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values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
dd2e53e6-9232-4f51-9a42-ee9f8cb6dc81 | simulate-time-integrated-coarse-grained | 2204.10348 | null | https://arxiv.org/abs/2204.10348v2 | https://arxiv.org/pdf/2204.10348v2.pdf | Simulate Time-integrated Coarse-grained Molecular Dynamics with Geometric Machine Learning | Molecular dynamics (MD) simulation is the workhorse of various scientific domains but is limited by high computational cost. Learning-based force fields have made major progress in accelerating ab-initio MD simulation but are still not fast enough for many real-world applications that require long-time MD simulation. I... | ['Tommi Jaakkola', 'Bradley D. Olsen', 'Nathan J. Rebello', 'Tian Xie', 'Xiang Fu'] | 2022-04-21 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [ 1.35253906e-01 -4.65442568e-01 -8.68071392e-02 -7.16357306e-02
-8.24834824e-01 -6.31738842e-01 4.77981538e-01 5.26350260e-01
-5.08345902e-01 1.17918646e+00 -5.35090327e-01 -8.92419577e-01
-8.72169361e-02 -7.25012779e-01 -8.66519630e-01 -1.40245855e+00
-4.48786765e-01 1.12530804e+00 3.41318637e-01 -4.27726179... | [5.074577331542969, 5.25032377243042] |
171917a9-0704-487b-8899-fb16bf02435b | machine-learning-in-downlink-coordinated | 1608.08306 | null | http://arxiv.org/abs/1608.08306v6 | http://arxiv.org/pdf/1608.08306v6.pdf | Machine Learning in Downlink Coordinated Multipoint in Heterogeneous Networks | We propose a method for downlink coordinated multipoint (DL CoMP) in
heterogeneous fifth generation New Radio (NR) networks. The primary
contribution of our paper is an algorithm to enhance the trigger of DL CoMP
using online machine learning. We use support vector machine (SVM) classifiers
to enhance the user downlink... | ['Brian L. Evans', 'Faris B. Mismar'] | 2016-08-30 | null | null | null | null | ['pico'] | ['natural-language-processing'] | [-2.01238245e-01 2.32819542e-01 -8.99144828e-01 -4.39723670e-01
-5.47073364e-01 -6.49125755e-01 1.27309710e-01 -5.72828114e-01
2.87728813e-02 2.09583473e+00 -3.27806175e-01 -8.63962948e-01
-2.81014293e-01 -7.84080565e-01 -8.57974738e-02 -7.53393412e-01
-6.46715283e-01 4.70958382e-01 5.84473461e-02 -5.70888638... | [6.123929023742676, 1.5284688472747803] |
4d8b01e7-b565-425c-abe6-bdd31aee9c4c | visual-prompt-tuning-for-few-shot-text | null | null | https://aclanthology.org/2022.coling-1.492 | https://aclanthology.org/2022.coling-1.492.pdf | Visual Prompt Tuning for Few-Shot Text Classification | Deploying large-scale pre-trained models in the prompt-tuning paradigm has demonstrated promising performance in few-shot learning. Particularly, vision-language pre-training models (VL-PTMs) have been intensively explored in various few-shot downstream tasks. However, most existing works only apply VL-PTMs to visual t... | ['Zhao Cao', 'Jie Jiang', 'Hao Jiang', 'Zhiwu Lu', 'Guoxing Yang', 'Nanyi Fei', 'Yutian Luo', 'Jingyuan Wen'] | null | null | null | null | coling-2022-10 | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 3.11690450e-01 -1.74634486e-01 -4.04000461e-01 -3.28123540e-01
-5.95597148e-01 -5.30478954e-02 1.02114868e+00 1.71766877e-01
-6.42643988e-01 2.35603452e-01 2.14374438e-01 -3.27334762e-01
4.04279232e-01 -6.48901880e-01 -5.48629761e-01 -5.82655251e-01
7.14595973e-01 3.32164049e-01 4.09677207e-01 -2.40718424... | [10.127581596374512, 2.354889392852783] |
47e5883d-2b9b-49e2-8f12-0999c76fb094 | inter-annotator-agreement-in-sentiment | null | null | https://aclanthology.org/R17-1015 | https://aclanthology.org/R17-1015.pdf | Inter-Annotator Agreement in Sentiment Analysis: Machine Learning Perspective | Manual text annotation is an essential part of Big Text analytics. Although annotators work with limited parts of data sets, their results are extrapolated by automated text classification and affect the final classification results. Reliability of annotations and adequacy of assigned labels are especially important in... | ['Victoria Bobicev', 'Marina Sokolova'] | 2017-09-01 | null | null | null | ranlp-2017-9 | ['text-annotation'] | ['natural-language-processing'] | [ 9.09641385e-02 5.41604578e-01 -1.83329836e-01 -8.40964913e-01
-6.70940042e-01 -1.06644428e+00 4.50144380e-01 1.11609924e+00
-5.47449887e-01 7.56614149e-01 4.60837305e-01 -7.40165636e-02
1.44905403e-01 -5.45685649e-01 -1.25266865e-01 -3.29809099e-01
6.07850611e-01 5.66251755e-01 1.62826389e-01 -1.96948186... | [10.82544994354248, 6.787208557128906] |
fe980b79-cf84-42ee-a26e-47d2cd88590a | expressive-body-capture-3d-hands-face-and | 1904.05866 | null | http://arxiv.org/abs/1904.05866v1 | http://arxiv.org/pdf/1904.05866v1.pdf | Expressive Body Capture: 3D Hands, Face, and Body from a Single Image | To facilitate the analysis of human actions, interactions and emotions, we
compute a 3D model of human body pose, hand pose, and facial expression from a
single monocular image. To achieve this, we use thousands of 3D scans to train
a new, unified, 3D model of the human body, SMPL-X, that extends SMPL with
fully articu... | ['Ahmed A. A. Osman', 'Vasileios Choutas', 'Georgios Pavlakos', 'Dimitrios Tzionas', 'Michael J. Black', 'Nima Ghorbani', 'Timo Bolkart'] | 2019-04-11 | expressive-body-capture-3d-hands-face-and-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Pavlakos_Expressive_Body_Capture_3D_Hands_Face_and_Body_From_a_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Pavlakos_Expressive_Body_Capture_3D_Hands_Face_and_Body_From_a_CVPR_2019_paper.pdf | cvpr-2019-6 | ['3d-human-reconstruction', '3d-multi-person-mesh-recovery'] | ['computer-vision', 'computer-vision'] | [-1.21229380e-01 2.39042155e-02 -3.66465212e-03 -4.59221452e-01
-5.72483599e-01 -4.60526139e-01 4.06716973e-01 -4.64956999e-01
-4.63668436e-01 5.52206397e-01 3.92685741e-01 3.95922989e-01
4.23079312e-01 -3.04626375e-01 -8.20135236e-01 -2.83889860e-01
-5.66339642e-02 7.65248895e-01 -4.03934605e-02 -6.35295734... | [7.09281063079834, -1.0978424549102783] |
365f4322-e442-41e3-bda2-7e614d312a5c | probabilistic-loss-and-its-online | 2105.05789 | null | https://arxiv.org/abs/2105.05789v1 | https://arxiv.org/pdf/2105.05789v1.pdf | Probabilistic Loss and its Online Characterization for Simplified Decision Making Under Uncertainty | It is a long-standing objective to ease the computation burden incurred by the decision making process. Identification of this mechanism's sensitivity to simplification has tremendous ramifications. Yet, algorithms for decision making under uncertainty usually lean on approximations or heuristics without quantifying th... | ['Vadim Indelman', 'Andrey Zhitnikov'] | 2021-05-12 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [ 2.46166229e-01 4.11424905e-01 -1.15350991e-01 -3.29148173e-01
-6.75892532e-01 -5.97101808e-01 5.49827754e-01 5.38148105e-01
-5.37916660e-01 9.16123390e-01 -7.12182298e-02 -5.26758134e-01
-6.67994201e-01 -1.09547114e+00 -7.15597391e-01 -5.61325490e-01
-1.19725130e-01 6.00710511e-01 1.24150023e-01 -6.48547411... | [4.721267223358154, 2.9532253742218018] |
91de13a7-a0d9-45f6-a3b5-22a8d847abbe | deep-semantic-role-labeling-what-works-and | null | null | https://aclanthology.org/P17-1044 | https://aclanthology.org/P17-1044.pdf | Deep Semantic Role Labeling: What Works and What's Next | We introduce a new deep learning model for semantic role labeling (SRL) that significantly improves the state of the art, along with detailed analyses to reveal its strengths and limitations. We use a deep highway BiLSTM architecture with constrained decoding, while observing a number of recent best practices for initi... | ['Luke Zettlemoyer', 'Mike Lewis', 'Kenton Lee', 'Luheng He'] | 2017-07-01 | null | null | null | acl-2017-7 | ['predicate-detection'] | ['natural-language-processing'] | [ 3.94054323e-01 5.64896464e-01 -3.93804491e-01 -6.19343996e-01
-1.13460135e+00 -6.91439867e-01 4.01552945e-01 1.57890216e-01
-8.02042842e-01 8.39180768e-01 6.03194535e-01 -6.34748399e-01
2.27113329e-02 -2.62255996e-01 -7.85803974e-01 -5.14303923e-01
-9.31102186e-02 5.08634090e-01 2.30793983e-01 -4.53078151... | [10.439498901367188, 9.400346755981445] |
caccb78d-cd2c-4b3f-bb7c-0f74853e6fe0 | continual-machine-reading-comprehension-via-1 | 2208.05217 | null | https://arxiv.org/abs/2208.05217v1 | https://arxiv.org/pdf/2208.05217v1.pdf | Continual Machine Reading Comprehension via Uncertainty-aware Fixed Memory and Adversarial Domain Adaptation | Continual Machine Reading Comprehension aims to incrementally learn from a continuous data stream across time without access the previous seen data, which is crucial for the development of real-world MRC systems. However, it is a great challenge to learn a new domain incrementally without catastrophically forgetting pr... | ['Kai Gao', 'Jingliang Fang', 'Hua Xu', 'Zhijing Wu'] | 2022-08-10 | continual-machine-reading-comprehension-via | https://aclanthology.org/2022.findings-naacl.179 | https://aclanthology.org/2022.findings-naacl.179.pdf | findings-naacl-2022-7 | ['machine-reading-comprehension'] | ['natural-language-processing'] | [ 2.88111448e-01 5.98342083e-02 -2.38741934e-01 -4.60055113e-01
-7.66332448e-01 -6.28235877e-01 3.67039740e-01 3.93151075e-01
-6.33658290e-01 1.16734028e+00 3.50661308e-01 -3.25506955e-01
-6.26225909e-03 -1.00597215e+00 -1.18422163e+00 -3.50975811e-01
6.23637661e-02 5.59417486e-01 6.01884723e-01 -3.47884268... | [9.895796775817871, 3.451050281524658] |
162dcfe7-f187-45b8-b0b0-daa0b8136ae8 | country-level-arabic-dialect-identification | null | null | https://aclanthology.org/2021.wanlp-1.30 | https://aclanthology.org/2021.wanlp-1.30.pdf | Country-level Arabic Dialect Identification Using Small Datasets with Integrated Machine Learning Techniques and Deep Learning Models | Arabic is characterised by a considerable number of varieties including spoken dialects. In this paper, we presented our models developed to participate in the NADI subtask 1.2 that requires building a system to distinguish between 21 country-level dialects. We investigated several classical machine learning approaches... | ['Maha J. Althobaiti'] | null | null | null | null | eacl-wanlp-2021-4 | ['dialect-identification'] | ['natural-language-processing'] | [-3.58978510e-01 3.11938912e-01 -1.90062504e-02 -7.39529550e-01
-6.62497401e-01 -8.28512669e-01 1.08174324e+00 2.92741090e-01
-7.02902377e-01 8.46564710e-01 2.73762643e-01 -2.61784226e-01
-3.01915795e-01 -9.74569678e-01 -2.42358759e-01 -7.31144965e-01
-3.04419845e-01 9.44807053e-01 -2.99888283e-01 -8.20373178... | [10.280868530273438, 10.544598579406738] |
642a5663-bfae-4de7-bd6b-4d8036baa86a | spatial-deep-learning-for-wireless-scheduling | 1808.01486 | null | https://arxiv.org/abs/1808.01486v3 | https://arxiv.org/pdf/1808.01486v3.pdf | Spatial Deep Learning for Wireless Scheduling | The optimal scheduling of interfering links in a dense wireless network with full frequency reuse is a challenging task. The traditional method involves first estimating all the interfering channel strengths then optimizing the scheduling based on the model. This model-based method is however resource intensive and com... | ['Wei Yu', 'Kaiming Shen', 'Wei Cui'] | 2018-08-04 | null | null | null | null | ['few-shot-camera-adaptive-color-constancy', 'few-shot-camera-adaptive-color-constancy'] | ['computer-vision', 'methodology'] | [ 1.56975865e-01 3.19296122e-01 -3.09060335e-01 -2.51556244e-02
-2.89230943e-01 -2.66878217e-01 -3.14288512e-02 -7.89423883e-02
-4.50403601e-01 1.13099360e+00 -3.30410570e-01 -7.06781447e-01
-7.23332226e-01 -9.63675141e-01 -3.68977726e-01 -1.08898091e+00
-1.09394169e+00 3.78380418e-01 -2.25311831e-01 -2.64206260... | [6.036064147949219, 1.541663408279419] |
57cd7c8e-f595-4af4-a697-fe06d3552355 | med-danet-dynamic-architecture-network-for | 2206.06575 | null | https://arxiv.org/abs/2206.06575v2 | https://arxiv.org/pdf/2206.06575v2.pdf | Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation | For 3D medical image (e.g. CT and MRI) segmentation, the difficulty of segmenting each slice in a clinical case varies greatly. Previous research on volumetric medical image segmentation in a slice-by-slice manner conventionally use the identical 2D deep neural network to segment all the slices of the same case, ignori... | ['Jiangyun Li', 'Yan Zhang', 'Sen Zha', 'Jing Wang', 'Chen Chen', 'Wenxuan Wang'] | 2022-06-14 | null | null | null | null | ['volumetric-medical-image-segmentation', 'brain-tumor-segmentation'] | ['medical', 'medical'] | [ 1.12499066e-01 2.90420055e-01 -2.26691678e-01 -6.11584008e-01
-7.87857175e-01 -3.04148197e-01 9.51881856e-02 5.06099500e-02
-6.95890844e-01 3.86703849e-01 -8.48321542e-02 -7.47283459e-01
-1.32303044e-01 -6.92054987e-01 -2.29050592e-01 -7.90371776e-01
-7.60906786e-02 1.04401565e+00 7.04781055e-01 2.16724381... | [14.561861991882324, -2.4257001876831055] |
ffbbbcb4-a16d-4ddb-abf7-57782c576661 | argscichat-a-dataset-for-argumentative | 2202.06690 | null | https://arxiv.org/abs/2202.06690v3 | https://arxiv.org/pdf/2202.06690v3.pdf | ArgSciChat: A Dataset for Argumentative Dialogues on Scientific Papers | The applications of conversational agents for scientific disciplines (as expert domains) are understudied due to the lack of dialogue data to train such agents. While most data collection frameworks, such as Amazon Mechanical Turk, foster data collection for generic domains by connecting crowd workers and task designer... | ['Iryna Gurevych', 'Mohsen Mesgar', 'Federico Ruggeri'] | 2022-02-14 | null | null | null | null | ['fact-selection'] | ['natural-language-processing'] | [-4.01938498e-01 6.90258563e-01 -1.26305491e-01 -4.57354963e-01
-6.74785078e-01 -1.29714048e+00 1.18472111e+00 3.35719466e-01
-5.26537299e-01 1.22115505e+00 5.20279348e-01 -6.70856416e-01
3.78127508e-02 -6.52703583e-01 -5.34522355e-01 -3.43449324e-01
5.46637416e-01 1.20789707e+00 1.74679741e-01 -3.75717044... | [12.553019523620605, 7.994938850402832] |
296dc339-5732-4de2-a86a-bb95610548e9 | resources-for-brewing-beir-reproducible | 2306.07471 | null | https://arxiv.org/abs/2306.07471v1 | https://arxiv.org/pdf/2306.07471v1.pdf | Resources for Brewing BEIR: Reproducible Reference Models and an Official Leaderboard | BEIR is a benchmark dataset for zero-shot evaluation of information retrieval models across 18 different domain/task combinations. In recent years, we have witnessed the growing popularity of a representation learning approach to building retrieval models, typically using pretrained transformers in a supervised setting... | ['Jimmy Lin', 'Jheng-Hong Yang', 'Xueguang Ma', 'Carlos Lassance', 'Nandan Thakur', 'Ehsan Kamalloo'] | 2023-06-13 | null | null | null | null | ['information-retrieval'] | ['natural-language-processing'] | [-9.62341204e-02 -3.61336619e-01 -5.28974652e-01 -1.09293342e-01
-1.23735499e+00 -7.54169405e-01 9.34932828e-01 3.66989255e-01
-8.07458580e-01 4.94965047e-01 5.72340310e-01 -5.30284822e-01
-5.04459202e-01 -6.36614919e-01 -3.89285028e-01 1.01510407e-02
1.91411674e-01 7.24512219e-01 2.07280919e-01 -5.71419537... | [11.372443199157715, 7.737179279327393] |
fa47c2bf-476a-4dd2-97e0-b4e10dbbc907 | todd-topological-compound-fingerprinting-in | 2211.03808 | null | https://arxiv.org/abs/2211.03808v1 | https://arxiv.org/pdf/2211.03808v1.pdf | ToDD: Topological Compound Fingerprinting in Computer-Aided Drug Discovery | In computer-aided drug discovery (CADD), virtual screening (VS) is used for identifying the drug candidates that are most likely to bind to a molecular target in a large library of compounds. Most VS methods to date have focused on using canonical compound representations (e.g., SMILES strings, Morgan fingerprints) or ... | ['Bulent Kiziltan', 'Yulia Gel', 'Yuzhou Chen', 'Ignacio Segovia-Dominguez', 'Baris Coskunuzer', 'Andac Demir'] | 2022-11-07 | null | null | null | null | ['graph-ranking'] | ['graphs'] | [ 3.33122581e-01 -8.65771174e-02 -5.64847767e-01 1.38767451e-01
-7.71578133e-01 -9.11109507e-01 6.96566880e-01 4.51552123e-01
-1.26321942e-01 1.01187956e+00 -4.24256921e-02 -5.84465802e-01
-4.71814573e-01 -9.10247445e-01 -8.44360471e-01 -8.41503978e-01
-4.91375327e-01 6.67973042e-01 1.68304920e-01 -2.85576284... | [5.182130813598633, 5.799956798553467] |
2fc00516-9246-462a-b97e-e2124e20e81c | tractable-probabilistic-graph-representation | 2305.10544 | null | https://arxiv.org/abs/2305.10544v1 | https://arxiv.org/pdf/2305.10544v1.pdf | Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks | We introduce Graph-Induced Sum-Product Networks (GSPNs), a new probabilistic framework for graph representation learning that can tractably answer probabilistic queries. Inspired by the computational trees induced by vertices in the context of message-passing neural networks, we build hierarchies of sum-product network... | ['Mathias Niepert', 'Federico Errica'] | 2023-05-17 | null | null | null | null | ['graph-classification', 'graph-representation-learning'] | ['graphs', 'methodology'] | [ 1.89513370e-01 9.39753890e-01 -1.87931895e-01 -3.26697916e-01
-5.78818142e-01 -4.20631588e-01 7.36946046e-01 3.96270841e-01
-1.46082953e-01 6.22915447e-01 2.36125588e-01 -5.35863400e-01
-5.84911108e-01 -1.31065667e+00 -1.11215532e+00 -4.67456698e-01
-5.24778068e-01 1.01300609e+00 4.29484397e-01 2.27783471... | [7.269837856292725, 6.4087934494018555] |
e9244f5d-d4e5-441b-9ed7-0868fd4b7af5 | 2211-09238 | 2211.09238 | null | https://arxiv.org/abs/2211.09238v1 | https://arxiv.org/pdf/2211.09238v1.pdf | Learning unfolded networks with a cyclic group structure | Deep neural networks lack straightforward ways to incorporate domain knowledge and are notoriously considered black boxes. Prior works attempted to inject domain knowledge into architectures implicitly through data augmentation. Building on recent advances on equivariant neural networks, we propose networks that explic... | ['Demba Ba', 'Emmanouil Theodosis'] | 2022-11-16 | null | null | null | null | ['rotated-mnist'] | ['computer-vision'] | [ 2.08732352e-01 8.64444554e-01 -4.11700219e-01 -7.00816095e-01
-1.73588887e-01 -7.66978800e-01 9.70901787e-01 -5.44947684e-01
-4.78435516e-01 7.39710689e-01 7.10145473e-01 -3.62169117e-01
-9.65761915e-02 -5.90409756e-01 -1.14813113e+00 -3.38986248e-01
-5.65587766e-02 4.54327404e-01 -5.16689122e-01 -6.97854757... | [9.389510154724121, 2.547260046005249] |
8c6a9cdf-99f9-409d-927c-984c709e76ca | density-aware-reinforcement-learning-to | 2306.08785 | null | https://arxiv.org/abs/2306.08785v1 | https://arxiv.org/pdf/2306.08785v1.pdf | Density-Aware Reinforcement Learning to Optimise Energy Efficiency in UAV-Assisted Networks | Unmanned aerial vehicles (UAVs) serving as aerial base stations can be deployed to provide wireless connectivity to mobile users, such as vehicles. However, the density of vehicles on roads often varies spatially and temporally primarily due to mobility and traffic situations in a geographical area, making it difficult... | ['Ivana Dusparic', 'Boris Galkin', 'Babatunji Omoniwa'] | 2023-06-14 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [-4.26083744e-01 2.48927116e-01 -2.22270817e-01 5.22290289e-01
-8.69926140e-02 -5.92507184e-01 3.18888336e-01 8.04720372e-02
-3.41989905e-01 1.29428387e+00 -5.77735186e-01 -5.28535664e-01
-7.21458733e-01 -1.21714067e+00 -5.01591444e-01 -1.10075974e+00
-7.15346515e-01 3.83792400e-01 1.31301865e-01 -4.63776588... | [5.828906536102295, 1.6099443435668945] |
81af64d0-6d5f-490e-a119-14eb11f14221 | short-term-solar-irradiance-forecasting-using | 2010.04715 | null | https://arxiv.org/abs/2010.04715v2 | https://arxiv.org/pdf/2010.04715v2.pdf | Short-Term Solar Irradiance Forecasting Using Calibrated Probabilistic Models | Advancing probabilistic solar forecasting methods is essential to supporting the integration of solar energy into the electricity grid. In this work, we develop a variety of state-of-the-art probabilistic models for forecasting solar irradiance. We investigate the use of post-hoc calibration techniques for ensuring wel... | ['Ram Rajagopal', 'Anand Avati', 'David Gagne', 'Andrew Y. Ng', 'Jack Kelly', 'Hao Sheng', 'Cooper Raterink', 'Jeremy Irvin', 'Sharon Zhou', 'Eric Zelikman'] | 2020-10-09 | null | null | null | null | ['solar-irradiance-forecasting'] | ['time-series'] | [-4.69414026e-01 -3.81589532e-01 -8.58993009e-02 -6.69489622e-01
-1.22957444e+00 -1.05649233e+00 9.08343136e-01 -5.05565666e-03
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-4.30591851e-01 -1.17815542e+00 -6.76997840e-01 -9.59553599e-01
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c7d028d3-85e3-4c48-9d41-8365ddd76b2d | transforming-clip-to-an-open-vocabulary-video | 2302.00624 | null | https://arxiv.org/abs/2302.00624v3 | https://arxiv.org/pdf/2302.00624v3.pdf | Open-VCLIP: Transforming CLIP to an Open-vocabulary Video Model via Interpolated Weight Optimization | Contrastive Language-Image Pretraining (CLIP) has demonstrated impressive zero-shot learning abilities for image understanding, yet limited effort has been made to investigate CLIP for zero-shot video recognition. We introduce Open-VCLIP, a simple yet effective approach that transforms CLIP into a strong zero-shot vide... | ['Yu-Gang Jiang', 'Zuxuan Wu', 'Ang Li', 'Xitong Yang', 'Zejia Weng'] | 2023-02-01 | null | null | null | null | ['video-recognition'] | ['computer-vision'] | [ 2.13640079e-01 -3.95170242e-01 -5.62323034e-01 -1.80607647e-01
-9.45479929e-01 -2.59042382e-01 5.43553591e-01 -3.31727743e-01
-4.08605635e-01 5.62623978e-01 1.46964192e-01 -1.94030881e-01
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-3.19683045e-01 -1.25073925e-01 4.92832273e-01 1.61732342... | [8.881330490112305, 0.8772662281990051] |
53a7198b-c480-448b-8083-de66cfbb25ce | online-spatiotemporal-action-detection-and | 2008.13759 | null | https://arxiv.org/abs/2008.13759v1 | https://arxiv.org/pdf/2008.13759v1.pdf | Online Spatiotemporal Action Detection and Prediction via Causal Representations | In this thesis, we focus on video action understanding problems from an online and real-time processing point of view. We start with the conversion of the traditional offline spatiotemporal action detection pipeline into an online spatiotemporal action tube detection system. An action tube is a set of bounding connecte... | ['Gurkirt Singh'] | 2020-08-31 | null | null | null | null | ['action-understanding'] | ['computer-vision'] | [ 5.02634943e-01 1.27823316e-02 -6.20457768e-01 -2.23899916e-01
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-3.74790907e-01 -5.82552314e-01 -7.55311489e-01 -3.10061723e-01
-6.09820664e-01 1.08282985e-02 6.29798889e-01 1.33932471... | [8.234606742858887, 0.5688027143478394] |
b202975a-a188-43bc-8a29-3408e4a2e528 | domain-adapting-speech-emotion-recognition | 2207.12248 | null | https://arxiv.org/abs/2207.12248v2 | https://arxiv.org/pdf/2207.12248v2.pdf | Domain Adapting Deep Reinforcement Learning for Real-world Speech Emotion Recognition | Computers can understand and then engage with people in an emotionally intelligent way thanks to speech-emotion recognition (SER). However, the performance of SER in cross-corpus and real-world live data feed scenarios can be significantly improved. The inability to adapt an existing model to a new domain is one of the... | ['Bjorn W. Schuller', 'Sara Khalifa', 'Rajib Rana', 'Thejan Rajapakshe'] | 2022-07-07 | null | null | null | null | ['cross-corpus'] | ['computer-vision'] | [-1.46782532e-01 -1.05717599e-01 1.21667311e-01 -6.56604469e-01
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1.02451421e-01 8.62962604e-01 -8.85200351e-02 -7.04959512... | [13.497001647949219, 5.9021172523498535] |
3f5ccaa0-e37b-41c6-a655-9850f65a3a32 | covidlies-detecting-covid-19-misinformation | null | null | https://aclanthology.org/2020.nlpcovid19-2.11 | https://aclanthology.org/2020.nlpcovid19-2.11.pdf | COVIDLies: Detecting COVID-19 Misinformation on Social Media | The ongoing pandemic has heightened the need for developing tools to flag COVID-19-related misinformation on the internet, specifically on social media such as Twitter. However, due to novel language and the rapid change of information, existing misinformation detection datasets are not effective for evaluating systems... | ['Sameer Singh', 'Sean Young', 'Yoshitomo Matsubara', 'Arjuna Ugarte', 'Robert L. Logan IV', 'Tamanna Hossain'] | null | null | null | null | emnlp-nlp-covid19-2020-12 | ['misconceptions'] | ['miscellaneous'] | [-1.10618897e-01 3.01239878e-01 -4.84400183e-01 -2.42191106e-01
-7.61344612e-01 -9.78652716e-01 1.09254265e+00 1.00442362e+00
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6.90227896e-02 5.74832082e-01 1.81234643e-01 -4.40475404... | [8.413398742675781, 9.81877326965332] |
7cb24021-6d24-44e8-8dcc-c4fc1149f3ea | painting-3d-nature-in-2d-view-synthesis-of | 2302.07224 | null | https://arxiv.org/abs/2302.07224v1 | https://arxiv.org/pdf/2302.07224v1.pdf | Painting 3D Nature in 2D: View Synthesis of Natural Scenes from a Single Semantic Mask | We introduce a novel approach that takes a single semantic mask as input to synthesize multi-view consistent color images of natural scenes, trained with a collection of single images from the Internet. Prior works on 3D-aware image synthesis either require multi-view supervision or learning category-level prior for sp... | ['Xiaowei Zhou', 'Yiyi Liao', 'Kaicheng Yu', 'Haotong Lin', 'Linzhan Mou', 'Tianrun Chen', 'Sida Peng', 'Shangzhan Zhang'] | 2023-02-14 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Painting_3D_Nature_in_2D_View_Synthesis_of_Natural_Scenes_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Painting_3D_Nature_in_2D_View_Synthesis_of_Natural_Scenes_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-aware-image-synthesis'] | ['computer-vision'] | [ 5.18774211e-01 -7.28845224e-02 6.87957928e-02 -4.11800832e-01
-5.63898563e-01 -8.84082019e-01 7.44040549e-01 -5.54458737e-01
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6.12717152e-01 3.71595830e-01 5.49030125e-01 -4.19272065... | [9.260663032531738, -3.0997822284698486] |
b19d4898-bb46-4932-a332-f83377aaf7b8 | improving-graph-neural-networks-on-multi-node | 2304.10074 | null | https://arxiv.org/abs/2304.10074v1 | https://arxiv.org/pdf/2304.10074v1.pdf | Improving Graph Neural Networks on Multi-node Tasks with Labeling Tricks | In this paper, we provide a theory of using graph neural networks (GNNs) for \textit{multi-node representation learning}, where we are interested in learning a representation for a set of more than one node such as a link. Existing GNNs are mainly designed to learn single-node representations. When we want to learn a n... | ['Muhan Zhang', 'Pan Li', 'Xiyuan Wang'] | 2023-04-20 | null | null | null | null | ['hyperedge-prediction'] | ['graphs'] | [ 4.31011409e-01 6.48590803e-01 -6.87775314e-01 -2.84131378e-01
-3.49765658e-01 -5.64602017e-01 4.32953358e-01 4.94558603e-01
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-5.89729846e-01 -1.44254315e+00 -7.29875624e-01 -6.55005157e-01
-4.45676208e-01 7.47106552e-01 1.06796868e-01 -4.26331580... | [7.079738616943359, 6.302099704742432] |
13947280-be52-4d95-9bd9-75d677bfc1da | auto-fedrl-federated-hyperparameter | 2203.06338 | null | https://arxiv.org/abs/2203.06338v2 | https://arxiv.org/pdf/2203.06338v2.pdf | Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation | Federated learning (FL) is a distributed machine learning technique that enables collaborative model training while avoiding explicit data sharing. The inherent privacy-preserving property of FL algorithms makes them especially attractive to the medical field. However, in case of heterogeneous client data distributions... | ['Holger R. Roth', 'Vishal M. Patel', 'Gianpaolo Carrafiello', 'Elvira Stellato', 'Francesca Patella', 'Bradford Wood', 'Baris Turkbey', 'Evrim Turkbey', 'Stephanie Harmon', 'Daguang Xu', 'Can Zhao', 'Wenqi Li', 'Ziyue Xu', 'An Xu', 'Ali Hatamizadeh', 'Dong Yang', 'Pengfei Guo'] | 2022-03-12 | null | null | null | null | ['pancreas-segmentation'] | ['medical'] | [-9.15608481e-02 -3.91897298e-02 -5.48272371e-01 -4.11589324e-01
-9.35119867e-01 -5.24948537e-01 1.07394278e-01 4.22182262e-01
-7.80135393e-01 9.88564730e-01 -3.86949778e-02 -3.54229003e-01
-3.97465438e-01 -5.92016160e-01 -4.40452188e-01 -1.26019692e+00
-2.33598322e-01 7.74031818e-01 -1.52165428e-01 3.30082864... | [6.072597980499268, 6.452235221862793] |
80d5623e-ed4e-4254-baa6-758b88326ad9 | cdiffmr-can-we-replace-the-gaussian-noise | 2306.14350 | null | https://arxiv.org/abs/2306.14350v1 | https://arxiv.org/pdf/2306.14350v1.pdf | CDiffMR: Can We Replace the Gaussian Noise with K-Space Undersampling for Fast MRI? | Deep learning has shown the capability to substantially accelerate MRI reconstruction while acquiring fewer measurements. Recently, diffusion models have gained burgeoning interests as a novel group of deep learning-based generative methods. These methods seek to sample data points that belong to a target distribution ... | ['Guang Yang', 'Carola-Bibiane Schönlieb', 'Angelica Aviles-Rivero', 'Jiahao Huang'] | 2023-06-25 | null | null | null | null | ['mri-reconstruction'] | ['computer-vision'] | [ 3.27136740e-02 4.87258509e-02 2.83205826e-02 -2.61967719e-01
-1.00950968e+00 -9.01368409e-02 6.70753479e-01 -1.55347630e-01
-4.95864540e-01 7.23130286e-01 2.50026703e-01 -2.00902164e-01
-2.43201420e-01 -6.93582892e-01 -6.87548161e-01 -1.18606877e+00
-8.75077918e-02 6.35588646e-01 1.81132793e-01 1.20127894... | [13.48302173614502, -2.3713948726654053] |
7e7c41aa-a9bf-48c3-9fd3-2bfeae445ae4 | self-attention-encoding-and-pooling-for | 2008.01077 | null | https://arxiv.org/abs/2008.01077v1 | https://arxiv.org/pdf/2008.01077v1.pdf | Self-attention encoding and pooling for speaker recognition | The computing power of mobile devices limits the end-user applications in terms of storage size, processing, memory and energy consumption. These limitations motivate researchers for the design of more efficient deep models. On the other hand, self-attention networks based on Transformer architecture have attracted rem... | ['Javier Hernando', 'Pooyan Safari', 'Miquel India'] | 2020-08-03 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [ 4.13923636e-02 -1.71630859e-01 1.52523875e-01 -5.84807336e-01
-6.27033830e-01 -1.13017187e-01 4.13211942e-01 -1.96365252e-01
-5.69175482e-01 2.47409940e-01 4.16789562e-01 -3.10844988e-01
1.47729889e-01 -4.65447992e-01 -4.80584949e-01 -8.18624914e-01
1.20324008e-01 -8.16242993e-02 -3.88953872e-02 -4.91131768... | [14.367122650146484, 6.065057277679443] |
8203159a-68d5-4311-bb79-f7bbaa584382 | classifier-calibration-with-implications-to | 2102.05143 | null | https://arxiv.org/abs/2102.05143v3 | https://arxiv.org/pdf/2102.05143v3.pdf | Classifier Calibration: with application to threat scores in cybersecurity | This paper explores the calibration of a classifier output score in binary classification problems. A calibrator is a function that maps the arbitrary classifier score, of a testing observation, onto $[0,1]$ to provide an estimate for the posterior probability of belonging to one of the two classes. Calibration is impo... | ['William Briguglio', 'Issa Traore', 'Waleed A. Yousef'] | 2021-02-09 | null | null | null | null | ['classifier-calibration', 'classifier-calibration'] | ['computer-vision', 'miscellaneous'] | [ 9.64565724e-02 -1.57092214e-01 -3.39763135e-01 -6.55730844e-01
-6.21984601e-01 -5.71843326e-01 2.70336568e-01 2.61491895e-01
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-5.00615239e-01 -9.31621611e-01 -3.81740749e-01 -7.75963604e-01
-2.66914256e-02 4.73251998e-01 4.20010597e-01 -1.60584152... | [8.343284606933594, 4.284278869628906] |
6a0edf64-b5ca-4876-97dd-3383879b1cc2 | cova-context-aware-visual-attention-for | 2110.12320 | null | https://arxiv.org/abs/2110.12320v1 | https://arxiv.org/pdf/2110.12320v1.pdf | CoVA: Context-aware Visual Attention for Webpage Information Extraction | Webpage information extraction (WIE) is an important step to create knowledge bases. For this, classical WIE methods leverage the Document Object Model (DOM) tree of a website. However, use of the DOM tree poses significant challenges as context and appearance are encoded in an abstract manner. To address this challeng... | ['Alexander Schwing', 'Kevin Chen-Chuan Chang', 'Jingjin Wang', 'Keval Morabia', 'Anurendra Kumar'] | 2021-10-24 | null | https://aclanthology.org/2022.ecnlp-1.11 | https://aclanthology.org/2022.ecnlp-1.11.pdf | ecnlp-acl-2022-5 | ['webpage-object-detection'] | ['computer-vision'] | [ 3.98822993e-01 -1.39337555e-01 -1.96388662e-01 -2.74760872e-01
-8.79707992e-01 -1.16270018e+00 7.32756555e-01 3.14201623e-01
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2.30764002e-01 -3.23100225e-03 3.59227598e-01 2.03992024... | [11.510533332824707, 2.3810975551605225] |
36825125-ccd4-4289-8e4d-39afb9ce82ad | klmo-knowledge-graph-enhanced-pretrained | null | null | https://aclanthology.org/2021.findings-emnlp.384 | https://aclanthology.org/2021.findings-emnlp.384.pdf | KLMo: Knowledge Graph Enhanced Pretrained Language Model with Fine-Grained Relationships | Interactions between entities in knowledge graph (KG) provide rich knowledge for language representation learning. However, existing knowledge-enhanced pretrained language models (PLMs) only focus on entity information and ignore the fine-grained relationships between entities. In this work, we propose to incorporate K... | ['Feng Zhang', 'Tao Yang', 'Suncong Zheng', 'Lei He'] | null | null | null | null | findings-emnlp-2021-11 | ['relation-classification'] | ['natural-language-processing'] | [-2.51385897e-01 3.90507430e-01 -7.28083014e-01 -4.79916394e-01
-2.79653639e-01 -3.51134658e-01 4.86192286e-01 6.41649961e-01
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-1.56734660e-01 -1.34771276e+00 -8.15831363e-01 -2.74383456e-01
-2.07716405e-01 4.49600667e-01 2.49202400e-01 -4.06179965... | [9.090047836303711, 8.24321460723877] |
f84ba9aa-e36f-4c1e-be7c-3cbe946b69f6 | neural-symbolic-argumentation-mining-an | 1905.09103 | null | https://arxiv.org/abs/1905.09103v3 | https://arxiv.org/pdf/1905.09103v3.pdf | Neural-Symbolic Argumentation Mining: an Argument in Favor of Deep Learning and Reasoning | Deep learning is bringing remarkable contributions to the field of argumentation mining, but the existing approaches still need to fill the gap toward performing advanced reasoning tasks. In this position paper, we posit that neural-symbolic and statistical relational learning could play a crucial role in the integrati... | ['Andrea Galassi', 'Xiaoting Shao', 'Marco Lippi', 'Kristian Kersting', 'Paolo Torroni'] | 2019-05-22 | null | null | null | null | ['component-classification'] | ['natural-language-processing'] | [-1.75838858e-01 6.30221069e-01 -5.15951455e-01 -4.53030974e-01
-3.12610060e-01 -8.24827626e-02 7.59482920e-01 5.93727708e-01
-6.21214919e-02 8.36194038e-01 1.23249702e-01 -8.80640626e-01
-4.58399326e-01 -1.14757562e+00 -5.81358910e-01 -3.04011226e-01
-8.94984007e-02 5.29542387e-01 2.34883517e-01 -8.79831493... | [9.039240837097168, 7.126823902130127] |
d0e28359-4703-4e50-bf7e-b0a8fb124a2c | improving-multitask-retrieval-by-promoting | 2307.00342 | null | https://arxiv.org/abs/2307.00342v1 | https://arxiv.org/pdf/2307.00342v1.pdf | Improving Multitask Retrieval by Promoting Task Specialization | In multitask retrieval, a single retriever is trained to retrieve relevant contexts for multiple tasks. Despite its practical appeal, naive multitask retrieval lags behind task-specific retrieval in which a separate retriever is trained for each task. We show that it is possible to train a multitask retriever that outp... | ['Arnold Overwijk', 'Karl Stratos', 'Chenyan Xiong', 'Wenzheng Zhang'] | 2023-07-01 | null | null | null | null | ['retrieval'] | ['methodology'] | [ 3.38325471e-01 -5.58101714e-01 -2.46952906e-01 -2.44394884e-01
-1.48910570e+00 -1.02051699e+00 6.89515173e-01 7.05938563e-02
-7.85711944e-01 5.40119231e-01 2.54024774e-01 -2.05014139e-01
-5.71389198e-01 -1.53116405e-01 -4.54988450e-01 -7.65382469e-01
1.46015108e-01 1.05242157e+00 3.77979010e-01 -3.94612074... | [11.393807411193848, 7.702139854431152] |
23dd13e0-1533-4120-ad41-17a40ca99254 | vote-from-the-center-6-dof-pose-estimation-in | 2104.02527 | null | https://arxiv.org/abs/2104.02527v4 | https://arxiv.org/pdf/2104.02527v4.pdf | Vote from the Center: 6 DoF Pose Estimation in RGB-D Images by Radial Keypoint Voting | We propose a novel keypoint voting scheme based on intersecting spheres, that is more accurate than existing schemes and allows for fewer, more disperse keypoints. The scheme is based upon the distance between points, which as a 1D quantity can be regressed more accurately than the 2D and 3D vector and offset quantitie... | ['Michael Greenspan', 'Ali Etemad', 'Mohsen Zand', 'Yangzheng Wu'] | 2021-04-06 | null | null | null | null | ['6d-pose-estimation-using-rgbd'] | ['computer-vision'] | [-2.80984819e-01 -2.39427745e-01 -2.79610723e-01 -9.04673412e-02
-8.96527410e-01 -5.13519764e-01 4.54124689e-01 6.12474792e-02
-4.33705896e-01 5.06660998e-01 -6.19005077e-02 -3.08104511e-02
3.04082944e-03 -5.82440197e-01 -9.75429416e-01 -7.39033103e-01
-7.61568546e-02 5.91674685e-01 4.65303957e-01 -1.04032807... | [7.571232795715332, -2.6258087158203125] |
2174a87d-a585-49ba-a052-239ade9914aa | a-bayesian-approach-to-robust-reinforcement | 1905.08188 | null | https://arxiv.org/abs/1905.08188v2 | https://arxiv.org/pdf/1905.08188v2.pdf | A Bayesian Approach to Robust Reinforcement Learning | Robust Markov Decision Processes (RMDPs) intend to ensure robustness with respect to changing or adversarial system behavior. In this framework, transitions are modeled as arbitrary elements of a known and properly structured uncertainty set and a robust optimal policy can be derived under the worst-case scenario. In t... | ['Daniel Mankowitz', 'Esther Derman', 'Timothy Mann', 'Shie Mannor'] | 2019-05-20 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 2.17694789e-02 4.34876174e-01 -4.11363840e-02 1.24016376e-02
-1.11523151e+00 -6.66109204e-01 6.50183618e-01 1.35266662e-01
-5.80236018e-01 1.16964614e+00 -7.06203505e-02 -2.96465814e-01
-5.93301892e-01 -6.70022130e-01 -7.98743784e-01 -8.88151586e-01
-3.96037489e-01 5.63073456e-01 5.45352578e-01 1.36140838... | [4.620416641235352, 2.394618511199951] |
74702137-3fc4-44ab-8d63-56a58ad67d94 | melbourne-at-semeval-2016-task-11-classifying | null | null | https://aclanthology.org/S16-1150 | https://aclanthology.org/S16-1150.pdf | Melbourne at SemEval 2016 Task 11: Classifying Type-level Word Complexity using Random Forests with Corpus and Word List Features | null | ['ra', 'Timothy Baldwin', 'Julian Brooke', 'Alex Uitdenbogerd'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['complex-word-identification'] | ['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
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-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.3356804847717285, 3.5935192108154297] |
e66bdf4d-1e85-4049-b5c2-0d90b9f2bdaa | unsupervised-syntactically-controlled | 2211.00881 | null | https://arxiv.org/abs/2211.00881v1 | https://arxiv.org/pdf/2211.00881v1.pdf | Unsupervised Syntactically Controlled Paraphrase Generation with Abstract Meaning Representations | Syntactically controlled paraphrase generation has become an emerging research direction in recent years. Most existing approaches require annotated paraphrase pairs for training and are thus costly to extend to new domains. Unsupervised approaches, on the other hand, do not need paraphrase pairs but suffer from relati... | ['Aram Galstyan', 'Kai-Wei Chang', 'Sriram Venkatapathy', 'Anoop Kumar', 'Varun Iyer', 'Kuan-Hao Huang'] | 2022-11-02 | null | null | null | null | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 3.64186823e-01 3.57589990e-01 -3.49805415e-01 -6.44233167e-01
-8.59923780e-01 -6.36116624e-01 5.37623167e-01 2.80767739e-01
-2.45769173e-02 6.95927322e-01 8.31270933e-01 -3.10641527e-01
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7.51513898e-01 3.85462314e-01 -1.97323784e-01 -2.54990637... | [11.672795295715332, 9.288416862487793] |
616a2590-c748-4af6-804e-3e08419c14ff | fedos-using-open-set-learning-to-stabilize | 2208.11512 | null | https://arxiv.org/abs/2208.11512v2 | https://arxiv.org/pdf/2208.11512v2.pdf | FedOS: using open-set learning to stabilize training in federated learning | Federated Learning is a recent approach to train statistical models on distributed datasets without violating privacy constraints. The data locality principle is preserved by sharing the model instead of the data between clients and the server. This brings many advantages but also poses new challenges. In this report, ... | ['Juan Segundo Argayo', 'Julian Neubert', 'Mohamad Mohamad'] | 2022-08-22 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [-1.81362942e-01 -1.41316056e-01 -3.24994028e-01 -6.55605137e-01
-7.64181614e-01 -6.94547236e-01 6.42632842e-01 -4.03596014e-02
-4.82696503e-01 8.91938746e-01 1.11194827e-01 -2.97815472e-01
-3.50155741e-01 -5.65752387e-01 -7.91751206e-01 -7.93855846e-01
-2.39133924e-01 3.83212179e-01 2.22718701e-01 2.54389107... | [5.912691593170166, 6.535826206207275] |
75f3ffc5-52bf-434e-871b-e1e9bcab79c6 | promises-and-challenges-of-causality-for | 2201.10683 | null | https://arxiv.org/abs/2201.10683v2 | https://arxiv.org/pdf/2201.10683v2.pdf | Promises and Challenges of Causality for Ethical Machine Learning | In recent years, there has been increasing interest in causal reasoning for designing fair decision-making systems due to its compatibility with legal frameworks, interpretability for human stakeholders, and robustness to spurious correlations inherent in observational data, among other factors. The recent attention to... | ['Alice Xiang', 'Aida Rahmattalabi'] | 2022-01-26 | null | null | null | null | ['econometrics'] | ['miscellaneous'] | [ 4.39365953e-01 2.39964113e-01 -8.07208002e-01 -5.27956128e-01
-1.91723481e-01 -5.12209833e-01 6.84501529e-01 5.73484659e-01
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1.43245012e-01 -7.67155811e-02 -5.39391935e-01 6.71245158... | [8.782029151916504, 5.546262741088867] |
050a87a7-1fdb-4529-ac42-bf7be89e798d | evaluation-of-interpretability-for-deep | 2111.13208 | null | https://arxiv.org/abs/2111.13208v6 | https://arxiv.org/pdf/2111.13208v6.pdf | Evaluation of Interpretability for Deep Learning algorithms in EEG Emotion Recognition: A case study in Autism | Current models on Explainable Artificial Intelligence (XAI) have shown an evident and quantified lack of reliability for measuring feature-relevance when statistically entangled features are proposed for training deep classifiers. There has been an increase in the application of Deep Learning in clinical trials to pred... | ['Giuseppe Riccardi', 'Matthew D. Lerner', 'Tessa Clarkson', 'Sara Medina-DeVilliers', 'Juan Manuel Mayor-Torres'] | 2021-11-25 | null | null | null | null | ['facial-emotion-recognition', 'eeg-emotion-recognition'] | ['computer-vision', 'miscellaneous'] | [ 5.20159841e-01 4.19872731e-01 3.58705938e-01 -7.15434790e-01
-1.10446885e-01 2.29025185e-01 3.35294455e-01 3.17179292e-01
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-8.03758502e-01 -2.04844847e-01 -4.45043683e-01 -5.49546659e-01
-4.48420584e-01 2.22191602e-01 -4.08994615e-01 -2.03465223... | [13.10433292388916, 3.4661197662353516] |
0087951a-b38c-427a-9399-b5feb009f49d | machine-learning-based-detection-of | 2303.11429 | null | https://arxiv.org/abs/2303.11429v1 | https://arxiv.org/pdf/2303.11429v1.pdf | Machine learning-based detection of cardiovascular disease using ECG signals: performance vs. complexity | Cardiovascular disease remains a significant problem in modern society. Among non-invasive techniques, the electrocardiogram (ECG) is one of the most reliable methods for detecting abnormalities in cardiac activities. However, ECG interpretation requires expert knowledge and it is time-consuming. Developing a novel met... | ['Semen Budennyy', 'Alexey Kazakov', 'Konstantin Egorov', 'Huy Pham'] | 2023-03-10 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [-8.46508592e-02 -4.63621244e-02 -2.81479657e-02 2.65280679e-02
-3.96593034e-01 -5.68586528e-01 8.84288251e-02 2.21356899e-01
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-7.25601137e-01 -8.96399558e-01 -4.42694813e-01 -5.44762254e-01
-6.19716167e-01 1.78405240e-01 -5.00493646e-01 1.50991857... | [14.214637756347656, 3.342275381088257] |
4c786085-c55c-412a-8778-b3a6fc5ad751 | pragmatically-appropriate-diversity-for | 2304.02812 | null | https://arxiv.org/abs/2304.02812v1 | https://arxiv.org/pdf/2304.02812v1.pdf | Pragmatically Appropriate Diversity for Dialogue Evaluation | Linguistic pragmatics state that a conversation's underlying speech acts can constrain the type of response which is appropriate at each turn in the conversation. When generating dialogue responses, neural dialogue agents struggle to produce diverse responses. Currently, dialogue diversity is assessed using automatic m... | ['Marti A. Hearst', 'Katherine Stasaski'] | 2023-04-06 | null | null | null | null | ['dialogue-evaluation'] | ['natural-language-processing'] | [ 2.03732178e-01 5.75210035e-01 -1.38588771e-01 -7.71173120e-01
-4.66581583e-01 -7.21450329e-01 1.06618369e+00 -7.77574182e-02
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-3.11254598e-02 -5.56020260e-01 1.18424639e-01 -3.44193667e-01
8.17440689e-01 5.99401057e-01 -3.03505272e-01 -7.07352459... | [12.79451847076416, 8.056619644165039] |
b2c38e10-6ae2-417e-959c-5ec09805083e | program-induction-by-rationale-generation-1 | null | null | https://aclanthology.org/P17-1015 | https://aclanthology.org/P17-1015.pdf | Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems | Solving algebraic word problems requires executing a series of arithmetic operations{---}a program{---}to obtain a final answer. However, since programs can be arbitrarily complicated, inducing them directly from question-answer pairs is a formidable challenge. To make this task more feasible, we solve these problems b... | ['Wang Ling', 'Chris Dyer', 'Phil Blunsom', 'Dani Yogatama'] | 2017-07-01 | null | null | null | acl-2017-7 | ['program-induction'] | ['computer-code'] | [ 2.68398941e-01 1.93134129e-01 -2.13949651e-01 -8.82754982e-01
-9.58351433e-01 -1.02006125e+00 3.64757478e-01 4.78315592e-01
-7.46226981e-02 5.11415541e-01 -5.64666130e-02 -1.10589325e+00
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4.12306450e-02 3.82729918e-01 3.32812041e-01 -3.86146933... | [9.482827186584473, 7.39417028427124] |
d5ba98b0-d89c-4730-919c-bb7e26e13709 | anticipating-human-actions-by-correlating | 2105.12414 | null | https://arxiv.org/abs/2105.12414v1 | https://arxiv.org/pdf/2105.12414v1.pdf | Anticipating human actions by correlating past with the future with Jaccard similarity measures | We propose a framework for early action recognition and anticipation by correlating past features with the future using three novel similarity measures called Jaccard vector similarity, Jaccard cross-correlation and Jaccard Frobenius inner product over covariances. Using these combinations of novel losses and using our... | ['Samitha Herath', 'Basura Fernando'] | 2021-05-26 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Fernando_Anticipating_Human_Actions_by_Correlating_Past_With_the_Future_With_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Fernando_Anticipating_Human_Actions_by_Correlating_Past_With_the_Future_With_CVPR_2021_paper.pdf | cvpr-2021-1 | ['action-anticipation'] | ['computer-vision'] | [ 2.31510460e-01 -2.51880646e-01 -4.08316582e-01 -4.76716071e-01
-9.83729899e-01 -1.38168335e-01 5.78504384e-01 2.65993506e-01
-6.99770749e-01 7.62898803e-01 5.44627726e-01 4.25394595e-01
-4.55449104e-01 -4.65376288e-01 -5.50652921e-01 -4.95569646e-01
-7.28595197e-01 -6.95785508e-02 3.92388731e-01 -8.52958634... | [8.13033676147461, 0.6182119250297546] |
a8201886-1158-4112-8437-afc0a22f5000 | chatlaw-open-source-legal-large-language | 2306.16092 | null | https://arxiv.org/abs/2306.16092v1 | https://arxiv.org/pdf/2306.16092v1.pdf | ChatLaw: Open-Source Legal Large Language Model with Integrated External Knowledge Bases | Large Language Models (LLMs) have shown the potential to revolutionize natural language processing tasks in various domains, sparking great interest in vertical-specific large models. However, unlike proprietary models such as BloombergGPT and FinGPT, which have leveraged their unique data accumulations to make strides... | ['Li Yuan', 'Bohua Chen', 'Yang Yan', 'Zongjian Li', 'Jiaxi Cui'] | 2023-06-28 | null | null | null | null | ['retrieval'] | ['methodology'] | [-5.64305365e-01 -8.21136460e-02 -3.11535001e-01 -2.36793458e-01
-1.20182478e+00 -4.14808989e-01 4.62027043e-01 -4.52510603e-02
-4.24957901e-01 4.33957487e-01 5.50114989e-01 -5.34794390e-01
-2.42739588e-01 -6.55147552e-01 -4.53039020e-01 -7.24188015e-02
3.92359436e-01 4.44740176e-01 -5.28897084e-02 -4.13234293... | [10.878803253173828, 8.501314163208008] |
739e7218-83bc-4099-b9c2-25141ad7c6aa | identifiability-of-label-noise-transition | 2202.02016 | null | https://arxiv.org/abs/2202.02016v3 | https://arxiv.org/pdf/2202.02016v3.pdf | Identifiability of Label Noise Transition Matrix | The noise transition matrix plays a central role in the problem of learning with noisy labels. Among many other reasons, a large number of existing solutions rely on access to it. Identifying and estimating the transition matrix without ground truth labels is a critical and challenging task. When label noise transition... | ['Kun Zhang', 'Hao Cheng', 'Yang Liu'] | 2022-02-04 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 5.40325642e-01 3.19215357e-02 -1.42738521e-01 -3.70515198e-01
-1.19226611e+00 -8.09056044e-01 4.99538660e-01 9.74376053e-02
-1.93054840e-01 5.76012075e-01 1.41242027e-01 -1.83584362e-01
-8.30930889e-01 -2.31181875e-01 -5.16322374e-01 -9.87101555e-01
-9.73746777e-02 6.37448251e-01 -4.24273729e-01 8.23068842... | [9.403112411499023, 4.142213821411133] |
2c9bc79c-5c1e-48eb-8520-852506ce5f74 | vcnet-a-self-explaining-model-for-realistic | 2212.10847 | null | https://arxiv.org/abs/2212.10847v1 | https://arxiv.org/pdf/2212.10847v1.pdf | VCNet: A self-explaining model for realistic counterfactual generation | Counterfactual explanation is a common class of methods to make local explanations of machine learning decisions. For a given instance, these methods aim to find the smallest modification of feature values that changes the predicted decision made by a machine learning model. One of the challenges of counterfactual expl... | ['Alexandre Termier', 'Tassadit Bouadi', 'Thomas Guyet', 'Françoise Fessant', 'Victor Guyomard'] | 2022-12-21 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 4.03303146e-01 1.02077603e+00 -2.54806578e-01 -4.02662784e-01
-6.20894432e-01 -2.22154289e-01 1.09122562e+00 -4.20741588e-02
-4.15475629e-02 1.34464192e+00 5.31815827e-01 -6.44980311e-01
4.99805957e-02 -9.09053922e-01 -1.07888460e+00 -5.60629010e-01
2.31750891e-01 8.08974862e-01 -3.48516047e-01 -7.71028623... | [8.628473281860352, 5.650996685028076] |
73f0cc9d-8953-426a-a773-251dcc385152 | multiverse-causal-reasoning-using-importance | 1910.08091 | null | https://arxiv.org/abs/1910.08091v2 | https://arxiv.org/pdf/1910.08091v2.pdf | MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming | We elaborate on using importance sampling for causal reasoning, in particular for counterfactual inference. We show how this can be implemented natively in probabilistic programming. By considering the structure of the counterfactual query, one can significantly optimise the inference process. We also consider design c... | ['Ciarán M. Lee', 'Logan Graham', 'Kostis Gourgoulias', 'Adam Baker', 'Yura Perov', 'Saurabh Johri', 'Jonathan G. Richens'] | 2019-10-17 | null | https://openreview.net/forum?id=S1xFcknVFS | https://openreview.net/pdf?id=S1xFcknVFS | pproximateinference-aabi-symposium-2019-12 | ['counterfactual-inference'] | ['miscellaneous'] | [-1.19926453e-01 8.02415133e-01 -4.45679247e-01 -6.83050871e-01
-7.66134560e-01 -5.20189881e-01 1.13500082e+00 3.72070551e-01
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-5.48213124e-01 -1.20212996e+00 -7.75252104e-01 -2.59513170e-01
-5.13606012e-01 9.78457153e-01 3.40911150e-01 1.21628359... | [8.32784366607666, 6.023369312286377] |
f085880a-0ddd-4204-86e0-dca12a234ea6 | deep-reinforcement-learning-for-detecting | 1905.09207 | null | https://arxiv.org/abs/1905.09207v1 | https://arxiv.org/pdf/1905.09207v1.pdf | Deep Reinforcement Learning for Detecting Malicious Websites | Phishing is the simplest form of cybercrime with the objective of baiting people into giving away delicate information such as individually recognizable data, banking and credit card details, or even credentials and passwords. This type of simple yet most effective cyber-attack is usually launched through emails, phone... | ['Akbar Siami Namin', 'Moitrayee Chatterjee'] | 2019-05-22 | null | null | null | null | ['phishing-website-detection'] | ['adversarial'] | [-1.87822089e-01 -2.63623744e-01 7.44039342e-02 -3.16010751e-02
-4.60381582e-02 -1.01887810e+00 4.88426894e-01 3.91837656e-01
-5.36182344e-01 8.13313663e-01 -4.38802361e-01 -4.20217216e-01
2.13717178e-01 -1.27758658e+00 -3.57567638e-01 -5.71691453e-01
-1.84523210e-01 3.37192804e-01 4.23359126e-01 -2.68442392... | [7.813441753387451, 9.994608879089355] |
f148bb45-db08-468a-b5e5-d4cec1973192 | ptrail-a-python-package-for-parallel | 2108.13202 | null | https://arxiv.org/abs/2108.13202v1 | https://arxiv.org/pdf/2108.13202v1.pdf | PTRAIL -- A python package for parallel trajectory data preprocessing | Trajectory data represent a trace of an object that changes its position in space over time. This kind of data is complex to handle and analyze, since it is generally produced in huge quantities, often prone to errors generated by the geolocation device, human mishandling, or area coverage limitation. Therefore, there ... | ['Amilcar Soares', 'Vinicius Prado da Fonseca', 'Chiara Renso', 'Vania Bogorny', 'Yaksh J. Haranwala', 'Salman Haidri'] | 2021-08-26 | null | null | null | null | ['trajectory-modeling'] | ['time-series'] | [-3.21056724e-01 -7.06742644e-01 5.33823185e-02 -1.00694895e-01
-3.15918714e-01 -8.66755843e-01 6.74824357e-01 1.00825107e+00
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-2.77690917e-01 -1.25910902e+00 -5.94935060e-01 -4.59932983e-01
-2.27410614e-01 5.02879143e-01 5.89186549e-01 -3.19035351... | [6.351580619812012, 1.7081716060638428] |
263982d2-ec53-4407-a8b9-86d3bf47a4f1 | cross-modal-fusion-distillation-for-fine | 2210.10486 | null | https://arxiv.org/abs/2210.10486v1 | https://arxiv.org/pdf/2210.10486v1.pdf | Cross-Modal Fusion Distillation for Fine-Grained Sketch-Based Image Retrieval | Representation learning for sketch-based image retrieval has mostly been tackled by learning embeddings that discard modality-specific information. As instances from different modalities can often provide complementary information describing the underlying concept, we propose a cross-attention framework for Vision Tran... | ['Anjan Dutta', 'Zeynep Akata', 'Yanbei Chen', 'Massimiliano Mancini', 'Abhra Chaudhuri'] | 2022-10-19 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 4.19337526e-02 -2.82781839e-01 -4.33666408e-01 -3.37424964e-01
-1.19118440e+00 -6.97446942e-01 1.16105592e+00 -3.66451144e-02
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-2.35556126e-01 -6.69325054e-01 -8.96675885e-01 -5.75920641e-01
3.78477901e-01 4.34398085e-01 -1.76045537e-01 -1.92625418... | [11.334315299987793, 0.8237981796264648] |
481e7b86-0de7-4232-81c2-d221d6103ce5 | streamlining-models-with-explanations-in-the | 2302.07760 | null | https://arxiv.org/abs/2302.07760v1 | https://arxiv.org/pdf/2302.07760v1.pdf | Streamlining models with explanations in the learning loop | Several explainable AI methods allow a Machine Learning user to get insights on the classification process of a black-box model in the form of local linear explanations. With such information, the user can judge which features are locally relevant for the classification outcome, and get an understanding of how the mode... | ['Elvio G. Amparore', 'Alan Perotti', 'Paolo Bajardi', 'Francesco Lomuscio'] | 2023-02-15 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [ 4.44174975e-01 6.04705632e-01 -4.48527753e-01 -6.73918366e-01
-1.95066452e-01 -5.58492184e-01 7.81892896e-01 6.33779109e-01
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-5.98791659e-01 -8.03909123e-01 -3.85694206e-01 -5.86110950e-01
1.66209012e-01 6.63012743e-01 7.51427710e-02 4.61381786... | [8.824880599975586, 5.789315700531006] |
d0706ec4-93dc-4c45-aebf-d2a644444b7d | deformable-mesh-transformer-for-3d-human-mesh | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yoshiyasu_Deformable_Mesh_Transformer_for_3D_Human_Mesh_Recovery_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yoshiyasu_Deformable_Mesh_Transformer_for_3D_Human_Mesh_Recovery_CVPR_2023_paper.pdf | Deformable Mesh Transformer for 3D Human Mesh Recovery | We present Deformable mesh transFormer (DeFormer), a novel vertex-based approach to monocular 3D human mesh recovery. DeFormer iteratively fits a body mesh model to an input image via a mesh alignment feedback loop formed within a transformer decoder that is equipped with efficient body mesh driven attention module... | ['Yusuke Yoshiyasu'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['human-mesh-recovery'] | ['computer-vision'] | [ 2.47388165e-02 2.99690157e-01 -4.92819324e-02 -2.51766622e-01
-9.98123705e-01 -2.64123827e-02 3.06538790e-01 -3.91131520e-01
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3.68272930e-01 -8.23269665e-01 -1.25394142e+00 -2.52267361e-01
2.69335508e-01 8.60345721e-01 9.81280804e-02 -2.63451099... | [7.082292079925537, -1.2029035091400146] |
51dfb313-3528-4508-b124-50c60d9b9915 | ecnuica-at-semeval-2021-task-11-rule-based | null | null | https://aclanthology.org/2021.semeval-1.185 | https://aclanthology.org/2021.semeval-1.185.pdf | ECNUICA at SemEval-2021 Task 11: Rule based Information Extraction Pipeline | This paper presents our endeavor for solving task11, NLPContributionGraph, of SemEval-2021. The purpose of the task was to extract triples from a paper in the Nature Language Processing field for constructing an Open Research Knowledge Graph. The task includes three sub-tasks: detecting the contribution sentences in pa... | ['Liang He', 'Qin Chen', 'Jiawei Liu', 'Zhiwei Wang', 'Jing Ling', 'Jiaju Lin'] | 2021-08-01 | null | null | null | semeval-2021 | ['open-information-extraction'] | ['natural-language-processing'] | [ 2.46824279e-01 6.91272795e-01 -4.80551869e-01 -2.57254690e-02
-7.24333405e-01 -6.98749721e-01 7.95784533e-01 5.93553662e-01
-2.08696067e-01 1.09636629e+00 1.24574527e-01 -6.25064671e-01
-2.37582445e-01 -8.96449625e-01 -9.77647245e-01 -8.04553181e-02
8.80491883e-02 4.37525034e-01 6.33699670e-02 2.01454997... | [9.436646461486816, 8.525623321533203] |
f8d10903-ae50-4802-b55c-e30924b9ced1 | the-search-for-stability-learning-dynamics-of | 2305.16695 | null | https://arxiv.org/abs/2305.16695v1 | https://arxiv.org/pdf/2305.16695v1.pdf | The Search for Stability: Learning Dynamics of Strategic Publishers with Initial Documents | We study a game-theoretic model of information retrieval, in which strategic publishers aim to maximize their chances of being ranked first by the search engine, while maintaining the integrity of their original documents. We show that the commonly used PRP ranking scheme results in an unstable environment where games ... | ['Moshe Tennenholtz', 'Omer Madmon'] | 2023-05-26 | null | null | null | null | ['information-retrieval'] | ['natural-language-processing'] | [-2.40934283e-01 3.08876604e-01 -3.92610729e-01 5.57561755e-01
-5.60149074e-01 -1.07414877e+00 7.53079295e-01 -3.20458263e-02
-7.14841723e-01 4.85975564e-01 9.49877799e-02 -4.91493315e-01
-9.80780721e-01 -7.48558521e-01 -3.10269028e-01 -5.11825681e-01
-4.92039829e-01 6.21221721e-01 4.86571223e-01 -4.29629713... | [4.2827277183532715, 2.9392666816711426] |
48f27819-d305-4a54-9f2b-eecdd6c2fa55 | evaluating-raw-waveforms-with-deep-learning | 2307.02820 | null | https://arxiv.org/abs/2307.02820v1 | https://arxiv.org/pdf/2307.02820v1.pdf | Evaluating raw waveforms with deep learning frameworks for speech emotion recognition | Speech emotion recognition is a challenging task in speech processing field. For this reason, feature extraction process has a crucial importance to demonstrate and process the speech signals. In this work, we represent a model, which feeds raw audio files directly into the deep neural networks without any feature extr... | ['Ayhan Kucukmanisa', 'Ulku Bayraktar', 'Zeynep Hilal Kilimci'] | 2023-07-06 | null | null | null | null | ['emotion-recognition', 'ensemble-learning', 'ensemble-learning', 'speech-emotion-recognition'] | ['computer-vision', 'computer-vision', 'methodology', 'speech'] | [-0.29788402 -0.3591448 0.44033724 -0.33984035 -0.29009116 -0.21239218
0.23048936 0.06058896 -0.58769214 0.8049107 0.08400458 -0.260679
-0.31282043 -0.5846425 -0.05666303 -0.49858293 -0.22427866 -0.10670739
-0.29213947 -0.52415 0.24884292 0.4865524 -1.9082059 0.5939202
0.5195187 1.6921331 -0.415... | [13.72793197631836, 5.523054122924805] |
a9d77c58-ffec-4a06-a03c-99846b351445 | semi-infinitely-constrained-markov-decision | 2305.00254 | null | https://arxiv.org/abs/2305.00254v1 | https://arxiv.org/pdf/2305.00254v1.pdf | Semi-Infinitely Constrained Markov Decision Processes and Efficient Reinforcement Learning | We propose a novel generalization of constrained Markov decision processes (CMDPs) that we call the \emph{semi-infinitely constrained Markov decision process} (SICMDP). Particularly, we consider a continuum of constraints instead of a finite number of constraints as in the case of ordinary CMDPs. We also devise two rei... | ['Zhihua Zhang', 'Wenhao Yang', 'Yang Peng', 'Liangyu Zhang'] | 2023-04-29 | null | null | null | null | ['model-based-reinforcement-learning'] | ['reasoning'] | [ 1.56770304e-01 3.72707248e-01 -5.45672297e-01 5.75782135e-02
-9.48331594e-01 -4.47719842e-01 2.32313424e-01 -8.92164186e-02
-4.87344474e-01 1.08330190e+00 -1.58882961e-01 -7.24042177e-01
-6.09102607e-01 -5.98501444e-01 -6.44673347e-01 -9.93757248e-01
-2.82800078e-01 6.67757630e-01 -5.00061549e-02 -2.89268848... | [4.241252422332764, 2.4687418937683105] |
f9f73329-2e01-4e70-8c53-a00f19e1c28b | multi-modal-representation-learning-for | 2306.07935 | null | https://arxiv.org/abs/2306.07935v1 | https://arxiv.org/pdf/2306.07935v1.pdf | Multi-modal Representation Learning for Social Post Location Inference | Inferring geographic locations via social posts is essential for many practical location-based applications such as product marketing, point-of-interest recommendation, and infector tracking for COVID-19. Unlike image-based location retrieval or social-post text embedding-based location inference, the combined effect o... | ['Fan Zhou', 'Wanlun Ma', 'Lisi Mo', 'Xucheng Luo', 'Jiayi Luo', 'Ruiting Dai'] | 2023-06-11 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [ 4.33354862e-02 -1.30039796e-01 -4.71037269e-01 -4.07191008e-01
-1.11736739e+00 -5.57642221e-01 1.11371553e+00 4.07123059e-01
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-5.93387969e-02 -8.80061328e-01 -8.47841203e-01 -5.91458678e-01
2.22446118e-02 3.47160071e-01 1.85603902e-01 2.17215791... | [10.785290718078613, 1.469226598739624] |
db10ea50-baaa-4ce2-b3f1-33822462bb56 | membership-inference-attack-and-defense-for | 2107.12173 | null | https://arxiv.org/abs/2107.12173v1 | https://arxiv.org/pdf/2107.12173v1.pdf | Membership Inference Attack and Defense for Wireless Signal Classifiers with Deep Learning | An over-the-air membership inference attack (MIA) is presented to leak private information from a wireless signal classifier. Machine learning (ML) provides powerful means to classify wireless signals, e.g., for PHY-layer authentication. As an adversarial machine learning attack, the MIA infers whether a signal of inte... | ['Yalin E. Sagduyu', 'Yi Shi'] | 2021-07-22 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 7.95463085e-01 2.84692228e-01 -1.48636967e-01 2.38745306e-02
-1.05733633e+00 -1.10043490e+00 1.88490719e-01 -2.81591376e-04
-7.27796461e-03 5.34232497e-01 -4.83309865e-01 -9.02946472e-01
2.15303395e-02 -1.12239218e+00 -8.14326882e-01 -1.29048252e+00
-5.32698274e-01 -2.72544086e-01 -3.29057388e-02 1.20114550... | [13.840845108032227, 5.80043888092041] |
67016273-5b2e-4e6c-8d63-d7338089972d | the-myth-of-culturally-agnostic-ai-models | 2211.15271 | null | https://arxiv.org/abs/2211.15271v2 | https://arxiv.org/pdf/2211.15271v2.pdf | The Myth of Culturally Agnostic AI Models | The paper discusses the potential of large vision-language models as objects of interest for empirical cultural studies. Focusing on the comparative analysis of outputs from two popular text-to-image synthesis models, DALL-E 2 and Stable Diffusion, the paper tries to tackle the pros and cons of striving towards cultura... | ['Eva Cetinic'] | 2022-11-28 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [-3.08067026e-03 2.94781417e-01 4.34044078e-02 -1.40727177e-01
-5.33021927e-01 -5.81748128e-01 1.24541676e+00 1.28112882e-02
-7.11224139e-01 4.94388103e-01 8.90833020e-01 -3.00855249e-01
1.37613351e-02 -4.39765036e-01 -6.01059794e-01 -5.02108097e-01
3.53066415e-01 1.78498983e-01 -5.99191606e-01 -4.60416853... | [11.624587059020996, 0.8943983912467957] |
475c9003-aaeb-429e-af0a-291317fcc3cd | learning-based-real-time-detection-of | 1911.00189 | null | https://arxiv.org/abs/1911.00189v1 | https://arxiv.org/pdf/1911.00189v1.pdf | Learning-based Real-time Detection of Intrinsic Reflectional Symmetry | Reflectional symmetry is ubiquitous in nature. While extrinsic reflectional symmetry can be easily parametrized and detected, intrinsic symmetry is much harder due to the high solution space. Previous works usually solve this problem by voting or sampling, which suffer from high computational cost and randomness. In th... | ['Yu-Kun Lai', 'Shu-Zhi Liu', 'Yi-Ling Qiao', 'Xilin Chen', 'Lin Gao', 'Ligang Liu'] | 2019-11-01 | null | null | null | null | ['symmetry-detection'] | ['computer-vision'] | [ 2.54156739e-01 -6.18633591e-02 -1.39833465e-01 -1.14752039e-01
-5.60069203e-01 -5.61356425e-01 4.35211629e-01 -1.71613693e-01
9.76839568e-03 5.14261127e-01 -7.67959803e-02 -8.94790590e-02
-3.62992465e-01 -1.18512702e+00 -8.26953888e-01 -8.17537904e-01
4.16874923e-02 7.14367509e-01 -1.48713917e-01 -2.27380276... | [8.348637580871582, -2.709261894226074] |
96891e56-ee91-4350-9ade-6218296dcbe4 | hyperspectral-image-denoising-based-on-multi | 2104.02304 | null | https://arxiv.org/abs/2104.02304v1 | https://arxiv.org/pdf/2104.02304v1.pdf | Hyperspectral Image Denoising Based On Multi-Stream Denoising Network | Hyperspectral images (HSIs) have been widely applied in many fields, such as military, agriculture, and environment monitoring. Nevertheless, HSIs commonly suffer from various types of noise during acquisition. Therefore, denoising is critical for HSI analysis and applications. In this paper, we propose a novel blind d... | ['Junyu Dong', 'Feng Gao', 'Yan Gao'] | 2021-04-06 | null | null | null | null | ['noise-estimation'] | ['medical'] | [ 5.61888993e-01 -1.00519788e+00 5.61802030e-01 -2.41343319e-01
-6.08619273e-01 -3.11612874e-01 1.25397503e-01 -3.13175581e-02
-1.41701356e-01 3.90743673e-01 1.01513453e-01 -8.60404149e-02
-1.80998668e-01 -1.05514419e+00 -2.95129847e-02 -1.35813010e+00
4.67595071e-01 -6.73480034e-01 3.25272590e-01 -3.22140634... | [10.522424697875977, -2.06819748878479] |
56fd7b6f-8204-4716-80fe-1c140ccd2f6a | zero-shot-grounding-of-objects-from-natural | 1908.07129 | null | https://arxiv.org/abs/1908.07129v1 | https://arxiv.org/pdf/1908.07129v1.pdf | Zero-Shot Grounding of Objects from Natural Language Queries | A phrase grounding system localizes a particular object in an image referred to by a natural language query. In previous work, the phrases were restricted to have nouns that were encountered in training, we extend the task to Zero-Shot Grounding(ZSG) which can include novel, "unseen" nouns. Current phrase grounding sys... | ['Kan Chen', 'Arka Sadhu', 'Ram Nevatia'] | 2019-08-20 | zero-shot-grounding-of-objects-from-natural-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Sadhu_Zero-Shot_Grounding_of_Objects_From_Natural_Language_Queries_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Sadhu_Zero-Shot_Grounding_of_Objects_From_Natural_Language_Queries_ICCV_2019_paper.pdf | iccv-2019-10 | ['phrase-grounding'] | ['natural-language-processing'] | [ 1.07797667e-01 2.95189232e-01 -1.21842146e-01 -2.91496068e-01
-1.14995289e+00 -5.85750222e-01 8.82795990e-01 9.86797661e-02
-5.71924686e-01 4.17408586e-01 1.83417663e-01 1.09102398e-01
2.45557740e-01 -7.43819475e-01 -8.57186437e-01 -5.39692581e-01
1.22383587e-01 5.43276906e-01 6.89488292e-01 -2.80519098... | [10.332995414733887, 1.5013929605484009] |
decad77b-30f1-48c0-a4d7-ce1ec5334144 | l-sa-learning-under-explored-targets-in-multi | 2305.13741 | null | https://arxiv.org/abs/2305.13741v1 | https://arxiv.org/pdf/2305.13741v1.pdf | L-SA: Learning Under-Explored Targets in Multi-Target Reinforcement Learning | Tasks that involve interaction with various targets are called multi-target tasks. When applying general reinforcement learning approaches for such tasks, certain targets that are difficult to access or interact with may be neglected throughout the course of training - a predicament we call Under-explored Target Proble... | ['Byoung-Tak Zhang', 'Minsu Lee', 'Moonheon Lee', 'Min Whoo Lee', 'Hyundo Lee', 'Kibeom Kim'] | 2023-05-23 | null | null | null | null | ['visual-navigation'] | ['robots'] | [ 1.37299478e-01 1.41511187e-01 -3.11233014e-01 -1.89414427e-01
-8.71094167e-01 -5.54805636e-01 2.62618035e-01 2.50430852e-01
-6.53370678e-01 8.83738041e-01 -1.80852786e-01 -9.13830325e-02
-5.41616738e-01 -9.04520512e-01 -5.91426790e-01 -7.01211870e-01
-1.81646541e-01 6.24164343e-01 5.70507407e-01 -4.82293516... | [4.0683488845825195, 1.7731633186340332] |
101b8a22-44f0-48c2-b7db-b0b89d724b59 | hooks-in-the-headline-learning-to-generate | 2004.01980 | null | https://arxiv.org/abs/2004.01980v3 | https://arxiv.org/pdf/2004.01980v3.pdf | Hooks in the Headline: Learning to Generate Headlines with Controlled Styles | Current summarization systems only produce plain, factual headlines, but do not meet the practical needs of creating memorable titles to increase exposure. We propose a new task, Stylistic Headline Generation (SHG), to enrich the headlines with three style options (humor, romance and clickbait), in order to attract mor... | ['Joey Tianyi Zhou', 'Zhijing Jin', 'Peter Szolovits', 'Lisa Orii', 'Di Jin'] | 2020-04-04 | hooks-in-the-headline-learning-to-generate-1 | https://aclanthology.org/2020.acl-main.456 | https://aclanthology.org/2020.acl-main.456.pdf | acl-2020-6 | ['headline-generation'] | ['natural-language-processing'] | [ 1.15946762e-01 1.87457249e-01 -1.46461114e-01 -1.12738319e-01
-1.29211164e+00 -6.05176926e-01 9.74187493e-01 5.78519888e-02
-3.98201048e-01 1.18002594e+00 9.62731600e-01 -1.74202755e-01
3.04726601e-01 -3.68257344e-01 -6.22418404e-01 -1.20063521e-01
6.61228597e-01 6.22737467e-01 2.06798643e-01 -5.10041595... | [12.272357940673828, 9.261817932128906] |
02bd294b-02e5-4602-b59d-1492e5185b80 | split-u-net-preventing-data-leakage-in-split | 2208.10553 | null | https://arxiv.org/abs/2208.10553v2 | https://arxiv.org/pdf/2208.10553v2.pdf | Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-Modal Brain Tumor Segmentation | Split learning (SL) has been proposed to train deep learning models in a decentralized manner. For decentralized healthcare applications with vertical data partitioning, SL can be beneficial as it allows institutes with complementary features or images for a shared set of patients to jointly develop more robust and gen... | ['Daguang Xu', 'Andriy Myronenko', 'Wenqi Li', 'Can Zhao', 'Ziyue Xu', 'Ali Hatamizadeh', 'Holger R. Roth'] | 2022-08-22 | null | null | null | null | ['brain-tumor-segmentation'] | ['medical'] | [-5.30950800e-02 4.51507032e-01 -3.47219527e-01 -5.91775775e-01
-7.68222034e-01 -7.10308611e-01 -5.40625080e-02 2.65706003e-01
-6.08204901e-01 9.37324345e-01 5.64159714e-02 -6.60919905e-01
7.35000893e-02 -7.14075983e-01 -7.34917760e-01 -7.30860233e-01
-5.96828721e-02 2.03489810e-02 -2.21853822e-01 2.99664587... | [6.1095805168151855, 6.469272136688232] |
9bd52fbd-7d11-44d8-85e2-e601d2108cb0 | casnet-investigating-channel-robustness-for | 2210.15370 | null | https://arxiv.org/abs/2210.15370v1 | https://arxiv.org/pdf/2210.15370v1.pdf | CasNet: Investigating Channel Robustness for Speech Separation | Recording channel mismatch between training and testing conditions has been shown to be a serious problem for speech separation. This situation greatly reduces the separation performance, and cannot meet the requirement of daily use. In this study, inheriting the use of our previously constructed TAT-2mix corpus, we ad... | ['Hsin-Min Wang', 'Yu Tsao', 'Hung-Shin Lee', 'Yao-Fei Cheng', 'Fan-Lin Wang'] | 2022-10-27 | null | null | null | null | ['speech-separation'] | ['speech'] | [ 2.44409889e-01 -7.88849518e-02 -5.11053428e-02 4.44537401e-03
-1.11153901e+00 -4.57314491e-01 2.35442400e-01 -1.76844195e-01
-1.14945561e-01 3.70125264e-01 4.40316498e-01 -5.38961887e-01
1.98545277e-01 -1.68076202e-01 -6.05347812e-01 -9.12982643e-01
-9.67628211e-02 -3.15012620e-03 5.61013371e-02 -5.70312329... | [14.81822681427002, 6.050320148468018] |
540b9445-9259-4b7b-9dfb-59405e5c3bb5 | false-negative-reduction-in-video-instance | 2106.14474 | null | https://arxiv.org/abs/2106.14474v1 | https://arxiv.org/pdf/2106.14474v1.pdf | False Negative Reduction in Video Instance Segmentation using Uncertainty Estimates | Instance segmentation of images is an important tool for automated scene understanding. Neural networks are usually trained to optimize their overall performance in terms of accuracy. Meanwhile, in applications such as automated driving, an overlooked pedestrian seems more harmful than a falsely detected one. In this w... | ['Kira Maag'] | 2021-06-28 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 5.35355031e-01 4.19788867e-01 1.58337966e-01 -8.37847948e-01
-7.03641117e-01 -3.09663355e-01 5.83860338e-01 6.89415157e-01
-9.06269789e-01 1.06360209e+00 -7.01183498e-01 -4.82978001e-02
-1.92536071e-01 -1.07735920e+00 -1.11802304e+00 -6.22128844e-01
-1.45859495e-01 5.47490954e-01 6.87968493e-01 3.21205437... | [8.025816917419434, -0.8582249283790588] |
bfefc8c6-2a3a-4792-918b-3d04e02c60f4 | data-incubation-synthesizing-missing-data-for | 2110.07040 | null | https://arxiv.org/abs/2110.07040v1 | https://arxiv.org/pdf/2110.07040v1.pdf | Data Incubation -- Synthesizing Missing Data for Handwriting Recognition | In this paper, we demonstrate how a generative model can be used to build a better recognizer through the control of content and style. We are building an online handwriting recognizer from a modest amount of training samples. By training our controllable handwriting synthesizer on the same data, we can synthesize hand... | ['Oncel Tuzel', 'Ryan Dixon', 'Thomas Deselaers', 'Adrien Delaye', 'Youssouf Chherawala', 'Martin Bresler', 'Jen-Hao Rick Chang'] | 2021-10-13 | null | null | null | null | ['handwriting-recognition'] | ['computer-vision'] | [ 3.76723140e-01 1.21332131e-01 -3.36128771e-01 -3.44673961e-01
-6.47335052e-01 -1.05612946e+00 5.51371396e-01 -4.92968291e-01
-7.57142575e-03 5.34045577e-01 3.23909372e-01 -3.11586440e-01
5.75602531e-01 -9.28248286e-01 -9.30837214e-01 -3.11544359e-01
7.27186322e-01 6.33641005e-01 -3.05543095e-01 -2.41650730... | [11.645824432373047, 0.05353802442550659] |
57f997a6-6cd3-459a-bc79-d636e3b55b5b | lightweight-alpha-matting-network-using | 2210.07760 | null | https://arxiv.org/abs/2210.07760v1 | https://arxiv.org/pdf/2210.07760v1.pdf | Lightweight Alpha Matting Network Using Distillation-Based Channel Pruning | Recently, alpha matting has received a lot of attention because of its usefulness in mobile applications such as selfies. Therefore, there has been a demand for a lightweight alpha matting model due to the limited computational resources of commercial portable devices. To this end, we suggest a distillation-based chann... | ['Donghyeon Cho', 'Jinsun Park', 'Donggeun Yoon'] | 2022-10-14 | null | null | null | null | ['image-matting'] | ['computer-vision'] | [ 4.57603186e-01 3.26872289e-01 -3.41001630e-01 -3.70075881e-01
-2.55981982e-01 -1.10578932e-01 2.61878341e-01 2.90205032e-01
-5.44998884e-01 7.82103956e-01 -3.84945027e-03 -5.75599253e-01
-3.02847885e-02 -1.07528973e+00 -9.87096131e-01 -5.92368364e-01
1.50259614e-01 9.15515870e-02 4.06144708e-01 4.57957089... | [8.779496192932129, 3.261981248855591] |
3fcd96f6-84b6-4d8f-b811-e8f55bf74668 | a-comparative-study-on-recent-neural-spoofing | 2103.11326 | null | https://arxiv.org/abs/2103.11326v2 | https://arxiv.org/pdf/2103.11326v2.pdf | A Comparative Study on Recent Neural Spoofing Countermeasures for Synthetic Speech Detection | A great deal of recent research effort on speech spoofing countermeasures has been invested into back-end neural networks and training criteria. We contribute to this effort with a comparative perspective in this study. Our comparison of countermeasure models on the ASVspoof 2019 logical access task takes into account ... | ['Junich Yamagishi', 'Xin Wang'] | 2021-03-21 | null | null | null | null | ['synthetic-speech-detection'] | ['audio'] | [ 2.35436246e-01 -2.00450450e-01 -3.63947332e-01 -3.41527969e-01
-9.14003491e-01 -4.71990556e-01 6.93373501e-01 2.71266587e-02
-8.12700033e-01 5.18906116e-01 1.41703948e-01 -9.90835309e-01
6.07780926e-02 -4.08163220e-01 -6.03138864e-01 -6.57046080e-01
-1.90011442e-01 4.36216183e-02 5.17835796e-01 -3.12775284... | [14.023295402526855, 5.842580795288086] |
184f02c3-1585-4402-84ad-ff61d32ddefd | information-fusion-via-symbolic-regression-a | 2306.00153 | null | https://arxiv.org/abs/2306.00153v1 | https://arxiv.org/pdf/2306.00153v1.pdf | Information Fusion via Symbolic Regression: A Tutorial in the Context of Human Health | This tutorial paper provides a general overview of symbolic regression (SR) with specific focus on standards of interpretability. We posit that interpretable modeling, although its definition is still disputed in the literature, is a practical way to support the evaluation of successful information fusion. In order to ... | ['Nitesh V. Chawla', 'Jennifer J. Schnur'] | 2023-05-31 | null | null | null | null | ['symbolic-regression'] | ['knowledge-base'] | [ 6.92098737e-01 5.40919244e-01 -6.49669707e-01 -8.57035041e-01
-4.47429478e-01 -2.26952489e-02 9.47116315e-02 8.80783856e-01
1.09780850e-02 5.68398833e-01 3.69779974e-01 -4.09547120e-01
-4.96104330e-01 -6.24723494e-01 -5.92570424e-01 -1.61766559e-01
-4.21824813e-01 2.74156004e-01 -5.63639641e-01 -2.23783836... | [8.116683006286621, 5.581523895263672] |
7c40ccad-0582-4e80-b0ca-ead0740b2c27 | toan-target-oriented-alignment-network-for | 2005.13820 | null | https://arxiv.org/abs/2005.13820v2 | https://arxiv.org/pdf/2005.13820v2.pdf | TOAN: Target-Oriented Alignment Network for Fine-Grained Image Categorization with Few Labeled Samples | The challenges of high intra-class variance yet low inter-class fluctuations in fine-grained visual categorization are more severe with few labeled samples, \textit{i.e.,} Fine-Grained categorization problems under the Few-Shot setting (FGFS). High-order features are usually developed to uncover subtle differences betw... | ['Jun-Jie Zhang', 'Chang Xu', 'Huaxi Huang', 'Qiang Wu', 'Jian Zhang'] | 2020-05-28 | null | null | null | null | ['image-categorization', 'fine-grained-visual-categorization'] | ['computer-vision', 'computer-vision'] | [ 1.16249040e-01 -4.60227787e-01 -2.89236009e-01 -6.64715469e-01
-6.29901886e-01 -3.56882691e-01 7.29886532e-01 7.47848153e-02
-4.75589991e-01 4.83076751e-01 3.68132353e-01 4.49201763e-01
-4.55462992e-01 -6.87921643e-01 -3.84879440e-01 -7.52074778e-01
2.59691298e-01 -1.69074144e-02 4.86924946e-01 -1.41193988... | [9.690065383911133, 2.0700182914733887] |
1a668e57-8bb6-4b0b-b8a4-3233e5b4f19f | a-clustering-based-method-for-automatic | 2010.04676 | null | https://arxiv.org/abs/2010.04676v1 | https://arxiv.org/pdf/2010.04676v1.pdf | A Clustering-Based Method for Automatic Educational Video Recommendation Using Deep Face-Features of Lecturers | Discovering and accessing specific content within educational video bases is a challenging task, mainly because of the abundance of video content and its diversity. Recommender systems are often used to enhance the ability to find and select content. But, recommendation mechanisms, especially those based on textual inf... | ['Sérgio Colcher', 'Antonio J. G. Busson', 'Álan L. V. Guedes', 'Eduardo S. Vieira', 'Paulo R. C. Mendes'] | 2020-10-09 | null | null | null | null | ['face-clustering'] | ['computer-vision'] | [-9.50413570e-02 -3.23035747e-01 -1.16223589e-01 -2.38766372e-01
-6.02668643e-01 -6.43662333e-01 4.47934508e-01 2.29802370e-01
-7.43095353e-02 3.75181943e-01 5.33536553e-01 1.11656025e-01
-7.39833891e-01 -7.49400973e-01 -4.76026684e-01 -8.91451836e-01
1.14487998e-01 -1.56192444e-02 1.91406101e-01 -8.62740949... | [10.148720741271973, 5.754115104675293] |
d24f6e1c-0806-4718-9c55-a5d1f77e58a9 | fact-checking-or-psycholinguistics-how-to | null | null | https://aclanthology.org/D19-6602 | https://aclanthology.org/D19-6602.pdf | Fact Checking or Psycholinguistics: How to Distinguish Fake and True Claims? | The goal of our paper is to compare psycholinguistic text features with fact checking approaches to distinguish lies from true statements. We examine both methods using data from a large ongoing study on deception and deception detection covering a mixture of factual and opinionated topics that polarize public opinion.... | ["Justyna Sarzy{\\'n}ska-Wawer", 'er', 'Aleks Wawer', 'Grzegorz Wojdyga'] | 2019-11-01 | null | null | null | ws-2019-11 | ['deception-detection'] | ['miscellaneous'] | [-3.83443654e-01 4.74859893e-01 -2.45269641e-01 -7.53706574e-01
-9.04974341e-01 -9.69805181e-01 1.02275753e+00 6.64230108e-01
-4.21464384e-01 1.14800322e+00 5.10385692e-01 -7.75372982e-01
1.09493241e-01 -3.37019891e-01 -5.73269486e-01 -6.16147369e-02
3.46881032e-01 1.61230206e-01 -5.69918565e-03 -5.78178167... | [8.199056625366211, 10.350325584411621] |
b75e4bf9-2500-4b48-9cf4-c7294c43f754 | cpnet-exploiting-clip-based-attention | 2305.13962 | null | https://arxiv.org/abs/2305.13962v1 | https://arxiv.org/pdf/2305.13962v1.pdf | CPNet: Exploiting CLIP-based Attention Condenser and Probability Map Guidance for High-fidelity Talking Face Generation | Recently, talking face generation has drawn ever-increasing attention from the research community in computer vision due to its arduous challenges and widespread application scenarios, e.g. movie animation and virtual anchor. Although persevering efforts have been undertaken to enhance the fidelity and lip-sync quality... | ['Meirong Ma', 'Minghao Li', 'Mingjie Wang', 'Benlai Tang', 'Jingning Xu'] | 2023-05-23 | null | null | null | null | ['talking-face-generation', 'face-generation'] | ['computer-vision', 'computer-vision'] | [ 2.24510923e-01 6.04082830e-02 -1.74784198e-01 -2.71842271e-01
-6.32191181e-01 -2.16304198e-01 6.73223197e-01 -4.75012898e-01
-2.50620488e-02 7.11372554e-01 3.42611194e-01 2.09178969e-01
-1.66700244e-01 -5.43188989e-01 -6.37475133e-01 -9.54384089e-01
1.76290527e-01 -1.56139657e-01 7.46952253e-04 -8.33964199... | [13.153016090393066, -0.3103271722793579] |
6886ca33-a207-41e4-8ff4-abf9491bf90f | grouped-attention-for-content-selection-and | null | null | https://aclanthology.org/2021.findings-emnlp.166 | https://aclanthology.org/2021.findings-emnlp.166.pdf | Grouped-Attention for Content-Selection and Content-Plan Generation | Content-planning is an essential part of data-to-text generation to determine the order of data mentioned in generated texts. Recent neural data-to-text generation models employ Pointer Networks to explicitly learn content-plan given a set of attributes as input. They use LSTM to encode the input, which assumes a seque... | ['Qingjun Cui', 'Rui Zhang', 'Jianzhong Qi', 'Xiaojie Wang', 'Bayu Distiawan Trisedya'] | null | null | null | null | findings-emnlp-2021-11 | ['data-to-text-generation'] | ['natural-language-processing'] | [ 3.03442925e-01 3.96634907e-01 -4.02554154e-01 -3.95278066e-01
-9.43673611e-01 -4.86353606e-01 8.96630347e-01 4.66733754e-01
-5.93119562e-01 8.09470236e-01 9.11114216e-01 -1.69894755e-01
2.59438716e-03 -1.08184218e+00 -8.33392143e-01 -6.34448946e-01
-1.77999586e-03 9.63698208e-01 1.17668010e-01 -2.25145489... | [11.703105926513672, 8.847733497619629] |
c79e9515-0f86-44f6-9c27-ee74a52349de | 3d-deep-shape-descriptor | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Fang_3D_Deep_Shape_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Fang_3D_Deep_Shape_2015_CVPR_paper.pdf | 3D Deep Shape Descriptor | Shape descriptor is a concise yet informative representation that provides a 3D object with an identification as a member of some category. This paper developed a concise deep shape descriptor for the first time to address challenging issues from ever-growing 3D datasets in areas as diverse as engineering, medicine, an... | ['Meng Wang', 'Fan Zhu', 'Yi Fang', 'Edward Wong', 'Guoxian Dai', 'Tiantian Xu', 'Jin Xie'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['3d-shape-retrieval'] | ['computer-vision'] | [-4.50146079e-01 -3.69475365e-01 1.43582121e-01 -4.26845759e-01
-8.71246338e-01 -7.70876706e-01 4.30654734e-01 1.36957303e-01
-3.50401476e-02 5.65589964e-02 1.09449141e-01 1.61865000e-02
-6.82621121e-01 -6.67077839e-01 -3.39825809e-01 -7.55842805e-01
-6.39345199e-02 7.81533599e-01 -1.59173116e-01 -1.94075946... | [8.126739501953125, -3.859771966934204] |
ef360c7d-85c5-4991-8cb6-3a896dab5fe0 | clera-a-unified-model-for-joint-cognitive | 2306.15073 | null | https://arxiv.org/abs/2306.15073v1 | https://arxiv.org/pdf/2306.15073v1.pdf | CLERA: A Unified Model for Joint Cognitive Load and Eye Region Analysis in the Wild | Non-intrusive, real-time analysis of the dynamics of the eye region allows us to monitor humans' visual attention allocation and estimate their mental state during the performance of real-world tasks, which can potentially benefit a wide range of human-computer interaction (HCI) applications. While commercial eye-track... | ['Bryan Reimer', 'Bruce Mehler', 'Lex Fridman', 'Meng Wang', 'Aishni Parab', 'Jack Terwilliger', 'Li Ding'] | 2023-06-26 | null | null | null | null | ['keypoint-detection', 'blink-estimation'] | ['computer-vision', 'computer-vision'] | [ 3.53211649e-02 -2.84105271e-01 -1.04192793e-01 -3.28127056e-01
-2.19606027e-01 -4.75904524e-01 2.61965245e-01 -5.85268345e-03
-4.82120693e-01 2.55390972e-01 1.08609855e-01 -2.80529529e-01
-1.01802617e-01 1.48829147e-01 -1.04872636e-01 -2.57756084e-01
1.36722088e-01 -1.45027533e-01 1.28881723e-01 3.81052077... | [14.103177070617676, 0.1168123185634613] |
5c9e0d1b-ce8e-4c69-b91a-046c15136df3 | grade-automatic-graph-enhanced-coherence | 2010.03994 | null | https://arxiv.org/abs/2010.03994v1 | https://arxiv.org/pdf/2010.03994v1.pdf | GRADE: Automatic Graph-Enhanced Coherence Metric for Evaluating Open-Domain Dialogue Systems | Automatically evaluating dialogue coherence is a challenging but high-demand ability for developing high-quality open-domain dialogue systems. However, current evaluation metrics consider only surface features or utterance-level semantics, without explicitly considering the fine-grained topic transition dynamics of dia... | ['Xiaodan Liang', 'Liang Lin', 'Jinghui Qin', 'Zheng Ye', 'Lishan Huang'] | 2020-10-08 | null | https://aclanthology.org/2020.emnlp-main.742 | https://aclanthology.org/2020.emnlp-main.742.pdf | emnlp-2020-11 | ['dialogue-evaluation'] | ['natural-language-processing'] | [ 6.70525953e-02 7.36546516e-01 -1.41409621e-01 -5.60653448e-01
-6.22011781e-01 -5.05855441e-01 1.11079013e+00 6.70664966e-01
-7.12637082e-02 8.39335859e-01 1.11749065e+00 -9.65863243e-02
-1.28510281e-01 -9.28723156e-01 9.23583657e-02 -1.57377318e-01
-2.03431189e-01 5.20637691e-01 2.11039320e-01 -9.76304412... | [12.70942211151123, 8.127300262451172] |
9131a627-12cf-4bb1-bd26-9ee2702c5682 | joint-learning-of-set-cardinality-and-state | 1709.04093 | null | http://arxiv.org/abs/1709.04093v2 | http://arxiv.org/pdf/1709.04093v2.pdf | Joint Learning of Set Cardinality and State Distribution | We present a novel approach for learning to predict sets using deep learning.
In recent years, deep neural networks have shown remarkable results in computer
vision, natural language processing and other related problems. Despite their
success, traditional architectures suffer from a serious limitation in that
they are... | ['S. Hamid Rezatofighi', 'Anton Milan', 'Anthony Dick', 'Qinfeng Shi', 'Ian Reid'] | 2017-09-13 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 5.95122039e-01 -5.13078153e-01 -2.43007466e-01 -6.20165765e-01
-4.49795187e-01 -7.50130057e-01 6.45312011e-01 2.30606124e-01
-3.53931159e-01 6.22001886e-01 -1.50468409e-01 -2.89777040e-01
-4.29534525e-01 -7.66790688e-01 -9.65152264e-01 -8.11785638e-01
5.18035851e-02 6.28416955e-01 -6.82385936e-02 -1.66591734... | [9.269378662109375, 2.8137853145599365] |
b9aa897d-5da7-4698-a91b-ec890c98a14a | mixture-kernel-graph-attention-network-for | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Suhail_Mixture-Kernel_Graph_Attention_Network_for_Situation_Recognition_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Suhail_Mixture-Kernel_Graph_Attention_Network_for_Situation_Recognition_ICCV_2019_paper.pdf | Mixture-Kernel Graph Attention Network for Situation Recognition | Understanding images beyond salient actions involves reasoning about scene context, objects, and the roles they play in the captured event. Situation recognition has recently been introduced as the task of jointly reasoning about the verbs (actions) and a set of semantic-role and entity (noun) pairs in the form of acti... | [' Leonid Sigal', 'Mohammed Suhail'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['grounded-situation-recognition', 'situation-recognition'] | ['computer-vision', 'computer-vision'] | [ 7.62206256e-01 4.31474537e-01 -2.99388200e-01 -5.34223557e-01
-2.67205060e-01 -4.13799733e-01 8.44863534e-01 3.31338078e-01
-4.07558888e-01 3.68508339e-01 8.40215087e-01 -1.52541742e-01
-6.32526055e-02 -5.70007563e-01 -9.56319094e-01 -4.87323314e-01
-8.42541829e-02 3.02298039e-01 1.69507936e-01 4.97653931... | [10.355497360229492, 1.4165197610855103] |
55e73192-9a0d-4687-acf2-e9241752df25 | leveraging-human-feedback-to-scale | 2305.12894 | null | https://arxiv.org/abs/2305.12894v1 | https://arxiv.org/pdf/2305.12894v1.pdf | Leveraging Human Feedback to Scale Educational Datasets: Combining Crowdworkers and Comparative Judgement | Machine Learning models have many potentially beneficial applications in education settings, but a key barrier to their development is securing enough data to train these models. Labelling educational data has traditionally relied on highly skilled raters using complex, multi-class rubrics, making the process expensive... | ['Owen Henkel Libby Hills'] | 2023-05-22 | null | null | null | null | ['reading-comprehension'] | ['natural-language-processing'] | [ 2.14325324e-01 2.52213687e-01 4.45259921e-02 -6.85392499e-01
-6.28719389e-01 -9.67329800e-01 3.78950864e-01 7.55850911e-01
-1.08956897e+00 5.84078610e-01 2.09508657e-01 -7.94200122e-01
-3.07563126e-01 -5.72380006e-01 -2.85247028e-01 -4.88134235e-01
7.30375528e-01 6.55321956e-01 1.03679486e-01 -9.84311476... | [11.689867973327637, 8.944730758666992] |
f50df864-ce84-4d69-b015-b7196bdf9ac0 | cross-lingual-semantic-specialization-via | null | null | https://aclanthology.org/D19-1226 | https://aclanthology.org/D19-1226.pdf | Cross-lingual Semantic Specialization via Lexical Relation Induction | Semantic specialization integrates structured linguistic knowledge from external resources (such as lexical relations in WordNet) into pretrained distributional vectors in the form of constraints. However, this technique cannot be leveraged in many languages, because their structured external resources are typically in... | ['Goran Glava{\\v{s}}', "Ivan Vuli{\\'c}", 'Roi Reichart', 'Edoardo Maria Ponti', 'Anna Korhonen'] | 2019-11-01 | null | null | null | ijcnlp-2019-11 | ['lexical-simplification'] | ['natural-language-processing'] | [-5.42674735e-02 1.30594462e-01 -6.61579728e-01 -3.62184048e-01
-5.90137124e-01 -9.43922043e-01 5.28088212e-01 2.08572865e-01
-6.71142936e-01 8.51173878e-01 6.29922271e-01 -4.05415863e-01
1.85135320e-01 -6.03588581e-01 -3.29604387e-01 2.69912984e-02
3.37247372e-01 8.85969043e-01 2.99028516e-01 -7.99961984... | [10.84362506866455, 9.72349739074707] |
cf8b7659-791c-43e9-a2c1-13840f054cac | multi-channel-speech-denoising-for-machine | 2202.08793 | null | https://arxiv.org/abs/2202.08793v1 | https://arxiv.org/pdf/2202.08793v1.pdf | Multi-Channel Speech Denoising for Machine Ears | This work describes a speech denoising system for machine ears that aims to improve speech intelligibility and the overall listening experience in noisy environments. We recorded approximately 100 hours of audio data with reverberation and moderate environmental noise using a pair of microphone arrays placed around eac... | ['Simon Carlile', 'Malcolm Slaney', 'Kyle Hoefer', 'E. Merve Kaya', 'Cong Han'] | 2022-02-17 | null | null | null | null | ['speech-denoising'] | ['speech'] | [-1.43995002e-01 -3.77541333e-01 7.29315817e-01 -3.94574195e-01
-9.08638179e-01 -1.87911570e-01 1.30410850e-01 -4.85110819e-01
-3.83039296e-01 2.52711713e-01 6.94958746e-01 -3.80356252e-01
-8.11251774e-02 -5.42495787e-01 -3.74228179e-01 -9.38624918e-01
4.48013842e-02 -3.01883340e-01 1.97619274e-02 -2.72221059... | [15.023606300354004, 5.863903522491455] |
ed7b4533-7fff-4db9-816f-64cbca08fd8b | on-the-effectiveness-of-iterative-learning | 2111.09434 | null | https://arxiv.org/abs/2111.09434v3 | https://arxiv.org/pdf/2111.09434v3.pdf | On the Effectiveness of Iterative Learning Control | Iterative learning control (ILC) is a powerful technique for high performance tracking in the presence of modeling errors for optimal control applications. There is extensive prior work showing its empirical effectiveness in applications such as chemical reactors, industrial robots and quadcopters. However, there is li... | ['J. Andrew Bagnell', 'Maxim Likhachev', 'Wen Sun', 'Anirudh Vemula'] | 2021-11-17 | null | null | null | null | ['industrial-robots'] | ['robots'] | [ 9.59320143e-02 4.75961208e-01 -5.09459198e-01 8.23209584e-01
-6.03077292e-01 -7.53937364e-01 3.49206358e-01 2.72001952e-01
-1.47925377e-01 9.90947664e-01 -4.64994818e-01 -6.53350055e-01
-5.33725202e-01 -4.59073186e-02 -1.17249143e+00 -8.88094366e-01
-2.11985126e-01 1.74467087e-01 4.84014377e-02 -6.20880902... | [5.027190685272217, 2.429328680038452] |
6a20f133-5500-47e8-ba2a-524f056f0357 | evaluation-of-three-deep-learning-models-for | null | null | https://www.mdpi.com/2072-4292/11/22/2673 | https://www.mdpi.com/2072-4292/11/22/2673/pdf | Evaluation of Three Deep Learning Models for Early Crop Classification Using Sentinel-1A Imagery Time Series—A Case Study in Zhanjiang, China | Timely and accurate estimation of the area and distribution of crops is vital for food security.
Optical remote sensing has been a key technique for acquiring crop area and conditions on regional
to global scales, but great challenges arise due to frequent cloudy days in southern China. This
makes optical remote sen... | ['Min Feng 6', 'Wenlong Jing', 'Hongwei Zhao', 'Liang Sun', 'Hao Jiang', 'Zhongxin Chen'] | 2019-11-15 | null | null | null | remote-sensing-issn-2072-4292-mdpi-2019-11 | ['crop-classification'] | ['miscellaneous'] | [ 2.13121116e-01 -5.20169973e-01 -2.70209134e-01 -1.12648174e-01
-4.40296829e-01 -6.31817997e-01 2.63687104e-01 3.68100256e-02
-2.99489617e-01 8.32559824e-01 -4.29540277e-01 -8.71986866e-01
-2.69235492e-01 -1.14203608e+00 -5.01820683e-01 -1.09670067e+00
-4.71833467e-01 -2.15312809e-01 -1.20103449e-01 -3.16661179... | [9.44820499420166, -1.708410620689392] |
f6a4b00a-b648-46a4-b6f0-9d2cd257bace | sentiment-analysis-and-opinion-mining-on | 2302.04359 | null | https://arxiv.org/abs/2302.04359v1 | https://arxiv.org/pdf/2302.04359v1.pdf | Sentiment analysis and opinion mining on educational data: A survey | Sentiment analysis AKA opinion mining is one of the most widely used NLP applications to identify human intentions from their reviews. In the education sector, opinion mining is used to listen to student opinions and enhance their learning-teaching practices pedagogically. With advancements in sentiment annotation tech... | ['Linda Galligan', 'Yan Li', 'Haoran Xie', 'Christopher Dann', 'Xiaohui Tao', 'Thanveer Shaik'] | 2023-02-08 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [ 2.10073832e-02 3.36457431e-01 -5.08706987e-01 -4.27509695e-01
-1.01006940e-01 -5.45237422e-01 8.39262232e-02 9.56948757e-01
-4.10100847e-01 5.65025389e-01 5.23980677e-01 -6.30800128e-01
9.60386544e-02 -7.72347331e-01 -1.50616914e-01 -4.84086871e-01
7.59444535e-01 -7.90995583e-02 -1.35800410e-02 -9.74292874... | [11.190085411071777, 6.881440162658691] |
ab9a5489-7c64-4cdf-9762-4072f16e3751 | evaluating-capability-of-deep-neural-networks | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Hao_Cheng_Evaluating_Capability_of_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Hao_Cheng_Evaluating_Capability_of_ECCV_2018_paper.pdf | Evaluating Capability of Deep Neural Networks for Image Classification via Information Plane | Inspired by the pioneering work of information bottleneck principle for Deep Neural Networks (DNNs) analysis, we design an information plane based framework to evaluate the capability of DNNs for image classification tasks, which not only helps understand the capability of DNNs, but also helps us choose a neural networ... | ['Shenghua Gao', 'Dongze Lian', 'Hao Cheng', 'Yanlin Geng'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['information-plane'] | ['methodology'] | [-6.00874051e-02 4.52198200e-02 -3.43595952e-01 -4.40795273e-01
9.55138281e-02 -4.70064491e-01 3.94994438e-01 -5.28820269e-02
-5.75397193e-01 5.88692725e-01 5.77653088e-02 -3.65116060e-01
-6.16678238e-01 -9.48211372e-01 -5.39656341e-01 -8.61431003e-01
-2.10006446e-01 1.57074317e-01 1.51379555e-01 2.17889741... | [8.006011009216309, 3.551642894744873] |
9e944aa7-358a-4d18-9498-bf9b7ab66b13 | unleashing-the-potential-of-unsupervised-pre | 2112.00317 | null | https://arxiv.org/abs/2112.00317v1 | https://arxiv.org/pdf/2112.00317v1.pdf | Unleashing the Potential of Unsupervised Pre-Training with Intra-Identity Regularization for Person Re-Identification | Existing person re-identification (ReID) methods typically directly load the pre-trained ImageNet weights for initialization. However, as a fine-grained classification task, ReID is more challenging and exists a large domain gap between ImageNet classification. Inspired by the great success of self-supervised represent... | ['Feng Zhao', 'Kecheng Zheng', 'Xin Jin', 'Zizheng Yang'] | 2021-12-01 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [-8.08905363e-02 -3.37519884e-01 6.76547270e-03 -7.04946756e-01
-3.68779689e-01 -4.77923512e-01 6.66857302e-01 -1.38874084e-01
-7.07168996e-01 5.67311585e-01 3.76033306e-01 1.74126774e-01
-1.12663344e-01 -6.39158189e-01 -5.87783098e-01 -6.16460681e-01
1.11657329e-01 3.07617962e-01 -2.44541079e-01 -2.55187899... | [14.708296775817871, 0.9282689094543457] |
c2f389d9-ac56-486d-abd9-e54a4dfbf2ae | ynu-hpcc-at-semeval-2022-task-5-multi-modal | null | null | https://aclanthology.org/2022.semeval-1.104 | https://aclanthology.org/2022.semeval-1.104.pdf | YNU-HPCC at SemEval-2022 Task 5: Multi-Modal and Multi-label Emotion Classification Based on LXMERT | This paper describes our system used in the SemEval-2022 Task5 Multimedia Automatic Misogyny Identification (MAMI). This task is to use the provided text-image pairs to classify emotions. In this paper, We propose a multi-label emotion classification model based on pre-trained LXMERT. We use Faster-RCNN to extract visu... | ['Xuejie Zhang', 'Jin Wang', 'Chao Han'] | null | null | null | null | semeval-naacl-2022-7 | ['emotion-classification', 'emotion-classification'] | ['computer-vision', 'natural-language-processing'] | [ 1.85394548e-02 -1.11756235e-01 -6.99514300e-02 -6.09582722e-01
-1.07916152e+00 -4.92152661e-01 4.55973834e-01 -1.73493087e-01
-8.11337531e-01 4.93685275e-01 2.08190039e-01 1.48385137e-01
6.13973498e-01 -5.67348980e-05 -5.43569446e-01 -4.88463432e-01
5.46385944e-01 1.57339647e-01 -4.56289560e-01 -6.52434751... | [13.271745681762695, 5.135117053985596] |
877d70ea-9ac4-410a-a0af-5e2f602c6d88 | an-expressive-deep-model-for-human-action | 1502.00501 | null | http://arxiv.org/abs/1502.00501v1 | http://arxiv.org/pdf/1502.00501v1.pdf | An Expressive Deep Model for Human Action Parsing from A Single Image | This paper aims at one newly raising task in vision and multimedia research:
recognizing human actions from still images. Its main challenges lie in the
large variations in human poses and appearances, as well as the lack of
temporal motion information. Addressing these problems, we propose to develop
an expressive dee... | ['Rui Huang', 'Liang Lin', 'Xiaolong Wang', 'Zhujin Liang'] | 2015-02-02 | null | null | null | null | ['action-understanding', 'action-parsing'] | ['computer-vision', 'natural-language-processing'] | [ 2.65359670e-01 2.83793025e-02 1.30157694e-01 -3.17204773e-01
-5.42113841e-01 -4.66889828e-01 5.54766059e-01 1.88241284e-02
-6.61974967e-01 5.45479894e-01 2.22936153e-01 3.23339313e-01
2.61592537e-01 -4.22998220e-01 -8.03133368e-01 -6.06262445e-01
-5.54508343e-02 3.77303392e-01 7.56183386e-01 -1.92706883... | [7.859068870544434, 0.10919881612062454] |
59ce6d84-3ff4-4635-baed-b147e35d8e96 | a-novel-plug-and-play-approach-for | 2208.09449 | null | https://arxiv.org/abs/2208.09449v2 | https://arxiv.org/pdf/2208.09449v2.pdf | A Novel Plug-and-Play Approach for Adversarially Robust Generalization | In this work, we propose a robust framework that employs adversarially robust training to safeguard the machine learning models against perturbed testing data. We achieve this by incorporating the worst-case additive adversarial error within a fixed budget for each sample during model estimation. Our main focus is to p... | ['Jean Honorio', 'Adarsh Barik', 'Deepak Maurya'] | 2022-08-19 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 4.96356606e-01 3.80837053e-01 -6.89208880e-02 -3.94493252e-01
-1.23904431e+00 -7.44984746e-01 2.48032898e-01 2.22102106e-01
-3.72082561e-01 9.29096699e-01 -2.33059466e-01 -4.71650094e-01
-4.19879377e-01 -4.45455939e-01 -1.22308779e+00 -7.60494888e-01
-2.86103994e-01 5.18587172e-01 -1.41242728e-01 -1.75608173... | [5.757019519805908, 7.813692092895508] |
b0c0226e-4fda-4717-b292-910d0da35ce2 | learning-object-level-point-augmentor-for | 2212.09273 | null | https://arxiv.org/abs/2212.09273v1 | https://arxiv.org/pdf/2212.09273v1.pdf | Learning Object-level Point Augmentor for Semi-supervised 3D Object Detection | Semi-supervised object detection is important for 3D scene understanding because obtaining large-scale 3D bounding box annotations on point clouds is time-consuming and labor-intensive. Existing semi-supervised methods usually employ teacher-student knowledge distillation together with an augmentation strategy to lever... | ['Ming-Hsuan Yang', 'Yen-Yu Lin', 'Yi-Hsuan Tsai', 'Chen-Hsuan Tai', 'Cheng-Ju Ho'] | 2022-12-19 | null | null | null | null | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 1.18220471e-01 1.06511883e-01 -2.37262219e-01 -4.37110901e-01
-8.26116323e-01 -5.50288439e-01 4.79413509e-01 3.44003767e-01
-2.49282837e-01 1.80363566e-01 -4.66148019e-01 -3.77553314e-01
3.82323444e-01 -6.44643843e-01 -9.08600748e-01 -6.72554374e-01
1.28021449e-01 6.50725782e-01 8.06808352e-01 7.59323016... | [7.883354187011719, -2.9079480171203613] |
79785774-7c88-4bf4-a880-81804c64782a | augmented-memory-capitalizing-on-experience | 2305.16160 | null | https://arxiv.org/abs/2305.16160v1 | https://arxiv.org/pdf/2305.16160v1.pdf | Augmented Memory: Capitalizing on Experience Replay to Accelerate De Novo Molecular Design | Sample efficiency is a fundamental challenge in de novo molecular design. Ideally, molecular generative models should learn to satisfy a desired objective under minimal oracle evaluations (computational prediction or wet-lab experiment). This problem becomes more apparent when using oracles that can provide increased p... | ['Philippe Schwaller', 'Jeff Guo'] | 2023-05-10 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 5.83538234e-01 1.09638847e-01 -4.06881660e-01 -2.15177551e-01
-1.19935799e+00 -7.17141747e-01 3.30727220e-01 3.50240737e-01
-5.35467029e-01 1.37620282e+00 -2.95034677e-01 -5.90401232e-01
-3.36960554e-01 -7.21775115e-01 -1.19215274e+00 -9.19773161e-01
-2.54985809e-01 9.25838292e-01 -1.71911851e-01 -8.52270611... | [5.096254348754883, 5.386468887329102] |
9b629fcc-8e87-4543-a168-8a621e86ef02 | viskositas-viscosity-prediction-of | 2208.01440 | null | https://arxiv.org/abs/2208.01440v5 | https://arxiv.org/pdf/2208.01440v5.pdf | Viskositas: Viscosity Prediction of Multicomponent Chemical Systems | Viscosity in the metallurgical and glass industry plays a fundamental role in its production processes, also in the area of geophysics. As its experimental measurement is financially expensive, also in terms of time, several mathematical models were built to provide viscosity results as a function of several variables,... | ['Patrick dos Anjos'] | 2022-08-02 | null | null | null | null | ['geophysics'] | ['miscellaneous'] | [-2.56961167e-01 -2.49728132e-02 1.03658102e-02 -3.71128023e-01
1.90663099e-01 -2.95976400e-01 4.44134980e-01 6.28115118e-01
-3.44241291e-01 9.56100285e-01 -6.77265450e-02 -3.74258995e-01
-5.36184728e-01 -1.06181252e+00 -4.79184121e-01 -8.00785959e-01
-8.99095908e-02 6.84332073e-01 2.73339570e-01 -2.64114082... | [6.262559413909912, 3.330704927444458] |
4574d277-939c-41b8-8de2-6986690bcfac | image-based-localization-using-hourglass | 1703.07971 | null | http://arxiv.org/abs/1703.07971v3 | http://arxiv.org/pdf/1703.07971v3.pdf | Image-based Localization using Hourglass Networks | In this paper, we propose an encoder-decoder convolutional neural network
(CNN) architecture for estimating camera pose (orientation and location) from a
single RGB-image. The architecture has a hourglass shape consisting of a chain
of convolution and up-convolution layers followed by a regression part. The
up-convolut... | ['Esa Rahtu', 'Juho Kannala', 'Juha Ylioinas', 'Iaroslav Melekhov'] | 2017-03-23 | null | null | null | null | ['image-based-localization'] | ['computer-vision'] | [ 1.76133916e-01 -9.43551511e-02 3.38723063e-01 -5.96736789e-01
-4.67273176e-01 -3.79874557e-01 4.73905832e-01 -5.47447503e-01
-6.29843891e-01 5.92372775e-01 3.10951829e-01 -2.38651708e-01
4.29414421e-01 -5.00347793e-01 -1.21141040e+00 -5.89808881e-01
1.03541590e-01 -3.09217781e-01 2.67399907e-01 -4.99623567... | [10.123032569885254, -2.384117364883423] |
a3f707b1-5b81-4413-9e12-9affc919caf1 | video-instance-segmentation-tracking-with-a | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Lin_Video_Instance_Segmentation_Tracking_With_a_Modified_VAE_Architecture_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Lin_Video_Instance_Segmentation_Tracking_With_a_Modified_VAE_Architecture_CVPR_2020_paper.pdf | Video Instance Segmentation Tracking With a Modified VAE Architecture | We propose a modified variational autoencoder (VAE) architecture built on top of Mask R-CNN for instance-level video segmentation and tracking. The method builds a shared encoder and three parallel decoders, yielding three disjoint branches for predictions of future frames, object detection boxes, and instance segmenta... | [' Linglin He', ' Rogerio Feris', ' Ying Hung', 'Chung-Ching Lin'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['video-instance-segmentation'] | ['computer-vision'] | [ 1.24092929e-01 2.53388822e-01 -3.11066151e-01 -2.05703408e-01
-9.16782081e-01 -4.78199303e-01 3.71886134e-01 -6.52044237e-01
-3.57598215e-01 4.77148682e-01 9.29544792e-02 -4.19321768e-02
1.84738457e-01 -4.79353786e-01 -1.16863525e+00 -7.22439706e-01
-9.53013375e-02 6.71797395e-01 8.39547038e-01 3.82131279... | [9.149539947509766, -0.03411867097020149] |
926a5d3f-6fce-40ad-8f05-4685b894d5b6 | from-image-collections-to-point-clouds-with | 2005.01939 | null | https://arxiv.org/abs/2005.01939v1 | https://arxiv.org/pdf/2005.01939v1.pdf | From Image Collections to Point Clouds with Self-supervised Shape and Pose Networks | Reconstructing 3D models from 2D images is one of the fundamental problems in computer vision. In this work, we propose a deep learning technique for 3D object reconstruction from a single image. Contrary to recent works that either use 3D supervision or multi-view supervision, we use only single view images with no po... | ['Wei-Chih Hung', 'Shashank Kashyap', 'R. Venkatesh Babu', 'K L Navaneet', 'Ansu Mathew', 'Varun Jampani'] | 2020-05-05 | from-image-collections-to-point-clouds-with-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Navaneet_From_Image_Collections_to_Point_Clouds_With_Self-Supervised_Shape_and_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Navaneet_From_Image_Collections_to_Point_Clouds_With_Self-Supervised_Shape_and_CVPR_2020_paper.pdf | cvpr-2020-6 | ['3d-point-cloud-reconstruction', 'point-cloud-reconstruction', '3d-object-reconstruction', '3d-object-reconstruction-from-a-single-image'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-9.59976092e-02 1.47826955e-01 8.92520230e-03 -4.00537342e-01
-5.70465744e-01 -7.35081017e-01 8.13159943e-01 -2.88082778e-01
-2.91521937e-01 1.98167056e-01 -4.40192044e-01 -1.40639767e-01
7.95902461e-02 -6.75724924e-01 -1.27495122e+00 -4.13174927e-01
3.38513076e-01 1.18713188e+00 3.15510809e-01 -2.49997899... | [8.3908052444458, -3.0051615238189697] |
9d045533-82ce-472c-a403-6c1e3eb7aa2a | transfer-learning-in-biomedical-natural | 1906.05474 | null | https://arxiv.org/abs/1906.05474v2 | https://arxiv.org/pdf/1906.05474v2.pdf | Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets | null | ['Yifan Peng', 'Zhiyong Lu', 'Shankai Yan'] | 2019-06-13 | transfer-learning-in-biomedical-natural-1 | https://aclanthology.org/W19-5006 | https://aclanthology.org/W19-5006.pdf | ws-2019-8 | ['medical-relation-extraction', 'drug-drug-interaction-extraction', 'medical-named-entity-recognition'] | ['medical', 'natural-language-processing', '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... | [-7.399508953094482, 3.747267007827759] |
67595424-a8e4-4f56-9f16-7dac84ec0966 | objectron-a-large-scale-dataset-of-object | 2012.09988 | null | https://arxiv.org/abs/2012.09988v1 | https://arxiv.org/pdf/2012.09988v1.pdf | Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild with Pose Annotations | 3D object detection has recently become popular due to many applications in robotics, augmented reality, autonomy, and image retrieval. We introduce the Objectron dataset to advance the state of the art in 3D object detection and foster new research and applications, such as 3D object tracking, view synthesis, and impr... | ['Matthias Grundmann', 'Artsiom Ablavatski', 'Jianing Wei', 'Liangkai Zhang', 'Adel Ahmadyan'] | 2020-12-18 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Ahmadyan_Objectron_A_Large_Scale_Dataset_of_Object-Centric_Videos_in_the_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Ahmadyan_Objectron_A_Large_Scale_Dataset_of_Object-Centric_Videos_in_the_CVPR_2021_paper.pdf | cvpr-2021-1 | ['3d-shape-representation', '3d-object-tracking'] | ['computer-vision', 'computer-vision'] | [-1.54207990e-01 -2.58425534e-01 -3.36068749e-01 -2.69282877e-01
-5.08812606e-01 -7.26198852e-01 7.66781807e-01 -6.18827641e-02
-3.05826247e-01 4.45443392e-02 2.98347056e-01 1.69288125e-02
2.34588474e-01 -1.63520411e-01 -9.93832588e-01 -1.89856932e-01
-3.24388385e-01 5.14314473e-01 6.28526628e-01 8.49501342... | [7.519432544708252, -2.6433167457580566] |
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