-01 7.77916729e-01 1.43763989e-01
-8.62863600e-01 -1.30867445e+00 -6.23840928e-01 7.75363445e-01
5.47112525e-03 4.72363010e-02 -5.24903655e-01 -4.46045399e-01
2.67509669e-01 9.53423858e-01 -7.86507368e-01 5.92810027e-02
-2.67126203e-01 -1.47838330e+00 9.95799154e-02 2.74561673e-01
1.29009694e-01 -2.64662951e-01 -2.84267902e-01 2.39999011e-01
-9.43853706e-02 -8.57499063e-01 -6.18744493e-01 9.80599895e-02
-7.77584016e-01 -1.08148634e+00 -9.82608318e-01 -3.00857991e-01
8.20600092e-01 -2.38350719e-01 9.72146928e-01 -3.07030290e-01
-1.04140183e-02 6.18272603e-01 -1.75993788e-04 -4.42988515e-01
-2.10194468e-01 -4.61492419e-01 3.28507841e-01 4.39859748e-01
-1.25120044e-01 -3.96597415e-01 -4.32140052e-01 3.63450021e-01
-9.98074174e-01 -5.64362168e-01 2.37916589e-01 9.81027544e-01
6.99373484e-01 2.56912678e-01 4.79626268e-01 -8.73790503e-01
7.12764680e-01 -7.09404767e-01 -9.92665827e-01 2.44266763e-01
-8.82874906e-01 5.55238090e-02 5.38492680e-01 -3.99097025e-01
-1.12196338e+00 -2.10930184e-01 9.41220820e-02 -5.40241480e-01
2.15731457e-01 6.23696089e-01 -2.61512667e-01 -3.24207954e-02
6.34199560e-01 -9.11507476e-03 1.95986658e-01 -5.09986341e-01
2.82695293e-01 3.90432119e-01 5.87941945e-01 -9.38709140e-01
6.69158757e-01 7.42643058e-01 1.57895535e-01 -8.45845401e-01
-9.18570876e-01 -3.32313269e-01 -3.84428173e-01 -1.20452866e-01
6.00703657e-01 -4.86225784e-01 -7.17991292e-01 3.55751991e-01
-8.29826236e-01 -6.02030084e-02 -8.44872177e-01 6.43330216e-01
-8.79800916e-01 7.12841451e-01 -3.80661011e-01 -1.17153752e+00
1.84817091e-01 -1.22731519e+00 6.71318889e-01 4.83941473e-02
-3.43135856e-02 -1.47881711e+00 -5.10566644e-02 2.37891614e-01
3.10486466e-01 5.67636192e-01 6.96262062e-01 -6.05761170e-01
-2.55666465e-01 -2.83445030e-01 -1.96566878e-04 7.33236969e-01
1.53724700e-01 9.91740376e-02 -7.69531429e-01 -2.04625785e-01
7.17337310e-01 1.18047103e-01 6.55130684e-01 8.29925060e-01
1.05799115e+00 -6.81315660e-01 -7.29372203e-02 7.51794577e-01
1.64473927e+00 -7.96648711e-02 1.85368136e-01 2.37319097e-01
3.43321979e-01 1.10356545e+00 5.33966601e-01 7.64239550e-01
1.66576058e-01 4.37216699e-01 5.16767263e-01 2.21948057e-01
5.79768419e-01 6.13308735e-02 2.03144580e-01 4.96560991e-01
-3.29738343e-03 -3.02162886e-01 -7.78324962e-01 6.48097813e-01
-1.81809187e+00 -9.23535645e-01 -1.88441038e-01 2.60810256e+00
7.92556465e-01 2.93000671e-03 1.13603443e-01 -3.35810147e-03
1.05227733e+00 -8.42549466e-03 -4.82932389e-01 -3.06903213e-01
-3.12238812e-01 -1.86538503e-01 8.71310115e-01 9.93872404e-01
-1.11989510e+00 3.06684971e-01 6.44292402e+00 7.82899916e-01
-7.84939289e-01 8.06333572e-02 8.15737128e-01 -2.05432341e-01
-5.09382486e-01 4.76164408e-02 -5.96964657e-01 6.82254314e-01
5.13478696e-01 -3.14815402e-01 2.97706693e-01 7.94054329e-01
3.97054464e-01 -1.71459392e-01 -1.00131953e+00 6.99152827e-01
-4.75699864e-02 -9.15930808e-01 -5.48659444e-01 2.93362796e-01
8.44450295e-01 -2.51461625e-01 4.64718163e-01 -1.11443907e-01
4.19842899e-01 -9.92006719e-01 8.13850224e-01 7.83051431e-01
5.68327606e-01 -7.49634385e-01 6.72096848e-01 3.65592152e-01
-6.38886511e-01 -2.12309241e-01 -3.07595700e-01 -6.05298877e-02
3.68172348e-01 1.00740802e+00 -2.95721501e-01 5.11481464e-01
4.68744457e-01 4.99390990e-01 7.12767765e-02 1.09745145e+00
4.39693742e-02 3.49823892e-01 -7.38563776e-01 1.28676280e-01
2.19750330e-01 -7.18964994e-01 9.08319473e-01 1.01434636e+00
3.26939613e-01 2.18957528e-01 1.85642749e-01 8.67533445e-01
3.51298273e-01 2.60961413e-01 -5.11524618e-01 2.36701742e-01
8.65488648e-02 8.81530941e-01 -7.14144409e-01 5.45662753e-02
-5.45531988e-01 6.54037297e-01 6.51048943e-02 6.59213543e-01
-7.44378686e-01 -1.70345843e-01 7.05347121e-01 2.14312621e-03
1.35821268e-01 -2.98304588e-01 -7.43274450e-01 -1.31830227e+00
5.19272506e-01 -6.60663068e-01 6.92908168e-01 -1.60513893e-01
-1.64553988e+00 2.38430306e-01 1.92974031e-01 -8.75044405e-01
-1.04180209e-01 -7.27567852e-01 -5.07189631e-01 8.68978679e-01
-1.34345555e+00 -5.01193345e-01 3.00089478e-01 5.40143192e-01
2.78713167e-01 1.83864161e-01 2.09349781e-01 3.20118189e-01
-7.55434394e-01 5.52399933e-01 6.57626152e-01 -2.83348620e-01
3.74987632e-01 -1.46418452e+00 -2.84637392e-01 1.04216039e+00
-1.77706525e-01 5.46079159e-01 1.29735708e+00 -4.49791938e-01
-1.08593428e+00 -8.28723907e-01 7.10713804e-01 -5.05856037e-01
9.24646974e-01 -1.64827853e-01 -9.66378391e-01 6.91399634e-01
-3.61292809e-01 3.38212065e-02 6.19861722e-01 1.96257308e-01
9.59040895e-02 -4.19991724e-02 -1.35003924e+00 5.38134038e-01
7.42181778e-01 -1.31098583e-01 -3.44853789e-01 7.17146039e-01
3.39021385e-01 -2.81194717e-01 -9.10096705e-01 5.45350790e-01
3.42422664e-01 -9.47355926e-01 8.86519253e-01 -7.49153972e-01
-8.29174295e-02 -1.51268229e-01 -5.05654931e-01 -9.63483691e-01
-2.43222695e-02 -9.88791585e-01 3.15444320e-01 1.03890777e+00
4.31798190e-01 -8.35939407e-01 7.12798476e-01 1.03976631e+00
-2.06495106e-01 -7.74448574e-01 -1.24910963e+00 -9.94989216e-01
4.39813077e-01 -6.95046961e-01 4.79851775e-02 9.84608650e-01
-1.55394211e-01 -2.48487890e-01 -4.08721387e-01 5.32202005e-01
8.70744407e-01 -2.83508580e-02 4.08466071e-01 -1.05457139e+00
-6.53431296e-01 -6.78407729e-01 -2.09762827e-01 -8.85513723e-01
1.50725275e-01 -6.78729594e-01 4.19054061e-01 -9.24952030e-01
-6.64419755e-02 -5.32547295e-01 -3.31133574e-01 -9.77586210e-02
-2.40038335e-01 4.42970823e-03 1.45502269e-01 5.96896484e-02
-2.39070430e-01 7.87305474e-01 9.47615445e-01 1.19774148e-01
-3.29636544e-01 5.60438633e-01 -7.62219965e-01 1.15738547e+00
6.02508724e-01 -4.34942305e-01 -4.86636668e-01 -3.38942230e-01
4.79826480e-01 1.29486024e-01 5.86337447e-01 -6.36670351e-01
1.03570461e-01 -4.56771910e-01 -1.80997383e-02 -5.41585833e-02
2.09537983e-01 -7.52870500e-01 1.64389282e-01 2.04463363e-01
-3.84972215e-01 2.70472467e-02 -6.97687194e-02 7.50907481e-01
-2.91791353e-02 -6.64343357e-01 1.04419160e+00 -3.79768051e-02
-1.17462337e-01 3.79887491e-01 -2.09121764e-01 3.29959154e-01
9.93586719e-01 -1.71538159e-01 1.16955519e-01 -4.85757560e-01
-1.33596754e+00 4.02060568e-01 5.39453745e-01 -1.73110083e-01
4.78000373e-01 -1.08265066e+00 -9.13255930e-01 -1.79519188e-02
-2.59706885e-01 7.63781667e-02 3.02670032e-01 1.27506268e+00
-3.36633265e-01 1.13266058e-01 4.02605981e-01 -5.91274202e-01
-7.85199225e-01 7.02783704e-01 4.90891486e-01 -1.48578778e-01
-4.40321058e-01 9.34791267e-01 4.89714175e-01 -2.47693092e-01
3.46090376e-01 -4.78048086e-01 1.92582607e-01 1.62129894e-01
1.76202580e-01 6.42333925e-01 -3.45817730e-02 -5.49955785e-01
-2.99378902e-01 5.29958308e-01 1.73826665e-01 -5.08248985e-01
9.69344676e-01 -6.06892824e-01 7.33447298e-02 5.94429314e-01
1.02547312e+00 3.55432570e-01 -1.62243581e+00 -1.43224686e-01
2.68628061e-01 -5.77052176e-01 -9.61226597e-02 -6.04383707e-01
-9.28473771e-01 6.90936387e-01 2.74645686e-01 1.75893262e-01
1.12857139e+00 -2.49581009e-01 2.71654725e-01 1.59032211e-01
2.08539844e-01 -1.30068386e+00 -3.75201792e-01 2.58514881e-01
1.08461058e+00 -1.25579274e+00 1.48265483e-02 -3.48573059e-01
-5.03859341e-01 9.90934908e-01 1.14999004e-01 -2.70646393e-01
9.06730592e-01 1.53598577e-01 -8.55777040e-02 1.48550138e-01
-3.96475405e-01 -1.36974275e-01 2.90006787e-01 4.91905928e-01
4.13453765e-02 -1.29873365e-01 -5.39183259e-01 6.73385680e-01
-9.25772041e-02 -2.67230362e-01 6.97131991e-01 7.48247147e-01
-2.22892895e-01 -6.36962652e-01 -5.57232022e-01 3.61679912e-01
-1.02078545e+00 -1.42206371e-01 2.08365411e-01 1.01053047e+00
-3.58871035e-02 7.88417459e-01 6.17005154e-02 2.56757587e-01
3.16346645e-01 -8.75848234e-02 4.14807260e-01 -4.92333353e-01
-1.52276143e-01 3.83899063e-01 -1.85423084e-02 -4.16689336e-01
-2.69780785e-01 -1.20348465e+00 -6.93868935e-01 -3.01587462e-01
-5.87285876e-01 3.37814659e-01 6.31063759e-01 1.01366889e+00
-2.10475866e-02 7.24927187e-02 6.47446096e-01 -5.24042368e-01
-1.42157841e+00 -5.77605605e-01 -9.85448122e-01 3.00274968e-01
6.39401317e-01 -5.41046083e-01 -9.06449974e-01 -1.53571397e-01] | [6.754144668579102, 4.3026204109191895] |
bcc541e9-843c-48e9-a425-2c13683a7a57 | learning-restoration-is-not-enough | 2305.10640 | null | https://arxiv.org/abs/2305.10640v1 | https://arxiv.org/pdf/2305.10640v1.pdf | Learning Restoration is Not Enough: Transfering Identical Mapping for Single-Image Shadow Removal | Shadow removal is to restore shadow regions to their shadow-free counterparts while leaving non-shadow regions unchanged. State-of-the-art shadow removal methods train deep neural networks on collected shadow & shadow-free image pairs, which are desired to complete two distinct tasks via shared weights, i.e., data restoration for shadow regions and identical mapping for non-shadow regions. We find that these two tasks exhibit poor compatibility, and using shared weights for these two tasks could lead to the model being optimized towards only one task instead of both during the training process. Note that such a key issue is not identified by existing deep learning-based shadow removal methods. To address this problem, we propose to handle these two tasks separately and leverage the identical mapping results to guide the shadow restoration in an iterative manner. Specifically, our method consists of three components: an identical mapping branch (IMB) for non-shadow regions processing, an iterative de-shadow branch (IDB) for shadow regions restoration based on identical results, and a smart aggregation block (SAB). The IMB aims to reconstruct an image that is identical to the input one, which can benefit the restoration of the non-shadow regions without explicitly distinguishing between shadow and non-shadow regions. Utilizing the multi-scale features extracted by the IMB, the IDB can effectively transfer information from non-shadow regions to shadow regions progressively, facilitating the process of shadow removal. The SAB is designed to adaptive integrate features from both IMB and IDB. Moreover, it generates a finely tuned soft shadow mask that guides the process of removing shadows. Extensive experiments demonstrate our method outperforms all the state-of-the-art shadow removal approaches on the widely used shadow removal datasets. | ['Song Wang', 'Ivor Tsang', 'Wei Feng', 'Pingping Cai', 'Qing Guo', 'Xiaoguang Li'] | 2023-05-18 | null | null | null | null | ['shadow-removal', 'image-shadow-removal'] | ['computer-vision', 'computer-vision'] | [ 5.65827549e-01 1.31973267e-01 3.61784816e-01 -3.89147580e-01
-3.23526233e-01 -3.23621035e-01 3.94556105e-01 -5.28261900e-01
-3.50750387e-02 7.99275994e-01 2.94043005e-01 -5.88235795e-01
2.36732498e-01 -8.42777014e-01 -5.79237342e-01 -1.16962886e+00
3.21652532e-01 2.70061642e-01 7.06611574e-01 -2.31331334e-01
6.99855536e-02 7.56881237e-01 -1.39777827e+00 3.48946214e-01
1.02580357e+00 1.01726961e+00 5.80028832e-01 6.83679163e-01
-1.26094282e-01 5.73941946e-01 -7.89275587e-01 9.67681631e-02
4.85374510e-01 -3.71021420e-01 -2.83050090e-01 -7.88905621e-02
3.15438092e-01 -8.97318006e-01 -4.33389813e-01 5.41400075e-01
5.48133612e-01 1.29248485e-01 5.35998940e-01 -1.16058493e+00
-5.42807162e-01 2.71831732e-02 -7.87611365e-01 -5.48336394e-02
-1.71745598e-01 -4.17245105e-02 6.73511922e-01 -9.75292742e-01
3.08004409e-01 1.19936323e+00 7.94969440e-01 2.26990879e-01
-9.00413692e-01 -8.18929732e-01 1.85171843e-01 1.93780549e-02
-1.06514978e+00 -5.24378657e-01 8.29151988e-01 1.44463731e-03
4.45472658e-01 4.17475164e-01 6.21336162e-01 8.01059186e-01
4.89026278e-01 8.41659427e-01 1.23171914e+00 -2.47990876e-01
1.47842392e-01 -2.27636974e-02 1.08530797e-01 6.88582838e-01
2.74221539e-01 3.37363750e-01 -6.07336521e-01 -1.18749559e-01
5.99048436e-01 2.65934229e-01 -7.27189720e-01 -4.40294921e-01
-8.52720797e-01 5.21164000e-01 5.72061658e-01 -8.28612074e-02
-4.25079942e-01 1.41774103e-01 8.16960484e-02 -1.48334885e-02
4.54198986e-01 -1.66588696e-03 -5.17300665e-01 5.29277325e-01
-9.26260650e-01 -1.86890122e-02 7.65427649e-01 7.44385958e-01
1.20649767e+00 1.28448993e-01 -4.42408115e-01 6.54712796e-01
2.37062454e-01 7.91548967e-01 1.14431707e-02 -7.72321463e-01
2.35421076e-01 4.08304542e-01 3.32413316e-01 -8.22414637e-01
-4.24784958e-01 -5.30610502e-01 -9.10831630e-01 6.85266197e-01
2.28317827e-01 -1.48116693e-01 -1.38297033e+00 1.59995854e+00
5.37811339e-01 3.34704310e-01 1.18795879e-01 9.58571672e-01
9.13179994e-01 7.09835231e-01 -4.17737514e-01 -1.30595669e-01
1.19521153e+00 -1.16520715e+00 -6.99884772e-01 -4.91603196e-01
1.35300770e-01 -9.25036848e-01 1.06561267e+00 9.75599587e-02
-7.19739497e-01 -4.20585573e-01 -1.34015930e+00 -1.52284265e-01
-3.96440238e-01 2.87195534e-01 5.82547367e-01 4.46918637e-01
-9.94268954e-01 2.72795618e-01 -5.18857181e-01 -1.99635521e-01
5.35827100e-01 2.05941752e-01 4.59417365e-02 -7.30458796e-02
-1.03696883e+00 7.47939527e-01 1.31561356e-02 4.36491221e-01
-1.12935126e+00 -8.28362882e-01 -5.93741894e-01 2.68412977e-01
7.10654497e-01 -5.19816101e-01 1.02976847e+00 -1.14233768e+00
-1.38413513e+00 4.88625199e-01 -4.81565207e-01 -1.89422280e-01
5.09401202e-01 -3.89644861e-01 -2.29601949e-01 -1.15093440e-01
2.12776080e-01 2.21582919e-01 1.29982865e+00 -2.22417712e+00
-7.57835448e-01 -1.57524899e-01 2.33176798e-02 5.00977516e-01
-1.30777627e-01 -2.93595344e-01 -7.62326956e-01 -7.53696978e-01
1.90455124e-01 -8.70801091e-01 2.22116306e-01 3.08119714e-01
-6.77145898e-01 3.53630692e-01 1.55272830e+00 -8.76846731e-01
1.04299426e+00 -2.17729020e+00 -1.54565766e-01 3.07532996e-01
5.53378582e-01 5.00177801e-01 -1.71752259e-01 2.91678131e-01
8.51864368e-02 -3.75419110e-01 -6.44247174e-01 -7.57726610e-01
-1.67580917e-01 5.62407553e-01 -9.12142515e-01 5.10580659e-01
-1.97712526e-01 8.42450798e-01 -7.68493235e-01 -3.12125534e-01
3.00004721e-01 5.69512010e-01 4.43074219e-02 4.64257300e-01
7.31650889e-02 2.74539232e-01 -2.87739456e-01 9.02493536e-01
1.27576482e+00 -5.27227856e-02 2.37172991e-01 -4.57472175e-01
-1.47405297e-01 1.11615218e-01 -9.58903015e-01 1.05512667e+00
-5.64203739e-01 8.72154117e-01 5.80368459e-01 -5.88316798e-01
7.56105363e-01 -4.83371988e-02 3.70910674e-01 -8.34401906e-01
8.71387273e-02 1.30450100e-01 -9.22494754e-02 -2.42224149e-02
6.27997994e-01 -1.11038521e-01 2.33473703e-01 8.96029472e-01
-5.18420160e-01 6.61331788e-02 -4.63369727e-01 2.52793044e-01
1.14046955e+00 2.22668082e-01 1.88640460e-01 -2.03994453e-01
4.09925073e-01 -3.28192681e-01 9.08428550e-01 9.38614786e-01
-1.97216541e-01 9.00110662e-01 1.20801158e-01 -3.47840816e-01
-5.46199799e-01 -1.35638988e+00 1.01363443e-01 1.26942348e+00
7.58672059e-01 2.32616328e-02 -6.98520124e-01 -8.58136117e-01
2.36506313e-01 5.79264164e-01 -8.51227045e-01 -1.49276748e-01
-8.19475234e-01 -8.52346063e-01 3.92540962e-01 4.20363218e-01
7.29192317e-01 -1.20673943e+00 -1.02090490e+00 3.23367007e-02
-9.57221463e-02 -8.58630121e-01 -5.36520779e-01 5.61699867e-01
-4.06883746e-01 -1.13004541e+00 -6.87356830e-01 -4.02978867e-01
8.54894757e-01 1.13908768e+00 1.09283388e+00 6.92752779e-01
-2.53014803e-01 1.09549843e-01 -3.96016657e-01 -7.01268196e-01
-3.03049326e-01 -1.85077205e-01 -2.27670953e-01 3.27484339e-01
-2.69019872e-01 -7.51304507e-01 -9.36223745e-01 4.65667278e-01
-1.09053814e+00 4.46107775e-01 1.02531862e+00 9.01017308e-01
4.33963209e-01 1.23470098e-01 2.85351455e-01 -1.05783737e+00
4.49361086e-01 -4.02061045e-01 -5.13234138e-01 4.06149566e-01
-7.66471863e-01 -8.60802606e-02 7.12666571e-01 -1.78509593e-01
-1.59786236e+00 1.22996718e-01 2.64043033e-01 -3.38940918e-01
-2.67744195e-02 -6.52337223e-02 -5.29059768e-01 -2.42380694e-01
3.63254786e-01 4.12186623e-01 -3.10073495e-01 -3.22829515e-01
3.18007857e-01 4.91784513e-01 6.34715974e-01 -2.53579944e-01
1.34799325e+00 1.16370034e+00 -9.38060135e-02 -7.13219285e-01
-1.01987767e+00 -4.10019606e-01 -6.19395196e-01 -3.18012387e-01
7.52031028e-01 -5.44965148e-01 -3.10038239e-01 8.18930447e-01
-1.14662278e+00 -9.21374202e-01 -1.80877820e-01 -2.71856725e-01
-1.01538170e-02 4.11817580e-01 -1.31946459e-01 -8.37828517e-01
-4.75361526e-01 -1.00525701e+00 1.41048193e+00 4.72468615e-01
2.22697467e-01 -8.08747470e-01 -8.96317735e-02 2.40075991e-01
5.15219271e-01 6.24327548e-02 9.37629521e-01 -1.08986326e-01
-1.00356460e+00 1.59906775e-01 -8.40920985e-01 3.37933540e-01
6.00032985e-01 -2.84409612e-01 -1.33434987e+00 -3.61370683e-01
4.53831665e-02 -3.74344178e-02 1.35096836e+00 4.37485874e-01
9.75893378e-01 -1.71087995e-01 -5.62747300e-01 9.16606843e-01
1.28911626e+00 2.39383399e-01 8.65959764e-01 3.62269461e-01
9.76771891e-01 4.01428312e-01 7.82243073e-01 3.10875744e-01
4.31784332e-01 5.50692856e-01 6.40380085e-01 -9.03088868e-01
-6.88218236e-01 2.47347623e-01 3.05580467e-01 1.74007684e-01
1.60469070e-01 -4.96792823e-01 -5.97929597e-01 4.14251357e-01
-1.96545601e+00 -6.92368925e-01 -5.53962439e-02 2.13136721e+00
6.80176198e-01 -3.48186344e-02 -5.20541489e-01 -5.97267263e-02
4.13251877e-01 7.03042209e-01 -7.60700107e-01 -1.47271097e-01
-2.49315545e-01 1.36058047e-01 8.05596411e-01 7.55273044e-01
-9.16346729e-01 1.08480585e+00 5.47058344e+00 6.88939035e-01
-1.19181728e+00 7.15695247e-02 3.89419675e-01 1.24939248e-01
-5.26853800e-01 3.96820068e-01 -6.06944799e-01 4.00376886e-01
8.80198553e-02 4.25954431e-01 7.38126397e-01 4.31907982e-01
2.75929600e-01 -7.67813861e-01 -6.73008263e-01 6.74302518e-01
2.17162520e-01 -1.21822393e+00 -9.94032323e-02 3.78591381e-02
7.65825510e-01 -1.40301445e-02 -1.66490749e-01 2.80990601e-01
3.48583907e-01 -8.16627800e-01 7.60755062e-01 7.63820350e-01
5.35292506e-01 -3.99230570e-01 7.31330872e-01 2.10758910e-01
-1.43762672e+00 -1.80498868e-01 -1.39336348e-01 1.86726630e-01
2.75338113e-01 1.00403762e+00 -9.15935993e-01 7.37029612e-01
8.41255307e-01 2.35768795e-01 -4.08867657e-01 6.42322659e-01
-3.31554174e-01 3.44621480e-01 -1.11760221e-01 5.75689673e-01
-2.91715451e-02 -3.79750043e-01 5.05564451e-01 1.12076747e+00
5.41207902e-02 1.11321039e-01 1.77672252e-01 8.56845975e-01
-6.42346889e-02 -4.52015162e-01 -3.07202011e-01 5.90180278e-01
6.38535559e-01 1.39780068e+00 -9.53597963e-01 -5.06593764e-01
-4.22434479e-01 1.35734224e+00 1.52066380e-01 7.80770302e-01
-1.13728714e+00 -4.44518477e-01 8.42788935e-01 -1.24364346e-02
2.98643440e-01 -1.02618597e-01 -6.72785163e-01 -6.94480777e-01
1.99774168e-02 -7.19772398e-01 -3.69700566e-02 -1.00476897e+00
-9.50825512e-01 4.97367233e-01 -1.76890343e-01 -9.94928718e-01
3.89566362e-01 -2.89064884e-01 -1.02517712e+00 1.14948928e+00
-2.15740252e+00 -1.46399415e+00 -1.15850484e+00 6.15588129e-01
4.07912612e-01 8.37805867e-02 5.20011425e-01 1.81378320e-01
-5.41395009e-01 4.33088779e-01 2.59216756e-01 -8.13684314e-02
1.08808708e+00 -1.23935258e+00 3.47005695e-01 1.18616605e+00
-3.89765024e-01 3.71312946e-01 5.87906778e-01 -9.80008841e-01
-1.24264205e+00 -1.16465092e+00 4.23799723e-01 -2.49168336e-01
2.81729341e-01 -4.39776480e-01 -1.03046060e+00 3.92933637e-01
-6.98015988e-02 -5.21918721e-02 3.22250724e-01 -2.49799594e-01
-2.10241199e-01 -5.22828817e-01 -9.18198287e-01 7.86868691e-01
1.08693647e+00 -5.03098071e-01 -2.96091199e-01 2.66963303e-01
7.19930470e-01 -6.06243193e-01 -4.87421034e-03 6.34709179e-01
7.42705882e-01 -1.51948118e+00 1.17057288e+00 1.99175209e-01
2.04356357e-01 -6.15791380e-01 -2.54640669e-01 -1.16051888e+00
-2.47230813e-01 -5.16457856e-01 -3.79716486e-01 1.12488592e+00
1.35600157e-02 -9.35925961e-01 7.97415197e-01 2.69120663e-01
-6.96449995e-01 -9.26584363e-01 -7.75576591e-01 -5.29523253e-01
-4.72159028e-01 -1.80871516e-01 8.13418508e-01 5.21320522e-01
-1.11048067e+00 4.57732156e-02 -5.83569467e-01 6.82582676e-01
6.13504291e-01 8.25527787e-01 1.19188011e+00 -1.14387059e+00
-3.16587269e-01 -2.24380821e-01 5.54244578e-01 -1.15667677e+00
2.61388570e-01 -4.26290393e-01 6.85069263e-01 -1.81758988e+00
3.76611769e-01 -8.37658107e-01 -2.58197576e-01 8.99534583e-01
-4.66118485e-01 3.83119464e-01 1.55058444e-01 4.74454999e-01
-2.12422907e-01 7.77643800e-01 1.35064614e+00 -8.49437842e-04
-4.90763426e-01 1.43432677e-01 -8.17718744e-01 7.26514578e-01
6.93047106e-01 -4.01135564e-01 -5.28426111e-01 -5.71235836e-01
-3.43345553e-01 -1.86191097e-01 6.73180461e-01 -8.57132375e-01
7.51347765e-02 -3.71874422e-01 5.53609431e-01 -8.31432283e-01
7.25839198e-01 -8.94981444e-01 -1.06047995e-01 3.75720412e-01
3.22698385e-01 -4.76746470e-01 9.12034437e-02 6.79063737e-01
2.28721380e-01 2.22457066e-01 7.94730604e-01 1.33333191e-01
-7.23603964e-01 2.13055119e-01 -3.53297025e-01 -4.39463645e-01
9.34731662e-01 -5.36538303e-01 -6.22658074e-01 -5.81229091e-01
-1.75969899e-01 1.40947387e-01 6.12008750e-01 2.43453890e-01
7.25236833e-01 -8.90500844e-01 -5.16404510e-01 4.54216778e-01
-1.99327216e-01 3.22489411e-01 2.96303630e-01 8.76599610e-01
-3.54246885e-01 9.88384336e-02 -5.49994316e-03 -3.69960338e-01
-1.52393270e+00 1.56370908e-01 5.21945775e-01 -3.55106682e-01
-8.93418014e-01 7.18357801e-01 1.12688839e+00 -4.63885009e-01
3.31578493e-01 -2.44784102e-01 1.69526085e-01 -7.33125061e-02
3.14962178e-01 3.56596410e-01 1.62019342e-01 -5.69182158e-01
-3.09122682e-01 3.60402673e-01 5.70369512e-03 6.05118237e-02
1.24092090e+00 -3.91844273e-01 -2.76822776e-01 1.06826149e-01
8.62312019e-01 3.35933924e-01 -1.76743519e+00 -2.37030774e-01
-5.30935347e-01 -6.32340908e-01 2.26942793e-01 -1.12010777e+00
-1.26610696e+00 5.67231655e-01 7.13054240e-01 -3.36792283e-02
1.64451194e+00 -1.21728405e-01 1.28937542e+00 2.83431262e-01
1.48214102e-01 -1.05648363e+00 2.17417270e-01 7.31973350e-01
1.01295829e+00 -1.18151999e+00 3.83773923e-01 -5.68821490e-01
-4.39441472e-01 9.49256659e-01 5.84960163e-01 4.70290370e-02
5.94417632e-01 6.26876950e-01 3.00776601e-01 -2.76423842e-01
-2.19521984e-01 -1.62511826e-01 3.14153522e-01 6.51924074e-01
-2.33101949e-01 1.90441862e-01 4.73522618e-02 4.60654855e-01
7.74123818e-02 -3.02915752e-01 4.69624788e-01 1.19836068e+00
-6.01012468e-01 -1.06859338e+00 -8.24824393e-01 5.63450396e-01
2.86169738e-01 -3.28884035e-01 -6.49120450e-01 7.51705706e-01
2.16923982e-01 8.19124460e-01 -1.33156300e-01 -4.69615966e-01
5.64499944e-03 -3.31866741e-01 3.34351719e-03 -5.12235999e-01
-3.68396997e-01 6.21900409e-02 -7.97815472e-02 -8.02872002e-01
-8.98717269e-02 -3.16235662e-01 -1.46124041e+00 -3.36937606e-01
-3.89153928e-01 -2.15638071e-01 5.29451132e-01 1.21704495e+00
3.21653545e-01 8.82983446e-01 7.53186166e-01 -1.38661301e+00
-9.37420875e-02 -6.30186677e-01 -5.61353624e-01 1.09874129e-01
7.92367458e-01 -8.32941651e-01 -4.11002606e-01 -1.53356075e-01] | [10.846863746643066, -4.100343704223633] |
c4660f6d-b917-4602-ab08-83fdb7807f3d | conditions-for-open-ended-evolution-in | 2004.02720 | null | https://arxiv.org/abs/2004.02720v1 | https://arxiv.org/pdf/2004.02720v1.pdf | Conditions for Open-Ended Evolution in Immigration Games | The Immigration Game (invented by Don Woods in 1971) extends the solitaire Game of Life (invented by John Conway in 1970) to enable two-player competition. The Immigration Game can be used in a model of evolution by natural selection, where fitness is measured with competitions. The rules for the Game of Life belong to the family of semitotalistic rules, a family with 262,144 members. Woods' method for converting the Game of Life into a two-player game generalizes to 8,192 members of the family of semitotalistic rules. In this paper, we call the original Immigration Game the Life Immigration Game and we call the 8,192 generalizations Immigration Games (including the Life Immigration Game). The question we examine here is, what are the conditions for one of the 8,192 Immigration Games to be suitable for modeling open-ended evolution? Our focus here is specifically on conditions for the rules, as opposed to conditions for other aspects of the model of evolution. In previous work, it was conjectured that Turing-completeness of the rules for the Game of Life may have been necessary for the success of evolution using the Life Immigration Game. Here we present evidence that Turing-completeness is a sufficient condition on the rules of Immigration Games, but not a necessary condition. The evidence suggests that a necessary and sufficient condition on the rules of Immigration Games, for open-ended evolution, is that the rules should allow growth. | ['Peter D. Turney'] | 2020-04-06 | null | null | null | null | ['solitaire'] | ['playing-games'] | [ 1.48127787e-02 4.00823094e-02 2.37762481e-01 2.69956499e-01
3.67028147e-01 -7.67931819e-01 5.22923052e-01 -2.46270329e-01
-7.62438893e-01 1.07304299e+00 -9.71653163e-02 -1.02435732e+00
-3.80787164e-01 -1.05158377e+00 -4.42226708e-01 -6.79020047e-01
-2.32877776e-01 4.52421248e-01 5.30228913e-01 -9.58944380e-01
2.53141105e-01 3.25335622e-01 -1.99489176e+00 -3.09685796e-01
8.41045499e-01 2.24711388e-01 2.54829198e-01 1.15608382e+00
-8.17207620e-02 5.02500951e-01 -7.35954344e-01 -4.74274457e-01
4.91525710e-01 -1.28405643e+00 -9.02158976e-01 -1.38191819e-01
-4.97524202e-01 1.21321186e-01 -6.23243824e-02 1.03208244e+00
3.08499664e-01 3.09935808e-01 7.38926768e-01 -1.10915625e+00
-5.07293284e-01 6.45110190e-01 -1.58966333e-01 4.03566003e-01
4.55400616e-01 3.36802714e-02 1.08649886e+00 -3.54615390e-01
6.75923645e-01 9.18680370e-01 5.83554268e-01 8.18601429e-01
-9.02009308e-01 -3.20092231e-01 -2.47791782e-01 -2.31864497e-01
-1.65569901e+00 -2.10044071e-01 -1.04314890e-02 -4.26235676e-01
1.03589463e+00 9.42795396e-01 1.46648991e+00 4.33448017e-01
6.90966010e-01 -1.73771568e-02 1.13308036e+00 -7.86533713e-01
4.09517646e-01 -1.62877798e-01 -2.14047685e-01 5.07783771e-01
9.38956618e-01 5.97155213e-01 -2.02773690e-01 -3.35702389e-01
1.18351626e+00 -7.40011275e-01 -4.36920486e-02 -1.07267216e-01
-6.73982561e-01 7.89388239e-01 -8.06607306e-02 6.27427459e-01
-1.46809116e-01 6.35965094e-02 1.24709271e-01 7.70574391e-01
1.60639033e-01 1.04644048e+00 -6.14462256e-01 -2.85662919e-01
-3.88259500e-01 4.15557384e-01 1.03556144e+00 5.66659689e-01
4.89910334e-01 2.57163569e-02 5.07483542e-01 3.67212921e-01
2.04368725e-01 1.46548837e-01 6.09439135e-01 -7.98233986e-01
-3.35945696e-01 4.27069008e-01 9.79225859e-02 -3.66506159e-01
-6.32938564e-01 -4.93465155e-01 -4.45784807e-01 2.64899701e-01
7.23188400e-01 -2.36113086e-01 -4.30663615e-01 2.18455291e+00
2.13764027e-01 -1.08052753e-01 2.37619251e-01 5.02903700e-01
4.45533127e-01 7.28590071e-01 -2.75818169e-01 -7.89452910e-01
1.17486012e+00 -3.00609440e-01 -1.57356799e-01 2.87057012e-01
8.83839965e-01 -6.30498409e-01 1.01309741e+00 3.85006100e-01
-1.12868857e+00 -2.47503236e-01 -1.08030665e+00 5.99637032e-01
-2.32312843e-01 -6.56909287e-01 7.72450507e-01 1.20169342e+00
-1.31802309e+00 4.24806207e-01 -4.71399456e-01 -9.34026122e-01
-7.31525660e-01 5.32263577e-01 -1.96188688e-01 5.90705276e-01
-1.47884452e+00 1.13129437e+00 5.66475928e-01 -2.68104851e-01
-7.80044019e-01 1.96538419e-01 -4.14806068e-01 1.00915059e-01
4.90784466e-01 -7.59639204e-01 1.02271128e+00 -1.17318535e+00
-1.42974031e+00 1.22323418e+00 4.78741616e-01 -2.66740233e-01
3.53394777e-01 7.43660927e-01 -3.39681685e-01 -1.54431313e-01
7.19397888e-02 2.66122311e-01 9.01810005e-02 -1.09384871e+00
-5.75794637e-01 -1.37480661e-01 6.83758378e-01 4.50832784e-01
-3.42525281e-02 3.62100750e-01 -1.28090426e-01 -4.26937371e-01
-1.10740075e-02 -1.15997612e+00 -3.52407366e-01 -4.89843220e-01
1.61870331e-01 -3.54204476e-01 -1.70724943e-01 -1.14606313e-01
1.33881497e+00 -2.10020113e+00 2.19263792e-01 1.00412086e-01
1.66670792e-02 2.37100441e-02 -1.27709821e-01 6.41838253e-01
-2.26333261e-01 6.65648341e-01 -2.42575929e-01 2.77231991e-01
2.65332032e-02 6.67308390e-01 2.07955599e-01 5.10559678e-01
-1.25299990e-01 5.70086479e-01 -6.27161622e-01 -2.44047880e-01
-3.23419988e-01 -5.50846532e-02 -8.40897202e-01 -7.54397213e-02
-2.20023721e-01 2.91283518e-01 -2.40194842e-01 1.76381290e-01
2.16929764e-01 1.50790244e-01 2.02322900e-01 1.00775647e+00
-6.54853344e-01 1.75929084e-01 -1.09333944e+00 1.06152880e+00
-2.61301035e-03 2.00398102e-01 1.79289039e-02 -6.97618961e-01
9.58262265e-01 3.47593427e-01 4.10375744e-01 -4.72431123e-01
6.00650966e-01 4.88073617e-01 8.26562941e-01 -3.28494728e-01
5.91382325e-01 -8.25533271e-01 -5.80867767e-01 8.15894067e-01
-3.92858386e-02 -6.07313156e-01 7.77400434e-01 1.06173784e-01
1.05926907e+00 8.25189129e-02 6.01710141e-01 -8.42834294e-01
4.72919464e-01 3.31502408e-02 9.84922886e-01 8.62784326e-01
-2.40035191e-01 2.93316185e-01 5.79569221e-01 -4.60805178e-01
-1.16378152e+00 -1.02737546e+00 -7.72626651e-03 1.00362277e+00
4.54144955e-01 -6.64173543e-01 -9.26518917e-01 6.78687170e-02
-3.60868484e-01 8.03284943e-01 -7.52145946e-01 -5.23815036e-01
-3.94728750e-01 -1.05964303e+00 8.68718624e-01 -3.94227430e-02
2.63798624e-01 -1.05801058e+00 -1.06751204e+00 1.48573920e-01
-1.89032525e-01 -3.55872154e-01 -3.52642626e-01 5.17909467e-01
-6.33325577e-01 -1.03447914e+00 -2.93690771e-01 -6.26978278e-01
4.43305761e-01 2.90805325e-02 8.97108138e-01 7.81157315e-01
-2.63733000e-01 3.25889796e-01 -5.51526904e-01 -4.52585876e-01
-8.74958992e-01 -7.89182410e-02 5.26478112e-01 -5.70503354e-01
7.46958032e-02 -6.02744877e-01 -7.66235888e-02 5.52401066e-01
-1.29979253e+00 -2.17355117e-02 7.64647946e-02 9.23494279e-01
9.49465036e-02 4.34776604e-01 3.11518431e-01 -3.40831339e-01
7.45539308e-01 -3.13568771e-01 -5.95587015e-01 1.95886508e-01
-4.26791847e-01 -2.50264108e-02 6.24463677e-01 -4.25557405e-01
-8.03088188e-01 -3.56703937e-01 -2.68054605e-01 5.28644145e-01
1.82045624e-01 7.50083029e-01 -2.13560343e-01 -2.56111950e-01
8.97431016e-01 3.62733632e-01 -6.70882091e-02 -2.08219811e-01
6.02157041e-02 3.69968057e-01 2.58462548e-01 -9.02522624e-01
4.39516008e-01 7.09693357e-02 1.78976342e-01 -1.02452481e+00
1.88996624e-02 1.32257575e-02 -4.75398868e-01 -4.07707512e-01
7.10361302e-01 -4.96168613e-01 -7.27161705e-01 4.74779278e-01
-7.54024804e-01 -4.64238703e-01 -8.59903514e-01 4.12947655e-01
-1.07903707e+00 2.75485367e-01 -5.93867242e-01 -1.13121569e+00
7.60445073e-02 -7.73791432e-01 1.71595618e-01 4.12017733e-01
-3.10375124e-01 -8.64385366e-01 4.60841924e-01 -8.48590657e-02
2.90974319e-01 2.66083740e-02 1.11450934e+00 -6.74136698e-01
2.52727559e-03 -1.93514023e-02 4.58481908e-01 -3.36776599e-02
-7.61144445e-04 4.58513528e-01 -1.40713099e-02 -2.31048629e-01
4.99416411e-01 4.85558398e-02 6.61142528e-01 2.70698220e-01
2.33426187e-02 -2.94198059e-02 8.35852996e-02 3.86199296e-01
1.47003138e+00 8.45101118e-01 7.90078402e-01 5.63180447e-01
-9.54938829e-02 6.85822070e-01 3.15175891e-01 4.96817112e-01
3.20787132e-01 5.03853858e-01 3.13618004e-01 1.94410086e-01
4.41182435e-01 6.14689477e-02 4.62596685e-01 1.31847394e+00
-5.88684201e-01 -5.55918932e-01 -1.00154424e+00 4.93789583e-01
-1.67464101e+00 -9.71232355e-01 -2.19317675e-01 2.46024179e+00
8.55711281e-01 2.91340917e-01 6.23883426e-01 2.06213221e-01
8.21555257e-01 -4.19818431e-01 -1.63454086e-01 -1.08692193e+00
-4.82787997e-01 2.17389315e-01 4.81277406e-01 8.62687588e-01
-4.18193132e-01 8.23189557e-01 7.96229839e+00 6.44860029e-01
-6.28877819e-01 1.73707157e-01 2.91837841e-01 -1.10464819e-01
-4.84951109e-01 5.54781377e-01 -6.09459341e-01 4.07095253e-01
8.44730616e-01 -7.65371978e-01 5.20828187e-01 1.24983013e-01
1.63844839e-01 -5.53090513e-01 -4.77774024e-01 3.48251343e-01
4.04744744e-02 -6.63449287e-01 -1.47120789e-01 4.07621950e-01
7.94086158e-01 -4.91570443e-01 -3.04253399e-01 3.42486173e-01
8.59214008e-01 -9.35143769e-01 8.35449040e-01 2.71685421e-01
6.49802089e-01 -9.01460588e-01 5.80455482e-01 8.79637718e-01
-1.06420732e+00 -8.55027512e-02 -5.03553152e-01 -9.38158214e-01
2.51204669e-01 6.30382672e-02 -2.65417337e-01 6.10875964e-01
3.61805201e-01 -2.99556017e-01 -3.38056892e-01 1.15392470e+00
-1.09008983e-01 4.56315011e-01 -5.85143209e-01 -3.90874803e-01
2.36897856e-01 -3.80943954e-01 8.17797303e-01 7.28085518e-01
6.15318239e-01 5.73824286e-01 -7.05167204e-02 6.21860206e-01
4.65700775e-01 3.41917068e-01 -5.73742330e-01 -2.42026567e-01
1.48319378e-01 5.75170219e-01 -1.18866885e+00 -9.68180448e-02
-1.22308090e-01 6.69865191e-01 -2.40677878e-01 -8.87789577e-02
-8.03782582e-01 -2.64848143e-01 7.41900444e-01 3.12683970e-01
1.35386989e-01 -3.61733168e-01 -1.81563064e-01 -1.22840655e+00
-5.19080281e-01 -8.41924608e-01 3.74646276e-01 -5.84383130e-01
-9.13033962e-01 5.85015059e-01 2.39349514e-01 -7.05579579e-01
-4.02542204e-01 -6.07901394e-01 -7.29456663e-01 7.83801496e-01
-3.51575226e-01 -5.91781616e-01 3.69362861e-01 5.40978611e-01
2.29635283e-01 -1.51969284e-01 7.90717363e-01 -2.67022312e-01
-6.30192220e-01 3.38311166e-01 2.78567612e-01 -4.27471638e-01
1.18074223e-01 -1.22100949e+00 6.09857321e-01 1.07985425e+00
6.32827654e-02 8.33044648e-01 1.14836657e+00 -8.95118535e-01
-1.12507927e+00 -3.35484415e-01 1.08465385e+00 -1.20118514e-01
4.98033375e-01 -3.09575945e-01 -3.81242067e-01 3.87548178e-01
1.92676842e-01 -7.63376057e-01 7.24352181e-01 -8.92740637e-02
-2.53071841e-02 4.47761178e-01 -1.07346082e+00 1.00457644e+00
1.38708603e+00 -2.09689841e-01 -6.30266428e-01 -2.74342429e-02
6.94693625e-01 -3.01748104e-02 -5.47556877e-01 2.91591555e-01
7.60150909e-01 -1.13042390e+00 5.38572252e-01 -6.46708131e-01
6.66307434e-02 -4.57881272e-01 -1.46840230e-01 -1.22008038e+00
-7.51767218e-01 -8.69172394e-01 7.50909865e-01 7.82467306e-01
4.94317442e-01 -1.02698660e+00 4.37015235e-01 4.15566742e-01
1.14910766e-01 -5.49383759e-01 -1.25635386e+00 -1.31329775e+00
6.91376567e-01 -1.23411760e-01 5.74991047e-01 9.41077232e-01
4.33763653e-01 4.02637929e-01 -4.17474002e-01 -4.88685399e-01
8.75536948e-02 -3.73024464e-01 5.45590341e-01 -1.48826849e+00
-9.98439193e-01 -7.24126577e-01 -6.82970107e-01 -6.00004077e-01
-2.16212511e-01 -6.16393924e-01 2.36972004e-01 -1.34033334e+00
1.07172310e-01 -3.71705592e-01 -1.47886783e-01 6.35482743e-02
2.94147767e-02 2.59578496e-01 4.78490204e-01 2.47493997e-01
-1.93085745e-01 2.92114526e-01 1.02715266e+00 3.90263587e-01
-2.92914987e-01 -4.39047702e-02 -1.06186426e+00 5.92542768e-01
8.50429118e-01 -4.15631235e-01 -4.37724769e-01 9.07972828e-02
1.08708084e+00 7.74518251e-02 -2.15175390e-01 -8.50070894e-01
2.05213636e-01 -4.82520908e-01 -2.98691034e-01 9.10757333e-02
1.28496408e-01 -4.17487919e-01 1.06401181e+00 9.96318519e-01
-5.25956228e-02 3.82220715e-01 2.62979776e-01 4.61116880e-02
1.90205857e-01 -8.15744877e-01 6.65347993e-01 -4.30779368e-01
-3.81467968e-01 -2.23853141e-01 -1.25762463e+00 1.62745640e-02
1.30757082e+00 -6.27297044e-01 -1.31821588e-01 -4.20518607e-01
-1.10785174e+00 -1.08068287e-01 1.18363762e+00 4.39409651e-02
5.47691360e-02 -1.00403666e+00 -7.65751481e-01 1.12107769e-01
-4.77272645e-02 -6.32913768e-01 -2.24112868e-01 6.73896790e-01
-1.05442595e+00 1.16320893e-01 -7.07652807e-01 6.24031387e-02
-1.31624317e+00 7.16747403e-01 5.24556637e-01 -1.81581646e-01
-3.99938263e-02 1.08193326e+00 4.11146581e-01 -4.11253750e-01
-3.97284031e-01 2.05009859e-02 -1.55599732e-02 -2.11387753e-01
3.44462991e-01 1.23268127e-01 -3.73689085e-01 -9.28966820e-01
-3.63934547e-01 5.62859058e-01 3.20993930e-01 -7.81420231e-01
1.09355474e+00 -2.40677029e-01 -6.68165147e-01 7.30922163e-01
4.34228599e-01 4.00389098e-02 -3.34975600e-01 2.94425279e-01
-3.47258300e-01 -2.22529098e-01 -5.60590804e-01 -6.15163743e-01
-4.21166480e-01 4.43065554e-01 1.97212044e-02 8.37172031e-01
1.38998520e+00 -1.39255419e-01 2.70797879e-01 1.11618273e-01
9.46893215e-01 -8.05129468e-01 -4.35317487e-01 8.24853420e-01
6.69001400e-01 -2.35355690e-01 -2.44000107e-01 -4.39486533e-01
-4.71853763e-01 8.72682333e-01 5.54073155e-01 -2.96640933e-01
6.35876298e-01 4.71316814e-01 -2.70415336e-01 -1.79059207e-02
-1.16532791e+00 -5.46858847e-01 -3.54788959e-01 4.02434021e-01
3.77549082e-01 3.29901516e-01 -1.44478774e+00 7.74191082e-01
-6.63771331e-01 -1.25402352e-02 9.62952375e-01 1.00535822e+00
-8.36771071e-01 -1.25517464e+00 -5.76785266e-01 3.26030821e-01
-3.31032306e-01 -1.09777309e-01 -1.08758724e+00 8.23599458e-01
7.64363587e-01 1.08468699e+00 1.99537665e-01 -7.14593709e-01
1.25846058e-01 -1.05819598e-01 7.18474627e-01 -6.22592330e-01
-1.04273796e+00 2.77561303e-02 2.66210377e-01 2.21564889e-01
-1.50568306e-01 -7.23603845e-01 -1.09510779e+00 -9.06622410e-01
-9.12621140e-01 7.34214604e-01 2.98093379e-01 9.45554972e-01
-1.77716017e-01 1.84285969e-01 2.75584519e-01 -1.72358766e-01
-3.49357069e-01 -6.85879350e-01 -1.18069208e+00 -9.10259485e-02
-4.77072418e-01 -4.84984428e-01 -6.09381139e-01 -8.93499106e-02] | [5.509002685546875, 4.08230447769165] |
3502ee6b-4db1-45d9-a86c-f366428ea2a5 | mixcycle-mixup-assisted-semi-supervised-3d | 2303.09219 | null | https://arxiv.org/abs/2303.09219v1 | https://arxiv.org/pdf/2303.09219v1.pdf | MixCycle: Mixup Assisted Semi-Supervised 3D Single Object Tracking with Cycle Consistency | 3D single object tracking (SOT) is an indispensable part of automated driving. Existing approaches rely heavily on large, densely labeled datasets. However, annotating point clouds is both costly and time-consuming. Inspired by the great success of cycle tracking in unsupervised 2D SOT, we introduce the first semi-supervised approach to 3D SOT. Specifically, we introduce two cycle-consistency strategies for supervision: 1) Self tracking cycles, which leverage labels to help the model converge better in the early stages of training; 2) forward-backward cycles, which strengthen the tracker's robustness to motion variations and the template noise caused by the template update strategy. Furthermore, we propose a data augmentation strategy named SOTMixup to improve the tracker's robustness to point cloud diversity. SOTMixup generates training samples by sampling points in two point clouds with a mixing rate and assigns a reasonable loss weight for training according to the mixing rate. The resulting MixCycle approach generalizes to appearance matching-based trackers. On the KITTI benchmark, based on the P2B tracker, MixCycle trained with $\textbf{10%}$ labels outperforms P2B trained with $\textbf{100%}$ labels, and achieves a $\textbf{28.4%}$ precision improvement when using $\textbf{1%}$ labels. Our code will be publicly released. | ['Mathieu Salzmann', 'Yanning Zhang', "Chu'ai Zhang", 'Kun Sun', 'Jiaqi Yang', 'Qiao Wu'] | 2023-03-16 | null | null | null | null | ['3d-single-object-tracking'] | ['computer-vision'] | [-1.16268426e-01 -1.06311016e-01 -2.92368531e-01 -2.94127434e-01
-6.82062984e-01 -4.90570813e-01 4.37296212e-01 -2.55501177e-02
-4.12542224e-01 4.23663020e-01 -4.63203639e-01 -3.04527223e-01
3.79779674e-02 -5.08499265e-01 -9.97544408e-01 -6.92226112e-01
9.91631076e-02 6.03602111e-01 5.72284579e-01 -3.95531692e-02
-5.04214549e-03 5.60752153e-01 -1.74249744e+00 -3.38994205e-01
9.36523199e-01 1.15233624e+00 2.57174551e-01 3.10143709e-01
-2.20674947e-01 3.90433848e-01 -4.46226388e-01 -4.92679596e-01
6.19242907e-01 -8.26850161e-02 -2.22587794e-01 1.61788374e-01
7.67143846e-01 -4.04640958e-02 -2.34214276e-01 1.22801614e+00
3.13910276e-01 2.58171886e-01 5.39114475e-01 -1.50676000e+00
-1.49223745e-01 5.00460789e-02 -7.60434091e-01 1.33111775e-01
-1.17880479e-01 4.53887939e-01 6.59215271e-01 -9.84669924e-01
3.81118923e-01 1.14663446e+00 1.05886769e+00 5.30863225e-01
-1.22606874e+00 -1.05423307e+00 4.00425434e-01 -5.62933087e-02
-1.30752134e+00 -3.98371249e-01 7.62372136e-01 -7.13142693e-01
5.64319015e-01 1.34011090e-01 6.46402121e-01 7.57266581e-01
-1.04947299e-01 6.76804125e-01 9.27772403e-01 -2.59560853e-01
1.71195164e-01 1.64487094e-01 1.03926674e-01 6.80364907e-01
4.64447141e-01 4.60958958e-01 -3.84380162e-01 1.10097580e-01
7.28879869e-01 1.40119093e-02 6.12969473e-02 -6.44457281e-01
-1.19962442e+00 6.65389240e-01 5.24688184e-01 -7.41911083e-02
-3.78006324e-02 3.69915694e-01 2.22699806e-01 1.18245497e-01
5.34053981e-01 1.96024477e-01 -2.86634773e-01 -1.35097101e-01
-9.90642130e-01 1.52557835e-01 2.42027760e-01 1.46048284e+00
1.04932356e+00 1.08667828e-01 -2.08081409e-01 7.34999537e-01
5.36027730e-01 9.43813443e-01 4.52666730e-02 -9.66966867e-01
5.23833513e-01 6.29226565e-01 4.11779761e-01 -7.34702826e-01
-3.57504964e-01 -4.94681776e-01 -5.61714411e-01 4.94158953e-01
4.44322735e-01 -8.61516818e-02 -1.00945902e+00 1.75736904e+00
7.38677561e-01 1.98367879e-01 -2.67560303e-01 9.11309659e-01
6.26681328e-01 2.74395406e-01 6.60525411e-02 -9.24546570e-02
1.18275476e+00 -9.84913945e-01 -4.54205394e-01 -3.79474640e-01
6.08304143e-01 -8.52082610e-01 9.30890322e-01 9.88271460e-02
-8.07711840e-01 -9.41124618e-01 -1.03750288e+00 2.45375395e-01
-4.92681861e-02 3.38219523e-01 4.69799042e-01 8.61344993e-01
-7.93607235e-01 5.10438561e-01 -1.11024117e+00 -1.62579805e-01
6.27824903e-01 3.74195665e-01 -1.56142250e-01 -1.17167898e-01
-6.76843822e-01 9.01890457e-01 1.98052272e-01 2.17482150e-01
-7.79856563e-01 -8.23635995e-01 -7.77805984e-01 -4.72292691e-01
4.51645732e-01 -5.23015261e-01 1.18728781e+00 -4.93534833e-01
-1.33172178e+00 8.46620858e-01 -3.70728016e-01 -5.16002417e-01
7.96862245e-01 -3.45153272e-01 -2.91720748e-01 -1.38052404e-01
3.08262140e-01 9.37728047e-01 1.03489137e+00 -1.32268596e+00
-7.53128469e-01 -4.31268483e-01 -2.86730319e-01 2.69830748e-02
-6.78813607e-02 -1.76663846e-01 -8.75920355e-01 -5.64571619e-01
2.87353337e-01 -1.43785083e+00 -2.90165007e-01 2.86170483e-01
-2.93309838e-01 -2.82186389e-01 9.86756623e-01 -2.41319180e-01
7.56473780e-01 -2.22894645e+00 -1.52406782e-01 1.99924648e-01
2.70728052e-01 3.08004290e-01 8.81476402e-02 -1.52332470e-01
1.55048326e-01 -2.91478902e-01 -7.42490962e-02 -8.16805422e-01
2.57452428e-01 2.08532751e-01 -1.35298774e-01 4.84441489e-01
1.70219362e-01 7.60847628e-01 -1.07236648e+00 -4.94214177e-01
4.72215056e-01 2.41177872e-01 -3.15278590e-01 -8.40378776e-02
-3.61447871e-01 6.28739893e-01 -5.10577857e-01 8.56998742e-01
8.37916553e-01 -3.55614185e-01 -3.66871834e-01 -1.72498807e-01
-3.69478434e-01 3.23506282e-03 -1.25418258e+00 1.55293226e+00
-1.57338202e-01 6.12134218e-01 -5.61856292e-02 -6.15683317e-01
1.26791823e+00 7.96783119e-02 7.06068933e-01 -5.02627909e-01
2.16017753e-01 2.65967965e-01 -2.48150617e-01 -1.05516993e-01
5.53301573e-01 -1.44662157e-01 1.12009114e-02 1.79627419e-01
-1.19619079e-01 -3.78108203e-01 1.39461517e-01 5.05207777e-02
9.52274740e-01 4.83152241e-01 -3.11379701e-01 -5.37007600e-02
3.38507295e-01 3.21711212e-01 9.63945627e-01 7.44866252e-01
-5.02029300e-01 5.41991591e-01 -2.73926230e-03 -4.32676673e-01
-1.05970454e+00 -9.19953883e-01 -3.02949160e-01 7.26807237e-01
5.61623454e-01 -1.85445160e-01 -5.26410341e-01 -8.42100680e-01
3.70555460e-01 5.85339546e-01 -4.88114089e-01 -3.17432612e-01
-4.75414008e-01 -5.35955191e-01 3.03873390e-01 7.02292681e-01
6.07775927e-01 -8.16064000e-01 -6.00444496e-01 9.37277153e-02
-5.24715241e-03 -1.35840940e+00 -7.15851128e-01 2.03493655e-01
-1.00763750e+00 -9.68563616e-01 -6.48700297e-01 -4.82227534e-01
8.24508786e-01 5.58829427e-01 9.16325331e-01 6.79759681e-02
-7.76684210e-02 3.65969658e-01 -3.65351796e-01 -7.40717471e-01
-3.07722300e-01 -2.55725123e-02 3.99117976e-01 -5.04058152e-02
5.79549789e-01 -3.52168560e-01 -5.50990939e-01 7.61305153e-01
-4.14573520e-01 8.04725587e-02 5.30459583e-01 5.44711411e-01
9.07557189e-01 -1.87232658e-01 1.58506900e-01 -5.89854181e-01
-1.74722135e-01 -1.07145905e-01 -1.11582696e+00 -9.13432017e-02
-6.53310478e-01 2.94591114e-02 2.33066142e-01 -6.81863129e-01
-7.57044494e-01 4.74973798e-01 -5.61136417e-02 -1.32528543e+00
-2.79545095e-02 -8.91942903e-02 -2.29877792e-02 -5.02648234e-01
6.66249275e-01 4.16614190e-02 6.34190068e-02 -4.65405315e-01
3.07422519e-01 1.93724945e-01 6.63309336e-01 -6.43559456e-01
1.36000180e+00 6.79606974e-01 2.03700271e-03 -5.82809389e-01
-9.07186925e-01 -6.44037008e-01 -6.34491384e-01 -5.73621511e-01
8.33992541e-01 -1.05064774e+00 -7.74271965e-01 4.25624371e-01
-8.24469686e-01 -5.07782817e-01 -4.29774284e-01 7.13701487e-01
-4.38921124e-01 3.54448915e-01 -6.03517964e-02 -9.49171901e-01
-1.87340379e-01 -1.17672944e+00 1.16561329e+00 3.78317118e-01
6.68751374e-02 -6.43612623e-01 6.16524667e-02 4.39780176e-01
1.46011174e-01 3.28427315e-01 1.87987432e-01 -3.31074566e-01
-7.83498526e-01 -3.39178294e-01 -3.02695692e-01 4.37871724e-01
1.40275538e-01 -6.93584532e-02 -9.95806396e-01 -4.89328176e-01
-1.13110386e-01 -1.19483709e-01 7.45608449e-01 2.76155263e-01
8.75568688e-01 2.23527744e-01 -7.23754048e-01 5.99125564e-01
1.09356177e+00 1.51029542e-01 4.04597402e-01 3.90262365e-01
9.06243622e-01 3.25634241e-01 1.06381750e+00 2.65369087e-01
4.40909088e-01 8.23711991e-01 6.38014555e-01 -5.94598241e-02
-4.06779766e-01 -3.16384435e-01 2.77078122e-01 6.91408336e-01
-1.27661854e-01 3.62397909e-01 -9.00371552e-01 6.39417112e-01
-1.72244978e+00 -7.21325696e-01 -3.03868353e-01 2.44957447e+00
6.97391748e-01 6.47958577e-01 2.38314420e-01 1.00904912e-01
8.19843590e-01 -3.59317474e-02 -8.04685295e-01 2.90192902e-01
6.07801415e-02 1.26563251e-01 7.88322747e-01 2.32958078e-01
-1.16686666e+00 1.03230572e+00 4.88150263e+00 7.04146087e-01
-1.13888967e+00 1.92154393e-01 1.61245957e-01 -1.05616063e-01
4.71835546e-02 1.82981700e-01 -1.35211504e+00 8.22055101e-01
5.63826799e-01 1.17609859e-01 5.22330077e-03 1.03506207e+00
1.56183720e-01 -8.55742842e-02 -9.99925196e-01 1.02175856e+00
-2.75290400e-01 -1.25494063e+00 -5.04529715e-01 1.62952036e-01
7.82544255e-01 4.52118039e-01 -6.02759980e-03 4.57592636e-01
4.91540074e-01 -4.20332253e-01 1.08313692e+00 3.99865746e-01
7.60647118e-01 -4.60701108e-01 4.52901214e-01 4.51580107e-01
-1.62937975e+00 -2.13053543e-02 -3.78970325e-01 2.94555455e-01
2.69208133e-01 7.55355954e-01 -7.51838028e-01 6.33257687e-01
1.06115210e+00 7.50873089e-01 -6.58334434e-01 1.35619164e+00
-2.32680798e-01 5.15525818e-01 -6.56584740e-01 6.28390014e-02
7.60719776e-02 -2.75559902e-01 8.25195193e-01 9.85270381e-01
3.41631293e-01 -1.49018124e-01 4.38739955e-01 7.87223816e-01
7.97266588e-02 -3.32231402e-01 -4.19777036e-01 3.56229991e-01
7.10695803e-01 1.19323230e+00 -8.00314784e-01 -1.88958988e-01
-2.42354944e-01 4.71785605e-01 1.42956659e-01 1.15478605e-01
-1.05119610e+00 -2.17398122e-01 7.24351168e-01 1.81687042e-01
6.63900375e-01 -4.37626153e-01 -3.21922600e-01 -9.34816778e-01
2.61788964e-01 -5.09295821e-01 1.67346179e-01 -7.69344747e-01
-1.06569207e+00 6.73985720e-01 1.54330149e-01 -1.79531479e+00
2.04903334e-01 -4.38481659e-01 -4.36040074e-01 6.90730274e-01
-1.62770009e+00 -1.13288164e+00 -7.16701627e-01 4.54516262e-01
3.36575359e-01 4.73793820e-02 3.09031427e-01 5.74712813e-01
-6.65398836e-01 6.76558733e-01 -6.54348359e-02 -1.11563085e-02
7.10979164e-01 -1.06777787e+00 5.83847702e-01 8.76166344e-01
1.07253157e-01 5.19090891e-01 7.72254229e-01 -7.67203808e-01
-1.23757041e+00 -1.46607208e+00 6.73641443e-01 -7.65536785e-01
4.82364655e-01 -3.93031240e-01 -9.53864634e-01 7.45879412e-01
-3.60636264e-01 3.64727139e-01 1.76149264e-01 -3.30173224e-02
-3.26654315e-01 -3.62367511e-01 -1.12745559e+00 4.21545833e-01
1.23627341e+00 -2.04887956e-01 -4.60588247e-01 4.24313396e-01
8.26649129e-01 -8.28384817e-01 -8.80216658e-01 6.22239530e-01
2.89434373e-01 -6.36001468e-01 9.08733368e-01 -4.91687879e-02
-3.80057871e-01 -8.60344946e-01 3.84751484e-02 -8.78137827e-01
-2.68399090e-01 -7.83066213e-01 -1.54312164e-01 1.23358464e+00
2.12767705e-01 -6.09516799e-01 1.12658405e+00 4.12928700e-01
-5.52225411e-01 -3.70920002e-01 -1.09576714e+00 -1.13853133e+00
-7.94474557e-02 -7.06238747e-01 5.29114723e-01 7.74885416e-01
-6.22265697e-01 1.25892200e-02 -1.84876278e-01 3.83057863e-01
1.11689544e+00 6.02628067e-02 1.20076346e+00 -1.51057076e+00
-6.70901164e-02 -3.45384657e-01 -3.36000413e-01 -1.27545786e+00
-9.15094241e-02 -6.38512194e-01 4.56715792e-01 -1.16326845e+00
-9.04908031e-02 -1.24328625e+00 -3.26236039e-02 6.17784917e-01
-1.56179890e-01 3.70127648e-01 4.00952220e-01 4.69885498e-01
-6.56633079e-01 6.25842094e-01 1.11377656e+00 -1.62609503e-01
-2.39573419e-01 4.51217175e-01 -2.68642575e-01 5.88198721e-01
5.90280414e-01 -7.16298461e-01 -2.37453669e-01 -4.40914273e-01
4.19809148e-02 -1.73397571e-01 5.04229546e-01 -1.39361715e+00
4.13720429e-01 -1.33055508e-01 2.81403244e-01 -1.14315546e+00
5.58082163e-01 -8.61510754e-01 3.39221090e-01 3.61318737e-01
2.56045222e-01 6.12222403e-03 5.95681727e-01 7.49749601e-01
3.28107923e-02 -1.94814578e-01 9.18796957e-01 1.15803510e-01
-6.15323901e-01 5.80980420e-01 8.47058147e-02 -5.19169569e-02
1.09798813e+00 -5.92953205e-01 -1.39501184e-01 1.10280160e-02
-5.45607626e-01 6.36573136e-01 6.96996152e-01 6.01396799e-01
2.35154942e-01 -1.51998949e+00 -3.54159474e-01 1.34408534e-01
3.40021819e-01 5.19405067e-01 9.99300107e-02 1.09353495e+00
-2.12614134e-01 3.00555706e-01 8.16091001e-02 -1.20919311e+00
-1.11714959e+00 5.09507418e-01 3.75388294e-01 6.69305539e-03
-7.56748497e-01 9.32883382e-01 3.64665836e-02 -4.83613551e-01
4.33850348e-01 -4.85232711e-01 1.42123431e-01 -2.56742835e-01
2.50973791e-01 3.27721745e-01 2.26500586e-01 -7.05450058e-01
-4.03310865e-01 8.81917477e-01 -6.09248430e-02 3.44351977e-02
9.72078562e-01 1.47039527e-02 4.26690876e-01 3.39467794e-01
8.07447076e-01 -1.03115156e-01 -1.92373133e+00 -4.12928253e-01
1.58663407e-01 -5.31207144e-01 -1.84333511e-02 -3.62028390e-01
-1.14664257e+00 6.66481256e-01 7.87746906e-01 1.23143652e-02
7.22981274e-01 2.66806446e-02 7.33271062e-01 1.13048762e-01
5.28657377e-01 -9.91542161e-01 1.14045858e-01 4.76408958e-01
5.41937947e-01 -1.40316367e+00 9.84862894e-02 -3.21422994e-01
-5.43846965e-01 6.19188964e-01 7.75973082e-01 -3.22701572e-03
4.85617757e-01 5.21074906e-02 1.45810172e-01 -2.48437956e-01
-3.74291629e-01 -4.14717793e-01 4.19009149e-01 5.60044587e-01
-1.22999057e-01 -2.00782195e-01 9.15688872e-02 2.77181983e-01
-1.46866441e-01 3.48746702e-02 -1.21993169e-01 1.07125247e+00
-4.24949288e-01 -1.04626358e+00 -6.46331131e-01 2.96188533e-01
1.32702768e-01 3.88790995e-01 -9.47880000e-02 8.97282720e-01
4.02108878e-01 7.91188121e-01 1.01959556e-01 -4.37866032e-01
6.34811997e-01 -3.46151739e-02 5.00118911e-01 -5.37536085e-01
-4.58378583e-01 2.11454600e-01 -1.58907458e-01 -4.67991889e-01
-5.85609734e-01 -1.07347381e+00 -1.31951284e+00 -2.21111611e-01
-6.31979823e-01 1.33913592e-01 8.61188591e-01 8.83752704e-01
4.03970301e-01 4.88534451e-01 6.94099665e-01 -1.15690100e+00
-4.56398219e-01 -9.35594559e-01 -1.76631108e-01 3.08207095e-01
3.37543756e-01 -1.23104501e+00 -3.27240676e-01 6.53748512e-02] | [6.644373893737793, -2.280320882797241] |
435bd1d9-8fc1-4e56-88e1-0ed33efa8b90 | night-time-haze-and-glow-removal-using-deep | 1902.00855 | null | http://arxiv.org/abs/1902.00855v1 | http://arxiv.org/pdf/1902.00855v1.pdf | Night Time Haze and Glow Removal using Deep Dilated Convolutional Network | In this paper, we address the single image haze removal problem in a
nighttime scene. The night haze removal is a severely ill-posed problem
especially due to the presence of various visible light sources with varying
colors and non-uniform illumination. These light sources are of different
shapes and introduce noticeable glow in night scenes. To address these effects
we introduce a deep learning based DeGlow-DeHaze iterative architecture which
accounts for varying color illumination and glows. First, our convolution
neural network (CNN) based DeGlow model is able to remove the glow effect
significantly and on top of it a separate DeHaze network is included to remove
the haze effect. For our recurrent network training, the hazy images and the
corresponding transmission maps are synthesized from the NYU depth datasets and
consequently restored a high-quality haze-free image. The experimental results
demonstrate that our hybrid CNN model outperforms other state-of-the-art
methods in terms of computation speed and image quality. We also show the
effectiveness of our model on a number of real images and compare our results
with the existing night haze heuristic models. | ['Shiba Kuanar', 'Monalisa Bilas', 'Dwarikanath Mahapatra', 'K. R. Rao'] | 2019-02-03 | null | null | null | null | ['single-image-haze-removal'] | ['computer-vision'] | [ 6.73230961e-02 -5.32543659e-01 8.28146458e-01 -2.89795280e-01
-2.52005070e-01 -2.58304745e-01 3.07869732e-01 -6.17367148e-01
-2.81656057e-01 7.51182199e-01 9.90985036e-02 -1.11591175e-01
-1.40491471e-01 -7.38807261e-01 -6.49197459e-01 -1.28697217e+00
1.91320628e-01 -1.65262043e-01 3.74109656e-01 -8.05625796e-01
1.31298512e-01 2.53087431e-01 -1.93303990e+00 1.40659556e-01
1.19654441e+00 7.65071571e-01 5.20391107e-01 8.30927908e-01
3.63149673e-01 9.54249978e-01 -8.78822982e-01 -1.75877698e-02
6.84315443e-01 -4.16692495e-01 3.24655287e-02 3.68934542e-01
7.73142576e-01 -6.70974672e-01 -5.56439757e-01 1.29248011e+00
5.97620249e-01 3.97684902e-01 3.29347759e-01 -1.00913918e+00
-7.33283281e-01 -2.05442280e-01 -5.25789142e-01 3.38462859e-01
-2.92170614e-01 2.14271650e-01 2.72538424e-01 -1.11730897e+00
1.61350071e-01 8.97724807e-01 3.01559657e-01 4.33595806e-01
-5.72812140e-01 -7.52509654e-01 1.04427755e-01 4.59317148e-01
-1.31732047e+00 -6.09177113e-01 7.65857339e-01 -2.03477964e-02
8.28784525e-01 2.41980612e-01 7.09868193e-01 6.96780562e-01
5.48847973e-01 4.34911937e-01 1.07914126e+00 -5.05276918e-01
2.91119158e-01 -1.06149232e-02 -1.45593300e-01 6.23036861e-01
4.76246536e-01 2.83689529e-01 -4.61356997e-01 4.77764338e-01
4.76919770e-01 4.92880732e-01 -6.00946963e-01 2.28925526e-01
-6.59136891e-01 6.65032506e-01 8.27261150e-01 -9.12415388e-04
-2.54314244e-01 2.81256318e-01 -3.66880327e-01 4.18489486e-01
7.71402657e-01 3.33076119e-01 -1.60457224e-01 5.24274409e-01
-9.73119736e-01 3.35517377e-01 4.93277401e-01 8.54600251e-01
1.07876921e+00 6.80960834e-01 -6.59078211e-02 9.35182691e-01
3.93999755e-01 8.47103655e-01 1.44349188e-01 -1.03302610e+00
2.37395167e-01 1.20066114e-01 4.53492582e-01 -8.33158255e-01
-2.64511466e-01 -4.64335471e-01 -1.24461496e+00 8.46096516e-01
-1.89801618e-01 -1.85109377e-01 -1.64838982e+00 1.24536920e+00
6.88756034e-02 4.22829151e-01 1.75803274e-01 1.37718332e+00
1.02893186e+00 1.16230571e+00 -6.53991282e-01 -3.52905840e-01
9.67076063e-01 -1.43063080e+00 -1.43137729e+00 -5.33880532e-01
3.21433470e-02 -9.59921837e-01 5.78522086e-01 7.48087168e-01
-1.22344184e+00 -4.89948660e-01 -1.39758253e+00 -3.06421727e-01
-7.59034157e-01 -1.16414689e-02 3.38274240e-01 5.85669219e-01
-1.57226455e+00 3.97746652e-01 -5.87756157e-01 -4.29726690e-02
1.44735456e-01 5.55871688e-02 1.33449212e-01 -6.98637605e-01
-1.17217588e+00 9.62236404e-01 6.02719076e-02 9.64608252e-01
-1.25605202e+00 -4.01893318e-01 -8.25875938e-01 -1.54881494e-03
3.48353595e-01 -6.37787282e-01 8.33706737e-01 -1.07757819e+00
-1.44245768e+00 4.46710050e-01 -2.86728024e-01 -2.79685199e-01
2.82617569e-01 -5.07551312e-01 -6.15401745e-01 -4.50847410e-02
-2.00882807e-01 3.36849689e-01 1.56206179e+00 -1.76585603e+00
-5.69474399e-01 -6.43533394e-02 9.57298279e-02 4.93842751e-01
1.12230415e-02 -1.82329729e-01 -7.52836227e-01 -6.91900373e-01
9.52390023e-03 -8.67559433e-01 -3.27057362e-01 1.09820934e-02
-1.90181717e-01 5.48621237e-01 1.08631277e+00 -7.27369308e-01
9.75910723e-01 -2.21276259e+00 6.91933706e-02 -1.26124650e-01
5.70234656e-01 5.22272050e-01 -1.46093011e-01 2.89315224e-01
1.28471432e-03 -1.82551518e-01 -4.71248776e-01 -7.59720325e-01
-4.30577487e-01 6.01359069e-01 -2.39408016e-01 7.10000575e-01
1.29980952e-01 5.62733233e-01 -7.87491560e-01 4.26770188e-03
6.41387939e-01 9.02435541e-01 -4.02517647e-01 6.73086166e-01
-8.93814638e-02 2.84485489e-01 3.16297621e-01 6.57903075e-01
1.35031939e+00 3.85669284e-02 -3.75020117e-01 -8.97106528e-03
-4.66280729e-01 -5.84358722e-03 -8.07523787e-01 1.51644480e+00
-5.51551104e-01 1.06511903e+00 3.80866319e-01 -3.74318361e-01
8.10787261e-01 7.08988532e-02 -2.05457583e-01 -1.07213211e+00
1.59529567e-01 2.81385273e-01 -1.47429749e-01 -6.76086783e-01
8.43246937e-01 -1.58194378e-01 5.88422060e-01 1.96806598e-03
-2.48917162e-01 -5.58201969e-01 -6.16132841e-02 -5.71592944e-03
8.77579451e-01 -1.25613287e-01 -2.91569799e-01 -3.33209515e-01
4.02286410e-01 -4.19247895e-01 6.47520661e-01 8.97723019e-01
-3.09085965e-01 1.23297989e+00 -2.29558513e-01 -7.21741676e-01
-9.99573350e-01 -1.04715407e+00 2.45294392e-01 8.93694222e-01
6.08696103e-01 -3.30272391e-02 -5.01216352e-01 6.59181774e-02
-5.11457443e-01 7.04728365e-01 -7.40816236e-01 -3.34728599e-01
-6.37179852e-01 -1.15294063e+00 -9.85902771e-02 -1.60631433e-01
9.88628030e-01 -9.51517701e-01 -5.15889347e-01 -4.77617490e-04
-3.61228049e-01 -1.27780378e+00 -1.98839918e-01 4.29121584e-01
-5.51074803e-01 -7.92106926e-01 -7.14302838e-01 -7.59394228e-01
5.51859021e-01 1.16771805e+00 1.16143322e+00 4.79870200e-01
-5.18578589e-01 -7.92558268e-02 -5.70573807e-01 -8.98161113e-01
-1.12983391e-01 -4.79416341e-01 -1.65983215e-01 3.26558024e-01
-4.62551527e-02 -6.78848386e-01 -1.18757558e+00 5.03995642e-02
-1.41331637e+00 2.31222898e-01 3.53138596e-01 7.25906312e-01
2.62146533e-01 7.84416378e-01 -2.38732785e-01 -6.14538848e-01
3.35186422e-01 -4.96818513e-01 -9.86727476e-01 -1.46289721e-01
-6.26701772e-01 -1.88563079e-01 5.29158175e-01 -5.93248904e-02
-1.35113633e+00 -1.74851656e-01 5.79573736e-02 -7.41859138e-01
2.76040491e-02 1.10330522e-01 1.04375528e-02 -3.47228914e-01
5.20763338e-01 4.21270043e-01 -3.30025554e-01 -3.85063499e-01
2.03299075e-01 4.45318460e-01 7.03772664e-01 1.32184580e-01
1.43563974e+00 1.07217479e+00 2.82574240e-02 -1.14954388e+00
-1.07010710e+00 -4.23307836e-01 -3.57385427e-01 -4.86973733e-01
1.05447817e+00 -1.29297972e+00 -3.57215017e-01 9.77175653e-01
-1.26150846e+00 -6.53624177e-01 1.90539300e-01 1.99765697e-01
4.78420481e-02 1.65662020e-01 -6.29687309e-01 -1.19307637e+00
-4.26442832e-01 -1.12756467e+00 1.05920029e+00 5.22389829e-01
9.79363799e-01 -8.28805864e-01 -1.52123958e-01 1.78453609e-01
8.25035393e-01 1.27062455e-01 6.51397228e-01 5.02914667e-01
-1.16300750e+00 2.44965926e-01 -4.86810982e-01 6.77361369e-01
5.18997312e-01 5.00511304e-02 -1.33296466e+00 -3.52296174e-01
3.81883144e-01 1.24250688e-01 1.39337122e+00 5.48348129e-01
8.72529805e-01 -2.77032137e-01 2.43695527e-01 1.30088866e+00
1.87828267e+00 1.84207663e-01 1.23293924e+00 7.04122007e-01
8.66192281e-01 4.29285645e-01 4.08783227e-01 4.96515930e-01
8.11503232e-02 4.43974048e-01 1.23365533e+00 -7.80886233e-01
-3.98067147e-01 6.29795253e-01 1.94058225e-01 7.63550460e-01
-3.59137893e-01 -8.25217545e-01 -5.04443526e-01 6.70744538e-01
-1.83254623e+00 -8.12097073e-01 -3.41793537e-01 2.04469562e+00
4.84630466e-01 -3.89243849e-03 -5.91970801e-01 -9.68800578e-03
3.41689199e-01 5.69023490e-01 -2.61074305e-01 -3.93383682e-01
-5.63351810e-01 2.38362312e-01 9.17206228e-01 7.75698185e-01
-1.04413414e+00 9.95329082e-01 6.05206394e+00 2.98545063e-01
-1.15381920e+00 1.47377729e-01 2.66441554e-01 -3.71085018e-01
-1.78108409e-01 -2.90502161e-01 -4.12777036e-01 6.15666628e-01
7.59762585e-01 2.96377867e-01 1.20549202e+00 1.70659825e-01
5.95556378e-01 -3.79552513e-01 -3.97831827e-01 1.30486703e+00
5.21428823e-01 -1.14328206e+00 2.98316963e-02 -1.34769216e-01
1.18465078e+00 4.69682753e-01 3.10507596e-01 1.03576127e-02
3.82409900e-01 -1.10280704e+00 7.34518409e-01 7.26742685e-01
3.96232694e-01 -6.98531926e-01 9.59267378e-01 7.99343511e-02
-7.60191858e-01 -7.38991871e-02 -7.73595214e-01 -2.36103848e-01
4.83474322e-02 9.44375098e-01 -3.74303550e-01 7.27077723e-01
1.22221088e+00 6.41412854e-01 -6.34008884e-01 1.20042014e+00
-5.00947356e-01 3.74805778e-01 -2.36614525e-01 3.92907292e-01
5.00774622e-01 -5.27624786e-01 2.83869237e-01 1.19246876e+00
4.01204318e-01 3.78778458e-01 -3.37775737e-01 8.75318527e-01
-3.83727551e-02 -7.52562702e-01 -5.73074698e-01 4.68344152e-01
-4.65857908e-02 1.22106373e+00 -3.98370743e-01 -2.90317386e-01
-5.24514437e-01 1.39699483e+00 -1.70615584e-01 9.73269582e-01
-8.79770875e-01 -4.08370137e-01 1.14726877e+00 -1.66916221e-01
4.39978778e-01 -3.71849358e-01 -1.10871390e-01 -1.22688842e+00
-7.76700024e-03 -6.96603239e-01 -5.95799126e-02 -1.50375593e+00
-1.16765583e+00 7.40257144e-01 -2.68395871e-01 -1.23678279e+00
2.51616776e-01 -6.64545298e-01 -6.38421059e-01 1.14454293e+00
-2.58599377e+00 -9.28741395e-01 -1.27188444e+00 8.43773186e-01
9.05659914e-01 1.26481459e-01 4.29070026e-01 5.69419503e-01
-6.69273794e-01 -3.94320004e-02 5.24627864e-01 -2.32386693e-01
8.24312091e-01 -1.18726087e+00 3.80939722e-01 1.54355204e+00
-3.45643461e-01 4.20406640e-01 1.14623678e+00 -4.29208547e-01
-1.49974132e+00 -1.57596517e+00 3.99073660e-01 -2.73978025e-01
1.42136797e-01 -5.33228040e-01 -1.10193801e+00 5.39300382e-01
6.88726902e-01 3.58345985e-01 9.00699422e-02 -6.35913968e-01
-2.79078275e-01 -4.19959575e-01 -7.84169555e-01 6.30540073e-01
7.40813315e-01 -2.15144500e-01 -3.14722389e-01 5.52793860e-01
9.15081084e-01 -5.87096930e-01 8.19083601e-02 3.83337289e-01
3.04573357e-01 -1.62131655e+00 9.33644414e-01 1.15257077e-01
4.99251068e-01 -6.97229624e-01 -2.59926021e-01 -1.53645205e+00
-5.19806921e-01 -8.36190581e-01 -5.10846749e-02 6.71010494e-01
1.42707705e-01 -7.09612191e-01 5.06333470e-01 1.55433744e-01
-5.62762260e-01 -1.58559948e-01 -6.07672036e-01 -6.36176765e-01
-3.75130504e-01 -1.58430174e-01 4.99715120e-01 8.45847905e-01
-9.86924350e-01 9.47180986e-02 -1.07049108e+00 8.48106146e-01
1.03956568e+00 8.64877179e-02 6.60641909e-01 -9.12606657e-01
-7.41086144e-04 -2.72172801e-02 4.03407551e-02 -5.63182056e-01
-2.67891318e-01 -1.53588533e-01 7.25731671e-01 -1.79320920e+00
-1.78950503e-01 5.18507101e-02 -5.00951469e-01 2.41227761e-01
-3.96942347e-01 6.23414159e-01 1.83053657e-01 1.52154654e-01
-4.03112739e-01 7.72577107e-01 1.22209764e+00 -3.79676521e-01
-3.56444061e-01 -2.94523090e-01 -3.53443325e-01 5.88892996e-01
7.09348440e-01 -5.38908899e-01 -4.72265720e-01 -1.17196083e+00
4.05144185e-01 -2.83719718e-01 3.78857553e-01 -1.16055405e+00
2.28472412e-01 -1.40776977e-01 5.73112547e-01 -6.12704754e-01
8.34257305e-01 -8.97734821e-01 -1.59153789e-02 3.18423867e-01
3.07601571e-01 -8.48908871e-02 2.35837013e-01 5.51126957e-01
-2.98716068e-01 5.78155071e-02 1.03259599e+00 -3.43302310e-01
-8.08976769e-01 3.00226331e-01 -5.32304704e-01 -4.36288327e-01
4.53727037e-01 -2.19278395e-01 -8.40250432e-01 -7.65951335e-01
-2.11672083e-01 9.56129804e-02 5.63674450e-01 3.97714019e-01
1.16775131e+00 -8.18943024e-01 -8.61652076e-01 5.93129814e-01
1.13512680e-01 1.89197630e-01 3.99196118e-01 6.08974814e-01
-1.02363145e+00 5.40768215e-03 -7.62601122e-02 -3.16975027e-01
-1.09989786e+00 6.44324660e-01 6.47301793e-01 2.80225933e-01
-8.00874949e-01 9.29416835e-01 7.68018723e-01 -7.19152093e-02
1.35171264e-01 -3.18062335e-01 -8.72926638e-02 -5.07492781e-01
8.27642322e-01 3.91505688e-01 4.33975220e-01 -2.91839659e-01
-9.76392552e-02 5.42326629e-01 9.57607105e-02 3.72967869e-02
1.66731286e+00 -5.73512256e-01 -4.53133166e-01 2.65564859e-01
9.48819339e-01 -9.42653324e-03 -1.34773910e+00 -1.06927581e-01
-7.85981238e-01 -8.77349615e-01 5.25586903e-01 -8.40435505e-01
-1.51887167e+00 9.52420652e-01 9.35579062e-01 1.39644712e-01
1.87576509e+00 -5.97734869e-01 8.04704309e-01 5.16554236e-01
-1.70298163e-02 -9.00663555e-01 2.06931442e-01 6.64346516e-01
8.14492166e-01 -1.35440791e+00 1.33999914e-01 -3.66294861e-01
-2.50046343e-01 1.07790267e+00 6.33327544e-01 -3.10956210e-01
6.03897870e-01 2.99074054e-01 5.88415802e-01 -4.05494511e-01
-6.86819553e-01 -5.21780074e-01 9.53809246e-02 6.47586226e-01
-8.24837908e-02 -2.85389841e-01 1.65688157e-01 -1.97868541e-01
-5.09245545e-02 -1.80743784e-01 1.15131521e+00 9.04183626e-01
-7.04564989e-01 -4.05062437e-01 -7.54516602e-01 -5.78799024e-02
-2.28505060e-01 -5.44377863e-01 -9.75478590e-02 5.95326304e-01
3.12561005e-01 1.39970005e+00 2.19136328e-02 -2.93283582e-01
1.21879876e-01 -4.12391037e-01 3.52826655e-01 -3.98656517e-01
-3.19644034e-01 4.00648147e-01 -2.88000435e-01 -6.00735784e-01
-5.09609640e-01 1.32551551e-01 -8.12361658e-01 -4.08554137e-01
-3.22473615e-01 -1.58441797e-01 9.02039051e-01 8.10028970e-01
7.02507496e-02 1.00438273e+00 9.07694042e-01 -1.22871494e+00
2.99451709e-01 -8.69136631e-01 -9.71055746e-01 2.16731310e-01
1.29796493e+00 -5.20635366e-01 -8.94365251e-01 1.34800464e-01] | [10.907658576965332, -3.192570209503174] |
a532cfe6-1e66-4641-8b38-1dd506a8c528 | systematic-review-for-ai-based-language | 2111.04455 | null | https://arxiv.org/abs/2111.04455v1 | https://arxiv.org/pdf/2111.04455v1.pdf | Systematic Review for AI-based Language Learning Tools | The Second Language Acquisition field has been significantly impacted by a greater emphasis on individualized learning and rapid developments in artificial intelligence (AI). Although increasingly adaptive language learning tools are being developed with the application of AI to the Computer Assisted Language Learning field, there have been concerns regarding insufficient information and teacher preparation. To effectively utilize these tools, teachers need an in-depth overview on recently developed AI-based language learning tools. Therefore, this review synthesized information on AI tools that were developed between 2017 and 2020. A majority of these tools utilized machine learning and natural language processing, and were used to identify errors, provide feedback, and assess language abilities. After using these tools, learners demonstrated gains in their language abilities and knowledge. This review concludes by presenting pedagogical implications and emerging themes in the future research of AI-based language learning tools. | ['Heeyoul Choi', 'Jin Ha Woo'] | 2021-10-29 | null | null | null | null | ['language-acquisition'] | ['natural-language-processing'] | [ 5.08688912e-02 5.03885388e-01 -5.05553544e-01 -2.16849044e-01
-5.64137459e-01 -7.27062285e-01 4.24103230e-01 8.58964205e-01
-5.97681403e-01 4.46135521e-01 3.17562699e-01 -6.53549254e-01
-3.16061825e-01 -6.20912313e-01 -3.53810668e-01 -1.21633410e-02
2.01458633e-01 4.75288719e-01 1.14107039e-02 -2.89427519e-01
6.09023929e-01 7.73709595e-01 -1.88512123e+00 3.57478231e-01
1.71690249e+00 8.59872252e-02 4.27822083e-01 6.35394275e-01
-9.54024732e-01 1.62488127e+00 -7.25926399e-01 -2.99750507e-01
-4.64243323e-01 -5.83225667e-01 -1.08338726e+00 -2.33493209e-01
4.40920889e-01 -6.43262327e-01 9.56126750e-02 7.60806501e-01
4.21808213e-01 1.73592553e-01 3.29143763e-01 -9.40757811e-01
-8.38349879e-01 9.07341480e-01 4.35698405e-02 3.81504357e-01
9.52127814e-01 1.98520094e-01 4.00067955e-01 -1.03153324e+00
4.01343852e-01 1.32517374e+00 4.63282645e-01 7.73921967e-01
-7.01794565e-01 -1.10604239e+00 9.74835381e-02 4.48016644e-01
-8.10928345e-01 -3.48682523e-01 4.99039829e-01 -8.79451990e-01
1.17826414e+00 -3.95825326e-01 1.57653880e+00 3.02484185e-01
2.92703807e-01 1.24025333e+00 1.23200297e+00 -1.23478568e+00
6.69606179e-02 7.65175343e-01 3.02542806e-01 6.89748526e-01
3.29594672e-01 -1.45481765e-01 -1.13148069e+00 5.00031769e-01
2.31679499e-01 -4.44090426e-01 2.44721144e-01 4.17165644e-02
-9.12423134e-01 8.87126088e-01 -7.37221763e-02 9.01583195e-01
-3.09495151e-01 -2.37274304e-01 4.22828406e-01 4.35733825e-01
4.07769471e-01 7.63148844e-01 -2.94262648e-01 -9.40244019e-01
-6.01841390e-01 5.51150665e-02 7.20054567e-01 7.72269964e-01
4.15337116e-01 6.84579194e-01 2.29170341e-02 9.82661903e-01
8.18858445e-01 6.07152402e-01 6.60741389e-01 -8.31499100e-01
1.06111355e-01 9.92647171e-01 -4.99252051e-01 -7.72136211e-01
-6.64543584e-02 -8.85795057e-02 8.92734006e-02 3.61020565e-01
3.37138891e-01 -1.83334962e-01 -9.31326330e-01 1.11939979e+00
2.22783089e-01 -1.69100001e-01 2.27803528e-01 1.31063879e-01
1.39388597e+00 4.99853164e-01 8.49622726e-01 -2.71174252e-01
9.98717904e-01 -9.15439725e-01 -1.11068344e+00 -4.41182166e-01
1.15941894e+00 -8.21827769e-01 1.20932853e+00 7.45126247e-01
-1.58919501e+00 -3.77284169e-01 -8.47491443e-01 -3.06400955e-01
-7.51850724e-01 -3.02407265e-01 8.26009214e-01 1.13600338e+00
-1.17603517e+00 1.00663044e-01 -8.09447885e-01 -4.92071003e-01
4.60368276e-01 2.56418884e-01 -6.91328421e-02 -2.13153839e-01
-1.06254184e+00 1.34160769e+00 5.74112952e-01 -4.18563157e-01
-6.23984814e-01 -1.08137107e+00 -1.06781209e+00 -1.59106463e-01
7.39160106e-02 -1.26786381e-01 1.93281853e+00 -1.18410373e+00
-2.04027081e+00 1.08391523e+00 1.25978485e-01 -2.65848905e-01
2.15306878e-02 -5.18502951e-01 -2.67901480e-01 2.88271159e-01
1.27248988e-01 6.95436597e-01 -1.79953545e-01 -7.66885161e-01
-1.09984529e+00 -2.56678015e-01 -1.30480871e-01 6.57451630e-01
-8.11201274e-01 7.44613945e-01 8.12029094e-02 -5.06403387e-01
-1.19261488e-01 -3.52911472e-01 2.17956662e-01 1.41528919e-01
8.06373119e-01 -9.56850410e-01 5.00473142e-01 -7.20862687e-01
1.54796755e+00 -1.72986329e+00 -5.09590089e-01 7.98750296e-02
2.63848186e-01 6.70789957e-01 3.09397299e-02 8.09200644e-01
2.39752442e-01 2.97514141e-01 3.10881257e-01 3.98317426e-01
-1.31416500e-01 2.45054513e-01 -3.56688015e-02 -1.82854250e-01
-8.46203715e-02 7.69869208e-01 -1.50938082e+00 -7.15567470e-01
6.20450020e-01 5.09991467e-01 -4.43155438e-01 5.10019541e-01
-2.17616223e-02 4.26160485e-01 -3.39987516e-01 6.58097863e-01
3.50301079e-02 1.93832576e-01 -6.80598840e-02 9.73008454e-01
-7.05542266e-01 8.64446938e-01 -6.89224958e-01 1.51712382e+00
-6.86034262e-01 9.27114904e-01 -1.76496040e-02 -9.22691047e-01
9.39408243e-01 7.18056858e-01 5.08624434e-01 -7.60230541e-01
-7.81168342e-02 5.03591537e-01 6.99782252e-01 -9.05803740e-01
2.48613451e-02 2.53843777e-02 4.93992001e-01 7.41197050e-01
1.78513646e-01 -1.19266641e+00 3.90181452e-01 2.04862222e-01
6.32564962e-01 2.91375041e-01 6.18648469e-01 -3.91015023e-01
6.74781978e-01 2.77330220e-01 2.77935237e-01 6.45001054e-01
-3.25887769e-01 -5.43318808e-01 -3.55860084e-01 -3.38119090e-01
-5.80221653e-01 -8.23229551e-01 4.86522876e-02 1.63532734e+00
-7.60576725e-01 -4.14520711e-01 -8.91477406e-01 -3.28934908e-01
-1.99790582e-01 1.40805328e+00 2.09252462e-01 -3.85296196e-01
-3.99769664e-01 3.22535932e-01 4.79825407e-01 4.68831688e-01
3.78807813e-01 -1.67852378e+00 -7.51587927e-01 5.60747683e-01
3.27573478e-01 -8.72399271e-01 1.24267533e-01 -2.49514297e-01
-7.77613461e-01 -6.00566030e-01 -3.92365038e-01 -1.48749995e+00
6.09061003e-01 2.57651240e-01 1.06319094e+00 3.99408668e-01
-1.61883935e-01 1.32875943e+00 -3.27512443e-01 -1.46102333e+00
-1.00097752e+00 -5.14855310e-02 1.81029961e-01 -1.08015358e+00
1.00855660e+00 -2.87402511e-01 1.27783582e-01 -7.07026839e-01
-7.81693578e-01 5.34928441e-01 5.51213980e-01 5.38011193e-01
-5.87461004e-03 2.40785703e-01 9.00490940e-01 -5.52197397e-01
1.03894424e+00 -2.77956635e-01 -2.72066027e-01 4.02333826e-01
-1.10939860e+00 -3.58118534e-01 4.73049283e-01 -7.03012884e-01
-1.24350774e+00 -1.89904064e-01 -2.19549894e-01 1.86727434e-01
-4.76519972e-01 9.33473170e-01 1.46187425e-01 -6.01746559e-01
5.85889935e-01 2.21265867e-01 2.17850208e-01 -5.40042333e-02
1.25398949e-01 1.03750968e+00 2.88378954e-01 -1.06899214e+00
5.97873688e-01 -6.04669631e-01 -4.87831801e-01 -1.29216444e+00
-7.97968149e-01 -4.09268290e-02 -6.41232073e-01 -8.27816367e-01
5.54856181e-01 -1.11476374e+00 -6.53438807e-01 5.81426680e-01
-7.87571132e-01 -7.35531628e-01 -5.54455578e-01 1.15731895e+00
-3.26928020e-01 3.76862623e-02 -5.10803640e-01 -1.20943773e+00
-4.48088139e-01 -1.23607397e+00 1.38710052e-01 9.52751100e-01
-5.64345956e-01 -1.30741704e+00 3.92694399e-02 7.45975673e-01
4.43383962e-01 -3.94896716e-01 1.22267699e+00 -9.72535729e-01
-8.90391022e-02 -9.59747434e-02 5.35362840e-01 4.03332293e-01
2.26810157e-01 2.81299919e-01 -6.58930063e-01 5.94827905e-02
-2.17860073e-01 -7.25908697e-01 -2.78250426e-01 9.08034816e-02
9.57580566e-01 -4.78311241e-01 1.10536389e-01 -6.66060373e-02
9.61127341e-01 9.63791370e-01 -1.53529808e-01 6.74101532e-01
3.81469280e-01 1.05005252e+00 6.35301352e-01 -7.01797754e-02
6.91006064e-01 1.22296445e-01 -3.49329442e-01 4.20240998e-01
-2.58861393e-01 -5.35341680e-01 6.43475950e-01 1.53230155e+00
1.13204062e-01 9.51575935e-02 -1.90941381e+00 1.01755333e+00
-1.16300488e+00 -6.92107379e-01 1.91387102e-01 2.02228141e+00
1.35235178e+00 1.20256551e-01 -4.26101983e-02 2.21077144e-01
9.78403017e-02 -5.11169553e-01 -5.14146090e-01 -1.11477554e+00
1.63248330e-01 8.16220224e-01 -2.39881709e-01 9.02272284e-01
-4.05344754e-01 1.57036185e+00 7.30792189e+00 5.21785676e-01
-1.14594758e+00 -2.66571492e-01 3.15951407e-01 3.45286518e-01
-7.81127572e-01 -3.41605008e-01 -7.46404290e-01 -8.23237468e-03
1.23923743e+00 -9.28779125e-01 3.90501559e-01 6.82594180e-01
3.53809386e-01 -1.67914614e-01 -7.35912561e-01 4.98192191e-01
5.99430576e-02 -9.99330103e-01 8.48150179e-02 -3.47391486e-01
7.70044208e-01 -3.40283155e-01 6.04845732e-02 5.46091616e-01
5.44206023e-01 -1.21877337e+00 4.90115047e-01 3.67982805e-01
6.49916112e-01 -8.41916203e-01 3.41891527e-01 3.86269301e-01
-6.99895382e-01 -2.08700195e-01 1.32501617e-01 -8.36047411e-01
-4.72467750e-01 1.20258760e-02 -1.46698964e+00 -1.71624437e-01
6.82702839e-01 6.15747511e-01 -4.89099294e-01 1.06724846e+00
-7.35455751e-01 1.15461671e+00 -2.63555110e-01 -6.41952693e-01
6.70177191e-02 -2.34834641e-01 3.20476979e-01 1.12339187e+00
1.72107279e-01 5.52718103e-01 3.68423223e-01 4.27342027e-01
4.76656735e-01 6.32405102e-01 -9.92209435e-01 -8.38928342e-01
1.04368615e+00 9.25189614e-01 -4.40965861e-01 -2.81105578e-01
-9.99337018e-01 2.42433190e-01 1.51501626e-01 1.42038748e-01
9.92625486e-03 -3.25349420e-01 5.70895314e-01 3.28951955e-01
-6.66793466e-01 -5.31284630e-01 -6.21217012e-01 -5.69590032e-01
-4.91088927e-01 -1.49117589e+00 1.80139914e-01 -7.78473794e-01
-8.37474883e-01 -9.49681997e-02 2.69352466e-01 -6.94113195e-01
-7.64656842e-01 -6.35789216e-01 -5.27973294e-01 6.57020867e-01
-8.34509373e-01 -1.03960180e+00 -5.34825586e-02 6.15658499e-02
8.71153295e-01 -6.77772999e-01 1.15889931e+00 3.39108817e-02
-3.48577857e-01 7.21167922e-01 -2.24656463e-02 -2.45622471e-02
4.13581222e-01 -1.07175958e+00 -2.27696270e-01 7.55431294e-01
-8.18289444e-02 5.28704822e-01 4.65962678e-01 -8.01906765e-01
-1.58467507e+00 -3.60110939e-01 1.29605651e+00 -1.74307585e-01
7.64167309e-01 1.18934112e-02 -9.23289061e-01 9.22307074e-01
7.14996219e-01 -7.46917427e-01 1.21178734e+00 -5.31601787e-01
3.79717350e-01 1.11856520e-01 -1.30385602e+00 8.19925845e-01
7.28109479e-01 -6.25085473e-01 -1.03970993e+00 4.02046531e-01
7.32183874e-01 -5.57700396e-01 -1.16944480e+00 4.26682472e-01
5.17458141e-01 -2.91337878e-01 7.44048774e-01 -5.79334617e-01
2.93062806e-01 2.42513880e-01 6.60158813e-01 -1.06819034e+00
-1.52840793e-01 -6.98309839e-01 2.26823613e-02 1.30991805e+00
2.26583511e-01 -6.03486955e-01 5.59105039e-01 1.14807844e+00
-4.39837962e-01 -8.70664835e-01 -6.46310508e-01 -8.80918130e-02
8.03129256e-01 -6.25795901e-01 3.22632998e-01 1.11502147e+00
5.37213922e-01 1.96531802e-01 4.80389714e-01 -2.31335834e-01
1.26191363e-01 -6.91126883e-01 6.68370366e-01 -1.34093118e+00
4.22236085e-01 -9.60041046e-01 -3.63641113e-01 -6.17748201e-01
4.53696698e-01 -7.75077224e-01 -5.33402376e-02 -1.81154335e+00
-3.25146556e-01 2.36598421e-02 1.58175364e-01 7.91656196e-01
-2.09630996e-01 -4.57853764e-01 1.75976962e-01 -4.38093215e-01
-1.38511389e-01 3.58737171e-01 1.28690195e+00 5.39154299e-02
-6.49786949e-01 -9.50002819e-02 -9.04628634e-01 1.16530871e+00
1.18606186e+00 -1.52883038e-01 -7.23432422e-01 -6.40835881e-01
2.68956035e-01 -2.99884290e-01 -4.02132422e-01 -1.20363033e+00
5.40599287e-01 -8.33904147e-01 2.06217870e-01 -3.96882832e-01
-3.61551702e-01 -8.14220667e-01 -4.45254564e-01 6.48052394e-01
-5.68788171e-01 3.13943595e-01 8.84769857e-01 -8.13920617e-01
-1.80720538e-01 -6.25560999e-01 6.81032419e-01 -4.93440069e-02
-8.95208657e-01 -2.23137498e-01 -1.28450966e+00 1.41487285e-01
1.26821911e+00 -3.79644752e-01 2.03254223e-01 -6.78813636e-01
-3.79905701e-01 6.03440702e-01 1.46385029e-01 6.86078370e-01
8.91676843e-01 -9.80920911e-01 -6.16981268e-01 2.90892035e-01
-8.62682685e-02 4.67814386e-01 -1.01703495e-01 4.13493186e-01
-9.33751464e-01 8.45516384e-01 -4.00776118e-01 -2.49570772e-01
-1.38169932e+00 1.63532913e-01 2.66103268e-01 -6.93587288e-02
-4.97772127e-01 8.25151742e-01 -1.64950773e-01 -7.00861752e-01
7.15620220e-01 -1.30156413e-01 -8.94417226e-01 -3.85539904e-02
9.61661518e-01 5.23464620e-01 -3.56227756e-01 -4.20175076e-01
-1.50705874e-01 6.00805700e-01 -1.21171281e-01 -3.95493388e-01
1.12038195e+00 -9.55284461e-02 -1.74872205e-01 9.44711566e-01
3.08620602e-01 2.96293944e-01 -2.98738182e-01 -2.18231246e-01
4.07294005e-01 -4.40375805e-02 9.46806967e-02 -1.23322499e+00
-5.27271271e-01 1.04391551e+00 6.06785655e-01 -1.80544317e-01
1.08488739e+00 -2.50223845e-01 2.34529600e-01 4.53140259e-01
3.01850252e-02 -1.43866670e+00 3.52105685e-02 8.86522651e-01
8.34949434e-01 -9.87953961e-01 2.19221219e-01 -2.03909680e-01
-4.56588566e-01 1.43692791e+00 1.28280735e+00 5.81800282e-01
7.33682156e-01 3.09494078e-01 4.70536023e-01 -6.88304305e-02
-5.49044132e-01 -6.35142773e-02 5.06443799e-01 7.67175555e-01
1.47088861e+00 2.65704636e-02 -9.87568080e-01 4.10073280e-01
-8.25328231e-01 4.73512948e-01 4.54855144e-01 1.31425631e+00
-9.41132963e-01 -1.23560214e+00 -5.70500970e-01 1.76093146e-01
-5.12397647e-01 -2.65882283e-01 -7.95521915e-01 7.54327357e-01
4.56564426e-02 1.32958901e+00 -6.62165508e-02 -5.21085523e-02
1.49466649e-01 5.69377601e-01 4.18370962e-01 -1.05720890e+00
-1.11028206e+00 -4.09830689e-01 1.06206555e-02 3.55918854e-02
-3.66411448e-01 -5.02508938e-01 -1.71382546e+00 -1.83307186e-01
-8.35272670e-02 3.43538225e-01 7.12802708e-01 1.10507452e+00
6.21148311e-02 4.46232766e-01 -1.62970468e-01 -1.45388260e-01
-2.59325236e-01 -1.08109355e+00 7.56349713e-02 -5.18821955e-01
6.96785823e-02 -2.66007900e-01 -1.62907109e-01 -2.09165648e-01] | [11.766698837280273, 8.350015640258789] |
80ee79c0-fcc0-483c-93d2-5466083f96ff | towards-learning-through-open-domain-dialog | 2202.03040 | null | https://arxiv.org/abs/2202.03040v1 | https://arxiv.org/pdf/2202.03040v1.pdf | Towards Learning Through Open-Domain Dialog | The development of artificial agents able to learn through dialog without domain restrictions has the potential to allow machines to learn how to perform tasks in a similar manner to humans and change how we relate to them. However, research in this area is practically nonexistent. In this paper, we identify the modifications required for a dialog system to be able to learn from the dialog and propose generic approaches that can be used to implement those modifications. More specifically, we discuss how knowledge can be extracted from the dialog, used to update the agent's semantic network, and grounded in action and observation. This way, we hope to raise awareness for this subject, so that it can become a focus of research in the future. | ['David Martins de Matos', 'Ricardo Ribeiro', 'Eugénio Ribeiro'] | 2022-02-07 | null | null | null | null | ['open-domain-dialog'] | ['natural-language-processing'] | [ 7.41359368e-02 8.66603076e-01 -1.65590309e-02 -8.74066770e-01
2.48840600e-01 -9.05658126e-01 9.89975214e-01 2.68769532e-01
-4.84371126e-01 1.14912248e+00 3.46861422e-01 -2.27986246e-01
-5.92394285e-02 -9.21104670e-01 -2.70029783e-01 -1.26783431e-01
1.58379346e-01 8.94732714e-01 8.42112660e-01 -6.55834019e-01
3.14165026e-01 6.67028785e-01 -1.27588379e+00 2.80334949e-01
4.29130524e-01 2.52110928e-01 3.55330825e-01 5.12502849e-01
-4.25176024e-01 1.10638571e+00 -7.18187571e-01 -2.49014631e-01
-3.59769724e-02 -5.41369557e-01 -1.57933474e+00 2.93827981e-01
-1.42569751e-01 -6.40009463e-01 -1.31709218e-01 7.42237926e-01
-4.36029956e-02 5.44585228e-01 4.69754934e-01 -9.59851563e-01
-2.80976981e-01 7.52337098e-01 6.35156929e-01 7.11330548e-02
6.49436533e-01 2.08850741e-01 5.99973202e-01 -2.48538300e-01
8.36926818e-01 1.65514338e+00 1.56668216e-01 1.13413739e+00
-9.65840518e-01 -2.29820251e-01 2.59743482e-01 2.92741477e-01
-6.55713141e-01 -6.06875658e-01 7.62116790e-01 -3.92279655e-01
1.01296210e+00 1.48225412e-01 7.06627905e-01 9.24781263e-01
1.19974499e-03 4.25219029e-01 1.20646572e+00 -7.91057944e-01
3.55358452e-01 8.84284437e-01 2.83889353e-01 6.95478976e-01
1.58104047e-01 2.37957034e-02 -4.79132682e-01 3.61311398e-02
7.57799387e-01 -4.15156305e-01 -7.24031404e-03 -6.04297936e-01
-9.66466367e-01 9.36248124e-01 3.64356309e-01 9.10987556e-01
-2.73745269e-01 -2.05303803e-01 2.82949567e-01 5.46088338e-01
1.60046130e-01 1.09196258e+00 -6.52662158e-01 -2.54983395e-01
-1.41855836e-01 3.96664619e-01 1.48752022e+00 6.59925461e-01
8.49617660e-01 -1.50523096e-01 3.07894379e-01 5.86595774e-01
5.76094210e-01 8.69325846e-02 4.57237989e-01 -1.46829164e+00
-5.60506321e-02 9.61885035e-01 2.96000689e-01 -6.52597547e-01
-6.09227419e-01 3.98830473e-01 2.40576133e-01 3.95823926e-01
5.66970170e-01 -4.32095975e-01 -3.95585567e-01 1.77305520e+00
4.34107333e-01 -1.80545613e-01 5.95292807e-01 6.15302205e-01
8.27886999e-01 5.18455207e-01 8.69972557e-02 -2.00649142e-01
1.42563510e+00 -7.50396490e-01 -7.36570954e-01 -5.81717789e-01
7.96443105e-01 -4.85881865e-01 7.94406533e-01 9.57759544e-02
-1.10143673e+00 -4.23028767e-01 -9.04983282e-01 6.08888967e-03
-6.59116387e-01 -5.24886966e-01 9.28267896e-01 6.34835124e-01
-1.27098620e+00 4.30234194e-01 -1.01366484e+00 -1.10066545e+00
-2.63220906e-01 5.10648072e-01 -1.28755301e-01 3.26396406e-01
-1.48053682e+00 1.63882494e+00 1.06256962e+00 -1.40638217e-01
-6.23357832e-01 -1.52680755e-01 -8.76007915e-01 -5.93973212e-02
7.66221583e-01 -6.92014754e-01 1.91669285e+00 -1.06392038e+00
-2.21572280e+00 7.79784381e-01 -1.96355674e-02 -6.91629410e-01
8.07369202e-02 1.87399104e-01 -3.80487800e-01 2.49697194e-01
-2.79604375e-01 7.49571383e-01 2.53693730e-01 -1.39555454e+00
-9.62207735e-01 -4.08317715e-01 8.99617493e-01 4.88669127e-01
-4.31841999e-01 1.84806570e-01 -1.86296090e-01 -7.71433040e-02
-1.49516925e-01 -1.07667243e+00 -2.57022798e-01 -1.14569716e-01
2.13952675e-01 -4.32424098e-01 8.20852220e-01 -3.59006256e-01
7.46090174e-01 -1.89985597e+00 2.35504150e-01 -5.39397448e-03
1.83333173e-01 5.16352415e-01 1.32769600e-01 8.94281387e-01
4.27022964e-01 -4.26974148e-02 -3.64337452e-02 -5.00313379e-02
1.16223320e-01 6.14400446e-01 -1.23690374e-01 -1.64672047e-01
2.81529091e-02 6.28197134e-01 -1.02554619e+00 -2.68490434e-01
6.63985252e-01 1.81869075e-01 -5.28227985e-01 4.92409289e-01
-6.57151997e-01 7.96880841e-01 -8.85501027e-01 -1.29422188e-01
3.06956731e-02 5.94994314e-02 6.45067930e-01 9.63312909e-02
-3.45404774e-01 7.43189692e-01 -7.80048072e-01 1.34598553e+00
-6.05580389e-01 7.11092651e-01 3.02971482e-01 -1.09089196e+00
1.03790748e+00 5.95789790e-01 8.98001194e-02 -3.03012431e-01
1.69676766e-01 6.18718229e-02 3.39930475e-01 -7.10559368e-01
3.55345726e-01 -4.43620056e-01 -1.30106751e-02 5.91828227e-01
1.01397455e-01 -4.16229397e-01 1.99679732e-01 6.15929887e-02
7.82115638e-01 -1.27935544e-01 6.75605953e-01 -4.15814146e-02
9.45333123e-01 3.94145578e-01 7.93219265e-03 6.41164243e-01
-2.57268041e-01 -6.12302303e-01 1.85407802e-01 -6.12926781e-01
-6.56918645e-01 -9.14859593e-01 1.33148536e-01 1.18226731e+00
1.53814286e-01 -1.86998159e-01 -8.28907847e-01 -7.04515994e-01
-4.14913714e-01 1.18813288e+00 -3.33417892e-01 -2.35481963e-01
-7.16958106e-01 -9.29361433e-02 2.04782933e-01 2.65985727e-01
8.17240775e-01 -1.56553328e+00 -7.81758070e-01 2.58481562e-01
1.21215954e-01 -1.04345882e+00 2.78941303e-01 1.47371426e-01
-1.06396723e+00 -1.01236534e+00 -1.16402037e-01 -1.04294646e+00
4.67371553e-01 8.56361538e-02 8.25846374e-01 2.72931904e-01
5.10632455e-01 8.98570657e-01 -6.27351344e-01 -5.84807992e-01
-1.17508066e+00 1.86338633e-01 -1.23318434e-01 -4.46271241e-01
6.60231590e-01 -4.93011534e-01 -2.22856924e-01 3.37514460e-01
-1.07924473e+00 -9.98510122e-02 1.88762128e-01 4.98453438e-01
-3.37808162e-01 1.67061269e-01 6.93739891e-01 -1.12624454e+00
9.46395338e-01 -2.57095486e-01 -4.30467129e-01 2.82013804e-01
-5.83142221e-01 2.17237949e-01 6.10809565e-01 -2.06612095e-01
-1.68300033e+00 1.89640313e-01 -3.04588191e-02 5.41886151e-01
-7.74328768e-01 5.31028509e-01 -2.80544758e-01 -3.08889538e-01
5.56132257e-01 1.85708702e-02 2.17376500e-01 -4.21260148e-01
5.45473397e-01 9.48054552e-01 3.62761080e-01 -6.28797591e-01
6.31883979e-01 4.64288294e-01 -3.63016903e-01 -9.36097801e-01
-7.06988394e-01 -4.24240828e-01 -7.58980513e-01 -3.23832661e-01
9.81467247e-01 -4.55843657e-01 -7.14886367e-01 1.32624313e-01
-1.16138732e+00 -7.80024111e-01 -3.06209892e-01 5.69647968e-01
-6.57892406e-01 2.71620184e-01 -4.73106802e-01 -8.28746438e-01
2.59153157e-01 -8.67044628e-01 3.47996980e-01 6.14411056e-01
-7.33848035e-01 -1.61081100e+00 2.77007401e-01 5.28664947e-01
3.98891062e-01 -2.73467094e-01 8.11164081e-01 -1.37076712e+00
-4.35738981e-01 2.38627642e-02 3.16337079e-01 3.05485457e-01
6.16666555e-01 -2.72514790e-01 -9.18129802e-01 -1.72954485e-01
5.18688619e-01 -2.36567944e-01 2.47931838e-01 -9.56121311e-02
4.60401565e-01 -4.22317982e-01 -4.76125032e-01 -2.31767088e-01
8.47430110e-01 8.42179954e-01 5.28584898e-01 5.98253429e-01
5.87382838e-02 1.12185621e+00 6.10149801e-01 1.20409250e-01
6.76628530e-01 9.98061717e-01 1.19913720e-01 1.80639714e-01
-1.73420027e-01 -1.20829664e-01 2.59588063e-01 6.88166738e-01
-8.57839063e-02 -1.22073494e-01 -1.03463089e+00 4.42696035e-01
-1.88307345e+00 -1.01893878e+00 3.60231847e-01 1.76962399e+00
1.01439631e+00 1.60308734e-01 2.06559896e-01 -2.39210159e-01
6.80132568e-01 5.24364747e-02 -3.62713248e-01 -7.80312419e-01
3.64219427e-01 2.31091812e-01 5.00788093e-02 9.79049265e-01
-7.97460318e-01 1.51833057e+00 6.50033998e+00 -1.84926882e-01
-8.00536871e-01 -1.61014926e-02 -1.61369219e-02 7.33144403e-01
-1.47640496e-01 6.09280705e-01 -6.35232449e-01 5.88555411e-02
1.17426634e+00 -3.75641257e-01 6.88860893e-01 7.14853823e-01
3.16931069e-01 -2.52351701e-01 -1.32571566e+00 1.54005632e-01
6.84117749e-02 -9.84155774e-01 7.85060227e-02 -1.34724081e-01
2.39215553e-01 -5.03147840e-01 -4.30990279e-01 4.66838986e-01
6.64162695e-01 -6.86496258e-01 1.25722855e-01 5.83952487e-01
-2.45608047e-01 -4.03085679e-01 7.59912312e-01 7.49738634e-01
-8.03200841e-01 -1.84174463e-01 -2.84073800e-01 -5.81772506e-01
1.98880732e-01 -3.30289155e-01 -1.81816447e+00 3.92452896e-01
1.83004305e-01 3.88502359e-01 -3.73413563e-01 8.10234308e-01
-4.26677734e-01 3.60903651e-01 -2.00718522e-01 -6.53676093e-01
1.89566955e-01 -9.10048708e-02 6.06401801e-01 9.04942274e-01
3.03884726e-02 5.13144076e-01 4.84024793e-01 5.81219435e-01
1.68921903e-01 4.05360619e-03 -7.43659616e-01 -7.70274177e-02
5.86990654e-01 8.51165056e-01 -8.00959110e-01 -5.58373988e-01
-5.28322160e-01 8.65787804e-01 1.78406104e-01 2.79592425e-01
-3.60004097e-01 -1.55611783e-01 5.52745223e-01 -3.82556953e-02
6.66062385e-02 -3.13923836e-01 1.57046705e-01 -1.01870298e+00
-3.82922649e-01 -1.06803977e+00 3.46395135e-01 -8.85987699e-01
-1.13441193e+00 6.10985994e-01 5.01995265e-01 -5.69003463e-01
-7.54532337e-01 -7.70877004e-01 -5.49192131e-01 5.28449357e-01
-1.14022338e+00 -8.04994941e-01 -2.47992873e-01 5.24669886e-01
7.24016905e-01 -2.77811259e-01 1.28521919e+00 -3.04058582e-01
-1.78623036e-03 1.49281090e-02 -4.61404085e-01 2.43472621e-01
7.19735742e-01 -1.03785217e+00 1.18474327e-01 2.51130849e-01
6.63207546e-02 7.60945499e-01 1.10902786e+00 -6.88984871e-01
-1.11529088e+00 -6.68967903e-01 9.42634284e-01 -7.35611081e-01
8.52238417e-01 -9.02151465e-02 -1.11303163e+00 1.05726647e+00
5.96660078e-01 -6.77828550e-01 5.53875864e-01 1.09751187e-01
-4.09297422e-02 4.70579080e-02 -1.27223575e+00 8.03055644e-01
7.23781645e-01 -5.16159177e-01 -1.55095017e+00 3.39026004e-01
8.58306229e-01 -3.90024155e-01 -8.54492068e-01 1.17222466e-01
2.56290287e-01 -9.29483771e-01 8.14334035e-01 -8.63137126e-01
-2.57804513e-01 -1.94298968e-01 -1.21240569e-02 -1.51671052e+00
-6.02476560e-02 -5.66708982e-01 5.05240001e-02 1.25701058e+00
5.53621352e-01 -1.11091912e+00 7.81576276e-01 1.01447022e+00
-2.36125976e-01 1.53526440e-02 -7.02832520e-01 -6.76591873e-01
2.02323988e-01 -1.33845925e-01 5.80492258e-01 9.39378738e-01
6.28143489e-01 6.59554899e-01 -1.00735642e-01 2.52168089e-01
-7.24835619e-02 -1.22803137e-01 9.03266251e-01 -1.59393489e+00
-2.43001968e-01 -2.37847075e-01 -3.98024559e-01 -1.25905252e+00
5.29405296e-01 -6.97407901e-01 1.55481547e-01 -1.63502109e+00
-9.35260355e-02 -2.63233066e-01 2.08234519e-01 4.54690784e-01
1.50648907e-01 -4.67421889e-01 4.57333356e-01 1.19083673e-01
-4.47506309e-01 3.53699386e-01 1.20544159e+00 1.05205305e-01
-6.35492206e-01 2.55150497e-01 -6.78868830e-01 1.19939733e+00
1.36412823e+00 -3.79406661e-01 -6.57064438e-01 -6.81691095e-02
-1.28566906e-01 3.72326374e-03 2.71098793e-01 -9.72648323e-01
5.75006068e-01 -5.79185128e-01 -8.31017364e-03 5.91127351e-02
6.52478576e-01 -1.18310273e+00 3.85231115e-02 5.66834271e-01
-6.40674233e-01 -1.12829790e-01 3.06748241e-01 3.39015335e-01
-2.42811009e-01 -8.31213355e-01 5.61812580e-01 -5.08320034e-01
-1.04673421e+00 -4.44069058e-01 -9.17645514e-01 -2.29004383e-01
1.31521761e+00 -1.13481455e-01 -3.48008424e-01 -7.48573422e-01
-9.16852295e-01 3.57406706e-01 6.16940260e-01 3.55271548e-01
3.39232206e-01 -8.06468248e-01 -2.15354070e-01 -1.70076147e-01
-1.14578335e-02 -3.02225292e-01 -1.03569567e-01 1.49898186e-01
-5.06375551e-01 7.78836966e-01 -4.33854073e-01 -1.08567789e-01
-1.26194930e+00 5.85834563e-01 5.37315786e-01 -1.55381739e-01
-4.75761026e-01 2.29127184e-01 1.07536957e-01 -7.47305751e-01
3.14719766e-01 -1.75948709e-01 -7.54711747e-01 -1.28828391e-01
6.53631210e-01 -5.59087358e-02 -2.11111203e-01 -4.14453477e-01
-3.49627912e-01 2.02577084e-01 -1.88819423e-01 -5.03434181e-01
1.35833418e+00 -5.22398174e-01 -2.15614244e-01 7.30562568e-01
4.64690030e-01 -8.18895996e-02 -1.08208263e+00 -2.63421446e-01
4.14118528e-01 -2.60231107e-01 -2.58101761e-01 -1.00251901e+00
-4.55127656e-01 6.40580058e-01 4.03415382e-01 9.39473331e-01
8.92490745e-01 4.62636441e-01 3.85478288e-01 1.14074838e+00
5.03931403e-01 -1.23055375e+00 1.33745670e-01 7.82833815e-01
7.99392998e-01 -1.14369941e+00 -1.24530471e-03 -5.72217047e-01
-7.60080993e-01 1.49164641e+00 7.37357974e-01 1.39160126e-01
5.40401816e-01 1.37801081e-01 2.44189054e-01 -4.67559755e-01
-9.51615751e-01 -2.92139351e-01 -7.29093924e-02 1.06365418e+00
5.20628929e-01 -1.18425652e-01 -3.16260606e-01 4.57830690e-02
-1.79207847e-01 6.39894232e-02 8.21353495e-01 1.10065103e+00
-9.56960142e-01 -1.64605939e+00 -3.49698722e-01 1.97844096e-02
3.42610329e-02 2.75320351e-01 -1.18847597e+00 1.10680115e+00
-1.54281838e-03 1.36191356e+00 -1.49810418e-01 -1.34667987e-02
5.32735288e-01 4.99199390e-01 4.92769063e-01 -1.14515531e+00
-6.75280154e-01 -3.74202728e-01 6.28190398e-01 -3.81881654e-01
-9.21576679e-01 -5.34319878e-01 -1.47518384e+00 -1.13239251e-01
-3.95158218e-04 4.47418302e-01 4.87521321e-01 1.18110681e+00
-7.37674832e-02 4.60225701e-01 3.51209402e-01 -5.01206815e-01
-5.79837084e-01 -9.68524575e-01 -1.61854759e-01 2.22322732e-01
7.07222745e-02 -8.39174926e-01 -3.81220520e-01 2.47523516e-01] | [12.856478691101074, 7.970150947570801] |
e0a927a1-8313-4568-8222-10708c483f73 | inferring-networks-from-random-walk-based | null | null | http://papers.nips.cc/paper/7628-inferring-networks-from-random-walk-based-node-similarities | http://papers.nips.cc/paper/7628-inferring-networks-from-random-walk-based-node-similarities.pdf | Inferring Networks From Random Walk-Based Node Similarities | Digital presence in the world of online social media entails significant privacy risks. In this work we consider a privacy threat to a social network in which an attacker has access to a subset of random walk-based node similarities, such as effective resistances (i.e., commute times) or personalized PageRank scores. Using these similarities, the attacker seeks to infer as much information as possible about the network, including unknown pairwise node similarities and edges.
For the effective resistance metric, we show that with just a small subset of measurements, one can learn a large fraction of edges in a social network. We also show that it is possible to learn a graph which accurately matches the underlying network on all other effective resistances. This second observation is interesting from a data mining perspective, since it can be expensive to compute all effective resistances or other random walk-based similarities. As an alternative, our graphs learned from just a subset of effective resistances can be used as surrogates in a range of applications that use effective resistances to probe graph structure, including for graph clustering, node centrality evaluation, and anomaly detection.
We obtain our results by formalizing the graph learning objective mathematically, using two optimization problems. One formulation is convex and can be solved provably in polynomial time. The other is not, but we solve it efficiently with projected gradient and coordinate descent. We demonstrate the effectiveness of these methods on a number of social networks obtained from Facebook. We also discuss how our methods can be generalized to other random walk-based similarities, such as personalized PageRank scores. Our code is available at https://github.com/cnmusco/graph-similarity-learning. | ['Babis Tsourakakis', 'Cameron Musco', 'Jeremy Hoskins', 'Christopher Musco'] | 2018-12-01 | null | null | null | neurips-2018-12 | ['graph-similarity'] | ['graphs'] | [ 6.48527592e-02 5.83648503e-01 -3.44357282e-01 -1.58056527e-01
-5.25548398e-01 -9.54977393e-01 2.39323765e-01 6.79962933e-01
-3.35048676e-01 5.30515552e-01 -6.29755929e-02 -5.15222549e-01
-5.71741045e-01 -1.26789093e+00 -6.89936697e-01 -4.67464119e-01
-7.67801881e-01 4.94152188e-01 6.48495778e-02 -1.24433875e-01
1.94782674e-01 6.64285243e-01 -6.72926188e-01 -2.65387565e-01
4.79046166e-01 5.83115458e-01 -4.87261593e-01 8.34491849e-01
2.97682822e-01 5.72455764e-01 -2.92885661e-01 -7.30569541e-01
6.22472465e-01 -3.56331691e-02 -8.67102444e-01 -1.45337656e-01
3.23556483e-01 -2.60084391e-01 -6.85545325e-01 1.28189290e+00
3.49074453e-01 -3.52508165e-02 2.82659709e-01 -1.77197707e+00
-2.32511967e-01 6.98732853e-01 -7.05074966e-01 8.93392265e-02
7.46604085e-01 -2.51958728e-01 1.43252039e+00 -1.44858897e-01
5.70393741e-01 9.80878115e-01 9.08362567e-01 4.37049940e-02
-1.48524249e+00 -6.32597566e-01 3.20208520e-02 6.20898511e-03
-1.41557252e+00 3.50131914e-02 8.51186633e-01 -3.36071461e-01
3.23813796e-01 8.32596600e-01 6.60469770e-01 1.01752543e+00
-1.67317703e-01 3.84391069e-01 8.93837988e-01 -5.05633801e-02
1.77183047e-01 2.49846414e-01 2.86647409e-01 9.32716310e-01
7.67887115e-01 -7.98517615e-02 -1.74398184e-01 -8.54905427e-01
4.92340177e-01 2.85699010e-01 -4.84577090e-01 -5.97098291e-01
-8.53897750e-01 1.00638962e+00 5.76775610e-01 1.45350397e-01
8.45149532e-02 8.82904083e-02 1.59696594e-01 6.68710947e-01
3.61232340e-01 6.05402887e-01 -4.55327809e-01 2.70567872e-02
-5.33845246e-01 -4.56163613e-03 1.60898292e+00 6.43623710e-01
1.21625710e+00 -5.31230986e-01 3.04517001e-01 2.79961944e-01
1.91323325e-01 5.07226884e-01 -2.42400959e-01 -1.04591334e+00
5.01548052e-01 4.60947514e-01 1.12497084e-01 -1.95402014e+00
-4.01874870e-01 -1.55608863e-01 -9.07703936e-01 -2.14296713e-01
7.43309140e-01 -3.78706485e-01 -1.77039146e-01 1.88504148e+00
5.46606600e-01 3.96390468e-01 -4.22516465e-01 5.62757730e-01
1.71894699e-01 2.81097591e-01 -4.32983488e-01 -2.75095344e-01
9.11879599e-01 -4.55835104e-01 -2.54597485e-01 -1.86800584e-01
8.81197453e-01 -1.98424056e-01 8.21046770e-01 2.00148210e-01
-7.49967456e-01 3.31720591e-01 -9.11939561e-01 1.30743682e-01
-4.64983582e-01 -6.48102105e-01 8.14016938e-01 1.11398757e+00
-1.20451021e+00 1.04650474e+00 -8.89930844e-01 -6.01267159e-01
3.99740458e-01 5.28437197e-01 -4.83457237e-01 3.29248980e-02
-1.19194973e+00 3.21726292e-01 -3.03352834e-03 -2.81927526e-01
-4.56089795e-01 -7.23116219e-01 -8.03433359e-01 1.60238802e-01
6.90135002e-01 -4.07580435e-01 6.82330370e-01 -6.91744387e-01
-1.00170684e+00 8.31470609e-01 -2.35703494e-02 -5.00228286e-01
7.24758863e-01 2.24757865e-01 -2.63257205e-01 3.84411067e-01
4.37202677e-02 -2.47504771e-01 5.45307875e-01 -9.36362445e-01
-1.19933695e-01 -6.02394760e-01 4.55392957e-01 -5.49278483e-02
-8.69054258e-01 -2.69628555e-01 -2.88457334e-01 -2.63466120e-01
1.11577995e-01 -1.16072404e+00 -4.79115307e-01 3.76221925e-01
-8.73286843e-01 1.91087142e-01 5.81134558e-01 -5.45673490e-01
1.30303907e+00 -1.79683745e+00 -6.76124915e-02 1.24780309e+00
9.10357594e-01 -7.36910701e-02 -1.57649904e-01 8.59241724e-01
7.72043504e-03 7.19026864e-01 -3.18935394e-01 -1.87033921e-01
-2.69483998e-02 -8.37916788e-03 -6.31454214e-02 1.04624414e+00
-4.08154488e-01 8.24940264e-01 -1.12547731e+00 -1.85546219e-01
-1.04920663e-01 1.32271335e-01 -5.21958292e-01 -5.48715182e-02
7.77807012e-02 3.58134151e-01 -7.04443753e-01 2.40030006e-01
7.75654256e-01 -6.40186489e-01 7.41707981e-01 1.85026348e-01
5.04359365e-01 2.00141788e-01 -1.25484478e+00 1.29469633e+00
-2.68808812e-01 5.85108936e-01 3.10138613e-01 -1.20369744e+00
6.35857344e-01 9.18349922e-02 7.48944163e-01 -1.98056474e-02
3.27302292e-02 1.17956465e-02 -1.98399916e-01 -2.86189973e-01
-4.37986478e-03 3.52717668e-01 -8.97417441e-02 9.94871974e-01
-3.77362788e-01 4.23829317e-01 4.92579862e-02 7.72790790e-01
1.97181690e+00 -8.68930161e-01 3.66789252e-01 -2.18333647e-01
3.45102489e-01 -3.89451861e-01 3.37474853e-01 8.00882757e-01
-3.43448482e-02 2.96449512e-01 1.22231686e+00 -1.67431727e-01
-9.79658902e-01 -1.37719786e+00 7.70313591e-02 5.69270730e-01
1.55081406e-01 -9.35234964e-01 -6.04796410e-01 -1.01256251e+00
4.21013951e-01 6.47127032e-02 -5.87866843e-01 -1.88749373e-01
-2.58542836e-01 -7.23485589e-01 5.11565506e-01 -1.17572226e-01
1.83870777e-01 -2.09995329e-01 3.48103315e-01 -1.79983169e-01
-1.62452295e-01 -1.16295135e+00 -6.72724366e-01 -3.41779053e-01
-7.37567067e-01 -1.61708033e+00 -4.87779379e-02 -2.89518267e-01
9.81072187e-01 4.95342106e-01 9.14621949e-01 6.21704221e-01
-1.02551132e-01 8.58915865e-01 -1.52064174e-01 7.37219453e-02
-1.73305482e-01 3.36393088e-01 9.29172710e-02 3.57822984e-01
3.35713387e-01 -1.31544352e+00 -5.34953177e-01 2.65574127e-01
-6.98970199e-01 -5.11619270e-01 1.48633793e-01 3.86371672e-01
3.10031891e-01 2.24633649e-01 2.84307569e-01 -1.63633144e+00
8.26171160e-01 -1.01362455e+00 -8.44631433e-01 1.06882647e-01
-7.53104031e-01 -5.72005361e-02 6.80491090e-01 -4.48385745e-01
-7.64496401e-02 -5.83077334e-02 1.80022344e-01 -1.90284058e-01
3.11770320e-01 5.88867843e-01 -2.89125741e-01 -5.92113554e-01
7.10106075e-01 -1.33467481e-01 1.47239372e-01 -2.10727796e-01
3.99676710e-01 5.62783480e-01 -9.61602479e-02 -5.26654065e-01
1.37464011e+00 7.40238547e-01 5.78143477e-01 -1.02587605e+00
-6.05719626e-01 -6.19122088e-01 -3.00694615e-01 1.32561438e-02
2.30050594e-01 -5.61231136e-01 -1.38894749e+00 9.91177037e-02
-8.40865493e-01 -2.36982346e-01 -2.30995104e-01 2.76504338e-01
-3.16122115e-01 9.19944108e-01 -6.57672942e-01 -8.22248399e-01
-1.51692048e-01 -5.52189410e-01 5.62966585e-01 -1.83084711e-01
-4.38562244e-01 -1.53138447e+00 1.61295950e-01 3.16759914e-01
1.23762481e-01 7.14816034e-01 6.40374362e-01 -1.03096783e+00
-8.47485960e-01 -7.63868392e-01 -2.85623610e-01 -4.99185361e-02
1.39025554e-01 -2.11261526e-01 -6.19484842e-01 -6.89683318e-01
-1.70376197e-01 -1.87006686e-02 5.03319025e-01 -9.76592824e-02
1.42331719e+00 -1.01649129e+00 -5.06308377e-01 8.67605209e-01
1.29357874e+00 -5.36367774e-01 5.20158350e-01 7.81328604e-02
9.52475190e-01 5.40995002e-01 1.68200552e-01 6.36644125e-01
5.00025213e-01 4.27478105e-01 4.50878501e-01 4.87196520e-02
5.80678344e-01 -6.55464828e-01 2.04895332e-01 4.98781294e-01
1.07179463e-01 -2.35496834e-01 -7.72751629e-01 3.08895409e-01
-1.86489773e+00 -8.81129324e-01 -2.36406490e-01 2.63428235e+00
5.39960921e-01 1.44701432e-02 5.08388698e-01 1.11181848e-01
8.78087103e-01 2.94300228e-01 -6.51557386e-01 -1.90935910e-01
3.41052786e-02 2.10631385e-01 1.08425570e+00 1.00001025e+00
-8.88899267e-01 3.71599764e-01 5.24801064e+00 5.13773382e-01
-7.36985683e-01 9.56325158e-02 7.27014005e-01 -1.13399446e-01
-6.21544838e-01 4.07870173e-01 -2.69079685e-01 4.89182472e-01
1.02570415e+00 -5.52271903e-01 1.06126308e+00 8.01869690e-01
3.28433476e-02 1.15900807e-01 -1.22669208e+00 9.07895565e-01
-2.13985458e-01 -1.10061777e+00 -3.90889078e-01 8.18336487e-01
6.44257665e-01 4.35271598e-02 -4.39963490e-02 -2.44657919e-01
6.37929082e-01 -9.46359754e-01 -1.80906728e-01 3.25352788e-01
7.67691612e-01 -7.79941320e-01 2.24597424e-01 3.61033320e-01
-1.21322644e+00 -7.39982724e-02 -3.43147159e-01 -3.48432809e-02
-1.70077413e-01 9.91387665e-01 -8.76274645e-01 4.69724596e-01
4.06575590e-01 8.27593923e-01 -5.07519305e-01 8.80734503e-01
-3.78780156e-01 6.33329868e-01 -8.86889279e-01 -1.53391629e-01
-1.96008295e-01 -5.48643112e-01 9.45474267e-01 6.83935106e-01
3.26755911e-01 7.42720813e-02 1.77415997e-01 7.35301018e-01
-5.73094606e-01 2.37740085e-01 -8.95127773e-01 -2.95942724e-01
7.68729508e-01 1.55541551e+00 -5.04656911e-01 2.23229140e-01
-3.73459309e-01 1.01869059e+00 7.57399023e-01 5.12146711e-01
-5.26787758e-01 -5.76824427e-01 9.65691388e-01 7.94005811e-01
-1.19508199e-01 -3.67999166e-01 1.11300368e-02 -1.45564282e+00
2.36018717e-01 -7.12387979e-01 5.94728231e-01 -1.34815276e-01
-1.56645703e+00 1.87610447e-01 -2.08956182e-01 -8.68495405e-01
-1.85223654e-01 -4.34479684e-01 -7.42267907e-01 6.11105919e-01
-1.06093264e+00 -6.76093340e-01 -1.70746401e-01 8.18975210e-01
-6.85676515e-01 1.49482936e-01 7.78270781e-01 2.20085397e-01
-5.13227940e-01 8.42579603e-01 1.90586463e-01 4.44773674e-01
3.78253698e-01 -1.52993059e+00 5.30696988e-01 9.12116289e-01
4.64659244e-01 6.04774058e-01 4.63783920e-01 -7.53729641e-01
-1.73890150e+00 -9.99620855e-01 8.21065664e-01 -5.78342974e-01
1.16391277e+00 -6.87909663e-01 -6.48656249e-01 1.08296144e+00
-4.11126703e-01 4.60459709e-01 8.59535515e-01 4.71210629e-01
-4.40910399e-01 -4.12342727e-01 -1.53687668e+00 7.68569469e-01
1.36666608e+00 -7.55941033e-01 3.68380576e-01 8.29129457e-01
5.39849699e-01 -1.63891256e-01 -1.06169987e+00 8.26194137e-03
4.35185254e-01 -8.13983142e-01 1.00635004e+00 -7.09478796e-01
-2.21499264e-01 -1.18371584e-02 -8.26640204e-02 -1.17242980e+00
-2.00100169e-01 -1.37565958e+00 -5.19423842e-01 9.64209795e-01
5.92135191e-01 -1.31921208e+00 1.33571708e+00 9.08002973e-01
9.74541962e-01 -8.14292789e-01 -8.66420031e-01 -6.73732579e-01
-9.44607556e-02 -2.91950524e-01 6.07111990e-01 1.37657368e+00
2.83387929e-01 3.36350590e-01 -4.19471592e-01 6.42597437e-01
1.00386655e+00 -8.81125629e-02 1.04618335e+00 -1.52745557e+00
-5.66353738e-01 -1.24000728e-01 -7.62745261e-01 -8.70853424e-01
2.90249556e-01 -1.21231079e+00 -8.87387395e-01 -1.18990529e+00
1.50427490e-01 -7.04623759e-01 -3.83584178e-03 3.11868489e-01
1.45616263e-01 5.72261736e-02 1.41523883e-01 3.73441204e-02
-5.35206735e-01 -4.89308946e-02 9.85695004e-01 -2.11940501e-02
-2.85601228e-01 6.01063371e-01 -9.79090631e-01 6.82534397e-01
1.05617750e+00 -6.78960979e-01 -5.45063913e-01 1.86657667e-01
7.88470984e-01 2.60175765e-01 3.73822510e-01 -6.97693884e-01
4.74202573e-01 -8.85553434e-02 4.94338870e-02 1.71160847e-02
4.19743925e-01 -9.70371902e-01 2.66259283e-01 4.26196426e-01
-3.19514066e-01 -7.91830570e-02 -3.61790419e-01 1.10924292e+00
3.82916123e-01 -5.77796660e-02 5.48020661e-01 -1.01213880e-01
2.10476860e-01 8.63149643e-01 3.15289572e-02 3.97351652e-01
1.12162852e+00 -1.64740235e-01 -4.65222895e-01 -1.16786146e+00
-7.63208747e-01 3.60737711e-01 6.94752038e-01 -7.98794790e-05
5.68233371e-01 -1.17672431e+00 -5.60926616e-01 3.00530959e-02
-5.94038665e-02 -4.32720870e-01 -7.37204850e-02 1.06339145e+00
-3.87643576e-01 -4.64429781e-02 2.39627808e-01 -2.15706304e-01
-1.36824811e+00 6.83629453e-01 3.53520960e-01 -4.04213697e-01
-3.28423798e-01 5.34562588e-01 -1.93451703e-01 -6.23154759e-01
1.73831478e-01 3.03111345e-01 3.28789741e-01 -1.84489936e-01
4.32779253e-01 6.89558089e-01 -2.67760545e-01 -4.62738872e-01
-2.98137277e-01 4.07068312e-01 -7.60742724e-02 -3.16282958e-02
1.25933802e+00 -3.47279936e-01 -4.42249984e-01 1.88513920e-01
1.77674556e+00 6.30088627e-01 -7.95879662e-01 -3.13703805e-01
1.66632041e-01 -8.75310004e-01 -2.31315225e-01 -2.44994760e-01
-1.36348236e+00 5.22092104e-01 8.68690684e-02 9.08338010e-01
9.79416788e-01 7.72531284e-03 8.04825425e-01 7.73309946e-01
5.16917646e-01 -6.08298659e-01 -1.62465394e-01 8.88229832e-02
3.23989034e-01 -1.20424294e+00 1.58275962e-01 -8.73966217e-01
-7.96368793e-02 9.52150285e-01 2.64423490e-01 -2.77452469e-01
1.23893464e+00 7.20418543e-02 -5.09680569e-01 -1.81704029e-01
-3.84106666e-01 4.18334715e-02 -4.93955845e-03 6.34684980e-01
3.79114300e-02 2.39738673e-01 -2.34630227e-01 3.71433824e-01
-3.16522032e-01 -5.60109854e-01 8.73437107e-01 6.09182477e-01
-2.62360841e-01 -1.40650940e+00 1.48784280e-01 9.26258504e-01
-6.82402909e-01 -4.43758443e-02 -8.01108658e-01 5.30041754e-01
-6.15915120e-01 1.06058681e+00 -2.76943117e-01 -5.66746771e-01
4.63674478e-02 -5.74020386e-01 2.36370236e-01 -6.38472021e-01
-2.32026517e-01 -6.77418888e-01 3.83763224e-01 -8.96584392e-01
2.05773506e-02 -6.61563456e-01 -8.09983611e-01 -1.11298358e+00
-2.09908962e-01 4.16460991e-01 5.14358163e-01 5.56290269e-01
4.60224479e-01 -3.52633715e-01 1.25955343e+00 -2.92431414e-01
-5.88616848e-01 -5.12030959e-01 -9.98817623e-01 3.94428492e-01
4.04445469e-01 -5.99703081e-02 -9.96471643e-01 -6.56368315e-01] | [6.845629692077637, 5.562986850738525] |
30c00fc9-e8ba-4892-bd7a-09b76bfcc9fa | modeling-dense-cross-modal-interactions-for | null | null | https://www.ijcai.org/Proceedings/2020/558 | https://www.ijcai.org/proceedings/2020/0558.pdf | Modeling Dense Cross-Modal Interactions for Joint Entity-Relation Extraction | Joint extraction of entities and their relations benefits from the close interaction between named entities and their relation information. Therefore, how to effectively model such cross-modal interactions is critical for the final performance. Previous works have used simple methods such as label-feature concatenation to perform coarse-grained semantic fusion among cross-modal instances, but fail to capture fine-grained correlations over token and label spaces, resulting in insufficient interactions. In this paper, we propose a deep Cross-Modal Attention Network (CMAN) for joint entity and relation extraction. The network is carefully constructed by stacking multiple attention units in depth to fully model dense interactions over token-label spaces, in which two basic attention units are proposed to explicitly capture fine-grained correlations across different modalities (e.g., token-to-token and labelto-token). Experiment results on CoNLL04 dataset show that our model obtains state-of-the-art results by achieving 90.62% F1 on entity recognition and 72.97% F1 on relation classification. In ADE dataset, our model surpasses existing approaches by more than 1.9% F1 on relation classification. Extensive analyses further confirm the effectiveness of our approach. | ['Fang Liu', 'Zhiping Cai', 'Minghao Hu', 'Shan Zhao'] | 2020-07-01 | null | null | null | null | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [-1.33276284e-01 2.44011447e-01 -3.74604374e-01 -6.21969640e-01
-8.55097950e-01 -4.04052794e-01 6.86231017e-01 3.09264511e-01
-4.62224722e-01 7.09136724e-01 2.94780612e-01 -1.53792962e-01
-1.79480702e-01 -8.34118783e-01 -7.64725745e-01 -5.20681918e-01
-4.76786569e-02 5.16371369e-01 -5.30109778e-02 -1.99026912e-01
-4.16156709e-01 2.39859298e-01 -1.34220409e+00 5.43448091e-01
1.01700675e+00 1.08103180e+00 -2.77531564e-01 3.39361906e-01
-4.79762465e-01 9.90666091e-01 -5.63527226e-01 -7.91165352e-01
-1.70175657e-01 -1.28033593e-01 -1.25264454e+00 -1.30001634e-01
2.30445608e-01 -1.75967321e-01 -4.27093387e-01 1.04350138e+00
3.84748936e-01 9.91272628e-02 6.91192269e-01 -1.21508980e+00
-1.00003207e+00 9.75274920e-01 -6.73615634e-01 -2.25580558e-02
2.41069838e-01 -1.88128203e-01 1.54726887e+00 -9.92550075e-01
4.66030896e-01 1.36024880e+00 6.38360083e-01 1.71258062e-01
-1.21613467e+00 -7.95864820e-01 3.39812428e-01 4.09804434e-01
-1.73859382e+00 -3.76889288e-01 4.36638951e-01 -2.79923469e-01
1.49435484e+00 1.72397360e-01 2.54619896e-01 9.10608649e-01
-5.09374365e-02 9.26024139e-01 8.61267149e-01 -5.04226565e-01
-4.65038955e-01 -1.35936037e-01 3.84414107e-01 5.55592358e-01
3.22487533e-01 -1.64299533e-01 -4.45019126e-01 1.18573815e-01
6.50403082e-01 -8.74924138e-02 -8.66312161e-02 1.12889409e-01
-1.35496759e+00 6.17277503e-01 8.45875859e-01 5.53421080e-01
-4.61112261e-01 1.50199980e-01 4.84701008e-01 3.93968783e-02
6.32900000e-01 4.52779293e-01 -8.15272510e-01 2.32218280e-02
-4.29395974e-01 5.07290401e-02 6.85582161e-01 1.20194900e+00
6.90583408e-01 -3.11498791e-01 -4.94071126e-01 1.17244959e+00
3.90456140e-01 3.47464293e-01 1.93851799e-01 -5.83557427e-01
7.50432849e-01 9.37455654e-01 -1.12384133e-01 -8.80249262e-01
-6.05305552e-01 -5.37011623e-01 -1.04911923e+00 -4.78962660e-01
1.02428004e-01 -2.09353805e-01 -1.04752147e+00 1.92456877e+00
4.40577090e-01 2.42661685e-01 2.23309830e-01 6.75797820e-01
1.40299833e+00 4.38499451e-01 6.75232649e-01 1.62881222e-02
1.69495189e+00 -1.15760195e+00 -1.09135902e+00 -1.74325585e-01
1.01020777e+00 -5.95608652e-01 6.43818140e-01 -2.91326165e-01
-9.47501302e-01 -6.62290752e-01 -7.87514746e-01 -4.42775875e-01
-6.98736012e-01 3.47152859e-01 8.89419734e-01 3.08421493e-01
-5.70127606e-01 3.70021433e-01 -7.13623226e-01 -3.71198475e-01
5.81434965e-01 6.34906828e-01 -6.85599148e-01 -1.29033178e-01
-1.75294602e+00 1.01489496e+00 7.93854773e-01 3.72863948e-01
-1.62516236e-01 -6.95466340e-01 -1.11381555e+00 4.49063450e-01
4.45715666e-01 -6.91391945e-01 1.04357493e+00 -2.27701709e-01
-1.05632257e+00 9.68492389e-01 -3.41276646e-01 -2.88428873e-01
1.06822159e-02 -3.61313492e-01 -6.74323142e-01 -1.52674556e-01
2.16344431e-01 8.51653099e-01 -5.35683893e-02 -1.22318554e+00
-7.74988234e-01 -1.74944982e-01 3.34502429e-01 3.29714388e-01
-4.00644898e-01 5.92150800e-02 -6.88324571e-01 -4.99748826e-01
1.19369172e-01 -7.59899437e-01 1.15453735e-01 -6.40553713e-01
-7.73261309e-01 -6.96222603e-01 6.41455412e-01 -6.02695167e-01
1.33341849e+00 -1.91617393e+00 -2.85215862e-02 -6.83427155e-02
4.10825610e-01 5.04373610e-01 -1.71865568e-01 4.19339001e-01
-3.56142104e-01 3.99948061e-01 1.68411862e-02 -6.41725600e-01
3.44571948e-01 2.18457401e-01 -5.54995202e-02 1.74929410e-01
6.66228235e-01 1.46982265e+00 -8.19097042e-01 -7.30437994e-01
2.60723941e-02 6.85157657e-01 -2.00703949e-01 2.68780977e-01
3.45384143e-02 2.05448583e-01 -4.45459485e-01 8.84617090e-01
7.50175059e-01 -5.40970385e-01 3.76992404e-01 -6.87059402e-01
3.52130741e-01 5.73640943e-01 -8.85033250e-01 1.45149982e+00
-6.07326984e-01 3.72307032e-01 -1.65553778e-01 -9.38970745e-01
8.68457794e-01 4.95695025e-01 5.13219893e-01 -6.78647041e-01
2.87109822e-01 1.83231860e-01 1.54545739e-01 -4.77219492e-01
4.38412100e-01 -2.13188350e-01 -2.75331020e-01 4.25674804e-02
4.59950447e-01 4.88555193e-01 2.93353975e-01 2.20253274e-01
9.87559438e-01 6.06677216e-03 4.78597283e-01 -8.00423920e-02
4.69597697e-01 -4.26800668e-01 7.45816767e-01 4.38688338e-01
-5.74391447e-02 3.03320646e-01 6.06841028e-01 -2.45916262e-01
-6.61252916e-01 -7.50584185e-01 -3.42347503e-01 1.18638062e+00
2.40728393e-01 -5.19613087e-01 -5.05672216e-01 -1.04358947e+00
4.59549166e-02 4.93545413e-01 -7.51834750e-01 -2.63103724e-01
-3.51731867e-01 -1.02857435e+00 7.75530457e-01 9.11928475e-01
8.57825875e-01 -1.16137958e+00 1.93416417e-01 2.80050963e-01
-5.25818586e-01 -1.65736842e+00 -2.70324081e-01 2.68458188e-01
-4.57654953e-01 -9.62897301e-01 -3.60366374e-01 -8.63840699e-01
4.18284446e-01 -9.10114795e-02 1.38187551e+00 9.25215036e-02
3.56384516e-02 -1.23815037e-01 -4.42500621e-01 -1.59794092e-01
9.09241885e-02 5.45398295e-01 -2.18162507e-01 8.92252252e-02
7.87409604e-01 -4.79359269e-01 -2.33044147e-01 2.39681214e-01
-6.33675992e-01 6.55334517e-02 8.14350307e-01 1.21585965e+00
4.56947923e-01 1.64918274e-01 7.26144075e-01 -1.20320332e+00
3.10102910e-01 -6.97414517e-01 -2.77222190e-02 6.37137890e-01
-4.03893709e-01 5.16246334e-02 3.31754059e-01 -3.61416340e-01
-1.21847034e+00 -8.74588862e-02 -2.27033705e-01 -2.42838621e-01
-3.97511661e-01 7.36997128e-01 -7.14460909e-01 2.51192600e-01
1.11528240e-01 -2.39279002e-01 -6.63583994e-01 -4.12088364e-01
5.57265699e-01 7.04743922e-01 5.48560977e-01 -7.92616248e-01
3.77268463e-01 1.54293999e-01 -4.58008535e-02 -3.04444909e-01
-1.27876103e+00 -4.81181830e-01 -8.44547451e-01 3.26334298e-01
1.14287710e+00 -1.14072657e+00 -9.28730845e-01 5.18274724e-01
-1.27798522e+00 -1.07059725e-01 -1.22393787e-01 5.97145498e-01
-1.36033148e-02 -5.78122437e-02 -1.04617703e+00 -6.23778701e-01
-3.37492913e-01 -1.01591957e+00 1.27941167e+00 3.24242115e-01
-1.07322074e-01 -1.16407704e+00 -1.90792635e-01 5.61498463e-01
2.39738718e-01 1.83363542e-01 8.49672079e-01 -9.72180963e-01
-5.03179252e-01 -2.63950855e-01 -8.84957075e-01 4.19697538e-02
2.72764146e-01 -9.95504633e-02 -1.15066183e+00 4.94824350e-02
-6.30349696e-01 -4.35061932e-01 1.10138655e+00 6.61961362e-02
9.66639936e-01 -6.78758174e-02 -7.35977113e-01 6.41981840e-01
1.10639322e+00 5.33876084e-02 6.36493385e-01 1.63198233e-01
1.35396588e+00 6.56344652e-01 7.05581069e-01 8.39091912e-02
9.71460760e-01 8.18343341e-01 3.55040938e-01 -4.40243006e-01
-2.23904803e-01 -1.32649899e-01 -6.41078278e-02 7.57768095e-01
-6.36357814e-02 -4.45199192e-01 -1.03207552e+00 7.56534815e-01
-1.98516119e+00 -8.30930889e-01 -2.70168871e-01 1.65771282e+00
1.10502493e+00 6.16259985e-02 -1.88603759e-01 -1.28571257e-01
8.53491902e-01 1.41954035e-01 -3.36250901e-01 -1.95219785e-01
-2.66086668e-01 2.15338856e-01 3.73962432e-01 5.46859443e-01
-1.56000996e+00 1.28227174e+00 5.16678572e+00 1.15858614e+00
-6.99701607e-01 1.38311148e-01 7.16825426e-01 2.54486710e-01
-2.60861039e-01 -7.55757317e-02 -1.18833768e+00 3.06131959e-01
8.29403520e-01 1.73038289e-01 -5.87630942e-02 4.82182860e-01
-4.58798170e-01 1.40117452e-01 -1.07487500e+00 8.18986714e-01
-2.15517506e-01 -1.07834589e+00 -7.38532096e-02 2.41243646e-01
8.38272512e-01 -4.52842042e-02 -2.13792726e-01 8.37193012e-01
6.68298185e-01 -1.23131680e+00 3.01913977e-01 3.76335859e-01
9.50648367e-01 -9.24187720e-01 1.30863726e+00 2.40620356e-02
-1.75498688e+00 1.17695294e-01 -1.08774938e-01 1.59521833e-01
2.94267118e-01 8.52400780e-01 -7.47029960e-01 9.85457420e-01
5.06229877e-01 8.83036077e-01 -4.53997046e-01 5.89193642e-01
-3.36659670e-01 4.51075017e-01 -3.89997929e-01 2.75644660e-01
2.03732267e-01 1.37539640e-01 1.05924802e-02 1.60312986e+00
1.11580275e-01 3.43616575e-01 2.20272362e-01 7.39113390e-01
-5.44413686e-01 1.18341155e-01 -3.33303362e-01 -2.30789274e-01
7.67567933e-01 1.42951524e+00 -5.22580981e-01 -5.11655927e-01
-5.94111323e-01 8.25740695e-01 8.59506607e-01 4.32345539e-01
-1.08092868e+00 -5.92656076e-01 8.00597250e-01 -3.80964488e-01
3.81532997e-01 -4.37225867e-03 -3.34655762e-01 -1.29846454e+00
-4.08117548e-02 -5.24680793e-01 6.02869332e-01 -4.43488598e-01
-1.60796452e+00 8.48079801e-01 -6.05039001e-02 -8.69932294e-01
-2.77558565e-02 -4.46303874e-01 -3.32175612e-01 1.01635146e+00
-1.50354576e+00 -1.83111966e+00 -2.39810780e-01 3.48007292e-01
6.87650144e-02 -4.80934978e-03 9.96206045e-01 7.92060077e-01
-8.93981814e-01 1.01405013e+00 -2.63724744e-01 6.82893336e-01
7.00696886e-01 -1.37580609e+00 4.65629786e-01 6.00459695e-01
3.35971057e-01 8.74763131e-01 2.39451081e-02 -6.58053637e-01
-9.18064594e-01 -1.20445180e+00 1.50307167e+00 -4.55525726e-01
6.06584191e-01 -3.74834806e-01 -1.03186893e+00 9.71384168e-01
2.93550313e-01 4.70635980e-01 8.50162268e-01 8.73002529e-01
-6.75563931e-01 -4.74079065e-02 -9.94199336e-01 4.08985823e-01
1.18057656e+00 -7.67018557e-01 -4.94025052e-01 1.76129743e-01
9.99234617e-01 -5.21163285e-01 -1.39661455e+00 9.78711069e-01
4.09892231e-01 -5.37551939e-01 1.09896910e+00 -7.93777227e-01
5.82135558e-01 -2.19541907e-01 -2.88830906e-01 -1.17753184e+00
-4.87488359e-01 -1.09891765e-01 -4.46055233e-01 1.96817994e+00
6.50171995e-01 -5.90029478e-01 4.38597709e-01 7.06243277e-01
-6.97809905e-02 -1.12299943e+00 -6.85506344e-01 -5.85548043e-01
5.02946191e-02 -3.56323063e-01 9.65583205e-01 1.48545098e+00
1.15560539e-01 8.97900403e-01 -2.16309965e-01 3.68699908e-01
3.70023787e-01 3.11319321e-01 3.83944064e-01 -1.08149946e+00
-1.02666371e-01 -5.51324069e-01 -3.49668324e-01 -1.06035161e+00
6.05930388e-01 -9.41623092e-01 -1.37430161e-01 -1.75024462e+00
3.67979646e-01 -7.86002398e-01 -5.62318027e-01 8.87274742e-01
-7.50888944e-01 2.58310348e-01 1.27028942e-01 -2.70447247e-02
-9.94250953e-01 7.54716218e-01 1.17132151e+00 -1.39151379e-01
7.07904249e-02 -3.74095768e-01 -7.85373926e-01 4.92661953e-01
6.26958430e-01 -3.85544002e-01 -1.73249587e-01 -5.37080169e-01
1.10884495e-01 -8.97757113e-02 1.09937087e-01 -6.29115164e-01
1.57750532e-01 5.53192664e-03 4.36852485e-01 -6.48773432e-01
4.72108036e-01 -7.84740269e-01 -6.39911294e-02 -4.17266190e-02
-4.49980587e-01 -3.35272402e-01 1.77728534e-01 4.56549138e-01
-6.43661320e-01 1.18141487e-01 3.86681229e-01 1.87823325e-01
-7.24284887e-01 4.47068185e-01 2.15642124e-01 1.36140123e-01
9.02458906e-01 3.38356972e-01 -5.89330494e-01 -9.05095935e-02
-9.91372168e-01 5.83139062e-01 2.81220991e-02 4.59924489e-01
1.38818681e-01 -1.80129969e+00 -8.14187050e-01 5.67176975e-02
2.97801644e-01 3.18061233e-01 5.47258973e-01 8.29793930e-01
-3.81518006e-02 5.93731105e-01 1.04968518e-01 -4.26727116e-01
-1.19665885e+00 4.61035848e-01 3.55341852e-01 -9.61078167e-01
-2.76109666e-01 1.33144796e+00 4.80555832e-01 -7.31223762e-01
4.64761183e-02 -3.52963954e-01 -3.43531042e-01 3.03854346e-01
3.21892262e-01 5.94795644e-02 1.41608655e-01 -9.17890668e-01
-6.31599247e-01 6.55223787e-01 -3.43238294e-01 2.52274036e-01
1.18120420e+00 -1.74662843e-01 -2.05725759e-01 2.88549513e-01
1.46603107e+00 -1.05828404e-01 -9.39052105e-01 -6.43643260e-01
3.83712687e-02 -2.18067035e-01 2.62408201e-02 -9.47378755e-01
-1.27426052e+00 9.75230575e-01 3.76238511e-03 3.07101578e-01
1.01384866e+00 3.28872621e-01 9.74121690e-01 1.75016269e-01
1.19137503e-01 -7.26635635e-01 -3.05872232e-01 7.87665844e-01
5.74234486e-01 -1.52288580e+00 -8.09727162e-02 -8.79620135e-01
-7.07725048e-01 7.45169103e-01 9.41158175e-01 2.31582731e-01
5.93786716e-01 3.57587755e-01 -9.09524038e-02 -3.81682277e-01
-8.44472647e-01 -7.57427990e-01 7.02126920e-01 3.80220085e-01
8.61830592e-01 3.22075874e-01 -1.76602721e-01 1.04049444e+00
8.53380039e-02 -3.28560233e-01 -2.14429379e-01 7.32396543e-01
2.41714977e-02 -1.28578043e+00 -1.96351707e-02 3.63973647e-01
-5.97160459e-01 -3.43559355e-01 -3.05676579e-01 8.72163355e-01
4.82530296e-01 9.72588420e-01 9.26599056e-02 -5.61938941e-01
4.59343672e-01 2.32095852e-01 3.23965520e-01 -6.32967234e-01
-7.14125514e-01 3.50782648e-03 5.44421315e-01 -4.19743657e-01
-5.49944699e-01 -5.43148875e-01 -1.47607994e+00 -4.00350004e-01
-6.49305940e-01 5.82932420e-02 2.61307895e-01 1.16697395e+00
6.09498799e-01 9.90367115e-01 3.36275101e-01 -5.21315455e-01
-9.99063477e-02 -1.26055312e+00 -5.12838423e-01 5.78217506e-01
1.52295992e-01 -9.86388683e-01 1.25324562e-01 -1.44620091e-01] | [9.222228050231934, 8.719186782836914] |
606d423f-ca54-4bc0-ae8a-fb439fe66afc | simplifying-and-empowering-transformers-for | 2306.10759 | null | https://arxiv.org/abs/2306.10759v1 | https://arxiv.org/pdf/2306.10759v1.pdf | Simplifying and Empowering Transformers for Large-Graph Representations | Learning representations on large-sized graphs is a long-standing challenge due to the inter-dependence nature involved in massive data points. Transformers, as an emerging class of foundation encoders for graph-structured data, have shown promising performance on small graphs due to its global attention capable of capturing all-pair influence beyond neighboring nodes. Even so, existing approaches tend to inherit the spirit of Transformers in language and vision tasks, and embrace complicated models by stacking deep multi-head attentions. In this paper, we critically demonstrate that even using a one-layer attention can bring up surprisingly competitive performance across node property prediction benchmarks where node numbers range from thousand-level to billion-level. This encourages us to rethink the design philosophy for Transformers on large graphs, where the global attention is a computation overhead hindering the scalability. We frame the proposed scheme as Simplified Graph Transformers (SGFormer), which is empowered by a simple attention model that can efficiently propagate information among arbitrary nodes in one layer. SGFormer requires none of positional encodings, feature/graph pre-processing or augmented loss. Empirically, SGFormer successfully scales to the web-scale graph ogbn-papers100M and yields up to 141x inference acceleration over SOTA Transformers on medium-sized graphs. Beyond current results, we believe the proposed methodology alone enlightens a new technical path of independent interest for building Transformers on large graphs. | ['Junchi Yan', 'Yatao Bian', 'Haitian Jiang', 'Fan Nie', 'Hengrui Zhang', 'Chenxiao Yang', 'Wentao Zhao', 'Qitian Wu'] | 2023-06-19 | null | null | null | null | ['property-prediction', 'philosophy'] | ['medical', 'miscellaneous'] | [ 1.22308828e-01 4.41658765e-01 -2.84242541e-01 -2.40745351e-01
-4.72681671e-01 -4.24567670e-01 5.56245625e-01 4.92044330e-01
-2.61308253e-01 6.47712469e-01 2.35873148e-01 -7.01660514e-01
-2.86783814e-01 -1.17610407e+00 -1.03602505e+00 -6.01363361e-01
-5.74178159e-01 4.38399464e-01 2.09730104e-01 -4.61887181e-01
-1.09333068e-01 4.69774693e-01 -1.13073671e+00 -4.99558784e-02
6.53450012e-01 8.96696031e-01 2.29315031e-02 7.01651156e-01
-4.26702984e-02 9.99540389e-01 -3.33085507e-01 -9.74899650e-01
2.12226391e-01 1.61269337e-01 -8.61176908e-01 -1.38859242e-01
7.10261106e-01 -2.96758413e-01 -8.09986115e-01 9.56698954e-01
4.84093487e-01 -2.33797118e-01 2.62282252e-01 -1.39083993e+00
-9.31993127e-01 1.23075581e+00 -7.25450993e-01 3.32965404e-01
1.12098493e-01 1.36328682e-01 1.73238420e+00 -6.03277445e-01
4.74156111e-01 1.20787430e+00 9.69966650e-01 1.10216454e-01
-1.06758499e+00 -4.61492717e-01 5.56392729e-01 3.43531281e-01
-1.37052178e+00 -1.75694644e-01 7.88474858e-01 -1.00179352e-01
1.33977997e+00 3.15553308e-01 6.17517710e-01 9.98427570e-01
3.08394730e-01 6.46454632e-01 4.62808490e-01 -5.97315319e-02
-9.94499996e-02 -3.55340540e-01 3.57510000e-01 1.02255476e+00
7.16055155e-01 -1.28436357e-01 -2.69938469e-01 1.38304858e-02
6.33427322e-01 -4.64421324e-02 -1.60667524e-01 -2.78790921e-01
-1.05834937e+00 8.29905987e-01 1.14460659e+00 2.31820196e-01
-3.71288031e-01 7.36788273e-01 7.17716992e-01 4.26025778e-01
3.08919609e-01 3.06029350e-01 -4.75356519e-01 2.46398877e-02
-6.89793527e-01 2.43927483e-02 6.77678168e-01 1.08283293e+00
5.85557163e-01 2.29839951e-01 -2.41090178e-01 3.99107963e-01
3.42407167e-01 3.41236889e-01 -4.25249338e-02 -3.15511644e-01
6.87008917e-01 9.19536471e-01 -5.09590864e-01 -1.34319115e+00
-5.44529974e-01 -1.02888179e+00 -1.26451838e+00 -2.32849330e-01
1.60928592e-01 1.13738678e-01 -9.83122170e-01 1.92892098e+00
1.49805844e-01 1.25012100e-01 -2.28969619e-01 6.54318690e-01
9.02944386e-01 5.01995623e-01 -6.01859996e-03 1.71207801e-01
1.24678385e+00 -1.03317082e+00 -3.32700133e-01 -1.78189576e-01
6.40253425e-01 -1.33822203e-01 1.15674341e+00 3.01497161e-01
-1.08022892e+00 -5.17126679e-01 -1.17306149e+00 -4.70530927e-01
-5.62769651e-01 -3.23393315e-01 1.22742033e+00 6.42769456e-01
-1.43095422e+00 5.55829048e-01 -7.47045994e-01 -5.00452280e-01
6.61086321e-01 6.41965508e-01 -5.27444839e-01 -1.52477533e-01
-1.22944272e+00 6.39341354e-01 3.14623654e-01 3.56546342e-01
-9.78443325e-01 -6.58981860e-01 -1.03034234e+00 6.21445358e-01
5.65356493e-01 -7.98275411e-01 7.22241879e-01 -5.01464546e-01
-9.57236707e-01 5.08895278e-01 4.09278553e-03 -8.25210035e-01
1.24781467e-01 -5.92895634e-02 -3.63898993e-01 -4.73685265e-02
-6.79311976e-02 4.58088011e-01 7.51618743e-01 -1.00082350e+00
-2.56925881e-01 -4.39003706e-01 4.79976714e-01 1.48009704e-02
-7.68441141e-01 -3.02582979e-01 -7.29731202e-01 -5.96423686e-01
-1.05952933e-01 -7.16666698e-01 -3.66490543e-01 -2.41935670e-01
-7.30937243e-01 -3.09991777e-01 6.85668588e-01 -3.62832814e-01
1.59310496e+00 -1.90049255e+00 2.26698309e-01 2.40928516e-01
9.40618277e-01 2.58105159e-01 -2.80730337e-01 7.24450231e-01
-9.85695049e-02 2.66632885e-01 -5.76673597e-02 -2.52853245e-01
2.42646500e-01 2.93133974e-01 -1.93815112e-01 4.50414389e-01
3.31678420e-01 1.41985965e+00 -9.20607626e-01 -3.11060220e-01
1.01552650e-01 4.65680033e-01 -7.51314938e-01 -7.93698505e-02
-2.23587081e-01 -2.52381086e-01 -3.98695678e-01 7.93520212e-01
7.24451065e-01 -8.75612736e-01 3.05933863e-01 -3.34661573e-01
2.55240500e-01 2.99001157e-01 -7.78591871e-01 1.65902507e+00
-2.05388948e-01 4.07387167e-01 3.17453802e-01 -1.15500724e+00
8.14338624e-01 -9.67773423e-03 3.10301393e-01 -6.21097147e-01
9.17096958e-02 -3.15893330e-02 2.87902176e-01 -9.00508314e-02
5.78398883e-01 7.04646334e-02 -2.72308916e-01 5.75763360e-02
3.22517902e-01 1.78317308e-01 3.99228096e-01 7.26827919e-01
1.64250505e+00 -2.21984729e-01 2.92263687e-01 -2.81526536e-01
4.76751804e-01 -3.50363344e-01 3.30437928e-01 8.13471317e-01
-1.16207795e-02 2.53400296e-01 9.34989214e-01 -7.67225623e-01
-7.98357725e-01 -1.06433260e+00 2.06346661e-01 1.31987321e+00
2.86678621e-03 -9.54495072e-01 -3.08110148e-01 -9.42360520e-01
2.52799332e-01 2.74532050e-01 -7.55964041e-01 -2.39200071e-01
-5.48560262e-01 -9.26979065e-01 7.26330876e-01 6.60924852e-01
2.99649060e-01 -8.34744453e-01 -4.24104072e-02 3.44979316e-01
2.71284461e-01 -1.12749565e+00 -3.49673778e-01 3.92863333e-01
-7.44082272e-01 -1.06756401e+00 -4.06443775e-01 -8.45137835e-01
6.05429530e-01 1.98808730e-01 1.53112900e+00 4.25414503e-01
-1.80352479e-01 1.22470655e-01 -2.91871816e-01 -2.45394945e-01
-1.24995112e-01 6.66557431e-01 -1.66428491e-01 -1.04620077e-01
6.85627908e-02 -1.11676073e+00 -4.35458809e-01 -9.76680592e-02
-6.93159878e-01 4.65876944e-02 8.78396571e-01 8.69024336e-01
3.45618844e-01 1.63710788e-01 7.20606089e-01 -1.16048431e+00
5.83432078e-01 -6.63951576e-01 -6.08548045e-01 2.86425680e-01
-7.87313998e-01 1.35475948e-01 9.98414040e-01 1.15282401e-01
-4.12070453e-01 -2.24891931e-01 -3.26234311e-01 -3.92130315e-01
2.87889540e-01 8.20767701e-01 -2.46462554e-01 -1.60481066e-01
2.96144068e-01 2.46121481e-01 -1.67946547e-01 -2.61077791e-01
5.64224899e-01 1.13317683e-01 4.90473390e-01 -4.94417757e-01
9.69209611e-01 3.69963199e-01 3.02807927e-01 -6.44371629e-01
-4.89174187e-01 -1.01850629e-01 -3.80897850e-01 9.38796923e-02
5.81660092e-01 -8.80385995e-01 -1.05767202e+00 3.88622791e-01
-9.39011395e-01 -2.05427557e-01 -1.35125875e-01 5.55889169e-03
-1.12173542e-01 4.67368990e-01 -9.67512250e-01 -4.70293343e-01
-6.96517944e-01 -1.06114161e+00 1.12248504e+00 -1.33477226e-01
2.04103142e-01 -1.20513415e+00 -3.24994862e-01 1.70599818e-01
6.00338876e-01 2.58275360e-01 1.29916537e+00 -6.04674876e-01
-9.83899236e-01 -2.13704348e-01 -5.56753278e-01 9.12980884e-02
4.93196249e-02 -1.55189574e-01 -7.38012671e-01 -7.78033793e-01
-7.14213908e-01 -3.70649695e-01 1.02243507e+00 1.64507389e-01
1.45607662e+00 -2.87551731e-01 -4.37953979e-01 8.72530699e-01
1.59668601e+00 -3.88905674e-01 5.02623081e-01 -3.83453853e-02
1.25362921e+00 1.13591412e-02 2.98721585e-02 3.58681202e-01
7.40662694e-01 4.21238452e-01 1.13542736e+00 -3.88335437e-01
-2.43343338e-01 -5.67896485e-01 1.77560180e-01 1.01152921e+00
-1.63808718e-01 -8.16859186e-01 -9.07257080e-01 5.17055631e-01
-1.58818758e+00 -7.74938881e-01 -1.78226829e-01 1.97448599e+00
2.93750912e-01 5.02532244e-01 -8.53601918e-02 1.70033634e-01
4.49832380e-01 6.18120492e-01 -5.86565435e-01 -4.83957827e-01
-6.63373247e-02 3.06628019e-01 8.11311066e-01 4.99271661e-01
-1.00813937e+00 9.95777249e-01 6.12742662e+00 8.81158531e-01
-1.21892810e+00 3.66376527e-02 6.65978134e-01 2.98817009e-02
-6.12455010e-01 -1.40122637e-01 -6.90856099e-01 3.72935891e-01
9.44123805e-01 -2.51610845e-01 5.17819762e-01 7.27791429e-01
-1.62323534e-01 3.43163967e-01 -1.16210139e+00 8.15947354e-01
-8.44489485e-02 -1.43750215e+00 3.22993129e-01 4.18055326e-01
4.22034711e-01 4.69197363e-01 7.80271217e-02 5.83425760e-01
6.08209193e-01 -1.45604932e+00 3.47353667e-01 7.55149573e-02
8.23301077e-01 -6.71656609e-01 8.49726081e-01 1.45007208e-01
-1.65051758e+00 -1.39005780e-01 -4.70812887e-01 -2.84295827e-01
1.54055387e-01 5.70598304e-01 -1.05233252e+00 9.74887788e-01
5.90992093e-01 9.22723413e-01 -8.23474169e-01 6.98790729e-01
2.63236091e-02 7.48399258e-01 -3.96337628e-01 -6.54313266e-02
5.56224406e-01 2.80582923e-02 3.71915251e-01 1.14248300e+00
1.47126257e-01 -3.04676354e-01 9.27479491e-02 6.73812151e-01
-5.93560457e-01 -1.03004873e-01 -7.31124997e-01 -3.60259563e-01
4.82009351e-01 1.35754275e+00 -7.23561287e-01 -2.36527085e-01
-6.91391647e-01 8.02624464e-01 8.88227344e-01 2.41898060e-01
-1.13961506e+00 -3.75402987e-01 5.71843743e-01 2.40758449e-01
7.26000845e-01 -2.18316838e-01 -2.08049670e-01 -9.29282308e-01
9.84969512e-02 -8.44121456e-01 5.76915979e-01 -4.05623734e-01
-1.35399616e+00 8.90180051e-01 -4.08883721e-01 -7.34915257e-01
1.57109991e-01 -6.50540650e-01 -5.99369407e-01 6.03514969e-01
-1.61350906e+00 -1.61676908e+00 -3.89770240e-01 7.11520493e-01
1.43581912e-01 5.32224625e-02 7.11140156e-01 5.75756073e-01
-5.47451019e-01 1.10769391e+00 -1.61418289e-01 2.28488162e-01
1.17561691e-01 -1.43655193e+00 9.57483113e-01 8.42781901e-01
3.93061012e-01 7.23650932e-01 4.43840146e-01 -5.29666424e-01
-1.97820771e+00 -1.19372869e+00 8.65774870e-01 -4.10601169e-01
9.10879970e-01 -6.90467894e-01 -8.25619042e-01 9.33464587e-01
2.41094083e-01 4.27208930e-01 2.96349138e-01 5.44395268e-01
-5.10665655e-01 -4.24806058e-01 -9.07781243e-01 4.61296529e-01
1.46749473e+00 -4.06171262e-01 -1.05676129e-01 2.56900251e-01
1.06231201e+00 -3.12064469e-01 -1.11653614e+00 5.31683624e-01
3.44352037e-01 -8.66323471e-01 1.00468826e+00 -7.22261548e-01
3.32251608e-01 -2.81542093e-02 -1.70633122e-01 -1.05927098e+00
-7.68573523e-01 -8.34875464e-01 -3.01088721e-01 1.14021099e+00
4.59153771e-01 -6.96897924e-01 1.05332100e+00 1.84995636e-01
-3.21412355e-01 -1.06291020e+00 -8.21506619e-01 -5.97161949e-01
4.33584191e-02 -4.16797727e-01 8.90956223e-01 8.80908370e-01
-8.81345496e-02 7.15066493e-01 -4.05975878e-01 4.56731647e-01
6.13049030e-01 1.12340450e-01 7.98853040e-01 -1.30118477e+00
-4.62736428e-01 -5.40454984e-01 -8.25739384e-01 -1.20210719e+00
6.31282479e-02 -1.27212155e+00 -3.59939605e-01 -1.72983587e+00
2.93241054e-01 -4.75565553e-01 -5.29379010e-01 6.36684775e-01
-2.14103535e-01 2.17445359e-01 1.16959356e-01 -3.30786556e-01
-8.10197473e-01 6.14784539e-01 1.20764863e+00 -3.93557787e-01
3.05150658e-01 -2.42447674e-01 -1.12874007e+00 4.23605502e-01
5.17289042e-01 -1.93953082e-01 -7.40750492e-01 -7.34180689e-01
5.81676185e-01 -4.04432565e-02 3.85092735e-01 -9.01749253e-01
2.82408535e-01 2.77767241e-01 5.88167831e-02 -5.39833605e-01
2.23201290e-01 -8.69418561e-01 6.44213483e-02 4.38957602e-01
-1.94490805e-01 3.68259072e-01 4.36346233e-02 7.94628143e-01
-2.00653613e-01 3.79398644e-01 3.33300740e-01 -1.50522277e-01
-7.86158144e-01 8.83582413e-01 1.55959100e-01 1.38398916e-01
8.48903060e-01 -1.28067657e-01 -4.38683122e-01 -4.50103432e-01
-6.60999298e-01 4.05919492e-01 3.19268972e-01 4.47714329e-01
4.08413202e-01 -1.17694259e+00 -7.78321922e-01 3.12337011e-01
1.12847313e-01 1.66492790e-01 1.66005865e-01 8.96081626e-01
-4.60227787e-01 5.50434113e-01 4.05352078e-02 -4.05224174e-01
-1.12577009e+00 8.68967116e-01 1.13674030e-01 -7.90162742e-01
-7.37604558e-01 1.23626626e+00 3.14178854e-01 -3.36986810e-01
2.65557498e-01 -5.93458354e-01 8.31492692e-02 -2.00701535e-01
2.27642775e-01 1.93748683e-01 3.82799536e-01 -4.65049177e-01
-4.44050372e-01 2.85069793e-01 -3.18954498e-01 6.70742929e-01
1.50671971e+00 5.55863567e-02 -2.98259646e-01 1.72862094e-02
1.26643217e+00 1.61122248e-01 -1.05937481e+00 -2.03190044e-01
-3.21170613e-02 -1.22254245e-01 1.42215893e-01 -6.28786027e-01
-1.51422656e+00 8.77517879e-01 -1.17475666e-01 5.37887216e-01
1.14046979e+00 2.64646083e-01 8.60484481e-01 4.25661236e-01
6.48244023e-01 -4.31455582e-01 -2.90448312e-02 5.64093411e-01
8.68042052e-01 -1.13346779e+00 1.39160439e-01 -6.09904468e-01
-2.24064901e-01 7.95587361e-01 7.49710083e-01 -1.92344457e-01
6.35045528e-01 4.78686780e-01 -6.08753860e-01 -5.34337401e-01
-1.09696341e+00 -2.00349510e-01 7.14331716e-02 5.40996909e-01
4.55801010e-01 1.92528039e-01 -1.27276396e-02 6.66506588e-01
-2.40459114e-01 -1.63783088e-01 4.14531618e-01 4.61230248e-01
-2.30809584e-01 -1.04947186e+00 1.97323233e-01 8.02732646e-01
-4.10684109e-01 -4.97921586e-01 -3.09298158e-01 1.00082028e+00
1.14941886e-02 6.43631160e-01 2.06906837e-03 -5.78781068e-01
1.56790048e-01 -3.71489823e-01 4.39737856e-01 -4.62091863e-01
-7.00365543e-01 -4.38325971e-01 2.21087500e-01 -6.76998079e-01
3.60264368e-02 -2.41502270e-01 -1.10414243e+00 -7.87954211e-01
-1.98223844e-01 7.58380964e-02 1.87522054e-01 4.29305434e-01
6.53977811e-01 8.65701556e-01 3.92921090e-01 -5.78004360e-01
-5.88117540e-01 -9.59653497e-01 -6.48470044e-01 2.05123439e-01
4.23773944e-01 -5.26416242e-01 -1.62459344e-01 -5.38807690e-01] | [6.982994556427002, 6.222074031829834] |
bb97dd07-cb3a-4275-8cd5-8800377b8b25 | spatiotemporal-capsule-neural-network-for | 2303.02880 | null | https://arxiv.org/abs/2303.02880v1 | https://arxiv.org/pdf/2303.02880v1.pdf | Spatiotemporal Capsule Neural Network for Vehicle Trajectory Prediction | Through advancement of the Vehicle-to-Everything (V2X) network, road safety, energy consumption, and traffic efficiency can be significantly improved. An accurate vehicle trajectory prediction benefits communication traffic management and network resource allocation for the real-time application of the V2X network. Recurrent neural networks and their variants have been reported in recent research to predict vehicle mobility. However, the spatial attribute of vehicle movement behavior has been overlooked, resulting in incomplete information utilization. To bridge this gap, we put forward for the first time a hierarchical trajectory prediction structure using the capsule neural network (CapsNet) with three sequential components. First, the geographic information is transformed into a grid map presentation, describing vehicle mobility distribution spatially and temporally. Second, CapsNet serves as the core model to embed local temporal and global spatial correlation through hierarchical capsules. Finally, extensive experiments conducted on actual taxi mobility data collected in Porto city (Portugal) and Singapore show that the proposed method outperforms the state-of-the-art methods. | ['Chau Yuen', 'Yong Liang Guan', 'Yan Qin'] | 2023-03-06 | null | null | null | null | ['trajectory-prediction'] | ['computer-vision'] | [-4.07479823e-01 -1.18178232e-02 -8.43813300e-01 -1.82000473e-01
-2.57122666e-01 -1.59622267e-01 5.59550047e-01 -2.56293535e-01
4.87931184e-02 8.19631338e-01 2.68194824e-01 -7.90385604e-01
-4.25954700e-01 -1.00044382e+00 -5.22199869e-01 -6.32543921e-01
-5.33245564e-01 -1.26338052e-02 2.34853134e-01 -1.21074706e-01
-6.26582354e-02 5.94231486e-01 -1.59819889e+00 -1.28746748e-01
8.89756441e-01 1.27847707e+00 3.73444468e-01 1.40802553e-02
-5.70194125e-02 9.62952316e-01 -1.67525381e-01 -1.74076945e-01
1.30334765e-01 9.59609970e-02 -2.83243537e-01 -3.29183757e-01
-2.85801113e-01 -2.12370202e-01 -1.17577136e+00 7.18395889e-01
1.48716837e-01 3.21737617e-01 4.37877983e-01 -1.84349692e+00
-4.24613208e-01 5.19578755e-01 -1.28542215e-01 5.30926399e-02
-4.56740350e-01 3.19148190e-02 6.26025319e-01 -7.69145310e-01
7.54655302e-01 8.50335658e-01 8.40418100e-01 5.24062932e-01
-7.40693331e-01 -9.10253584e-01 3.48138124e-01 8.49696815e-01
-1.71171784e+00 -3.21541429e-01 8.94680083e-01 -2.93985605e-01
1.18289602e+00 3.71242285e-01 7.91925192e-01 1.07613266e+00
3.53104800e-01 1.06554437e+00 4.22963053e-01 3.29286546e-01
9.21302438e-02 2.55351931e-01 2.39338521e-02 2.77708262e-01
-4.69706543e-02 3.33801359e-01 -2.27715105e-01 1.03005446e-01
2.32587382e-01 5.28232753e-01 -8.51978809e-02 -3.95157635e-02
-7.58825243e-01 5.69414496e-01 6.94065869e-01 9.39683169e-02
-7.76102722e-01 4.36730295e-01 6.07652724e-01 6.81616440e-02
5.46781659e-01 -2.74957508e-01 -1.71245530e-01 -4.54343677e-01
-1.09555113e+00 1.97301567e-01 5.21475673e-01 1.35626686e+00
5.06278872e-01 6.66355491e-01 -6.58435151e-02 5.10592878e-01
3.33126605e-01 6.51158810e-01 2.81100452e-01 -1.14605820e+00
7.03747690e-01 6.18075371e-01 -1.11829869e-01 -1.36798692e+00
-5.83701253e-01 -5.27575374e-01 -1.31364155e+00 -3.84806432e-02
-1.07653789e-01 -2.06489861e-01 -5.93754292e-01 1.56494796e+00
3.45784463e-02 6.01808608e-01 2.10892975e-01 7.18410432e-01
7.32665539e-01 1.08174670e+00 1.93232149e-01 -1.49290293e-01
8.99638772e-01 -1.13214874e+00 -1.04534757e+00 2.12033004e-01
7.48686254e-01 -1.38059080e-01 3.50663662e-01 -5.95331676e-02
-1.14902282e+00 -5.33602834e-01 -1.06590939e+00 2.86188394e-01
-7.54948914e-01 2.79137105e-01 5.20524442e-01 5.90742469e-01
-1.11812198e+00 4.26718920e-01 -9.36262310e-01 -3.22656959e-01
3.92283499e-01 4.38325614e-01 1.86058525e-02 2.07877513e-02
-1.54845917e+00 7.73189783e-01 2.01918393e-01 3.74498487e-01
-6.97392464e-01 -9.51238394e-01 -6.38493955e-01 3.67478400e-01
4.01260287e-01 -1.66611061e-01 8.86318266e-01 -4.44056153e-01
-1.37697637e+00 -1.77232951e-01 -3.71012211e-01 -7.56743908e-01
4.04925913e-01 3.26161772e-01 -9.41092253e-01 -1.48415834e-01
-3.70722562e-02 6.93500578e-01 1.45303309e-01 -1.03264010e+00
-1.07473886e+00 -2.17824001e-02 -2.79271007e-01 3.88383977e-02
-3.61927480e-01 -2.62688011e-01 -7.60107994e-01 -4.62678730e-01
-2.03722149e-01 -1.25899315e+00 -2.84187883e-01 -2.65304178e-01
-3.10409665e-01 -5.35313606e-01 1.57450449e+00 -8.51103008e-01
1.61478198e+00 -2.31243992e+00 -1.94007680e-01 7.15552509e-01
3.28871042e-01 3.49167734e-01 -1.03285320e-01 6.08256102e-01
2.11333781e-01 7.79484883e-02 3.37839454e-01 -4.91162777e-01
2.02887818e-01 2.88881183e-01 -4.89404976e-01 4.06883687e-01
-2.11731091e-01 1.12445819e+00 -6.45472884e-01 -2.07044080e-01
3.69215190e-01 6.45753920e-01 -2.69056141e-01 -1.81515783e-01
9.82824937e-02 9.47840661e-02 -5.06076276e-01 7.83988476e-01
5.95574379e-01 -1.38762489e-01 1.83753371e-01 -5.42721748e-02
-4.30747688e-01 1.71021029e-01 -7.03957796e-01 9.14965451e-01
-3.72575432e-01 1.24718559e+00 1.22415014e-02 -1.02231014e+00
9.80037034e-01 5.60102284e-01 9.70962882e-01 -1.30291796e+00
-8.02494884e-02 1.68242261e-01 -3.52087587e-01 -5.28925955e-01
9.28195596e-01 7.13337302e-01 -6.28125891e-02 1.74622927e-02
-4.54928935e-01 8.60009730e-01 -3.26496512e-02 2.05257028e-01
9.93574083e-01 -1.68319568e-01 -2.92331159e-01 -4.41717356e-02
2.81600714e-01 7.97283724e-02 8.71200800e-01 4.28804696e-01
-3.09091985e-01 9.46336091e-02 3.35179061e-01 -4.45922554e-01
-1.32156038e+00 -9.05851185e-01 7.57197142e-02 5.56737483e-01
4.33742642e-01 -4.38616514e-01 -6.26278222e-01 -2.01800287e-01
9.40093026e-03 8.24063897e-01 -4.60736454e-01 -1.25480130e-01
-8.18953097e-01 -4.27869231e-01 6.33293152e-01 7.69249439e-01
7.65778780e-01 -7.91693151e-01 -1.78426221e-01 4.96011406e-01
-3.07327867e-01 -1.27443707e+00 -6.04253232e-01 -3.17427158e-01
-5.71598411e-01 -6.31914616e-01 -3.99289489e-01 -5.96387684e-01
3.01009119e-01 6.43867195e-01 5.06419063e-01 9.82722640e-02
2.58670390e-01 9.15707946e-02 -6.96072802e-02 -1.34093717e-01
-1.16465054e-01 4.47750062e-01 4.69650120e-01 1.18395485e-01
4.47681278e-01 -6.84011579e-01 -7.51615167e-01 6.42398894e-01
-3.74795288e-01 1.22246780e-01 3.75143915e-01 3.90735745e-01
4.19988483e-01 3.25212240e-01 9.62765813e-01 -1.99670181e-01
5.50206900e-01 -1.11937284e+00 -7.20272839e-01 1.46413922e-01
-9.13346291e-01 -5.16369462e-01 6.97098672e-01 -3.76034528e-01
-9.51497972e-01 -9.76756588e-02 -3.43200006e-02 -6.24446034e-01
9.43368152e-02 8.00037622e-01 -2.64060199e-01 -7.99174607e-02
-1.50212809e-01 4.27450627e-01 1.88610196e-01 -1.13619596e-01
2.10363582e-01 7.90194094e-01 6.06154144e-01 8.33210871e-02
7.36276507e-01 5.42116284e-01 1.80859357e-01 -9.58241403e-01
2.77699292e-01 -3.52223873e-01 -2.26506680e-01 -6.79705918e-01
7.90776134e-01 -1.11131644e+00 -1.35944498e+00 2.86111891e-01
-1.14834106e+00 -3.29068094e-01 3.65916342e-02 7.86016881e-01
-4.45676833e-01 -9.89419445e-02 -4.99551177e-01 -9.04451430e-01
-5.57058193e-02 -1.16934693e+00 3.86328518e-01 2.01006293e-01
1.02188624e-01 -9.90964234e-01 -3.07219923e-01 7.57517517e-02
8.31339717e-01 3.25269327e-02 7.82677889e-01 -4.28179651e-01
-1.24029064e+00 -3.30126375e-01 -5.63342869e-01 -9.11715478e-02
-7.34576583e-02 -6.20245263e-02 -6.11713648e-01 -1.93521127e-01
-3.70496333e-01 4.95722950e-01 5.24409413e-01 6.10440731e-01
1.32415891e+00 -6.50346637e-01 -1.08061755e+00 7.82697320e-01
1.24997759e+00 5.58150411e-01 8.69694293e-01 3.17677736e-01
7.36450672e-01 7.17362285e-01 4.80110466e-01 6.75541312e-02
9.78725672e-01 9.26795721e-01 5.77920616e-01 -1.14919551e-01
-1.85744748e-01 -4.90594476e-01 4.50263441e-01 9.98926222e-01
-1.31961808e-01 -4.76154894e-01 -8.41704130e-01 9.13368225e-01
-2.21107006e+00 -1.33697379e+00 -2.43183374e-01 1.75425649e+00
-2.63812002e-02 -4.78743650e-02 1.43728927e-01 -9.08165798e-02
6.52451336e-01 1.33493483e-01 -5.73747516e-01 -9.17715803e-02
-1.77228242e-01 -8.18475783e-01 1.11494100e+00 4.60881889e-01
-7.95408428e-01 8.12959552e-01 6.06665993e+00 1.40171814e+00
-1.27054298e+00 3.09962064e-01 5.17018318e-01 -3.18139762e-01
-4.39104974e-01 -3.39774549e-01 -9.17519510e-01 8.19328666e-01
1.53475833e+00 -3.94178092e-01 5.17617762e-01 9.38351333e-01
9.45722401e-01 5.42555630e-01 -5.69741488e-01 9.60957408e-01
-2.35101685e-01 -2.01728487e+00 -2.68888503e-01 5.42013824e-01
5.87509513e-01 6.90578818e-01 2.79040247e-01 4.35551226e-01
6.69852123e-02 -1.01518309e+00 5.35467386e-01 8.01842093e-01
7.08989322e-01 -1.11046493e+00 6.27542853e-01 2.75897384e-01
-1.84727323e+00 -5.12698293e-01 -3.80678140e-02 2.55614012e-01
7.79544771e-01 1.89806849e-01 -6.25608504e-01 5.72666526e-01
7.43169248e-01 9.38642442e-01 -2.16900080e-01 1.08109224e+00
4.30487901e-01 8.04675400e-01 -4.23421592e-01 -2.64704436e-01
8.44042182e-01 -2.99431920e-01 6.99693561e-01 1.13377154e+00
6.80506229e-01 9.37733650e-02 -1.26631116e-03 8.54392231e-01
-1.05673023e-01 -2.18608499e-01 -1.05123484e+00 2.87978113e-01
9.47927594e-01 9.96864021e-01 -3.91194999e-01 -2.84503073e-01
-6.58019185e-01 1.87760815e-01 -1.08394630e-01 9.44929242e-01
-1.04845977e+00 -4.89841223e-01 1.04840457e+00 1.94018170e-01
4.52183217e-01 -4.52713251e-01 -2.41421163e-01 -7.04367697e-01
8.62449408e-02 -2.00690180e-01 -1.97600082e-01 -5.89044452e-01
-8.28340352e-01 6.42297029e-01 2.67650280e-03 -1.61010146e+00
-3.05501163e-01 -3.62220377e-01 -6.87901497e-01 5.79110444e-01
-1.68399525e+00 -1.16843545e+00 -1.58868268e-01 5.12893081e-01
5.13167799e-01 -6.84690654e-01 3.20799738e-01 9.61022019e-01
-7.51159012e-01 7.55822718e-01 7.50051022e-01 5.50573766e-02
-5.22208400e-02 -5.23046851e-01 5.36608458e-01 7.14730382e-01
-5.53014219e-01 2.02826962e-01 3.84347200e-01 -8.66737962e-01
-1.43119085e+00 -1.83481693e+00 1.09925044e+00 -7.84979984e-02
6.53137207e-01 -2.73802578e-01 -8.77683461e-01 7.45497406e-01
2.52455026e-01 -1.46595791e-01 4.22115326e-01 -2.60671377e-01
1.77279532e-01 -4.95526344e-01 -8.94412816e-01 9.36245918e-01
9.92908418e-01 -6.09953105e-01 3.49725246e-01 5.36070578e-02
9.10162210e-01 -1.08249448e-01 -8.75513792e-01 2.63322800e-01
6.30077004e-01 -4.38183546e-01 9.41870451e-01 -2.60562450e-01
-2.11642042e-01 -3.69979024e-01 -3.91205013e-01 -1.03495932e+00
-2.52820522e-01 -7.96041667e-01 -4.06299293e-01 1.11478531e+00
4.44016248e-01 -7.68050075e-01 1.02941537e+00 7.28959203e-01
-4.93382037e-01 -8.27081859e-01 -1.35772884e+00 -9.34385121e-01
-2.21209601e-01 -8.84753048e-01 1.02708435e+00 6.58485770e-01
1.73089415e-01 -1.15191706e-01 -7.66001821e-01 2.68708169e-01
5.23067296e-01 -2.90224522e-01 4.83050317e-01 -1.05589044e+00
6.89552605e-01 -6.43863738e-01 -6.46944225e-01 -1.27349699e+00
4.79361951e-01 -9.37977672e-01 -2.18030542e-01 -1.27529788e+00
-7.32070394e-03 -5.87173879e-01 -1.35205269e-01 1.37107834e-01
4.80252683e-01 4.82466519e-02 2.56695569e-01 3.83990645e-01
-5.35685003e-01 9.52454865e-01 6.54420614e-01 -3.53474408e-01
-4.02365863e-01 2.61268944e-01 -2.51533955e-01 3.99729639e-01
9.37410176e-01 -2.74336785e-01 -6.71162486e-01 -3.16666991e-01
9.87664424e-03 5.14764011e-01 4.43805844e-01 -1.01428318e+00
1.01497328e+00 -1.93850219e-01 -1.27174526e-01 -1.36656475e+00
5.43579221e-01 -1.26506627e+00 6.46087110e-01 3.81724179e-01
-5.75076267e-02 3.87799472e-01 2.52780467e-01 8.27602446e-01
-3.20914209e-01 4.62353051e-01 1.49864912e-01 4.95533526e-01
-8.81975591e-01 8.29212666e-01 -9.73738253e-01 -7.21010864e-01
1.23902500e+00 -5.48662364e-01 -3.83724958e-01 -5.84875882e-01
-6.04632258e-01 7.96496987e-01 2.54824702e-02 8.04718256e-01
8.20316315e-01 -1.89771461e+00 -4.35991168e-01 1.66325569e-01
-7.39031509e-02 -5.89817107e-01 6.44989550e-01 1.05748904e+00
-1.39049113e-01 1.20431876e+00 -1.32561952e-01 -5.77014267e-01
-1.00441086e+00 6.33180201e-01 4.03331757e-01 -1.31053692e-02
-7.73438871e-01 -1.30037472e-01 -2.65965939e-01 -4.44922626e-01
4.94268596e-01 -2.03206971e-01 -4.28987026e-01 -2.25444860e-03
4.45186645e-01 9.84017909e-01 -1.86479449e-01 -9.96050537e-01
-3.16094875e-01 4.78241265e-01 2.92649776e-01 6.94110468e-02
1.17015183e+00 -8.39086175e-01 2.12593019e-01 1.44171774e-01
1.49026370e+00 -5.06929338e-01 -1.25003994e+00 -2.10768044e-01
-4.40134406e-02 -2.56224573e-02 2.55472749e-01 -3.88466656e-01
-1.50759518e+00 6.73635662e-01 6.64304197e-01 4.23868030e-01
9.46622610e-01 -2.92234302e-01 1.28271639e+00 4.23349828e-01
3.38893801e-01 -1.30613744e+00 -6.43753231e-01 4.55388904e-01
3.80784750e-01 -9.92274761e-01 -6.20922565e-01 -2.05813721e-01
-5.80457747e-01 9.20545697e-01 5.98199785e-01 1.33890077e-01
9.95786250e-01 1.25736892e-01 -2.19445974e-01 -1.26505896e-01
-1.11181760e+00 3.23651321e-02 2.21077800e-01 6.87865019e-01
-2.57498741e-01 3.75196040e-01 -1.42426351e-02 6.16727173e-01
4.29374389e-02 1.57513186e-01 3.71623069e-01 4.52582330e-01
-1.60134822e-01 -6.99647963e-01 2.03889031e-02 3.91720951e-01
-1.51524305e-01 1.35040298e-01 3.73432904e-01 9.91558015e-01
-1.15044311e-01 1.00158262e+00 3.48214209e-01 -8.74332190e-01
1.92466870e-01 -2.60901570e-01 -4.82413501e-01 4.00022626e-01
-2.63257056e-01 -2.45585427e-01 1.94609031e-01 -8.99045467e-01
-8.24744552e-02 -6.72556996e-01 -1.29921651e+00 -1.00983262e+00
1.11053847e-01 3.77954364e-01 9.98342872e-01 9.35844779e-01
8.71026754e-01 7.98682690e-01 8.16429615e-01 -9.34311450e-01
3.96192111e-02 -6.49423063e-01 -5.92485189e-01 -9.52087939e-02
6.76471055e-01 -5.65706015e-01 -1.28703326e-01 -3.06358576e-01] | [6.207125663757324, 1.7025973796844482] |
ca18b4ce-d273-4a2c-b79a-9a460ff7584b | towards-versatile-embodied-navigation | 2210.16822 | null | https://arxiv.org/abs/2210.16822v1 | https://arxiv.org/pdf/2210.16822v1.pdf | Towards Versatile Embodied Navigation | With the emergence of varied visual navigation tasks (e.g, image-/object-/audio-goal and vision-language navigation) that specify the target in different ways, the community has made appealing advances in training specialized agents capable of handling individual navigation tasks well. Given plenty of embodied navigation tasks and task-specific solutions, we address a more fundamental question: can we learn a single powerful agent that masters not one but multiple navigation tasks concurrently? First, we propose VXN, a large-scale 3D dataset that instantiates four classic navigation tasks in standardized, continuous, and audiovisual-rich environments. Second, we propose Vienna, a versatile embodied navigation agent that simultaneously learns to perform the four navigation tasks with one model. Building upon a full-attentive architecture, Vienna formulates various navigation tasks as a unified, parse-and-query procedure: the target description, augmented with four task embeddings, is comprehensively interpreted into a set of diversified goal vectors, which are refined as the navigation progresses, and used as queries to retrieve supportive context from episodic history for decision making. This enables the reuse of knowledge across navigation tasks with varying input domains/modalities. We empirically demonstrate that, compared with learning each visual navigation task individually, our multitask agent achieves comparable or even better performance with reduced complexity. | ['Wenguan Wang', 'Luc van Gool', 'Wei Liang', 'Hanqing Wang'] | 2022-10-30 | null | null | null | null | ['vision-language-navigation'] | ['computer-vision'] | [-1.28876820e-01 -2.51834869e-01 1.34770095e-01 -4.78859991e-02
-7.61294663e-01 -8.10823679e-01 8.51585388e-01 -5.24781011e-02
-8.35810661e-01 4.58183736e-01 3.81861031e-01 -2.75484264e-01
-2.14194655e-01 -4.49648768e-01 -5.42380333e-01 -7.22409368e-01
-2.16770127e-01 5.82715631e-01 3.32904994e-01 -5.31087220e-01
2.74572015e-01 2.24174410e-01 -1.79954028e+00 -6.38569193e-03
6.76473379e-01 1.05762339e+00 9.30288732e-01 6.54800832e-01
-2.68808037e-01 6.71024740e-01 -4.56491619e-01 -4.90959771e-02
-4.04190160e-02 -1.55559599e-01 -9.87699509e-01 -1.39038429e-01
3.42858583e-01 -3.59854460e-01 -3.84196043e-01 7.34358132e-01
7.31374443e-01 8.81322026e-01 7.04874158e-01 -1.30318153e+00
-1.15577006e+00 2.71846145e-01 -4.58763912e-02 3.08941066e-01
6.66376114e-01 4.70177323e-01 1.08748901e+00 -1.09226739e+00
7.10824966e-01 1.20712078e+00 3.82199585e-01 8.66016924e-01
-1.29479373e+00 -1.88398048e-01 6.98129296e-01 2.36540422e-01
-1.08907366e+00 -4.65132594e-01 4.68857884e-01 -3.90963376e-01
1.28459489e+00 -1.07205316e-01 8.60998034e-01 1.63132954e+00
3.28883342e-02 8.51855755e-01 7.29751348e-01 -8.17495286e-02
3.78754854e-01 -1.84415922e-01 6.13591149e-02 8.21534336e-01
-9.78395045e-02 3.78466338e-01 -8.72105837e-01 2.29721777e-02
7.96992660e-01 -3.15209590e-02 -4.71332431e-01 -8.64639759e-01
-1.46663249e+00 8.27969313e-01 5.88126659e-01 2.43721321e-01
-5.14535010e-01 4.21296865e-01 4.02816713e-01 4.78419006e-01
-1.69686526e-01 7.10418582e-01 -4.21213418e-01 -1.14591673e-01
-4.91822869e-01 3.66866708e-01 6.57108724e-01 9.72861469e-01
8.21894705e-01 3.43194276e-01 -2.03762516e-01 7.04496384e-01
3.68198097e-01 5.94271243e-01 6.34410679e-01 -1.10660195e+00
2.46443614e-01 1.97290689e-01 1.48067504e-01 -6.53108597e-01
-7.80815840e-01 -4.36310768e-01 -4.67528552e-01 5.47546029e-01
2.34694868e-01 -3.11640836e-02 -1.12432480e+00 2.33035135e+00
3.14198375e-01 -2.55338132e-01 3.93666357e-01 1.04910874e+00
1.04675496e+00 5.77136338e-01 4.94510710e-01 3.40256542e-01
1.53848743e+00 -1.24745047e+00 -4.40516591e-01 -6.13652468e-01
3.96762311e-01 -3.43877941e-01 1.49615991e+00 3.11767578e-01
-9.85269129e-01 -7.04512000e-01 -1.08101714e+00 -4.28313285e-01
-8.03042173e-01 -2.90660173e-01 7.46836603e-01 1.44060805e-01
-1.64498746e+00 1.82219688e-02 -5.83117366e-01 -6.50603473e-01
1.43632829e-01 2.19316721e-01 -6.34594440e-01 1.82100087e-02
-1.14716911e+00 1.23753595e+00 2.58234352e-01 2.05265693e-02
-1.65091574e+00 -5.34302294e-01 -1.17781687e+00 6.69961050e-02
6.69773698e-01 -1.30215430e+00 1.48323703e+00 -6.06588781e-01
-1.42846060e+00 8.17178488e-01 -1.09421983e-01 -1.85984179e-01
7.31272921e-02 -1.82614535e-01 -2.65803546e-01 2.00699300e-01
3.56524438e-01 9.58466470e-01 9.19045508e-01 -1.45960724e+00
-7.12152719e-01 -4.36886072e-01 5.46356320e-01 7.32561946e-01
1.04204146e-02 -4.56466913e-01 -8.58108342e-01 -5.32622457e-01
8.16718861e-02 -8.41888905e-01 -2.28522658e-01 3.03601205e-01
-5.36389127e-02 -2.73293972e-01 5.30854166e-01 -3.51899117e-01
8.91209543e-01 -2.20089674e+00 8.66623402e-01 -1.35709956e-01
4.35666263e-01 -2.18822390e-01 -5.85400581e-01 4.62998927e-01
1.50794044e-01 -5.60143031e-02 -5.80850393e-02 -6.55737221e-01
2.33734861e-01 3.24734688e-01 -2.86570698e-01 2.35608555e-02
-6.01758249e-02 1.18289101e+00 -1.21217239e+00 -1.81273535e-01
2.21209258e-01 6.09162390e-01 -4.73872781e-01 3.15153837e-01
-4.32817698e-01 5.94461560e-01 -5.39851308e-01 6.41091883e-01
-3.75172161e-02 -2.99731821e-01 -1.64154842e-01 -7.72540197e-02
-1.94182768e-02 1.03243224e-01 -9.32218432e-01 2.54783654e+00
-7.26992130e-01 6.54630542e-01 2.70672321e-01 -7.47679412e-01
5.37231863e-01 2.04751000e-01 8.61394405e-02 -1.24600911e+00
-8.01292434e-02 -1.24492673e-02 -3.67420316e-01 -7.77754903e-01
6.44365847e-01 1.28636017e-01 -3.38274539e-01 5.02995491e-01
6.15342796e-01 -2.00990707e-01 3.76645997e-02 2.74919719e-01
1.04231775e+00 4.01436776e-01 2.42670149e-01 -5.22211054e-03
2.96890587e-01 1.65914282e-01 -3.16176191e-02 1.14736927e+00
-3.58145088e-01 4.78976399e-01 1.75025269e-01 -4.05534834e-01
-7.10533679e-01 -1.59724152e+00 2.94561654e-01 1.94740343e+00
4.27411765e-01 -3.03409696e-01 -2.20198378e-01 -4.76633668e-01
1.39055373e-02 5.43853402e-01 -9.11761820e-01 -1.34826809e-01
-3.90499443e-01 -2.72638679e-01 3.74404609e-01 6.41034901e-01
4.25277561e-01 -1.55634093e+00 -1.04453003e+00 1.35099918e-01
-5.12449145e-02 -7.28667617e-01 -4.63073730e-01 6.49406314e-01
-4.11360383e-01 -1.15184772e+00 -8.32611501e-01 -1.23719931e+00
3.48773569e-01 5.82631528e-01 1.48931682e+00 9.13313776e-02
-2.95550954e-02 1.28432441e+00 -4.13494051e-01 -1.81606393e-02
4.12224047e-03 1.13401428e-01 5.13169281e-02 -5.61781466e-01
-5.64294569e-02 -6.71837747e-01 -6.45050228e-01 1.08279184e-01
-8.90076041e-01 -4.01900634e-02 5.67417085e-01 9.95945275e-01
5.97370684e-01 -5.66300452e-01 5.70599139e-01 -3.88066262e-01
1.02072155e+00 -7.16184497e-01 -4.70087588e-01 2.98688561e-01
-4.46577489e-01 3.42762917e-01 2.91121572e-01 -5.44567466e-01
-9.81294751e-01 -1.33124709e-01 -3.03381443e-01 -3.51470649e-01
-2.58223712e-01 7.98675060e-01 -9.49739143e-02 -1.93263795e-02
7.70343125e-01 6.81200862e-01 -2.96664387e-02 -3.11488032e-01
9.70628202e-01 -4.00363095e-02 9.33966875e-01 -8.26876402e-01
6.69494748e-01 3.34116012e-01 -3.54645491e-01 -6.27246618e-01
-5.02571166e-01 -3.16365212e-01 -3.73314857e-01 -1.78017139e-01
1.28496814e+00 -9.55614924e-01 -1.01374948e+00 4.45763111e-01
-1.03103805e+00 -9.25271153e-01 -2.81241298e-01 2.51496822e-01
-8.40944827e-01 1.80194080e-02 -3.15547675e-01 -5.53514183e-01
-1.75223857e-01 -1.37467623e+00 1.09906459e+00 3.60089272e-01
-2.38957927e-01 -1.06038463e+00 2.35298976e-01 -2.17818677e-01
6.93352103e-01 -1.25846267e-01 1.14931953e+00 -6.89467788e-01
-7.44208813e-01 2.89472938e-01 -6.33155778e-02 -1.84577152e-01
-8.66643712e-02 -5.06268978e-01 -7.96924055e-01 -3.21884662e-01
-1.51147306e-01 -7.91904330e-01 1.04635811e+00 2.69465476e-01
8.38254154e-01 1.09499894e-01 -3.52081478e-01 9.43675756e-01
1.27128053e+00 5.23297668e-01 2.48159781e-01 6.78265452e-01
4.88558292e-01 5.05700290e-01 3.21463972e-01 1.78425923e-01
8.60245287e-01 6.83569849e-01 7.74497926e-01 2.37321883e-01
-9.27165002e-02 -2.05291286e-01 3.81998718e-01 3.55532259e-01
3.32905278e-02 -3.72609019e-01 -8.64375532e-01 5.14636457e-01
-1.86310947e+00 -1.00701690e+00 8.49731445e-01 1.89203858e+00
6.28230393e-01 1.96644552e-02 1.40132368e-01 -3.89415532e-01
2.12444708e-01 6.64390802e-01 -9.57865238e-01 -1.84218735e-01
-1.37753829e-01 1.01204522e-01 -1.24699280e-01 5.55132151e-01
-1.06369114e+00 1.05467522e+00 6.85899258e+00 2.74815798e-01
-1.24374485e+00 1.11806966e-01 -4.44638617e-02 -1.26263663e-01
-6.08125985e-01 -3.38755816e-01 -4.69414562e-01 5.52261919e-02
5.43199241e-01 -1.16070986e-01 6.91385508e-01 8.78118813e-01
-3.11719060e-01 -2.10197419e-01 -1.31202662e+00 1.26481450e+00
2.30584100e-01 -1.37098217e+00 2.32900456e-01 -2.30938226e-01
1.89108804e-01 2.92900085e-01 6.55160248e-01 8.84817004e-01
6.24308586e-01 -1.10024643e+00 1.07505310e+00 5.51627576e-01
6.52307451e-01 -2.45387807e-01 7.88527802e-02 2.07002833e-01
-1.35705292e+00 -3.35258067e-01 -4.44215350e-03 1.03483282e-01
2.88888603e-01 -4.40934956e-01 -2.26975933e-01 3.68623018e-01
9.37397838e-01 4.88897681e-01 -4.56531823e-01 1.08948851e+00
-3.34260821e-01 -2.56174684e-01 -8.46160948e-02 -6.23593107e-02
4.79890555e-01 -4.87738736e-02 5.44028819e-01 8.21849823e-01
3.96548837e-01 7.34560117e-02 2.55068928e-01 6.98493302e-01
1.40123129e-01 -1.21790275e-01 -7.87188172e-01 1.89997196e-01
7.41053581e-01 1.04434729e+00 -3.96855921e-01 -1.89553007e-01
-5.06144524e-01 1.16348088e+00 7.21122384e-01 9.40935016e-01
-7.15892136e-01 -1.32287666e-01 9.52938735e-01 -4.26928729e-01
4.73268896e-01 -6.98897839e-01 1.68772921e-01 -1.05144215e+00
-1.36593804e-01 -7.81054258e-01 4.25826758e-01 -1.29366457e+00
-1.12471735e+00 1.02485609e+00 -1.77955657e-01 -7.99797833e-01
-4.48907197e-01 -7.11781263e-01 -5.69955468e-01 8.32497895e-01
-1.53153396e+00 -1.16903985e+00 -6.49652719e-01 1.00063014e+00
8.90657127e-01 -2.65122831e-01 1.17706168e+00 1.41847525e-02
-3.41694206e-01 2.91492432e-01 -3.38167846e-01 -6.81372136e-02
6.57587886e-01 -1.38390315e+00 5.20788312e-01 4.88550782e-01
2.99113601e-01 7.06914425e-01 5.97864270e-01 -3.67689222e-01
-1.62205195e+00 -6.48322642e-01 4.34757560e-01 -7.32260108e-01
7.06358016e-01 -4.95418608e-01 -8.48643541e-01 8.62408996e-01
3.81628871e-01 -1.96541131e-01 5.10516465e-01 2.36038908e-01
-7.13444650e-01 1.90982819e-01 -7.83602118e-01 9.63501215e-01
1.55680859e+00 -9.84083235e-01 -1.05602276e+00 2.11797538e-04
1.06129301e+00 -6.38950884e-01 -4.54467297e-01 -9.16403607e-02
6.65952086e-01 -8.95209789e-01 1.35244536e+00 -7.31781602e-01
3.66447009e-02 -3.17042738e-01 -5.11639833e-01 -1.50973988e+00
-4.81120139e-01 -4.42815959e-01 -1.10311061e-01 7.29496658e-01
4.65453029e-01 -5.32525182e-01 4.11530733e-01 3.79784822e-01
-4.36757565e-01 -5.96553147e-01 -1.03195429e+00 -4.71359104e-01
-1.96709573e-01 -5.41981459e-01 4.17708486e-01 6.34885490e-01
-1.10548161e-01 5.68233192e-01 -2.30208069e-01 1.86615840e-01
3.38154018e-01 2.34027281e-02 6.64737046e-01 -1.25010300e+00
-1.59182534e-01 -6.76554322e-01 -1.33489236e-01 -1.48538542e+00
3.34077299e-01 -8.82951081e-01 3.00022244e-01 -1.79794312e+00
-1.41110972e-01 -4.05234367e-01 -4.72177982e-01 6.66941166e-01
-3.29560079e-02 -8.68067071e-02 3.21399659e-01 1.64917126e-01
-1.03064609e+00 8.13726366e-01 1.23630106e+00 -2.51185238e-01
-4.27346230e-01 -3.27099681e-01 -1.01707458e+00 4.35248941e-01
3.32193732e-01 5.45411408e-02 -8.87518823e-01 -1.03571033e+00
3.90001267e-01 9.52467397e-02 6.32218957e-01 -1.19815683e+00
5.79981685e-01 6.00291863e-02 3.89433175e-01 -4.97457772e-01
6.47100747e-01 -7.66544700e-01 -8.43334123e-02 8.32326263e-02
-4.33027923e-01 6.04083478e-01 3.60730082e-01 8.05815935e-01
-1.73279390e-01 6.55706301e-02 2.89715916e-01 -3.45026642e-01
-1.66476011e+00 4.05781865e-01 -6.15403056e-01 2.35339776e-01
9.06815052e-01 -1.68712541e-01 -6.00000858e-01 -4.72834617e-01
-1.43579900e+00 6.90690160e-01 4.48214889e-01 7.95088649e-01
8.13450813e-01 -1.34208179e+00 -1.97697565e-01 1.78009316e-01
4.22232002e-01 8.97885337e-02 4.28042442e-01 4.91726309e-01
-7.07881302e-02 4.07207608e-01 -4.74351227e-01 -6.61873162e-01
-6.97089791e-01 8.65316808e-01 4.46550518e-01 7.51285851e-02
-5.78766108e-01 1.11504722e+00 5.21178842e-01 -4.63451266e-01
6.45269811e-01 -2.33683661e-01 -4.49319690e-01 3.33964765e-01
4.73410547e-01 -1.08552754e-01 -2.07522973e-01 -5.00542581e-01
-3.70152235e-01 6.30029857e-01 7.61554241e-02 -4.63279843e-01
1.25687122e+00 -5.27860820e-01 2.84366965e-01 4.77187604e-01
9.12076592e-01 -4.05605942e-01 -1.55802131e+00 -3.16833794e-01
-1.58381224e-01 5.52576110e-02 -1.65460467e-01 -1.05551994e+00
-8.76322031e-01 7.88032055e-01 5.70552170e-01 4.96023521e-02
1.11997235e+00 3.31399947e-01 3.56207341e-01 8.45645070e-01
5.90675831e-01 -7.52192020e-01 6.30364239e-01 8.94257426e-01
1.24843013e+00 -1.26365244e+00 -5.27423203e-01 4.46135730e-01
-8.37111413e-01 8.20733011e-01 9.26068902e-01 1.88711092e-01
4.55269635e-01 -8.19934607e-02 2.35760808e-01 -3.99353832e-01
-9.71978247e-01 -6.84539557e-01 1.89313069e-01 1.21988440e+00
-8.55023861e-02 -3.54985923e-01 4.38817084e-01 7.61548162e-01
-8.72233063e-02 -3.02109808e-01 7.21599534e-02 9.67545331e-01
-7.12636888e-01 -7.71971643e-01 9.72155947e-03 7.42967501e-02
2.51334041e-01 -3.05081874e-01 -3.20806280e-02 1.07934439e+00
-6.02400340e-02 7.04592705e-01 7.73831904e-02 -4.04636025e-01
5.04742086e-01 3.11966687e-01 5.60438454e-01 -6.37374341e-01
-5.16623080e-01 -1.52613968e-01 -7.48886541e-02 -9.26068783e-01
-3.76450837e-01 -3.86916220e-01 -1.32905209e+00 1.16772465e-01
3.50099206e-01 9.90104675e-02 6.10038996e-01 8.96152139e-01
4.35011327e-01 6.40938461e-01 -4.55110483e-02 -1.31062663e+00
-4.79123175e-01 -4.45821881e-01 -3.67749810e-01 4.12210882e-01
7.48834610e-01 -1.18133616e+00 -3.49150956e-01 -1.87475666e-01] | [4.465583801269531, 0.609258770942688] |
516ead46-5f47-48d2-b51d-6dd4c82b3815 | 3d-oocs-learning-prostate-segmentation-with | 2110.15664 | null | https://arxiv.org/abs/2110.15664v2 | https://arxiv.org/pdf/2110.15664v2.pdf | 3D-OOCS: Learning Prostate Segmentation with Inductive Bias | Despite the great success of convolutional neural networks (CNN) in 3D medical image segmentation tasks, the methods currently in use are still not robust enough to the different protocols utilized by different scanners, and to the variety of image properties or artefacts they produce. To this end, we introduce OOCS-enhanced networks, a novel architecture inspired by the innate nature of visual processing in the vertebrates. With different 3D U-Net variants as the base, we add two 3D residual components to the second encoder blocks: on and off center-surround (OOCS). They generalise the ganglion pathways in the retina to a 3D setting. The use of 2D-OOCS in any standard CNN network complements the feedforward framework with sharp edge-detection inductive biases. The use of 3D-OOCS also helps 3D U-Nets to scrutinise and delineate anatomical structures present in 3D images with increased accuracy.We compared the state-of-the-art 3D U-Nets with their 3D-OOCS extensions and showed the superior accuracy and robustness of the latter in automatic prostate segmentation from 3D Magnetic Resonance Images (MRIs). For a fair comparison, we trained and tested all the investigated 3D U-Nets with the same pipeline, including automatic hyperparameter optimisation and data augmentation. | ['Anca-Ligia Grosu', 'Radu Grosu', 'Constantinos Zamboglou', 'Tobias Fechter', 'Dejan Kostyszyn', 'Zahra Babaiee', 'Shrajan Bhandary'] | 2021-10-29 | null | null | null | null | ['prostate-zones-segmentation'] | ['computer-vision'] | [ 5.5991435e-01 5.3219074e-01 5.9846360e-03 -3.1986746e-01
-1.4620817e-01 -4.3424338e-01 6.7098743e-01 2.6836365e-01
-6.4912796e-01 3.9812595e-01 2.8640414e-03 -4.5687890e-01
-8.9230254e-02 -5.3291065e-01 -8.2757342e-01 -6.1678368e-01
-4.7454694e-01 2.4336670e-01 4.8953587e-01 -2.7489272e-01
9.1936283e-02 9.1991419e-01 -1.1650456e+00 2.7707461e-01
5.8177966e-01 1.1669073e+00 2.3709646e-01 8.9782864e-01
-2.2979103e-01 2.3355880e-01 -3.4957686e-01 -8.9772247e-02
4.3414810e-01 -3.6377212e-01 -7.1506840e-01 -4.4872709e-02
4.3896437e-01 -2.4268800e-01 -2.4397817e-01 8.3826292e-01
7.9885292e-01 -1.5546630e-01 6.0589200e-01 -5.7686871e-01
-8.2733226e-01 3.8609567e-01 -3.7752259e-01 6.8610018e-01
6.9913410e-02 2.5008094e-01 2.8939557e-01 -4.4804794e-01
1.0152009e+00 1.0666529e+00 9.4628394e-01 8.2979339e-01
-1.5602708e+00 -2.5439015e-01 -1.6942683e-01 -3.1853971e-01
-8.2670760e-01 -1.7325191e-01 5.1816672e-01 -5.1961648e-01
1.1526232e+00 2.8155926e-01 7.6961380e-01 1.1851006e+00
6.6373128e-01 7.4265575e-01 1.3124013e+00 -3.9994207e-01
2.3219076e-01 2.9023161e-02 9.6466757e-02 5.2455431e-01
1.2736887e-01 2.9473618e-01 1.4321883e-01 1.2076610e-01
1.3679200e+00 5.5478524e-02 -2.0794025e-01 -5.9378946e-01
-9.7861910e-01 6.7128515e-01 1.1094259e+00 7.2466767e-01
-2.5328621e-01 1.8483834e-01 6.7804688e-01 -9.0484954e-02
3.0619481e-01 5.6027955e-01 -2.4102998e-01 3.6562851e-01
-8.6748487e-01 8.3478563e-02 3.8990447e-01 5.7249898e-01
3.3984464e-01 5.3042807e-02 -4.4642696e-01 6.9024426e-01
1.8358880e-01 -7.0977509e-03 8.1888729e-01 -7.2084188e-01
-8.3539173e-02 8.1317735e-01 -6.2945116e-01 -6.2574846e-01
-1.0833181e+00 -9.5902550e-01 -1.0514944e+00 5.0447440e-01
5.3682208e-01 -3.5459436e-02 -1.4888070e+00 1.4326892e+00
2.8755814e-01 -2.2496800e-01 -8.9469105e-02 8.9529580e-01
1.0443730e+00 -1.8684964e-01 3.2159351e-02 2.1179694e-01
1.3600366e+00 -5.9754634e-01 -6.3438314e-01 -4.4204304e-01
8.8030720e-01 -3.7550211e-01 7.4544042e-01 1.1701078e-02
-1.0830257e+00 -4.7759080e-01 -1.1997947e+00 -1.0925124e-01
-6.8904114e-01 1.6684059e-02 8.4292871e-01 9.4908190e-01
-1.4191504e+00 9.1047186e-01 -9.3926269e-01 -4.3366742e-01
9.6040010e-01 7.5399745e-01 -5.7425463e-01 1.4949389e-01
-1.1323534e+00 1.1397187e+00 4.1718429e-01 5.1420063e-01
-6.9637710e-01 -6.7866838e-01 -9.5290160e-01 -1.2367190e-01
2.4934983e-01 -7.5084645e-01 1.1674440e+00 -8.1821567e-01
-1.3004752e+00 1.3317703e+00 4.6506289e-01 -8.7096071e-01
6.0337806e-01 2.2072352e-01 8.0883810e-03 1.6521962e-01
-6.9814913e-02 9.5147681e-01 4.4912571e-01 -1.1959008e+00
-8.0365889e-02 -8.1971240e-01 -1.7156912e-02 -1.6012926e-03
1.9004266e-01 -3.0041325e-01 -4.2531264e-01 -6.4079148e-01
2.5655171e-01 -8.2467753e-01 -6.2398511e-01 4.5614999e-02
-3.4368911e-01 1.7857356e-01 6.3158768e-01 -4.5631695e-01
7.1623200e-01 -2.1205771e+00 5.1872097e-02 1.4282270e-01
3.2599193e-01 5.2709937e-01 1.3868013e-01 -1.7506823e-01
-4.5033264e-01 1.2523080e-01 -5.8881867e-01 -1.3726796e-01
-1.0273586e-01 3.5000932e-01 4.2960072e-01 6.3303757e-01
3.9152408e-01 1.1809466e+00 -8.8955367e-01 -3.8409376e-01
3.6931971e-01 6.5266991e-01 -4.6813497e-01 -1.9759127e-01
1.0066248e-01 5.9431839e-01 -3.5792294e-01 6.1400783e-01
6.5662491e-01 -1.2362241e-01 -8.1618890e-02 -1.0141456e-01
-1.1061831e-01 2.5761472e-02 -8.0803883e-01 2.1638029e+00
-2.7514026e-01 5.5166620e-01 3.6454841e-01 -1.0823034e+00
9.6643949e-01 3.0598873e-01 3.7874490e-01 -9.8635024e-01
6.1066157e-01 5.7979703e-01 5.4206532e-01 -4.4689184e-01
4.7543157e-02 -3.4113148e-01 9.9168345e-02 1.0060815e-01
6.3458353e-01 -2.4698794e-01 1.9706008e-01 -8.5031621e-02
1.0257279e+00 3.1198645e-01 3.0678606e-01 -5.4872864e-01
5.9110129e-01 -1.5560643e-01 3.0530089e-01 7.5553590e-01
-5.9453106e-01 9.3721086e-01 8.2726479e-01 -6.9731116e-01
-8.6530834e-01 -9.7280562e-01 -6.7008501e-01 4.2137307e-01
-2.4266617e-01 1.4326146e-01 -8.8146633e-01 -8.6243057e-01
-8.7028749e-02 3.9605609e-01 -1.2250254e+00 -1.3899136e-01
-5.5835098e-01 -8.6458635e-01 8.4793299e-01 5.7699019e-01
4.5177755e-01 -1.1681871e+00 -1.1476365e+00 3.7240332e-01
5.4167265e-01 -9.4363475e-01 -5.5587888e-02 1.0614535e+00
-1.2665346e+00 -9.9605119e-01 -1.2863375e+00 -7.6937246e-01
7.2698849e-01 -2.9616690e-01 1.1106483e+00 -4.0503066e-02
-6.8114340e-01 2.1472523e-01 -1.4959545e-01 -6.1438066e-01
-3.9474785e-01 6.6388667e-02 -3.1794792e-01 -5.8371502e-01
1.9143574e-01 -5.4134113e-01 -5.3894722e-01 -2.0470042e-02
-1.2412047e+00 -6.0053717e-02 9.1373360e-01 9.4081688e-01
6.2098372e-01 -4.2096788e-01 2.3309565e-01 -1.1886833e+00
5.5160475e-01 -5.2690815e-02 -2.7672696e-01 -1.3866983e-01
-2.9227242e-01 8.2478814e-02 4.5458868e-01 -1.5191354e-01
-5.5646670e-01 3.1371358e-01 -4.8472187e-01 -3.4314218e-01
-5.9591961e-01 1.4752829e-01 2.3912200e-01 -4.1139549e-01
9.9668241e-01 8.8275231e-02 2.8099421e-01 -2.2752108e-01
1.5395941e-01 2.9803902e-01 6.8824178e-01 -4.4970017e-02
3.7792373e-01 6.6205621e-01 2.5027794e-01 -8.6732775e-01
-4.8797801e-01 -2.6607466e-01 -1.0594823e+00 -1.4001714e-01
1.2181686e+00 -3.8512295e-01 -4.0188184e-01 6.2776774e-01
-1.1407791e+00 -5.6402612e-01 -5.1140845e-01 3.3910698e-01
-6.2364036e-01 2.3186021e-01 -6.2581676e-01 -5.8602709e-01
-5.2734363e-01 -1.5979536e+00 1.1090513e+00 4.8923740e-01
-7.6231748e-02 -1.1560605e+00 -5.7629734e-02 -9.6558884e-02
6.1999953e-01 7.1639991e-01 9.4366407e-01 -7.4730092e-01
2.1707427e-02 -2.6776507e-01 -2.8915563e-01 3.0912900e-01
-5.0155222e-03 -4.4206795e-01 -1.2487378e+00 1.2683900e-01
4.2625077e-02 -2.1568121e-01 9.8174649e-01 1.0126134e+00
1.1919323e+00 2.5920609e-01 -4.1540423e-01 8.8272393e-01
1.3727778e+00 2.9114807e-01 7.5351137e-01 6.7346615e-01
2.8124046e-01 6.2862778e-01 -1.9205469e-01 -9.4306372e-02
-2.0976265e-01 4.3804184e-01 8.7267542e-01 -7.5959861e-01
-2.7653694e-01 3.8240808e-01 -1.9767715e-01 1.2647304e-01
-2.1533930e-01 2.3800123e-01 -9.8678637e-01 6.3898873e-01
-1.3659859e+00 -3.5878378e-01 -3.2396170e-01 2.0472503e+00
6.6916329e-01 7.0282125e-01 -5.9570656e-03 5.8365613e-02
5.4828525e-01 2.6368748e-02 -5.4860562e-01 -7.3935181e-01
-7.7188537e-02 4.2856655e-01 8.1927121e-01 1.5871564e-01
-1.2522742e+00 4.0431064e-01 6.4651365e+00 5.9914553e-01
-1.2651881e+00 1.9385647e-02 9.5647782e-01 -2.2786623e-03
8.4340334e-02 -4.5349458e-01 -5.8937228e-01 7.0057213e-02
7.0245856e-01 5.9125984e-01 4.5127884e-02 5.8661038e-01
4.0436629e-02 -2.7595729e-01 -1.1329011e+00 7.4919003e-01
-2.8668664e-02 -1.6536112e+00 -4.2925850e-01 1.9254363e-01
6.3611591e-01 3.4474531e-01 6.5337550e-03 2.0243651e-01
-6.2927462e-02 -1.3807542e+00 4.0022698e-01 3.6444241e-01
8.0988157e-01 -5.0244451e-01 1.0679544e+00 1.0983453e-01
-7.4230868e-01 -1.0591571e-02 -2.8685677e-01 2.0890436e-01
-1.6182903e-02 4.1512483e-01 -9.7774833e-01 5.5030358e-01
9.5389837e-01 3.3599210e-01 -7.1701705e-01 1.1714481e+00
1.0345729e-02 8.2039580e-02 -3.5727242e-01 1.1052103e-01
9.0748137e-01 1.1806151e-01 4.4802269e-01 1.5030681e+00
-2.5159335e-02 -2.8236681e-01 -4.1623682e-01 1.1312178e+00
2.6617082e-02 -2.8031399e-02 -6.8085372e-01 2.3450773e-02
-2.7299389e-01 1.5457957e+00 -1.4058249e+00 -1.8382972e-01
-2.5244319e-01 9.6309781e-01 4.4450689e-02 1.4672513e-01
-4.7831175e-01 -4.9436721e-01 2.7353933e-01 1.7514531e-01
4.0590382e-01 1.3021273e-02 -5.0230664e-01 -4.4301644e-01
-1.5323451e-01 -5.5864298e-01 3.0522236e-01 -6.1664391e-01
-1.2663567e+00 9.3393672e-01 -1.2881747e-01 -8.0642337e-01
-6.2660165e-02 -1.0704849e+00 -6.4448881e-01 9.0013212e-01
-1.6398891e+00 -9.9197525e-01 -3.7287440e-02 3.2910445e-01
2.7772573e-01 2.6392049e-01 9.2293131e-01 9.6524723e-02
-2.7082798e-01 2.3550709e-01 3.6036413e-02 2.9594547e-01
5.6247431e-01 -1.7580140e+00 5.3085715e-01 4.8918307e-01
-2.6765305e-01 6.1909783e-01 3.9988288e-01 -5.7522810e-01
-1.0354011e+00 -7.6817489e-01 6.2061661e-01 -1.1728080e-01
4.9756512e-01 -5.1541710e-01 -7.8182572e-01 6.5875715e-01
2.9836002e-01 5.6138253e-01 6.1308593e-01 -1.7449048e-01
1.3447271e-01 2.7736986e-01 -1.3391466e+00 4.9021131e-01
8.2339376e-01 -1.2184301e-01 -6.5139097e-01 1.5416744e-01
4.7225872e-01 -8.7632984e-01 -9.8481739e-01 4.8094365e-01
4.9385720e-01 -1.3025093e+00 1.1659749e+00 -3.3346081e-01
4.9816138e-01 -1.5575613e-02 2.4560857e-01 -1.3013712e+00
-3.1558955e-01 -2.1021532e-01 2.5751984e-01 7.1818757e-01
4.5338020e-01 -5.2233714e-01 9.1539884e-01 2.3702008e-01
-6.9522500e-01 -1.0077407e+00 -1.2113926e+00 -5.4080820e-01
2.7999860e-01 -3.6911514e-01 1.3469225e-01 6.7915457e-01
-2.2896464e-01 -3.6468312e-02 2.3528633e-01 -2.8408444e-01
4.4436496e-01 -2.8928199e-01 2.3635420e-01 -1.2947633e+00
-1.9237056e-01 -8.3775687e-01 -7.3192507e-01 -6.1419207e-01
-3.3463711e-01 -1.3437275e+00 -1.1825900e-01 -1.6950389e+00
-1.4865386e-01 -4.0147871e-01 -1.7625336e-01 3.7317526e-01
-1.6606456e-02 6.3377374e-01 1.1235869e-01 -5.2950069e-02
-3.1042157e-02 2.7351794e-01 1.7770379e+00 -7.1410023e-02
-3.6251414e-01 2.7107794e-02 -6.0360891e-01 7.7595890e-01
6.1368078e-01 -3.0132973e-01 -7.8990839e-02 -3.7689096e-01
-1.3385613e-01 -2.0218025e-01 5.2585936e-01 -1.1427671e+00
7.1277015e-02 7.6252055e-01 1.0347880e+00 -5.7460177e-01
5.6304488e-02 -9.2307729e-01 -2.4712719e-01 9.5505685e-01
-2.2098303e-01 -2.2706825e-01 6.9835377e-01 2.8379363e-01
2.2209916e-02 -4.3006089e-01 8.8713694e-01 -7.5147367e-01
-5.7003063e-01 2.2515522e-01 -6.1834925e-01 -2.8360310e-01
9.4428742e-01 -7.9764193e-01 -7.7441692e-02 2.2399235e-01
-1.0323869e+00 1.0384100e-01 3.4180880e-01 1.3753532e-01
3.7084487e-01 -1.1566744e+00 -3.8743237e-01 4.7377998e-01
-1.0240380e-01 4.2759550e-01 5.4263973e-01 1.4538153e+00
-8.0700552e-01 7.3392481e-01 -7.6310402e-01 -1.0007637e+00
-8.9096874e-01 4.2413577e-01 8.2047415e-01 -5.9937638e-01
-7.5341934e-01 9.0178114e-01 3.6977318e-01 -9.8176026e-01
2.4742992e-01 -7.2969776e-01 -6.0020518e-01 -7.9118542e-02
2.3661633e-01 2.7925861e-03 5.4321575e-01 -3.0293182e-01
-4.7208595e-01 3.5023186e-01 -2.7881540e-02 2.4310228e-01
1.5773137e+00 1.7388333e-01 1.7092031e-01 3.1665480e-01
1.0945520e+00 -5.0816131e-01 -1.3378083e+00 1.6592160e-02
7.6179288e-02 1.1554936e-02 4.0821368e-01 -9.2116827e-01
-1.2365860e+00 1.0174611e+00 1.0134686e+00 2.6032585e-01
1.1094232e+00 -3.1642005e-02 4.4467735e-01 -1.4134113e-01
-7.7139057e-02 -8.4422827e-01 -3.1420594e-01 4.4308650e-01
7.6905352e-01 -1.1441433e+00 -8.9819327e-02 -2.7296862e-01
-4.7811595e-01 1.4324688e+00 5.1444614e-01 -4.6856019e-01
5.0249738e-01 4.9311209e-01 1.1494595e-01 -4.6355131e-01
-1.1235387e-01 -3.3472383e-01 4.3378380e-01 9.4046521e-01
7.6722944e-01 -2.9627898e-01 -3.9008012e-01 5.3932875e-01
5.7482913e-02 8.0676280e-02 5.3797042e-01 1.0264633e+00
-4.7257364e-02 -9.6663076e-01 -4.5492348e-01 4.9256462e-01
-6.7915034e-01 7.5457104e-02 -4.6070680e-01 1.2655994e+00
4.1172084e-01 1.5380797e-01 9.0756252e-02 -6.3237198e-02
3.6600271e-01 7.0166327e-03 6.8027663e-01 -5.3590602e-01
-1.2695445e+00 4.2878345e-01 -2.2378643e-01 -5.6532824e-01
-4.0897569e-01 -7.1446961e-01 -1.3945335e+00 2.7431208e-01
-3.0672270e-01 -3.6951393e-01 9.9313676e-01 8.2153070e-01
9.2856787e-02 1.2016143e+00 2.9728467e-02 -1.3215898e+00
-1.9135131e-01 -1.0914375e+00 -7.5791359e-01 1.8723632e-01
3.2066625e-01 -6.1022109e-01 -1.4594427e-01 -2.7545097e-01] | [14.339847564697266, -2.552886962890625] |
1c6d8f6c-5508-4259-9418-ae885e2df8bc | tunable-convolutions-with-parametric-multi | 2304.00898 | null | https://arxiv.org/abs/2304.00898v1 | https://arxiv.org/pdf/2304.00898v1.pdf | Tunable Convolutions with Parametric Multi-Loss Optimization | Behavior of neural networks is irremediably determined by the specific loss and data used during training. However it is often desirable to tune the model at inference time based on external factors such as preferences of the user or dynamic characteristics of the data. This is especially important to balance the perception-distortion trade-off of ill-posed image-to-image translation tasks. In this work, we propose to optimize a parametric tunable convolutional layer, which includes a number of different kernels, using a parametric multi-loss, which includes an equal number of objectives. Our key insight is to use a shared set of parameters to dynamically interpolate both the objectives and the kernels. During training, these parameters are sampled at random to explicitly optimize all possible combinations of objectives and consequently disentangle their effect into the corresponding kernels. During inference, these parameters become interactive inputs of the model hence enabling reliable and consistent control over the model behavior. Extensive experimental results demonstrate that our tunable convolutions effectively work as a drop-in replacement for traditional convolutions in existing neural networks at virtually no extra computational cost, outperforming state-of-the-art control strategies in a wide range of applications; including image denoising, deblurring, super-resolution, and style transfer. | ['Aleš Leonardis', 'Steven McDonagh', 'Francesca Babiloni', 'Thomas Tanay', 'Matteo Maggioni'] | 2023-04-03 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Maggioni_Tunable_Convolutions_With_Parametric_Multi-Loss_Optimization_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Maggioni_Tunable_Convolutions_With_Parametric_Multi-Loss_Optimization_CVPR_2023_paper.pdf | cvpr-2023-1 | ['deblurring'] | ['computer-vision'] | [ 1.92273825e-01 -4.02097046e-01 -1.79897174e-02 -3.80178005e-01
-4.67828363e-01 -6.44032776e-01 3.84380519e-01 -1.46737933e-01
-6.08159900e-01 5.60364902e-01 -8.84280875e-02 -8.33717808e-02
-1.38484076e-01 -6.08955145e-01 -8.08671117e-01 -8.14564884e-01
6.88759312e-02 3.40750605e-01 1.87496990e-01 -1.90897942e-01
1.50457174e-01 6.01889133e-01 -1.34785604e+00 1.61428437e-01
1.12903261e+00 1.09652591e+00 4.07867879e-01 7.22838402e-01
2.29318693e-01 5.08850753e-01 -4.58177835e-01 -3.60790223e-01
2.30294243e-01 -1.62004739e-01 -3.71972412e-01 5.62686510e-02
6.00194573e-01 -4.02289301e-01 -8.58305022e-02 1.17120409e+00
5.45781553e-01 3.02419156e-01 5.22591114e-01 -8.15520942e-01
-7.21122980e-01 3.93053025e-01 -3.52687806e-01 1.89976931e-01
-3.20154160e-01 4.24696237e-01 8.82429063e-01 -7.32864857e-01
3.18112195e-01 1.21942985e+00 5.64849079e-01 4.09420669e-01
-1.79231262e+00 -8.01272810e-01 3.54814142e-01 2.01316401e-01
-1.34284675e+00 -6.77820623e-01 7.55405128e-01 -4.45308685e-01
4.21795309e-01 2.15239435e-01 4.64990079e-01 1.03497934e+00
1.50992170e-01 4.03043836e-01 9.87913609e-01 -2.85304040e-01
3.37791741e-01 3.58846068e-01 -3.89698446e-01 6.18125260e-01
3.89717519e-02 2.83033140e-02 -6.22090161e-01 -2.36455411e-01
1.22515535e+00 -3.11358184e-01 -6.45796001e-01 -5.67813396e-01
-9.52971697e-01 6.51776969e-01 3.78034562e-01 -4.14377004e-02
-3.17805767e-01 3.94291073e-01 2.53040493e-01 4.64660406e-01
4.27524686e-01 7.05090463e-01 -7.47717083e-01 -7.10789710e-02
-7.68043995e-01 4.57007766e-01 5.47363997e-01 4.55360085e-01
7.97682166e-01 -1.26786781e-02 -4.57322329e-01 1.13279188e+00
7.92303681e-02 1.35614574e-01 2.95720309e-01 -1.18962634e+00
5.53204715e-01 2.76219487e-01 4.22624201e-01 -1.07940233e+00
-8.85121152e-02 -8.27963889e-01 -7.86645651e-01 5.52014172e-01
4.11204219e-01 -1.94611207e-01 -1.00198519e+00 2.10898542e+00
4.91124183e-01 2.35495105e-01 -4.39475566e-01 1.14233077e+00
2.19318479e-01 4.05535132e-01 9.00468528e-02 1.40797332e-01
1.29206502e+00 -6.52840197e-01 -5.40483117e-01 -4.01310056e-01
2.02958703e-01 -7.34896421e-01 1.47390759e+00 3.48378569e-01
-1.31544971e+00 -5.35123110e-01 -1.05498660e+00 -4.65266444e-02
-7.45830238e-02 3.94373029e-01 3.54862154e-01 4.37317789e-01
-9.99597073e-01 9.67061043e-01 -1.04807210e+00 1.82582304e-01
5.61974645e-01 6.74354076e-01 6.34709597e-02 2.18451396e-01
-1.11974204e+00 8.90754342e-01 2.59639174e-01 3.43100399e-01
-8.40172172e-01 -1.15890944e+00 -4.53542829e-01 3.48426759e-01
4.03673500e-01 -9.02276397e-01 1.18235552e+00 -1.16451633e+00
-1.79622543e+00 6.17290080e-01 1.65751353e-01 -1.83393091e-01
8.25653076e-01 -2.64922023e-01 -5.85497357e-02 -3.03025972e-02
-3.17269892e-01 7.23239601e-01 1.30001736e+00 -1.20292914e+00
-2.72361457e-01 -1.68068528e-01 2.73198038e-01 3.29712391e-01
-5.39261460e-01 -4.27122675e-02 -6.88803911e-01 -8.06252301e-01
-1.83579043e-01 -1.04575539e+00 -1.71093568e-01 6.88388109e-01
-1.90424770e-01 2.90653884e-01 8.38293850e-01 -5.25396168e-01
9.88339126e-01 -2.15348434e+00 4.00115937e-01 9.64727774e-02
1.90923691e-01 3.78660053e-01 -2.34332457e-01 1.28510222e-01
1.15584163e-02 -3.64923254e-02 -2.46052578e-01 -4.87986714e-01
-1.99650705e-01 1.47463113e-01 -1.63184464e-01 4.53629464e-01
4.24562424e-01 7.33241200e-01 -7.12273777e-01 -2.70626932e-01
1.72698677e-01 9.44992185e-01 -8.44098330e-01 3.48103285e-01
-4.65050638e-01 7.26298034e-01 -6.15016937e-01 1.51202291e-01
6.76877558e-01 -3.26415718e-01 2.56741457e-02 -4.76103038e-01
-3.45442854e-02 2.44222403e-01 -1.25182796e+00 1.48814344e+00
-8.26245487e-01 6.36155605e-01 4.72043604e-01 -9.00739372e-01
6.37719691e-01 2.02651858e-01 1.61579862e-01 -3.24930876e-01
2.22318947e-01 3.49419750e-02 8.99633840e-02 -3.60755742e-01
3.23310971e-01 -2.41173897e-02 3.49280894e-01 2.06845388e-01
-1.82484891e-02 -1.98951308e-02 -8.08890015e-02 -1.49309143e-01
8.96335721e-01 -1.94867272e-02 -1.93726510e-01 -4.46790069e-01
4.13449913e-01 -4.50408936e-01 6.33428097e-01 7.19129026e-01
9.10008699e-02 8.82151067e-01 7.01607943e-01 -3.11662734e-01
-1.09279764e+00 -9.38690007e-01 -3.09271723e-01 1.08760500e+00
-6.07322194e-02 6.23734631e-02 -9.14776504e-01 -2.64504880e-01
-9.55348089e-02 6.11625314e-01 -6.65248930e-01 -3.99707735e-01
-6.32823944e-01 -7.50205576e-01 2.00065762e-01 4.02368933e-01
5.72663844e-01 -8.10805142e-01 -6.97715759e-01 2.58326232e-01
-8.41407031e-02 -1.03002620e+00 -9.56734776e-01 2.13393018e-01
-7.83845723e-01 -7.30414033e-01 -5.40874660e-01 -4.83881742e-01
8.71397853e-01 7.83772618e-02 1.09381950e+00 -3.57817076e-02
-2.13571310e-01 1.47853240e-01 1.16276674e-01 -6.70758486e-02
-4.20075864e-01 1.63491264e-01 -1.95743456e-01 3.99457723e-01
-3.83662701e-01 -9.92641449e-01 -9.62020993e-01 4.41982895e-01
-1.21060455e+00 1.27428249e-01 3.94079685e-01 1.02518284e+00
4.88554209e-01 1.94854125e-01 2.08825514e-01 -8.11507344e-01
8.61584604e-01 -2.57104129e-01 -1.03623068e+00 2.79004633e-01
-4.64503765e-01 3.45962107e-01 7.94036388e-01 -1.14624453e+00
-1.02230930e+00 -1.19515434e-01 2.24078804e-01 -9.49994266e-01
1.70144394e-01 4.84208792e-01 -3.46003324e-01 -2.98160613e-01
5.29943049e-01 1.51706776e-02 5.36059886e-02 -4.57643181e-01
3.90082836e-01 2.83445120e-01 4.85096037e-01 -9.07474875e-01
7.06717134e-01 3.20248246e-01 -5.47435172e-02 -5.82592070e-01
-6.89632297e-01 3.80275734e-02 -4.53828365e-01 -1.54460356e-01
5.77232599e-01 -8.61167312e-01 -7.08489656e-01 7.82084525e-01
-1.13839912e+00 -7.34173715e-01 -1.09232582e-01 1.70481518e-01
-4.29403126e-01 -1.87745273e-01 -5.01749158e-01 -3.64463508e-01
-2.43883848e-01 -1.57273746e+00 9.06461060e-01 2.09596395e-01
-1.68637052e-01 -1.09948802e+00 -3.07922363e-01 1.94126502e-01
8.38418007e-01 1.86602652e-01 1.11257768e+00 -1.38485789e-01
-8.08306873e-01 -2.73643099e-02 -3.31572890e-01 5.96304655e-01
2.29576245e-01 2.36769598e-02 -8.70642662e-01 -5.76563835e-01
-3.94246308e-03 -2.94472247e-01 8.31547439e-01 4.73818272e-01
1.60040843e+00 -5.91342628e-01 6.06589802e-02 9.77775812e-01
1.31966174e+00 -1.12326123e-01 6.34229541e-01 2.73578405e-01
7.32239723e-01 4.06628698e-01 9.13549587e-02 4.19610351e-01
1.44418120e-01 9.43161964e-01 5.08163035e-01 6.17988817e-02
8.58432874e-02 1.08308643e-02 1.17033042e-01 2.13066116e-01
1.09929860e-01 -1.06940947e-01 -6.91043556e-01 2.40800157e-01
-1.67552125e+00 -7.59800375e-01 3.67207974e-01 2.55742526e+00
1.39207804e+00 1.81294903e-01 -1.42160237e-01 -2.88625509e-01
6.08177662e-01 2.99554288e-01 -9.28220570e-01 -2.23813683e-01
2.88680494e-01 2.18158007e-01 5.80308199e-01 6.56288981e-01
-1.12881637e+00 7.19730675e-01 5.56454277e+00 9.45803583e-01
-1.64833283e+00 -8.86040181e-02 8.29818785e-01 -3.51292938e-01
-4.99230444e-01 -5.54617383e-02 -5.69455743e-01 5.98952174e-01
6.74073577e-01 -1.09461285e-02 9.97504056e-01 4.80481029e-01
5.42496681e-01 6.23235852e-02 -1.12026942e+00 6.31590545e-01
-3.40004742e-01 -1.38889909e+00 6.39288649e-02 1.23023624e-02
6.47881150e-01 -1.52372509e-01 4.77491647e-01 -1.46775112e-01
1.30745366e-01 -9.97765422e-01 9.91225243e-01 6.01042509e-01
7.78704464e-01 -6.80637717e-01 6.85286373e-02 2.55579084e-01
-8.22193503e-01 -1.27580196e-01 -1.66660696e-01 1.79613739e-01
-6.75368235e-02 7.77633071e-01 -4.66932148e-01 -2.50587344e-01
5.51515460e-01 1.52902842e-01 -1.49856627e-01 9.01013494e-01
-1.08815230e-01 6.49846971e-01 -1.62201971e-01 1.94700330e-01
1.27481073e-01 -3.61732364e-01 6.64149702e-01 9.27784145e-01
9.73168686e-02 1.02950908e-01 -5.15539236e-02 1.34599769e+00
-3.22876364e-01 -2.58534074e-01 5.52233355e-03 9.25295800e-02
7.06766784e-01 1.11648726e+00 -3.70014071e-01 7.74597423e-03
-1.04821391e-01 8.99880588e-01 5.45355916e-01 6.12582207e-01
-9.27023292e-01 -3.04200858e-01 1.27472901e+00 3.02169442e-01
6.09931707e-01 -4.30292368e-01 -2.95310378e-01 -1.11850607e+00
2.83522993e-01 -9.61021841e-01 -1.87627062e-01 -6.35770321e-01
-1.14140987e+00 4.23067331e-01 5.75527251e-02 -1.03721440e+00
2.81599183e-02 -4.80949521e-01 -6.74439073e-01 1.20376241e+00
-1.53018653e+00 -9.16356385e-01 -2.75980175e-01 4.33902204e-01
2.76627362e-01 1.89664900e-01 5.67582488e-01 3.72435272e-01
-7.26746440e-01 7.37982512e-01 2.21205667e-01 -4.16221730e-02
8.07480454e-01 -1.01591301e+00 3.74740839e-01 6.57419503e-01
-1.95024431e-01 8.09386492e-01 1.03107452e+00 -2.63872415e-01
-1.23094916e+00 -1.07997012e+00 3.46500546e-01 -1.18860379e-01
6.86926842e-01 -3.81247431e-01 -1.31361806e+00 3.96938562e-01
7.67512154e-03 2.90848404e-01 3.48622561e-01 -5.82623743e-02
-4.72092271e-01 -5.23095250e-01 -1.06588089e+00 8.07837129e-01
7.07295597e-01 -4.75710779e-01 2.00814217e-01 5.90578327e-03
7.50833273e-01 -7.54669845e-01 -6.46236300e-01 3.29804629e-01
6.64378583e-01 -8.43496919e-01 1.19839060e+00 -5.29736042e-01
5.93431830e-01 -2.34003648e-01 6.38056099e-02 -1.62377131e+00
-4.20327336e-01 -6.49872780e-01 -8.34457278e-02 1.20451045e+00
3.56726885e-01 -6.89805865e-01 6.07100129e-01 1.04197228e+00
8.53725746e-02 -1.03874218e+00 -7.94885516e-01 -3.79449666e-01
3.76377779e-04 -2.64262378e-01 4.92258102e-01 8.77389669e-01
-6.75691307e-01 1.92269702e-02 -4.22670335e-01 3.81585449e-01
7.56792843e-01 -2.15395525e-01 5.48830211e-01 -9.33964431e-01
-6.82036996e-01 -6.30948067e-01 3.64088640e-03 -1.25985074e+00
-6.92465901e-02 -3.59170675e-01 7.37575665e-02 -8.44879031e-01
-5.81811108e-02 -8.56018543e-01 -3.26118320e-01 4.48073357e-01
-5.07484615e-01 -8.45065638e-02 1.24238439e-01 1.04716070e-01
-9.89673585e-02 7.18939841e-01 1.45771956e+00 -5.54314926e-02
-4.77127850e-01 9.62599516e-02 -7.68992722e-01 5.62461853e-01
7.73446321e-01 -3.51831734e-01 -6.87168837e-01 -9.94566679e-01
1.18253171e-01 1.29747078e-01 5.67549706e-01 -8.31370652e-01
1.89296052e-01 -5.48993766e-01 3.03318858e-01 7.31383264e-02
4.92387623e-01 -7.63116419e-01 1.87405989e-01 1.01041412e-02
-6.93481684e-01 -6.52269050e-02 3.11469972e-01 5.67055225e-01
3.76080535e-02 -1.12252399e-01 1.20047545e+00 -6.70319572e-02
-2.17756450e-01 4.34761882e-01 -1.72893882e-01 2.12617576e-01
5.86615443e-01 -1.13018975e-01 -4.58592176e-03 -4.15926993e-01
-5.14205933e-01 2.23312244e-01 5.04357636e-01 4.36783850e-01
3.83251309e-01 -1.13622272e+00 -6.32446826e-01 3.08261275e-01
-1.56446725e-01 3.01354557e-01 2.20198348e-01 6.72656000e-01
-3.25657040e-01 -1.74815953e-01 -1.63473502e-01 -4.67974037e-01
-1.05362582e+00 -2.49164831e-02 8.10458004e-01 -2.13295192e-01
-3.87968898e-01 8.39821219e-01 2.91761309e-01 -2.23093793e-01
4.32829976e-01 -3.08413357e-01 1.42996252e-01 -1.24233045e-01
4.95577663e-01 1.52043208e-01 1.23168640e-01 -7.04605803e-02
-6.22337945e-02 4.02304620e-01 -2.45120719e-01 -1.10623799e-01
1.46033192e+00 -1.58942819e-01 -1.06062226e-01 3.84670109e-01
1.21629894e+00 -2.10442245e-01 -2.02740049e+00 -3.33093911e-01
-5.01641512e-01 -6.71200693e-01 5.15275240e-01 -8.80157173e-01
-1.37206805e+00 7.28382647e-01 6.17090106e-01 -4.08988260e-02
1.30236375e+00 -2.29479954e-01 6.44836426e-01 1.36185899e-01
2.34288931e-01 -9.56131935e-01 3.14344615e-01 2.99684882e-01
9.76323187e-01 -9.78931487e-01 -1.40064880e-01 -1.73484802e-01
-4.64984626e-01 1.02073145e+00 5.94051242e-01 -1.91541940e-01
5.82469046e-01 4.62159842e-01 1.78716660e-01 1.89894363e-01
-8.77425313e-01 3.93045455e-01 4.02021199e-01 3.13744992e-01
3.56879354e-01 -1.62194788e-01 -5.69015071e-02 1.61373556e-01
-1.28599852e-02 4.27591391e-02 2.63179153e-01 6.68648601e-01
-3.02964956e-01 -1.34575522e+00 -3.34201038e-01 4.39750105e-01
-4.14524794e-01 -1.69563383e-01 1.34530738e-01 4.56182331e-01
1.56179503e-01 5.15018106e-01 -7.79884693e-04 -3.28285955e-02
3.62333030e-01 -2.78892845e-01 5.17869592e-01 -3.57317597e-01
-5.32900393e-01 -1.11490309e-01 -9.90211815e-02 -5.20705700e-01
-1.00125141e-01 -7.06827044e-01 -8.03843737e-01 -3.19869697e-01
-3.50164503e-01 -2.02088013e-01 5.64129889e-01 9.23016429e-01
4.85539466e-01 5.97076058e-01 6.02194667e-01 -1.05982971e+00
-1.07069325e+00 -6.24903977e-01 -3.03322583e-01 3.44792753e-01
6.75907195e-01 -7.27571607e-01 -2.47687191e-01 1.48460835e-01] | [11.369669914245605, -1.814149022102356] |
ae58b59d-32d2-4993-a34e-8699317309ee | learning-decomposed-representation-for | 2006.07040 | null | https://arxiv.org/abs/2006.07040v2 | https://arxiv.org/pdf/2006.07040v2.pdf | Learning Decomposed Representation for Counterfactual Inference | The fundamental problem in treatment effect estimation from observational data is confounder identification and balancing. Most of the previous methods realized confounder balancing by treating all observed pre-treatment variables as confounders, ignoring further identifying confounders and non-confounders. In general, not all the observed pre-treatment variables are confounders that refer to the common causes of the treatment and the outcome, some variables only contribute to the treatment and some only contribute to the outcome. Balancing those non-confounders, including instrumental variables and adjustment variables, would generate additional bias for treatment effect estimation. By modeling the different causal relations among observed pre-treatment variables, treatment and outcome, we propose a synergistic learning framework to 1) identify confounders by learning decomposed representations of both confounders and non-confounders, 2) balance confounder with sample re-weighting technique, and simultaneously 3) estimate the treatment effect in observational studies via counterfactual inference. Empirical results on synthetic and real-world datasets demonstrate that the proposed method can precisely decompose confounders and achieve a more precise estimation of treatment effect than baselines. | ['Fei Wu', 'Yueting Zhuang', 'Qiang Zhu', 'Runze Wu', 'Kun Kuang', 'Bo Li', 'Anpeng Wu', 'Junkun Yuan'] | 2020-06-12 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 3.08846712e-01 -1.76547449e-02 -1.27431214e+00 -3.95010173e-01
-7.51478016e-01 -3.30557257e-01 4.06457394e-01 2.60396987e-01
-3.44583303e-01 1.27297592e+00 9.90741909e-01 -5.30366600e-01
-4.58805352e-01 -8.91065896e-01 -7.56148517e-01 -8.21052849e-01
-3.77038956e-01 5.23460150e-01 -5.38650930e-01 3.70971829e-01
9.28545594e-02 6.37954995e-02 -9.80467975e-01 3.25030349e-02
1.03067064e+00 2.15867549e-01 -3.43199849e-01 2.64296263e-01
-4.22187522e-02 7.42234707e-01 -3.94471467e-01 -2.45655313e-01
2.73024980e-02 -5.01373708e-01 -4.47778851e-01 -2.08235607e-01
1.66998714e-01 -4.04648215e-01 -1.65774032e-01 7.53785610e-01
5.90926707e-01 -1.62132770e-01 1.28365231e+00 -1.47862220e+00
-5.85023463e-01 1.09008145e+00 -1.19015849e+00 -8.42642877e-03
-1.16387375e-01 2.53191054e-01 8.06744754e-01 -4.03668880e-01
1.75583750e-01 1.67603576e+00 5.63849926e-01 3.71154904e-01
-1.54471827e+00 -1.43753612e+00 3.51863474e-01 -5.38300239e-02
-9.36949790e-01 -4.45170730e-01 6.32273912e-01 -7.22553551e-01
6.30549043e-02 3.01016420e-01 3.38736743e-01 1.19891858e+00
3.49932015e-01 4.20714259e-01 1.12393570e+00 -3.65458786e-01
1.42872229e-01 -1.59096554e-01 2.75935918e-01 2.66418904e-01
6.18127167e-01 8.53286803e-01 -1.06626697e-01 -5.84828377e-01
8.73540103e-01 5.35716474e-01 -2.54714310e-01 -3.12052518e-01
-1.37647438e+00 1.05520761e+00 3.57562572e-01 -1.75758362e-01
-8.15684021e-01 3.26065212e-01 5.07795811e-01 1.25157312e-01
4.20401901e-01 5.05970083e-02 -8.02917063e-01 5.65003574e-01
-6.19119823e-01 4.33569372e-01 2.89474964e-01 7.00348318e-01
6.81388676e-01 -1.81690380e-01 -8.07486594e-01 5.68114579e-01
1.36005446e-01 9.56981659e-01 4.30767447e-01 -8.32647800e-01
7.45435834e-01 6.91788435e-01 6.21207893e-01 -6.43368959e-01
-6.61665499e-01 -3.03981155e-01 -1.48851025e+00 3.14067863e-02
7.08763361e-01 -5.81762075e-01 -9.94636834e-01 2.30355072e+00
5.10177016e-01 4.64924246e-01 -2.46846676e-01 7.54102230e-01
7.37072885e-01 2.07953542e-01 8.33751380e-01 -9.38338161e-01
1.47828722e+00 -4.52402174e-01 -1.06676340e+00 -8.48971456e-02
6.93160057e-01 -5.53061604e-01 6.88071132e-01 -1.07403740e-01
-1.17070699e+00 -3.78766179e-01 -5.79485774e-01 1.97460949e-01
-1.08113699e-02 1.42690688e-01 7.39944458e-01 6.01748943e-01
2.18311860e-03 4.06314284e-01 -3.92443120e-01 2.81790018e-01
5.56505919e-01 6.07729793e-01 -2.59264261e-01 1.91232622e-01
-1.44688809e+00 5.97105682e-01 1.66107029e-01 -1.38236731e-01
-1.17918980e+00 -1.53717649e+00 -6.47039592e-01 3.92696589e-01
6.70744419e-01 -1.33114624e+00 1.04807484e+00 -9.13306355e-01
-1.07538581e+00 6.19849443e-01 -6.03664756e-01 -9.71715823e-02
5.49581468e-01 -7.72643313e-02 -2.78706163e-01 -6.19642437e-01
3.32766354e-01 5.95950931e-02 5.02248645e-01 -1.07994843e+00
-8.07325125e-01 -6.45803273e-01 -1.97578534e-01 2.25830331e-01
2.27736995e-01 2.20976900e-02 2.55840600e-01 -8.55884373e-01
-1.92518473e-01 -6.12018406e-01 -5.39369643e-01 -3.06044161e-01
-5.01428723e-01 -1.40277922e-01 3.28666240e-01 -5.90116799e-01
1.41945755e+00 -1.92360854e+00 1.41821146e-01 5.40148653e-02
5.12208223e-01 2.86451764e-02 -1.20733775e-01 3.45479310e-01
-7.99230754e-01 4.02667403e-01 -2.55818576e-01 8.04642066e-02
-2.46722475e-01 -6.73876330e-03 -5.37387729e-01 8.17099035e-01
-6.32705390e-02 9.28652227e-01 -1.01784098e+00 -4.76502925e-01
4.57861185e-01 4.58982959e-02 -5.93824983e-01 4.01279926e-01
1.79124638e-01 6.95083857e-01 -6.15597248e-01 1.46740004e-01
9.40001488e-01 -3.34346667e-02 4.01717484e-01 -1.37503341e-01
-9.14216936e-02 3.52497041e-01 -1.57969773e+00 7.59796441e-01
-4.55553830e-01 -1.36186063e-01 9.49414149e-02 -1.31578255e+00
5.17030776e-01 6.65981948e-01 6.16337776e-01 -5.14130712e-01
2.27404088e-01 7.61311352e-02 1.16811149e-01 -7.88570821e-01
-2.94286400e-01 -9.45752501e-01 -1.48191884e-01 5.18226802e-01
-2.29565948e-01 4.08425570e-01 -1.14105947e-01 -1.98391184e-01
6.66388750e-01 -3.66004825e-01 1.00949728e+00 -2.64203340e-01
6.76694691e-01 -2.85582602e-01 1.01468086e+00 9.42106128e-01
-3.69748361e-02 3.07330191e-01 7.31366217e-01 -3.02383661e-01
-7.98921704e-01 -1.31705880e+00 -1.89236283e-01 9.41306114e-01
-5.21458732e-03 4.39293653e-01 -1.91187799e-01 -6.95637763e-01
4.30975020e-01 8.78900111e-01 -1.11674356e+00 -4.21408266e-01
-5.48922777e-01 -1.36975992e+00 3.39470029e-01 6.26434326e-01
4.30714935e-02 -7.86501169e-01 8.18956196e-02 2.81104684e-01
-3.04098189e-01 8.19692481e-03 -6.96644127e-01 4.33793738e-02
-1.07497060e+00 -1.59278011e+00 -8.18104088e-01 -3.54722023e-01
5.56784511e-01 2.99157739e-01 1.09591722e+00 -3.82867791e-02
5.99098280e-02 -2.61646092e-01 3.32581908e-01 -9.19590235e-01
-4.00035411e-01 -3.97052675e-01 1.11547723e-01 -3.41679491e-02
3.66673976e-01 -6.33037865e-01 -9.88092065e-01 1.97629809e-01
-5.64227819e-01 -4.13682275e-02 5.21895766e-01 1.17266107e+00
2.85237610e-01 -8.91299471e-02 1.00285649e+00 -1.20721686e+00
3.99722070e-01 -9.28311467e-01 -6.98013663e-01 3.08074117e-01
-7.81399846e-01 3.85842728e-03 4.40374076e-01 -8.69932115e-01
-1.45478392e+00 -4.03258413e-01 5.38450003e-01 -1.11933045e-01
-9.34607685e-02 4.95794058e-01 -5.78388393e-01 8.13925624e-01
5.39075911e-01 -2.69446373e-01 -1.01520933e-01 -4.48543817e-01
4.52484787e-01 6.11352801e-01 1.82185799e-01 -6.32608593e-01
4.64352906e-01 6.62775040e-01 4.84793574e-01 1.35791674e-01
-7.68177152e-01 -5.14863849e-01 -3.88947040e-01 4.33176607e-01
7.36676455e-01 -1.22073913e+00 -1.36307180e+00 2.46674597e-01
-8.90360475e-01 -1.73554197e-01 -2.07449168e-01 1.20103395e+00
-4.31270301e-01 -3.84231843e-02 -1.75630540e-01 -1.07279849e+00
-3.43761027e-01 -1.13623166e+00 8.72829437e-01 1.21436983e-01
-2.99738526e-01 -1.18735731e+00 4.42812890e-01 1.76487997e-01
-5.06223179e-02 4.03533578e-01 1.47192061e+00 -3.97657067e-01
-3.37996125e-01 -2.13007964e-02 -5.06713569e-01 -2.84479111e-01
6.56354129e-01 -1.70390308e-01 -6.35004342e-01 -1.81301102e-01
4.56779413e-02 6.94661513e-02 7.01034606e-01 1.42841637e+00
1.24653208e+00 -6.84576750e-01 -6.99132323e-01 5.08382857e-01
1.39257884e+00 3.25114399e-01 5.07606626e-01 -1.98713332e-01
7.02932060e-01 8.02653551e-01 3.33314985e-01 4.93776947e-01
5.88754058e-01 5.00126004e-01 2.57499456e-01 -5.57422936e-01
1.40190080e-01 -4.65282291e-01 -1.00941718e-01 1.29800171e-01
-1.46088585e-01 -9.62299258e-02 -6.29109025e-01 8.78644586e-01
-1.87722421e+00 -1.13835597e+00 -8.82484674e-01 2.67413878e+00
1.21308851e+00 -3.81314814e-01 3.22789401e-01 -7.90120140e-02
9.40080702e-01 -1.52735561e-01 -5.39037645e-01 -1.14017427e-01
-7.37713799e-02 1.20386675e-01 9.97511566e-01 6.81264162e-01
-9.77629006e-01 1.75143942e-01 7.12021303e+00 5.36781073e-01
-7.74716496e-01 1.01727836e-01 9.02836800e-01 -1.53885797e-01
-5.25722563e-01 2.80340195e-01 -4.02957350e-01 5.76640606e-01
6.94216669e-01 -7.16293097e-01 2.03504339e-01 2.94139832e-01
1.00659335e+00 -1.38171673e-01 -1.34846079e+00 4.71572369e-01
-6.59846067e-01 -1.13828611e+00 1.07341677e-01 1.71339706e-01
1.08872080e+00 -5.16308308e-01 -1.10853292e-01 3.89466465e-01
8.88698876e-01 -1.22581053e+00 3.98089558e-01 6.32871747e-01
9.21993971e-01 -5.81779778e-01 9.08432364e-01 3.91166866e-01
-8.04491520e-01 -3.49493891e-01 -3.32677335e-01 -5.04689574e-01
1.21226586e-01 8.97813618e-01 -4.59269136e-01 7.06336856e-01
2.71256149e-01 5.63742101e-01 1.01694740e-01 9.80870545e-01
-3.14409316e-01 8.85022342e-01 1.34142965e-01 5.11226296e-01
-1.98395818e-01 -2.42179841e-01 2.60767072e-01 7.59918332e-01
1.82555899e-01 3.36828440e-01 3.24707925e-02 7.75366783e-01
-2.83315212e-01 2.50016749e-01 -4.64621454e-01 4.45362806e-01
6.63904071e-01 5.55593491e-01 -1.09474950e-01 -5.86609781e-01
-4.86710817e-01 -3.83335687e-02 1.31818339e-01 7.98299134e-01
-1.00558543e+00 2.07151785e-01 8.98000658e-01 8.16604644e-02
-2.95929819e-01 6.62601292e-01 -9.09215152e-01 -1.11254752e+00
-4.18873906e-01 -1.02116358e+00 8.91073525e-01 -4.00650233e-01
-1.50350225e+00 -7.23354101e-01 4.42527324e-01 -9.63834286e-01
6.55493587e-02 -1.12247892e-01 -8.31887186e-01 1.62326729e+00
-1.39399827e+00 -1.08596957e+00 5.65754808e-02 4.82349545e-01
1.50714681e-01 1.66291654e-01 7.59391010e-01 4.14549708e-01
-7.03466237e-01 4.54118222e-01 2.54511714e-01 -4.73347213e-03
1.07732236e+00 -1.15527928e+00 -3.33171397e-01 2.35168353e-01
-5.87607145e-01 9.05850470e-01 6.22344375e-01 -1.00678420e+00
-7.87196994e-01 -1.13240063e+00 1.09329450e+00 -3.75968963e-01
5.91406584e-01 -8.74872506e-02 -8.57236326e-01 9.27440643e-01
-1.08328454e-01 -4.37287927e-01 8.16168904e-01 7.35614479e-01
-3.87031883e-01 -3.02250713e-01 -1.08365059e+00 8.04450750e-01
9.41500425e-01 -1.74173072e-01 -8.55194986e-01 2.65382826e-01
6.74157739e-01 -1.13833204e-01 -6.04240835e-01 7.49827743e-01
6.92062676e-01 -6.63670719e-01 1.24159026e+00 -1.26797009e+00
7.76382685e-01 -8.52262676e-02 2.95171976e-01 -1.37872875e+00
-7.42202103e-01 -2.69047558e-01 6.77515343e-02 1.39057553e+00
2.43114099e-01 -6.24376178e-01 4.77783829e-01 6.77367151e-01
4.96262103e-01 -2.40596041e-01 -9.86479044e-01 -4.00169194e-01
6.60753012e-01 -1.48336127e-01 1.25265419e+00 1.26955009e+00
-1.90807998e-01 4.48278278e-01 -8.50567520e-01 2.52252907e-01
9.05800998e-01 3.57540399e-01 8.09808016e-01 -1.19397628e+00
-1.27027333e-01 -6.64179027e-01 2.93120086e-01 -3.00588667e-01
1.75297007e-01 -6.41378820e-01 -3.15889090e-01 -1.34633601e+00
9.56818640e-01 -5.57074904e-01 -4.60467517e-01 3.75091374e-01
-1.13072455e+00 -4.57463562e-01 -3.25690031e-01 5.49622029e-02
2.68124968e-01 5.59871197e-01 1.49224925e+00 -3.14073622e-01
-3.78643394e-01 2.34841436e-01 -9.84961510e-01 6.41142666e-01
4.73677576e-01 -9.36581492e-01 -5.81145883e-01 -1.81262478e-01
-1.86276987e-01 5.98187804e-01 5.29096723e-01 -5.08142337e-02
-3.00456136e-01 -1.10589802e+00 4.63655144e-01 -2.76847899e-01
-4.34383661e-01 -1.00485814e+00 5.50550282e-01 7.95179546e-01
-6.82080507e-01 -2.98869520e-01 1.37904122e-01 5.98910630e-01
1.25119835e-01 -1.93922088e-01 4.97242779e-01 -4.25152062e-03
2.05792040e-01 3.00438672e-01 -2.31523991e-01 2.21542642e-01
9.26487267e-01 3.59844983e-01 -4.61873651e-01 -3.12112361e-01
-6.78727448e-01 6.50591314e-01 -4.65166345e-02 2.77395964e-01
-9.48576722e-03 -1.57017100e+00 -1.12010205e+00 1.10124312e-02
-2.32938025e-02 -1.83296621e-01 7.06591129e-01 8.96691918e-01
3.45635861e-01 4.43313539e-01 8.61940160e-02 -1.24904722e-01
-1.20128059e+00 1.13805854e+00 3.90574068e-01 -7.38534629e-01
-1.76320374e-01 3.54911685e-01 1.35688412e+00 -4.55685318e-01
2.03756437e-01 -1.57704502e-01 -2.59020150e-01 2.37131566e-01
6.64786518e-01 8.41593087e-01 -5.64343512e-01 -2.45923474e-01
-3.27195734e-01 4.16315734e-01 3.13948780e-01 1.02388389e-01
1.36359370e+00 -4.12396103e-01 -2.21858799e-01 4.88330632e-01
1.14488602e+00 1.00651309e-01 -9.11651492e-01 -3.76297474e-01
-1.98775426e-01 -6.03928328e-01 -1.14958342e-02 -9.18408930e-01
-9.91077125e-01 7.95807958e-01 5.46372056e-01 -1.68684483e-01
1.26477456e+00 -2.37899750e-01 1.23320865e-02 -4.40996438e-01
5.23756668e-02 -6.16940260e-01 -3.91456991e-01 -2.87952274e-01
9.16400135e-01 -1.45321071e+00 4.09955323e-01 -4.31474954e-01
-2.68092662e-01 4.83259916e-01 3.80386561e-01 -8.77958909e-02
6.89281583e-01 5.58565855e-02 6.75613433e-02 -8.60859677e-02
-7.56500840e-01 7.37529472e-02 3.61895472e-01 6.27388179e-01
7.25447297e-01 5.10152757e-01 -9.58230793e-01 9.51248646e-01
2.20399126e-01 4.73739147e-01 2.99660355e-01 2.09887639e-01
3.48616034e-01 -1.16850746e+00 -8.98815870e-01 6.98005855e-01
-6.55884683e-01 -1.58516467e-01 1.56496018e-01 1.00322413e+00
4.66939926e-01 9.08261895e-01 6.09126128e-02 3.39962065e-01
7.53145218e-01 -4.03656811e-02 2.05879033e-01 -4.73108083e-01
-6.47970319e-01 2.67906934e-01 -8.14103335e-02 -2.73420721e-01
-6.63396776e-01 -5.65467775e-01 -9.04336333e-01 -5.54039299e-01
-4.81966943e-01 2.04891831e-01 1.40697792e-01 9.97290015e-01
2.48645712e-02 8.73160779e-01 1.03522563e+00 -4.11010951e-01
-8.19752216e-01 -1.05062330e+00 -6.37895048e-01 7.06385434e-01
7.45262563e-01 -1.01231170e+00 -5.55119216e-01 8.60093459e-02] | [8.02977466583252, 5.3528947830200195] |
6884b080-1281-4253-98c0-b2e9e4e137ed | evaluation-of-self-supervised-pre-training | 2305.09366 | null | https://arxiv.org/abs/2305.09366v1 | https://arxiv.org/pdf/2305.09366v1.pdf | Evaluation of self-supervised pre-training for automatic infant movement classification using wearable movement sensors | The recently-developed infant wearable MAIJU provides a means to automatically evaluate infants' motor performance in an objective and scalable manner in out-of-hospital settings. This information could be used for developmental research and to support clinical decision-making, such as detection of developmental problems and guiding of their therapeutic interventions. MAIJU-based analyses rely fully on the classification of infant's posture and movement; it is hence essential to study ways to increase the accuracy of such classifications, aiming to increase the reliability and robustness of the automated analysis. Here, we investigated how self-supervised pre-training improves performance of the classifiers used for analyzing MAIJU recordings, and we studied whether performance of the classifier models is affected by context-selective quality-screening of pre-training data to exclude periods of little infant movement or with missing sensors. Our experiments show that i) pre-training the classifier with unlabeled data leads to a robust accuracy increase of subsequent classification models, and ii) selecting context-relevant pre-training data leads to substantial further improvements in the classifier performance. | ['Okko Räsänen', 'Sampsa Vanhatalo', 'Manu Airaksinen', 'Einari Vaaras'] | 2023-05-16 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [ 3.98027360e-01 -7.14561716e-02 -2.68351138e-01 -5.94489932e-01
-6.63442314e-01 -3.98908734e-01 8.46732333e-02 7.82198429e-01
-6.35955572e-01 3.47354680e-01 2.20299110e-01 -2.60689348e-01
-4.02384341e-01 -5.89219272e-01 -7.44440377e-01 -5.01775324e-01
-2.11547151e-01 3.88467252e-01 4.54878241e-01 2.21309483e-01
-6.48121312e-02 5.67397296e-01 -2.02367973e+00 6.40071869e-01
8.05200815e-01 7.66973376e-01 2.81847358e-01 7.73001015e-01
2.45688319e-01 5.51098704e-01 -4.17126209e-01 5.60020320e-02
-1.33911997e-01 -5.24690330e-01 -6.72065318e-01 -2.47299910e-01
3.01512703e-02 -3.73259574e-01 3.84488940e-01 6.89390659e-01
7.04426050e-01 -1.93777800e-01 5.71692348e-01 -6.06613517e-01
4.07656096e-02 3.94845575e-01 5.74189760e-02 3.58138233e-01
5.47406018e-01 9.64861661e-02 3.39216620e-01 -4.90129709e-01
5.21588206e-01 3.73279631e-01 7.25053251e-01 6.73614502e-01
-1.22331703e+00 -5.08794606e-01 3.80397514e-02 4.79687095e-01
-1.04806042e+00 -7.00665295e-01 7.39257574e-01 -7.77664602e-01
8.13492239e-01 3.66386026e-01 1.02988350e+00 1.04067516e+00
-3.19255769e-01 2.97982693e-01 9.51288521e-01 -7.31350541e-01
5.18447399e-01 7.01228604e-02 5.19723818e-02 4.84830886e-01
5.39489448e-01 1.29753351e-02 -5.97702682e-01 5.66519387e-02
2.95107454e-01 -1.73877999e-01 -1.24315277e-01 -2.25717962e-01
-9.70808983e-01 4.17756915e-01 -4.62123044e-02 8.29122126e-01
-6.62598491e-01 -2.40499854e-01 4.76261199e-01 2.31638491e-01
6.41199589e-01 5.58113754e-01 -6.43010676e-01 -6.89104140e-01
-1.03508508e+00 -1.55199736e-01 4.83962178e-01 5.81042647e-01
2.77421176e-01 -3.02454799e-01 -1.49992287e-01 9.66043413e-01
-1.93746835e-02 4.54267532e-01 7.03880370e-01 -7.74746954e-01
4.93952781e-01 8.01817119e-01 -1.62746176e-01 -4.43407267e-01
-9.38003957e-01 -6.80187345e-02 -1.32556275e-01 5.17222919e-02
5.01540303e-01 -3.71598870e-01 -6.73574746e-01 1.68535352e+00
5.01962900e-01 -3.27692688e-01 -1.89687714e-01 6.11093640e-01
7.59393811e-01 -4.23656218e-02 3.05359095e-01 -6.14760935e-01
9.77705598e-01 -2.66693175e-01 -4.60189134e-01 1.01974830e-01
1.02397978e+00 -5.74539065e-01 8.79756451e-01 5.72875202e-01
-9.43611860e-01 -3.87897283e-01 -8.96486700e-01 3.93180192e-01
-1.96510166e-01 3.98101509e-01 3.32863510e-01 6.89990997e-01
-8.19191575e-01 9.56682324e-01 -1.30463576e+00 -6.43175244e-01
4.86907303e-01 7.62552679e-01 -7.39301801e-01 1.83590949e-01
-6.59898281e-01 1.04469228e+00 2.28169709e-01 5.17413802e-02
-4.18432385e-01 -4.11177605e-01 -6.54489934e-01 -1.92325130e-01
1.19312458e-01 -1.55325562e-01 1.08165276e+00 -9.19540763e-01
-1.26486874e+00 1.00781667e+00 -1.85891166e-01 -2.17464343e-01
5.88370621e-01 -2.73509413e-01 -4.32982475e-01 3.63359392e-01
6.05297536e-02 2.74861813e-01 6.64532304e-01 -6.38761342e-01
-6.10373199e-01 -8.44204366e-01 -3.23566079e-01 -9.39679444e-02
-4.09641773e-01 3.66862208e-01 2.76251938e-02 -4.73139733e-01
3.54419827e-01 -8.85532856e-01 8.28348771e-02 -1.65595770e-01
1.82943106e-01 -4.51294295e-02 5.15140235e-01 -1.01553214e+00
1.14242053e+00 -1.87586260e+00 -2.12530538e-01 2.65802532e-01
3.34021300e-02 6.71132088e-01 2.58198120e-02 2.90713012e-01
-3.29712480e-01 -2.93168515e-01 6.56390116e-02 -7.49354884e-02
-4.82386738e-01 9.82405320e-02 5.08141696e-01 5.35289347e-01
2.31941298e-01 6.65979207e-01 -8.72780263e-01 -2.82139570e-01
6.17935479e-01 3.93967599e-01 -6.40656471e-01 5.81552327e-01
1.23682339e-03 9.49706316e-01 -3.93422723e-01 4.75896209e-01
-1.44822568e-01 2.68467814e-01 1.16438717e-02 -8.16901866e-03
-2.50039697e-01 6.24536693e-01 -8.57515037e-01 1.47011960e+00
-3.26767325e-01 7.28959560e-01 -1.95142195e-01 -1.18357158e+00
6.99621260e-01 5.79662859e-01 8.39020371e-01 -7.27590024e-01
4.29211676e-01 3.00740778e-01 2.65680790e-01 -1.26940286e+00
-2.30074883e-01 2.42691249e-01 5.87434113e-01 3.25580537e-01
-1.65818423e-01 1.79269299e-01 2.32243195e-01 -5.30662119e-01
1.17889106e+00 1.66550621e-01 1.87972441e-01 -3.33912402e-01
4.45300072e-01 -1.41362399e-01 3.69958937e-01 3.39703530e-01
-3.49888429e-02 6.77295625e-01 1.31190866e-01 -2.47999161e-01
-7.25276172e-01 -6.60730302e-01 -2.41841406e-01 9.75428224e-01
-5.42176664e-01 -6.60052150e-02 -1.02788448e+00 -5.18405139e-01
-9.98770818e-02 6.66437030e-01 -7.30644584e-01 -4.47797090e-01
-4.87809300e-01 -6.66344345e-01 2.92823315e-01 9.69730139e-01
-1.33272678e-01 -1.24512982e+00 -1.27406013e+00 4.37735230e-01
1.64885327e-01 -7.80648112e-01 8.55212137e-02 5.56535482e-01
-9.82393086e-01 -1.41295373e+00 -6.07316911e-01 -6.79681480e-01
8.84434700e-01 -2.44798809e-01 3.49105120e-01 1.76464133e-02
-1.63499802e-01 6.17943406e-01 -9.95735168e-01 -4.63947862e-01
-6.59496903e-01 4.53636386e-02 3.36255878e-01 -1.94973856e-01
6.02094769e-01 -8.76688182e-01 -6.82561994e-01 1.17400564e-01
-4.47531939e-01 -1.77979574e-01 3.33376795e-01 5.09452701e-01
4.91497695e-01 -5.15364707e-01 8.57434809e-01 -6.18304491e-01
5.67643821e-01 -2.28699625e-01 -5.27130306e-01 3.77263486e-01
-9.94711697e-01 2.57294863e-01 4.88334656e-01 -8.11386883e-01
-5.71758926e-01 2.01114044e-01 -7.09538758e-01 -1.55517727e-01
-5.80459535e-01 3.31958354e-01 1.79287214e-02 -4.34068702e-02
6.72480822e-01 -9.27162822e-03 1.14999242e-01 -7.81730652e-01
-1.96493685e-01 9.34465349e-01 6.28356516e-01 -6.49821460e-01
1.10974528e-01 1.21254824e-01 -3.22085209e-02 -7.71965027e-01
-2.61095703e-01 -6.98440254e-01 -9.54313457e-01 -5.62641561e-01
9.44777727e-01 -4.48588699e-01 -6.03444815e-01 3.27483475e-01
-1.01263833e+00 -4.06133264e-01 -2.49824896e-01 9.92989182e-01
-5.91118276e-01 7.76361525e-02 -1.08422458e-01 -9.51259553e-01
-7.07914412e-01 -1.14455211e+00 7.49685585e-01 9.92919356e-02
-6.84688449e-01 -6.51008666e-01 4.40989524e-01 5.73097944e-01
2.02329352e-01 2.62293905e-01 8.95988345e-01 -1.06736016e+00
3.10632467e-01 -6.28017068e-01 2.66173661e-01 6.41178608e-01
3.64432395e-01 8.14919397e-02 -1.13828135e+00 -1.18721955e-01
-4.09088992e-02 -8.83357897e-02 2.27301612e-01 3.60879332e-01
1.14516282e+00 -3.86364460e-02 -8.84005800e-02 2.17713878e-01
8.52381825e-01 7.28187859e-01 3.22904229e-01 2.06804901e-01
5.44515789e-01 1.04565513e+00 5.98859906e-01 1.82735622e-01
3.22394103e-01 7.00800896e-01 2.46959969e-01 1.54004291e-01
-4.07192893e-02 -4.78154086e-02 1.89695120e-01 7.03644097e-01
-7.53460646e-01 2.92001843e-01 -8.85478020e-01 8.53785992e-01
-1.72658527e+00 -9.10678625e-01 -2.48642907e-01 2.60094833e+00
6.68666005e-01 4.57531177e-02 6.27708852e-01 8.02031755e-01
5.69236636e-01 -5.18161654e-01 -3.66983652e-01 -6.28215134e-01
4.91309553e-01 4.80407208e-01 3.71552378e-01 -4.60268334e-02
-6.40448749e-01 2.70378292e-01 6.66599512e+00 2.20744818e-01
-1.42350507e+00 2.51354933e-01 3.27747345e-01 -2.86806136e-01
2.34991625e-01 -4.95031387e-01 -3.02205384e-01 7.39489675e-01
1.01795328e+00 2.57989734e-01 4.01285410e-01 9.24059093e-01
7.03893542e-01 -3.96030992e-01 -1.49689353e+00 5.67510605e-01
-2.05196470e-01 -9.00175571e-01 -6.82515562e-01 -2.10220411e-01
4.34612662e-01 1.86734304e-01 -5.49289048e-01 -1.69122294e-01
-5.64546525e-01 -6.97208047e-01 8.89297485e-01 5.84560275e-01
9.19385076e-01 -5.06685019e-01 6.51745260e-01 5.34187734e-01
-9.02392209e-01 -1.02791697e-01 2.52577096e-01 -2.30642259e-01
-2.19272196e-01 3.30205113e-01 -1.07521558e+00 1.49872169e-01
7.94496953e-01 3.11662465e-01 -5.54697692e-01 1.01620328e+00
-2.43375227e-01 1.02364159e+00 -5.60615957e-01 -3.00004125e-01
-3.90105039e-01 1.08419560e-01 4.10461843e-01 1.03533530e+00
1.48722097e-01 2.55391270e-01 -3.81697506e-01 4.62306917e-01
5.24340510e-01 4.53337044e-01 -4.17297900e-01 -1.08238526e-01
3.15535456e-01 6.55321360e-01 -7.16964006e-01 7.23647252e-02
-6.29997194e-01 3.98735166e-01 3.00931036e-01 -5.86719513e-02
-3.99903148e-01 -1.35924593e-01 3.35699052e-01 7.95281112e-01
3.05059910e-01 4.55565937e-02 -5.48047960e-01 -7.51740336e-01
3.34047735e-01 -7.02980161e-01 3.31565231e-01 -4.78144854e-01
-4.66876984e-01 3.09935600e-01 1.97231218e-01 -1.15772116e+00
-7.15735674e-01 -4.63906437e-01 -8.76098931e-01 5.21201670e-01
-8.22563589e-01 -9.63563442e-01 -2.21842498e-01 2.82897055e-01
3.63751173e-01 7.89859146e-02 8.10131133e-01 4.34085548e-01
-6.37891233e-01 6.37757659e-01 -6.33202270e-02 1.25653416e-01
3.54338527e-01 -8.39435101e-01 -2.29897231e-01 8.49934757e-01
9.69526544e-02 4.24360752e-01 8.10291231e-01 -5.89410901e-01
-1.01923490e+00 -8.42987061e-01 8.58803272e-01 -4.67486054e-01
2.88579345e-01 -1.11716241e-02 -8.79172981e-01 2.93442607e-01
-5.31327784e-01 -9.76689160e-02 6.85591638e-01 1.20572045e-01
1.67620346e-01 -4.54438686e-01 -1.33355105e+00 9.65012237e-02
1.09157312e+00 -4.68475997e-01 -7.74876714e-01 1.49641559e-01
1.76000714e-01 -2.11338848e-01 -1.03929222e+00 8.58755589e-01
9.54720974e-01 -7.94380128e-01 5.49362063e-01 -6.85473621e-01
2.46452764e-01 8.49823877e-02 7.44969174e-02 -1.05203116e+00
3.40548791e-02 -1.18303053e-01 -4.66197841e-02 1.19354677e+00
4.89980072e-01 -5.85107207e-01 7.06256568e-01 8.95857334e-01
-1.55783236e-01 -1.00750399e+00 -9.49578345e-01 -5.30572653e-01
-1.60023674e-01 -8.93712521e-01 5.00177622e-01 5.37554026e-01
4.37829077e-01 -1.99624583e-01 1.29655302e-01 2.56846100e-02
4.50620390e-02 -2.03871638e-01 4.17375535e-01 -1.18370938e+00
-1.05075456e-01 -3.02248150e-01 -8.50397766e-01 -1.42157763e-01
-3.49820554e-01 -5.64824760e-01 2.26049140e-01 -1.55073965e+00
-1.54923320e-01 -4.64752406e-01 -3.23833823e-01 6.91603124e-01
-1.79679468e-01 1.94839984e-01 -3.23263377e-01 -2.20299557e-01
-3.20801586e-01 -3.86929773e-02 7.01371551e-01 2.59084016e-01
-6.34218872e-01 6.26912415e-01 -4.34754759e-01 6.08277798e-01
5.76512635e-01 -7.32126474e-01 -5.78289866e-01 -3.32891345e-01
1.29865736e-01 -1.67636592e-02 1.24489345e-01 -1.13497031e+00
-5.55462651e-02 1.45980548e-02 4.39692587e-01 -5.40755093e-01
1.96219590e-02 -7.94972062e-01 1.66198760e-01 6.28092051e-01
-3.06792200e-01 -1.55351892e-01 1.59610346e-01 -5.73075823e-02
1.04010336e-01 -4.45586801e-01 6.56455874e-01 3.07152301e-01
-3.86921793e-01 -1.07651852e-01 -6.20272219e-01 -5.38243726e-02
1.08899319e+00 -4.14630532e-01 1.63207144e-01 -5.02992831e-02
-9.48324800e-01 -3.55241522e-02 5.20739079e-01 3.57313097e-01
2.10335672e-01 -8.10496330e-01 -3.92297536e-01 4.52193707e-01
3.64204466e-01 2.03950375e-01 -5.68383150e-02 1.21931660e+00
-5.97132683e-01 3.38662952e-01 -1.49622083e-01 -7.16657817e-01
-1.80889070e+00 3.97319973e-01 1.39725059e-01 3.09296727e-01
-6.11391306e-01 6.47045612e-01 -4.71018881e-01 1.18156567e-01
5.40145278e-01 -8.42115819e-01 -6.72650158e-01 1.84112087e-01
5.40533185e-01 7.06056416e-01 7.65547752e-01 -7.10798919e-01
-7.15143442e-01 4.84830469e-01 1.25783414e-01 1.54578567e-01
1.52352297e+00 1.28897116e-01 2.02508777e-01 5.40792346e-01
9.07880306e-01 -1.53254926e-01 -9.20112908e-01 3.69702667e-01
1.98308259e-01 -8.60063657e-02 5.02538644e-02 -8.28855395e-01
-9.69259024e-01 9.25303459e-01 9.33196902e-01 1.30311221e-01
1.44846189e+00 1.50357664e-01 3.66100609e-01 2.11104274e-01
5.78924716e-01 -1.25466979e+00 -3.86951953e-01 -2.90115058e-01
7.73774624e-01 -1.12642455e+00 6.12585247e-02 -8.55339095e-02
-4.55848545e-01 1.05198205e+00 3.39568853e-01 1.85507968e-01
5.85358799e-01 1.45543605e-01 1.65178999e-01 -7.70522580e-02
-6.38760865e-01 -4.54285085e-01 8.17572534e-01 7.70614326e-01
4.35561299e-01 2.87643790e-01 -8.41927648e-01 1.05516088e+00
-8.36261809e-02 3.81884903e-01 1.11808203e-01 1.12110007e+00
-3.71189058e-01 -1.07070518e+00 -2.27467120e-01 9.83492017e-01
-6.49694502e-01 4.12354678e-01 -3.48376930e-01 5.47887385e-01
5.50411999e-01 1.09686744e+00 -2.16589302e-01 -5.81854939e-01
6.76599205e-01 3.54417771e-01 8.09764445e-01 -8.55109453e-01
-6.15697086e-01 5.41687272e-02 5.61202586e-01 -6.45416558e-01
-4.49652612e-01 -1.17649198e+00 -1.26349044e+00 3.52334172e-01
-5.72962999e-01 1.09355263e-01 1.11371279e+00 1.45737648e+00
1.54775411e-01 4.81681764e-01 6.33393109e-01 -5.83462596e-01
-2.67114908e-01 -1.03052735e+00 -1.80982009e-01 2.64459908e-01
4.60665494e-01 -8.26297820e-01 -1.71593934e-01 1.83094770e-01] | [13.09050464630127, 3.2119007110595703] |
6b1e0744-543f-400b-ab50-639050c86f59 | road-segmentation-of-remotely-sensed-images | null | null | https://www.mdpi.com/2072-4292/9/7/680 | https://www.mdpi.com/2072-4292/9/7/680/pdf | Road Segmentation of Remotely-Sensed Images Using Deep Convolutional Neural Networks with Landscape Metrics and Conditional Random Fields | Object segmentation of remotely-sensed aerial (or very-high resolution, VHS) images and satellite (or high-resolution, HR) images, has been applied to many application domains, especially in road extraction in which the segmented objects are served as a mandatory layer in geospatial databases. Several attempts at applying the deep convolutional neural network (DCNN) to extract roads from remote sensing images have been made; however, the accuracy is still limited. In this paper, we present an enhanced DCNN framework specifically tailored for road extraction of remote sensing images by applying landscape metrics (LMs) and conditional random fields (CRFs). To improve the DCNN, a modern activation function called the exponential linear unit (ELU), is employed in our network, resulting in a higher number of, and yet more accurate, extracted roads. To further reduce falsely classified road objects, a solution based on an adoption of LMs is proposed. Finally, to sharpen the extracted roads, a CRF method is added to our framework. The experiments were conducted on Massachusetts road aerial imagery as well as the Thailand Earth Observation System (THEOS) satellite imagery data sets. The results showed that our proposed framework outperformed Segnet, a state-of-the-art object segmentation technique, on any kinds of remote sensing imagery, in most of the cases in terms of precision, recall, and F1. | ['Teerapong Panboonyuen'] | 2017-07-01 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [ 5.70687473e-01 -6.75533935e-02 9.02259201e-02 -4.67612833e-01
-4.62808222e-01 -3.71986330e-01 5.65294087e-01 -1.02865867e-01
-6.25810146e-01 1.01255357e+00 -2.53564328e-01 -7.09538639e-01
-5.33849001e-01 -1.40912259e+00 -5.84794402e-01 -6.14682376e-01
-2.51511425e-01 1.23974063e-01 4.00130481e-01 -1.27541393e-01
2.22134829e-01 9.34890747e-01 -1.63649452e+00 -2.85433948e-01
1.29815161e+00 8.58350039e-01 5.14331579e-01 5.55294156e-01
-3.02435905e-01 4.62050915e-01 -4.50123698e-02 -4.39946949e-02
4.42226976e-01 1.48070278e-02 -1.05699229e+00 1.47893623e-01
3.42280716e-01 -4.54334080e-01 2.81336159e-02 1.10932279e+00
2.21087083e-01 3.61620575e-01 7.42760539e-01 -6.83552623e-01
-4.58468705e-01 7.51042724e-01 -8.75398219e-01 2.93817490e-01
-3.06300223e-01 -1.00280412e-01 6.43900692e-01 -8.50016594e-01
2.66773283e-01 1.03668928e+00 7.07786858e-01 -6.38579801e-02
-9.68319952e-01 -6.92618072e-01 2.08148077e-01 -1.37523459e-02
-1.86312735e+00 -1.89307705e-01 5.19276857e-01 -3.89111996e-01
7.88031697e-01 1.89227998e-01 4.47267830e-01 2.73959171e-02
9.50511098e-02 4.79075551e-01 1.30900824e+00 -3.50855142e-01
-8.16484690e-02 8.79671723e-02 2.58173466e-01 4.36205387e-01
3.32625180e-01 3.88237163e-02 4.34135467e-01 2.75683910e-01
9.21904325e-01 1.72726810e-01 -1.28346279e-01 1.53212398e-01
-7.07390010e-01 9.07441556e-01 9.99539435e-01 5.12924194e-01
-9.09937441e-01 -1.71440884e-01 -1.76886432e-02 -2.38292038e-01
6.78315341e-01 1.13541082e-01 -4.40380543e-01 4.86521304e-01
-1.23248446e+00 2.64669545e-02 3.03074479e-01 5.56765079e-01
1.04682040e+00 1.89401433e-01 1.28137305e-01 7.69402444e-01
4.24961299e-01 5.78058958e-01 1.32293016e-01 -3.87213379e-01
4.79777664e-01 9.36242640e-01 -2.28366349e-02 -1.29835474e+00
-4.67631012e-01 -5.24734616e-01 -1.20411193e+00 1.47672847e-01
-6.99172243e-02 -2.91335732e-01 -1.55606425e+00 1.13238394e+00
3.77407074e-01 1.92746893e-01 8.58210847e-02 9.41697598e-01
7.71319151e-01 8.53194118e-01 5.46973586e-01 1.88907623e-01
1.17930496e+00 -4.49768871e-01 -4.64148462e-01 -2.64676571e-01
3.23945284e-01 -5.01982689e-01 7.85466552e-01 5.46354130e-02
-4.51746613e-01 -8.13194454e-01 -9.25897062e-01 2.97212869e-01
-9.79221940e-01 4.95756984e-01 8.40222955e-01 6.27662599e-01
-1.03331399e+00 6.33784950e-01 -8.42734456e-01 -5.39845705e-01
8.97847831e-01 4.84417379e-01 -2.76733726e-01 7.60341361e-02
-1.21165454e+00 8.26975405e-01 7.19573677e-01 9.23676193e-01
-6.04915440e-01 -1.20628141e-01 -8.93624187e-01 1.13738813e-01
4.80903238e-01 -9.84125510e-02 3.79702151e-01 -9.59064662e-01
-1.07286906e+00 7.23019600e-01 2.13267252e-01 -4.23307747e-01
1.30462185e-01 -3.27870190e-01 -5.65312505e-01 1.42815158e-01
1.56644404e-01 9.21213269e-01 4.23176557e-01 -1.24895442e+00
-9.30857539e-01 -5.57362735e-01 -6.62537152e-03 1.54874042e-01
-6.59290105e-02 1.23098038e-01 6.50640875e-02 -5.67957580e-01
4.85000014e-01 -7.49871850e-01 -5.73179960e-01 -4.19192702e-01
-4.63787109e-01 -7.12674335e-02 1.02231133e+00 -1.09735262e+00
1.24818778e+00 -1.85930514e+00 -2.97605187e-01 6.65516615e-01
-4.89426553e-02 8.17894340e-01 6.76143691e-02 -1.71447266e-02
-1.38168737e-01 5.98900557e-01 -1.06826007e+00 4.25662845e-01
-4.96259212e-01 2.79524684e-01 -1.71604782e-01 3.88633490e-01
5.11370301e-01 6.62795722e-01 -5.23520172e-01 -6.20300710e-01
5.42767644e-01 5.32730997e-01 1.12485610e-01 -2.33566202e-02
7.52297640e-02 4.49052721e-01 -6.49638414e-01 6.57415330e-01
1.31522822e+00 1.59270048e-01 -1.82082072e-01 -2.97630969e-02
-6.03047669e-01 -1.26127064e-01 -1.34674954e+00 1.06043029e+00
-2.71184325e-01 4.83291745e-01 -3.81060541e-02 -9.96269703e-01
1.34202993e+00 1.98056519e-01 3.99514258e-01 -5.21317482e-01
1.20235677e-03 1.32328078e-01 -1.52084723e-01 -5.66824377e-01
8.90215337e-01 1.39832243e-01 3.23076546e-01 -6.04893714e-02
-3.65700483e-01 5.17582372e-02 5.96079938e-02 -1.27120599e-01
2.27444261e-01 5.12564778e-01 3.23969811e-01 -2.68148541e-01
7.53579438e-01 4.25474882e-01 3.64638329e-01 5.42391002e-01
-3.13050346e-03 4.10997599e-01 -1.14577077e-02 -5.42786658e-01
-9.02567506e-01 -6.87597811e-01 -5.60730696e-01 7.18822360e-01
7.45162144e-02 4.30585116e-01 -8.25616241e-01 -5.55172622e-01
-1.84012368e-01 5.17647564e-01 -2.96597362e-01 5.37765801e-01
-5.74722052e-01 -1.29593015e+00 6.81722164e-01 5.42753041e-01
1.17231393e+00 -1.36218798e+00 -6.30000472e-01 3.91160935e-01
-7.09042475e-02 -1.18758810e+00 2.25519359e-01 2.44827680e-02
-1.11342227e+00 -1.00819945e+00 -7.22878218e-01 -5.66264153e-01
6.37931347e-01 5.32018006e-01 6.06726885e-01 5.88610917e-02
-2.47515097e-01 -2.00316995e-01 -4.78416830e-01 -2.11891428e-01
-4.09482047e-02 4.94668841e-01 -4.87033397e-01 1.35712504e-01
3.75088960e-01 -6.03830099e-01 -4.05433595e-01 3.48635674e-01
-1.13626790e+00 3.91675606e-02 1.02420890e+00 5.06368876e-01
6.18593216e-01 5.90742588e-01 6.53041244e-01 -9.55757320e-01
2.51691073e-01 -5.23000717e-01 -8.88548374e-01 1.55592233e-01
-6.45834982e-01 -3.43576014e-01 4.20033455e-01 6.13285080e-02
-1.32466352e+00 4.15175080e-01 -4.36181396e-01 2.29845852e-01
-7.81553805e-01 1.02036345e+00 -7.37724155e-02 -1.76660970e-01
6.14600420e-01 1.69384927e-01 -2.83991396e-01 -4.31864351e-01
2.83333302e-01 1.23521650e+00 5.07336199e-01 -1.18915886e-01
8.20542157e-01 4.02229846e-01 1.39440019e-02 -1.09485734e+00
-6.38779402e-01 -7.02793479e-01 -1.08858252e+00 -1.65829495e-01
1.17279077e+00 -9.40291047e-01 -1.72133863e-01 7.87743092e-01
-8.65045965e-01 -2.68003762e-01 1.18329406e-01 7.00279057e-01
-1.49152145e-01 1.72851458e-01 -1.29001409e-01 -1.18480921e+00
-5.26129186e-01 -9.71601665e-01 6.95404887e-01 7.95065105e-01
4.21735734e-01 -6.77309096e-01 -2.69038796e-01 2.86649764e-01
6.02527678e-01 5.35845160e-01 7.70614088e-01 -4.25171316e-01
-6.45171106e-01 -3.82476896e-02 -8.85985553e-01 6.22294128e-01
2.90718287e-01 3.58852744e-01 -8.95719469e-01 1.38545424e-01
-4.44308192e-01 7.07967430e-02 1.20075202e+00 5.84960818e-01
1.13062799e+00 -2.28015497e-01 -4.23697770e-01 5.86890161e-01
1.89521194e+00 2.88244098e-01 1.16891515e+00 5.51186860e-01
8.76758099e-01 7.46877670e-01 7.62969673e-01 2.93911397e-01
4.27392751e-01 1.66171730e-01 7.01664031e-01 -6.01342559e-01
1.57990530e-01 1.43748194e-01 7.32341260e-02 1.83750942e-01
-5.88272512e-01 -1.20542377e-01 -1.10861981e+00 8.16907644e-01
-1.65614378e+00 -9.69109714e-01 -7.48271465e-01 2.14131761e+00
4.45761174e-01 8.02522874e-04 5.02863675e-02 2.17028648e-01
1.05766225e+00 1.82357401e-01 -3.69132847e-01 -1.83626667e-01
-2.35142753e-01 4.58613902e-01 1.19231749e+00 3.57128829e-01
-1.63594961e+00 1.36580157e+00 5.22050905e+00 7.40063429e-01
-1.28909898e+00 -1.21917211e-01 5.39136231e-01 8.63401473e-01
1.02101438e-01 1.45987958e-01 -8.16331327e-01 1.51878059e-01
8.46852303e-01 5.53457141e-01 2.97241092e-01 6.95774734e-01
4.65676069e-01 -5.80217659e-01 1.12028189e-01 2.32912809e-01
-4.39860016e-01 -9.89337027e-01 1.58836007e-01 1.62180468e-01
7.26501584e-01 2.73311645e-01 -2.26237968e-01 1.89537525e-01
4.48423356e-01 -1.09696913e+00 1.99305356e-01 7.18960583e-01
7.27344573e-01 -9.25960898e-01 1.17544460e+00 3.55373532e-01
-1.57135272e+00 -1.43512830e-01 -4.77407157e-01 5.52673750e-02
-1.19788423e-02 5.33211887e-01 -8.65526021e-01 8.98412049e-01
9.14813399e-01 5.71698070e-01 -6.76390707e-01 1.13955021e+00
-2.55802304e-01 7.34628558e-01 -4.86418337e-01 2.30726361e-01
7.18047798e-01 -4.89089757e-01 2.53234386e-01 1.25351834e+00
2.33784646e-01 4.22099233e-01 2.73342162e-01 9.84202266e-01
1.70969650e-01 2.79547662e-01 -6.61128044e-01 9.41804424e-02
3.60367954e-01 1.61102343e+00 -1.08017027e+00 -2.10698694e-01
-7.09074214e-02 4.31651950e-01 -6.99482039e-02 3.17731470e-01
-7.98089564e-01 -8.14025223e-01 9.44715738e-02 1.74983740e-01
3.24553400e-01 -4.29684788e-01 -3.03944856e-01 -6.47269309e-01
-1.39001340e-01 -4.25130934e-01 2.37979457e-01 -7.68266797e-01
-8.88010919e-01 7.36333609e-01 2.88079888e-01 -1.08940303e+00
1.39729515e-01 -3.78496289e-01 -5.55263638e-01 1.20397556e+00
-2.24586868e+00 -1.53624749e+00 -7.50069559e-01 5.03970027e-01
3.48073959e-01 1.17946044e-01 4.86952871e-01 4.94870126e-01
-6.42343521e-01 -7.11914524e-02 -3.02203707e-02 4.67067212e-01
1.48793802e-01 -1.01170552e+00 3.41695100e-01 1.12070298e+00
-2.54534006e-01 3.01980376e-01 1.57445535e-01 -8.21780801e-01
-7.09264457e-01 -1.75517118e+00 6.86511457e-01 3.43018353e-01
2.90994614e-01 3.89312953e-01 -1.09165144e+00 7.21909285e-01
-1.59182638e-01 -8.52668956e-02 3.29410881e-01 -2.61986971e-01
3.60343933e-01 -2.62600243e-01 -1.46591616e+00 2.27275088e-01
7.08334982e-01 -2.43157402e-01 -4.05413419e-01 1.13861583e-01
3.68988961e-01 -1.42318951e-02 -8.92374575e-01 7.96753287e-01
5.56960881e-01 -7.33975708e-01 8.99575531e-01 -3.00402999e-01
4.50680882e-01 -6.23475909e-01 -2.67990977e-01 -1.18251014e+00
-4.30298507e-01 1.96844429e-01 6.23140335e-01 1.44047368e+00
4.58745658e-01 -7.14813590e-01 5.93749106e-01 3.28239948e-01
-2.85701483e-01 -4.58610654e-01 -6.16248965e-01 -5.18148661e-01
-1.33987322e-01 -2.37311617e-01 6.80526435e-01 8.83894920e-01
-9.84003186e-01 3.45315263e-02 -2.46810555e-01 6.18737161e-01
4.30342704e-01 -3.17526236e-02 7.12473392e-01 -1.56863630e+00
4.77671206e-01 -4.04905528e-01 -3.70474637e-01 -8.05779874e-01
-1.86608601e-02 -7.08024085e-01 1.67204320e-01 -1.98827565e+00
-1.85252920e-01 -9.26903605e-01 -1.38358399e-01 6.83719039e-01
-3.59414190e-01 4.17972207e-01 -9.02273282e-02 2.82621741e-01
3.54423784e-02 5.04474044e-01 1.08158207e+00 -1.25224087e-02
-5.97573698e-01 3.39152664e-01 -5.32859743e-01 8.42822194e-01
1.02017975e+00 -5.54268360e-01 -4.41929400e-02 -3.58679563e-01
-3.78462784e-02 -1.45713851e-01 4.97543871e-01 -1.09367514e+00
1.49781078e-01 -3.14909816e-01 4.78971660e-01 -1.29767168e+00
-1.19733810e-01 -9.81685579e-01 3.23529899e-01 2.18103662e-01
7.86524042e-02 -1.31436020e-01 3.28978568e-01 2.48327047e-01
-2.22314730e-01 -2.43892640e-01 9.26966906e-01 -2.13107780e-01
-1.13181615e+00 3.78900796e-01 -4.32871640e-01 -5.54520369e-01
1.10842443e+00 -5.71643174e-01 -2.02987298e-01 2.21174166e-01
-6.06791019e-01 3.25270593e-01 1.32372871e-03 9.91019011e-02
6.91631675e-01 -8.67532372e-01 -9.21346545e-01 1.59433246e-01
-1.23607174e-01 4.52075034e-01 3.97310823e-01 8.70698810e-01
-8.63565683e-01 4.70665425e-01 -4.66659755e-01 -5.68151653e-01
-8.99890900e-01 1.51410982e-01 3.05473447e-01 -3.23614776e-01
-4.50072318e-01 3.89098614e-01 -3.73435825e-01 -7.36243069e-01
-2.19896466e-01 -4.30775136e-01 -1.00748563e+00 2.57821679e-01
1.05950780e-01 5.60630798e-01 8.48224312e-02 -1.09804606e+00
-3.62888128e-01 8.32056999e-01 2.44790956e-01 3.40094268e-02
1.54003608e+00 -3.47621620e-01 -2.44460687e-01 -7.35008791e-02
6.13874257e-01 -2.01884463e-01 -1.04553282e+00 -1.91317290e-01
1.69992164e-01 -3.30617219e-01 5.65427899e-01 -7.23717868e-01
-1.29489374e+00 8.77481461e-01 8.25762033e-01 2.42952093e-01
1.28884649e+00 -3.97237837e-01 6.88258946e-01 4.14421469e-01
1.65640160e-01 -9.44045246e-01 -9.52680528e-01 4.38046455e-01
4.47862774e-01 -1.47506773e+00 2.36040935e-01 -5.30424774e-01
-5.18315315e-01 1.27292979e+00 5.52297175e-01 -1.05674632e-01
8.49593043e-01 1.88652065e-03 -1.10316455e-01 -1.61617741e-01
2.98233807e-01 -8.92605066e-01 2.52597839e-01 5.22270739e-01
2.90449709e-01 3.92162174e-01 -3.25597137e-01 2.30664194e-01
5.43076955e-02 3.48440468e-01 4.83609259e-01 8.87942195e-01
-9.68392253e-01 -6.30948544e-01 -6.22961462e-01 6.71392560e-01
-5.07043183e-01 -3.65321666e-01 -1.71227723e-01 9.35696304e-01
4.15224105e-01 9.66360509e-01 1.50062799e-01 -3.22708249e-01
2.19451144e-01 -2.99137950e-01 -4.89008576e-02 -5.31383216e-01
-5.80839217e-01 -1.41205922e-01 -3.75629142e-02 -9.32915732e-02
-1.11812568e+00 -3.13157022e-01 -1.37671781e+00 -2.82718807e-01
-7.75299609e-01 7.05045536e-02 1.05764365e+00 1.08109558e+00
1.40746102e-01 3.98580194e-01 9.12013948e-01 -8.21590245e-01
-6.88328743e-02 -1.25436246e+00 -7.59498954e-01 -2.71212161e-01
1.12748422e-01 -6.32291317e-01 -1.59531340e-01 -1.12775974e-02] | [9.331293106079102, -1.4283013343811035] |
85451c06-61ba-4d0e-bad2-7eba46ea2c17 | translation-consistent-semi-supervised | 2203.14523 | null | https://arxiv.org/abs/2203.14523v2 | https://arxiv.org/pdf/2203.14523v2.pdf | Translation Consistent Semi-supervised Segmentation for 3D Medical Images | 3D medical image segmentation methods have been successful, but their dependence on large amounts of voxel-level annotated data is a disadvantage that needs to be addressed given the high cost to obtain such annotation. Semi-supervised learning (SSL) solve this issue by training models with a large unlabelled and a small labelled dataset. The most successful SSL approaches are based on consistency learning that minimises the distance between model responses obtained from perturbed views of the unlabelled data. These perturbations usually keep the spatial input context between views fairly consistent, which may cause the model to learn segmentation patterns from the spatial input contexts instead of the segmented objects. In this paper, we introduce the Translation Consistent Co-training (TraCoCo) which is a consistency learning SSL method that perturbs the input data views by varying their spatial input context, allowing the model to learn segmentation patterns from visual objects. Furthermore, we propose the replacement of the commonly used mean squared error (MSE) semi-supervised loss by a new Cross-model confident Binary Cross entropy (CBC) loss, which improves training convergence and keeps the robustness to co-training pseudo-labelling mistakes. We also extend CutMix augmentation to 3D SSL to further improve generalisation. Our TraCoCo shows state-of-the-art results for the Left Atrium (LA) and Brain Tumor Segmentation (BRaTS19) datasets with different backbones. Our code is available at https://github.com/yyliu01/TraCoCo. | ['Gustavo Carneiro', 'Vasileios Belagiannis', 'Fengbei Liu', 'Yuanhong Chen', 'Chong Wang', 'Yu Tian', 'Yuyuan Liu'] | 2022-03-28 | null | null | null | null | ['brain-tumor-segmentation'] | ['medical'] | [ 4.28065032e-01 5.13750255e-01 -1.42434388e-01 -5.32992601e-01
-1.08416188e+00 -5.86880744e-01 5.82833886e-01 1.48468941e-01
-5.01501024e-01 7.37803459e-01 -7.44636357e-02 -2.53179103e-01
1.64604783e-02 -3.06877822e-01 -7.81481326e-01 -8.96496892e-01
9.29302499e-02 6.22027814e-01 4.58333999e-01 3.09050560e-01
-2.83109071e-03 4.49669570e-01 -1.29587626e+00 4.40960974e-01
9.53554273e-01 8.06554735e-01 3.37350160e-01 6.10971272e-01
-5.78365885e-02 6.67611837e-01 -4.16024923e-01 -7.48180971e-02
3.67566079e-01 -7.29682744e-01 -1.00640774e+00 3.37881863e-01
5.09087384e-01 1.53662100e-01 3.02561700e-01 1.05732715e+00
5.89384854e-01 -1.82781592e-01 7.27048874e-01 -1.19888055e+00
-6.80217519e-02 2.18989804e-01 -6.60929918e-01 1.75123036e-01
4.20202725e-02 9.89744887e-02 6.17231429e-01 -7.72092462e-01
8.41874182e-01 8.33388746e-01 8.48216414e-01 6.41082168e-01
-1.66240478e+00 -4.93892670e-01 6.78058565e-02 -2.86706984e-02
-1.28389037e+00 -2.54813522e-01 8.14317405e-01 -6.05223000e-01
6.30229175e-01 5.18829346e-01 6.36826396e-01 1.05114603e+00
2.76807159e-01 7.40806401e-01 1.62670493e+00 -6.15129828e-01
4.71255064e-01 3.38177711e-01 -1.47118881e-01 6.79161787e-01
4.02757823e-02 2.13384300e-01 -5.72995618e-02 -1.94583684e-01
7.47939229e-01 -1.42546624e-01 -5.52893639e-01 -1.01562095e+00
-1.18643820e+00 7.14762151e-01 5.66454411e-01 4.27443862e-01
-1.25760540e-01 -2.67902792e-01 4.35816258e-01 1.29727721e-01
5.47184706e-01 4.30649102e-01 -5.11034131e-01 2.25180298e-01
-1.19626057e+00 -3.73682193e-02 6.09386623e-01 7.45463073e-01
5.05939662e-01 -2.83080697e-01 -6.04988970e-02 8.64890516e-01
4.61776793e-01 2.13785708e-01 6.70490205e-01 -1.00167203e+00
2.86601186e-01 5.63149273e-01 -4.24756557e-01 -6.08720243e-01
-4.83393639e-01 -4.88530815e-01 -8.86328757e-01 5.71361363e-01
7.13329554e-01 8.57174620e-02 -1.35571170e+00 1.74249268e+00
5.35309732e-01 1.72005177e-01 -1.16090551e-01 8.57996583e-01
6.77156568e-01 1.96356952e-01 1.53610473e-02 -3.51125747e-01
9.82738554e-01 -8.21751416e-01 -6.19642556e-01 -2.84581751e-01
8.80505145e-01 -6.06911361e-01 1.02252686e+00 3.01272720e-01
-1.14007699e+00 -4.25800592e-01 -9.02932942e-01 1.67357340e-01
-3.42557222e-01 -1.15704715e-01 1.16800666e-01 6.70457780e-01
-9.75157678e-01 6.45710170e-01 -1.22573423e+00 -1.73541024e-01
8.08154643e-01 4.38288927e-01 -4.92544442e-01 -1.18941441e-03
-9.46298718e-01 1.01648235e+00 3.99789959e-01 1.49861500e-01
-5.59746385e-01 -8.32351506e-01 -9.19990778e-01 -4.47870880e-01
4.57255453e-01 -4.22770590e-01 1.03710079e+00 -1.32367027e+00
-1.25009263e+00 1.40306032e+00 9.99241769e-02 -4.08033580e-01
1.08061433e+00 3.07231098e-01 -6.00613505e-02 2.13383496e-01
2.28144407e-01 9.51390445e-01 8.17660689e-01 -1.61152899e+00
-3.47835213e-01 -4.52429354e-01 -5.06426930e-01 1.21462621e-01
3.84387642e-01 -3.41482371e-01 -3.84964198e-01 -7.82053828e-01
4.19764191e-01 -1.20703351e+00 -4.63369042e-01 1.13267265e-01
-5.33550322e-01 2.22644433e-01 7.30243564e-01 -6.45932913e-01
8.49946380e-01 -2.16195560e+00 2.67854691e-01 3.85040671e-01
7.80040771e-02 2.69631654e-01 1.33249924e-01 -1.29528105e-01
-4.50859636e-01 1.91364929e-01 -1.01092935e+00 -6.06260121e-01
-3.88384968e-01 4.39466774e-01 1.13840058e-01 5.66420972e-01
1.13320023e-01 9.38120544e-01 -8.60192120e-01 -8.53498697e-01
4.41776097e-01 4.20857877e-01 -3.33944589e-01 1.39106825e-01
-1.46554738e-01 1.00315988e+00 -1.35877907e-01 3.84565085e-01
6.80637300e-01 -1.99517503e-01 1.78334802e-01 -1.07753739e-01
7.81260952e-02 -4.63780239e-02 -1.11845207e+00 1.90043056e+00
-3.19240659e-01 2.70150870e-01 -1.14974082e-02 -1.11098123e+00
6.74572110e-01 4.69518274e-01 7.07219183e-01 -6.72104299e-01
3.58182862e-02 4.06075597e-01 -2.10493021e-02 -4.52658832e-01
-3.91603470e-01 -4.40200090e-01 9.70593393e-02 2.09875718e-01
6.37497529e-02 -4.78490144e-01 -1.38938054e-01 9.21262726e-02
7.53866792e-01 3.04278851e-01 4.05237377e-01 -2.72639722e-01
6.02875173e-01 9.49908197e-02 6.91160202e-01 5.34020364e-01
-4.60357159e-01 1.15364707e+00 6.28462374e-01 -2.75283068e-01
-9.68688130e-01 -1.03727758e+00 -5.47114611e-01 1.24058552e-01
-3.38418148e-02 -1.84147328e-01 -9.20477629e-01 -1.38698018e+00
-1.91299811e-01 6.83999836e-01 -8.35640907e-01 -4.54752408e-02
-5.70620298e-01 -7.10790157e-01 3.07313323e-01 5.00881076e-01
4.72956002e-01 -1.08532310e+00 -8.81761014e-01 6.13575615e-02
-1.50200024e-01 -1.02337742e+00 -4.37605143e-01 5.74898183e-01
-1.10250735e+00 -1.18394077e+00 -9.24001157e-01 -8.01426828e-01
1.06334651e+00 -3.53608459e-01 1.08263052e+00 -4.26895395e-02
-4.07605529e-01 3.73068482e-01 -3.01815987e-01 -3.88812870e-01
-6.96978152e-01 -2.31394451e-02 -1.93867788e-01 -1.11524917e-01
-8.89234841e-02 -4.27045017e-01 -3.93594533e-01 4.56119686e-01
-1.00498796e+00 3.80918056e-01 5.04278123e-01 1.10563350e+00
1.18418586e+00 -1.59163192e-01 3.02607864e-01 -1.37637258e+00
2.94609219e-02 -2.44706303e-01 -5.19583583e-01 2.51217365e-01
-7.13249564e-01 -1.14247976e-02 4.44187939e-01 -4.09977823e-01
-8.19391966e-01 4.12968010e-01 -5.75041361e-02 -7.71577954e-01
-5.04257798e-01 3.11071217e-01 -7.14635998e-02 -1.10365987e-01
7.25291610e-01 -6.81127328e-03 3.67966324e-01 -3.19681704e-01
2.32863128e-01 3.91943961e-01 4.41938490e-01 -2.53855079e-01
5.57479560e-01 6.47465110e-01 2.42232189e-01 -6.00937247e-01
-8.37560177e-01 -4.30282176e-01 -1.14794338e+00 -1.86629817e-01
1.05017793e+00 -5.15837133e-01 9.29031223e-02 4.07672286e-01
-7.80391276e-01 -7.28245378e-01 -6.80113614e-01 5.05063415e-01
-8.36084425e-01 4.39486653e-01 -1.59368455e-01 -5.20388186e-01
-1.02515481e-01 -1.34350729e+00 1.01485777e+00 -6.35808408e-02
-3.64554524e-01 -1.18087542e+00 2.27542639e-01 4.16279912e-01
1.06273964e-01 7.62999415e-01 8.03864419e-01 -7.69297123e-01
-3.23059678e-01 -1.23713009e-01 1.94881894e-02 7.42053747e-01
2.91393369e-01 -2.48804152e-01 -1.02484167e+00 -4.05347437e-01
2.49232933e-01 -3.38008463e-01 7.50210822e-01 7.91594625e-01
1.23977494e+00 -4.29780260e-02 -4.40944552e-01 7.03237116e-01
1.29441309e+00 7.87644461e-02 5.29175222e-01 3.44080567e-01
7.88986564e-01 8.48314047e-01 6.49045944e-01 -8.94928873e-02
-4.38632444e-03 6.11366570e-01 4.52628613e-01 -5.86779594e-01
-2.68512398e-01 -7.84658361e-03 -1.40643656e-01 5.63133359e-01
1.18755780e-01 5.97819388e-02 -1.08161271e+00 5.13619781e-01
-1.81666112e+00 -6.92737639e-01 -2.00307712e-01 2.40074062e+00
9.61070836e-01 2.93829083e-01 8.28256011e-02 3.49032044e-01
5.47859967e-01 1.95148997e-02 -5.58417797e-01 -2.19155699e-01
1.20338514e-01 1.65539801e-01 5.52349925e-01 6.67053103e-01
-1.30159318e+00 5.44543386e-01 5.20139599e+00 8.18132281e-01
-1.16132092e+00 2.73173153e-01 1.04806352e+00 -1.19002089e-01
-4.48664688e-02 -8.66469070e-02 -3.40886623e-01 6.10216856e-01
7.58267164e-01 4.01677161e-01 -7.39971697e-02 6.20800138e-01
2.42651328e-01 -3.96453440e-01 -1.22107315e+00 8.91072810e-01
9.89378467e-02 -1.15760624e+00 -2.89374530e-01 9.09284279e-02
7.86603272e-01 1.78585835e-02 2.82607004e-02 4.22472507e-02
-1.55317664e-01 -1.08282208e+00 5.77702463e-01 4.86687988e-01
9.09059107e-01 -5.83980322e-01 8.19049656e-01 4.69369411e-01
-8.45842242e-01 2.41072476e-01 1.94856860e-02 6.01508319e-01
1.74198464e-01 5.57325661e-01 -9.62826192e-01 5.47723830e-01
7.32037604e-01 6.51534081e-01 -6.92111790e-01 1.13320804e+00
-2.28691325e-01 5.77430964e-01 -3.47967833e-01 5.27619243e-01
2.76606888e-01 -1.64718926e-01 6.88241422e-01 1.03728890e+00
-1.63091585e-01 -6.44730404e-02 2.34660178e-01 8.76928926e-01
2.58447379e-01 5.09857722e-02 -4.78444219e-01 5.38455188e-01
7.23531321e-02 1.05299664e+00 -1.08921027e+00 -2.05039799e-01
-3.00709963e-01 1.06244004e+00 1.44750699e-01 1.90453127e-01
-7.32447863e-01 8.29699188e-02 5.60526960e-02 3.90330344e-01
1.84793249e-01 1.91995457e-01 -5.34039915e-01 -9.46128309e-01
1.26515761e-01 -8.17516506e-01 6.46741092e-01 -6.50554419e-01
-1.25147665e+00 6.19613707e-01 3.15721571e-01 -1.45204890e+00
-1.31849945e-01 -3.27469289e-01 -4.17625725e-01 8.02763104e-01
-1.50312698e+00 -1.13557613e+00 -1.51984587e-01 3.16166818e-01
6.15481555e-01 1.62240863e-01 8.01461041e-01 7.93547928e-02
-3.51293832e-01 4.82500523e-01 4.58555575e-03 9.48821828e-02
7.51953542e-01 -1.69682467e+00 1.67414799e-01 5.50269783e-01
1.73451394e-01 1.05957985e-01 5.30779958e-01 -7.20226347e-01
-5.15985191e-01 -1.16750216e+00 7.01306105e-01 -6.63766682e-01
1.95655167e-01 -2.95955151e-01 -1.19983900e+00 7.06431091e-01
4.43853252e-02 6.09042645e-01 6.29379511e-01 -5.07914543e-01
2.78051328e-02 1.69114336e-01 -1.58141339e+00 4.33176368e-01
7.44079113e-01 -2.55181193e-01 -5.14768481e-01 2.95329541e-01
3.70874882e-01 -7.33855546e-01 -8.31348062e-01 6.87911034e-01
3.15646321e-01 -1.11864066e+00 9.06641781e-01 -4.18726087e-01
1.46206468e-01 -3.81353736e-01 7.93665498e-02 -1.32629502e+00
-1.19232098e-02 -3.21762711e-01 9.69037414e-02 9.57008302e-01
5.84054470e-01 -5.82963228e-01 8.70503366e-01 7.87730992e-01
-1.13152467e-01 -1.10999560e+00 -1.21985281e+00 -8.09030950e-01
3.65414411e-01 -4.09814477e-01 4.89875302e-02 1.20538855e+00
-6.19888231e-02 -1.50181232e-02 -1.60996336e-02 1.00883543e-01
7.81649590e-01 -8.46772864e-02 4.20753896e-01 -1.11494577e+00
-2.87422270e-01 -2.75291920e-01 -5.34469783e-01 -4.24049854e-01
1.13833509e-01 -1.24520779e+00 1.14897951e-01 -1.34568846e+00
1.06879257e-01 -6.41604662e-01 -1.37172356e-01 5.56243002e-01
-1.14806950e-01 4.06968683e-01 7.39087984e-02 3.47845256e-01
-3.21480274e-01 3.24087858e-01 1.50608075e+00 4.21511009e-02
-3.58122170e-01 2.44185641e-01 -1.79431066e-01 8.42293859e-01
8.70976210e-01 -7.71890044e-01 -5.41070819e-01 -1.34708494e-01
-2.70585358e-01 3.61454370e-03 5.89065731e-01 -8.80531609e-01
1.19854033e-01 1.64317682e-01 6.76595211e-01 -4.84584600e-01
1.10527262e-01 -1.11857784e+00 2.11175591e-01 6.27686679e-01
-5.04042625e-01 -1.08230263e-01 3.66885602e-01 5.06387770e-01
-9.47725624e-02 -3.39932948e-01 1.31095755e+00 -4.30119872e-01
-2.68625766e-01 1.73163697e-01 -2.14241281e-01 2.69160241e-01
1.29572153e+00 -4.87067610e-01 2.80212909e-01 -7.32388049e-02
-1.24063241e+00 3.01093817e-01 6.04453385e-01 5.73137254e-02
4.76997197e-01 -1.08708882e+00 -5.35368681e-01 4.90663290e-01
4.14549001e-02 5.86430132e-01 2.22234145e-01 1.24277294e+00
-4.89689291e-01 2.13121340e-01 -7.59954080e-02 -1.09485626e+00
-1.40458012e+00 5.05507052e-01 8.25969756e-01 -3.91151905e-01
-8.21891546e-01 8.36986303e-01 1.12242572e-01 -7.39644289e-01
2.69247472e-01 -5.33745587e-01 -9.84521583e-02 -7.24596977e-02
1.38182834e-01 2.01723754e-01 3.13203126e-01 -6.77100360e-01
-4.93416756e-01 7.11546361e-01 -1.78950980e-01 -2.72328258e-01
1.07577419e+00 -5.42296038e-04 1.08838245e-01 5.46409070e-01
1.35566521e+00 -2.18424574e-01 -1.46803212e+00 -2.83021390e-01
1.08126640e-01 -4.62169170e-01 2.57310033e-01 -1.06271708e+00
-1.18670559e+00 7.67686486e-01 9.59480226e-01 8.04590508e-02
1.01874220e+00 2.10373268e-01 4.06750202e-01 -2.69254118e-01
1.46019906e-01 -1.00929976e+00 -9.35234502e-02 1.76915433e-02
8.56540978e-01 -1.45129502e+00 8.50312039e-02 -5.50369322e-01
-9.72705603e-01 8.31653833e-01 5.21010518e-01 -3.34728211e-02
8.18604052e-01 3.96890730e-01 4.24203515e-01 -2.70309299e-01
-3.66868764e-01 -1.04721732e-01 4.51298237e-01 6.45841062e-01
2.80407786e-01 -7.33637437e-02 -1.56179443e-01 2.63698190e-01
6.26227632e-02 -1.44041255e-01 1.83047637e-01 1.09820449e+00
1.09059259e-01 -1.22185373e+00 -4.13931310e-01 4.45563525e-01
-4.19290483e-01 1.59171686e-01 -4.23825264e-01 9.99609411e-01
3.16803247e-01 4.86220866e-01 3.12781148e-02 9.31362212e-02
4.33734357e-01 2.32022122e-01 6.09098852e-01 -7.77521133e-01
-4.99101788e-01 3.39728534e-01 -3.02615136e-01 -5.87332547e-01
-6.21739686e-01 -9.51179504e-01 -1.44101763e+00 4.44306880e-01
-3.75404030e-01 2.98424270e-02 5.57240486e-01 9.35433269e-01
7.91495815e-02 4.86104429e-01 6.98964715e-01 -8.94045413e-01
-3.52435946e-01 -7.79695213e-01 -4.30578262e-01 6.23781741e-01
4.68550086e-01 -6.76466227e-01 -6.22002304e-01 2.27725893e-01] | [14.54741096496582, -2.1239633560180664] |
dd4dc3dc-3142-4d1d-81d4-0e1f96643be4 | noddle-node2vec-based-deep-learning-model-for | 2305.16421 | null | https://arxiv.org/abs/2305.16421v1 | https://arxiv.org/pdf/2305.16421v1.pdf | NODDLE: Node2vec based deep learning model for link prediction | Computing the probability of an edge's existence in a graph network is known as link prediction. While traditional methods calculate the similarity between two given nodes in a static network, recent research has focused on evaluating networks that evolve dynamically. Although deep learning techniques and network representation learning algorithms, such as node2vec, show remarkable improvements in prediction accuracy, the Stochastic Gradient Descent (SGD) method of node2vec tends to fall into a mediocre local optimum value due to a shortage of prior network information, resulting in failure to capture the global structure of the network. To tackle this problem, we propose NODDLE (integration of NOde2vec anD Deep Learning mEthod), a deep learning model which incorporates the features extracted by node2vec and feeds them into a four layer hidden neural network. NODDLE takes advantage of adaptive learning optimizers such as Adam, Adamax, Adadelta, and Adagrad to improve the performance of link prediction. Experimental results show that this method yields better results than the traditional methods on various social network datasets. | ['Vijay Mago', 'Aditya Singhal', 'Kazi Zainab Khanam'] | 2023-05-25 | null | null | null | null | ['link-prediction'] | ['graphs'] | [-6.25948370e-01 1.41035870e-01 -2.16999292e-01 -2.07824603e-01
1.83284864e-01 -1.86469451e-01 5.87234855e-01 3.14299285e-01
-2.18117669e-01 6.77841842e-01 3.23213562e-02 -3.87373149e-01
-2.63177454e-01 -1.23552418e+00 -4.54047680e-01 -4.57892537e-01
-4.67233866e-01 7.28056550e-01 2.18424767e-01 -3.48655224e-01
-9.99626815e-02 3.82855415e-01 -9.17940259e-01 -3.09151322e-01
8.27592909e-01 9.73137796e-01 -1.51135936e-01 5.95011652e-01
-4.57318842e-01 8.67289007e-01 -4.80521441e-01 -8.40187490e-01
3.46692950e-01 -3.80230784e-01 -8.23172510e-01 -3.56564790e-01
-3.76415402e-02 -1.59055293e-01 -7.73103178e-01 1.05307186e+00
4.34989452e-01 2.30829373e-01 5.49993396e-01 -1.61203110e+00
-6.98674381e-01 8.89278591e-01 -4.70061541e-01 3.10030490e-01
2.23344430e-01 -9.83127430e-02 1.38892245e+00 -4.03696597e-01
7.04841852e-01 1.28783286e+00 1.05649817e+00 1.73415482e-01
-1.11893654e+00 -3.85840982e-01 1.55778736e-01 2.87219644e-01
-1.30316424e+00 9.12010670e-02 9.91014540e-01 -4.36589301e-01
9.59067762e-01 1.52032571e-02 9.69502926e-01 9.58071709e-01
5.37852943e-01 7.62966752e-01 4.60493147e-01 -1.68991894e-01
4.81890365e-02 1.97231680e-01 1.12578973e-01 1.05250204e+00
9.93938744e-02 1.06400726e-02 -9.58738774e-02 -1.80767596e-01
5.94758272e-01 2.12256327e-01 2.96878852e-02 -4.72670645e-01
-7.61460602e-01 9.99992251e-01 1.07553756e+00 3.38552922e-01
-6.58728480e-01 2.51030177e-01 4.27277118e-01 6.97960973e-01
7.00258970e-01 4.03652936e-01 -3.83586824e-01 -1.49387764e-02
-6.83603704e-01 9.10732523e-02 1.15892494e+00 3.45346719e-01
8.70305836e-01 8.23650807e-02 2.68122666e-02 8.37238073e-01
4.76955444e-01 -3.65195312e-02 5.66767991e-01 -5.72954416e-01
2.45070010e-01 1.08728051e+00 -4.87266600e-01 -1.72213554e+00
-4.51469749e-01 -8.30222845e-01 -8.86105835e-01 1.14642866e-01
7.47460499e-03 -4.70464855e-01 -6.10288560e-01 1.52883720e+00
4.69571918e-01 3.86062980e-01 -3.44803594e-02 7.66734123e-01
1.00866306e+00 7.70403385e-01 -3.36387791e-02 4.35949750e-02
4.26952004e-01 -1.30772352e+00 -5.22886813e-01 9.13449675e-02
5.80865145e-01 -2.27681488e-01 4.87988710e-01 1.12030925e-02
-7.28764892e-01 -3.63708854e-01 -1.00788248e+00 1.70926332e-01
-6.55955315e-01 -5.09413719e-01 8.88847232e-01 4.36490357e-01
-1.26643622e+00 1.08442748e+00 -7.52959609e-01 -4.73685384e-01
4.03973877e-01 4.73481178e-01 -4.50152665e-01 1.51318610e-01
-1.38462365e+00 8.40276301e-01 5.23599386e-01 2.25256115e-01
-7.18235731e-01 -4.82155323e-01 -8.73389602e-01 4.32735980e-01
4.28387791e-01 -6.94647849e-01 6.55673623e-01 -1.17639661e+00
-1.54157293e+00 3.86820883e-01 2.38750950e-01 -6.21365368e-01
4.97234523e-01 6.55286908e-02 -5.69905698e-01 -1.84035346e-01
-2.93823510e-01 4.82598841e-01 7.69060135e-01 -1.06835794e+00
-3.44747394e-01 -1.65011078e-01 2.27895707e-01 2.31697187e-01
-7.94448197e-01 -1.73203066e-01 -5.93993485e-01 -3.49595517e-01
-2.16959007e-02 -8.43350947e-01 -4.79878247e-01 3.43295150e-02
-6.95354819e-01 -6.16485536e-01 8.60645354e-01 -6.02151036e-01
1.46424305e+00 -1.76013207e+00 3.71624827e-01 6.40882432e-01
6.94700956e-01 5.87869644e-01 -2.94790447e-01 5.98154902e-01
4.92029765e-04 2.79489726e-01 1.44179597e-01 -2.37453774e-01
-1.94432929e-01 2.44014800e-01 3.29983652e-01 3.17796409e-01
-1.83952972e-02 1.00913370e+00 -1.09296703e+00 -6.26290381e-01
2.02842597e-02 6.28715992e-01 -4.93841350e-01 1.70216307e-01
-1.33552253e-01 -1.94699958e-01 -4.88119394e-01 3.81845415e-01
3.35468501e-01 -6.09514475e-01 5.06863952e-01 3.97087932e-02
3.41571867e-01 8.81880820e-02 -1.09914839e+00 1.16009760e+00
-1.53152794e-01 9.39150989e-01 -1.16810337e-01 -1.13116920e+00
1.22951961e+00 2.31646746e-01 7.28593767e-01 -4.39989358e-01
3.40709746e-01 -1.51346669e-01 1.98782966e-01 -5.42806864e-01
2.31805786e-01 2.37556890e-01 4.04000849e-01 4.61829931e-01
1.28477290e-01 2.81597197e-01 2.66829073e-01 4.66825545e-01
1.32157004e+00 -3.31387669e-01 4.36366089e-02 4.23346981e-02
4.89323080e-01 -3.54389101e-01 6.43925548e-01 4.48115200e-01
-2.55592823e-01 1.35936290e-01 8.92988503e-01 -9.01714683e-01
-8.59928846e-01 -7.94939399e-01 2.96902090e-01 9.16723967e-01
6.56492487e-02 -7.36402273e-01 -5.55617213e-01 -9.91402209e-01
3.98657501e-01 3.09931278e-01 -8.06825459e-01 -5.41783452e-01
-3.00284296e-01 -7.92335927e-01 1.83156118e-01 3.18752915e-01
4.37636316e-01 -1.06361985e+00 5.84967621e-02 4.58016157e-01
1.55082703e-01 -6.91290379e-01 -8.07384700e-02 -2.49222219e-02
-9.07156348e-01 -1.26779413e+00 -4.23183650e-01 -7.20359862e-01
5.62017620e-01 1.74252759e-03 1.20811260e+00 5.17718315e-01
-1.54099181e-01 1.37900084e-01 -3.39298129e-01 -2.12681331e-02
-3.36188436e-01 4.72377807e-01 5.00525609e-02 1.75466150e-01
2.79334664e-01 -6.71902299e-01 -5.34156561e-01 2.55992971e-02
-5.83097696e-01 -3.61368209e-01 5.05724907e-01 9.27010834e-01
1.45509467e-01 4.27030653e-01 4.73081380e-01 -9.43314850e-01
1.11754549e+00 -8.69963229e-01 -3.29465657e-01 2.84797698e-01
-1.23560500e+00 1.88629508e-01 5.89862168e-01 -3.17261696e-01
-5.70044339e-01 -3.65229279e-01 -3.20147094e-03 -5.17100811e-01
6.18633091e-01 1.17733359e+00 2.11599603e-01 -2.60755271e-01
4.75714207e-01 1.12099666e-02 3.28953862e-01 -4.30248648e-01
1.57765344e-01 3.12404901e-01 -6.36627674e-02 8.63794144e-03
8.46612453e-01 8.38221535e-02 -1.36338258e-02 -6.47938311e-01
-5.90241730e-01 -1.65218860e-01 -4.17253882e-01 -5.35127342e-01
4.50464666e-01 -6.19863033e-01 -8.47830236e-01 3.55504155e-01
-7.49566495e-01 -2.85647273e-01 6.71274886e-02 3.60157281e-01
1.38417840e-01 3.61752063e-01 -7.58734763e-01 -4.81136918e-01
-5.98827839e-01 -8.81324172e-01 1.73105672e-01 3.94856066e-01
-1.23042889e-01 -1.67520165e+00 3.67111355e-01 2.05206290e-01
5.56718767e-01 5.42431355e-01 8.96029949e-01 -8.78246665e-01
-4.16878045e-01 -7.16694951e-01 -1.83235198e-01 4.33926195e-01
1.76762026e-02 5.03679454e-01 -3.19452316e-01 -4.78181213e-01
-5.95349967e-01 -1.50528491e-01 9.22022521e-01 3.51001054e-01
1.03876340e+00 -4.00448233e-01 -6.65298998e-01 7.18063712e-01
1.49872756e+00 -2.08320655e-02 4.48192477e-01 4.20723170e-01
8.47243190e-01 3.95257831e-01 1.20708302e-01 4.19218659e-01
6.69985056e-01 5.16925871e-01 8.03811371e-01 -7.29635134e-02
-9.72912461e-02 -4.46008265e-01 2.02760816e-01 1.06027555e+00
-1.39182746e-01 -5.70610583e-01 -1.05375397e+00 5.00287652e-01
-1.85727894e+00 -9.00972068e-01 -3.95176634e-02 1.71827388e+00
5.29253483e-01 5.20296335e-01 2.33128488e-01 6.22802638e-02
7.71886051e-01 3.91569644e-01 -6.40280962e-01 -5.68252027e-01
-5.97733306e-03 -2.34606743e-01 3.21625620e-01 4.61230487e-01
-1.03485417e+00 1.05718553e+00 6.48311853e+00 4.81082171e-01
-1.36984849e+00 -5.15937619e-02 5.75186610e-01 8.82964581e-02
-3.68700981e-01 -2.56861188e-02 -4.17777389e-01 6.00066781e-01
9.19341624e-01 -2.52629340e-01 5.12522995e-01 8.67442906e-01
-7.21762553e-02 3.49385738e-01 -8.52260232e-01 8.50109756e-01
3.59471403e-02 -1.32293057e+00 -1.48412257e-01 1.37604296e-01
8.75753105e-01 3.67372334e-01 -4.76623103e-02 5.33437312e-01
7.05398500e-01 -9.86247361e-01 1.20243080e-01 6.50718212e-01
1.07726954e-01 -9.43643808e-01 1.00054789e+00 1.11439168e-01
-1.05575502e+00 -1.90209553e-01 -3.84530514e-01 -1.51984572e-01
-2.10701558e-03 8.09507072e-01 -1.23194456e+00 5.34844577e-01
6.30683064e-01 1.03039873e+00 -7.16063440e-01 1.25651836e+00
-1.82617620e-01 6.36751890e-01 -2.42474318e-01 -5.01882434e-01
4.06815231e-01 -3.98610204e-01 7.39379287e-01 7.89062619e-01
-7.72952801e-03 -3.11122358e-01 2.98207045e-01 5.82544804e-01
-4.64276910e-01 1.09267697e-01 -5.33837497e-01 -4.47374105e-01
4.59941119e-01 1.49323547e+00 -6.96386456e-01 -1.04574285e-01
-2.90873557e-01 7.85763979e-01 9.01833713e-01 3.85601610e-01
-7.52860904e-01 -6.46582186e-01 7.34814644e-01 1.07800245e-01
2.93232471e-01 -2.20151767e-01 -9.74042434e-03 -8.79890263e-01
-1.66176394e-01 -4.94214296e-01 4.41041291e-01 -4.72148210e-01
-1.27961326e+00 8.85389805e-01 -5.49525499e-01 -8.05171847e-01
-2.48851269e-01 -5.69337308e-01 -1.07190025e+00 5.49228489e-01
-1.30907774e+00 -8.24521005e-01 -2.71713644e-01 5.24908781e-01
6.69019595e-02 -5.42754531e-01 6.33053064e-01 3.36137056e-01
-1.02838230e+00 7.85637259e-01 5.79039752e-01 4.93023872e-01
3.24526966e-01 -1.28104174e+00 4.33149576e-01 4.24939901e-01
1.79854140e-01 2.60038465e-01 5.81889927e-01 -8.11320126e-01
-1.33126092e+00 -1.14539886e+00 1.13810027e+00 4.36601825e-02
8.37566495e-01 -7.38286525e-02 -8.27150583e-01 7.33760297e-01
1.67731941e-01 1.86513469e-01 6.69750512e-01 5.27503014e-01
-1.39769793e-01 -4.56881225e-01 -1.02256358e+00 6.43255234e-01
9.33903873e-01 -4.38436568e-01 -1.21220656e-01 4.53109592e-01
6.41020834e-01 1.25893336e-02 -1.20980990e+00 2.88716078e-01
5.20024776e-01 -8.35215032e-01 9.31700349e-01 -1.01736879e+00
6.32688880e-01 3.52843441e-02 2.58403033e-01 -1.73735845e+00
-5.80160856e-01 -4.71973777e-01 -5.72219849e-01 1.07990801e+00
6.08003020e-01 -7.04378843e-01 1.01990569e+00 5.15511990e-01
1.80669889e-01 -1.25676262e+00 -5.38380325e-01 -6.60355568e-01
-2.28205189e-01 1.37656212e-01 7.32891262e-01 1.26628232e+00
5.31958379e-02 5.18096626e-01 -3.62824649e-01 -1.37968048e-01
4.55906123e-01 2.59692594e-02 6.88093722e-01 -1.72721577e+00
-1.68938220e-01 -6.50185764e-01 -9.07078087e-01 -5.18119514e-01
4.34271306e-01 -1.20207596e+00 -3.61392349e-01 -1.86405993e+00
2.89843306e-02 -5.85352063e-01 -6.50876939e-01 3.88283253e-01
-3.02328676e-01 -4.29746648e-03 3.04321945e-02 7.40340352e-02
-8.70078027e-01 8.88183475e-01 1.19621038e+00 -3.39887887e-01
-3.03602993e-01 9.56136808e-02 -5.72636247e-01 4.62265611e-01
7.80723929e-01 -4.85437423e-01 -3.08923125e-01 -5.66635966e-01
5.60906947e-01 9.20614228e-03 9.47342813e-02 -9.87752974e-01
5.13308644e-01 2.22269699e-01 5.21434963e-01 -2.63646752e-01
1.71547532e-01 -7.99147010e-01 3.15154523e-01 6.84498608e-01
-4.02823865e-01 2.60133505e-01 -3.39166880e-01 8.16860974e-01
-2.84333676e-01 -1.96187451e-01 4.31566507e-01 2.91607194e-02
-5.58013320e-01 8.29619765e-01 -2.00557321e-01 -9.72821340e-02
9.40555215e-01 -1.26782492e-01 -8.46294388e-02 -6.44090712e-01
-7.70347357e-01 7.54483759e-01 1.93742767e-01 6.34949148e-01
5.93591869e-01 -1.57631350e+00 -8.62514436e-01 -6.82633519e-02
-3.24434489e-01 -2.26120412e-01 -2.35775989e-02 6.83863163e-01
-7.29749322e-01 9.17015597e-02 -1.50283709e-01 -3.87038320e-01
-1.15956926e+00 5.28004825e-01 5.14419198e-01 -5.89100599e-01
-4.92623240e-01 1.13398242e+00 -6.33538485e-01 -7.28024304e-01
3.44466656e-01 3.56900156e-01 -5.57088256e-01 1.93673894e-01
9.87386256e-02 5.43833315e-01 -3.48150954e-02 -4.29961562e-01
-2.58336961e-01 1.60272509e-01 -3.26742619e-01 4.23182994e-01
1.65596867e+00 1.17574826e-01 -4.11144137e-01 2.39269421e-01
1.63201213e+00 -4.56308246e-01 -1.06243789e+00 -4.95731324e-01
1.45019069e-01 -2.74735272e-01 3.37208301e-01 -5.23272693e-01
-1.68133938e+00 5.21583140e-01 4.76785690e-01 7.47781277e-01
7.43031621e-01 -1.97168887e-01 9.65859354e-01 5.90129375e-01
-5.65790795e-02 -1.16153347e+00 1.24299265e-01 7.11529136e-01
5.22437632e-01 -1.48327172e+00 1.22503206e-01 -4.80697118e-02
-3.55145454e-01 1.22571194e+00 7.98092008e-01 -3.62142742e-01
1.20872283e+00 -1.04055978e-01 -7.89898410e-02 -2.59710938e-01
-9.43307400e-01 -7.42800906e-02 2.44368836e-01 3.52087647e-01
3.47642899e-01 9.95632038e-02 -1.59997568e-01 1.66973785e-01
-2.61175483e-01 -3.22757334e-01 1.74891159e-01 6.60180032e-01
-2.93417364e-01 -1.14291644e+00 2.90082902e-01 7.85776794e-01
-3.13809216e-01 2.47100070e-02 -5.66220343e-01 5.93303680e-01
-1.40569746e-01 7.19303846e-01 2.41771638e-01 -7.53736734e-01
3.96659747e-02 -1.17332585e-01 2.52890699e-02 -2.40539715e-01
-8.58664215e-01 -4.71243232e-01 1.24877729e-01 -4.53602046e-01
-1.01849526e-01 -3.89858276e-01 -8.87254059e-01 -8.83898854e-01
-6.21658027e-01 3.67637306e-01 6.62538528e-01 7.93109596e-01
3.64580005e-01 7.44803607e-01 9.32294428e-01 -5.24228394e-01
-4.07215685e-01 -8.39414775e-01 -5.25173843e-01 4.24795061e-01
1.27554655e-01 -5.44152617e-01 -2.58624583e-01 -6.19605780e-01] | [7.181975364685059, 6.110024452209473] |
258bb43d-0fda-413e-8fe3-9770b1d19881 | decentralised-semi-supervised-onboard | 2305.04059 | null | https://arxiv.org/abs/2305.04059v1 | https://arxiv.org/pdf/2305.04059v1.pdf | Decentralised Semi-supervised Onboard Learning for Scene Classification in Low-Earth Orbit | Onboard machine learning on the latest satellite hardware offers the potential for significant savings in communication and operational costs. We showcase the training of a machine learning model on a satellite constellation for scene classification using semi-supervised learning while accounting for operational constraints such as temperature and limited power budgets based on satellite processor benchmarks of the neural network. We evaluate mission scenarios employing both decentralised and federated learning approaches. All scenarios achieve convergence to high accuracy (around 91% on EuroSAT RGB dataset) within a one-day mission timeframe. | ['Gabriele Meoni', 'Vinutha Magal Shreenath', 'Pablo Gomez', 'Johan Östman'] | 2023-05-06 | null | null | null | null | ['scene-classification'] | ['computer-vision'] | [ 1.03184529e-01 1.65754229e-01 -4.75541949e-01 -8.49080026e-01
-7.35509634e-01 -5.95108211e-01 4.88410383e-01 -4.15371507e-02
-6.28407419e-01 8.28371584e-01 -3.60900730e-01 -5.75881362e-01
-4.42270368e-01 -6.74611032e-01 -7.00000823e-01 -9.55199838e-01
-1.03127110e+00 4.16410863e-01 -2.12660894e-01 -2.54623204e-01
-3.37812096e-01 8.07951570e-01 -1.66498399e+00 1.47702405e-02
5.94542623e-01 1.64892888e+00 -5.40101044e-02 9.38600957e-01
5.99884033e-01 7.68823624e-01 -6.17042601e-01 2.73630768e-01
1.01199043e+00 1.83417544e-01 -8.43384802e-01 1.33252978e-01
3.76717538e-01 -3.28130335e-01 -5.27968109e-01 8.83739173e-01
4.15464103e-01 1.82684604e-02 4.51534316e-02 -1.35397768e+00
8.73272598e-01 1.89201713e-01 7.94289559e-02 3.75463367e-01
-2.55099088e-01 3.86306942e-01 7.59651840e-01 -6.57959282e-02
4.08623330e-02 5.19388795e-01 9.53942478e-01 -2.16895565e-01
-1.19457841e+00 -4.84005153e-01 -2.67365098e-01 4.19207811e-02
-1.43873024e+00 -8.82349908e-01 7.16429129e-02 -2.28569612e-01
1.64066648e+00 7.31165528e-01 9.03259754e-01 3.95330191e-02
5.37687600e-01 3.75171989e-01 1.25093901e+00 -3.59615713e-01
7.73697495e-01 9.89448801e-02 -9.83951539e-02 4.59928662e-01
1.09312885e-01 4.14409399e-01 -3.65089595e-01 -3.00003767e-01
3.38125467e-01 -3.87408048e-01 -2.41783351e-01 4.45483811e-02
-1.08588254e+00 8.51119936e-01 8.52520525e-01 -7.32725412e-02
-6.09556317e-01 1.99098393e-01 6.34922206e-01 7.80364513e-01
6.11042917e-01 4.13672298e-01 -8.58911514e-01 1.80636835e-03
-1.45232928e+00 -2.70029873e-01 8.79310131e-01 1.00158525e+00
1.11304522e+00 7.99619734e-01 5.37775755e-01 1.61879107e-01
2.26457402e-01 7.28359342e-01 5.29083550e-01 -9.85219717e-01
1.71024531e-01 3.07394862e-01 9.16651413e-02 -2.90387183e-01
-8.85830104e-01 -8.49289596e-01 -8.80589366e-01 3.37154001e-01
-2.55714923e-01 -6.85206473e-01 -8.54407191e-01 7.50544369e-01
4.12421852e-01 4.45609055e-02 5.85525036e-01 1.22498751e+00
3.20237190e-01 9.41736221e-01 -1.62323385e-01 -2.62910813e-01
9.94890034e-01 -9.20596123e-01 -1.47547886e-01 -8.26512277e-01
1.18467271e+00 -2.90426284e-01 2.65558839e-01 6.98392570e-01
-5.77781916e-01 -2.57573575e-01 -1.55435252e+00 4.79475349e-01
-9.95383859e-02 3.61474752e-01 1.23273933e+00 1.05981386e+00
-1.32035089e+00 7.23404527e-01 -1.02816606e+00 -4.20208305e-01
2.66402662e-01 9.13228750e-01 -2.31839612e-01 -2.16026176e-02
-9.64796245e-01 1.05726480e+00 8.63962591e-01 2.97618032e-01
-1.26774585e+00 -5.84710538e-01 -6.13057017e-01 -2.78345913e-01
-1.99006274e-01 -3.96454334e-01 1.43128777e+00 -1.51968408e+00
-1.48375571e+00 8.17780137e-01 5.89647830e-01 -1.31135416e+00
-3.43734249e-02 8.51750076e-02 -7.50060976e-01 -3.50733548e-02
-4.48494434e-01 4.93750453e-01 1.00096714e+00 -5.52233398e-01
-1.09718108e+00 -3.90097290e-01 -1.96441904e-01 4.11774367e-01
-4.21978176e-01 4.30749170e-02 1.15901075e-01 1.58168224e-03
1.05694741e-01 -1.08563495e+00 -3.72593254e-01 2.20615938e-02
3.03610295e-01 2.16901615e-01 1.00517774e+00 -9.05598462e-01
3.91430438e-01 -2.11003423e+00 -1.11221597e-01 3.24712157e-01
-4.97439235e-01 -1.39070168e-01 1.03869028e-01 2.54782010e-03
-1.86120316e-01 -5.09214640e-01 -1.72790095e-01 1.09288596e-01
-2.35172808e-01 7.32008398e-01 -6.10020280e-01 1.04593420e+00
-1.42048717e-01 1.39521286e-01 -5.22236288e-01 -1.86541796e-01
7.52443522e-02 2.51068827e-02 -1.21112674e-01 2.49652565e-01
-4.10544842e-01 2.47568026e-01 -1.88544448e-02 1.11355293e+00
4.02808666e-01 -1.28645793e-01 6.94710076e-01 -3.01077664e-01
-1.50358081e-01 3.06943953e-01 -1.04310405e+00 1.80124640e+00
-7.51754344e-01 8.51314723e-01 8.47830534e-01 -1.22391856e+00
8.93832743e-01 5.65783232e-02 1.74730003e-01 -7.31063008e-01
3.06781232e-01 3.91067863e-01 -1.43989593e-01 -9.60210636e-02
7.19728708e-01 -2.93233216e-01 -2.79914379e-01 4.52118695e-01
6.06918693e-01 -3.68423432e-01 -3.90748829e-01 1.55177647e-02
8.89349401e-01 5.90203293e-02 2.42457449e-01 -7.38930106e-01
4.83288728e-02 7.36731410e-01 3.75159919e-01 3.63749355e-01
-8.77703130e-02 -2.80926824e-01 -1.35199070e-01 -9.46478605e-01
-1.12896574e+00 -1.03013821e-01 -3.28466386e-01 1.19773841e+00
-2.71248251e-01 -6.03029467e-02 -3.41922998e-01 -2.11867854e-01
1.17582016e-01 6.75068080e-01 6.82287151e-04 -2.59990506e-02
-1.82698682e-01 -1.36292458e+00 6.86517775e-01 2.66216487e-01
5.71734607e-01 -8.26483071e-02 -1.07639468e+00 4.99683730e-02
2.68329442e-01 -1.12803304e+00 5.98566413e-01 9.13101912e-01
-1.43544054e+00 -6.53339863e-01 -8.33884105e-02 -4.16630954e-01
3.98692697e-01 1.51559204e-01 1.04067862e+00 4.68720719e-02
-3.52525979e-01 3.09853375e-01 -5.85101582e-02 -3.71099591e-01
8.57911333e-02 1.02501743e-01 5.98317087e-01 -2.32411042e-01
-4.52913754e-02 -3.47957015e-01 -4.48263407e-01 1.40178025e-01
-5.85194230e-01 -2.01636195e-01 5.45965135e-01 6.65407419e-01
3.51670027e-01 5.80991805e-01 -2.88041323e-01 -1.81517482e-01
-2.75845617e-01 -7.51025736e-01 -1.35737741e+00 8.75729322e-02
-8.69309425e-01 1.32321730e-01 3.58357310e-01 2.25550145e-01
-7.86090672e-01 7.08891749e-01 3.94194484e-01 -1.62325025e-01
-8.19419622e-02 6.57027185e-01 1.77637562e-01 -1.04760325e+00
8.97596538e-01 4.42785025e-01 3.75545084e-01 -2.84649819e-01
3.89067307e-02 1.29888308e+00 7.32003331e-01 -5.77957034e-01
7.48193681e-01 6.94617450e-01 7.67507479e-02 -1.11836481e+00
-6.48191452e-01 -1.72495544e-01 -4.40904319e-01 -5.63672662e-01
-5.62552772e-02 -2.00311971e+00 -5.41233897e-01 2.82622457e-01
-6.32235467e-01 -7.08505929e-01 -1.57619730e-01 8.42415214e-01
-4.84286964e-01 2.73493230e-02 -1.18343852e-01 -8.65958810e-01
-8.40606570e-01 -6.06836259e-01 7.68763006e-01 2.49533594e-01
1.24712400e-01 -9.32846427e-01 -1.50811337e-02 3.85257035e-01
7.37443745e-01 3.28727573e-01 2.88812548e-01 -5.17137587e-01
-7.73713350e-01 -5.92618823e-01 1.11924291e-01 3.64915341e-01
-3.41792524e-01 -3.43936443e-01 -1.28215230e+00 -1.18395817e+00
6.76250607e-02 -8.09121847e-01 4.94765341e-01 3.76613028e-02
7.34905064e-01 -5.71081579e-01 -2.31597811e-01 1.03374016e+00
1.91453779e+00 -4.37317103e-01 3.86228442e-01 8.83614004e-01
-1.75751835e-01 5.25088429e-01 9.44412947e-01 7.53602087e-01
-2.35173050e-02 3.91539633e-01 9.38072801e-01 4.54320237e-02
5.21748543e-01 4.72691625e-01 3.64208043e-01 5.27321577e-01
-2.60083098e-02 3.22488576e-01 -1.51186109e+00 7.07162499e-01
-1.52035642e+00 -3.41149300e-01 1.52240936e-02 2.46812105e+00
5.78731835e-01 1.94174528e-01 1.29909739e-02 1.28777046e-02
3.08933467e-01 1.20733216e-01 -6.82118237e-01 -3.09895664e-01
-2.70558089e-01 3.04434672e-02 1.64389920e+00 1.97601780e-01
-1.25360787e+00 4.92585331e-01 7.72151756e+00 5.77659845e-01
-1.62656951e+00 3.40412378e-01 4.98079896e-01 -6.16896212e-01
3.92490059e-01 3.87641549e-01 -7.45545745e-01 2.90014535e-01
2.20789671e+00 -3.45468856e-02 4.80851382e-01 1.31461906e+00
-9.41207558e-02 -4.08862889e-01 -7.54711390e-01 9.71656919e-01
-4.84288074e-02 -1.54256940e+00 -5.59279978e-01 1.20611385e-01
5.49772441e-01 1.26015043e+00 -5.23249447e-01 4.59395438e-01
5.00909626e-01 -8.65532041e-01 7.53011525e-01 3.21658224e-01
1.10493159e+00 -1.04017580e+00 9.31854367e-01 6.65956855e-01
-7.46907473e-01 -5.06207526e-01 -2.56199747e-01 -6.34511232e-01
-4.32105809e-01 5.88465452e-01 -8.70782614e-01 8.52611065e-01
1.19113159e+00 4.46735024e-01 -5.28622091e-01 1.27365446e+00
8.12905282e-02 7.88806498e-01 -1.13980162e+00 2.55392045e-01
5.42740226e-01 1.54617339e-01 2.38160610e-01 1.08629334e+00
3.19264740e-01 4.48299885e-01 2.30894908e-01 -3.29692304e-01
2.77083069e-01 -3.08217436e-01 -3.82083565e-01 6.59056976e-02
3.79604936e-01 1.52789569e+00 -3.75770718e-01 -2.68887490e-01
-2.40664512e-01 5.98950326e-01 -5.72335906e-02 -1.50342524e-01
-3.30767930e-01 -3.43712628e-01 7.27237940e-01 -2.14274898e-01
1.13806762e-01 -5.53057015e-01 -2.90291339e-01 -8.15428674e-01
-3.62056404e-01 -9.18527544e-01 5.43023646e-01 -8.99544001e-01
-6.65195227e-01 7.41019726e-01 -1.95602313e-01 -1.55734396e+00
-2.55652249e-01 -6.97732091e-01 -4.63595748e-01 7.41485476e-01
-1.67216408e+00 -1.16294169e+00 -4.68133301e-01 5.95483720e-01
-1.33016640e-02 -7.57312119e-01 1.24736142e+00 -1.56915635e-02
-4.70573217e-01 1.93331838e-01 5.77236295e-01 -2.28927121e-01
2.16446385e-01 -7.07381070e-01 1.30244523e-01 9.45441544e-01
-2.39275679e-01 -2.19897017e-01 9.40887153e-01 -3.65469426e-01
-2.13238525e+00 -1.35749829e+00 3.93798709e-01 3.14717382e-01
8.26773643e-01 -1.27079129e-01 -4.34899718e-01 1.09390342e+00
6.41063809e-01 4.41373527e-01 9.33465421e-01 1.32419661e-01
-7.59386793e-02 -6.20499849e-01 -1.29298329e+00 -2.97895491e-01
1.45646811e-01 -3.85153502e-01 -2.21079633e-01 1.18104935e+00
4.26855594e-01 -6.98010445e-01 -1.46947289e+00 3.45997065e-01
4.35220934e-02 -4.83892173e-01 4.31747377e-01 -6.65719748e-01
-5.62793314e-01 -4.51637596e-01 -5.19660056e-01 -1.33733213e+00
-5.19056953e-02 -1.01551461e+00 -1.85159877e-01 5.08276820e-01
2.25707680e-01 -7.36220241e-01 8.62283289e-01 6.64125741e-01
-1.98808029e-01 -6.24996275e-02 -1.71312618e+00 -1.12046540e+00
-3.16782266e-01 -4.50748682e-01 5.59633851e-01 1.07211018e+00
2.57660728e-02 -7.28166774e-02 -2.94789344e-01 7.33963251e-01
1.11180604e+00 3.66291523e-01 7.29263604e-01 -1.23279560e+00
-3.20334047e-01 3.57998371e-01 -9.34597075e-01 -5.52770734e-01
1.40674353e-01 -7.74406135e-01 4.03210111e-02 -7.91548252e-01
-3.20057601e-01 -7.39857614e-01 -1.16583601e-01 1.00298440e+00
9.13899660e-01 3.57345521e-01 -1.28390327e-01 7.78346002e-01
-6.59474611e-01 4.57970172e-01 -2.46787563e-01 -3.07732344e-01
3.07400078e-01 -7.45718777e-02 -9.64519903e-02 4.37555104e-01
1.02198589e+00 -4.99315888e-01 9.34855044e-02 -8.49137664e-01
3.86116236e-01 1.90699860e-01 5.44495046e-01 -1.59442520e+00
6.25276625e-01 -5.25818020e-02 3.89520317e-01 -2.03382239e-01
3.40353519e-01 -1.24176395e+00 4.05441880e-01 9.02392507e-01
3.02878439e-01 -4.88134831e-01 7.06983626e-01 5.64086080e-01
-3.75472099e-01 8.16699937e-02 8.47448349e-01 6.23368844e-02
-1.26232815e+00 -1.58007398e-01 -5.85307777e-01 -6.47352815e-01
1.30897069e+00 -2.88523491e-02 -4.26082015e-01 -1.04936808e-01
-7.27758408e-01 6.49725914e-01 3.76479894e-01 7.30321854e-02
4.39061224e-02 -1.05009186e+00 -6.48962855e-01 3.87848854e-01
1.04929112e-01 -9.63909850e-02 7.85172209e-02 8.34625363e-01
-8.96667063e-01 8.98685038e-01 -4.40375984e-01 -9.13599014e-01
-1.24923706e+00 -2.87496839e-02 1.00646889e+00 3.62283587e-01
-3.14752460e-01 7.83828616e-01 -1.24941623e+00 -8.25881243e-01
1.91994384e-01 7.89995044e-02 5.54561794e-01 3.05448379e-02
5.39264619e-01 1.81761548e-01 9.15460825e-01 -4.05765593e-01
-5.43962836e-01 -2.77763121e-02 2.18909830e-01 9.70197693e-02
1.65616357e+00 6.26762435e-02 -2.57013470e-01 2.15966851e-01
1.02669585e+00 -9.98215735e-01 -1.42703843e+00 -7.17567921e-01
6.31362870e-02 -3.21672380e-01 1.33711576e+00 -7.78141081e-01
-1.02108431e+00 3.52259457e-01 1.34199107e+00 -1.38718173e-01
1.47328746e+00 -3.31178933e-01 4.77496535e-01 1.03086555e+00
9.80351329e-01 -1.46496809e+00 -7.85813928e-01 3.59561712e-01
5.75111687e-01 -1.20104253e+00 8.91767919e-01 2.18655780e-01
-4.12043482e-01 1.41974413e+00 2.65246987e-01 -1.96965650e-01
7.12861300e-01 5.95144749e-01 3.08357310e-02 -6.00202493e-02
-1.07888532e+00 6.23779930e-02 -2.20945939e-01 4.40212071e-01
-1.32317111e-01 4.25150663e-01 2.84822941e-01 1.73197985e-01
-3.44204813e-01 -3.23000401e-02 3.39559287e-01 1.63202918e+00
-8.66184652e-01 -2.47742414e-01 -7.31182635e-01 6.06314778e-01
7.70019442e-02 -1.00232288e-01 1.70858473e-01 5.36405265e-01
-2.51757294e-01 5.94133019e-01 6.29437625e-01 -5.22133708e-01
-1.99174508e-02 3.64797898e-02 2.99931824e-01 -2.29539111e-01
-7.20013857e-01 -8.58389884e-02 6.18400335e-01 -7.17203915e-01
-6.16538346e-01 -8.36232722e-01 -1.11704719e+00 -3.79219294e-01
-3.47664446e-01 3.14837128e-01 1.49930990e+00 9.84202087e-01
4.76920098e-01 1.12028956e-01 1.16232967e+00 -1.37508130e+00
-1.10341775e+00 -9.79497313e-01 -9.04878378e-01 -9.04333949e-01
4.58457023e-01 1.15360478e-02 -6.55274570e-01 -1.15413949e-01] | [9.566845893859863, -1.459147572517395] |
fdc7e7a4-41e9-4c04-969e-e0dcf010e5e9 | deep-neural-networks-can-predict-mortality | 1904.07032 | null | https://arxiv.org/abs/1904.07032v3 | https://arxiv.org/pdf/1904.07032v3.pdf | Deep neural networks can predict mortality from 12-lead electrocardiogram voltage data | The electrocardiogram (ECG) is a widely-used medical test, typically consisting of 12 voltage versus time traces collected from surface recordings over the heart. Here we hypothesize that a deep neural network can predict an important future clinical event (one-year all-cause mortality) from ECG voltage-time traces. We show good performance for predicting one-year mortality with an average AUC of 0.85 from a model cross-validated on 1,775,926 12-lead resting ECGs, that were collected over a 34-year period in a large regional health system. Even within the large subset of ECGs interpreted as 'normal' by a physician (n=297,548), the model performance to predict one-year mortality remained high (AUC=0.84), and Cox Proportional Hazard model revealed a hazard ratio of 6.6 (p<0.005) for the two predicted groups (dead vs alive one year after ECG) over a 30-year follow-up period. A blinded survey of three cardiologists suggested that the patterns captured by the model were generally not visually apparent to cardiologists even after being shown 240 paired examples of labeled true positives (dead) and true negatives (alive). In summary, deep learning can add significant prognostic information to the interpretation of 12-lead resting ECGs, even in cases that are interpreted as 'normal' by physicians. | ['Aalpen A. Patel', 'David P. vanMaanen', 'Sushravya Raghunath', 'Joshua Stough', 'H. Lester Kirchner', 'Brian P. Delisle', 'Alvaro E. Ulloa Cerna', 'Joseph B. Leader', 'Dominik Beer', 'Brandon K. Fornwalt', 'Dustin N. Hartzel', 'Christopher W. Good', 'Christopher M. Haggerty', 'Linyuan Jing', 'Amro Alsaid'] | 2019-04-15 | null | null | null | null | ['electrocardiography-ecg'] | ['methodology'] | [ 1.51321545e-01 7.72075653e-02 2.51039155e-02 -4.86681461e-01
-1.17138016e+00 -6.39479876e-01 -1.74747586e-01 7.55187631e-01
-4.16742802e-01 9.17685449e-01 6.41234368e-02 -9.75689054e-01
-4.82077301e-01 -7.50181317e-01 -5.10358334e-01 -4.10898358e-01
-1.05680001e+00 5.64637542e-01 -3.72285783e-01 4.28950250e-01
-2.16232732e-01 5.24345279e-01 -5.42005062e-01 2.09289908e-01
4.60397869e-01 9.49279785e-01 -5.13762176e-01 9.92935240e-01
7.14309096e-01 5.84895670e-01 -7.88358450e-01 -2.55653977e-01
7.03403577e-02 -8.39814126e-01 -5.12756467e-01 -3.32680404e-01
2.48049378e-01 -5.76296985e-01 -3.20284277e-01 3.14648986e-01
1.09941244e+00 -4.30552959e-01 6.26118302e-01 -8.30655634e-01
-1.96046919e-01 6.30773485e-01 -3.90858024e-01 5.45163751e-01
4.03208612e-03 3.82204294e-01 7.56838262e-01 -7.00579941e-01
3.55475098e-01 4.11338061e-01 1.47937524e+00 2.71019369e-01
-1.53426433e+00 -6.41341627e-01 -5.95517755e-01 -4.88764010e-02
-1.65636516e+00 -1.78137079e-01 4.89416242e-01 -4.48033303e-01
6.78539395e-01 5.52481115e-01 1.13381994e+00 7.42451727e-01
9.12586570e-01 -1.06448822e-01 9.56547439e-01 -9.31769162e-02
-1.64629333e-02 -2.03451455e-01 2.13000074e-01 4.52215523e-01
5.23910165e-01 3.83330345e-01 -1.12065479e-01 -8.51663172e-01
9.26854551e-01 1.43018603e-01 -4.64793563e-01 2.98567191e-02
-1.38830805e+00 4.67529833e-01 1.87706411e-01 1.29694253e-01
-6.19388402e-01 6.19561449e-02 6.59868062e-01 5.24422884e-01
1.74583972e-01 3.79102290e-01 -7.12811351e-01 -2.07168713e-01
-1.04177332e+00 -2.54786648e-02 7.39283264e-01 8.91007558e-02
1.08293541e-01 1.98576748e-01 -2.46445954e-01 4.75754857e-01
1.38852820e-01 5.84314525e-01 4.10864860e-01 -9.43135023e-01
5.21499105e-02 3.69007319e-01 2.11849198e-01 -9.38136518e-01
-1.05450189e+00 -1.12194586e+00 -1.36847436e+00 9.22918916e-02
5.80093145e-01 -7.37225592e-01 -6.88418508e-01 1.48584390e+00
-2.44067788e-01 -8.41157883e-02 1.41654927e-02 7.88743377e-01
7.25546181e-01 2.46104330e-01 3.12021941e-01 -5.80258131e-01
1.37414384e+00 1.60443380e-01 -3.59947890e-01 4.09960411e-02
8.07839572e-01 -3.08985705e-03 6.77485704e-01 5.76746941e-01
-1.07147861e+00 -4.58058268e-01 -9.58868444e-01 4.65595096e-01
2.43072942e-01 2.02708334e-01 1.70611441e-01 7.50516474e-01
-1.12005210e+00 1.05764341e+00 -9.54798877e-01 -3.31972510e-01
7.46831834e-01 3.56083632e-01 -3.51405412e-01 2.69663453e-01
-1.41336429e+00 8.53336692e-01 -5.00871092e-02 1.47050485e-01
-1.13960040e+00 -8.45413864e-01 -5.43265820e-01 1.25221223e-01
-1.74598798e-01 -1.03186285e+00 8.36409271e-01 -6.47603333e-01
-5.58607757e-01 1.10023987e+00 -1.22303449e-01 -7.69572496e-01
7.08645463e-01 -1.07133962e-01 -5.97157359e-01 3.11453223e-01
-1.88757731e-05 -4.30587605e-02 3.67569596e-01 -1.02301764e+00
-4.34989095e-01 -7.22036302e-01 -3.90976757e-01 -9.85576399e-03
4.67920899e-02 1.22376457e-01 2.24776298e-01 -6.93989813e-01
9.63538960e-02 -6.26933217e-01 -5.75688183e-01 -3.08047570e-02
-2.46194601e-01 2.66844332e-01 3.25053513e-01 -1.08426106e+00
1.37174404e+00 -2.04020405e+00 -6.59366965e-01 2.96278089e-01
8.92079651e-01 7.27582537e-03 4.10413146e-01 2.49999195e-01
-6.33920968e-01 5.02988040e-01 -4.32495236e-01 1.21755622e-01
-5.98935664e-01 -1.75933972e-01 1.60377808e-02 8.70822489e-01
4.52362411e-02 1.20944357e+00 -7.84715295e-01 -3.34742010e-01
1.97112992e-01 5.34913838e-01 1.17054664e-01 6.36850521e-02
8.34503591e-01 6.05952561e-01 4.72487994e-02 3.85711879e-01
3.59837204e-01 -7.74658918e-01 6.22267485e-01 -1.90753222e-03
2.28807449e-01 9.10404474e-02 -5.89911580e-01 1.32429409e+00
1.15410410e-01 7.81337023e-01 -2.94010609e-01 -9.65292275e-01
1.19109571e+00 7.80927002e-01 6.36859536e-01 -4.63462710e-01
1.76899940e-01 9.73783731e-02 4.52409059e-01 -2.76828527e-01
-5.01013458e-01 -6.99138463e-01 -9.91795361e-02 5.97962677e-01
-4.55479532e-01 3.91649425e-01 -3.24543059e-01 4.55235094e-02
1.55586445e+00 -3.95632207e-01 5.62782168e-01 -2.60630339e-01
2.66150627e-02 1.83090940e-01 8.84501576e-01 1.23484731e+00
-5.09771466e-01 9.52169895e-01 8.23800206e-01 -1.28586650e+00
-8.54097545e-01 -1.32826471e+00 -6.17257893e-01 1.14038020e-01
-3.03119838e-01 -3.25045288e-01 -1.79933861e-01 -5.96044004e-01
4.33332426e-03 4.09076184e-01 -5.98105907e-01 -4.42985982e-01
-5.32073855e-01 -1.20116329e+00 1.18634605e+00 9.45513070e-01
9.17810872e-02 -8.70749414e-01 -1.28623712e+00 6.01966798e-01
-1.57214120e-01 -4.27584767e-01 1.99964195e-01 3.46064866e-01
-1.29679739e+00 -1.30964541e+00 -1.14951301e+00 -5.01023769e-01
3.19957495e-01 -5.94894886e-01 1.62492776e+00 2.69824028e-01
-4.62853253e-01 2.10743576e-01 4.67485823e-02 -5.65606534e-01
-3.93935353e-01 -3.72151554e-01 2.13444248e-01 -2.21982211e-01
2.25812688e-01 -7.53244877e-01 -1.07828271e+00 7.63647035e-02
-2.03318015e-01 -1.18718095e-01 6.32697046e-01 9.57238197e-01
6.38460755e-01 -2.75727272e-01 1.05435491e+00 -7.81146884e-01
5.16698301e-01 -4.82335746e-01 -1.72831386e-01 1.77518323e-01
-1.05864418e+00 -8.08920860e-01 5.89646399e-01 -2.82687455e-01
-2.86764711e-01 -1.06286161e-01 8.12626779e-02 -2.69973218e-01
-4.47697073e-01 8.22937965e-01 3.95829976e-01 3.99578869e-01
9.16708887e-01 1.07312486e-01 5.19569628e-02 -2.97527760e-01
-5.38204730e-01 3.81462276e-01 7.50758350e-01 -1.36660099e-01
3.33847791e-01 3.40844601e-01 4.02517945e-01 -6.46477878e-01
-1.90405130e-01 -1.20753072e-01 -5.40017188e-01 -1.15639105e-01
7.29536474e-01 -9.59207892e-01 -9.41253126e-01 2.93528765e-01
-9.10302758e-01 -4.19659764e-01 -4.01030421e-01 6.96528614e-01
-2.16341928e-01 3.21228385e-01 -6.63971364e-01 -9.09313440e-01
-8.92454147e-01 -6.04044497e-01 5.46138227e-01 -1.42405868e-01
-9.05051708e-01 -1.06527674e+00 2.37331435e-01 -3.20277989e-01
3.46796423e-01 1.02326190e+00 1.15276361e+00 -8.42482269e-01
1.40483201e-01 -6.39218509e-01 -2.18845800e-01 9.76321921e-02
3.56521279e-01 -9.85025242e-02 -7.93341994e-01 -2.98218995e-01
4.32596467e-02 1.03558362e-01 5.19863307e-01 1.01305819e+00
1.29083574e+00 1.82022844e-02 -3.83501440e-01 6.84181333e-01
1.27511120e+00 7.69804418e-01 7.21668422e-01 -5.30487811e-03
5.93316138e-01 1.25781342e-01 -1.33403102e-02 3.95294517e-01
3.66764933e-01 1.89033687e-01 1.96526587e-01 -7.02522218e-01
3.36361259e-01 7.27469921e-02 -6.65708408e-02 2.55368441e-01
-3.61376166e-01 -1.91238411e-02 -1.47898591e+00 6.76085174e-01
-1.49186242e+00 -7.50313878e-01 -7.35313654e-01 2.55771852e+00
4.43227142e-01 3.58617842e-01 2.82987773e-01 4.11363244e-01
7.69509435e-01 -1.68770388e-01 -8.55754972e-01 -3.27471316e-01
-2.25345358e-01 2.54441112e-01 4.81571943e-01 1.09942369e-01
-8.72533500e-01 -1.61834896e-01 7.61882925e+00 -1.27218366e-01
-1.27953815e+00 -8.24586749e-02 1.46999776e+00 -3.98851857e-02
9.35728848e-02 2.05063121e-03 1.17352873e-01 3.36759090e-01
1.55601919e+00 -4.45203334e-01 -1.93485498e-01 3.66234213e-01
6.07208908e-01 4.48831171e-02 -1.12985432e+00 1.06819594e+00
-6.71532527e-02 -1.55510831e+00 -4.07084286e-01 -4.21562977e-02
2.49026895e-01 -2.71949731e-02 -1.63400039e-01 1.37553304e-01
-1.35152444e-01 -1.31018913e+00 3.76035303e-01 6.99044406e-01
1.87051165e+00 -6.76575124e-01 9.89760995e-01 3.04837316e-01
-8.38131905e-01 -2.31378898e-02 1.53524667e-01 -3.73380274e-01
3.40202361e-01 7.73438036e-01 -9.01569307e-01 3.87720972e-01
1.04597819e+00 8.26636553e-01 -5.08493960e-01 1.04754853e+00
8.13063011e-02 1.15534246e+00 -2.12023765e-01 4.51603740e-01
-2.97701806e-01 4.49643075e-01 4.64738905e-01 1.04104376e+00
2.03896597e-01 4.22697276e-01 -2.49605820e-01 6.82011008e-01
5.81995994e-02 -6.81618601e-02 -7.74576843e-01 2.03667030e-01
2.39311934e-01 1.12644911e+00 -8.60686600e-01 -6.92631602e-01
-2.98674375e-01 5.44284403e-01 -2.49152526e-01 3.62598509e-01
-7.30515659e-01 -7.08016932e-01 -5.11172377e-02 6.30226552e-01
-4.34884071e-01 2.73507774e-01 -1.07551682e+00 -7.85582960e-01
-9.45823081e-03 -8.11936677e-01 7.56828904e-01 -8.49273920e-01
-1.21374965e+00 6.27833903e-01 -1.95451349e-01 -1.33475828e+00
-3.82301241e-01 -2.88839579e-01 -8.43721628e-01 1.35762513e+00
-7.68114746e-01 -4.67713267e-01 -1.67815179e-01 2.82384247e-01
-1.60599817e-02 4.63684760e-02 1.34444106e+00 2.67220467e-01
-3.70875984e-01 6.89841032e-01 -1.64126337e-01 5.14624715e-01
7.90796459e-01 -1.48417044e+00 5.35622120e-01 4.59172040e-01
-2.27514386e-01 8.17060947e-01 3.66667479e-01 -8.01228404e-01
-8.24387670e-01 -1.02633166e+00 1.16108453e+00 -6.92640722e-01
1.79885462e-01 3.37823749e-01 -1.05999482e+00 6.46238625e-01
-1.09591626e-01 2.31139019e-01 1.09151888e+00 1.88409701e-01
1.72231272e-01 -1.81238204e-01 -1.20723057e+00 2.69178182e-01
5.48442960e-01 -4.10807580e-01 -6.78196788e-01 -1.57290667e-01
3.80135365e-02 -3.87954324e-01 -1.45441604e+00 8.62620950e-01
1.30043805e+00 -1.04860711e+00 9.52775717e-01 -8.57085168e-01
4.21081424e-01 1.22820295e-03 4.38967258e-01 -1.06916070e+00
-5.03385663e-01 -6.41723096e-01 -2.89403275e-03 4.67855006e-01
6.06275618e-01 -7.70753205e-01 7.36378908e-01 6.27142549e-01
-2.21438017e-02 -1.20843685e+00 -6.96348548e-01 -3.39331955e-01
1.27970845e-01 -4.27484542e-01 3.81352782e-01 1.10346305e+00
-3.99368182e-02 2.92963415e-01 -2.85754919e-01 2.11481094e-01
5.84756434e-01 -2.21730266e-02 3.80157530e-01 -1.64644158e+00
-2.72309422e-01 -1.36251211e-01 -5.38795054e-01 -1.91757590e-01
-4.74127680e-01 -7.77805328e-01 -3.97301376e-01 -1.83934212e+00
5.36469340e-01 -5.12093365e-01 -9.87008989e-01 6.12635612e-01
-2.89296836e-01 4.60032552e-01 -2.39722669e-01 2.87363589e-01
-2.03832835e-02 -1.97371259e-01 7.71660864e-01 7.16751739e-02
-3.67432505e-01 1.73548922e-01 -8.66349518e-01 8.96924198e-01
8.82352591e-01 -7.64727533e-01 -2.55540639e-01 -2.03533560e-01
1.51420429e-01 9.34915602e-01 7.97658145e-01 -1.09983265e+00
-3.41176152e-01 1.66092947e-01 1.33919656e+00 -6.43531203e-01
-1.19477259e-02 -5.42089224e-01 6.97377682e-01 9.76015747e-01
-3.39885503e-01 5.99797964e-01 4.34904665e-01 3.87371451e-01
5.14047220e-02 4.39439982e-01 5.83142817e-01 -2.38818452e-01
-9.37206373e-02 2.83357620e-01 -6.47248566e-01 2.39873111e-01
8.25073242e-01 -3.83907914e-01 -1.07728675e-01 -5.42094469e-01
-1.32366407e+00 -8.56820643e-02 1.71966150e-01 -1.05810277e-01
7.05828428e-01 -1.14871407e+00 -9.91214037e-01 1.19866535e-01
7.31877089e-02 -1.54042363e-01 4.56712842e-01 1.32214165e+00
-7.88753092e-01 1.10179789e-01 -2.10046157e-01 -8.01317811e-01
-1.02276850e+00 1.68618828e-01 7.20596254e-01 -2.30482623e-01
-1.14282310e+00 5.55570722e-01 1.17209300e-01 3.60606015e-01
1.48047179e-01 -1.81254044e-01 2.60422938e-02 -1.34664342e-01
6.54290378e-01 4.68288720e-01 2.30778843e-01 -4.29942906e-01
-6.18150055e-01 4.74506646e-01 2.65544295e-01 -1.01079755e-01
1.17158091e+00 -7.09256530e-02 2.05603912e-02 9.49046731e-01
1.05530250e+00 -3.19092989e-01 -8.21691334e-01 3.94756347e-01
-3.13242435e-01 -5.57570569e-02 -1.51388094e-01 -1.15440631e+00
-9.96323764e-01 1.04805040e+00 1.16304445e+00 3.34322572e-01
1.05873561e+00 -2.46657580e-01 5.58184922e-01 1.48078173e-01
2.39832804e-01 -2.94911444e-01 -5.87389648e-01 -1.25302881e-01
5.10728836e-01 -9.76625264e-01 -1.84169859e-02 3.14801782e-01
-7.56313443e-01 9.64516878e-01 6.91929162e-02 -1.15322821e-01
8.52872610e-01 1.01700537e-02 5.30178666e-01 -4.41784710e-01
-7.23651588e-01 7.02675343e-01 1.06727295e-01 6.85088754e-01
7.83530951e-01 3.93581808e-01 -6.09171987e-01 1.01504910e+00
1.12178020e-01 2.38855720e-01 5.24919868e-01 7.92699635e-01
-5.70426285e-02 -6.13836884e-01 -3.25913638e-01 1.18835700e+00
-9.88595366e-01 -2.71668583e-02 -2.46085674e-01 6.39260411e-01
-1.99038945e-02 8.11046958e-01 1.48913309e-01 -3.42665017e-01
3.02198291e-01 4.41168159e-01 1.23735383e-01 -4.15488422e-01
-7.32105792e-01 1.27481118e-01 1.99874461e-01 -2.58861780e-01
-1.21793211e-01 -5.99354088e-01 -1.17279053e+00 -4.93566599e-03
6.74342737e-02 1.41792461e-01 1.35727063e-01 6.92885458e-01
4.02290434e-01 8.27254593e-01 4.41503018e-01 -2.95753717e-01
-4.17771548e-01 -8.73374403e-01 -1.01140583e+00 2.43148908e-01
8.08486104e-01 -2.13857189e-01 -4.63516921e-01 2.54326135e-01] | [14.319972038269043, 3.2851905822753906] |
2df7f92b-bbec-4989-83fa-f5bdd4fa2769 | fedhealth-a-federated-transfer-learning | 1907.09173 | null | https://arxiv.org/abs/1907.09173v2 | https://arxiv.org/pdf/1907.09173v2.pdf | FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare | With the rapid development of computing technology, wearable devices such as smart phones and wristbands make it easy to get access to people's health information including activities, sleep, sports, etc. Smart healthcare achieves great success by training machine learning models on a large quantity of user data. However, there are two critical challenges. Firstly, user data often exists in the form of isolated islands, making it difficult to perform aggregation without compromising privacy security. Secondly, the models trained on the cloud fail on personalization. In this paper, we propose FedHealth, the first federated transfer learning framework for wearable healthcare to tackle these challenges. FedHealth performs data aggregation through federated learning, and then builds personalized models by transfer learning. It is able to achieve accurate and personalized healthcare without compromising privacy and security. Experiments demonstrate that FedHealth produces higher accuracy (5.3% improvement) for wearable activity recognition when compared to traditional methods. FedHealth is general and extensible and has the potential to be used in many healthcare applications. | ['Chaohui Yu', 'Yiqiang Chen', 'Wen Gao', 'Jindong Wang', 'Xin Qin'] | 2019-07-22 | null | null | null | null | ['wearable-activity-recognition'] | ['time-series'] | [-2.93908012e-03 -2.30371603e-03 -4.60599899e-01 -4.22815174e-01
-6.48458123e-01 -2.13430941e-01 8.35130662e-02 2.34494582e-01
-1.81382194e-01 8.62987995e-01 4.50648576e-01 -2.44988531e-01
9.42603126e-03 -9.95156765e-01 -4.54088688e-01 -7.06995070e-01
-8.87290388e-02 1.13135502e-01 -2.31365517e-01 3.09351176e-01
-4.65665042e-01 3.63899842e-02 -1.43147957e+00 5.51138997e-01
9.40497875e-01 1.24807429e+00 -2.93600172e-01 3.80410612e-01
2.27859601e-01 4.75250095e-01 -4.76621091e-01 -4.52379614e-01
3.18212472e-02 -4.58319217e-01 -6.37593687e-01 -3.81940693e-01
4.16237153e-02 -4.88093317e-01 6.34349361e-02 7.52226472e-01
8.26595247e-01 -2.17211723e-01 1.21570602e-01 -1.09771025e+00
-6.04330063e-01 2.82146275e-01 -9.57552493e-02 -1.61857247e-01
7.29859829e-01 4.17336896e-02 4.87167031e-01 -3.19361180e-01
1.11608572e-01 4.82905984e-01 1.20828426e+00 7.14098871e-01
-8.85149717e-01 -6.03899240e-01 -3.65975469e-01 2.67194331e-01
-1.22038317e+00 -3.18811595e-01 2.73072392e-01 -2.93502927e-01
8.04148614e-01 7.51282573e-01 1.10068631e+00 1.16425681e+00
3.56580853e-01 6.12179220e-01 9.99549389e-01 -2.05693394e-01
5.03586650e-01 2.41952434e-01 -1.66440737e-02 6.01405203e-01
4.97057170e-01 -1.87167346e-01 -5.36639094e-01 -6.93541050e-01
2.86603689e-01 8.25627327e-01 -2.28555486e-01 -2.87227351e-02
-1.16346645e+00 4.60444391e-01 6.03799343e-01 4.82895017e-01
-6.43869996e-01 -2.27616936e-01 4.10892606e-01 1.16222791e-01
4.48240101e-01 -4.47460040e-02 -6.29502773e-01 -3.68581593e-01
-7.26370454e-01 -9.36231092e-02 9.31201696e-01 7.60292470e-01
7.80253053e-01 -3.81382942e-01 -1.45629346e-01 4.50816900e-01
1.80628911e-01 4.37058240e-01 9.63843644e-01 -7.16897964e-01
2.07032919e-01 1.02139306e+00 1.07401907e-01 -8.67432356e-01
-4.83909160e-01 -1.97350323e-01 -1.22530627e+00 -4.27549630e-01
1.65499464e-01 -6.18672729e-01 -4.90961432e-01 1.28223443e+00
7.26890922e-01 2.63234556e-01 3.93271856e-02 6.27651751e-01
7.87081540e-01 5.35517812e-01 4.00235802e-01 -2.82687664e-01
1.54313195e+00 -7.32634604e-01 -9.04444873e-01 8.89789760e-02
6.76765978e-01 -1.77465379e-01 1.03303194e+00 3.26185435e-01
-8.57255459e-01 -3.32978547e-01 -9.07667518e-01 1.78650156e-01
-4.60001647e-01 -2.65914828e-01 8.27152908e-01 1.37368333e+00
-7.83607304e-01 5.96816361e-01 -1.28657258e+00 -6.57205582e-01
8.42311442e-01 5.43135285e-01 -3.67914706e-01 -1.66328788e-01
-8.77835751e-01 4.56833541e-01 1.71212271e-01 -3.51811707e-01
-3.84298712e-01 -8.45624804e-01 -6.79980040e-01 9.49712247e-02
-5.20423874e-02 -1.18818533e+00 1.06340528e+00 -7.62817383e-01
-1.45056272e+00 6.79005861e-01 9.29720402e-02 -4.28448796e-01
4.71269041e-01 -4.87851769e-01 -7.64785111e-01 -1.39825672e-01
-9.97744873e-02 -1.41035885e-01 5.07219851e-01 -5.71707726e-01
-7.77058721e-01 -9.59920585e-01 -6.00532293e-01 -8.29706416e-02
-1.15586591e+00 -9.52883661e-02 -1.36564940e-01 -9.63596031e-02
-3.74759257e-01 -7.66427219e-01 -7.05129504e-02 -1.85019329e-01
-3.44412513e-02 -1.33737370e-01 9.59186435e-01 -9.66964126e-01
1.36936629e+00 -2.08368921e+00 -4.88777161e-01 8.68303031e-02
2.73375124e-01 5.06468713e-01 5.04022717e-01 3.97386611e-01
4.06642944e-01 5.07623143e-02 -7.23677725e-02 -1.13560632e-01
-1.88931778e-01 3.97959292e-01 2.85870761e-01 4.21084583e-01
-5.33164620e-01 1.14095998e+00 -7.88122833e-01 -4.38230723e-01
1.37517273e-01 7.81909049e-01 -5.50437987e-01 4.98909563e-01
2.42720783e-01 8.59965563e-01 -6.36203468e-01 9.36802983e-01
2.48565972e-01 -5.00496984e-01 4.66396421e-01 -1.91387713e-01
9.27096307e-02 8.05395544e-02 -6.54810786e-01 1.79651260e+00
-3.42401534e-01 -1.96486220e-01 2.99676619e-02 -9.09055412e-01
7.50448227e-01 7.15274274e-01 1.13481879e+00 -5.93794465e-01
3.18510622e-01 6.67905733e-02 -4.38784570e-01 -9.28556144e-01
-1.44110441e-01 7.42172152e-02 -1.49563193e-01 6.37676835e-01
1.96570847e-02 8.16174448e-01 -6.34469509e-01 -2.17958704e-01
1.34733784e+00 5.63262478e-02 7.12363422e-01 2.55067064e-03
3.22206050e-01 -3.40651810e-01 7.91604221e-01 2.19763055e-01
-4.04488266e-01 1.85597003e-01 -3.99534017e-01 -9.08827484e-01
-6.43734813e-01 -9.25075352e-01 4.18935344e-02 1.19068742e+00
-2.13792011e-01 -7.01211751e-01 -9.01692510e-01 -8.02852333e-01
9.72763076e-02 1.46294177e-01 -5.68321168e-01 -3.67790520e-01
-3.58073533e-01 -1.03505492e+00 5.82529902e-01 6.26036286e-01
8.64858985e-01 -9.45839167e-01 -1.06705666e+00 2.80719548e-01
-5.43436646e-01 -6.64223135e-01 -5.80345035e-01 -1.22574553e-01
-1.09475708e+00 -1.08817971e+00 -5.88236988e-01 -4.81790721e-01
4.64147359e-01 1.11456655e-01 9.69177008e-01 1.25000209e-01
-5.17553329e-01 4.35937345e-01 -3.31413835e-01 -5.90259135e-01
2.16451613e-03 2.10330531e-01 1.68072999e-01 5.61599731e-01
7.77397215e-01 -6.95111692e-01 -1.14609718e+00 2.45422691e-01
-5.96584022e-01 -3.63634348e-01 3.83358359e-01 8.00942183e-01
5.19696593e-01 5.69925904e-02 8.77932489e-01 -1.07020056e+00
5.42072892e-01 -9.00813103e-01 9.69257057e-02 3.86441201e-01
-1.12141418e+00 -4.97131795e-01 4.76450861e-01 -3.17013413e-01
-9.97270823e-01 2.74117976e-01 -3.11908424e-01 1.16880894e-01
-3.75650048e-01 3.42611253e-01 -5.21576643e-01 2.55323146e-02
8.19095492e-01 1.11698337e-01 1.38859257e-01 -8.34073484e-01
-2.01350413e-02 1.20781434e+00 4.35079545e-01 -2.67972797e-01
1.12808153e-01 4.28968966e-01 -5.04990220e-01 -6.64553285e-01
-5.71479082e-01 -5.91721117e-01 -2.23025694e-01 -1.72500625e-01
1.00430489e+00 -9.45650697e-01 -1.08199310e+00 3.45870823e-01
-3.98570538e-01 -2.60146916e-01 -2.92785287e-01 4.13020581e-01
-3.62835556e-01 2.18372330e-01 -5.98347545e-01 -8.47576380e-01
-1.14246488e+00 -3.66511047e-01 7.20999837e-01 3.64369005e-01
-6.09592021e-01 -9.94664788e-01 3.21852833e-01 7.80446172e-01
6.22213364e-01 6.02822721e-01 5.82454681e-01 -5.82667589e-01
-9.16916579e-02 -6.28762126e-01 2.14210585e-01 1.68264478e-01
8.34978223e-01 -6.01106703e-01 -1.10983014e+00 -6.58830881e-01
1.35072976e-01 -2.09701777e-01 2.05493420e-01 2.01469868e-01
1.28663421e+00 -9.31417108e-01 -6.84064269e-01 7.91107476e-01
1.22903156e+00 1.45193070e-01 8.25285673e-01 5.95209859e-02
5.99050105e-01 3.28505069e-01 2.37762630e-01 6.07690275e-01
7.56668150e-01 3.95332754e-01 3.51283163e-01 -1.95435151e-01
1.69155478e-01 -2.79500782e-01 1.44700199e-01 8.61279666e-01
-4.74853992e-01 3.46665084e-01 -8.48123968e-01 5.11563063e-01
-2.06182027e+00 -8.32112968e-01 1.56757072e-01 2.51118708e+00
8.90295327e-01 -5.80190122e-01 6.94225788e-01 2.85513818e-01
3.78657430e-01 -4.79143679e-01 -7.65573204e-01 -2.93769538e-01
3.90393764e-01 2.59130478e-01 3.53779972e-01 -2.91631371e-01
-1.03009760e+00 1.60013407e-01 6.35323715e+00 -4.69659306e-02
-1.14365053e+00 8.56586874e-01 6.66643143e-01 -2.25504041e-01
1.04315557e-01 -5.94225049e-01 -3.67046237e-01 8.22356939e-01
1.25074899e+00 -1.40847461e-02 2.80557871e-01 9.96373177e-01
-9.81292054e-02 1.67172924e-01 -1.01478684e+00 1.20191371e+00
-7.08010495e-02 -1.11784041e+00 -4.47751969e-01 3.15107018e-01
8.61973107e-01 6.21123798e-02 -1.66944623e-01 2.11123466e-01
1.72326550e-01 -8.85671616e-01 -8.84787142e-02 6.35726273e-01
8.93397868e-01 -8.42470467e-01 7.93350160e-01 6.18903041e-01
-9.33387637e-01 -4.18529838e-01 4.66802754e-02 -1.33302554e-01
-1.10059261e-01 4.99034762e-01 -8.93769503e-01 6.26343191e-01
1.39130223e+00 6.32477760e-01 -2.71881133e-01 1.03127766e+00
4.13843453e-01 7.79302359e-01 -1.19836092e-01 -7.03076497e-02
-4.34588760e-01 -3.97241786e-02 -2.14222610e-01 1.07406080e+00
7.49544442e-01 1.54890716e-01 2.78374881e-01 2.17928573e-01
-6.34406283e-02 4.92260963e-01 -5.63906848e-01 1.21074162e-01
1.76056430e-01 1.32145786e+00 -1.72654033e-01 -3.37766498e-01
-5.95810533e-01 1.08955884e+00 1.67519733e-01 -4.20993753e-02
-6.27632022e-01 -1.56463727e-01 8.00520539e-01 4.33950573e-01
-7.84111023e-02 1.69522285e-01 -3.95009398e-01 -1.30675828e+00
-8.32070634e-02 -1.00173497e+00 9.39724684e-01 -2.04836741e-01
-1.39191985e+00 4.06974852e-01 -5.49493849e-01 -1.12180054e+00
-2.02675790e-01 -1.33221567e-01 -6.27700150e-01 5.59052765e-01
-9.26901162e-01 -1.42374778e+00 -7.03442097e-01 1.19563103e+00
-1.61724836e-01 -7.24964663e-02 1.58023989e+00 8.00145090e-01
-6.03353858e-01 7.58035958e-01 9.65843722e-02 5.36078289e-02
7.80128717e-01 -9.47161555e-01 8.96687061e-03 3.33317816e-01
-7.44871721e-02 6.79580927e-01 1.20744266e-01 -6.97920918e-01
-1.67502773e+00 -1.37195599e+00 8.97016525e-01 -4.46368068e-01
1.86722409e-02 -2.14818180e-01 -7.87892699e-01 8.28089595e-01
2.67994940e-01 1.93576828e-01 1.72329688e+00 4.34045941e-01
-1.86376259e-01 -7.65706778e-01 -1.69689691e+00 2.38957718e-01
8.21506202e-01 -2.83636302e-01 -3.33590657e-01 4.22807157e-01
2.74321765e-01 -1.03741176e-01 -1.44079661e+00 3.36063445e-01
9.27455425e-01 -8.84363472e-01 8.35822284e-01 -7.58743584e-01
-2.59061217e-01 8.54100939e-03 -6.58786148e-02 -1.04252493e+00
-4.91855592e-01 -8.05238307e-01 -8.35760057e-01 1.19326818e+00
4.61310223e-02 -1.04983401e+00 1.03573382e+00 1.17665899e+00
2.21927568e-01 -6.67104661e-01 -9.54995453e-01 -4.66450393e-01
-4.16264862e-01 -9.46973860e-02 1.23890114e+00 1.23020959e+00
5.83712816e-01 1.62512839e-01 -7.08128572e-01 -3.86172496e-02
8.13274920e-01 1.01032771e-01 8.53042960e-01 -1.40826035e+00
-3.15172434e-01 2.15988562e-01 -4.41536576e-01 -3.29028219e-01
-5.99866331e-01 -7.13084877e-01 -3.69931221e-01 -1.62266874e+00
1.70985073e-01 -5.99707901e-01 -7.09938228e-01 1.13166618e+00
-5.17177507e-02 6.46066964e-01 -1.98189825e-01 2.00639978e-01
-6.15432322e-01 9.55821872e-02 8.70886743e-01 -2.55476147e-01
-4.72608835e-01 4.98357922e-01 -9.57000077e-01 4.96617019e-01
1.41574860e+00 -1.24499366e-01 -1.84805468e-01 -2.80247748e-01
1.61826640e-01 1.21246792e-01 2.46364459e-01 -1.27307105e+00
2.93391019e-01 -8.97640642e-03 7.69630909e-01 -1.72675267e-01
1.70214102e-01 -1.23620665e+00 7.76540160e-01 8.64310384e-01
2.10796744e-01 -2.64907330e-01 -1.12373576e-01 5.67161918e-01
1.82513803e-01 5.45283020e-01 3.79938334e-01 -1.90139472e-01
-1.28677532e-01 4.62674111e-01 -3.58182162e-01 -5.31426609e-01
1.28129244e+00 -1.23586200e-01 -1.36164412e-01 -2.32938156e-01
-1.01693809e+00 8.83193165e-02 3.52692097e-01 3.38702917e-01
4.64682341e-01 -1.49320126e+00 -3.59302938e-01 5.39072692e-01
2.48259753e-01 -2.26281032e-01 3.65817100e-01 9.01339471e-01
-1.52335450e-01 2.22339332e-01 -3.56158823e-01 -5.18557131e-01
-1.47987056e+00 7.52152979e-01 2.21785054e-01 2.52837408e-02
-9.21350241e-01 1.26976669e-01 -5.08993983e-01 -4.14729595e-01
4.00147676e-01 -2.46191904e-01 5.28527685e-02 -1.65720046e-01
1.04219306e+00 6.84179485e-01 4.27507490e-01 -3.28836799e-01
-4.55065459e-01 1.46395862e-01 3.35741818e-01 5.33584356e-01
1.50671983e+00 -1.21888667e-01 -1.42467096e-01 2.61779845e-01
1.14943266e+00 -4.53671627e-02 -9.71734464e-01 -1.97549999e-01
-7.09745288e-02 -3.51613104e-01 -2.31601849e-01 -1.01375735e+00
-1.10230434e+00 5.99084914e-01 1.08026338e+00 3.98129493e-01
1.38060451e+00 -2.14952976e-01 1.37151790e+00 1.48507506e-01
6.46168411e-01 -9.91091907e-01 -2.32987046e-01 -2.45271668e-01
2.26266772e-01 -1.13928020e+00 -1.46088898e-02 3.50857154e-02
-3.93455446e-01 6.77588463e-01 3.88050348e-01 3.27377588e-01
8.76430035e-01 2.77701914e-01 1.59511104e-01 1.07288621e-01
-4.42447096e-01 2.41052121e-01 2.45739713e-01 9.43300545e-01
4.48417544e-01 4.46062237e-01 -3.34769726e-01 1.02027249e+00
-6.68653995e-02 5.23342550e-01 -3.33404422e-01 9.94362116e-01
-2.99843282e-01 -1.34520102e+00 -4.70562458e-01 8.04738462e-01
-8.87452066e-01 3.14548761e-01 -1.95359573e-01 1.34948753e-02
4.64623570e-01 1.05829537e+00 -1.28101453e-01 -5.47828138e-01
1.91407174e-01 4.89700735e-01 3.32370102e-01 -4.32025731e-01
-1.02487588e+00 -2.76270676e-02 -8.32609013e-02 -7.85640657e-01
-5.16259611e-01 -5.80719888e-01 -9.98837054e-01 -3.65854055e-01
7.57160261e-02 2.41316929e-01 8.52231979e-01 7.55070210e-01
9.25837696e-01 4.58685040e-01 5.40843844e-01 -4.99637067e-01
-4.38892722e-01 -7.80492544e-01 -4.88174289e-01 4.60155576e-01
2.94713616e-01 -1.23270452e-01 3.41171265e-01 3.37315708e-01] | [6.172980785369873, 6.283412456512451] |
2142c61c-295e-4d73-af4a-64643023678b | 190910148 | 1909.10148 | null | https://arxiv.org/abs/1909.10148v1 | https://arxiv.org/pdf/1909.10148v1.pdf | Dependency-Guided LSTM-CRF for Named Entity Recognition | Dependency tree structures capture long-distance and syntactic relationships between words in a sentence. The syntactic relations (e.g., nominal subject, object) can potentially infer the existence of certain named entities. In addition, the performance of a named entity recognizer could benefit from the long-distance dependencies between the words in dependency trees. In this work, we propose a simple yet effective dependency-guided LSTM-CRF model to encode the complete dependency trees and capture the above properties for the task of named entity recognition (NER). The data statistics show strong correlations between the entity types and dependency relations. We conduct extensive experiments on several standard datasets and demonstrate the effectiveness of the proposed model in improving NER and achieving state-of-the-art performance. Our analysis reveals that the significant improvements mainly result from the dependency relations and long-distance interactions provided by dependency trees. | ['Zhanming Jie', 'Wei Lu'] | 2019-09-23 | dependency-guided-lstm-crf-for-named-entity | https://aclanthology.org/D19-1399 | https://aclanthology.org/D19-1399.pdf | ijcnlp-2019-11 | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [-5.68034947e-01 -3.16503868e-02 -2.39069611e-01 -9.26286519e-01
-3.66232485e-01 -4.92569238e-01 3.64181966e-01 3.34818453e-01
-6.18857861e-01 8.41194212e-01 6.94200575e-01 -4.24015075e-01
-2.23748554e-02 -8.74896586e-01 -6.05673611e-01 -5.15358984e-01
-6.54068708e-01 1.80600345e-01 -6.69745952e-02 -1.27650782e-01
-1.28350407e-02 5.89782774e-01 -8.17079723e-01 1.10892549e-01
8.97089124e-01 6.68532133e-01 3.67577463e-01 3.13246697e-01
-7.63487697e-01 7.61096895e-01 -6.89868093e-01 -5.81068575e-01
-3.16749036e-01 -1.51388496e-02 -1.04049766e+00 -3.77465397e-01
-1.54307708e-01 -8.96813050e-02 -2.62373865e-01 9.33855653e-01
3.95824283e-01 3.26130062e-01 3.37532938e-01 -6.08441770e-01
-7.98363030e-01 1.24828470e+00 -3.59577686e-01 3.53642315e-01
1.90632448e-01 -4.30752486e-01 1.43392563e+00 -9.80943859e-01
5.66835701e-01 1.12460208e+00 4.31459188e-01 3.84126902e-01
-5.83508849e-01 -7.39618421e-01 3.74981791e-01 1.42427146e-01
-1.37482059e+00 -3.11525702e-01 4.32573587e-01 -1.54541314e-01
1.50496483e+00 -2.07568318e-01 -4.61912621e-03 9.70061481e-01
4.80658948e-01 6.14496589e-01 8.35787356e-01 -4.86599863e-01
-1.21756263e-01 -1.15460880e-01 8.78049076e-01 6.62362874e-01
2.97506422e-01 -1.16811275e-01 -4.43543881e-01 -5.04465960e-03
5.76898813e-01 -1.79164633e-01 -1.49271600e-02 4.95710760e-01
-9.95169580e-01 6.05383098e-01 7.71194696e-01 9.42130148e-01
-4.43649501e-01 -3.08353156e-02 4.42941457e-01 -1.37656346e-01
5.31001329e-01 2.96244860e-01 -1.06872296e+00 3.47450301e-02
-3.61900181e-01 -5.18967092e-01 9.78404880e-01 1.06916213e+00
6.24963224e-01 4.34373412e-03 -3.45876217e-01 8.31929088e-01
4.23608661e-01 4.07251775e-01 3.29826146e-01 -2.59737879e-01
7.25089610e-01 5.56013703e-01 -1.23133985e-02 -9.28540051e-01
-6.82993591e-01 -5.48410058e-01 -8.56554806e-01 -5.99669218e-01
1.50404572e-01 -6.00339472e-01 -9.73174155e-01 1.84472311e+00
2.84253746e-01 3.03773135e-01 3.69874746e-01 4.74065810e-01
1.33306873e+00 8.10737848e-01 8.02244902e-01 -2.37203494e-01
1.54733002e+00 -7.72258937e-01 -9.68544066e-01 -4.20833379e-01
1.11917031e+00 -4.83498573e-01 4.77702171e-01 -5.14263630e-01
-7.06839800e-01 -4.87937629e-01 -5.88722825e-01 -1.50544494e-01
-5.54091752e-01 2.22604156e-01 8.50313306e-01 3.29517752e-01
-7.08496690e-01 5.94576657e-01 -1.00266683e+00 -2.64720470e-01
2.03392163e-01 6.63325369e-01 -6.17439210e-01 5.11948541e-02
-1.70290267e+00 1.13344121e+00 8.50390553e-01 6.13959074e-01
-2.44989544e-01 -2.79881418e-01 -1.15176761e+00 4.03803200e-01
7.82989487e-02 -3.28515857e-01 9.48479891e-01 -3.64510506e-01
-9.95860398e-01 7.33683705e-01 -6.58112705e-01 -3.69947463e-01
-3.62366736e-01 -6.16893589e-01 -7.44400084e-01 -3.81797194e-01
8.40765759e-02 3.12056750e-01 -1.69641390e-01 -9.20296311e-01
-7.55542755e-01 -3.66529703e-01 7.32835159e-02 1.10140011e-01
-4.78476077e-01 6.14382863e-01 -2.88372636e-01 -3.68328601e-01
-1.16397008e-01 -6.56884432e-01 -3.99716347e-01 -9.58157778e-01
-8.27680945e-01 -8.13759327e-01 3.98435146e-01 -8.62134039e-01
1.51879644e+00 -2.09970665e+00 -1.83931947e-01 -4.26580347e-02
-2.54472550e-02 3.25714320e-01 -1.49609491e-01 5.25248587e-01
-2.24425837e-01 5.35308063e-01 -4.57591265e-02 -2.20121071e-01
-1.30137816e-01 5.64459562e-01 -3.15945596e-01 2.05623612e-01
4.91386682e-01 1.17646980e+00 -7.36499727e-01 -6.66412294e-01
-1.32054850e-01 6.73145294e-01 1.75351277e-01 4.41235751e-01
2.60698050e-02 5.47471702e-01 -7.67299950e-01 3.49629641e-01
5.71911633e-01 -3.35828364e-01 5.02385139e-01 -2.02950269e-01
-1.98586494e-01 1.08305597e+00 -6.55215502e-01 1.50852191e+00
-6.61959887e-01 3.82374346e-01 -2.34170988e-01 -7.46825099e-01
1.16947448e+00 5.33142686e-01 1.07790930e-02 -5.52871644e-01
2.57253975e-01 1.46363303e-01 3.16033721e-01 -5.01142502e-01
4.22808260e-01 -1.53294921e-01 -3.83198142e-01 1.72948852e-01
2.44700864e-01 7.44544923e-01 2.64306188e-01 8.31981078e-02
1.02044189e+00 3.68271582e-02 6.12325072e-01 -2.87100136e-01
4.38922852e-01 -3.57468754e-01 8.62528443e-01 4.77767557e-01
5.86976409e-02 -2.20746458e-01 3.45851094e-01 -2.79985964e-01
-5.44191897e-01 -9.08519208e-01 -2.99052030e-01 1.30161464e+00
2.81352978e-02 -3.72839212e-01 -4.18738872e-01 -1.04429269e+00
-4.74585712e-01 9.64789748e-01 -4.71610904e-01 1.54781237e-01
-1.14243019e+00 -8.10376167e-01 7.23487377e-01 1.05965030e+00
6.08079493e-01 -1.45387065e+00 -1.30927533e-01 4.16945845e-01
-2.63352454e-01 -1.67582190e+00 -2.15235814e-01 6.98930919e-01
-1.01590633e+00 -5.64871371e-01 -1.83892801e-01 -1.23917961e+00
6.73285961e-01 -1.97022334e-01 1.25755310e+00 1.41723618e-01
3.63600940e-01 -4.17142868e-01 -6.16475582e-01 -1.65896982e-01
-1.27903327e-01 4.24973071e-01 1.59147400e-02 -2.80774325e-01
6.48614824e-01 -6.35967612e-01 -1.17872402e-01 1.14518322e-01
-4.66190219e-01 -3.27725232e-01 7.58309484e-01 7.89087415e-01
3.33482414e-01 2.28963822e-01 4.90009069e-01 -1.02709925e+00
4.08216029e-01 -6.73422873e-01 -2.94813186e-01 4.37967271e-01
-2.55246460e-01 5.01655042e-01 8.01531136e-01 -2.22037524e-01
-1.52323103e+00 6.83151037e-02 -4.72378314e-01 3.21066260e-01
-6.10554397e-01 9.86057758e-01 -5.30694902e-01 4.44939196e-01
1.48982868e-01 7.01764086e-03 -1.20324719e+00 -6.60687268e-01
4.47437316e-01 6.12780809e-01 4.70817000e-01 -6.70478284e-01
6.33532286e-01 5.21484092e-02 -3.92906256e-02 -7.35383272e-01
-1.48172867e+00 -4.60909784e-01 -1.03148293e+00 4.09819514e-01
1.08104110e+00 -9.93743002e-01 -6.21795833e-01 2.24116206e-01
-1.72687709e+00 2.13596132e-02 1.48088843e-01 7.28621364e-01
3.61941308e-01 2.76758343e-01 -1.11474395e+00 -7.50853419e-01
-5.54670215e-01 -5.85420847e-01 6.87719822e-01 5.84087193e-01
-1.56348705e-01 -1.47096753e+00 6.59612194e-02 -8.37070718e-02
1.29094824e-01 2.02479940e-02 1.11924636e+00 -1.29005289e+00
-3.15960258e-01 -2.53201295e-02 -3.03933352e-01 1.51749700e-01
2.42212608e-01 -1.36500821e-02 -7.25308299e-01 1.37417302e-01
-1.82068855e-01 1.12303376e-01 8.17438960e-01 3.50863904e-01
5.76991498e-01 -2.13797212e-01 -5.78418434e-01 4.44378883e-01
1.29893124e+00 3.10102612e-01 3.95909160e-01 1.03366569e-01
1.00854611e+00 8.00550103e-01 6.15524232e-01 1.98275611e-01
5.70532858e-01 2.62299538e-01 2.31163632e-02 -2.10392430e-01
1.97113305e-01 -3.28554571e-01 3.41471732e-01 1.35403717e+00
-1.01592645e-01 -4.87706095e-01 -1.11175871e+00 6.12762690e-01
-1.66702223e+00 -7.37272143e-01 -6.97800636e-01 1.82129538e+00
9.73695219e-01 2.25278407e-01 -5.66197753e-01 -4.53615040e-01
1.05038190e+00 2.22969845e-01 -2.40335003e-01 -6.05209649e-01
-2.56613672e-01 4.78281349e-01 4.67738569e-01 3.98154616e-01
-1.20561385e+00 1.43550551e+00 6.16362047e+00 6.00657880e-01
-9.39091980e-01 8.87380317e-02 3.76544237e-01 5.32296836e-01
-1.53041750e-01 6.40633032e-02 -1.52262557e+00 1.78058490e-01
1.29620588e+00 -2.39997998e-01 -1.61070690e-01 7.38379955e-01
6.65346012e-02 1.18405402e-01 -1.06669831e+00 4.38449174e-01
-2.85206616e-01 -9.21020329e-01 -1.03864662e-01 9.32490975e-02
5.52609384e-01 4.40304667e-01 -4.52262491e-01 3.90643537e-01
7.53397822e-01 -1.12400651e+00 2.00481609e-01 2.61680424e-01
5.11306047e-01 -8.31417918e-01 1.09310031e+00 3.98616076e-01
-1.62468660e+00 -1.01457434e-02 -3.82948488e-01 -1.74072176e-01
5.73140025e-01 9.85014975e-01 -7.25655019e-01 9.02597845e-01
5.64942658e-01 7.63252139e-01 -3.65135461e-01 7.52507746e-01
-1.04966688e+00 9.17717040e-01 -2.81276494e-01 -2.51183987e-01
2.33710676e-01 -7.98878893e-02 1.84717432e-01 1.65889060e+00
6.65457845e-02 3.85405809e-01 7.67343491e-02 6.24518931e-01
-4.13428903e-01 3.94330889e-01 -3.78185958e-01 -2.03649953e-01
8.06966722e-01 1.34838724e+00 -7.23122835e-01 -3.72613013e-01
-6.37104332e-01 7.07859218e-01 9.74261582e-01 3.34283322e-01
-8.13457191e-01 -5.43566585e-01 5.26205182e-01 -4.99737591e-01
6.91633224e-01 -6.95805728e-01 -4.10093129e-01 -1.20644319e+00
1.96716741e-01 -2.55394995e-01 4.83333915e-01 -4.39036220e-01
-1.61690354e+00 1.08495498e+00 -2.33156383e-01 -5.51924288e-01
-1.48615465e-01 -6.00318313e-01 -7.54720271e-01 1.05350649e+00
-1.62104559e+00 -1.09149218e+00 8.94033462e-02 3.72438878e-01
2.10231766e-01 3.37442130e-01 1.24640620e+00 3.22253853e-01
-8.79438043e-01 4.55615491e-01 -7.12333471e-02 1.03347743e+00
4.46671188e-01 -1.10444009e+00 9.81652558e-01 1.00016928e+00
5.07996976e-01 1.19371176e+00 3.09988439e-01 -8.36925209e-01
-8.93568397e-01 -1.07145727e+00 1.86924076e+00 -4.07000393e-01
7.81266212e-01 -4.99174863e-01 -1.08224487e+00 9.77920353e-01
2.87642181e-01 4.31293584e-02 1.01122868e+00 7.65192628e-01
-5.83690643e-01 1.42461509e-01 -6.91697240e-01 3.67432952e-01
1.19927740e+00 -6.45002425e-01 -1.00847185e+00 2.13411935e-02
1.06065583e+00 -2.44685844e-01 -1.01236236e+00 4.95415211e-01
4.21447933e-01 -5.73715448e-01 7.03689337e-01 -9.88988936e-01
5.06342351e-01 -2.20307112e-02 -1.33253232e-01 -1.26005435e+00
-4.80564147e-01 -7.21147582e-02 -1.31668359e-01 1.86984479e+00
8.45706165e-01 -5.32803953e-01 3.14525276e-01 9.34861720e-01
-7.07581192e-02 -6.90299273e-01 -7.80239582e-01 -9.08633769e-01
1.83592677e-01 -3.64993483e-01 6.52641535e-01 1.28379488e+00
1.62979186e-01 9.21723366e-01 -2.04363018e-01 5.44135451e-01
2.62888312e-01 1.70087069e-01 8.24371129e-02 -1.30127072e+00
-1.32796243e-01 9.82960537e-02 -1.89782083e-01 -1.33111429e+00
8.69150817e-01 -8.53812099e-01 2.39887014e-01 -1.81067598e+00
2.65257299e-01 -6.80228412e-01 -6.52256131e-01 8.30624640e-01
-5.89604735e-01 -4.98184592e-01 -1.87268034e-02 1.55923411e-01
-5.65068781e-01 5.44181168e-01 9.08955097e-01 2.56720364e-01
-3.55159998e-01 -1.82331637e-01 -5.91554821e-01 7.86265194e-01
8.84909749e-01 -9.83179212e-01 1.78359717e-01 -8.83538485e-01
2.89846987e-01 1.62538931e-01 -1.87138215e-01 -4.61106479e-01
3.66826296e-01 -8.43584016e-02 3.77976686e-01 -5.44223189e-01
-1.83123555e-02 -6.52784109e-01 -2.45537132e-01 1.97131827e-01
-3.77396226e-01 1.09704427e-01 4.99464050e-02 3.33028793e-01
-3.92002076e-01 -2.18653396e-01 3.65277976e-01 1.20763127e-02
-7.87829340e-01 2.29801834e-01 -1.68611065e-01 2.10824251e-01
6.22165978e-01 3.66196990e-01 -3.86323243e-01 -3.41311172e-02
-8.22775722e-01 2.49872983e-01 -1.64309651e-01 5.97525954e-01
2.78195709e-01 -1.11263013e+00 -7.13932991e-01 -1.35465130e-01
-1.51327014e-01 1.36109561e-01 1.65053606e-02 4.16357368e-01
6.90748692e-02 6.05975747e-01 -6.33415580e-02 -9.07846540e-02
-1.33535361e+00 4.29655403e-01 1.03037260e-01 -8.29556704e-01
-4.21170145e-01 1.12050498e+00 2.57725924e-01 -5.12871265e-01
9.88713428e-02 -4.05992180e-01 -6.22423172e-01 -1.25743121e-01
3.83303404e-01 1.58934947e-02 -8.17326605e-02 -1.00230730e+00
-8.07951152e-01 3.24861974e-01 -2.69540817e-01 1.69297084e-01
1.46404386e+00 -1.33999303e-01 -4.68905896e-01 4.49194819e-01
1.18373477e+00 2.30826452e-01 -5.51685810e-01 -3.37051094e-01
6.37958705e-01 6.02838881e-02 -1.36557147e-01 -6.80145860e-01
-9.57584381e-01 9.41801667e-01 -6.70650378e-02 7.17539489e-02
9.17615592e-01 4.47846681e-01 1.13789022e+00 6.63202524e-01
4.06814843e-01 -6.51301086e-01 -6.46833956e-01 1.22119594e+00
2.78328389e-01 -9.17122543e-01 -2.29882464e-01 -7.24644065e-01
-4.90441501e-01 9.82660353e-01 6.70624852e-01 -2.27730647e-02
7.75595367e-01 5.78016102e-01 1.93022847e-01 -2.46370807e-01
-6.62931740e-01 -5.37540853e-01 2.86664218e-01 3.43401819e-01
1.11927712e+00 1.93770707e-01 -6.27399623e-01 8.86308849e-01
-1.68292567e-01 -3.84816289e-01 1.19339317e-01 7.12306261e-01
-3.63568187e-01 -1.37144589e+00 1.08624674e-01 1.58124045e-01
-8.74115586e-01 -6.61561608e-01 -3.49238187e-01 5.65158665e-01
7.46887550e-02 1.32366729e+00 7.37787262e-02 -2.83404469e-01
3.95698845e-01 2.34449074e-01 1.06536821e-01 -8.61488760e-01
-8.39472115e-01 -2.29320109e-01 6.05647027e-01 -3.24644059e-01
-5.68821132e-01 -2.72139907e-01 -2.05306792e+00 -5.25805876e-02
-7.78145671e-01 5.19497156e-01 6.86351597e-01 1.44778156e+00
5.40980935e-01 4.86331582e-01 6.15134001e-01 -1.29878700e-01
-2.48878777e-01 -1.14150691e+00 -6.30192876e-01 3.34953606e-01
2.33704112e-02 -3.48864645e-01 -1.37313798e-01 -5.67210885e-03] | [9.653787612915039, 9.567285537719727] |
12a82613-d162-41ba-b147-6d8efb7d4de4 | efficient-movie-scene-detection-using-state | 2212.14427 | null | https://arxiv.org/abs/2212.14427v2 | https://arxiv.org/pdf/2212.14427v2.pdf | Efficient Movie Scene Detection using State-Space Transformers | The ability to distinguish between different movie scenes is critical for understanding the storyline of a movie. However, accurately detecting movie scenes is often challenging as it requires the ability to reason over very long movie segments. This is in contrast to most existing video recognition models, which are typically designed for short-range video analysis. This work proposes a State-Space Transformer model that can efficiently capture dependencies in long movie videos for accurate movie scene detection. Our model, dubbed TranS4mer, is built using a novel S4A building block, which combines the strengths of structured state-space sequence (S4) and self-attention (A) layers. Given a sequence of frames divided into movie shots (uninterrupted periods where the camera position does not change), the S4A block first applies self-attention to capture short-range intra-shot dependencies. Afterward, the state-space operation in the S4A block is used to aggregate long-range inter-shot cues. The final TranS4mer model, which can be trained end-to-end, is obtained by stacking the S4A blocks one after the other multiple times. Our proposed TranS4mer outperforms all prior methods in three movie scene detection datasets, including MovieNet, BBC, and OVSD, while also being $2\times$ faster and requiring $3\times$ less GPU memory than standard Transformer models. We will release our code and models. | ['Gedas Bertasius', 'Tony Braskich', 'Kishan Shamsundar Athrey', 'Mahmudul Hasan', 'Md Mohaiminul Islam'] | 2022-12-29 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Islam_Efficient_Movie_Scene_Detection_Using_State-Space_Transformers_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Islam_Efficient_Movie_Scene_Detection_Using_State-Space_Transformers_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-recognition'] | ['computer-vision'] | [ 3.57535511e-01 -7.07089603e-01 -1.04738131e-01 -3.86535734e-01
-5.97138166e-01 -7.56463468e-01 5.98445773e-01 5.77275082e-02
-2.72053748e-01 3.60545889e-02 2.95772433e-01 -1.73779458e-01
1.46076888e-01 -5.02110898e-01 -9.13672984e-01 -5.88707030e-01
-4.04569730e-02 -1.98277712e-01 6.86297178e-01 -8.20431113e-02
3.89468759e-01 2.91910559e-01 -1.24360991e+00 7.82203972e-01
1.94983840e-01 1.40565038e+00 3.70562464e-01 9.93155658e-01
3.64838503e-02 1.51287985e+00 -3.49309176e-01 -3.05362046e-01
2.13315383e-01 -6.91821992e-01 -5.62840641e-01 4.39956188e-01
6.82994008e-01 -6.49767876e-01 -7.74417996e-01 9.53947008e-01
1.07372388e-01 3.95949244e-01 1.70363396e-01 -8.19122612e-01
-2.28794366e-01 3.46273720e-01 -5.96257329e-01 8.30935419e-01
5.86553633e-01 1.43625915e-01 1.04491520e+00 -1.00533628e+00
5.33913553e-01 1.07951510e+00 4.87782210e-01 3.11654598e-01
-8.95885825e-01 -6.57507658e-01 4.80772257e-01 6.28676236e-01
-1.12705290e+00 -5.11428714e-01 8.82200718e-01 -6.71133935e-01
1.24014187e+00 2.81666219e-01 6.24243855e-01 1.13034916e+00
1.17834851e-01 1.13019598e+00 7.30303109e-01 -1.19012222e-01
1.09500460e-01 -3.51167381e-01 3.47913146e-01 6.32140875e-01
-3.75773728e-01 -2.37261191e-01 -7.40769207e-01 1.90638319e-01
1.03962517e+00 4.25452292e-01 -2.00783804e-01 -3.12473446e-01
-1.18782485e+00 6.21551692e-01 2.61136562e-01 1.79425210e-01
-4.46644306e-01 6.92825094e-02 6.27362490e-01 1.09019667e-01
1.50937557e-01 2.54480600e-01 -1.18313730e-01 -5.60844898e-01
-1.13880742e+00 1.21968128e-01 4.08494204e-01 9.54651892e-01
3.68957371e-01 1.39060155e-01 -3.01295519e-01 7.22821951e-01
1.61539111e-02 2.26843953e-01 4.07728821e-01 -1.03582394e+00
5.61665058e-01 4.25707370e-01 1.71577066e-01 -1.15888584e+00
-4.84191477e-02 -3.43128681e-01 -7.37805665e-01 -2.71345019e-01
1.10769294e-01 2.29918316e-01 -8.62869501e-01 1.42117059e+00
1.28526762e-01 6.31263077e-01 -2.43487284e-01 1.19710529e+00
7.89370000e-01 1.08758152e+00 -1.80742055e-01 -3.84984672e-01
1.38282025e+00 -1.46081460e+00 -7.07933187e-01 -4.67423171e-01
1.97723120e-01 -7.11756587e-01 8.68754745e-01 5.18678069e-01
-1.32397544e+00 -8.94209087e-01 -1.11257398e+00 -2.52911180e-01
-1.00527190e-01 1.01179175e-01 3.25095505e-01 2.22870614e-03
-7.84696341e-01 5.48695087e-01 -9.79976356e-01 -4.45685446e-01
3.70049745e-01 4.51804884e-02 -3.13731015e-01 -1.70337513e-01
-1.06620634e+00 4.68701065e-01 1.06268413e-01 1.15317784e-01
-1.41241908e+00 -4.35194373e-01 -1.12764764e+00 2.07809418e-01
7.33492017e-01 -3.64657551e-01 1.26410460e+00 -1.09761393e+00
-1.42044187e+00 6.13473058e-01 -4.42777574e-01 -4.90569562e-01
1.73373133e-01 -5.55189729e-01 -5.21739125e-01 4.83225942e-01
1.30127370e-01 1.98128849e-01 1.14355946e+00 -9.56159770e-01
-5.38988829e-01 -1.83132559e-01 3.10783565e-01 2.27726102e-01
-2.13501334e-01 7.36698985e-01 -1.21714389e+00 -9.92392838e-01
1.20048942e-02 -9.09672499e-01 -1.12198777e-01 -2.58701771e-01
-3.15139294e-01 1.29834682e-01 9.14225876e-01 -8.33526731e-01
1.61619842e+00 -2.41985202e+00 3.23608309e-01 -1.94679022e-01
1.49192125e-01 4.10768867e-01 -1.34462863e-01 4.59971130e-01
-2.93521702e-01 -2.68332958e-01 -2.36590281e-02 -5.43863773e-01
-3.31470281e-01 1.50969233e-02 -5.28962135e-01 4.87399817e-01
8.99996832e-02 6.08906806e-01 -8.17927182e-01 -2.98219681e-01
5.05055130e-01 3.39154869e-01 -5.61795413e-01 5.24512112e-01
1.65222138e-02 1.71485469e-01 -2.16619298e-01 5.56657374e-01
2.61493623e-01 -5.40703654e-01 4.07104939e-02 -3.27170372e-01
-2.74535149e-01 3.91437083e-01 -1.16328776e+00 1.81014180e+00
-2.97142595e-01 8.26557934e-01 6.65377825e-02 -1.05653203e+00
5.76862395e-01 3.19720268e-01 4.92123157e-01 -6.76224351e-01
3.72193992e-01 -4.13690537e-01 -2.55916595e-01 -6.40144885e-01
5.14165759e-01 1.19321361e-01 -1.66934401e-01 1.79195851e-01
6.04538806e-02 2.59054840e-01 5.14110386e-01 5.32396317e-01
1.22952545e+00 2.05150232e-01 3.25985849e-01 2.25109428e-01
5.58076680e-01 -2.43218586e-01 9.05976892e-01 6.95263863e-01
-3.79593790e-01 9.60636556e-01 5.49772322e-01 -4.73323196e-01
-9.39578891e-01 -9.83126581e-01 3.85422438e-01 1.31154025e+00
4.82167274e-01 -8.74925435e-01 -7.01243341e-01 -6.87485933e-01
-3.54145736e-01 6.33269966e-01 -7.16584504e-01 -1.66710392e-01
-6.62253082e-01 -3.42175245e-01 1.26564950e-01 8.67876053e-01
5.32300413e-01 -1.05407989e+00 -7.46964157e-01 3.35325688e-01
-3.04729968e-01 -1.53148663e+00 -9.44631577e-01 -7.11148679e-02
-5.99743783e-01 -1.07530367e+00 -4.90460008e-01 -5.96058965e-01
4.08334196e-01 7.32175350e-01 9.67920125e-01 -1.79269105e-01
-1.50284022e-01 3.05779666e-01 -5.31313956e-01 1.59061313e-01
-7.32842386e-02 -4.68698531e-01 1.93227500e-01 5.25173664e-01
3.18593293e-01 -4.29653406e-01 -6.66812956e-01 4.69450504e-01
-8.21632266e-01 3.06847364e-01 3.83108824e-01 7.46413708e-01
7.01101542e-01 8.03459659e-02 1.57588184e-01 -8.08547795e-01
4.49818447e-02 -5.47768295e-01 -4.12523985e-01 1.89612538e-01
-2.04220533e-01 -4.88213688e-01 9.58706200e-01 -3.04554313e-01
-9.84683514e-01 1.43259034e-01 -4.92114313e-02 -1.17872310e+00
-2.10050419e-01 2.23146141e-01 -1.85565397e-01 3.17010731e-01
1.16037220e-01 5.07661760e-01 -2.70073146e-01 -6.89299107e-01
1.28896479e-02 4.91299272e-01 8.92319083e-01 -7.65720308e-02
4.67059314e-01 5.88967562e-01 -3.35281223e-01 -9.42850530e-01
-1.11041260e+00 -9.47481215e-01 -7.41668522e-01 -5.78687370e-01
1.21551251e+00 -1.04864848e+00 -4.55666959e-01 6.92499399e-01
-1.07334745e+00 -2.50531644e-01 3.74750718e-02 4.89562571e-01
-5.16810536e-01 6.23238146e-01 -9.52860177e-01 -7.43582904e-01
-7.23964423e-02 -1.21109200e+00 1.09695280e+00 2.13884875e-01
-1.47326589e-01 -6.44350171e-01 -7.39738420e-02 7.69502401e-01
1.37706175e-01 1.46216303e-01 4.89224970e-01 -5.21482229e-01
-5.25564551e-01 -3.05323511e-01 -2.03356579e-01 4.71255124e-01
4.43375744e-02 8.39915276e-02 -8.34278762e-01 -4.54488784e-01
1.69548079e-01 -1.43649518e-01 1.07576323e+00 3.90239507e-01
1.44284546e+00 -3.12219769e-01 1.22298149e-03 7.60173857e-01
1.19043374e+00 4.93373036e-01 6.53077126e-01 3.67144555e-01
1.03731763e+00 2.92648673e-01 7.84289300e-01 5.07597744e-01
4.31994945e-01 9.72823203e-01 4.52152103e-01 -8.64367858e-02
-6.76099136e-02 -2.72922218e-01 9.32058573e-01 1.04715741e+00
-1.14721127e-01 -3.71885508e-01 -5.75146079e-01 5.26151597e-01
-1.94585085e+00 -1.28402555e+00 -2.15947144e-02 2.08093667e+00
3.96991372e-01 4.59506392e-01 3.00540090e-01 9.28572118e-02
6.93303704e-01 7.55723834e-01 -7.38082588e-01 -3.63738328e-01
-1.82313532e-01 -1.96696490e-01 2.59207994e-01 3.40116918e-01
-1.40151465e+00 9.17842209e-01 5.44938707e+00 7.39633620e-01
-1.07702827e+00 5.54183498e-02 5.56283355e-01 -6.13296330e-01
2.92786717e-01 7.39052966e-02 -6.50416255e-01 6.59516156e-01
7.41819918e-01 1.90316513e-01 4.50178653e-01 1.00301540e+00
5.12450576e-01 -1.04670329e-02 -1.22105527e+00 1.22192109e+00
6.25253022e-01 -1.43649936e+00 3.26541066e-03 -3.97119701e-01
5.33367038e-01 -8.15265253e-02 -1.83258176e-01 2.39273906e-01
-3.33524719e-02 -6.87110782e-01 9.51544523e-01 2.84398019e-01
7.55835414e-01 -5.22316635e-01 5.19119442e-01 2.70496964e-01
-1.46369958e+00 -3.16284806e-01 -1.09185919e-01 -2.84171611e-01
5.44968188e-01 2.04508632e-01 -2.88897038e-01 2.75343120e-01
1.11654389e+00 1.43206429e+00 -4.65417534e-01 8.18819344e-01
-1.36585310e-01 8.91577482e-01 3.04673500e-02 2.55403638e-01
3.62994492e-01 -2.65933454e-01 7.37282574e-01 1.46216941e+00
1.89995661e-01 3.13025355e-01 2.56941885e-01 3.35785389e-01
3.65806073e-02 -2.47311294e-01 -2.89223820e-01 -2.83260476e-02
1.06952421e-01 1.16306877e+00 -7.45040953e-01 -7.22230256e-01
-8.50509286e-01 1.50612581e+00 3.49341817e-02 3.69468153e-01
-1.25945580e+00 -3.38305116e-01 6.97925866e-01 2.63093531e-01
8.84005904e-01 -4.04200137e-01 1.79523304e-01 -1.46368873e+00
1.79271884e-02 -9.72029984e-01 6.54086590e-01 -9.65982318e-01
-1.08116162e+00 6.82763040e-01 -2.01959997e-01 -1.41893375e+00
-5.74091822e-02 -4.30833757e-01 -6.58442378e-01 4.00923431e-01
-1.12352538e+00 -9.86336708e-01 -3.17967057e-01 7.34457254e-01
1.22709608e+00 3.50121185e-02 4.20372695e-01 4.84732836e-01
-1.13234901e+00 4.55898076e-01 8.35227221e-03 4.43807930e-01
5.48391759e-01 -1.10977733e+00 5.07617831e-01 1.43449163e+00
3.69897008e-01 5.92399478e-01 6.57986820e-01 -5.40390372e-01
-1.51480055e+00 -1.16685915e+00 6.92588806e-01 -3.86091024e-01
7.94038951e-01 -6.77054107e-01 -9.44823623e-01 8.07653785e-01
1.64591178e-01 9.70265195e-02 6.63820326e-01 -1.18129097e-01
-5.75405002e-01 -3.06711972e-01 -4.75802392e-01 3.90359402e-01
1.06421196e+00 -7.61686563e-01 -6.57040358e-01 1.91046327e-01
5.92066646e-01 -3.64403725e-01 -7.93346524e-01 3.20986122e-01
5.20032167e-01 -1.18533373e+00 1.07881379e+00 -7.18104661e-01
7.04616129e-01 -4.49574500e-01 -2.28228241e-01 -7.46319711e-01
-7.58554876e-01 -7.87729502e-01 -5.78953028e-01 1.07689142e+00
-1.22429868e-02 5.03142886e-02 5.85059941e-01 4.34814960e-01
-2.25494534e-01 -7.26428330e-01 -6.97363496e-01 -7.47789204e-01
-5.79247355e-01 -7.15691388e-01 1.31690681e-01 9.33333695e-01
-2.46186974e-03 6.48668885e-01 -9.09449458e-01 2.93592691e-01
4.56052780e-01 5.12049139e-01 7.29352295e-01 -4.33551192e-01
-5.64592719e-01 -4.20829475e-01 -4.36303616e-01 -1.71524036e+00
-3.25002730e-01 -2.68814176e-01 1.75804928e-01 -1.39739633e+00
5.92827082e-01 1.03350103e-01 -6.66840136e-01 1.95932001e-01
-2.82600671e-01 4.75265354e-01 4.47616309e-01 3.04375201e-01
-1.25279367e+00 4.88722980e-01 9.53726232e-01 6.54811412e-02
-1.74501929e-02 -7.38079548e-02 -3.48119318e-01 1.04648411e+00
3.51095974e-01 -2.07064718e-01 -3.48934978e-01 -5.78196406e-01
4.23355661e-02 4.10423845e-01 4.04615015e-01 -1.20472121e+00
3.79563630e-01 -2.32657537e-01 3.28388125e-01 -8.75904858e-01
6.07654333e-01 -5.80689907e-01 -4.12072018e-02 3.87231484e-02
-2.96136647e-01 1.77485704e-01 -4.02242579e-02 7.36907721e-01
-4.65357274e-01 2.48955309e-01 7.93610096e-01 -4.28736061e-02
-1.11150253e+00 5.54381907e-01 -6.06175125e-01 -1.49108365e-01
1.23879516e+00 -2.73006052e-01 -9.41015333e-02 -5.94498158e-01
-6.66540504e-01 1.97670430e-01 5.09968579e-01 7.76784897e-01
7.21019566e-01 -1.18875575e+00 -4.21680599e-01 1.50896773e-01
3.43733951e-02 -7.31367245e-02 8.85891974e-01 7.42954373e-01
-4.93563473e-01 4.22357470e-01 9.87773482e-03 -6.83022082e-01
-1.54386353e+00 1.01942110e+00 1.08464241e-01 -1.61969572e-01
-6.63969576e-01 1.10918152e+00 5.57746470e-01 4.51618314e-01
2.78646469e-01 -3.24637413e-01 -3.86504829e-01 5.82402758e-02
8.79674256e-01 2.96980441e-01 -2.74452955e-01 -9.77651417e-01
-4.76106435e-01 6.25275195e-01 -3.45911443e-01 1.80670559e-01
1.33758295e+00 -3.47215503e-01 4.44476828e-02 6.87954843e-01
1.26802111e+00 3.76540720e-02 -1.76950479e+00 -2.62261927e-01
-3.55815768e-01 -8.34481597e-01 1.02515228e-01 -4.60143268e-01
-1.22701430e+00 9.82712984e-01 2.55852699e-01 1.98686153e-01
1.50414586e+00 1.05734758e-01 1.16556573e+00 -3.20726410e-02
1.08818471e-01 -1.03819025e+00 3.15852344e-01 5.44290841e-01
7.48663008e-01 -1.17275631e+00 2.41967048e-02 -3.43106210e-01
-9.04082119e-01 9.20288086e-01 7.74475932e-01 -2.61976331e-01
4.58196908e-01 2.47309282e-01 -1.52699828e-01 -2.77120411e-01
-9.05339003e-01 9.21564028e-02 3.11573982e-01 -1.01850525e-01
1.16304509e-01 -2.41890833e-01 2.50682086e-01 8.71436596e-01
2.34203100e-01 7.42064510e-03 4.41295624e-01 9.82944906e-01
-2.80464411e-01 -7.14762032e-01 -1.23823188e-01 2.80977190e-01
-6.08708262e-01 -8.94609019e-02 -3.13681364e-01 3.47356647e-01
1.10560894e-01 9.87449110e-01 3.94254059e-01 -6.74524367e-01
3.93609703e-01 -2.72926509e-01 2.47478828e-01 -6.46651328e-01
-5.19789279e-01 3.24398518e-01 7.60881379e-02 -1.16733754e+00
-4.38614964e-01 -8.99041116e-01 -1.00473189e+00 -1.53904438e-01
-6.52888641e-02 -4.55468111e-02 2.86251932e-01 8.36741269e-01
4.47447181e-01 6.24458969e-01 8.62691522e-01 -7.80556202e-01
1.86943319e-02 -8.81938338e-01 -4.57968354e-01 7.34241009e-01
4.90267277e-01 -6.26608253e-01 -2.51162708e-01 4.20684010e-01] | [8.901327133178711, 0.5182328224182129] |
b2fcfd76-19dd-45a8-a73b-27088b14efcd | multimodal-forgery-detection-using-ensemble | null | null | http://www.apsipa.org/proceedings/2022/APSIPA%202022/ThAM1-6/1570840386.pdf | http://www.apsipa.org/proceedings/2022/APSIPA%202022/ThAM1-6/1570840386.pdf | Multimodal Forgery Detection Using Ensemble Learning | The recent rapid revolution in Artificial Intelligence (AI) technology has enabled the creation of hyper-realistic deepfakes, and detecting deepfake videos (also known as AIsynthesized videos) has become a critical task. The existing systems generally do not fully consider the unified processing of audio and video data, so there is still room for further improvement. In this paper, we focus on the multimodal forgery detection task and propose a deep forgery detection method based on audiovisual ensemble learning. The proposed method consists of four parts, namely a Video Network, an Audio Network, an Audiovisual Network, and a Voting Module. Given a video, the proposed multimodal and ensemble learning system can identify whether it is fake or real. Experimental results on a recently released multimodal FakeAVCeleb dataset show that the proposed method achieves 89% accuracy, significantly outperforming existing models. | ['Hsin-Min Wang', 'Yu Tsao', 'Chia Wen Lin', 'Wasim Ahmad', 'Sahibzada Adil Shahzad', 'Ammarah Hashmi'] | 2022-11-07 | null | null | null | asia-pacific-signal-and-information-1 | ['multimodal-forgery-detection', 'face-swapping'] | ['computer-vision', 'computer-vision'] | [ 1.05774023e-01 -4.53823805e-01 -5.60793690e-02 -6.62665144e-02
-7.13307381e-01 -3.72259974e-01 5.05449533e-01 -2.41163433e-01
-2.98873752e-01 4.22977507e-01 1.47668093e-01 -2.06374064e-01
2.58864701e-01 -5.11293113e-01 -6.02543175e-01 -7.86187470e-01
1.17729120e-01 -2.01974422e-01 3.25090319e-01 -1.91929832e-01
4.00503993e-01 1.07375689e-01 -1.70686448e+00 8.21673572e-01
7.01207757e-01 1.33639646e+00 -1.80439234e-01 8.44822943e-01
3.04703712e-01 1.20165157e+00 -9.10670996e-01 -7.88514972e-01
-9.64682698e-02 -3.55250090e-01 -6.31742120e-01 1.79667160e-01
4.58666444e-01 -9.34864819e-01 -8.81454706e-01 1.07914639e+00
6.10650003e-01 -7.39383548e-02 3.87670696e-01 -1.80332506e+00
-4.43928480e-01 5.21637619e-01 -5.86814761e-01 3.09691578e-01
3.11158866e-01 5.53937145e-02 7.02927232e-01 -1.02296615e+00
1.54751644e-01 1.24681234e+00 6.39256716e-01 5.58177769e-01
-5.19131005e-01 -1.06427085e+00 -2.04104796e-01 1.01156187e+00
-1.40072250e+00 -5.99672019e-01 8.88680160e-01 -4.15712893e-01
5.85692227e-01 1.83866128e-01 5.85246205e-01 1.46959329e+00
7.76552185e-02 1.21153653e+00 7.74878263e-01 -3.41562390e-01
8.67118593e-04 1.57826781e-01 -1.80057555e-01 6.27804399e-01
9.38858390e-02 1.42769635e-01 -7.10445285e-01 -2.40928903e-01
5.35597563e-01 -9.63311419e-02 -4.30340409e-01 -2.30101153e-01
-9.55587685e-01 8.16354513e-01 1.24155737e-01 2.72870868e-01
-9.21046510e-02 1.76745370e-01 8.15661669e-01 4.70020652e-01
2.17703775e-01 -3.77224460e-02 7.08622187e-02 -1.90778360e-01
-1.11792839e+00 1.71522439e-01 4.39763129e-01 5.56138694e-01
2.17594326e-01 1.56459227e-01 2.84975737e-01 7.43760109e-01
4.23462212e-01 2.60705888e-01 7.52130210e-01 -8.25716197e-01
5.60445666e-01 4.21239048e-01 1.28946632e-01 -1.65621865e+00
-1.04785889e-01 3.96343879e-02 -8.14777434e-01 9.49096028e-03
1.66070670e-01 -1.13762267e-01 -5.35988748e-01 1.29973328e+00
2.46729344e-01 6.02463543e-01 4.97747399e-02 1.14187038e+00
1.10175610e+00 6.82659984e-01 -1.48087576e-01 2.71860119e-02
1.07418096e+00 -8.91178310e-01 -8.56868505e-01 -2.97333878e-02
4.89115268e-01 -8.90688777e-01 4.83538598e-01 1.00067019e+00
-8.95686030e-01 -6.40520155e-01 -1.28710020e+00 -7.39615783e-03
-2.44053513e-01 3.71962547e-01 4.80912298e-01 9.81127560e-01
-8.63937736e-01 2.62611181e-01 -3.83951426e-01 -1.77758858e-02
4.89910156e-01 2.76175380e-01 -6.26317501e-01 -2.45343059e-01
-1.40647626e+00 6.14529133e-01 7.73976564e-01 3.21835309e-01
-1.33330595e+00 1.89855620e-02 -6.28763914e-01 -2.50519123e-02
4.87713754e-01 -2.48433560e-01 1.16251194e+00 -1.66755497e+00
-1.31191266e+00 7.04539478e-01 3.16192657e-01 -3.42045069e-01
6.64660871e-01 -1.64516881e-01 -9.96233225e-01 4.09867287e-01
-4.97186273e-01 4.89300966e-01 1.54124701e+00 -1.27052391e+00
-8.64307880e-01 -2.42232516e-01 1.69691965e-02 -1.05525060e-02
-7.56270051e-01 2.87326545e-01 -4.05714214e-01 -7.91056812e-01
-1.89859122e-01 -6.88524485e-01 5.62602341e-01 -5.95329516e-02
-2.07466602e-01 -2.63686657e-01 1.31598330e+00 -1.07452285e+00
1.43959463e+00 -2.32101488e+00 2.26670265e-01 -2.08661966e-02
4.15883601e-01 6.08055174e-01 -2.50190467e-01 4.13239300e-01
-2.18505710e-01 -1.44412473e-01 1.28678307e-01 -3.51003349e-01
-1.31759018e-01 -1.93590805e-01 -4.15781528e-01 5.91524899e-01
1.00669987e-01 4.18029040e-01 -1.02787304e+00 -4.92042065e-01
2.90342093e-01 5.14327586e-01 -4.81386960e-01 2.18312114e-01
2.13109590e-02 1.31440699e-01 -1.54348388e-01 9.63163733e-01
9.07338142e-01 3.68127711e-02 2.25790486e-01 -4.36640292e-01
4.61555570e-02 -3.02509099e-01 -1.07651985e+00 1.47577870e+00
6.46573007e-02 1.03190005e+00 8.01584348e-02 -1.21761382e+00
6.62250340e-01 5.44448614e-01 3.21907908e-01 -4.31770593e-01
5.46917140e-01 1.99710220e-01 5.40123843e-02 -1.01943195e+00
8.32658470e-01 1.51271690e-02 3.41829471e-02 3.39150637e-01
3.27651560e-01 3.70170385e-01 -1.47740379e-01 4.12966460e-01
1.15463495e+00 -7.74261914e-03 -1.25520334e-01 4.76782948e-01
7.79777765e-01 -2.02427939e-01 4.39608425e-01 4.31289494e-01
-6.34600282e-01 3.34363222e-01 5.06221831e-01 -2.56650031e-01
-9.62907970e-01 -7.54408181e-01 3.51733059e-01 9.97522712e-01
4.92150813e-01 -4.42951530e-01 -7.76149571e-01 -8.89366388e-01
1.60037667e-01 3.10238123e-01 -3.87846053e-01 -5.77917397e-01
-3.72769445e-01 -5.44956744e-01 1.05006266e+00 2.95540601e-01
8.33803236e-01 -8.99806798e-01 -3.70310307e-01 1.11346617e-02
-7.07894683e-01 -1.31117022e+00 -3.48450929e-01 -6.33537531e-01
-4.94166106e-01 -1.23532820e+00 -5.37605464e-01 -8.61133277e-01
8.76240805e-02 6.46081984e-01 6.05489850e-01 3.66831303e-01
-2.43321821e-01 4.01822567e-01 -6.59540355e-01 -2.16814741e-01
-6.56901240e-01 -5.18766165e-01 8.13293532e-02 5.88067591e-01
4.28905159e-01 -8.36910605e-02 -5.63646913e-01 4.83541727e-01
-1.04587233e+00 -3.29889432e-02 5.55710077e-01 9.17504370e-01
-7.67747536e-02 4.99983490e-01 7.54066229e-01 -2.12712929e-01
4.51740116e-01 -6.87581539e-01 -3.61403257e-01 3.43123436e-01
3.12754512e-02 -5.92565775e-01 4.06195343e-01 -6.67406857e-01
-1.12142241e+00 -1.54931143e-01 -3.96743491e-02 -8.16057086e-01
-1.77973121e-01 4.58691180e-01 -5.02684057e-01 -4.98341501e-01
2.91569591e-01 3.25335145e-01 4.33097780e-02 -1.92369282e-01
1.63472280e-01 1.41901219e+00 7.40177989e-01 -4.92999144e-02
5.26367128e-01 4.78419453e-01 -4.83256400e-01 -1.08445013e+00
-2.19717905e-01 -3.94144684e-01 -2.66027540e-01 -9.76329386e-01
5.92163026e-01 -1.10382032e+00 -9.21169043e-01 1.17778742e+00
-1.22894228e+00 2.33418524e-01 6.07001364e-01 6.36496603e-01
-3.83082062e-01 1.06062019e+00 -6.38898313e-01 -9.01131868e-01
-5.56768738e-02 -1.26271737e+00 9.93978560e-01 -1.06817029e-01
7.88865015e-02 -7.20420241e-01 -8.83300677e-02 1.05694640e+00
1.16349347e-01 1.50571316e-01 5.66206157e-01 -6.64940834e-01
-5.97425044e-01 -4.44047213e-01 -4.07340199e-01 7.49705851e-01
-1.21785946e-01 1.69375420e-01 -1.13004422e+00 -4.18721765e-01
-4.68192771e-02 -6.83822572e-01 1.15334225e+00 -8.75269622e-02
1.37024152e+00 -1.61614060e-01 -1.58530876e-01 4.09582227e-01
1.10229886e+00 2.09453776e-01 9.15514946e-01 1.60990089e-01
6.24501050e-01 4.01422709e-01 5.07371604e-01 6.69456065e-01
3.57163072e-01 5.11096418e-01 9.80667293e-01 2.59171307e-01
-1.20024104e-02 -1.19860135e-01 8.43380511e-01 9.66878533e-01
5.50925918e-02 -7.66360343e-01 -6.98342204e-01 4.39363569e-01
-1.89425182e+00 -1.32876647e+00 -6.20745495e-02 2.02502465e+00
3.18858594e-01 -2.21313491e-01 3.30404758e-01 6.95924163e-01
9.79512691e-01 8.61865878e-02 -3.86020631e-01 -2.05926135e-01
-2.31092855e-01 -1.32498696e-01 1.44539773e-01 2.33894680e-02
-1.59895277e+00 8.99925113e-01 5.66334963e+00 1.11151361e+00
-1.17747748e+00 2.52340078e-01 3.40240180e-01 -9.52091739e-02
1.13729894e-01 -3.83579671e-01 -3.26709986e-01 8.03611994e-01
7.00063586e-01 2.62212276e-01 3.68883818e-01 5.31305611e-01
2.01546356e-01 -8.39123353e-02 -6.96875989e-01 1.43297231e+00
8.27898443e-01 -1.16207421e+00 9.28210989e-02 -1.34384157e-02
4.60411817e-01 -2.96987295e-01 1.94113731e-01 3.14588904e-01
-1.73685461e-01 -9.48293924e-01 7.42201805e-01 4.88276631e-01
5.35625041e-01 -1.02082312e+00 1.03461540e+00 3.55972409e-01
-7.57985353e-01 -3.79198670e-01 -2.00102851e-01 2.31709883e-01
-6.01301789e-02 2.88348615e-01 -5.44820487e-01 6.48822784e-01
9.43278670e-01 8.86923730e-01 -4.20683652e-01 1.27210426e+00
7.18963742e-02 5.72487056e-01 1.39892787e-01 6.31766692e-02
2.47514471e-02 1.95005581e-01 5.06255269e-01 1.09560812e+00
4.63285029e-01 -2.71638818e-02 6.73131738e-03 2.25986868e-01
-2.87935734e-01 5.22838049e-02 -5.00553966e-01 -4.31753039e-01
2.51764178e-01 1.28019476e+00 -4.35645670e-01 -3.52902561e-01
-5.42991102e-01 1.27716947e+00 4.10216264e-02 1.13730617e-01
-1.35555935e+00 -4.79676157e-01 5.52854598e-01 -5.34265995e-01
3.94007355e-01 4.15352359e-02 3.00557911e-01 -1.41206872e+00
-1.06042109e-01 -1.44206727e+00 4.85081911e-01 -9.63296294e-01
-1.16751826e+00 4.69723880e-01 -4.35364634e-01 -1.39083195e+00
9.46835950e-02 -7.43988395e-01 -3.14832449e-01 2.03268930e-01
-1.15968382e+00 -1.10704708e+00 -6.88072145e-01 8.01275909e-01
4.56177026e-01 -5.37200272e-01 4.24238831e-01 7.14701951e-01
-7.42125928e-01 8.19831133e-01 1.74118802e-01 4.26793963e-01
1.03412437e+00 -5.95350206e-01 -2.74270982e-01 8.46733928e-01
1.66513305e-02 1.68232787e-02 6.60657763e-01 -6.08440697e-01
-1.49005747e+00 -9.42627788e-01 5.44309378e-01 -1.89941406e-01
8.19121122e-01 -1.19087026e-01 -8.96860063e-01 4.41321433e-01
4.84187335e-01 -2.65155822e-01 6.91878200e-01 -5.44530332e-01
-4.97578710e-01 -6.77020922e-02 -1.22103727e+00 1.59303084e-01
6.53639913e-01 -6.66025996e-01 -5.61143577e-01 2.38407850e-01
2.86013663e-01 -1.52444080e-01 -5.75662673e-01 3.67713153e-01
9.09515321e-01 -1.27773547e+00 8.03652406e-01 -6.73438191e-01
4.75716442e-01 -2.35981524e-01 -2.67232746e-01 -1.29882395e+00
-6.75048679e-02 -3.82279813e-01 -5.05206585e-01 1.07469380e+00
-2.73556679e-01 -2.43619755e-01 8.17371070e-01 -8.56951904e-03
-5.88536188e-02 -1.46540180e-01 -1.03834510e+00 -6.96141005e-01
-4.62976664e-01 -6.39339507e-01 5.05067587e-01 1.31987703e+00
2.10628957e-01 1.05919652e-01 -1.00280726e+00 3.52631807e-01
6.96271896e-01 -2.97579587e-01 7.75545597e-01 -1.01143956e+00
-2.64236242e-01 -4.91551042e-01 -9.73444045e-01 -9.23293352e-01
2.74279952e-01 -6.19067073e-01 -2.26086542e-01 -8.48713398e-01
3.47806334e-01 6.83583096e-02 -4.20965672e-01 2.85391569e-01
-6.45886362e-03 5.61845601e-01 6.46957010e-02 9.90258157e-02
-7.60077178e-01 6.85537159e-01 8.31351936e-01 -3.14576745e-01
3.43695521e-01 -1.71369240e-01 -3.24084997e-01 8.53922069e-01
7.67281294e-01 -1.62174806e-01 -1.63051516e-01 -5.43304980e-01
1.02038920e-01 3.86455119e-01 7.58877099e-01 -1.05430949e+00
2.62163758e-01 1.49045393e-01 4.87694591e-01 -6.75287127e-01
5.86970031e-01 -8.45525384e-01 -2.64216084e-02 4.42858934e-01
-1.19186662e-01 1.16356097e-01 1.89457908e-01 6.72352612e-01
-5.36158144e-01 -1.02138542e-01 7.22276211e-01 2.50863731e-01
-9.96311486e-01 1.08431444e-01 -7.25631475e-01 -4.48048830e-01
1.23493898e+00 -1.91340476e-01 -5.79756320e-01 -6.71848536e-01
-5.75740755e-01 1.15198836e-01 2.96001852e-01 7.89098203e-01
1.17838192e+00 -1.35780084e+00 -6.89704955e-01 1.90052807e-01
2.45464146e-01 -7.83706427e-01 7.44894147e-01 7.45613396e-01
-6.02752805e-01 2.18517348e-01 -2.26924092e-01 -4.26279873e-01
-1.81858301e+00 6.36064708e-01 2.70628363e-01 2.79271930e-01
-9.34528112e-02 8.98935974e-01 -1.26700059e-01 -3.72701474e-02
4.39671993e-01 2.04258069e-01 -3.13715816e-01 2.23702192e-01
8.01094294e-01 6.54337704e-01 1.72499850e-01 -1.14242899e+00
-4.06330258e-01 6.42229691e-02 6.36130422e-02 1.69127077e-01
8.77095640e-01 1.17309447e-02 -1.57034323e-01 1.76812693e-01
1.10938823e+00 -1.44774884e-01 -8.36839259e-01 -6.90131858e-02
-1.83761299e-01 -6.99277818e-01 4.16326672e-01 -7.80454993e-01
-1.26518667e+00 1.13018870e+00 7.32069790e-01 1.77840605e-01
1.33200014e+00 -2.78117597e-01 1.07703745e+00 1.73629165e-01
3.81366074e-01 -1.18522429e+00 6.01514995e-01 3.24638247e-01
8.17780733e-01 -1.44437563e+00 -1.30186319e-01 -1.42761558e-01
-6.87678218e-01 1.26945758e+00 6.67766452e-01 -5.18236645e-02
4.88398194e-01 -1.36225536e-01 -1.55383516e-02 1.12299062e-01
-8.08729470e-01 2.15157375e-01 3.07018101e-01 4.49781328e-01
9.90545098e-03 -7.88562372e-02 -1.46137495e-02 5.98796248e-01
1.00413546e-01 1.20001040e-01 6.26998484e-01 7.21083283e-01
-5.60288012e-01 -8.45697463e-01 -6.77627981e-01 2.51563609e-01
-5.51252306e-01 1.82793394e-01 -6.96425557e-01 8.14266801e-01
4.30878162e-01 1.34665275e+00 1.40438542e-01 -1.18451130e+00
-3.86341624e-02 -6.69864863e-02 7.12122023e-01 8.63270164e-02
-5.75687647e-01 4.29842770e-02 3.58254500e-02 -5.82657695e-01
-5.43207347e-01 -6.56735241e-01 -6.31486237e-01 -7.29481697e-01
-6.97316289e-01 5.76290078e-02 5.98278463e-01 9.15850639e-01
2.98904210e-01 1.78418979e-01 8.51914942e-01 -8.38570654e-01
-3.43283147e-01 -7.97464430e-01 -4.43798721e-01 3.36366981e-01
5.35466492e-01 -7.69685924e-01 -6.22312546e-01 4.27372725e-04] | [13.028109550476074, 1.3521004915237427] |
49b1e6bf-af1d-4269-98c2-4919059374a8 | multiscale-audio-spectrogram-transformer-for | 2303.10757 | null | https://arxiv.org/abs/2303.10757v1 | https://arxiv.org/pdf/2303.10757v1.pdf | Multiscale Audio Spectrogram Transformer for Efficient Audio Classification | Audio event has a hierarchical architecture in both time and frequency and can be grouped together to construct more abstract semantic audio classes. In this work, we develop a multiscale audio spectrogram Transformer (MAST) that employs hierarchical representation learning for efficient audio classification. Specifically, MAST employs one-dimensional (and two-dimensional) pooling operators along the time (and frequency domains) in different stages, and progressively reduces the number of tokens and increases the feature dimensions. MAST significantly outperforms AST~\cite{gong2021ast} by 22.2\%, 4.4\% and 4.7\% on Kinetics-Sounds, Epic-Kitchens-100 and VGGSound in terms of the top-1 accuracy without external training data. On the downloaded AudioSet dataset, which has over 20\% missing audios, MAST also achieves slightly better accuracy than AST. In addition, MAST is 5x more efficient in terms of multiply-accumulates (MACs) with 42\% reduction in the number of parameters compared to AST. Through clustering metrics and visualizations, we demonstrate that the proposed MAST can learn semantically more separable feature representations from audio signals. | ['Mohamed Omar', 'Wentao Zhu'] | 2023-03-19 | null | null | null | null | ['audio-classification'] | ['audio'] | [ 5.05095795e-02 -4.41939056e-01 4.21920568e-01 -3.42936277e-01
-1.19856954e+00 -5.34441769e-01 -7.30480924e-02 5.21203756e-01
-4.22515303e-01 3.90414745e-01 3.13975155e-01 9.55439359e-02
-8.89794603e-02 -6.75356150e-01 -3.47489238e-01 -4.11291510e-01
-6.22373521e-01 -1.92117080e-01 4.95959342e-01 -6.18138835e-02
2.91838851e-02 8.35365281e-02 -2.01351714e+00 7.81826437e-01
4.11892325e-01 1.45050204e+00 9.57219824e-02 8.19949806e-01
-1.18371984e-02 6.50500774e-01 -9.15884316e-01 1.22490585e-01
-7.90636614e-03 -3.01563323e-01 -8.25740099e-01 -2.60725230e-01
3.64406139e-01 2.66767181e-02 -1.90739051e-01 7.76465952e-01
8.84410083e-01 5.14393449e-01 5.59591711e-01 -1.16376197e+00
-1.55890867e-01 1.04500687e+00 -6.50353551e-01 4.59187090e-01
4.19791818e-01 -3.09509546e-01 1.31652546e+00 -1.02535045e+00
-6.81828633e-02 1.50731754e+00 8.38238239e-01 9.35125798e-02
-1.24379075e+00 -1.05844212e+00 4.92050797e-02 3.64313245e-01
-1.80963945e+00 -4.07609969e-01 6.02143943e-01 -4.92031008e-01
1.04267406e+00 5.03083646e-01 4.68583375e-01 6.37443244e-01
-1.34969234e-01 6.98575020e-01 8.86237204e-01 -3.74669969e-01
4.33956921e-01 -2.80126750e-01 3.44360471e-01 4.42527503e-01
-3.78360868e-01 -3.14167410e-01 -9.48470771e-01 -3.10591429e-01
6.28728688e-01 -1.80456430e-01 -2.46580869e-01 4.28546965e-01
-1.13080204e+00 6.29434586e-01 3.98204535e-01 3.83226275e-01
-5.59275672e-02 3.54125649e-01 6.82127178e-01 4.57576424e-01
4.48333144e-01 5.00092506e-01 -5.08764207e-01 -4.72935021e-01
-9.19465840e-01 2.53111750e-01 4.31153268e-01 8.79134178e-01
5.69810152e-01 5.40957689e-01 2.58586518e-02 1.22667313e+00
1.28439784e-01 1.81795359e-01 8.85918140e-01 -1.00517368e+00
4.47407812e-01 4.47698593e-01 -1.98733702e-01 -9.49897945e-01
-5.78848183e-01 -5.56986570e-01 -8.67429912e-01 -7.58409426e-02
3.09559256e-01 2.28445455e-02 -7.61156261e-01 1.71181953e+00
1.19834185e-01 4.16794032e-01 -1.00247681e-01 7.87049055e-01
9.99133587e-01 9.24438179e-01 1.88859522e-01 -9.40207168e-02
1.56038737e+00 -6.10700488e-01 -6.84798777e-01 9.48119015e-02
4.43675190e-01 -8.58455181e-01 1.53460324e+00 7.37231731e-01
-1.27165318e+00 -9.05439973e-01 -1.13054657e+00 -6.40421286e-02
-3.33740711e-01 1.19766250e-01 4.66067642e-01 5.91449440e-01
-9.66354847e-01 7.95143425e-01 -8.32462311e-01 -1.50724396e-01
2.72614360e-01 4.20555741e-01 -4.91408817e-02 3.69781226e-01
-1.29253006e+00 3.78704071e-02 4.48768824e-01 -4.69725966e-01
-9.42021906e-01 -1.11468375e+00 -7.67143071e-01 4.77840990e-01
-5.66405663e-03 -1.68612897e-01 1.24476993e+00 -2.79685348e-01
-1.60727835e+00 2.93705583e-01 -1.06599860e-01 -5.50442219e-01
-4.05205674e-02 -4.54666793e-01 -7.15345800e-01 4.10457075e-01
9.00483597e-03 8.35626304e-01 9.39238012e-01 -6.36799514e-01
-8.65341067e-01 -2.90959597e-01 -1.39318913e-01 -5.10191582e-02
-7.59099603e-01 3.47682714e-01 -1.89767927e-01 -1.27481043e+00
3.69185805e-01 -8.11262071e-01 8.29135180e-02 -2.46435329e-01
-2.76608944e-01 -2.79579163e-01 6.61930680e-01 -6.36007249e-01
1.72056782e+00 -2.72571135e+00 6.31476492e-02 6.47205412e-02
1.50861368e-01 -1.61563009e-01 -8.39630663e-02 2.55157381e-01
-2.51997709e-01 1.69119552e-01 -2.05630004e-01 -3.98318321e-01
9.30714086e-02 -1.61696315e-01 -4.91079986e-01 8.77551064e-02
1.19869867e-02 3.38704586e-01 -5.90615571e-01 -4.76811707e-01
-4.01448347e-02 4.64677393e-01 -8.54652107e-01 8.60921517e-02
1.73663393e-01 2.18022674e-01 -5.81210926e-02 4.92619246e-01
3.63016903e-01 -6.82291240e-02 -7.32903630e-02 -8.58030096e-02
-1.33242726e-01 6.26271486e-01 -1.36796200e+00 1.95631242e+00
-4.32127655e-01 6.17058873e-01 -4.79425713e-02 -6.67236328e-01
1.01850343e+00 6.87565267e-01 5.83034933e-01 -4.15175468e-01
1.21375755e-03 1.38680100e-01 -1.33182213e-01 -2.13301286e-01
5.20203173e-01 -7.67247304e-02 -5.40693820e-01 3.85218590e-01
3.45811039e-01 -1.88288301e-01 1.44378275e-01 2.91015860e-02
1.19093978e+00 -4.34464514e-01 4.84789982e-02 -3.81048083e-01
3.39601010e-01 -5.21720648e-01 7.13365614e-01 4.02247638e-01
2.87621822e-02 6.71217620e-01 2.76433945e-01 -2.57899851e-01
-5.81783593e-01 -1.37869513e+00 -1.14256449e-01 1.74497795e+00
-2.18862399e-01 -1.02426374e+00 -7.75354803e-01 -3.16338018e-02
-2.68072914e-02 5.86496413e-01 -2.45593995e-01 -9.17540789e-02
-4.31250185e-01 -6.08543456e-01 1.12930620e+00 6.37044668e-01
4.88353401e-01 -9.61696386e-01 -6.51761949e-01 3.67862433e-01
-3.32325578e-01 -9.69404221e-01 -3.85912448e-01 3.81890804e-01
-9.13028836e-01 -7.12684810e-01 -2.84961164e-01 -7.56915152e-01
-3.07830912e-03 1.02469489e-01 8.52535129e-01 -3.76761913e-01
-4.67502177e-01 5.67756779e-02 -6.10326171e-01 -5.04957736e-01
9.24202055e-02 1.99117199e-01 3.93888324e-01 9.89001021e-02
1.98183462e-01 -1.06748986e+00 -5.16896605e-01 3.55136544e-01
-8.28498065e-01 -2.22781405e-01 1.57995615e-02 5.75508296e-01
6.26966774e-01 4.50228155e-01 8.10809612e-01 -1.73024476e-01
5.72086573e-01 -3.79696310e-01 -2.52488285e-01 -2.46515051e-01
-5.85931428e-02 -2.53989220e-01 7.75540292e-01 -5.24589241e-01
-6.44673407e-01 -3.70223001e-02 -3.32455374e-02 -5.60214281e-01
-1.76296353e-01 2.58236498e-01 -2.61581019e-02 3.62095803e-01
7.81088173e-01 2.01401085e-01 -4.45341825e-01 -1.10330701e+00
2.06075817e-01 8.64095628e-01 6.30126059e-01 -7.52767622e-01
4.27491456e-01 3.49449098e-01 -3.76353890e-01 -1.11450696e+00
-5.85966468e-01 -4.64904398e-01 -4.80391413e-01 -1.24836214e-01
8.53270173e-01 -1.24513805e+00 -8.24403942e-01 4.27011162e-01
-7.84810364e-01 -1.12323061e-01 -4.81894493e-01 7.25991070e-01
-4.77876782e-01 1.66881740e-01 -9.00133193e-01 -8.42075765e-01
-4.13490295e-01 -9.52962697e-01 1.07553315e+00 -1.31091535e-01
-6.85391188e-01 -3.64928931e-01 -3.27605873e-01 1.44983098e-01
3.15687776e-01 2.25994363e-01 1.10195291e+00 -5.05124092e-01
-3.64785381e-02 9.03773680e-02 -1.16723645e-02 4.68060613e-01
9.95786861e-02 -2.16038480e-01 -1.40351164e+00 -3.96968246e-01
1.24254353e-01 -3.53704333e-01 8.21060836e-01 3.05373937e-01
1.68789196e+00 -1.81004122e-01 1.23199746e-01 4.95331734e-01
9.45151150e-01 5.16693652e-01 3.70619416e-01 -7.09241182e-02
5.06281257e-01 6.63909137e-01 4.24132198e-01 8.67348135e-01
2.17514873e-01 7.55385101e-01 4.94792722e-02 1.47937804e-01
-2.04635203e-01 -1.02595806e-01 6.41483665e-01 1.65796101e+00
6.58378974e-02 4.85148653e-02 -7.56960273e-01 6.82444811e-01
-1.53781581e+00 -9.06104386e-01 -9.99419093e-02 2.06750393e+00
1.05671287e+00 1.70069724e-01 4.51734543e-01 1.01687133e+00
7.20058382e-01 -5.06465398e-02 -2.65693218e-01 -3.31563175e-01
8.41200352e-02 7.22581565e-01 -1.39773384e-01 2.49709561e-01
-1.20835578e+00 7.47101843e-01 6.45253611e+00 1.18199027e+00
-9.57002938e-01 2.22636342e-01 3.83602381e-01 -5.92506766e-01
1.21703506e-01 -3.54962289e-01 -5.39651155e-01 6.62345052e-01
1.33189964e+00 -1.02348939e-01 4.51970786e-01 6.22855961e-01
2.17769906e-01 2.08800748e-01 -1.16961861e+00 1.38187075e+00
-2.91158408e-01 -1.10887372e+00 2.28705928e-02 -2.93475807e-01
2.06435278e-01 -4.60081212e-02 8.50584134e-02 5.01663864e-01
4.66859266e-02 -9.25089419e-01 9.59832013e-01 2.24561915e-01
9.24755156e-01 -1.04433048e+00 3.92804205e-01 -4.35200483e-02
-1.99147630e+00 -1.97561190e-01 -2.82684207e-01 -2.47756571e-01
-8.02337378e-02 5.68315625e-01 -6.17648125e-01 5.41932702e-01
1.40752101e+00 6.07142806e-01 -5.33782244e-01 9.30906832e-01
1.44426227e-01 1.06442845e+00 -6.59080088e-01 9.17687789e-02
-3.83214988e-02 2.50774056e-01 4.19134349e-01 1.33965158e+00
5.89989364e-01 1.87962726e-01 4.06112462e-01 4.89381552e-01
-2.99887005e-02 1.86756507e-01 9.33241993e-02 2.74954569e-02
8.10783148e-01 9.60979939e-01 -6.07564807e-01 -3.57561141e-01
-1.10203259e-01 6.73449099e-01 -1.43197710e-02 3.16199571e-01
-9.55551326e-01 -1.04630816e+00 9.56053793e-01 1.37348399e-01
3.64606172e-01 -1.56015709e-01 -1.81205243e-01 -8.57166409e-01
-2.04381011e-02 -6.58439338e-01 6.90998793e-01 -7.44503856e-01
-1.06656730e+00 8.47099006e-01 2.92832758e-02 -1.45240819e+00
-2.46510878e-01 -3.46636057e-01 -2.85027504e-01 5.60693920e-01
-9.72949624e-01 -6.20202661e-01 -2.44102865e-01 8.78613353e-01
6.66671157e-01 -1.77806288e-01 1.11799574e+00 8.21645379e-01
-4.90696728e-01 8.14969301e-01 -2.06263274e-01 6.77745715e-02
6.41020060e-01 -1.36110246e+00 3.46307635e-01 3.28964502e-01
3.69930774e-01 3.99825782e-01 4.47672874e-01 4.01380425e-03
-9.61897969e-01 -1.27184176e+00 6.74571335e-01 -5.74296415e-02
8.41654062e-01 -6.07721806e-01 -1.07623637e+00 2.43910253e-01
-7.27047846e-02 -7.72332996e-02 1.23306882e+00 1.40449688e-01
-6.71257794e-01 -4.47994828e-01 -1.01873791e+00 4.64644879e-01
1.05764890e+00 -8.96568120e-01 -6.55330718e-01 5.48062548e-02
1.17523360e+00 -7.66059011e-02 -1.45612836e+00 3.53106499e-01
4.40835536e-01 -8.48757327e-01 9.42146003e-01 -4.84074086e-01
-1.53057333e-02 -5.67802668e-01 -6.30363643e-01 -1.04563999e+00
-5.05017042e-01 -8.58218849e-01 -6.93889260e-02 1.39367855e+00
2.45957762e-01 -4.01603609e-01 3.94177288e-01 -2.30824918e-01
-3.91227365e-01 -6.47697151e-01 -1.20510709e+00 -9.36513364e-01
-7.33275265e-02 -1.11437261e+00 7.04609632e-01 8.28599513e-01
3.06007862e-01 3.57391208e-01 -1.08953446e-01 1.60843477e-01
3.80858332e-01 7.59433955e-02 4.81814831e-01 -1.44683945e+00
-3.75055283e-01 -3.40605319e-01 -6.56459093e-01 -8.75432312e-01
-2.36083120e-01 -8.44108343e-01 -1.03305541e-01 -8.64175856e-01
-2.77138472e-01 -3.09731722e-01 -9.30675983e-01 8.74107301e-01
1.32448792e-01 4.96432364e-01 2.70867169e-01 1.50778621e-01
-5.07151544e-01 5.91166914e-01 6.23644710e-01 -8.20942074e-02
-3.96683604e-01 -1.04465567e-01 -4.86204296e-01 8.49609494e-01
9.44181442e-01 -5.42281449e-01 -4.93816912e-01 -3.21752906e-01
-2.57881545e-02 5.09875193e-02 1.69292375e-01 -1.58842719e+00
-1.59835424e-02 4.25189108e-01 3.41901541e-01 -7.00777888e-01
7.46912122e-01 -4.22224641e-01 2.12756142e-01 2.31976613e-01
-5.94276190e-01 2.54352629e-01 6.63159847e-01 4.43304509e-01
-6.02223635e-01 1.18812174e-01 7.27936447e-01 9.04731974e-02
-5.37769794e-01 -7.77522475e-03 -5.24630606e-01 1.96387440e-01
6.64793611e-01 -1.24670297e-01 3.95062305e-02 -2.97470927e-01
-1.14544976e+00 -4.21857722e-02 -2.45329678e-01 5.81886709e-01
6.37348890e-01 -1.56299460e+00 -6.12347007e-01 2.21845299e-01
1.43483400e-01 -8.46362859e-02 4.42707270e-01 6.44795060e-01
-3.08141321e-01 1.41426876e-01 -1.47830158e-01 -6.89733565e-01
-1.43149412e+00 1.32264048e-01 -8.46274346e-02 -2.90462673e-02
-5.61991096e-01 1.11792588e+00 2.35020831e-01 -1.77690640e-01
5.67760825e-01 -9.21366572e-01 -1.66695297e-01 4.95691180e-01
7.71118164e-01 7.50702024e-01 2.51093984e-01 -3.85039777e-01
-4.70494568e-01 5.32090902e-01 3.17888148e-02 -3.87942642e-01
1.43939710e+00 1.17023408e-01 -3.69342640e-02 8.97903740e-01
1.26569128e+00 -3.05677559e-02 -1.07380188e+00 -1.25944272e-01
1.33107886e-01 -2.62708008e-01 3.63589302e-02 -5.35835922e-01
-9.80719149e-01 1.17632163e+00 8.19311142e-01 6.11654043e-01
1.39390790e+00 -5.79080693e-02 8.64005625e-01 1.74024895e-01
4.58653033e-01 -1.13346481e+00 2.69197553e-01 5.08241296e-01
9.57271755e-01 -4.06004161e-01 -1.47902876e-01 -4.20577437e-01
-4.65092123e-01 1.03047585e+00 3.57714325e-01 -1.67794019e-01
8.24078619e-01 3.95342886e-01 -1.18035994e-01 -6.30300269e-02
-8.00912261e-01 1.08223306e-02 3.65038931e-01 2.67135471e-01
5.79139233e-01 2.53624916e-01 9.11002159e-02 1.05590522e+00
-8.34989727e-01 -4.79539186e-01 1.49993867e-01 7.50768423e-01
-6.22355282e-01 -7.82278657e-01 -5.43475389e-01 3.23861092e-01
-7.20638752e-01 -1.89543471e-01 -1.65325925e-01 5.00897527e-01
2.55453348e-01 1.27409232e+00 3.44355941e-01 -7.92149961e-01
4.35470849e-01 3.65549654e-01 2.28792131e-01 -7.10843682e-01
-8.53002429e-01 5.01032531e-01 -6.50622100e-02 -6.80502236e-01
-2.04197943e-01 -5.14434755e-01 -1.61843157e+00 -1.05533309e-01
-1.52752906e-01 3.87877792e-01 4.10200894e-01 4.32067603e-01
6.01536393e-01 9.91374612e-01 6.55747294e-01 -9.08797979e-01
-3.53692621e-01 -1.19315910e+00 -7.93222308e-01 2.43620560e-01
8.60280022e-02 -6.16808951e-01 -4.60498512e-01 2.94653118e-01] | [15.208403587341309, 5.2109808921813965] |
53224710-d2c0-4ce1-9b10-54dba23acdb9 | black-box-generation-of-adversarial-text | 1801.04354 | null | http://arxiv.org/abs/1801.04354v5 | http://arxiv.org/pdf/1801.04354v5.pdf | Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers | Although various techniques have been proposed to generate adversarial
samples for white-box attacks on text, little attention has been paid to
black-box attacks, which are more realistic scenarios. In this paper, we
present a novel algorithm, DeepWordBug, to effectively generate small text
perturbations in a black-box setting that forces a deep-learning classifier to
misclassify a text input. We employ novel scoring strategies to identify the
critical tokens that, if modified, cause the classifier to make an incorrect
prediction. Simple character-level transformations are applied to the
highest-ranked tokens in order to minimize the edit distance of the
perturbation, yet change the original classification. We evaluated DeepWordBug
on eight real-world text datasets, including text classification, sentiment
analysis, and spam detection. We compare the result of DeepWordBug with two
baselines: Random (Black-box) and Gradient (White-box). Our experimental
results indicate that DeepWordBug reduces the prediction accuracy of current
state-of-the-art deep-learning models, including a decrease of 68\% on average
for a Word-LSTM model and 48\% on average for a Char-CNN model. | ['Yanjun Qi', 'Jack Lanchantin', 'Ji Gao', 'Mary Lou Soffa'] | 2018-01-13 | null | null | null | null | ['adversarial-text', 'spam-detection'] | ['adversarial', 'natural-language-processing'] | [ 4.61444557e-01 1.81429163e-01 5.34629412e-02 -4.03742045e-01
-9.08591092e-01 -7.04795241e-01 6.79302812e-01 2.69647390e-01
-5.25065005e-01 6.97136223e-01 9.98960137e-02 -7.63480723e-01
5.72697699e-01 -8.52746367e-01 -9.26002562e-01 -6.76822603e-01
4.23323572e-01 1.63704708e-01 1.69104740e-01 -3.77562910e-01
3.10926259e-01 3.80258739e-01 -9.01121259e-01 7.41901338e-01
8.68530750e-01 6.61816061e-01 -3.93301636e-01 9.00157571e-01
-1.50269911e-01 6.79205000e-01 -1.24901354e+00 -1.02133524e+00
4.03040498e-01 -5.29873192e-01 -6.44391477e-01 -4.80055869e-01
7.57118165e-01 -5.09551644e-01 -6.23738110e-01 1.26454818e+00
8.27280819e-01 1.03530064e-01 6.24305785e-01 -9.96219993e-01
-7.33992875e-01 1.08547640e+00 -4.25130039e-01 2.00230524e-01
1.17024556e-01 3.80634964e-01 8.92823040e-01 -7.72122920e-01
3.97901297e-01 1.40404284e+00 7.23789155e-01 1.11960232e+00
-1.24297512e+00 -9.75120425e-01 2.94563591e-01 6.67140121e-03
-8.32619548e-01 -3.03520888e-01 5.76874256e-01 -2.14391947e-01
9.60579395e-01 3.87377828e-01 6.72638789e-02 1.59200370e+00
6.96176708e-01 7.91467965e-01 8.64698291e-01 -5.29684246e-01
2.99398750e-01 1.13218233e-01 2.19646722e-01 4.85145181e-01
4.17255580e-01 1.87513739e-01 -4.96372789e-01 -4.40433741e-01
-1.68048605e-01 -8.77930224e-02 -2.21011460e-01 2.64107972e-01
-7.26146996e-01 9.99678314e-01 3.30949455e-01 6.74446747e-02
-9.78543758e-02 4.80811238e-01 8.24091077e-01 4.27960753e-01
7.23895013e-01 6.36232316e-01 -4.95245546e-01 -1.41425893e-01
-8.40883255e-01 5.85815609e-01 8.18362653e-01 5.60468376e-01
3.14407855e-01 5.15794754e-01 -6.51981711e-01 5.78189135e-01
-1.90399930e-01 5.37227094e-01 8.77471149e-01 -1.45948112e-01
8.33117425e-01 3.83870661e-01 -4.23982888e-02 -1.05711973e+00
-4.48633358e-02 -1.48399472e-01 -8.46891105e-01 4.81118798e-01
5.94906390e-01 -6.02562010e-01 -1.01476347e+00 1.47816515e+00
6.81585222e-02 1.98331755e-02 -5.93703650e-02 5.53452373e-01
5.63665330e-01 7.06286967e-01 -1.16070705e-02 3.17047894e-01
9.32445109e-01 -9.28399444e-01 -4.74990875e-01 -4.06034589e-01
9.80495989e-01 -7.68066406e-01 1.50857055e+00 4.24637705e-01
-8.49204600e-01 -3.90388042e-01 -1.23146975e+00 2.01236606e-01
-7.58971035e-01 -1.66552559e-01 2.21744124e-02 1.18306255e+00
-6.13602102e-01 8.50442946e-01 -6.30154967e-01 2.91062705e-02
3.75482231e-01 2.04176843e-01 -1.85770497e-01 2.23173812e-01
-1.56496382e+00 9.60080981e-01 3.36741626e-01 -5.52890636e-02
-9.72683370e-01 -7.23087132e-01 -5.62661529e-01 2.07170218e-01
5.89256361e-02 -1.38361126e-01 1.41222847e+00 -9.17873442e-01
-1.58339584e+00 6.70528114e-01 -4.73466981e-03 -1.02908444e+00
9.32261944e-01 -5.21891534e-01 -4.22890335e-01 -1.87837273e-01
-4.82389927e-01 2.89638937e-01 1.29614353e+00 -1.16901672e+00
-3.61713946e-01 -2.56799936e-01 -1.40494734e-01 -1.68127522e-01
-7.27063894e-01 8.92954469e-02 8.32641944e-02 -1.26829565e+00
-6.07238531e-01 -1.00723386e+00 -2.85908490e-01 -2.11297333e-01
-8.94888699e-01 -6.15539514e-02 1.07825649e+00 -6.60381854e-01
1.43480134e+00 -2.05233026e+00 -2.42220253e-01 1.56093404e-01
9.70754251e-02 8.79358947e-01 -3.35881591e-01 5.49359500e-01
-2.64086276e-01 5.26098847e-01 -2.93522090e-01 -4.14440006e-01
1.88452944e-01 -8.34701806e-02 -1.20563841e+00 3.48091722e-01
6.37339950e-02 9.79109704e-01 -6.00789845e-01 3.96565953e-03
2.82676946e-02 1.16923042e-01 -6.70122266e-01 3.43830474e-02
-5.92922747e-01 -2.41559267e-01 -1.59748703e-01 2.65158594e-01
7.61911154e-01 2.82439619e-01 -1.51248664e-01 1.98863879e-01
4.58010495e-01 5.19534886e-01 -7.48977780e-01 8.90788317e-01
-5.78326464e-01 9.03246701e-01 -3.69472474e-01 -5.86410463e-01
1.06846774e+00 8.34275689e-03 -2.60772496e-01 -3.32974076e-01
3.72471333e-01 6.54562414e-02 1.69383883e-01 -8.79138187e-02
7.08155990e-01 1.26721084e-01 -2.68658847e-01 7.41310954e-01
-3.29265773e-01 -2.55556226e-01 -1.35766380e-02 4.18499202e-01
1.31471860e+00 -2.57065117e-01 -2.22977847e-02 1.40345236e-02
5.33413708e-01 -3.11198533e-01 2.18669727e-01 1.28737724e+00
-1.81707233e-01 4.76605654e-01 7.59551525e-01 -6.12505078e-01
-1.11810887e+00 -7.35078692e-01 2.40382239e-01 1.25114977e+00
-2.87603080e-01 -4.73898321e-01 -1.30163980e+00 -1.28384817e+00
3.59565347e-01 1.23749447e+00 -7.88949609e-01 -7.98560381e-01
-6.90483510e-01 -9.04182136e-01 1.17925501e+00 3.95368338e-01
5.24479330e-01 -1.24016976e+00 -3.63298386e-01 5.76836132e-02
1.82906762e-01 -9.31858242e-01 -7.97237813e-01 2.82310784e-01
-6.23876512e-01 -7.60112166e-01 -4.44620878e-01 -5.31260252e-01
7.22769439e-01 -1.88932374e-01 8.17251861e-01 3.09025943e-02
-1.99515179e-01 -4.43621129e-01 -6.09432518e-01 -5.71517825e-01
-1.02782333e+00 4.40683037e-01 6.04633130e-02 -2.97460780e-02
6.33125186e-01 -4.95322347e-02 -3.87674659e-01 3.15188080e-01
-1.15243781e+00 -3.60192090e-01 4.94090557e-01 1.12320507e+00
1.13215782e-01 -8.76807049e-03 6.54759288e-01 -1.33642626e+00
1.14705729e+00 -1.26318097e-01 -5.22867858e-01 3.58015411e-02
-6.66486263e-01 1.69252023e-01 1.41202188e+00 -8.37982357e-01
-7.86696613e-01 -1.66052073e-01 -3.75860810e-01 -2.97919542e-01
-4.99467999e-02 2.96498120e-01 -1.95343360e-01 -1.13311879e-01
1.22271025e+00 4.11523759e-01 -2.24934891e-01 -3.18149596e-01
5.27847230e-01 1.00775552e+00 4.26169097e-01 -4.34912175e-01
1.21269763e+00 2.66332716e-01 -2.62709051e-01 -4.84022468e-01
-7.86117077e-01 2.86811382e-01 -3.72797757e-01 1.66988343e-01
5.26865661e-01 -4.24314052e-01 -5.49408138e-01 9.23460782e-01
-1.14094627e+00 -7.08919466e-01 -2.25052133e-01 -8.73457715e-02
-1.12968691e-01 5.38260102e-01 -6.75497234e-01 -5.59051037e-01
-9.61653650e-01 -1.04696941e+00 8.08507562e-01 -1.36191532e-01
-4.02552605e-01 -8.48116040e-01 -1.66319460e-01 1.27844989e-01
4.02981997e-01 2.06923023e-01 1.32240593e+00 -1.27066064e+00
-1.86631661e-02 -7.40293980e-01 2.56885830e-02 8.96046162e-01
-7.40719289e-02 6.56003058e-02 -9.51840997e-01 -5.79091489e-01
-2.18399718e-01 -3.51137936e-01 8.99117827e-01 -6.10485636e-02
1.67866266e+00 -6.57936811e-01 -2.60249019e-01 5.86569011e-01
8.88589084e-01 3.20838630e-01 8.01133513e-01 4.24655944e-01
6.47144794e-01 1.00556873e-01 3.73725802e-01 4.54789817e-01
-3.89680535e-01 5.28605103e-01 4.26611394e-01 -1.56784374e-02
6.65226951e-02 -5.15292466e-01 7.97954023e-01 3.94724399e-01
7.78205633e-01 -7.28218377e-01 -9.15003777e-01 2.37365305e-01
-1.44225943e+00 -9.20902610e-01 -8.55963230e-02 2.17375660e+00
1.18569779e+00 7.63118446e-01 -1.63176373e-01 4.08293337e-01
8.42284501e-01 4.15205806e-01 -7.80299127e-01 -1.01651573e+00
4.87723462e-02 5.78374326e-01 7.08919942e-01 5.33171535e-01
-1.17661262e+00 1.35233581e+00 6.44247293e+00 1.08397257e+00
-1.32998693e+00 -1.32984772e-01 7.88677037e-01 -3.12545776e-01
-2.39776105e-01 -3.18178356e-01 -1.16235185e+00 8.10220838e-01
1.08079576e+00 -4.13816214e-01 3.79153341e-01 1.00021410e+00
2.57330418e-01 3.47720951e-01 -1.02542841e+00 5.90917170e-01
2.93279022e-01 -1.14719379e+00 5.80053270e-01 -1.37757987e-01
8.19673121e-01 -1.55747309e-01 3.68075103e-01 4.74745512e-01
7.35865653e-01 -1.06235123e+00 5.61133742e-01 1.59052745e-01
7.65425384e-01 -1.06731880e+00 7.62836695e-01 3.09083909e-01
-4.00227457e-01 -2.39844881e-02 -4.00406361e-01 6.74906820e-02
-1.73427090e-01 6.91163898e-01 -1.15409184e+00 4.92763333e-03
5.81414640e-01 1.71628416e-01 -7.48381674e-01 4.87679690e-01
-4.69450474e-01 1.08962178e+00 -1.90269481e-02 -6.26225471e-01
3.26166391e-01 2.44366065e-01 5.44317186e-01 1.39929855e+00
1.51118925e-02 -3.82396847e-01 -1.43607780e-01 7.10876226e-01
-6.33725107e-01 3.92097682e-02 -4.51986760e-01 -1.79113001e-01
5.14351368e-01 9.93102252e-01 -1.76845968e-01 -3.62279773e-01
-5.79935573e-02 1.15521073e+00 1.54315278e-01 3.45072687e-01
-9.55401182e-01 -1.18415129e+00 8.41787457e-01 1.39511898e-01
3.22682530e-01 3.31361243e-03 -6.14602864e-01 -9.92478669e-01
1.36632830e-01 -1.42541921e+00 2.55013555e-01 -5.66803217e-01
-1.32761490e+00 7.62190640e-01 -3.30341637e-01 -9.27210212e-01
-4.20188993e-01 -7.48756468e-01 -9.10790205e-01 1.06472564e+00
-1.08520210e+00 -6.57052517e-01 -1.66932032e-01 4.01095867e-01
6.21497989e-01 -6.28275573e-01 8.93901944e-01 1.35290669e-03
-7.58479118e-01 1.51819253e+00 4.59352344e-01 6.70903385e-01
8.34894478e-01 -1.22161961e+00 1.49549866e+00 1.18624353e+00
-6.97188033e-03 6.63935721e-01 8.37862730e-01 -7.95308292e-01
-1.05586433e+00 -1.46166623e+00 7.92577267e-01 -4.49755847e-01
7.49245763e-01 -8.42080832e-01 -1.01893246e+00 6.11552715e-01
9.66851190e-02 -9.40326825e-02 5.87405503e-01 -2.79906094e-01
-7.38249600e-01 -9.03319344e-02 -1.32639468e+00 1.13602018e+00
5.69697499e-01 -5.41826308e-01 -5.27726054e-01 3.59875262e-01
9.63027477e-01 -5.87952852e-01 -3.02695036e-01 1.69004947e-01
3.99412483e-01 -7.09185064e-01 6.41657948e-01 -1.09517741e+00
7.38261223e-01 4.17216048e-02 1.39937267e-01 -1.76080942e+00
1.36352805e-02 -9.26315427e-01 -3.07311177e-01 9.26469266e-01
5.45755565e-01 -8.94461095e-01 1.16469097e+00 3.19486976e-01
-3.64197344e-02 -8.22210133e-01 -8.82784605e-01 -7.44002819e-01
8.10538828e-01 -3.27310622e-01 6.99692190e-01 8.17831874e-01
-7.48904124e-02 -7.50286728e-02 -4.44491386e-01 -3.38569321e-02
2.03310356e-01 -3.68126541e-01 1.13505292e+00 -6.82827652e-01
-3.69582742e-01 -6.07067585e-01 -3.99245858e-01 -9.57148552e-01
4.99588132e-01 -8.73975933e-01 9.56803709e-02 -1.01210964e+00
-2.14918569e-01 1.02357768e-01 -6.52200505e-02 6.71076238e-01
-6.04237199e-01 1.65388778e-01 2.88142174e-01 -1.57995403e-01
1.23715118e-01 5.22977531e-01 8.01092744e-01 -4.60851252e-01
-5.26914671e-02 1.71952873e-01 -7.33078778e-01 5.93900561e-01
1.10234606e+00 -7.33611166e-01 -1.19710021e-01 -4.22751725e-01
2.72879958e-01 -5.92018843e-01 1.56555146e-01 -9.57402587e-01
-4.29538526e-02 -3.01300269e-02 3.63499373e-01 -5.35794914e-01
-1.47124985e-02 -5.18712699e-01 -7.06279039e-01 7.40515888e-01
-9.38462317e-01 2.64917284e-01 4.29896414e-01 4.66558665e-01
7.90398717e-02 -6.17108405e-01 1.09385800e+00 1.91389188e-01
-1.18820325e-01 1.51899338e-01 -7.46946752e-01 2.42561758e-01
9.51105237e-01 8.77887458e-02 -6.95971072e-01 -5.42984784e-01
-2.78764904e-01 -1.39500648e-01 2.95639724e-01 6.03410661e-01
6.02069974e-01 -9.72923100e-01 -7.97031999e-01 2.80860156e-01
-5.49022518e-02 -2.07989633e-01 -8.22413489e-02 -9.41335112e-02
-7.06864893e-01 2.66303569e-01 9.65390056e-02 -2.14180946e-02
-1.42592311e+00 5.66000938e-01 6.48568153e-01 -3.90869111e-01
-5.41842997e-01 9.59507883e-01 -1.84703618e-01 -6.15969300e-01
4.76696789e-01 -3.40402722e-01 3.38251412e-01 -2.14275181e-01
6.03217661e-01 3.24293762e-01 4.65602547e-01 -1.02189794e-01
-5.68885505e-02 1.95275694e-02 -7.60099649e-01 -3.83633189e-02
7.29954660e-01 5.56558669e-01 1.50635809e-01 1.25285864e-01
1.08511651e+00 1.86070919e-01 -9.96903360e-01 -2.06260830e-01
-2.00322941e-01 -5.97328246e-01 3.56633216e-02 -1.11292970e+00
-9.11709309e-01 1.12406361e+00 4.40763086e-01 3.87670130e-01
8.47200811e-01 -7.24175274e-01 1.18852878e+00 8.14182520e-01
-9.54042375e-02 -1.02512240e+00 3.78437191e-01 8.58697891e-01
7.92890608e-01 -8.42340529e-01 -1.01943687e-01 -8.16934407e-02
-7.34677672e-01 1.03651929e+00 9.89856958e-01 -1.70354575e-01
3.90077353e-01 3.96261394e-01 3.14375758e-01 4.00445640e-01
-8.91835093e-01 4.04262513e-01 -1.06438354e-01 4.54653919e-01
2.28314698e-01 -8.74053836e-02 -1.01878487e-01 4.26175684e-01
-6.45275772e-01 -2.82959998e-01 7.59826303e-01 7.79068470e-01
-3.92458588e-01 -1.08925605e+00 -4.45153207e-01 7.41589725e-01
-8.35039020e-01 -4.77046877e-01 -7.74409652e-01 5.60999095e-01
-2.89001644e-01 9.19547558e-01 6.26065284e-02 -7.37282038e-01
4.34592009e-01 3.83699208e-01 -5.90508580e-02 -5.82189739e-01
-1.11648774e+00 -5.60571313e-01 2.45009344e-02 -1.61423430e-01
6.60627127e-01 -4.34520245e-01 -1.05425537e+00 -7.62659252e-01
-3.49139810e-01 5.66438772e-02 6.25358999e-01 7.65496135e-01
4.16655421e-01 5.75690448e-01 7.97535419e-01 -6.34897292e-01
-1.37068522e+00 -1.29585934e+00 -2.48423398e-01 6.62628770e-01
1.82689980e-01 -8.35074559e-02 -7.83247650e-01 -9.72699672e-02] | [5.985134124755859, 8.118221282958984] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.