paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
56ba82d5-424b-49b0-a060-317d6845f9c3 | 11-teraflops-per-second-photonic | 2011.07393 | null | https://arxiv.org/abs/2011.07393v1 | https://arxiv.org/pdf/2011.07393v1.pdf | 11 TeraFLOPs per second photonic convolutional accelerator for deep learning optical neural networks | Convolutional neural networks (CNNs), inspired by biological visual cortex systems, are a powerful category of artificial neural networks that can extract the hierarchical features of raw data to greatly reduce the network parametric complexity and enhance the predicting accuracy. They are of significant interest for m... | ['David J. Moss', 'Arnan Mitchell', 'Roberto Morandotti', 'Damien G. Hicks', 'Brent E. Little', 'Sai T. Chu', 'Thach G. Nguyen', 'Andreas Boes', 'Jiayang Wu', 'Bill Corcoran', 'Mengxi Tan', 'Xingyuan Xu'] | 2020-11-14 | null | null | null | null | ['board-games'] | ['playing-games'] | [ 2.32297033e-01 -7.39165917e-02 -2.27861434e-01 -1.86973661e-01
3.81165564e-01 -3.18719685e-01 3.64500910e-01 -2.51015842e-01
-8.90900254e-01 4.91889983e-01 -4.77736712e-01 -7.43822157e-01
5.55071235e-02 -7.77114987e-01 -5.58806479e-01 -8.13177526e-01
-1.69623822e-01 -5.35913445e-02 3.39757740e-01 -6.44741431... | [8.339064598083496, 2.5719544887542725] |
9cc4d30d-e41a-4005-ab54-1d87e09adf35 | residual-guide-feature-fusion-network-for | 1804.07493 | null | http://arxiv.org/abs/1804.07493v1 | http://arxiv.org/pdf/1804.07493v1.pdf | Residual-Guide Feature Fusion Network for Single Image Deraining | Single image rain streaks removal is extremely important since rainy images
adversely affect many computer vision systems. Deep learning based methods have
found great success in image deraining tasks. In this paper, we propose a novel
residual-guide feature fusion network, called ResGuideNet, for single image
derainin... | ['Huafeng Wu', 'Zhiwen Fan', 'Yue Hunag', 'Xueyang Fu', 'Xinghao Ding'] | 2018-04-20 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [ 2.05765456e-01 -2.14236721e-01 4.03306484e-01 -5.31551838e-01
-3.47789586e-01 -1.11012198e-01 1.80827409e-01 -6.18204832e-01
-2.46431559e-01 8.33868802e-01 -2.25276891e-02 -2.09880501e-01
3.37593883e-01 -8.30422282e-01 -8.32090676e-01 -9.93559241e-01
2.10492730e-01 -2.81290263e-01 3.16069752e-01 -3.92942220... | [10.924304008483887, -3.203662395477295] |
f40dce37-e7f7-4de9-bda7-cee0aaff18f5 | learning-to-refine-object-contours-with-a-top | 1705.04456 | null | http://arxiv.org/abs/1705.04456v1 | http://arxiv.org/pdf/1705.04456v1.pdf | Learning to Refine Object Contours with a Top-Down Fully Convolutional Encoder-Decoder Network | We develop a novel deep contour detection algorithm with a top-down fully
convolutional encoder-decoder network. Our proposed method, named TD-CEDN,
solves two important issues in this low-level vision problem: (1) learning
multi-scale and multi-level features; and (2) applying an effective top-down
refined approach in... | ['Jian Yao', 'Yahui Liu', 'Xiaohu Lu', 'Li Li', 'Jing Han'] | 2017-05-12 | null | null | null | null | ['contour-detection'] | ['computer-vision'] | [ 2.61743188e-01 8.84498507e-02 1.04819514e-01 -3.29965353e-01
-6.07384443e-01 -1.96777098e-02 5.62541544e-01 -2.59997081e-02
-5.65887630e-01 5.01398563e-01 1.33315712e-01 -5.30075841e-02
1.92981973e-01 -8.90047431e-01 -7.88501859e-01 -6.54653966e-01
1.86480880e-01 7.90872201e-02 7.79685557e-01 2.87005957... | [9.714241981506348, -0.3996181786060333] |
8602f452-b244-4c4e-8436-139383fbcf2b | poly-gan-multi-conditioned-gan-for-fashion | 1909.02165 | null | https://arxiv.org/abs/1909.02165v1 | https://arxiv.org/pdf/1909.02165v1.pdf | Poly-GAN: Multi-Conditioned GAN for Fashion Synthesis | We present Poly-GAN, a novel conditional GAN architecture that is motivated by Fashion Synthesis, an application where garments are automatically placed on images of human models at an arbitrary pose. Poly-GAN allows conditioning on multiple inputs and is suitable for many tasks, including image alignment, image stitch... | ['Nilesh Pandey', 'Andreas Savakis'] | 2019-09-05 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 6.45215988e-01 1.66729569e-01 4.79549244e-02 -1.33606300e-01
-4.20613140e-01 -6.27024472e-01 5.30045033e-01 -5.02586305e-01
-1.64335951e-01 3.92632753e-01 1.57488763e-01 2.57475853e-01
4.31041598e-01 -8.83728802e-01 -1.28113878e+00 -6.31173432e-01
4.98847604e-01 7.24179089e-01 -1.33956894e-01 -2.95431733... | [11.754449844360352, -0.7409340739250183] |
a399f181-a620-4bbc-b113-231401a0ba24 | globaltrack-a-simple-and-strong-baseline-for | 1912.08531 | null | https://arxiv.org/abs/1912.08531v1 | https://arxiv.org/pdf/1912.08531v1.pdf | GlobalTrack: A Simple and Strong Baseline for Long-term Tracking | A key capability of a long-term tracker is to search for targets in very large areas (typically the entire image) to handle possible target absences or tracking failures. However, currently there is a lack of such a strong baseline for global instance search. In this work, we aim to bridge this gap. Specifically, we pr... | ['Xin Zhao', 'Lianghua Huang', 'Kaiqi Huang'] | 2019-12-18 | null | null | null | null | ['instance-search'] | ['computer-vision'] | [-4.95544642e-01 -4.64907646e-01 -4.42671984e-01 -2.32453048e-02
-9.35846269e-01 -9.71628368e-01 6.36822641e-01 -2.41804924e-02
-5.23331881e-01 5.35229802e-01 -2.69229025e-01 -2.14819193e-01
1.82837605e-01 -4.50749874e-01 -8.59550834e-01 -5.83128273e-01
-2.65147030e-01 2.44027808e-01 1.17096663e+00 1.07380539... | [6.334116458892822, -2.0992865562438965] |
492279cc-669f-48fd-a083-a4037d3a2d12 | discriminative-language-model-as-semantic | 2210.12763 | null | https://arxiv.org/abs/2210.12763v1 | https://arxiv.org/pdf/2210.12763v1.pdf | Discriminative Language Model as Semantic Consistency Scorer for Prompt-based Few-Shot Text Classification | This paper proposes a novel prompt-based finetuning method (called DLM-SCS) for few-shot text classification by utilizing the discriminative language model ELECTRA that is pretrained to distinguish whether a token is original or generated. The underlying idea is that the prompt instantiated with the true label should h... | ['Yahe Li', 'Zhipeng Xie'] | 2022-10-23 | null | null | null | null | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 9.36196074e-02 -5.56815341e-02 -4.38813210e-01 -5.29918909e-01
-8.35686386e-01 -3.35224569e-01 9.42746282e-01 5.51432669e-01
-5.32302499e-01 5.30856729e-01 2.62029827e-01 1.42613679e-01
1.13111496e-01 -6.72470212e-01 -1.31900474e-01 -5.43501079e-01
3.91505361e-01 6.02032781e-01 5.87361336e-01 -2.70221084... | [10.71043872833252, 7.584021091461182] |
4df8fbde-732c-42d2-85b4-23c771e08e07 | phoenix-a-self-optimizing-chess-engine | 1603.09051 | null | http://arxiv.org/abs/1603.09051v4 | http://arxiv.org/pdf/1603.09051v4.pdf | Phoenix: A Self-Optimizing Chess Engine | Since the advent of computers, many tasks which required humans to spend a
lot of time and energy have been trivialized by the computers' ability to
perform repetitive tasks extremely quickly. Playing chess is one such task. It
was one of the first games which was `solved' using AI. With the advent of deep
learning, ch... | ['G. Srinivasaraghavan', 'Rahul Aralikatte'] | 2016-03-30 | null | null | null | null | ['game-of-chess'] | ['playing-games'] | [-1.26883358e-01 -8.81351233e-02 2.91714877e-01 7.97753111e-02
-4.77843761e-01 -6.91630125e-01 4.30562407e-01 1.44622937e-01
-9.00644243e-01 1.05999720e+00 -3.80330086e-01 -2.83740729e-01
-2.88284063e-01 -9.91041243e-01 -7.08446860e-01 -6.98053241e-01
-1.51554989e-02 1.01980376e+00 5.20859957e-01 -9.01200891... | [3.539407730102539, 1.5707497596740723] |
be722937-1b40-4db1-bf48-d131355c4ef7 | parallel-data-helps-neural-entity-coreference | 2305.17709 | null | https://arxiv.org/abs/2305.17709v1 | https://arxiv.org/pdf/2305.17709v1.pdf | Parallel Data Helps Neural Entity Coreference Resolution | Coreference resolution is the task of finding expressions that refer to the same entity in a text. Coreference models are generally trained on monolingual annotated data but annotating coreference is expensive and challenging. Hardmeier et al.(2013) have shown that parallel data contains latent anaphoric knowledge, but... | ['Christian Hardmeier', 'Gongbo Tang'] | 2023-05-28 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [-8.84145349e-02 5.71327090e-01 -7.78363585e-01 -4.99496132e-01
-1.21622598e+00 -8.27339590e-01 6.20785356e-01 1.02234453e-01
-6.65594935e-01 9.25308883e-01 8.13783586e-01 1.76145405e-01
-2.30161861e-01 -3.84721696e-01 -7.41044700e-01 -2.79965341e-01
6.29207566e-02 1.12003994e+00 8.03007837e-03 -4.59214002... | [9.297536849975586, 9.533158302307129] |
6acf3617-a49a-4732-bac8-318d67eb8b33 | point-cloud-registration-based-on-graph | 2302.05844 | null | https://arxiv.org/abs/2302.05844v3 | https://arxiv.org/pdf/2302.05844v3.pdf | Graph Matching Optimization Network for Point Cloud Registration | Point Cloud Registration is a fundamental and challenging problem in 3D computer vision. Recent works often utilize the geometric structure information in point feature embedding or outlier rejection for registration while neglecting to consider explicitly isometry-preserving constraint ($e.g.,$ point pair linked edge'... | ['Jin Xie', 'Haobo Jiang', 'Jian Yang', 'Lei Luo', 'Yaqing Ding', 'Guofeng Mei', 'Yaqi Shen', 'Qianliang Wu'] | 2023-02-12 | null | null | null | null | ['point-cloud-registration', 'graph-matching'] | ['computer-vision', 'graphs'] | [-2.21285626e-01 -1.06369406e-01 -8.24781284e-02 -3.60037327e-01
-7.98236370e-01 -3.72314245e-01 3.52630734e-01 7.20356554e-02
-2.65038282e-01 5.51917069e-02 -3.88728708e-01 -1.10826157e-01
-4.58554655e-01 -6.47970796e-01 -1.04838693e+00 -6.85982823e-01
-8.79468024e-02 5.91872454e-01 -1.99786369e-02 -2.49547899... | [7.686346054077148, -3.0574164390563965] |
364a96b3-d4ae-47fc-9d80-731cdd4e6a5f | maximum-margin-learning-of-t-spns-for-cell | 2303.09065 | null | https://arxiv.org/abs/2303.09065v3 | https://arxiv.org/pdf/2303.09065v3.pdf | Maximum margin learning of t-SPNs for cell classification with filtered input | An algorithm based on a deep probabilistic architecture referred to as a tree-structured sum-product network (t-SPN) is considered for cell classification. The t-SPN is constructed such that the unnormalized probability is represented as conditional probabilities of a subset of most similar cell classes. The constructe... | ['Yongcheon Na', 'Chang D. Yoo', 'Haeyong Kang'] | 2023-03-16 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [-3.75061557e-02 9.52601507e-02 4.85253371e-02 -1.65856287e-01
-4.95477349e-01 -1.34346381e-01 5.45968890e-01 5.02477646e-01
-7.06102014e-01 9.77927148e-01 -1.18820764e-01 -1.35200471e-01
-2.37603769e-01 -9.76976335e-01 -6.04017615e-01 -1.14813268e+00
-2.31347769e-01 3.37690562e-01 1.53016657e-01 2.95952279... | [14.990592002868652, -3.0549333095550537] |
ebab565d-5ee8-45dd-8bf6-5fe2a2416c6b | guts-generalized-uncertainty-aware-thompson | 2304.02075 | null | https://arxiv.org/abs/2304.02075v1 | https://arxiv.org/pdf/2304.02075v1.pdf | GUTS: Generalized Uncertainty-Aware Thompson Sampling for Multi-Agent Active Search | Robotic solutions for quick disaster response are essential to ensure minimal loss of life, especially when the search area is too dangerous or too vast for human rescuers. We model this problem as an asynchronous multi-agent active-search task where each robot aims to efficiently seek objects of interest (OOIs) in an ... | ['Jeff Schneider', 'Ramina Ghods', 'Tejus Gupta', 'Nikhil Angad Bakshi'] | 2023-04-04 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 2.19196025e-02 3.30809593e-01 3.56611684e-02 1.75673142e-01
-1.08347869e+00 -7.36962199e-01 4.02540833e-01 3.76288474e-01
-6.33874893e-01 1.02472281e+00 -9.60558429e-02 -2.63088018e-01
-7.04936326e-01 -8.23915780e-01 -5.52122235e-01 -6.35505617e-01
-9.19187188e-01 1.26333570e+00 5.67908287e-01 -5.32955289... | [5.100680351257324, 1.2574794292449951] |
051f133c-2337-4da4-bd47-add357e4ab09 | synthesis-unit-and-question-set-definition | null | null | https://aclanthology.org/O13-1008 | https://aclanthology.org/O13-1008.pdf | 合成單元與問題集之定義於隱藏式馬可夫模型中文歌聲合成系統之建立 (Synthesis Unit and Question Set Definition for Mandarin HMM-based Singing Voice Synthesis) | null | ['Chung-Hsien Wu', 'Yi-chin Huang', 'Ju-Yun Cheng'] | 2013-10-01 | synthesis-unit-and-question-set-definition-1 | https://aclanthology.org/O13-1008 | https://aclanthology.org/O13-1008.pdf | roclingijclclp-2013-10 | ['singing-voice-synthesis'] | ['speech'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.226997375488281, 3.630502700805664] |
0ae2790f-5ca6-40fc-869d-0eb802d9efde | hierarchical-classification-of-pulmonary | 2010.04049 | null | https://arxiv.org/abs/2010.04049v1 | https://arxiv.org/pdf/2010.04049v1.pdf | Hierarchical Classification of Pulmonary Lesions: A Large-Scale Radio-Pathomics Study | Diagnosis of pulmonary lesions from computed tomography (CT) is important but challenging for clinical decision making in lung cancer related diseases. Deep learning has achieved great success in computer aided diagnosis (CADx) area for lung cancer, whereas it suffers from label ambiguity due to the difficulty in the r... | ['Chang Chen', 'Dong Xie', 'Yunlang She', 'Bingbing Ni', 'Kaiming Kuang', 'Mingze Gao', 'Jiancheng Yang'] | 2020-10-08 | null | null | null | null | ['lung-cancer-diagnosis'] | ['medical'] | [ 2.41251200e-01 1.50803208e-01 -7.80398488e-01 4.80892975e-03
-1.21584952e+00 -3.31609160e-01 2.23365784e-01 2.28708550e-01
-9.74923074e-02 8.22359920e-01 3.32259357e-01 -7.90269315e-01
-4.89744961e-01 -8.53077292e-01 -1.64486170e-01 -1.02997565e+00
-9.15751010e-02 1.38648748e+00 2.64736801e-01 4.86092836... | [15.401811599731445, -2.309955358505249] |
afb97fba-c275-4999-aec3-f0fcb6ed761b | using-document-summarization-techniques-for | null | null | https://aclanthology.org/N13-1086 | https://aclanthology.org/N13-1086.pdf | Using Document Summarization Techniques for Speech Data Subset Selection | null | ['Kai Wei', 'Jeff Bilmes', 'Katrin Kirchhoff', 'Yuzong Liu'] | 2013-06-01 | null | null | null | naacl-2013-6 | ['extractive-document-summarization'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.3657307624816895, 3.6632490158081055] |
c051f8c9-ad20-4909-ae6f-e924bec3be2e | learning-monocular-visual-odometry-with-dense | 1803.02286 | null | http://arxiv.org/abs/1803.02286v2 | http://arxiv.org/pdf/1803.02286v2.pdf | Learning monocular visual odometry with dense 3D mapping from dense 3D flow | This paper introduces a fully deep learning approach to monocular SLAM, which
can perform simultaneous localization using a neural network for learning
visual odometry (L-VO) and dense 3D mapping. Dense 2D flow and a depth image
are generated from monocular images by sub-networks, which are then used by a
3D flow assoc... | ['Pulak Purkait', 'Tom Duckett', 'Cheng Zhao', 'Rustam Stolkin', 'Li Sun'] | 2018-03-06 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-5.82311749e-01 2.04508491e-02 -3.89000267e-01 -4.10816938e-01
-4.19887304e-01 -4.38683152e-01 7.51623333e-01 -3.93248945e-01
-3.89946997e-01 6.17017329e-01 5.93591556e-02 -8.29657093e-02
2.03408748e-01 -8.07929814e-01 -1.09372520e+00 -4.71587896e-01
-8.11058059e-02 7.72964835e-01 -2.60106623e-01 2.03036234... | [8.132007598876953, -2.2330729961395264] |
c71a41a6-71a9-4a87-aacf-ec25266ff17e | custom-pretrainings-and-adapted-3d-convnext | 2206.15073 | null | https://arxiv.org/abs/2206.15073v2 | https://arxiv.org/pdf/2206.15073v2.pdf | COVID Detection and Severity Prediction with 3D-ConvNeXt and Custom Pretrainings | Since COVID strongly affects the respiratory system, lung CT-scans can be used for the analysis of a patients health. We introduce a neural network for the prediction of the severity of lung damage and the detection of a COVID-infection using three-dimensional CT-data. Therefore, we adapt the recent ConvNeXt model to p... | ['Rainer Lienhart', 'Katja Ludwig', 'Robin Schön', 'Julian Lorenz', 'Daniel Kienzle'] | 2022-06-30 | null | null | null | null | ['severity-prediction'] | ['computer-vision'] | [-1.47573769e-01 -4.39230293e-01 4.85334210e-02 -2.45440185e-01
-5.10258257e-01 -3.01843971e-01 1.79165587e-01 -4.88173552e-02
-8.17532122e-01 4.32482690e-01 1.36425212e-01 -3.42233270e-01
-2.74883598e-01 -6.09402478e-01 -4.18554544e-01 -6.27739251e-01
-1.89275742e-01 1.11368322e+00 3.86978120e-01 3.49726856... | [15.451578140258789, -1.8137915134429932] |
f31259b2-29a2-474a-90df-a0c67f4fdcc2 | the-value-of-ai-guidance-in-human-examination | 2208.10544 | null | https://arxiv.org/abs/2208.10544v1 | https://arxiv.org/pdf/2208.10544v1.pdf | The Value of AI Guidance in Human Examination of Synthetically-Generated Faces | Face image synthesis has progressed beyond the point at which humans can effectively distinguish authentic faces from synthetically generated ones. Recently developed synthetic face image detectors boast "better-than-human" discriminative ability, especially those guided by human perceptual intelligence during the mode... | ['Adam Czajka', 'Kevin Bowyer', 'Patrick Tinsley', 'Aidan Boyd'] | 2022-08-22 | null | null | null | null | ['face-detection', 'synthetic-image-detection'] | ['computer-vision', 'computer-vision'] | [ 4.66167569e-01 6.27131343e-01 1.32932439e-01 -6.62392616e-01
-5.21338880e-01 -3.61756086e-01 6.38000667e-01 -1.73353955e-01
-3.93196791e-01 6.31753087e-01 -6.59627244e-02 -1.48106009e-01
2.91319579e-01 -4.82104897e-01 -7.44522512e-01 -5.35009563e-01
2.19657436e-01 5.23428202e-01 -7.93059468e-02 -1.89587682... | [10.14278507232666, 2.2725541591644287] |
8c817d5a-d7ea-4d70-9d73-f0fe807f4417 | multi-stage-neural-networks-with-single-sided | 1703.00311 | null | http://arxiv.org/abs/1703.00311v3 | http://arxiv.org/pdf/1703.00311v3.pdf | Multi-stage Neural Networks with Single-sided Classifiers for False Positive Reduction and its Evaluation using Lung X-ray CT Images | Lung nodule classification is a class imbalanced problem because nodules are
found with much lower frequency than non-nodules. In the class imbalanced
problem, conventional classifiers tend to be overwhelmed by the majority class
and ignore the minority class. We therefore propose cascaded convolutional
neural networks... | ['Taro Sekiyama', 'Masaharu Sakamoto', 'Kun Zhao', 'Hiroki Nakano'] | 2017-03-01 | null | null | null | null | ['lung-nodule-classification'] | ['medical'] | [ 2.49985471e-01 4.56583530e-01 -6.62559032e-01 -3.67225915e-01
-5.45960128e-01 -1.48711398e-01 2.35688221e-02 2.11695820e-01
-3.67758363e-01 6.80058539e-01 -2.08811805e-01 -6.56952024e-01
-2.14761034e-01 -1.01656687e+00 -3.93620312e-01 -5.01720488e-01
3.77784297e-02 5.03530860e-01 4.64242548e-01 2.87496954... | [15.398285865783691, -2.2215757369995117] |
784d98a2-3b2b-42d9-8dd7-e0d782625e6e | constrained-online-two-stage-stochastic | 2302.00997 | null | https://arxiv.org/abs/2302.00997v2 | https://arxiv.org/pdf/2302.00997v2.pdf | Constrained Online Two-stage Stochastic Optimization: Near Optimal Algorithms via Adversarial Learning | We consider an online two-stage stochastic optimization with long-term constraints over a finite horizon of $T$ periods. At each period, we take the first-stage action, observe a model parameter realization and then take the second-stage action from a feasible set that depends both on the first-stage decision and the m... | ['Jiashuo Jiang'] | 2023-02-02 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [ 1.48625091e-01 3.19719225e-01 -3.19718093e-01 -1.64960966e-01
-1.26334381e+00 -1.01763153e+00 -3.71107310e-02 6.42458871e-02
-6.40950978e-01 9.63612318e-01 -3.30236524e-01 -3.93942952e-01
-3.94658357e-01 -8.98461163e-01 -1.20802295e+00 -9.48600173e-01
-3.33928078e-01 3.80326778e-01 -1.78909197e-01 -4.96804751... | [4.600570201873779, 3.2571535110473633] |
e3989450-2368-421c-90fc-fa15dbf8667a | learning-to-rank-visual-stories-from-human | null | null | https://openreview.net/forum?id=b8lMsO5YtpR | https://openreview.net/pdf?id=b8lMsO5YtpR | Learning to Rank Visual Stories From Human Ranking Data | Visual storytelling (VIST) is a typical vision and language task that has seen extensive development in the natural language generation research domain. However, it remains unclear whether conventional automatic evaluation metrics for text generation are applicable on VIST.
In this paper, we present the VHED (VIST Hum... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['visual-storytelling'] | ['natural-language-processing'] | [ 1.76391512e-01 2.30899110e-01 -1.10398903e-01 -1.45583436e-01
-1.06526196e+00 -8.05005491e-01 1.26916683e+00 2.92997807e-01
-1.05757713e-01 9.59010303e-01 8.44764292e-01 -2.13313639e-01
-1.31541848e-01 -7.54342973e-01 -2.57644534e-01 -1.66701078e-01
1.87220141e-01 7.29143620e-01 1.15957044e-01 -7.12893188... | [11.74596118927002, 8.869836807250977] |
a3f63fbe-cbcd-4bd9-96ae-eb8e5a7f5058 | morphological-analysis-and-disambiguation-for | null | null | https://aclanthology.org/N13-1044 | https://aclanthology.org/N13-1044.pdf | Morphological Analysis and Disambiguation for Dialectal Arabic | null | ['Esk', 'Ryan Roth', 'Owen Rambow', 'Nadi Tomeh', 'Ramy er', 'Nizar Habash'] | 2013-06-01 | null | null | null | naacl-2013-6 | ['morphological-tagging'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.487101078033447, 3.5685601234436035] |
6a01be34-4399-49f4-8b1a-30650dba3ff1 | hybridfusion-lidar-and-vision-cross-source | 2304.04508 | null | https://arxiv.org/abs/2304.04508v1 | https://arxiv.org/pdf/2304.04508v1.pdf | HybridFusion: LiDAR and Vision Cross-Source Point Cloud Fusion | Recently, cross-source point cloud registration from different sensors has become a significant research focus. However, traditional methods confront challenges due to the varying density and structure of cross-source point clouds. In order to solve these problems, we propose a cross-source point cloud fusion algorithm... | ['Ke Li', 'Xuefeng Cao', 'Kun Li', 'Yifei Dong', 'Lin Chen', 'Shuhui Bu', 'Yu Wang'] | 2023-04-10 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [-3.96023579e-02 -6.24072611e-01 1.09678231e-01 -1.49087682e-01
-1.13400578e+00 -4.87316251e-01 4.78754371e-01 5.50964355e-01
-2.02332407e-01 2.33521670e-01 -3.48291725e-01 5.06752551e-01
-2.50790566e-01 -9.60040987e-01 -6.45902216e-01 -6.56587899e-01
1.56285882e-01 6.32560372e-01 6.01173639e-01 -3.13845217... | [7.72562837600708, -2.8780770301818848] |
63aa22aa-3744-4aa7-b97f-f96bc9565da8 | unsupervised-episode-generation-for-graph | 2306.15217 | null | https://arxiv.org/abs/2306.15217v1 | https://arxiv.org/pdf/2306.15217v1.pdf | Unsupervised Episode Generation for Graph Meta-learning | In this paper, we investigate Unsupervised Episode Generation methods to solve Few-Shot Node-Classification (FSNC) problem via Meta-learning without labels. Dominant meta-learning methodologies for FSNC were developed under the existence of abundant labeled nodes for training, which however may not be possible to obtai... | ['Chanyoung Park', 'Sungwon Kim', 'Sangwoo Seo', 'Jihyeong Jung'] | 2023-06-27 | null | null | null | null | ['self-supervised-learning', 'node-classification', 'meta-learning'] | ['computer-vision', 'graphs', 'methodology'] | [ 4.50832367e-01 4.14909899e-01 -6.80863142e-01 -4.75463942e-02
-4.75202054e-01 -3.86533320e-01 6.43004358e-01 3.69379640e-01
-1.41103104e-01 8.63789141e-01 6.11422583e-02 -4.02221411e-01
-2.16284662e-01 -1.25987029e+00 -3.94235134e-01 -8.05986583e-01
-1.66069083e-02 2.20176488e-01 2.69140929e-01 -2.79467344... | [7.436803817749023, 6.161898612976074] |
8b499b3c-f744-4298-94f4-60548973cdc6 | meshtalk-3d-face-animation-from-speech-using | 2104.08223 | null | https://arxiv.org/abs/2104.08223v2 | https://arxiv.org/pdf/2104.08223v2.pdf | MeshTalk: 3D Face Animation from Speech using Cross-Modality Disentanglement | This paper presents a generic method for generating full facial 3D animation from speech. Existing approaches to audio-driven facial animation exhibit uncanny or static upper face animation, fail to produce accurate and plausible co-articulation or rely on person-specific models that limit their scalability. To improve... | ['Yaser Sheikh', 'Fernando de la Torre', 'Yandong Wen', 'Michael Zollhoefer', 'Alexander Richard'] | 2021-04-16 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Richard_MeshTalk_3D_Face_Animation_From_Speech_Using_Cross-Modality_Disentanglement_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Richard_MeshTalk_3D_Face_Animation_From_Speech_Using_Cross-Modality_Disentanglement_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-face-animation'] | ['computer-vision'] | [ 2.38498840e-02 4.09912497e-01 -1.17433503e-01 -1.58408448e-01
-1.13834631e+00 -3.79071087e-01 6.56964481e-01 -8.10975790e-01
3.42451990e-01 4.88506615e-01 7.22957194e-01 6.19634055e-02
2.50188559e-01 -1.25056431e-01 -4.46304381e-01 -5.67751050e-01
-9.42601115e-02 1.43646738e-02 -1.28196150e-01 -2.41813183... | [13.228921890258789, -0.42888572812080383] |
0c37f821-ccc9-47d0-a4da-b2760298675a | the-case-for-hierarchical-deep-learning | 2304.11763 | null | https://arxiv.org/abs/2304.11763v1 | https://arxiv.org/pdf/2304.11763v1.pdf | The Case for Hierarchical Deep Learning Inference at the Network Edge | Resource-constrained Edge Devices (EDs), e.g., IoT sensors and microcontroller units, are expected to make intelligent decisions using Deep Learning (DL) inference at the edge of the network. Toward this end, there is a significant research effort in developing tinyML models - Deep Learning (DL) models with reduced com... | ['Jaya Prakash Champati', 'James Gross', 'Vishnu Narayanan Moothedath', 'Adarsh Prasad Behera', 'Andrea Fresa', 'Ghina Al-Atat'] | 2023-04-23 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [-3.62789124e-01 2.54493058e-01 -3.34126145e-01 -1.21097803e-01
-1.87294334e-01 -3.89187723e-01 -1.25235081e-01 -2.70302836e-02
9.64697972e-02 7.30353057e-01 -4.81923372e-01 -7.12850988e-01
-2.11943254e-01 -1.15326726e+00 -8.64145935e-01 -6.36382282e-01
1.13652445e-01 8.74513209e-01 2.46658802e-01 5.87076545... | [8.001446723937988, 2.742945432662964] |
0295b0c8-0fa1-4794-872d-ca0b64c4ef0f | evaluation-of-peppermint-leaf-flavonoids-as | 2102.12651 | null | https://arxiv.org/abs/2102.12651v2 | https://arxiv.org/pdf/2102.12651v2.pdf | Evaluation of Peppermint Leaf Flavonoids as SARS-CoV-2 Spike Receptor-Binding Domain Attachment Inhibitors to the Human ACE2 Receptor: A Molecular Docking Study | Virtual screening is a computational technique widely used for identifying small molecules which are most likely to bind to a protein target. Here, we performed a molecular docking study to propose potential candidates to prevent the RBD/ACE2 attachment. These candidates are sixteen different flavonoids present in the ... | ['L. A. Ribeiro Júnior', 'W. F. Giozza', 'G. D. Amvame Nze', 'R. T. de Sousa Junior', 'M. L. Pereira Júnior'] | 2021-02-25 | null | null | null | null | ['molecular-docking'] | ['medical'] | [-1.09500319e-01 9.66334864e-02 -3.24359745e-01 2.88675539e-02
-9.71918702e-02 -4.64086443e-01 4.26197611e-02 5.60990989e-01
-3.11986089e-01 1.33917069e+00 -1.65940627e-01 -4.01660293e-01
2.50012994e-01 -6.21971726e-01 -5.85727632e-01 -9.13222373e-01
-4.42312360e-01 1.59443587e-01 3.51493686e-01 -5.08042812... | [4.628389835357666, 5.076449871063232] |
70f7c16a-7520-42f6-9708-1c2aca513936 | hypernetworks | 1609.09106 | null | http://arxiv.org/abs/1609.09106v4 | http://arxiv.org/pdf/1609.09106v4.pdf | HyperNetworks | This work explores hypernetworks: an approach of using a one network, also
known as a hypernetwork, to generate the weights for another network.
Hypernetworks provide an abstraction that is similar to what is found in
nature: the relationship between a genotype - the hypernetwork - and a
phenotype - the main network. T... | ['David Ha', 'Andrew Dai', 'Quoc V. Le'] | 2016-09-27 | null | null | null | null | ['handwriting-generation'] | ['computer-vision'] | [ 6.96280599e-01 6.97200358e-01 -1.34189025e-01 -1.85949445e-01
-1.66991532e-01 -4.57286388e-01 6.72945321e-01 -5.53905189e-01
-4.20235008e-01 5.92463195e-01 1.41301349e-01 -5.15482783e-01
8.93548355e-02 -8.99799168e-01 -1.14645481e+00 -8.14869463e-01
-6.93565235e-02 7.48529315e-01 4.86565791e-02 -6.53029263... | [10.739361763000488, 7.197402000427246] |
108d40d7-7195-4e0d-b021-a6fd99c08c6b | image-retargetability | 1802.04392 | null | https://arxiv.org/abs/1802.04392v2 | https://arxiv.org/pdf/1802.04392v2.pdf | Image Retargetability | Real-world applications could benefit from the ability to automatically retarget an image to different aspect ratios and resolutions, while preserving its visually and semantically important content. However, not all images can be equally well processed that way. In this work, we introduce the notion of image retargeta... | ['Wei-Ming Dong', 'Tong-Yee Lee', 'Fan Tang', 'Yiping Meng', 'Fuzhang Wu', 'Xinrui Li', 'Chongyang Ma'] | 2018-02-12 | null | null | null | null | ['image-retargeting'] | ['computer-vision'] | [ 4.42005068e-01 1.09603845e-01 -1.47169024e-01 -5.36145091e-01
-8.72816622e-01 -7.78939188e-01 4.40732151e-01 2.67057955e-01
-4.81409699e-01 5.26368380e-01 2.26112798e-01 -5.04462458e-02
1.55845523e-01 -6.80286407e-01 -8.18083346e-01 -3.49637240e-01
5.52657127e-01 6.29659882e-03 1.87369436e-01 -7.38772377... | [11.284735679626465, -1.022826910018921] |
8422db8d-8b10-4ccd-8785-f8440379710e | tuning-computer-vision-models-with-task | 2302.08242 | null | https://arxiv.org/abs/2302.08242v1 | https://arxiv.org/pdf/2302.08242v1.pdf | Tuning computer vision models with task rewards | Misalignment between model predictions and intended usage can be detrimental for the deployment of computer vision models. The issue is exacerbated when the task involves complex structured outputs, as it becomes harder to design procedures which address this misalignment. In natural language processing, this is often ... | ['Xiaohua Zhai', 'Lucas Beyer', 'Yuge Shi', 'Alexander Kolesnikov', 'André Susano Pinto'] | 2023-02-16 | null | null | null | null | ['panoptic-segmentation', 'colorization'] | ['computer-vision', 'computer-vision'] | [ 5.90419531e-01 9.93557274e-02 -1.27768606e-01 -6.32535577e-01
-6.63337648e-01 -9.04036701e-01 8.36827993e-01 1.26216620e-01
-6.50011897e-01 3.78808320e-01 -6.84145391e-02 -7.39672363e-01
1.61479920e-01 -8.71987417e-02 -6.31362975e-01 -2.34255388e-01
4.23468351e-01 3.28099132e-01 -3.76489535e-02 -3.76888663... | [10.548222541809082, 1.8114323616027832] |
650fdbc2-9f56-4430-8dd0-88432c14d070 | community-detection-attack-against | 2306.08929 | null | https://arxiv.org/abs/2306.08929v1 | https://arxiv.org/pdf/2306.08929v1.pdf | Community Detection Attack against Collaborative Learning-based Recommender Systems | Collaborative-learning based recommender systems emerged following the success of collaborative learning techniques such as Federated Learning (FL) and Gossip Learning (GL). In these systems, users participate in the training of a recommender system while keeping their history of consumed items on their devices. While ... | ['Anthony Simonet-Boulogne', 'Mohamed Maouche', 'Sonia Ben Mokhtar', 'Yacine Belal'] | 2023-06-15 | null | null | null | null | ['community-detection'] | ['graphs'] | [-2.39417568e-01 1.56227559e-01 1.04973681e-01 -2.69853085e-01
-4.61547494e-01 -1.35179210e+00 7.45989323e-01 3.47012132e-01
-2.97498733e-01 3.85437876e-01 1.77799210e-01 -6.35859311e-01
-5.66469431e-01 -1.00472260e+00 -5.33711016e-01 -8.62504363e-01
-5.31760335e-01 1.98456362e-01 1.34440929e-01 -1.43579781... | [5.857906341552734, 6.715753555297852] |
3d799326-8339-4185-9ff7-b887b78737d6 | bounce-a-reliable-bayesian-optimization | 2307.00618 | null | https://arxiv.org/abs/2307.00618v1 | https://arxiv.org/pdf/2307.00618v1.pdf | Bounce: a Reliable Bayesian Optimization Algorithm for Combinatorial and Mixed Spaces | Impactful applications such as materials discovery, hardware design, neural architecture search, or portfolio optimization require optimizing high-dimensional black-box functions with mixed and combinatorial input spaces. While Bayesian optimization has recently made significant progress in solving such problems, an in... | ['Matthias Poloczek', 'Luigi Nardi', 'Leonard Papenmeier'] | 2023-07-02 | null | null | null | null | ['architecture-search', 'bayesian-optimization', 'portfolio-optimization'] | ['methodology', 'methodology', 'time-series'] | [-1.62627742e-01 -2.65843987e-01 -4.52395409e-01 -2.95948356e-01
-9.24723446e-01 -4.66361374e-01 5.28747141e-01 -4.39831503e-02
-2.44811818e-01 9.57954526e-01 5.24062850e-02 -3.91803533e-01
-6.46681309e-01 -5.80097377e-01 -5.16639531e-01 -1.00491560e+00
-1.67045355e-01 9.47874010e-01 -2.56431778e-03 7.05948025... | [6.565055847167969, 4.009541988372803] |
515bfb2a-d765-4b5f-91eb-58ed5e66fc6a | binary-patterns-encoded-convolutional-neural | 1706.01171 | null | http://arxiv.org/abs/1706.01171v2 | http://arxiv.org/pdf/1706.01171v2.pdf | Binary Patterns Encoded Convolutional Neural Networks for Texture Recognition and Remote Sensing Scene Classification | Designing discriminative powerful texture features robust to realistic
imaging conditions is a challenging computer vision problem with many
applications, including material recognition and analysis of satellite or
aerial imagery. In the past, most texture description approaches were based on
dense orderless statistica... | ['Jorma Laaksonen', 'Joost Van de Weijer', 'Fahad Shahbaz Khan', 'Rao Muhammad Anwer', 'Matthieu Molinier'] | 2017-06-05 | null | null | null | null | ['material-recognition'] | ['computer-vision'] | [ 6.01488292e-01 -5.32222748e-01 7.01335743e-02 -7.21449554e-01
-4.98003453e-01 -3.71952921e-01 5.27202785e-01 -3.55443835e-01
-3.13011736e-01 4.01614249e-01 -4.25880134e-01 -5.28634369e-01
-4.53406423e-01 -1.22261298e+00 -6.64055824e-01 -1.01114392e+00
-1.69593230e-01 2.30192661e-01 1.61666796e-01 -2.99585551... | [10.181258201599121, -0.2533702254295349] |
60bed26e-b2e3-42e4-9035-f13f8b0bd8be | unifying-consciousness-and-time-to-enhance | 2301.08742 | null | https://arxiv.org/abs/2301.08742v1 | https://arxiv.org/pdf/2301.08742v1.pdf | Unifying Consciousness and Time to Enhance Artificial Intelligence | Consciousness is a sequential process of awareness which can focus on one piece of information at a time. This process of awareness experiences causation which underpins the notion of time while it interplays with matter and energy, forming reality. The study of Consciousness, time and reality is complex and evolving f... | ['Mahendra Samarawickrama'] | 2023-01-10 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [ 8.42663124e-02 -1.66887626e-01 2.82207310e-01 -3.34266722e-02
8.46510589e-01 -7.55747795e-01 1.06211519e+00 1.05526391e-03
-3.69490653e-01 9.46354330e-01 7.94938266e-01 -4.66637593e-03
-1.72915339e-01 -7.29473591e-01 -4.37393636e-01 -8.25869441e-01
-3.43501680e-02 -1.03515811e-01 1.40533879e-01 -5.33353031... | [5.679319381713867, 4.205467224121094] |
54372206-21ec-4853-bb3b-b3e57cf1ce76 | attention-based-feature-decomposition | 2111.14340 | null | https://arxiv.org/abs/2111.14340v1 | https://arxiv.org/pdf/2111.14340v1.pdf | Attention-based Feature Decomposition-Reconstruction Network for Scene Text Detection | Recently, scene text detection has been a challenging task. Texts with arbitrary shape or large aspect ratio are usually hard to detect. Previous segmentation-based methods can describe curve text more accurately but suffer from over segmentation and text adhesion. In this paper, we propose attention-based feature deco... | ['Lijiang Chen', 'Shuchang Lyu', 'YuFei Wang', 'Qi Zhao'] | 2021-11-29 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 4.18902725e-01 -5.63764393e-01 5.30594178e-02 -1.75390065e-01
-8.76304805e-01 -2.10393891e-01 4.52277452e-01 3.49891514e-01
-3.47716928e-01 1.05266251e-01 4.65863585e-01 -2.15029288e-02
7.76830837e-02 -8.32894504e-01 -3.40644568e-01 -7.75527418e-01
8.01690876e-01 4.70399886e-01 5.63962042e-01 -2.73772031... | [12.084006309509277, 2.3113744258880615] |
5ba528ae-590d-4303-8768-0864c3607222 | sodeep-a-sorting-deep-net-to-learn-ranking | 1904.04272 | null | http://arxiv.org/abs/1904.04272v1 | http://arxiv.org/pdf/1904.04272v1.pdf | SoDeep: a Sorting Deep net to learn ranking loss surrogates | Several tasks in machine learning are evaluated using non-differentiable
metrics such as mean average precision or Spearman correlation. However, their
non-differentiability prevents from using them as objective functions in a
learning framework. Surrogate and relaxation methods exist but tend to be
specific to a given... | ['Patrick Pérez', 'Matthieu Cord', 'Martin Engilberge', 'Louis Chevallier'] | 2019-04-08 | sodeep-a-sorting-deep-net-to-learn-ranking-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Engilberge_SoDeep_A_Sorting_Deep_Net_to_Learn_Ranking_Loss_Surrogates_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Engilberge_SoDeep_A_Sorting_Deep_Net_to_Learn_Ranking_Loss_Surrogates_CVPR_2019_paper.pdf | cvpr-2019-6 | ['multi-label-image-classification'] | ['computer-vision'] | [ 6.21063635e-02 -6.25472590e-02 -7.42698163e-02 -8.33024383e-01
-1.23978949e+00 -5.46068966e-01 7.70374060e-01 3.43499392e-01
-6.32224381e-01 7.29815722e-01 -1.88159436e-01 -2.23715678e-02
-5.50574839e-01 -6.93469763e-01 -7.91595757e-01 -5.50079644e-01
-3.88550609e-02 8.86896074e-01 1.14030220e-01 -2.30953157... | [9.458087921142578, 3.1212213039398193] |
bfb80f1c-9ab0-4986-952d-6d24dedb418c | depth-estimation-and-image-restoration-by | 2302.10730 | null | https://arxiv.org/abs/2302.10730v1 | https://arxiv.org/pdf/2302.10730v1.pdf | Depth Estimation and Image Restoration by Deep Learning from Defocused Images | Monocular depth estimation and image deblurring are two fundamental tasks in computer vision, given their crucial role in understanding 3D scenes. Performing any of them by relying on a single image is an ill-posed problem. The recent advances in the field of deep convolutional neural networks (DNNs) have revolutionize... | ['Daniela Coltuc', 'Víctor M. Brea', 'Manuel Mucientes', 'Lorenzo Vaquero', 'Saqib Nazir'] | 2023-02-21 | null | null | null | null | ['deblurring'] | ['computer-vision'] | [ 5.29411197e-01 -3.51713151e-01 1.76718295e-01 -3.53340358e-01
-1.71174765e-01 -3.94834697e-01 6.51815414e-01 -5.57972908e-01
-4.88663375e-01 7.92437315e-01 3.72176170e-01 -2.22088784e-01
7.44965598e-02 -6.13127887e-01 -6.68834090e-01 -1.20636797e+00
2.01271310e-01 6.26967102e-02 2.73858339e-01 1.65071487... | [11.20882511138916, -2.8027353286743164] |
6b6bf285-e7a8-420b-bb76-6796cac59cef | set-transformer-a-framework-for-attention | 1810.00825 | null | https://arxiv.org/abs/1810.00825v3 | https://arxiv.org/pdf/1810.00825v3.pdf | Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks | Many machine learning tasks such as multiple instance learning, 3D shape recognition, and few-shot image classification are defined on sets of instances. Since solutions to such problems do not depend on the order of elements of the set, models used to address them should be permutation invariant. We present an attenti... | ['Yee Whye Teh', 'Jungtaek Kim', 'Seungjin Choi', 'Juho Lee', 'Adam R. Kosiorek', 'Yoonho Lee'] | 2018-10-01 | set-transformer | https://openreview.net/forum?id=Hkgnii09Ym | https://openreview.net/pdf?id=Hkgnii09Ym | null | ['3d-shape-recognition'] | ['computer-vision'] | [ 5.5756587e-01 1.3671972e-01 -4.0636059e-02 -2.6778072e-01
-7.6739508e-01 -3.8243133e-01 7.2096235e-01 1.7127383e-01
-2.2776608e-01 3.1058368e-01 7.0345461e-02 -8.6950911e-03
-4.2772362e-01 -7.8804469e-01 -1.0560277e+00 -6.6854501e-01
2.3410225e-02 9.7736478e-01 8.1925116e-02 4.4264525e-02
5.4135072e-01... | [9.247145652770996, 2.5324504375457764] |
b0d8eaa6-141f-4d9d-905d-42304a8a3893 | are-there-intelligent-turing-machines | 1503.03787 | null | http://arxiv.org/abs/1503.03787v1 | http://arxiv.org/pdf/1503.03787v1.pdf | Are there intelligent Turing machines? | This paper introduces a new computing model based on the cooperation among
Turing machines called orchestrated machines. Like universal Turing machines,
orchestrated machines are also designed to simulate Turing machines but they
can also modify the original operation of the included Turing machines to
create a new lay... | ['Norbert Bátfai'] | 2015-03-12 | null | null | null | null | ['emotional-intelligence'] | ['natural-language-processing'] | [-3.47269028e-01 7.69731104e-01 4.05646503e-01 -3.39234382e-01
5.24085760e-01 -8.74301255e-01 9.93009210e-01 -1.03242666e-01
-1.26046523e-01 5.58866084e-01 -2.14464933e-01 -1.48596969e-02
-6.28851429e-02 -1.13787234e+00 -3.06304842e-01 -7.56266654e-01
-6.08944237e-01 5.93675077e-01 -1.04203798e-01 -7.61154652... | [5.569382190704346, 4.1619696617126465] |
1a1a0450-4ac1-4755-bd90-4b6dc5e521c9 | benchmarking-probabilistic-deep-learning | 2302.01427 | null | https://arxiv.org/abs/2302.01427v1 | https://arxiv.org/pdf/2302.01427v1.pdf | Benchmarking Probabilistic Deep Learning Methods for License Plate Recognition | Learning-based algorithms for automated license plate recognition implicitly assume that the training and test data are well aligned. However, this may not be the case under extreme environmental conditions, or in forensic applications where the system cannot be trained for a specific acquisition device. Predictions on... | ['Christian Riess', 'Anatol Maier', 'Benedikt Lorch', 'Franziska Schirrmacher'] | 2023-02-02 | null | null | null | null | ['license-plate-recognition', 'probabilistic-deep-learning'] | ['computer-vision', 'computer-vision'] | [ 2.67975450e-01 -1.93548024e-01 3.20938021e-01 -4.59558427e-01
-1.24967504e+00 -5.91422856e-01 4.35345441e-01 -1.94816753e-01
-1.31746873e-01 9.89692450e-01 -4.82878268e-01 -5.01500890e-02
-1.86976641e-01 -6.05973184e-01 -8.20582151e-01 -9.18104470e-01
4.21733409e-01 7.97849417e-01 5.19170940e-01 3.70705158... | [9.829955101013184, -4.860540866851807] |
aaf464f9-a4a9-4083-984d-8d18e5c390fe | cross-lingual-and-cross-domain-discourse-1 | 1704.04100 | null | http://arxiv.org/abs/1704.04100v2 | http://arxiv.org/pdf/1704.04100v2.pdf | Cross-lingual and cross-domain discourse segmentation of entire documents | Discourse segmentation is a crucial step in building end-to-end discourse
parsers. However, discourse segmenters only exist for a few languages and
domains. Typically they only detect intra-sentential segment boundaries,
assuming gold standard sentence and token segmentation, and relying on
high-quality syntactic parse... | ['Anders Søgaard', 'Ophélie Lacroix', 'Chloé Braud'] | 2017-04-13 | null | null | null | null | ['discourse-segmentation'] | ['natural-language-processing'] | [ 2.14459822e-01 7.27021515e-01 -5.94824731e-01 -4.07072991e-01
-1.31693947e+00 -1.10661972e+00 7.32856631e-01 5.29521406e-01
-6.46619201e-01 1.09980285e+00 5.11713088e-01 -5.92992067e-01
4.31870520e-01 -6.17548764e-01 -5.44103086e-01 -8.38294625e-02
1.68532170e-02 7.90223539e-01 7.04095960e-01 -3.31566274... | [10.795724868774414, 9.544035911560059] |
45afb061-7733-4b57-a27a-8a0397fc010a | editable-indoor-lighting-estimation | 2211.03928 | null | https://arxiv.org/abs/2211.03928v2 | https://arxiv.org/pdf/2211.03928v2.pdf | Editable Indoor Lighting Estimation | We present a method for estimating lighting from a single perspective image of an indoor scene. Previous methods for predicting indoor illumination usually focus on either simple, parametric lighting that lack realism, or on richer representations that are difficult or even impossible to understand or modify after pred... | ['Jean-François Lalonde', 'Mathieu Garon', 'Henrique Weber'] | 2022-11-08 | null | null | null | null | ['lighting-estimation'] | ['computer-vision'] | [ 4.95051742e-01 -8.68292451e-02 7.20964015e-01 -6.85534358e-01
-2.35814124e-01 -8.23385537e-01 4.97128665e-01 1.11157060e-01
1.89666420e-01 7.84950316e-01 1.15645073e-01 -2.46810764e-01
2.41872609e-01 -7.29554713e-01 -7.07798123e-01 -4.55601394e-01
1.58963516e-01 4.40332770e-01 3.42587858e-01 -1.48076965... | [9.719807624816895, -3.0487024784088135] |
4c62c85e-4f4a-43c7-ac3b-a85cfc1ab12f | programming-by-example-and-text-to-code | 2211.11554 | null | https://arxiv.org/abs/2211.11554v3 | https://arxiv.org/pdf/2211.11554v3.pdf | Programming by Example and Text-to-Code Translation for Conversational Code Generation | Dialogue systems is an increasingly popular task of natural language processing. However, the dialogue paths tend to be deterministic, restricted to the system rails, regardless of the given request or input text. Recent advances in program synthesis have led to systems which can synthesize programs from very general s... | ['Marc Franco-Salvador', 'Yauhen Klimovich', 'William Gerard', 'Eli Whitehouse'] | 2022-11-21 | null | null | null | null | ['code-translation', 'program-synthesis'] | ['computer-code', 'computer-code'] | [ 2.00407580e-01 6.06282115e-01 -1.09355398e-01 -5.24433374e-01
-8.02698195e-01 -7.91878939e-01 7.46109962e-01 2.63597757e-01
-1.94410346e-02 4.95100796e-01 -3.04506719e-02 -9.02554870e-01
1.48437247e-01 -1.05062544e+00 -3.26898277e-01 -8.33539814e-02
1.15961730e-01 6.49235964e-01 6.27150714e-01 -7.36388445... | [8.522127151489258, 7.355160713195801] |
4caac0f2-f860-4985-9317-a7bdf8b10132 | skin-lesion-segmentation-using-segnet-with | null | null | https://raw.githubusercontent.com/hashbanger/Skin_Lesion_Segmentation/master/abstract.txt | https://drive.google.com/file/d/1pgAXmKgY2NerSMzvaS9M8PKnP0cTrbQM/view?usp=sharing | Skin Lesion Segmentation using SegNet with Binary Cross-Entropy | In this paper a simple and computationally efficient approach as per the complexity has been presented for Automatic Skin Lesion Segmentation using a Deep Learning architecture called SegNet including some additional specifications for the improvisation of the results. The secondary objective is to keep the pre/post -p... | ['Prashant Brahmbhatt', 'Siddhi Nath Rajan'] | 2019-11-15 | null | null | null | international-conference-on-artificial | ['skin-lesion-segmentation', 'skin-cancer-segmentation'] | ['medical', 'medical'] | [ 5.04354239e-01 5.18909931e-01 3.61650348e-01 -3.75782251e-01
-2.88585693e-01 -3.92650098e-01 4.27800596e-01 5.09256721e-01
-8.21423233e-01 5.62104046e-01 -5.39533257e-01 -2.76043117e-01
-5.16618729e-01 -6.59074306e-01 -1.91060811e-01 -7.68109560e-01
-1.50891781e-01 5.20729482e-01 2.43654013e-01 1.29694551... | [15.61436653137207, -3.0147128105163574] |
4403d1b0-eba3-4e44-ab5a-059836097cad | gram-regularization-for-multi-view-3d-shape | 2011.07733 | null | https://arxiv.org/abs/2011.07733v1 | https://arxiv.org/pdf/2011.07733v1.pdf | Gram Regularization for Multi-view 3D Shape Retrieval | How to obtain the desirable representation of a 3D shape is a key challenge in 3D shape retrieval task. Most existing 3D shape retrieval methods focus on capturing shape representation with different neural network architectures, while the learning ability of each layer in the network is neglected. A common and tough i... | ['Zhaoqun Li'] | 2020-11-16 | null | null | null | null | ['multi-view-3d-shape-retrieval', '3d-object-retrieval', 'l2-regularization'] | ['computer-vision', 'computer-vision', 'methodology'] | [-3.65221471e-01 -3.12920064e-01 -2.40883827e-01 -3.22014391e-01
-4.68737543e-01 -5.31822681e-01 3.92968476e-01 -8.67544860e-02
-1.50604591e-01 8.96538198e-02 3.15463580e-02 -3.96211259e-02
-3.54490221e-01 -7.35383451e-01 -5.55217505e-01 -8.80736291e-01
1.97664991e-01 2.09444568e-01 2.68795639e-01 -2.95615457... | [8.15885066986084, -3.8784873485565186] |
aa04442b-0b29-48d1-9d32-606576768eb7 | learning-structured-representations-of-visual | 2207.04200 | null | https://arxiv.org/abs/2207.04200v1 | https://arxiv.org/pdf/2207.04200v1.pdf | Learning Structured Representations of Visual Scenes | As the intermediate-level representations bridging the two levels, structured representations of visual scenes, such as visual relationships between pairwise objects, have been shown to not only benefit compositional models in learning to reason along with the structures but provide higher interpretability for model de... | ['Meng-Jiun Chiou'] | 2022-07-09 | null | null | null | null | ['visual-relationship-detection', 'scene-graph-generation', 'unbiased-scene-graph-generation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 6.27102673e-01 2.72493511e-01 -4.80596155e-01 -9.78911936e-01
-2.20255628e-01 -5.62870979e-01 6.81828320e-01 1.76929966e-01
1.98620602e-01 4.44153488e-01 7.26898193e-01 -1.79186955e-01
-4.18134719e-01 -4.70315546e-01 -8.22068691e-01 -5.67539752e-01
-1.26060307e-01 2.61031985e-01 -2.05756739e-01 1.88220758... | [10.082708358764648, 1.1988552808761597] |
72f70ee0-49bc-41a8-8c30-f3bb831d9f36 | salient-skin-lesion-segmentation-via-dilated | 2205.10272 | null | https://arxiv.org/abs/2205.10272v2 | https://arxiv.org/pdf/2205.10272v2.pdf | Salient Skin Lesion Segmentation via Dilated Scale-Wise Feature Fusion Network | Skin lesion detection in dermoscopic images is essential in the accurate and early diagnosis of skin cancer by a computerized apparatus. Current skin lesion segmentation approaches show poor performance in challenging circumstances such as indistinct lesion boundaries, low contrast between the lesion and the surroundin... | ['Huiyu Zhou', 'Eric Granger', 'Masoumeh Zareapoor', 'Pourya Shamsolmoali'] | 2022-05-20 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 5.47525704e-01 -3.46755773e-01 -3.94922793e-01 3.18477824e-02
-5.11310756e-01 -3.40081483e-01 2.58683920e-01 6.46090880e-02
-4.11511749e-01 3.98805559e-01 -2.58204728e-01 -1.99032515e-01
-5.30306138e-02 -5.70228398e-01 -8.04339349e-03 -8.86185944e-01
2.12203905e-01 -2.31480494e-01 6.50430024e-01 -2.85117831... | [15.623198509216309, -2.9975175857543945] |
0816a0ea-f6a1-4d1e-8de0-9e2d30579b68 | does-syntax-help-discourse-segmentation-not | null | null | https://aclanthology.org/D17-1258 | https://aclanthology.org/D17-1258.pdf | Does syntax help discourse segmentation? Not so much | Discourse segmentation is the first step in building discourse parsers. Most work on discourse segmentation does not scale to real-world discourse parsing across languages, for two reasons: (i) models rely on constituent trees, and (ii) experiments have relied on gold standard identification of sentence and token bound... | ['Anders S{\\o}gaard', "Oph{\\'e}lie Lacroix", "Chlo{\\'e} Braud"] | 2017-09-01 | null | null | null | emnlp-2017-9 | ['discourse-segmentation'] | ['natural-language-processing'] | [ 2.78884500e-01 5.15440404e-01 -2.52713352e-01 -2.87063241e-01
-1.01359272e+00 -1.10200655e+00 7.80625820e-01 5.69736302e-01
-5.58265686e-01 9.40498829e-01 8.28672707e-01 -8.92691553e-01
3.04910004e-01 -5.88461578e-01 -4.43619579e-01 -1.89534366e-01
9.84010920e-02 5.97763062e-01 9.08919692e-01 -4.24534142... | [10.749577522277832, 9.499356269836426] |
03dea23c-f90a-4e52-bb2c-f8d05282e79d | a-comparison-of-modeling-units-in-sequence-to | 1805.06239 | null | http://arxiv.org/abs/1805.06239v2 | http://arxiv.org/pdf/1805.06239v2.pdf | A Comparison of Modeling Units in Sequence-to-Sequence Speech Recognition with the Transformer on Mandarin Chinese | The choice of modeling units is critical to automatic speech recognition
(ASR) tasks. Conventional ASR systems typically choose context-dependent states
(CD-states) or context-dependent phonemes (CD-phonemes) as their modeling
units. However, it has been challenged by sequence-to-sequence attention-based
models, which ... | ['Bo Xu', 'Shuang Xu', 'Shiyu Zhou', 'Linhao Dong'] | 2018-05-16 | null | null | null | null | ['sequence-to-sequence-speech-recognition'] | ['speech'] | [ 4.25042301e-01 -1.58676431e-01 -6.56114705e-03 -2.22859532e-01
-1.17149007e+00 -3.29508752e-01 4.90295351e-01 -1.65146459e-02
-6.73875213e-01 5.35918057e-01 2.79156297e-01 -8.18926930e-01
6.09842718e-01 -3.93639684e-01 -5.23738265e-01 -5.21251082e-01
1.59288332e-01 3.89840633e-01 3.05419834e-03 -4.07379627... | [14.423758506774902, 6.861808776855469] |
ee77c56f-e13f-405a-87ff-4a318bfdba73 | vision-infused-deep-audio-inpainting-1 | 1910.10997 | null | https://arxiv.org/abs/1910.10997v1 | https://arxiv.org/pdf/1910.10997v1.pdf | Vision-Infused Deep Audio Inpainting | Multi-modality perception is essential to develop interactive intelligence. In this work, we consider a new task of visual information-infused audio inpainting, \ie synthesizing missing audio segments that correspond to their accompanying videos. We identify two key aspects for a successful inpainter: (1) It is desirab... | ['Ping Luo', 'Xudong Xu', 'Hang Zhou', 'Ziwei Liu', 'Xiaogang Wang'] | 2019-10-24 | vision-infused-deep-audio-inpainting | http://openaccess.thecvf.com/content_ICCV_2019/html/Zhou_Vision-Infused_Deep_Audio_Inpainting_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Zhou_Vision-Infused_Deep_Audio_Inpainting_ICCV_2019_paper.pdf | iccv-2019-10 | ['audio-inpainting'] | ['audio'] | [ 4.05490726e-01 -3.04137290e-01 3.11551690e-02 -4.69776578e-02
-1.16922069e+00 -6.13211572e-01 2.46374890e-01 -3.90007019e-01
1.74469650e-01 5.50368309e-01 4.86060083e-01 2.15817094e-01
4.56244089e-02 -2.98985809e-01 -1.03837335e+00 -4.57689643e-01
1.11205593e-01 -6.67649582e-02 -1.53060645e-01 -1.63932130... | [15.386474609375, 5.219402313232422] |
b1c290d5-fb8c-4228-a7f1-2e4421419170 | skipdecode-autoregressive-skip-decoding-with | 2307.02628 | null | https://arxiv.org/abs/2307.02628v1 | https://arxiv.org/pdf/2307.02628v1.pdf | SkipDecode: Autoregressive Skip Decoding with Batching and Caching for Efficient LLM Inference | Autoregressive large language models (LLMs) have made remarkable progress in various natural language generation tasks. However, they incur high computation cost and latency resulting from the autoregressive token-by-token generation. To address this issue, several approaches have been proposed to reduce computational ... | ['Subhabrata Mukherjee', 'Ahmed Awadallah', 'Bin Yu', 'Sahaj Agarwal', 'Allie Del Giorno', 'Luciano del Corro'] | 2023-07-05 | null | null | null | null | ['text-generation'] | ['natural-language-processing'] | [ 4.01445851e-02 -8.06934834e-02 -2.23859459e-01 -2.35376999e-01
-9.60400224e-01 -5.41856527e-01 6.67800486e-01 3.00109923e-01
-6.67849958e-01 7.84600616e-01 6.68916777e-02 -8.22859585e-01
2.73215681e-01 -9.69022989e-01 -6.82322919e-01 -4.39523607e-01
-2.56788637e-02 5.37215352e-01 3.61703485e-01 3.07082590... | [8.679705619812012, 3.5608575344085693] |
ad8578af-6dd4-461b-81ce-fdcc4c131026 | adaptive-sharpness-aware-pruning-for-robust | 2306.14306 | null | https://arxiv.org/abs/2306.14306v1 | https://arxiv.org/pdf/2306.14306v1.pdf | Adaptive Sharpness-Aware Pruning for Robust Sparse Networks | Robustness and compactness are two essential components of deep learning models that are deployed in the real world. The seemingly conflicting aims of (i) generalization across domains as in robustness, and (ii) specificity to one domain as in compression, are why the overall design goal of achieving robust compact mod... | ['Jose Alvarez', 'Pavlo Molchanov', 'Maying Shen', 'Hongxu Yin', 'Anna Bair'] | 2023-06-25 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [ 3.48408699e-01 8.06844831e-02 -2.20542312e-01 -2.85899282e-01
-4.60015923e-01 -3.14202279e-01 5.31267464e-01 1.05097666e-01
-6.18285477e-01 5.96181095e-01 2.09444299e-01 -7.59943724e-02
-5.56402922e-01 -4.96825665e-01 -6.92864537e-01 -5.24205804e-01
3.00483629e-02 1.29292667e-01 4.87127453e-01 -2.76942074... | [8.644311904907227, 3.306295156478882] |
79514af2-1a3d-4a8c-8e80-64720f40cb97 | cooperative-lane-changing-in-mixed-traffic | 2303.16948 | null | https://arxiv.org/abs/2303.16948v1 | https://arxiv.org/pdf/2303.16948v1.pdf | Cooperative Lane Changing in Mixed Traffic can be Robust to Human Driver Behavior | We derive time and energy-optimal control policies for a Connected Autonomous Vehicle (CAV) to complete lane change maneuvers in mixed traffic. The interaction between CAVs and Human-Driven Vehicles (HDVs) requires designing the best possible response of a CAV to actions by its neighboring HDVs. This interaction is for... | ['Christos G. Cassandras', 'Andres S. Chavez Armijos', 'Anni Li'] | 2023-03-29 | null | null | null | null | ['bilevel-optimization'] | ['methodology'] | [-2.23516330e-01 5.87104261e-01 -3.04872096e-01 1.57211408e-01
-2.17477247e-01 -7.10775912e-01 3.16539943e-01 -1.63789809e-01
-4.00266886e-01 9.61816549e-01 -4.47845608e-01 -6.30059481e-01
-3.88393342e-01 -7.75048733e-01 -8.39833617e-01 -1.04992402e+00
-3.38753581e-01 3.63440186e-01 5.76488376e-01 -4.03228611... | [5.537360191345215, 1.6408413648605347] |
316e4a3c-ad9f-4429-b7f6-9fb5373957eb | gps-reviving-the-art-of-message-passing-for | 2302.02947 | null | https://arxiv.org/abs/2302.02947v2 | https://arxiv.org/pdf/2302.02947v2.pdf | GPS++: Reviving the Art of Message Passing for Molecular Property Prediction | We present GPS++, a hybrid Message Passing Neural Network / Graph Transformer model for molecular property prediction. Our model integrates a well-tuned local message passing component and biased global attention with other key ideas from prior literature to achieve state-of-the-art results on large-scale molecular dat... | ['Dominique Beaini', 'Ladislav Rampášek', 'Shenyang Huang', 'Andrew Fitzgibbon', 'Deniz Beker', 'Hatem Helal', 'Adam Sanders', 'Sam Maddrell-Mander', 'Zhiyi Li', 'Kerstin Klaser', 'Josef Dean', 'Dominic Masters'] | 2023-02-06 | null | null | null | null | ['molecular-property-prediction'] | ['miscellaneous'] | [ 3.89879972e-01 2.23783515e-02 -5.90374589e-01 1.04727084e-02
-8.28934133e-01 -3.95416975e-01 4.92302656e-01 8.12432945e-01
-3.20077211e-01 9.55451608e-01 9.68487039e-02 -7.67437220e-01
-3.08806449e-01 -5.96026897e-01 -1.18713021e+00 -7.27973342e-01
-4.72106874e-01 5.35137892e-01 3.13164532e-01 -4.76482064... | [5.289738178253174, 5.784573078155518] |
4ba1f742-6391-4863-9a8c-e80c313f51c3 | intelligence-of-astronomical-optical | 2306.16834 | null | https://arxiv.org/abs/2306.16834v1 | https://arxiv.org/pdf/2306.16834v1.pdf | Intelligence of Astronomical Optical Telescope: Present Status and Future Perspectives | Artificial intelligence technology has been widely used in astronomy, and new artificial intelligence technologies and application scenarios are constantly emerging. There have been a large number of papers reviewing the application of artificial intelligence technology in astronomy. However, relevant articles seldom m... | ['Xiangqun Cui', 'Huaiqing Wang', 'Yong Zhang', 'Yonghui Hou', 'Xiushan Pang', 'Jingyi Cai', 'Kang Huang', 'Tianzhu Hu'] | 2023-06-29 | null | null | null | null | ['astronomy'] | ['miscellaneous'] | [-4.92348671e-01 -4.18616951e-01 2.71278005e-02 -1.29712494e-02
5.36532402e-01 -8.08456063e-01 3.49887282e-01 -8.41911554e-01
-3.75648797e-01 3.15619588e-01 -1.19359992e-01 -7.00730145e-01
-6.91289306e-01 -8.27702284e-01 -1.46740690e-01 -8.39638948e-01
2.97499210e-01 5.68144858e-01 1.31529734e-01 9.88447145... | [7.754026889801025, 3.10526180267334] |
03d7e78f-994a-4ac0-90bc-8618ad84f369 | sundown-model-driven-per-panel-solar-anomaly | 2005.12181 | null | https://arxiv.org/abs/2005.12181v1 | https://arxiv.org/pdf/2005.12181v1.pdf | SunDown: Model-driven Per-Panel Solar Anomaly Detection for Residential Arrays | There has been significant growth in both utility-scale and residential-scale solar installations in recent years, driven by rapid technology improvements and falling prices. Unlike utility-scale solar farms that are professionally managed and maintained, smaller residential-scale installations often lack sensing and i... | ['Prashant Shenoy', 'Menghong Feng', 'Noman Bashir', 'David Irwin', 'Beka Kosanovic'] | 2020-05-25 | null | null | null | null | ['anomaly-classification'] | ['computer-vision'] | [ 2.62380362e-01 -1.12600856e-01 2.69373238e-01 -5.16325086e-02
-6.47602558e-01 -8.19748640e-01 1.88303038e-01 3.18318635e-01
9.13386703e-01 8.98429334e-01 -8.81433263e-02 -2.81173170e-01
-1.58716828e-01 -1.32518303e+00 -7.26837993e-01 -6.53192282e-01
-5.93763217e-02 2.10759059e-01 3.61153215e-01 1.11406073... | [6.355451583862305, 2.679643154144287] |
54c82644-393a-4c71-8871-204883a3ed8d | mitigating-embedding-and-class-assignment | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4802_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123690749.pdf | Mitigating Embedding and Class Assignment Mismatch in Unsupervised Image Classification | Unsupervised image classification is a challenging computer vision task. Deep learning-based algorithms have achieved superb results, where the latest approach adopts unified losses from embedding and class assignment processes. Since these processes inherently have different goals, jointly optimizing them may lead to ... | ['Sungwon Park', 'Sungwon Han', 'Sungkyu Park', 'Sundong Kim', 'Meeyoung Cha'] | null | null | null | null | eccv-2020-8 | ['image-clustering', 'unsupervised-image-classification'] | ['computer-vision', 'computer-vision'] | [-5.48486710e-02 1.44843468e-02 -2.61738598e-01 -4.10307527e-01
-8.12150300e-01 -2.61307985e-01 5.77387393e-01 8.79058763e-02
-8.60059500e-01 6.74719214e-01 -1.54315516e-01 -2.18662262e-01
6.62471130e-02 -6.98296785e-01 -4.42110986e-01 -7.80322373e-01
2.37678047e-02 4.87832278e-01 8.45096111e-02 2.61322379... | [9.440829277038574, 2.881216287612915] |
cc2d4753-65d2-4563-bdbb-09d1c1f1f19b | characterization-multimodal-connectivity-of | 2107.09953 | null | https://arxiv.org/abs/2107.09953v1 | https://arxiv.org/pdf/2107.09953v1.pdf | Characterization Multimodal Connectivity of Brain Network by Hypergraph GAN for Alzheimer's Disease Analysis | Using multimodal neuroimaging data to characterize brain network is currently an advanced technique for Alzheimer's disease(AD) Analysis. Over recent years the neuroimaging community has made tremendous progress in the study of resting-state functional magnetic resonance imaging (rs-fMRI) derived from blood-oxygen-leve... | ['Shuqiang Wang', 'Zhiguang Feng', 'Yong liu', 'Yanyan Shen', 'Baiying Lei', 'Junren Pan'] | 2021-07-21 | null | null | null | null | ['white-matter-fiber-tractography'] | ['medical'] | [ 2.56679475e-01 -1.56689420e-01 2.20110029e-01 -6.03489101e-01
-3.10714662e-01 -3.26561242e-01 4.20940131e-01 -5.75766802e-01
-3.21376324e-01 8.57837498e-01 2.68629164e-01 -1.59390897e-01
-3.68023008e-01 -7.51468301e-01 -1.49896011e-01 -5.90232134e-01
-5.74078798e-01 3.22477102e-01 -5.95951416e-02 -6.73563266... | [12.440800666809082, 3.361058473587036] |
1a8564fc-a6d5-4c12-b3d7-bad0c0827c23 | analyzing-impact-of-socio-economic-factors-on | 2303.00517 | null | https://arxiv.org/abs/2303.00517v1 | https://arxiv.org/pdf/2303.00517v1.pdf | Analyzing Impact of Socio-Economic Factors on COVID-19 Mortality Prediction Using SHAP Value | This paper applies multiple machine learning (ML) algorithms to a dataset of de-identified COVID-19 patients provided by the COVID-19 Research Database. The dataset consists of 20,878 COVID-positive patients, among which 9,177 patients died in the year 2020. This paper aims to understand and interpret the association o... | ['Ying Ding', 'Justin F Rousseau', 'Jooyeong Kang', 'Redoan Rahman'] | 2023-02-27 | null | null | null | null | ['mortality-prediction'] | ['medical'] | [-2.37605184e-01 -1.78854018e-01 -5.52850604e-01 -4.13797915e-01
-2.35069692e-01 7.74720237e-02 3.66211593e-01 8.76138389e-01
-7.74821222e-01 1.09259474e+00 2.49530375e-01 -4.37635362e-01
-4.26496804e-01 -8.75461519e-01 -2.97972828e-01 -5.65880835e-01
-6.03485465e-01 8.99208724e-01 -7.78522849e-01 1.88070938... | [7.917239665985107, 5.977105140686035] |
4e55e1dd-4539-484b-bacc-425fb2e59abd | understanding-grounded-language-learning-1 | null | null | https://openreview.net/forum?id=ByZmGjkA- | https://openreview.net/pdf?id=ByZmGjkA- | Understanding Grounded Language Learning Agents | Neural network-based systems can now learn to locate the referents of words and phrases in images, answer questions about visual scenes, and even execute symbolic instructions as first-person actors in partially-observable worlds. To achieve this so-called grounded language learning, models must overcome certain well-s... | ['Karl Moritz Hermann', 'Felix Hill', 'Stephen Clark', 'Phil Blunsom'] | 2018-01-01 | null | null | null | iclr-2018-1 | ['grounded-language-learning'] | ['natural-language-processing'] | [ 4.51897919e-01 6.97317183e-01 1.66710634e-02 -5.40524125e-01
-2.27341101e-01 -5.70420980e-01 9.58303690e-01 3.96813005e-01
-7.17925012e-01 4.27270979e-01 3.63773495e-01 -4.45661664e-01
-3.54712829e-02 -6.24073803e-01 -1.05508006e+00 -4.67763960e-01
-1.16399907e-01 4.98901278e-01 1.62256956e-02 -2.30894580... | [10.15075397491455, 8.497102737426758] |
739ab4f3-62d1-4c04-a979-89841b2f224e | real-time-salient-object-detection-with-a | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Tu_Real-Time_Salient_Object_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Tu_Real-Time_Salient_Object_CVPR_2016_paper.pdf | Real-Time Salient Object Detection With a Minimum Spanning Tree | In this paper, we present a real-time salient object detection system based on the minimum spanning tree. Due to the fact that background regions are typically connected to the image boundaries, salient objects can be extracted by computing the distances to the boundaries. However, measuring the image boundary connecti... | ['Shao-Yi Chien', 'Wei-Chih Tu', 'Shengfeng He', 'Qingxiong Yang'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['video-salient-object-detection'] | ['computer-vision'] | [ 3.85738701e-01 -2.67809451e-01 -1.27555549e-01 -2.00671777e-01
-3.62604260e-01 -3.57241452e-01 1.24907024e-01 1.82145134e-01
-3.04004848e-01 3.64944488e-01 -1.91686228e-01 -1.49069220e-01
-3.96703631e-02 -9.49435115e-01 -4.50825274e-01 -6.20857775e-01
1.31585225e-01 -4.56689149e-02 9.71000195e-01 4.81631374... | [9.637250900268555, -0.598028838634491] |
38205aa6-f24c-4d1a-bcce-be35a499f3d6 | rendezvous-attention-mechanisms-for-the | 2109.03223 | null | https://arxiv.org/abs/2109.03223v2 | https://arxiv.org/pdf/2109.03223v2.pdf | Rendezvous: Attention Mechanisms for the Recognition of Surgical Action Triplets in Endoscopic Videos | Out of all existing frameworks for surgical workflow analysis in endoscopic videos, action triplet recognition stands out as the only one aiming to provide truly fine-grained and comprehensive information on surgical activities. This information, presented as <instrument, verb, target> combinations, is highly challengi... | ['Nicolas Padoy', 'Jacques Marescaux', 'Didier Mutter', 'Pietro Mascagni', 'Barbara Seeliger', 'Cristians Gonzalez', 'Tong Yu', 'Chinedu Innocent Nwoye'] | 2021-09-07 | null | null | null | null | ['action-triplet-recognition'] | ['computer-vision'] | [ 3.00289631e-01 -4.02585901e-02 -3.06105345e-01 4.09131078e-03
-8.68111312e-01 -5.53979039e-01 4.83666807e-01 1.95001215e-01
-3.04620117e-01 2.01237023e-01 6.06157482e-01 -2.11343065e-01
-4.39955205e-01 -4.75488514e-01 -6.94402158e-01 -7.66109765e-01
2.38651276e-01 2.34859541e-01 1.19001165e-01 -1.47259250... | [14.161773681640625, -3.2656912803649902] |
2db146b0-ca56-4707-aa55-39343aa9f1dd | tetratsdf-3d-human-reconstruction-from-a | 2004.10534 | null | https://arxiv.org/abs/2004.10534v1 | https://arxiv.org/pdf/2004.10534v1.pdf | TetraTSDF: 3D human reconstruction from a single image with a tetrahedral outer shell | Recovering the 3D shape of a person from its 2D appearance is ill-posed due to ambiguities. Nevertheless, with the help of convolutional neural networks (CNN) and prior knowledge on the 3D human body, it is possible to overcome such ambiguities to recover detailed 3D shapes of human bodies from single images. Current s... | ['Rin-ichiro Taniguchi', 'Zehra Hayirci', 'Hideaki Uchiyama', 'Diego Thomas', 'Akihiro Sugimoto', 'Hayato Onizuka'] | 2020-04-22 | tetratsdf-3d-human-reconstruction-from-a-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Onizuka_TetraTSDF_3D_Human_Reconstruction_From_a_Single_Image_With_a_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Onizuka_TetraTSDF_3D_Human_Reconstruction_From_a_Single_Image_With_a_CVPR_2020_paper.pdf | cvpr-2020-6 | ['3d-human-reconstruction'] | ['computer-vision'] | [-5.26146889e-02 -3.13412920e-02 4.87723172e-01 -3.43102485e-01
-3.24715301e-02 -1.43206552e-01 6.38505667e-02 -1.06667206e-01
-3.19595039e-01 5.39647400e-01 -1.57424539e-01 2.59975672e-01
7.15965331e-02 -6.88481331e-01 -7.38869309e-01 -5.32371700e-01
1.28590986e-01 1.04884529e+00 3.51218618e-02 -3.23234469... | [7.0415167808532715, -1.1969175338745117] |
5de5139c-ccaf-45fd-9132-edcb8a7b7fc8 | natural-scene-text-editing-based-on-ai | 2111.15475 | null | https://arxiv.org/abs/2111.15475v1 | https://arxiv.org/pdf/2111.15475v1.pdf | Natural Scene Text Editing Based on AI | In a recorded situation, textual information is crucial for scene interpretation and decision making. The ability to edit text directly on images has a number of advantages, including error correction, text restoration, and image reusability. This research shows how to change image text at the letter and digits level. ... | ['Yujie Zhang'] | 2021-11-26 | null | null | null | null | ['scene-text-editing'] | ['computer-vision'] | [ 8.32480431e-01 -4.94755238e-01 1.91781640e-01 -4.76458490e-01
4.23896164e-01 -5.58672607e-01 3.83400351e-01 -2.18819216e-01
-4.08638328e-01 8.62712264e-01 2.27741078e-01 -4.20294583e-01
2.01748893e-01 -7.33338833e-01 -7.56791472e-01 -3.78386021e-01
4.95186627e-01 -6.29708171e-02 2.77010500e-01 -2.14330032... | [11.758821487426758, 1.899789571762085] |
9a4f49d4-ea10-4c70-831c-bb6ef92f20ba | a-survey-of-numerical-algorithms-that-can | 2303.03576 | null | https://arxiv.org/abs/2303.03576v1 | https://arxiv.org/pdf/2303.03576v1.pdf | A Survey of Numerical Algorithms that can Solve the Lasso Problems | In statistics, the least absolute shrinkage and selection operator (Lasso) is a regression method that performs both variable selection and regularization. There is a lot of literature available, discussing the statistical properties of the regression coefficients estimated by the Lasso method. However, there lacks a c... | ['Xiaoming Huo', 'Yujie Zhao'] | 2023-03-07 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 1.07618429e-01 -3.34683716e-01 -5.29143572e-01 -5.99874735e-01
-9.19683397e-01 -4.34930444e-01 3.35726552e-02 1.01280525e-01
-1.25451922e-01 1.02826309e+00 1.69869989e-01 -3.40369344e-01
-2.72589475e-01 -3.00067008e-01 -3.13934565e-01 -1.02397156e+00
-4.11374122e-01 2.26783946e-01 -5.98885000e-01 -6.67290315... | [7.052743434906006, 4.394947052001953] |
ece5acc1-50d1-412f-8d5c-8732f333d6f6 | robust-autoregressive-hidden-semi-markov | 2010.08641 | null | https://arxiv.org/abs/2010.08641v2 | https://arxiv.org/pdf/2010.08641v2.pdf | Deep Neural Dynamic Bayesian Networks applied to EEG sleep spindles modeling | We propose a generative model for single-channel EEG that incorporates the constraints experts actively enforce during visual scoring. The framework takes the form of a dynamic Bayesian network with depth in both the latent variables and the observation likelihoods-while the hidden variables control the durations, stat... | ['Laura L. Colgin', 'Carlos A. Loza'] | 2020-10-16 | null | null | null | null | ['sleep-spindles-detection'] | ['time-series'] | [ 2.01400757e-01 1.29020140e-01 -1.05176911e-01 -4.54831541e-01
-6.01066768e-01 -7.72441745e-01 6.85855269e-01 2.92397082e-01
-6.93778753e-01 6.65297151e-01 6.99248835e-02 -3.35281819e-01
-5.79976559e-01 -4.00240719e-01 -5.56016922e-01 -7.88763642e-01
-5.48762560e-01 4.07168299e-01 1.26779944e-01 3.53476584... | [6.954540252685547, 3.8724544048309326] |
2f385a93-dcf7-4dfc-8f34-05c00a8c60d5 | umfa-a-photorealistic-style-transfer-method | 2108.06113 | null | https://arxiv.org/abs/2108.06113v1 | https://arxiv.org/pdf/2108.06113v1.pdf | UMFA: A photorealistic style transfer method based on U-Net and multi-layer feature aggregation | In this paper, we propose a photorealistic style transfer network to emphasize the natural effect of photorealistic image stylization. In general, distortion of the image content and lacking of details are two typical issues in the style transfer field. To this end, we design a novel framework employing the U-Net struc... | ['T. Y. Xu', 'J. Kittler', 'H. Li', 'X. J. Wu', 'D. Y. Rao'] | 2021-08-13 | null | null | null | null | ['image-stylization'] | ['computer-vision'] | [ 1.72881693e-01 -1.78978980e-01 2.15799436e-01 -3.61550808e-01
-3.47113907e-02 -2.29397818e-01 5.02983332e-01 -4.68560308e-01
-2.78917402e-01 4.74957913e-01 4.23401028e-01 1.74055219e-01
1.86971843e-01 -1.02892566e+00 -8.94389570e-01 -8.19400609e-01
7.37700462e-01 -4.61228669e-01 2.72473723e-01 -3.35209459... | [11.41497802734375, -0.8755183219909668] |
a7ea1037-b031-4286-9712-129f62d213d1 | density-aware-chamfer-distance-as-a | 2111.12702 | null | https://arxiv.org/abs/2111.12702v1 | https://arxiv.org/pdf/2111.12702v1.pdf | Density-aware Chamfer Distance as a Comprehensive Metric for Point Cloud Completion | Chamfer Distance (CD) and Earth Mover's Distance (EMD) are two broadly adopted metrics for measuring the similarity between two point sets. However, CD is usually insensitive to mismatched local density, and EMD is usually dominated by global distribution while overlooks the fidelity of detailed structures. Besides, th... | ['Dahua Lin', 'Ziwei Liu', 'Tai Wang', 'Junzhe Zhang', 'Liang Pan', 'Tong Wu'] | 2021-11-24 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [-4.07936066e-01 -4.30534035e-01 -2.02165380e-01 -3.43872815e-01
-9.90124166e-01 -3.37814301e-01 6.95343971e-01 4.22070503e-01
-2.85783112e-01 4.53377813e-01 3.45129892e-03 -4.34257388e-02
-3.02794993e-01 -1.03764033e+00 -5.74675918e-01 -5.96375763e-01
-7.01324120e-02 6.38227582e-01 6.95354879e-01 -1.83243126... | [7.871311664581299, -3.0925214290618896] |
898c9199-951a-4972-9312-e1e1ceaafc00 | contrastive-representation-learning-for | 2109.01484 | null | https://arxiv.org/abs/2109.01484v1 | https://arxiv.org/pdf/2109.01484v1.pdf | Contrastive Representation Learning for Exemplar-Guided Paraphrase Generation | Exemplar-Guided Paraphrase Generation (EGPG) aims to generate a target sentence which conforms to the style of the given exemplar while encapsulating the content information of the source sentence. In this paper, we propose a new method with the goal of learning a better representation of the style andthe content. This... | ['Piji Li', 'Wai Lam', 'Haoran Yang'] | 2021-09-03 | null | https://aclanthology.org/2021.findings-emnlp.409 | https://aclanthology.org/2021.findings-emnlp.409.pdf | findings-emnlp-2021-11 | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 2.79264838e-01 3.31321508e-01 -3.31674665e-02 -4.35255826e-01
-4.52448070e-01 -2.28311643e-01 7.87417948e-01 5.36452755e-02
-3.96430135e-01 8.47001672e-01 5.50756693e-01 3.23385805e-01
2.64505427e-02 -8.16179872e-01 -7.26432025e-01 -7.35782027e-01
4.98970449e-01 2.85972238e-01 -1.73487127e-01 -5.58450878... | [11.704755783081055, 9.39675521850586] |
b960f334-940d-4885-9acb-0c2e6f0d2a05 | automated-identification-of-disaster-news-for | 2301.09896 | null | https://arxiv.org/abs/2301.09896v1 | https://arxiv.org/pdf/2301.09896v1.pdf | Automated Identification of Disaster News For Crisis Management Using Machine Learning | A lot of news sources picked up on Typhoon Rai (also known locally as Typhoon Odette), along with fake news outlets. The study honed in on the issue, to create a model that can identify between legitimate and illegitimate news articles. With this in mind, we chose the following machine learning algorithms in our develo... | ['Angie M. Ceniza-Canillo', 'Ai Matsushita', 'Lord Christian Carl H. Regacho'] | 2023-01-24 | null | null | null | null | ['lemmatization'] | ['natural-language-processing'] | [-4.84465927e-01 1.81486249e-01 -5.95621765e-01 9.66880023e-02
-6.71478570e-01 -7.33972013e-01 1.03147364e+00 4.40692872e-01
-4.48110193e-01 9.34533417e-01 3.47717077e-01 -5.93364894e-01
-2.16390472e-02 -1.06780481e+00 -4.71590549e-01 -4.52524006e-01
-6.84376583e-02 5.22490025e-01 1.91163540e-01 -1.94224015... | [8.273897171020508, 10.14286994934082] |
098dbd47-3b3f-4d4e-9159-42b4f368cfc0 | plwordnet-in-word-sense-disambiguation-task | null | null | https://aclanthology.org/2016.gwc-1.41 | https://aclanthology.org/2016.gwc-1.41.pdf | plWordNet in Word Sense Disambiguation task | The paper explores the application of plWordNet, a very large wordnet of Polish, in weakly supervised Word Sense Disambiguation (WSD). Because plWordNet provides only partial descriptions by glosses and usage examples, and does not include sense-disambiguated glosses, PageRank-based WSD methods perform slightly worse t... | ['Marlena Orlińska', 'Paweł Kędzia', 'Maciej Piasecki'] | null | null | null | null | gwc-2016-1 | ['word-sense-disambiguation'] | ['natural-language-processing'] | [-5.51990345e-02 1.76722720e-01 -3.85861486e-01 -7.08013475e-02
-4.61813480e-01 -7.19633281e-01 8.74285877e-01 7.66145706e-01
-1.05030429e+00 1.17655528e+00 7.77311385e-01 -3.89574319e-01
-5.75920999e-01 -8.53218317e-01 3.76583219e-01 -4.23691213e-01
1.00223228e-01 9.68839705e-01 7.86884010e-01 -9.56943929... | [10.170938491821289, 9.197247505187988] |
a0708bc8-b54e-4426-9f4e-570f3e35a880 | maskplace-fast-chip-placement-via-reinforced | 2211.13382 | null | https://arxiv.org/abs/2211.13382v1 | https://arxiv.org/pdf/2211.13382v1.pdf | MaskPlace: Fast Chip Placement via Reinforced Visual Representation Learning | Placement is an essential task in modern chip design, aiming at placing millions of circuit modules on a 2D chip canvas. Unlike the human-centric solution, which requires months of intense effort by hardware engineers to produce a layout to minimize delay and energy consumption, deep reinforcement learning has become a... | ['Ping Luo', 'Yao Mu', 'Yao Lai'] | 2022-11-24 | null | null | null | null | ['layout-design'] | ['computer-vision'] | [-2.05738410e-01 2.56727159e-01 -5.11231005e-01 2.26922911e-02
-7.05696642e-01 -5.93272507e-01 5.77536114e-02 -1.41421873e-02
-4.74160649e-02 9.77156222e-01 -2.00368762e-01 -6.45868421e-01
-4.11608145e-02 -9.15548265e-01 -8.27058256e-01 -5.42315781e-01
-1.19765304e-01 4.12704259e-01 -1.34417236e-01 -1.79699883... | [5.700962066650391, 3.1229538917541504] |
7b608840-69c3-472c-b6c3-4dbae174ff2d | sketch-a-net-that-beats-humans | 1501.07873 | null | http://arxiv.org/abs/1501.07873v3 | http://arxiv.org/pdf/1501.07873v3.pdf | Sketch-a-Net that Beats Humans | We propose a multi-scale multi-channel deep neural network framework that,
for the first time, yields sketch recognition performance surpassing that of
humans. Our superior performance is a result of explicitly embedding the unique
characteristics of sketches in our model: (i) a network architecture designed
for sketch... | ['Yi-Zhe Song', 'Timothy Hospedales', 'Qian Yu', 'Yongxin Yang', 'Tao Xiang'] | 2015-01-30 | null | null | null | null | ['sketch-recognition'] | ['computer-vision'] | [ 1.41767010e-01 -2.50240386e-01 -3.88637558e-02 -3.14400315e-01
-6.54412568e-01 -5.79508603e-01 1.16377711e+00 -3.16788554e-01
-1.77748650e-01 4.77248788e-01 3.24735880e-01 -1.50056660e-01
-1.50192469e-01 -7.89359093e-01 -7.85162091e-01 -5.48778534e-01
-1.28740057e-01 6.93640411e-01 -8.00705422e-03 5.23273572... | [11.72197437286377, 0.45582032203674316] |
40416335-85ce-4865-ae00-919a25e2cdc1 | bandit-based-model-selection-for-deformable | 1703.10254 | null | http://arxiv.org/abs/1703.10254v1 | http://arxiv.org/pdf/1703.10254v1.pdf | Bandit-Based Model Selection for Deformable Object Manipulation | We present a novel approach to deformable object manipulation that does not
rely on highly-accurate modeling. The key contribution of this paper is to
formulate the task as a Multi-Armed Bandit problem, with each arm representing
a model of the deformable object. To "pull" an arm and evaluate its utility, we
use the ar... | ['Dmitry Berenson', 'Dale McConachie'] | 2017-03-29 | null | null | null | null | ['deformable-object-manipulation'] | ['robots'] | [ 1.64141804e-01 1.49645343e-01 -8.11105132e-01 2.55779088e-01
-1.01625407e+00 -9.84231174e-01 2.67104775e-01 -5.03785551e-01
-2.90700108e-01 9.69364583e-01 3.43490727e-02 -1.25860170e-01
-5.79326630e-01 -5.08342326e-01 -1.16402018e+00 -8.32826436e-01
-2.61110812e-01 1.00375330e+00 1.05021290e-01 -4.15511318... | [4.732168674468994, 0.6768501996994019] |
754f66a2-2fc4-4ce1-84a7-ce07ef6ca5bb | xcit-cross-covariance-image-transformers | 2106.09681 | null | https://arxiv.org/abs/2106.09681v2 | https://arxiv.org/pdf/2106.09681v2.pdf | XCiT: Cross-Covariance Image Transformers | Following their success in natural language processing, transformers have recently shown much promise for computer vision. The self-attention operation underlying transformers yields global interactions between all tokens ,i.e. words or image patches, and enables flexible modelling of image data beyond the local intera... | ['Hervé Jegou', 'Jakob Verbeek', 'Gabriel Synnaeve', 'Natalia Neverova', 'Ivan Laptev', 'Armand Joulin', 'Matthijs Douze', 'Piotr Bojanowski', 'Mathilde Caron', 'Hugo Touvron', 'Alaaeldin El-Nouby'] | 2021-06-17 | null | http://proceedings.neurips.cc/paper/2021/hash/a655fbe4b8d7439994aa37ddad80de56-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/a655fbe4b8d7439994aa37ddad80de56-Paper.pdf | neurips-2021-12 | ['self-supervised-image-classification'] | ['computer-vision'] | [ 3.19921404e-01 -1.97411671e-01 2.10151941e-01 -4.67523456e-01
-7.10014522e-01 -6.69382215e-01 7.84824729e-01 9.77104902e-02
-8.70056272e-01 3.72679867e-02 -7.76231065e-02 -4.37239707e-01
-3.97908017e-02 -7.99872518e-01 -1.03858256e+00 -6.25971377e-01
-1.22944154e-01 2.38226146e-01 4.70955431e-01 5.62315844... | [9.454320907592773, 1.2930957078933716] |
96fad8bd-005e-4c20-a4fa-4134bdfb8519 | mild-modeling-the-instance-learning-dynamics | 2306.11560 | null | https://arxiv.org/abs/2306.11560v1 | https://arxiv.org/pdf/2306.11560v1.pdf | MILD: Modeling the Instance Learning Dynamics for Learning with Noisy Labels | Despite deep learning has achieved great success, it often relies on a large amount of training data with accurate labels, which are expensive and time-consuming to collect. A prominent direction to reduce the cost is to learn with noisy labels, which are ubiquitous in the real-world applications. A critical challenge ... | ['Xuming He', 'Zhitong Gao', 'Shipeng Yan', 'Chuanyang Hu'] | 2023-06-20 | null | null | null | null | ['learning-with-noisy-labels', 'memorization', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing', 'natural-language-processing'] | [ 1.76253840e-01 -2.30735675e-01 6.08188361e-02 -4.49444026e-01
-8.39959383e-01 -3.61570507e-01 3.80753785e-01 7.44313478e-01
-6.71580434e-01 1.02457428e+00 -2.86036879e-01 -1.60460267e-03
-3.28745395e-01 -8.24064910e-01 -8.73370767e-01 -9.99974430e-01
2.41289243e-01 3.17699552e-01 9.39549282e-02 2.79893249... | [9.35533618927002, 3.8885581493377686] |
745178be-8b51-4098-9aaf-9e84526e1370 | grids-interactive-layout-design-with-integer | 2001.02921 | null | https://arxiv.org/abs/2001.02921v1 | https://arxiv.org/pdf/2001.02921v1.pdf | GRIDS: Interactive Layout Design with Integer Programming | Grid layouts are used by designers to spatially organise user interfaces when sketching and wireframing. However, their design is largely time consuming manual work. This is challenging due to combinatorial explosion and complex objectives, such as alignment, balance, and expectations regarding positions. This paper pr... | ['Antti Oulasvirta', 'Taru Saarelainen', 'Niraj Dayama', 'Kashyap Todi'] | 2020-01-09 | grids-interactive-layout-design-with-integer-1 | https://dl.acm.org/doi/abs/10.1145/3313831.3376553 | https://dl.acm.org/doi/pdf/10.1145/3313831.3376553 | null | ['layout-design'] | ['computer-vision'] | [ 2.33322665e-01 1.84799924e-01 -2.80736804e-01 -1.93857849e-01
-4.97105867e-01 -8.79796505e-01 1.28406420e-01 3.28631878e-01
1.40112668e-01 7.02531934e-01 4.88247663e-01 -6.56842172e-01
-8.80183935e-01 -6.71438932e-01 -2.96034813e-01 -1.76292419e-01
-3.04120511e-01 5.03069878e-01 -2.97840327e-01 -3.57167482... | [5.834203720092773, 3.4632794857025146] |
c943e46d-8063-4066-bde2-a22cb02cea18 | endomapper-dataset-of-complete-calibrated | 2204.14240 | null | https://arxiv.org/abs/2204.14240v1 | https://arxiv.org/pdf/2204.14240v1.pdf | EndoMapper dataset of complete calibrated endoscopy procedures | Computer-assisted systems are becoming broadly used in medicine. In endoscopy, most research focuses on automatic detection of polyps or other pathologies, but localization and navigation of the endoscope is completely performed manually by physicians. To broaden this research and bring spatial Artificial Intelligence ... | ['José M. M. Montiel', 'Angel Lanas', 'Ana Cristina Murillo', 'Juan D. Tardós', 'Javier Civera', 'Cristina Oriol', 'Julia López', 'Richard Elvira', 'Juan J. Gómez-Rodríguez', 'Victor M. Batlle', 'David Recasens', 'Javier Morlana', 'Oscar León Barbed', 'Clara Tomasini', 'Luis Riazuelo', 'Ángel Ferrandez', 'Carlos Sostre... | 2022-04-29 | null | null | null | null | ['medical-image-retrieval', 'medical-image-retrieval'] | ['computer-vision', 'medical'] | [-3.23022634e-01 6.65098876e-02 -1.03222691e-01 -1.34297013e-01
-3.28385174e-01 -1.16866410e+00 -1.43941632e-03 2.38889068e-01
-4.95851040e-01 2.02703834e-01 9.42759067e-02 -6.08665466e-01
-1.11792661e-01 -3.78726512e-01 -5.86174369e-01 -6.29182041e-01
-5.42384028e-01 4.10034657e-01 3.55166137e-01 -4.21204157... | [13.990654945373535, -3.1394505500793457] |
b98a65bf-3584-4e83-8b5b-6b00e96606d8 | embodied-concept-learner-self-supervised | 2304.03767 | null | https://arxiv.org/abs/2304.03767v1 | https://arxiv.org/pdf/2304.03767v1.pdf | Embodied Concept Learner: Self-supervised Learning of Concepts and Mapping through Instruction Following | Humans, even at a very early age, can learn visual concepts and understand geometry and layout through active interaction with the environment, and generalize their compositions to complete tasks described by natural languages in novel scenes. To mimic such capability, we propose Embodied Concept Learner (ECL) in an in... | ['Chuang Gan', 'Joshua B. Tenenbaum', 'Ping Luo', 'David Daniel Cox', 'Zhenfang Chen', 'Yan Xu', 'Mingyu Ding'] | 2023-04-07 | null | null | null | null | ['instruction-following'] | ['natural-language-processing'] | [-9.75799561e-02 8.50149751e-01 2.57226452e-02 -4.70382541e-01
-2.30594546e-01 -8.22873175e-01 7.27101862e-01 2.82338738e-01
-3.82191718e-01 3.67739290e-01 1.17994666e-01 -4.69612926e-01
2.67956048e-01 -1.07857728e+00 -1.20785069e+00 -4.99213427e-01
-3.84567261e-01 7.87721872e-01 2.60082901e-01 -2.80171752... | [4.461153507232666, 0.7084499001502991] |
ea05b1b3-8a83-45b1-ae54-c00a76e9f66d | zero-shot-and-few-shot-learning-for-lung | 2205.15290 | null | https://arxiv.org/abs/2205.15290v2 | https://arxiv.org/pdf/2205.15290v2.pdf | Zero-Shot and Few-Shot Learning for Lung Cancer Multi-Label Classification using Vision Transformer | Lung cancer is the leading cause of cancer-related death worldwide. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are the most common histologic subtypes of non-small-cell lung cancer (NSCLC). Histology is an essential tool for lung cancer diagnosis. Pathologists make classifications according to t... | ['Yingfang Fan', 'Fu-Ming Guo'] | 2022-05-30 | null | null | null | null | ['lung-cancer-diagnosis'] | ['medical'] | [-3.36759202e-02 -2.44876876e-01 -5.33698380e-01 2.10295200e-01
-1.31916499e+00 -4.73367095e-01 5.33363879e-01 2.31920645e-01
-3.26401204e-01 5.22022963e-01 -1.08252518e-01 -4.86735612e-01
9.33647081e-02 -7.67741501e-01 -7.97842443e-02 -1.03346491e+00
3.78673524e-01 7.01520026e-01 6.57096744e-01 2.16072738... | [15.369316101074219, -2.3397629261016846] |
33b4ec82-9a78-4440-9181-581cf7a0fb1f | image-based-detection-of-surface-defects-in | 2208.02313 | null | https://arxiv.org/abs/2208.02313v2 | https://arxiv.org/pdf/2208.02313v2.pdf | Image-based Detection of Surface Defects in Concrete during Construction | Defects increase the cost and duration of construction projects as they require significant inspection and documentation efforts. Automating defect detection could significantly reduce these efforts. This work focuses on detecting honeycombs, a substantial defect in concrete structures that may affect structural integr... | ['Olaf Hellwich', 'Monika Kwiatkowski', 'Dominik Kuhnke'] | 2022-08-03 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 3.18073303e-01 3.86870146e-01 5.49327731e-01 -6.96647028e-03
-3.25263113e-01 -1.58580676e-01 1.40817240e-02 2.99384922e-01
2.60900557e-01 4.18513790e-02 -4.13220972e-01 -2.61933327e-01
-3.20312709e-01 -1.35213125e+00 -5.38999796e-01 -3.57475162e-01
-6.31606132e-02 4.23671812e-01 8.00574064e-01 -4.45395887... | [7.382139682769775, 1.7931948900222778] |
9fec4cb1-3873-4337-b02a-309f359953b0 | question-answering-and-question-generation | 2211.13794 | null | https://arxiv.org/abs/2211.13794v1 | https://arxiv.org/pdf/2211.13794v1.pdf | Question Answering and Question Generation for Finnish | Recent advances in the field of language modeling have improved the state-of-the-art in question answering (QA) and question generation (QG). However, the development of modern neural models, their benchmarks, and datasets for training them has mainly focused on English. Finnish, like many other languages, faces a shor... | ['Roman Yangarber', 'Ilmari Kylliäinen'] | 2022-11-24 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [ 1.1543890e-01 2.0125560e-01 3.1118444e-01 -3.8322964e-01
-1.4814317e+00 -6.7851317e-01 4.9781582e-01 -7.7196442e-02
-3.5327002e-01 9.9969178e-01 4.5609990e-01 -7.7270949e-01
6.4091249e-03 -9.2735595e-01 -7.4195743e-01 -2.1440738e-01
5.9486562e-01 1.3029346e+00 1.8142547e-01 -8.1833994e-01
-1.3952866e-01... | [11.371939659118652, 8.348283767700195] |
b916b8cf-50f9-47ef-aedc-f4c2e72372f0 | detecting-out-of-context-multimodal | 2304.07633 | null | https://arxiv.org/abs/2304.07633v1 | https://arxiv.org/pdf/2304.07633v1.pdf | Detecting Out-of-Context Multimodal Misinformation with interpretable neural-symbolic model | Recent years have witnessed the sustained evolution of misinformation that aims at manipulating public opinions. Unlike traditional rumors or fake news editors who mainly rely on generated and/or counterfeited images, text and videos, current misinformation creators now more tend to use out-of-context multimedia conten... | ['Yan Liu', 'Zijun Cui', 'Defu Cao', 'Loc Trinh', 'Yizhou Zhang'] | 2023-04-15 | null | null | null | null | ['misinformation', 'fake-news-detection'] | ['miscellaneous', 'natural-language-processing'] | [ 2.68173099e-01 3.65581393e-01 -3.21771115e-01 -2.00310424e-01
-7.81523049e-01 -6.78670764e-01 1.02300155e+00 3.39772731e-01
-7.04098195e-02 6.58684313e-01 4.89734501e-01 -3.43636245e-01
3.78135741e-01 -5.40368438e-01 -8.55152905e-01 -2.68350869e-01
4.88911152e-01 3.34108174e-01 1.91706404e-01 -3.73649001... | [8.202559471130371, 10.26128101348877] |
d45496af-34c9-4feb-a478-c2d080b2690c | an-fnet-based-auto-encoder-for-long-sequence | 2211.08295 | null | https://arxiv.org/abs/2211.08295v2 | https://arxiv.org/pdf/2211.08295v2.pdf | An FNet based Auto Encoder for Long Sequence News Story Generation | In this paper, we design an auto encoder based off of Google's FNet Architecture in order to generate text from a subset of news stories contained in Google's C4 dataset. We discuss previous attempts and methods to generate text from autoencoders and non LLM Models. FNET poses multiple advantages to BERT based encoders... | ['Rakeshkumar Mahto', 'Paul K. Mandal'] | 2022-11-15 | null | null | null | null | ['story-generation'] | ['natural-language-processing'] | [-1.26232475e-01 6.17288113e-01 1.14374757e-01 -3.04819673e-01
-6.02111340e-01 -5.46732247e-01 1.07747328e+00 -3.85130912e-01
-3.84758711e-01 1.00934374e+00 7.30247796e-01 -4.27688122e-01
4.54256147e-01 -1.23309278e+00 -1.14046323e+00 2.17649490e-02
2.81686723e-01 6.45810068e-01 -2.01269001e-01 -5.50133586... | [11.75180435180664, 9.117317199707031] |
a3e25c6c-09ac-4fc8-b22d-4839f778b6ea | modeling-arterial-pulse-waves-in-healthy | null | null | https://doi.org/10.1152/ajpheart.00218.2019 | https://journals.physiology.org/doi/full/10.1152/ajpheart.00218.2019 | Modeling arterial pulse waves in healthy aging: a database for in silico evaluation of hemodynamics and pulse wave indexes | The arterial pulse wave (PW) is a rich source of information on cardiovascular (CV) health. It is widely measured by both consumer and clinical devices. However, the physical determinants of the PW are not yet fully understood, and the development of PW analysis algorithms is limited by a lack of PW data sets containin... | ['and Jordi Alastruey', 'Phil Chowienczyk', 'Ye Li', 'Samuel Vennin', 'Jorge Mariscal Harana', 'Peter H. Charlton'] | 2019-10-24 | null | null | null | null | ['photoplethysmography-ppg', 'pulse-wave-simulation'] | ['medical', 'medical'] | [-4.34112772e-02 -2.34474257e-01 -3.34736630e-02 -2.38146737e-01
-3.38474423e-01 -8.09641302e-01 1.04399249e-02 3.67242843e-01
-2.00853035e-01 9.43471193e-01 2.09324777e-01 -7.26856232e-01
-4.49112877e-02 -8.13940406e-01 5.10580977e-03 -4.23833132e-01
-5.74270666e-01 5.77904940e-01 3.38012785e-01 1.92591026... | [14.077539443969727, 2.9858264923095703] |
4ec7e962-0883-4401-b014-bb5927b7eaca | dreem-open-datasets-multi-scored-sleep | 1911.03221 | null | https://arxiv.org/abs/1911.03221v4 | https://arxiv.org/pdf/1911.03221v4.pdf | Dreem Open Datasets: Multi-Scored Sleep Datasets to compare Human and Automated sleep staging | Sleep stage classification constitutes an important element of sleep disorder diagnosis. It relies on the visual inspection of polysomnography records by trained sleep technologists. Automated approaches have been designed to alleviate this resource-intensive task. However, such approaches are usually compared to a sin... | ['Emmanuel H. During', 'Fabien Sauvet', 'Antoine Guillot', 'Valentin Thorey'] | 2019-10-31 | null | null | null | null | ['sleep-stage-detection', 'multimodal-sleep-stage-detection', 'sleep-staging', 'automatic-sleep-stage-classification'] | ['medical', 'medical', 'medical', 'medical'] | [-1.42234743e-01 1.46716684e-01 -1.87377930e-01 -5.96131980e-01
-5.93036890e-01 -3.41403246e-01 -4.36876208e-01 1.07843757e-01
-7.93347359e-01 9.68841136e-01 4.21063229e-02 -9.11304206e-02
-1.17207438e-01 -1.14119656e-01 3.81216675e-01 -4.92939770e-01
-1.41330257e-01 8.53204370e-01 2.88004130e-01 2.56914776... | [13.454463005065918, 3.5661118030548096] |
fae243fc-cc62-464b-b0d2-69cf3ae93cec | hitmi-t-at-semeval-2021-task-5-integrating | null | null | https://aclanthology.org/2021.semeval-1.117 | https://aclanthology.org/2021.semeval-1.117.pdf | HITMI\&T at SemEval-2021 Task 5: Integrating Transformer and CRF for Toxic Spans Detection | This paper introduces our system at SemEval-2021 Task 5: Toxic Spans Detection. The task aims to accurately locate toxic spans within a text. Using BIO tagging scheme, we model the task as a token-level sequence labeling task. Our system uses a single model built on the model of multi-layer bidirectional transformer en... | ['Tiejun Zhao', 'Tianshu Liu', 'Chenyi Wang'] | 2021-08-01 | null | null | null | semeval-2021 | ['toxic-spans-detection'] | ['natural-language-processing'] | [ 1.58104792e-01 4.03401516e-02 -2.85938084e-01 -2.03268871e-01
-9.65141892e-01 -5.26322305e-01 5.03014505e-01 8.97261128e-02
-6.63706422e-01 1.10741031e+00 3.34182888e-01 -3.14040184e-01
6.36921465e-01 -7.63752878e-01 -9.54126894e-01 -4.41117734e-01
-1.24303371e-01 1.09029688e-01 2.59656221e-01 -2.59732436... | [8.945273399353027, 10.598735809326172] |
4109f72b-af5a-479d-b359-c1f783ce5a8b | a-hybrid-feature-selection-and-construction | 2306.09491 | null | https://arxiv.org/abs/2306.09491v1 | https://arxiv.org/pdf/2306.09491v1.pdf | A Hybrid Feature Selection and Construction Method for Detection of Wind Turbine Generator Heating Faults | Preprocessing of information is an essential step for the effective design of machine learning applications. Feature construction and selection are powerful techniques used for this aim. In this paper, a feature selection and construction approach is presented for the detection of wind turbine generator heating faults.... | ['Burak Barutcu', 'Ayse Gokcen Kavaz'] | 2023-06-15 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [ 1.27743930e-01 -3.60678852e-01 4.99211460e-01 -1.88368037e-01
1.35201022e-01 -4.23537225e-01 3.36629480e-01 6.14558756e-01
-2.87232101e-01 6.63482606e-01 -1.60186663e-01 -1.95472687e-01
-9.13750589e-01 -9.86865640e-01 1.38549030e-01 -8.85919094e-01
-1.80772245e-01 3.56923312e-01 2.51624584e-01 -1.47559136... | [6.729666709899902, 2.3856687545776367] |
8a49dcb2-9b35-4c14-b504-1a2fb6e9c924 | semi-weakly-supervised-object-kinematic | 2303.17774 | null | https://arxiv.org/abs/2303.17774v2 | https://arxiv.org/pdf/2303.17774v2.pdf | Semi-Weakly Supervised Object Kinematic Motion Prediction | Given a 3D object, kinematic motion prediction aims to identify the mobile parts as well as the corresponding motion parameters. Due to the large variations in both topological structure and geometric details of 3D objects, this remains a challenging task and the lack of large scale labeled data also constrain the perf... | ['Ruizhen Hu', 'Hui Huang', 'Li Yi', 'Yulan Guo', 'Chongyang Ma', 'Haibin Huang', 'Qian Sun', 'Gengxin Liu'] | 2023-03-31 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Liu_Semi-Weakly_Supervised_Object_Kinematic_Motion_Prediction_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Liu_Semi-Weakly_Supervised_Object_Kinematic_Motion_Prediction_CVPR_2023_paper.pdf | cvpr-2023-1 | ['motion-prediction'] | ['computer-vision'] | [ 1.07691042e-01 5.23267925e-01 -7.01277077e-01 -3.79420400e-01
-5.65289021e-01 -4.61973906e-01 4.15208101e-01 -1.22152731e-01
-1.25607207e-01 2.56965548e-01 7.45181888e-02 -9.26394239e-02
-1.41773611e-01 -6.66309297e-01 -9.05512035e-01 -4.56295192e-01
-2.59180099e-01 1.27370715e+00 1.04667890e+00 -2.35622704... | [8.03491497039795, -3.1458725929260254] |
044b38d2-58e0-434a-bc1b-6540d296aacd | a-quantum-kernel-learning-approach-to | 2211.01263 | null | https://arxiv.org/abs/2211.01263v1 | https://arxiv.org/pdf/2211.01263v1.pdf | A Quantum Kernel Learning Approach to Acoustic Modeling for Spoken Command Recognition | We propose a quantum kernel learning (QKL) framework to address the inherent data sparsity issues often encountered in training large-scare acoustic models in low-resource scenarios. We project acoustic features based on classical-to-quantum feature encoding. Different from existing quantum convolution techniques, we u... | ['Chin-Hui Lee', 'Sabato Marco Siniscalchi', 'Tara N. Sainath', 'Nanxin Chen', 'Yu Zhang', 'Bo Li', 'Chao-Han Huck Yang'] | 2022-11-02 | null | null | null | null | ['spoken-command-recognition'] | ['speech'] | [ 7.77564868e-02 -2.88080424e-01 1.34750381e-01 -6.77454650e-01
-1.26406717e+00 -3.77823591e-01 5.43482363e-01 -1.63511381e-01
-8.26139152e-01 5.28365970e-01 -2.07533374e-01 -4.05566871e-01
-1.02939196e-02 -6.60015464e-01 -3.64637107e-01 -8.46283734e-01
-3.22421312e-01 1.46191090e-01 3.84178087e-02 -3.51766199... | [5.57249641418457, 4.973879337310791] |
26acb018-04f6-468d-a7c7-bb1ea5a9ef2e | deep-human-parsing-with-active-template | 1503.02391 | null | http://arxiv.org/abs/1503.02391v1 | http://arxiv.org/pdf/1503.02391v1.pdf | Deep Human Parsing with Active Template Regression | In this work, the human parsing task, namely decomposing a human image into
semantic fashion/body regions, is formulated as an Active Template Regression
(ATR) problem, where the normalized mask of each fashion/body item is expressed
as the linear combination of the learned mask templates, and then morphed to a
more pr... | ['Luoqi Liu', 'Liang Lin', 'Xiaohui Shen', 'Xiaodan Liang', 'Jianchao Yang', 'Si Liu', 'Jian Dong', 'Shuicheng Yan'] | 2015-03-09 | null | null | null | null | ['human-parsing'] | ['computer-vision'] | [ 5.36106288e-01 5.19986808e-01 -3.94689851e-02 -6.41368389e-01
-8.49229813e-01 -4.22237128e-01 2.44651772e-02 -1.88205495e-01
-4.61985320e-01 8.02903101e-02 7.68869892e-02 1.67218670e-01
4.44646686e-01 -8.19247901e-01 -6.89777792e-01 -7.81488836e-01
4.10079420e-01 2.50869811e-01 3.44073594e-01 -6.81525692... | [8.727819442749023, 0.061181943863630295] |
f10cd973-7f12-4edf-ad16-e74a301a582e | detecting-heart-disease-from-multi-view | 2306.00003 | null | https://arxiv.org/abs/2306.00003v1 | https://arxiv.org/pdf/2306.00003v1.pdf | Detecting Heart Disease from Multi-View Ultrasound Images via Supervised Attention Multiple Instance Learning | Aortic stenosis (AS) is a degenerative valve condition that causes substantial morbidity and mortality. This condition is under-diagnosed and under-treated. In clinical practice, AS is diagnosed with expert review of transthoracic echocardiography, which produces dozens of ultrasound images of the heart. Only some of t... | ['Michael C. Hughes', 'Benjamin S. Wessler', 'Zhe Huang'] | 2023-05-25 | null | null | null | null | ['multiple-instance-learning'] | ['methodology'] | [ 2.14301452e-01 3.73107284e-01 -2.72382349e-01 -2.80622482e-01
-1.21259618e+00 -6.33251727e-01 4.97234054e-02 9.49247479e-02
-1.95747524e-01 5.52010655e-01 2.23817483e-01 -7.27208257e-01
-1.54098079e-01 -3.52948010e-01 -7.08730161e-01 -3.46668631e-01
-2.40046263e-01 8.39742362e-01 2.70743817e-01 1.76776677... | [14.777129173278809, -2.229907274246216] |
512bebe6-4afc-4d71-a689-ba707dd11c29 | an-online-semantic-enhanced-dirichlet-model | null | null | https://aclanthology.org/2020.acl-main.70 | https://aclanthology.org/2020.acl-main.70.pdf | An Online Semantic-enhanced Dirichlet Model for Short Text Stream Clustering | Clustering short text streams is a challenging task due to its unique properties: infinite length, sparse data representation and cluster evolution. Existing approaches often exploit short text streams in a batch way. However, determine the optimal batch size is usually a difficult task since we have no priori knowledg... | ['Salah Uddin', 'Jay Kumar', 'Wazir Ali', 'Junming Shao'] | 2020-07-01 | null | null | null | acl-2020-6 | ['text-clustering', 'short-text-clustering'] | ['natural-language-processing', 'natural-language-processing'] | [-4.03844416e-02 -6.70958698e-01 4.35348786e-02 -4.78692621e-01
-4.10681456e-01 -3.70851815e-01 5.43260753e-01 4.01243687e-01
-3.09639782e-01 3.20160270e-01 1.61414787e-01 1.28279835e-01
-1.69843227e-01 -5.55452824e-01 -2.32041314e-01 -8.88421357e-01
-1.31599292e-01 9.70269978e-01 4.12817597e-01 8.77024457... | [10.349706649780273, 6.865638256072998] |
823b7d39-3fe5-47b8-a6f5-74882050e2d0 | advsmo-black-box-adversarial-attack-by | 2206.10988 | null | https://arxiv.org/abs/2206.10988v1 | https://arxiv.org/pdf/2206.10988v1.pdf | AdvSmo: Black-box Adversarial Attack by Smoothing Linear Structure of Texture | Black-box attacks usually face two problems: poor transferability and the inability to evade the adversarial defense. To overcome these shortcomings, we create an original approach to generate adversarial examples by smoothing the linear structure of the texture in the benign image, called AdvSmo. We construct the adve... | ['Zi Kang', 'Shuliang Jiang', 'Rui Zhang', 'Hui Xia'] | 2022-06-22 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 3.44604403e-01 3.70621502e-01 2.63892502e-01 5.23244701e-02
-6.53805792e-01 -9.68238831e-01 8.56839061e-01 -6.76837146e-01
-2.56263256e-01 5.80414772e-01 -1.88550830e-01 -4.61114138e-01
2.67483503e-01 -9.64681566e-01 -7.60084867e-01 -8.89508009e-01
-3.26501429e-01 -1.46739319e-01 2.43082285e-01 -5.05895317... | [5.587649345397949, 7.841855525970459] |
eaf67d20-52ff-449b-a921-301eb0468bcb | speaker-identification-from-emotional-and | 2210.12701 | null | https://arxiv.org/abs/2210.12701v1 | https://arxiv.org/pdf/2210.12701v1.pdf | Speaker Identification from emotional and noisy speech data using learned voice segregation and Speech VGG | Speech signals are subjected to more acoustic interference and emotional factors than other signals. Noisy emotion-riddled speech data is a challenge for real-time speech processing applications. It is essential to find an effective way to segregate the dominant signal from other external influences. An ideal system sh... | ['Naoufel Werghi', 'Ernesto Damiani', 'Youssef Iraqi', 'Ismail Shahin', 'Shibani Hamsa'] | 2022-10-23 | null | null | null | null | ['speaker-identification'] | ['speech'] | [ 3.00334506e-02 -2.71553636e-01 7.52125382e-01 -3.74467790e-01
-6.13916099e-01 -3.44710112e-01 1.72633752e-01 6.92516342e-02
-4.25268918e-01 6.05606318e-01 2.54421204e-01 -1.77284002e-01
-4.43882458e-02 -1.78443804e-01 -1.57294542e-01 -7.75131404e-01
-1.21580072e-01 9.83205661e-02 -2.23467648e-01 -5.02942145... | [14.272773742675781, 5.93170690536499] |
20ce49fe-698c-4707-ad3f-f29fefe78413 | controlled-random-search-improves-hyper | 1809.01712 | null | http://arxiv.org/abs/1809.01712v3 | http://arxiv.org/pdf/1809.01712v3.pdf | Coverage-Based Designs Improve Sample Mining and Hyper-Parameter Optimization | Sampling one or more effective solutions from large search spaces is a
recurring idea in machine learning, and sequential optimization has become a
popular solution. Typical examples include data summarization, sample mining
for predictive modeling and hyper-parameter optimization. Existing solutions
attempt to adaptiv... | ['Peer-Timo Bremer', 'Bhavya Kailkhura', 'Jayaraman J. Thiagarajan', 'Gowtham Muniraju', 'Andreas Spanias', 'Cihan Tepedelenlioglu'] | 2018-09-05 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [ 4.02360886e-01 1.45937055e-01 -9.28043902e-01 -3.21796030e-01
-1.23898852e+00 -4.47772056e-01 3.77798975e-01 3.98074567e-01
-2.39893124e-01 1.08570850e+00 1.90219596e-01 -3.81661564e-01
-4.56736326e-01 -6.87894762e-01 -5.56299269e-01 -8.24781299e-01
-2.64422536e-01 9.66923177e-01 3.12987864e-02 3.93237710... | [6.908023357391357, 4.298558712005615] |
dd31775f-1db3-4070-9739-e633cf94f365 | probing-neural-dialog-models-for | 2006.08331 | null | https://arxiv.org/abs/2006.08331v1 | https://arxiv.org/pdf/2006.08331v1.pdf | Probing Neural Dialog Models for Conversational Understanding | The predominant approach to open-domain dialog generation relies on end-to-end training of neural models on chat datasets. However, this approach provides little insight as to what these models learn (or do not learn) about engaging in dialog. In this study, we analyze the internal representations learned by neural ope... | ['Yonatan Belinkov', 'Abdelrhman Saleh', 'Stuart Shieber', 'Tovly Deutsch', 'Stephen Casper'] | 2020-06-07 | probing-neural-dialog-models-for-1 | https://aclanthology.org/2020.nlp4convai-1.15 | https://aclanthology.org/2020.nlp4convai-1.15.pdf | ws-2020-7 | ['open-domain-dialog'] | ['natural-language-processing'] | [-1.03571445e-01 8.16939712e-01 -1.53258350e-02 -6.51610732e-01
-3.96799654e-01 -9.12206113e-01 9.64726210e-01 -6.39416203e-02
3.99947315e-02 9.97173429e-01 9.09095526e-01 -5.46865344e-01
1.42343342e-01 -9.10968959e-01 -1.71705216e-01 3.63010019e-02
2.28404924e-01 9.52925980e-01 -2.20334940e-02 -8.40064466... | [12.804779052734375, 8.029428482055664] |
37a69e80-498d-4952-8f9c-991321511339 | bactrian-x-a-multilingual-replicable | 2305.15011 | null | https://arxiv.org/abs/2305.15011v1 | https://arxiv.org/pdf/2305.15011v1.pdf | Bactrian-X : A Multilingual Replicable Instruction-Following Model with Low-Rank Adaptation | Instruction tuning has shown great promise in the field of natural language processing. However, the research on multilingual instruction tuning has been limited due to the scarcity of high-quality instruction-response datasets. To address this gap, we present Bactrian-X, a comprehensive multilingual parallel dataset o... | ['Timothy Baldwin', 'Alham Fikri Aji', 'Minghao Wu', 'Fajri Koto', 'Haonan Li'] | 2023-05-24 | null | null | null | null | ['instruction-following'] | ['natural-language-processing'] | [-5.21510780e-01 -7.43611872e-01 -7.77126551e-01 -6.71501756e-01
-1.31100309e+00 -5.97348511e-01 4.15883482e-01 6.16328530e-02
-6.71362698e-01 6.68037653e-01 5.70385516e-01 -8.81351650e-01
5.24334490e-01 -5.14633179e-01 -8.95269632e-01 -2.51529366e-01
5.99680804e-02 4.39514309e-01 3.49190116e-01 -6.56443477... | [10.660781860351562, 8.359701156616211] |
3b9a4eb6-5ef7-4e32-afc3-bef589c336e3 | cooperative-thresholded-lasso-for-sparse | 2305.19161 | null | https://arxiv.org/abs/2305.19161v1 | https://arxiv.org/pdf/2305.19161v1.pdf | Cooperative Thresholded Lasso for Sparse Linear Bandit | We present a novel approach to address the multi-agent sparse contextual linear bandit problem, in which the feature vectors have a high dimension $d$ whereas the reward function depends on only a limited set of features - precisely $s_0 \ll d$. Furthermore, the learning follows under information-sharing constraints. T... | ['Setareh Maghsudi', 'Xiaotong Cheng', 'Haniyeh Barghi'] | 2023-05-30 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [-7.51186088e-02 1.84167385e-01 -7.05351651e-01 -1.51775435e-01
-1.05484939e+00 -4.69534159e-01 1.69703647e-01 2.78626531e-01
-5.73241770e-01 1.15470123e+00 -1.88388705e-01 -3.03447917e-02
-7.71082640e-01 -9.26997423e-01 -9.65271473e-01 -8.73453081e-01
-6.89302027e-01 1.03975391e+00 -3.65710706e-01 1.30234286... | [4.609948635101318, 3.372896909713745] |
2f395d21-0cd8-4897-8a9e-e3006c29ca90 | leapfrog-diffusion-model-for-stochastic | 2303.10895 | null | https://arxiv.org/abs/2303.10895v1 | https://arxiv.org/pdf/2303.10895v1.pdf | Leapfrog Diffusion Model for Stochastic Trajectory Prediction | To model the indeterminacy of human behaviors, stochastic trajectory prediction requires a sophisticated multi-modal distribution of future trajectories. Emerging diffusion models have revealed their tremendous representation capacities in numerous generation tasks, showing potential for stochastic trajectory predictio... | ['Yanfeng Wang', 'Siheng Chen', 'Qi Zhu', 'Chenxin Xu', 'Weibo Mao'] | 2023-03-20 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Mao_Leapfrog_Diffusion_Model_for_Stochastic_Trajectory_Prediction_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Mao_Leapfrog_Diffusion_Model_for_Stochastic_Trajectory_Prediction_CVPR_2023_paper.pdf | cvpr-2023-1 | ['trajectory-prediction'] | ['computer-vision'] | [-2.77794600e-01 -4.13641781e-01 -4.39924061e-01 -3.63036484e-01
-6.98845446e-01 -5.89331031e-01 6.90641642e-01 -2.80545175e-01
-2.98296213e-01 9.20052946e-01 3.74675453e-01 -3.90976012e-01
-3.32839757e-01 -9.64614332e-01 -7.61620045e-01 -7.81787574e-01
-1.51404053e-01 5.18440127e-01 1.84287772e-01 -1.07257530... | [6.612987041473389, 1.6153903007507324] |
58c023d2-f8ac-4cc0-b01b-b85744bd55f6 | ospc-online-sequential-photometric | 2305.17673 | null | https://arxiv.org/abs/2305.17673v2 | https://arxiv.org/pdf/2305.17673v2.pdf | OSPC: Online Sequential Photometric Calibration | Photometric calibration is essential to many computer vision applications. One of its key benefits is enhancing the performance of Visual SLAM, especially when it depends on a direct method for tracking, such as the standard KLT algorithm. Another advantage could be in retrieving the sensor irradiance values from measu... | ['Daniel Asmar', 'Douaa Khalil', 'Jawad Haidar'] | 2023-05-28 | null | null | null | null | ['visual-odometry'] | ['robots'] | [ 1.39693186e-01 -5.26554167e-01 8.53945613e-02 -3.55559230e-01
-4.45396185e-01 -5.20987093e-01 2.99609363e-01 -9.23031121e-02
-5.89440942e-01 7.30836153e-01 -3.16895515e-01 -1.47973806e-01
-2.57279947e-02 -6.59461856e-01 -5.57490110e-01 -9.82648313e-01
6.09025657e-01 5.19434929e-01 3.62235725e-01 -1.06682532... | [7.760224342346191, -2.2083234786987305] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.