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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fe775551-548c-4a24-9b9b-ed0822cbf437 | fau-facial-expressions-valence-and-arousal-a | 2002.03557 | null | https://arxiv.org/abs/2002.03557v2 | https://arxiv.org/pdf/2002.03557v2.pdf | Multitask Emotion Recognition with Incomplete Labels | We train a unified model to perform three tasks: facial action unit detection, expression classification, and valence-arousal estimation. We address two main challenges of learning the three tasks. First, most existing datasets are highly imbalanced. Second, most existing datasets do not contain labels for all three ta... | ['Zhaokang Chen', 'Didan Deng', 'Bertram E. Shi'] | 2020-02-10 | null | null | null | null | ['action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 4.13388640e-01 6.33983687e-02 -4.82239783e-01 -6.94727480e-01
-1.11354399e+00 -4.90290552e-01 3.07921588e-01 -7.50832260e-02
-3.62599224e-01 7.61274040e-01 -9.97040793e-02 1.09824039e-01
3.37795258e-01 -2.61176497e-01 -6.23379946e-01 -8.38617086e-01
4.22032237e-01 2.69861788e-01 4.12995592e-02 3.65354754... | [13.576349258422852, 1.7806358337402344] |
b4afc24a-8384-42d4-8676-91c99e7eee82 | actor-identified-spatiotemporal-action | 2208.1294 | null | https://arxiv.org/abs/2208.12940v2 | https://arxiv.org/pdf/2208.12940v2.pdf | Actor-identified Spatiotemporal Action Detection --- Detecting Who Is Doing What in Videos | The success of deep learning on video Action Recognition (AR) has motivated researchers to progressively promote related tasks from the coarse level to the fine-grained level. Compared with conventional AR which only predicts an action label for the entire video, Temporal Action Detection (TAD) has been investigated fo... | ['Satoshi Nakamura', 'Sakriani Sakti', 'Norimichi Ukita', 'Fan Yang'] | 2022-08-27 | null | null | null | null | ['action-classification'] | ['computer-vision'] | [ 3.74519885e-01 -3.55370194e-01 -4.18965548e-01 -1.42829835e-01
-6.25635564e-01 -3.91290277e-01 5.78882337e-01 7.43235946e-02
-2.57934868e-01 4.97623950e-01 1.94574222e-01 1.07581779e-01
-5.64546138e-02 -4.48416740e-01 -4.46403861e-01 -7.93466985e-01
6.93796203e-02 3.77889425e-02 6.24726474e-01 3.57039928... | [8.497082710266113, 0.48382073640823364] |
c7010492-2407-435f-9279-15d97c18b89e | neural-math-word-problem-solver-with | null | null | https://aclanthology.org/C18-1018 | https://aclanthology.org/C18-1018.pdf | Neural Math Word Problem Solver with Reinforcement Learning | Sequence-to-sequence model has been applied to solve math word problems. The model takes math problem descriptions as input and generates equations as output. The advantage of sequence-to-sequence model requires no feature engineering and can generate equations that do not exist in training data. However, our experimen... | ['Chin-Yew Lin', 'Jian Yin', 'Danqing Huang', 'Jing Liu'] | 2018-08-01 | neural-math-word-problem-solver-with-1 | https://aclanthology.org/C18-1018 | https://aclanthology.org/C18-1018.pdf | coling-2018-8 | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [ 2.65792519e-01 -2.05396339e-01 9.64079425e-02 -2.59697467e-01
-7.35540926e-01 -6.61067426e-01 1.95677698e-01 -2.33424932e-01
-2.74114788e-01 1.06145656e+00 -1.55413717e-01 -5.55278957e-01
-2.82286465e-01 -9.10413921e-01 -8.71422172e-01 -1.95506200e-01
2.46559501e-01 2.73999125e-01 -1.01645000e-03 -4.72824931... | [9.808761596679688, 7.474664688110352] |
dee100f6-fa34-41a0-a8a9-cd8462f122c0 | epileptic-seizure-prediction-using-pearson-s | 2006.01359 | null | https://arxiv.org/abs/2006.01359v1 | https://arxiv.org/pdf/2006.01359v1.pdf | Epileptic seizure prediction using Pearson's product-moment correlation coefficient of a linear classifier from generalized Gaussian modeling | To predict an epileptic event means the ability to determine in advance the time of the seizure with the highest possible accuracy. A correct prediction benchmark for epilepsy events in clinical applications is a typical problem in biomedical signal processing that helps to an appropriate diagnosis and treatment of thi... | ["Carlos D'Giano", 'Antonio Quintero-Rincon', 'Marcelo Risk'] | 2020-06-02 | null | null | null | null | ['seizure-prediction'] | ['medical'] | [-1.97760344e-01 -1.78719521e-01 3.89770329e-01 -3.83808583e-01
-6.51625097e-01 -2.58764654e-01 2.55088449e-01 2.35397637e-01
-3.64089847e-01 9.31092322e-01 2.92490646e-02 -2.69391894e-01
-7.12457895e-01 -4.10332471e-01 -2.79090311e-02 -7.69280374e-01
-9.80192363e-01 4.01687890e-01 1.86439887e-01 -1.61162333... | [13.239418983459473, 3.5168986320495605] |
69bb31cd-6b0b-4173-9bc4-3caed8b6de82 | deep-structured-output-regression-learning | 1607.03856 | null | http://arxiv.org/abs/1607.03856v2 | http://arxiv.org/pdf/1607.03856v2.pdf | Deep Structured-Output Regression Learning for Computational Color Constancy | Computational color constancy that requires esti- mation of illuminant colors
of images is a fundamental yet active problem in computer vision, which can be
formulated into a regression problem. To learn a robust regressor for color
constancy, obtaining meaningful imagery features and capturing latent
correlations acro... | ['Jiri Matas', 'Joni-Kristian Kamarainen', 'Yanlin Qian', 'Ke Chen', 'Jarno Nikkanen'] | 2016-07-13 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 2.00160131e-01 -7.08582163e-01 -1.51909485e-01 -4.39518601e-01
-3.70918274e-01 -5.10447264e-01 4.43877608e-01 -5.22441089e-01
-1.42153904e-01 5.57175398e-01 -8.38914141e-02 -2.63464212e-01
1.73829554e-03 -3.97184968e-01 -5.97545326e-01 -9.70043957e-01
8.85461569e-02 -3.20201755e-01 -2.27292225e-01 -2.77430058... | [10.492703437805176, -2.6026763916015625] |
68eeb2d4-7d30-4b8b-aa53-ba75a62498bd | fabric-surface-characterization-assessment-of | 2003.07725 | null | https://arxiv.org/abs/2003.07725v1 | https://arxiv.org/pdf/2003.07725v1.pdf | Fabric Surface Characterization: Assessment of Deep Learning-based Texture Representations Using a Challenging Dataset | Tactile sensing or fabric hand plays a critical role in an individual's decision to buy a certain fabric from the range of available fabrics for a desired application. Therefore, textile and clothing manufacturers have long been in search of an objective method for assessing fabric hand, which can then be used to engin... | ['Sundaresan Jayaraman', 'Sungmee Park', 'Ghassan AlRegib', 'Anirudha Sundaresan', 'Yuting Hu', 'Zhiling Long', 'Motaz Alfarraj'] | 2020-03-16 | null | null | null | null | ['material-recognition', 'texture-classification'] | ['computer-vision', 'computer-vision'] | [ 6.07674658e-01 -6.05755031e-01 -3.36974151e-02 -3.67405534e-01
-5.40185928e-01 -4.43968683e-01 2.16989681e-01 2.39063561e-01
1.46592394e-01 4.64205176e-01 -2.66774982e-01 -8.74861032e-02
-5.47149539e-01 -1.32008123e+00 -6.59689188e-01 -9.18280363e-01
5.55705316e-02 4.56549823e-01 -1.39406964e-01 -3.88022184... | [10.19558334350586, -0.19423720240592957] |
b9355416-0678-4a2d-b82e-db89e6b5356e | nlpbk-at-vlsp-2020-shared-task-compose | 2101.12672 | null | https://arxiv.org/abs/2101.12672v1 | https://arxiv.org/pdf/2101.12672v1.pdf | NLPBK at VLSP-2020 shared task: Compose transformer pretrained models for Reliable Intelligence Identification on Social network | This paper describes our method for tuning a transformer-based pretrained model, to adaptation with Reliable Intelligence Identification on Vietnamese SNSs problem. We also proposed a model that combines bert-base pretrained models with some metadata features, such as the number of comments, number of likes, images of ... | ['Van Nha Nguyen', 'Thanh Chinh Nguyen'] | 2021-01-29 | null | null | null | null | ['reliable-intelligence-identification'] | ['natural-language-processing'] | [-1.27382323e-01 2.02717572e-01 -3.62126678e-01 -6.95598602e-01
-1.14689100e+00 -7.06762075e-01 6.20020270e-01 -1.50939569e-01
-6.66421413e-01 1.21439791e+00 2.90165450e-02 1.80449225e-02
-4.01346952e-01 -8.31286728e-01 -4.19236600e-01 -4.34728444e-01
-4.75125723e-02 1.30253053e+00 1.75211966e-01 -4.03774500... | [10.140763282775879, 10.649721145629883] |
aaa64e0b-c267-4758-b088-1308f85f2bbc | pyramid-real-image-denoising-network | 1908.00273 | null | https://arxiv.org/abs/1908.00273v2 | https://arxiv.org/pdf/1908.00273v2.pdf | Pyramid Real Image Denoising Network | While deep Convolutional Neural Networks (CNNs) have shown extraordinary capability of modelling specific noise and denoising, they still perform poorly on real-world noisy images. The main reason is that the real-world noise is more sophisticated and diverse. To tackle the issue of blind denoising, in this paper, we p... | ['Zhuqing Jiang', 'Guodong Ju', 'Yiyun Zhao', 'Aidong Men'] | 2019-08-01 | null | null | null | null | ['noise-estimation'] | ['medical'] | [ 1.29105344e-01 -8.03229749e-01 5.46085954e-01 -2.63439357e-01
-8.90110195e-01 -2.40830898e-01 3.07891518e-01 -1.40871495e-01
-5.66360533e-01 5.01704097e-01 4.51054782e-01 -2.85898969e-02
-4.18222062e-02 -8.26604307e-01 -4.98606592e-01 -1.05241930e+00
2.21866041e-01 -4.70995218e-01 2.97501236e-01 -3.84760618... | [11.493109703063965, -2.3854479789733887] |
1d840c54-aa99-4428-b852-ffee3eea76ba | attentional-feature-pair-relation-networks | 1908.06255 | null | https://arxiv.org/abs/1908.06255v1 | https://arxiv.org/pdf/1908.06255v1.pdf | Attentional Feature-Pair Relation Networks for Accurate Face Recognition | Human face recognition is one of the most important research areas in biometrics. However, the robust face recognition under a drastic change of the facial pose, expression, and illumination is a big challenging problem for its practical application. Such variations make face recognition more difficult. In this paper, ... | ['Bong-Nam Kang', 'Yonghyun Kim', 'Daijin Kim', 'Bongjin Jun'] | 2019-08-17 | attentional-feature-pair-relation-networks-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Kang_Attentional_Feature-Pair_Relation_Networks_for_Accurate_Face_Recognition_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Kang_Attentional_Feature-Pair_Relation_Networks_for_Accurate_Face_Recognition_ICCV_2019_paper.pdf | iccv-2019-10 | ['robust-face-recognition'] | ['computer-vision'] | [ 1.35242119e-01 -5.71604311e-01 3.41022983e-02 -5.60987234e-01
-4.44038212e-01 5.61810583e-02 4.24488276e-01 -5.83328545e-01
-3.27755153e-01 6.07647896e-01 1.32497832e-01 1.06960513e-01
-5.79562724e-01 -4.68577385e-01 -4.40839410e-01 -1.12072647e+00
-3.23137119e-02 3.23610641e-02 2.69967178e-03 -2.35950440... | [13.172743797302246, 0.6984913349151611] |
7507dfca-7cae-43c7-97dc-66e0d321595a | wavemixsr-a-resource-efficient-neural-network | 2307.0043 | null | https://arxiv.org/abs/2307.00430v1 | https://arxiv.org/pdf/2307.00430v1.pdf | WaveMixSR: A Resource-efficient Neural Network for Image Super-resolution | Image super-resolution research recently been dominated by transformer models which need higher computational resources than CNNs due to the quadratic complexity of self-attention. We propose a new neural network -- WaveMixSR -- for image super-resolution based on WaveMix architecture which uses a 2D-discrete wavelet t... | ['Amit Sethi', 'Pasunuri Prathiba', 'Akella Srinidhi', 'Pranav Jeevan'] | 2023-07-01 | null | null | null | null | ['image-super-resolution', 'super-resolution'] | ['computer-vision', 'computer-vision'] | [ 3.80888402e-01 -5.01231700e-02 -6.08890541e-02 -9.65607613e-02
-1.28096759e+00 1.51141435e-01 4.95885670e-01 -4.79238123e-01
-4.08365548e-01 6.82886481e-01 5.31010330e-01 3.87802608e-02
1.44264564e-01 -1.01524997e+00 -8.37300062e-01 -6.00803554e-01
3.94685604e-02 1.57907590e-01 5.53123891e-01 -4.12333548... | [11.086946487426758, -1.9382935762405396] |
401345d1-308c-422b-838e-5395edfed681 | learning-invariant-representations-for-2 | 2209.10944 | null | https://arxiv.org/abs/2209.10944v1 | https://arxiv.org/pdf/2209.10944v1.pdf | Learning Invariant Representations for Equivariant Neural Networks Using Orthogonal Moments | The convolutional layers of standard convolutional neural networks (CNNs) are equivariant to translation. However, the convolution and fully-connected layers are not equivariant or invariant to other affine geometric transformations. Recently, a new class of CNNs is proposed in which the conventional layers of CNNs are... | ['Chandan Singh', 'Jaspreet Singh'] | 2022-09-22 | null | null | null | null | ['rotated-mnist'] | ['computer-vision'] | [-7.62506425e-02 -2.53350168e-01 2.51700908e-01 -7.16381192e-01
1.64538473e-01 -7.00397491e-01 7.74975538e-01 -3.05467546e-01
-8.82630885e-01 4.55126375e-01 2.04011664e-01 7.00281858e-02
-2.13684097e-01 -9.51503694e-01 -8.36370051e-01 -7.68567085e-01
-6.12366274e-02 -3.78685355e-01 3.24212432e-01 -3.82634491... | [8.94102954864502, 2.338167905807495] |
ec50ece5-874c-48b5-8a66-530e0f5e24cc | habitat-a-platform-for-embodied-ai-research | 1904.01201 | null | https://arxiv.org/abs/1904.01201v2 | https://arxiv.org/pdf/1904.01201v2.pdf | Habitat: A Platform for Embodied AI Research | We present Habitat, a platform for research in embodied artificial intelligence (AI). Habitat enables training embodied agents (virtual robots) in highly efficient photorealistic 3D simulation. Specifically, Habitat consists of: (i) Habitat-Sim: a flexible, high-performance 3D simulator with configurable agents, sensor... | ['Julian Straub', 'Bhavana Jain', 'Abhishek Kadian', 'Manolis Savva', 'Yili Zhao', 'Vladlen Koltun', 'Erik Wijmans', 'Oleksandr Maksymets', 'Jia Liu', 'Dhruv Batra', 'Jitendra Malik', 'Devi Parikh'] | 2019-04-02 | habitat-a-platform-for-embodied-ai-research-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Savva_Habitat_A_Platform_for_Embodied_AI_Research_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Savva_Habitat_A_Platform_for_Embodied_AI_Research_ICCV_2019_paper.pdf | iccv-2019-10 | ['pointgoal-navigation'] | ['robots'] | [ 2.13802643e-02 -5.24783395e-02 5.55494070e-01 -1.16623513e-01
-3.43863964e-01 -8.91935229e-01 6.28627598e-01 1.88124776e-02
-8.48029494e-01 5.48801064e-01 3.76215146e-04 -5.44535220e-01
-2.82304622e-02 -7.84004033e-01 -1.08306170e+00 -6.13555193e-01
-5.86613536e-01 5.98984301e-01 2.57382810e-01 -6.38527513... | [4.621621608734131, 0.8042087554931641] |
4ac1af28-f036-4e30-a4d4-92e2b5f1d1ce | sked-sketch-guided-text-based-3d-editing | 2303.10735 | null | https://arxiv.org/abs/2303.10735v3 | https://arxiv.org/pdf/2303.10735v3.pdf | SKED: Sketch-guided Text-based 3D Editing | Text-to-image diffusion models are gradually introduced into computer graphics, recently enabling the development of Text-to-3D pipelines in an open domain. However, for interactive editing purposes, local manipulations of content through a simplistic textual interface can be arduous. Incorporating user guided sketches... | ['Ali Mahdavi-Amiri', 'Mehdi Safaee', 'Daniel Cohen-Or', 'Or Perel', 'Aryan Mikaeili'] | 2023-03-19 | null | null | null | null | ['text-to-3d'] | ['computer-vision'] | [ 4.09344435e-01 -6.39145914e-03 4.97422665e-01 -5.71584046e-01
-2.95464635e-01 -7.63310254e-01 9.35393870e-01 -5.99766411e-02
-5.00463396e-02 3.19478780e-01 9.61621478e-02 -1.40886664e-01
2.31481776e-01 -1.02251327e+00 -7.33565271e-01 -3.35364044e-01
2.96324402e-01 2.24161252e-01 2.83037603e-01 -2.54839480... | [9.374850273132324, -3.172276020050049] |
e4531165-5f60-4c33-9a27-b93da78b2b60 | scale-equivalent-distillation-for-semi | 2203.12244 | null | https://arxiv.org/abs/2203.12244v2 | https://arxiv.org/pdf/2203.12244v2.pdf | Scale-Equivalent Distillation for Semi-Supervised Object Detection | Recent Semi-Supervised Object Detection (SS-OD) methods are mainly based on self-training, i.e., generating hard pseudo-labels by a teacher model on unlabeled data as supervisory signals. Although they achieved certain success, the limited labeled data in semi-supervised learning scales up the challenges of object dete... | ['Ping Luo', 'Yizhou Yu', 'Tianqi Wang', 'Jianyu Chen', 'Yao Mu', 'Qiushan Guo'] | 2022-03-23 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Guo_Scale-Equivalent_Distillation_for_Semi-Supervised_Object_Detection_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Guo_Scale-Equivalent_Distillation_for_Semi-Supervised_Object_Detection_CVPR_2022_paper.pdf | cvpr-2022-1 | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 1.67718068e-01 8.40551779e-02 -3.42027664e-01 -2.54632115e-01
-9.21506941e-01 -3.56899977e-01 5.13142943e-01 -1.70305327e-01
-4.65573639e-01 6.60825133e-01 -2.54943341e-01 -3.05018835e-02
1.98285714e-01 -4.10396695e-01 -8.80547225e-01 -9.96577203e-01
3.64966303e-01 5.18732786e-01 7.63151467e-01 1.18739396... | [9.191727638244629, 1.2656563520431519] |
2810f026-d7c2-4ca7-8d8a-9a8722261cef | towards-open-vocabulary-video-instance | 2304.01715 | null | https://arxiv.org/abs/2304.01715v1 | https://arxiv.org/pdf/2304.01715v1.pdf | Towards Open-Vocabulary Video Instance Segmentation | Video Instance Segmentation(VIS) aims at segmenting and categorizing objects in videos from a closed set of training categories, lacking the generalization ability to handle novel categories in real-world videos. To address this limitation, we make the following three contributions. First, we introduce the novel task o... | ['Efstratios Gavves', 'Weidi Xie', 'Yao Hu', 'Xu Tang', 'XiaoLong Jiang', 'Cilin Yan', 'Shuai Wang', 'Haochen Wang'] | 2023-04-04 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 4.44124937e-01 6.35843053e-02 -4.55566853e-01 -5.42793810e-01
-9.12182510e-01 -8.57611418e-01 3.37272644e-01 -3.09173346e-01
-2.37389669e-01 5.01221836e-01 -2.12370902e-02 -1.21855438e-01
1.98996156e-01 -3.75670493e-01 -1.11695445e+00 -3.15330684e-01
-1.62862018e-01 5.05852759e-01 3.67845446e-01 2.50819981... | [9.35136890411377, 0.3187369108200073] |
b84c96b7-c81c-4f8a-8d5c-5e3802d06e78 | sound2synth-interpreting-sound-via-fm | 2205.03043 | null | https://arxiv.org/abs/2205.03043v2 | https://arxiv.org/pdf/2205.03043v2.pdf | Sound2Synth: Interpreting Sound via FM Synthesizer Parameters Estimation | Synthesizer is a type of electronic musical instrument that is now widely used in modern music production and sound design. Each parameters configuration of a synthesizer produces a unique timbre and can be viewed as a unique instrument. The problem of estimating a set of parameters configuration that best restore a so... | ['Hang Zhao', 'Jian Wu', 'Yifei Xu', 'Shengcheng Yuan', 'Yansen Jing', 'Zui Chen'] | 2022-05-06 | null | null | null | null | ['audio-signal-processing'] | ['audio'] | [-3.82449813e-02 -7.43267894e-01 1.66545346e-01 2.41075352e-01
-7.80668736e-01 -8.42773616e-01 6.41451627e-02 -7.34024167e-01
8.32087025e-02 3.77669960e-01 1.55748963e-01 -1.32885769e-01
-2.54269123e-01 -5.41145384e-01 -6.34869337e-01 -6.80936396e-01
1.70366451e-01 5.05293846e-01 -4.58356351e-01 -4.88525808... | [15.686894416809082, 5.900119304656982] |
6586ddbe-0b61-40c0-9827-78586b0075d5 | online-structured-sparsity-based-moving | 1911.12989 | null | https://arxiv.org/abs/1911.12989v3 | https://arxiv.org/pdf/1911.12989v3.pdf | Online Structured Sparsity-based Moving Object Detection from Satellite Videos | Inspired by the recent developments in computer vision, low-rank and structured sparse matrix decomposition can be potentially be used for extract moving objects in satellite videos. This set of approaches seeks for rank minimization on the background that typically requires batch-based optimization over a sequence of ... | ['Junpeng Zhang', 'Xiuping Jia', 'Jocelyn Chanussot', 'Jiankun Hu'] | 2019-11-29 | null | null | null | null | ['moving-object-detection'] | ['computer-vision'] | [ 2.26530686e-01 -6.99001253e-01 2.60212928e-01 -1.23932414e-01
-7.20436752e-01 -5.07151723e-01 2.56717354e-01 -3.83093596e-01
-2.41680518e-01 4.11969006e-01 2.39738762e-01 -1.18615545e-01
-3.08674335e-01 -1.29731104e-01 -4.91143525e-01 -1.07713974e+00
-3.32387745e-01 -8.72499570e-02 2.82209456e-01 2.30471864... | [8.999110221862793, -0.8348671793937683] |
0d243c3b-df29-4361-87f1-e6f774d6ff2b | medal-deep-active-learning-sampling-method | 1809.09287 | null | http://arxiv.org/abs/1809.09287v2 | http://arxiv.org/pdf/1809.09287v2.pdf | MedAL: Deep Active Learning Sampling Method for Medical Image Analysis | Deep learning models have been successfully used in medical image analysis
problems but they require a large amount of labeled images to obtain good
performance.Deep learning models have been successfully used in medical image
analysis problems but they require a large amount of labeled images to obtain
good performanc... | ['Adrián Galdrán', 'Pedro Costa', 'Asim Smailagic', 'Susu Xu', 'Mostafa Mirshekari', 'Kartik Khandelwal', 'Hae Young Noh', 'Jonathon Fagert', 'Devesh Walawalkar'] | 2018-09-25 | null | null | null | null | ['diabetic-retinopathy-detection'] | ['medical'] | [ 1.32496536e-01 2.79609382e-01 -2.87815005e-01 -9.08332586e-01
-1.27236700e+00 -1.91502884e-01 2.27467313e-01 4.00198400e-01
-8.89028430e-01 5.85084736e-01 -2.44342119e-01 3.84067260e-02
-2.24686041e-01 -8.73406589e-01 -6.11828268e-01 -8.84533823e-01
1.68201849e-01 7.87416279e-01 2.10754469e-01 6.39778793... | [14.800943374633789, -2.2836427688598633] |
1e595f8b-878b-40cb-98cd-a6dcaa914814 | evaluating-openai-s-whisper-asr-for | 2305.1458 | null | https://arxiv.org/abs/2305.14580v2 | https://arxiv.org/pdf/2305.14580v2.pdf | Evaluating OpenAI's Whisper ASR for Punctuation Prediction and Topic Modeling of life histories of the Museum of the Person | Automatic speech recognition (ASR) systems play a key role in applications involving human-machine interactions. Despite their importance, ASR models for the Portuguese language proposed in the last decade have limitations in relation to the correct identification of punctuation marks in automatic transcriptions, which... | ['Sandra Maria Aluísio', 'Anderson Soares', 'Edresson Casanova', 'Arnaldo Candido Junior', 'Ricardo Marcacini', 'Lucas Rafael Stefanel Gris'] | 2023-05-23 | null | null | null | null | ['automatic-speech-recognition'] | ['speech'] | [ 4.41224240e-02 1.29746526e-01 2.19005242e-01 -2.18411341e-01
-9.66754436e-01 -5.71722150e-01 8.52056265e-01 3.17104936e-01
-5.98665476e-01 7.13282883e-01 5.97929716e-01 -3.35449338e-01
3.26208584e-02 -2.37687677e-01 -3.26732934e-01 -5.82529783e-01
1.12724587e-01 6.27783060e-01 4.91987586e-01 -6.53281212... | [14.287074089050293, 6.958329200744629] |
875706ea-3128-46e7-a033-81d509573d50 | a-novel-twitter-sentiment-analysis-model-with | 2003.08137 | null | https://arxiv.org/abs/2003.08137v2 | https://arxiv.org/pdf/2003.08137v2.pdf | A Novel Twitter Sentiment Analysis Model with Baseline Correlation for Financial Market Prediction with Improved Efficiency | A novel social networks sentiment analysis model is proposed based on Twitter sentiment score (TSS) for real-time prediction of the future stock market price FTSE 100, as compared with conventional econometric models of investor sentiment based on closed-end fund discount (CEFD). The proposed TSS model features a new b... | ['Xinyi Guo', 'Jinfeng Li'] | 2020-03-18 | null | null | null | null | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-6.75920129e-01 -1.22513384e-01 -2.53599346e-01 -4.43155378e-01
-2.08994791e-01 -5.61604619e-01 6.65729225e-01 2.59445995e-01
-4.46170568e-01 6.21615708e-01 -5.71154850e-03 -6.15184844e-01
-2.35265512e-02 -1.22930861e+00 -2.08838861e-02 -4.30159479e-01
-1.34090856e-01 1.89313754e-01 6.35990873e-02 -7.08438873... | [4.481029033660889, 4.333154678344727] |
64e0a369-23cd-4a61-bbb6-008aedec9156 | parallel-detection-for-efficient-video | 2107.12563 | null | https://arxiv.org/abs/2107.12563v1 | https://arxiv.org/pdf/2107.12563v1.pdf | Parallel Detection for Efficient Video Analytics at the Edge | Deep Neural Network (DNN) trained object detectors are widely deployed in many mission-critical systems for real time video analytics at the edge, such as autonomous driving and video surveillance. A common performance requirement in these mission-critical edge services is the near real-time latency of online object de... | ['Ramana Kompella', 'Ling Liu', 'Yanzhao Wu'] | 2021-07-27 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [-5.45128575e-03 -5.15422285e-01 -3.04860294e-01 7.38161802e-02
-1.58837214e-01 -3.12002927e-01 1.11451596e-01 -2.41353661e-02
-7.53513277e-01 -1.64357036e-01 -4.53182161e-01 -5.48758268e-01
3.20130408e-01 -6.44764125e-01 -9.34314132e-01 -4.35043275e-01
-2.82230377e-01 1.46522045e-01 1.14992011e+00 2.46701762... | [8.411710739135742, -0.47293657064437866] |
76bcef52-6dac-46d0-bfa5-477a06676ad4 | a-practical-method-for-constructing | 2104.09459 | null | https://arxiv.org/abs/2104.09459v1 | https://arxiv.org/pdf/2104.09459v1.pdf | A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups | Symmetries and equivariance are fundamental to the generalization of neural networks on domains such as images, graphs, and point clouds. Existing work has primarily focused on a small number of groups, such as the translation, rotation, and permutation groups. In this work we provide a completely general algorithm for... | ['Andrew Gordon Wilson', 'Max Welling', 'Marc Finzi'] | 2021-04-19 | a-practical-method-for-constructing-1 | https://arxiv.org/abs/2104.09459 | https://arxiv.org/pdf/2104.09459.pdf | null | ['rubik-s-cube'] | ['graphs'] | [ 2.50935167e-01 2.49906957e-01 1.41731739e-01 -1.68460950e-01
-3.77359055e-02 -6.95838451e-01 7.32449174e-01 -2.27081746e-01
-2.91145235e-01 4.31676090e-01 1.41521573e-01 -5.69822550e-01
-4.47870046e-01 -7.55445182e-01 -1.08413517e+00 -7.33241975e-01
-5.78738987e-01 5.20956635e-01 -8.78263041e-02 -5.65597713... | [8.799604415893555, 2.491394281387329] |
85bfb024-cf45-4b32-bb2c-454a02505963 | ecg-arrhythmia-classification-using-a-2-d | 1804.06812 | null | http://arxiv.org/abs/1804.06812v1 | http://arxiv.org/pdf/1804.06812v1.pdf | ECG arrhythmia classification using a 2-D convolutional neural network | In this paper, we propose an effective electrocardiogram (ECG) arrhythmia
classification method using a deep two-dimensional convolutional neural network
(CNN) which recently shows outstanding performance in the field of pattern
recognition. Every ECG beat was transformed into a two-dimensional grayscale
image as an in... | ['Young-Hak Kim', 'Tae Joon Jun', 'Dohyeun Kim', 'Hoang Minh Nguyen', 'Daeyoun Kang', 'Daeyoung Kim'] | 2018-04-18 | null | null | null | null | ['arrhythmia-detection', 'electrocardiography-ecg'] | ['medical', 'methodology'] | [ 2.61329710e-01 -3.65563244e-01 4.54742879e-01 -5.68492234e-01
-4.38538373e-01 -2.22461835e-01 -2.58640707e-01 2.78461903e-01
-8.26838911e-01 6.77547753e-01 -5.08681715e-01 -3.58087480e-01
-1.02639005e-01 -7.44878948e-01 -3.05098504e-01 -5.79425275e-01
-2.36295119e-01 3.07030566e-02 -3.56118619e-01 1.14790253... | [14.26003646850586, 3.246610403060913] |
a15ad65a-0988-4b73-8f58-35fd3c748c8f | mcgnet-partial-multi-view-few-shot-learning | 2105.02046 | null | https://arxiv.org/abs/2105.02046v4 | https://arxiv.org/pdf/2105.02046v4.pdf | Few-shot Partial Multi-view Learning | It is often the case that data are with multiple views in real-world applications. Fully exploring the information of each view is significant for making data more representative. However, due to various limitations and failures in data collection and pre-processing, it is inevitable for real data to suffer from view m... | ['Jiebo Luo', 'Richang Hong', 'Shijie Hao', 'Yanrong Guo', 'Yuan Zhou'] | 2021-05-05 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [-4.44409586e-02 -3.40682417e-01 -1.33825347e-01 -3.27797055e-01
-5.27727127e-01 -2.63407081e-01 3.96613687e-01 -2.71410435e-01
-1.79364473e-01 6.47278965e-01 4.68380809e-01 1.54194638e-01
-1.83779240e-01 -6.28388345e-01 -5.51801443e-01 -7.50091076e-01
3.59618276e-01 8.80326852e-02 1.35839069e-02 -2.61777371... | [8.488686561584473, 4.5418219566345215] |
c8b40a8e-7817-46ba-92d7-dfb0a875ada2 | schema-independent-relational-learning | 1508.03846 | null | http://arxiv.org/abs/1508.03846v2 | http://arxiv.org/pdf/1508.03846v2.pdf | Schema Independent Relational Learning | Learning novel concepts and relations from relational databases is an
important problem with many applications in database systems and machine
learning. Relational learning algorithms learn the definition of a new relation
in terms of existing relations in the database. Nevertheless, the same data set
may be represente... | ['Parisa Ataei', 'Alan Fern', 'Jose Picado', 'Arash Termehchy'] | 2015-08-16 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 1.36296436e-01 1.00845210e-01 -7.40998983e-01 -8.31545711e-01
-5.93729019e-01 -7.58978784e-01 3.05662006e-01 7.07055330e-01
-2.46370584e-01 5.24330199e-01 -2.08255574e-01 -4.11343485e-01
-6.93166375e-01 -1.42433274e+00 -1.00051129e+00 -4.85704035e-01
-2.38351896e-01 1.09861934e+00 4.60098594e-01 -3.76336545... | [9.267040252685547, 7.688653469085693] |
517f0635-aa52-44bd-b481-2fdc24b221c4 | a-deep-moving-camera-background-model | 2209.07923 | null | https://arxiv.org/abs/2209.07923v1 | https://arxiv.org/pdf/2209.07923v1.pdf | A Deep Moving-camera Background Model | In video analysis, background models have many applications such as background/foreground separation, change detection, anomaly detection, tracking, and more. However, while learning such a model in a video captured by a static camera is a fairly-solved task, in the case of a Moving-camera Background Model (MCBM), the ... | ['Oren Freifeld', 'Ron Shapira Weber', 'Guy Erez'] | 2022-09-16 | null | null | null | null | ['video-background-subtraction'] | ['computer-vision'] | [ 1.09563850e-01 -5.15044868e-01 -1.93452947e-02 -2.01351568e-02
-6.02046609e-01 -5.17193377e-01 6.59445286e-01 -3.43043625e-01
-3.72556388e-01 5.48298538e-01 -1.36607990e-01 -2.86137342e-01
1.30439429e-02 -4.14073080e-01 -7.80215681e-01 -9.39020216e-01
1.17069818e-01 1.53687507e-01 5.74406207e-01 -7.13787600... | [8.935518264770508, -0.6837957501411438] |
2e5be8ee-d10b-4305-a498-5f6b06e6ad63 | mdi-a-flexible-random-forest-based-feature | 2307.01932 | null | https://arxiv.org/abs/2307.01932v1 | https://arxiv.org/pdf/2307.01932v1.pdf | MDI+: A Flexible Random Forest-Based Feature Importance Framework | Mean decrease in impurity (MDI) is a popular feature importance measure for random forests (RFs). We show that the MDI for a feature $X_k$ in each tree in an RF is equivalent to the unnormalized $R^2$ value in a linear regression of the response on the collection of decision stumps that split on $X_k$. We use this inte... | ['Bin Yu', 'Tiffany M. Tang', 'Yan Shuo Tan', 'Ana M. Kenney', 'Abhineet Agarwal'] | 2023-07-04 | null | null | null | null | ['drug-response-prediction', 'feature-importance'] | ['medical', 'methodology'] | [ 6.79458082e-01 -6.19826093e-02 -5.97149014e-01 -6.98367298e-01
-8.96907270e-01 -2.60658622e-01 4.51707691e-01 1.74752533e-01
-2.88738385e-02 1.10612869e+00 1.46263823e-01 -4.72370774e-01
-7.33103812e-01 -7.97829628e-01 -5.64734221e-01 -9.86119509e-01
-3.41745675e-01 2.05493525e-01 -1.78003341e-01 1.17935807... | [7.970056533813477, 4.8347344398498535] |
fbef7e97-c8df-4ff7-94b3-49fbe779749b | composition-loss-for-counting-density-map | 1808.0105 | null | http://arxiv.org/abs/1808.01050v1 | http://arxiv.org/pdf/1808.01050v1.pdf | Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds | With multiple crowd gatherings of millions of people every year in events
ranging from pilgrimages to protests, concerts to marathons, and festivals to
funerals; visual crowd analysis is emerging as a new frontier in computer
vision. In particular, counting in highly dense crowds is a challenging problem
with far-reach... | ['Somaya Al-Maadeed', 'Dong Zhang', 'Nasir Rajpoot', 'Kishan Athrey', 'Muhmmad Tayyab', 'Mubarak Shah', 'Haroon Idrees'] | 2018-08-02 | composition-loss-for-counting-density-map-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Haroon_Idrees_Composition_Loss_for_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Haroon_Idrees_Composition_Loss_for_ECCV_2018_paper.pdf | eccv-2018-9 | ['visual-crowd-analysis'] | ['computer-vision'] | [-4.42997545e-01 -4.88288313e-01 4.00560707e-01 -1.21274471e-01
-4.85530198e-01 -5.46338379e-01 8.30243647e-01 3.41489315e-01
-1.26199484e+00 1.05583894e+00 3.53566617e-01 2.07138266e-02
4.44284081e-01 -5.98888397e-01 -6.12472951e-01 -4.19355214e-01
-2.36074366e-02 9.54263389e-01 5.72387099e-01 -2.69946575... | [8.404275894165039, -0.3269347548484802] |
d1f0d348-6e60-4c59-bb9c-7f9205fa24bf | training-data-efficient-image-transformers | 2012.12877 | null | https://arxiv.org/abs/2012.12877v2 | https://arxiv.org/pdf/2012.12877v2.pdf | Training data-efficient image transformers & distillation through attention | Recently, neural networks purely based on attention were shown to address image understanding tasks such as image classification. However, these visual transformers are pre-trained with hundreds of millions of images using an expensive infrastructure, thereby limiting their adoption. In this work, we produce a competit... | ['Hervé Jégou', 'Alexandre Sablayrolles', 'Francisco Massa', 'Matthijs Douze', 'Matthieu Cord', 'Hugo Touvron'] | 2020-12-23 | null | null | null | null | ['document-image-classification', 'document-layout-analysis'] | ['computer-vision', 'computer-vision'] | [ 2.34593064e-01 3.67680103e-01 1.85660437e-01 -1.97937518e-01
-4.65994984e-01 -5.83199620e-01 7.53893197e-01 -2.02098072e-01
-6.46512449e-01 2.99466491e-01 -3.13293189e-01 -8.19244921e-01
3.88325512e-01 -8.56782317e-01 -1.27496338e+00 -4.41025078e-01
1.25978678e-01 3.47002685e-01 4.47841942e-01 -1.15955554... | [9.452077865600586, 1.5363589525222778] |
67434265-2201-4393-a5db-90ee4ac29bec | model-based-offline-reinforcement-learning-1 | 2301.11426 | null | https://arxiv.org/abs/2301.11426v1 | https://arxiv.org/pdf/2301.11426v1.pdf | Model-based Offline Reinforcement Learning with Local Misspecification | We present a model-based offline reinforcement learning policy performance lower bound that explicitly captures dynamics model misspecification and distribution mismatch and we propose an empirical algorithm for optimal offline policy selection. Theoretically, we prove a novel safe policy improvement theorem by establi... | ['Emma Brunskill', 'Allen Nie', 'Yannis Flet-Berliac', 'Kefan Dong'] | 2023-01-26 | null | null | null | null | ['d4rl'] | ['robots'] | [-3.38917136e-01 1.64111659e-01 -8.93953800e-01 1.50079802e-01
-9.77066159e-01 -9.39390123e-01 2.65531123e-01 1.30724818e-01
-6.54147625e-01 1.08761990e+00 1.11392133e-01 -6.77896500e-01
-4.57097292e-01 -3.66424859e-01 -9.60482359e-01 -6.94450676e-01
-4.61771578e-01 7.22069502e-01 4.68878150e-02 -1.91959277... | [4.089269638061523, 2.3319191932678223] |
b39b6e50-3dfc-4a8f-8c7a-ba0f2087cb16 | a-configuration-space-decomposition-scheme | 1911.08581 | null | https://arxiv.org/abs/1911.08581v1 | https://arxiv.org/pdf/1911.08581v1.pdf | A Configuration-Space Decomposition Scheme for Learning-based Collision Checking | Motion planning for robots of high degrees-of-freedom (DOFs) is an important problem in robotics with sampling-based methods in configuration space C as one popular solution. Recently, machine learning methods have been introduced into sampling-based motion planning methods, which train a classifier to distinguish coll... | ['Yong-Jin Liu', 'Yiheng Han', 'Wang Zhao', 'Ran Yi', 'Jia Pan', 'Zipeng Ye'] | 2019-11-17 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [ 1.52633205e-01 1.40437752e-01 -4.85023677e-01 7.63437226e-02
-6.95805103e-02 -5.59109688e-01 4.98010457e-01 4.79009040e-02
-3.10063586e-02 5.18500686e-01 -5.30544341e-01 -4.45051044e-01
-6.89183354e-01 -6.80044889e-01 -5.51070213e-01 -1.06552088e+00
-2.08869979e-01 9.40236866e-01 4.33445007e-01 -4.55680281... | [4.876671314239502, 1.266391396522522] |
3116a91b-ae33-4d1b-a05d-10fb24d7bd2b | characterization-of-lung-nodule-malignancy | 1609.06668 | null | http://arxiv.org/abs/1609.06668v1 | http://arxiv.org/pdf/1609.06668v1.pdf | Characterization of Lung Nodule Malignancy using Hybrid Shape and Appearance Features | Computed tomography imaging is a standard modality for detecting and
assessing lung cancer. In order to evaluate the malignancy of lung nodules,
clinical practice often involves expert qualitative ratings on several criteria
describing a nodule's appearance and shape. Translating these features for
computer-aided diagn... | ['Daniel J. Mollura', 'Ulas Bagci', 'Mingchen Gao', 'Ziyue Xu', 'Aaron Wu', 'Mario Buty'] | 2016-09-21 | null | null | null | null | ['lung-cancer-diagnosis'] | ['medical'] | [ 1.43751279e-01 2.55852610e-01 -3.98265153e-01 -3.16324174e-01
-9.82202411e-01 -6.23822510e-01 4.77057785e-01 2.72908628e-01
-1.94761589e-01 2.60155529e-01 1.20463610e-01 -4.59596306e-01
-2.03472357e-02 -6.86487317e-01 -5.12131974e-02 -6.91732049e-01
1.20887928e-01 9.67142582e-01 3.48477006e-01 2.77361244... | [15.379424095153809, -2.155588388442993] |
0c6329b7-68b7-4b1c-87bf-fe1e240a0393 | hypertransformer-a-textural-and-spectral | 2203.02503 | null | https://arxiv.org/abs/2203.02503v3 | https://arxiv.org/pdf/2203.02503v3.pdf | HyperTransformer: A Textural and Spectral Feature Fusion Transformer for Pansharpening | Pansharpening aims to fuse a registered high-resolution panchromatic image (PAN) with a low-resolution hyperspectral image (LR-HSI) to generate an enhanced HSI with high spectral and spatial resolution. Existing pansharpening approaches neglect using an attention mechanism to transfer HR texture features from PAN to LR... | ['Vishal M. Patel', 'Wele Gedara Chaminda Bandara'] | 2022-03-04 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Bandara_HyperTransformer_A_Textural_and_Spectral_Feature_Fusion_Transformer_for_Pansharpening_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Bandara_HyperTransformer_A_Textural_and_Spectral_Feature_Fusion_Transformer_for_Pansharpening_CVPR_2022_paper.pdf | cvpr-2022-1 | ['pansharpening'] | ['computer-vision'] | [ 7.90252149e-01 -5.33044696e-01 2.90648602e-02 -2.44097084e-01
-1.26991081e+00 -5.14630318e-01 3.71060371e-01 -3.49735588e-01
3.52534764e-02 4.78805751e-01 3.13848406e-01 8.71883333e-03
-4.74290520e-01 -1.22110176e+00 -5.48286796e-01 -1.18854272e+00
3.85235846e-01 -1.76645473e-01 -4.94968332e-02 -3.00527900... | [10.193822860717773, -1.9335129261016846] |
1b9e56b1-3c7f-4ba4-b442-20c05bd8a61e | analysis-of-benfords-law-for-no-reference | null | null | https://www.mdpi.com/2079-9292/10/19/2378 | https://www.mdpi.com/2079-9292/10/19/2378 | Analysis of Benford’s Law for No-Reference Quality Assessment of Natural, Screen-Content, and Synthetic Images | With the tremendous growth and usage of digital images, no-reference image quality assessment is becoming increasingly important. This paper presents in-depth analysis of Benford’s law inspired first digit distribution feature vectors for no-reference quality assessment of natural, screen-content, and synthetic images ... | ['Domonkos Varga'] | 2021-09-21 | null | null | null | electronics-2021-9 | ['no-reference-image-quality-assessment', 'image-forensics'] | ['computer-vision', 'computer-vision'] | [ 1.76732749e-01 -5.38157523e-01 -2.41107166e-01 -2.24345669e-01
-8.82211208e-01 -3.97583902e-01 7.34831989e-01 4.23288137e-01
-3.86800736e-01 4.41320807e-01 1.77402824e-01 -1.49276406e-01
-2.66094565e-01 -7.79512346e-01 -3.37097019e-01 -4.33429182e-01
-6.25555664e-02 -1.14695445e-01 2.26229414e-01 -6.96145520... | [11.826131820678711, -1.7998625040054321] |
49229b18-b4b0-4c74-b664-d627d95db8b3 | approximate-fpga-based-lstms-under | 1801.0219 | null | http://arxiv.org/abs/1801.02190v1 | http://arxiv.org/pdf/1801.02190v1.pdf | Approximate FPGA-based LSTMs under Computation Time Constraints | Recurrent Neural Networks and in particular Long Short-Term Memory (LSTM)
networks have demonstrated state-of-the-art accuracy in several emerging
Artificial Intelligence tasks. However, the models are becoming increasingly
demanding in terms of computational and memory load. Emerging latency-sensitive
applications inc... | ['Christos-Savvas Bouganis', 'Alexandros Kouris', 'Michalis Rizakis', 'Stylianos I. Venieris'] | 2018-01-07 | null | null | null | null | ['low-rank-compression'] | ['computer-code'] | [ 5.70437491e-01 -3.28749418e-02 -7.50122443e-02 -3.54246795e-01
-9.23313737e-01 2.75457576e-02 5.27498722e-01 -6.78459108e-02
-7.57354259e-01 4.02663022e-01 -3.38854402e-01 -5.82673967e-01
-5.91032282e-02 -4.94907886e-01 -8.91779542e-01 -5.91097176e-01
-1.14426101e-02 5.88330865e-01 6.44679815e-02 2.19218917... | [8.475311279296875, 2.917058229446411] |
c1df2e0b-210d-46bf-b9cf-14d8410cb80a | diagnosis-of-covid-19-using-chest-x-ray | null | null | https://link.springer.com/article/10.1007/s12065-021-00679-7 | https://link.springer.com/content/pdf/10.1007/s12065-021-00679-7.pdf | Diagnosis of COVID-19 using chest X-ray images based on modified DarkCovidNet model | Coronavirus disease, also known as COVID-19, is an infectious disease caused by SARS-CoV-2. It has a direct impact on the upper and lower respiratory tract and threatened the health of many people around the world. The latest statistics show that the number of people diagnosed with COVID-19 is growing exponentially. Di... | ['Om Prakash Verma & Tarun Kumar Sharma', 'Vimal Kumar Shrivastava', 'Semagn Sisay Teferi', 'Tensaie Melkamu Demissie', 'Abdulhakim Edao Sirko', 'Dawit Kiros Redie'] | 2022-03-09 | null | null | null | journal-2022-3 | ['covid-19-detection'] | ['medical'] | [-3.68422866e-02 -6.87021971e-01 1.35972217e-01 -1.32143766e-01
-2.54061639e-01 -6.22660160e-01 1.36526423e-02 3.20897400e-01
-5.77715993e-01 6.83842123e-01 -3.32262278e-01 -7.29575455e-01
2.37441272e-03 -8.09804916e-01 -3.93739760e-01 -6.96346641e-01
-1.88112870e-01 7.76440442e-01 3.69241685e-02 7.46234432... | [15.588909149169922, -1.6648826599121094] |
91d038f7-db43-4c9f-a875-0d10232de9e1 | fully-transformer-networks-for-semantic | 2106.04108 | null | https://arxiv.org/abs/2106.04108v3 | https://arxiv.org/pdf/2106.04108v3.pdf | Fully Transformer Networks for Semantic Image Segmentation | Transformers have shown impressive performance in various natural language processing and computer vision tasks, due to the capability of modeling long-range dependencies. Recent progress has demonstrated that combining such Transformers with CNN-based semantic image segmentation models is very promising. However, it i... | ['Guodong Guo', 'Shengwei Tian', 'Fangjian Lin', 'Tianyi Wu', 'Sitong Wu'] | 2021-06-08 | null | null | null | null | ['face-parsing'] | ['computer-vision'] | [ 3.23335350e-01 2.72633523e-01 -4.11380231e-02 -7.26834297e-01
-8.53974283e-01 -5.56382775e-01 4.75924641e-01 -2.45003968e-01
-1.23532861e-01 2.27533922e-01 -2.37117466e-02 -3.51375908e-01
2.10654899e-01 -8.36567998e-01 -9.44865644e-01 -5.30916631e-01
3.87205482e-01 4.88031805e-01 5.61350226e-01 -1.36069536... | [9.578731536865234, 0.4277397096157074] |
45751355-c0a7-471e-a9af-3ab742db45e7 | low-complexity-multidimensional-dct | 2306.11724 | null | https://arxiv.org/abs/2306.11724v1 | https://arxiv.org/pdf/2306.11724v1.pdf | Low-complexity Multidimensional DCT Approximations | In this paper, we introduce low-complexity multidimensional discrete cosine transform (DCT) approximations. Three dimensional DCT (3D DCT) approximations are formalized in terms of high-order tensor theory. The formulation is extended to higher dimensions with arbitrary lengths. Several multiplierless $8\times 8\times ... | ['F. M. Bayer', 'R. J. Cintra', 'V. A. Coutinho'] | 2023-06-20 | null | null | null | null | ['visual-tracking', 'quantization'] | ['computer-vision', 'methodology'] | [-2.15418592e-01 -3.27560633e-01 -8.81685019e-02 1.78292133e-02
-4.16244149e-01 -4.47609723e-01 7.01308608e-01 -2.84509584e-02
-3.40502530e-01 3.03431451e-01 1.93997249e-01 -4.99756366e-01
8.00631419e-02 -1.96239114e-01 -1.59259215e-01 -5.49217939e-01
-4.58465308e-01 1.59752354e-01 3.23790193e-01 -1.47599548... | [11.440278053283691, -2.190136671066284] |
044b4ffa-6e82-4147-9add-5e52a902f82a | horizon-lines-in-the-wild | 1604.02129 | null | http://arxiv.org/abs/1604.02129v2 | http://arxiv.org/pdf/1604.02129v2.pdf | Horizon Lines in the Wild | The horizon line is an important contextual attribute for a wide variety of
image understanding tasks. As such, many methods have been proposed to estimate
its location from a single image. These methods typically require the image to
contain specific cues, such as vanishing points, coplanar circles, and regular
textur... | ['Menghua Zhai', 'Scott Workman', 'Nathan Jacobs'] | 2016-04-07 | null | null | null | null | ['horizon-line-estimation'] | ['computer-vision'] | [ 3.47629815e-01 6.19439296e-02 -6.94430992e-02 -4.86860335e-01
-5.27842224e-01 -6.01762176e-01 9.16921079e-01 2.29362801e-01
-4.35876817e-01 2.68553287e-01 -1.82753503e-01 -2.06320956e-01
3.15880850e-02 -7.90523112e-01 -9.35822606e-01 -3.51399601e-01
-1.10160872e-01 2.93917030e-01 6.45655453e-01 -2.79869586... | [8.249381065368652, -1.9886410236358643] |
5377532f-71cc-4ca3-8725-aed0c83930a6 | efficient-annotation-and-learning-for-3d-hand | 2206.02257 | null | https://arxiv.org/abs/2206.02257v3 | https://arxiv.org/pdf/2206.02257v3.pdf | Efficient Annotation and Learning for 3D Hand Pose Estimation: A Survey | In this survey, we present a systematic review of 3D hand pose estimation from the perspective of efficient annotation and learning. 3D hand pose estimation has been an important research area owing to its potential to enable various applications, such as video understanding, AR/VR, and robotics. However, the performan... | ['Yoichi Sato', 'Ryosuke Furuta', 'Takehiko Ohkawa'] | 2022-06-05 | null | null | null | null | ['3d-hand-pose-estimation', '3d-hand-pose-estimation'] | ['computer-vision', 'graphs'] | [ 3.57313678e-02 -5.62417544e-02 -5.36703348e-01 -7.28666931e-02
-5.12256086e-01 -7.43616104e-01 -3.33227031e-02 -1.59069479e-01
-3.15111428e-01 6.25324607e-01 2.96452701e-01 -2.08545546e-03
-7.36669078e-02 -1.13926068e-01 -5.41298151e-01 -4.40427572e-01
7.86528811e-02 8.65537524e-01 2.11063087e-01 -7.73873329... | [6.578194618225098, -0.7599847316741943] |
83e6e5f7-1e59-4a65-a3cf-21c6480b748e | logical-form-generation-via-multi-task | null | null | https://aclanthology.org/2022.coling-1.145 | https://aclanthology.org/2022.coling-1.145.pdf | Logical Form Generation via Multi-task Learning for Complex Question Answering over Knowledge Bases | Question answering over knowledge bases (KBQA) for complex questions is a challenging task in natural language processing. Recently, generation-based methods that translate natural language questions to executable logical forms have achieved promising performance. These methods use auxiliary information to augment the ... | ['Yuzhong Qu', 'Yiheng Shu', 'Xuan Wu', 'Xixin Hu'] | null | null | null | null | coling-2022-10 | ['relation-classification', 'entity-disambiguation'] | ['natural-language-processing', 'natural-language-processing'] | [-1.27955243e-01 3.24528068e-01 -1.49293423e-01 -3.74899805e-01
-1.51359749e+00 -6.50115967e-01 2.92609304e-01 2.38458946e-01
-3.31058770e-01 1.35047948e+00 1.48784757e-01 -4.38948095e-01
-4.09894288e-01 -1.33603251e+00 -8.49371016e-01 -7.73951560e-02
2.49342412e-01 1.17835557e+00 5.68378508e-01 -8.43217194... | [10.63388442993164, 7.938277721405029] |
67f848ed-14f9-4624-af61-660e3221566f | intrinsic-dimensionality-explains-the | 2012.13255 | null | https://arxiv.org/abs/2012.13255v1 | https://arxiv.org/pdf/2012.13255v1.pdf | Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning | Although pretrained language models can be fine-tuned to produce state-of-the-art results for a very wide range of language understanding tasks, the dynamics of this process are not well understood, especially in the low data regime. Why can we use relatively vanilla gradient descent algorithms (e.g., without strong re... | ['Sonal Gupta', 'Luke Zettlemoyer', 'Armen Aghajanyan'] | 2020-12-22 | null | https://aclanthology.org/2021.acl-long.568 | https://aclanthology.org/2021.acl-long.568.pdf | acl-2021-5 | ['paraphrase-identification'] | ['natural-language-processing'] | [-1.89553797e-01 1.33900359e-01 -4.14858520e-01 -3.11181694e-01
-7.63474107e-01 -7.74174750e-01 6.93499386e-01 -3.70226474e-03
-6.67643964e-01 6.83420122e-01 3.37751627e-01 -3.79001200e-01
-3.54036629e-01 -6.94403231e-01 -7.13655949e-01 -6.23538256e-01
2.10985821e-02 7.68815041e-01 -2.16257825e-01 -5.13079524... | [8.413430213928223, 3.776336908340454] |
ce851b5e-e9b0-40ec-a913-5d0eecdba1f2 | ratatouille-a-tool-for-novel-recipe | 2206.08267 | null | https://arxiv.org/abs/2206.08267v1 | https://arxiv.org/pdf/2206.08267v1.pdf | Ratatouille: A tool for Novel Recipe Generation | Due to availability of a large amount of cooking recipes online, there is a growing interest in using this as data to create novel recipes. Novel Recipe Generation is a problem in the field of Natural Language Processing in which our main interest is to generate realistic, novel cooking recipes. To come up with such no... | ['Ganesh Bagler', 'Aakanksha Saini', 'Sritanaya Tatipamala', 'Minnet Khan', 'Vijay Ponnaganti', 'Pallab Chakraborty', 'Mansi Goel'] | 2022-05-10 | null | null | null | null | ['recipe-generation'] | ['miscellaneous'] | [-2.00442180e-01 -1.65296923e-02 4.67090338e-01 -3.22072744e-01
-3.80855381e-01 -6.24211490e-01 3.49881917e-01 2.34154135e-01
-1.28692165e-01 7.37969697e-01 5.91871798e-01 -1.64425597e-01
4.63343769e-01 -1.45185769e+00 -1.04413188e+00 -5.01522005e-01
-3.30585726e-02 -8.50183610e-03 -3.62424701e-01 -8.45558763... | [11.515787124633789, 4.552906513214111] |
55da0d99-6cbf-467b-9777-9678563c3094 | a-multiresolution-clinical-decision-support | 1512.08051 | null | http://arxiv.org/abs/1512.08051v1 | http://arxiv.org/pdf/1512.08051v1.pdf | A Multiresolution Clinical Decision Support System Based on Fractal Model Design for Classification of Histological Brain Tumours | Tissue texture is known to exhibit a heterogeneous or non-stationary nature,
therefore using a single resolution approach for optimum classification might
not suffice. A clinical decision support system that exploits the subband
textural fractal characteristics for best bases selection of meningioma brain
histopatholog... | ['Omar S. Al-Kadi'] | 2015-12-25 | null | null | null | null | ['histopathological-image-classification'] | ['medical'] | [ 4.19413030e-01 -1.36187430e-02 -6.69950768e-02 -1.15348868e-01
-5.05707324e-01 -1.51712433e-01 5.46154857e-01 6.73296094e-01
-5.88298202e-01 8.11536849e-01 -1.05118513e-01 -1.65088579e-01
-8.37690949e-01 -8.34155738e-01 1.33821741e-01 -1.43584406e+00
-3.99730891e-01 3.72957259e-01 4.98136550e-01 -2.05964535... | [15.056410789489746, -2.6725783348083496] |
a7489a91-0203-454e-890d-66d36f47bc91 | construction-of-hierarchical-structured | null | null | https://aclanthology.org/2022.dialdoc-1.9 | https://aclanthology.org/2022.dialdoc-1.9.pdf | Construction of Hierarchical Structured Knowledge-based Recommendation Dialogue Dataset and Dialogue System | We work on a recommendation dialogue system to help a user understand the appealing points of some target (e.g., a movie). In such dialogues, the recommendation system needs to utilize structured external knowledge to make informative and detailed recommendations. However, there is no dialogue dataset with structured e... | ['Sadao Kurohashi', 'Ribeka Tanaka', 'Takashi Kodama'] | null | null | null | null | dialdoc-acl-2022-5 | ['movie-recommendation'] | ['miscellaneous'] | [-3.50767612e-01 3.99618566e-01 -3.47481340e-01 -7.96001732e-01
-3.87003094e-01 -8.63668382e-01 5.58470547e-01 -1.04278862e-01
-1.34284467e-01 5.71548641e-01 8.04325044e-01 -2.42133602e-01
4.84052561e-02 -7.50447512e-01 -1.00438990e-01 -3.07868004e-01
6.11327648e-01 4.40235585e-01 4.91167545e-01 -6.81735218... | [12.378247261047363, 7.508142471313477] |
8296c44c-0b8e-403c-ae3f-92833d1ae93d | adaptively-learning-facial-expression | null | null | https://ieeexplore.ieee.org/abstract/document/9321757 | https://ieeexplore.ieee.org/abstract/document/9321757 | Adaptively learning facial expression representation via cf labels and distillation. | Facial expression recognition is of significant importance in criminal investigation and digital entertainment. Under unconstrained conditions, existing expression datasets are highly class-imbalanced, and the similarity between expressions is high. Previous methods tend to improve the performance of facial expression ... | ['Hangyu Li; Nannan Wang; Xinpeng Ding; Xi Yang; Xinbo Gao'] | 2021-01-13 | null | null | null | ieee-transactions-on-image-processing-2021-1 | ['facial-expression-recognition'] | ['computer-vision'] | [ 1.91924259e-01 -9.36975703e-02 -3.36674899e-01 -8.25826526e-01
-3.80620658e-02 1.49258614e-01 2.21206099e-01 -3.02008241e-01
-4.97766793e-01 7.07448483e-01 -4.55137268e-02 1.98568434e-01
-3.66756953e-02 -8.12365115e-01 -2.80991256e-01 -7.73973465e-01
4.04080451e-02 7.91949853e-02 -2.61515468e-01 -4.01436210... | [13.604606628417969, 1.6858042478561401] |
3b5d12b2-f37c-4943-b367-77a47134f2aa | inner-ear-augmented-metal-artifact-reduction | 2104.1251 | null | https://arxiv.org/abs/2104.12510v1 | https://arxiv.org/pdf/2104.12510v1.pdf | Inner-ear Augmented Metal Artifact Reduction with Simulation-based 3D Generative Adversarial Networks | Metal Artifacts creates often difficulties for a high quality visual assessment of post-operative imaging in {c}omputed {t}omography (CT). A vast body of methods have been proposed to tackle this issue, but {these} methods were designed for regular CT scans and their performance is usually insufficient when imaging tin... | ['Delingette Herve', 'Guevara Nicolas', 'Raffaelli Charles', 'Gnansia Dan', 'Demarcy Thomas', 'Vandersteen Clair', 'Wang Zihao'] | 2021-04-26 | null | null | null | null | ['metal-artifact-reduction'] | ['medical'] | [ 5.08559167e-01 4.02013272e-01 8.53904009e-01 9.52445995e-03
-1.12494099e+00 -2.97804266e-01 1.15522027e-01 -1.79932103e-01
-4.76625741e-01 4.70113426e-01 4.66129333e-02 -2.64815778e-01
-2.74161547e-01 -3.47772628e-01 -7.20485449e-01 -6.59096420e-01
-2.63710972e-03 6.89402521e-01 1.10719815e-01 7.04797134... | [13.449530601501465, -2.555616855621338] |
94c5b379-146d-4ece-bd49-420208faeb08 | object-discovery-via-cohesion-measurement | 1704.08944 | null | http://arxiv.org/abs/1704.08944v1 | http://arxiv.org/pdf/1704.08944v1.pdf | Object Discovery via Cohesion Measurement | Color and intensity are two important components in an image. Usually, groups
of image pixels, which are similar in color or intensity, are an informative
representation for an object. They are therefore particularly suitable for
computer vision tasks, such as saliency detection and object proposal
generation. However,... | ['Xuelong. Li', 'Wan-Lei Zhao', 'Yan Yan', 'Hanzi Wang', 'Guanjun Guo'] | 2017-04-28 | null | null | null | null | ['object-proposal-generation'] | ['computer-vision'] | [ 4.67317134e-01 -2.73100048e-01 -1.12262316e-01 -3.95275801e-01
-3.82037222e-01 -2.10581541e-01 2.60888606e-01 4.52843994e-01
-4.40203458e-01 4.65921134e-01 -7.38790724e-03 1.14072755e-01
-2.66394675e-01 -7.26910889e-01 -5.98850191e-01 -8.86329234e-01
2.51814872e-01 2.33205911e-02 7.62217999e-01 -4.95418124... | [9.820380210876465, -0.6004092693328857] |
4066b942-021c-45d3-987d-a48b83a83c62 | energy-based-models-for-atomic-resolution-1 | 2004.13167 | null | https://arxiv.org/abs/2004.13167v1 | https://arxiv.org/pdf/2004.13167v1.pdf | Energy-based models for atomic-resolution protein conformations | We propose an energy-based model (EBM) of protein conformations that operates at atomic scale. The model is trained solely on crystallized protein data. By contrast, existing approaches for scoring conformations use energy functions that incorporate knowledge of physical principles and features that are the complex pro... | ['Rob Fergus', 'Alexander Rives', 'Yilun Du', 'Jerry Ma', 'Joshua Meier'] | 2020-04-27 | null | https://openreview.net/forum?id=S1e_9xrFvS | https://openreview.net/pdf?id=S1e_9xrFvS | iclr-2020-1 | ['protein-design'] | ['medical'] | [ 1.98381767e-01 5.40216826e-02 -2.56462246e-01 -5.60416579e-01
-8.70926023e-01 -6.00059927e-01 3.27553630e-01 4.24453557e-01
-3.65297973e-01 1.03816843e+00 3.81101668e-01 -6.97400570e-01
2.84798384e-01 -5.28544664e-01 -1.03890395e+00 -9.80683863e-01
-2.11722478e-01 4.83226150e-01 1.37915760e-02 -4.57152188... | [4.753408432006836, 5.594182014465332] |
4a441619-ee12-46f2-98d6-ce66418124d7 | unsupervised-question-answering-via-answer | 2208.10813 | null | https://arxiv.org/abs/2208.10813v1 | https://arxiv.org/pdf/2208.10813v1.pdf | Unsupervised Question Answering via Answer Diversifying | Unsupervised question answering is an attractive task due to its independence on labeled data. Previous works usually make use of heuristic rules as well as pre-trained models to construct data and train QA models. However, most of these works regard named entity (NE) as the only answer type, which ignores the high div... | ['Xian-Ling Mao', 'Zewen Chi', 'Heyan Huang', 'Yuxiang Nie'] | 2022-08-23 | null | https://aclanthology.org/2022.coling-1.149 | https://aclanthology.org/2022.coling-1.149.pdf | coling-2022-10 | ['triviaqa'] | ['miscellaneous'] | [ 4.61444929e-02 -2.38173688e-03 1.78687990e-01 -5.56186795e-01
-9.08758938e-01 -3.29357356e-01 3.68903577e-01 7.96849951e-02
-6.39494181e-01 7.92016387e-01 5.95770717e-01 -9.42788199e-02
8.88636708e-02 -1.22488868e+00 -2.83022970e-01 -6.43293083e-01
7.51653075e-01 4.61659133e-01 5.23689806e-01 -6.09797776... | [11.032135963439941, 8.046451568603516] |
6a5b80ad-8380-434c-aed4-1cc5dfb05b5d | shakkil-an-automatic-diacritization-system | null | null | https://aclanthology.org/W17-1311 | https://aclanthology.org/W17-1311.pdf | SHAKKIL: An Automatic Diacritization System for Modern Standard Arabic Texts | This paper sheds light on a system that would be able to diacritize Arabic texts automatically (SHAKKIL). In this system, the diacritization problem will be handled through two levels; morphological and syntactic processing levels. The adopted morphological disambiguation algorithm depends on four layers; Uni-morpholog... | ['Sameh Alansary', 'Amany Fashwan'] | 2017-04-01 | null | null | null | ws-2017-4 | ['morphological-disambiguation'] | ['natural-language-processing'] | [-1.17136724e-01 3.40924352e-01 2.63476104e-01 -1.59091994e-01
-3.21300119e-01 -6.04413807e-01 6.17329061e-01 7.65077412e-01
-6.45769536e-01 7.05681980e-01 -1.09399088e-01 -7.72091627e-01
-4.70276177e-01 -1.06741726e+00 -2.69485801e-01 -6.67607844e-01
6.21232670e-03 6.61211014e-01 2.89787591e-01 -6.90157890... | [10.39285659790039, 10.234641075134277] |
d8f40842-559e-4333-a866-c0ffa2b352d3 | constrained-few-shot-class-incremental | 2203.16588 | null | https://arxiv.org/abs/2203.16588v1 | https://arxiv.org/pdf/2203.16588v1.pdf | Constrained Few-shot Class-incremental Learning | Continually learning new classes from fresh data without forgetting previous knowledge of old classes is a very challenging research problem. Moreover, it is imperative that such learning must respect certain memory and computational constraints such as (i) training samples are limited to only a few per class, (ii) the... | ['Abbas Rahimi', 'Abu Sebastian', 'Luca Benini', 'Giovanni Cherubini', 'Geethan Karunaratne', 'Michael Hersche'] | 2022-03-30 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Hersche_Constrained_Few-Shot_Class-Incremental_Learning_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Hersche_Constrained_Few-Shot_Class-Incremental_Learning_CVPR_2022_paper.pdf | cvpr-2022-1 | ['few-shot-class-incremental-learning'] | ['methodology'] | [ 1.65991649e-01 7.24022975e-03 -4.08124536e-01 -3.61621737e-01
-5.52125156e-01 -5.37178278e-01 4.10781443e-01 3.08374286e-01
-6.77330315e-01 8.12709510e-01 -2.25925326e-01 -7.10907355e-02
-2.34069094e-01 -9.56312239e-01 -8.01800311e-01 -8.10159683e-01
-2.48158127e-01 4.21275973e-01 4.99753654e-01 7.70319477... | [9.817824363708496, 3.383467674255371] |
59b2f1e4-0a8f-4a09-9842-74ba83b04fc9 | multi-feature-data-fusion-based-load | 2301.13774 | null | https://arxiv.org/abs/2301.13774v1 | https://arxiv.org/pdf/2301.13774v1.pdf | Multi Feature Data Fusion-Based Load Forecasting of Electric Vehicle Charging Stations Using a Deep Learning Model | We propose a forecasting technique based on multi-feature data fusion to enhance the accuracy of an electric vehicle (EV) charging station load forecasting deep-learning model. The proposed method uses multi-feature inputs based on observations of historical weather (wind speed, temperature, and humidity) data as multi... | ['Ameena S. Al Sumaiti', 'Zhibo Zhang', 'Prince Aduama'] | 2023-01-31 | null | null | null | null | ['load-forecasting'] | ['miscellaneous'] | [-3.66637588e-01 -2.93518215e-01 5.82871288e-02 -8.44540238e-01
-1.74887076e-01 -2.86341697e-01 7.91635573e-01 1.40475318e-01
-3.26310724e-01 7.50119805e-01 6.62532449e-02 -5.31782985e-01
-2.24532232e-01 -1.04709649e+00 -7.04872787e-01 -8.02812755e-01
-2.00832427e-01 6.01746380e-01 -3.01035583e-01 -4.76398021... | [6.208680629730225, 2.7830095291137695] |
437377e8-569f-4362-a68f-1d1d9dd25518 | a-novel-light-field-coding-scheme-based-on | 2210.01447 | null | https://arxiv.org/abs/2210.01447v2 | https://arxiv.org/pdf/2210.01447v2.pdf | A Novel Light Field Coding Scheme Based on Deep Belief Network & Weighted Binary Images for Additive Layered Displays | Light-field displays create an immersive experience by providing binocular depth sensation and motion parallax. Stacking light attenuating layers is one approach to implement a light field display with a broader depth of field, wide viewing angles and high resolution. Due to the transparent holographic optical element ... | ['Mansi Sharma', 'Sally Khaidem'] | 2022-10-04 | null | null | null | null | ['mixed-reality'] | ['computer-vision'] | [ 5.47734022e-01 -3.32449198e-01 -1.23727195e-01 -1.92096919e-01
-2.04450503e-01 -1.88267678e-01 2.06739604e-01 -4.30424482e-01
-1.93177402e-01 6.93221569e-01 5.16176999e-01 9.39990976e-04
-1.39817461e-01 -9.06114757e-01 -4.25507754e-01 -8.30449820e-01
1.21055387e-01 -6.41459882e-01 4.30144727e-01 -6.58306330... | [10.125919342041016, -2.592113494873047] |
4c5a9282-ae75-48db-9750-a2bfa6e1a178 | evaluation-guidelines-to-deal-with-implicit | null | null | https://aclanthology.org/2021.unimplicit-1.3 | https://aclanthology.org/2021.unimplicit-1.3.pdf | Evaluation Guidelines to Deal with Implicit Phenomena to Assess Factuality in Data-to-Text Generation | Data-to-text generation systems are trained on large datasets, such as WebNLG, Ro-toWire, E2E or DART. Beyond traditional token-overlap evaluation metrics (BLEU or METEOR), a key concern faced by recent generators is to control the factuality of the generated text with respect to the input data specification. We report... | ['Michael Elhadad', 'Roy Eisenstadt'] | null | null | null | null | acl-unimplicit-2021-8 | ['data-to-text-generation'] | ['natural-language-processing'] | [ 1.82669014e-01 8.91272843e-01 1.48823380e-01 -4.57491487e-01
-1.29280543e+00 -8.34040344e-01 1.25638866e+00 5.51374614e-01
-4.38237965e-01 1.45102787e+00 9.16826844e-01 -5.64461291e-01
-8.05105940e-02 -6.77745819e-01 -5.51540315e-01 -5.92910983e-02
5.48497379e-01 9.19073522e-01 -6.29236400e-02 -2.74500519... | [11.715166091918945, 9.017182350158691] |
add9762b-3762-45fe-82e5-d9fdcc6a7f44 | causally-aware-intraoperative-imputation-for | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_Causally-Aware_Intraoperative_Imputation_for_Overall_Survival_Time_Prediction_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_Causally-Aware_Intraoperative_Imputation_for_Overall_Survival_Time_Prediction_CVPR_2023_paper.pdf | Causally-Aware Intraoperative Imputation for Overall Survival Time Prediction | Previous efforts in vision community are mostly made on learning good representations from visual patterns. Beyond this, this paper emphasizes the high-level ability of causal reasoning. We thus present a case study of solving the challenging task of Overall Survival (OS) time in primary liver cancers. Critically, ... | ['Yanwei Fu', 'Xiuzhong Yao', 'Dingxia Liu', 'Jiejun Chen', 'Qiaole Dong', 'Lingjie Kong', 'Litian Liang', 'Xuelin Qian', 'Xiang Li'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['causal-inference', 'causal-discovery', 'causal-inference'] | ['knowledge-base', 'knowledge-base', 'miscellaneous'] | [ 1.72196582e-01 4.62741941e-01 -8.52885723e-01 -5.04554510e-01
-5.67308009e-01 -1.41586557e-01 6.07279062e-01 3.08165282e-01
1.39049321e-01 8.59986901e-01 8.35714817e-01 -5.35753369e-01
-5.75226247e-01 -6.14658535e-01 -8.02388012e-01 -9.34765160e-01
-3.21127027e-01 1.99016362e-01 -3.06574047e-01 2.80305207... | [8.588251113891602, 5.538060188293457] |
6216a043-cf80-4f2e-a441-af33faef3e19 | enhancing-robustness-of-pre-trained-language | null | null | https://openreview.net/forum?id=3FIjaX458P | https://openreview.net/pdf?id=3FIjaX458P | Enhancing Robustness of Pre-trained Language Model with Lexical Simplification | For both human readers and pre-trained language models (PrLMs), lexical diversity may lead to confusion and inaccuracy when understanding the underlying semantic meanings of given sentences. By substituting complex words with simple alternatives, lexical simplification (LS) is a recognized method to reduce such lexical... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['lexical-simplification'] | ['natural-language-processing'] | [ 5.63163579e-01 4.06696230e-01 -9.62480828e-02 -6.03349209e-01
-6.86124504e-01 -3.39438438e-01 6.28353417e-01 5.29517293e-01
-7.15617657e-01 7.77268410e-01 5.61017990e-01 -3.10969055e-01
4.06122476e-01 -6.56511128e-01 -4.53077585e-01 -2.34770238e-01
7.52756715e-01 1.82515189e-01 4.51145172e-02 -5.04003167... | [11.16778564453125, 9.200353622436523] |
94a0779f-9501-4288-827a-5324a07312c2 | open-domain-question-answering-over-virtual-1 | null | null | https://openreview.net/forum?id=tKTRPNNc7A3 | https://openreview.net/pdf?id=tKTRPNNc7A3 | Open Domain Question Answering over Virtual Documents: A Unified Approach for Data and Text | Due to its potential for a universal interface over both data and text, data-to-text generation is becoming increasingly popular.
However, few prior work has focused on its application to downstream tasks, e.g. using the converted data for grounding or reasoning.
In this work, we bridge this gap and use the data-to-te... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['data-to-text-generation'] | ['natural-language-processing'] | [ 4.52833949e-03 8.68156374e-01 -8.33984837e-02 -1.82731345e-01
-1.62402773e+00 -9.35836077e-01 6.87182665e-01 4.41888779e-01
-2.96584636e-01 9.21051860e-01 7.73265541e-01 -7.17151344e-01
-1.17828809e-01 -1.09888601e+00 -1.00411701e+00 8.69894400e-02
5.18783629e-01 1.20999968e+00 3.59106123e-01 -8.33251595... | [10.792928695678711, 7.979024410247803] |
013b84f8-2f16-4b8e-9a53-e14652108a37 | forecasting-crude-oil-price-using-event | 2111.09111 | null | https://arxiv.org/abs/2111.09111v1 | https://arxiv.org/pdf/2111.09111v1.pdf | Forecasting Crude Oil Price Using Event Extraction | Research on crude oil price forecasting has attracted tremendous attention from scholars and policymakers due to its significant effect on the global economy. Besides supply and demand, crude oil prices are largely influenced by various factors, such as economic development, financial markets, conflicts, wars, and poli... | ['Xiaohong Huang', 'Jiangwei Liu'] | 2021-11-14 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [-3.74092042e-01 -4.59938109e-01 -2.56253392e-01 -3.32156181e-01
-4.35521632e-01 -3.80881488e-01 8.78436625e-01 1.21207476e-01
-3.63378555e-01 6.43771291e-01 7.72555351e-01 -3.76550734e-01
7.94583857e-02 -1.36869621e+00 -3.17909688e-01 -7.52530634e-01
-3.61484855e-01 7.64469579e-02 -5.79973981e-02 -3.68649364... | [4.420652389526367, 4.2763800621032715] |
678fb346-a9a0-4c60-920e-ce7a31bbffb4 | artificial-color-constancy-via-googlenet-with | 1811.08456 | null | https://arxiv.org/abs/1811.08456v2 | https://arxiv.org/pdf/1811.08456v2.pdf | Artificial Color Constancy via GoogLeNet with Angular Loss Function | Color Constancy is the ability of the human visual system to perceive colors unchanged independently of the illumination. Giving a machine this feature will be beneficial in many fields where chromatic information is used. Particularly, it significantly improves scene understanding and object recognition. In this paper... | ['Oleksii Sidorov'] | 2018-11-20 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 5.03537850e-03 -5.00522137e-01 -8.85299221e-02 -4.23331022e-01
-3.18547860e-02 -4.67326164e-01 5.57179928e-01 -3.03572983e-01
-6.40250027e-01 8.65176499e-01 -5.70556045e-01 -3.12129378e-01
4.81350161e-02 -6.60258174e-01 -5.96019506e-01 -8.07550311e-01
1.14856593e-01 -1.32655606e-01 3.69419664e-01 -2.92547107... | [10.37661075592041, -2.489915132522583] |
0301ecb9-45bd-4dcb-a920-2475e17da4d5 | augmenting-neural-response-generation-with | 1811.01063 | null | https://arxiv.org/abs/1811.01063v2 | https://arxiv.org/pdf/1811.01063v2.pdf | Augmenting Neural Response Generation with Context-Aware Topical Attention | Sequence-to-Sequence (Seq2Seq) models have witnessed a notable success in generating natural conversational exchanges. Notwithstanding the syntactically well-formed responses generated by these neural network models, they are prone to be acontextual, short and generic. In this work, we introduce a Topical Hierarchical ... | ['Osmar Zaiane', 'Nouha Dziri', 'Ehsan Kamalloo', 'Kory W. Mathewson'] | 2018-11-02 | augmenting-neural-response-generation-with-1 | https://aclanthology.org/W19-4103 | https://aclanthology.org/W19-4103.pdf | ws-2019-8 | ['open-domain-dialog'] | ['natural-language-processing'] | [ 5.24143934e-01 4.15004641e-01 2.23528758e-01 -7.27570832e-01
-1.31208181e+00 -6.03216410e-01 1.12676144e+00 -1.55145526e-01
-1.90623134e-01 1.22605586e+00 1.11177826e+00 -4.05688062e-02
2.63666362e-01 -6.45854175e-01 -3.52290481e-01 -3.34902942e-01
3.80100489e-01 8.01121473e-01 -6.65505007e-02 -9.27252948... | [12.551469802856445, 8.276959419250488] |
ffdee8cf-80bd-45fe-80eb-46c6837bf457 | bi-lstm-neural-networks-for-chinese | null | null | https://aclanthology.org/W16-4919 | https://aclanthology.org/W16-4919.pdf | Bi-LSTM Neural Networks for Chinese Grammatical Error Diagnosis | Grammatical Error Diagnosis for Chinese has always been a challenge for both foreign learners and NLP researchers, for the variousity of grammar and the flexibility of expression. In this paper, we present a model based on Bidirectional Long Short-Term Memory(Bi-LSTM) neural networks, which treats the task as a sequenc... | ['Shen Huang', 'Houfeng Wang'] | 2016-12-01 | null | null | null | ws-2016-12 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-4.56489511e-02 -2.07171783e-01 3.82555991e-01 -5.02986789e-01
-6.53885663e-01 -2.05225706e-01 -3.93295914e-01 1.68688774e-01
-7.50573814e-01 8.49041998e-01 2.38037631e-01 -7.69122839e-01
2.03135282e-01 -5.88728845e-01 -6.63504541e-01 -2.92050868e-01
2.88144685e-02 4.76279318e-01 1.73548430e-01 -2.08696380... | [11.039663314819336, 10.784902572631836] |
b5fd0148-32c8-4842-b3ef-f856c1a4b1a4 | cooperative-task-and-motion-planning-for | 2203.02475 | null | https://arxiv.org/abs/2203.02475v1 | https://arxiv.org/pdf/2203.02475v1.pdf | Cooperative Task and Motion Planning for Multi-Arm Assembly Systems | Multi-robot assembly systems are becoming increasingly appealing in manufacturing due to their ability to automatically, flexibly, and quickly construct desired structural designs. However, effectively planning for these systems in a manner that ensures each robot is simultaneously productive, and not idle, is challeng... | ['Brian C. Williams', 'Sven Koenig', 'Caitlin Mueller', 'Andreas Hofmann', 'Chuchu Fan', 'Dawei Sun', 'Caelan Garrett', 'Yijiang Huang', 'Jiaoyang Li', 'Jingkai Chen'] | 2022-03-04 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 2.29013234e-01 3.54044557e-01 -8.06553811e-02 1.31260514e-01
-2.55478501e-01 -9.92075384e-01 9.25221220e-02 4.56842482e-01
1.38590336e-01 1.00461042e+00 -2.86087424e-01 -3.13228220e-01
-1.05963194e+00 -7.73795307e-01 -6.59813106e-01 -4.68623519e-01
-5.63503385e-01 1.21190250e+00 3.27767491e-01 -4.65708196... | [4.922003269195557, 1.530883550643921] |
b7fcbf43-2839-49dc-ae7e-4000744b7a91 | predicting-the-future-a-jointly-learnt-model-1 | 1912.07148 | null | https://arxiv.org/abs/1912.07148v1 | https://arxiv.org/pdf/1912.07148v1.pdf | Predicting the Future: A Jointly Learnt Model for Action Anticipation | Inspired by human neurological structures for action anticipation, we present an action anticipation model that enables the prediction of plausible future actions by forecasting both the visual and temporal future. In contrast to current state-of-the-art methods which first learn a model to predict future video feature... | ['Harshala Gammulle', 'Clinton Fookes', 'Sridha Sridharan', 'Simon Denman'] | 2019-12-16 | predicting-the-future-a-jointly-learnt-model | http://openaccess.thecvf.com/content_ICCV_2019/html/Gammulle_Predicting_the_Future_A_Jointly_Learnt_Model_for_Action_Anticipation_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Gammulle_Predicting_the_Future_A_Jointly_Learnt_Model_for_Action_Anticipation_ICCV_2019_paper.pdf | iccv-2019-10 | ['action-anticipation'] | ['computer-vision'] | [ 4.74690586e-01 6.92130685e-01 -5.74285798e-02 -4.26834434e-01
-4.17035133e-01 -2.44699255e-01 1.36533642e+00 -5.12419462e-01
-2.06580162e-01 6.77526534e-01 9.15754616e-01 7.17637539e-02
2.26929069e-01 -4.75867689e-01 -9.30740774e-01 -3.40836316e-01
-3.76558155e-01 2.76316971e-01 4.02473286e-02 5.16277775... | [7.863069534301758, 0.33628591895103455] |
c3607514-25bf-4122-abb7-82d67bfc7104 | knowledge-and-data-driven-services-for-energy | 2103.07248 | null | https://arxiv.org/abs/2103.07248v1 | https://arxiv.org/pdf/2103.07248v1.pdf | Knowledge- and Data-driven Services for Energy Systems using Graph Neural Networks | The transition away from carbon-based energy sources poses several challenges for the operation of electricity distribution systems. Increasing shares of distributed energy resources (e.g. renewable energy generators, electric vehicles) and internet-connected sensing and control devices (e.g. smart heating and cooling)... | ['Seshu Tirupathi', 'Mark Purcell', 'Robert Gormally', 'Bradley Eck', 'Francesco Fusco'] | 2021-03-12 | null | null | null | null | ['physical-simulations'] | ['miscellaneous'] | [-2.87081778e-01 2.18555629e-01 -2.97929257e-01 -2.24697903e-01
-2.06020996e-01 -7.25283325e-01 8.06922972e-01 1.92570686e-01
2.48643294e-01 1.10988796e+00 -7.33369738e-02 -6.32491291e-01
-6.67327642e-01 -1.28555191e+00 -5.16413808e-01 -8.78779829e-01
-4.34992015e-01 7.76422679e-01 -4.74074632e-01 -2.07854155... | [6.003544330596924, 2.741778612136841] |
93fbf9ea-de41-49a4-b60a-f8168d17f3b5 | learning-efficient-explainable-and | 2101.07429 | null | https://arxiv.org/abs/2101.07429v1 | https://arxiv.org/pdf/2101.07429v1.pdf | Learning Efficient, Explainable and Discriminative Representations for Pulmonary Nodules Classification | Automatic pulmonary nodules classification is significant for early diagnosis of lung cancers. Recently, deep learning techniques have enabled remarkable progress in this field. However, these deep models are typically of high computational complexity and work in a black-box manner. To combat these challenges, in this ... | ['Weidong Han', 'Fei Gao', 'Fuhao Shen', 'Hanliang Jiang'] | 2021-01-19 | null | null | null | null | ['pulmonary-nodules-classification', 'lung-nodule-classification'] | ['medical', 'medical'] | [-1.35094807e-01 2.81119794e-01 -4.81489748e-01 -4.00175333e-01
-7.11763978e-01 -1.08923621e-01 1.82457328e-01 -3.71706694e-01
-1.19740717e-01 3.59094799e-01 1.14880867e-01 -6.55388474e-01
-2.95241505e-01 -6.67830288e-01 -6.73325777e-01 -6.82447255e-01
4.33639467e-01 4.83666599e-01 2.73845047e-01 1.47180498... | [15.304022789001465, -2.1139957904815674] |
ca631b2c-16d6-45cb-a89a-e6a209b29aee | chatgpt-vision-and-challenges | 2305.15323 | null | https://arxiv.org/abs/2305.15323v1 | https://arxiv.org/pdf/2305.15323v1.pdf | ChatGPT: Vision and Challenges | Artificial intelligence (AI) and machine learning have changed the nature of scientific inquiry in recent years. Of these, the development of virtual assistants has accelerated greatly in the past few years, with ChatGPT becoming a prominent AI language model. In this study, we examine the foundations, vision, research... | ['Rupinder Kaur', 'Sukhpal Singh Gill'] | 2023-05-08 | null | null | null | null | ['ethics'] | ['miscellaneous'] | [-1.29167646e-01 5.67642689e-01 -3.34644943e-01 6.79619983e-02
4.85394657e-01 -4.04374152e-01 7.14725494e-01 -1.58996448e-01
-3.70838463e-01 7.06463754e-01 2.26366609e-01 -4.30317640e-01
-2.10789770e-01 -9.54125822e-01 -2.87559837e-01 -5.81085443e-01
2.04070985e-01 4.19793695e-01 -4.21061143e-02 -2.81623572... | [9.266037940979004, 6.437495231628418] |
2944ba86-4799-49ef-bdb5-f8d8d0d10131 | rolling-shutter-camera-synchronization-with | 1902.11084 | null | http://arxiv.org/abs/1902.11084v1 | http://arxiv.org/pdf/1902.11084v1.pdf | Rolling Shutter Camera Synchronization with Sub-millisecond Accuracy | A simple method for synchronization of video streams with a precision better
than one millisecond is proposed. The method is applicable to any number of
rolling shutter cameras and when a few photographic flashes or other abrupt
lighting changes are present in the video. The approach exploits the rolling
shutter sensor... | ['Jiri Matas', 'Matej Smid'] | 2019-02-28 | null | null | null | null | ['video-synchronization'] | ['computer-vision'] | [ 4.55751002e-01 -5.20542324e-01 6.88881949e-02 -3.44865881e-02
-3.20943624e-01 -7.27997482e-01 6.53257012e-01 3.21711779e-01
-7.96173096e-01 6.56122148e-01 -3.84647280e-01 1.32128909e-01
1.27448022e-01 -3.69007498e-01 -7.55404949e-01 -4.70866323e-01
-2.98693299e-01 5.08300737e-02 9.68681514e-01 4.70568873... | [8.71487808227539, -1.9046692848205566] |
4be7c459-449e-4f30-8323-feafddce5351 | enhancing-early-lung-cancer-detection-on | 2208.14742 | null | https://arxiv.org/abs/2208.14742v1 | https://arxiv.org/pdf/2208.14742v1.pdf | Enhancing Early Lung Cancer Detection on Chest Radiographs with AI-assistance: A Multi-Reader Study | Objectives: The present study evaluated the impact of a commercially available explainable AI algorithm in augmenting the ability of clinicians to identify lung cancer on chest X-rays (CXR). Design: This retrospective study evaluated the performance of 11 clinicians for detecting lung cancer from chest radiographs, wit... | ['Simon Rasalingham', 'George Pearse', 'Jordan Smith', 'Liliana Garcia-Mondragon', 'Paul Williams', 'Tom Naunton Morgan', 'Qaiser Malik', 'Amanda Stockham', 'Stephanie Patterson', 'Jackson J. Pat', 'James Hoare', 'David Doyne', 'Richard Dittrich', 'Matthew Tam', 'Tom Dyer', 'Nicole Tay', 'Gaetan Dissez'] | 2022-08-31 | null | null | null | null | ['lung-cancer-diagnosis'] | ['medical'] | [ 4.17726129e-01 4.10857320e-01 -3.15309554e-01 -1.18911207e-01
-1.50892305e+00 -7.67514467e-01 2.63844490e-01 4.96615499e-01
-6.35769367e-01 6.27503097e-01 5.15111506e-01 -1.13858223e+00
-6.75552905e-01 -5.94659686e-01 -2.97522873e-01 -7.11567819e-01
3.60816747e-01 1.16619158e+00 3.53324205e-01 8.15052986... | [15.491170883178711, -2.0266013145446777] |
13ff6911-0f5a-452e-bccc-72e03a8327c3 | eeg-synthetic-data-generation-using | 2303.06068 | null | https://arxiv.org/abs/2303.06068v1 | https://arxiv.org/pdf/2303.06068v1.pdf | EEG Synthetic Data Generation Using Probabilistic Diffusion Models | Electroencephalography (EEG) plays a significant role in the Brain Computer Interface (BCI) domain, due to its non-invasive nature, low cost, and ease of use, making it a highly desirable option for widespread adoption by the general public. This technology is commonly used in conjunction with deep learning techniques,... | ['Francesco Fumagalli', 'Cesare M. Dalbagno', 'Giulio Tosato'] | 2023-03-06 | null | null | null | null | ['synthetic-data-generation', 'eeg', 'synthetic-data-generation', 'eeg'] | ['medical', 'methodology', 'miscellaneous', 'time-series'] | [ 2.98308372e-01 -1.50449753e-01 4.98837352e-01 -3.36527050e-01
-4.85426903e-01 -2.83281654e-01 3.54396492e-01 3.83363038e-01
-6.62592292e-01 1.09727049e+00 -2.62281727e-02 2.95618977e-02
-2.91114479e-01 -6.21072888e-01 -4.79655057e-01 -9.60066617e-01
-1.59223944e-01 9.57579166e-02 -4.57132667e-01 1.85303181... | [13.158992767333984, 3.3995816707611084] |
875127b1-529f-4bcb-b7d9-1f7f795af58f | deep-linear-networks-dynamics-low-rank-biases | 2106.15933 | null | https://arxiv.org/abs/2106.15933v2 | https://arxiv.org/pdf/2106.15933v2.pdf | Saddle-to-Saddle Dynamics in Deep Linear Networks: Small Initialization Training, Symmetry, and Sparsity | The dynamics of Deep Linear Networks (DLNs) is dramatically affected by the variance $\sigma^2$ of the parameters at initialization $\theta_0$. For DLNs of width $w$, we show a phase transition w.r.t. the scaling $\gamma$ of the variance $\sigma^2=w^{-\gamma}$ as $w\to\infty$: for large variance ($\gamma<1$), $\theta_0... | ['Berfin Şimşek', 'Clément Hongler', 'Franck Gabriel', 'François Ged', 'Arthur Jacot'] | 2021-06-30 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [-2.69956768e-01 3.18866551e-01 -9.39975455e-02 9.21790488e-03
-4.37300980e-01 -4.24385577e-01 1.73857868e-01 1.46842986e-01
-6.89297616e-01 7.33819187e-01 -2.43715674e-01 -4.32197273e-01
-7.48786986e-01 -8.41770947e-01 -9.93147194e-01 -1.25013292e+00
-8.52231324e-01 4.27396983e-01 2.74377197e-01 -5.82713962... | [7.743269443511963, 3.7522168159484863] |
7e2bfb10-0bad-4ff8-a00e-97235662b96c | dual-stream-time-delay-neural-network-with | 2303.1102 | null | https://arxiv.org/abs/2303.11020v2 | https://arxiv.org/pdf/2303.11020v2.pdf | Dual-stream Time-Delay Neural Network with Dynamic Global Filter for Speaker Verification | The time-delay neural network (TDNN) is one of the state-of-the-art models for text-independent speaker verification. However, it is difficult for conventional TDNN to capture global context that has been proven critical for robust speaker representations and long-duration speaker verification in many recent works. Bes... | ['Xiaodan Lin', 'Yangfu Li'] | 2023-03-20 | null | null | null | null | ['text-independent-speaker-verification', 'speaker-verification'] | ['speech', 'speech'] | [-7.16058165e-02 -3.39386225e-01 2.20938735e-02 -4.96912032e-01
-7.90561140e-01 -2.97998846e-01 2.82099247e-01 -3.66368771e-01
-4.07599479e-01 3.90171289e-01 4.27876800e-01 -5.33446133e-01
-7.72270858e-02 -9.82560813e-02 -3.75764906e-01 -8.83906901e-01
-2.56604124e-02 -2.11698234e-01 -6.81543946e-02 -2.41583362... | [14.572232246398926, 5.974699020385742] |
8aefd15d-b744-4448-80ac-670d508e0456 | fault-detection-and-localization-in-active | 2204.0569 | null | https://arxiv.org/abs/2204.05690v1 | https://arxiv.org/pdf/2204.05690v1.pdf | Fault Detection and Localization in Active Distribution Networks using Optimally Placed Phasor Measurements Units | This paper introduces an algorithm able to detect and localize the occurrance of a fault in an Active Distribution Network, using the measurements collected by Phasor Measurement Units (PMUs). First, a basic algorithm that works under the assumption that all grid buses are equipped with a PMU is designed. Then, formal ... | ['Federico Silvestro', 'Giacomo-Piero Schiapparelli', 'Bruno Gabriele', "Fabio D'Agostino", 'Francesco Conte'] | 2022-04-12 | null | null | null | null | ['fault-localization'] | ['computer-code'] | [-2.21916869e-01 3.25888023e-02 9.52363536e-02 9.08819884e-02
-1.62752554e-01 -7.86148846e-01 4.03425872e-01 7.15936720e-01
1.53445944e-01 1.04811895e+00 -5.71375787e-01 -1.27935663e-01
-5.01445055e-01 -1.08656085e+00 -2.93851376e-01 -8.28427315e-01
-5.36357462e-01 5.73994398e-01 1.67724475e-01 -1.68325529... | [5.985929489135742, 2.5238261222839355] |
6d94cba8-a180-462e-8b81-3fb886bdf06a | protein-complex-invariant-embedding-with | 2305.0948 | null | https://arxiv.org/abs/2305.09480v3 | https://arxiv.org/pdf/2305.09480v3.pdf | Cross-Gate MLP with Protein Complex Invariant Embedding is A One-Shot Antibody Designer | Antibodies are crucial proteins produced by the immune system in response to foreign substances or antigens. The specificity of an antibody is determined by its complementarity-determining regions (CDRs), which are located in the variable domains of the antibody chains and form the antigen-binding site. Previous studie... | ['Stan Z. Li', 'Zhangyang Gao', 'Cheng Tan'] | 2023-04-21 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [ 3.89909387e-01 -2.61673242e-01 -1.55583382e-01 -2.95253485e-01
-4.72334474e-01 -6.95713103e-01 2.41073206e-01 3.04640502e-01
-1.84119582e-01 8.89230847e-01 5.43305054e-02 -6.99237883e-01
2.29644701e-01 -6.89417601e-01 -9.37636018e-01 -9.67485070e-01
7.76952365e-03 5.84386468e-01 8.33440870e-02 -3.37865084... | [4.746662616729736, 5.65386438369751] |
7129cb15-ad1f-44b0-aad5-241814e79bd0 | 190511807 | 1905.11807 | null | https://arxiv.org/abs/1905.11807v1 | https://arxiv.org/pdf/1905.11807v1.pdf | Artificial Consciousness and Security | This paper describes a possible way to improve computer security by implementing a program which implements the following three features related to a weak notion of artificial consciousness: (partial) self-monitoring, ability to compute the truth of quantifier-free propositions and the ability to communicate with the u... | ['Andrew Powell'] | 2019-05-11 | null | null | null | null | ['computer-security'] | ['miscellaneous'] | [-1.54824957e-01 4.26495463e-01 2.55371273e-01 -4.28842723e-01
-1.33376449e-01 -6.10983670e-01 9.32207704e-01 4.91600305e-01
-7.55399823e-01 6.56784475e-01 -1.22233313e-02 -7.92284071e-01
2.26039529e-01 -1.09637260e+00 -3.83178681e-01 -4.12813693e-01
-2.48525083e-01 2.34337121e-01 8.74620736e-01 -6.53334618... | [5.791286945343018, 4.796272277832031] |
02609003-d21f-4760-9de2-6285c276b8d8 | heart-rate-estimation-from-face-videos-for | 2006.00825 | null | https://arxiv.org/abs/2006.00825v1 | https://arxiv.org/pdf/2006.00825v1.pdf | Heart Rate Estimation from Face Videos for Student Assessment: Experiments on edBB | In this study we estimate the heart rate from face videos for student assessment. This information could be very valuable to track their status along time and also to estimate other data such as their attention level or the presence of stress that may be caused by cheating attempts. The recent edBBplat, a platform for ... | ['Javier Hernandez-Ortega', 'Ruben Tolosana', 'Roberto Daza', 'Julian Fierrez', 'Aythami Morales'] | 2020-06-01 | null | null | null | null | ['heart-rate-estimation'] | ['medical'] | [-1.80269882e-01 -1.08210199e-01 2.67734319e-01 -4.13982034e-01
-6.84229508e-02 -4.57858622e-01 -1.71265051e-01 1.79159790e-01
-4.05704796e-01 6.61144853e-01 -1.45951018e-01 -4.91937548e-02
-1.64413527e-01 -4.65804040e-01 -1.77939713e-01 -7.86840439e-01
4.37937409e-01 -3.02566528e-01 -3.62834036e-01 6.00378923... | [13.600967407226562, 2.7526755332946777] |
4ce5c463-422c-412a-998b-9de606821f93 | evolutionary-multi-objective-algorithms-for | 2303.01695 | null | https://arxiv.org/abs/2303.01695v1 | https://arxiv.org/pdf/2303.01695v1.pdf | Evolutionary Multi-Objective Algorithms for the Knapsack Problems with Stochastic Profits | Evolutionary multi-objective algorithms have been widely shown to be successful when utilized for a variety of stochastic combinatorial optimization problems. Chance constrained optimization plays an important role in complex real-world scenarios, as it allows decision makers to take into account the uncertainty of the... | ['Frank Neumann', 'Aneta Neumann', 'Kokila Perera'] | 2023-03-03 | null | null | null | null | ['combinatorial-optimization'] | ['methodology'] | [ 1.40742078e-01 -2.59841889e-01 -4.18001376e-02 -2.33212382e-01
-5.04947722e-01 -6.96848214e-01 -8.39030668e-02 4.69638169e-01
-6.05826974e-01 1.22398698e+00 -3.28920960e-01 -3.06203868e-02
-9.77634728e-01 -9.70153809e-01 -8.27058733e-01 -1.05463672e+00
-2.80658733e-02 8.16513181e-01 1.28023490e-01 -2.10926831... | [5.661288261413574, 3.47473406791687] |
101e2196-6eed-45b6-b401-bcfb57cc1da2 | differentiable-spike-rethinking-gradient | null | null | http://proceedings.neurips.cc/paper/2021/hash/c4ca4238a0b923820dcc509a6f75849b-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/c4ca4238a0b923820dcc509a6f75849b-Paper.pdf | Differentiable Spike: Rethinking Gradient-Descent for Training Spiking Neural Networks | Spiking Neural Networks (SNNs) have emerged as a biology-inspired method mimicking the spiking nature of brain neurons. This bio-mimicry derives SNNs' energy efficiency of inference on neuromorphic hardware. However, it also causes an intrinsic disadvantage in training high-performing SNNs from scratch since the discre... | ['Shi Gu', 'Yongqing Hai', 'Shikuang Deng', 'Shanghang Zhang', 'Yufei Guo', 'Yuhang Li'] | 2021-12-01 | null | https://openreview.net/forum?id=H4e7mBnC9f0 | https://openreview.net/pdf?id=H4e7mBnC9f0 | neurips-2021-12 | ['event-data-classification'] | ['computer-vision'] | [ 2.44827494e-01 -2.14996964e-01 2.44479761e-01 -3.21669400e-01
4.65703830e-02 -2.38692015e-01 3.99722755e-01 -3.20796192e-01
-7.85526693e-01 1.07942319e+00 -5.18497825e-01 -1.03569575e-01
3.11065884e-03 -8.32096398e-01 -1.09764874e+00 -9.00087774e-01
-1.01312995e-01 -1.86732307e-01 5.57307899e-01 -3.56229097... | [8.201112747192383, 2.519321918487549] |
70d61ba9-1e67-49cb-a3a2-22411e4dc0c3 | fine-grained-visual-classification-using-self | 2205.10529 | null | https://arxiv.org/abs/2205.10529v1 | https://arxiv.org/pdf/2205.10529v1.pdf | Fine-Grained Visual Classification using Self Assessment Classifier | Extracting discriminative features plays a crucial role in the fine-grained visual classification task. Most of the existing methods focus on developing attention or augmentation mechanisms to achieve this goal. However, addressing the ambiguity in the top-k prediction classes is not fully investigated. In this paper, ... | ['Anh Nguyen', 'Quang D. Tran', 'Erman Tjiputra', 'Huy Tran', 'Tuong Do'] | 2022-05-21 | null | null | null | null | ['fine-grained-image-classification'] | ['computer-vision'] | [-4.22012098e-02 -2.50332236e-01 -3.66856515e-01 -5.23400426e-01
-8.34496796e-01 -6.74537182e-01 5.67985177e-01 8.92581791e-02
-5.62724061e-02 4.61175591e-01 2.69125909e-01 -2.01416723e-02
-1.38965368e-01 -6.14237309e-01 -8.20260763e-01 -6.56164587e-01
3.04329604e-01 3.35717827e-01 6.00519180e-01 -9.30971131... | [9.585640907287598, 2.0689191818237305] |
1cb5b281-ac50-4e9e-80fa-403b05614bcf | graph-neural-networks-and-representation | 2208.11203 | null | https://arxiv.org/abs/2208.11203v1 | https://arxiv.org/pdf/2208.11203v1.pdf | Graph Neural Networks and Representation Embedding for Table Extraction in PDF Documents | Tables are widely used in several types of documents since they can bring important information in a structured way. In scientific papers, tables can sum up novel discoveries and summarize experimental results, making the research comparable and easily understandable by scholars. Several methods perform table analysis ... | ['Simone Marinai', 'Emanuele Vivoli', 'Andrea Gemelli'] | 2022-08-23 | null | null | null | null | ['table-extraction'] | ['miscellaneous'] | [ 7.43714571e-02 1.67977646e-01 -2.61123091e-01 -6.23840466e-02
-3.15575361e-01 -7.90928185e-01 7.02146232e-01 1.28175938e+00
-2.87470698e-01 1.14697552e+00 2.37615302e-01 -2.40310520e-01
-4.76357430e-01 -1.35474360e+00 -6.71326101e-01 -3.58845234e-01
-5.77058531e-02 4.78480726e-01 2.59640627e-02 -1.11145407... | [11.672059059143066, 2.91666579246521] |
7fd55761-4c2a-457b-ab83-5e6711811f88 | leveraging-smartphone-sensors-for-detecting | 2208.01876 | null | https://arxiv.org/abs/2208.01876v1 | https://arxiv.org/pdf/2208.01876v1.pdf | Leveraging Smartphone Sensors for Detecting Abnormal Gait for Smart Wearable Mobile Technologies | Walking is one of the most common modes of terrestrial locomotion for humans. Walking is essential for humans to perform most kinds of daily activities. When a person walks, there is a pattern in it, and it is known as gait. Gait analysis is used in sports and healthcare. We can analyze this gait in different ways, lik... | ['Ahmed Al Marouf', 'Md Shahriar Tasjid'] | 2022-08-03 | null | null | null | null | ['electromyography-emg'] | ['medical'] | [ 1.51237011e-01 -3.50344747e-01 -1.80901721e-01 1.05928935e-01
3.96253586e-01 -1.88019246e-01 -2.13702247e-01 -2.20099941e-01
-3.94743532e-01 6.54686630e-01 3.20484310e-01 3.74846533e-02
2.33110234e-01 -1.09118080e+00 -4.35912162e-01 -5.30124724e-01
-1.42349349e-02 -1.85275570e-01 7.40261078e-01 -4.31253731... | [7.1389360427856445, 0.5251425504684448] |
148819fa-c85c-4ffa-b01f-26bd6f1f8fbf | refined-plane-segmentation-for-cuboid-shaped | 2003.1287 | null | https://arxiv.org/abs/2003.12870v1 | https://arxiv.org/pdf/2003.12870v1.pdf | Refined Plane Segmentation for Cuboid-Shaped Objects by Leveraging Edge Detection | Recent advances in the area of plane segmentation from single RGB images show strong accuracy improvements and now allow a reliable segmentation of indoor scenes into planes. Nonetheless, fine-grained details of these segmentation masks are still lacking accuracy, thus restricting the usability of such techniques on a ... | ['Kai Furmans', 'Laura Dörr', 'Niels Ole Salscheider', 'Alexander Naumann'] | 2020-03-28 | null | null | null | null | ['robust-object-detection'] | ['computer-vision'] | [ 6.47636771e-01 2.35277683e-01 1.78838581e-01 -5.14436126e-01
-7.42348075e-01 -4.61277664e-01 3.88680965e-01 1.96137633e-02
-5.31881452e-01 6.77176416e-01 -1.28073439e-01 -1.12156026e-01
-1.40050054e-02 -8.54627013e-01 -8.56348097e-01 -4.58031476e-01
7.38393236e-03 3.20703059e-01 7.06105769e-01 -8.21089223... | [8.694480895996094, -2.718822717666626] |
82c6de55-91b3-4fd9-81ff-68391ee7567c | bits-and-pieces-understanding-information | 2008.09535 | null | https://arxiv.org/abs/2008.09535v2 | https://arxiv.org/pdf/2008.09535v2.pdf | Bits and Pieces: Understanding Information Decomposition from Part-whole Relationships and Formal Logic | Partial information decomposition (PID) seeks to decompose the multivariate mutual information that a set of source variables contains about a target variable into basic pieces, the so called "atoms of information". Each atom describes a distinct way in which the sources may contain information about the target. In thi... | ['Michael Wibral', 'Abdullah Makkeh', 'Aaron J. Gutknecht'] | 2020-08-21 | null | null | null | null | ['formal-logic'] | ['reasoning'] | [ 1.95968315e-01 6.25845253e-01 -3.21374238e-01 -3.20936620e-01
-2.32225284e-01 -7.67251551e-01 9.61620450e-01 3.72607172e-01
-6.83114454e-02 6.82010472e-01 4.31924790e-01 -4.48440790e-01
-7.05452979e-01 -1.06074023e+00 -3.32897723e-01 -8.12239707e-01
-1.68731198e-01 6.94698036e-01 2.58441597e-01 -5.22803724... | [8.140904426574707, 5.843266010284424] |
50a7bf86-7370-435d-885a-031138fbbf13 | from-word-models-to-world-models-translating | 2306.12672 | null | https://arxiv.org/abs/2306.12672v2 | https://arxiv.org/pdf/2306.12672v2.pdf | From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought | How does language inform our downstream thinking? In particular, how do humans make meaning from language--and how can we leverage a theory of linguistic meaning to build machines that think in more human-like ways? In this paper, we propose rational meaning construction, a computational framework for language-informed... | ['Joshua B. Tenenbaum', 'Jacob Andreas', 'Vikash K. Mansinghka', 'Noah D. Goodman', 'Alexander K. Lew', 'Gabriel Grand', 'Lionel Wong'] | 2023-06-22 | null | null | null | null | ['bayesian-inference', 'probabilistic-programming', 'relational-reasoning'] | ['methodology', 'methodology', 'natural-language-processing'] | [ 1.16152711e-01 9.48243916e-01 2.97606569e-02 -6.96002901e-01
-5.15900433e-01 -6.77919865e-01 1.12674820e+00 7.38869160e-02
9.19247493e-02 2.56199598e-01 8.57650876e-01 -8.07045817e-01
-2.27384150e-01 -1.49316502e+00 -6.50634885e-01 -1.44388020e-01
2.86210358e-01 9.14122343e-01 1.47638038e-01 -5.34437656... | [9.221184730529785, 7.233150005340576] |
48f0b3b6-3325-4041-ae80-4af1c8b00924 | armbench-an-object-centric-benchmark-dataset | 2303.16382 | null | https://arxiv.org/abs/2303.16382v1 | https://arxiv.org/pdf/2303.16382v1.pdf | ARMBench: An Object-centric Benchmark Dataset for Robotic Manipulation | This paper introduces Amazon Robotic Manipulation Benchmark (ARMBench), a large-scale, object-centric benchmark dataset for robotic manipulation in the context of a warehouse. Automation of operations in modern warehouses requires a robotic manipulator to deal with a wide variety of objects, unstructured storage, and d... | ['Manikantan Nambi', 'Felipe Polido', 'Tyler Garaas', 'Vikedo Terhuja', 'Shiyang Lu', 'Fan Wang', 'Chaitanya Mitash'] | 2023-03-29 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [-5.66906929e-02 -6.21392429e-01 2.62280367e-02 -3.01249772e-01
-2.38221258e-01 -1.06210172e+00 1.39952347e-01 3.80307466e-01
-4.32475321e-02 2.49265842e-02 -3.37893933e-01 1.28184959e-01
-3.54038566e-01 -4.95141566e-01 -1.02869332e+00 -4.81528938e-01
-2.37403542e-01 8.98706615e-01 3.05634856e-01 -3.77048463... | [5.836464881896973, -0.9153764247894287] |
a417a357-e8bc-4f8f-88fb-3ac424882b43 | scandeval-a-benchmark-for-scandinavian | 2304.00906 | null | https://arxiv.org/abs/2304.00906v1 | https://arxiv.org/pdf/2304.00906v1.pdf | ScandEval: A Benchmark for Scandinavian Natural Language Processing | This paper introduces a Scandinavian benchmarking platform, ScandEval, which can benchmark any pretrained model on four different tasks in the Scandinavian languages. The datasets used in two of the tasks, linguistic acceptability and question answering, are new. We develop and release a Python package and command-line... | ['Dan Saattrup Nielsen'] | 2023-04-03 | null | null | null | null | ['cross-lingual-transfer', 'linguistic-acceptability'] | ['natural-language-processing', 'natural-language-processing'] | [-8.84962261e-01 2.14172378e-01 1.65541083e-01 -5.45675635e-01
-1.09545279e+00 -1.06710196e+00 6.16711020e-01 1.39175355e-01
-9.99427855e-01 8.16092670e-01 6.53104007e-01 -8.98938239e-01
1.48841947e-01 -2.77363211e-01 -7.93796778e-01 -1.00696586e-01
1.27268359e-01 8.88115168e-01 1.61351323e-01 -9.13074553... | [10.907868385314941, 9.93319320678711] |
72ae9124-123e-4673-a847-faa283beb9b9 | non-linear-phase-retrieval-algorithms-for-x | 2305.00334 | null | https://arxiv.org/abs/2305.00334v1 | https://arxiv.org/pdf/2305.00334v1.pdf | Non-Linear Phase-Retrieval Algorithms for X-ray Propagation-Based Phase-Contrast Tomography | X-ray phase-contrast tomography (XPCT) is widely used for high-contrast 3D micron-scale imaging using nearly monochromatic X-rays at synchrotron beamlines. XPCT enables an order of magnitude improvement in image contrast of the reconstructed material interfaces with low X-ray absorption contrast. The dominant approache... | ['Dilworth Parkinson', 'Jefferson A. Cuadra', 'Venkatesh Sridhar', 'Jean-Baptiste Forien', 'K. Aditya Mohan'] | 2023-04-29 | null | null | null | null | ['3d-reconstruction'] | ['computer-vision'] | [ 5.88373542e-01 -5.85859716e-02 1.87687144e-01 -1.96073949e-01
-9.53264952e-01 -1.22297809e-01 4.05787885e-01 -4.62801792e-02
-5.46075165e-01 7.40080237e-01 -2.26877749e-01 -2.01643094e-01
-3.69075090e-01 -7.14738667e-01 -3.57097328e-01 -1.02202296e+00
2.35040024e-01 8.85812938e-01 5.41628897e-01 2.53200740... | [12.878005027770996, -2.7746024131774902] |
99adbda4-b322-4291-b603-bcfaf0f84ab1 | analysis-of-convolutional-neural-networks-for | 1708.03273 | null | http://arxiv.org/abs/1708.03273v1 | http://arxiv.org/pdf/1708.03273v1.pdf | Analysis of Convolutional Neural Networks for Document Image Classification | Convolutional Neural Networks (CNNs) are state-of-the-art models for document
image classification tasks. However, many of these approaches rely on
parameters and architectures designed for classifying natural images, which
differ from document images. We question whether this is appropriate and
conduct a large empiric... | ['Chris Tensmeyer', 'Tony Martinez'] | 2017-08-10 | null | null | null | null | ['document-image-classification'] | ['computer-vision'] | [ 1.61037296e-01 -1.53781369e-01 -4.86287653e-01 -4.39492047e-01
-2.57245332e-01 -9.26545084e-01 9.08691525e-01 8.56694132e-02
-4.42486912e-01 6.29100651e-02 3.08473825e-01 -7.40330815e-01
-9.48267058e-02 -8.63219440e-01 -9.26278830e-01 -2.76386082e-01
-2.03706650e-03 3.15717518e-01 4.67859171e-02 -2.05595747... | [11.516401290893555, 2.6286261081695557] |
6d84a493-6a20-4347-b46d-07d0fe6cc266 | transmomo-invariance-driven-unsupervised | 2003.14401 | null | https://arxiv.org/abs/2003.14401v2 | https://arxiv.org/pdf/2003.14401v2.pdf | TransMoMo: Invariance-Driven Unsupervised Video Motion Retargeting | We present a lightweight video motion retargeting approach TransMoMo that is capable of transferring motion of a person in a source video realistically to another video of a target person. Without using any paired data for supervision, the proposed method can be trained in an unsupervised manner by exploiting invarianc... | ['Zhuoqian Yang', 'Wayne Wu', 'Wentao Zhu', 'Qiang Zhou', 'Chen Qian', 'Bolei Zhou', 'Chen Change Loy'] | 2020-03-31 | transmomo-invariance-driven-unsupervised-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Yang_TransMoMo_Invariance-Driven_Unsupervised_Video_Motion_Retargeting_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Yang_TransMoMo_Invariance-Driven_Unsupervised_Video_Motion_Retargeting_CVPR_2020_paper.pdf | cvpr-2020-6 | ['motion-retargeting'] | ['computer-vision'] | [ 2.12261543e-01 -1.24044232e-01 -3.10432673e-01 -9.87907127e-02
-7.54492581e-01 -7.74039030e-01 4.22845572e-01 -6.28210008e-01
-2.65495658e-01 5.01226366e-01 3.59625101e-01 1.29255325e-01
1.68509096e-01 -3.65465224e-01 -9.13525343e-01 -8.08237851e-01
-1.40237153e-01 -1.25902101e-01 3.36066574e-01 8.58719200... | [10.551694869995117, -0.9153048396110535] |
ace2c9a1-014b-4f7f-ae26-d32679c074b6 | boltzmann-tuning-of-generative-models | 2104.05252 | null | https://arxiv.org/abs/2104.05252v1 | https://arxiv.org/pdf/2104.05252v1.pdf | Boltzmann Tuning of Generative Models | The paper focuses on the a posteriori tuning of a generative model in order to favor the generation of good instances in the sense of some external differentiable criterion. The proposed approach, called Boltzmann Tuning of Generative Models (BTGM), applies to a wide range of applications. It covers conditional generat... | ['Michele Sebag', 'Victor Berger'] | 2021-04-12 | null | null | null | null | ['robust-design'] | ['miscellaneous'] | [ 3.76580179e-01 4.23809260e-01 -6.82186931e-02 -5.93163520e-02
-9.81302142e-01 -1.14242941e-01 8.43904734e-01 6.75380602e-03
-5.55163920e-01 9.72086549e-01 -1.46962792e-01 2.56086327e-02
-7.17365980e-01 -9.39561129e-01 -4.87575114e-01 -1.26393986e+00
1.89952776e-01 8.58881295e-01 -2.37980098e-01 -1.73810989... | [6.094314098358154, 3.6384620666503906] |
2a148dfd-039b-4cad-8636-f939f9e7d5c7 | evaluating-multilingual-bert-for-estonian | 2010.00454 | null | https://arxiv.org/abs/2010.00454v2 | https://arxiv.org/pdf/2010.00454v2.pdf | Evaluating Multilingual BERT for Estonian | Recently, large pre-trained language models, such as BERT, have reached state-of-the-art performance in many natural language processing tasks, but for many languages, including Estonian, BERT models are not yet available. However, there exist several multilingual BERT models that can handle multiple languages simultan... | ['Kairit Sirts', 'Claudia Kittask', 'Kirill Milintsevich'] | 2020-10-01 | null | null | null | null | ['morphological-tagging'] | ['natural-language-processing'] | [-5.91351151e-01 1.02411538e-01 -1.38555050e-01 -3.76064837e-01
-1.04182601e+00 -9.12714005e-01 7.70953357e-01 6.74416780e-01
-1.12607408e+00 9.64092374e-01 4.03614134e-01 -5.52382827e-01
1.24034442e-01 -3.91289115e-01 -4.31266904e-01 -1.43315375e-01
1.64939631e-02 1.12727439e+00 3.00514817e-01 -3.04616213... | [10.230796813964844, 9.940140724182129] |
e8717dad-cf8f-43c7-8a37-e3b4a98dc544 | an-interpretable-federated-learning-based | 2201.03134 | null | https://arxiv.org/abs/2201.03134v1 | https://arxiv.org/pdf/2201.03134v1.pdf | An Interpretable Federated Learning-based Network Intrusion Detection Framework | Learning-based Network Intrusion Detection Systems (NIDSs) are widely deployed for defending various cyberattacks. Existing learning-based NIDS mainly uses Neural Network (NN) as a classifier that relies on the quality and quantity of cyberattack data. Such NN-based approaches are also hard to interpret for improving e... | ['Jialiang Lu', 'Han Qiu', 'Song Li', 'Tian Dong'] | 2022-01-10 | null | null | null | null | ['network-intrusion-detection'] | ['miscellaneous'] | [-1.41676188e-01 -4.49566454e-01 -4.54293936e-01 -7.68126726e-01
-1.46758318e-01 -8.61836076e-01 5.01231611e-01 5.90294041e-02
-3.36677700e-01 7.63314664e-01 -2.81410366e-01 -1.14732730e+00
-3.04539472e-01 -8.82766664e-01 -4.32835668e-01 -5.08416235e-01
1.00421727e-01 1.04801003e-02 2.95885921e-01 -1.80169940... | [5.386078834533691, 7.187366485595703] |
27f348ae-8285-45fd-8f8e-d04f44319b5b | novel-deep-learning-framework-for-bovine-iris | 2212.11439 | null | https://arxiv.org/abs/2212.11439v1 | https://arxiv.org/pdf/2212.11439v1.pdf | Novel Deep Learning Framework For Bovine Iris Segmentation | Iris segmentation is the initial step to identify biometric of animals to establish a traceability system of livestock. In this study, we propose a novel deep learning framework for pixel-wise segmentation with minimum use of annotation labels using BovineAAEyes80 public dataset. In the experiment, U-Net with VGG16 bac... | ['Sang-Hee Lee', 'Mira Park', 'Heemoon Yoon'] | 2022-12-22 | null | null | null | null | ['iris-segmentation'] | ['medical'] | [ 3.17474663e-01 -8.22101533e-02 -2.84544677e-01 -7.59686470e-01
-1.76093012e-01 -2.49596983e-01 3.81102003e-02 -1.98212609e-01
-4.43438381e-01 6.47490919e-01 -2.28657991e-01 -1.89947486e-01
8.27179253e-02 -7.16241062e-01 -6.88352406e-01 -7.24402606e-01
3.06333870e-01 1.68555945e-01 -2.13934228e-01 1.49382144... | [3.7500877380371094, -3.6304140090942383] |
301158ed-aeaa-49c2-af24-117f0c1e17ae | building-a-parallel-corpus-and-training | 2301.02773 | null | https://arxiv.org/abs/2301.02773v1 | https://arxiv.org/pdf/2301.02773v1.pdf | Building a Parallel Corpus and Training Translation Models Between Luganda and English | Neural machine translation (NMT) has achieved great successes with large datasets, so NMT is more premised on high-resource languages. This continuously underpins the low resource languages such as Luganda due to the lack of high-quality parallel corpora, so even 'Google translate' does not serve Luganda at the time of... | ['Heeyoul Choi', 'Daniela N. Rim', 'Richard Kimera'] | 2023-01-07 | null | null | null | null | ['nmt'] | ['computer-code'] | [-1.15477175e-01 -5.06153964e-02 -4.58012611e-01 -2.12616041e-01
-1.15518105e+00 -6.74801648e-01 7.86106765e-01 -3.85590643e-01
-5.07309854e-01 1.15841472e+00 4.89122957e-01 -8.81218493e-01
3.12134862e-01 -5.12170196e-01 -8.02064598e-01 -5.11884689e-01
3.38977963e-01 1.08407378e+00 -3.58573467e-01 -5.68929017... | [11.540536880493164, 10.365926742553711] |
4ed7b02a-dea0-49d7-89d8-95d7a2ec1ab6 | a-multi-stage-memory-augmented-neural-network | null | null | https://aclanthology.org/W18-2603 | https://aclanthology.org/W18-2603.pdf | A Multi-Stage Memory Augmented Neural Network for Machine Reading Comprehension | Reading Comprehension (RC) of text is one of the fundamental tasks in natural language processing. In recent years, several end-to-end neural network models have been proposed to solve RC tasks. However, most of these models suffer in reasoning over long documents. In this work, we propose a novel Memory Augmented Mach... | ['Haejun Lee', 'Seohyun Back', 'Sathish Reddy Indurthi', 'Seunghak Yu'] | 2018-07-01 | null | null | null | ws-2018-7 | ['triviaqa'] | ['miscellaneous'] | [ 3.43110770e-01 2.78179310e-02 4.00613993e-01 -4.98440474e-01
-9.83685255e-01 -4.81716365e-01 7.86666811e-01 6.72153831e-01
-7.36548185e-01 7.26135850e-01 6.16839230e-01 -7.01337755e-01
-2.97477841e-01 -8.08694303e-01 -7.02378035e-01 -2.07327425e-01
2.20507145e-01 7.38657355e-01 3.46104801e-01 -4.16663736... | [11.197235107421875, 8.212535858154297] |
ed493d8c-e317-4bb8-9996-d1fc8acdc0c6 | road-reality-oriented-adaptation-for-semantic | 1711.11556 | null | http://arxiv.org/abs/1711.11556v2 | http://arxiv.org/pdf/1711.11556v2.pdf | ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes | Exploiting synthetic data to learn deep models has attracted increasing
attention in recent years. However, the intrinsic domain difference between
synthetic and real images usually causes a significant performance drop when
applying the learned model to real world scenarios. This is mainly due to two
reasons: 1) the m... | ['Luc van Gool', 'Yuhua Chen', 'Wen Li'] | 2017-11-30 | road-reality-oriented-adaptation-for-semantic-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Chen_ROAD_Reality_Oriented_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_ROAD_Reality_Oriented_CVPR_2018_paper.pdf | cvpr-2018-6 | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 3.88758957e-01 1.64149746e-01 1.05187513e-01 -5.62844932e-01
-5.58182001e-01 -4.84766632e-01 6.99467719e-01 -4.26882505e-01
-4.95173514e-01 6.68235481e-01 8.00106674e-02 6.64947331e-02
1.44231126e-01 -1.04034042e+00 -9.75733221e-01 -5.39191246e-01
5.59728980e-01 4.96572584e-01 6.17450178e-01 -4.09620434... | [9.732667922973633, 1.1428358554840088] |
7459761c-b33b-46df-a1b1-a726c8f9d889 | 3-dimensional-dense-reconstruction-a-review | 2304.09371 | null | https://arxiv.org/abs/2304.09371v1 | https://arxiv.org/pdf/2304.09371v1.pdf | 3 Dimensional Dense Reconstruction: A Review of Algorithms and Dataset | 3D dense reconstruction refers to the process of obtaining the complete shape and texture features of 3D objects from 2D planar images. 3D reconstruction is an important and extensively studied problem, but it is far from being solved. This work systematically introduces classical methods of 3D dense reconstruction bas... | ['Yangming Li'] | 2023-04-19 | null | null | null | null | ['3d-reconstruction'] | ['computer-vision'] | [-2.28264779e-01 -9.73479077e-02 1.56807527e-01 -3.85820985e-01
-5.50216138e-01 4.28831438e-03 6.67965889e-01 -3.09092581e-01
-6.62317351e-02 3.97921681e-01 2.63746470e-01 5.73425181e-02
-7.39279315e-02 -7.95285046e-01 -7.86961675e-01 -6.34683967e-01
-2.88089991e-01 1.14957905e+00 2.73415521e-02 1.62058517... | [8.437551498413086, -3.4756767749786377] |
be889c32-d404-4d37-8eeb-cab01f1867f4 | coupled-physics-informed-neural-networks-for | 2301.08618 | null | https://arxiv.org/abs/2301.08618v3 | https://arxiv.org/pdf/2301.08618v3.pdf | Solving PDEs with Unmeasurable Source Terms Using Coupled Physics-Informed Neural Network with Recurrent Prediction for Soft Sensors | Partial differential equations (PDEs) are a model candidate for soft sensors in industrial processes with spatiotemporal dependence. Although physics-informed neural networks (PINNs) are a promising machine learning method for solving PDEs, they are infeasible for the nonhomogeneous PDEs with unmeasurable source terms.... | ['Xi-Ming Sun', 'Pan Qin', 'Aina Wang'] | 2023-01-20 | null | null | null | null | ['sensor-modeling'] | ['computer-vision'] | [-4.26744372e-02 2.26160914e-01 -2.93587483e-02 8.40021006e-04
-3.37122113e-01 4.78793234e-02 8.32192376e-02 -1.29520744e-01
1.90017730e-01 8.44135880e-01 -2.77563959e-01 -1.56694353e-01
-6.25648677e-01 -8.74850035e-01 -9.84368443e-01 -9.36510980e-01
1.83182538e-01 1.98223993e-01 2.22100258e-01 -3.13464962... | [6.619194507598877, 3.5012874603271484] |
db717a1c-cab6-40cd-9d70-c0488cf74919 | localization-of-deep-inpainting-using-high | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Li_Localization_of_Deep_Inpainting_Using_High-Pass_Fully_Convolutional_Network_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Li_Localization_of_Deep_Inpainting_Using_High-Pass_Fully_Convolutional_Network_ICCV_2019_paper.pdf | Localization of Deep Inpainting Using High-Pass Fully Convolutional Network | Image inpainting has been substantially improved with deep learning in the past years. Deep inpainting can fill image regions with plausible contents, which are not visually apparent. Although inpainting is originally designed to repair images, it can even be used for malicious manipulations, e.g., removal of specific ... | [' Jiwu Huang', 'Haodong Li'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['image-manipulation-detection'] | ['computer-vision'] | [ 5.94706595e-01 3.34118456e-02 1.52453342e-02 -8.72203484e-02
-5.61281025e-01 -2.96691898e-02 2.64922112e-01 -2.46240478e-02
-1.43720314e-01 7.31291234e-01 8.76154304e-02 1.24972060e-01
2.85998642e-01 -8.30696642e-01 -1.07692885e+00 -9.77980852e-01
2.14364961e-01 -3.01262736e-01 6.04097843e-02 -1.13282464... | [11.4028902053833, -1.2865592241287231] |
0bb366e0-948f-4316-b09f-7bce9bd99afc | embedded-deep-regularized-block-hsic | 2106.02106 | null | https://arxiv.org/abs/2106.02106v1 | https://arxiv.org/pdf/2106.02106v1.pdf | Embedded Deep Regularized Block HSIC Thermomics for Early Diagnosis of Breast Cancer | Thermography has been used extensively as a complementary diagnostic tool in breast cancer detection. Among thermographic methods matrix factorization (MF) techniques show an unequivocal capability to detect thermal patterns corresponding to vasodilation in cancer cases. One of the biggest challenges in such techniques... | ['Xavier P. V. Maldague', 'Hossein Memarzadeh Sharifipour', 'Bardia Yousefi'] | 2021-06-03 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 2.95231849e-01 -1.38319820e-01 -4.04258966e-01 -1.95911750e-01
-8.17283511e-01 -4.03444409e-01 7.22247139e-02 -3.42748135e-01
-2.37529054e-01 3.48050654e-01 4.92410213e-01 -2.93265879e-01
-3.68529975e-01 -5.05546868e-01 -7.76295364e-02 -1.44789839e+00
-2.28089824e-01 2.43576363e-01 -5.82229555e-01 -1.65910646... | [12.314820289611816, 0.32027676701545715] |
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