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e7766c9b-62d3-4873-88b9-ed208eb40dca | photometric-identification-of-compact | 2211.08388 | null | https://arxiv.org/abs/2211.08388v1 | https://arxiv.org/pdf/2211.08388v1.pdf | Photometric identification of compact galaxies, stars and quasars using multiple neural networks | We present MargNet, a deep learning-based classifier for identifying stars, quasars and compact galaxies using photometric parameters and images from the Sloan Digital Sky Survey (SDSS) Data Release 16 (DR16) catalogue. MargNet consists of a combination of Convolutional Neural Network (CNN) and Artificial Neural Networ... | ['Ajit Kembhavi', 'M. Vivek', 'Kaushal Sharma', 'Rishi Gondkar', 'Anish Deshpande', 'Atharva Bagul', 'Siddharth Chaini'] | 2022-11-15 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [-4.40940350e-01 -1.79499224e-01 2.39963844e-01 -5.91599584e-01
-3.34591925e-01 -6.93081260e-01 9.58459258e-01 -2.66308159e-01
-4.60622162e-01 4.48017031e-01 4.37458195e-02 -5.33756196e-01
-1.98606372e-01 -7.83670545e-01 -1.07292809e-01 -6.90316796e-01
-7.22135454e-02 6.31594479e-01 4.04583663e-01 2.84310151... | [7.892989635467529, 2.9581546783447266] |
ce1702eb-4711-4af1-91cd-73738448975a | a-new-insight-into-the-secondary-path | 1811.03755 | null | http://arxiv.org/abs/1811.03755v2 | http://arxiv.org/pdf/1811.03755v2.pdf | A new insight into the secondary path modeling problem in active noise control | The close relationship between the feedforward ANC system and the stereo
acoustic echo cancellation system is revealed in this paper. Accordingly, the
convergence behavior of the ANC system can be analyzed by investigating the
joint auto-correlation matrix of the reference and the filtered reference
signal. It is prove... | [] | 2019-01-18 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 2.93035895e-01 7.82194808e-02 6.52642965e-01 1.59202203e-01
-1.61505893e-01 -5.74034989e-01 5.61751664e-01 -3.73313218e-01
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-6.67942762e-01 -4.33580279e-01 -2.15914309e-01 -1.06087565e+00
3.08058798e-01 -3.40909868e-01 7.91589022e-02 -1.33589938... | [15.17230224609375, 5.678704261779785] |
2e84b643-d486-4260-9a5a-8919d7701a17 | domain-adaptation-strategies-for-cancer | 2207.06193 | null | https://arxiv.org/abs/2207.06193v1 | https://arxiv.org/pdf/2207.06193v1.pdf | Domain adaptation strategies for cancer-independent detection of lymph node metastases | Recently, large, high-quality public datasets have led to the development of convolutional neural networks that can detect lymph node metastases of breast cancer at the level of expert pathologists. Many cancers, regardless of the site of origin, can metastasize to lymph nodes. However, collecting and annotating high-v... | ['Geert Litjens', 'Jeroen van der Laak', 'Bram van Ginneken', 'Marcory van Dijk', 'Maschenka Balkenhol', 'Péter Bándi'] | 2022-07-13 | null | null | null | null | ['cancer-metastasis-detection'] | ['medical'] | [ 8.25092494e-02 -1.22914284e-01 -7.23942876e-01 -8.23058039e-02
-1.45625210e+00 -3.45493346e-01 2.47358188e-01 5.81432581e-01
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-1.48268610e-01 -5.97176254e-01 -5.90247154e-01 -8.95977497e-01
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13e2eb92-eb6e-44de-a16d-d8b0694c7673 | do-transformers-really-perform-bad-for-graph | 2106.05234 | null | https://arxiv.org/abs/2106.05234v5 | https://arxiv.org/pdf/2106.05234v5.pdf | Do Transformers Really Perform Bad for Graph Representation? | The Transformer architecture has become a dominant choice in many domains, such as natural language processing and computer vision. Yet, it has not achieved competitive performance on popular leaderboards of graph-level prediction compared to mainstream GNN variants. Therefore, it remains a mystery how Transformers cou... | ['Tie-Yan Liu', 'Yanming Shen', 'Di He', 'Guolin Ke', 'Shuxin Zheng', 'Shengjie Luo', 'Tianle Cai', 'Chengxuan Ying'] | 2021-06-09 | null | null | null | null | ['graph-property-prediction', 'graph-regression'] | ['graphs', 'graphs'] | [ 2.01332405e-01 3.27925891e-01 -3.82574022e-01 2.16990281e-02
-2.22272605e-01 -5.72212338e-01 4.93552625e-01 3.37103665e-01
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-3.40244502e-01 6.72476053e-01 1.68714114e-02 -5.70342422... | [6.938317775726318, 6.213719844818115] |
f8fd9f0d-bbe0-4dc5-855e-858a51319dd9 | neural-keyphrase-generation-via-reinforcement | 1906.04106 | null | https://arxiv.org/abs/1906.04106v1 | https://arxiv.org/pdf/1906.04106v1.pdf | Neural Keyphrase Generation via Reinforcement Learning with Adaptive Rewards | Generating keyphrases that summarize the main points of a document is a fundamental task in natural language processing. Although existing generative models are capable of predicting multiple keyphrases for an input document as well as determining the number of keyphrases to generate, they still suffer from the problem... | ['Hou Pong Chan', 'Irwin King', 'Wang Chen', 'Lu Wang'] | 2019-06-10 | neural-keyphrase-generation-via-reinforcement-1 | https://aclanthology.org/P19-1208 | https://aclanthology.org/P19-1208.pdf | acl-2019-7 | ['keyphrase-generation'] | ['natural-language-processing'] | [-6.86332062e-02 3.06802560e-02 -2.91729838e-01 4.78582904e-02
-1.37467360e+00 -9.19059753e-01 1.16596711e+00 5.19813836e-01
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3.20981741e-01 6.13820374e-01 2.30681017e-01 -5.19577205... | [12.323601722717285, 8.896717071533203] |
06ae982b-8348-4d7e-8fea-b51f4be65d22 | catastrophic-interference-in-reinforcement | 2109.00525 | null | https://arxiv.org/abs/2109.00525v2 | https://arxiv.org/pdf/2109.00525v2.pdf | Catastrophic Interference in Reinforcement Learning: A Solution Based on Context Division and Knowledge Distillation | The powerful learning ability of deep neural networks enables reinforcement learning agents to learn competent control policies directly from continuous environments. In theory, to achieve stable performance, neural networks assume i.i.d. inputs, which unfortunately does no hold in the general reinforcement learning pa... | ['Bo Yuan', 'Bin Liang', 'Xueqian Wang', 'Tiantian Zhang'] | 2021-09-01 | null | null | null | null | ['online-clustering'] | ['computer-vision'] | [-1.85040057e-01 -1.53671816e-01 -5.29242456e-02 7.40869045e-02
-5.65476060e-01 -4.36352968e-01 4.06884789e-01 -2.14814603e-01
-6.57315493e-01 1.00927496e+00 -9.62534249e-02 -2.67045438e-01
-4.92161274e-01 -6.30913794e-01 -8.21648002e-01 -1.16483855e+00
-1.03209205e-01 2.00914860e-01 -4.13482189e-02 -1.85539573... | [3.987497329711914, 2.215881586074829] |
5869291d-9d1c-4548-8965-729523ce55dc | heart-rate-estimation-in-intense-exercise | 2208.02509 | null | https://arxiv.org/abs/2208.02509v1 | https://arxiv.org/pdf/2208.02509v1.pdf | Heart rate estimation in intense exercise videos | Estimating heart rate from video allows non-contact health monitoring with applications in patient care, human interaction, and sports. Existing work can robustly measure heart rate under some degree of motion by face tracking. However, this is not always possible in unconstrained settings, as the face might be occlude... | ['Jan van Gemert', 'Thijs Eijsvogels', 'Puck Alkemade', 'Nergis Tomen', 'Anwesh Marwade', 'Yeshwanth Napolean'] | 2022-08-04 | null | null | null | null | ['heart-rate-estimation'] | ['medical'] | [ 3.22125167e-01 9.96539090e-03 -2.66183943e-01 -2.41839170e-01
-3.95216405e-01 -3.70335668e-01 -3.13352972e-01 -5.06023526e-01
-2.25688815e-01 7.75842845e-01 4.21504006e-02 4.65427823e-02
4.16684568e-01 -3.15575570e-01 -3.27948928e-01 -6.28412127e-01
-6.79723620e-02 7.72449449e-02 -3.12952399e-02 4.06688958... | [13.890326499938965, 2.8031251430511475] |
1a4684a1-22e2-4f53-bf2e-90cd86357008 | language-model-detoxification-in-dialogue | 2301.10368 | null | https://arxiv.org/abs/2301.10368v1 | https://arxiv.org/pdf/2301.10368v1.pdf | Language Model Detoxification in Dialogue with Contextualized Stance Control | To reduce the toxic degeneration in a pretrained Language Model (LM), previous work on Language Model detoxification has focused on reducing the toxicity of the generation itself (self-toxicity) without consideration of the context. As a result, a type of implicit offensive language where the generations support the of... | ['Xifeng Yan', 'Jing Qian'] | 2023-01-25 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [ 5.27209103e-01 8.23209658e-02 -4.08253223e-01 -1.97351173e-01
-2.40302205e-01 -6.21679842e-01 6.68917477e-01 2.12483838e-01
-4.67050582e-01 6.53803587e-01 4.80476290e-01 -1.60391837e-01
4.19903025e-02 -1.04317808e+00 -4.71673220e-01 -8.52283835e-01
5.45973063e-01 4.70225625e-02 3.21824029e-02 -4.71948475... | [11.748701095581055, 9.194592475891113] |
f40bd620-baf4-4b76-9c72-08fe6cd5d043 | uncertainty-estimation-in-medical-image | 2008.08837 | null | https://arxiv.org/abs/2008.08837v1 | https://arxiv.org/pdf/2008.08837v1.pdf | Uncertainty Estimation in Medical Image Denoising with Bayesian Deep Image Prior | Uncertainty quantification in inverse medical imaging tasks with deep learning has received little attention. However, deep models trained on large data sets tend to hallucinate and create artifacts in the reconstructed output that are not anatomically present. We use a randomly initialized convolutional network as par... | ['Malte Tölle', 'Max-Heinrich Laves', 'Tobias Ortmaier'] | 2020-08-20 | null | null | null | null | ['medical-image-denoising'] | ['computer-vision'] | [ 1.82986781e-01 5.59759200e-01 2.91004300e-01 -3.08661669e-01
-9.27550912e-01 -8.56153220e-02 4.68566537e-01 1.32503659e-01
-5.60746968e-01 9.61074769e-01 3.60681534e-01 2.08159581e-01
-1.55373409e-01 -6.36106789e-01 -9.86476898e-01 -1.02371037e+00
1.81498706e-01 4.78258610e-01 -1.02442846e-01 4.24656332... | [13.558259963989258, -2.306161880493164] |
d8fd3bcb-c826-4622-8b45-b4aec7883517 | une-comparaison-des-algorithmes-d | 2303.13590 | null | https://arxiv.org/abs/2303.13590v1 | https://arxiv.org/pdf/2303.13590v1.pdf | Une comparaison des algorithmes d'apprentissage pour la survie avec données manquantes | Survival analysis is an essential tool for the study of health data. An inherent component of such data is the presence of missing values. In recent years, researchers proposed new learning algorithms for survival tasks based on neural networks. Here, we studied the predictive performance of such algorithms coupled wit... | ['Sébastien Benzekry', 'Paul Dufossé'] | 2023-03-23 | null | null | null | null | ['feature-engineering', 'survival-analysis'] | ['methodology', 'miscellaneous'] | [ 4.16124538e-02 -1.92019999e-01 3.17768961e-01 -4.35844928e-01
-3.67435932e-01 -4.06854570e-01 4.64359164e-01 8.43968451e-01
-7.62129128e-01 1.10833216e+00 -3.80594023e-02 -1.56120330e-01
-7.94986188e-02 -1.09768486e+00 -7.73683310e-01 -6.97956681e-01
-2.27495313e-01 3.74784410e-01 -3.54399860e-01 -2.38232940... | [14.103219032287598, 13.317512512207031] |
ae7e93c6-a2ef-4dbd-82bf-dc57b733a818 | occ-bev-multi-camera-unified-pre-training-via | 2305.18829 | null | https://arxiv.org/abs/2305.18829v2 | https://arxiv.org/pdf/2305.18829v2.pdf | Occ-BEV: Multi-Camera Unified Pre-training via 3D Scene Reconstruction | Multi-camera 3D perception has emerged as a prominent research field in autonomous driving, offering a viable and cost-effective alternative to LiDAR-based solutions. However, existing multi-camera algorithms primarily rely on monocular image pre-training, which overlooks the spatial and temporal correlations among dif... | ['Bin Dai', 'Jiaolong Xu', 'Jimei Li', 'Zhichao Zhang', 'Weizhong Jiang', 'Hanzhang Xue', 'Shubin Si', 'Fuyang Li', 'Yiming Nie', 'Liang Xiao', 'Dawei Zhao', 'Xinli Xu', 'Chen Min'] | 2023-05-30 | null | null | null | null | ['3d-scene-reconstruction'] | ['computer-vision'] | [-4.76834178e-02 -3.77171963e-01 -7.96524435e-02 -5.21906078e-01
-8.48016441e-01 -7.51204967e-01 5.48877001e-01 -4.47960943e-02
-5.03881872e-01 2.85282046e-01 -2.00099692e-01 -4.17108864e-01
1.26946688e-01 -7.65224218e-01 -9.18027341e-01 -4.35315669e-01
5.79394519e-01 3.99674058e-01 5.38698852e-01 -2.84163743... | [8.019513130187988, -2.2745542526245117] |
0c7d5937-8d9f-49eb-9480-cbc3b31a7905 | reducing-large-internet-topologies-for-faster | null | null | https://link.springer.com/chapter/10.1007/11422778_27 | http://www.cs.ucr.edu/~michalis/PAPERS/sampling-networking-05.pdf | Reducing Large Internet Topologies for Faster Simulations | In this paper, we develop methods to “sample” a small realistic graph from a large real network. Despite recent activity, the modeling and generation of realistic graphs is still not a resolved issue. All previous work has attempted to grow a graph from scratch. We address the complementary problem of shrinking a graph... | ['J. -H. Cui', 'M. Faloutsos', 'A. G. Percus', 'V. Krishnamurthy', 'M. Chrobak', 'L. Lao'] | 2020-05-13 | null | null | null | 2020-5 | ['graph-sampling'] | ['graphs'] | [ 5.47187209e-01 8.89538050e-01 -1.03499793e-01 1.78598478e-01
-6.54058993e-01 -7.89231718e-01 6.38822913e-01 2.08048239e-01
-3.40207405e-02 1.04648781e+00 -8.37220103e-02 -5.57062507e-01
-5.24281301e-02 -1.12760651e+00 -7.87953436e-01 -3.75941873e-01
-5.30947030e-01 7.90538549e-01 4.36681032e-01 -2.19354644... | [6.939284324645996, 5.375678539276123] |
8803e4fe-c1d0-497f-90be-eb7d24f3b482 | improved-transition-based-parsing-and-tagging | null | null | https://aclanthology.org/D15-1159 | https://aclanthology.org/D15-1159.pdf | Improved Transition-Based Parsing and Tagging with Neural Networks | null | ['Slav Petrov', 'David Weiss', 'Chris Alberti', 'Greg Coppola'] | 2015-09-01 | null | null | null | emnlp-2015-9 | ['transition-based-dependency-parsing'] | ['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... | [-7.263052463531494, 3.811129093170166] |
cab90a9b-535e-4c96-a6c2-e3cbac97354d | cluster-based-point-set-saliency | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Tasse_Cluster-Based_Point_Set_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Tasse_Cluster-Based_Point_Set_ICCV_2015_paper.pdf | Cluster-Based Point Set Saliency | We propose a cluster-based approach to point set saliency detection, a challenge since point sets lack topological information. A point set is first decomposed into small clusters, using fuzzy clustering. We evaluate cluster uniqueness and spatial distribution of each cluster and combine these values into a cluster sal... | ['Jiri Kosinka', 'Neil Dodgson', 'Flora Ponjou Tasse'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['interest-point-detection'] | ['computer-vision'] | [-2.07520336e-01 -1.41902328e-01 -2.14568526e-01 -3.72537225e-02
-5.70539594e-01 -6.32184625e-01 4.47862685e-01 6.74700260e-01
-1.67672306e-01 3.13718796e-01 -4.45214957e-02 1.38619214e-01
2.25242861e-02 -7.34863698e-01 -6.10152483e-01 -3.76778573e-01
-4.74490106e-01 2.45053381e-01 1.17035413e+00 -2.03458354... | [9.77799129486084, -0.31913456320762634] |
083e564d-8808-409b-a694-9ea441726f7d | rfpose-ot-rf-based-3d-human-pose-estimation | 2301.13013 | null | https://arxiv.org/abs/2301.13013v1 | https://arxiv.org/pdf/2301.13013v1.pdf | RFPose-OT: RF-Based 3D Human Pose Estimation via Optimal Transport Theory | This paper introduces a novel framework, i.e., RFPose-OT, to enable the 3D human pose estimation from Radio Frequency (RF) signals. Different from existing methods that predict human poses from RF signals on the signal level directly, we consider the structure difference between the RF signals and the human poses, prop... | ['Yan Chen', 'Yang Hu', 'Chunyang Xie', 'Zhi Lu', 'Zhi Wu', 'Dongheng Zhang', 'Cong Yu'] | 2022-12-26 | null | null | null | null | ['3d-human-pose-estimation'] | ['computer-vision'] | [ 2.24951342e-01 -2.50343144e-01 2.81198800e-01 -4.75160837e-01
-4.25942600e-01 -2.78273761e-01 1.55983627e-01 -7.93141186e-01
-2.10000440e-01 6.82641685e-01 2.63379812e-01 1.31873861e-01
-3.33061308e-01 -9.70279574e-01 -6.77582145e-01 -4.59144682e-01
-1.86237603e-01 2.73683131e-01 1.22850180e-01 -3.99601698... | [6.789117813110352, 0.3975635766983032] |
ded43742-e72c-4302-91e0-b57ce82ab689 | ol-a-bonjour-salve-xformal-a-benchmark-for | null | null | https://aclanthology.org/2021.naacl-main.256 | https://aclanthology.org/2021.naacl-main.256.pdf | Ol\'a, Bonjour, Salve! XFORMAL: A Benchmark for Multilingual Formality Style Transfer | We take the first step towards multilingual style transfer by creating and releasing XFORMAL, a benchmark of multiple formal reformulations of informal text in Brazilian Portuguese, French, and Italian. Results on XFORMAL suggest that state-of-the-art style transfer approaches perform close to simple baselines, indicat... | ['Joel Tetreault', 'Ke Zhang', 'Di Lu', 'Eleftheria Briakou'] | 2021-06-01 | null | null | null | naacl-2021-4 | ['formality-style-transfer'] | ['natural-language-processing'] | [-1.50335729e-01 5.19240424e-02 -2.11466849e-01 -5.13697326e-01
-1.23000085e+00 -1.24418974e+00 9.60355282e-01 -3.83363515e-01
-8.20731163e-01 1.72740078e+00 5.13379395e-01 -6.21233940e-01
4.42091137e-01 -3.90193999e-01 -6.87795818e-01 -3.24971341e-02
2.97920287e-01 8.81958306e-01 -4.13628221e-02 -9.63507175... | [11.461576461791992, 10.027562141418457] |
03f1ec6f-a89d-4045-8752-fa33a4b1d8c6 | casapose-class-adaptive-and-semantic-aware | 2210.05318 | null | https://arxiv.org/abs/2210.05318v3 | https://arxiv.org/pdf/2210.05318v3.pdf | CASAPose: Class-Adaptive and Semantic-Aware Multi-Object Pose Estimation | Applications in the field of augmented reality or robotics often require joint localisation and 6D pose estimation of multiple objects. However, most algorithms need one network per object class to be trained in order to provide the best results. Analysing all visible objects demands multiple inferences, which is memor... | ['Peter Eisert', 'Anna Hilsmann', 'Niklas Gard'] | 2022-10-11 | null | null | null | null | ['6d-pose-estimation-1', '6d-pose-estimation'] | ['computer-vision', 'computer-vision'] | [ 4.39327121e-01 1.93171188e-01 2.46724501e-01 -4.91553485e-01
-9.90005195e-01 -5.11345685e-01 2.78087109e-01 1.39971226e-01
-5.74484229e-01 2.79663473e-01 -4.54636455e-01 -9.46866721e-02
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2.73904055e-01 9.42010581e-01 8.26997519e-01 1.31033227... | [7.55289363861084, -2.6053624153137207] |
3c0602c8-e8b7-4f77-a693-8171c17acf93 | sequential-experimental-design-for-spectral | 2305.07040 | null | https://arxiv.org/abs/2305.07040v1 | https://arxiv.org/pdf/2305.07040v1.pdf | Sequential Experimental Design for Spectral Measurement: Active Learning Using a Parametric Model | In this study, we demonstrate a sequential experimental design for spectral measurements by active learning using parametric models as predictors. In spectral measurements, it is necessary to reduce the measurement time because of sample fragility and high energy costs. To improve the efficiency of experiments, sequent... | ['Masato Okada', 'Masaichiro Mizumaki', 'Shun Katakami', 'Kenji Nagata', 'Tomohiro Nabika'] | 2023-05-11 | null | null | null | null | ['experimental-design', 'bayesian-inference'] | ['methodology', 'methodology'] | [ 5.74997067e-01 -1.67392835e-01 -3.33551504e-02 -5.09051085e-01
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-4.63549823e-01 -7.42938161e-01 -4.50345248e-01 -1.41090417e+00
2.28315771e-01 6.81192279e-01 1.41868457e-01 3.36000741... | [6.427456855773926, 3.7162351608276367] |
f38d9a18-86ce-445d-9af2-7dd02a1b30bb | curricular-subgoals-for-inverse-reinforcement | 2306.08232 | null | https://arxiv.org/abs/2306.08232v1 | https://arxiv.org/pdf/2306.08232v1.pdf | Curricular Subgoals for Inverse Reinforcement Learning | Inverse Reinforcement Learning (IRL) aims to reconstruct the reward function from expert demonstrations to facilitate policy learning, and has demonstrated its remarkable success in imitation learning. To promote expert-like behavior, existing IRL methods mainly focus on learning global reward functions to minimize the... | ['Mingli Song', 'YunFu Liu', 'Tianhao Chen', 'Jingyuan Cong', 'Jiangtao Zhang', 'Hongyan Wu', 'Shuqi Xu', 'Yunpeng Qing', 'Shunyu Liu'] | 2023-06-14 | null | null | null | null | ['imitation-learning', 'd4rl'] | ['methodology', 'robots'] | [-2.00165167e-01 1.16861850e-01 -4.59896773e-01 5.52186333e-02
-5.17381966e-01 -4.15878564e-01 6.53428078e-01 -3.45421791e-01
-5.45236349e-01 7.92681098e-01 1.39248930e-02 -2.59612501e-01
-4.12558079e-01 -4.41597581e-01 -7.19295919e-01 -8.66963625e-01
-6.00590892e-02 4.48480755e-01 1.60060182e-01 -5.47262192... | [4.231594085693359, 1.6467595100402832] |
6319ce87-5388-4e57-b667-5f496b72df6f | supervised-adaptation-of-sequence-to-sequence | null | null | https://aclanthology.org/2020.lifelongnlp-1.2 | https://aclanthology.org/2020.lifelongnlp-1.2.pdf | Supervised Adaptation of Sequence-to-Sequence Speech Recognition Systems using Batch-Weighting | When training speech recognition systems, one often faces the situation that sufficient amounts of training data for the language in question are available but only small amounts of data for the domain in question. This problem is even bigger for end-to-end speech recognition systems that only accept transcribed speech... | ['Alexander Waibel', 'Sebastian Stüker', 'Kaihang Song', 'Tuan-Nam Nguyen', 'Juan Hussain', 'Christian Huber'] | null | null | null | null | aacl-lifelongnlp-2020-12 | ['sequence-to-sequence-speech-recognition'] | ['speech'] | [ 1.54251233e-01 1.13892801e-01 6.39994070e-02 -8.01762938e-01
-7.41974413e-01 -5.05575001e-01 5.62054992e-01 3.37082855e-02
-8.52496564e-01 7.72733808e-01 2.17007205e-01 -7.88775444e-01
8.88510793e-02 -5.50777316e-01 -2.87630349e-01 -4.30844158e-01
2.64355510e-01 7.68986344e-01 3.70362371e-01 -6.41629875... | [14.420239448547363, 6.665634632110596] |
df857f1f-774a-4fef-bc31-5bc2eedd8a3f | cross-database-micro-expression-recognition-a | 1812.07742 | null | https://arxiv.org/abs/1812.07742v2 | https://arxiv.org/pdf/1812.07742v2.pdf | Cross-Database Micro-Expression Recognition: A Benchmark | Cross-database micro-expression recognition (CDMER) is one of recently emerging and interesting problem in micro-expression analysis. CDMER is more challenging than the conventional micro-expression recognition (MER), because the training and testing samples in CDMER come from different micro-expression databases, resu... | ['Chuangao Tang', 'Yuan Zong', 'Xiaopeng Hong', 'Zhen Cui', 'Wenming Zheng', 'Tong Zhang', 'Guoying Zhao'] | 2018-12-19 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [-2.34543364e-02 -7.32842147e-01 -2.24536479e-01 -3.84210110e-01
-9.73471522e-01 -2.01718882e-01 4.17716622e-01 -2.25996792e-01
-3.39898556e-01 5.89553833e-01 7.42921082e-04 4.98896569e-01
-8.97521079e-02 -5.49368322e-01 -4.56174910e-01 -1.25797856e+00
2.09135517e-01 -9.88569669e-03 5.25451079e-03 -4.93958503... | [13.66688060760498, 1.6871615648269653] |
807cc2bb-ee3c-4594-aa4b-f1b9b330d765 | second-order-winobias-sowinobias-test-set-for | 2109.14047 | null | https://arxiv.org/abs/2109.14047v1 | https://arxiv.org/pdf/2109.14047v1.pdf | Second Order WinoBias (SoWinoBias) Test Set for Latent Gender Bias Detection in Coreference Resolution | We observe an instance of gender-induced bias in a downstream application, despite the absence of explicit gender words in the test cases. We provide a test set, SoWinoBias, for the purpose of measuring such latent gender bias in coreference resolution systems. We evaluate the performance of current debiasing methods o... | ['Hillary Dawkins'] | 2021-09-28 | null | https://aclanthology.org/2021.gebnlp-1.12 | https://aclanthology.org/2021.gebnlp-1.12.pdf | acl-gebnlp-2021-8 | ['gender-bias-detection', 'gender-bias-detection'] | ['miscellaneous', 'natural-language-processing'] | [-2.31982842e-01 5.17201900e-01 -5.55586755e-01 -5.96246421e-01
-7.44995475e-01 -7.66779065e-01 1.03921592e+00 3.69267240e-02
-4.86952007e-01 9.78548110e-01 8.90301824e-01 -4.26133782e-01
-2.26000443e-01 -4.71687704e-01 -3.15922976e-01 -6.13210082e-01
1.84505343e-01 7.10433722e-01 -3.58942151e-01 -4.07867491... | [9.396224975585938, 10.254012107849121] |
9dbff942-c21e-41f6-b4e1-e7558d0348ab | benchmark-of-data-preprocessing-methods-for | 2303.03094 | null | https://arxiv.org/abs/2303.03094v1 | https://arxiv.org/pdf/2303.03094v1.pdf | Benchmark of Data Preprocessing Methods for Imbalanced Classification | Severe class imbalance is one of the main conditions that make machine learning in cybersecurity difficult. A variety of dataset preprocessing methods have been introduced over the years. These methods modify the training dataset by oversampling, undersampling or a combination of both to improve the predictive performa... | ['Tomáš Komárek', 'Jan Brabec', 'Radovan Haluška'] | 2023-03-06 | null | null | null | null | ['automl', 'imbalanced-classification'] | ['methodology', 'miscellaneous'] | [ 3.43802512e-01 -1.20149627e-01 -4.88605618e-01 -1.78796917e-01
-3.63273561e-01 -7.70665228e-01 6.26789868e-01 4.92693126e-01
-3.75431597e-01 7.12284446e-01 -1.01255938e-01 -6.25897884e-01
-4.33028996e-01 -9.04578149e-01 -3.85052919e-01 -7.02070236e-01
-1.05037376e-01 2.37313628e-01 2.56626159e-02 -2.54685104... | [5.31766939163208, 7.182766437530518] |
39b81033-9176-4d7c-a064-24e4b2114bd1 | hc-search-for-structured-prediction-in | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Lam_HC-Search_for_Structured_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Lam_HC-Search_for_Structured_2015_CVPR_paper.pdf | HC-Search for Structured Prediction in Computer Vision | The mainstream approach to structured prediction problems in computer vision is to learn an energy function such that the solution minimizes that function. At prediction time, this approach must solve an often-challenging optimization problem. Search-based methods provide an alternative that has the potential to achi... | ['Sinisa Todorovic', 'Janardhan Rao Doppa', 'Thomas G. Dietterich', 'Michael Lam'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['scene-labeling'] | ['computer-vision'] | [ 4.04770702e-01 1.05064712e-01 -2.47135714e-01 -6.48003519e-01
-1.13281083e+00 -5.38505852e-01 6.44320548e-01 2.67169416e-01
-5.73594987e-01 2.72650361e-01 -2.67799646e-01 -1.73731208e-01
1.92539483e-01 -6.89359725e-01 -7.48688579e-01 -7.46602476e-01
2.91217893e-01 9.44048524e-01 7.89974928e-01 2.58479029... | [9.462553024291992, 0.27206525206565857] |
11727da7-538c-44d7-b694-68c75c28595d | multimodal-emotion-recognition-with-high | 2111.10202 | null | https://arxiv.org/abs/2111.10202v1 | https://arxiv.org/pdf/2111.10202v1.pdf | Multimodal Emotion Recognition with High-level Speech and Text Features | Automatic emotion recognition is one of the central concerns of the Human-Computer Interaction field as it can bridge the gap between humans and machines. Current works train deep learning models on low-level data representations to solve the emotion recognition task. Since emotion datasets often have a limited amount ... | ['Koichi Shinoda', 'Kuniaki Uto', 'Mariana Rodrigues Makiuchi'] | 2021-09-29 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 1.79072872e-01 -6.67814165e-02 -2.59615481e-02 -7.16803193e-01
-8.40168715e-01 -1.85340092e-01 5.52982748e-01 1.33974209e-01
-4.38825905e-01 2.98250794e-01 5.00216544e-01 2.00353144e-03
1.70871124e-01 -3.54095548e-01 -1.12421408e-01 -5.58871746e-01
2.94361800e-01 3.12919319e-01 -5.27309775e-01 -4.59341496... | [13.409242630004883, 5.531747341156006] |
c0331c71-79ad-4596-9164-b3f675fdef95 | elimination-of-central-artefacts-of-l-spect | 2008.07893 | null | http://arxiv.org/abs/2008.07893v1 | http://arxiv.org/pdf/2008.07893v1.pdf | Elimination of Central Artefacts of L-SPECT with Modular Partial Ring Detectors by Shifting Center of Scanning | The Lightfield Single Photon Emission Computed Tomography (L-SPECT) system is
developed to overcome some of the drawbacks in conventional SPECT by applying
the idea of plenoptic imaging. This system displayed improved performance in
terms of reduced information loss and scanning time when compared to the SPECT
system w... | [] | 2020-08-18 | null | null | null | null | ['lightfield'] | ['computer-vision'] | [ 1.16112240e-01 3.98197584e-02 2.47500762e-01 -4.64251637e-02
-4.33987588e-01 -6.94893375e-02 5.47732353e-01 -7.10331127e-02
-9.34752703e-01 8.42920959e-01 2.73263156e-01 -1.72686264e-01
-2.47736022e-01 -4.94715631e-01 -2.82127187e-02 -1.09106028e+00
5.82414031e-01 5.53273618e-01 8.19917679e-01 3.10250431... | [13.101956367492676, -2.6862292289733887] |
59f3bf7b-9e20-4928-b37a-5ac6fc997197 | high-frequency-space-diffusion-models-for | 2208.05481 | null | https://arxiv.org/abs/2208.05481v4 | https://arxiv.org/pdf/2208.05481v4.pdf | High-Frequency Space Diffusion Models for Accelerated MRI | Diffusion models with continuous stochastic differential equations (SDEs) have shown superior performances in image generation. It can be used as a deep generative prior to solve the inverse problem in MR reconstruction. However, the existing VP-SDE can be treated as maximizing the energy of the MR image to be reconstr... | ['Yanjie Zhu', 'Dong Liang', 'Hairong Zheng', 'Shaonan Liu', 'Zhuo-Xu Cui', 'Chentao Cao'] | 2022-08-10 | null | null | null | null | ['mri-reconstruction'] | ['computer-vision'] | [ 6.51838332e-02 -3.03124011e-01 3.01245391e-01 -2.41468787e-01
-6.28322482e-01 7.98861831e-02 4.27637190e-01 -5.44818401e-01
-3.82635534e-01 8.35866511e-01 4.61863607e-01 -1.45497099e-01
-3.51092607e-01 -7.59168923e-01 -3.98780972e-01 -1.23550534e+00
4.78874967e-02 6.00864828e-01 4.08577293e-01 -2.34327435... | [13.510953903198242, -2.389096736907959] |
746f0f45-8835-4c97-9adb-6c59c31d97ff | domain-transfer-based-data-augmentation-for | null | null | https://aclanthology.org/2020.coling-main.399 | https://aclanthology.org/2020.coling-main.399.pdf | Domain Transfer based Data Augmentation for Neural Query Translation | Query translation (QT) serves as a critical factor in successful cross-lingual information retrieval (CLIR). Due to the lack of parallel query samples, neural-based QT models are usually optimized with synthetic data which are derived from large-scale monolingual queries. Nevertheless, such kind of pseudo corpus is mos... | ['Weihua Luo', 'Boxing Chen', 'Haibo Zhang', 'Baosong Yang', 'Liang Yao'] | 2020-12-01 | null | null | null | coling-2020-8 | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [ 5.60960770e-02 -2.65258342e-01 -5.20702720e-01 -2.77649432e-01
-1.74106121e+00 -7.75677502e-01 9.80561495e-01 -1.40041104e-02
-6.27168238e-01 8.17119956e-01 2.99237102e-01 -2.85908431e-01
6.52919337e-02 -3.98494065e-01 -9.37103271e-01 -3.43296111e-01
5.66609025e-01 1.07960117e+00 7.81076774e-02 -6.43415928... | [11.62764835357666, 10.030182838439941] |
35200f3f-3680-4fa2-bc2d-2cc5612364a5 | demystifying-how-self-supervised-features-1 | 2110.09022 | null | https://arxiv.org/abs/2110.09022v3 | https://arxiv.org/pdf/2110.09022v3.pdf | Mitigating Memorization of Noisy Labels via Regularization between Representations | Designing robust loss functions is popular in learning with noisy labels while existing designs did not explicitly consider the overfitting property of deep neural networks (DNNs). As a result, applying these losses may still suffer from overfitting/memorizing noisy labels as training proceeds. In this paper, we first ... | ['Yang Liu', 'Xing Sun', 'Zhaowei Zhu', 'Hao Cheng'] | 2021-10-18 | demystifying-how-self-supervised-features | https://openreview.net/forum?id=R5sVzzXhW8n | https://openreview.net/pdf?id=R5sVzzXhW8n | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 1.81858271e-01 3.08735549e-01 -7.20964372e-02 -7.39120305e-01
-8.40705335e-01 -5.28318763e-01 1.57046646e-01 7.98630938e-02
-6.25656188e-01 9.07381356e-01 -5.97515404e-02 -2.99503237e-01
-1.86664790e-01 -7.19101489e-01 -8.66440773e-01 -9.55200791e-01
2.44083151e-01 6.34646863e-02 -1.23756222e-01 1.32238492... | [9.219600677490234, 3.7363762855529785] |
55f84b50-4d35-4787-b8c7-3f30a0a6737b | localized-data-fusion-for-kernel-k-means | null | null | http://papers.nips.cc/paper/5236-localized-data-fusion-for-kernel-k-means-clustering-with-application-to-cancer-biology | http://papers.nips.cc/paper/5236-localized-data-fusion-for-kernel-k-means-clustering-with-application-to-cancer-biology.pdf | Localized Data Fusion for Kernel k-Means Clustering with Application to Cancer Biology | In many modern applications from, for example, bioinformatics and computer vision, samples have multiple feature representations coming from different data sources. Multiview learning algorithms try to exploit all these available information to obtain a better learner in such scenarios. In this paper, we propose a nove... | ['Mehmet Gönen', 'Adam A. Margolin'] | 2014-12-01 | null | null | null | neurips-2014-12 | ['multiview-learning'] | ['computer-vision'] | [-1.0385326e-01 -3.8466486e-01 -5.7769048e-01 -5.5034810e-01
-1.2380606e+00 -3.2438469e-01 3.3466777e-01 9.6751153e-01
-2.8146845e-01 5.7118458e-01 6.4847386e-01 1.5722859e-01
-6.1619383e-01 -4.5747906e-01 -4.0705004e-01 -1.0759423e+00
-1.7969681e-02 2.8177252e-01 -1.1815049e-01 2.9800719e-01
5.3016096e-02... | [6.781291484832764, 5.315694332122803] |
b1824971-77ec-493b-8707-1184befdd46c | gpu-based-parallel-optimization-for-real-time | 1810.03988 | null | http://arxiv.org/abs/1810.03988v2 | http://arxiv.org/pdf/1810.03988v2.pdf | GPU based Parallel Optimization for Real Time Panoramic Video Stitching | Panoramic video is a sort of video recorded at the same point of view to
record the full scene. With the development of video surveillance and the
requirement for 3D converged video surveillance in smart cities, CPU and GPU
are required to possess strong processing abilities to make panoramic video.
The traditional pan... | ['Lin Li', 'Jingling Yuan', 'Chengyao Du', 'Tao Li', 'Mincheng Chen', 'Jiansheng Dong'] | 2018-10-04 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 3.36704373e-01 -9.47181523e-01 -2.85624564e-02 2.04945192e-01
-5.03864996e-02 -2.76606172e-01 4.44614261e-01 -4.58933860e-01
-3.05820823e-01 7.25147054e-02 8.80837515e-02 -2.92307734e-01
-2.07796134e-03 -1.00613570e+00 -4.07350749e-01 -8.12959552e-01
2.39387587e-01 -6.16100654e-02 9.47214365e-01 -2.50689328... | [9.019108772277832, -2.18414306640625] |
96527fd7-354d-4b13-9d3b-c2a66421bc44 | large-scale-radio-frequency-signal | 2207.09918 | null | https://arxiv.org/abs/2207.09918v1 | https://arxiv.org/pdf/2207.09918v1.pdf | Large Scale Radio Frequency Signal Classification | Existing datasets used to train deep learning models for narrowband radio frequency (RF) signal classification lack enough diversity in signal types and channel impairments to sufficiently assess model performance in the real world. We introduce the Sig53 dataset consisting of 5 million synthetically-generated samples ... | ['Robert D. Miller', 'Craig Lennon', 'Silvija Kokalj-Filipovic', 'Bradley Comar', 'Phillip Vallance', 'Garrett Vanhoy', 'Manbir Gulati', 'Luke Boegner'] | 2022-07-20 | null | null | null | null | ['classification'] | ['methodology'] | [ 3.11752647e-01 -1.95402116e-01 6.03796169e-02 -5.29614687e-01
-8.39406908e-01 -4.25499171e-01 4.11411732e-01 -7.31565952e-01
-1.12152316e-01 7.06650436e-01 1.03013545e-01 -7.83377886e-01
-2.85266906e-01 -5.88983476e-01 -6.07800841e-01 -5.56380451e-01
-4.36749130e-01 7.83749074e-02 -3.13595891e-01 -2.69454718... | [15.243663787841797, 5.269145488739014] |
6cd71865-2ef6-4fcc-a3fe-fc258264940a | multi-target-regression-via-random-linear | 1404.5065 | null | http://arxiv.org/abs/1404.5065v1 | http://arxiv.org/pdf/1404.5065v1.pdf | Multi-Target Regression via Random Linear Target Combinations | Multi-target regression is concerned with the simultaneous prediction of
multiple continuous target variables based on the same set of input variables.
It arises in several interesting industrial and environmental application
domains, such as ecological modelling and energy forecasting. This paper
presents an ensemble ... | ['Ioannis Vlahavas', 'Eleftherios Spyromitros-Xioufis', 'Grigorios Tsoumakas', 'Aikaterini Vrekou'] | 2014-04-20 | null | null | null | null | ['multi-target-regression'] | ['miscellaneous'] | [ 7.61134982e-01 -3.84853743e-02 -5.60853243e-01 -6.57091975e-01
-1.14704704e+00 -3.40436220e-01 7.69477963e-01 7.66257346e-02
-1.10987276e-01 1.28641963e+00 -8.17802325e-02 -3.75842959e-01
-3.68139684e-01 -9.04746294e-01 -6.70152187e-01 -1.06085134e+00
5.51001839e-02 7.85101473e-01 9.41416025e-02 -2.06297010... | [9.128877639770508, 4.308846473693848] |
2c04150d-c035-4818-9519-7407debc988a | cardiacgen-a-hierarchical-deep-generative | 2211.08385 | null | https://arxiv.org/abs/2211.08385v1 | https://arxiv.org/pdf/2211.08385v1.pdf | CardiacGen: A Hierarchical Deep Generative Model for Cardiac Signals | We present CardiacGen, a Deep Learning framework for generating synthetic but physiologically plausible cardiac signals like ECG. Based on the physiology of cardiovascular system function, we propose a modular hierarchical generative model and impose explicit regularizing constraints for training each module using mult... | ['Emre Ertin', 'Tushar Agarwal'] | 2022-11-15 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 1.13763371e-02 3.18282872e-01 1.72372222e-01 -3.73493254e-01
-7.37310171e-01 -3.51294190e-01 1.93574950e-01 -5.31745069e-02
1.58017129e-01 8.55484009e-01 -1.02993762e-02 -1.73052743e-01
2.77667820e-01 -7.16117799e-01 -5.86627364e-01 -6.28806114e-01
-3.14502507e-01 2.58327216e-01 -5.31982601e-01 1.09403264... | [14.280726432800293, 3.043760299682617] |
ea2f95dc-0ee4-4501-830c-ac6cf7db3e2d | weakly-supervised-instance-segmentation-by | 2001.11207 | null | https://arxiv.org/abs/2001.11207v3 | https://arxiv.org/pdf/2001.11207v3.pdf | Weakly Supervised Instance Segmentation by Deep Community Learning | We present a weakly supervised instance segmentation algorithm based on deep community learning with multiple tasks. This task is formulated as a combination of weakly supervised object detection and semantic segmentation, where individual objects of the same class are identified and segmented separately. We address th... | ['Jeany Son', 'Jaedong Hwang', 'Bohyung Han', 'Seohyun Kim'] | 2020-01-30 | null | null | null | null | ['weakly-supervised-instance-segmentation', 'image-level-supervised-instance-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.40460262e-01 4.12825257e-01 -3.84730220e-01 -3.91893327e-01
-9.30486977e-01 -6.70686305e-01 3.67860138e-01 1.19690098e-01
-4.58398163e-01 4.75557029e-01 -1.83302477e-01 -1.25365973e-01
3.63924950e-01 -7.41738498e-01 -1.04163694e+00 -7.79343843e-01
2.35559177e-02 7.17010736e-01 5.89334846e-01 3.37769717... | [9.490427017211914, 0.5747250914573669] |
f5ad4cb5-4589-4f90-b19e-4c533760f4cc | covid-19-event-extraction-from-twitter-via | 2303.10659 | null | https://arxiv.org/abs/2303.10659v2 | https://arxiv.org/pdf/2303.10659v2.pdf | COVID-19 event extraction from Twitter via extractive question answering with continuous prompts | As COVID-19 ravages the world, social media analytics could augment traditional surveys in assessing how the pandemic evolves and capturing consumer chatter that could help healthcare agencies in addressing it. This typically involves mining disclosure events that mention testing positive for the disease or discussions... | ['Ramakanth Kavuluru', 'Yuhang Jiang'] | 2023-03-19 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [ 1.95679948e-01 4.18974787e-01 -4.76446569e-01 -2.68805534e-01
-1.43563235e+00 -7.45776892e-01 1.13107800e+00 1.24314845e+00
-6.52217507e-01 8.34378242e-01 1.04104996e+00 -4.06297863e-01
8.12927261e-02 -8.68093193e-01 -4.85391915e-01 -5.97711019e-02
-2.93744564e-01 6.17384911e-01 -6.18763380e-02 -2.35264823... | [8.526542663574219, 9.383091926574707] |
0e0e50d8-fa2a-481d-8ad2-d7689327bb52 | two-view-geometry-scoring-without-1 | 2306.01596 | null | https://arxiv.org/abs/2306.01596v1 | https://arxiv.org/pdf/2306.01596v1.pdf | Two-View Geometry Scoring Without Correspondences | Camera pose estimation for two-view geometry traditionally relies on RANSAC. Normally, a multitude of image correspondences leads to a pool of proposed hypotheses, which are then scored to find a winning model. The inlier count is generally regarded as a reliable indicator of "consensus". We examine this scoring heuris... | ['Daniyar Turmukhambetov', 'Gabriel J. Brostow', 'Victor Adrian Prisacariu', 'Eric Brachmann', 'Axel Barroso-Laguna'] | 2023-06-02 | two-view-geometry-scoring-without | http://openaccess.thecvf.com//content/CVPR2023/html/Barroso-Laguna_Two-View_Geometry_Scoring_Without_Correspondences_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Barroso-Laguna_Two-View_Geometry_Scoring_Without_Correspondences_CVPR_2023_paper.pdf | cvpr-2023-1 | ['pose-estimation'] | ['computer-vision'] | [-1.92172974e-02 -1.09128170e-01 -2.84389913e-04 -4.31759417e-01
-8.55146050e-01 -6.86837733e-01 6.73578203e-01 -2.93998569e-01
-1.37092486e-01 3.93000662e-01 2.18569279e-01 1.80366728e-02
-1.26833677e-01 -4.02100027e-01 -6.17547989e-01 -6.02302492e-01
2.53361911e-01 4.73844469e-01 2.62668043e-01 -4.49077666... | [7.897251129150391, -2.337676763534546] |
32f46c1f-d757-4b9e-b851-86bdccc937c1 | negotiated-reasoning-on-provably-addressing | 2306.05353 | null | https://arxiv.org/abs/2306.05353v1 | https://arxiv.org/pdf/2306.05353v1.pdf | Negotiated Reasoning: On Provably Addressing Relative Over-Generalization | Over-generalization is a thorny issue in cognitive science, where people may become overly cautious due to past experiences. Agents in multi-agent reinforcement learning (MARL) also have been found to suffer relative over-generalization (RO) as people do and stuck to sub-optimal cooperation. Recent methods have shown t... | ['Xiangfeng Wang', 'Jun Wang', 'Hongyuan Zha', 'Bo Jin', 'Wenhao Li', 'Junjie Sheng'] | 2023-06-08 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [-2.01816395e-01 5.00461221e-01 -2.03875840e-01 -5.45284431e-03
-4.96934474e-01 -3.89287263e-01 5.95441282e-01 -8.14421102e-02
-8.12438726e-01 1.08594096e+00 5.05771488e-02 -2.32920974e-01
-7.79885769e-01 -7.76796937e-01 -3.99637699e-01 -8.11605334e-01
-2.36837417e-01 6.26969576e-01 -1.17686987e-01 -7.37849951... | [3.819293737411499, 1.8177495002746582] |
cb1ceaa2-8fd4-4240-b920-8cf08d2a9e3a | 191104470 | 1911.04470 | null | https://arxiv.org/abs/1911.04470v1 | https://arxiv.org/pdf/1911.04470v1.pdf | Semi-Heterogeneous Three-Way Joint Embedding Network for Sketch-Based Image Retrieval | Sketch-based image retrieval (SBIR) is a challenging task due to the large cross-domain gap between sketches and natural images. How to align abstract sketches and natural images into a common high-level semantic space remains a key problem in SBIR. In this paper, we propose a novel semi-heterogeneous three-way joint e... | ['Yi-Zhe Song', 'Zhanyu Ma', 'Yuxin Song', 'Bo Peng', 'Ling Shao', 'Jianjun Lei'] | 2019-11-10 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 1.57281607e-01 -3.17908853e-01 -2.63537854e-01 -3.29433650e-01
-7.37218797e-01 -4.76759315e-01 8.85663867e-01 -4.80722666e-01
-6.86844531e-03 3.52320641e-01 3.68432611e-01 3.98282766e-01
-4.23194021e-01 -7.80426860e-01 -7.09758282e-01 -6.15041256e-01
2.37536505e-01 2.21033931e-01 2.42878512e-01 -2.54362553... | [11.616800308227539, 0.6859695911407471] |
ca10e2c0-408a-42f1-b5b6-d947403ccf56 | textcraft-zero-shot-generation-of-high | 2211.01427 | null | https://arxiv.org/abs/2211.01427v4 | https://arxiv.org/pdf/2211.01427v4.pdf | CLIP-Sculptor: Zero-Shot Generation of High-Fidelity and Diverse Shapes from Natural Language | Recent works have demonstrated that natural language can be used to generate and edit 3D shapes. However, these methods generate shapes with limited fidelity and diversity. We introduce CLIP-Sculptor, a method to address these constraints by producing high-fidelity and diverse 3D shapes without the need for (text, shap... | ['Daniel Ritchie', 'Srinath Sridhar', 'Amir Hosein Khasahmadi', 'Hooman Shayani', 'Karl Willis', 'Vivian Liu', 'Rao Fu', 'Aditya Sanghi'] | 2022-11-02 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Sanghi_CLIP-Sculptor_Zero-Shot_Generation_of_High-Fidelity_and_Diverse_Shapes_From_Natural_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Sanghi_CLIP-Sculptor_Zero-Shot_Generation_of_High-Fidelity_and_Diverse_Shapes_From_Natural_CVPR_2023_paper.pdf | cvpr-2023-1 | ['text-to-shape-generation', 'text-to-3d'] | ['computer-vision', 'computer-vision'] | [ 2.85397470e-01 -1.31342322e-01 8.51399973e-02 -4.14617509e-01
-1.17316604e+00 -1.07447684e+00 9.24333274e-01 -1.54401809e-01
8.96025077e-02 4.77292359e-01 4.76329774e-01 -1.12495475e-01
2.95936704e-01 -8.74073088e-01 -8.57374966e-01 -4.86559033e-01
3.60757113e-01 6.66981220e-01 1.00625999e-01 -6.86830059... | [9.06436824798584, -3.5412328243255615] |
650d405c-592b-4c3e-a235-042050073725 | combining-experience-replay-with-exploration | 1905.07579 | null | https://arxiv.org/abs/1905.07579v1 | https://arxiv.org/pdf/1905.07579v1.pdf | Combining Experience Replay with Exploration by Random Network Distillation | Our work is a simple extension of the paper "Exploration by Random Network Distillation". More in detail, we show how to efficiently combine Intrinsic Rewards with Experience Replay in order to achieve more efficient and robust exploration (with respect to PPO/RND) and consequently better results in terms of agent perf... | ['Francesco Sovrano'] | 2019-05-18 | null | null | null | null | ['montezumas-revenge'] | ['playing-games'] | [-1.78540602e-01 5.54205954e-01 -2.72024989e-01 2.08843514e-01
-4.87423033e-01 -6.48130476e-01 7.85820305e-01 -1.44229710e-01
-1.06175053e+00 1.36623979e+00 2.43348390e-01 -2.11628556e-01
-6.86638832e-01 -9.01673675e-01 -5.56146562e-01 -9.12563801e-01
-6.65033340e-01 7.11143672e-01 8.72362580e-04 -6.51087105... | [3.88181209564209, 1.8015285730361938] |
7ad3e7ce-2c2a-4394-ab2a-28aadbecbbee | deep-learning-for-cardiologist-level | 1912.07618 | null | https://arxiv.org/abs/1912.07618v3 | https://arxiv.org/pdf/1912.07618v3.pdf | Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiograms | Myocardial infarction is the leading cause of death worldwide. In this paper, we design domain-inspired neural network models to detect myocardial infarction. First, we study the contribution of various leads. This systematic analysis, first of its kind in the literature, indicates that out of 15 ECG leads, data from t... | ['Arjun Gupta', 'Issam Moussa', 'Zhizhen Zhao', 'E. A. Huerta'] | 2019-12-16 | deep-learning-for-cardiologist-level-1 | null | null | arxiv-2019-12 | ['myocardial-infarction-detection'] | ['medical'] | [ 1.03288218e-01 -1.14835359e-01 1.27095133e-01 -2.54587233e-01
-7.21870244e-01 -1.88786536e-01 -4.18757379e-01 4.71399933e-01
-6.83122575e-01 6.92445755e-01 -2.86182724e-02 -7.63779759e-01
-6.08362138e-01 -9.04426277e-01 -7.93727189e-02 -5.21303654e-01
-6.33163691e-01 2.10906520e-01 -1.34444326e-01 -3.38605195... | [14.324128150939941, 3.279284715652466] |
b9825f6e-18b9-4391-b4b6-37c2a460ba22 | deep-structured-prediction-for-facial-1 | 2010.09035 | null | https://arxiv.org/abs/2010.09035v1 | https://arxiv.org/pdf/2010.09035v1.pdf | Deep Structured Prediction for Facial Landmark Detection | Existing deep learning based facial landmark detection methods have achieved excellent performance. These methods, however, do not explicitly embed the structural dependencies among landmark points. They hence cannot preserve the geometric relationships between landmark points or generalize well to challenging conditio... | ['Qiang Ji', 'Hui Su', 'Lisha Chen'] | 2020-10-18 | deep-structured-prediction-for-facial | http://papers.nips.cc/paper/8515-deep-structured-prediction-for-facial-landmark-detection | http://papers.nips.cc/paper/8515-deep-structured-prediction-for-facial-landmark-detection.pdf | neurips-2019-12 | ['face-alignment'] | ['computer-vision'] | [-3.12534571e-01 -1.88393787e-01 -1.89373776e-01 -7.51247287e-01
-9.13888931e-01 -2.95952708e-01 6.43188775e-01 2.35721236e-03
-4.17531550e-01 3.20921808e-01 -1.00388020e-01 1.25172585e-01
-3.00368969e-03 -6.05162740e-01 -5.97399533e-01 -5.80848396e-01
-3.84937257e-01 5.40421605e-01 3.37780923e-01 -5.98953143... | [13.463530540466309, 0.4785809814929962] |
816610f2-e221-4104-921e-c033fb0f873e | pragmatically-informative-text-generation | 1904.01301 | null | http://arxiv.org/abs/1904.01301v2 | http://arxiv.org/pdf/1904.01301v2.pdf | Pragmatically Informative Text Generation | We improve the informativeness of models for conditional text generation
using techniques from computational pragmatics. These techniques formulate
language production as a game between speakers and listeners, in which a
speaker should generate output text that a listener can use to correctly
identify the original inpu... | ['Daniel Fried', 'Sheng Shen', 'Jacob Andreas', 'Dan Klein'] | 2019-04-02 | pragmatically-informative-text-generation-1 | https://aclanthology.org/N19-1410 | https://aclanthology.org/N19-1410.pdf | naacl-2019-6 | ['conditional-text-generation', 'grounded-language-learning'] | ['natural-language-processing', 'natural-language-processing'] | [ 6.96569979e-01 1.15158784e+00 -2.14345455e-01 -3.70235652e-01
-9.67253983e-01 -6.50557101e-01 1.16127169e+00 4.94015545e-01
-2.35387325e-01 9.02262509e-01 1.38887608e+00 -4.23473299e-01
9.87524316e-02 -7.14198709e-01 -4.77123320e-01 -3.63760620e-01
4.22138274e-01 8.22298527e-01 -1.10158585e-01 -4.90991443... | [11.536991119384766, 8.949480056762695] |
5b075136-d0ba-4953-a0b6-d89f115c0882 | hippocluster-an-efficient-hippocampus | 2205.12338 | null | https://arxiv.org/abs/2205.12338v1 | https://arxiv.org/pdf/2205.12338v1.pdf | Hippocluster: an efficient, hippocampus-inspired algorithm for graph clustering | Random walks can reveal communities or clusters in networks, because they are more likely to stay within a cluster than leave it. Thus, one family of community detection algorithms uses random walks to measure distance between pairs of nodes in various ways, and then applies K-Means or other generic clustering methods ... | ['Artur Luczak', 'Eric Chalmers'] | 2022-05-19 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [ 6.64223209e-02 2.53125340e-01 1.92125365e-01 -1.59158587e-01
2.86595762e-01 -7.72122324e-01 7.09595740e-01 6.62301183e-01
-6.60558105e-01 3.47042710e-01 -3.29440325e-01 -1.45629808e-01
-3.28577459e-01 -1.25712991e+00 -6.43075883e-01 -1.10514903e+00
-7.71540821e-01 8.04289460e-01 8.49063039e-01 4.14820984... | [6.986321926116943, 5.777878761291504] |
a24476ab-35d6-4ab0-90df-4ac8489bd36a | large-vocabulary-audio-visual-speech | 2109.04894 | null | https://arxiv.org/abs/2109.04894v1 | https://arxiv.org/pdf/2109.04894v1.pdf | Large-vocabulary Audio-visual Speech Recognition in Noisy Environments | Audio-visual speech recognition (AVSR) can effectively and significantly improve the recognition rates of small-vocabulary systems, compared to their audio-only counterparts. For large-vocabulary systems, however, there are still many difficulties, such as unsatisfactory video recognition accuracies, that make it hard ... | ['Dorothea Kolossa', 'Steffen Zeiler', 'Wentao Yu'] | 2021-09-10 | null | null | null | null | ['lipreading', 'audio-visual-speech-recognition'] | ['computer-vision', 'speech'] | [ 4.12009865e-01 -1.71159565e-01 -6.50558993e-02 7.09798634e-02
-1.62679458e+00 -3.10370505e-01 6.69469476e-01 5.35017066e-03
-5.34397721e-01 4.58346933e-01 3.17716151e-01 -1.55597553e-01
6.17721900e-02 -1.06444187e-01 -5.39426029e-01 -9.86542940e-01
2.43981406e-01 9.48026180e-02 1.27911732e-01 -1.82587683... | [14.452394485473633, 5.259712219238281] |
70153a96-e414-4d4f-90c4-c6e5f0428347 | using-word-embeddings-for-italian-crime-news | null | null | https://ieeexplore.ieee.org/document/9555723 | https://annals-csis.org/proceedings/2021/drp/pdf/118.pdf | Using Word Embeddings for Italian Crime News Categorization | Several studies have shown that the use of embeddings improves outcomes in many Natural Language Processing (NLP) activities, including text categorization. This paper focuses on how word embeddings can be used on newspaper articles related to crimes. The scope is the categorization of the news articles based on the ty... | ['Laura Po', 'Giovanni Bonisoli', 'Federica Rollo'] | 2021-10-08 | null | null | null | conference-on-computer-science-and | ['text-categorization'] | ['natural-language-processing'] | [-3.95198971e-01 -7.82398358e-02 -5.60045123e-01 -2.64829725e-01
-2.06329823e-01 -4.37487155e-01 1.11473930e+00 1.27817500e+00
-1.20107651e+00 3.78470480e-01 1.02989316e+00 -4.23621505e-01
-1.18083455e-01 -1.04999280e+00 2.16919586e-01 -2.58433014e-01
-3.63714807e-02 5.81864476e-01 -7.19021335e-02 -3.97309542... | [10.371769905090332, 8.791936874389648] |
4e2c32c8-1110-40c8-b7f0-ccdbac3863d1 | renovating-parsing-r-cnn-for-accurate-1 | 2009.09447 | null | https://arxiv.org/abs/2009.09447v1 | https://arxiv.org/pdf/2009.09447v1.pdf | Renovating Parsing R-CNN for Accurate Multiple Human Parsing | Multiple human parsing aims to segment various human parts and associate each part with the corresponding instance simultaneously. This is a very challenging task due to the diverse human appearance, semantic ambiguity of different body parts, and complex background. Through analysis of multiple human parsing task, we ... | ['Qing Song', 'Xueshi Xin', 'Wenhe Jia', 'Songcen Xu', 'Lu Yang', 'Chun Liu', 'Mengjie Hu', 'Zhihui Wang'] | 2020-09-20 | renovating-parsing-r-cnn-for-accurate | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1600_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123570409.pdf | eccv-2020-8 | ['human-parsing'] | ['computer-vision'] | [ 2.02116415e-01 2.57225245e-01 7.69439489e-02 -6.17906928e-01
-1.08849287e+00 -3.10320348e-01 1.00019649e-01 -5.07336743e-02
-3.45692575e-01 3.87262225e-01 2.94543296e-01 2.41664574e-01
4.05214190e-01 -8.49838436e-01 -8.15936029e-01 -2.34897405e-01
2.07336873e-01 4.64159608e-01 7.45396197e-01 -2.66256899... | [8.622200965881348, -0.008342829532921314] |
dd535711-8a20-40c1-97fd-852b27a0bd25 | semantic-dependency-parsing-via-book | null | null | https://aclanthology.org/P17-1077 | https://aclanthology.org/P17-1077.pdf | Semantic Dependency Parsing via Book Embedding | We model a dependency graph as a book, a particular kind of topological space, for semantic dependency parsing. The spine of the book is made up of a sequence of words, and each page contains a subset of noncrossing arcs. To build a semantic graph for a given sentence, we design new Maximum Subgraph algorithms to gener... | ['Weiwei Sun', 'Junjie Cao', 'Xiaojun Wan'] | 2017-07-01 | null | null | null | acl-2017-7 | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [ 6.87278137e-02 9.04332876e-01 -7.09841192e-01 -4.95178908e-01
-7.31215537e-01 -8.90634596e-01 4.50496793e-01 1.38507023e-01
1.14053287e-01 7.37354457e-01 2.93384194e-01 -4.41204667e-01
-7.04388097e-02 -1.22765672e+00 -9.99594212e-01 -2.34840721e-01
-2.12672383e-01 9.59469616e-01 7.52167940e-01 -1.96174532... | [10.327214241027832, 9.583195686340332] |
f012b781-7580-44f4-8ba1-b20d36378b36 | universal-adversarial-perturbations-and-image | 2103.05469 | null | https://arxiv.org/abs/2103.05469v1 | https://arxiv.org/pdf/2103.05469v1.pdf | Universal Adversarial Perturbations and Image Spam Classifiers | As the name suggests, image spam is spam email that has been embedded in an image. Image spam was developed in an effort to evade text-based filters. Modern deep learning-based classifiers perform well in detecting typical image spam that is seen in the wild. In this chapter, we evaluate numerous adversarial techniques... | ['Mark Stamp', 'Andy Phung'] | 2021-03-07 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [ 5.88045418e-01 -2.21456498e-01 4.97232944e-01 -2.39682361e-01
-6.10230267e-01 -8.89492452e-01 1.02632916e+00 -1.20942533e-01
-2.49048084e-01 2.61314541e-01 4.30012308e-02 -5.36642253e-01
4.28937167e-01 -8.47286284e-01 -1.10774601e+00 -8.32901239e-01
5.81371859e-02 3.12808633e-01 4.96719211e-01 -5.11001348... | [7.664395332336426, 9.867128372192383] |
715267f6-014f-4a4e-859a-cb34310f0aaa | learning-monocular-3d-human-pose-estimation | 1803.04775 | null | http://arxiv.org/abs/1803.04775v2 | http://arxiv.org/pdf/1803.04775v2.pdf | Learning Monocular 3D Human Pose Estimation from Multi-view Images | Accurate 3D human pose estimation from single images is possible with
sophisticated deep-net architectures that have been trained on very large
datasets. However, this still leaves open the problem of capturing motions for
which no such database exists. Manual annotation is tedious, slow, and
error-prone. In this paper... | ['Mathieu Salzmann', 'Erich Müller', 'Frédéric Meyer', 'Jörg Spörri', 'Pascal Fua', 'Isinsu Katircioglu', 'Victor Constantin', 'Helge Rhodin'] | 2018-03-13 | learning-monocular-3d-human-pose-estimation-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Rhodin_Learning_Monocular_3D_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Rhodin_Learning_Monocular_3D_CVPR_2018_paper.pdf | cvpr-2018-6 | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [ 1.70727316e-02 8.50450844e-02 4.38832399e-03 -3.31919432e-01
-7.86415339e-01 -6.70949161e-01 3.15589517e-01 -2.99998432e-01
-6.98458612e-01 7.41145790e-01 -1.35408835e-02 1.22027129e-01
2.58967966e-01 -3.29976112e-01 -9.72509086e-01 -4.50959921e-01
3.46502811e-01 6.74692988e-01 2.08874226e-01 -1.25937074... | [7.155930995941162, -0.964701235294342] |
53f4e5f0-0410-4e0c-977e-6bfaedefa073 | quantum-artificial-vision-for-defect | 2208.04988 | null | https://arxiv.org/abs/2208.04988v1 | https://arxiv.org/pdf/2208.04988v1.pdf | Quantum artificial vision for defect detection in manufacturing | In this paper we consider several algorithms for quantum computer vision using Noisy Intermediate-Scale Quantum (NISQ) devices, and benchmark them for a real problem against their classical counterparts. Specifically, we consider two approaches: a quantum Support Vector Machine (QSVM) on a universal gate-based quantum ... | ['Roman Orus', 'Josu Bilbao', 'Aizea Lojo', 'Ana Adell', 'Xabier De Carlos', 'Daniel Estepa', 'Samuel Mugel', 'Gianni Del Bimbo', 'Victor Onofre', 'Daniel Guijo'] | 2022-08-09 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 5.13444245e-01 1.93070117e-02 3.32517475e-01 -1.21207349e-01
-1.07835066e+00 -4.74396080e-01 3.59652817e-01 2.63117552e-01
-5.19146800e-01 3.83218020e-01 -7.16370940e-01 -2.52586186e-01
-3.35725397e-01 -1.03057015e+00 -5.62156558e-01 -1.03408694e+00
3.04411978e-01 8.30851376e-01 3.17775279e-01 -8.25509906... | [5.551692962646484, 4.940662384033203] |
6b47c553-6e25-4056-81f8-0dbfe06608c7 | binding-actions-to-objects-in-world-models | 2204.13022 | null | https://arxiv.org/abs/2204.13022v1 | https://arxiv.org/pdf/2204.13022v1.pdf | Binding Actions to Objects in World Models | We study the problem of binding actions to objects in object-factored world models using action-attention mechanisms. We propose two attention mechanisms for binding actions to objects, soft attention and hard attention, which we evaluate in the context of structured world models for five environments. Our experiments ... | ['Thomas Kipf', 'Lawson L. S. Wong', 'Jan-Willem van de Meent', 'Robert Platt', 'Ondrej Biza'] | 2022-04-27 | null | null | null | null | ['hard-attention'] | ['methodology'] | [ 1.45880565e-01 5.01440585e-01 -1.77366421e-01 -1.38703227e-01
-2.89351285e-01 -2.96948403e-01 6.13278568e-01 -4.71842527e-01
-4.12114441e-01 6.43579960e-01 6.71952784e-01 2.12406859e-01
-4.88911986e-01 -5.79760075e-01 -1.08698130e+00 -6.05389714e-01
-1.91034004e-01 9.33341444e-01 4.20766503e-01 -3.29462081... | [4.563510417938232, 0.5553707480430603] |
b95b21f3-e8bb-4852-9f3b-9b876e74ea99 | marrnet-3d-shape-reconstruction-via-25d | 1711.03129 | null | http://arxiv.org/abs/1711.03129v1 | http://arxiv.org/pdf/1711.03129v1.pdf | MarrNet: 3D Shape Reconstruction via 2.5D Sketches | 3D object reconstruction from a single image is a highly under-determined
problem, requiring strong prior knowledge of plausible 3D shapes. This
introduces challenges for learning-based approaches, as 3D object annotations
are scarce in real images. Previous work chose to train on synthetic data with
ground truth 3D in... | ['William T. Freeman', 'Joshua B. Tenenbaum', 'Jiajun Wu', 'Yifan Wang', 'Xingyuan Sun', 'Tianfan Xue'] | 2017-11-08 | marrnet-3d-shape-reconstruction-via-25d-1 | http://papers.nips.cc/paper/6657-marrnet-3d-shape-reconstruction-via-25d-sketches | http://papers.nips.cc/paper/6657-marrnet-3d-shape-reconstruction-via-25d-sketches.pdf | neurips-2017-12 | ['3d-object-reconstruction', '3d-object-reconstruction-from-a-single-image'] | ['computer-vision', 'computer-vision'] | [ 3.63723710e-02 2.61601925e-01 -6.43897057e-02 -2.53566921e-01
-9.09445167e-01 -9.21346366e-01 6.61618829e-01 -5.77305555e-01
-4.38845903e-02 4.81241614e-01 -4.17168252e-02 -7.41767511e-02
2.52684265e-01 -5.92468143e-01 -1.06007600e+00 -3.75683814e-01
3.37793171e-01 1.11451459e+00 7.17116594e-02 -9.85294208... | [8.655791282653809, -3.2461225986480713] |
3d1ad776-41d9-4df4-a5f5-f7b9501a882a | a-reminder-of-its-brittleness-language-reward | 2305.16621 | null | https://arxiv.org/abs/2305.16621v1 | https://arxiv.org/pdf/2305.16621v1.pdf | A Reminder of its Brittleness: Language Reward Shaping May Hinder Learning for Instruction Following Agents | Teaching agents to follow complex written instructions has been an important yet elusive goal. One technique for improving learning efficiency is language reward shaping (LRS), which is used in reinforcement learning (RL) to reward actions that represent progress towards a sparse reward. We argue that the apparent succ... | ['Trevor Cohn', 'Nir Lipovetzky', 'Sukai Huang'] | 2023-05-26 | null | null | null | null | ['instruction-following'] | ['natural-language-processing'] | [ 5.06526046e-02 2.26903707e-01 -3.39771479e-01 7.56672323e-02
-7.62955844e-01 -7.32064307e-01 9.87295568e-01 1.22081518e-01
-8.65653515e-01 1.08580387e+00 6.41420186e-01 -5.06811857e-01
-2.63439924e-01 -1.72882706e-01 -7.75664926e-01 -5.65737605e-01
-2.61166066e-01 2.31073365e-01 -1.27314568e-01 -5.87949753... | [4.069272994995117, 1.8309383392333984] |
80d54261-eddc-493d-b08d-916f10f1b02b | autoextend-combining-word-embeddings-with | null | null | https://aclanthology.org/J17-3004 | https://aclanthology.org/J17-3004.pdf | AutoExtend: Combining Word Embeddings with Semantic Resources | We present AutoExtend, a system that combines word embeddings with semantic resources by learning embeddings for non-word objects like synsets and entities and learning word embeddings that incorporate the semantic information from the resource. The method is based on encoding and decoding the word embeddings and is fl... | ['Hinrich Sch{\\"u}tze', 'Sascha Rothe'] | 2017-09-01 | null | null | null | cl-2017-9 | ['learning-word-embeddings'] | ['methodology'] | [-6.06940985e-01 -8.56212899e-02 -5.87737620e-01 -2.43791863e-01
-3.13822567e-01 -6.46488190e-01 6.29615307e-01 7.45970249e-01
-1.13067865e+00 3.26477438e-01 8.92597377e-01 -2.34676555e-01
-6.34862855e-02 -1.00838625e+00 -1.34086102e-01 -3.13841909e-01
-1.62936613e-01 6.45079553e-01 4.02170390e-01 -7.19110131... | [10.42634391784668, 8.749483108520508] |
24d739f2-ccfc-411e-819a-aa06d986cefe | the-effects-of-varying-penetration-rates-of | 2306.01177 | null | https://arxiv.org/abs/2306.01177v1 | https://arxiv.org/pdf/2306.01177v1.pdf | The Effects of Varying Penetration Rates of L4-L5 Autonomous Vehicles on Fuel Efficiency and Mobility of Traffic Networks | Microscopic traffic simulators that simulate realistic traffic flow are crucial in studying, understanding and evaluating the fuel usage and mobility effects of having a higher number of autonomous vehicles (AVs) in traffic under realistic mixed traffic conditions including both autonomous and non-autonomous vehicles. ... | ['Levent Guvenc', 'Bilin Aksun-Guvenc', 'Karina Meneses Cime', 'M. Ridvan Cantas', 'Ozgenur Kavas-Torris'] | 2023-06-01 | null | null | null | null | ['autonomous-vehicles'] | ['computer-vision'] | [-6.75327241e-01 -1.29048869e-01 -8.94762874e-02 7.71728680e-02
6.86012730e-02 -6.60175681e-01 5.43466687e-01 3.67913730e-02
-5.79555988e-01 1.04929149e+00 -4.08104628e-01 -1.18234444e+00
-4.63769883e-01 -1.14958453e+00 -4.91717488e-01 -6.35707319e-01
-3.70764732e-01 3.38436872e-01 7.38441229e-01 -4.32789266... | [5.611453056335449, 1.5503485202789307] |
cf2b8366-39c6-4309-9bbb-f92e71861eb4 | aspect-based-sentiment-analysis-through-edu | 2202.02535 | null | https://arxiv.org/abs/2202.02535v1 | https://arxiv.org/pdf/2202.02535v1.pdf | Aspect-based Sentiment Analysis through EDU-level Attentions | A sentence may express sentiments on multiple aspects. When these aspects are associated with different sentiment polarities, a model's accuracy is often adversely affected. We observe that multiple aspects in such hard sentences are mostly expressed through multiple clauses, or formally known as elementary discourse u... | ['Yequan Wang', 'Aixin Sun', 'Ting Lin'] | 2022-02-05 | null | null | null | null | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [ 2.53612906e-01 5.30441046e-01 -5.58610320e-01 -6.58378303e-01
-9.57256913e-01 -6.88562632e-01 4.75301981e-01 3.71671826e-01
-2.23719060e-01 6.79683864e-01 8.47620189e-01 -2.13810325e-01
6.71087503e-01 -5.99263966e-01 -7.78216124e-01 -3.66339952e-01
5.31498313e-01 2.02718318e-01 -1.21702440e-01 -3.58952880... | [11.481767654418945, 6.701155185699463] |
3429d433-2538-4a83-8678-0316f2ed6ef7 | towards-end-to-end-text-spotting-with | 1707.03985 | null | http://arxiv.org/abs/1707.03985v1 | http://arxiv.org/pdf/1707.03985v1.pdf | Towards End-to-end Text Spotting with Convolutional Recurrent Neural Networks | In this work, we jointly address the problem of text detection and
recognition in natural scene images based on convolutional recurrent neural
networks. We propose a unified network that simultaneously localizes and
recognizes text with a single forward pass, avoiding intermediate processes
like image cropping and feat... | ['Chunhua Shen', 'Peng Wang', 'Hui Li'] | 2017-07-13 | towards-end-to-end-text-spotting-with-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Li_Towards_End-To-End_Text_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Li_Towards_End-To-End_Text_ICCV_2017_paper.pdf | iccv-2017-10 | ['text-spotting', 'image-cropping'] | ['computer-vision', 'computer-vision'] | [ 6.42599106e-01 -4.51541603e-01 -8.79501458e-03 -2.97688365e-01
-6.53909266e-01 -5.71754158e-01 7.11290836e-01 2.49808490e-01
-8.29522610e-01 2.47526646e-01 -1.55769080e-01 -2.77415574e-01
3.12908977e-01 -6.50472343e-01 -4.66409653e-01 -6.98860705e-01
6.71320558e-01 2.92086959e-01 2.70138204e-01 3.58245134... | [11.93722915649414, 2.2552406787872314] |
5f34576a-71f1-4149-bf06-810ffe0b334c | logic-and-commonsense-guided-temporal | 2211.16865 | null | https://arxiv.org/abs/2211.16865v2 | https://arxiv.org/pdf/2211.16865v2.pdf | Logic and Commonsense-Guided Temporal Knowledge Graph Completion | A temporal knowledge graph (TKG) stores the events derived from the data involving time. Predicting events is extremely challenging due to the time-sensitive property of events. Besides, the previous TKG completion (TKGC) approaches cannot represent both the timeliness and the causality properties of events, simultaneo... | ['Bo Li', 'Guanglin Niu'] | 2022-11-30 | null | null | null | null | ['temporal-knowledge-graph-completion'] | ['knowledge-base'] | [-9.10369605e-02 6.85945675e-02 -4.56021219e-01 -4.56768036e-01
-2.96668410e-01 -4.10641432e-01 7.37115204e-01 3.91387761e-01
-6.22127615e-02 7.14641333e-01 7.49234438e-01 -2.76507378e-01
-5.15430331e-01 -1.13329923e+00 -6.34691954e-01 -5.28572738e-01
-3.20639938e-01 4.13254276e-02 1.26205653e-01 -1.28891692... | [8.84278678894043, 8.483184814453125] |
bbade855-97fb-477d-b307-26da749d1230 | tracking-based-semi-automatic-annotation-for | 2103.15488 | null | https://arxiv.org/abs/2103.15488v1 | https://arxiv.org/pdf/2103.15488v1.pdf | Tracking Based Semi-Automatic Annotation for Scene Text Videos | Recently, video scene text detection has received increasing attention due to its comprehensive applications. However, the lack of annotated scene text video datasets has become one of the most important problems, which hinders the development of video scene text detection. The existing scene text video datasets are no... | ['Shan Cao', 'Shugong Xu', 'Zhiwei Jia', 'Xiufeng Jiang', 'Jiajun Zhu'] | 2021-03-29 | null | null | null | null | ['scene-text-detection', 'text-annotation'] | ['computer-vision', 'natural-language-processing'] | [ 3.04299176e-01 -7.79970825e-01 1.20355271e-01 -8.83581638e-02
-7.16411233e-01 -3.56951028e-01 4.27306145e-01 -2.46104464e-01
-4.21611518e-01 4.02135819e-01 1.70308352e-01 1.22542627e-01
2.37268537e-01 -3.43097657e-01 -5.90935826e-01 -8.75424027e-01
7.72510409e-01 -5.17504523e-03 7.63181686e-01 3.12798351... | [9.273470878601074, -0.20563088357448578] |
1d761c2f-3085-4823-b00b-f0f5db62dba1 | a-generalized-framework-for-critical-heat | 2212.09107 | null | https://arxiv.org/abs/2212.09107v3 | https://arxiv.org/pdf/2212.09107v3.pdf | A Framework for Generalizing Critical Heat Flux Detection Models Using Unsupervised Image-to-Image Translation | The detection of critical heat flux (CHF) is crucial in heat boiling applications as failure to do so can cause rapid temperature ramp leading to device failures. Many machine learning models exist to detect CHF, but their performance reduces significantly when tested on data from different domains. To deal with datase... | ['Md Mahfuzur Rahman Siddiquee', 'Tejaswi Soori', 'Ying Sun', 'Teresa Wu', 'Hyunsoo Yoon', 'Han Hu', 'Firas Al-Hindawi'] | 2022-12-18 | null | null | null | null | ['unsupervised-image-to-image-translation'] | ['computer-vision'] | [ 6.51446521e-01 3.78359668e-02 2.48088259e-02 -5.72423518e-01
-5.26361406e-01 -6.64855003e-01 3.64708573e-01 2.16642618e-01
-7.28505105e-02 6.98270500e-01 -5.87220490e-01 -2.41612032e-01
4.05733973e-01 -8.34649086e-01 -8.00050437e-01 -7.49675333e-01
4.18354511e-01 5.71948230e-01 4.86100972e-01 -1.54124692... | [9.79812240600586, 2.343776226043701] |
0abb447c-eb91-4de7-858f-aa1d9504f6a6 | enlarged-large-margin-loss-for-imbalanced | 2306.09132 | null | https://arxiv.org/abs/2306.09132v1 | https://arxiv.org/pdf/2306.09132v1.pdf | Enlarged Large Margin Loss for Imbalanced Classification | We propose a novel loss function for imbalanced classification. LDAM loss, which minimizes a margin-based generalization bound, is widely utilized for class-imbalanced image classification. Although, by using LDAM loss, it is possible to obtain large margins for the minority classes and small margins for the majority c... | ['Kazuhiro Hotta', 'Sota Kato'] | 2023-06-15 | null | null | null | null | ['imbalanced-classification'] | ['miscellaneous'] | [-1.42550528e-01 1.05539240e-01 -5.39786279e-01 -7.94789314e-01
-5.90103686e-01 -5.59584908e-02 -1.51422061e-02 4.68872130e-01
-6.25773966e-01 1.05900395e+00 -2.14207023e-01 -2.40275860e-01
-2.12015286e-01 -7.86545396e-01 -5.56173921e-01 -7.49025404e-01
-4.34676707e-02 -1.66460685e-02 2.18233883e-01 7.28314370... | [9.098267555236816, 3.9285833835601807] |
bb0fbe56-b814-4b1d-a77b-35479cfcd507 | a-survey-on-energy-optimization-techniques-in | 2204.07967 | null | https://arxiv.org/abs/2204.07967v1 | https://arxiv.org/pdf/2204.07967v1.pdf | A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches | Wireless communication networks have been witnessing an unprecedented demand due to the increasing number of connected devices and emerging bandwidth-hungry applications. Albeit many competent technologies for capacity enhancement purposes, such as millimeter wave communications and network densification, there is stil... | ['Muhammad Ali Imran', 'Sajjad Hussain', 'Qammer H. Abbasi', 'Michael S. Mollel', 'Ali Makine Abdel-Salam', 'Cihat Ozturk', 'Metin Ozturk', 'Kenechi G. Omeke', 'Iftikhar Ahmad', 'Attai Ibrahim Abubakar'] | 2022-04-17 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 2.75762826e-01 -9.71529484e-02 -3.47502470e-01 1.97643027e-01
1.91400245e-01 -4.74693149e-01 5.81021868e-02 5.40114678e-02
-4.12858039e-01 1.06766129e+00 -4.71624017e-01 -1.99034721e-01
-7.96334445e-01 -1.08421659e+00 -2.22432077e-01 -9.41436589e-01
-4.55123156e-01 5.86163290e-02 -4.69498374e-02 -3.52448553... | [5.934391498565674, 1.57746422290802] |
a4df6370-3954-4a14-9806-2cf5cf431ba8 | coarse-to-fine-entity-representations-for | 2012.02507 | null | https://arxiv.org/abs/2012.02507v2 | https://arxiv.org/pdf/2012.02507v2.pdf | Coarse-to-Fine Entity Representations for Document-level Relation Extraction | Document-level Relation Extraction (RE) requires extracting relations expressed within and across sentences. Recent works show that graph-based methods, usually constructing a document-level graph that captures document-aware interactions, can obtain useful entity representations thus helping tackle document-level RE. ... | ['Zhifang Sui', 'Baobao Chang', 'Shuang Zeng', 'Jing Ren', 'Damai Dai'] | 2020-12-04 | null | null | null | null | ['document-level-relation-extraction'] | ['natural-language-processing'] | [ 1.02699675e-01 3.20409149e-01 -5.11728764e-01 -1.84839353e-01
-5.61201811e-01 -5.63395500e-01 6.72589064e-01 7.60880709e-01
1.27106979e-01 8.76586676e-01 4.43683743e-01 -1.91074371e-01
-4.82455760e-01 -1.46193552e+00 -4.77985233e-01 -3.15009922e-01
-2.20348045e-01 3.56617510e-01 4.08412009e-01 -3.61987770... | [9.194870948791504, 8.544050216674805] |
24abb3d1-8383-458a-8ec2-81a8dffa19db | last-at-semeval-2021-task-1-improving-multi | 2105.09653 | null | https://arxiv.org/abs/2105.09653v1 | https://arxiv.org/pdf/2105.09653v1.pdf | LAST at SemEval-2021 Task 1: Improving Multi-Word Complexity Prediction Using Bigram Association Measures | This paper describes the system developed by the Laboratoire d'analyse statistique des textes (LAST) for the Lexical Complexity Prediction shared task at SemEval-2021. The proposed system is made up of a LightGBM model fed with features obtained from many word frequency lists, published lexical norms and psychometric d... | ['Yves Bestgen'] | 2021-05-20 | null | https://aclanthology.org/2021.semeval-1.71 | https://aclanthology.org/2021.semeval-1.71.pdf | semeval-2021 | ['lexical-complexity-prediction'] | ['natural-language-processing'] | [-1.99110627e-01 -6.37074411e-02 -2.88629174e-01 -3.10740322e-01
-5.62798619e-01 -2.06623048e-01 6.53411448e-01 5.44414580e-01
-1.13887596e+00 9.67143476e-01 2.35720247e-01 -4.43749726e-01
-1.30796626e-01 -6.39738202e-01 -5.58695309e-02 -2.33384579e-01
2.23508686e-01 6.07391834e-01 2.32330352e-01 -5.41880727... | [10.640741348266602, 10.515192985534668] |
f8fd7a2b-5d61-426b-926c-9b5a52778e43 | lipschitz-normalization-for-self-attention | 2103.04886 | null | https://arxiv.org/abs/2103.04886v3 | https://arxiv.org/pdf/2103.04886v3.pdf | Lipschitz Normalization for Self-Attention Layers with Application to Graph Neural Networks | Attention based neural networks are state of the art in a large range of applications. However, their performance tends to degrade when the number of layers increases. In this work, we show that enforcing Lipschitz continuity by normalizing the attention scores can significantly improve the performance of deep attentio... | ['Aladin Virmaux', 'Kevin Scaman', 'George Dasoulas'] | 2021-03-08 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [-6.12744242e-02 4.56223696e-01 -3.54760557e-01 -5.43591678e-01
-5.04841030e-01 -3.90478313e-01 3.43155771e-01 3.05317193e-01
-4.39231992e-01 5.26213229e-01 2.41964936e-01 -5.24230242e-01
3.84468995e-02 -7.05537796e-01 -9.80784714e-01 -6.43583298e-01
-2.64139920e-01 5.07306159e-01 3.45449597e-02 -2.57310629... | [6.963697910308838, 6.215373516082764] |
47f764d7-d62a-436a-a51b-b4d63861e4dc | noisy-positive-unlabeled-learning-with-self | 2306.07512 | null | https://arxiv.org/abs/2306.07512v1 | https://arxiv.org/pdf/2306.07512v1.pdf | Noisy Positive-Unlabeled Learning with Self-Training for Speculative Knowledge Graph Reasoning | This paper studies speculative reasoning task on real-world knowledge graphs (KG) that contain both \textit{false negative issue} (i.e., potential true facts being excluded) and \textit{false positive issue} (i.e., unreliable or outdated facts being included). State-of-the-art methods fall short in the speculative reas... | ['Tarek F. Abdelzaher', 'Hanghang Tong', 'Shengzhong Liu', 'Yuchen Yan', 'Jinning Li', 'Dachun Sun', 'Yichen Lu', 'Baoyu Li', 'Ruijie Wang'] | 2023-06-13 | null | null | null | null | ['knowledge-graphs'] | ['knowledge-base'] | [-1.71503481e-02 9.67454612e-01 -7.41704404e-01 -3.83860111e-01
-6.37726426e-01 -6.68046951e-01 7.26592898e-01 3.54280293e-01
-2.19920605e-01 1.27974665e+00 3.80118974e-02 -5.05169511e-01
-1.32515326e-01 -1.18985963e+00 -1.34477675e+00 -4.96133804e-01
-2.86341012e-02 8.41278553e-01 5.73692560e-01 1.03518344... | [9.010615348815918, 7.899378776550293] |
9be4e9cc-6de9-4ae9-af42-8ef3e09d7c27 | adaptive-low-precision-training-for | 2212.05735 | null | https://arxiv.org/abs/2212.05735v1 | https://arxiv.org/pdf/2212.05735v1.pdf | Adaptive Low-Precision Training for Embeddings in Click-Through Rate Prediction | Embedding tables are usually huge in click-through rate (CTR) prediction models. To train and deploy the CTR models efficiently and economically, it is necessary to compress their embedding tables at the training stage. To this end, we formulate a novel quantization training paradigm to compress the embeddings from the... | ['Ruixuan Li', 'Rui Zhang', 'Ruiming Tang', 'Xing Tang', 'Wei zhang', 'Lu Hou', 'Huifeng Guo', 'Shiwei Li'] | 2022-12-12 | null | null | null | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [-5.25480621e-02 -3.72185290e-01 -7.23599434e-01 -2.34708369e-01
-8.20023954e-01 -2.24193051e-01 -2.16718981e-04 4.35719520e-01
-4.60483164e-01 3.81041914e-01 4.38193604e-02 -8.53713930e-01
-4.14538197e-02 -1.06284463e+00 -7.88561523e-01 -4.60721999e-01
-8.61563012e-02 7.84148723e-02 3.86519641e-01 -1.31507590... | [8.652063369750977, 3.311443328857422] |
2b1f4162-1a4a-44cf-be24-9d70158e2543 | uncertainty-aware-reward-based-deep | 2302.10195 | null | https://arxiv.org/abs/2302.10195v1 | https://arxiv.org/pdf/2302.10195v1.pdf | Uncertainty-Aware Reward-based Deep Reinforcement Learning for Intent Analysis of Social Media Information | Due to various and serious adverse impacts of spreading fake news, it is often known that only people with malicious intent would propagate fake news. However, it is not necessarily true based on social science studies. Distinguishing the types of fake news spreaders based on their intent is critical because it will ef... | ['Jin-Hee Cho', 'Dong H. Jeong', 'Feng Chen', 'Lance M. Kaplan', 'Audun Jøsang', 'Qisheng Zhang', 'Xinwei An', 'Qi Zhang', 'Zhen Guo'] | 2023-02-19 | null | null | null | null | ['intent-classification'] | ['natural-language-processing'] | [-1.00632265e-01 8.00118148e-02 -5.90889037e-01 -4.94104207e-01
-5.24589062e-01 -4.21868682e-01 7.34471142e-01 9.51462090e-02
-4.02522087e-01 8.24198902e-01 5.26951849e-01 -3.93175095e-01
2.28770345e-01 -1.01281655e+00 -7.65628397e-01 -3.24857652e-01
7.91493952e-02 3.32014292e-01 -2.02320263e-01 -5.78434944... | [8.114317893981934, 10.23215389251709] |
9f8e0bfa-8591-45a3-9cb4-c1886764b4ff | leveraging-human-computation-for-quality | null | null | https://epub.ub.uni-muenchen.de/91046/ | https://epub.ub.uni-muenchen.de/91046/1/BA_Johannah.Sprinz.pdf | Leveraging Human Computation for Quality Assurance in Open Source Communities | Software developed under the open source development model (OSSD) has risen to significant importance over the recent decades. With more and more critical components being developed under the OSSD, the need for extensive quality assurance (QA) increases. This thesis investigates any potential for conducting formalized ... | ['Johannah Sprinz'] | 2022-01-29 | null | null | null | open-access-lmu-2022-1 | ['manufacturing-quality-control'] | ['computer-vision'] | [-7.18861282e-01 4.14148629e-01 3.73267263e-01 -4.00129586e-01
-6.57879770e-01 -8.43399525e-01 2.59027004e-01 4.01077330e-01
-2.47303113e-01 7.34959841e-01 6.70787692e-02 -5.21678030e-01
-5.65884821e-02 -5.75164080e-01 -3.13013732e-01 2.50285804e-01
1.17784843e-01 5.26464701e-01 4.44242954e-01 -4.17766988... | [9.233014106750488, 6.531495094299316] |
f26dd1d9-5cf0-4270-b217-909bbed4c2f1 | from-shortcuts-to-triggers-backdoor-defense | 2305.14910 | null | https://arxiv.org/abs/2305.14910v1 | https://arxiv.org/pdf/2305.14910v1.pdf | From Shortcuts to Triggers: Backdoor Defense with Denoised PoE | Language models are often at risk of diverse backdoor attacks, especially data poisoning. Thus, it is important to investigate defense solutions for addressing them. Existing backdoor defense methods mainly focus on backdoor attacks with explicit triggers, leaving a universal defense against various backdoor attacks wi... | ['Muhao Chen', 'Chaowei Xiao', 'Fei Wang', 'Qin Liu'] | 2023-05-24 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [-7.68796653e-02 -4.46771681e-01 -2.60979265e-01 -1.43195182e-01
-1.00728405e+00 -1.24722886e+00 5.64853847e-01 2.12168377e-02
-1.60720989e-01 2.19150752e-01 2.70507902e-01 -8.41528893e-01
4.27636728e-02 -7.98086464e-01 -7.11220384e-01 -7.46395290e-01
8.31556916e-02 -9.28672329e-02 3.93060774e-01 -4.94699001... | [5.971005916595459, 7.8669962882995605] |
a48de1d3-5b98-412a-b82a-971d45968027 | twitter-user-geolocation-using-deep-multiview | 1805.04612 | null | http://arxiv.org/abs/1805.04612v1 | http://arxiv.org/pdf/1805.04612v1.pdf | Twitter User Geolocation using Deep Multiview Learning | Predicting the geographical location of users on social networks like Twitter
is an active research topic with plenty of methods proposed so far. Most of the
existing work follows either a content-based or a network-based approach. The
former is based on user-generated content while the latter exploits the
structure of... | ['Nikos Deligiannis', 'Bruno Cornelis', 'Tien Huu Do', 'Duc Minh Nguyen', 'Evaggelia Tsiligianni'] | 2018-05-11 | null | null | null | null | ['multiview-learning'] | ['computer-vision'] | [-4.06034887e-01 -2.32419103e-01 -5.65524936e-01 -3.57746422e-01
-3.59035194e-01 -3.90990704e-01 1.07798350e+00 6.65843368e-01
-5.86665869e-01 4.91754025e-01 6.33239031e-01 -1.04182042e-01
-2.32730329e-01 -1.14807224e+00 -3.34581316e-01 -3.97066802e-01
1.73898991e-02 2.84202397e-01 2.86312759e-01 -4.57744896... | [9.983735084533691, 6.9288716316223145] |
8de7fedf-b717-43b5-a366-d8831dab4089 | wizardlm-empowering-large-language-models-to | 2304.12244 | null | https://arxiv.org/abs/2304.12244v2 | https://arxiv.org/pdf/2304.12244v2.pdf | WizardLM: Empowering Large Language Models to Follow Complex Instructions | Training large language models (LLMs) with open-domain instruction following data brings colossal success. However, manually creating such instruction data is very time-consuming and labor-intensive. Moreover, humans may struggle to produce high-complexity instructions. In this paper, we show an avenue for creating lar... | ['Daxin Jiang', 'Chongyang Tao', 'Jiazhan Feng', 'Pu Zhao', 'Xiubo Geng', 'Kai Zheng', 'Qingfeng Sun', 'Can Xu'] | 2023-04-24 | null | null | null | null | ['instruction-following'] | ['natural-language-processing'] | [ 5.53712286e-02 1.54012769e-01 1.26054063e-01 -3.99049491e-01
-9.86928463e-01 -6.67757154e-01 3.43883455e-01 9.86928865e-03
-6.04722440e-01 6.43416286e-01 3.91950980e-02 -7.23806620e-01
1.53014839e-01 -7.88848341e-01 -9.17593062e-01 -3.75029981e-01
1.24761775e-01 9.11167443e-01 7.80383572e-02 -6.42283022... | [10.751492500305176, 8.395049095153809] |
75416c51-ecad-4b40-b672-3a1ad9f12b31 | synchronous-image-label-diffusion-probability | 2307.01740 | null | https://arxiv.org/abs/2307.01740v1 | https://arxiv.org/pdf/2307.01740v1.pdf | Synchronous Image-Label Diffusion Probability Model with Application to Stroke Lesion Segmentation on Non-contrast CT | Stroke lesion volume is a key radiologic measurement for assessing the prognosis of Acute Ischemic Stroke (AIS) patients, which is challenging to be automatically measured on Non-Contrast CT (NCCT) scans. Recent diffusion probabilistic models have shown potentials of being used for image segmentation. In this paper, a ... | ['Qiu Wu', 'Aravind Ganesh', 'Ethan MacDonald', 'Tonghua Wan', 'Jianhai Zhang'] | 2023-07-04 | null | null | null | null | ['lesion-segmentation'] | ['medical'] | [ 3.27507257e-01 6.22183690e-03 -3.93112659e-01 -5.68740487e-01
-1.31720948e+00 -3.92281532e-01 6.83065414e-01 9.08919722e-02
-6.72992408e-01 5.97705007e-01 4.45114553e-01 -4.03592646e-01
-1.91318274e-01 -6.58646524e-01 -3.07805181e-01 -9.85136569e-01
9.65017229e-02 1.08622158e+00 7.05241799e-01 7.55914629... | [14.31957721710205, -2.0866751670837402] |
5382d7db-8421-4ab9-95cd-43d56ab2279a | can-we-still-use-peaq-a-performance-analysis | 2212.01467 | null | https://arxiv.org/abs/2212.01467v1 | https://arxiv.org/pdf/2212.01467v1.pdf | Can we still use PEAQ? A Performance Analysis of the ITU Standard for the Objective Assessment of Perceived Audio Quality | The Perceptual Evaluation of Audio Quality (PEAQ) method as described in the International Telecommunication Union (ITU) recommendation ITU-R BS.1387 has been widely used for computationally estimating the quality of perceptually coded audio signals without the need for extensive subjective listening tests. However, ma... | ['Jürgen Herre', 'Pablo M. Delgado'] | 2022-12-02 | null | null | null | null | ['bandwidth-extension', 'bandwidth-extension'] | ['audio', 'speech'] | [ 6.66167438e-02 -3.67691368e-01 1.99986905e-01 -1.57645375e-01
-9.27080214e-01 -4.74457115e-01 3.05951715e-01 4.95863795e-01
-4.01803911e-01 8.17403495e-01 3.37862462e-01 -3.42511773e-01
-6.40151680e-01 -4.73736465e-01 -1.21434040e-01 -7.23820448e-01
-1.36799216e-01 7.61826232e-04 4.51948673e-01 -1.77413434... | [15.126378059387207, 5.683314800262451] |
94974dd3-58db-405b-a7c3-f2bebb06e690 | minirbt-a-two-stage-distilled-small-chinese | 2304.00717 | null | https://arxiv.org/abs/2304.00717v1 | https://arxiv.org/pdf/2304.00717v1.pdf | MiniRBT: A Two-stage Distilled Small Chinese Pre-trained Model | In natural language processing, pre-trained language models have become essential infrastructures. However, these models often suffer from issues such as large size, long inference time, and challenging deployment. Moreover, most mainstream pre-trained models focus on English, and there are insufficient studies on smal... | ['Shijin Wang', 'Yiming Cui', 'Ziqing Yang', 'Xin Yao'] | 2023-04-03 | null | null | null | null | ['reading-comprehension', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.55302489e-01 -2.03938425e-01 -1.95413485e-01 -4.67675298e-01
-8.61466289e-01 -4.84614879e-01 3.20406795e-01 3.93327028e-01
-8.94387603e-01 4.61565703e-01 4.72142756e-01 -1.17322779e+00
5.77312350e-01 -7.54723072e-01 -5.36460400e-01 -2.42209703e-01
2.61703432e-01 1.52865559e-01 2.20506385e-01 -3.22418392... | [10.912589073181152, 9.1555757522583] |
762e3ce4-23cb-4d2c-919b-d3712c02c3bc | 190411093 | 1904.11093 | null | http://arxiv.org/abs/1904.11093v1 | http://arxiv.org/pdf/1904.11093v1.pdf | Deep Sparse Representation-based Classification | We present a transductive deep learning-based formulation for the sparse
representation-based classification (SRC) method. The proposed network consists
of a convolutional autoencoder along with a fully-connected layer. The role of
the autoencoder network is to learn robust deep features for classification. On
the othe... | ['Vishal M. Patel', 'Mahdi Abavisani'] | 2019-04-24 | null | null | null | null | ['sparse-representation-based-classification'] | ['computer-vision'] | [-1.85999855e-01 9.34028029e-02 -4.13605928e-01 -3.70001763e-01
-5.88292837e-01 5.61975352e-02 4.33945447e-01 -1.56697571e-01
1.05211705e-01 5.86941481e-01 4.79859710e-01 1.51010066e-01
1.08586892e-01 -9.03075576e-01 -7.82773376e-01 -9.07416344e-01
-5.17924607e-04 2.59193331e-01 -9.72105563e-03 -1.42352387... | [9.319314002990723, 2.746812105178833] |
96e184f1-711e-448b-9715-827dd50d8dd2 | an-efficient-short-time-discrete-cosine | null | null | https://ieeexplore.ieee.org/document/9947051?source=authoralert | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9947051 | An Efficient Short-Time Discrete Cosine Transform and Attentive MultiResUNet Framework for Music Source Separation | The music source separation problem, where the task at hand is to estimate the audio components that are present in a mixture, has been at the centre of research activity for a long time. In more recent frameworks, the problem is tackled by creating deep learning models, which attempt to extract information from each c... | ['N. Mitianoudis', 'A. Bousis', 'T. Sgouros'] | 2022-11-14 | null | null | null | ieee-access-2022-11 | ['music-source-separation'] | ['music'] | [ 4.25422698e-01 -1.02255508e-01 -1.28302234e-03 6.22037463e-02
-6.87569678e-01 -3.78535628e-01 5.09536207e-01 1.74708501e-01
-5.81084073e-01 6.71349406e-01 4.81172949e-02 -3.95075753e-02
-5.65015733e-01 -4.94577438e-01 -5.92460811e-01 -8.88745248e-01
-2.10531652e-01 2.60445118e-01 2.04266831e-01 -8.47393125... | [15.298248291015625, 5.520060062408447] |
7bf8e16a-6fdc-47c2-b65f-51b8c2aa2974 | reinforced-labels-multi-agent-deep | 2303.01388 | null | https://arxiv.org/abs/2303.01388v1 | https://arxiv.org/pdf/2303.01388v1.pdf | Reinforced Labels: Multi-Agent Deep Reinforcement Learning for Point-feature Label Placement | Over the past few years, Reinforcement Learning combined with Deep Learning techniques has successfully proven to solve complex problems in various domains including robotics, self-driving cars, finance, and gaming. In this paper, we are introducing Reinforcement Learning (RL) to another domain - visualization. Our nov... | ['Martin Čadík', 'Ladislav Čmolík', 'Petr Bobák'] | 2023-03-02 | null | null | null | null | ['self-driving-cars'] | ['computer-vision'] | [-2.29670256e-01 2.80479252e-01 -6.43900558e-02 -1.31571293e-01
-4.52067256e-01 -5.88754833e-01 5.36650598e-01 1.84654266e-01
-6.38519526e-01 7.38087595e-01 -6.10754639e-02 -3.54088098e-01
-9.54526588e-02 -7.40580559e-01 -3.95848900e-01 -5.82095325e-01
-7.53474087e-02 5.98605752e-01 4.00642902e-02 -1.70950517... | [4.421448230743408, 1.6320067644119263] |
f601632b-1e94-4008-98ca-0d0ef29dfa49 | hplflownet-hierarchical-permutohedral-lattice-1 | 1906.05332 | null | https://arxiv.org/abs/1906.05332v1 | https://arxiv.org/pdf/1906.05332v1.pdf | HPLFlowNet: Hierarchical Permutohedral Lattice FlowNet for Scene Flow Estimation on Large-scale Point Clouds | We present a novel deep neural network architecture for end-to-end scene flow estimation that directly operates on large-scale 3D point clouds. Inspired by Bilateral Convolutional Layers (BCL), we propose novel DownBCL, UpBCL, and CorrBCL operations that restore structural information from unstructured point clouds, an... | ['Yong-Jae lee', 'Yijie Wang', 'Xiuye Gu', 'Panqu Wang', 'Chongruo wu'] | 2019-06-12 | hplflownet-hierarchical-permutohedral-lattice | http://openaccess.thecvf.com/content_CVPR_2019/html/Gu_HPLFlowNet_Hierarchical_Permutohedral_Lattice_FlowNet_for_Scene_Flow_Estimation_on_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Gu_HPLFlowNet_Hierarchical_Permutohedral_Lattice_FlowNet_for_Scene_Flow_Estimation_on_CVPR_2019_paper.pdf | cvpr-2019-6 | ['scene-flow-estimation'] | ['computer-vision'] | [-2.88301319e-01 -4.52431828e-01 1.34762123e-01 -1.99230343e-01
-3.09136778e-01 -6.36280596e-01 4.62596506e-01 -1.08676575e-01
-4.38547581e-01 4.00971383e-01 1.68713018e-01 -3.46234828e-01
-1.24320202e-02 -1.04630780e+00 -9.38632905e-01 -1.79640949e-01
-1.51825666e-01 5.48131108e-01 4.74633634e-01 -2.91829467... | [8.534579277038574, -2.050877332687378] |
2142d4f6-4194-4bd3-a545-da60be3d11f8 | deep-learning-technique-for-human-parsing-a | 2301.00394 | null | https://arxiv.org/abs/2301.00394v1 | https://arxiv.org/pdf/2301.00394v1.pdf | Deep Learning Technique for Human Parsing: A Survey and Outlook | Human parsing aims to partition humans in image or video into multiple pixel-level semantic parts. In the last decade, it has gained significantly increased interest in the computer vision community and has been utilized in a broad range of practical applications, from security monitoring, to social media, to visual sp... | ['Qing Song', 'Shan Li', 'Wenhe Jia', 'Lu Yang'] | 2023-01-01 | null | null | null | null | ['human-parsing'] | ['computer-vision'] | [ 5.40996671e-01 1.99100539e-01 -2.80095905e-01 -4.59363401e-01
-8.27051520e-01 -4.93208081e-01 2.68053889e-01 -9.57936570e-02
-4.20024186e-01 3.69446099e-01 3.60211506e-02 -1.36613682e-01
3.56228828e-01 -5.79663336e-01 -5.44657946e-01 -5.27915239e-01
1.49369776e-01 2.22547725e-01 4.98242199e-01 1.48289157... | [8.419771194458008, -0.12578485906124115] |
d6b985a1-9da1-4d8a-b331-3d0c777cd8c2 | copynext-explicit-span-copying-and-alignment | 2010.15266 | null | https://arxiv.org/abs/2010.15266v1 | https://arxiv.org/pdf/2010.15266v1.pdf | CopyNext: Explicit Span Copying and Alignment in Sequence to Sequence Models | Copy mechanisms are employed in sequence to sequence models (seq2seq) to generate reproductions of words from the input to the output. These frameworks, operating at the lexical type level, fail to provide an explicit alignment that records where each token was copied from. Further, they require contiguous token sequen... | ['Benjamin Van Durme', 'Mahsa Yarmohammadi', 'Guanghui Qin', 'Patrick Xia', 'Abhinav Singh'] | 2020-10-28 | null | https://aclanthology.org/2020.spnlp-1.2 | https://aclanthology.org/2020.spnlp-1.2.pdf | emnlp-spnlp-2020-11 | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [ 6.74942195e-01 2.49308467e-01 -2.00773895e-01 -2.11427823e-01
-1.09058201e+00 -8.76325488e-01 4.94394034e-01 3.07221919e-01
-4.95327145e-01 9.05795157e-01 2.47570187e-01 -7.42825210e-01
3.91176581e-01 -8.76104236e-01 -8.35311770e-01 -2.19654620e-01
-9.72791389e-03 2.73999900e-01 1.00223988e-01 -2.67160147... | [11.13813304901123, 9.113529205322266] |
df983ea7-2b73-4fa0-8ab9-6131602f7da8 | machine-learning-approaches-for-type-2 | 2104.07820 | null | https://arxiv.org/abs/2104.07820v2 | https://arxiv.org/pdf/2104.07820v2.pdf | Machine Learning Approaches for Type 2 Diabetes Prediction and Care Management | Prediction of diabetes and its various complications has been studied in a number of settings, but a comprehensive overview of problem setting for diabetes prediction and care management has not been addressed in the literature. In this document we seek to remedy this omission in literature with an encompassing overvie... | ['Ankur Teredesai', 'Muhammad Aurangzeb Ahmad', 'Vikas Kumar', 'Carly Eckert', 'Jody Chiam', 'Ashish Singh', 'Aloysius Lim'] | 2021-04-15 | null | null | null | null | ['diabetes-prediction'] | ['medical'] | [ 5.24431705e-01 1.95962533e-01 -7.29508102e-01 -8.93748581e-01
-5.34142554e-01 -3.60222496e-02 2.34397218e-01 9.45206344e-01
8.84144306e-02 9.09047842e-01 5.05731285e-01 -6.95459306e-01
-7.83295333e-01 -6.92965984e-01 -1.86652347e-01 -4.09055024e-01
-3.92471790e-01 1.05656755e+00 -6.93363547e-01 1.85872406... | [8.176926612854004, 5.407768726348877] |
941e2391-babd-4fe8-be06-769947495db0 | bootstrapping-weakly-supervised-segmentation | 2003.11087 | null | https://arxiv.org/abs/2003.11087v1 | https://arxiv.org/pdf/2003.11087v1.pdf | Bootstrapping Weakly Supervised Segmentation-free Word Spotting through HMM-based Alignment | Recent work in word spotting in handwritten documents has yielded impressive results. This progress has largely been made by supervised learning systems, which are dependent on manually annotated data, making deployment to new collections a significant effort. In this paper, we propose an approach that utilises transcr... | ['Tomas Wilkinson', 'Carl Nettelblad'] | 2020-03-24 | null | null | null | null | ['word-spotting-in-handwritten-documents'] | ['computer-vision'] | [ 7.17847228e-01 3.05395395e-01 -1.34369761e-01 -4.31596845e-01
-1.59627759e+00 -8.55699420e-01 5.59350073e-01 1.66489825e-01
-7.52092421e-01 7.59823501e-01 1.41429588e-01 -4.46054757e-01
3.12283188e-01 -4.84099716e-01 -5.12781143e-01 -7.27809668e-01
2.46654660e-01 1.04202831e+00 4.97665435e-01 -2.01114699... | [11.795819282531738, 2.7258548736572266] |
f45e2be9-4b55-4cd5-bf4b-8ce570661706 | foveated-downsampling-techniques | null | null | https://openreview.net/forum?id=rkldVXKU8H | https://openreview.net/pdf?id=rkldVXKU8H | Foveated Downsampling Techniques | Foveation is an important part of human vision, and a number of deep networks have also used foveation. However, there have been few systematic comparisons between foveating and non-foveating deep networks, and between different variable-resolution downsampling methods. Here we define several such methods, and compare ... | ['Anonymous'] | 2019-09-11 | null | null | null | null | ['foveation'] | ['computer-vision'] | [-1.32802501e-01 -1.21497959e-01 4.03781608e-02 -2.88386703e-01
2.24792153e-01 -1.90810010e-01 8.25269043e-01 -5.32789230e-01
-9.09700453e-01 8.01957607e-01 1.18432514e-01 -1.52344987e-01
-6.76892474e-02 -9.60556149e-01 -6.57463849e-01 -4.75029737e-01
4.02340293e-02 -2.56660711e-02 6.04492188e-01 -1.85326651... | [9.266857147216797, 1.8984194993972778] |
6238be7a-b97a-48d0-9fb9-aa50573bf85b | coherent-comment-generation-for-chinese | 1906.01231 | null | https://arxiv.org/abs/1906.01231v1 | https://arxiv.org/pdf/1906.01231v1.pdf | Coherent Comment Generation for Chinese Articles with a Graph-to-Sequence Model | Automatic article commenting is helpful in encouraging user engagement and interaction on online news platforms. However, the news documents are usually too long for traditional encoder-decoder based models, which often results in general and irrelevant comments. In this paper, we propose to generate comments with a gr... | ['ShengLi Yan', 'Yunfang Wu', 'Yancheng He', 'Xu sun', 'Jingjing Xu', 'Wei Li'] | 2019-06-04 | null | null | null | null | ['graph-to-sequence', 'comment-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 9.81194898e-02 7.89821804e-01 -5.14822781e-01 -3.03500473e-01
-7.62490869e-01 -4.59236771e-01 7.04699039e-01 3.13279390e-01
1.32630140e-01 7.76438773e-01 1.52045429e+00 -4.21657205e-01
7.28083968e-01 -5.63479662e-01 -5.82643628e-01 -8.36282149e-02
2.05259651e-01 3.35784495e-01 1.03004701e-01 -6.81221366... | [12.320806503295898, 9.247392654418945] |
0eda7834-9631-498a-96c5-942a39b34995 | disasternets-embedding-machine-learning-in | 2306.09815 | null | https://arxiv.org/abs/2306.09815v1 | https://arxiv.org/pdf/2306.09815v1.pdf | DisasterNets: Embedding Machine Learning in Disaster Mapping | Disaster mapping is a critical task that often requires on-site experts and is time-consuming. To address this, a comprehensive framework is presented for fast and accurate recognition of disasters using machine learning, termed DisasterNets. It consists of two stages, space granulation and attribute granulation. The s... | ['Xiao Xiang Zhu', 'Yilei Shi', 'Qingsong Xu'] | 2023-06-16 | null | null | null | null | ['change-detection', 'scene-recognition'] | ['computer-vision', 'computer-vision'] | [-3.72965708e-02 -2.49019250e-01 4.77008261e-02 -2.73546785e-01
-7.12158799e-01 -3.68267775e-01 7.17310965e-01 7.97489107e-01
-5.56954265e-01 5.84797680e-01 5.07232904e-01 -2.70138174e-01
-7.14563802e-02 -1.41470468e+00 -2.05557987e-01 -5.24293482e-01
-4.70015258e-01 5.52259743e-01 2.02576458e-01 -4.01597321... | [9.499809265136719, -1.3182255029678345] |
06439763-e183-4f35-8ddd-040bdc14c2ba | instructzero-efficient-instruction | 2306.03082 | null | https://arxiv.org/abs/2306.03082v1 | https://arxiv.org/pdf/2306.03082v1.pdf | InstructZero: Efficient Instruction Optimization for Black-Box Large Language Models | Large language models~(LLMs) are instruction followers, but it can be challenging to find the best instruction for different situations, especially for black-box LLMs on which backpropagation is forbidden. Instead of directly optimizing the discrete instruction, we optimize a low-dimensional soft prompt applied to an o... | ['Tianyi Zhou', 'Heng Huang', 'Tom Goldstein', 'Jiuhai Chen', 'Lichang Chen'] | 2023-06-05 | null | null | null | null | ['bayesian-optimization'] | ['methodology'] | [-1.09372556e-01 -2.12303121e-02 -5.32824636e-01 -6.06679857e-01
-1.09935629e+00 -4.14491624e-01 4.74095643e-01 9.55361128e-02
-4.32001352e-01 4.71053660e-01 6.89994544e-02 -9.30801690e-01
3.26176554e-01 -6.44721627e-01 -1.08138895e+00 -5.65804720e-01
1.22786082e-01 5.69491863e-01 3.29460084e-01 -1.92192987... | [10.586843490600586, 8.396305084228516] |
809ab3ca-ec07-4c08-9ea0-1dee20433507 | an-automatic-learning-of-an-algerian-dialect | null | null | https://aclanthology.org/L18-1133 | https://aclanthology.org/L18-1133.pdf | An Automatic Learning of an Algerian Dialect Lexicon by using Multilingual Word Embeddings | null | ['Kamel Sma{\\"\\i}li', 'Abidi Karima'] | 2018-05-01 | an-automatic-learning-of-an-algerian-dialect-1 | https://aclanthology.org/L18-1133 | https://aclanthology.org/L18-1133.pdf | lrec-2018-5 | ['multilingual-word-embeddings'] | ['methodology'] | [-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... | [-7.22589111328125, 3.865901470184326] |
b2e8bb8c-9c69-4d80-8263-b54ca54eb595 | turning-large-language-models-into-cognitive | 2306.03917 | null | https://arxiv.org/abs/2306.03917v1 | https://arxiv.org/pdf/2306.03917v1.pdf | Turning large language models into cognitive models | Large language models are powerful systems that excel at many tasks, ranging from translation to mathematical reasoning. Yet, at the same time, these models often show unhuman-like characteristics. In the present paper, we address this gap and ask whether large language models can be turned into cognitive models. We fi... | ['Eric Schulz', 'Marcel Binz'] | 2023-06-06 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [ 8.23458284e-02 2.14693740e-01 -1.64291501e-01 -4.13534820e-01
-1.87537283e-01 -5.19851923e-01 4.30028200e-01 4.51787442e-01
-5.56448340e-01 4.79091465e-01 -4.18446846e-02 -5.27903378e-01
-1.83462441e-01 -7.13793874e-01 -5.48792422e-01 -2.30620325e-01
9.46032703e-02 6.74213171e-01 2.86397319e-02 -3.72285187... | [9.707118034362793, 7.350860595703125] |
c6db8d86-46b4-41d4-8412-a0c09a78ac01 | deep-learning-with-partially-labeled-data-for | 2306.05294 | null | https://arxiv.org/abs/2306.05294v1 | https://arxiv.org/pdf/2306.05294v1.pdf | Deep Learning with Partially Labeled Data for Radio Map Reconstruction | In this paper, we address the problem of Received Signal Strength map reconstruction based on location-dependent radio measurements and utilizing side knowledge about the local region; for example, city plan, terrain height, gateway position. Depending on the quantity of such prior side information, we employ Neural Ar... | ['Christophe Villien', 'Benoit Denis', 'Massih-Reza Amini', 'Alkesandra Malkova'] | 2023-06-07 | null | null | null | null | ['architecture-search'] | ['methodology'] | [ 9.94280167e-03 -4.62222099e-02 -2.25137711e-01 -5.84487021e-01
-8.68554533e-01 -2.24771783e-01 4.44300503e-01 8.38841721e-02
-5.31004190e-01 8.26306343e-01 3.83798361e-01 -6.70380831e-01
-5.76754689e-01 -1.13616216e+00 -6.15856111e-01 -7.26150155e-01
-4.30939615e-01 4.19593811e-01 -2.00965032e-01 -4.68138248... | [6.299079895019531, 1.157138466835022] |
ba7b959a-bdaf-4a70-bcc8-169d0f19c6f1 | multi-lingual-and-cross-genre-discourse-unit | null | null | https://aclanthology.org/W19-2714 | https://aclanthology.org/W19-2714.pdf | Multi-lingual and Cross-genre Discourse Unit Segmentation | We describe a series of experiments applied to data sets from different languages and genres annotated for coherence relations according to different theoretical frameworks. Specifically, we investigate the feasibility of a unified (theory-neutral) approach toward discourse segmentation; a process which divides a text ... | ['Robin Sch{\\"a}fer', 'Peter Bourgonje'] | 2019-06-01 | null | null | null | ws-2019-6 | ['discourse-segmentation'] | ['natural-language-processing'] | [ 2.77829736e-01 6.73074782e-01 -3.93561542e-01 -3.98013800e-01
-9.76505101e-01 -9.61654782e-01 1.04306924e+00 3.80217761e-01
-4.76039469e-01 9.33564067e-01 9.51840937e-01 -5.57957292e-01
3.32402512e-02 -6.74370408e-01 -6.45997167e-01 -4.32267278e-01
7.38510629e-03 6.68141186e-01 2.13356480e-01 -6.39701843... | [10.982114791870117, 9.365667343139648] |
780f46a5-c8e1-437b-9c0b-475f0aea3e22 | abode-ab-initio-antibody-design-using | 2306.01005 | null | https://arxiv.org/abs/2306.01005v1 | https://arxiv.org/pdf/2306.01005v1.pdf | AbODE: Ab Initio Antibody Design using Conjoined ODEs | Antibodies are Y-shaped proteins that neutralize pathogens and constitute the core of our adaptive immune system. De novo generation of new antibodies that target specific antigens holds the key to accelerating vaccine discovery. However, this co-design of the amino acid sequence and the 3D structure subsumes and accen... | ['Vikas Garg', 'Markus Heinonen', 'Yogesh Verma'] | 2023-05-31 | null | null | null | null | ['graph-matching', 'protein-folding'] | ['graphs', 'natural-language-processing'] | [ 3.54377538e-01 -1.72757387e-01 -1.60938829e-01 -1.21707603e-01
4.28781658e-02 -9.26135957e-01 5.61722815e-01 2.18344584e-01
-5.56029156e-02 8.84892404e-01 1.45209298e-01 -5.29639304e-01
-1.49426907e-01 -6.55377209e-01 -9.71936226e-01 -8.80292296e-01
-4.18655008e-01 7.77212560e-01 1.73471227e-01 -5.63797176... | [4.806307792663574, 5.608367443084717] |
3f5b7179-5a11-4cab-8548-0d1b2f6f4325 | improving-compositional-generalization-with | 2110.08467 | null | https://arxiv.org/abs/2110.08467v2 | https://arxiv.org/pdf/2110.08467v2.pdf | Improving Compositional Generalization with Self-Training for Data-to-Text Generation | Data-to-text generation focuses on generating fluent natural language responses from structured meaning representations (MRs). Such representations are compositional and it is costly to collect responses for all possible combinations of atomic meaning schemata, thereby necessitating few-shot generalization to novel MRs... | ['Emma Strubell', 'Ankur P. Parikh', 'Mihir Kale', 'Yi Tay', 'Jinfeng Rao', 'Sanket Vaibhav Mehta'] | 2021-10-16 | null | https://aclanthology.org/2022.acl-long.289 | https://aclanthology.org/2022.acl-long.289.pdf | acl-2022-5 | ['data-to-text-generation'] | ['natural-language-processing'] | [ 8.00661087e-01 5.11055887e-01 -2.40299538e-01 -5.96457839e-01
-1.31468189e+00 -3.82788509e-01 7.31262684e-01 1.16447516e-01
-2.59475857e-01 9.15013433e-01 6.70433164e-01 -2.76725590e-01
1.89787388e-01 -1.14208722e+00 -5.58127403e-01 -3.97122383e-01
4.31747824e-01 7.81483114e-01 1.97616085e-01 -8.41181338... | [11.451175689697266, 8.714180946350098] |
1de8e5dd-c1b1-471d-9ed5-2569de316118 | engineering-a-direct-k-way-hypergraph | null | null | https://epubs.siam.org/doi/10.1137/1.9781611974768.3 | https://epubs.siam.org/doi/10.1137/1.9781611974768.3 | Engineering a direct k-way Hypergraph Partitioning Algorithm | We develop a fast and high quality multilevel algorithm that directly partitions hypergraphs into k balanced blocks – without the detour over recursive bipartitioning. In particular, our algorithm efficiently implements the powerful FM local search heuristics for the complicated k-way case. This is important for object... | ['Sebastian Schlag', 'Yaroslav Akhremtsev', 'Tobias Heuer', 'Peter Sanders'] | 2017-01-18 | null | null | null | alenex-2017-2017-1 | ['hypergraph-partitioning'] | ['graphs'] | [-4.21553478e-02 7.39115551e-02 -6.03247106e-01 -8.94686282e-02
-8.68514121e-01 -6.76687777e-01 9.94063988e-02 3.37453783e-01
-1.82194501e-01 1.24924040e+00 1.62616923e-01 -4.50519800e-01
-5.57365417e-01 -1.28225660e+00 -5.98447025e-01 -8.78321171e-01
-2.26430416e-01 1.14480591e+00 7.90946364e-01 -1.33988723... | [6.99375581741333, 5.197354316711426] |
66bdf572-bc77-4ad0-864e-375a34c215f7 | composed-image-retrieval-with-text-feedback | 2211.07394 | null | https://arxiv.org/abs/2211.07394v4 | https://arxiv.org/pdf/2211.07394v4.pdf | Composed Image Retrieval with Text Feedback via Multi-grained Uncertainty Regularization | We investigate composed image retrieval with text feedback. Users gradually look for the target of interest by moving from coarse to fine-grained feedback. However, existing methods merely focus on the latter, i.e., fine-grained search, by harnessing positive and negative pairs during training. This pair-based paradigm... | ['Tat-Seng Chua', 'Leigang Qu', 'Wei Ji', 'Zhedong Zheng', 'Yiyang Chen'] | 2022-11-14 | null | null | null | null | ['composed-image-retrieval', 'multi-modal'] | ['computer-vision', 'miscellaneous'] | [-1.60955518e-01 -5.13995051e-01 -4.16357875e-01 -4.37814265e-01
-1.39887261e+00 -6.03495121e-01 6.09430075e-01 2.81473607e-01
-5.22485256e-01 5.33062339e-01 1.88352734e-01 1.52960047e-01
-5.03861666e-01 -7.72864163e-01 -7.35039055e-01 -6.63722217e-01
3.70535105e-01 4.63133186e-01 3.63634080e-01 -1.98035225... | [10.098140716552734, 5.217386245727539] |
38d72e22-1dcf-45f8-b0d2-7ad968d11449 | a-knowledge-aware-sequence-to-tree-network | null | null | https://aclanthology.org/2020.emnlp-main.579 | https://aclanthology.org/2020.emnlp-main.579.pdf | A Knowledge-Aware Sequence-to-Tree Network for Math Word Problem Solving | With the advancements in natural language processing tasks, math word problem solving has received increasing attention. Previous methods have achieved promising results but ignore background common-sense knowledge not directly provided by the problem. In addition, during generation, they focus on local features while ... | ['Xuanjing Huang', 'Jinlan Fu', 'Qi Zhang', 'Qinzhuo Wu'] | null | null | null | null | emnlp-2020-11 | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [ 3.24507326e-01 1.18892990e-01 -2.18830168e-01 -5.32832026e-01
-4.86870795e-01 -5.99622667e-01 2.04444200e-01 5.74073613e-01
-1.91778079e-01 7.71098614e-01 2.94968635e-01 -2.86959767e-01
-1.62980348e-01 -1.19895494e+00 -8.97629976e-01 -1.77171141e-01
8.19615275e-02 3.61753166e-01 2.99419463e-01 -3.19116622... | [9.790555953979492, 7.854576587677002] |
40d8cfb9-f53e-493d-9134-c45fa1a974a0 | toward-improving-confidence-in-autonomous | null | null | https://doi.org/10.1109/MC.2021.3075054 | https://hull-repository.worktribe.com/preview/3757643/SafeML_II_Author_Version.pdf | Toward Improving Confidence in Autonomous Vehicle Software: A Study on Traffic Sign Recognition Systems | The application of artificial intelligence (AI) and data-driven decision-making systems in autonomous vehicles is growing rapidly. As autonomous vehicles operate in dynamic environments, the risk that they can face an unknown observation is relatively high due to insufficient training data, distributional shift, or cyb... | ['Yiannis Papadopoulos', 'Vinod Vasudevan Nair', 'Amr Abdullatif', 'Sohag Kabir', 'Koorosh Aslansefat'] | 2021-08-03 | null | null | null | computer-2021-8 | ['traffic-sign-recognition'] | ['computer-vision'] | [ 9.46168154e-02 1.64904311e-01 -2.03002259e-01 -5.90912163e-01
-2.31926262e-01 -3.02330434e-01 8.26653004e-01 4.66404967e-02
-5.23087084e-01 6.22088492e-01 -6.29573047e-01 -1.05586183e+00
-4.74928677e-01 -8.97143006e-01 -6.66258216e-01 -7.28687644e-01
1.31132051e-01 5.19904554e-01 4.27942067e-01 -2.94789076... | [5.7136688232421875, 1.2399060726165771] |
2cb391b4-a755-4d47-9efb-029686919b30 | u-need-a-fine-grained-dataset-for-user-needs | 2305.04774 | null | https://arxiv.org/abs/2305.04774v1 | https://arxiv.org/pdf/2305.04774v1.pdf | U-NEED: A Fine-grained Dataset for User Needs-Centric E-commerce Conversational Recommendation | Conversational recommender systems (CRSs) aim to understand the information needs and preferences expressed in a dialogue to recommend suitable items to the user. Most of the existing conversational recommendation datasets are synthesized or simulated with crowdsourcing, which has a large gap with real-world scenarios.... | ['Wanxiang Che', 'Yongbin Li', 'Hengbin Cui', 'Ziyu Zhuang', 'Yifan Chen', 'Fan Feng', 'Hang Wang', 'Yan Fan', 'Baohua Dong', 'Weinan Zhang', 'Yuanxing Liu'] | 2023-05-05 | null | null | null | null | ['dialogue-evaluation', 'dialogue-generation', 'dialogue-understanding', 'dialogue-generation'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'speech'] | [-2.26379305e-01 1.91842943e-01 -1.88956305e-01 -7.63196051e-01
-5.73495865e-01 -7.60217130e-01 7.59668887e-01 -1.38186947e-01
-1.66294172e-01 5.17127573e-01 9.65398967e-01 -2.76750952e-01
-1.15987487e-01 -7.22605944e-01 -1.58978507e-01 -2.86551952e-01
2.14530319e-01 9.57654774e-01 1.40393913e-01 -1.22788608... | [12.358187675476074, 7.46424674987793] |
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