paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
54c7bcf0-2c61-4fe1-aedc-2e3d13e69aa3 | hinet-half-instance-normalization-network-for | 2105.06086 | null | https://arxiv.org/abs/2105.06086v2 | https://arxiv.org/pdf/2105.06086v2.pdf | HINet: Half Instance Normalization Network for Image Restoration | In this paper, we explore the role of Instance Normalization in low-level vision tasks. Specifically, we present a novel block: Half Instance Normalization Block (HIN Block), to boost the performance of image restoration networks. Based on HIN Block, we design a simple and powerful multi-stage network named HINet, whic... | ['Chengpeng Chen', 'Xiaojie Chu', 'Jie Zhang', 'Xin Lu', 'Liangyu Chen'] | 2021-05-13 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [ 1.66625515e-01 -3.36544693e-01 6.29221722e-02 -6.76502958e-02
-7.30835855e-01 -1.49660558e-01 4.64346647e-01 -2.59052604e-01
-8.52002740e-01 4.99261260e-01 3.10106933e-01 -5.24159431e-01
2.03905478e-01 -5.06349325e-01 -9.18427408e-01 -7.85971820e-01
-1.21854648e-01 -5.16524851e-01 3.68419975e-01 -3.48018527... | [11.15610122680664, -2.195042133331299] |
d4220fbe-d5bc-4b46-99dd-4d9e5dce26b5 | dense-steerable-filter-cnns-for-exploiting | 2004.03037 | null | https://arxiv.org/abs/2004.03037v2 | https://arxiv.org/pdf/2004.03037v2.pdf | Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images | Histology images are inherently symmetric under rotation, where each orientation is equally as likely to appear. However, this rotational symmetry is not widely utilised as prior knowledge in modern Convolutional Neural Networks (CNNs), resulting in data hungry models that learn independent features at each orientation... | ['Simon Graham', 'Nasir Rajpoot', 'David Epstein'] | 2020-04-06 | null | null | null | null | ['colorectal-gland-segmentation', 'breast-tumour-classification', 'tumour-classification', 'multi-tissue-nucleus-segmentation', 'nuclear-segmentation'] | ['medical', 'medical', 'medical', 'medical', 'medical'] | [ 3.22695971e-01 2.72466123e-01 -1.16761336e-02 -4.36225235e-01
-3.27940196e-01 -5.96050680e-01 5.79576552e-01 -4.47379723e-02
-7.95868158e-01 4.37313944e-01 1.39100641e-01 -4.63370442e-01
-1.85101882e-01 -6.23993754e-01 -9.20483351e-01 -1.05563021e+00
4.54403721e-02 2.48854339e-01 3.46105754e-01 -2.85246581... | [15.030021667480469, -2.8232991695404053] |
e1d84e5c-61e1-4e20-9295-7e5ad2439291 | deepmatching-hierarchical-deformable-dense | 1506.07656 | null | http://arxiv.org/abs/1506.07656v2 | http://arxiv.org/pdf/1506.07656v2.pdf | DeepMatching: Hierarchical Deformable Dense Matching | We introduce a novel matching algorithm, called DeepMatching, to compute
dense correspondences between images. DeepMatching relies on a hierarchical,
multi-layer, correlational architecture designed for matching images and was
inspired by deep convolutional approaches. The proposed matching algorithm can
handle non-rig... | ['Cordelia Schmid', 'Zaid Harchaoui', 'Jerome Revaud', 'Philippe Weinzaepfel'] | 2015-06-25 | null | null | null | null | ['dense-pixel-correspondence-estimation'] | ['computer-vision'] | [-3.17462504e-01 -5.90048492e-01 -1.15122125e-01 -8.46610218e-02
-3.53272468e-01 -5.44382572e-01 6.59022629e-01 -1.05359234e-01
-3.48636031e-01 4.22486156e-01 3.98754537e-01 1.48574427e-01
-2.71497905e-01 -8.79927456e-01 -6.46298110e-01 -3.75367552e-01
-3.71194303e-01 4.52622294e-01 3.34689856e-01 -2.63502032... | [8.827816009521484, -1.895565390586853] |
05020b4b-826d-4f82-8ead-d1f4e8716f3e | dreambooth3d-subject-driven-text-to-3d | 2303.13508 | null | https://arxiv.org/abs/2303.13508v2 | https://arxiv.org/pdf/2303.13508v2.pdf | DreamBooth3D: Subject-Driven Text-to-3D Generation | We present DreamBooth3D, an approach to personalize text-to-3D generative models from as few as 3-6 casually captured images of a subject. Our approach combines recent advances in personalizing text-to-image models (DreamBooth) with text-to-3D generation (DreamFusion). We find that naively combining these methods fails... | ['Varun Jampani', 'Yuanzhen Li', 'Jonathan Barron', 'Michael Rubinstein', 'Kfir Aberman', 'Shiran Zada', 'Ben Mildenhall', 'Nataniel Ruiz', 'Michael Niemeyer', 'Ben Poole', 'Srinivas Kaza', 'Amit Raj'] | 2023-03-23 | null | null | null | null | ['text-to-3d'] | ['computer-vision'] | [ 1.14813946e-01 9.91170704e-02 5.25137067e-01 -6.63099647e-01
-8.15336168e-01 -7.44378030e-01 8.94428730e-01 -5.88994503e-01
3.72387096e-02 4.60011512e-01 2.39885896e-01 2.09654853e-01
4.66223657e-02 -4.33095813e-01 -8.08644831e-01 -5.67823768e-01
2.09128693e-01 7.62421370e-01 7.00259656e-02 -1.44556895... | [9.268279075622559, -3.128288745880127] |
65690f63-72d0-4001-818e-a4ac450dae1d | where-does-the-stimulus-go-deep-generative | 2101.0923 | null | https://arxiv.org/abs/2101.09230v1 | https://arxiv.org/pdf/2101.09230v1.pdf | Where does the Stimulus go? Deep Generative Model for Commercial Banking Deposits | This paper examines deposits of individuals ("retail") and large companies ("wholesale") in the U.S. banking industry, and how these deposit types are impacted by macroeconomic factors, such as quantitative easing (QE). Actual data for deposits by holder are unavailable. We use a dataset on banks' financial information... | ['Ni Zhan'] | 2021-01-22 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [-1.03222191e+00 1.32543445e-01 -1.17367446e-01 -3.60781431e-01
-6.01416230e-01 -7.05286086e-01 7.14930296e-01 -1.78011626e-01
-2.37689331e-01 1.03526342e+00 9.21168387e-01 -8.09494436e-01
1.46304801e-01 -1.52683377e+00 -3.18460196e-01 -6.60434067e-01
3.85246813e-01 7.91592121e-01 -3.58419299e-01 -2.71157864... | [4.840504169464111, 4.108068943023682] |
834e05d7-619a-4039-b6cb-16147d29a8db | information-directed-exploration-for-deep | 1812.07544 | null | http://arxiv.org/abs/1812.07544v2 | http://arxiv.org/pdf/1812.07544v2.pdf | Information-Directed Exploration for Deep Reinforcement Learning | Efficient exploration remains a major challenge for reinforcement learning.
One reason is that the variability of the returns often depends on the current
state and action, and is therefore heteroscedastic. Classical exploration
strategies such as upper confidence bound algorithms and Thompson sampling fail
to appropri... | ['Andreas Krause', 'Nikolay Nikolov', 'Johannes Kirschner', 'Felix Berkenkamp'] | 2018-12-18 | information-directed-exploration-for-deep-1 | https://openreview.net/forum?id=Byx83s09Km | https://openreview.net/pdf?id=Byx83s09Km | iclr-2019-5 | ['distributional-reinforcement-learning'] | ['methodology'] | [-2.77045190e-01 -6.67437613e-02 -7.16196001e-01 -1.92992255e-01
-1.21069980e+00 -5.37434161e-01 5.65249622e-01 6.25866130e-02
-5.27138412e-01 1.41726601e+00 1.89502686e-01 -5.43129861e-01
-5.54313838e-01 -9.21879888e-01 -8.91761124e-01 -6.21566117e-01
-1.35241672e-01 8.08514655e-01 -1.25304654e-01 1.34785786... | [4.211427688598633, 2.6523420810699463] |
0371bc1f-4bdb-4712-a3a3-8e07559f2b9d | world-consistent-video-to-video-synthesis | 2007.08509 | null | https://arxiv.org/abs/2007.08509v1 | https://arxiv.org/pdf/2007.08509v1.pdf | World-Consistent Video-to-Video Synthesis | Video-to-video synthesis (vid2vid) aims for converting high-level semantic inputs to photorealistic videos. While existing vid2vid methods can achieve short-term temporal consistency, they fail to ensure the long-term one. This is because they lack knowledge of the 3D world being rendered and generate each frame only b... | ['Ming-Yu Liu', 'Ting-Chun Wang', 'Karan Sapra', 'Arun Mallya'] | 2020-07-16 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/587_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123530358.pdf | eccv-2020-8 | ['video-to-video-synthesis'] | ['computer-vision'] | [ 7.69370794e-02 -5.75762205e-02 1.29657954e-01 -1.72476187e-01
-3.34306687e-01 -6.23602748e-01 6.56432986e-01 -4.52742338e-01
-5.06986640e-02 5.37731767e-01 2.78331906e-01 -1.03647865e-01
2.74234802e-01 -9.18451011e-01 -9.89930749e-01 -3.56055111e-01
2.32476011e-01 5.84689528e-02 5.00822306e-01 -1.04034692... | [9.757499694824219, -2.2445871829986572] |
d33a0f6b-bd66-40ec-aaa8-34c3ee2cbddd | ris-gan-explore-residual-and-illumination | 1911.09178 | null | https://arxiv.org/abs/1911.09178v2 | https://arxiv.org/pdf/1911.09178v2.pdf | RIS-GAN: Explore Residual and Illumination with Generative Adversarial Networks for Shadow Removal | Residual images and illumination estimation have been proved very helpful in image enhancement. In this paper, we propose a general and novel framework RIS-GAN which explores residual and illumination with Generative Adversarial Networks for shadow removal. Combined with the coarse shadow-removal image, the estimated n... | ['Chunxia Xiao', 'Ling Zhang', 'Chengjiang Long', 'Xiaolong Zhang'] | 2019-11-20 | null | null | null | null | ['shadow-removal'] | ['computer-vision'] | [ 8.26919675e-01 1.84433758e-01 3.94022495e-01 -6.21303134e-02
-5.60919583e-01 -4.85870570e-01 4.37956899e-01 -1.04235518e+00
1.05253175e-01 1.16206205e+00 -6.66849613e-02 -3.05343926e-01
4.55401719e-01 -7.58056402e-01 -6.27348840e-01 -1.09638083e+00
4.63824987e-01 -5.31363394e-03 3.34863901e-01 -3.56308937... | [10.848403930664062, -4.100964069366455] |
936c73d0-a303-49fb-bf6d-d92f6c7648d9 | the-effects-of-system-initiative-during | 2202.09728 | null | https://arxiv.org/abs/2202.09728v1 | https://arxiv.org/pdf/2202.09728v1.pdf | The Effects of System Initiative during Conversational Collaborative Search | Our research in this paper lies at the intersection of collaborative and conversational search. We report on a Wizard of Oz lab study in which 27 pairs of participants collaborated on search tasks over the Slack messaging platform. To complete tasks, pairs of collaborators interacted with a so-called \emph{searchbot} w... | ['Jaime Arguello', 'Bogeum Choi', 'Sandeep Avula'] | 2022-02-20 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [-4.15598676e-02 6.31591082e-01 -1.92149468e-02 -3.68557304e-01
-6.00447834e-01 -7.65171945e-01 8.70159507e-01 4.27243680e-01
-6.02309108e-01 5.51748395e-01 6.80920959e-01 -6.56286240e-01
-3.01850766e-01 -3.65800291e-01 9.87293720e-02 -2.93360114e-01
5.07353783e-01 3.81909013e-01 7.54505247e-02 -4.57440674... | [12.45665168762207, 7.849400043487549] |
aa8a7be0-7e66-4d52-be5d-3b854d6d7847 | attention-based-transformer-networks-for | 2305.05433 | null | https://arxiv.org/abs/2305.05433v1 | https://arxiv.org/pdf/2305.05433v1.pdf | Attention-Based Transformer Networks for Quantum State Tomography | Neural networks have been actively explored for quantum state tomography (QST) due to their favorable expressibility. To further enhance the efficiency of reconstructing quantum states, we explore the similarity between language modeling and quantum state tomography and propose an attention-based QST method that utiliz... | ['Herschel Rabitz', 'Chunlin Chen', 'Daoyi Dong', 'Zhenhong Sun', 'Hailan Ma'] | 2023-05-09 | null | null | null | null | ['quantum-state-tomography'] | ['medical'] | [ 1.11005269e-01 -2.29879275e-01 1.03614010e-01 -2.13573456e-01
-1.24130106e+00 -3.31257343e-01 7.00653493e-01 -1.36721551e-01
-5.43477833e-01 7.38204658e-01 2.75148392e-01 -6.06163919e-01
-8.74290690e-02 -9.94803488e-01 -7.29070425e-01 -9.32835400e-01
3.65338683e-01 4.73952621e-01 -1.21953994e-01 -2.99442053... | [5.591667652130127, 4.965409278869629] |
e1931e39-69fe-4604-bb36-474bb5deb6a7 | self-supervised-learning-and-graph | 2306.08469 | null | https://arxiv.org/abs/2306.08469v1 | https://arxiv.org/pdf/2306.08469v1.pdf | Self-supervised Learning and Graph Classification under Heterophily | Self-supervised learning has shown its promising capability in graph representation learning in recent work. Most existing pre-training strategies usually choose the popular Graph neural networks (GNNs), which can be seen as a special form of low-pass filter, fail to effectively capture heterophily. In this paper, we f... | ['Hao Hao', 'Zhen Liu', 'Yilin Ding'] | 2023-06-14 | null | null | null | null | ['graph-classification', 'property-prediction', 'protein-function-prediction', 'classification-1', 'graph-representation-learning', 'molecular-property-prediction'] | ['graphs', 'medical', 'medical', 'methodology', 'methodology', 'miscellaneous'] | [ 1.44003555e-01 3.78170982e-02 -3.19645166e-01 -7.07716346e-02
2.01630265e-01 -2.49271020e-01 4.40453082e-01 5.29508770e-01
-2.54037920e-02 6.58243835e-01 1.48154691e-01 -2.80419111e-01
-4.51203585e-01 -1.22783589e+00 -6.17385983e-01 -8.92302096e-01
-3.38088751e-01 3.15531611e-01 1.44120902e-01 -4.47730333... | [7.025129795074463, 6.196854591369629] |
080df7ae-4d86-4e62-b470-ddacc2ad0b21 | aunet-attention-guided-dense-upsampling | 1810.10151 | null | https://arxiv.org/abs/1810.10151v3 | https://arxiv.org/pdf/1810.10151v3.pdf | AUNet: Attention-guided dense-upsampling networks for breast mass segmentation in whole mammograms | Mammography is one of the most commonly applied tools for early breast cancer screening. Automatic segmentation of breast masses in mammograms is essential but challenging due to the low signal-to-noise ratio and the wide variety of mass shapes and sizes. Existing methods deal with these challenges mainly by extracting... | ['Shan-Shan Wang', 'David Dagan Feng', 'Hui Sun', 'Boqiang Liu', 'Hairong Zheng', 'Cheng Li'] | 2018-10-24 | null | null | null | null | ['breast-mass-segmentation-in-whole-mammograms'] | ['medical'] | [ 5.28813481e-01 1.72136232e-01 -2.90457636e-01 -4.55553472e-01
-9.43484068e-01 3.03020775e-01 2.17248932e-01 3.60450029e-01
-5.36849141e-01 5.14227271e-01 1.31345898e-01 -4.17519778e-01
3.51907574e-02 -8.23014677e-01 -6.95580602e-01 -8.25898826e-01
3.88853438e-02 2.72089660e-01 4.48451519e-01 7.69075826... | [15.085184097290039, -2.51940655708313] |
47e73742-8c59-4d31-be36-6c320d7c3435 | intent-generation-for-goal-oriented-dialogue | 1807.01292 | null | http://arxiv.org/abs/1807.01292v1 | http://arxiv.org/pdf/1807.01292v1.pdf | Intent Generation for Goal-Oriented Dialogue Systems based on Schema.org Annotations | Goal-oriented dialogue systems typically communicate with a backend (e.g.
database, Web API) to complete certain tasks to reach a goal. The intents that
a dialogue system can recognize are mostly included to the system by the
developer statically. For an open dialogue system that can work on more than a
small set of we... | ['Umutcan Şimşek', 'Dieter Fensel'] | 2018-07-03 | null | null | null | null | ['goal-oriented-dialogue-systems'] | ['natural-language-processing'] | [-1.43497474e-02 1.12924051e+00 4.58054274e-01 -8.28649998e-01
-6.04098260e-01 -9.21173573e-01 8.70036364e-01 2.53140271e-01
-1.68617755e-01 7.72836685e-01 4.87405896e-01 -3.55135769e-01
4.50704731e-02 -8.28181744e-01 -1.33055598e-01 3.38234067e-01
3.21093351e-01 8.37292492e-01 6.63445890e-01 -9.69655693... | [12.84169864654541, 7.908576011657715] |
c1af86a5-2799-4376-abf9-725a5799514d | multi-modal-fusion-for-end-to-end-rgb-t | 1908.11714 | null | https://arxiv.org/abs/1908.11714v1 | https://arxiv.org/pdf/1908.11714v1.pdf | Multi-Modal Fusion for End-to-End RGB-T Tracking | We propose an end-to-end tracking framework for fusing the RGB and TIR modalities in RGB-T tracking. Our baseline tracker is DiMP (Discriminative Model Prediction), which employs a carefully designed target prediction network trained end-to-end using a discriminative loss. We analyze the effectiveness of modality fusio... | ['Joost Van de Weijer', 'Abel Gonzalez-Garcia', 'Martin Danelljan', 'Lichao Zhang', 'Fahad Shahbaz Khan'] | 2019-08-30 | null | null | null | null | ['rgb-t-tracking'] | ['computer-vision'] | [ 2.75891930e-01 -3.44363600e-02 -2.28852749e-01 -2.43649855e-01
-1.30139041e+00 -6.61854029e-01 7.15768158e-01 -5.03777981e-01
-5.21905482e-01 3.23217750e-01 -1.77207440e-01 6.62352797e-03
2.32134297e-01 -1.66326374e-01 -1.04312706e+00 -7.95707405e-01
3.83138329e-01 2.05891192e-01 4.38831598e-01 6.01853244... | [6.354137420654297, -2.189080238342285] |
c2ccde7e-bc30-4583-8d45-0afac7cd0de5 | mdd-eval-self-training-on-augmented-data-for | 2112.07194 | null | https://arxiv.org/abs/2112.07194v2 | https://arxiv.org/pdf/2112.07194v2.pdf | MDD-Eval: Self-Training on Augmented Data for Multi-Domain Dialogue Evaluation | Chatbots are designed to carry out human-like conversations across different domains, such as general chit-chat, knowledge exchange, and persona-grounded conversations. To measure the quality of such conversational agents, a dialogue evaluator is expected to conduct assessment across domains as well. However, most of t... | ['Haizhou Li', 'Thomas Friedrichs', "Luis Fernando D'Haro", 'Chen Zhang'] | 2021-12-14 | null | null | null | null | ['dialogue-evaluation'] | ['natural-language-processing'] | [-1.80019617e-01 2.82713473e-01 1.60208493e-01 -7.36608684e-01
-9.06301498e-01 -6.43943548e-01 8.92744482e-01 2.73783691e-02
-3.33759695e-01 1.07258046e+00 3.65335286e-01 1.74071435e-02
7.06852898e-02 -5.85376620e-01 1.19079009e-01 -3.76205951e-01
3.19610894e-01 1.09535193e+00 2.75043935e-01 -7.79634118... | [12.725916862487793, 8.114681243896484] |
1b1e016e-823d-475e-bc1b-5e37efd5503c | mobile-microphone-array-speech-detection-and | 2106.14787 | null | https://arxiv.org/abs/2106.14787v1 | https://arxiv.org/pdf/2106.14787v1.pdf | Mobile Microphone Array Speech Detection and Localization in Diverse Everyday Environments | Joint sound event localization and detection (SELD) is an integral part of developing context awareness into communication interfaces of mobile robots, smartphones, and home assistants. For example, an automatic audio focus for video capture on a mobile phone requires robust detection of relevant acoustic events around... | ['Antti Eronen', 'Archontis Politis', 'Tuomas Virtanen', 'Eemi Fagerlund', 'Aapo Hakala', 'Emre Cakir', 'Pasi Pertilä'] | 2021-06-28 | null | null | null | null | ['sound-event-localization-and-detection'] | ['audio'] | [ 5.60717046e-01 -2.18839645e-01 4.81637955e-01 -7.79973492e-02
-1.09701943e+00 -3.77169549e-01 4.94167060e-01 3.04482132e-01
-5.86908638e-01 4.43796575e-01 3.28538269e-01 -1.12475686e-01
-7.78867155e-02 -3.78117830e-01 -5.44607341e-01 -7.55622923e-01
-1.09004378e-01 5.73181883e-02 5.38881242e-01 2.02731371... | [14.915132522583008, 5.643947124481201] |
361386fb-2e43-416f-a45f-54ae5587dbb6 | hashing-on-nonlinear-manifolds | 1412.0826 | null | http://arxiv.org/abs/1412.0826v1 | http://arxiv.org/pdf/1412.0826v1.pdf | Hashing on Nonlinear Manifolds | Learning based hashing methods have attracted considerable attention due to
their ability to greatly increase the scale at which existing algorithms may
operate. Most of these methods are designed to generate binary codes preserving
the Euclidean similarity in the original space. Manifold learning techniques,
in contra... | ['Anton Van Den Hengel', 'Qinfeng Shi', 'Chunhua Shen', 'Fumin Shen', 'Zhenmin Tang', 'Heng Tao Shen'] | 2014-12-02 | null | null | null | null | ['semantic-retrieval'] | ['natural-language-processing'] | [-5.18637113e-02 -7.33189732e-02 -4.84759957e-01 -2.99473405e-01
-1.02377963e+00 -5.50004423e-01 6.86494648e-01 4.12648171e-01
-4.66730177e-01 5.07984102e-01 -2.82450188e-02 5.93952052e-02
-3.49562407e-01 -9.38588262e-01 -4.62090522e-01 -9.54580843e-01
-3.14457029e-01 5.08699596e-01 3.06791123e-02 -2.41414428... | [8.027703285217285, 4.038346767425537] |
0d8be60f-0acf-4a61-aba3-c962709e662e | single-exposure-absorption-imaging-of | 2003.01643 | null | https://arxiv.org/abs/2003.01643v2 | https://arxiv.org/pdf/2003.01643v2.pdf | Single-exposure absorption imaging of ultracold atoms using deep learning | Absorption imaging is the most common probing technique in experiments with ultracold atoms. The standard procedure involves the division of two frames acquired at successive exposures, one with the atomic absorption signal and one without. A well-known problem is the presence of residual structured noise in the final ... | ['Gal Ness', 'Yoav Sagi', 'Yanay Florshaim', 'Constantine Shkedrov', 'Anastasiya Vainbaum'] | 2020-03-03 | null | null | null | null | ['physical-attribute-prediction'] | ['computer-vision'] | [ 4.92795616e-01 -1.58496067e-01 7.28847444e-01 -2.65545368e-01
-6.06075764e-01 -2.20098197e-01 4.48735058e-01 -9.67747793e-02
-8.78101230e-01 8.71820211e-01 -3.10514957e-01 -9.07728449e-02
2.66035408e-01 -8.34842622e-01 -8.52566898e-01 -1.36529696e+00
2.21016347e-01 7.35918224e-01 2.08483726e-01 -1.88965835... | [12.251259803771973, -2.6500351428985596] |
0894c76e-40ae-4d04-9686-d363751f16b2 | scrib-set-classifier-with-class-specific-risk | 2103.03945 | null | https://arxiv.org/abs/2103.03945v1 | https://arxiv.org/pdf/2103.03945v1.pdf | SCRIB: Set-classifier with Class-specific Risk Bounds for Blackbox Models | Despite deep learning (DL) success in classification problems, DL classifiers do not provide a sound mechanism to decide when to refrain from predicting. Recent works tried to control the overall prediction risk with classification with rejection options. However, existing works overlook the different significance of d... | ['Jimeng Sun', 'M. Brandon Westover', 'Lucas Glass', 'Cao Xiao', 'Zhen Lin'] | 2021-03-05 | null | null | null | null | ['sleep-staging', 'atrial-fibrillation-detection'] | ['medical', 'medical'] | [ 3.17172110e-01 2.38600314e-01 -4.21442717e-01 -8.02991271e-01
-6.58598483e-01 -2.18082711e-01 9.33327898e-02 3.65263909e-01
-4.44420040e-01 9.69612896e-01 -2.79785633e-01 -5.77234030e-01
-5.64306200e-01 -6.81771576e-01 -3.60735923e-01 -7.60446429e-01
-3.51321101e-01 5.43529987e-01 -6.48621023e-02 1.49889395... | [8.872971534729004, 4.034825325012207] |
4c19f93a-53ee-485a-b8ab-93f4bb6e655e | the-past-mistake-is-the-future-wisdom-error-1 | 2203.00991 | null | https://arxiv.org/abs/2203.00991v1 | https://arxiv.org/pdf/2203.00991v1.pdf | The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checking | Chinese Spell Checking (CSC) aims to detect and correct Chinese spelling errors, which are mainly caused by the phonological or visual similarity. Recently, pre-trained language models (PLMs) promote the progress of CSC task. However, there exists a gap between the learned knowledge of PLMs and the goal of CSC task. PL... | ['Hai-Tao Zheng', 'Yunbo Cao', 'Chao Li', 'Zizhen Wang', 'Rongyi Sun', 'Ruiyang Liu', 'Zhongli Li', 'Yangning Li', 'Qingyu Zhou', 'Yinghui Li'] | 2022-03-02 | null | https://aclanthology.org/2022.findings-acl.252 | https://aclanthology.org/2022.findings-acl.252.pdf | findings-acl-2022-5 | ['chinese-spell-checking'] | ['natural-language-processing'] | [ 2.21018642e-01 -3.63823503e-01 -3.84216616e-03 -5.78790382e-02
-4.57337111e-01 -1.87599942e-01 4.80034620e-01 2.12100863e-01
-5.76315165e-01 5.75887680e-01 5.41246891e-01 -2.62370110e-01
2.95256674e-01 -4.89734322e-01 -6.07931435e-01 -4.54519987e-01
6.38105035e-01 1.98427185e-01 5.76441526e-01 -1.25868499... | [10.939549446105957, 10.83447551727295] |
df528e26-2ec5-4e23-bfcf-f4eb2dc84370 | few-shot-learning-with-siamese-networks-and-1 | 2203.14655 | null | https://arxiv.org/abs/2203.14655v2 | https://arxiv.org/pdf/2203.14655v2.pdf | Few-Shot Learning with Siamese Networks and Label Tuning | We study the problem of building text classifiers with little or no training data, commonly known as zero and few-shot text classification. In recent years, an approach based on neural textual entailment models has been found to give strong results on a diverse range of tasks. In this work, we show that with proper pre... | ['Marc Franco-Salvador', 'Guillermo Pérez-Torró', 'Thomas Müller'] | 2022-03-28 | null | https://aclanthology.org/2022.acl-long.584 | https://aclanthology.org/2022.acl-long.584.pdf | acl-2022-5 | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 2.92846262e-01 5.71299121e-02 -3.81717116e-01 -6.70244396e-01
-8.01096976e-01 -4.80407864e-01 9.02029514e-01 4.97217894e-01
-9.19995546e-01 5.92413068e-01 1.27670079e-01 -1.67107522e-01
4.07500044e-02 -6.12367749e-01 -5.48415780e-01 -5.61900198e-01
2.85143971e-01 5.79792082e-01 3.30421031e-01 -1.44155100... | [10.689817428588867, 7.871866703033447] |
5af36480-a753-4334-b885-2ed80c1ecebd | causal-temporal-graph-convolutional-neural | 2303.09634 | null | https://arxiv.org/abs/2303.09634v1 | https://arxiv.org/pdf/2303.09634v1.pdf | Causal Temporal Graph Convolutional Neural Networks (CTGCN) | Many large-scale applications can be elegantly represented using graph structures. Their scalability, however, is often limited by the domain knowledge required to apply them. To address this problem, we propose a novel Causal Temporal Graph Convolutional Neural Network (CTGCN). Our CTGCN architecture is based on a cau... | ['Joern Ploennigs', 'Christopher Lohse', 'Fabio Lorenzi', 'Amadou Ba', "Fearghal O'Donncha", 'Abigail Langbridge'] | 2023-03-16 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 1.85403705e-01 4.38265979e-01 -4.40132290e-01 -2.30572268e-01
-9.84268188e-02 -5.04566491e-01 9.31896150e-01 3.56578827e-01
1.93802908e-01 8.60323548e-01 5.66753328e-01 -7.56521583e-01
-6.04223490e-01 -1.11751759e+00 -8.94978642e-01 -1.17685318e-01
-5.12274146e-01 4.49555486e-01 4.81408358e-01 -2.21176073... | [7.664956092834473, 5.928220748901367] |
fb0c66dc-4881-404b-a52b-6cdc370b8c03 | implementing-facial-landmark-tracking-for | 2211.12723 | null | https://arxiv.org/abs/2211.12723v3 | https://arxiv.org/pdf/2211.12723v3.pdf | A Classification Model Utilizing Facial Landmark Tracking to Determine Sentence Types for American Sign Language Recognition | The deaf and hard of hearing community relies on American Sign Language (ASL) as their primary mode of communication, but communication with others who do not know ASL can be difficult, especially during emergencies where no interpreter is available. As an effort to alleviate this problem, research in computer vision b... | ['Y. Curtis Wang', 'Janice Nguyen'] | 2022-11-23 | null | null | null | null | ['sign-language-recognition', 'landmark-tracking'] | ['computer-vision', 'computer-vision'] | [ 1.89234689e-01 -7.76488632e-02 -2.64524817e-01 -6.42754734e-01
-9.35949564e-01 -5.96987069e-01 2.98494935e-01 -1.39141589e-01
-5.78923225e-01 4.18257028e-01 4.58527714e-01 -4.50397223e-01
1.09834418e-01 -2.63905674e-01 9.25566256e-03 -6.25520527e-01
2.64381349e-01 1.50645867e-01 -2.72649843e-02 -1.35666236... | [9.069245338439941, -6.359238147735596] |
0600dae0-2bca-4117-8898-7bc7bbc5af19 | vitmatte-boosting-image-matting-with | 2305.15272 | null | https://arxiv.org/abs/2305.15272v2 | https://arxiv.org/pdf/2305.15272v2.pdf | ViTMatte: Boosting Image Matting with Pretrained Plain Vision Transformers | Recently, plain vision Transformers (ViTs) have shown impressive performance on various computer vision tasks, thanks to their strong modeling capacity and large-scale pretraining. However, they have not yet conquered the problem of image matting. We hypothesize that image matting could also be boosted by ViTs and pres... | ['Baoyuan Wang', 'Shusheng Yang', 'Xinggang Wang', 'Jingfeng Yao'] | 2023-05-24 | null | null | null | null | ['image-matting'] | ['computer-vision'] | [ 2.13069350e-01 2.77877022e-02 -6.49429560e-02 -4.26219195e-01
-6.24385655e-01 -1.04351699e-01 7.03532517e-01 -4.96832252e-01
-3.90933782e-01 3.33365083e-01 1.05459765e-01 -5.24924755e-01
4.62472558e-01 -6.69395268e-01 -1.31069803e+00 -5.81781507e-01
5.01869977e-01 3.37118834e-01 2.34282330e-01 -3.31762433... | [10.635175704956055, -0.8332929015159607] |
a5a7a498-c8ec-4e0c-b18a-abb0f534c911 | liar-liar-pants-on-fire-a-new-benchmark | 1705.00648 | null | http://arxiv.org/abs/1705.00648v1 | http://arxiv.org/pdf/1705.00648v1.pdf | "Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection | Automatic fake news detection is a challenging problem in deception
detection, and it has tremendous real-world political and social impacts.
However, statistical approaches to combating fake news has been dramatically
limited by the lack of labeled benchmark datasets. In this paper, we present
liar: a new, publicly av... | ['William Yang Wang'] | 2017-05-01 | null | null | null | acl-2017-7 | ['deception-detection'] | ['miscellaneous'] | [-1.56271011e-02 -9.55954101e-03 -7.92847276e-01 -3.11325908e-01
-1.02470446e+00 -8.15588415e-01 1.12642348e+00 3.75666827e-01
-1.67679951e-01 8.95206034e-01 7.73552775e-01 -5.62157631e-01
5.27305841e-01 -7.74241865e-01 -9.87186491e-01 -2.08909482e-01
2.91956007e-01 3.58134300e-01 1.11029841e-01 -7.10928738... | [8.143072128295898, 10.207809448242188] |
f365cd8f-f9dc-4fd2-95c5-b18394c42a9b | exploring-the-relationship-between-alignment | 2306.0279 | null | https://arxiv.org/abs/2306.02790v1 | https://arxiv.org/pdf/2306.02790v1.pdf | Exploring the Relationship between Alignment and Cross-lingual Transfer in Multilingual Transformers | Without any explicit cross-lingual training data, multilingual language models can achieve cross-lingual transfer. One common way to improve this transfer is to perform realignment steps before fine-tuning, i.e., to train the model to build similar representations for pairs of words from translated sentences. But such ... | ['Yannick Toussaint', 'Parisa Rastin', 'Patricio Cerda', 'Félix Gaschi'] | 2023-06-05 | null | null | null | null | ['cross-lingual-transfer', 'xlm-r'] | ['natural-language-processing', 'natural-language-processing'] | [ 6.78265393e-02 -6.09404333e-02 -3.10569286e-01 -4.88737255e-01
-1.38938391e+00 -9.80101705e-01 6.99709654e-01 2.08666891e-01
-9.34395790e-01 9.06003833e-01 3.20237964e-01 -7.20757544e-01
3.04411501e-01 -6.27939284e-01 -1.14896035e+00 -2.75700748e-01
2.65762180e-01 5.95913947e-01 4.93294410e-02 -4.80031401... | [10.958650588989258, 10.013030052185059] |
10b93781-c6cc-43f1-b93e-bd12c4f0b212 | bayesian-mixtures-of-spatial-spline | 1508.00635 | null | http://arxiv.org/abs/1508.00635v1 | http://arxiv.org/pdf/1508.00635v1.pdf | Bayesian mixtures of spatial spline regressions | This work relates the framework of model-based clustering for spatial
functional data where the data are surfaces. We first introduce a Bayesian
spatial spline regression model with mixed-effects (BSSR) for modeling spatial
function data. The BSSR model is based on Nodal basis functions for spatial
regression and accom... | ['Faicel Chamroukhi'] | 2015-08-04 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 8.39124545e-02 -1.08186319e-01 1.36454508e-01 -2.81457573e-01
-6.87187493e-01 -3.15859877e-02 8.26453507e-01 -5.55193834e-02
-3.69178891e-01 1.09027481e+00 5.83801530e-02 -2.81688511e-01
-7.10578561e-01 -1.10094011e+00 -8.53773892e-01 -1.15343654e+00
-1.98206186e-01 8.91858935e-01 4.45472836e-01 1.09721690... | [6.752548694610596, 3.9409313201904297] |
d6d53c65-b5c8-47c4-8396-f1eec03a2baa | a-new-class-of-explanations-for-classifiers | 2304.1476 | null | https://arxiv.org/abs/2304.14760v1 | https://arxiv.org/pdf/2304.14760v1.pdf | A New Class of Explanations for Classifiers with Non-Binary Features | Two types of explanations have received significant attention in the literature recently when analyzing the decisions made by classifiers. The first type explains why a decision was made and is known as a sufficient reason for the decision, also an abductive or PI-explanation. The second type explains why some other de... | ['Adnan Darwiche', 'Chunxi Ji'] | 2023-04-28 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 4.47505534e-01 7.68742263e-01 -3.91536772e-01 -5.90640962e-01
1.25442892e-01 -6.64403260e-01 9.97456074e-01 6.22265160e-01
-2.97377199e-01 9.54090059e-01 2.88956642e-01 -7.23984122e-01
-6.66041076e-01 -9.28021610e-01 -5.97094119e-01 -7.57390857e-01
1.17430449e-01 4.11686689e-01 2.37852171e-01 -3.68278086... | [8.732662200927734, 5.792830467224121] |
7017ba58-336d-4f41-beae-599af3d44ce4 | back-to-the-drawing-board-a-critical | 2108.10241 | null | https://arxiv.org/abs/2108.10241v2 | https://arxiv.org/pdf/2108.10241v2.pdf | Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning | While recent works have indicated that federated learning (FL) may be vulnerable to poisoning attacks by compromised clients, their real impact on production FL systems is not fully understood. In this work, we aim to develop a comprehensive systemization for poisoning attacks on FL by enumerating all possible threat m... | ['Daniel Ramage', 'Peter Kairouz', 'Amir Houmansadr', 'Virat Shejwalkar'] | 2021-08-23 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [-1.30527884e-01 -2.67798096e-01 1.00166604e-01 8.71082842e-02
-7.37573922e-01 -1.17395878e+00 7.41536975e-01 -1.27769634e-02
-3.84388655e-01 5.79913437e-01 1.24043584e-01 -8.34369123e-01
-1.82200179e-01 -5.82215726e-01 -7.30333209e-01 -6.93393111e-01
-3.46812308e-01 2.66786605e-01 4.27395672e-01 -2.75122225... | [5.774926662445068, 7.538160800933838] |
28bfa73c-a471-44ec-8b49-ca6f3da3c87b | divide-and-conquer-based-large-scale-spectral | null | null | https://www.researchgate.net/publication/351270623_Divide-and-conquer_based_Large-Scale_Spectral_Clustering | https://www.researchgate.net/profile/Hongmin-Li-7/publication/351270623_Divide-and-conquer_based_Large-Scale_Spectral_Clustering/links/608eb38a458515d315efa6ec/Divide-and-conquer-based-Large-Scale-Spectral-Clustering.pdf?_sg%5B0%5D=afU-HPOj3kpiSK8_UmQ5jVrrRNuCh7_Q3GujgkM7XaWsiGsIi1kJ8pHQEcENHYg9XZ0SDLQY7Rd3x77HT2KPcA.n... | Divide-and-conquer based Large-Scale Spectral Clustering | Spectral clustering is one of the most popular clustering methods. However, how to balance the efficiency and effectiveness of the large-scale spectral clustering with limited computing resources has not been properly solved for a long time. In this paper, we propose a divide-and-conquer based large-scale spectral clus... | ['Tetsuya Sakurai', 'Akira Imakura', 'Xiucai Ye', 'Hongmin Li'] | 2021-05-02 | null | null | null | null | ['image-clustering', 'imagedocument-clustering'] | ['computer-vision', 'computer-vision'] | [-3.12911958e-01 -4.59122539e-01 -1.72387898e-01 -2.31059507e-01
-8.35836112e-01 -5.17925739e-01 8.42472985e-02 1.01355016e-01
-2.46727347e-01 4.65711921e-01 -1.30289569e-02 -2.13728771e-01
-5.62833548e-01 -7.98951089e-01 -1.86868787e-01 -9.15235043e-01
7.65229613e-02 4.94987577e-01 6.05738282e-01 3.56105238... | [7.5129876136779785, 4.771225452423096] |
78a38dd2-ba8b-41ff-b1bb-108680a574a8 | bidirectional-learning-for-domain-adaptation | 1904.1062 | null | http://arxiv.org/abs/1904.10620v1 | http://arxiv.org/pdf/1904.10620v1.pdf | Bidirectional Learning for Domain Adaptation of Semantic Segmentation | Domain adaptation for semantic image segmentation is very necessary since
manually labeling large datasets with pixel-level labels is expensive and time
consuming. Existing domain adaptation techniques either work on limited
datasets, or yield not so good performance compared with supervised learning.
In this paper, we... | ['Nuno Vasconcelos', 'Yunsheng Li', 'Lu Yuan'] | 2019-04-24 | bidirectional-learning-for-domain-adaptation-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Li_Bidirectional_Learning_for_Domain_Adaptation_of_Semantic_Segmentation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Li_Bidirectional_Learning_for_Domain_Adaptation_of_Semantic_Segmentation_CVPR_2019_paper.pdf | cvpr-2019-6 | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 3.24737638e-01 3.70906144e-02 -7.53106833e-01 -6.01025224e-01
-8.83319676e-01 -6.65960729e-01 2.78984696e-01 -1.76186919e-01
-4.66810316e-01 8.15624237e-01 -3.71096060e-02 -3.40182453e-01
2.81104803e-01 -6.94828272e-01 -8.49377871e-01 -6.42976761e-01
6.45009220e-01 7.35217690e-01 6.13237143e-01 1.32994264... | [9.639220237731934, 1.3979662656784058] |
e96b8755-5cb4-4e8c-ab3b-a2431b4a16c7 | robo3d-towards-robust-and-reliable-3d | 2303.17597 | null | https://arxiv.org/abs/2303.17597v3 | https://arxiv.org/pdf/2303.17597v3.pdf | Robo3D: Towards Robust and Reliable 3D Perception against Corruptions | The robustness of 3D perception systems under natural corruptions from environments and sensors is pivotal for safety-critical applications. Existing large-scale 3D perception datasets often contain data that are meticulously cleaned. Such configurations, however, cannot reflect the reliability of perception models dur... | ['Ziwei Liu', 'Kai Chen', 'Liang Pan', 'Jiawei Ren', 'Wenwei Zhang', 'Runnan Chen', 'Xin Li', 'Youquan Liu', 'Lingdong Kong'] | 2023-03-30 | null | null | null | null | ['robust-3d-semantic-segmentation', 'robust-3d-object-detection'] | ['computer-vision', 'computer-vision'] | [ 2.01519415e-01 -4.55006883e-02 1.90551266e-01 -1.08403504e-01
-6.32008433e-01 -8.85442495e-01 7.46156454e-01 3.54763210e-01
-1.72156006e-01 2.90645480e-01 1.63587928e-01 -4.53900307e-01
-2.08561420e-02 -7.64497459e-01 -1.04824615e+00 -7.14491725e-01
-4.60437119e-01 1.03603482e-01 5.42208374e-01 -3.69716614... | [7.953094005584717, -1.6790202856063843] |
e8f27815-1bcd-41b8-abb0-60ea869d6e6b | semantic-models-for-the-first-stage-retrieval | 2103.04831 | null | https://arxiv.org/abs/2103.04831v4 | https://arxiv.org/pdf/2103.04831v4.pdf | Semantic Models for the First-stage Retrieval: A Comprehensive Review | Multi-stage ranking pipelines have been a practical solution in modern search systems, where the first-stage retrieval is to return a subset of candidate documents, and latter stages attempt to re-rank those candidates. Unlike re-ranking stages going through quick technique shifts during past decades, the first-stage r... | ['Yixing Fan', 'Yinqiong Cai', 'Jiafeng Guo', 'Xueqi Cheng', 'Ruqing Zhang', 'Fei Sun'] | 2021-03-08 | null | null | null | null | ['semantic-retrieval'] | ['natural-language-processing'] | [ 1.54343814e-01 -4.07143921e-01 -4.17672902e-01 -3.23813945e-01
-1.08722329e+00 -5.06887257e-01 9.40182149e-01 4.71518397e-01
-7.20816195e-01 3.14979076e-01 2.63175279e-01 7.34406263e-02
-7.00834453e-01 -7.84379065e-01 -5.75479716e-02 -1.99943542e-01
1.08061478e-01 9.92277205e-01 5.24786294e-01 -6.68067873... | [11.46572208404541, 7.656174659729004] |
39f7011e-a0f0-4c97-bf30-595a12967620 | deep-clustering-and-conventional-networks-for | 1611.06265 | null | http://arxiv.org/abs/1611.06265v2 | http://arxiv.org/pdf/1611.06265v2.pdf | Deep Clustering and Conventional Networks for Music Separation: Stronger Together | Deep clustering is the first method to handle general audio separation
scenarios with multiple sources of the same type and an arbitrary number of
sources, performing impressively in speaker-independent speech separation
tasks. However, little is known about its effectiveness in other challenging
situations such as mus... | ['Zhuo Chen', 'Nima Mesgarani', 'John R. Hershey', 'Yi Luo', 'Jonathan Le Roux'] | 2016-11-18 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 1.71266079e-01 -2.46827096e-01 5.42468540e-02 2.53505856e-02
-1.30679882e+00 -8.83618295e-01 5.35918891e-01 -2.68704176e-01
-1.88139379e-01 3.69187593e-01 6.34847283e-01 -2.36326419e-02
-5.87127268e-01 6.31304905e-02 -2.67163485e-01 -9.26062822e-01
-2.59558052e-01 4.95664626e-01 -1.18207209e-01 -8.91752914... | [15.399054527282715, 5.592049598693848] |
df25b4b0-e426-4167-b4fc-7afba06bf101 | toward-a-neural-semantic-parsing-system-for | 2211.04569 | null | https://arxiv.org/abs/2211.04569v1 | https://arxiv.org/pdf/2211.04569v1.pdf | Toward a Neural Semantic Parsing System for EHR Question Answering | Clinical semantic parsing (SP) is an important step toward identifying the exact information need (as a machine-understandable logical form) from a natural language query aimed at retrieving information from electronic health records (EHRs). Current approaches to clinical SP are largely based on traditional machine lea... | ['Kirk Roberts', 'Sarvesh Soni'] | 2022-11-08 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [ 4.42820013e-01 7.05219090e-01 -1.00821503e-01 -7.95263469e-01
-1.43633699e+00 -4.22845513e-01 -1.97452873e-01 7.85016894e-01
-4.39231664e-01 4.18139786e-01 4.75730240e-01 -9.62449193e-01
-2.33674452e-01 -6.72989607e-01 -6.75387383e-01 1.71768412e-01
5.42875305e-02 7.97233701e-01 6.25398606e-02 -9.04202685... | [8.751130104064941, 8.538110733032227] |
d50fbb9f-892d-4c2e-a23e-24226fb207d6 | the-meccano-dataset-understanding-human | 2010.05654 | null | https://arxiv.org/abs/2010.05654v1 | https://arxiv.org/pdf/2010.05654v1.pdf | The MECCANO Dataset: Understanding Human-Object Interactions from Egocentric Videos in an Industrial-like Domain | Wearable cameras allow to collect images and videos of humans interacting with the world. While human-object interactions have been thoroughly investigated in third person vision, the problem has been understudied in egocentric settings and in industrial scenarios. To fill this gap, we introduce MECCANO, the first data... | ['Giovanni Maria Farinella', 'Salvatore Livatino', 'Antonino Furnari', 'Francesco Ragusa'] | 2020-10-12 | null | null | null | null | ['active-object-detection'] | ['computer-vision'] | [ 1.88522279e-01 -1.93309218e-01 -9.80427265e-02 -8.06964040e-02
7.91944191e-02 -5.68481743e-01 7.24943817e-01 -3.83006692e-01
-3.41577142e-01 2.99471706e-01 2.01734781e-01 3.11280578e-01
-8.53117108e-02 -1.95538253e-01 -7.51925528e-01 -7.12523818e-01
-1.28326237e-01 4.65515614e-01 3.01753283e-01 7.78514445... | [8.027946472167969, 0.35835838317871094] |
44a0b2b6-cb07-4b5e-9944-58cdae3f85b7 | leveraging-gpt-4-for-automatic-translation | 2305.14878 | null | https://arxiv.org/abs/2305.14878v1 | https://arxiv.org/pdf/2305.14878v1.pdf | Leveraging GPT-4 for Automatic Translation Post-Editing | While Neural Machine Translation (NMT) represents the leading approach to Machine Translation (MT), the outputs of NMT models still require translation post-editing to rectify errors and enhance quality, particularly under critical settings. In this work, we formalize the task of translation post-editing with Large Lan... | ['Arul Menezes', 'Hany Hassan Awadallah', 'Amr Sharaf', 'Vikas Raunak'] | 2023-05-24 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 6.01463079e-01 1.42443195e-01 -1.98447585e-01 -2.99445629e-01
-1.40787828e+00 -7.06492126e-01 7.49978125e-01 1.12883244e-02
-5.49100757e-01 1.00399125e+00 2.47369185e-01 -8.42681348e-01
4.15330172e-01 -4.36293960e-01 -1.05079341e+00 2.70152360e-01
3.88008296e-01 9.82568145e-01 -4.79693204e-01 -7.06635535... | [11.684884071350098, 10.270073890686035] |
3d061656-0c29-410f-92f0-b0f8b54d808c | a-health-monitoring-system-for-elder-and-sick | 1304.4652 | null | http://arxiv.org/abs/1304.4652v1 | http://arxiv.org/pdf/1304.4652v1.pdf | A Health Monitoring System for Elder and Sick Persons | This paper discusses a vision based health monitoring system which would be
very easy in use and deployment. Elder and sick people who are not able to talk
or walk they are dependent on other human beings for their daily needs and need
continuous monitoring. The developed system provides facility to the sick or
elder p... | ['Jagdish L. Raheja', 'Ankit Chaudhary'] | 2013-04-17 | null | null | null | null | ['fingertip-detection'] | ['computer-vision'] | [-2.53738463e-01 -4.12486255e-01 -2.91867852e-01 -5.85373640e-01
1.79715604e-01 -4.61774826e-01 6.26632422e-02 -4.99770567e-02
-8.41579497e-01 8.37862968e-01 1.74492896e-01 -2.34400243e-01
1.39381826e-01 -5.40166199e-01 5.62784910e-01 -5.05870104e-01
3.39722298e-02 6.92726493e-01 3.41184884e-01 -2.56790578... | [6.488065242767334, -0.2487478256225586] |
a1a22a50-2360-4bf0-9c05-8c17a4c669f2 | uob-at-semeval-2021-task-5-extending-pre-1 | 2110.0373 | null | https://arxiv.org/abs/2110.03730v1 | https://arxiv.org/pdf/2110.03730v1.pdf | UoB at SemEval-2021 Task 5: Extending Pre-Trained Language Models to Include Task and Domain-Specific Information for Toxic Span Prediction | Toxicity is pervasive in social media and poses a major threat to the health of online communities. The recent introduction of pre-trained language models, which have achieved state-of-the-art results in many NLP tasks, has transformed the way in which we approach natural language processing. However, the inherent natu... | ['Harish Tayyar Madabushi', 'Erik Yan'] | 2021-10-07 | uob-at-semeval-2021-task-5-extending-pre | https://aclanthology.org/2021.semeval-1.28 | https://aclanthology.org/2021.semeval-1.28.pdf | semeval-2021 | ['toxic-spans-detection'] | ['natural-language-processing'] | [ 2.31733784e-01 -1.21885367e-01 -3.07107210e-01 -7.85920322e-02
-1.27661717e+00 -6.62044525e-01 8.93196762e-01 1.07073426e+00
-9.14499640e-01 9.13169265e-01 4.57750142e-01 -2.23601922e-01
1.72078852e-02 -7.06282616e-01 -6.54943943e-01 -3.47694546e-01
-2.08908662e-01 2.76094794e-01 3.47221911e-01 1.52965918... | [8.980640411376953, 10.572006225585938] |
d14861b3-b2f1-43f2-9d90-47b8ae80cacd | a-comparative-study-of-deep-learning-loss | 2009.13935 | null | https://arxiv.org/abs/2009.13935v1 | https://arxiv.org/pdf/2009.13935v1.pdf | A Comparative Study of Deep Learning Loss Functions for Multi-Label Remote Sensing Image Classification | This paper analyzes and compares different deep learning loss functions in the framework of multi-label remote sensing (RS) image scene classification problems. We consider seven loss functions: 1) cross-entropy loss; 2) focal loss; 3) weighted cross-entropy loss; 4) Hamming loss; 5) Huber loss; 6) ranking loss; and 7)... | ['Begüm Demir', 'Hichame Yessou', 'Gencer Sumbul'] | 2020-09-29 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 4.26684290e-01 -1.40283540e-01 -1.58903003e-01 -5.15862346e-01
-9.39898551e-01 -1.60352871e-01 2.86740661e-01 8.96937490e-01
-7.44876027e-01 7.58628428e-01 -2.94459045e-01 3.76550555e-02
-7.76451409e-01 -8.15333784e-01 -4.91106570e-01 -1.02009845e+00
-3.08765739e-01 2.86774635e-01 -1.91483706e-01 1.62500083... | [9.119155883789062, 3.842449188232422] |
ab01b3b6-bac6-452b-8dce-f877932d3baf | clip4clip-an-empirical-study-of-clip-for-end | 2104.0886 | null | https://arxiv.org/abs/2104.08860v2 | https://arxiv.org/pdf/2104.08860v2.pdf | CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval | Video-text retrieval plays an essential role in multi-modal research and has been widely used in many real-world web applications. The CLIP (Contrastive Language-Image Pre-training), an image-language pre-training model, has demonstrated the power of visual concepts learning from web collected image-text datasets. In t... | ['Tianrui Li', 'Nan Duan', 'Wen Lei', 'Yang Chen', 'Ming Zhong', 'Lei Ji', 'Huaishao Luo'] | 2021-04-18 | null | null | null | null | ['video-text-retrieval'] | ['computer-vision'] | [-1.56228617e-01 -1.07143509e+00 -6.11479521e-01 -7.78534710e-02
-9.91343677e-01 -4.47248369e-01 5.85716963e-01 -1.22041747e-01
-5.77648103e-01 1.57743096e-01 2.64371425e-01 -1.42708912e-01
-9.36838165e-02 -2.52013832e-01 -7.15604007e-01 -5.47790468e-01
-9.72771868e-02 8.78690258e-02 4.60936129e-01 -6.15670718... | [10.324419975280762, 0.9445454478263855] |
1ffee44d-4b62-467e-b44d-6fac6aba5364 | transformers-for-headline-selection-for | 2106.10487 | null | https://arxiv.org/abs/2106.10487v1 | https://arxiv.org/pdf/2106.10487v1.pdf | Transformers for Headline Selection for Russian News Clusters | In this paper, we explore various multilingual and Russian pre-trained transformer-based models for the Dialogue Evaluation 2021 shared task on headline selection. Our experiments show that the combined approach is superior to individual multilingual and monolingual models. We present an analysis of a number of ways to... | ['Olga Sopilnyak', 'Pavel Voropaev'] | 2021-06-19 | null | null | null | null | ['dialogue-evaluation'] | ['natural-language-processing'] | [-3.69951129e-01 3.12721342e-01 -8.52166787e-02 -8.11263740e-01
-1.43023360e+00 -6.86558783e-01 1.06896091e+00 2.33241528e-01
-9.85306263e-01 1.23513985e+00 8.25090766e-01 -4.44283038e-01
1.70552045e-01 -2.77102500e-01 1.03035616e-02 -1.23572037e-01
1.13055460e-01 9.12545741e-01 2.68506378e-01 -9.07856703... | [12.44701862335205, 8.382412910461426] |
a7371b61-2d50-4069-bf51-2de2ca514e5f | machine-learning-models-for-dota-2-outcomes | 2106.01782 | null | https://arxiv.org/abs/2106.01782v1 | https://arxiv.org/pdf/2106.01782v1.pdf | Machine learning models for DOTA 2 outcomes prediction | Prediction of the real-time multiplayer online battle arena (MOBA) games' match outcome is one of the most important and exciting tasks in Esports analytical research. This research paper predominantly focuses on building predictive machine and deep learning models to identify the outcome of the Dota 2 MOBA game using ... | ['Anh Huy Phan', 'Kodirjon Akhmedov'] | 2021-06-03 | null | null | null | null | ['dota-2'] | ['playing-games'] | [-3.65319222e-01 -1.21557005e-01 2.79531274e-02 -1.51222795e-01
-5.24789035e-01 -1.17497154e-01 4.68610108e-01 5.43673225e-02
-8.49565506e-01 6.80932820e-01 1.55277759e-01 -3.62633854e-01
-7.26675212e-01 -1.03355038e+00 -4.36382949e-01 -5.08995235e-01
-4.07096267e-01 8.12585711e-01 3.52769911e-01 -8.75571668... | [6.724055767059326, 0.3714500069618225] |
ceed2b79-ed31-4db7-9ac4-39d3be820f36 | cendernet-center-and-curvature | 2208.09829 | null | https://arxiv.org/abs/2208.09829v1 | https://arxiv.org/pdf/2208.09829v1.pdf | CenDerNet: Center and Curvature Representations for Render-and-Compare 6D Pose Estimation | We introduce CenDerNet, a framework for 6D pose estimation from multi-view images based on center and curvature representations. Finding precise poses for reflective, textureless objects is a key challenge for industrial robotics. Our approach consists of three stages: First, a fully convolutional neural network predic... | ['Francis wyffels', 'Joris de Hoog', 'Taoufik Bourgana', 'Jonathan Croenen', 'Rembert Daems', 'Peter De Roovere'] | 2022-08-21 | null | null | null | null | ['6d-pose-estimation-1'] | ['computer-vision'] | [-1.10359170e-01 -6.18455857e-02 1.35466963e-01 -2.91303217e-01
-7.19288588e-01 -9.03517306e-01 5.70801556e-01 9.01944637e-02
2.17842132e-01 -5.21369539e-02 -1.26151040e-01 3.17502953e-02
-1.47944957e-01 -3.83641094e-01 -9.59197283e-01 -2.74378538e-01
6.07078783e-02 9.93692577e-01 1.85555324e-01 2.14811061... | [7.461577892303467, -2.6132845878601074] |
a4149b1f-d833-47ba-99cb-fbcff11d341f | natural-language-processing-state-of-the-art | 1708.05148 | null | http://arxiv.org/abs/1708.05148v1 | http://arxiv.org/pdf/1708.05148v1.pdf | Natural Language Processing: State of The Art, Current Trends and Challenges | Natural language processing (NLP) has recently gained much attention for
representing and analysing human language computationally. It has spread its
applications in various fields such as machine translation, email spam
detection, information extraction, summarization, medical, and question
answering etc. The paper di... | ['Sukhdev Singh', 'Kiran Khatter', 'Diksha Khurana', 'Aditya Koli'] | 2017-08-17 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [ 6.13419235e-01 7.02209473e-01 -1.15321867e-01 -2.59095547e-03
-7.23220825e-01 -7.98240364e-01 1.19766092e+00 1.07153964e+00
-4.29155022e-01 1.18530226e+00 7.83803821e-01 -3.46072108e-01
-2.77871937e-02 -6.16534531e-01 -1.04859143e-01 -1.09515168e-01
-9.58718136e-02 6.83768332e-01 2.59963304e-01 -3.52049351... | [12.412054061889648, 9.450925827026367] |
fa02bb91-0488-4866-8f84-7269a47242cf | group-gated-fusion-on-attention-based | 2201.06309 | null | https://arxiv.org/abs/2201.06309v1 | https://arxiv.org/pdf/2201.06309v1.pdf | Group Gated Fusion on Attention-based Bidirectional Alignment for Multimodal Emotion Recognition | Emotion recognition is a challenging and actively-studied research area that plays a critical role in emotion-aware human-computer interaction systems. In a multimodal setting, temporal alignment between different modalities has not been well investigated yet. This paper presents a new model named as Gated Bidirectiona... | ['Helen Meng', 'Kun Li', 'PengFei Liu'] | 2022-01-17 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 2.69364029e-01 -3.06086093e-01 -1.99803293e-01 -6.90319836e-01
-7.95255542e-01 -1.55173406e-01 7.73613393e-01 -1.18139103e-01
-4.37615335e-01 4.03679073e-01 5.41893959e-01 -6.18983768e-02
2.67826408e-01 -1.56250194e-01 -5.62255085e-01 -7.57364452e-01
1.01035953e-01 2.30602741e-01 -2.43332982e-01 -3.39815348... | [13.223611831665039, 5.282009124755859] |
75bfd7f7-ccbc-4c1a-ba66-c599b92c6a0c | fine-grained-image-analysis-with-deep | 2111.06119 | null | https://arxiv.org/abs/2111.06119v2 | https://arxiv.org/pdf/2111.06119v2.pdf | Fine-Grained Image Analysis with Deep Learning: A Survey | Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition, and underpins a diverse set of real-world applications. The task of FGIA targets analyzing visual objects from subordinate categories, e.g., species of birds or models of cars. The small inter-class ... | ['Serge Belongie', 'Jian Yang', 'Jinhui Tang', 'Yuxin Peng', 'Jianxin Wu', 'Oisin Mac Aodha', 'Yi-Zhe Song', 'Xiu-Shen Wei'] | 2021-11-11 | null | null | null | null | ['fine-grained-image-recognition'] | ['computer-vision'] | [ 1.70016527e-01 -6.70588851e-01 -1.34845704e-01 -5.32739878e-01
-5.83442628e-01 -7.46502578e-01 6.82186246e-01 5.20892330e-02
-2.51738191e-01 5.85286140e-01 1.17445670e-01 1.23762697e-01
-4.32606608e-01 -8.86090636e-01 -5.94767451e-01 -7.01146245e-01
4.76303920e-02 2.82279074e-01 7.02645183e-02 9.80357639... | [9.63174819946289, 2.0216856002807617] |
a4cf044a-0e82-4d46-be19-fe3f4c2f4ef6 | analogy-as-nonparametric-bayesian-inference | 2006.04156 | null | https://arxiv.org/abs/2006.04156v1 | https://arxiv.org/pdf/2006.04156v1.pdf | Analogy as Nonparametric Bayesian Inference over Relational Systems | Much of human learning and inference can be framed within the computational problem of relational generalization. In this project, we propose a Bayesian model that generalizes relational knowledge to novel environments by analogically weighting predictions from previously encountered relational structures. First, we sh... | ['Ruairidh M. Battleday', 'Thomas L. Griffiths'] | 2020-06-07 | null | null | null | null | ['analogical-similarity'] | ['reasoning'] | [ 1.95324644e-02 5.51568985e-01 7.44676515e-02 -5.90019822e-01
-2.02303022e-01 -5.69695652e-01 9.87940609e-01 6.78324044e-01
-8.44943821e-02 5.27670801e-01 7.05451488e-01 -4.37615395e-01
-8.01186025e-01 -1.15731740e+00 -1.15356028e+00 3.38232182e-02
-3.42491537e-01 9.03240085e-01 4.37776625e-01 -4.86027449... | [9.533038139343262, 6.9905686378479] |
33c045ff-6373-4930-b0f1-1dfc08a0eeb7 | hierarchical-regression-network-for-spectral | 2005.04703 | null | https://arxiv.org/abs/2005.04703v1 | https://arxiv.org/pdf/2005.04703v1.pdf | Hierarchical Regression Network for Spectral Reconstruction from RGB Images | Capturing visual image with a hyperspectral camera has been successfully applied to many areas due to its narrow-band imaging technology. Hyperspectral reconstruction from RGB images denotes a reverse process of hyperspectral imaging by discovering an inverse response function. Current works mainly map RGB images direc... | ['Lai-Man Po', 'Tingyu Lin', 'Yuzhi Zhao', 'Wei Liu', 'Qiong Yan'] | 2020-05-10 | null | null | null | null | ['spectral-reconstruction'] | ['computer-vision'] | [ 8.96802187e-01 -1.55021399e-01 1.56665370e-01 -4.02975768e-01
-9.34183478e-01 -4.61298674e-01 1.39521703e-01 -6.88647151e-01
-1.58424288e-01 4.99346703e-01 2.01092750e-01 -4.39323187e-01
-1.80861995e-01 -8.00974727e-01 -9.77905273e-01 -8.87831688e-01
3.76972705e-01 -3.05819631e-01 -3.81587535e-01 -1.91338509... | [10.21755313873291, -2.046236276626587] |
43540870-51f1-4311-bc18-7982b33380d8 | pushing-the-frontiers-of-unconstrained-face | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Klare_Pushing_the_Frontiers_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Klare_Pushing_the_Frontiers_2015_CVPR_paper.pdf | Pushing the Frontiers of Unconstrained Face Detection and Recognition: IARPA Janus Benchmark A | Rapid progress in unconstrained face recognition has resulted in a saturation in recognition accuracy for current benchmark datasets. While important for early progress, a chief limitation in most benchmark datasets is the use of a commodity face detector to select face imagery. The implication of this strategy is rest... | ['Jordan Cheney', 'Austin Blanton', 'Alan Mah', 'Kristen Allen', 'Anil K. Jain', 'Patrick Grother', 'Emma Taborsky', 'Brendan F. Klare', 'Ben Klein'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['robust-face-recognition'] | ['computer-vision'] | [ 3.91219109e-02 -2.97702789e-01 -1.56963784e-02 -7.47689903e-01
-9.17170584e-01 -7.09637105e-01 7.46347189e-01 -5.99175334e-01
-4.90472853e-01 6.53371274e-01 1.58135459e-01 4.39845212e-02
2.60051078e-04 -2.51737505e-01 -4.59207803e-01 -6.19169891e-01
-3.19450915e-01 6.91133380e-01 -2.35607237e-01 3.72030102... | [13.390953063964844, 0.8259932398796082] |
521373b5-a880-43dd-8058-929e7581166d | diverse-and-relevant-visual-storytelling-with | null | null | https://aclanthology.org/2020.conll-1.34 | https://aclanthology.org/2020.conll-1.34.pdf | Diverse and Relevant Visual Storytelling with Scene Graph Embeddings | A problem in automatically generated stories for image sequences is that they use overly generic vocabulary and phrase structure and fail to match the distributional characteristics of human-generated text. We address this problem by introducing explicit representations for objects and their relations by extracting sce... | ['Bernt Schiele', 'Vera Demberg', 'Khushboo Mehra', 'Asad Sayeed', 'Rakshith Shetty', 'Xudong Hong'] | 2020-11-01 | null | null | null | conll-2020 | ['visual-storytelling'] | ['natural-language-processing'] | [ 3.53880316e-01 3.90571579e-02 -2.80931801e-01 -3.88325363e-01
-6.95582449e-01 -6.46318972e-01 1.40622365e+00 4.21386659e-01
-3.02693814e-01 5.56752980e-01 1.02769601e+00 2.65227258e-01
-2.15836223e-02 -8.69729757e-01 -4.49993521e-01 -2.07438201e-01
1.81770474e-01 3.79527271e-01 4.92339611e-01 -2.86011994... | [11.001441955566406, 1.0040435791015625] |
212cf36a-f301-4328-b8c4-f82db2d1402e | dense-multi-path-u-net-for-ischemic-stroke | 1810.07003 | null | http://arxiv.org/abs/1810.07003v1 | http://arxiv.org/pdf/1810.07003v1.pdf | Dense Multi-path U-Net for Ischemic Stroke Lesion Segmentation in Multiple Image Modalities | Delineating infarcted tissue in ischemic stroke lesions is crucial to
determine the extend of damage and optimal treatment for this life-threatening
condition. However, this problem remains challenging due to high variability of
ischemic strokes' location and shape. Recently, fully-convolutional neural
networks (CNN), ... | ['Christian Desrosiers', 'Jose Dolz', 'Ismail Ben Ayed'] | 2018-10-16 | null | null | null | null | ['ischemic-stroke-lesion-segmentation'] | ['medical'] | [ 8.77413005e-02 -1.63278788e-01 2.40013702e-03 -2.09662870e-01
-4.43050265e-01 -6.21780276e-01 2.90580899e-01 3.84442881e-02
-8.69392633e-01 8.45086932e-01 8.39479342e-02 -5.07141948e-01
3.36192027e-02 -1.16429222e+00 -7.13619888e-01 -6.36931300e-01
-2.60317206e-01 1.59462407e-01 7.22363114e-01 3.10934726... | [14.360928535461426, -2.071216583251953] |
888e010e-24ef-42d3-b55f-709aa1c1a6d4 | three-dependency-and-boundary-models-for | null | null | https://aclanthology.org/D12-1063 | https://aclanthology.org/D12-1063.pdf | Three Dependency-and-Boundary Models for Grammar Induction | null | ['Valentin I. Spitkovsky', 'Hiyan Alshawi', 'Daniel Jurafsky'] | 2012-07-01 | null | null | null | emnlp-2012-7 | ['dependency-grammar-induction'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.310418605804443, 3.7660129070281982] |
e330bd29-f5a4-4c06-83ed-bee428ee5136 | bootstrap-your-flow | 2111.1151 | null | https://arxiv.org/abs/2111.11510v4 | https://arxiv.org/pdf/2111.11510v4.pdf | Bootstrap Your Flow | Normalizing flows are flexible, parameterized distributions that can be used to approximate expectations from intractable distributions via importance sampling. However, current flow-based approaches are limited on challenging targets where they either suffer from mode seeking behaviour or high variance in the training... | ['José Miguel Hernández-Lobato', 'Gregor N. C. Simm', 'Vincent Stimper', 'Laurence Illing Midgley'] | 2021-11-22 | null | https://openreview.net/forum?id=Rzwf6LeM-6E | https://openreview.net/pdf?id=Rzwf6LeM-6E | pproximateinference-aabi-symposium-2022-2 | ['normalising-flows'] | ['methodology'] | [ 1.33314684e-01 -3.20350647e-01 -1.46942884e-01 -3.86310309e-01
-1.10044229e+00 -6.63807690e-01 4.78269428e-01 5.42726144e-02
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-1.18600771e-01 -7.69875348e-01 -6.90145433e-01 -6.92075431e-01
2.01249734e-01 7.94062734e-01 2.87429214e-01 3.24532986... | [7.022350311279297, 3.916487455368042] |
72d34a86-f4a0-489d-ad83-72f12aafcb4c | middle-level-fusion-for-lightweight-rgb-d | 2104.11543 | null | https://arxiv.org/abs/2104.11543v3 | https://arxiv.org/pdf/2104.11543v3.pdf | Middle-level Fusion for Lightweight RGB-D Salient Object Detection | Most existing lightweight RGB-D salient object detection (SOD) models are based on two-stream structure or single-stream structure. The former one first uses two sub-networks to extract unimodal features from RGB and depth images, respectively, and then fuses them for SOD. While, the latter one directly extracts multi-... | ['Jungong Han', 'Qiang Zhang', 'Nianchang Huang'] | 2021-04-23 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 2.23644361e-01 -1.54094398e-01 -1.64828405e-01 -1.19378209e-01
-6.74125969e-01 -3.83710600e-02 3.11868966e-01 1.62860155e-01
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-2.42346585e-01 -8.30371320e-01 -4.10233736e-01 -9.69201684e-01
1.14022240e-01 -4.26216006e-01 1.00644529e+00 -2.67877758... | [9.676178932189941, -0.8601583242416382] |
1780eb5d-b6ce-40b0-a518-8285f4dbeabf | arabsign-a-multi-modality-dataset-and | 2210.03951 | null | https://arxiv.org/abs/2210.03951v1 | https://arxiv.org/pdf/2210.03951v1.pdf | ArabSign: A Multi-modality Dataset and Benchmark for Continuous Arabic Sign Language Recognition | Sign language recognition has attracted the interest of researchers in recent years. While numerous approaches have been proposed for European and Asian sign languages recognition, very limited attempts have been made to develop similar systems for the Arabic sign language (ArSL). This can be attributed partly to the l... | ['Hamzah Luqman'] | 2022-10-08 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [-1.11022800e-01 -3.34304273e-01 -2.40406301e-02 -4.80118454e-01
-8.35026264e-01 -3.54159921e-01 6.23093009e-01 -4.68379378e-01
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1.59994334e-01 2.63771862e-01 1.13365866e-01 -7.61856362... | [9.116056442260742, -6.421206474304199] |
bb07989b-1584-4e70-961e-432dd8cb18ff | mind-the-gap-alleviating-local-imbalance-for | 2205.11888 | null | https://arxiv.org/abs/2205.11888v2 | https://arxiv.org/pdf/2205.11888v2.pdf | Mind The Gap: Alleviating Local Imbalance for Unsupervised Cross-Modality Medical Image Segmentation | Unsupervised cross-modality medical image adaptation aims to alleviate the severe domain gap between different imaging modalities without using the target domain label. A key in this campaign relies upon aligning the distributions of source and target domain. One common attempt is to enforce the global alignment betwee... | ['Kaizhu Huang', 'Jie Sun', 'Yuyao Yan', 'Qiufeng Wang', 'Xi Yang', 'Kai Yao', 'Zixian Su'] | 2022-05-24 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 4.41654503e-01 -1.10156111e-01 -4.41195279e-01 -5.07533014e-01
-1.08907902e+00 -5.26989102e-01 3.75970930e-01 2.22884431e-01
-4.04094011e-01 5.24052918e-01 2.74719834e-01 1.08419821e-01
-2.48948276e-01 -6.07238889e-01 -5.27459681e-01 -9.99179900e-01
4.66321737e-01 2.59700477e-01 3.64543885e-01 -1.76425979... | [14.501206398010254, -1.9448487758636475] |
a7cbdfd5-4617-4fa3-bf20-4bac703351af | intent-recognition-and-unsupervised-slot | 2104.01287 | null | https://arxiv.org/abs/2104.01287v3 | https://arxiv.org/pdf/2104.01287v3.pdf | Intent Recognition and Unsupervised Slot Identification for Low Resourced Spoken Dialog Systems | Intent Recognition and Slot Identification are crucial components in spoken language understanding (SLU) systems. In this paper, we present a novel approach towards both these tasks in the context of low resourced and unwritten languages. We present an acoustic based SLU system that converts speech to its phonetic tran... | ['Sai Krishna Rallabandi', 'William Zeng', 'Saloni Mittal', 'Akruti Kushwaha', 'Olivia Deng', 'Alan W Black', 'Akshat Gupta'] | 2021-04-03 | null | null | null | null | ['intent-recognition'] | ['natural-language-processing'] | [ 6.37035072e-01 4.75335330e-01 -5.99802658e-02 -6.82583451e-01
-1.25664485e+00 -4.55269396e-01 7.20734119e-01 1.04486711e-01
-6.95691705e-01 6.22235894e-01 7.67333567e-01 -6.57704353e-01
3.96063179e-01 -5.27617335e-01 -2.95854419e-01 -4.11201119e-01
2.88339585e-01 9.62248445e-01 1.05063714e-01 -3.40910405... | [14.065542221069336, 6.912117958068848] |
d87e2df1-1eef-442d-a1c4-22a13a19891f | mga-medical-generalist-agent-through-text | 2303.08562 | null | https://arxiv.org/abs/2303.08562v1 | https://arxiv.org/pdf/2303.08562v1.pdf | MGA: Medical generalist agent through text-guided knowledge transformation | Multi-modal representation methods have achieved advanced performance in medical applications by extracting more robust features from multi-domain data. However, existing methods usually need to train additional branches for downstream tasks, which may increase the model complexities in clinical applications as well as... | ['Shanshan Wang', 'Rui Yang', 'Mingtong Dai', 'Cheng Li', 'Hao Yang', 'Weijian Huang'] | 2023-03-15 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [ 4.52650078e-02 1.77805498e-01 -5.00459611e-01 -4.30359900e-01
-1.26062548e+00 -3.86605531e-01 3.76535147e-01 4.49520856e-01
-2.91018516e-01 8.40260148e-01 4.78451043e-01 -4.88420427e-01
-5.25957346e-01 -7.84026206e-01 -5.12125075e-01 -7.03718483e-01
1.86594471e-01 6.41487598e-01 -4.58187796e-02 -3.04563679... | [14.917399406433105, -1.8517369031906128] |
167b0cac-d140-40e3-8a85-7e11da9817e5 | constrained-clustering-and-its-application-to | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Wu_Constrained_Clustering_and_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Wu_Constrained_Clustering_and_2013_CVPR_paper.pdf | Constrained Clustering and Its Application to Face Clustering in Videos | In this paper, we focus on face clustering in videos. Given the detected faces from real-world videos, we partition all faces into K disjoint clusters. Different from clustering on a collection of facial images, the faces from videos are organized as face tracks and the frame index of each face is also provided. As a r... | ['Bao-Gang Hu', 'Baoyuan Wu', 'Yifan Zhang', 'Qiang Ji'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['face-clustering'] | ['computer-vision'] | [-1.03425540e-01 -1.61838755e-01 -1.09126136e-01 -6.60597742e-01
-4.15993363e-01 -3.32008988e-01 3.50021958e-01 -4.29062396e-01
-1.87251836e-01 3.84044200e-01 -8.60763267e-02 4.64512318e-01
-3.19983929e-01 -4.77830797e-01 -6.87482834e-01 -1.26263213e+00
-2.28125602e-01 3.38887364e-01 2.09945485e-01 3.09211344... | [13.46142578125, 1.0875228643417358] |
297244f1-7b71-475c-bd46-738426807c2c | towards-incremental-learning-of-word | null | null | https://aclanthology.org/P19-2022 | https://aclanthology.org/P19-2022.pdf | Towards Incremental Learning of Word Embeddings Using Context Informativeness | In this paper, we investigate the task of learning word embeddings from very sparse data in an incremental, cognitively-plausible way. We focus on the notion of {`}informativeness{'}, that is, the idea that some content is more valuable to the learning process than other. We further highlight the challenges of online l... | ["Aur{\\'e}lie Herbelot", 're', 'Alex Kabbach', 'Kristina Gulordava'] | 2019-07-01 | null | null | null | acl-2019-7 | ['learning-word-embeddings'] | ['methodology'] | [ 4.63715792e-01 1.95329189e-01 -1.61240682e-01 -3.59843314e-01
-8.39037299e-01 -5.88219702e-01 7.17340529e-01 4.67731804e-01
-1.12558317e+00 7.79139102e-01 6.94227219e-01 -4.80071217e-01
-5.29421568e-02 -6.02281392e-01 -6.89779401e-01 -4.15436119e-01
-2.23605022e-01 3.66611391e-01 5.29579110e-02 -2.20874593... | [10.636524200439453, 8.559222221374512] |
3b7c0722-3640-4c1d-a8cf-dc4190efb4c4 | learning-joint-latent-space-ebm-prior-model-1 | 2306.06323 | null | https://arxiv.org/abs/2306.06323v1 | https://arxiv.org/pdf/2306.06323v1.pdf | Learning Joint Latent Space EBM Prior Model for Multi-layer Generator | This paper studies the fundamental problem of learning multi-layer generator models. The multi-layer generator model builds multiple layers of latent variables as a prior model on top of the generator, which benefits learning complex data distribution and hierarchical representations. However, such a prior model usuall... | ['Tian Han', 'Ying Nian Wu', 'Jiali Cui'] | 2023-06-10 | learning-joint-latent-space-ebm-prior-model | http://openaccess.thecvf.com//content/CVPR2023/html/Cui_Learning_Joint_Latent_Space_EBM_Prior_Model_for_Multi-Layer_Generator_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Cui_Learning_Joint_Latent_Space_EBM_Prior_Model_for_Multi-Layer_Generator_CVPR_2023_paper.pdf | cvpr-2023-1 | ['outlier-detection'] | ['methodology'] | [ 1.29259899e-01 6.77103698e-02 -1.86541930e-01 -2.17168376e-01
-1.01309073e+00 -1.26176067e-02 5.53824544e-01 -3.19998324e-01
-3.15232426e-02 6.74978316e-01 2.22156808e-01 1.65132552e-01
7.49332532e-02 -9.22608316e-01 -9.44623768e-01 -1.03482866e+00
3.10027841e-02 3.94717067e-01 1.31073639e-01 5.26531994... | [7.142113208770752, 3.8205103874206543] |
b7b6a8d9-437b-467b-bfcf-6dd787e9b73d | upop-unified-and-progressive-pruning-for | 2301.13741 | null | https://arxiv.org/abs/2301.13741v3 | https://arxiv.org/pdf/2301.13741v3.pdf | UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers | Real-world data contains a vast amount of multimodal information, among which vision and language are the two most representative modalities. Moreover, increasingly heavier models, \textit{e}.\textit{g}., Transformers, have attracted the attention of researchers to model compression. However, how to compress multimodal... | ['Jiaqi Wang', 'Chun Yuan', 'Zhendong Yang', 'Ying Jin', 'Chaofan Tao', 'Dachuan Shi'] | 2023-01-31 | null | null | null | null | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [ 4.91831452e-01 -8.24009720e-03 -4.26424295e-01 -2.58625656e-01
-7.06944823e-01 -4.14991081e-01 4.03067052e-01 4.32411442e-03
-4.74458009e-01 4.23799127e-01 1.71515152e-01 -1.97869852e-01
-3.34135234e-01 -4.93055016e-01 -7.38116562e-01 -7.19146371e-01
3.32471222e-01 6.09020710e-01 2.36645788e-02 1.69824511... | [10.230467796325684, 0.9294155240058899] |
d15cf739-6fc2-49bf-b792-523fad16b369 | speech-enhancement-guided-by-contextual | 2011.07442 | null | https://arxiv.org/abs/2011.07442v5 | https://arxiv.org/pdf/2011.07442v5.pdf | Improving Speech Enhancement Performance by Leveraging Contextual Broad Phonetic Class Information | Previous studies have confirmed that by augmenting acoustic features with the place/manner of articulatory features, the speech enhancement (SE) process can be guided to consider the broad phonetic properties of the input speech when performing enhancement to attain performance improvements. In this paper, we explore t... | ['Yu Tsao', 'Shinji Watanabe', 'Jeih-weih Hung', 'Ching-Feng Liu', 'Cheng Yu', 'Chia-Yu Chang', 'Yen-Ju Lu'] | 2020-11-15 | null | null | null | null | ['speech-denoising', 'speech-dereverberation'] | ['speech', 'speech'] | [ 3.95772278e-01 -1.83043443e-02 1.86891496e-01 -5.13558626e-01
-1.29550231e+00 -2.78306842e-01 2.68491209e-01 -2.93451667e-01
-4.81013536e-01 2.14869007e-01 7.27419019e-01 -5.05866647e-01
-1.03078440e-01 -3.44286859e-01 -6.33685648e-01 -7.08009243e-01
2.99993038e-01 -2.23701909e-01 -6.72390983e-02 -4.87959415... | [14.868931770324707, 6.1361613273620605] |
4f2d2a06-cb35-4357-929e-9b2abfaa461a | contrastive-distillation-is-a-sample | 2212.11353 | null | https://arxiv.org/abs/2212.11353v1 | https://arxiv.org/pdf/2212.11353v1.pdf | Contrastive Distillation Is a Sample-Efficient Self-Supervised Loss Policy for Transfer Learning | Traditional approaches to RL have focused on learning decision policies directly from episodic decisions, while slowly and implicitly learning the semantics of compositional representations needed for generalization. While some approaches have been adopted to refine representations via auxiliary self-supervised losses ... | ['Charysse Redwood', 'François Charton', 'Kurt Shuster', 'Hugh Leather', 'Amy Zhang', 'Gabriel Synnaeve', 'Chris Lengerich'] | 2022-12-21 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [ 1.11142725e-01 2.64948130e-01 -5.22224247e-01 -4.98960167e-01
-7.77786553e-01 -4.86810982e-01 9.90314543e-01 -1.34311423e-01
-5.52440703e-01 1.06289566e+00 3.48577529e-01 2.05139462e-02
-1.32852912e-01 -8.75014424e-01 -7.58876264e-01 -7.04990506e-01
-3.61170739e-01 8.16239476e-01 1.20171003e-01 -3.42329264... | [4.254665374755859, 1.6076141595840454] |
5c323702-2e1f-43ae-8a5c-f722730fc0e3 | automatic-differentiation-to-simultaneously | 2009.0881 | null | https://arxiv.org/abs/2009.08810v2 | https://arxiv.org/pdf/2009.08810v2.pdf | Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from Data | The sparse identification of nonlinear dynamics (SINDy) is a regression framework for the discovery of parsimonious dynamic models and governing equations from time-series data. As with all system identification methods, noisy measurements compromise the accuracy and robustness of the model discovery procedure. In this... | ['J. Nathan Kutz', 'Steven L. Brunton', 'Kadierdan Kaheman'] | 2020-09-12 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [ 1.30636171e-02 -4.15045589e-01 4.12825495e-01 2.56393909e-01
-6.07350767e-01 -7.61613011e-01 7.23907650e-01 -2.45431542e-01
-2.27881327e-01 8.87106538e-01 -8.87285024e-02 -3.90606821e-01
-6.51503086e-01 -2.86925286e-01 -2.24976808e-01 -1.10467768e+00
-4.69583452e-01 6.15032256e-01 -1.06533259e-01 -8.86091813... | [6.551994323730469, 3.517589569091797] |
a93cb22e-db6f-4b2e-ab0c-916be30d1d8e | zero-shot-bird-species-recognition-by | 2206.01466 | null | https://arxiv.org/abs/2206.01466v1 | https://arxiv.org/pdf/2206.01466v1.pdf | Zero-Shot Bird Species Recognition by Learning from Field Guides | We exploit field guides to learn bird species recognition, in particular zero-shot recognition of unseen species. The illustrations contained in field guides deliberately focus on discriminative properties of a species, and can serve as side information to transfer knowledge from seen to unseen classes. We study two ap... | ['Konrad Schindler', 'Jan D. Wegner', 'Rodrigo Caye Daudt', "Stefano D'Aronco", 'Andrés C. Rodríguez'] | 2022-06-03 | null | null | null | null | ['generalized-zero-shot-learning', 'generalized-zero-shot-learning'] | ['computer-vision', 'methodology'] | [ 2.52631366e-01 -2.32261315e-01 6.60836324e-02 -4.08373356e-01
-5.85045755e-01 -1.11844933e+00 7.23328531e-01 7.61964992e-02
-6.62238479e-01 7.28008091e-01 3.48610282e-01 8.73577446e-02
-1.26489744e-01 -8.38508964e-01 -9.27625000e-01 -6.14730418e-01
-4.95406061e-01 -1.82871241e-02 2.37239152e-01 -1.48167819... | [9.837918281555176, 2.2608370780944824] |
c7acc28b-39f4-429f-b547-f810182aa6c0 | texture-features-in-medical-image-analysis-a | 2208.02046 | null | https://arxiv.org/abs/2208.02046v1 | https://arxiv.org/pdf/2208.02046v1.pdf | Texture features in medical image analysis: a survey | The texture is defined as spatial structure of the intensities of the pixels in an image that is repeated periodically in the whole image or regions, and makes the concept of the image. Texture, color and shape are three main components which are used by human visual system to recognize image contents. In this paper, f... | ['Faeze Kiani'] | 2022-08-02 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 1.75584868e-01 -5.72341979e-01 -9.52495113e-02 -2.70701468e-01
-1.72569692e-01 -8.77203569e-02 3.29150707e-01 1.22813970e-01
-2.26381212e-01 4.82527912e-01 -1.37997329e-01 6.71731532e-02
-3.24360609e-01 -9.99605894e-01 3.73711996e-02 -1.15628111e+00
-3.95655558e-02 1.55717418e-01 4.29905027e-01 -2.05009207... | [10.384881973266602, -0.4168902635574341] |
19fd68a3-8960-4f13-ba8e-a9c926932eb9 | s3net-3d-lidar-sparse-semantic-segmentation | 2103.08745 | null | https://arxiv.org/abs/2103.08745v1 | https://arxiv.org/pdf/2103.08745v1.pdf | S3Net: 3D LiDAR Sparse Semantic Segmentation Network | Semantic Segmentation is a crucial component in the perception systems of many applications, such as robotics and autonomous driving that rely on accurate environmental perception and understanding. In literature, several approaches are introduced to attempt LiDAR semantic segmentation task, such as projection-based (r... | ['Liu Bingbing', 'Yuan Ren', 'Ryan Razani', 'Ran Cheng'] | 2021-03-15 | s3net-3d-lidar-sparse-semantic-segmentation-1 | https://arxiv.org/abs/2103.08745 | https://arxiv.org/pdf/2103.08745 | null | ['lidar-semantic-segmentation'] | ['computer-vision'] | [ 2.25048929e-01 -9.66462716e-02 4.55622599e-02 -7.33814895e-01
-5.57897627e-01 -1.92652807e-01 4.17756587e-01 -1.87568232e-01
-3.84279132e-01 3.32217962e-01 -1.10044345e-01 -2.88218230e-01
-1.97211742e-01 -9.44068968e-01 -1.08118439e+00 -4.10069644e-01
3.25721920e-01 5.98637938e-01 5.80070078e-01 -2.26515666... | [8.186901092529297, -2.733492612838745] |
91a7db31-b1c6-48ac-ac4b-898a80bd68e7 | maestro-open-ended-environment-design-for | 2303.03376 | null | https://arxiv.org/abs/2303.03376v1 | https://arxiv.org/pdf/2303.03376v1.pdf | MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning | Open-ended learning methods that automatically generate a curriculum of increasingly challenging tasks serve as a promising avenue toward generally capable reinforcement learning agents. Existing methods adapt curricula independently over either environment parameters (in single-agent settings) or co-player policies (i... | ['Tim Rocktäschel', 'Roberta Raileanu', 'Jakob Foerster', 'Jack Parker-Holder', 'Minqi Jiang', 'Michael Dennis', 'Akbir Khan', 'Mikayel Samvelyan'] | 2023-03-06 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [ 4.50874902e-02 2.15538263e-01 2.25858856e-02 5.09163439e-02
-9.67230916e-01 -1.06715727e+00 6.99332952e-01 1.10886991e-01
-8.95060897e-01 1.21127725e+00 1.47537440e-01 -1.35561898e-01
-5.83820999e-01 -9.30445611e-01 -9.37722683e-01 -8.34773958e-01
-4.03940827e-01 9.15029883e-01 7.89443925e-02 -7.56877244... | [3.7963340282440186, 1.762798547744751] |
288a8e8f-1781-4a45-b7ba-0c6788661057 | tsfd-net-tissue-specific-feature-distillation | null | null | https://www.sciencedirect.com/science/article/pii/S0893608022000612?via%3Dihub | https://www.sciencedirect.com/science/article/pii/S0893608022000612?via%3Dihub | TSFD-Net: Tissue specific feature distillation network for nuclei segmentation and classification | Nuclei segmentation and classification of hematoxylin and eosin-stained histology images is a challenging task due to a variety of issues, such as color inconsistency that results from the non-uniform manual staining operations, clustering of nuclei, and blurry and overlapping nuclei boundaries. Existing approaches inv... | ['Friso De Boer', 'Hyongsuk Kim', 'Sami Azam', 'Abbas Khan', 'Zubaer Ibna Mannan', 'Talha Ilyas'] | 2022-03-09 | null | null | null | elsevier-neural-networks-2022-3 | ['panoptic-segmentation'] | ['computer-vision'] | [ 2.39389122e-01 -1.51868090e-01 -3.11046909e-03 -3.32541496e-01
-9.46976483e-01 -5.83359480e-01 1.32203281e-01 3.32848310e-01
-7.21018910e-01 8.07194948e-01 -7.26444498e-02 -5.43881170e-02
-5.84295020e-02 -5.70063114e-01 -1.96400791e-01 -1.32842588e+00
-8.01057369e-02 2.84534067e-01 1.89204752e-01 1.15390331... | [15.038466453552246, -3.059568166732788] |
7bc60c7a-dbcc-45f2-8b81-daf71857f0ce | deep-neural-network-based-respiratory | 2106.12174 | null | https://arxiv.org/abs/2106.12174v1 | https://arxiv.org/pdf/2106.12174v1.pdf | Deep Neural Network Based Respiratory Pathology Classification Using Cough Sounds | Intelligent systems are transforming the world, as well as our healthcare system. We propose a deep learning-based cough sound classification model that can distinguish between children with healthy versus pathological coughs such as asthma, upper respiratory tract infection (URTI), and lower respiratory tract infectio... | ['Jer Ming Chen', 'Dorien Herremans', 'Khai Pin Lee', 'Sung Shin Teng', 'Oon Hoe Teoh', 'Saumitra Kapoor', 'Hwan Ing Hee', 'Balamurali B T'] | 2021-06-23 | null | null | null | null | ['sound-classification'] | ['audio'] | [ 3.12988281e-01 -1.14644229e-01 -2.53301173e-01 -3.30252163e-02
-3.40333909e-01 -5.27473032e-01 4.65240553e-02 2.67328292e-01
-5.87881804e-01 4.99201953e-01 1.75508428e-02 -5.19106388e-01
-1.44129813e-01 -7.89440453e-01 -4.33150619e-01 -9.36634004e-01
1.60317719e-01 6.92183137e-01 3.46724242e-02 3.43471259... | [14.515698432922363, 3.841136932373047] |
f95ce77b-0d88-4fc7-80f9-00c804718296 | multi-modal-multi-kernel-graph-learning-for | 2303.03388 | null | https://arxiv.org/abs/2303.03388v2 | https://arxiv.org/pdf/2303.03388v2.pdf | Multi-modal Multi-kernel Graph Learning for Autism Prediction and Biomarker Discovery | Due to its complexity, graph learning-based multi-modal integration and classification is one of the most challenging obstacles for disease prediction. To effectively offset the negative impact between modalities in the process of multi-modal integration and extract heterogeneous information from graphs, we propose a n... | ['Shirui Pan', 'Yi Pan', 'Hulin Kuang', 'Hanhe Lin', 'Jin Liu', 'Junbin Mao'] | 2023-03-03 | null | null | null | null | ['disease-prediction'] | ['medical'] | [ 1.01626813e-01 2.55623549e-01 6.18762597e-02 -1.24222428e-01
-4.76041108e-01 -1.13907486e-01 3.19486946e-01 6.62796378e-01
-8.25638846e-02 1.95732996e-01 4.06708449e-01 2.16080979e-01
-4.65311140e-01 -7.57278144e-01 -3.63604009e-01 -7.85721064e-01
-2.33069122e-01 5.37045181e-01 2.75220633e-01 -2.27878526... | [12.350234985351562, 3.3848330974578857] |
774892c9-04db-4b4c-a06c-8cf278c67cb4 | variation-of-gender-biases-in-visual | 2303.07615 | null | https://arxiv.org/abs/2303.07615v1 | https://arxiv.org/pdf/2303.07615v1.pdf | Variation of Gender Biases in Visual Recognition Models Before and After Finetuning | We introduce a framework to measure how biases change before and after fine-tuning a large scale visual recognition model for a downstream task. Deep learning models trained on increasing amounts of data are known to encode societal biases. Many computer vision systems today rely on models typically pretrained on large... | ['Vicente Ordonez', 'Baishakhi Ray', 'Tianlu Wang', 'Jaspreet Ranjit'] | 2023-03-14 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [ 2.95228601e-01 5.15710190e-02 -2.20437795e-01 -8.66955638e-01
-2.99914856e-03 -7.56825268e-01 9.01684046e-01 1.94516063e-01
-8.88870239e-01 5.54625809e-01 6.24355197e-01 -1.08967155e-01
7.16722235e-02 -8.47959995e-01 -1.21352565e+00 -2.55184919e-01
4.67835516e-02 3.27701509e-01 1.30626485e-01 -3.50685984... | [10.206670761108398, 2.2266547679901123] |
dbebca04-1a8a-4b08-82d3-b82c2a4b7ecf | aifb-webscience-at-semeval-2022-task-12 | 2203.05325 | null | https://arxiv.org/abs/2203.05325v2 | https://arxiv.org/pdf/2203.05325v2.pdf | AIFB-WebScience at SemEval-2022 Task 12: Relation Extraction First -- Using Relation Extraction to Identify Entities | In this paper, we present an end-to-end joint entity and relation extraction approach based on transformer-based language models. We apply the model to the task of linking mathematical symbols to their descriptions in LaTeX documents. In contrast to existing approaches, which perform entity and relation extraction in s... | ['Michael Färber', 'Walter Laurito', 'Nicholas Popovic'] | 2022-03-10 | null | null | null | null | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [-2.71623787e-02 4.44423020e-01 -1.75948814e-01 -5.02225757e-01
-8.18669379e-01 -7.46994734e-01 7.53463447e-01 5.75945854e-01
-3.38220984e-01 9.58560348e-01 -1.30911589e-01 -5.86627603e-01
-1.89930797e-01 -9.40949142e-01 -8.18654835e-01 3.15090045e-02
-1.44039933e-02 7.40200818e-01 3.30998152e-01 -1.48219556... | [9.527030944824219, 8.824071884155273] |
52e8fa24-743c-4141-bda2-aaa4d4a04179 | data-and-knowledge-dual-driven-automatic | 2206.15035 | null | https://arxiv.org/abs/2206.15035v1 | https://arxiv.org/pdf/2206.15035v1.pdf | Data-and-Knowledge Dual-Driven Automatic Modulation Recognition for Wireless Communication Networks | Automatic modulation classification is of crucial importance in wireless communication networks. Deep learning based automatic modulation classification schemes have attracted extensive attention due to the superior accuracy. However, the data-driven method relies on a large amount of training samples and the classific... | ['Zhu Han', 'Qihui Wu', 'Fuhui Zhou', 'Hao Zhang', 'Rui Ding'] | 2022-06-30 | null | null | null | null | ['automatic-modulation-recognition'] | ['time-series'] | [ 5.70619702e-01 -3.66882741e-01 -3.51211041e-01 -4.55022663e-01
-7.92618334e-01 1.46777593e-02 5.12222707e-01 -4.74289106e-03
-1.89091712e-01 7.51707196e-01 1.06867971e-02 -3.71978372e-01
-5.42723656e-01 -1.00454187e+00 -1.59855753e-01 -1.20699584e+00
5.73994853e-02 -1.97340429e-01 -9.07395855e-02 -1.58512846... | [6.546184539794922, 1.4743773937225342] |
d4bb0814-d23f-46e4-944d-ceb5620c5f42 | towards-bengali-word-embedding-corpus | null | null | https://aclanthology.org/2020.icon-main.61 | https://aclanthology.org/2020.icon-main.61.pdf | Towards Bengali Word Embedding: Corpus Creation, Intrinsic and Extrinsic Evaluations | Distributional word vector representation or word embedding has become an essential ingredient in many natural language processing (NLP) tasks such as machine translation, document classification, information retrieval and question answering. Investigation of embedding model helps to reduce the feature space and improv... | ['Mohammed Moshiul Hoque', 'Md. Rajib Hossain'] | null | towards-bengali-word-embedding-corpus-1 | https://aclanthology.org/2020.icon-main.61/ | https://aclanthology.org/2020.icon-main.61 | icon-17th-international-conference-on-natural | ['word-similarity'] | ['natural-language-processing'] | [-2.25231364e-01 2.56502777e-01 -4.82241102e-02 -2.94825077e-01
-5.02797723e-01 -5.23826957e-01 9.83955801e-01 6.50432229e-01
-1.01517045e+00 5.40551007e-01 6.39190614e-01 -4.71989572e-01
-3.16075057e-01 -8.62514853e-01 7.42236301e-02 -4.05189931e-01
3.32156109e-04 4.01727885e-01 -8.75852406e-02 -5.51465034... | [10.477995872497559, 8.708149909973145] |
9277c6d7-ae6d-445f-8009-ef339d591f67 | learning-end-to-end-goal-oriented-dialog-with-2 | 1907.07638 | null | https://arxiv.org/abs/1907.07638v1 | https://arxiv.org/pdf/1907.07638v1.pdf | Learning End-to-End Goal-Oriented Dialog with Maximal User Task Success and Minimal Human Agent Use | Neural end-to-end goal-oriented dialog systems showed promise to reduce the workload of human agents for customer service, as well as reduce wait time for users. However, their inability to handle new user behavior at deployment has limited their usage in real world. In this work, we propose an end-to-end trainable met... | ['Jatin Ganhotra', 'Lazaros Polymenakos', 'Janarthanan Rajendran'] | 2019-07-17 | learning-end-to-end-goal-oriented-dialog-with-3 | https://aclanthology.org/Q19-1024 | https://aclanthology.org/Q19-1024.pdf | tacl-2019-3 | ['goal-oriented-dialog'] | ['natural-language-processing'] | [-2.45493278e-01 5.76859236e-01 5.74541569e-01 -7.88134575e-01
-6.74033821e-01 -7.56302655e-01 2.23824099e-01 -2.13283479e-01
-6.78396285e-01 8.23335767e-01 1.52953252e-01 -3.52471083e-01
-2.11141575e-02 -5.90024948e-01 -1.35364071e-01 -5.10163486e-01
2.61120014e-02 1.25207853e+00 3.11097413e-01 -8.01687062... | [12.877631187438965, 8.011702537536621] |
e272e176-d153-4423-8d02-531358b6c37e | deep-denoising-prior-based-spectral | 2306.17096 | null | https://arxiv.org/abs/2306.17096v1 | https://arxiv.org/pdf/2306.17096v1.pdf | Deep Denoising Prior-Based Spectral Estimation for Phaseless Synthetic Aperture Radar | Incoherent processing for synthetic aperture radar (SAR) is a promising approach that enables low implementation costs, simplified hardware designs and operations in high frequency spectrum compared to the conventional imaging methods using coherent processing. Existing non-convex phaseless imaging algorithms offer rec... | ['Birsen Yazıcı', 'Bariscan Yonel', 'Samia Kazemi'] | 2023-06-29 | null | null | null | null | ['retrieval'] | ['methodology'] | [ 7.71857738e-01 -2.31109008e-01 6.17566466e-01 -3.37789536e-01
-1.15794647e+00 -2.14743093e-01 3.95411015e-01 -5.78969955e-01
-2.64439851e-01 6.81566119e-01 3.36589158e-01 -1.64120737e-02
-9.14424419e-01 -6.28652990e-01 -3.94056410e-01 -1.19401109e+00
-4.39887762e-01 4.81630474e-01 -3.48500967e-01 -1.18061803... | [10.555087089538574, -2.1955349445343018] |
e1d4b62f-d4cd-421c-90c1-42d31e90f312 | deep-learning-for-ecg-classification | null | null | http://doi.org/10.1088/1742-6596/913/1/012004 | http://iopscience.iop.org/article/10.1088/1742-6596/913/1/012004/pdf | Deep Learning for ECG Classification | The importance of ECG classification is very high now due to many current medical applications where this problem can be stated. Currently, there are many machine learning (ML) solutions which can be used for analyzing and classifying ECG data. However, the main disadvantages of these ML results is use of heuristic han... | ['Natasha Kazachenko', 'Nick Mikhailovsky', 'Boris Pyakillya'] | 2017-01-01 | null | null | null | journal-of-physics-conference-series-2017-1 | ['ecg-classification', 'electrocardiography-ecg'] | ['medical', 'methodology'] | [ 2.49576956e-01 2.25329310e-01 1.86188668e-01 -6.37380421e-01
-1.40591100e-01 -2.43764855e-02 3.43441188e-01 5.16280711e-01
-6.11199200e-01 7.87580311e-01 -1.46129504e-01 -2.08514646e-01
-5.29897392e-01 -8.91910732e-01 -1.75184488e-01 -5.99345505e-01
-3.21750551e-01 3.48000556e-01 1.85548469e-01 -1.78699896... | [14.24092960357666, 3.2976226806640625] |
fa5df241-5721-4d1a-970f-1ae07fb502db | condition-random-fields-based-grammatical | null | null | https://aclanthology.org/W15-4416 | https://aclanthology.org/W15-4416.pdf | Condition Random Fields-based Grammatical Error Detection for Chinese as Second Language | null | ['Wan-Ling Tsai', 'Ya-Ting Li', 'Kai-Hsiang Yu', 'Chan-Kun Yeh', 'Jui-Feng Yeh'] | 2015-07-01 | null | null | null | ws-2015-7 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.3621439933776855, 3.7058639526367188] |
87d17beb-1108-42ad-9f02-0cdd63a7729b | superbench-a-super-resolution-benchmark | 2306.1407 | null | https://arxiv.org/abs/2306.14070v1 | https://arxiv.org/pdf/2306.14070v1.pdf | SuperBench: A Super-Resolution Benchmark Dataset for Scientific Machine Learning | Super-Resolution (SR) techniques aim to enhance data resolution, enabling the retrieval of finer details, and improving the overall quality and fidelity of the data representation. There is growing interest in applying SR methods to complex spatiotemporal systems within the Scientific Machine Learning (SciML) community... | ['Michael W. Mahoney', 'Zarija Lukic', 'Omer San', 'Shashank Subramanian', 'N. Benjamin Erichson', 'Pu Ren'] | 2023-06-24 | null | null | null | null | ['super-resolution', 'retrieval'] | ['computer-vision', 'methodology'] | [-5.26237637e-02 -5.40550888e-01 9.08690915e-02 -7.19393566e-02
-5.55699885e-01 -6.47406518e-01 9.87058163e-01 3.01984161e-01
-1.50751188e-01 1.01636004e+00 3.54693562e-01 -2.03592837e-01
-3.28617096e-01 -9.73153949e-01 -6.68500602e-01 -6.91143334e-01
-3.66439939e-01 2.84161955e-01 5.01622148e-02 -1.18432760... | [6.573493480682373, 3.139309883117676] |
233b10d4-5840-454e-a658-915603d5d321 | balanced-supervised-contrastive-learning-for | 2305.16687 | null | https://arxiv.org/abs/2305.16687v1 | https://arxiv.org/pdf/2305.16687v1.pdf | Balanced Supervised Contrastive Learning for Few-Shot Class-Incremental Learning | Few-shot class-incremental learning (FSCIL) presents the primary challenge of balancing underfitting to a new session's task and forgetting the tasks from previous sessions. To address this challenge, we develop a simple yet powerful learning scheme that integrates effective methods for each core component of the FSCIL... | ['Jong-Hwan Kim', 'Young-Min Kim', 'Tae-Min Choi', 'In-Ug Yoon'] | 2023-05-26 | null | null | null | null | ['class-incremental-learning', 'few-shot-class-incremental-learning', 'incremental-learning'] | ['computer-vision', 'methodology', 'methodology'] | [ 2.21930549e-01 -1.71743780e-01 -1.14983842e-01 -4.26214069e-01
-7.84991324e-01 -4.59334582e-01 5.40349782e-01 2.40870863e-02
-5.38242817e-01 7.73645222e-01 1.80322498e-01 -6.37867972e-02
-2.98272759e-01 -3.52016032e-01 -6.64085984e-01 -6.99369013e-01
-1.61964502e-02 6.74271490e-03 4.49084848e-01 -1.28523842... | [9.792716979980469, 3.406764268875122] |
a08c03fa-582c-45b7-ad6a-188e66df65e0 | ksconf-a-light-weight-test-if-a-convnet | 1804.04171 | null | http://arxiv.org/abs/1804.04171v1 | http://arxiv.org/pdf/1804.04171v1.pdf | KS(conf ): A Light-Weight Test if a ConvNet Operates Outside of Its Specifications | Computer vision systems for automatic image categorization have become
accurate and reliable enough that they can run continuously for days or even
years as components of real-world commercial applications. A major open problem
in this context, however, is quality control. Good classification performance
can only be ex... | ['Christoph H. Lampert', 'Rémy Sun'] | 2018-04-11 | null | null | null | null | ['image-categorization'] | ['computer-vision'] | [-1.19106565e-02 -2.54208714e-01 1.43842444e-01 -7.35715389e-01
-3.91017854e-01 -6.60146594e-01 6.18071675e-01 3.17574441e-01
-6.71155095e-01 5.51050246e-01 -7.74552047e-01 -5.77359676e-01
-5.80850951e-02 -6.39517069e-01 -7.12427437e-01 -6.85188532e-01
-3.61842752e-01 5.13637722e-01 7.69854426e-01 -1.29837707... | [8.91755199432373, 2.667086124420166] |
2a51ea3d-4ebf-4204-bfc3-ac905d942b05 | symmetric-uncertainty-aware-feature | 2306.00386 | null | https://arxiv.org/abs/2306.00386v1 | https://arxiv.org/pdf/2306.00386v1.pdf | Symmetric Uncertainty-Aware Feature Transmission for Depth Super-Resolution | Color-guided depth super-resolution (DSR) is an encouraging paradigm that enhances a low-resolution (LR) depth map guided by an extra high-resolution (HR) RGB image from the same scene. Existing methods usually use interpolation to upscale the depth maps before feeding them into the network and transfer the high-freque... | ['Bo Du', 'Mang Ye', 'Wuxuan Shi'] | 2023-06-01 | null | null | null | null | ['super-resolution'] | ['computer-vision'] | [ 4.67408061e-01 -9.81328413e-02 2.71495610e-01 -2.34245926e-01
-1.00641322e+00 -5.61550073e-02 2.27473974e-01 -4.19123232e-01
-5.80384098e-02 8.50082517e-01 4.14306581e-01 2.34720513e-01
-1.36357665e-01 -1.08172417e+00 -7.11886942e-01 -8.62435997e-01
2.46047392e-01 -1.94926590e-01 5.16190827e-01 -2.31778771... | [9.704601287841797, -2.4474592208862305] |
0ca97a4d-41c3-4731-9fd2-ade4096e85cc | combinatorial-multi-armed-bandit-with | 1707.07443 | null | http://arxiv.org/abs/1707.07443v1 | http://arxiv.org/pdf/1707.07443v1.pdf | Combinatorial Multi-armed Bandit with Probabilistically Triggered Arms: A Case with Bounded Regret | In this paper, we study the combinatorial multi-armed bandit problem (CMAB)
with probabilistically triggered arms (PTAs). Under the assumption that the arm
triggering probabilities (ATPs) are positive for all arms, we prove that a
class of upper confidence bound (UCB) policies, named Combinatorial UCB with
exploration ... | ['A. Ömer Sarıtaç', 'Cem Tekin'] | 2017-07-24 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [-6.59666955e-02 1.41319498e-01 -6.98466718e-01 -1.86914057e-01
-1.04089153e+00 -9.95425940e-01 -1.17725387e-01 7.13927448e-02
-3.35090369e-01 1.03397417e+00 -1.76394939e-01 -7.66495943e-01
-1.09780931e+00 -9.90447700e-01 -1.20958126e+00 -8.69547725e-01
-2.33635813e-01 7.94131219e-01 -5.69176488e-02 -1.35409623... | [4.57266902923584, 3.3716907501220703] |
a79d9c4f-93c9-42cc-a4fb-83491c7b78c4 | an-energy-activity-dataset-for-smart-homes | 2208.13416 | null | https://arxiv.org/abs/2208.13416v2 | https://arxiv.org/pdf/2208.13416v2.pdf | An Energy Activity Dataset for Smart Homes | A smart home energy dataset that records miscellaneous energy consumption data is publicly offered. The proposed energy activity dataset (EAD) has a high data type diversity in contrast to existing load monitoring datasets. In EAD, a simple data point is labeled with the appliance, brand, and event information, whereas... | ['Chen Li'] | 2022-08-29 | null | null | null | null | ['miscellaneous'] | ['miscellaneous'] | [ 1.09392237e-02 -6.64439857e-01 -3.05392832e-01 -6.35994434e-01
-3.81589085e-01 -6.06341958e-01 3.77358735e-01 3.43805730e-01
-2.32367873e-01 5.20322263e-01 1.38565898e-01 -2.03071460e-01
-1.22109540e-01 -1.08338964e+00 -4.17448819e-01 -8.06169033e-01
2.96766132e-01 1.34179994e-01 -3.45233560e-01 2.91951030... | [6.001276016235352, 2.581291913986206] |
e5382308-b3f4-4f4a-a762-5ab58d751a96 | high-fidelity-speech-regeneration-with | 2102.00429 | null | https://arxiv.org/abs/2102.00429v1 | https://arxiv.org/pdf/2102.00429v1.pdf | High Fidelity Speech Regeneration with Application to Speech Enhancement | Speech enhancement has seen great improvement in recent years mainly through contributions in denoising, speaker separation, and dereverberation methods that mostly deal with environmental effects on vocal audio. To enhance speech beyond the limitations of the original signal, we take a regeneration approach, in which ... | ['Yaniv Taigman', 'Ori Kabeli', 'Yossi Adi', 'Lior Wolf', 'Adam Polyak'] | 2021-01-31 | null | null | null | null | ['speaker-separation'] | ['speech'] | [ 2.60003597e-01 1.43607646e-01 2.62816638e-01 -2.71437645e-01
-1.07234836e+00 -5.90374708e-01 3.02635401e-01 -4.12881583e-01
-5.09949513e-02 4.33410436e-01 9.44166780e-01 -1.63216516e-01
3.11069548e-01 -3.70041341e-01 -4.25102711e-01 -7.36347795e-01
2.97087550e-01 -4.74339336e-01 -2.30595842e-01 -5.71058571... | [15.041340827941895, 6.099411487579346] |
20991ea1-a9fe-4d65-8e7e-b140261610fd | from-key-points-to-key-point-hierarchy | 2306.03853 | null | https://arxiv.org/abs/2306.03853v1 | https://arxiv.org/pdf/2306.03853v1.pdf | From Key Points to Key Point Hierarchy: Structured and Expressive Opinion Summarization | Key Point Analysis (KPA) has been recently proposed for deriving fine-grained insights from collections of textual comments. KPA extracts the main points in the data as a list of concise sentences or phrases, termed key points, and quantifies their prevalence. While key points are more expressive than word clouds and k... | ['Roy Bar-Haim', 'Yoav Kantor', 'Lilach Eden', 'Arie Cattan'] | 2023-06-06 | null | null | null | null | ['natural-language-inference', 'specificity'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.92558537e-02 1.72911718e-01 -6.57897115e-01 -4.85508054e-01
-1.04368126e+00 -1.00361276e+00 7.69082129e-01 1.15370870e+00
-4.76243645e-02 3.29853743e-01 9.72351015e-01 -4.03516859e-01
-2.23712176e-01 -6.99486315e-01 -7.93515801e-01 -3.90349299e-01
1.23386413e-01 4.89873379e-01 1.45019591e-01 -3.29255193... | [11.269048690795898, 8.786035537719727] |
1ccd9ccd-8e24-46ba-a933-f541099f04f8 | results-of-semtab-2022 | null | null | https://www.semanticscholar.org/paper/Results-of-SemTab-2022-Abdelmageed-Chen/64dfbc1da6ad7402a6365c3e41667069a63599a6 | https://ceur-ws.org/Vol-3320/paper0.pdf | Results of SemTab 2022 | SemTab 2022 was the fourth edition of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, successfully collocated with the 21st International Semantic Web Conference (ISWC) and the 17th Ontology Matching (OM) Workshop. SemTab provides a common framework to conduct a systematic evaluation of state-of... | ['Kavitha Srinivas', 'Juan Sequeda', 'Ernesto Jiménez-Ruiz', 'Madelon Hulsebos', 'Oktie Hassanzadeh', 'Vasilis Efthymiou', 'Vincenzo Cutrona', 'Jiaoyan Chen', 'Nora Abdelmageed'] | 2022-10-25 | null | null | null | semtab-iswc-2022-10 | ['graph-matching', 'ontology-matching', 'column-type-annotation', 'cell-entity-annotation'] | ['graphs', 'knowledge-base', 'natural-language-processing', 'natural-language-processing'] | [-3.18961106e-02 5.54221034e-01 -4.72445995e-01 -8.52105021e-02
-2.36348778e-01 -6.61282122e-01 8.64572763e-01 6.14779532e-01
-1.44431710e-01 4.67115551e-01 3.58165652e-01 -2.10117385e-01
-8.01894367e-01 -1.05928421e+00 -3.66127372e-01 6.44774258e-01
-1.54915107e-02 9.96121883e-01 7.83684194e-01 -7.19540298... | [9.319365501403809, 8.074041366577148] |
405e5419-f66a-4be5-ab0f-aa75df9cd977 | examining-deep-learning-architectures-for | 1812.00602 | null | http://arxiv.org/abs/1812.00602v1 | http://arxiv.org/pdf/1812.00602v1.pdf | Examining Deep Learning Architectures for Crime Classification and Prediction | In this paper, a detailed study on crime classification and prediction using
deep learning architectures is presented. We examine the effectiveness of deep
learning algorithms on this domain and provide recommendations for designing
and training deep learning systems for predicting crime areas, using open data
from pol... | ['Theodoros Semertzidis', 'Petros Daras', 'Panagiotis Stalidis'] | 2018-12-03 | null | null | null | null | ['crime-prediction'] | ['miscellaneous'] | [-3.80224228e-01 -2.20835954e-01 -1.50617078e-01 -7.08592594e-01
-6.55911684e-01 -1.27697974e-01 5.83239257e-01 5.33787966e-01
-7.95015991e-01 6.23700023e-01 5.40387571e-01 -5.84631741e-01
-4.04741138e-01 -1.27657521e+00 -5.41009128e-01 -2.80837297e-01
-3.03321183e-01 5.95132053e-01 -2.10461125e-01 -2.02786177... | [6.738507270812988, 1.9519312381744385] |
d1f36613-efe9-48ca-a46f-9e34ebeddbbe | using-poisson-binomial-glms-to-reveal-voter | 1802.01053 | null | http://arxiv.org/abs/1802.01053v1 | http://arxiv.org/pdf/1802.01053v1.pdf | Using Poisson Binomial GLMs to Reveal Voter Preferences | We present a new modeling technique for solving the problem of ecological
inference, in which individual-level associations are inferred from labeled
data available only at the aggregate level. We model aggregate count data as
arising from the Poisson binomial, the distribution of the sum of independent
but not identic... | ['Evan Rosenman', 'Nitin Viswanathan'] | 2018-02-04 | null | null | null | null | ['holdout-set'] | ['computer-vision'] | [ 2.60149777e-01 8.46718997e-02 -4.89015281e-01 -5.05469620e-01
-6.83304489e-01 -7.41245210e-01 5.24638712e-01 4.11743999e-01
-9.21322823e-01 1.33084857e+00 3.32644045e-01 -6.24344945e-01
-2.04113945e-01 -8.08046401e-01 -1.08088195e+00 -4.66796607e-01
-5.55599511e-01 5.72348595e-01 -6.30508423e-01 2.29086310... | [7.910074710845947, 4.710550785064697] |
5ffbea3b-66a6-414d-b9a9-02e094cf79d9 | adjust-a-dictionary-based-joint | 2112.11406 | null | https://arxiv.org/abs/2112.11406v2 | https://arxiv.org/pdf/2112.11406v2.pdf | ADJUST: A Dictionary-Based Joint Reconstruction and Unmixing Method for Spectral Tomography | Advances in multi-spectral detectors are causing a paradigm shift in X-ray Computed Tomography (CT). Spectral information acquired from these detectors can be used to extract volumetric material composition maps of the object of interest. If the materials and their spectral responses are known a priori, the image recon... | ['Kees Joost Batenburg', 'Tristan van Leeuwen', 'Ajinkya Kadu', 'Mathé T. Zeegers'] | 2021-12-21 | null | null | null | null | ['inference-optimization', 'spectral-reconstruction', 'multispectral-object-detection', 'low-dose-x-ray-ct-reconstruction'] | ['audio', 'computer-vision', 'computer-vision', 'medical'] | [ 4.60075378e-01 -3.29942137e-01 1.52394414e-01 -1.32168725e-01
-1.02679837e+00 -2.09249094e-01 1.89891025e-01 2.20157340e-01
-4.70805287e-01 5.80829740e-01 2.91603088e-01 3.14758122e-02
-1.71193257e-01 -5.93000174e-01 -5.30176640e-01 -1.14531207e+00
1.39457420e-01 7.00741529e-01 2.62087375e-01 9.73113701... | [12.991758346557617, -2.65207839012146] |
b1911603-fe9f-47b5-8a24-69fabe77f8bf | end-to-end-supervised-multilabel-contrastive | 2307.03967 | null | https://arxiv.org/abs/2307.03967v1 | https://arxiv.org/pdf/2307.03967v1.pdf | End-to-End Supervised Multilabel Contrastive Learning | Multilabel representation learning is recognized as a challenging problem that can be associated with either label dependencies between object categories or data-related issues such as the inherent imbalance of positive/negative samples. Recent advances address these challenges from model- and data-centric viewpoints. ... | ['Mahdi S. Hosseini', 'Konstantinos N. Plataniotis', 'Samir Khaki', 'Ahmad Sajedi'] | 2023-07-08 | null | null | null | null | ['contrastive-learning', 'image-classification', 'contrastive-learning', 'representation-learning'] | ['computer-vision', 'computer-vision', 'methodology', 'methodology'] | [ 1.86383024e-01 1.01732194e-01 -3.89996439e-01 -6.19789064e-01
-9.88092482e-01 -2.17260748e-01 3.48286361e-01 7.80742392e-02
-3.09020758e-01 6.45083904e-01 -1.38620973e-01 -2.57757008e-01
-1.36418432e-01 -4.95140702e-01 -7.66038358e-01 -9.19312775e-01
2.73497105e-01 2.79479384e-01 -1.42640993e-01 1.74637780... | [9.501811981201172, 3.7284326553344727] |
840b6f65-0bfd-46e8-9d71-0cdb606ed10a | multi-task-neural-network-for-non-discrete | 1708.04828 | null | http://arxiv.org/abs/1708.04828v1 | http://arxiv.org/pdf/1708.04828v1.pdf | Multi-task Neural Network for Non-discrete Attribute Prediction in Knowledge Graphs | Many popular knowledge graphs such as Freebase, YAGO or DBPedia maintain a
list of non-discrete attributes for each entity. Intuitively, these attributes
such as height, price or population count are able to richly characterize
entities in knowledge graphs. This additional source of information may help to
alleviate th... | ['Luu Anh Tuan', 'Yi Tay', 'Siu Cheung Hui', 'Minh C. Phan'] | 2017-08-16 | null | null | null | null | ['value-prediction'] | ['computer-code'] | [-7.43869841e-02 3.73794317e-01 -8.57799649e-01 -5.99227250e-01
-5.99028945e-01 -4.92523313e-01 6.33070052e-01 9.69849885e-01
-1.18340217e-01 1.04331791e+00 1.32548928e-01 -1.59653768e-01
-6.54861867e-01 -1.59547102e+00 -1.09551167e+00 -4.51307118e-01
-2.34221682e-01 9.92223799e-01 1.31351640e-02 -3.88619900... | [8.762755393981934, 7.872171401977539] |
884473d3-6dfb-4205-9f7f-e572d6ed369b | stochastic-planner-actor-critic-for | 2112.07415 | null | https://arxiv.org/abs/2112.07415v2 | https://arxiv.org/pdf/2112.07415v2.pdf | Stochastic Planner-Actor-Critic for Unsupervised Deformable Image Registration | Large deformations of organs, caused by diverse shapes and nonlinear shape changes, pose a significant challenge for medical image registration. Traditional registration methods need to iteratively optimize an objective function via a specific deformation model along with meticulous parameter tuning, but which have lim... | ['Siwei Lyu', 'Xi Wu', 'Qi Song', 'Youbing Yin', 'Bin Kong', 'Shu Hu', 'Xin Wang', 'Jing Hu', 'Ziwei Luo'] | 2021-12-14 | null | null | null | null | ['deformable-medical-image-registration'] | ['medical'] | [ 2.77476668e-01 3.13620836e-01 -2.90161341e-01 -1.65243015e-01
-1.20671380e+00 -3.11356366e-01 5.25064886e-01 7.24081025e-02
-4.99262631e-01 5.06249368e-01 2.98769444e-01 2.78411098e-02
-2.98964471e-01 -4.81843531e-01 -7.16496408e-01 -1.01983356e+00
-3.30876261e-01 8.10129642e-01 -6.25389069e-02 -4.38623101... | [14.17306900024414, -2.5496015548706055] |
03d0f791-33ea-483b-83f0-b23381704a74 | adversarial-learning-with-mask-reconstruction | null | null | https://github.com/GaranWu/ALMR | https://pan.baidu.com/s/1-FZHhxtGMxKexVY82_Ceyw?pwd=6jj5 | Adversarial Learning with Mask Reconstruction for Text-Guided Image Inpainting | Text-guided image inpainting aims to complete the corrupted patches coherent with both visual and textual context. On one hand, existing works focus on surrounding pixels of the corrupted patches without considering the objects in the image, resulting in the characteristics of objects described in text being painted on... | ['and Wenyin Liu', 'Qing Li', 'Yi Yu', 'Zhenguo Yang', 'Jiaqi Zeng', 'Yucheng Xie', 'Xingcai Wu'] | 2021-07-28 | null | null | null | conference-2021-7 | ['image-inpainting'] | ['computer-vision'] | [ 3.05302203e-01 1.35979299e-02 1.65910125e-01 -1.37115017e-01
-1.08164728e+00 -2.12941572e-01 4.22206789e-01 -3.33488524e-01
-1.67249367e-01 8.54660749e-01 3.03460568e-01 8.46223682e-02
3.17493111e-01 -8.41320932e-01 -9.22447324e-01 -8.92965198e-01
5.55090547e-01 -8.04250017e-02 -4.36509959e-02 -2.58936822... | [11.362399101257324, -1.1649285554885864] |
ef202f17-8ac4-429f-80a7-3a864d2ff54b | graph-similarity-learning-for-change-point | 2203.1547 | null | https://arxiv.org/abs/2203.15470v1 | https://arxiv.org/pdf/2203.15470v1.pdf | Graph similarity learning for change-point detection in dynamic networks | Dynamic networks are ubiquitous for modelling sequential graph-structured data, e.g., brain connectome, population flows and messages exchanges. In this work, we consider dynamic networks that are temporal sequences of graph snapshots, and aim at detecting abrupt changes in their structure. This task is often termed ne... | ['Xiaowen Dong', 'Mihai Cucuringu', 'Henry Kenlay', 'Deborah Sulem'] | 2022-03-29 | null | null | null | null | ['graph-similarity', 'temporal-sequences'] | ['graphs', 'reasoning'] | [ 1.96258307e-01 -1.24803595e-01 -3.00718069e-01 3.77453752e-02
2.51444757e-01 -6.38091326e-01 7.30156898e-01 6.22751355e-01
-2.63746083e-01 4.17208254e-01 -9.08031613e-02 -3.09605122e-01
-4.09779161e-01 -1.01583779e+00 -5.81047177e-01 -4.13995177e-01
-8.79368126e-01 5.37412465e-01 6.77227795e-01 -4.29424763... | [7.030505180358887, 5.9414215087890625] |
d391207f-3541-407d-8aa1-1d4f12c6957b | the-clear-benchmark-continual-learning-on | 2201.06289 | null | https://arxiv.org/abs/2201.06289v3 | https://arxiv.org/pdf/2201.06289v3.pdf | The CLEAR Benchmark: Continual LEArning on Real-World Imagery | Continual learning (CL) is widely regarded as crucial challenge for lifelong AI. However, existing CL benchmarks, e.g. Permuted-MNIST and Split-CIFAR, make use of artificial temporal variation and do not align with or generalize to the real-world. In this paper, we introduce CLEAR, the first continual image classificat... | ['Deva Ramanan', 'Deepak Pathak', 'Jia Shi', 'Zhiqiu Lin'] | 2022-01-17 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [ 1.77255459e-02 -4.87719297e-01 -2.24874869e-01 -6.62105262e-01
-9.29915488e-01 -1.05896366e+00 9.28893030e-01 5.92730008e-02
-8.79833817e-01 8.61080468e-01 -1.01653516e-01 -4.97318119e-01
-3.36585678e-02 -3.72417271e-01 -1.05556345e+00 -4.42128211e-01
-1.32415980e-01 5.56916058e-01 2.25012809e-01 -2.88752973... | [9.90649700164795, 1.8781208992004395] |
2d517f6e-3cfa-4763-8450-a9e534b8c4fb | bridging-speech-and-textual-pre-trained | 2211.03025 | null | https://arxiv.org/abs/2211.03025v1 | https://arxiv.org/pdf/2211.03025v1.pdf | Bridging Speech and Textual Pre-trained Models with Unsupervised ASR | Spoken language understanding (SLU) is a task aiming to extract high-level semantics from spoken utterances. Previous works have investigated the use of speech self-supervised models and textual pre-trained models, which have shown reasonable improvements to various SLU tasks. However, because of the mismatched modalit... | ['Hung-Yi Lee', 'Ann Lee', 'Shinji Watanabe', 'Paola Garcia', 'Dongji Gao', 'Holam Chung', 'Chan-Jan Hsu', 'Jiatong Shi'] | 2022-11-06 | null | null | null | null | ['spoken-language-understanding', 'spoken-language-understanding'] | ['natural-language-processing', 'speech'] | [ 5.07433295e-01 6.12923324e-01 4.66130339e-02 -7.76953101e-01
-1.10747409e+00 -2.95844883e-01 9.55994010e-01 1.43225923e-01
-4.44225997e-01 3.96575511e-01 6.95045888e-01 -3.52579117e-01
1.80636913e-01 -5.75372696e-01 -8.70569229e-01 -2.42943496e-01
3.65187347e-01 4.78341907e-01 2.35349283e-01 -3.57649088... | [14.010128021240234, 6.984279632568359] |
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