paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
f9f5d544-0c0b-407b-9410-b778eaedcf7e | tener-adapting-transformer-encoder-for-name | 1911.04474 | null | https://arxiv.org/abs/1911.04474v3 | https://arxiv.org/pdf/1911.04474v3.pdf | TENER: Adapting Transformer Encoder for Named Entity Recognition | The Bidirectional long short-term memory networks (BiLSTM) have been widely used as an encoder in models solving the named entity recognition (NER) task. Recently, the Transformer is broadly adopted in various Natural Language Processing (NLP) tasks owing to its parallelism and advantageous performance. Nevertheless, t... | ['Bocao Deng', 'Xipeng Qiu', 'Xiaonan Li', 'Hang Yan'] | 2019-11-10 | null | null | null | null | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [-2.44400203e-01 -2.30850533e-01 2.40812432e-02 -2.95275480e-01
-6.54576540e-01 -3.57131362e-01 4.39681411e-01 1.39909178e-01
-1.07838118e+00 8.12622488e-01 6.18366957e-01 -3.29495311e-01
1.01560719e-01 -8.84249926e-01 -5.17632723e-01 -5.49410403e-01
-2.84720454e-02 1.73736155e-01 2.84382999e-01 -3.34931016... | [9.896418571472168, 9.633264541625977] |
0bdc365f-1ea7-416b-99da-b74245a714ca | understanding-important-features-of-deep | 1912.06077 | null | https://arxiv.org/abs/1912.06077v1 | https://arxiv.org/pdf/1912.06077v1.pdf | Understanding Important Features of Deep Learning Models for Transmission Electron Microscopy Image Segmentation | Cutting edge deep learning techniques allow for image segmentation with great speed and accuracy. However, application to problems in materials science is often difficult since these complex models may have difficultly learning physical parameters. In situ electron microscopy provides a clear platform for utilizing aut... | ['James P. Horwath', 'Remi Megret', 'Eric A. Stach', 'Dmitri N. Zakharov'] | 2019-12-12 | null | null | null | null | ['electron-microscopy-image-segmentation'] | ['computer-vision'] | [ 3.60132754e-01 -3.77643853e-01 -2.87939347e-02 1.03322044e-01
-5.42788446e-01 -3.32520038e-01 3.22757274e-01 2.07579777e-01
-4.69819605e-01 8.95308554e-01 -6.09620810e-01 -7.09266305e-01
-2.02225819e-01 -6.15993142e-01 -8.60825360e-01 -1.17056000e+00
1.78989217e-01 7.34543622e-01 2.82490049e-02 2.72716191... | [14.128927230834961, -2.8544318675994873] |
953ff21d-201a-4694-9552-0a82ca3ea40a | regression-forest-based-atlas-localization | 2005.03345 | null | https://arxiv.org/abs/2005.03345v1 | https://arxiv.org/pdf/2005.03345v1.pdf | Regression Forest-Based Atlas Localization and Direction Specific Atlas Generation for Pancreas Segmentation | This paper proposes a fully automated atlas-based pancreas segmentation method from CT volumes utilizing atlas localization by regression forest and atlas generation using blood vessel information. Previous probabilistic atlas-based pancreas segmentation methods cannot deal with spatial variations that are commonly fou... | ['Kensaku MORI', 'Takayuki Kitasaka', "Ken'ichi Karasawa", 'Masahiro Oda', 'Yukitaka Nimura', 'Michitaka Fujiwara', 'Daniel Rueckert', 'Kazunari Misawa', 'Natsuki Shimizu'] | 2020-05-07 | null | null | null | null | ['pancreas-segmentation', 'automated-pancreas-segmentation'] | ['medical', 'medical'] | [-4.48929787e-01 1.75844654e-01 1.47428215e-01 -4.68820661e-01
-5.02601564e-01 -7.72127986e-01 3.19236517e-01 5.24354219e-01
-3.83299649e-01 7.40087271e-01 3.16315889e-01 -7.75060132e-02
-1.50410622e-01 -6.99415326e-01 -4.69107538e-01 -9.53710556e-01
-2.51659870e-01 1.03115296e+00 5.07070184e-01 3.12469929... | [14.43752670288086, -2.731698989868164] |
aa4fde92-b2a7-44d2-a2fc-af2977ffceaf | topic-modeling-of-hierarchical-corpora | 1409.3518 | null | http://arxiv.org/abs/1409.3518v2 | http://arxiv.org/pdf/1409.3518v2.pdf | Topic Modeling of Hierarchical Corpora | We study the problem of topic modeling in corpora whose documents are
organized in a multi-level hierarchy. We explore a parametric approach to this
problem, assuming that the number of topics is known or can be estimated by
cross-validation. The models we consider can be viewed as special
(finite-dimensional) instance... | ['Geoffrey M. Voelker', 'Do-kyum Kim', 'Lawrence K. Saul'] | 2014-09-11 | null | null | null | null | ['computer-security'] | ['miscellaneous'] | [-1.94199950e-01 5.72834671e-01 -3.88916314e-01 -3.97985458e-01
-1.28463376e+00 -4.18932885e-01 9.20329392e-01 1.53651625e-01
-3.47305864e-01 8.66018236e-01 4.36123490e-01 -4.58948702e-01
1.39412642e-01 -8.90059531e-01 -6.93389118e-01 -7.99644172e-01
-3.30438763e-01 1.42251468e+00 6.83274209e-01 1.26201898... | [10.282029151916504, 6.837799549102783] |
9d8d5fde-c5eb-4e52-ba42-89c17131a55f | caibc-capturing-all-round-information-beyond | 2209.05773 | null | https://arxiv.org/abs/2209.05773v1 | https://arxiv.org/pdf/2209.05773v1.pdf | CAIBC: Capturing All-round Information Beyond Color for Text-based Person Retrieval | Given a natural language description, text-based person retrieval aims to identify images of a target person from a large-scale person image database. Existing methods generally face a \textbf{color over-reliance problem}, which means that the models rely heavily on color information when matching cross-modal data. Ind... | ['Yifeng Li', 'Tian Wang', 'Chao Liu', 'Xili Wan', 'Jingyi Xue', 'Aichun Zhu', 'Zijie Wang'] | 2022-09-13 | null | null | null | null | ['person-retrieval', 'nlp-based-person-retrival'] | ['computer-vision', 'computer-vision'] | [ 2.96063181e-02 -5.94385862e-01 -7.90910125e-02 -4.02093887e-01
-8.30141187e-01 -4.21405166e-01 6.23134553e-01 -4.95349383e-03
-6.17223978e-01 6.37970507e-01 -2.02161670e-01 1.09463491e-01
-2.37546071e-01 -7.03171074e-01 -2.31472000e-01 -8.17684293e-01
2.08548397e-01 7.33907700e-01 -2.08971262e-01 -2.86061019... | [14.567193984985352, 0.8137559294700623] |
8d0cfd18-4774-45f1-874f-9330eab6a8d8 | weighing-features-of-lung-and-heart-regions | 2105.12430 | null | https://arxiv.org/abs/2105.12430v1 | https://arxiv.org/pdf/2105.12430v1.pdf | Weighing Features of Lung and Heart Regions for Thoracic Disease Classification | Chest X-rays are the most commonly available and affordable radiological examination for screening thoracic diseases. According to the domain knowledge of screening chest X-rays, the pathological information usually lay on the lung and heart regions. However, it is costly to acquire region-level annotation in practice,... | ['Jiang Liu', 'Junling Liu', 'Yuguang Yan', 'Yitian Zhao', 'Yanwu Xu', 'Jiansheng Fang'] | 2021-05-26 | null | null | null | null | ['thoracic-disease-classification'] | ['computer-vision'] | [ 4.23477083e-01 1.65540054e-01 -4.82722431e-01 -2.84698159e-01
-1.01358891e+00 -2.10207626e-01 2.03209236e-01 2.65558511e-01
-4.50336099e-01 4.16138202e-01 9.38242525e-02 -4.29967523e-01
-1.34026229e-01 -8.80265176e-01 -6.03253663e-01 -7.63742447e-01
1.53240249e-01 2.99944282e-01 5.84583938e-01 2.01807141... | [15.098577499389648, -2.1361100673675537] |
15dbf58b-5dfd-4d7c-925b-f36e83a287f4 | a-transformer-based-approach-for-translating | null | null | https://www.dre.vanderbilt.edu/~schmidt/PDF/A_Transformer_based_Approach_for_TranslatingNatural_Language_to_Bash_Commands.pdf | https://www.dre.vanderbilt.edu/~schmidt/PDF/A_Transformer_based_Approach_for_TranslatingNatural_Language_to_Bash_Commands.pdf | A Transformer-based Approach for Translating Natural Language to Bash Commands | This paper explores the translation of natural language into Bash Commands, which developers commonly use to accomplish command-line tasks in a terminal. In our approach a terminal takes a command as a sentence in plain English and translates it into the corresponding string of Bash Commands. The paper analyzes the per... | ['Douglas C. Schmidt', 'Jules White', 'Zhongwei Teng', 'Quchen Fu'] | 2021-12-14 | null | null | null | 20th-ieee-international-conference-on-machine | ['code-translation'] | ['computer-code'] | [ 3.70906144e-01 6.81970716e-02 -1.85636356e-01 -8.01252306e-01
-1.18018425e+00 -6.92610085e-01 7.58318603e-01 -3.67776491e-02
-7.11608529e-01 8.02740216e-01 1.03583194e-01 -9.08277512e-01
5.48092127e-01 -4.57776010e-01 -7.34748721e-01 7.56803080e-02
2.01829538e-01 9.09750581e-01 1.44639805e-01 -8.66126001... | [11.543485641479492, 10.348734855651855] |
3d8924b0-4beb-4b30-9028-166218670f4c | comparative-analysis-of-non-blind-deblurring | 2205.03464 | null | https://arxiv.org/abs/2205.03464v1 | https://arxiv.org/pdf/2205.03464v1.pdf | Comparative Analysis of Non-Blind Deblurring Methods for Noisy Blurred Images | Image blurring refers to the degradation of an image wherein the image's overall sharpness decreases. Image blurring is caused by several factors. Additionally, during the image acquisition process, noise may get added to the image. Such a noisy and blurred image can be represented as the image resulting from the convo... | ['Poorna Banerjee Dasgupta'] | 2022-05-06 | null | null | null | null | ['blind-image-deblurring'] | ['computer-vision'] | [ 2.86023766e-01 -7.32930303e-01 5.18506408e-01 1.02348059e-01
-4.54931818e-02 -6.96577787e-01 5.09564936e-01 -6.08832479e-01
-3.46677303e-01 9.79211330e-01 5.67458868e-01 -2.81345308e-01
-4.43725675e-01 -1.41361415e-01 -3.38154018e-01 -9.09465313e-01
1.55410767e-01 -2.80076742e-01 -2.26716742e-01 9.11516175... | [11.614234924316406, -2.7362046241760254] |
3d1215e6-c56f-444a-9405-5d54ec53b6ba | stochastic-partial-swap-enhanced-model | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Huang_Stochastic_Partial_Swap_Enhanced_Model_Generalization_and_Interpretability_for_Fine-Grained_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Huang_Stochastic_Partial_Swap_Enhanced_Model_Generalization_and_Interpretability_for_Fine-Grained_ICCV_2021_paper.pdf | Stochastic Partial Swap: Enhanced Model Generalization and Interpretability for Fine-Grained Recognition | Learning mid-level representation for fine-grained recognition is easily dominated by a limited number of highly discriminative patterns, degrading its robustness and generalization capability. To this end, we propose a novel Stochastic Partial Swap (SPS) scheme to address this issue. Our method performs element-wi... | ['DaCheng Tao', 'Xinchao Wang', 'Shaoli Huang'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['material-recognition', 'scene-recognition'] | ['computer-vision', 'computer-vision'] | [ 3.90785754e-01 -2.89324373e-01 -2.24451900e-01 -6.30159557e-01
-4.33024228e-01 -6.17688358e-01 6.70003057e-01 -4.29045521e-02
-2.12008134e-01 7.29809701e-01 1.97844788e-01 -2.53123101e-02
-3.66031200e-01 -9.13973033e-01 -8.44503462e-01 -8.14420104e-01
6.95935860e-02 -9.02549457e-03 1.09502293e-01 2.80649848... | [9.606565475463867, 2.1203911304473877] |
717807e4-27ce-4f9d-aee3-e8c423316d97 | metaviewer-towards-a-unified-multi-view | 2303.06329 | null | https://arxiv.org/abs/2303.06329v1 | https://arxiv.org/pdf/2303.06329v1.pdf | MetaViewer: Towards A Unified Multi-View Representation | Existing multi-view representation learning methods typically follow a specific-to-uniform pipeline, extracting latent features from each view and then fusing or aligning them to obtain the unified object representation. However, the manually pre-specify fusion functions and view-private redundant information mixed in ... | ['Yilong Yin', 'Xiaoming Xi', 'Yuling Ma', 'Haoliang Sun', 'Ren Wang'] | 2023-03-11 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_MetaViewer_Towards_a_Unified_Multi-View_Representation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_MetaViewer_Towards_a_Unified_Multi-View_Representation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['multi-view-learning'] | ['computer-vision'] | [ 1.95131022e-02 -1.05403498e-01 -3.59780341e-01 -6.76322281e-01
-1.40069926e+00 -7.08798230e-01 6.27226830e-01 -1.05063289e-01
2.83271730e-01 1.73179716e-01 4.94351327e-01 3.11285496e-01
-7.12783486e-02 -5.88270009e-01 -6.94010079e-01 -8.14901829e-01
3.78025949e-01 6.62458599e-01 -9.69652086e-02 3.00759017... | [8.553354263305664, 4.463045597076416] |
988ab1f3-340e-4661-8d5a-52d89571ba41 | ceres-pretraining-of-graph-conditioned-1 | 2204.04303 | null | https://arxiv.org/abs/2204.04303v1 | https://arxiv.org/pdf/2204.04303v1.pdf | CERES: Pretraining of Graph-Conditioned Transformer for Semi-Structured Session Data | User sessions empower many search and recommendation tasks on a daily basis. Such session data are semi-structured, which encode heterogeneous relations between queries and products, and each item is described by the unstructured text. Despite recent advances in self-supervised learning for text or graphs, there lack o... | ['Chao Zhang', 'Tuo Zhao', 'Bing Yin', 'Qingyu Yin', 'Chen Luo', 'Rui Feng'] | 2022-04-08 | null | https://aclanthology.org/2022.naacl-main.16 | https://aclanthology.org/2022.naacl-main.16.pdf | naacl-2022-7 | ['session-search'] | ['natural-language-processing'] | [ 3.04909348e-01 3.27694625e-01 -7.53578126e-01 -7.10115910e-01
-4.59831536e-01 -8.35481822e-01 7.44864941e-01 5.64811766e-01
-1.09553300e-01 3.10212702e-01 5.94364405e-01 -3.68182153e-01
-2.14347348e-01 -8.27219784e-01 -8.98318052e-01 1.35080501e-01
-3.06832850e-01 9.83140171e-01 1.88862726e-01 -5.30466020... | [10.92813777923584, 7.354452133178711] |
09ad7d57-afdc-4ec8-acba-d9e883199853 | tinyml-design-contest-for-life-threatening | 2305.05105 | null | https://arxiv.org/abs/2305.05105v2 | https://arxiv.org/pdf/2305.05105v2.pdf | TinyML Design Contest for Life-Threatening Ventricular Arrhythmia Detection | The first ACM/IEEE TinyML Design Contest (TDC) held at the 41st International Conference on Computer-Aided Design (ICCAD) in 2022 is a challenging, multi-month, research and development competition. TDC'22 focuses on real-world medical problems that require the innovation and implementation of artificial intelligence/m... | ['Yiyu Shi', 'Lichuan Ping', 'Xiaowei Xu', 'Liqi Liao', 'Cong Liu', 'Dawei Li', 'Zhenge Jia'] | 2023-05-09 | null | null | null | null | ['arrhythmia-detection'] | ['medical'] | [ 1.65909857e-01 5.68239614e-02 4.80777360e-02 1.33038372e-01
-7.36491323e-01 -5.20821571e-01 -2.52384692e-01 1.63099289e-01
9.52034593e-02 8.34180892e-01 -8.90820548e-02 -7.68622816e-01
-2.66668081e-01 -1.74176663e-01 -2.37890452e-01 -3.96192312e-01
-5.02308965e-01 5.02261341e-01 -5.76318920e-01 4.08665717... | [14.270562171936035, 3.264667272567749] |
7ebe1ec4-8c86-4091-85d3-a9334f1be114 | beyond-512-tokens-siamese-multi-depth | 2004.12297 | null | https://arxiv.org/abs/2004.12297v2 | https://arxiv.org/pdf/2004.12297v2.pdf | Beyond 512 Tokens: Siamese Multi-depth Transformer-based Hierarchical Encoder for Long-Form Document Matching | Many natural language processing and information retrieval problems can be formalized as the task of semantic matching. Existing work in this area has been largely focused on matching between short texts (e.g., question answering), or between a short and a long text (e.g., ad-hoc retrieval). Semantic matching between l... | ['Liu Yang', 'Michael Bendersky', 'Mingyang Zhang', 'Marc Najork', 'Cheng Li'] | 2020-04-26 | null | null | null | null | ['2048'] | ['playing-games'] | [ 3.00159156e-01 -6.92367107e-02 -1.45266116e-01 -4.38161105e-01
-1.19226933e+00 -2.43986711e-01 6.52294219e-01 6.05925143e-01
-7.04115629e-01 1.59807414e-01 5.09023130e-01 -4.56966996e-01
-2.11336151e-01 -8.04953456e-01 -6.45461321e-01 -1.61160260e-01
3.91815454e-01 7.46936023e-01 4.71884340e-01 -5.20976603... | [11.143756866455078, 8.228445053100586] |
0a8c582a-e04f-46e9-95b4-2e5a0fb1adf4 | hybrik-x-hybrid-analytical-neural-inverse | 2304.05690 | null | https://arxiv.org/abs/2304.05690v1 | https://arxiv.org/pdf/2304.05690v1.pdf | HybrIK-X: Hybrid Analytical-Neural Inverse Kinematics for Whole-body Mesh Recovery | Recovering whole-body mesh by inferring the abstract pose and shape parameters from visual content can obtain 3D bodies with realistic structures. However, the inferring process is highly non-linear and suffers from image-mesh misalignment, resulting in inaccurate reconstruction. In contrast, 3D keypoint estimation met... | ['Cewu Lu', 'Lixin Yang', 'Zhicun Chen', 'Chao Xu', 'Siyuan Bian', 'Jiefeng Li'] | 2023-04-12 | null | null | null | null | ['3d-human-pose-estimation', '3d-human-reconstruction'] | ['computer-vision', 'computer-vision'] | [-2.24400833e-01 8.44305307e-02 -3.46134007e-01 3.26445736e-02
-8.28684270e-01 -3.79728138e-01 2.29603872e-01 -5.02721548e-01
1.25346467e-01 4.07250166e-01 3.23914438e-01 4.40112650e-01
1.13725476e-01 -6.02353156e-01 -8.61750722e-01 -4.82538998e-01
2.69028872e-01 7.39331126e-01 3.67246531e-02 -3.87526125... | [7.080688953399658, -1.1747326850891113] |
e3dc7503-db4f-404c-b9e5-b5956dff3942 | what-s-behind-the-mask-estimating-uncertainty | 2211.15211 | null | https://arxiv.org/abs/2211.15211v1 | https://arxiv.org/pdf/2211.15211v1.pdf | What's Behind the Mask: Estimating Uncertainty in Image-to-Image Problems | Estimating uncertainty in image-to-image networks is an important task, particularly as such networks are being increasingly deployed in the biological and medical imaging realms. In this paper, we introduce a new approach to this problem based on masking. Given an existing image-to-image network, our approach computes... | ['Daniel Freedman', 'Michael Elad', 'Regev Cohen', 'Gilad Kutiel'] | 2022-11-28 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 7.15374172e-01 5.55813134e-01 4.71260659e-02 -5.33990204e-01
-1.04889989e+00 -5.18604159e-01 3.70611757e-01 -2.44749226e-02
-4.53330606e-01 7.72080898e-01 -1.19256303e-01 -1.92008503e-02
-2.46878102e-01 -6.74266398e-01 -8.46335769e-01 -6.37346745e-01
-2.61572421e-01 3.59022260e-01 2.65596300e-01 8.65715817... | [11.572304725646973, -1.776789665222168] |
ec433fd1-9f07-48c9-b167-f5618fcd1b3b | verbal-and-nonverbal-clues-for-real-life | null | null | https://aclanthology.org/D15-1281 | https://aclanthology.org/D15-1281.pdf | Verbal and Nonverbal Clues for Real-life Deception Detection | null | ["Ver{\\'o}nica P{\\'e}rez-Rosas", 'Mohamed Abouelenien', 'Mihai Burzo', 'Yao Xiao', 'Rada Mihalcea', 'CJ Linton'] | 2015-09-01 | null | null | null | emnlp-2015-9 | ['deception-detection'] | ['miscellaneous'] | [-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... | [-7.398003101348877, 3.6464171409606934] |
54b2f87a-061c-4d7e-864e-cabd82c772d6 | dkm-differentiable-k-means-clustering-layer | 2108.12659 | null | https://arxiv.org/abs/2108.12659v4 | https://arxiv.org/pdf/2108.12659v4.pdf | DKM: Differentiable K-Means Clustering Layer for Neural Network Compression | Deep neural network (DNN) model compression for efficient on-device inference is becoming increasingly important to reduce memory requirements and keep user data on-device. To this end, we propose a novel differentiable k-means clustering layer (DKM) and its application to train-time weight clustering-based DNN model c... | ['Mohammad Rastegari', 'Saurabh Adya', 'Keivan A. Vahid', 'Minsik Cho'] | 2021-08-28 | dkm-differentiable-k-means-clustering-layer-1 | https://openreview.net/forum?id=J_F_qqCE3Z5 | https://openreview.net/pdf?id=J_F_qqCE3Z5 | iclr-2022-4 | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [-5.31964861e-02 -2.30364930e-02 -3.44803333e-01 -6.18039370e-01
-7.58170068e-01 -2.32615113e-01 4.14167136e-01 -8.81825536e-02
-1.18896043e+00 3.77386272e-01 -5.89559227e-03 -6.10039473e-01
-1.26409560e-01 -6.29788220e-01 -1.09069347e+00 -4.90990549e-01
1.37161300e-01 7.71798432e-01 -7.23869400e-03 2.35359907... | [8.580625534057617, 3.069040536880493] |
29a39a38-6550-41d8-981e-9864fbd88d46 | safe-collaborative-filtering | 2306.05292 | null | https://arxiv.org/abs/2306.05292v1 | https://arxiv.org/pdf/2306.05292v1.pdf | Safe Collaborative Filtering | Excellent tail performance is crucial for modern machine learning tasks, such as algorithmic fairness, class imbalance, and risk-sensitive decision making, as it ensures the effective handling of challenging samples within a dataset. Tail performance is also a vital determinant of success for personalised recommender s... | ['Tetsuro Morimura', 'Naoto Ohsaka', 'Tatsushi Oka', 'Riku Togashi'] | 2023-06-08 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [ 9.48046520e-02 -1.66004390e-01 -5.14046550e-01 -6.88777506e-01
-7.11532533e-01 -2.58573145e-01 9.16585997e-02 4.48431283e-01
-6.37111723e-01 7.10288882e-01 1.80930436e-01 -6.40218139e-01
-6.02250278e-01 -6.50627136e-01 -9.01575536e-02 -4.97517407e-01
-3.05422433e-02 3.65987092e-01 -1.27850771e-01 -1.27948180... | [9.512308120727539, 5.673984050750732] |
08d7efcd-d8a8-439a-8e8f-9b6df3286a56 | leveraging-off-the-shelf-diffusion-model-for | 2210.05872 | null | https://arxiv.org/abs/2210.05872v1 | https://arxiv.org/pdf/2210.05872v1.pdf | Leveraging Off-the-shelf Diffusion Model for Multi-attribute Fashion Image Manipulation | Fashion attribute editing is a task that aims to convert the semantic attributes of a given fashion image while preserving the irrelevant regions. Previous works typically employ conditional GANs where the generator explicitly learns the target attributes and directly execute the conversion. These approaches, however, ... | ['Nojun Kwak', 'Ohjoon Kwon', 'Donghyeon Jeon', 'Chaerin Kong'] | 2022-10-12 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [ 6.10055447e-01 9.02561098e-02 -2.27223992e-01 -6.71662927e-01
-7.15960026e-01 -8.73553038e-01 8.33121717e-01 -5.97957820e-02
-7.63416588e-02 6.04777753e-01 1.92960724e-01 -1.49223721e-03
1.03557691e-01 -9.50123489e-01 -1.00493670e+00 -5.65983951e-01
3.65222335e-01 3.91857922e-01 -6.21155761e-02 -3.51970345... | [11.607929229736328, -0.40011367201805115] |
3f65ecd4-8fe0-4b66-92fb-a4fe09b23801 | representing-input-transformations-by-low | 2305.13536 | null | https://arxiv.org/abs/2305.13536v1 | https://arxiv.org/pdf/2305.13536v1.pdf | Representing Input Transformations by Low-Dimensional Parameter Subspaces | Deep models lack robustness to simple input transformations such as rotation, scaling, and translation, unless they feature a particular invariant architecture or undergo specific training, e.g., learning the desired robustness from data augmentations. Alternatively, input transformations can be treated as a domain shi... | ['Lothar Thiele', 'Xiaoxi He', 'Dong Wang', 'Olga Saukh'] | 2023-05-22 | null | null | null | null | ['audio-signal-processing'] | ['audio'] | [ 3.61990511e-01 7.05452040e-02 -2.53608227e-01 -1.54019922e-01
-2.35178351e-01 -1.04889703e+00 7.00243056e-01 -2.34857947e-01
-3.52383822e-01 4.45025146e-01 2.38818944e-01 -3.07687432e-01
-2.76767939e-01 -3.27450484e-01 -8.47063124e-01 -6.90390885e-01
-5.22409156e-02 5.98010004e-01 2.11520563e-03 -2.27467582... | [9.028987884521484, 2.6753180027008057] |
2aac02fa-c467-4499-a68c-9f36ed204439 | efficient-explorative-key-term-selection | 2303.00315 | null | https://arxiv.org/abs/2303.00315v1 | https://arxiv.org/pdf/2303.00315v1.pdf | Efficient Explorative Key-term Selection Strategies for Conversational Contextual Bandits | Conversational contextual bandits elicit user preferences by occasionally querying for explicit feedback on key-terms to accelerate learning. However, there are aspects of existing approaches which limit their performance. First, information gained from key-term-level conversations and arm-level recommendations is not ... | ['John C. S. Lui', 'Shuai Li', 'Xutong Liu', 'Zhiyong Wang'] | 2023-03-01 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 5.35714719e-03 -1.88204199e-01 -7.92841077e-01 -4.35304433e-01
-1.27177751e+00 -8.05275500e-01 6.92477729e-03 7.37820119e-02
-4.43813056e-01 1.24990129e+00 3.06973010e-01 -7.02972531e-01
-7.77336001e-01 -6.23734236e-01 -8.01694989e-01 -8.26783836e-01
-2.36577570e-01 5.74303508e-01 6.88926652e-02 -2.02496678... | [4.639928817749023, 3.337019205093384] |
9ccad644-1023-43c8-85f3-7a0e6e566058 | positive-unlabeled-classification-under-class | 1809.07011 | null | https://arxiv.org/abs/1809.07011v4 | https://arxiv.org/pdf/1809.07011v4.pdf | Positive-Unlabeled Classification under Class Prior Shift and Asymmetric Error | Bottlenecks of binary classification from positive and unlabeled data (PU classification) are the requirements that given unlabeled patterns are drawn from the test marginal distribution, and the penalty of the false positive error is identical to the false negative error. However, such requirements are often not fulfi... | ['Masashi Sugiyama', 'Nontawat Charoenphakdee'] | 2018-09-19 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [ 4.28087056e-01 2.37431094e-01 -5.23420632e-01 -6.21230304e-01
-6.33493483e-01 -5.03451645e-01 1.39628425e-01 6.97395578e-02
-2.29306206e-01 1.15907860e+00 -6.19539440e-01 -5.27787328e-01
-1.61769301e-01 -8.13414037e-01 -7.21584618e-01 -8.04107070e-01
5.20623326e-01 4.77349132e-01 1.77817255e-01 6.52370393... | [9.0558443069458, 4.049476623535156] |
b40b644c-5d99-43e0-87ca-d8175735ee61 | to-root-artificial-intelligence-deeply-in | 2009.05678 | null | https://arxiv.org/abs/2009.05678v1 | https://arxiv.org/pdf/2009.05678v1.pdf | To Root Artificial Intelligence Deeply in Basic Science for a New Generation of AI | One of the ambitions of artificial intelligence is to root artificial intelligence deeply in basic science while developing brain-inspired artificial intelligence platforms that will promote new scientific discoveries. The challenges are essential to push artificial intelligence theory and applied technologies research... | ['Jingan Yang', 'Yang Peng'] | 2020-09-11 | null | null | null | null | ['visual-commonsense-reasoning'] | ['reasoning'] | [ 2.60108620e-01 6.89368546e-02 1.29708841e-01 -2.55238593e-01
9.50731874e-01 -2.54714698e-01 3.60664636e-01 -4.08087462e-01
-2.17129469e-01 5.51756084e-01 -6.68460801e-02 -5.51870286e-01
-5.38562357e-01 -1.08277249e+00 -4.43054557e-01 -4.11783904e-01
-1.65487945e-01 3.26468766e-01 -5.74807748e-02 -6.78336918... | [9.157012939453125, 6.414112567901611] |
e5d379b1-79aa-425a-ad07-6c90d3089727 | clirmatrix-a-massively-large-collection-of | null | null | https://aclanthology.org/2020.emnlp-main.340 | https://aclanthology.org/2020.emnlp-main.340.pdf | CLIRMatrix: A massively large collection of bilingual and multilingual datasets for Cross-Lingual Information Retrieval | We present CLIRMatrix, a massively large collection of bilingual and multilingual datasets for Cross-Lingual Information Retrieval extracted automatically from Wikipedia. CLIRMatrix comprises (1) BI-139, a bilingual dataset of queries in one language matched with relevant documents in another language for 139x138=19,18... | ['Kevin Duh', 'Shuo Sun'] | null | null | null | null | emnlp-2020-11 | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [-4.27031428e-01 -5.29116035e-01 -7.34114766e-01 -2.46889666e-01
-1.96581817e+00 -1.03296423e+00 8.99443507e-01 2.00901255e-01
-1.00749660e+00 7.78811932e-01 5.48674643e-01 -7.49828741e-02
-2.15921327e-01 -3.22138578e-01 -6.99660003e-01 -1.69311147e-02
3.24310482e-01 1.05925035e+00 -1.32905051e-01 -3.32870513... | [11.391020774841309, 9.770442008972168] |
78f499f4-3d26-4b45-840b-ea271e08af36 | retrosynthesis-prediction-with-local-template | 2306.04123 | null | https://arxiv.org/abs/2306.04123v1 | https://arxiv.org/pdf/2306.04123v1.pdf | Retrosynthesis Prediction with Local Template Retrieval | Retrosynthesis, which predicts the reactants of a given target molecule, is an essential task for drug discovery. In recent years, the machine learing based retrosynthesis methods have achieved promising results. In this work, we introduce RetroKNN, a local reaction template retrieval method to further boost the perfor... | ['Tao Qin', 'Lijun Wu', 'Yingce Xia', 'Junliang Guo', 'Rui Yan', 'Shufang Xie'] | 2023-06-07 | null | null | null | null | ['drug-discovery', 'retrosynthesis'] | ['medical', 'medical'] | [ 4.00512099e-01 -2.47907177e-01 -8.67593229e-01 -1.56485066e-01
-8.24148417e-01 -6.01756752e-01 4.91347194e-01 9.41223279e-02
-2.21439481e-01 9.53649938e-01 2.81385899e-01 -3.33862901e-01
-8.17749277e-03 -7.85226345e-01 -7.99799860e-01 -1.08659267e+00
2.76488572e-01 2.20870405e-01 3.17074001e-01 -8.29064995... | [4.499020099639893, 6.106736660003662] |
1a283830-67f7-4534-bb47-04ec1800d851 | old-is-mathbf-mathcal-g-old-redefining-the | 2004.07657 | null | https://arxiv.org/abs/2004.07657v4 | https://arxiv.org/pdf/2004.07657v4.pdf | Old is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm | A popular method for anomaly detection is to use the generator of an adversarial network to formulate anomaly scores over reconstruction loss of input. Due to the rare occurrence of anomalies, optimizing such networks can be a cumbersome task. Another possible approach is to use both generator and discriminator for ano... | ['Seung-Ik Lee', 'Jin-ha Lee', 'Muhammad Zaigham Zaheer', 'Marcella Astrid'] | 2020-04-16 | old-is-gold-redefining-the-adversarially | http://openaccess.thecvf.com/content_CVPR_2020/html/Zaheer_Old_Is_Gold_Redefining_the_Adversarially_Learned_One-Class_Classifier_Training_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Zaheer_Old_Is_Gold_Redefining_the_Adversarially_Learned_One-Class_Classifier_Training_CVPR_2020_paper.pdf | cvpr-2020-6 | ['one-class-classifier'] | ['methodology'] | [ 2.87395626e-01 -1.57531440e-01 3.83871496e-01 -4.02175151e-02
-7.31056392e-01 -6.78520024e-01 6.27789497e-01 2.25855425e-01
-3.98381948e-01 5.66581786e-01 -4.33821648e-01 -1.78501919e-01
2.88704634e-01 -8.66902113e-01 -9.68884051e-01 -8.48955631e-01
-8.50344002e-02 1.38800204e-01 2.93043286e-01 -1.01055078... | [7.703464508056641, 2.2436957359313965] |
a9227d1d-a04b-4708-85ed-85bede0dc496 | improving-language-identification-of-accented | 2203.16972 | null | https://arxiv.org/abs/2203.16972v3 | https://arxiv.org/pdf/2203.16972v3.pdf | Improving Language Identification of Accented Speech | Language identification from speech is a common preprocessing step in many spoken language processing systems. In recent years, this field has seen fast progress, mostly due to the use of self-supervised models pretrained on multilingual data and the use of large training corpora. This paper shows that for speech with ... | ['Tanel Alumäe', 'Kunnar Kukk'] | 2022-03-31 | null | null | null | null | ['spoken-language-identification'] | ['speech'] | [ 4.34897803e-02 6.27800301e-02 -2.39651158e-01 -7.34559000e-01
-1.15626812e+00 -8.24851036e-01 6.07842505e-01 5.01459278e-02
-7.54767060e-01 5.89312494e-01 4.47023302e-01 -4.27735448e-01
3.37040424e-01 -2.21137285e-01 -3.15345943e-01 -5.02551556e-01
1.57075912e-01 7.73507833e-01 1.01308666e-01 -4.18080270... | [14.164530754089355, 6.629953861236572] |
73f02050-9b4d-4dd0-8baa-f93d50fa4824 | segregated-temporal-assembly-recurrent | 1811.07460 | null | http://arxiv.org/abs/1811.07460v1 | http://arxiv.org/pdf/1811.07460v1.pdf | Segregated Temporal Assembly Recurrent Networks for Weakly Supervised Multiple Action Detection | This paper proposes a segregated temporal assembly recurrent (STAR) network
for weakly-supervised multiple action detection. The model learns from
untrimmed videos with only supervision of video-level labels and makes
prediction of intervals of multiple actions. Specifically, we first assemble
video clips according to ... | ['ShiLiang Pu', 'Zhanzhan Cheng', 'Yunlu Xu', 'Yi Niu', 'Jianwen Xie', 'Fei Wu', 'Chengwei Zhang'] | 2018-11-19 | null | null | null | null | ['multiple-action-detection'] | ['computer-vision'] | [ 3.86517286e-01 6.59520328e-02 -4.98467356e-01 -4.05663341e-01
-8.76554012e-01 -2.75091588e-01 5.68693519e-01 -5.20390570e-01
-4.47546870e-01 5.13676167e-01 5.49526989e-01 1.70092002e-01
1.86724171e-01 -3.48334610e-01 -8.31589341e-01 -8.31784606e-01
-3.89298499e-01 1.57964617e-01 6.06114507e-01 1.38735354... | [8.411823272705078, 0.5530698895454407] |
b3986740-85dd-4cc3-acaf-3ee41df69ce1 | s3m-scalable-statistical-shape-modeling | 2304.07515 | null | https://arxiv.org/abs/2304.07515v1 | https://arxiv.org/pdf/2304.07515v1.pdf | S3M: Scalable Statistical Shape Modeling through Unsupervised Correspondences | Statistical shape models (SSMs) are an established way to geometrically represent the anatomy of a population with various clinically relevant applications. However, they typically require domain expertise and labor-intensive manual segmentations or landmark annotations to generate. Methods to estimate correspondences ... | ['Nassir Navab', 'Benjamin Busam', 'Mahdi Saleh', 'Ha Young Kim', 'Vincent Bürgin', 'Emily Hoppe', 'Alexander Bauman', 'Lennart Bastian'] | 2023-04-15 | null | null | null | null | ['anatomy'] | ['miscellaneous'] | [ 2.8788078e-01 5.6183410e-01 -1.1540575e-01 -6.1800206e-01
-1.1292760e+00 -7.0797008e-01 2.7805391e-01 4.8800325e-01
3.0066535e-02 4.5005742e-01 1.7465553e-01 -3.0468491e-01
-1.8011231e-02 -4.4840014e-01 -5.9235871e-01 -2.2288935e-01
-2.3398812e-01 9.6514368e-01 1.3834345e-01 4.0104073e-02
1.5039930e-01... | [14.199416160583496, -2.4564080238342285] |
d02092e3-3e2a-4a68-ada7-1bd34c8524f5 | unsupervised-image-to-image-translation-with-3 | 2204.03641 | null | https://arxiv.org/abs/2204.03641v1 | https://arxiv.org/pdf/2204.03641v1.pdf | Unsupervised Image-to-Image Translation with Generative Prior | Unsupervised image-to-image translation aims to learn the translation between two visual domains without paired data. Despite the recent progress in image translation models, it remains challenging to build mappings between complex domains with drastic visual discrepancies. In this work, we present a novel framework, G... | ['Chen Change Loy', 'Ziwei Liu', 'Liming Jiang', 'Shuai Yang'] | 2022-04-07 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Yang_Unsupervised_Image-to-Image_Translation_With_Generative_Prior_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Yang_Unsupervised_Image-to-Image_Translation_With_Generative_Prior_CVPR_2022_paper.pdf | cvpr-2022-1 | ['unsupervised-image-to-image-translation'] | ['computer-vision'] | [ 5.85389733e-01 -4.02650833e-02 -2.60838479e-01 -3.61238301e-01
-1.00330460e+00 -6.35773122e-01 8.91927600e-01 -5.24151325e-01
1.37435257e-01 8.40574026e-01 3.05288941e-01 1.96608886e-01
2.00006023e-01 -8.04566920e-01 -1.01619232e+00 -7.14714706e-01
7.40165234e-01 5.06419063e-01 1.03007160e-01 -2.10062698... | [11.625657081604004, -0.4575669765472412] |
800e9b20-7bd3-4cb4-b5c4-62ce0eed74dd | is-mapping-necessary-for-realistic-pointgoal | 2206.00997 | null | https://arxiv.org/abs/2206.00997v2 | https://arxiv.org/pdf/2206.00997v2.pdf | Is Mapping Necessary for Realistic PointGoal Navigation? | Can an autonomous agent navigate in a new environment without building an explicit map? For the task of PointGoal navigation ('Go to $\Delta x$, $\Delta y$') under idealized settings (no RGB-D and actuation noise, perfect GPS+Compass), the answer is a clear 'yes' - map-less neural models composed of task-agnostic compo... | ['Oleksandr Maksymets', 'Dhruv Batra', 'Oles Dobosevych', 'Naoki Yokoyama', 'Erik Wijmans', 'Ruslan Partsey'] | 2022-06-02 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Partsey_Is_Mapping_Necessary_for_Realistic_PointGoal_Navigation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Partsey_Is_Mapping_Necessary_for_Realistic_PointGoal_Navigation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['pointgoal-navigation'] | ['robots'] | [-3.08288485e-02 2.12874532e-01 1.52716100e-01 -2.07670286e-01
-6.65552855e-01 -7.56696343e-01 6.07813597e-01 -7.11655170e-02
-1.00553715e+00 9.09111440e-01 -1.23217948e-01 -6.33804083e-01
-6.60650432e-02 -7.95401216e-01 -1.18763983e+00 -5.00174165e-01
-4.53491569e-01 6.21332705e-01 3.96608472e-01 -1.05809236... | [4.7040629386901855, 0.6989579200744629] |
31f452ad-3a63-4396-87c8-aedc40ae020a | compm-context-modeling-with-speaker-s-pre | 2108.11626 | null | https://arxiv.org/abs/2108.11626v3 | https://arxiv.org/pdf/2108.11626v3.pdf | CoMPM: Context Modeling with Speaker's Pre-trained Memory Tracking for Emotion Recognition in Conversation | As the use of interactive machines grow, the task of Emotion Recognition in Conversation (ERC) became more important. If the machine-generated sentences reflect emotion, more human-like sympathetic conversations are possible. Since emotion recognition in conversation is inaccurate if the previous utterances are not tak... | ['Wooin Lee', 'Joosung Lee'] | 2021-08-26 | null | null | null | null | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [-1.86536491e-01 2.66001821e-01 -2.21907511e-01 -6.24940097e-01
-6.68114603e-01 -5.86854160e-01 5.50218403e-01 -1.42459497e-01
-4.43313122e-01 6.56544030e-01 6.86909318e-01 -1.12011224e-01
6.42112017e-01 -5.40236294e-01 -3.67634684e-01 -2.77319759e-01
9.92993191e-02 3.33669811e-01 -6.87642395e-02 -5.37360251... | [12.990486145019531, 6.260193347930908] |
31814033-61fd-4c3b-8aa5-82221844285e | weakly-supervised-headline-dependency-parsing | 2301.10371 | null | https://arxiv.org/abs/2301.10371v1 | https://arxiv.org/pdf/2301.10371v1.pdf | Weakly Supervised Headline Dependency Parsing | English news headlines form a register with unique syntactic properties that have been documented in linguistics literature since the 1930s. However, headlines have received surprisingly little attention from the NLP syntactic parsing community. We aim to bridge this gap by providing the first news headline corpus of U... | ['Igor Malioutov', 'Ozan İrsoy', 'Tianze Shi', 'Adrian Benton'] | 2023-01-25 | null | null | null | null | ['dependency-parsing'] | ['natural-language-processing'] | [-2.53514230e-01 5.52843750e-01 -6.17438674e-01 -6.28166795e-01
-1.49967790e+00 -9.15596128e-01 2.46704757e-01 5.26587844e-01
-6.01680219e-01 7.81346142e-01 1.12340367e+00 -6.62059009e-01
3.21802229e-01 -5.96406221e-01 -8.12803566e-01 -2.84942966e-02
1.95692509e-01 3.78021985e-01 2.01222628e-01 -3.50211024... | [10.231389999389648, 9.77672290802002] |
5edb0f70-19b2-460b-8514-616923dc541f | casp-net-rethinking-video-saliency-prediction | 2303.06357 | null | https://arxiv.org/abs/2303.06357v1 | https://arxiv.org/pdf/2303.06357v1.pdf | CASP-Net: Rethinking Video Saliency Prediction from an Audio-VisualConsistency Perceptual Perspective | Incorporating the audio stream enables Video Saliency Prediction (VSP) to imitate the selective attention mechanism of human brain. By focusing on the benefits of joint auditory and visual information, most VSP methods are capable of exploiting semantic correlation between vision and audio modalities but ignoring the n... | ['Guangtao Zhai', 'Yufei zha', 'Wei Huang', 'Peng Zhang', 'Ganglai Wang', 'Junwen Xiong'] | 2023-03-11 | null | null | null | null | ['saliency-prediction'] | ['computer-vision'] | [ 3.72205347e-01 -1.15744323e-01 -7.88846333e-03 -3.09792906e-01
-6.21309102e-01 3.87317240e-02 3.38917851e-01 7.15384707e-02
-1.00603536e-01 4.92039084e-01 5.50844550e-01 3.27769220e-01
3.13338302e-02 -6.10167533e-02 -9.20912206e-01 -4.22377765e-01
2.32080042e-01 -5.24891376e-01 8.47424030e-01 -5.52305579... | [9.765751838684082, -0.24203626811504364] |
663aba92-961a-461e-94f6-1df4ffc096fb | cmcgan-a-uniform-framework-for-cross-modal | 1711.08102 | null | http://arxiv.org/abs/1711.08102v2 | http://arxiv.org/pdf/1711.08102v2.pdf | CMCGAN: A Uniform Framework for Cross-Modal Visual-Audio Mutual Generation | Visual and audio modalities are two symbiotic modalities underlying videos,
which contain both common and complementary information. If they can be mined
and fused sufficiently, performances of related video tasks can be
significantly enhanced. However, due to the environmental interference or
sensor fault, sometimes, ... | ['Zhao-Xiang Zhang', 'Wangli Hao', 'He Guan'] | 2017-11-22 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 2.53268987e-01 -5.80588058e-02 -3.49748731e-02 1.63802937e-01
-7.94281483e-01 -4.66942132e-01 5.82485557e-01 -7.67740250e-01
1.65831387e-01 7.95127094e-01 3.75584632e-01 1.04619391e-01
-3.67347617e-03 -6.88336551e-01 -8.26286256e-01 -1.14083207e+00
2.34712139e-01 -2.50571162e-01 -3.76490131e-02 -2.15323776... | [11.382927894592285, 1.1058692932128906] |
7b0c7184-5baa-4afc-b36f-91763f215a83 | leaf-counting-with-deep-convolutional-and | 1708.07570 | null | http://arxiv.org/abs/1708.07570v2 | http://arxiv.org/pdf/1708.07570v2.pdf | Leaf Counting with Deep Convolutional and Deconvolutional Networks | In this paper, we investigate the problem of counting rosette leaves from an
RGB image, an important task in plant phenotyping. We propose a data-driven
approach for this task generalized over different plant species and imaging
setups. To accomplish this task, we use state-of-the-art deep learning
architectures: a dec... | ['Ian Stavness', 'Shubhra Aich'] | 2017-08-24 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [ 3.36018711e-01 -2.76285142e-01 8.54179710e-02 -2.10655257e-01
-5.44918358e-01 -1.11687779e+00 2.84601212e-01 6.33668154e-02
-3.42765450e-01 4.31960762e-01 -8.03293586e-01 -4.90426689e-01
4.64780003e-01 -8.64460468e-01 -6.53515637e-01 -8.29142749e-01
9.74418744e-02 6.89362586e-01 3.45966011e-01 3.20787162... | [9.109793663024902, -1.4988937377929688] |
d89b1c1b-303e-4566-9a2f-9e57b965f0ed | self-supervised-3d-human-pose-estimation-in | 2210.04514 | null | https://arxiv.org/abs/2210.04514v1 | https://arxiv.org/pdf/2210.04514v1.pdf | Self-Supervised 3D Human Pose Estimation in Static Video Via Neural Rendering | Inferring 3D human pose from 2D images is a challenging and long-standing problem in the field of computer vision with many applications including motion capture, virtual reality, surveillance or gait analysis for sports and medicine. We present preliminary results for a method to estimate 3D pose from 2D video contain... | ['Bernhard Kainz', 'Athanasios Vlontzos', 'Benjamin Hou', 'Luca Schmidtke'] | 2022-10-10 | null | null | null | null | ['3d-human-pose-estimation'] | ['computer-vision'] | [ 4.72817183e-01 1.36096641e-01 2.97825873e-01 -2.76609600e-01
-6.19099140e-01 -3.06946248e-01 5.46966434e-01 -2.52408236e-01
-8.07759285e-01 4.48819041e-01 -8.75525996e-02 -1.35205641e-01
2.40353778e-01 -4.25001770e-01 -7.51169622e-01 -2.61224568e-01
-1.76054135e-01 8.72187972e-01 4.65043604e-01 -6.96796924... | [7.122154712677002, -1.0201588869094849] |
de7833b7-4877-46fb-9d47-51f92e494382 | proknow-process-knowledge-for-safety | 2305.08010 | null | https://arxiv.org/abs/2305.08010v2 | https://arxiv.org/pdf/2305.08010v2.pdf | ProKnow: Process Knowledge for Safety Constrained and Explainable Question Generation for Mental Health Diagnostic Assistance | Current Virtual Mental Health Assistants (VMHAs) provide counseling and suggestive care. They refrain from patient diagnostic assistance because they lack training in safety-constrained and specialized clinical process knowledge. In this work, we define Proknow as an ordered set of information that maps to evidence-bas... | ['Amit Sheth', 'Ashwin Kalyan', 'Vipula Rawte', 'Misagh Soltani', 'Manas Gaur', 'Kaushik Roy'] | 2023-05-13 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [ 2.00303406e-01 1.25700128e+00 -3.07560444e-01 -5.59845090e-01
-8.97978425e-01 -5.22277176e-01 3.43347102e-01 9.01079655e-01
3.79753509e-03 7.59852290e-01 7.76782751e-01 -6.84824228e-01
-7.79414237e-01 -7.47904360e-01 -2.42089137e-01 1.82618737e-01
2.20602825e-01 1.05657375e+00 -2.64780879e-01 -2.81752974... | [9.341038703918457, 7.802137851715088] |
5a486e9c-c0ad-4325-87fb-ea3807fd1176 | improving-weakly-supervised-temporal-action | 2304.07978 | null | https://arxiv.org/abs/2304.07978v1 | https://arxiv.org/pdf/2304.07978v1.pdf | Improving Weakly Supervised Temporal Action Localization by Bridging Train-Test Gap in Pseudo Labels | The task of weakly supervised temporal action localization targets at generating temporal boundaries for actions of interest, meanwhile the action category should also be classified. Pseudo-label-based methods, which serve as an effective solution, have been widely studied recently. However, existing methods generate p... | ['Hongsheng Li', 'Si Liu', 'Liang Wang', 'Linjiang Huang', 'Jingqiu Zhou'] | 2023-04-17 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhou_Improving_Weakly_Supervised_Temporal_Action_Localization_by_Bridging_Train-Test_Gap_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhou_Improving_Weakly_Supervised_Temporal_Action_Localization_by_Bridging_Train-Test_Gap_CVPR_2023_paper.pdf | cvpr-2023-1 | ['weakly-supervised-temporal-action', 'action-localization', 'action-recognition', 'pseudo-label'] | ['computer-vision', 'computer-vision', 'computer-vision', 'miscellaneous'] | [ 6.01905525e-01 1.14775412e-01 -3.19638878e-01 -5.16530752e-01
-9.16211307e-01 -3.02962065e-01 4.68410432e-01 9.65052471e-03
-4.16552871e-01 9.52416301e-01 1.37103319e-01 1.96660325e-01
-3.78100462e-02 -5.62914371e-01 -6.51224613e-01 -8.15084755e-01
1.59750953e-01 1.99246570e-01 6.45306766e-01 9.78492871... | [8.503715515136719, 0.6392882466316223] |
f7fb64ee-886c-4607-8ad7-3fa9c8ca6700 | challenges-and-trends-in-user-trust-discourse | 2305.11876 | null | https://arxiv.org/abs/2305.11876v2 | https://arxiv.org/pdf/2305.11876v2.pdf | Challenges and Trends in User Trust Discourse in AI | The Internet revolution in 1990, followed by the data-driven and information revolution, has transformed the world as we know it. Nowadays, what seam to be 10 to 20 years ago, a science fiction idea (i.e., machines dominating the world) is seen as possible. This revolution also brought a need for new regulatory practic... | ['Paulo Martins', 'Jose Cravino', 'Sonia Sousa'] | 2023-05-05 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [-2.15009347e-01 5.94711959e-01 -1.99498013e-01 -4.60334927e-01
2.57797956e-01 -5.32447994e-01 6.59202814e-01 5.86507618e-01
-4.94378924e-01 4.68228191e-01 4.21614796e-01 -7.51337469e-01
1.94260538e-01 -4.29940253e-01 -4.97903436e-01 -3.24690878e-01
5.29673517e-01 -9.20335501e-02 -2.29083344e-01 -3.96300673... | [9.063992500305176, 6.312570095062256] |
5c99ff66-c9e3-4603-99f3-773c2bb41ac5 | sampling-individually-fair-rankings-that-are | 2306.11964 | null | https://arxiv.org/abs/2306.11964v1 | https://arxiv.org/pdf/2306.11964v1.pdf | Sampling Individually-Fair Rankings that are Always Group Fair | Rankings on online platforms help their end-users find the relevant information -- people, news, media, and products -- quickly. Fair ranking tasks, which ask to rank a set of items to maximize utility subject to satisfying group-fairness constraints, have gained significant interest in the Algorithmic Fairness, Inform... | ['Anand Louis', 'Amit Deshpande', 'Anay Mehrotra', 'Sruthi Gorantla'] | 2023-06-21 | null | null | null | null | ['fairness', 'retrieval', 'fairness', 'information-retrieval'] | ['computer-vision', 'methodology', 'miscellaneous', 'natural-language-processing'] | [-2.02865183e-01 1.00515792e-02 -6.47514939e-01 -5.83626628e-01
-9.31344628e-01 -7.91207016e-01 1.17346905e-01 3.85245144e-01
-6.84216201e-01 7.85077631e-01 4.23783094e-01 -1.64020896e-01
-5.48515201e-01 -7.12945461e-01 -1.87158465e-01 -3.78369898e-01
-2.03446046e-01 7.47950137e-01 -1.88031569e-01 -1.09988697... | [9.364383697509766, 5.538438320159912] |
afc3af89-e7c1-46fe-88cb-fc0a9b4371f3 | inter-patient-ecg-heartbeat-classification | null | null | https://doi.org/10.1038/s41598-017-09837-3 | https://www.nature.com/articles/s41598-017-09837-3.pdf | Inter-Patient ECG Heartbeat Classification with Temporal VCG Optimized by PSO | Classifying arrhythmias can be a tough task for a human being and automating this task is highly desirable. Nevertheless fully automatic arrhythmia classification through Electrocardiogram (ECG) signals is a challenging task when the inter-patient paradigm is considered. For the inter-patient paradigm, classifiers are ... | ['Gladston Moreira', 'Eduardo Luz', 'David Menotti', 'Gabriel Garcia'] | 2017-09-05 | null | null | null | scientific-reports-2017-9 | ['arrhythmia-detection', 'ecg-classification', 'heartbeat-classification', 'electrocardiography-ecg'] | ['medical', 'medical', 'medical', 'methodology'] | [ 4.63287473e-01 -3.29728872e-01 8.68025050e-02 -2.02619005e-02
-3.04618299e-01 -5.03921330e-01 7.60152265e-02 4.51076776e-01
-3.46909106e-01 9.09077585e-01 -3.58571917e-01 -4.16381478e-01
-5.38470089e-01 -4.60435539e-01 -1.77304193e-01 -8.92197549e-01
-3.50352794e-01 5.87112784e-01 -2.93222219e-02 -7.04910383... | [14.20549488067627, 3.1997601985931396] |
b1808b1f-c6f4-40ad-a3d6-974c87232d2f | fast-training-method-for-stochastic | null | null | http://proceedings.neurips.cc/paper/2021/hash/d5397f1497b5cdaad7253fdc92db610b-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/d5397f1497b5cdaad7253fdc92db610b-Paper.pdf | Fast Training Method for Stochastic Compositional Optimization Problems | The stochastic compositional optimization problem covers a wide range of machine learning models, such as sparse additive models and model-agnostic meta-learning. Thus, it is necessary to develop efficient methods for its optimization. Existing methods for the stochastic compositional optimization problem only focus o... | ['Heng Huang', 'Hongchang Gao'] | 2021-12-01 | null | null | null | neurips-2021-12 | ['additive-models'] | ['methodology'] | [-6.33398667e-02 -3.23861897e-01 -6.24868810e-01 -2.58765340e-01
-9.95876312e-01 -1.48671582e-01 2.45433912e-01 -1.57323942e-01
-8.87854844e-02 7.52894342e-01 6.38781637e-02 -3.74831975e-01
-3.41493301e-02 -5.56585312e-01 -1.10242891e+00 -8.07695150e-01
8.17226395e-02 6.29785061e-01 4.22443449e-02 -1.72151159... | [6.251470565795898, 5.006959438323975] |
e95cbb6e-d5a9-4e86-b557-91e283a6b066 | ultra-light-deep-mir-by-trimming-lottery | 2007.16187 | null | https://arxiv.org/abs/2007.16187v1 | https://arxiv.org/pdf/2007.16187v1.pdf | Ultra-light deep MIR by trimming lottery tickets | Current state-of-the-art results in Music Information Retrieval are largely dominated by deep learning approaches. These provide unprecedented accuracy across all tasks. However, the consistently overlooked downside of these models is their stunningly massive complexity, which seems concomitantly crucial to their succe... | ['Theis Bazin', 'Philippe Esling', 'Adrien Bitton', 'Tristan Carsault', 'Ninon Devis'] | 2020-07-31 | null | null | null | null | ['drum-transcription'] | ['music'] | [ 2.94460118e-01 -7.48061910e-02 2.39319131e-02 1.45791486e-01
-7.57876337e-01 -6.12653434e-01 3.50416183e-01 1.62250727e-01
-6.41098738e-01 3.90310585e-01 2.33096983e-02 -1.84450328e-01
-2.55379468e-01 -4.93520260e-01 -5.21975040e-01 -6.52418196e-01
-1.07979812e-01 3.05020928e-01 2.10363746e-01 -2.85410464... | [15.766778945922852, 5.30109977722168] |
01592014-b79e-4a5a-92a4-78ba2d6f32aa | advancing-direct-convolution-using | 2303.04739 | null | https://arxiv.org/abs/2303.04739v1 | https://arxiv.org/pdf/2303.04739v1.pdf | Advancing Direct Convolution using Convolution Slicing Optimization and ISA Extensions | Convolution is one of the most computationally intensive operations that must be performed for machine-learning model inference. A traditional approach to compute convolutions is known as the Im2Col + BLAS method. This paper proposes SConv: a direct-convolution algorithm based on a MLIR/LLVM code-generation toolchain t... | ['Guido Araujo', 'José Moreira', 'José Nelson Amaral', 'João P. L. de Carvalho', 'Marcio Pereira', 'Rafael Sousa', 'Victor Ferrari'] | 2023-03-08 | null | null | null | null | ['blocking'] | ['natural-language-processing'] | [ 6.80616423e-02 -2.21861809e-01 -2.16714218e-01 -4.35669601e-01
-3.88001233e-01 -3.25289428e-01 3.42592150e-01 1.14748538e-01
-6.44981623e-01 3.43666762e-01 -1.17419370e-01 -1.29866874e+00
-1.04883406e-02 -9.20343637e-01 -8.04008543e-01 -6.15166128e-01
-1.55277058e-01 4.91568863e-01 7.55251572e-02 1.54249236... | [8.422927856445312, 3.0197155475616455] |
f30da945-479d-4fda-8a1e-77023b2ecd96 | online-video-super-resolution-with | 2208.02470 | null | https://arxiv.org/abs/2208.02470v1 | https://arxiv.org/pdf/2208.02470v1.pdf | Online Video Super-Resolution with Convolutional Kernel Bypass Graft | Deep learning-based models have achieved remarkable performance in video super-resolution (VSR) in recent years, but most of these models are less applicable to online video applications. These methods solely consider the distortion quality and ignore crucial requirements for online applications, e.g., low latency and ... | ['Kin-Man Lam', 'Dongsheng Li', 'Yuqing Yang', 'Yifan Yang', 'Huan Yang', 'Ningxin Zheng', 'Xinyang Jiang', 'Jun Xiao'] | 2022-08-04 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 2.95140028e-01 -3.19116265e-01 -2.07963377e-01 -3.13466012e-01
-4.99522418e-01 -4.09741104e-02 -1.08981943e-02 -4.02686298e-01
-4.99169618e-01 5.83785355e-01 -1.17537603e-01 -4.15666848e-01
-6.10666573e-02 -9.32109296e-01 -1.01973128e+00 -4.29208666e-01
-7.33932406e-02 -2.93408066e-01 9.67077672e-01 -2.36296132... | [11.099864959716797, -1.773682713508606] |
495bc1c2-003c-4f6a-8e4d-2e46003f5518 | direction-aware-feature-level-frequency | 2106.07941 | null | https://arxiv.org/abs/2106.07941v1 | https://arxiv.org/pdf/2106.07941v1.pdf | Direction-aware Feature-level Frequency Decomposition for Single Image Deraining | We present a novel direction-aware feature-level frequency decomposition network for single image deraining. Compared with existing solutions, the proposed network has three compelling characteristics. First, unlike previous algorithms, we propose to perform frequency decomposition at feature-level instead of image-lev... | ['Jing Qin', 'Xiao-Ping Zhang', 'Jonathan Li', 'Yiping Chen', 'Haoran Xie', 'Mingqiang Wei', 'Yidan Feng', 'Sen Deng'] | 2021-06-15 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [ 1.83120459e-01 -1.97683781e-01 4.00652021e-01 -2.92423695e-01
-3.56447786e-01 -5.82871735e-01 2.04476580e-01 -9.18303654e-02
-1.78224668e-01 7.74827838e-01 4.38934714e-01 1.46308377e-01
-3.33196640e-01 -1.19034851e+00 -9.09554422e-01 -8.88771713e-01
-5.00468731e-01 -3.95104229e-01 2.21460134e-01 -4.19075280... | [10.920806884765625, -3.2304866313934326] |
1d0b175e-81c7-4065-9984-47909a9155b4 | dual-generator-face-reenactment | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Hsu_Dual-Generator_Face_Reenactment_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Hsu_Dual-Generator_Face_Reenactment_CVPR_2022_paper.pdf | Dual-Generator Face Reenactment | We propose the Dual-Generator (DG) network for large-pose face reenactment. Given a source face and a reference face as inputs, the DG network can generate an output face that has the same pose and expression as of the reference face, and has the same identity as of the source face. As most approaches do not partic... | ['Hung-Yi Wu', 'Chun-Hung Tsai', 'Gee-Sern Hsu'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['face-reenactment'] | ['computer-vision'] | [ 2.87396789e-01 6.50316536e-01 1.79967657e-01 -2.76867568e-01
-6.60068810e-01 -7.29855597e-01 4.35743332e-01 -5.77651322e-01
2.65127212e-01 2.50174463e-01 2.06263751e-01 3.62226784e-01
2.93257058e-01 -7.88478732e-01 -8.56130481e-01 -8.51279914e-01
1.91048846e-01 3.93178284e-01 -7.63739496e-02 -1.18905909... | [12.690739631652832, -0.1153566986322403] |
4f93a29a-84e0-4f48-8ab2-5fa328c6ca7a | ils-summ-iterated-local-search-for | 1912.03650 | null | https://arxiv.org/abs/1912.03650v1 | https://arxiv.org/pdf/1912.03650v1.pdf | ILS-SUMM: Iterated Local Search for Unsupervised Video Summarization | In recent years, there has been an increasing interest in building video summarization tools, where the goal is to automatically create a short summary of an input video that properly represents the original content. We consider shot-based video summarization where the summary consists of a subset of the video shots wh... | ['Daniel Rotman', 'Yair Shemer', 'Nahum Shimkin'] | 2019-12-08 | null | null | null | null | ['unsupervised-video-summarization', 'metaheuristic-optimization'] | ['computer-vision', 'methodology'] | [ 3.81818473e-01 -5.01232669e-02 -3.63410234e-01 -1.39623970e-01
-9.53591764e-01 -5.31704307e-01 9.33524072e-02 3.86296302e-01
-2.44615257e-01 9.32732940e-01 3.89007777e-01 1.57226309e-01
-3.72824669e-01 -6.38896286e-01 -8.23551178e-01 -6.32596195e-01
-1.88198283e-01 1.48357719e-01 4.45437610e-01 -3.56385186... | [10.437253952026367, 0.4373369812965393] |
75ab3287-3387-4b31-8513-567792acf3b1 | malicious-network-traffic-detection-via-deep | 2009.07753 | null | https://arxiv.org/abs/2009.07753v1 | https://arxiv.org/pdf/2009.07753v1.pdf | Malicious Network Traffic Detection via Deep Learning: An Information Theoretic View | The attention that deep learning has garnered from the academic community and industry continues to grow year over year, and it has been said that we are in a new golden age of artificial intelligence research. However, neural networks are still often seen as a "black box" where learning occurs but cannot be understood... | ['Erick Galinkin'] | 2020-09-16 | null | null | null | null | ['information-plane'] | ['methodology'] | [ 2.93027908e-01 1.81894675e-01 -1.55732960e-01 -4.13577735e-01
8.13926905e-02 -6.63685501e-01 6.17084444e-01 -1.37320071e-01
-4.22159255e-01 5.07549822e-01 9.89824347e-03 -4.55627948e-01
-4.15939689e-01 -8.45311582e-01 -8.14265847e-01 -7.39681900e-01
-2.19787136e-01 1.30735338e-01 -8.67348462e-02 -3.37567180... | [8.129749298095703, 3.7217400074005127] |
b4333e7a-51f1-4978-bcf7-596540099746 | decorate-the-examples-a-simple-method-of | 2204.10360 | null | https://arxiv.org/abs/2204.10360v1 | https://arxiv.org/pdf/2204.10360v1.pdf | Decorate the Examples: A Simple Method of Prompt Design for Biomedical Relation Extraction | Relation extraction is a core problem for natural language processing in the biomedical domain. Recent research on relation extraction showed that prompt-based learning improves the performance on both fine-tuning on full training set and few-shot training. However, less effort has been made on domain-specific tasks wh... | ['Pierre Zweigenbaum', 'Thomas Lavergne', 'Hui-Syuan Yeh'] | 2022-04-21 | null | https://aclanthology.org/2022.lrec-1.403 | https://aclanthology.org/2022.lrec-1.403.pdf | lrec-2022-6 | ['cloze-test'] | ['natural-language-processing'] | [ 4.40703183e-01 6.75643742e-01 -5.26757658e-01 -5.09148836e-01
-1.33249795e+00 -3.20106000e-01 5.71339548e-01 7.34636307e-01
-5.44654906e-01 1.03219485e+00 4.18992847e-01 -4.02972102e-01
-2.96619207e-01 -5.73678672e-01 -4.57081914e-01 -2.89282084e-01
7.25858063e-02 6.54491603e-01 2.47864008e-01 -3.28339010... | [8.670714378356934, 8.71225357055664] |
28120caf-1161-4ac6-81e4-2327192e2077 | scar-sentence-compression-using-autoencoders | null | null | https://aclanthology.org/2020.acl-srw.13 | https://aclanthology.org/2020.acl-srw.13.pdf | SCAR: Sentence Compression using Autoencoders for Reconstruction | Sentence compression is the task of shortening a sentence while retaining its meaning. Most methods proposed for this task rely on labeled or paired corpora (containing pairs of verbose and compressed sentences), which is often expensive to collect. To overcome this limitation, we present a novel unsupervised deep lear... | ['Manish Shrivastava', 'Tirth Maniar', 'Chanakya Malireddy'] | 2020-07-01 | null | null | null | acl-2020-6 | ['sentence-compression'] | ['natural-language-processing'] | [ 6.69877291e-01 4.26255941e-01 -3.05442214e-01 -6.01080120e-01
-9.96262312e-01 -3.55749041e-01 2.75215328e-01 6.25043392e-01
-6.51225030e-01 7.10100949e-01 7.12884426e-01 -4.26962525e-01
3.75225872e-01 -6.13382220e-01 -8.43530774e-01 -2.95213223e-01
1.37096643e-01 4.27632540e-01 -1.81391567e-01 -6.43010065... | [12.121528625488281, 9.207356452941895] |
972e7436-2396-4494-93a1-8d8cf11eb9da | learning-accurate-template-matching-with | 2303.08438 | null | https://arxiv.org/abs/2303.08438v1 | https://arxiv.org/pdf/2303.08438v1.pdf | Learning Accurate Template Matching with Differentiable Coarse-to-Fine Correspondence Refinement | Template matching is a fundamental task in computer vision and has been studied for decades. It plays an essential role in manufacturing industry for estimating the poses of different parts, facilitating downstream tasks such as robotic grasping. Existing methods fail when the template and source images have different ... | ['Kai Xu', 'Chenyang Zhu', 'Yunfan Ye', 'Zheng Qin', 'Renjiao Yi', 'Zhirui Gao'] | 2023-03-15 | null | null | null | null | ['template-matching', 'robotic-grasping'] | ['computer-vision', 'robots'] | [ 8.32413435e-01 -1.69147477e-01 1.09891169e-01 -3.01428318e-01
-5.57260573e-01 -6.85413718e-01 6.16355598e-01 -9.89762172e-02
-1.46713838e-01 3.81062120e-01 -1.53900743e-01 8.76128301e-02
-1.53338864e-01 -7.01446831e-01 -9.14963901e-01 -5.82488537e-01
2.92920858e-01 6.43659174e-01 5.24261832e-01 -3.38970661... | [8.625284194946289, -2.391019821166992] |
78954bd6-f035-426c-b7bc-79ca934792b9 | the-hidden-language-of-diffusion-models | 2306.00966 | null | https://arxiv.org/abs/2306.00966v2 | https://arxiv.org/pdf/2306.00966v2.pdf | The Hidden Language of Diffusion Models | Text-to-image diffusion models have demonstrated an unparalleled ability to generate high-quality, diverse images from a textual concept (e.g., "a doctor", "love"). However, the internal process of mapping text to a rich visual representation remains an enigma. In this work, we tackle the challenge of understanding con... | ['Lior Wolf', 'Inbar Mosseri', 'Michal Irani', 'Assaf Shocher', 'Volodymyr Polosukhin', 'Mor Geva', 'Oran Lang', 'Hila Chefer'] | 2023-06-01 | null | null | null | null | ['image-manipulation', 'bias-detection'] | ['computer-vision', 'natural-language-processing'] | [ 2.68667340e-01 1.30964473e-01 -5.85245080e-02 -2.94758558e-01
-5.08774579e-01 -5.95222235e-01 1.03768456e+00 2.37452567e-01
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3.95632610e-02 -8.01728487e-01 -7.69693255e-01 -8.51181149e-01
2.25919276e-01 4.13687646e-01 -4.75288332e-01 -3.99664849... | [11.337724685668945, 0.14457295835018158] |
7d88c929-0111-404a-b75d-3b5a50b7a2d5 | analysing-lexical-semantic-change-with | 2004.14118 | null | https://arxiv.org/abs/2004.14118v1 | https://arxiv.org/pdf/2004.14118v1.pdf | Analysing Lexical Semantic Change with Contextualised Word Representations | This paper presents the first unsupervised approach to lexical semantic change that makes use of contextualised word representations. We propose a novel method that exploits the BERT neural language model to obtain representations of word usages, clusters these representations into usage types, and measures change alon... | ['Raquel Fernández', 'Marco del Tredici', 'Mario Giulianelli'] | 2020-04-29 | analysing-lexical-semantic-change-with-1 | https://aclanthology.org/2020.acl-main.365 | https://aclanthology.org/2020.acl-main.365.pdf | acl-2020-6 | ['contextualised-word-representations'] | ['natural-language-processing'] | [ 2.32597873e-01 -1.94554366e-02 -5.38879216e-01 -6.23705268e-01
-1.34637386e-01 -7.13708162e-01 1.20790446e+00 6.15206122e-01
-6.82789385e-01 5.14564633e-01 8.09493482e-01 -2.60730475e-01
-6.22295402e-02 -9.36908901e-01 -3.19742531e-01 -3.45555335e-01
-6.94817603e-02 1.99094549e-01 2.13431731e-01 -6.10909283... | [10.233344078063965, 8.944576263427734] |
2a968089-ab85-4cab-ad65-2160d31380b2 | an-open-unified-deep-graph-learning-framework | 2301.03424 | null | https://arxiv.org/abs/2301.03424v2 | https://arxiv.org/pdf/2301.03424v2.pdf | An open unified deep graph learning framework for discovering drug leads | Computational discovery of ideal lead compounds is a critical process for modern drug discovery. It comprises multiple stages: hit screening, molecular property prediction, and molecule optimization. Current efforts are disparate, involving the establishment of models for each stage, followed by multi-stage multi-model... | ['Wilson Wen Bin Goh', 'JianSheng Wu', 'Chun Ye', 'Jitao Yang', 'Zhen Yang', 'Haifeng Hu', 'Yueming Yin'] | 2022-12-06 | null | null | null | null | ['graph-reconstruction', 'molecular-property-prediction'] | ['graphs', 'miscellaneous'] | [ 1.46242261e-01 -1.66774020e-01 -4.52397048e-01 9.72632095e-02
-8.17892134e-01 -7.97410071e-01 4.11017120e-01 2.86438286e-01
9.05010626e-02 7.55726337e-01 -1.60236910e-01 -9.29125965e-01
-2.09497392e-01 -6.20893121e-01 -7.75173485e-01 -6.15120590e-01
-8.28758180e-02 6.30192876e-01 -2.29335606e-01 -2.60039661... | [5.079238414764404, 5.826414108276367] |
6c3c5787-dbdb-42ed-bf3c-bcc4fb0b97d5 | cross-domain-adaptation-for-animal-pose | 1908.05806 | null | https://arxiv.org/abs/1908.05806v2 | https://arxiv.org/pdf/1908.05806v2.pdf | Cross-Domain Adaptation for Animal Pose Estimation | In this paper, we are interested in pose estimation of animals. Animals usually exhibit a wide range of variations on poses and there is no available animal pose dataset for training and testing. To address this problem, we build an animal pose dataset to facilitate training and evaluation. Considering the heavy labor ... | ['Yu-Wing Tai', 'Hao-Shu Fang', 'Xiaoyong Shen', 'Jinkun Cao', 'Hongyang Tang', 'Cewu Lu'] | 2019-08-16 | cross-domain-adaptation-for-animal-pose-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Cao_Cross-Domain_Adaptation_for_Animal_Pose_Estimation_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Cao_Cross-Domain_Adaptation_for_Animal_Pose_Estimation_ICCV_2019_paper.pdf | iccv-2019-10 | ['animal-pose-estimation'] | ['computer-vision'] | [ 2.93071065e-02 -2.13838160e-01 -4.02637005e-01 -7.35974491e-01
-4.97370601e-01 -6.31535113e-01 1.43899813e-01 3.99207510e-02
-4.66469705e-01 8.20363700e-01 -1.53425887e-01 1.87771350e-01
1.46103144e-01 -6.53347135e-01 -1.10065782e+00 -3.04343671e-01
-2.69812614e-01 4.79990691e-01 4.65017110e-01 -2.02371940... | [7.588891983032227, -0.9202432036399841] |
356784fe-242b-418f-a525-e476cbbed9c9 | metrabs-metric-scale-truncation-robust | 2007.07227 | null | https://arxiv.org/abs/2007.07227v2 | https://arxiv.org/pdf/2007.07227v2.pdf | MeTRAbs: Metric-Scale Truncation-Robust Heatmaps for Absolute 3D Human Pose Estimation | Heatmap representations have formed the basis of human pose estimation systems for many years, and their extension to 3D has been a fruitful line of recent research. This includes 2.5D volumetric heatmaps, whose X and Y axes correspond to image space and Z to metric depth around the subject. To obtain metric-scale pred... | ['István Sárándi', 'Timm Linder', 'Bastian Leibe', 'Kai O. Arras'] | 2020-07-12 | null | null | null | null | ['3d-absolute-human-pose-estimation'] | ['computer-vision'] | [-9.38255191e-02 4.37722951e-01 -2.08375275e-01 -5.38321316e-01
-7.67141640e-01 -4.93619412e-01 2.77038485e-01 -6.27125055e-02
-3.86304915e-01 4.55783337e-01 2.50647515e-01 1.04747750e-01
-2.86874212e-02 -6.06649280e-01 -8.11188936e-01 -2.46250704e-01
-4.18048799e-01 9.60067570e-01 1.02931961e-01 -3.29777271... | [6.979128837585449, -0.9774619340896606] |
1877b7e2-e69a-459c-8c15-52b4fff5f2fa | language-based-colorization-of-scene-sketches | null | null | https://sketchyscene.github.io/SketchySceneColorization/ | http://mo-haoran.com/files/SIGA19/SketchColorization_paper_SA2019.pdf | Language-based Colorization of Scene Sketches | Being natural, touchless, and fun-embracing, language-based inputs have been demonstrated effective for various tasks from image generation to literacy education for children. This paper for the first time presents a language-based system for interactive colorization of scene sketches, based on semantic comprehension. ... | ['Ruofei Du', 'Hongbo Fu', 'Changqing Zou', 'Haoran Mo', 'Chengying Gao'] | 2019-11-17 | null | null | null | transactions-on-graphics-2019-11 | ['sketch'] | ['computer-vision'] | [ 3.90524089e-01 -3.38620603e-01 1.85047507e-01 -3.66189510e-01
-2.57208377e-01 -7.22544789e-01 6.96303427e-01 2.57534329e-02
-3.00291181e-01 2.61812478e-01 -1.19877726e-01 -5.55575848e-01
1.65464640e-01 -9.13522840e-01 -5.94618917e-01 -2.31971651e-01
3.05223882e-01 2.85105139e-01 1.18705556e-01 -3.12602282... | [11.437841415405273, -0.7394251823425293] |
cadb6362-e340-422b-98c9-258f237ef06e | virtual-node-tuning-for-few-shot-node | 2306.06063 | null | https://arxiv.org/abs/2306.06063v1 | https://arxiv.org/pdf/2306.06063v1.pdf | Virtual Node Tuning for Few-shot Node Classification | Few-shot Node Classification (FSNC) is a challenge in graph representation learning where only a few labeled nodes per class are available for training. To tackle this issue, meta-learning has been proposed to transfer structural knowledge from base classes with abundant labels to target novel classes. However, existin... | ['Huan Liu', 'Kaize Ding', 'Ruocheng Guo', 'Zhen Tan'] | 2023-06-09 | null | null | null | null | ['meta-learning', 'graph-representation-learning'] | ['methodology', 'methodology'] | [ 6.08952284e-01 5.90281367e-01 -6.88291848e-01 -2.76123852e-01
-3.45792860e-01 -4.46438849e-01 7.31613934e-01 2.49687359e-01
-5.97729422e-02 5.99634409e-01 1.39033556e-01 -7.52806067e-02
-1.54284276e-02 -1.06991088e+00 -5.44987857e-01 -7.98262656e-01
9.20913666e-02 3.96923661e-01 3.06670219e-01 -3.65487278... | [7.429309844970703, 6.171928405761719] |
9b6633a6-535a-45c6-9a08-d67b8e3bb023 | unsupervised-face-recognition-using-unlabeled | 2211.07371 | null | https://arxiv.org/abs/2211.07371v1 | https://arxiv.org/pdf/2211.07371v1.pdf | Unsupervised Face Recognition using Unlabeled Synthetic Data | Over the past years, the main research innovations in face recognition focused on training deep neural networks on large-scale identity-labeled datasets using variations of multi-class classification losses. However, many of these datasets are retreated by their creators due to increased privacy and ethical concerns. V... | ['Naser Damer', 'Arjan Kuijper', 'Meiling Fang', 'Marcel Klemt', 'Fadi Boutros'] | 2022-11-14 | null | null | null | null | ['unsupervised-face-recognition'] | ['computer-vision'] | [ 4.88006026e-01 3.01083446e-01 7.25353928e-03 -1.12596738e+00
-4.83256370e-01 -3.59909654e-01 5.47148585e-01 -6.15194261e-01
-4.06829298e-01 1.00642300e+00 -6.48508370e-02 8.57523456e-02
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3.25626075e-01 2.49307036e-01 -6.41341090e-01 1.47698045... | [12.823098182678223, 0.6797406077384949] |
61de76af-827e-4f15-8c5c-c14aeaa3dbec | stochastic-unrolled-federated-learning | 2305.15371 | null | https://arxiv.org/abs/2305.15371v1 | https://arxiv.org/pdf/2305.15371v1.pdf | Stochastic Unrolled Federated Learning | Algorithm unrolling has emerged as a learning-based optimization paradigm that unfolds truncated iterative algorithms in trainable neural-network optimizers. We introduce Stochastic UnRolled Federated learning (SURF), a method that expands algorithm unrolling to a federated learning scenario. Our proposed method tackle... | ['Alejandro Ribeiro', 'Navid Naderializadeh', 'Samar Hadou'] | 2023-05-24 | null | null | null | null | ['unrolling'] | ['computer-vision'] | [-2.20642343e-01 3.69661063e-01 -9.71928462e-02 -3.53265673e-01
-6.52237415e-01 -8.00289869e-01 1.77329212e-01 -1.29052298e-02
-5.34152031e-01 6.29362881e-01 2.56360974e-03 -6.58120513e-01
-4.47923034e-01 -6.36711180e-01 -1.27418160e+00 -7.20896721e-01
-1.53114438e-01 2.94152886e-01 -4.35702473e-01 3.62574384... | [6.126292705535889, 5.303414821624756] |
d0febb90-1068-4d5f-b4ae-0b4c9cb71840 | universal-battery-performance-and-degradation | 2008.01527 | null | https://arxiv.org/abs/2008.01527v2 | https://arxiv.org/pdf/2008.01527v2.pdf | Universal Battery Performance and Degradation Model for Electric Aircraft | Development of Urban Air Mobility (UAM) concepts has been primarily focused on electric vertical takeoff and landing aircraft (eVTOLs), small aircraft which can land and takeoff vertically, and which are powered by rechargeable (typically lithium-ion) batteries. Design, analysis, and operation of eVTOLs requires fast a... | ['Venkatasubramanian Viswanathan', 'Evan Frank', 'William L. Fredericks', 'Alexander Bills', 'Matthew Guttenberg', 'Devin Charles', 'Shashank Sripad'] | 2020-07-06 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [-4.44368809e-01 -8.80446792e-01 -4.88737971e-01 2.55667478e-01
-2.87904531e-01 -5.17238975e-01 4.29637522e-01 9.17340349e-03
-7.10652769e-02 1.09392285e+00 -2.50946909e-01 -1.15520370e+00
-2.57515937e-01 -7.01042831e-01 -8.38559568e-01 -7.01855481e-01
-5.35880588e-03 7.63000607e-01 2.83939838e-01 -5.22968709... | [6.385476589202881, 2.8084516525268555] |
ab04f1f0-6fee-4eab-aa8f-31cd958edaaa | on-the-diagnostic-of-road-pathway-visibility | 1601.05535 | null | http://arxiv.org/abs/1601.05535v1 | http://arxiv.org/pdf/1601.05535v1.pdf | On the Diagnostic of Road Pathway Visibility | Visibility distance on the road pathway plays a significant role in road
safety and in particular, has a clear impact on the choice of speed limits.
Visibility distance is thus of importance for road engineers and authorities.
While visibility distance criteria are routinely taken into account in road
design, only a fe... | ['Jean-Philippe Tarel', 'Pierre Charbonnier', 'Francois Goulette'] | 2016-01-21 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [ 1.66857049e-01 -5.08405603e-02 4.45261374e-02 -3.42362434e-01
-1.87921762e-01 -6.69620097e-01 7.32159078e-01 1.74806148e-01
-7.02817261e-01 5.22905409e-01 -4.96180981e-01 -9.20115769e-01
-4.01491344e-01 -1.36831379e+00 -3.29898089e-01 -4.67830420e-01
2.03867376e-01 7.42737889e-01 7.60117114e-01 -3.57467115... | [7.953003883361816, -1.4967306852340698] |
37b1fd7a-d7fa-4c86-a5d2-1bbe8c4cc8ff | character-grounding-and-re-identification-in | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3913_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500528.pdf | Character Grounding and Re-Identification in Story of Videos and Text Descriptions | We address character grounding and re-identification in multiple story-based videos like movies and associated text descriptions. In order to solve these related tasks in a mutually rewarding way, we propose a model named Character in Story Identification Network (CiSIN). Our method builds two semantically informative ... | ['Jongseok Kim', 'Youngjae Yu', 'Jiwan Chung', 'Heeseung Yun', 'Gunhee Kim'] | null | null | null | null | eccv-2020-8 | ['gender-prediction'] | ['computer-vision'] | [ 4.27639633e-01 -1.13833562e-01 -5.68475246e-01 -3.75076771e-01
-1.11903560e+00 -6.43777549e-01 8.44899893e-01 2.35956982e-01
-3.60495985e-01 4.30088401e-01 5.03898978e-01 2.90522903e-01
2.64887065e-01 -1.45614937e-01 -9.55851018e-01 -3.55353236e-01
3.00912082e-01 9.35997725e-01 -8.10630172e-02 3.16740751... | [10.588286399841309, 1.0012539625167847] |
770a00c6-16c0-46c0-a744-8aaa9f4f67c9 | discourse-parsing-of-contentious-non | 2012.04585 | null | https://arxiv.org/abs/2012.04585v1 | https://arxiv.org/pdf/2012.04585v1.pdf | Discourse Parsing of Contentious, Non-Convergent Online Discussions | Online discourse is often perceived as polarized and unproductive. While some conversational discourse parsing frameworks are available, they do not naturally lend themselves to the analysis of contentious and polarizing discussions. Inspired by the Bakhtinian theory of Dialogism, we propose a novel theoretical and com... | ['Oren Tsur', 'Yifat Ben-David Kolikant', 'Dina Grossman', 'Tovit Hakak', 'Omri Hadar', 'Stepan Zakharov'] | 2020-12-08 | null | null | null | null | ['discourse-parsing'] | ['natural-language-processing'] | [ 4.67979237e-02 7.34196961e-01 -4.71572459e-01 -3.06777716e-01
-4.58595663e-01 -9.57331002e-01 1.17390597e+00 5.33110499e-01
-2.70572811e-01 7.17582464e-01 9.15540576e-01 -5.03302932e-01
-1.33390740e-01 -6.30419374e-01 -4.68887575e-02 -8.02174866e-01
1.16992705e-01 4.69775051e-01 1.98618814e-01 -3.47462088... | [12.349077224731445, 8.12497615814209] |
da655250-d604-4ef9-8491-6b951e6d0027 | disarm-detecting-the-victims-targeted-by-1 | 2205.05738 | null | https://arxiv.org/abs/2205.05738v1 | https://arxiv.org/pdf/2205.05738v1.pdf | DISARM: Detecting the Victims Targeted by Harmful Memes | Internet memes have emerged as an increasingly popular means of communication on the Web. Although typically intended to elicit humour, they have been increasingly used to spread hatred, trolling, and cyberbullying, as well as to target specific individuals, communities, or society on political, socio-cultural, and psy... | ['Tanmoy Chakraborty', 'Preslav Nakov', 'Md. Shad Akhtar', 'Shivam Sharma'] | 2022-05-11 | null | https://aclanthology.org/2022.findings-naacl.118 | https://aclanthology.org/2022.findings-naacl.118.pdf | findings-naacl-2022-7 | ['person-identification'] | ['computer-vision'] | [-5.64264841e-02 8.14654864e-03 2.97614057e-02 1.14958338e-01
-3.95410508e-01 -8.44954848e-01 9.71096277e-01 5.00743508e-01
-4.78305906e-01 7.19600737e-01 3.70320857e-01 -1.03916086e-01
3.56237441e-01 -7.84758627e-01 -3.59545588e-01 -5.48575699e-01
1.67149961e-01 3.55209976e-01 1.09101571e-01 -3.11615616... | [8.498302459716797, 10.668750762939453] |
c6c5fd91-93bb-43b4-9c91-8fd2ec422cfe | optical-flow-estimation-in-360-circ-videos | 2301.11880 | null | https://arxiv.org/abs/2301.11880v1 | https://arxiv.org/pdf/2301.11880v1.pdf | Optical Flow Estimation in 360$^\circ$ Videos: Dataset, Model and Application | Optical flow estimation has been a long-lasting and fundamental problem in the computer vision community. However, despite the advances of optical flow estimation in perspective videos, the 360$^\circ$ videos counterpart remains in its infancy, primarily due to the shortage of benchmark datasets and the failure to acco... | ['Yan Yan', 'Gaowen Liu', 'Keshav Bhandari', 'Bin Duan'] | 2023-01-27 | null | null | null | null | ['egocentric-activity-recognition'] | ['computer-vision'] | [-2.10401654e-01 -5.04773498e-01 -1.84526280e-01 -4.61953916e-02
-2.23649636e-01 -5.15329778e-01 3.40911716e-01 -6.37832344e-01
-3.91790360e-01 8.16635132e-01 2.19390407e-01 -2.65638262e-01
-3.07826132e-01 -5.71987510e-01 -6.50893807e-01 -7.17201531e-01
-3.92329603e-01 -2.58022487e-01 -1.16669573e-01 -1.08102672... | [8.798748016357422, -1.7992980480194092] |
4f2c33fe-b9e3-42ad-bded-5ebca1c2bbe0 | improving-stain-invariance-of-cnns-for | 2304.11445 | null | https://arxiv.org/abs/2304.11445v1 | https://arxiv.org/pdf/2304.11445v1.pdf | Improving Stain Invariance of CNNs for Segmentation by Fusing Channel Attention and Domain-Adversarial Training | Variability in staining protocols, such as different slide preparation techniques, chemicals, and scanner configurations, can result in a diverse set of whole slide images (WSIs). This distribution shift can negatively impact the performance of deep learning models on unseen samples, presenting a significant challenge ... | ['Abdulmotaleb El Saddik', 'Mustaqeem Khan', 'Numan Saeed', 'Kudaibergen Abutalip'] | 2023-04-22 | null | null | null | null | ['whole-slide-images'] | ['computer-vision'] | [ 5.46198130e-01 -1.99241579e-01 6.29858598e-02 -4.78523701e-01
-1.25264800e+00 -9.71327066e-01 2.71183401e-01 1.99164882e-01
-6.32264256e-01 7.04851389e-01 -1.73232764e-01 -4.71093178e-01
2.66112268e-01 -4.69609827e-01 -9.01362479e-01 -1.08637595e+00
2.49273360e-01 3.38671863e-01 3.47641766e-01 -2.79866979... | [15.091075897216797, -2.9744458198547363] |
89edc229-8bb9-4e35-8b3d-aa02db36fe6c | enabling-noninvasive-physical-assault | null | null | https://doi.org/10.1155/2019/8186573 | http://downloads.hindawi.com/journals/wcmc/2019/8186573.pdf | Enabling Noninvasive Physical Assault Monitoring in Smart School with Commercial Wi-Fi Devices | Monitoring physical assault is critical for the prevention of juvenile delinquency and promotion of school harmony. A large portion of assault events, particularly school violence among teenagers, usually happen at indoor secluded places. Pioneering approaches employ always-on-body sensors or cameras in the limited sur... | ['Shuo Zhao', 'Qizhen Zhou', 'Jianchun Xing', 'Chenshu Wu', 'and Qiliang Yang'] | 2019-04-01 | null | null | null | wireless-communications-and-mobile-computing | ['rf-based-pose-estimation'] | ['computer-vision'] | [ 4.18439031e-01 -1.88568264e-01 -8.86851490e-01 -9.35066419e-05
-6.02672875e-01 -4.81550723e-01 6.27593324e-02 -6.01304807e-02
-8.88390467e-02 6.63772821e-01 1.14382468e-01 -3.79123002e-01
-6.43739104e-01 -9.91703153e-01 -2.88152575e-01 -8.44488919e-01
-4.22236234e-01 -5.55440247e-01 2.78957784e-01 1.65682048... | [6.705839157104492, 0.7117884159088135] |
3c30d517-5de3-45d7-a8cb-f5d6f6db76d0 | automatic-face-understanding-recognizing | 2102.08941 | null | https://arxiv.org/abs/2102.08941v1 | https://arxiv.org/pdf/2102.08941v1.pdf | Automatic Face Understanding: Recognizing Families in Photos | We built the largest database for kinship recognition. The data were labeled using a novel clustering algorithm that used label proposals as side information to guide more accurate clusters. Great savings in time and human input was had. Statistically, FIW shows enormous gains over its predecessors. We have several ben... | ['Joseph P Robinson'] | 2021-01-10 | null | null | null | null | ['face-alignment'] | ['computer-vision'] | [ 2.69648787e-02 2.26744175e-01 -2.34768942e-01 -8.17593575e-01
-9.31792796e-01 -5.43862283e-01 3.90158892e-01 -5.06131388e-02
-4.82132405e-01 5.92550218e-01 8.21642876e-02 -2.93553583e-02
-1.02258965e-01 -7.82620907e-01 -7.25010514e-01 -6.48147523e-01
-4.08464134e-01 4.49186504e-01 -1.93698019e-01 7.72855384... | [13.34774112701416, 0.8785491585731506] |
a184e15a-6718-4d29-b9da-2a50c701e87d | lightweight-learning-from-label-proportions | 2306.12461 | null | https://arxiv.org/abs/2306.12461v1 | https://arxiv.org/pdf/2306.12461v1.pdf | Lightweight learning from label proportions on satellite imagery | This work addresses the challenge of producing chip level predictions on satellite imagery when only label proportions at a coarser spatial geometry are available, typically from statistical or aggregated data from administrative divisions (such as municipalities or communes). This kind of tabular data is usually widel... | ['Fabio A. González', 'Raúl Ramos-Pollán'] | 2023-06-21 | null | null | null | null | ['benchmarking', 'benchmarking'] | ['miscellaneous', 'robots'] | [-6.77914992e-02 1.27171930e-02 -4.29659426e-01 -4.03554827e-01
-8.84790480e-01 -6.16013706e-01 9.34739470e-01 2.92092681e-01
-4.18711603e-01 1.15446532e+00 3.16570401e-01 -4.96198535e-01
-3.35793197e-01 -1.07253313e+00 -6.16501272e-01 -9.05277550e-01
-4.04029429e-01 6.69209301e-01 -5.25800735e-02 -2.89175570... | [9.518356323242188, -1.4895392656326294] |
568540a5-a9a5-4885-b1f0-217423ef10c8 | eeg-based-emotion-recognition-using | 1907.07835 | null | https://arxiv.org/abs/1907.07835v4 | https://arxiv.org/pdf/1907.07835v4.pdf | EEG-Based Emotion Recognition Using Regularized Graph Neural Networks | Electroencephalography (EEG) measures the neuronal activities in different brain regions via electrodes. Many existing studies on EEG-based emotion recognition do not fully exploit the topology of EEG channels. In this paper, we propose a regularized graph neural network (RGNN) for EEG-based emotion recognition. RGNN c... | ['Peixiang Zhong', 'Di Wang', 'Chunyan Miao'] | 2019-07-18 | null | null | null | null | ['eeg-emotion-recognition'] | ['miscellaneous'] | [ 4.15785238e-02 -1.50121465e-01 3.86531383e-01 -5.35015881e-01
1.17924139e-01 -3.74738336e-01 3.33465412e-02 -9.79201943e-02
-1.39594868e-01 9.22585607e-01 1.92737937e-01 1.44038033e-02
-4.08611149e-01 -5.49847245e-01 -8.35144043e-01 -7.52335310e-01
-6.67515278e-01 -1.68102533e-01 -5.10134220e-01 -6.18361607... | [13.091193199157715, 3.4941980838775635] |
9582203d-debb-4b2d-891e-c5a01e50b8c4 | coarse-to-fine-multi-label-image | 2012.13662 | null | https://arxiv.org/abs/2012.13662v1 | https://arxiv.org/pdf/2012.13662v1.pdf | Coarse to Fine: Multi-label Image Classification with Global/Local Attention | In our daily life, the scenes around us are always with multiple labels especially in a smart city, i.e., recognizing the information of city operation to response and control. Great efforts have been made by using Deep Neural Networks to recognize multi-label images. Since multi-label image classification is very comp... | ['Baochuan Fu', 'Qiming Fu', 'Zhengtian Wu', 'Victor S. Sheng', 'Fuyuan Hu', 'Fan Lyu'] | 2020-12-26 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 2.40060747e-01 -3.67052257e-01 -1.60640553e-01 -5.58945417e-01
-6.62216246e-01 -4.36631680e-01 2.98148632e-01 8.86284746e-03
-3.46680045e-01 4.06781971e-01 -9.77898240e-02 -1.38127774e-01
1.66728467e-01 -6.45601749e-01 -5.20389795e-01 -7.92727351e-01
7.85129786e-01 3.46051872e-01 1.31524265e-01 3.16762067... | [9.80652904510498, 3.95959734916687] |
e65ce18f-8873-471e-98cc-5e39fbcf1698 | a-semantics-based-approach-to-disclosure | null | null | https://aclanthology.org/2020.findings-emnlp.312 | https://aclanthology.org/2020.findings-emnlp.312.pdf | A Semantics-based Approach to Disclosure Classification in User-Generated Online Content | As users engage in public discourse, the rate of voluntarily disclosed personal information has seen a steep increase. So-called self-disclosure can result in a number of privacy concerns. Users are often unaware of the sheer amount of personal information they share across online forums, commentaries, and social netwo... | ['Sarah Rajtmajer', 'Anna Squicciarini', 'Chandan Akiti'] | 2020-11-01 | null | null | null | findings-of-the-association-for-computational | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 1.95813403e-01 7.68372238e-01 -7.24201798e-01 -7.25286722e-01
-6.49818122e-01 -6.39449537e-01 7.66967952e-01 5.30153930e-01
-2.09050596e-01 8.03413093e-01 1.14896488e+00 1.07472152e-01
4.77217346e-01 -4.14477557e-01 -7.14316219e-02 2.33555343e-02
1.03382722e-01 -7.87704065e-02 -3.36064905e-01 -3.54060322... | [8.799736022949219, 10.087385177612305] |
67b53ef2-32e8-4b62-bf26-932a75528f76 | beyond-appearance-a-semantic-controllable | 2303.17602 | null | https://arxiv.org/abs/2303.17602v1 | https://arxiv.org/pdf/2303.17602v1.pdf | Beyond Appearance: a Semantic Controllable Self-Supervised Learning Framework for Human-Centric Visual Tasks | Human-centric visual tasks have attracted increasing research attention due to their widespread applications. In this paper, we aim to learn a general human representation from massive unlabeled human images which can benefit downstream human-centric tasks to the maximum extent. We call this method SOLIDER, a Semantic ... | ['Xiuyu Sun', 'Rong Jin', 'Fan Wang', 'Yaohua Wang', 'Hao Luo', 'Jian Jia', 'Xianzhe Xu', 'Weihua Chen'] | 2023-03-30 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chen_Beyond_Appearance_A_Semantic_Controllable_Self-Supervised_Learning_Framework_for_Human-Centric_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chen_Beyond_Appearance_A_Semantic_Controllable_Self-Supervised_Learning_Framework_for_Human-Centric_CVPR_2023_paper.pdf | cvpr-2023-1 | ['person-re-identification', 'pedestrian-attribute-recognition', 'pedestrian-detection', 'human-parsing', 'person-search'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-2.25142129e-02 2.04938471e-01 -2.15387121e-01 -8.17608476e-01
-2.82277822e-01 -3.91355604e-01 3.93703222e-01 -2.69043177e-01
-4.84244704e-01 4.27286506e-01 2.90922999e-01 5.09748869e-02
5.04999816e-01 -5.19713283e-01 -5.84976554e-01 -4.30392236e-01
4.70472634e-01 4.64530766e-01 2.41512731e-01 -1.25180647... | [10.920047760009766, 1.273616075515747] |
dd47dadc-5cfe-4a21-b949-d65e575ad543 | leveraging-joint-sparsity-in-hierarchical | 2303.16954 | null | https://arxiv.org/abs/2303.16954v1 | https://arxiv.org/pdf/2303.16954v1.pdf | Leveraging joint sparsity in hierarchical Bayesian learning | We present a hierarchical Bayesian learning approach to infer jointly sparse parameter vectors from multiple measurement vectors. Our model uses separate conditionally Gaussian priors for each parameter vector and common gamma-distributed hyper-parameters to enforce joint sparsity. The resulting joint-sparsity-promotin... | ['Anne Gelb', 'Jan Glaubitz'] | 2023-03-29 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [ 1.19040072e-01 3.23141180e-02 -4.00779575e-01 -7.73638666e-01
-1.20900357e+00 1.00948289e-02 4.81984973e-01 -1.92550227e-01
-3.24371696e-01 8.85039866e-01 5.12991309e-01 -1.31260991e-01
-3.55653644e-01 -2.81069189e-01 -4.39204067e-01 -9.43251193e-01
-4.01835203e-01 7.99376726e-01 3.54779959e-01 4.91832882... | [6.999424457550049, 3.992400884628296] |
a0b52389-50e9-4787-a349-b537fe24faef | operationalising-representation-in-natural | 2306.08193 | null | https://arxiv.org/abs/2306.08193v1 | https://arxiv.org/pdf/2306.08193v1.pdf | Operationalising Representation in Natural Language Processing | Despite its centrality in the philosophy of cognitive science, there has been little prior philosophical work engaging with the notion of representation in contemporary NLP practice. This paper attempts to fill that lacuna: drawing on ideas from cognitive science, I introduce a framework for evaluating the representati... | ['Jacqueline Harding'] | 2023-06-14 | null | null | null | null | ['philosophy'] | ['miscellaneous'] | [ 4.88345027e-01 7.93746710e-01 -6.39279962e-01 -3.30393851e-01
-5.08375108e-01 -9.25125718e-01 1.10869443e+00 3.79771322e-01
-2.43487313e-01 2.40805298e-01 9.76212144e-01 -1.14676225e+00
-4.10516918e-01 -9.37208235e-01 -6.29988313e-01 -4.19860661e-01
3.62558693e-01 4.36259001e-01 -7.81593993e-02 -7.52099082... | [9.350739479064941, 6.997243404388428] |
33d4c888-3676-4bce-b072-72171e4261d3 | inductive-relation-prediction-using-analogy | null | null | https://openreview.net/forum?id=PTRo58zPt3P | https://openreview.net/pdf?id=PTRo58zPt3P | Inductive Relation Prediction Using Analogy Subgraph Embeddings | Prevailing methods for relation prediction in heterogeneous graphs aim at learning latent representations (i.e., embeddings) of observed nodes and relations, and thus are limited to the transductive setting where the relation types must be known during training. Here, we propose ANalogy SubGraphEmbeddingLearning (Gr... | ['Quan Gan', 'Yong Yu', 'David Wipf', 'Zheng Zhang', 'Weinan Zhang', 'Kounianhua Du', 'Yangkun Wang', 'Jiarui Jin'] | 2021-09-29 | null | null | null | iclr-2022-4 | ['inductive-relation-prediction'] | ['graphs'] | [ 2.50295579e-01 1.20428753e+00 -7.91505039e-01 -3.44591022e-01
9.83091816e-02 -4.57113296e-01 8.48637462e-01 5.05079389e-01
5.13197482e-01 6.74392998e-01 5.66896796e-01 -5.20648897e-01
-3.61143112e-01 -1.56521237e+00 -9.96350646e-01 -3.43946815e-01
-2.69923896e-01 8.78774822e-01 2.31501851e-02 -3.51727724... | [8.836387634277344, 7.816189765930176] |
b9884019-a852-4509-a517-1ba99740e482 | navigation-of-micro-robot-swarms-for-targeted | 2306.17598 | null | https://arxiv.org/abs/2306.17598v1 | https://arxiv.org/pdf/2306.17598v1.pdf | Navigation of micro-robot swarms for targeted delivery using reinforcement learning | Micro robotics is quickly emerging to be a promising technological solution to many medical treatments with focus on targeted drug delivery. They are effective when working in swarms whose individual control is mostly infeasible owing to their minute size. Controlling a number of robots with a single controller is thus... | ['Manoj Varma', 'Akshatha Jagadish'] | 2023-06-30 | null | null | null | null | ['reinforcement-learning-1', 'navigate'] | ['methodology', 'reasoning'] | [ 1.52037758e-02 9.23540369e-02 8.75496417e-02 4.22551811e-01
-4.18754034e-02 -5.83114445e-01 5.87444484e-01 3.32313746e-01
-8.40878963e-01 1.33893704e+00 -3.20378542e-01 -2.95161426e-01
-5.52939355e-01 -4.11360890e-01 -7.65928924e-01 -1.34496188e+00
-5.57625949e-01 7.27429569e-01 1.06096931e-01 -7.22434402... | [4.012686252593994, 2.0085997581481934] |
a15ba904-3537-4962-8355-6af1050e83db | provably-personalized-and-robust-federated | 2306.08393 | null | https://arxiv.org/abs/2306.08393v1 | https://arxiv.org/pdf/2306.08393v1.pdf | Provably Personalized and Robust Federated Learning | Clustering clients with similar objectives and learning a model per cluster is an intuitive and interpretable approach to personalization in federated learning. However, doing so with provable and optimal guarantees has remained an open challenge. In this work, we formalize personalized federated learning as a stochast... | ['Martin Jaggi', 'Michael Jordan', 'Sai Praneeth Karimireddy', 'Lie He', 'Mariel Werner'] | 2023-06-14 | null | null | null | null | ['clustering', 'stochastic-optimization', 'personalized-federated-learning'] | ['methodology', 'methodology', 'methodology'] | [-5.20631313e-01 1.16042364e-02 -2.49166936e-01 -5.44737935e-01
-1.06414258e+00 -8.76847327e-01 2.17410356e-01 -5.95642580e-03
-5.50626636e-01 6.99512899e-01 3.12081784e-01 -2.27488890e-01
-3.65964681e-01 -5.84435642e-01 -1.12082744e+00 -1.16480076e+00
-4.34981704e-01 9.29176509e-01 -1.10955246e-01 3.37115407... | [5.88787841796875, 6.1480607986450195] |
d76e73e0-66a2-4d4e-b6b2-3d319fa1b6d7 | aitom-open-source-ai-platform-for-cryo | 1911.03044 | null | https://arxiv.org/abs/1911.03044v2 | https://arxiv.org/pdf/1911.03044v2.pdf | AITom: Open-source AI platform for cryo-electron tomography data analysis | Cryo-electron tomography (cryo-ET) is an emerging technology for the 3D visualization of structural organizations and interactions of subcellular components at near-native state and sub-molecular resolution. Tomograms captured by cryo-ET contain heterogeneous structures representing the complex and dynamic subcellular ... | ['Xiangrui Zeng', 'Min Xu'] | 2019-11-08 | null | null | null | null | ['electron-tomography'] | ['medical'] | [-2.71140754e-01 -6.57832563e-01 3.76365036e-01 -2.53675938e-01
-7.64927268e-01 -6.90613866e-01 4.22625989e-02 1.54697478e-01
-5.57468355e-01 9.79546070e-01 -4.26439226e-01 -2.67080903e-01
3.04777890e-01 -3.12161356e-01 -4.52149868e-01 -1.09003043e+00
-1.79681346e-01 1.04688191e+00 1.26978174e-01 2.30635464... | [13.494174003601074, -3.0942296981811523] |
e2fbfb0b-4388-488a-87df-27ec8526d19e | drone-path-following-in-gps-denied | 1905.01658 | null | https://arxiv.org/abs/1905.01658v1 | https://arxiv.org/pdf/1905.01658v1.pdf | Drone Path-Following in GPS-Denied Environments using Convolutional Networks | his paper presents a simple approach for drone navigation to follow a predetermined path using visual input only without reliance on a Global Positioning System (GPS). A Convolutional Neural Network (CNN) is used to output the steering command of the drone in an end-to-end approach. We tested our approach in two simula... | ['M. Shaker', 'M. ElHelw', 'K. Amer', 'M. Samy'] | 2019-05-05 | null | null | null | null | ['drone-navigation'] | ['computer-vision'] | [-2.88796782e-01 1.75883427e-01 3.54409814e-01 -4.25975770e-01
1.90855801e-01 -1.18754065e+00 7.23389149e-01 -3.37092370e-01
-6.74723923e-01 8.32744360e-01 -1.72221914e-01 -1.13553715e+00
-6.33883551e-02 -1.06876957e+00 -5.13816774e-01 -1.45369917e-01
-4.13252056e-01 -1.25560477e-01 3.86670262e-01 -9.28371131... | [7.237626075744629, -1.8911594152450562] |
f82808f7-6c97-45f3-adbb-853b706fb199 | multi-level-matching-and-aggregation-network | 1906.06678 | null | https://arxiv.org/abs/1906.06678v1 | https://arxiv.org/pdf/1906.06678v1.pdf | Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification | This paper presents a multi-level matching and aggregation network (MLMAN) for few-shot relation classification. Previous studies on this topic adopt prototypical networks, which calculate the embedding vector of a query instance and the prototype vector of each support set independently. In contrast, our proposed MLMA... | ['Zhen-Hua Ling', 'Zhi-Xiu Ye'] | 2019-06-16 | multi-level-matching-and-aggregation-network-1 | https://aclanthology.org/P19-1277 | https://aclanthology.org/P19-1277.pdf | acl-2019-7 | ['few-shot-relation-classification', 'few-shot-relation-classification'] | ['methodology', 'natural-language-processing'] | [ 1.51469097e-01 3.28754991e-01 -7.61155009e-01 -4.49744344e-01
-4.44503367e-01 5.73092028e-02 6.65925205e-01 6.78922713e-01
-2.78595865e-01 4.98970360e-01 1.24922611e-01 4.16068882e-01
-5.58988512e-01 -1.21993470e+00 -1.23123221e-01 -4.03133422e-01
-3.89710426e-01 7.50315547e-01 5.86561143e-01 -3.03707212... | [9.188006401062012, 8.499299049377441] |
044dbbf8-db20-4ccf-85dc-ef01f148cc02 | reproducing-activation-function-for-deep | 2101.04844 | null | https://arxiv.org/abs/2101.04844v2 | https://arxiv.org/pdf/2101.04844v2.pdf | Reproducing Activation Function for Deep Learning | We propose reproducing activation functions (RAFs) to improve deep learning accuracy for various applications ranging from computer vision to scientific computing. The idea is to employ several basic functions and their learnable linear combination to construct neuron-wise data-driven activation functions for each neur... | ['Haizhao Yang', 'Chunmei Wang', 'Liyao Lyu', 'Senwei Liang'] | 2021-01-13 | null | null | null | null | ['video-reconstruction'] | ['computer-vision'] | [-3.72723877e-01 5.57133891e-02 1.46429971e-01 7.98269734e-02
-7.10252047e-01 -8.86258669e-03 5.73042892e-02 -5.35019696e-01
-3.84537935e-01 6.11423254e-01 -2.14613397e-02 -1.97356075e-01
6.19002581e-02 -5.40306628e-01 -1.14475918e+00 -8.09343934e-01
1.71648383e-01 -1.20695010e-02 -2.35376611e-01 -1.60501808... | [11.25480842590332, -1.6514348983764648] |
992aab46-f59f-4f0b-9ade-3ffd4bbbd450 | layered-tpot-speeding-up-tree-based-pipeline | 1801.06007 | null | http://arxiv.org/abs/1801.06007v2 | http://arxiv.org/pdf/1801.06007v2.pdf | Layered TPOT: Speeding up Tree-based Pipeline Optimization | With the demand for machine learning increasing, so does the demand for tools
which make it easier to use. Automated machine learning (AutoML) tools have
been developed to address this need, such as the Tree-Based Pipeline
Optimization Tool (TPOT) which uses genetic programming to build optimal
pipelines. We introduce ... | ['Randal S. Olson', 'Pieter Gijsbers', 'Joaquin Vanschoren'] | 2018-01-18 | null | null | null | null | ['automated-feature-engineering'] | ['methodology'] | [-1.26285464e-01 3.10798913e-01 1.21595405e-01 -1.89012170e-01
-9.15440381e-01 -8.77009988e-01 1.83707774e-01 1.57669410e-01
-4.44538563e-01 2.64830232e-01 4.67043258e-02 -2.93671638e-01
-1.09254360e-01 -4.89417344e-01 -3.51548016e-01 -4.39031124e-01
1.27576683e-02 9.51633155e-01 8.64900053e-01 1.35687843... | [8.466976165771484, 4.423255920410156] |
373f2640-27d0-4d76-a4ef-f8bd3e4394ce | practical-equivariances-via-relational | 2306.10915 | null | https://arxiv.org/abs/2306.10915v1 | https://arxiv.org/pdf/2306.10915v1.pdf | Practical Equivariances via Relational Conditional Neural Processes | Conditional Neural Processes (CNPs) are a class of metalearning models popular for combining the runtime efficiency of amortized inference with reliable uncertainty quantification. Many relevant machine learning tasks, such as spatio-temporal modeling, Bayesian Optimization and continuous control, contain equivariances... | ['Luigi Acerbi', 'Samuel Kaski', 'Kevin Sebastian Luck', 'Grégoire Clarté', 'ST John', 'Ulpu Remes', 'Manuel Haussmann', 'Daolang Huang'] | 2023-06-19 | null | null | null | null | ['bayesian-optimization', 'continuous-control'] | ['methodology', 'playing-games'] | [ 4.67286631e-02 2.19958816e-02 -6.79305848e-03 -3.21200341e-01
-9.33772087e-01 -5.34388781e-01 1.17768550e+00 1.82298217e-02
-4.54927474e-01 8.13352406e-01 1.66972548e-01 -5.00442922e-01
-4.29049104e-01 -8.22155476e-01 -9.42516804e-01 -6.66031480e-01
-3.69342327e-01 7.14478672e-01 2.96699345e-01 2.31480092... | [7.034091949462891, 3.7741708755493164] |
a6944b1c-d4f8-437b-a417-3b9c8a3fa283 | a-robust-and-efficient-method-for-improving | 1407.6705 | null | http://arxiv.org/abs/1407.6705v2 | http://arxiv.org/pdf/1407.6705v2.pdf | A Robust and Efficient Method for Improving Accuracy of License Plate Characters Recognition | License Plate Recognition (LPR) plays an important role on the traffic
monitoring and parking management. A robust and efficient method for enhancing
accuracy of license plate characters recognition based on K Nearest Neighbours
(K-NN) classifier is presented in this paper. The system first prepares a
contour form of t... | ['Reza Azad', 'Hamed Amiri', 'Hamid Reza Shayegh'] | 2014-07-24 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [ 9.32447240e-02 -7.74812520e-01 -2.09259585e-01 -4.17239010e-01
-5.77392340e-01 -5.80539465e-01 4.32977378e-01 -2.75474757e-01
-6.39614463e-01 6.36589944e-01 -1.95630863e-01 -3.36097360e-01
-6.81650788e-02 -9.74263072e-01 -2.46059909e-01 -6.00760996e-01
2.50914931e-01 4.35402036e-01 7.16471136e-01 -1.40589714... | [9.791455268859863, -5.002038955688477] |
3acee780-3888-495c-bed1-0e05c8b2f544 | few-shot-text-generation-with-pattern | 2012.11926 | null | https://arxiv.org/abs/2012.11926v2 | https://arxiv.org/pdf/2012.11926v2.pdf | Few-Shot Text Generation with Pattern-Exploiting Training | Providing pretrained language models with simple task descriptions in natural language enables them to solve some tasks in a fully unsupervised fashion. Moreover, when combined with regular learning from examples, this idea yields impressive few-shot results for a wide range of text classification tasks. It is also a p... | ['Hinrich Schütze', 'Timo Schick'] | 2020-12-22 | null | null | null | null | ['headline-generation'] | ['natural-language-processing'] | [ 5.91680169e-01 3.40595335e-01 -2.97982395e-01 -3.06285590e-01
-1.24327636e+00 -4.14998949e-01 9.70169485e-01 1.27825677e-01
-3.24289858e-01 1.01437235e+00 5.60239792e-01 -1.53910905e-01
1.94059163e-01 -7.46581614e-01 -7.50856340e-01 -5.16663373e-01
3.34809691e-01 8.68361533e-01 8.51988420e-02 -5.55239975... | [11.668352127075195, 8.864684104919434] |
27a1893e-995c-43a4-b811-7e35042f3b68 | combining-metric-learning-and-attention-heads | 2209.06585 | null | https://arxiv.org/abs/2209.06585v2 | https://arxiv.org/pdf/2209.06585v2.pdf | Combining Metric Learning and Attention Heads For Accurate and Efficient Multilabel Image Classification | Multi-label image classification allows predicting a set of labels from a given image. Unlike multiclass classification, where only one label per image is assigned, such a setup is applicable for a broader range of applications. In this work we revisit two popular approaches to multilabel classification: transformer-ba... | ['Vladislav Sovrasov', 'Kirill Prokofiev'] | 2022-09-14 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 3.9410645e-01 2.1827719e-01 -6.6705465e-01 -7.9869729e-01
-1.0690231e+00 -7.5656700e-01 4.4660670e-01 6.7020881e-01
-6.1381567e-01 1.0210927e+00 -2.5332949e-01 -3.8771400e-01
-1.2126815e-01 -7.1423131e-01 -6.4633447e-01 -8.7874377e-01
1.1386984e-01 5.9752470e-01 1.4228781e-02 6.6624790e-02
-4.0619593e-02... | [9.58692741394043, 4.110672950744629] |
bd491c7a-c482-41dc-8dc4-3f56014f35fa | run-off-election-improved-provable-defense | 2302.02300 | null | https://arxiv.org/abs/2302.02300v3 | https://arxiv.org/pdf/2302.02300v3.pdf | Run-Off Election: Improved Provable Defense against Data Poisoning Attacks | In data poisoning attacks, an adversary tries to change a model's prediction by adding, modifying, or removing samples in the training data. Recently, ensemble-based approaches for obtaining provable defenses against data poisoning have been proposed where predictions are done by taking a majority vote across multiple ... | ['Soheil Feizi', 'Atoosa Chegini', 'Kiarash Banihashem', 'Keivan Rezaei'] | 2023-02-05 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 1.68892384e-01 -1.10787414e-01 1.86281335e-02 -8.91612396e-02
-1.19422245e+00 -8.86291325e-01 5.45917869e-01 4.35925275e-01
-5.72274089e-01 8.53185713e-01 -1.40046567e-01 -6.08482242e-01
-9.67843831e-03 -9.68907773e-01 -9.72996533e-01 -8.87991250e-01
-1.03928052e-01 5.51242113e-01 6.47983611e-01 -2.49427631... | [5.827052593231201, 7.4989705085754395] |
1d816422-1c81-48b0-902c-fc3b57ea0c22 | hypergraph-artificial-benchmark-for-community | 2210.15009 | null | https://arxiv.org/abs/2210.15009v3 | https://arxiv.org/pdf/2210.15009v3.pdf | Hypergraph Artificial Benchmark for Community Detection (h-ABCD) | The Artificial Benchmark for Community Detection (ABCD) graph is a recently introduced random graph model with community structure and power-law distribution for both degrees and community sizes. The model generates graphs with similar properties as the well-known LFR one, and its main parameter can be tuned to mimic i... | ['François Théberge', 'Paweł Prałat', 'Bogumił Kamiński'] | 2022-10-26 | null | null | null | null | ['community-detection'] | ['graphs'] | [ 3.34642194e-02 3.20153236e-01 -3.27903442e-02 4.44875330e-01
-1.01244457e-01 -9.80822980e-01 6.49394810e-01 4.40285951e-01
1.18013673e-01 7.04325497e-01 -1.22994334e-01 -4.24432188e-01
-2.26726115e-01 -1.36651528e+00 -4.39984053e-01 -7.07095742e-01
-6.05883718e-01 8.97486508e-01 7.85313785e-01 -2.31063530... | [6.958712577819824, 5.252319812774658] |
197bf601-8385-4b0c-817a-3cfb66644a79 | topic-detection-in-continuous-sign-language | 2209.02402 | null | https://arxiv.org/abs/2209.02402v1 | https://arxiv.org/pdf/2209.02402v1.pdf | Topic Detection in Continuous Sign Language Videos | Significant progress has been made recently on challenging tasks in automatic sign language understanding, such as sign language recognition, translation and production. However, these works have focused on datasets with relatively few samples, short recordings and limited vocabulary and signing space. In this work, we... | ['Xavier Giro-i-Nieto', 'Jordi Torres', 'Francesc Moreno-Noguer', 'Gerard I. Gallego', 'Laia Tarres', 'Alvaro Budria'] | 2022-09-01 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [ 2.55130261e-01 -3.59375596e-01 -5.27705312e-01 -3.67907822e-01
-8.45871031e-01 -5.34065127e-01 8.98169756e-01 -6.35002851e-01
-3.93751889e-01 4.66700464e-01 9.64049459e-01 -9.84750502e-03
3.35444421e-01 -7.31595606e-02 -4.56578881e-01 -4.71648425e-01
1.00870669e-01 1.84800848e-01 5.82519710e-01 8.40165317... | [9.154878616333008, -6.467281818389893] |
70c903e4-3e48-4d5e-b6f8-fd9d0af13920 | dont-just-scratch-the-surface-enhancing-word | 1908.09282 | null | https://arxiv.org/abs/1908.09282v3 | https://arxiv.org/pdf/1908.09282v3.pdf | Don't Just Scratch the Surface: Enhancing Word Representations for Korean with Hanja | We propose a simple yet effective approach for improving Korean word representations using additional linguistic annotation (i.e. Hanja). We employ cross-lingual transfer learning in training word representations by leveraging the fact that Hanja is closely related to Chinese. We evaluate the intrinsic quality of repre... | ['Sang-goo Lee', 'Kang Min Yoo', 'Taeuk Kim'] | 2019-08-25 | dont-just-scratch-the-surface-enhancing-word-1 | https://aclanthology.org/D19-1358 | https://aclanthology.org/D19-1358.pdf | ijcnlp-2019-11 | ['headline-generation'] | ['natural-language-processing'] | [-1.13991179e-01 -7.25862607e-02 -5.62578559e-01 -3.84317040e-01
-1.48083401e+00 -6.29606307e-01 4.98441368e-01 7.19767958e-02
-9.16675150e-01 1.08208489e+00 1.07982516e+00 -4.18665469e-01
4.02788401e-01 -8.22012722e-01 -6.82424843e-01 -1.54105335e-01
1.98106840e-01 1.33959249e-01 -2.50858545e-01 -7.64558017... | [10.951261520385742, 9.206174850463867] |
3b5f5d43-3887-43e5-a551-2c2f2fa5191f | real-time-and-robust-3d-object-detection | 2204.00132 | null | https://arxiv.org/abs/2204.00132v2 | https://arxiv.org/pdf/2204.00132v2.pdf | Real-Time and Robust 3D Object Detection Within Road-Side LiDARs Using Domain Adaptation | This work aims to address the challenges in domain adaptation of 3D object detection using infrastructure LiDARs. We design a model DASE-ProPillars that can detect vehicles in infrastructure-based LiDARs in real-time. Our model uses PointPillars as the baseline model with additional modules to improve the 3D detection ... | ['Alois Knoll', 'Marcus Grabler', 'Walter Zimmer'] | 2022-03-31 | null | null | null | null | ['robust-3d-object-detection'] | ['computer-vision'] | [-2.53884882e-01 -8.08655545e-02 7.94446766e-02 -5.39011121e-01
-1.06827986e+00 -5.66345453e-01 7.13236809e-01 -1.48159057e-01
-8.31997693e-01 4.99855161e-01 -8.10068429e-01 -6.51542127e-01
2.51366258e-01 -1.09217286e+00 -1.20687532e+00 -2.21737668e-01
-1.62579566e-01 1.08209074e+00 1.15142560e+00 1.86410751... | [7.771960735321045, -2.4629149436950684] |
a094abf8-47ac-415d-8418-e4f98cdcd9de | discriminative-sentence-modeling-for-story | 1912.09008 | null | https://arxiv.org/abs/1912.09008v1 | https://arxiv.org/pdf/1912.09008v1.pdf | Discriminative Sentence Modeling for Story Ending Prediction | Story Ending Prediction is a task that needs to select an appropriate ending for the given story, which requires the machine to understand the story and sometimes needs commonsense knowledge. To tackle this task, we propose a new neural network called Diff-Net for better modeling the differences of each ending in this ... | ['Wei-Nan Zhang', 'Yiming Cui', 'Shijin Wang', 'Ting Liu', 'Wanxiang Che', 'Guoping Hu'] | 2019-12-19 | null | null | null | null | ['cloze-test'] | ['natural-language-processing'] | [ 3.39939028e-01 -1.81181803e-01 -5.92764020e-01 -5.88332951e-01
-6.68889999e-01 -7.49382317e-01 6.36011720e-01 4.00607893e-03
-2.34288812e-01 6.61019206e-01 8.30590069e-01 -4.90868054e-02
5.14551103e-02 -6.31710947e-01 -2.79922664e-01 -2.77465194e-01
2.93870747e-01 4.07490104e-01 9.84502211e-02 -4.86095428... | [11.27477741241455, 8.841788291931152] |
8a62fb45-2c97-4a03-a1e0-026680bb3677 | mris-a-multi-modal-retrieval-approach-for | 2303.10249 | null | https://arxiv.org/abs/2303.10249v1 | https://arxiv.org/pdf/2303.10249v1.pdf | MRIS: A Multi-modal Retrieval Approach for Image Synthesis on Diverse Modalities | Multiple imaging modalities are often used for disease diagnosis, prediction, or population-based analyses. However, not all modalities might be available due to cost, different study designs, or changes in imaging technology. If the differences between the types of imaging are small, data harmonization approaches can ... | ['Marc Niethammer', 'Boqi Chen'] | 2023-03-17 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [ 2.96484232e-01 -3.06683797e-02 -3.69489878e-01 -3.86092573e-01
-1.51859248e+00 -1.60562605e-01 3.79897118e-01 1.77855372e-01
-5.51710963e-01 6.24943793e-01 5.21821558e-01 1.04443379e-01
-5.20931363e-01 -7.20508933e-01 -4.39872354e-01 -6.40928388e-01
-4.10506278e-02 6.24454141e-01 2.68319845e-01 -8.03692415... | [14.356610298156738, -1.6409391164779663] |
d35a09da-26f7-47d0-9428-840c66b7f04d | neural-machine-translation-for-low-resource-3 | 2304.07869 | null | https://arxiv.org/abs/2304.07869v2 | https://arxiv.org/pdf/2304.07869v2.pdf | Neural Machine Translation For Low Resource Languages | Neural Machine translation is a challenging task due to the inherent complex nature and the fluidity that natural languages bring. Nonetheless, in recent years, it has achieved state-of-the-art performance in several language pairs. Although, a lot of traction can be seen in the areas of multilingual neural machine tra... | ['Utsa Chattopadhyay', 'Kannan Girija Ravikumar', 'Parvathy Krishnaswamy', 'Kartikay Goyle', 'Vakul Goyle'] | 2023-04-16 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 2.21417710e-01 -2.41222866e-02 -5.88741422e-01 -2.29945928e-01
-1.03275001e+00 -4.44118649e-01 1.00756514e+00 -4.68267977e-01
-5.56676388e-01 1.09405708e+00 1.71688661e-01 -9.42266405e-01
1.19173117e-01 -6.04502916e-01 -8.33653629e-01 -4.15892422e-01
3.35895330e-01 9.99361157e-01 -2.96125948e-01 -7.63859034... | [11.541364669799805, 10.300251007080078] |
a1344d5f-ec22-469b-8952-9c5771c7b80d | the-best-path-algorithm-automatic-variables | 2211.07267 | null | https://arxiv.org/abs/2211.07267v2 | https://arxiv.org/pdf/2211.07267v2.pdf | The Best Path Algorithm automatic variables selection via High Dimensional Graphical Models | This paper proposes a new algorithm for an automatic variable selection procedure in High Dimensional Graphical Models. The algorithm selects the relevant variables for the node of interest on the basis of mutual information. Several contributions in literature have investigated the use of mutual information in selecti... | ['Consuelo R. Nava', 'Maria G. Zoia', 'Luigi Riso'] | 2022-11-14 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 2.74102747e-01 1.56217009e-01 -3.57907474e-01 -4.53667581e-01
-5.52273214e-01 -3.35766673e-01 7.00366974e-01 4.84682530e-01
-3.94640237e-01 9.55652475e-01 -8.70461911e-02 -4.20544207e-01
-1.02101159e+00 -9.13602412e-01 5.72805703e-02 -8.30067098e-01
-5.01836121e-01 5.85704505e-01 -3.19763087e-02 1.33745730... | [7.8263726234436035, 4.701794624328613] |
959bf045-2827-485c-8e16-cfa17c8c7b4b | flow-edge-guided-video-completion | 2009.01835 | null | https://arxiv.org/abs/2009.01835v1 | https://arxiv.org/pdf/2009.01835v1.pdf | Flow-edge Guided Video Completion | We present a new flow-based video completion algorithm. Previous flow completion methods are often unable to retain the sharpness of motion boundaries. Our method first extracts and completes motion edges, and then uses them to guide piecewise-smooth flow completion with sharp edges. Existing methods propagate colors a... | ['Jia-Bin Huang', 'Johannes Kopf', 'Ayush Saraf', 'Chen Gao'] | 2020-09-03 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1715_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123570698.pdf | eccv-2020-8 | ['video-inpainting'] | ['computer-vision'] | [-4.62598540e-02 -5.30119538e-01 -2.16406003e-01 1.00110807e-01
-2.05345482e-01 -8.80353868e-01 3.83122534e-01 -2.27446839e-01
-3.68890405e-01 9.64590788e-01 3.57960820e-01 -1.42983347e-01
1.98544860e-01 -5.99822462e-01 -4.58244681e-01 -1.85525060e-01
-6.23273313e-01 -2.11683959e-01 9.81100619e-01 -1.86317302... | [10.643553733825684, -1.4595510959625244] |
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