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
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
5afa9db3-590a-42fe-b434-e7de6320be35 | nn-copula-cd-a-copula-guided-interpretable | 2303.17448 | null | https://arxiv.org/abs/2303.17448v1 | https://arxiv.org/pdf/2303.17448v1.pdf | NN-Copula-CD: A Copula-Guided Interpretable Neural Network for Change Detection in Heterogeneous Remote Sensing Images | Change detection (CD) in heterogeneous remote sensing images is a practical and challenging issue for real-life emergencies. In the past decade, the heterogeneous CD problem has significantly benefited from the development of deep neural networks (DNN). However, the data-driven DNNs always perform like a black box wher... | ['Gang Li', 'Xueqian Wang', 'Weiming Li'] | 2023-03-30 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 1.39547512e-01 -4.85336691e-01 2.95245320e-01 -2.37534270e-01
-3.50440204e-01 -3.19867015e-01 3.54319930e-01 -1.25338897e-01
-1.62713736e-01 6.02557540e-01 -2.92108625e-01 -3.19586009e-01
-6.64993346e-01 -9.21779215e-01 -5.53444266e-01 -1.31134903e+00
-2.60131061e-01 6.87805340e-02 -4.50380659e-03 -2.82237113... | [9.974052429199219, -1.5468181371688843] |
ea88a203-8f4a-4367-b61a-2f780700616a | hyperspectral-pansharpening-based-on-improved | 2107.02630 | null | https://arxiv.org/abs/2107.02630v1 | https://arxiv.org/pdf/2107.02630v1.pdf | Hyperspectral Pansharpening Based on Improved Deep Image Prior and Residual Reconstruction | Hyperspectral pansharpening aims to synthesize a low-resolution hyperspectral image (LR-HSI) with a registered panchromatic image (PAN) to generate an enhanced HSI with high spectral and spatial resolution. Recently proposed HS pansharpening methods have obtained remarkable results using deep convolutional networks (Co... | ['Vishal M. Patel', 'Jeya Maria Jose Valanarasu', 'Wele Gedara Chaminda Bandara'] | 2021-07-06 | null | null | null | null | ['pansharpening'] | ['computer-vision'] | [ 9.14022088e-01 -2.63785958e-01 2.01610595e-01 -3.32280070e-01
-1.03246844e+00 -4.00423437e-01 2.94138908e-01 -4.93615657e-01
-1.48884058e-01 5.99168718e-01 6.51959181e-02 -5.24534248e-02
-2.44522557e-01 -1.28429008e+00 -7.73839414e-01 -1.18222737e+00
2.00084582e-01 -1.56996086e-01 -1.18379351e-02 -2.63446629... | [10.210897445678711, -1.9791988134384155] |
8b291889-ca4b-4b15-a36a-252bff3746e1 | is-risk-sensitive-reinforcement-learning | 2307.00547 | null | https://arxiv.org/abs/2307.00547v1 | https://arxiv.org/pdf/2307.00547v1.pdf | Is Risk-Sensitive Reinforcement Learning Properly Resolved? | Due to the nature of risk management in learning applicable policies, risk-sensitive reinforcement learning (RSRL) has been realized as an important direction. RSRL is usually achieved by learning risk-sensitive objectives characterized by various risk measures, under the framework of distributional reinforcement learn... | ['Dongsheng Li', 'Weinan Zhang', 'Xufang Luo', 'Kan Ren', 'Minghuan Liu', 'Ruiwen Zhou'] | 2023-07-02 | null | null | null | null | ['q-learning', 'distributional-reinforcement-learning', 'management'] | ['methodology', 'methodology', 'miscellaneous'] | [-0.25563127 0.05381701 -0.40415555 -0.3425316 -1.13269 -0.5166749
0.21181712 0.21696894 -0.55161244 1.0222887 0.07470082 -0.50763685
-0.857947 -0.95018387 -0.59449756 -0.72467846 -0.32043287 0.2717622
0.01793152 -0.1662441 0.45398498 0.43934125 -1.3411688 -0.3447203
1.2634718 1.0044807 -0.072... | [4.238212585449219, 2.571080446243286] |
123e64e6-be8a-43f5-ae72-394689ee42e1 | occlusion-net-2d3d-occluded-keypoint | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Reddy_Occlusion-Net_2D3D_Occluded_Keypoint_Localization_Using_Graph_Networks_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Reddy_Occlusion-Net_2D3D_Occluded_Keypoint_Localization_Using_Graph_Networks_CVPR_2019_paper.pdf | Occlusion-Net: 2D/3D Occluded Keypoint Localization Using Graph Networks | We present Occlusion-Net, a framework to predict 2D and 3D locations of occluded keypoints for objects, in a largely self-supervised manner. We use an off-the-shelf detector as input (like MaskRCNN) that is trained only on visible key point annotations. This is the only supervision used in this work. A graph encoder ne... | [' Srinivasa G. Narasimhan', ' Minh Vo', 'N. Dinesh Reddy'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['3d-car-instance-understanding', '3d-object-reconstruction-from-a-single-image', 'vehicle-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-2.72838563e-01 3.89359027e-01 -2.63422906e-01 -4.59006488e-01
-8.59920740e-01 -7.62209713e-01 5.57342052e-01 -1.74950525e-01
-1.50941968e-01 2.27767885e-01 -9.85172391e-02 -2.22324401e-01
4.18362528e-01 -3.90882671e-01 -1.21693206e+00 -2.67259389e-01
7.23201856e-02 7.99431443e-01 5.07330775e-01 9.69428718... | [7.707833290100098, -2.555600166320801] |
9579963c-1013-4343-9c4c-3e6b02bfa692 | a-comparison-of-audio-preprocessing | 2212.05335 | null | https://arxiv.org/abs/2212.05335v1 | https://arxiv.org/pdf/2212.05335v1.pdf | A Comparison of Audio Preprocessing Techniques and Deep Learning Algorithms for Raga Recognition | Ragas form the foundation for Indian Classical Music. The task of Raga Recognition has gained traction in the Music Information Retrieval community in the recent past, which can be attributed to the nuances of Indian Classical Music that have resulted in a plethora of research problems in Computing. In this work, we us... | ['Vandana Jagtap', 'Devayani Hebbar'] | 2022-12-10 | null | null | null | null | ['audio-signal-processing', 'music-information-retrieval'] | ['audio', 'music'] | [ 2.38270894e-01 -4.16373491e-01 1.08200088e-01 1.53639540e-02
-9.01031494e-01 -6.48131728e-01 6.11031175e-01 1.98708653e-01
-2.85124600e-01 2.44165391e-01 2.97493100e-01 1.12203456e-01
-4.72025096e-01 -8.85054529e-01 -3.02818567e-01 -8.07262361e-01
-2.37029552e-01 3.58487487e-01 3.28356326e-02 -4.72377509... | [15.895161628723145, 5.208682537078857] |
7c123ab5-055e-42e1-bb46-f63098abbfed | texpose-neural-texture-learning-for-self | 2212.12902 | null | https://arxiv.org/abs/2212.12902v2 | https://arxiv.org/pdf/2212.12902v2.pdf | TexPose: Neural Texture Learning for Self-Supervised 6D Object Pose Estimation | In this paper, we introduce neural texture learning for 6D object pose estimation from synthetic data and a few unlabelled real images. Our major contribution is a novel learning scheme which removes the drawbacks of previous works, namely the strong dependency on co-modalities or additional refinement. These have been... | ['Benjamin Busam', 'Nassir Navab', 'Fabian Manhardt', 'Hanzhi Chen'] | 2022-12-25 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chen_TexPose_Neural_Texture_Learning_for_Self-Supervised_6D_Object_Pose_Estimation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chen_TexPose_Neural_Texture_Learning_for_Self-Supervised_6D_Object_Pose_Estimation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['6d-pose-estimation'] | ['computer-vision'] | [ 7.00767756e-01 6.56556845e-01 3.69692683e-01 -3.69028658e-01
-1.33439481e+00 -6.57236397e-01 8.17726851e-01 -3.08248401e-01
-5.60286283e-01 7.47817636e-01 -3.35912257e-01 1.60662830e-01
-8.91152769e-02 -6.16176605e-01 -1.29779029e+00 -9.02742147e-01
3.05978090e-01 1.00750542e+00 3.63391340e-01 -6.80997670... | [8.052486419677734, -2.7173879146575928] |
9e4218fa-ab1c-432c-b7c7-77c95ef28a05 | a-dataset-for-research-on-short-text | null | null | https://aclanthology.org/D13-1096 | https://aclanthology.org/D13-1096.pdf | A Dataset for Research on Short-Text Conversations | null | ['Hang Li', 'Hao Wang', 'Enhong Chen', 'Zhengdong Lu'] | 2013-10-01 | null | null | null | emnlp-2013-10 | ['short-text-conversation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.3513875007629395, 3.76395583152771] |
d1f6cc24-a660-4c2b-a39d-26f2002d6335 | semi-equivariant-gnn-architectures-for-jet | 2202.06941 | null | https://arxiv.org/abs/2202.06941v1 | https://arxiv.org/pdf/2202.06941v1.pdf | Semi-Equivariant GNN Architectures for Jet Tagging | Composing Graph Neural Networks (GNNs) of operations that respect physical symmetries has been suggested to give better model performance with a smaller number of learnable parameters. However, real-world applications, such as in high energy physics have not born this out. We present the novel architecture VecNet that ... | ['Jason Wong', 'Savannah Thais', 'Daniel Murnane'] | 2022-02-14 | null | null | null | null | ['jet-tagging'] | ['graphs'] | [ 3.19885314e-02 1.32626981e-01 -3.46264154e-01 -1.73211619e-01
1.08235665e-01 -5.32058358e-01 5.52538574e-01 -8.16775709e-02
-3.95310014e-01 7.03010142e-01 -4.97176573e-02 -7.13680208e-01
-5.87549388e-01 -9.99110818e-01 -8.86239350e-01 -6.80373132e-01
-3.60745698e-01 5.81655800e-01 2.03711048e-01 -5.13284326... | [6.553464412689209, 5.917568206787109] |
ec36c1a4-420f-4a08-b11e-a2908ac3eacc | learning-to-simulate-tree-branch-dynamics-for | 2306.03410 | null | https://arxiv.org/abs/2306.03410v1 | https://arxiv.org/pdf/2306.03410v1.pdf | Learning to Simulate Tree-Branch Dynamics for Manipulation | We propose to use a simulation driven inverse inference approach to model the joint dynamics of tree branches under manipulation. Learning branch dynamics and gaining the ability to manipulate deformable vegetation can help with occlusion-prone tasks, such as fruit picking in dense foliage, as well as moving overhangin... | ['Fabio Ramos', 'Paulo Borges', 'Jason Williams', 'Tirthankar Bandyopadhyay', 'Jayadeep Jacob'] | 2023-06-06 | null | null | null | null | ['density-estimation'] | ['methodology'] | [ 3.42961311e-01 2.60047823e-01 -1.25884786e-01 2.36537695e-01
-4.57492977e-01 -9.81319666e-01 4.23942119e-01 -7.07089826e-02
-9.85970050e-02 8.26922357e-01 -5.93611225e-02 -1.11096062e-01
-5.24211764e-01 -9.86243427e-01 -1.18972552e+00 -7.80092001e-01
-2.91594267e-01 8.84321868e-01 1.81241304e-01 -3.38039249... | [5.503587245941162, -0.3750949203968048] |
768e7794-661c-44b8-b412-77094f4ff6d5 | hinted-networks | 1812.06297 | null | http://arxiv.org/abs/1812.06297v1 | http://arxiv.org/pdf/1812.06297v1.pdf | Hinted Networks | We present Hinted Networks: a collection of architectural transformations for
improving the accuracies of neural network models for regression tasks, through
the injection of a prior for the output prediction (i.e. a hint). We ground our
investigations within the camera relocalization domain, and propose two
variants, ... | ['Joel Lamy-Poirier', 'Anqi Xu'] | 2018-12-15 | null | null | null | null | ['camera-relocalization'] | ['computer-vision'] | [ 1.48366034e-01 2.05610901e-01 -3.24432760e-01 -5.35047650e-01
-5.78754961e-01 -7.10195839e-01 7.13641107e-01 -3.13055694e-01
-3.88500154e-01 6.18966103e-01 2.36989185e-01 -2.92614430e-01
-3.36925417e-01 -3.13859969e-01 -1.07322788e+00 -5.69532692e-01
-2.34283552e-01 5.77425100e-02 -9.77985635e-02 -2.67399281... | [7.666461944580078, -2.142548084259033] |
b1621242-1939-4523-bb62-4943427e69b5 | infer-intermediate-representations-for-future | 1903.10641 | null | http://arxiv.org/abs/1903.10641v1 | http://arxiv.org/pdf/1903.10641v1.pdf | INFER: INtermediate representations for FuturE pRediction | In urban driving scenarios, forecasting future trajectories of surrounding
vehicles is of paramount importance. While several approaches for the problem
have been proposed, the best-performing ones tend to require extremely detailed
input representations (eg. image sequences). But, such methods do not
generalize to dat... | ['Madhava Krishna K', 'Krishna Murthy J.', 'Junaid Ahmed Ansari', 'Shashank Srikanth', 'Karnik Ram R', 'Sarthak Sharma'] | 2019-03-26 | null | null | null | null | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 1.76303804e-01 -6.27243370e-02 -1.88388735e-01 -6.28458858e-01
-5.07675767e-01 -5.46750486e-01 1.09861827e+00 -1.03279755e-01
-4.13216680e-01 6.72972143e-01 3.65630895e-01 -5.65061808e-01
6.65050000e-02 -9.52897906e-01 -8.80760550e-01 -4.56463754e-01
-2.08768457e-01 4.23874646e-01 3.97086859e-01 -5.33463240... | [6.266190528869629, 0.6481020450592041] |
9182651b-7430-4de8-9063-b84cdddaa78e | dissect-disentangled-simultaneous | 2105.15164 | null | https://arxiv.org/abs/2105.15164v4 | https://arxiv.org/pdf/2105.15164v4.pdf | DISSECT: Disentangled Simultaneous Explanations via Concept Traversals | Explaining deep learning model inferences is a promising venue for scientific understanding, improving safety, uncovering hidden biases, evaluating fairness, and beyond, as argued by many scholars. One of the principal benefits of counterfactual explanations is allowing users to explore "what-if" scenarios through what... | ['Rosalind W. Picard', 'Brian Eoff', 'Brendan Jou', 'Chun-Liang Li', 'Been Kim', 'Asma Ghandeharioun'] | 2021-05-31 | dissect-disentangled-simultaneous-1 | https://openreview.net/forum?id=qY79G8jGsep | https://openreview.net/pdf?id=qY79G8jGsep | iclr-2022-4 | ['interpretability-techniques-for-deep-learning'] | ['miscellaneous'] | [ 3.83671045e-01 4.50029671e-01 -5.31160295e-01 -5.14768600e-01
-5.85668266e-01 -7.52789259e-01 9.82553959e-01 2.64587283e-01
-6.17225282e-02 1.02736712e+00 4.45001692e-01 -9.21072721e-01
-2.56728262e-01 -8.12971950e-01 -7.45908976e-01 -5.40640056e-01
8.53239670e-02 5.12074471e-01 -2.28542596e-01 1.78918213... | [8.724835395812988, 5.591401100158691] |
a9330ef3-f778-4cf6-9998-4a7dc448f09b | music-auto-tagging-using-cnns-and-mel | 1911.04824 | null | https://arxiv.org/abs/1911.04824v3 | https://arxiv.org/pdf/1911.04824v3.pdf | How Low Can You Go? Reducing Frequency and Time Resolution in Current CNN Architectures for Music Auto-tagging | Automatic tagging of music is an important research topic in Music Information Retrieval and audio analysis algorithms proposed for this task have achieved improvements with advances in deep learning. In particular, many state-of-the-art systems use Convolutional Neural Networks and operate on mel-spectrogram represent... | ['Dmitry Bogdanov', 'Jay Ho Jeon', 'Andres Ferraro', 'Xavier Serra', 'Jason Yoon'] | 2019-11-12 | null | null | null | null | ['music-auto-tagging'] | ['music'] | [-1.39952414e-02 -7.25228667e-01 -1.20618105e-01 -6.04786798e-02
-7.75305688e-01 -7.48359263e-01 7.75699764e-02 2.79728800e-01
-6.41516805e-01 4.55938339e-01 1.62441537e-01 5.91973215e-02
-4.36704129e-01 -6.06811762e-01 -2.61670172e-01 -3.72145057e-01
-3.62529427e-01 2.10396945e-01 1.52483806e-01 -7.51123354... | [15.757858276367188, 5.256225109100342] |
96417aa4-a0c3-48ad-8f67-96c2de69f1fe | grassmannian-learning-mutual-subspace-method | 2111.04352 | null | https://arxiv.org/abs/2111.04352v1 | https://arxiv.org/pdf/2111.04352v1.pdf | Grassmannian learning mutual subspace method for image set recognition | This paper addresses the problem of object recognition given a set of images as input (e.g., multiple camera sources and video frames). Convolutional neural network (CNN)-based frameworks do not exploit these sets effectively, processing a pattern as observed, not capturing the underlying feature distribution as it doe... | ['Kazuhiro Fukui', 'Takumi Kobayashi', 'Bernardo B. Gatto', 'Naoya Sogi', 'Lincon S. Souza'] | 2021-11-08 | null | null | null | null | ['facial-emotion-recognition', 'face-identification'] | ['computer-vision', 'computer-vision'] | [ 6.01794291e-03 -1.73439398e-01 -1.28916368e-01 -5.82499981e-01
-1.34768724e-01 -6.63115382e-01 4.76685375e-01 -5.70188165e-01
-3.65142137e-01 -1.16151735e-01 8.63649771e-02 2.07122475e-01
-3.94170374e-01 -4.00269747e-01 -5.84134042e-01 -8.39448869e-01
8.53774622e-02 1.86005041e-01 -5.60290456e-01 6.36959672... | [8.052536964416504, 3.866126537322998] |
6b81e60d-dd41-4bca-8d1d-c0b1d62b25e0 | training-a-deep-learning-model-for-single | null | null | https://www.nature.com/articles/s41598-021-03299-4 | https://www.nature.com/articles/s41598-021-03299-4 | Training a deep learning model for single-cell segmentation without manual annotation | Advances in the artificial neural network have made machine learning techniques increasingly more important in image analysis tasks. Recently, convolutional neural networks (CNN) have been applied to the problem of cell segmentation from microscopy images. However, previous methods used a supervised training paradigm i... | ['Ji Yu', 'Nizam Ud Din'] | 2021-12-14 | null | null | null | scientific-reports-article-number-23995-2021 | ['cell-segmentation'] | ['medical'] | [ 6.10632896e-01 -6.40213937e-02 4.00792688e-01 -3.94151926e-01
-6.46285713e-01 -6.14985645e-01 2.00201824e-01 4.29310530e-01
-1.06855357e+00 1.11136675e+00 -7.27530718e-01 -3.78704697e-01
3.55302483e-01 -7.54778802e-01 -6.70888960e-01 -1.07171571e+00
3.10461909e-01 5.12439072e-01 5.45024097e-01 5.68913408... | [14.52804183959961, -3.1511824131011963] |
a1c2f308-ad1b-4d3d-8d94-19d448b09125 | alive-caricature-from-2d-to-3d | 1803.06802 | null | http://arxiv.org/abs/1803.06802v3 | http://arxiv.org/pdf/1803.06802v3.pdf | Alive Caricature from 2D to 3D | Caricature is an art form that expresses subjects in abstract, simple and
exaggerated view. While many caricatures are 2D images, this paper presents an
algorithm for creating expressive 3D caricatures from 2D caricature images with
a minimum of user interaction. The key idea of our approach is to introduce an
intrinsi... | ['Yu-Kun Lai', 'Qianyi Wu', 'Juyong Zhang', 'Jianfei Cai', 'Jianmin Zheng'] | 2018-03-19 | alive-caricature-from-2d-to-3d-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Wu_Alive_Caricature_From_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Wu_Alive_Caricature_From_CVPR_2018_paper.pdf | cvpr-2018-6 | ['caricature'] | ['computer-vision'] | [ 3.35380696e-02 5.37448227e-01 1.70216829e-01 -4.39603895e-01
2.51163952e-02 -3.92557085e-01 5.87077200e-01 -9.45634425e-01
2.92621464e-01 3.72273862e-01 -3.09410840e-02 1.06153943e-01
-1.65985063e-01 -7.42485940e-01 -7.08234012e-01 -4.28134561e-01
2.77604729e-01 4.39508379e-01 -5.22390008e-01 -3.00066113... | [12.734289169311523, -0.247842475771904] |
bcc859fb-86cd-4010-bef2-6d98fa3d9041 | a-computer-vision-based-optical-method-for | 2303.14233 | null | https://arxiv.org/abs/2303.14233v1 | https://arxiv.org/pdf/2303.14233v1.pdf | A computer vision based optical method for measuring fluid level in cell culture plates | For a transparent well with a known volume capacity, changes in fluid level result in predictable changes in magnification of an overhead light source. For a given well size and fluid, the relationship between volume and magnification can be calculated if the fluid's index of refraction is known or in a naive fashion w... | ['Mircea Teodorescu', 'Pierre V. Baudin'] | 2023-03-24 | null | null | null | null | ['culture'] | ['speech'] | [ 1.63381666e-01 -1.78784579e-01 3.60289037e-01 2.76911929e-02
3.83053005e-01 -7.02661037e-01 1.02980144e-01 1.91599935e-01
-5.71190953e-01 6.94478810e-01 -1.79796681e-01 -2.26345927e-01
2.80320913e-01 -9.85012412e-01 -4.68946636e-01 -2.72582740e-01
3.76651466e-01 4.17212248e-01 6.63521588e-01 1.12325042... | [13.090763092041016, -2.9535398483276367] |
7a362d84-c68f-4473-a68f-fb61ae417f56 | semantic-sentence-similarity-size-does-not | 2106.08648 | null | https://arxiv.org/abs/2106.08648v1 | https://arxiv.org/pdf/2106.08648v1.pdf | Semantic sentence similarity: size does not always matter | This study addresses the question whether visually grounded speech recognition (VGS) models learn to capture sentence semantics without access to any prior linguistic knowledge. We produce synthetic and natural spoken versions of a well known semantic textual similarity database and show that our VGS model produces emb... | ['Mirjam Ernestus', 'Stefan L. Frank', 'Danny Merkx'] | 2021-06-16 | null | null | null | null | ['learning-semantic-representations', 'grounded-language-learning'] | ['methodology', 'natural-language-processing'] | [ 3.76120657e-01 3.50576073e-01 -4.25843149e-03 -5.35373926e-01
-1.01919758e+00 -5.48676431e-01 8.79995167e-01 3.13550442e-01
-7.07189381e-01 5.80031574e-01 8.34359229e-01 -3.77680331e-01
2.45270625e-01 -5.87049246e-01 -7.93311059e-01 -3.99906754e-01
3.68165582e-01 5.40678740e-01 4.57964003e-01 -5.20469844... | [10.901494979858398, 1.716861605644226] |
11235a48-75b8-464a-a84c-99b589217f03 | effective-data-augmentation-with-multi-domain | 1912.11597 | null | https://arxiv.org/abs/1912.11597v1 | https://arxiv.org/pdf/1912.11597v1.pdf | Effective Data Augmentation with Multi-Domain Learning GANs | For deep learning applications, the massive data development (e.g., collecting, labeling), which is an essential process in building practical applications, still incurs seriously high costs. In this work, we propose an effective data augmentation method based on generative adversarial networks (GANs), called Domain Fu... | ["Shin'ya Yamaguchi", 'Sekitoshi Kanai', 'Takeharu Eda'] | 2019-12-25 | null | null | null | null | ['scene-recognition'] | ['computer-vision'] | [ 4.05517548e-01 -6.72534630e-02 -7.88516700e-02 -3.89353901e-01
-9.69702780e-01 -5.55228770e-01 5.48251629e-01 -2.99299419e-01
-2.68571705e-01 1.07664979e+00 -2.09144130e-02 -5.65335061e-03
4.01159376e-01 -1.20610833e+00 -9.29567873e-01 -8.96525741e-01
6.11920595e-01 5.49124658e-01 -1.08935580e-01 -5.53425774... | [11.63126277923584, -0.26114383339881897] |
4cdb928f-1a63-4621-a1ee-dcd2974867ab | entire-space-counterfactual-learning-tuning | 2210.11039 | null | https://arxiv.org/abs/2210.11039v2 | https://arxiv.org/pdf/2210.11039v2.pdf | Entire Space Counterfactual Learning: Tuning, Analytical Properties and Industrial Applications | As a basic research problem for building effective recommender systems, post-click conversion rate (CVR) estimation has long been plagued by sample selection bias and data sparsity issues. To address the data sparsity issue, prevalent methods based on entire space multi-task model leverage the sequential pattern of use... | ['Xinggao Liu', 'Weiming Liu', 'Yuxin Huang', 'Jiajun Fan', 'Zhichao Chen', 'Hao Wang'] | 2022-10-20 | null | null | null | null | ['auxiliary-learning', 'selection-bias'] | ['methodology', 'natural-language-processing'] | [ 2.47980461e-01 -2.89143860e-01 -5.42479634e-01 -1.17444299e-01
-1.12397683e+00 -3.25485677e-01 4.51617211e-01 -3.80313963e-01
-2.48989522e-01 1.09348249e+00 1.25482604e-01 -7.06632555e-01
-5.22575319e-01 -5.17887950e-01 -8.42025220e-01 -5.56498528e-01
9.64416414e-02 1.36263268e-02 -3.47330719e-01 -4.93680462... | [9.675785064697266, 5.379745960235596] |
658b057b-2925-4a74-9f11-5a6b458a8ad3 | achieving-connectivity-between-wide-areas | 1807.04505 | null | http://arxiv.org/abs/1807.04505v1 | http://arxiv.org/pdf/1807.04505v1.pdf | Achieving Connectivity Between Wide Areas Through Self-Organising Robot Swarm Using Embodied Evolution | Abruptions to the communication infrastructure happens occasionally, where
manual dedicated personnel will go out to fix the interruptions, restoring
communication abilities. However, sometimes this can be dangerous to the
personnel carrying out the task, which can be the case in war situations,
environmental disasters... | ['Asieh Abolpour Mofrad', 'Hårek Haugerud', 'Anis Yazidi', 'Stefano Nichele', 'Erik Aaron Hansen', 'Alex Alcocer'] | 2018-07-12 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 9.37197506e-02 3.45850587e-01 5.29057860e-01 2.48047695e-01
7.02589571e-01 -5.28985858e-01 5.65389693e-01 2.52586573e-01
-6.03976846e-01 1.29420650e+00 -4.68959838e-01 -1.04110673e-01
-8.64320457e-01 -8.07183325e-01 -4.80118454e-01 -9.77046967e-01
-6.05199337e-01 6.56588674e-01 4.19603199e-01 -1.03077042... | [4.941695690155029, 1.7709872722625732] |
2b542738-8802-405f-adf4-289b97c52eed | cluzh-at-sigmorphon-2022-shared-tasks-on | null | null | https://aclanthology.org/2022.sigmorphon-1.21 | https://aclanthology.org/2022.sigmorphon-1.21.pdf | CLUZH at SIGMORPHON 2022 Shared Tasks on Morpheme Segmentation and Inflection Generation | This paper describes the submissions of the team of the Department of Computational Linguistics, University of Zurich, to the SIGMORPHON 2022 Shared Tasks on Morpheme Segmentation and Inflection Generation. Our submissions use a character-level neural transducer that operates over traditional edit actions. While this m... | ['Peter Makarov', 'Simon Clematide', 'Silvan Wehrli'] | null | null | null | null | naacl-sigmorphon-2022-7 | ['morpheme-segmentaiton', 'morphological-inflection'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.19706386e-01 1.92665696e-01 1.32934526e-01 -3.40378940e-01
-1.32720947e+00 -7.15935826e-01 4.55914497e-01 3.10478181e-01
-8.22641313e-01 6.87663436e-01 2.49329254e-01 -7.63003707e-01
4.56049770e-01 -7.80375361e-01 -7.75092185e-01 -2.14601249e-01
-2.85172075e-01 7.57145107e-01 1.13006108e-01 -6.37117088... | [10.675230979919434, 9.868005752563477] |
604d6918-571b-417b-91e0-dc46bf3890b2 | banknote-net-open-dataset-for-assistive | 2204.03738 | null | https://arxiv.org/abs/2204.03738v1 | https://arxiv.org/pdf/2204.03738v1.pdf | BankNote-Net: Open dataset for assistive universal currency recognition | Millions of people around the world have low or no vision. Assistive software applications have been developed for a variety of day-to-day tasks, including optical character recognition, scene identification, person recognition, and currency recognition. This last task, the recognition of banknotes from different denom... | ['Juan Lavista Ferres', 'Saqib Shaikh', 'Hemant Malhotra', 'Eugene Seleznev', 'Srinivas Vinnakota', 'Felipe Oviedo'] | 2022-04-07 | null | null | null | null | ['vision-based-navigation-with-language-based'] | ['robots'] | [-1.51387796e-01 -5.38800836e-01 -1.05496496e-01 -2.13488549e-01
-5.56809902e-01 -7.84489036e-01 6.49781287e-01 -1.78197473e-01
-5.57785451e-01 6.81406200e-01 4.17121440e-01 2.09044991e-03
5.48145652e-01 -8.62154007e-01 -4.62724715e-01 -3.86463225e-01
4.42285478e-01 3.66075248e-01 -2.91148484e-01 -2.28760973... | [10.08338737487793, 2.0955958366394043] |
19648563-58db-4001-8d3c-39393b4892a5 | anticipation-and-next-action-forecasting-in | 1901.03728 | null | http://arxiv.org/abs/1901.03728v1 | http://arxiv.org/pdf/1901.03728v1.pdf | Anticipation and next action forecasting in video: an end-to-end model with memory | Action anticipation and forecasting in videos do not require a hat-trick, as
far as there are signs in the context to foresee how actions are going to be
deployed. Capturing these signs is hard because the context includes the past.
We propose an end-to-end network for action anticipation and forecasting with
memory, t... | ['Valsamis Ntouskos', 'Lorenzo Mauro', 'Elham Omrani', 'Fiora Pirri', 'Edoardo Alati', 'Mahdieh Izadpanahkakhk'] | 2019-01-11 | null | null | null | null | ['action-anticipation'] | ['computer-vision'] | [ 4.69805419e-01 -3.71051580e-02 -5.68949163e-01 -6.37518525e-01
-2.25418672e-01 -3.55087370e-01 7.09545016e-01 -3.28208774e-01
-3.31656426e-01 5.66765487e-01 8.92617345e-01 -1.01627395e-01
1.20179608e-01 -3.46861064e-01 -6.86190307e-01 -4.53512818e-01
-7.46713042e-01 5.73910438e-02 4.24019605e-01 -1.72428787... | [8.057389259338379, 0.4959203898906708] |
84edc4be-65b0-4caf-8193-68d817ebc6c8 | leveraging-intra-and-inter-dataset-variations | null | null | http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/html/Wu_Leveraging_Intra_and_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Wu_Leveraging_Intra_and_CVPR_2017_paper.pdf | Leveraging intra and inter-dataset variations for robust face alignment | Face alignment is a critical topic in the computer vision community. Numerous efforts have been made and various benchmark datasets have been released in recent decades. However, two significant issues remain in recent datasets, e.g., Intra-Dataset Variation and Inter-Dataset Variation. Inter-Dataset Variation refers t... | ['Wenyan Wu', 'Shuo Yang'] | 2017-07-21 | null | null | null | cvpr-2017-2017-7 | ['robust-face-alignment'] | ['computer-vision'] | [ 6.26622811e-02 -2.36933544e-01 -1.94637671e-01 -1.02559805e+00
-3.74769360e-01 -1.31551683e-01 5.26096702e-01 -5.61176360e-01
-8.94134566e-02 5.64748347e-01 1.84244052e-01 2.86332160e-01
-2.38257438e-01 -3.65071416e-01 -2.72071451e-01 -5.92019141e-01
1.27547055e-01 2.89281309e-01 1.56786680e-01 -2.62941360... | [13.362777709960938, 0.6002155542373657] |
9485c4e2-47e1-4c4e-926f-cf3e0df6616c | dense-siamese-network | 2203.11075 | null | https://arxiv.org/abs/2203.11075v2 | https://arxiv.org/pdf/2203.11075v2.pdf | Dense Siamese Network for Dense Unsupervised Learning | This paper presents Dense Siamese Network (DenseSiam), a simple unsupervised learning framework for dense prediction tasks. It learns visual representations by maximizing the similarity between two views of one image with two types of consistency, i.e., pixel consistency and region consistency. Concretely, DenseSiam fi... | ['Chen Change Loy', 'Kai Chen', 'Jiangmiao Pang', 'Wenwei Zhang'] | 2022-03-21 | null | null | null | null | ['unsupervised-semantic-segmentation'] | ['computer-vision'] | [ 2.19199006e-02 4.81765151e-01 -3.55654299e-01 -5.48564315e-01
-6.46011710e-01 -2.99808204e-01 4.68206167e-01 -6.15604185e-02
-4.72462147e-01 4.20250863e-01 5.19363806e-02 1.42216921e-01
1.29341036e-01 -6.81580007e-01 -1.01888335e+00 -5.95483005e-01
2.13746112e-02 4.26128834e-01 6.11864150e-01 -7.19270781... | [9.665946960449219, 0.4939509332180023] |
dd7003e6-fd4e-421d-8fc1-fbf72b530a25 | monopair-monocular-3d-object-detection-using | 2003.00504 | null | https://arxiv.org/abs/2003.00504v1 | https://arxiv.org/pdf/2003.00504v1.pdf | MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships | Monocular 3D object detection is an essential component in autonomous driving while challenging to solve, especially for those occluded samples which are only partially visible. Most detectors consider each 3D object as an independent training target, inevitably resulting in a lack of useful information for occluded sa... | ['Mingyang Li', 'Lei Tai', 'Kai Sun', 'Yongjian Chen'] | 2020-03-01 | monopair-monocular-3d-object-detection-using-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Chen_MonoPair_Monocular_3D_Object_Detection_Using_Pairwise_Spatial_Relationships_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Chen_MonoPair_Monocular_3D_Object_Detection_Using_Pairwise_Spatial_Relationships_CVPR_2020_paper.pdf | cvpr-2020-6 | ['vehicle-pose-estimation'] | ['computer-vision'] | [-2.28144914e-01 1.60218123e-03 -2.44556472e-01 -5.30593514e-01
-7.39779353e-01 -4.25171673e-01 3.58047038e-01 -8.75792801e-02
-6.09926224e-01 3.74050170e-01 -1.91829488e-01 -2.39975438e-01
1.24779955e-01 -2.89395899e-01 -9.23409939e-01 -8.23537409e-01
1.32882714e-01 3.86661291e-01 7.14581370e-01 2.31922746... | [7.93573522567749, -2.3988301753997803] |
80360a08-6bb9-4d31-ba78-9e4e6c1f9ad7 | sam-helps-shadow-when-segment-anything-model | 2306.06113 | null | https://arxiv.org/abs/2306.06113v1 | https://arxiv.org/pdf/2306.06113v1.pdf | SAM-helps-Shadow:When Segment Anything Model meet shadow removal | The challenges surrounding the application of image shadow removal to real-world images and not just constrained datasets like ISTD/SRD have highlighted an urgent need for zero-shot learning in this field. In this study, we innovatively adapted the SAM (Segment anything model) for shadow removal by introducing SAM-help... | ['Shanying Zhu', 'Chaochen Gu', 'Xiaofeng Zhang'] | 2023-06-01 | null | null | null | null | ['shadow-removal', 'shadow-detection-and-removal', 'shadow-detection', 'image-shadow-removal'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 4.95604843e-01 2.13050425e-01 3.27370346e-01 -3.66746217e-01
-3.32448632e-01 -2.43576109e-01 5.62883496e-01 -4.75046605e-01
-2.70510852e-01 5.73338568e-01 1.96169838e-01 -6.87842071e-01
4.10583884e-01 -5.39677262e-01 -5.26022136e-01 -7.49949515e-01
8.92680138e-02 1.06878906e-01 7.49161363e-01 -1.16465271... | [10.847152709960938, -4.1112775802612305] |
80ae1741-dcc9-410a-a5eb-bb45d13263be | fast-localization-and-single-pixel-imaging-of | 2208.07371 | null | https://arxiv.org/abs/2208.07371v1 | https://arxiv.org/pdf/2208.07371v1.pdf | Fast localization and single-pixel imaging of the moving object using time-division multiplexing | When imaging moving objects, single-pixel imaging produces motion blur. This paper proposes a new single-pixel imaging method, which can achieve anti-motion blur imaging of a fast-moving object. The geometric moment patterns and Hadamard patterns are used to alternately encode the position information and the image inf... | ['Yingjian Wang', 'Yafeng Chen', 'Jian Huang', 'Wei Yang', 'Linbin Zha', 'Dongfeng Shi', 'Wenwen Meng', 'Zijun Guo'] | 2022-08-15 | null | null | null | null | ['motion-compensation'] | ['computer-vision'] | [ 3.73227775e-01 -5.84161341e-01 1.81779668e-01 7.15562031e-02
-1.53057575e-01 -4.07382071e-01 3.44873160e-01 -8.88429582e-01
-7.00261533e-01 8.51085603e-01 4.87342961e-02 -1.63929731e-01
-2.47770667e-01 -2.56510496e-01 -2.26142168e-01 -1.22523928e+00
2.08328664e-01 -6.36500865e-02 5.66202283e-01 7.15602458... | [11.454476356506348, -2.650191068649292] |
0928108e-5c3d-4ed4-addf-dc567cf2c627 | a-contrario-horizon-first-vanishing-point | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Gilles_Simon_A_Contrario_Horizon-First_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Gilles_Simon_A_Contrario_Horizon-First_ECCV_2018_paper.pdf | A-Contrario Horizon-First Vanishing Point Detection Using Second-Order Grouping Laws | We show that, in images of man-made environments, the horizon line can usually be hypothesized based on an a contrario detection of second-order grouping events. This allows constraining the extraction of the horizontal vanishing points on that line, thus reducing false detections. Experiments made on three datasets sh... | ['Marie-Odile Berger', 'Antoine Fond', 'Gilles Simon'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['line-detection', 'horizon-line-estimation'] | ['computer-vision', 'computer-vision'] | [ 1.79055870e-01 1.51778519e-01 1.62976414e-01 -2.42531046e-01
-3.79462183e-01 -5.82795858e-01 8.85766745e-01 3.77922207e-01
-2.75502354e-01 3.23993176e-01 4.34900969e-02 -1.26238421e-01
-4.74972241e-02 -6.28028929e-01 -7.74945259e-01 -6.30408823e-01
-8.34329247e-01 3.09287161e-01 1.13565528e+00 -2.43057355... | [8.130681037902832, -1.8470226526260376] |
c8025f0b-a292-4318-be8e-f10ad07f254b | removing-radio-frequency-interference-from | 2210.12931 | null | https://arxiv.org/abs/2210.12931v3 | https://arxiv.org/pdf/2210.12931v3.pdf | Removing Radio Frequency Interference from Auroral Kilometric Radiation with Stacked Autoencoders | Radio frequency data in astronomy enable scientists to analyze astrophysical phenomena. However, these data can be corrupted by radio frequency interference (RFI) that limits the observation of underlying natural processes. In this study, we extend recent developments in deep learning algorithms to astronomy data. We r... | ['Philip J. Erickson', 'Ryan Volz', 'John Swoboda', 'James LaBelle', 'Mary Knapp', 'Allen Chang'] | 2022-10-24 | null | null | null | null | ['astronomy'] | ['miscellaneous'] | [ 3.25381905e-02 -4.44017857e-01 6.12493336e-01 5.54884858e-02
-6.57073438e-01 -5.06141543e-01 9.37998891e-01 -6.44403696e-01
-1.74618602e-01 7.39640832e-01 4.53830808e-01 -2.82198131e-01
-3.16612124e-01 -8.77497077e-01 -6.90941811e-01 -1.13540196e+00
-5.67195043e-02 -5.07415980e-02 -4.43900466e-01 -3.17036152... | [11.23192310333252, -2.540212869644165] |
968edd1a-d022-455c-9ac4-bb09b9185a0c | detecting-and-accommodating-novel-types-and | 2211.04555 | null | https://arxiv.org/abs/2211.04555v1 | https://arxiv.org/pdf/2211.04555v1.pdf | Detecting and Accommodating Novel Types and Concepts in an Embodied Simulation Environment | In this paper, we present methods for two types of metacognitive tasks in an AI system: rapidly expanding a neural classification model to accommodate a new category of object, and recognizing when a novel object type is observed instead of misclassifying the observation as a known class. Our methods take numerical dat... | ['Nikhil Krishnaswamy', 'Sadaf Ghaffari'] | 2022-11-08 | null | null | null | null | ['type'] | ['speech'] | [ 2.73812771e-01 4.40841280e-02 2.49148920e-01 -2.52671063e-01
1.38469249e-01 -8.73202980e-01 7.16542542e-01 6.64887905e-01
-6.04110658e-01 5.64435065e-01 -1.34756729e-01 -3.34429950e-01
-1.54222578e-01 -8.00295651e-01 -6.05773449e-01 -2.55048394e-01
-5.56570351e-01 4.98961896e-01 3.29367042e-01 -1.85085356... | [4.378599643707275, 1.272653579711914] |
174f6ebd-bd95-4b75-bd09-386f77cb496c | bottlenecks-club-unifying-information | 2207.04895 | null | https://arxiv.org/abs/2207.04895v1 | https://arxiv.org/pdf/2207.04895v1.pdf | Bottlenecks CLUB: Unifying Information-Theoretic Trade-offs Among Complexity, Leakage, and Utility | Bottleneck problems are an important class of optimization problems that have recently gained increasing attention in the domain of machine learning and information theory. They are widely used in generative models, fair machine learning algorithms, design of privacy-assuring mechanisms, and appear as information-theor... | ['Slava Voloshynovskiy', 'Deniz Gunduz', 'Flavio P. Calmon', 'Behrooz Razeghi'] | 2022-07-11 | null | null | null | null | ['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning'] | ['methodology', 'natural-language-processing'] | [ 3.25040787e-01 2.61083663e-01 -2.12005779e-01 -1.89990133e-01
-1.06793618e+00 -5.70649266e-01 6.77078605e-01 -6.68248385e-02
-2.58415371e-01 8.89486313e-01 2.64232963e-01 -5.32399714e-01
-5.18298745e-01 -8.91376138e-01 -8.23439837e-01 -1.04369521e+00
-2.28142310e-02 3.50598454e-01 -3.05103779e-01 -1.29509969... | [7.234932899475098, 3.997911214828491] |
0b4abf6a-fe7e-49e4-a991-e80d9edfcced | a-light-transformer-for-speech-to-intent | null | null | https://www.researchgate.net/publication/350395426_A_Light_Transformer_For_Speech-To-Intent_Applications | https://ieeexplore.ieee.org/document/9383559 | A Light Transformer For Speech-To-Intent Applications | Spoken language understanding (SLU) systems can make life more agreeable, safer (e.g. in a car) or can increase the independence of physically challenged users. However, due to the many sources of variation in speech, a well-trained system is hard to transfer to other conditions like a different language or to speech i... | ['Hugo', 'Pu; Van hamme', 'Wang'] | 2021-01-19 | null | null | null | ieee-spoken-language-technology-workshop-slt | ['spoken-language-understanding', 'spoken-language-understanding'] | ['natural-language-processing', 'speech'] | [ 1.23223348e-03 3.54556203e-01 -3.51812430e-02 -5.06242275e-01
-7.49770403e-01 -3.31748664e-01 4.01359111e-01 -2.72725731e-01
-4.06923294e-01 8.32804263e-01 6.82576820e-02 -4.42199469e-01
2.56892323e-01 -5.70396125e-01 -7.99402833e-01 -3.41029406e-01
4.06039596e-01 4.41395044e-01 5.81877351e-01 -4.11985159... | [14.258269309997559, 6.813375949859619] |
0e4581b6-36b8-4b28-8b8a-f74f355254a5 | voint-cloud-multi-view-point-cloud | 2111.15363 | null | https://arxiv.org/abs/2111.15363v2 | https://arxiv.org/pdf/2111.15363v2.pdf | Voint Cloud: Multi-View Point Cloud Representation for 3D Understanding | Multi-view projection methods have demonstrated promising performance on 3D understanding tasks like 3D classification and segmentation. However, it remains unclear how to combine such multi-view methods with the widely available 3D point clouds. Previous methods use unlearned heuristics to combine features at the poin... | ['Bernard Ghanem', 'Silvio Giancola', 'Abdullah Hamdi'] | 2021-11-30 | null | null | null | null | ['3d-classification', '3d-part-segmentation'] | ['computer-vision', 'computer-vision'] | [-4.66316342e-01 -2.81231515e-02 -1.08338639e-01 -6.05202377e-01
-8.15593302e-01 -1.00548935e+00 7.10813940e-01 9.98593122e-03
1.64700329e-01 -3.10653567e-01 2.57244438e-01 -1.10815905e-01
-9.56928765e-04 -8.61039460e-01 -9.68414009e-01 -3.72303337e-01
1.96373999e-01 7.83260524e-01 4.17869717e-01 -1.81607857... | [8.123398780822754, -3.4784271717071533] |
94f09e1e-af01-4c52-b3c3-3f300506f33c | lxl-lidar-exclusive-lean-3d-object-detection | 2307.00724 | null | https://arxiv.org/abs/2307.00724v2 | https://arxiv.org/pdf/2307.00724v2.pdf | LXL: LiDAR Excluded Lean 3D Object Detection with 4D Imaging Radar and Camera Fusion | As an emerging technology and a relatively affordable device, the 4D imaging radar has already been confirmed effective in performing 3D object detection in autonomous driving. Nevertheless, the sparsity and noisiness of 4D radar point clouds hinder further performance improvement, and in-depth studies about its fusion... | ['Bing Zhu', 'Yuxuan Xia', 'Qing-Long Han', 'Tao Huang', 'Jianan Liu', 'Weiyi Xiong'] | 2023-07-03 | null | null | null | null | ['3d-object-detection', 'depth-estimation'] | ['computer-vision', 'computer-vision'] | [ 1.97587952e-01 -3.69208395e-01 -1.01437457e-01 -5.02335489e-01
-4.20522541e-01 -3.70897382e-01 7.15712488e-01 -3.76842350e-01
-5.12395859e-01 3.37387353e-01 -3.09795469e-01 -4.64952022e-01
-2.20787134e-02 -8.17015111e-01 -5.08861661e-01 -7.67093360e-01
2.26329505e-01 3.30443174e-01 5.65750659e-01 -2.68834531... | [7.78403377532959, -1.597353219985962] |
b9189b55-2d30-4be7-ae70-c544c8a0a504 | 190503297 | 1905.03297 | null | https://arxiv.org/abs/1905.03297v3 | https://arxiv.org/pdf/1905.03297v3.pdf | Interpretable Subgroup Discovery in Treatment Effect Estimation with Application to Opioid Prescribing Guidelines | The dearth of prescribing guidelines for physicians is one key driver of the current opioid epidemic in the United States. In this work, we analyze medical and pharmaceutical claims data to draw insights on characteristics of patients who are more prone to adverse outcomes after an initial synthetic opioid prescription... | ['Sara E. Berger', 'Monica Shekhar', 'Kush R. Varshney', 'Chirag Nagpal', 'Subhro Das', 'Dennis Wei', 'Bhanukiran Vinzamuri'] | 2019-05-08 | null | null | null | null | ['subgroup-discovery'] | ['methodology'] | [ 3.07105511e-01 2.80757278e-01 -1.17721426e+00 -4.14348096e-01
-5.80697477e-01 -5.37501872e-01 1.85918838e-01 4.96953189e-01
-1.08542897e-01 9.61586356e-01 8.82411718e-01 -8.82806242e-01
-5.46536565e-01 -6.69491649e-01 -7.35518634e-01 -3.39269996e-01
-3.25386912e-01 6.72983050e-01 -8.45836759e-01 1.71965972... | [8.051410675048828, 5.440039157867432] |
b4e7e3f1-2ac4-423c-af2b-9f904aaf84e9 | a-coarse-to-fine-multi-stream-hybrid | 1908.10521 | null | https://arxiv.org/abs/1908.10521v1 | https://arxiv.org/pdf/1908.10521v1.pdf | A Coarse-to-Fine Multi-stream Hybrid Deraining Network for Single Image Deraining | Single image deraining task is still a very challenging task due to its ill-posed nature in reality. Recently, researchers have tried to fix this issue by training the CNN-based end-to-end models, but they still cannot extract the negative rain streaks from rainy images precisely, which usually leads to an over de-rain... | ['Richang Hong', 'Haijun Zhang', 'Yanyan Wei', 'Meng Wang', 'Zhao Zhang'] | 2019-08-28 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [-1.01560145e-03 -3.39829981e-01 2.96811581e-01 -6.17289424e-01
-4.92055297e-01 1.53466966e-02 1.31380588e-01 -4.14032340e-01
-4.32913870e-01 8.69601250e-01 1.04759812e-01 5.38425222e-02
1.81781366e-01 -9.36488569e-01 -7.48789907e-01 -9.37757969e-01
1.99989140e-01 -2.41558641e-01 4.03739631e-01 -4.90542889... | [10.899300575256348, -3.258986473083496] |
c6b933ef-b95b-40db-8c63-3643e63d15ac | mquake-assessing-knowledge-editing-in | 2305.14795 | null | https://arxiv.org/abs/2305.14795v1 | https://arxiv.org/pdf/2305.14795v1.pdf | MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questions | The information stored in large language models (LLMs) falls out of date quickly, and retraining from scratch is often not an option. This has recently given rise to a range of techniques for injecting new facts through updating model weights. Current evaluation paradigms are extremely limited, mainly validating the re... | ['Danqi Chen', 'Christopher Potts', 'Christopher D. Manning', 'Zhengxuan Wu', 'Zexuan Zhong'] | 2023-05-24 | null | null | null | null | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 1.96332365e-01 5.69064081e-01 -6.98628128e-02 -4.09000546e-01
-1.12549269e+00 -7.32021451e-01 7.36645699e-01 5.96313179e-01
-5.28040051e-01 1.06836367e+00 5.03863275e-01 -6.15032911e-01
-3.59084129e-01 -1.16149628e+00 -9.85855639e-01 -1.65126026e-02
2.97521770e-01 8.17921817e-01 6.19952619e-01 -6.28823936... | [10.69953441619873, 8.056760787963867] |
95cd24d3-7e6c-4268-8706-0c87d6e5091a | dual-spls-a-family-of-dual-sparse-partial | 2301.07206 | null | https://arxiv.org/abs/2301.07206v1 | https://arxiv.org/pdf/2301.07206v1.pdf | Dual-sPLS: a family of Dual Sparse Partial Least Squares regressions for feature selection and prediction with tunable sparsity; evaluation on simulated and near-infrared (NIR) data | Relating a set of variables X to a response y is crucial in chemometrics. A quantitative prediction objective can be enriched by qualitative data interpretation, for instance by locating the most influential features. When high-dimensional problems arise, dimension reduction techniques can be used. Most notable are pro... | ['François Wahl', 'Rami El Haddad', 'Clément Marteau', 'Laurent Duval', 'Louna Alsouki'] | 2023-01-17 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 6.64210379e-01 3.31616774e-02 -4.58603948e-01 -5.23808420e-01
-7.10679591e-01 -6.40286863e-01 6.07060790e-01 3.28047037e-01
-1.69197127e-01 1.07453573e+00 6.49987981e-02 -2.74162650e-01
-4.77284342e-01 -5.63511729e-01 -5.76029420e-01 -1.06363451e+00
4.07867357e-02 5.10232210e-01 -1.19765982e-01 -5.20484298... | [7.734650135040283, 4.478766441345215] |
e5ed0508-1420-42c9-b08e-d0279f1c953c | learning-sentiment-lexicons-in-spanish | null | null | https://aclanthology.org/L12-1645 | https://aclanthology.org/L12-1645.pdf | Learning Sentiment Lexicons in Spanish | In this paper we present a framework to derive sentiment lexicons in a target language by using manually or automatically annotated data available in an electronic resource rich language, such as English. We show that bridging the language gap using the multilingual sense-level aligned WordNet structure allows us to ge... | ["Ver{\\'o}nica P{\\'e}rez-Rosas", 'Rada Mihalcea', 'Carmen Banea'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['subjectivity-analysis'] | ['natural-language-processing'] | [ 1.63143203e-01 5.23171008e-01 -2.70897210e-01 -2.07779363e-01
-8.83602083e-01 -1.12245297e+00 4.55452561e-01 7.74372935e-01
-8.15483630e-01 1.16697085e+00 4.78095025e-01 -3.64816129e-01
4.09385301e-02 -8.33795905e-01 -3.65835577e-01 -5.37349433e-02
5.37326992e-01 6.46853089e-01 3.69387791e-02 -9.78910506... | [10.466434478759766, 9.699620246887207] |
55ee6d9f-daa6-42d7-9dfd-ab356786b618 | temp-temporal-message-passing-for-temporal | 2010.03526 | null | https://arxiv.org/abs/2010.03526v1 | https://arxiv.org/pdf/2010.03526v1.pdf | TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion | Inferring missing facts in temporal knowledge graphs (TKGs) is a fundamental and challenging task. Previous works have approached this problem by augmenting methods for static knowledge graphs to leverage time-dependent representations. However, these methods do not explicitly leverage multi-hop structural information ... | ['William L. Hamilton', 'Jackie Chi Kit Cheung', 'Meng Cao', 'Jiapeng Wu'] | 2020-10-07 | null | https://aclanthology.org/2020.emnlp-main.462 | https://aclanthology.org/2020.emnlp-main.462.pdf | emnlp-2020-11 | ['temporal-knowledge-graph-completion'] | ['knowledge-base'] | [-2.52237003e-02 1.85094208e-01 -8.93806040e-01 -1.12138398e-01
-6.81967914e-01 -3.67866606e-01 7.71392226e-01 3.98609072e-01
-4.88481522e-02 1.04674900e+00 6.42332733e-01 -3.58128846e-01
-4.67552692e-01 -1.07462656e+00 -1.05302823e+00 -2.46992886e-01
-6.85818672e-01 4.99551117e-01 6.10959947e-01 -2.77793020... | [8.571038246154785, 7.903960227966309] |
78778948-8447-4248-b7f5-dc3ce0895322 | pseudoreasoner-leveraging-pseudo-labels-for | 2210.07988 | null | https://arxiv.org/abs/2210.07988v1 | https://arxiv.org/pdf/2210.07988v1.pdf | PseudoReasoner: Leveraging Pseudo Labels for Commonsense Knowledge Base Population | Commonsense Knowledge Base (CSKB) Population aims at reasoning over unseen entities and assertions on CSKBs, and is an important yet hard commonsense reasoning task. One challenge is that it requires out-of-domain generalization ability as the source CSKB for training is of a relatively smaller scale (1M) while the who... | ['Simon See', 'Ginny Y. Wong', 'Yangqiu Song', 'Hongming Zhang', 'Quyet V. Do', 'Tianqing Fang'] | 2022-10-14 | null | null | null | null | ['knowledge-base-population'] | ['natural-language-processing'] | [-2.24725664e-01 6.12737775e-01 -5.31369269e-01 -4.18988794e-01
-7.65805066e-01 -5.06447375e-01 3.21112752e-01 -3.85225751e-03
-3.31046820e-01 1.14295161e+00 3.04271262e-02 -2.81188309e-01
-9.94781852e-02 -1.06695092e+00 -9.01965380e-01 -3.88255358e-01
3.00493151e-01 8.78727615e-01 6.03573263e-01 -5.10476232... | [9.875195503234863, 8.15687370300293] |
a68386aa-0a1f-44f8-871d-530cf99072a5 | generative-adversarial-network-for-text-to | null | null | https://ieeexplore.ieee.org/document/9666791 | https://ieeexplore.ieee.org/document/9666791 | Generative Adversarial Network for Text-to-Face Synthesis and Manipulation with Pretrained BERT Model | This work proposes a cyclic generative adversarial network with spatial-wise and channel-wise attention modules for text-to-face synthesis and manipulation. Then, we explore the pre-trained transformer-based BERT model to obtain text embedding. Furthermore, dual-layer perceptual loss and SSIM loss are introduced to rei... | ['Yutong Zhou，Nobutaka Shimada'] | 2022-01-12 | null | null | null | fg-2022-1 | ['text-to-face-generation'] | ['computer-vision'] | [ 4.62065369e-01 2.77615130e-01 3.15628499e-01 -5.41284859e-01
-8.20380569e-01 -4.53893334e-01 8.44797373e-01 -1.00655138e+00
6.58144429e-02 4.85016167e-01 2.38795117e-01 2.60171175e-01
1.87792808e-01 -7.27499187e-01 -1.01059043e+00 -7.34383404e-01
3.79146457e-01 6.40626997e-02 -5.91651380e-01 -2.47630030... | [12.531699180603027, -0.19000831246376038] |
ecf2294d-62a5-48d0-ab6a-5cc4e8b2f5d4 | tnn7-a-custom-macro-suite-for-implementing | 2205.07410 | null | https://arxiv.org/abs/2205.07410v2 | https://arxiv.org/pdf/2205.07410v2.pdf | TNN7: A Custom Macro Suite for Implementing Highly Optimized Designs of Neuromorphic TNNs | Temporal Neural Networks (TNNs), inspired from the mammalian neocortex, exhibit energy-efficient online sensory processing capabilities. Recent works have proposed a microarchitecture framework for implementing TNNs and demonstrated competitive performance on vision and time-series applications. Building on these previ... | ['John Paul Shen', 'Santha Bhasuthkar', 'Prabhu Vellaisamy', 'Harideep Nair'] | 2022-05-16 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [ 3.51856589e-01 -9.95493978e-02 -1.72063056e-02 -1.81057557e-01
1.56754442e-02 -2.35403091e-01 2.11145476e-01 3.83177623e-02
-8.59240234e-01 3.74409229e-01 -3.03812265e-01 -2.57463574e-01
-4.33222242e-02 -6.69961929e-01 -4.25392091e-01 -6.96616054e-01
-1.59810763e-02 -2.21867353e-01 6.47799313e-01 1.14948437... | [8.260205268859863, 2.515133857727051] |
dd6c34de-af9f-490a-9c60-e0b79a6cdb32 | logistic-regression-models-for-aggregated | 1912.03805 | null | https://arxiv.org/abs/1912.03805v2 | https://arxiv.org/pdf/1912.03805v2.pdf | Logistic regression models for aggregated data | Logistic regression models are a popular and effective method to predict the probability of categorical response data. However inference for these models can become computationally prohibitive for large datasets. Here we adapt ideas from symbolic data analysis to summarise the collection of predictor variables into his... | ['Scott A. Sisson', 'Tom Whitaker', 'Boris Beranger'] | 2019-12-09 | null | null | null | null | ['crop-classification'] | ['miscellaneous'] | [ 6.65931880e-01 1.30632622e-02 -4.68325883e-01 -8.45927894e-01
-1.28514540e+00 -5.39544821e-01 5.91286004e-01 3.55203092e-01
-2.00110361e-01 1.23902309e+00 -1.63278177e-01 -7.22500801e-01
-4.30887699e-01 -1.01680434e+00 -8.22749376e-01 -8.36896956e-01
-3.65951449e-01 6.20695710e-01 -3.75864506e-02 3.87028337... | [7.420284748077393, 4.2454423904418945] |
873ad3ff-d988-4267-a587-955d8e537554 | random-forests-versus-neural-networks-whats | 1609.05797 | null | http://arxiv.org/abs/1609.05797v3 | http://arxiv.org/pdf/1609.05797v3.pdf | Random Forests versus Neural Networks - What's Best for Camera Localization? | This work addresses the task of camera localization in a known 3D scene given
a single input RGB image. State-of-the-art approaches accomplish this in two
steps: firstly, regressing for every pixel in the image its 3D scene coordinate
and subsequently, using these coordinates to estimate the final 6D camera pose
via RA... | ['Alexander Krull', 'Philip H. S. Torr', 'Eric Brachmann', 'Carsten Rother', 'Daniela Massiceti'] | 2016-09-19 | null | null | null | null | ['camera-localization'] | ['computer-vision'] | [ 3.90721768e-01 -4.66247052e-01 -6.91530406e-02 -4.93471563e-01
-9.96268332e-01 -7.85865963e-01 4.24199998e-01 -5.66758990e-01
-6.33204162e-01 4.71692383e-01 -1.05149828e-01 -3.55921447e-01
-2.61863787e-02 -4.59483355e-01 -1.01329422e+00 -8.36923659e-01
2.68203795e-01 4.63257134e-01 7.93036446e-03 2.97298074... | [7.738099575042725, -2.1276705265045166] |
c11d6859-92b8-4396-9132-03ffbab5dee3 | equity-beyond-bias-in-language-technologies | null | null | https://aclanthology.org/W19-4446 | https://aclanthology.org/W19-4446.pdf | Equity Beyond Bias in Language Technologies for Education | There is a long record of research on equity in schools. As machine learning researchers begin to study fairness and bias in earnest, language technologies in education have an unusually strong theoretical and applied foundation to build on. Here, we introduce concepts from culturally relevant pedagogy and other framew... | ['Alan W. black', "Ezekiel Dixon-Rom{\\'a}n", 'Shrimai Prabhumoye', 'Michael Madaio', 'Brittany McLaughlin', 'David Gerritsen', 'Elijah Mayfield'] | 2019-08-01 | null | null | null | ws-2019-8 | ['automated-essay-scoring'] | ['natural-language-processing'] | [-9.87236053e-02 4.24486160e-01 -1.10224593e+00 -7.56862164e-01
-1.58418998e-01 -5.02224743e-01 2.52704382e-01 9.11824763e-01
-5.87894499e-01 8.74190032e-01 6.72705591e-01 -9.28609073e-01
-2.53582299e-01 -7.20792472e-01 -2.85662591e-01 4.69183996e-02
6.17757976e-01 5.30033484e-02 -2.47448772e-01 -4.96225268... | [11.235286712646484, 9.119438171386719] |
778dff11-791f-4bfc-b911-211efff2b727 | an-efficient-recurrent-adversarial-framework | 2012.13033 | null | https://arxiv.org/abs/2012.13033v2 | https://arxiv.org/pdf/2012.13033v2.pdf | An Efficient Recurrent Adversarial Framework for Unsupervised Real-Time Video Enhancement | Video enhancement is a challenging problem, more than that of stills, mainly due to high computational cost, larger data volumes and the difficulty of achieving consistency in the spatio-temporal domain. In practice, these challenges are often coupled with the lack of example pairs, which inhibits the application of su... | ['Radu Timofte', 'Luc van Gool', 'Danda Pani Paudel', 'Zhiwu Huang', 'Dario Fuoli'] | 2020-12-24 | null | null | null | null | ['video-enhancement'] | ['computer-vision'] | [ 2.96348214e-01 -3.74110848e-01 -3.60026583e-02 -2.63581667e-02
-8.27216923e-01 -5.05683601e-01 4.81033862e-01 -1.92202032e-01
-5.91802955e-01 8.19324613e-01 2.20583186e-01 -9.24707353e-02
-1.74753219e-02 -7.25024581e-01 -9.10419226e-01 -8.57451916e-01
-1.59313977e-01 -4.49679375e-01 2.87491143e-01 -2.77624547... | [11.016698837280273, -1.6350346803665161] |
d6f09f35-a6a6-485e-99b1-3791109de944 | temporal-logic-guided-safe-reinforcement | 1903.09885 | null | http://arxiv.org/abs/1903.09885v1 | http://arxiv.org/pdf/1903.09885v1.pdf | Temporal Logic Guided Safe Reinforcement Learning Using Control Barrier Functions | Using reinforcement learning to learn control policies is a challenge when
the task is complex with potentially long horizons. Ensuring adequate but safe
exploration is also crucial for controlling physical systems. In this paper, we
use temporal logic to facilitate specification and learning of complex tasks.
We combi... | ['Calin Belta', 'Xiao Li'] | 2019-03-23 | null | null | null | null | ['safe-exploration'] | ['robots'] | [-5.77252358e-02 2.80447572e-01 -3.37212235e-01 6.84409514e-02
-5.02075195e-01 -9.67232764e-01 5.65266013e-01 6.39742389e-02
-4.56653386e-01 1.23917198e+00 -2.95054436e-01 -6.44628346e-01
-6.88257992e-01 -6.80635691e-01 -6.55549586e-01 -5.18637121e-01
-7.25459158e-01 2.85419583e-01 6.08913302e-01 -4.38672215... | [4.590867042541504, 2.0818052291870117] |
0500f57f-e71e-4239-be55-377405a1b71f | on-training-targets-and-activation-functions | 2201.06426 | null | https://arxiv.org/abs/2201.06426v1 | https://arxiv.org/pdf/2201.06426v1.pdf | On Training Targets and Activation Functions for Deep Representation Learning in Text-Dependent Speaker Verification | Deep representation learning has gained significant momentum in advancing text-dependent speaker verification (TD-SV) systems. When designing deep neural networks (DNN) for extracting bottleneck features, key considerations include training targets, activation functions, and loss functions. In this paper, we systematic... | ['Zheng-Hua Tan', 'Achintya kr. Sarkar'] | 2022-01-17 | null | null | null | null | ['text-dependent-speaker-verification'] | ['speech'] | [-1.09402591e-03 -2.36758307e-01 -3.77003849e-02 -6.31187201e-01
-9.04659152e-01 -2.46807516e-01 6.33569300e-01 -4.16358039e-02
-5.83786011e-01 4.91877079e-01 1.84329927e-01 -7.33532906e-01
-4.76916544e-02 -1.19179383e-01 -3.44056457e-01 -9.80883837e-01
-1.68070570e-02 1.00423016e-01 -1.71979651e-01 -1.80015370... | [14.327168464660645, 6.07444429397583] |
7cdddb45-468c-4644-9fca-6d5fc33ffdb8 | knowledge-enhanced-agents-for-interactive | 2305.05091 | null | https://arxiv.org/abs/2305.05091v1 | https://arxiv.org/pdf/2305.05091v1.pdf | Knowledge-enhanced Agents for Interactive Text Games | Communication via natural language is a crucial aspect of intelligence, and it requires computational models to learn and reason about world concepts, with varying levels of supervision. While there has been significant progress made on fully-supervised non-interactive tasks, such as question-answering and procedural t... | ['Kaixin Ma', 'Jonathan Francis', 'Filip Ilievski', 'Jiarui Zhang', 'Prateek Chhikara'] | 2023-05-08 | null | null | null | null | ['instruction-following', 'text-based-games'] | ['natural-language-processing', 'playing-games'] | [ 2.38256752e-01 5.20519257e-01 8.58187210e-03 -1.60441652e-03
-2.01819256e-01 -8.80851150e-01 8.82207930e-01 5.71875870e-01
-6.35490358e-01 7.08131015e-01 1.65391058e-01 -6.23357058e-01
-3.54024947e-01 -1.34490478e+00 -8.05008292e-01 -2.46754169e-01
-2.28586212e-01 8.09271574e-01 7.97644317e-01 -7.88207650... | [3.916210412979126, 1.2467976808547974] |
a2c1a8a7-e900-4cb4-97f3-c4e61f706dcf | ris-assisted-device-activity-detection-with | 2206.06805 | null | https://arxiv.org/abs/2206.06805v1 | https://arxiv.org/pdf/2206.06805v1.pdf | RIS Assisted Device Activity Detection with Statistical Channel State Information | This paper studies reconfigurable intelligent surface (RIS) assisted device activity detection for grant-free (GF) uplink transmission in wireless communication networks. In particular, we consider mobile devices located in an area where the direct link to an access point (AP) is blocked. Thus, the devices try to conne... | ['Robert Schober', 'Vahid Jamali', 'Friedemann Laue'] | 2022-06-14 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [ 5.81425726e-01 6.90939069e-01 -7.96376169e-02 1.43457025e-01
-5.36242366e-01 -4.70935524e-01 -5.42192422e-02 2.21147195e-01
-2.46670887e-01 6.09082818e-01 -4.30810660e-01 -7.25415230e-01
-6.03315353e-01 -1.09505439e+00 -6.81944132e-01 -1.06250608e+00
-3.68345171e-01 7.94934705e-02 1.69247597e-01 -1.96660832... | [6.254181861877441, 1.2242292165756226] |
8c8f703b-845d-48ee-817c-7f7257b91b7e | self-sufficient-framework-for-continuous-sign | 2303.11771 | null | https://arxiv.org/abs/2303.11771v1 | https://arxiv.org/pdf/2303.11771v1.pdf | Self-Sufficient Framework for Continuous Sign Language Recognition | The goal of this work is to develop self-sufficient framework for Continuous Sign Language Recognition (CSLR) that addresses key issues of sign language recognition. These include the need for complex multi-scale features such as hands, face, and mouth for understanding, and absence of frame-level annotations. To this ... | ['Joon Son Chung', 'In So Kweon', 'Dong-Jin Kim', 'Myungchul Kim', 'Jae Won Cho', 'Youngtaek Oh', 'Youngjoon Jang'] | 2023-03-21 | null | null | null | null | ['sign-language-recognition', 'pseudo-label'] | ['computer-vision', 'miscellaneous'] | [ 3.39888394e-01 -1.82829767e-01 -5.09161036e-04 -5.53650856e-01
-1.01353228e+00 -4.65655833e-01 5.98878562e-01 -6.40914857e-01
-7.60724545e-01 6.11316800e-01 4.84552830e-01 -9.07120258e-02
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1.69139579e-01 1.50174975e-01 4.41801190e-01 -3.83038595... | [9.17138671875, -6.485856533050537] |
c46cc823-63be-4687-8025-3e3e1b6fd916 | pemp-leveraging-physics-properties-to-enhance | 2211.01978 | null | https://arxiv.org/abs/2211.01978v1 | https://arxiv.org/pdf/2211.01978v1.pdf | PEMP: Leveraging Physics Properties to Enhance Molecular Property Prediction | Molecular property prediction is essential for drug discovery. In recent years, deep learning methods have been introduced to this area and achieved state-of-the-art performances. However, most of existing methods ignore the intrinsic relations between molecular properties which can be utilized to improve the performan... | ['Yanyan Lan', 'Wei-Ying Ma', 'ZhiMing Ma', 'Kang Liu', 'Wenhao Huang', 'Weizhi Ma', 'Yimeng Chen', 'Yuancheng Sun'] | 2022-10-18 | null | null | null | null | ['molecular-property-prediction'] | ['miscellaneous'] | [ 2.97071844e-01 -2.26524502e-01 -5.24546504e-01 -3.18812728e-01
-5.60506344e-01 -2.24759370e-01 5.60665667e-01 5.96337557e-01
-3.68087143e-01 1.18956995e+00 5.78257516e-02 -3.31780106e-01
-2.34446555e-01 -1.08120251e+00 -9.02292073e-01 -9.91947234e-01
7.00395703e-02 1.39605612e-01 4.74524081e-01 -1.18207641... | [5.111632823944092, 5.909398555755615] |
2c5bf9ed-b4b0-4277-9f52-4daab38060a8 | mesh-r-cnn | 1906.02739 | null | https://arxiv.org/abs/1906.02739v2 | https://arxiv.org/pdf/1906.02739v2.pdf | Mesh R-CNN | Rapid advances in 2D perception have led to systems that accurately detect objects in real-world images. However, these systems make predictions in 2D, ignoring the 3D structure of the world. Concurrently, advances in 3D shape prediction have mostly focused on synthetic benchmarks and isolated objects. We unify advance... | ['Georgia Gkioxari', 'Justin Johnson', 'Jitendra Malik'] | 2019-06-06 | mesh-r-cnn-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Gkioxari_Mesh_R-CNN_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Gkioxari_Mesh_R-CNN_ICCV_2019_paper.pdf | iccv-2019-10 | ['3d-shape-modeling'] | ['computer-vision'] | [ 3.49564314e-01 5.34061015e-01 1.84940830e-01 -2.82409102e-01
-3.61639261e-01 -5.19473970e-01 6.61240518e-01 2.47200295e-01
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4.29564357e-01 -1.20508885e+00 -1.02213240e+00 1.64596945e-01
-3.28332067e-01 1.12970769e+00 8.83557141e-01 -1.76741719... | [8.326817512512207, -3.7125790119171143] |
6c4ddc82-b1b4-4cb1-b5cb-daf3213a531d | learning-semantic-representations-for | null | null | https://icml.cc/Conferences/2018/Schedule?showEvent=1961 | http://proceedings.mlr.press/v80/xie18c/xie18c.pdf | Learning Semantic Representations for Unsupervised Domain Adaptation |
It is important to transfer the knowledge from label-rich source domain to unlabeled target domain due to the expensive cost of manual labeling efforts. Prior domain adaptation methods address this problem through aligning the global distribution statistics between source domain and target domain, but a drawback o... | ['Zibin Zheng', 'Shaoan Xie', 'Liang Chen', 'Chuan Chen'] | 2018-07-01 | null | null | null | icml-2018-7 | ['learning-semantic-representations'] | ['methodology'] | [ 1.89236194e-01 3.87786776e-02 -6.07921004e-01 -9.79899228e-01
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3.32065701e-01 7.31932402e-01 4.47749943e-01 1.94436967... | [10.33004379272461, 2.8516030311584473] |
c8de20a5-05c1-4a2b-9854-f13996136f0d | towards-implicit-content-introducing-for | null | null | https://aclanthology.org/D17-1233 | https://aclanthology.org/D17-1233.pdf | Towards Implicit Content-Introducing for Generative Short-Text Conversation Systems | The study on human-computer conversation systems is a hot research topic nowadays. One of the prevailing methods to build the system is using the generative Sequence-to-Sequence (Seq2Seq) model through neural networks. However, the standard Seq2Seq model is prone to generate trivial responses. In this paper, we aim to ... | ['Yaoyuan Zhang', 'Rui Yan', 'Lili Yao', 'Dongyan Zhao', 'Yansong Feng'] | 2017-09-01 | null | null | null | emnlp-2017-9 | ['short-text-conversation'] | ['natural-language-processing'] | [ 4.63452190e-01 1.77473396e-01 3.39264631e-01 -5.64312458e-01
-1.16604745e+00 -5.67287028e-01 7.59291291e-01 -2.16340512e-01
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4.77087587e-01 -5.43274701e-01 -3.45624715e-01 -7.17296422e-01
7.18530476e-01 4.43099469e-01 2.34235868e-01 -7.33284116... | [12.524234771728516, 8.28912353515625] |
d86baef4-2e8b-4653-829c-1d027cb109c5 | mailex-email-event-and-argument-extraction | 2305.13469 | null | https://arxiv.org/abs/2305.13469v1 | https://arxiv.org/pdf/2305.13469v1.pdf | MAILEX: Email Event and Argument Extraction | In this work, we present the first dataset, \dataset, for performing event extraction from conversational email threads. To this end, we first proposed a new taxonomy covering 10 event types and 76 arguments in the email domain. Our final dataset includes $\sim$4K emails annotated with $\sim$9K event instances. To unde... | ['Ziyu Yao', 'Joshua Poore', 'Paulo Costa', 'Ali Raz', 'Shou Matsumoto', 'Gaurav Singh', 'Saurabh Srivastava'] | 2023-05-22 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [ 3.13661963e-01 3.78498852e-01 8.79176483e-02 -5.03308654e-01
-1.15054941e+00 -8.10885668e-01 8.71646643e-01 3.47471744e-01
-2.88924247e-01 9.57386672e-01 5.99554002e-01 -2.77868509e-01
-6.29168451e-02 -7.22716510e-01 -4.36708272e-01 -3.61131370e-01
-2.13778093e-02 6.78358376e-01 2.90906698e-01 -8.14654529... | [9.086450576782227, 9.260741233825684] |
9bd20296-1059-4047-a5f3-3eb44415f461 | graph-combined-coreference-resolution-methods | null | null | https://aclanthology.org/2022.dialdoc-1.8 | https://aclanthology.org/2022.dialdoc-1.8.pdf | Graph-combined Coreference Resolution Methods on Conversational Machine Reading Comprehension with Pre-trained Language Model | Coreference resolution such as for anaphora has been an essential challenge that is commonly found in conversational machine reading comprehension (CMRC). This task aims to determine the referential entity to which a pronoun refers on the basis of contextual information. Existing approaches based on pre-trained languag... | ['Kazunori Komatani', 'Zhaodong Wang'] | null | null | null | null | dialdoc-acl-2022-5 | ['machine-reading-comprehension'] | ['natural-language-processing'] | [ 5.22699594e-01 8.10644746e-01 -4.29860651e-02 -4.33985084e-01
-1.06437969e+00 -5.43754160e-01 1.07140708e+00 5.75156987e-01
-4.15646762e-01 7.18288302e-01 9.07429039e-01 -3.93029600e-01
-3.27261806e-01 -8.88121247e-01 -6.13459945e-01 -1.74375311e-01
1.71582446e-01 1.14941621e+00 4.73820686e-01 -8.12943101... | [9.413331031799316, 9.28394603729248] |
77e610e6-8aee-43b5-b549-bd72ec315db2 | triangular-contrastive-learning-on-molecular | 2205.13279 | null | https://arxiv.org/abs/2205.13279v1 | https://arxiv.org/pdf/2205.13279v1.pdf | Triangular Contrastive Learning on Molecular Graphs | Recent contrastive learning methods have shown to be effective in various tasks, learning generalizable representations invariant to data augmentation thereby leading to state of the art performances. Regarding the multifaceted nature of large unlabeled data used in self-supervised learning while majority of real-word ... | ['Sun Kim', 'Yijingxiu Lu', 'Wonseok Shin', 'MinGyu Choi'] | 2022-05-26 | null | null | null | null | ['molecular-property-prediction'] | ['miscellaneous'] | [ 5.15055418e-01 -7.94453919e-02 -5.68380058e-01 -3.82560074e-01
-8.41629565e-01 -9.17123497e-01 9.94603336e-01 3.09846401e-01
-5.74843168e-01 9.08339739e-01 1.99225366e-01 -2.32727453e-01
-2.81729102e-01 -4.32880193e-01 -8.29922318e-01 -1.15159428e+00
-1.42067686e-01 2.65173972e-01 -1.12327732e-01 -5.15795112... | [9.665116310119629, 2.8160207271575928] |
00fbc089-b134-4259-b4e9-a9dca0816e21 | using-gaze-for-behavioural-biometrics | null | null | https://www.mdpi.com/1424-8220/23/3/1262 | https://www.mdpi.com/1424-8220/23/3/1262 | Using Gaze for Behavioural Biometrics | A principled approach to the analysis of eye movements for behavioural biometrics is laid down. The approach grounds in foraging theory, which provides a sound basis to capture the uniqueness of individual eye movement behaviour. We propose a composite Ornstein-Uhlenbeck process for quantifying the exploration/exploita... | ['Giuseppe Boccignone', 'Vittorio Cuculo', 'Sathya Bursic', 'Sabrina Patania', 'Alessandro D’Amelio'] | 2023-01-22 | null | null | null | sensors-2023-1 | ['gaze-estimation'] | ['computer-vision'] | [ 3.19500148e-01 -6.77794814e-02 -5.71336411e-02 -2.45027632e-01
4.92186025e-02 -5.16181231e-01 8.00366521e-01 -2.07974672e-01
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-8.49678338e-01 -1.09740824e-01 -2.11036205e-01 -1.11887360e+00
-1.92976028e-01 -8.24724436e-02 -9.96390432e-02 -3.49903405... | [10.118263244628906, 1.6666233539581299] |
aafb4116-3d74-45ed-b5c0-4f802f2d4a8f | a-joint-model-for-structure-based-news-genre | null | null | https://aclanthology.org/2021.findings-acl.295 | https://aclanthology.org/2021.findings-acl.295.pdf | A Joint Model for Structure-based News Genre Classification with Application to Text Summarization | null | ['Ruihong Huang', 'Zeyu Dai'] | null | null | null | null | findings-acl-2021-8 | ['genre-classification'] | ['computer-vision'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.274194240570068, 3.7999160289764404] |
4071c718-dacb-4c43-96b5-937b58f16303 | divide-and-conquer-large-scale-capacitated | 1912.12667 | null | https://arxiv.org/abs/1912.12667v2 | https://arxiv.org/pdf/1912.12667v2.pdf | Divide-and-Conquer Large Scale Capacitated Arc Routing Problems with Route Cutting Off Decomposition | The capacitated arc routing problem is a very important problem with many practical applications. This paper focuses on the large scale capacitated arc routing problem. Traditional solution optimization approaches usually fail because of their poor scalability. The divide-and-conquer strategy has achieved great success... | ['Keqin Jiang', 'Buzhong Zhang', 'Yuzhou Zhang', 'Yi Mei'] | 2019-12-29 | null | null | null | null | ['problem-decomposition'] | ['miscellaneous'] | [ 2.42603257e-01 -7.51259476e-02 -9.85267162e-02 -4.85868081e-02
-6.55763626e-01 -7.30400503e-01 -1.85866449e-02 1.78513959e-01
-2.77200192e-01 9.17173088e-01 -1.99233547e-01 -3.47901672e-01
-7.23889649e-01 -7.43209064e-01 -4.84484911e-01 -9.36784744e-01
-1.06558166e-01 9.03994977e-01 3.25677246e-01 -2.18777150... | [5.1996893882751465, 2.8969240188598633] |
58bed502-8d6b-420e-9508-269aa2fec9cb | learning-to-substitute-ingredients-in-recipes | 2302.07960 | null | https://arxiv.org/abs/2302.07960v1 | https://arxiv.org/pdf/2302.07960v1.pdf | Learning to Substitute Ingredients in Recipes | Recipe personalization through ingredient substitution has the potential to help people meet their dietary needs and preferences, avoid potential allergens, and ease culinary exploration in everyone's kitchen. To address ingredient substitution, we build a benchmark, composed of a dataset of substitution pairs with sta... | ['Adriana Romero-Soriano', 'Michal Drozdzal', 'Rohit Girdhar', 'Quentin Duval', 'Bahare Fatemi'] | 2023-02-15 | null | null | null | null | ['recipe-generation'] | ['miscellaneous'] | [ 3.86377484e-01 6.17834218e-02 -4.07079279e-01 -3.22469622e-01
-4.36701179e-01 -8.73808563e-01 2.93610573e-01 6.84194326e-01
1.82721525e-01 1.12393811e-01 1.01311886e+00 1.30842909e-01
6.20198585e-02 -1.05315781e+00 -8.94777894e-01 -1.43492669e-01
8.00678879e-02 1.63075905e-02 -1.94259956e-01 -5.73420942... | [11.51645278930664, 4.532201766967773] |
44c746dd-cb7c-4103-ae30-4a682ce50d25 | simgans-simulator-based-generative | 2006.15353 | null | https://arxiv.org/abs/2006.15353v1 | https://arxiv.org/pdf/2006.15353v1.pdf | SimGANs: Simulator-Based Generative Adversarial Networks for ECG Synthesis to Improve Deep ECG Classification | Generating training examples for supervised tasks is a long sought after goal in AI. We study the problem of heart signal electrocardiogram (ECG) synthesis for improved heartbeat classification. ECG synthesis is challenging: the generation of training examples for such biological-physiological systems is not straightfo... | ['Kira Radinsky', 'Daniel Freedman', 'Tomer Golany'] | 2020-06-27 | null | https://proceedings.icml.cc/static/paper_files/icml/2020/2829-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/2829-Paper.pdf | icml-2020-1 | ['ecg-classification', 'heartbeat-classification'] | ['medical', 'medical'] | [ 7.23958731e-01 3.85388404e-01 5.41584671e-01 -4.77022529e-02
-4.01724398e-01 -7.09142983e-01 4.13864344e-01 -7.66793191e-02
-5.92021458e-02 8.61392617e-01 -2.07290143e-01 -3.70264560e-01
4.92099263e-02 -5.36477327e-01 -7.01725602e-01 -7.22930670e-01
-1.98658988e-01 4.42354023e-01 -3.72078151e-01 -1.35868862... | [14.280930519104004, 3.0417656898498535] |
2143a436-0366-4e70-9213-3bfe883b2c57 | imad-image-augmented-multi-modal-dialogue | 2305.10512 | null | https://arxiv.org/abs/2305.10512v1 | https://arxiv.org/pdf/2305.10512v1.pdf | IMAD: IMage-Augmented multi-modal Dialogue | Currently, dialogue systems have achieved high performance in processing text-based communication. However, they have not yet effectively incorporated visual information, which poses a significant challenge. Furthermore, existing models that incorporate images in dialogue generation focus on discussing the image itself... | ['Kuznetsov Denis', 'Frolov Anton', 'Moskvoretskii Viktor'] | 2023-05-17 | null | null | null | null | ['dialogue-generation', 'dialogue-generation'] | ['natural-language-processing', 'speech'] | [ 3.15167069e-01 2.19834596e-01 2.44152322e-01 -4.57376987e-01
-7.06404746e-01 -6.01136208e-01 1.09718251e+00 1.93264019e-02
-5.00692666e-01 7.17602968e-01 3.77717346e-01 -1.48150653e-01
4.95946646e-01 -8.21397007e-01 -3.44983369e-01 -4.80737925e-01
7.33553171e-01 5.88530540e-01 5.36951900e-01 -5.20616114... | [11.007502555847168, 1.3197672367095947] |
8bb709d0-8884-4e54-b851-a7df8d36ecfe | automated-vehicle-highway-merging-motion | 2211.02225 | null | https://arxiv.org/abs/2211.02225v1 | https://arxiv.org/pdf/2211.02225v1.pdf | Automated Vehicle Highway Merging: Motion Planning via Adaptive Interactive Mixed-Integer MPC | A new motion planning framework for automated highway merging is presented in this paper. To plan the merge and predict the motion of the neighboring vehicle, the ego automated vehicle solves a joint optimization of both vehicle costs over a receding horizon. The non-convex nature of feasible regions and lane disciplin... | ['Ardalan Vahidi', 'Viranjan Bhattacharyya'] | 2022-11-04 | null | null | null | null | ['motion-planning'] | ['robots'] | [-1.64159909e-02 8.13433707e-01 -8.12391698e-01 -3.13322872e-01
-7.04897940e-01 -3.68561953e-01 3.28300416e-01 -6.81984052e-02
-4.38573003e-01 1.13245690e+00 -1.77084088e-01 -5.26501119e-01
-5.66237271e-01 -7.88136959e-01 -7.92565465e-01 -8.34438622e-01
-2.78639287e-01 5.20466805e-01 1.56425521e-01 -1.29782381... | [5.449989318847656, 1.7033543586730957] |
72c0a88b-be8f-4e76-9028-28192a00ea6c | is-cross-modal-information-retrieval-possible | 2304.11095 | null | https://arxiv.org/abs/2304.11095v1 | https://arxiv.org/pdf/2304.11095v1.pdf | Is Cross-modal Information Retrieval Possible without Training? | Encoded representations from a pretrained deep learning model (e.g., BERT text embeddings, penultimate CNN layer activations of an image) convey a rich set of features beneficial for information retrieval. Embeddings for a particular modality of data occupy a high-dimensional space of its own, but it can be semanticall... | ['Youngjune L. Gwon', 'Seongho Joe', 'Hyunjae Lee', 'Hyunjin Choi'] | 2023-04-20 | null | null | null | null | ['cross-modal-information-retrieval'] | ['miscellaneous'] | [ 2.23881364e-01 -1.02975243e-03 -2.56276667e-01 -4.59588796e-01
-1.05822861e+00 -5.30134559e-01 1.01443124e+00 2.51378149e-01
-7.38466203e-01 2.18765005e-01 2.86798477e-01 -2.24248856e-01
-1.79704264e-01 -7.47469723e-01 -1.06866062e+00 -4.76833194e-01
1.37182131e-01 5.47830224e-01 -4.56604622e-02 -3.06499481... | [10.629119873046875, 1.7162545919418335] |
b48b83e8-e93a-4490-a0cc-43f77df9f1aa | a-priori-compression-of-convolutional-neural | 2304.04964 | null | https://arxiv.org/abs/2304.04964v2 | https://arxiv.org/pdf/2304.04964v2.pdf | A priori compression of convolutional neural networks for wave simulators | Convolutional neural networks are now seeing widespread use in a variety of fields, including image classification, facial and object recognition, medical imaging analysis, and many more. In addition, there are applications such as physics-informed simulators in which accurate forecasts in real time with a minimal lag ... | ['David Ryckelynck', 'Fabien Casenave', 'Nissrine Akkari', 'Hamza Boukraichi'] | 2023-04-11 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [ 1.50116652e-01 1.05887137e-01 3.37524354e-01 -4.79233563e-01
1.80138499e-01 -5.96283898e-02 8.72352496e-02 -6.11237995e-02
-5.44701338e-01 5.58194101e-01 -3.37023020e-01 -6.02495253e-01
-4.59693968e-01 -1.04098535e+00 -1.07203019e+00 -6.12376690e-01
-2.47018650e-01 3.02873969e-01 1.81552470e-01 -2.65376002... | [8.527362823486328, 2.9968721866607666] |
bd932d10-82cd-426e-ae3a-772dccd82f22 | deep-reformulated-laplacian-tone-mapping | 2102.00348 | null | https://arxiv.org/abs/2102.00348v1 | https://arxiv.org/pdf/2102.00348v1.pdf | Deep Reformulated Laplacian Tone Mapping | Wide dynamic range (WDR) images contain more scene details and contrast when compared to common images. However, it requires tone mapping to process the pixel values in order to display properly. The details of WDR images can diminish during the tone mapping process. In this work, we address the problem by combining a ... | ['Orly Yadid-Pecht', 'Svetlana Yanushkevich', 'Mengchen Lin', 'Ziyi Liu', 'Jie Yang'] | 2021-01-31 | null | null | null | null | ['tone-mapping'] | ['computer-vision'] | [ 4.23974395e-01 -5.05086660e-01 -1.42638907e-01 -9.67956409e-02
-6.46315336e-01 -2.98419446e-01 1.43638954e-01 -4.89975661e-01
-1.38160259e-01 6.14008844e-01 3.32274526e-01 4.94509339e-02
-1.27732992e-01 -1.19241405e+00 -5.80591977e-01 -7.53178120e-01
1.66898802e-01 -4.31554586e-01 5.45419872e-01 -3.36361736... | [10.963947296142578, -2.210054874420166] |
a71cf2d5-8215-4991-809a-46f95f09ae81 | temporal-label-smoothing-for-early-prediction | 2208.13764 | null | https://arxiv.org/abs/2208.13764v2 | https://arxiv.org/pdf/2208.13764v2.pdf | Temporal Label Smoothing for Early Event Prediction | Models that can predict the occurrence of events ahead of time with low false-alarm rates are critical to the acceptance of decision support systems in the medical community. This challenging task is typically treated as a simple binary classification, ignoring temporal dependencies between samples, whereas we propose ... | ['Rita Kuznetsova', 'Gunnar Rätsch', 'Alizée Pace', 'Hugo Yèche'] | 2022-08-29 | null | null | null | null | ['respiratory-failure', 'circulatory-failure', 'survival-analysis'] | ['medical', 'medical', 'miscellaneous'] | [ 5.38224220e-01 1.56441107e-01 -6.50247276e-01 -5.68860054e-01
-9.79221523e-01 -2.59658933e-01 8.30448568e-01 1.03609359e+00
-6.32697105e-01 8.01402450e-01 4.50412363e-01 -4.66232508e-01
-5.50118864e-01 -4.61014956e-01 -2.45227516e-01 -5.93470514e-01
-5.74310899e-01 4.08956766e-01 5.34228802e-01 1.53809831... | [7.844993591308594, 5.716399669647217] |
e7af91e7-75e8-4df0-a0eb-21178364dc5f | learning-transferable-deep-models-for-land | 1807.05713 | null | https://arxiv.org/abs/1807.05713v3 | https://arxiv.org/pdf/1807.05713v3.pdf | Land-Cover Classification with High-Resolution Remote Sensing Images Using Transferable Deep Models | In recent years, large amount of high spatial-resolution remote sensing (HRRS) images are available for land-cover mapping. However, due to the complex information brought by the increased spatial resolution and the data disturbances caused by different conditions of image acquisition, it is often difficult to find an ... | ['Huanfeng Shen', 'Qikai Lu', 'Gui-Song Xia', 'Xin-Yi Tong', 'Shucheng You', 'Shengyang Li', 'Liangpei Zhang'] | 2018-07-16 | null | null | null | null | ['segmentation-of-remote-sensing-imagery', 'remote-sensing-image-classification'] | ['miscellaneous', 'miscellaneous'] | [ 4.81969804e-01 -3.08864057e-01 -2.65930623e-01 -6.40756428e-01
-9.05512989e-01 -3.64378959e-01 1.29806533e-01 4.49278653e-02
-4.21669543e-01 9.83069003e-01 -2.71687061e-01 -4.36253458e-01
-1.96681485e-01 -1.58584881e+00 -7.01456666e-01 -8.37798536e-01
-4.45937738e-02 3.19538534e-01 1.00657381e-01 -3.93742293... | [9.608356475830078, -1.42849862575531] |
8f685749-1cfc-4788-9732-f42342ddb86c | textbugger-generating-adversarial-text | 1812.05271 | null | http://arxiv.org/abs/1812.05271v1 | http://arxiv.org/pdf/1812.05271v1.pdf | TextBugger: Generating Adversarial Text Against Real-world Applications | Deep Learning-based Text Understanding (DLTU) is the backbone technique
behind various applications, including question answering, machine translation,
and text classification. Despite its tremendous popularity, the security
vulnerabilities of DLTU are still largely unknown, which is highly concerning
given its increas... | ['Ting Wang', 'Bo Li', 'Shouling Ji', 'Jinfeng Li', 'Tianyu Du'] | 2018-12-13 | null | null | null | null | ['adversarial-text'] | ['adversarial'] | [ 3.20135683e-01 -7.61870891e-02 1.54066026e-01 -1.21289052e-01
-8.63476872e-01 -1.31002629e+00 7.56446719e-01 4.12030250e-01
-3.58121485e-01 3.17444474e-01 2.20509887e-01 -8.53283703e-01
3.30870539e-01 -1.09126103e+00 -7.14954555e-01 -5.75497270e-01
1.92797959e-01 4.43662763e-01 2.69125775e-02 -6.50083482... | [6.006949424743652, 8.04949951171875] |
17f46da6-d3aa-4d4d-b5e4-818d37825221 | adaptive-statistical-learning-with-bayesian | 1911.00765 | null | https://arxiv.org/abs/1911.00765v1 | https://arxiv.org/pdf/1911.00765v1.pdf | Adaptive Statistical Learning with Bayesian Differential Privacy | In statistical learning, a dataset is often partitioned into two parts: the training set and the holdout (i.e., testing) set. For instance, the training set is used to learn a predictor, and then the holdout set is used for estimating the accuracy of the predictor on the true distribution. However, often in practice, t... | ['Jun Zhao'] | 2019-11-02 | null | null | null | null | ['holdout-set'] | ['computer-vision'] | [ 2.19764307e-01 -6.91726059e-02 -3.72390240e-01 -3.91017854e-01
-7.26258993e-01 -8.28993917e-01 8.52240175e-02 4.37645346e-01
-6.04618013e-01 1.01250052e+00 -2.32498080e-01 -1.08040042e-01
-2.72227764e-01 -9.36906815e-01 -9.51822937e-01 -1.03697693e+00
-2.70488411e-01 3.78505677e-01 1.14104394e-02 2.20778316... | [6.084505558013916, 6.670045852661133] |
1dcfd664-43b9-417e-b190-981b1b740a7b | prevention-and-resolution-of-conflicts-in | 2106.12113 | null | https://arxiv.org/abs/2106.12113v3 | https://arxiv.org/pdf/2106.12113v3.pdf | Conflict Avoidance in Social Navigation -- a Survey | A major goal in robotics is to enable intelligent mobile robots to operate smoothly in shared human-robot environments. One of the most fundamental capabilities in service of this goal is competent navigation in this ``social" context. As a result, there has been a recent surge of research on social navigation; and esp... | ['Peter Stone', 'Justin Hart', 'Xuesu Xiao', 'Reuth Mirsky'] | 2021-06-23 | null | null | null | null | ['social-navigation'] | ['robots'] | [ 1.19893327e-01 5.97707391e-01 -4.91156757e-01 -3.51912141e-01
-5.05932532e-02 -5.39261460e-01 8.44895840e-01 -2.41617918e-01
-7.10291505e-01 9.88968968e-01 4.46084663e-02 -4.34747726e-01
-6.04839802e-01 -7.07005739e-01 -2.36204192e-01 -6.95018232e-01
-1.94413483e-01 5.76250553e-01 4.47297364e-01 -1.10966945... | [4.829054355621338, 1.0058289766311646] |
3a326d36-7219-4cb7-bb20-4da83115abe1 | chatgpt-as-your-personal-data-scientist | 2305.13657 | null | https://arxiv.org/abs/2305.13657v1 | https://arxiv.org/pdf/2305.13657v1.pdf | ChatGPT as your Personal Data Scientist | The rise of big data has amplified the need for efficient, user-friendly automated machine learning (AutoML) tools. However, the intricacy of understanding domain-specific data and defining prediction tasks necessitates human intervention making the process time-consuming while preventing full automation. Instead, envi... | ['Shubhra Kanti Karmaker Santu', 'Alex Knipper', 'Md Mahadi Hassan'] | 2023-05-23 | null | null | null | null | ['data-visualization', 'automl', 'data-visualization'] | ['methodology', 'methodology', 'miscellaneous'] | [-1.39221072e-01 4.07871723e-01 5.42027876e-02 -4.61306334e-01
-4.93360817e-01 -7.30089068e-01 9.30432379e-01 3.08679700e-01
-1.36169598e-01 5.24783432e-01 4.93749887e-01 -6.90152287e-01
-1.37166560e-01 -5.42022765e-01 -1.25165984e-01 -1.89863339e-01
1.27672046e-01 8.88916194e-01 -2.90772110e-01 -4.45868224... | [12.59626293182373, 7.842702865600586] |
32457427-6acf-419a-9144-4f8dae229297 | prophetnet-x-large-scale-pre-training-models | 2104.08006 | null | https://arxiv.org/abs/2104.08006v2 | https://arxiv.org/pdf/2104.08006v2.pdf | ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation | Now, the pre-training technique is ubiquitous in natural language processing field. ProphetNet is a pre-training based natural language generation method which shows powerful performance on English text summarization and question generation tasks. In this paper, we extend ProphetNet into other domains and languages, an... | ['Nan Duan', 'Houqiang Li', 'Ruofei Zhang', 'Jiusheng Chen', 'Daxin Jiang', 'Biao Cheng', 'Bartuer Zhou', 'Bolun Yao', 'Can Xu', 'Yu Yan', 'Yeyun Gong', 'Weizhen Qi'] | 2021-04-16 | null | https://aclanthology.org/2021.acl-demo.28 | https://aclanthology.org/2021.acl-demo.28.pdf | acl-2021-5 | ['open-domain-dialog'] | ['natural-language-processing'] | [-1.89115494e-01 5.96898496e-01 -2.78056189e-02 -2.59872049e-01
-9.44380283e-01 -5.64273894e-01 1.01442266e+00 1.36321068e-01
-1.95654362e-01 1.36544418e+00 8.76942873e-01 -4.55700636e-01
4.16493893e-01 -1.09499264e+00 -2.94360310e-01 -2.31722206e-01
3.29388767e-01 9.78752017e-01 2.03948557e-01 -8.17394137... | [11.8883056640625, 9.054323196411133] |
2b99184f-dcc6-4a11-89df-45c2a071006c | learning-joint-2d-3d-diffusion-models-for | 2305.12347 | null | https://arxiv.org/abs/2305.12347v2 | https://arxiv.org/pdf/2305.12347v2.pdf | Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generation | Designing new molecules is essential for drug discovery and material science. Recently, deep generative models that aim to model molecule distribution have made promising progress in narrowing down the chemical research space and generating high-fidelity molecules. However, current generative models only focus on model... | ['Weifeng Lv', 'Bowen Du', 'Leilei Sun', 'Han Huang'] | 2023-05-21 | null | null | null | null | ['drug-discovery', '3d-molecule-generation'] | ['medical', 'medical'] | [ 4.19259667e-02 -7.94224069e-02 -2.55605400e-01 -7.85943270e-02
-7.30780721e-01 -6.60348594e-01 7.45631516e-01 3.27564895e-01
1.04708388e-01 1.10962653e+00 3.12224746e-01 -4.93921191e-01
2.26293262e-02 -1.30037248e+00 -8.73696148e-01 -9.65769529e-01
5.08563034e-02 6.02484345e-01 -2.87456840e-01 -2.96018362... | [5.017292022705078, 5.758340835571289] |
4dee732d-8151-48ea-98ab-0e55c00da884 | active-dictionary-learning-in-sparse | 1409.5763 | null | http://arxiv.org/abs/1409.5763v2 | http://arxiv.org/pdf/1409.5763v2.pdf | Active Dictionary Learning in Sparse Representation Based Classification | Sparse representation, which uses dictionary atoms to reconstruct input
vectors, has been studied intensively in recent years. A proper dictionary is a
key for the success of sparse representation. In this paper, an active
dictionary learning (ADL) method is introduced, in which classification error
and reconstruction ... | ['Jin Xu', 'Hong Man', 'Haibo He'] | 2014-09-19 | null | null | null | null | ['sparse-representation-based-classification'] | ['computer-vision'] | [ 1.86630502e-01 -3.21120322e-01 -6.64290905e-01 -2.63261765e-01
-5.19625306e-01 -1.23901302e-02 3.30793381e-01 2.86453307e-01
-3.15863132e-01 8.16044927e-01 3.99499416e-01 2.24613741e-01
-1.11430302e-01 -8.61127496e-01 4.43862006e-02 -1.00421262e+00
5.80784939e-02 2.27836311e-01 -1.10597670e-01 -1.63228244... | [12.396055221557617, 0.40833550691604614] |
017e0e0e-a125-463e-81b4-706c1a8dbded | embedding-graphs-on-grassmann-manifold | 2205.15068 | null | https://arxiv.org/abs/2205.15068v1 | https://arxiv.org/pdf/2205.15068v1.pdf | Embedding Graphs on Grassmann Manifold | Learning efficient graph representation is the key to favorably addressing downstream tasks on graphs, such as node or graph property prediction. Given the non-Euclidean structural property of graphs, preserving the original graph data's similarity relationship in the embedded space needs specific tools and a similarit... | ['Junbin Gao', 'Ming Li', 'Yu Guang Wang', 'Xuebin Zheng', 'Bingxin Zhou'] | 2022-05-30 | null | null | null | null | ['graph-property-prediction'] | ['graphs'] | [-6.49789795e-02 6.97501361e-01 -2.05972925e-01 -1.19994283e-01
-1.93783656e-01 -6.70095921e-01 6.23984039e-01 2.88659990e-01
1.73068747e-01 1.21676520e-01 3.32409918e-01 -3.19499791e-01
-4.62588131e-01 -9.80175555e-01 -6.86897635e-01 -8.68557930e-01
-6.13835931e-01 1.91491574e-01 -3.72745216e-01 9.65345930... | [7.091531753540039, 5.96779203414917] |
05a39c8c-4198-48ec-88d7-f690e18daaea | exact-how-to-train-your-accuracy | 2205.09615 | null | https://arxiv.org/abs/2205.09615v3 | https://arxiv.org/pdf/2205.09615v3.pdf | EXACT: How to Train Your Accuracy | Classification tasks are usually evaluated in terms of accuracy. However, accuracy is discontinuous and cannot be directly optimized using gradient ascent. Popular methods minimize cross-entropy, hinge loss, or other surrogate losses, which can lead to suboptimal results. In this paper, we propose a new optimization fr... | ['Sergey Kolesnikov', 'Stanislav Dereka', 'Ivan Karpukhin'] | 2022-05-19 | null | null | null | null | ['classification'] | ['methodology'] | [-3.24465960e-01 -1.20810293e-01 -3.87961000e-01 -7.55803525e-01
-1.01401508e+00 -2.13309154e-01 8.58585313e-02 2.19862312e-01
-6.19167209e-01 1.25084877e+00 -4.72905010e-01 -4.56413515e-02
-1.39916807e-01 -6.24200463e-01 -6.85631573e-01 -7.53776670e-01
2.44268894e-01 3.16579461e-01 -2.03134447e-01 2.60981679... | [7.9060282707214355, 3.8024120330810547] |
cae67ab3-8a8a-4912-af82-afe4ad37ac15 | label-decoupling-framework-for-salient-object-1 | 2008.11048 | null | https://arxiv.org/abs/2008.11048v1 | https://arxiv.org/pdf/2008.11048v1.pdf | Label Decoupling Framework for Salient Object Detection | To get more accurate saliency maps, recent methods mainly focus on aggregating multi-level features from fully convolutional network (FCN) and introducing edge information as auxiliary supervision. Though remarkable progress has been achieved, we observe that the closer the pixel is to the edge, the more difficult it i... | ['Jun Wei', 'Chi Su', 'Shuhui Wang', 'Qi Tian', 'Zhe Wu', 'Qingming Huang'] | 2020-08-25 | label-decoupling-framework-for-salient-object | http://openaccess.thecvf.com/content_CVPR_2020/html/Wei_Label_Decoupling_Framework_for_Salient_Object_Detection_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Wei_Label_Decoupling_Framework_for_Salient_Object_Detection_CVPR_2020_paper.pdf | cvpr-2020-6 | ['salient-object-detection'] | ['computer-vision'] | [ 3.38916898e-01 1.54269114e-01 -3.29639494e-01 -3.89573365e-01
-1.38251990e-01 -2.86699142e-02 3.99731696e-01 2.80939966e-01
-2.28616208e-01 5.99173248e-01 2.48171166e-01 2.40591913e-02
1.29510030e-01 -8.29512239e-01 -7.01922536e-01 -7.51359701e-01
2.60044754e-01 -9.02894810e-02 9.75081503e-01 -2.76224583... | [9.777902603149414, -0.3974379301071167] |
ab6fe854-9f25-489f-ae05-285f980831da | large-scale-electron-microscopy-image | 1604.00385 | null | http://arxiv.org/abs/1604.00385v1 | http://arxiv.org/pdf/1604.00385v1.pdf | Large-Scale Electron Microscopy Image Segmentation in Spark | The emerging field of connectomics aims to unlock the mysteries of the brain
by understanding the connectivity between neurons. To map this connectivity, we
acquire thousands of electron microscopy (EM) images with nanometer-scale
resolution. After aligning these images, the resulting dataset has the
potential to revea... | ['Stephen M. Plaza', 'Stuart E. Berg'] | 2016-04-01 | null | null | null | null | ['electron-microscopy-image-segmentation'] | ['computer-vision'] | [ 9.90554020e-02 -1.60634205e-01 4.83590424e-01 -2.12356195e-01
-3.27692300e-01 -7.72662044e-01 2.81214863e-01 2.52505928e-01
-6.36796296e-01 5.88747144e-01 -4.94930387e-01 -5.66210330e-01
1.05119191e-01 -7.19343245e-01 -7.23024845e-01 -6.04912043e-01
1.21237569e-01 7.98637629e-01 5.70849717e-01 2.71173537... | [14.208995819091797, -3.071223020553589] |
d2d38125-6a4d-4aaf-befc-46c3cb255e5d | embedding-convolutions-for-short-text-extreme | 2109.07319 | null | https://arxiv.org/abs/2109.07319v2 | https://arxiv.org/pdf/2109.07319v2.pdf | InceptionXML: A Lightweight Framework with Synchronized Negative Sampling for Short Text Extreme Classification | Automatic annotation of short-text data to a large number of target labels, referred to as Short Text Extreme Classification, has found numerous applications including prediction of related searches and product recommendation tasks. In this paper, we propose a convolutional architecture InceptionXML which is light-weig... | ['Devaansh Gupta', 'Rohit Babbar', 'Akash Palrecha', 'Atmadeep Banerjee', 'Siddhant Kharbanda'] | 2021-09-13 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [ 3.31428975e-01 -1.49598375e-01 -4.97075498e-01 -6.65670991e-01
-3.52580905e-01 -8.17734063e-01 7.07740426e-01 2.39417210e-01
-8.68680596e-01 2.67261088e-01 -7.93917477e-02 -8.37579846e-01
-1.88078582e-01 -6.29268944e-01 -4.81549412e-01 -3.28408688e-01
4.09937143e-01 5.83287597e-01 8.61310810e-02 -3.21567319... | [9.612666130065918, 4.483583450317383] |
c1dad21c-4db7-4a45-b3fa-718185396129 | q-how-to-specialize-large-vision-language-1 | 2306.03932 | null | https://arxiv.org/abs/2306.03932v1 | https://arxiv.org/pdf/2306.03932v1.pdf | Q: How to Specialize Large Vision-Language Models to Data-Scarce VQA Tasks? A: Self-Train on Unlabeled Images! | Finetuning a large vision language model (VLM) on a target dataset after large scale pretraining is a dominant paradigm in visual question answering (VQA). Datasets for specialized tasks such as knowledge-based VQA or VQA in non natural-image domains are orders of magnitude smaller than those for general-purpose VQA. W... | ['Manmohan Chandraker', 'Yun Fu', 'Xiang Yu', 'Samuel Schulter', 'Vijay Kumar BG', 'Zaid Khan'] | 2023-06-06 | q-how-to-specialize-large-vision-language | http://openaccess.thecvf.com//content/CVPR2023/html/Khan_Q_How_To_Specialize_Large_Vision-Language_Models_to_Data-Scarce_VQA_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Khan_Q_How_To_Specialize_Large_Vision-Language_Models_to_Data-Scarce_VQA_CVPR_2023_paper.pdf | cvpr-2023-1 | ['visual-question-answering-1', 'domain-generalization'] | ['computer-vision', 'methodology'] | [ 3.46261382e-01 3.73954713e-01 -5.90845421e-02 -4.61428523e-01
-1.06661701e+00 -1.07158148e+00 5.76582849e-01 -2.93592904e-02
-6.42556965e-01 7.59054661e-01 6.93178177e-02 -5.53565025e-01
3.28411937e-01 -7.43108571e-01 -1.16630936e+00 -3.69999409e-01
6.11425519e-01 6.38989389e-01 2.20162243e-01 -2.99485058... | [10.762890815734863, 1.7824578285217285] |
a74fe7b7-102d-466e-af96-3eab33602e24 | triangulation-learning-network-from-monocular-1 | 1906.01193 | null | https://arxiv.org/abs/1906.01193v1 | https://arxiv.org/pdf/1906.01193v1.pdf | Triangulation Learning Network: from Monocular to Stereo 3D Object Detection | In this paper, we study the problem of 3D object detection from stereo images, in which the key challenge is how to effectively utilize stereo information. Different from previous methods using pixel-level depth maps, we propose employing 3D anchors to explicitly construct object-level correspondences between the regio... | ['Jinglu Wang', 'Zengyi Qin', 'Yan Lu'] | 2019-06-04 | triangulation-learning-network-from-monocular | http://openaccess.thecvf.com/content_CVPR_2019/html/Qin_Triangulation_Learning_Network_From_Monocular_to_Stereo_3D_Object_Detection_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Qin_Triangulation_Learning_Network_From_Monocular_to_Stereo_3D_Object_Detection_CVPR_2019_paper.pdf | cvpr-2019-6 | ['3d-object-detection-from-stereo-images'] | ['computer-vision'] | [ 2.49231204e-01 -7.50774816e-02 8.58717933e-02 -2.73709476e-01
-8.32599044e-01 -6.00399971e-01 5.99847317e-01 -2.53124088e-01
-6.41199887e-01 2.21704528e-01 1.54749369e-02 -7.07127973e-02
3.01296771e-01 -4.83493030e-01 -1.06324279e+00 -5.16352594e-01
1.53702451e-02 4.72156852e-01 3.70668352e-01 1.61006123... | [7.837341785430908, -2.6352646350860596] |
2aab104e-0686-4aeb-9322-0093bcf4a252 | flexibert-are-current-transformer | 2205.11656 | null | https://arxiv.org/abs/2205.11656v1 | https://arxiv.org/pdf/2205.11656v1.pdf | FlexiBERT: Are Current Transformer Architectures too Homogeneous and Rigid? | The existence of a plethora of language models makes the problem of selecting the best one for a custom task challenging. Most state-of-the-art methods leverage transformer-based models (e.g., BERT) or their variants. Training such models and exploring their hyperparameter space, however, is computationally expensive. ... | ['Niraj K. Jha', 'Shreshth Tuli', 'Bhishma Dedhia', 'Shikhar Tuli'] | 2022-05-23 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [-2.62139380e-01 -1.80678740e-02 -4.72372770e-01 -1.31801754e-01
-7.80578792e-01 -4.33913261e-01 4.71713781e-01 -4.30144906e-01
-1.09892823e-01 3.19082230e-01 1.67862132e-01 -5.94935119e-01
-3.53310615e-01 -6.96689844e-01 -6.62383974e-01 -4.72045928e-01
-1.38341263e-01 5.11152089e-01 1.04836628e-01 -3.94491017... | [8.714158058166504, 3.4158711433410645] |
28822391-5dba-436f-80fe-b3c9ba89c7a4 | uda-cope-unsupervised-domain-adaptation-for | 2111.12580 | null | https://arxiv.org/abs/2111.12580v2 | https://arxiv.org/pdf/2111.12580v2.pdf | UDA-COPE: Unsupervised Domain Adaptation for Category-level Object Pose Estimation | Learning to estimate object pose often requires ground-truth (GT) labels, such as CAD model and absolute-scale object pose, which is expensive and laborious to obtain in the real world. To tackle this problem, we propose an unsupervised domain adaptation (UDA) for category-level object pose estimation, called UDA-COPE.... | ['Kuk-Jin Yoon', 'In So Kweon', 'Ukcheol Shin', 'Jaesung Choe', 'Inkyu Shin', 'Byeong-Uk Lee', 'Taeyeop Lee'] | 2021-11-24 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Lee_UDA-COPE_Unsupervised_Domain_Adaptation_for_Category-Level_Object_Pose_Estimation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Lee_UDA-COPE_Unsupervised_Domain_Adaptation_for_Category-Level_Object_Pose_Estimation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['6d-pose-estimation-using-rgbd'] | ['computer-vision'] | [ 4.18967977e-02 1.27148584e-01 -1.11723594e-01 -5.74735343e-01
-1.04587281e+00 -5.82847834e-01 4.71890748e-01 5.30766835e-03
-2.11659044e-01 4.37462032e-01 -2.47397542e-01 7.92488605e-02
-1.80735633e-01 -8.02459359e-01 -1.11936855e+00 -5.87222219e-01
3.87972236e-01 9.95413065e-01 5.49946249e-01 1.62147358... | [7.6106743812561035, -2.653414249420166] |
34ea8fef-4869-41c7-afb7-d64e53035cd9 | particle-transformer-for-jet-tagging | 2202.03772 | null | https://arxiv.org/abs/2202.03772v2 | https://arxiv.org/pdf/2202.03772v2.pdf | Particle Transformer for Jet Tagging | Jet tagging is a critical yet challenging classification task in particle physics. While deep learning has transformed jet tagging and significantly improved performance, the lack of a large-scale public dataset impedes further enhancement. In this work, we present JetClass, a new comprehensive dataset for jet tagging.... | ['Sitian Qian', 'Congqiao Li', 'Huilin Qu'] | 2022-02-08 | null | null | null | null | ['jet-tagging'] | ['graphs'] | [-4.38098520e-01 -9.14568752e-02 -3.45645308e-01 -2.51400530e-01
-1.15764403e+00 -1.03648651e+00 9.26107168e-01 1.54778650e-02
-3.31547439e-01 9.36540663e-01 5.82612574e-01 -4.36447948e-01
3.55274417e-02 -7.53840864e-01 -6.78211153e-01 -6.79054201e-01
4.01079208e-02 1.01531124e+00 1.01533496e+00 -1.11696661... | [15.701129913330078, 2.9201831817626953] |
90ecc2ab-48f1-4c20-a631-32a7a09b5791 | clio-role-interactive-multi-event-head | null | null | https://aclanthology.org/2022.coling-1.221 | https://aclanthology.org/2022.coling-1.221.pdf | CLIO: Role-interactive Multi-event Head Attention Network for Document-level Event Extraction | Transforming the large amounts of unstructured text on the Internet into structured event knowledge is a critical, yet unsolved goal of NLP, especially when addressing document-level text. Existing methods struggle in Document-level Event Extraction (DEE) due to its two intrinsic challenges: (a) Nested arguments, which... | ['Yi Liu', 'Wei Ma', 'Zheng Lin', 'Ping Guo', 'Fang Fang', 'Yanan Cao', 'Yubing Ren'] | null | null | null | null | coling-2022-10 | ['event-extraction', 'document-level-event-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.63586491e-01 2.31759809e-03 -2.02484593e-01 -1.74642593e-01
-9.48036849e-01 -6.66174173e-01 6.80839300e-01 4.14038956e-01
-4.81959552e-01 7.34755933e-01 1.06278813e+00 -1.18118688e-01
-2.57237583e-01 -8.64288390e-01 -7.62534857e-01 -5.18723845e-01
5.74408881e-02 5.30250967e-01 2.57883102e-01 -2.95636263... | [9.058799743652344, 9.163472175598145] |
db91ab91-c1db-4a2e-affc-56e1acba95e7 | relational-model-for-parameter-description-in | 2005.05046 | null | https://arxiv.org/abs/2005.05046v1 | https://arxiv.org/pdf/2005.05046v1.pdf | Relational Model for Parameter Description in Automatic Semantic Web Service Composition | Automatic Service Composition is a research direction aimed at facilitating the usage of atomic web services. Particularly, the goal is to build workflows of services that solve specific queries, which cannot be resolved by any single service from a known repository. Each of these services is described independently by... | ['Andrei Netedu', 'Liana Ţucăr', 'Paul Diac'] | 2020-05-08 | null | null | null | null | ['service-composition'] | ['miscellaneous'] | [-1.93906296e-02 2.80952960e-01 1.29988059e-01 -5.53880632e-01
9.94096920e-02 -6.73086524e-01 9.43746090e-01 1.92552865e-01
-3.19427341e-01 6.92624748e-01 1.76904798e-02 -2.91996509e-01
-6.97741330e-01 -1.26560462e+00 -1.82643726e-01 -4.96630162e-01
7.58755952e-02 9.02844191e-01 9.19426739e-01 -6.87334895... | [8.672204971313477, 7.010528087615967] |
579ea15c-e3cc-49b1-b7b2-4daa50651273 | generating-compressed-combinatory-proof | 2209.12592 | null | https://arxiv.org/abs/2209.12592v1 | https://arxiv.org/pdf/2209.12592v1.pdf | Generating Compressed Combinatory Proof Structures -- An Approach to Automated First-Order Theorem Proving | Representing a proof tree by a combinator term that reduces to the tree lets subtle forms of duplication within the tree materialize as duplicated subterms of the combinator term. In a DAG representation of the combinator term these straightforwardly factor into shared subgraphs. To search for proofs, combinator terms ... | ['Christoph Wernhard'] | 2022-09-26 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 4.67397928e-01 8.82810414e-01 -1.82806045e-01 7.02661127e-02
-1.90620705e-01 -1.01738763e+00 9.91130590e-01 3.57282519e-01
2.87415832e-01 8.58433843e-01 -6.64255843e-02 -1.18315208e+00
-5.65075278e-01 -1.19643450e+00 -5.90570450e-01 -2.53651410e-01
-5.21787107e-01 5.77920496e-01 7.19098210e-01 -3.07849765... | [8.77391242980957, 6.851644992828369] |
8e4e4c6c-ed1f-4a55-a088-d1299c3187fc | anomaly-detection-inspired-few-shot-medical | 2203.02048 | null | https://arxiv.org/abs/2203.02048v1 | https://arxiv.org/pdf/2203.02048v1.pdf | Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With Supervoxels | Recent work has shown that label-efficient few-shot learning through self-supervision can achieve promising medical image segmentation results. However, few-shot segmentation models typically rely on prototype representations of the semantic classes, resulting in a loss of local information that can degrade performance... | ['Michael Kampffmeyer', 'Robert Jenssen', 'Srishti Gautam', 'Stine Hansen'] | 2022-03-03 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 5.48877537e-01 2.94302255e-01 -1.84908912e-01 -4.98637468e-01
-7.75735199e-01 -1.03236102e-01 4.32407767e-01 7.97049224e-01
-4.52278048e-01 3.77103060e-01 -1.04828291e-01 1.26801595e-01
1.02134332e-01 -6.59948707e-01 -5.92331767e-01 -9.81643856e-01
5.12219071e-02 7.33561277e-01 8.35134149e-01 -4.86269733... | [14.63752269744873, -2.19032883644104] |
56d98fbd-73e9-4097-a6b1-92986c1ea873 | continuous-decomposition-of-granularity-for | 2209.01765 | null | https://arxiv.org/abs/2209.01765v2 | https://arxiv.org/pdf/2209.01765v2.pdf | Continuous Decomposition of Granularity for Neural Paraphrase Generation | While Transformers have had significant success in paragraph generation, they treat sentences as linear sequences of tokens and often neglect their hierarchical information. Prior work has shown that decomposing the levels of granularity~(e.g., word, phrase, or sentence) for input tokens has produced substantial improv... | ['Jung-Woo Ha', 'Kang Min Yoo', 'Sang-Woo Lee', 'Zhaowei Zhang', 'Xiaodong Gu'] | 2022-09-05 | null | https://aclanthology.org/2022.coling-1.554 | https://aclanthology.org/2022.coling-1.554.pdf | coling-2022-10 | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 1.12491407e-01 4.23545420e-01 -2.60780126e-01 -1.56809092e-01
-1.21459079e+00 -6.51208758e-01 8.88414383e-01 3.22566181e-01
-7.61220083e-02 7.64330983e-01 1.06894565e+00 -4.77569818e-01
4.83556002e-01 -1.22057307e+00 -1.03580403e+00 -2.94532239e-01
3.27752411e-01 4.21463370e-01 1.36706069e-01 -2.65622348... | [11.73184585571289, 8.961880683898926] |
b3b7d160-1d1a-4e6c-b223-c95c55ec6c47 | learning-customized-visual-models-with | 2301.07094 | null | https://arxiv.org/abs/2301.07094v1 | https://arxiv.org/pdf/2301.07094v1.pdf | Learning Customized Visual Models with Retrieval-Augmented Knowledge | Image-text contrastive learning models such as CLIP have demonstrated strong task transfer ability. The high generality and usability of these visual models is achieved via a web-scale data collection process to ensure broad concept coverage, followed by expensive pre-training to feed all the knowledge into model weigh... | ['Chunyuan Li', 'Yong Jae Lee', 'Jianfeng Gao', 'Ce Liu', 'Jianwei Yang', 'Kilho Son', 'Haotian Liu'] | 2023-01-17 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Liu_Learning_Customized_Visual_Models_With_Retrieval-Augmented_Knowledge_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Liu_Learning_Customized_Visual_Models_With_Retrieval-Augmented_Knowledge_CVPR_2023_paper.pdf | cvpr-2023-1 | ['zero-shot-transfer-image-classification', 'semi-supervised-image-classification'] | ['computer-vision', 'computer-vision'] | [ 3.23948205e-01 -2.26963863e-01 -3.62857342e-01 -3.67185265e-01
-1.08539510e+00 -6.08655751e-01 4.09611821e-01 4.51387800e-02
-6.68609321e-01 2.92153388e-01 -7.33224601e-02 3.25584337e-02
8.21591392e-02 -4.46435422e-01 -8.94790411e-01 -4.38197523e-01
1.25590414e-01 4.82776523e-01 7.19957471e-01 -2.02619866... | [10.257715225219727, 1.7929413318634033] |
cf3e9b25-1c65-4384-8c80-35a82858438b | on-decoder-only-architecture-for-speech-to | 2307.03917 | null | https://arxiv.org/abs/2307.03917v1 | https://arxiv.org/pdf/2307.03917v1.pdf | On decoder-only architecture for speech-to-text and large language model integration | Large language models (LLMs) have achieved remarkable success in the field of natural language processing, enabling better human-computer interaction using natural language. However, the seamless integration of speech signals into LLMs has not been explored well. The "decoder-only" architecture has also not been well s... | ['Yu Wu', 'Linquan Liu', 'Bo Ren', 'Shujie Liu', 'Jinyu Li', 'Tianrui Wang', 'Yimeng Zhu', 'Long Zhou', 'Zhuo Chen', 'Yashesh Gaur', 'Jian Wu'] | 2023-07-08 | null | null | null | null | ['speech-to-text-translation', 'language-modelling'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.45592916e-01 2.47685134e-01 -5.24793044e-02 -5.99419653e-01
-1.54134154e+00 -4.39054638e-01 8.46591353e-01 -1.40922427e-01
-3.60759586e-01 3.26959610e-01 5.87876618e-01 -6.95681393e-01
6.41976953e-01 -2.22265765e-01 -9.43912864e-01 -3.96983325e-01
2.78276712e-01 6.45706952e-01 -1.38459161e-01 -2.16051146... | [14.47337532043457, 7.045506954193115] |
6c24bfe7-d7f6-41a6-87d4-cde2bbdbeb26 | query-based-industrial-analytics-over | 2209.11089 | null | https://arxiv.org/abs/2209.11089v1 | https://arxiv.org/pdf/2209.11089v1.pdf | Query-based Industrial Analytics over Knowledge Graphs with Ontology Reshaping | Industrial analytics that includes among others equipment diagnosis and anomaly detection heavily relies on integration of heterogeneous production data. Knowledge Graphs (KGs) as the data format and ontologies as the unified data schemata are a prominent solution that offers high quality data integration and a conveni... | ['Evgeny Kharlamo', 'Ahmet Soylu', 'Ernesto Jiménez-Ruiz', 'Gong Cheng', 'Dongzhuoran Zhou', 'Baifan Zhou', 'Zhuoxun Zheng'] | 2022-09-22 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [-2.42919043e-01 1.87038839e-01 -1.15503632e-01 -3.87185872e-01
-1.21101163e-01 -5.80263197e-01 9.52678397e-02 1.03550971e+00
1.51964411e-01 6.75721526e-01 -4.55280572e-01 -2.34109581e-01
-8.74619067e-01 -1.46515036e+00 -5.70471644e-01 7.84014538e-03
7.29890242e-02 7.61321783e-01 5.70937514e-01 -4.68411595... | [9.120279312133789, 7.7033538818359375] |
c1b03033-11ab-4295-b48a-d998083c7a3f | transferring-from-formal-newswire-domain-with | null | null | https://aclanthology.org/D18-1275 | https://aclanthology.org/D18-1275.pdf | Transferring from Formal Newswire Domain with Hypernet for Twitter POS Tagging | Part-of-Speech (POS) tagging for Twitter has received considerable attention in recent years. Because most POS tagging methods are based on supervised models, they usually require a large amount of labeled data for training. However, the existing labeled datasets for Twitter are much smaller than those for newswire tex... | ['Xuanjing Huang', 'Keyu Ding', 'Di Liang', 'Tao Gui', 'Minlong Peng', 'Qi Zhang', 'Jingjing Gong'] | 2018-10-01 | null | null | null | emnlp-2018-10 | ['stock-prediction'] | ['time-series'] | [-2.49452055e-01 1.31468788e-01 -6.97222412e-01 -6.91337407e-01
-3.29894304e-01 -6.50630772e-01 6.37680829e-01 3.62001926e-01
-4.14740622e-01 6.71306968e-01 4.32760894e-01 -8.46361443e-02
4.73953664e-01 -7.04340398e-01 -3.17639589e-01 -5.30182838e-01
3.73321891e-01 4.84615654e-01 5.09384573e-01 -3.34520280... | [9.74415111541748, 9.473616600036621] |
6e884b01-1470-487b-8e6e-77f74d4d44d2 | off-beat-multi-agent-reinforcement-learning | 2205.13718 | null | https://arxiv.org/abs/2205.13718v2 | https://arxiv.org/pdf/2205.13718v2.pdf | Off-Beat Multi-Agent Reinforcement Learning | We investigate model-free multi-agent reinforcement learning (MARL) in environments where off-beat actions are prevalent, i.e., all actions have pre-set execution durations. During execution durations, the environment changes are influenced by, but not synchronised with, action execution. Such a setting is ubiquitous i... | ['Changjie Fan', 'Yingfeng Chen', 'Jianye Hao', 'Zinovi Rabinovich', 'Svetlana Obraztsova', 'Yujing Hu', 'Bo An', 'Rundong Wang', 'Weixun Wang', 'Wei Qiu'] | 2022-05-27 | null | null | null | null | ['starcraft-ii'] | ['playing-games'] | [-1.16594970e-01 -2.11886212e-01 -3.99537623e-01 1.06905058e-01
-5.31382084e-01 -3.54534686e-01 6.72508597e-01 2.68838316e-01
-8.18685174e-01 1.31252742e+00 6.12045564e-02 8.92970040e-02
-4.70160216e-01 -9.09010351e-01 -8.30967903e-01 -9.06488299e-01
-5.77562988e-01 9.92333651e-01 2.61373669e-01 -3.22977811... | [3.7826409339904785, 2.007352352142334] |
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