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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
80237570-6a3a-4ebf-838c-a9b8672c7374 | first-arrival-picking-using-u-net-with-lovasz | 2104.02805 | null | https://arxiv.org/abs/2104.02805v1 | https://arxiv.org/pdf/2104.02805v1.pdf | First arrival picking using U-net with Lovasz loss and nearest point picking method | We proposed a robust segmentation and picking workflow to solve the first arrival picking problem for seismic signal processing. Unlike traditional classification algorithm, image segmentation method can utilize the location information by outputting a prediction map which has the same size of the input image. A parame... | ['Hien Van Nguyen', 'Jiefu Chen', 'Xuqing Wu', 'Wenyi Hu', 'Pengyu Yuan'] | 2021-04-06 | null | null | null | null | ['contour-detection'] | ['computer-vision'] | [ 1.77676409e-01 -3.21267657e-02 4.66065288e-01 -2.76769698e-01
-1.23136234e+00 -4.69800264e-01 2.78832883e-01 2.74094939e-01
-7.22652853e-01 5.15479863e-01 -1.17507584e-01 -4.04913649e-02
-5.33534884e-01 -8.36901784e-01 -7.76874304e-01 -9.80944157e-01
-4.25399601e-01 3.40445846e-01 5.74357510e-01 -2.30388433... | [7.209409236907959, 2.106745958328247] |
b78a1573-7d97-402b-9357-0bc568388c45 | a-model-oriented-approach-for-lifting | 2208.03095 | null | https://arxiv.org/abs/2208.03095v1 | https://arxiv.org/pdf/2208.03095v1.pdf | A Model-Oriented Approach for Lifting Symmetries in Answer Set Programming | When solving combinatorial problems, pruning symmetric solution candidates from the search space is essential. Most of the existing approaches are instance-specific and focus on the automatic computation of Symmetry Breaking Constraints (SBCs) for each given problem instance. However, the application of such approaches... | ['Alice Tarzariol'] | 2022-08-05 | null | null | null | null | ['inductive-logic-programming'] | ['methodology'] | [ 6.55334234e-01 5.08775592e-01 -3.43558550e-01 -3.38816643e-01
-5.82614660e-01 -6.23076618e-01 4.70386803e-01 7.60748327e-01
-2.53695231e-02 1.04838717e+00 -4.39462483e-01 -7.69454539e-01
-6.45967364e-01 -1.34070659e+00 -5.26682258e-01 -4.03486997e-01
-2.13624999e-01 1.08494914e+00 4.72369403e-01 -1.88392058... | [8.608901023864746, 6.663442611694336] |
a7504d07-6c4f-4770-add7-75e99999f7b6 | image-deconvolution-with-deep-image-and | 1910.08386 | null | https://arxiv.org/abs/1910.08386v1 | https://arxiv.org/pdf/1910.08386v1.pdf | Image Deconvolution with Deep Image and Kernel Priors | Image deconvolution is the process of recovering convolutional degraded images, which is always a hard inverse problem because of its mathematically ill-posed property. On the success of the recently proposed deep image prior (DIP), we build an image deconvolution model with deep image and kernel priors (DIKP). DIP is ... | ['Zipei Wang', 'Zhunxuan Wang', 'Hakan Bilen', 'Qiqi Li'] | 2019-10-18 | null | null | null | null | ['image-deconvolution'] | ['computer-vision'] | [ 3.64469051e-01 -1.55457288e-01 2.75238961e-01 -1.01855762e-01
-4.34612900e-01 -1.28343076e-01 4.74817067e-01 -8.11753511e-01
-2.77427584e-01 8.95271122e-01 4.65557277e-01 -5.70384078e-02
-1.21572025e-01 -7.35429823e-01 -1.02680016e+00 -9.47955906e-01
1.95267662e-01 -4.25012857e-02 1.64759472e-01 -2.84593910... | [11.481878280639648, -2.430739164352417] |
71bdce57-ee23-4e4a-8376-ea22838b2104 | observability-blocking-for-functional-privacy | 2304.07928 | null | https://arxiv.org/abs/2304.07928v2 | https://arxiv.org/pdf/2304.07928v2.pdf | Observability Blocking for Functional Privacy of Linear Dynamic Networks | This paper addresses the problem of determining the minimum set of state variables in a network that need to be blocked from direct measurements in order to protect functional privacy with respect to {\emph{any}} output matrices. The goal is to prevent adversarial observers or eavesdroppers from inferring a linear func... | ['Yuanqing Xia', 'Ranbo Cheng', 'Yuan Zhang'] | 2023-04-17 | null | null | null | null | ['blocking'] | ['natural-language-processing'] | [ 5.71147859e-01 4.64320719e-01 8.67848191e-03 -1.30202189e-01
-5.80047190e-01 -1.26120329e+00 1.41623691e-01 -3.62848900e-02
-3.74917567e-01 6.69905901e-01 -2.14226142e-01 -8.86496007e-01
-5.97029686e-01 -6.65483415e-01 -7.51899898e-01 -1.08036816e+00
-3.29074562e-01 -1.45034924e-01 -5.68716265e-02 -5.64973988... | [5.913848400115967, 6.930883884429932] |
3dd9ddc5-ce82-4b37-a0fe-f877c5820ab3 | generic-dependency-modeling-for-multi-party | 2302.10680 | null | https://arxiv.org/abs/2302.10680v1 | https://arxiv.org/pdf/2302.10680v1.pdf | Generic Dependency Modeling for Multi-Party Conversation | To model the dependencies between utterances in multi-party conversations, we propose a simple and generic framework based on the dependency parsing results of utterances. Particularly, we present an approach to encoding the dependencies in the form of relative dependency encoding (ReDE) and illustrate how to implement... | ['Ke Yang', 'Xiaojun Quan', 'Weizhou Shen'] | 2023-02-21 | null | null | null | null | ['dependency-parsing'] | ['natural-language-processing'] | [-1.31182507e-01 3.67412090e-01 -5.20899072e-02 -8.76913488e-01
-1.01280737e+00 -5.50207973e-01 7.54523814e-01 -1.15352087e-01
-3.60107832e-02 5.86098373e-01 8.13323498e-01 -5.02848744e-01
3.03305417e-01 -5.06369710e-01 -5.21159708e-01 -4.76154864e-01
-6.46925867e-02 7.42906094e-01 1.68601990e-01 -5.94687819... | [12.488269805908203, 7.757236957550049] |
b687f8ae-005f-4755-b947-af6729c1094b | rng-kbqa-generation-augmented-iterative | 2109.08678 | null | https://arxiv.org/abs/2109.08678v2 | https://arxiv.org/pdf/2109.08678v2.pdf | RnG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering | Existing KBQA approaches, despite achieving strong performance on i.i.d. test data, often struggle in generalizing to questions involving unseen KB schema items. Prior ranking-based approaches have shown some success in generalization, but suffer from the coverage issue. We present RnG-KBQA, a Rank-and-Generate approac... | ['Caiming Xiong', 'Yingbo Zhou', 'Kazuma Hashimoto', 'Semih Yavuz', 'Xi Ye'] | 2021-09-17 | null | https://aclanthology.org/2022.acl-long.417 | https://aclanthology.org/2022.acl-long.417.pdf | acl-2022-5 | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-1.61560193e-01 1.65286317e-01 -2.91039258e-01 -4.31031048e-01
-1.58388317e+00 -8.63329589e-01 3.29475284e-01 4.59482163e-01
-3.58131915e-01 1.09574306e+00 1.46184281e-01 -1.25123784e-01
-7.09939778e-01 -1.16784012e+00 -1.02046442e+00 -3.88278723e-01
1.24171913e-01 1.18906748e+00 8.02987397e-01 -8.52407515... | [10.499441146850586, 7.951272487640381] |
e7a8b529-2125-4cd5-b7b9-ada70b07723b | iterative-zero-shot-llm-prompting-for | 2307.01128 | null | https://arxiv.org/abs/2307.01128v1 | https://arxiv.org/pdf/2307.01128v1.pdf | Iterative Zero-Shot LLM Prompting for Knowledge Graph Construction | In the current digitalization era, capturing and effectively representing knowledge is crucial in most real-world scenarios. In this context, knowledge graphs represent a potent tool for retrieving and organizing a vast amount of information in a properly interconnected and interpretable structure. However, their gener... | ['Sandro Gabriele Tiddia', 'Livio Pompianu', 'Alessandro Sebastian Podda', 'Leonardo Piano', 'Alessandro Giuliani', 'Salvatore Carta'] | 2023-07-03 | null | null | null | null | ['graph-construction', 'graph-generation', 'knowledge-graphs'] | ['graphs', 'graphs', 'knowledge-base'] | [ 5.70500828e-02 4.83001202e-01 -1.81091636e-01 2.66431272e-01
-5.06975770e-01 -8.84714007e-01 8.98823559e-01 5.85638702e-01
-1.54126598e-03 6.02869570e-01 1.54076681e-01 -5.02684534e-01
-6.01017475e-01 -1.14566076e+00 -5.12917399e-01 -3.27353835e-01
6.35590628e-02 5.56389093e-01 2.10261926e-01 -3.91320556... | [9.326194763183594, 8.125299453735352] |
f17632d0-28dc-4d42-8da9-9e3929333278 | cross-domain-few-shot-graph-classification | 2201.08265 | null | https://arxiv.org/abs/2201.08265v1 | https://arxiv.org/pdf/2201.08265v1.pdf | Cross-Domain Few-Shot Graph Classification | We study the problem of few-shot graph classification across domains with nonequivalent feature spaces by introducing three new cross-domain benchmarks constructed from publicly available datasets. We also propose an attention-based graph encoder that uses three congruent views of graphs, one contextual and two topolog... | ['Kaveh Hassani'] | 2022-01-20 | null | null | null | null | ['cross-domain-few-shot'] | ['computer-vision'] | [ 7.24643469e-02 1.09308742e-01 -6.45214498e-01 -2.64100641e-01
-9.23098326e-01 -4.88270730e-01 7.42037654e-01 2.54141778e-01
-1.12366505e-01 7.56162882e-01 2.50453621e-01 1.67728271e-02
-2.32693478e-01 -9.43952203e-01 -7.25241542e-01 -3.69902939e-01
-2.56111473e-01 5.58821142e-01 2.37803221e-01 -3.03045481... | [9.963494300842285, 3.0825862884521484] |
61cdf053-016c-4c2b-a596-b5a2a759a060 | pixel-level-equalized-matching-for-video | 2209.03139 | null | https://arxiv.org/abs/2209.03139v2 | https://arxiv.org/pdf/2209.03139v2.pdf | Pixel-Level Equalized Matching for Video Object Segmentation | Feature similarity matching, which transfers the information of the reference frame to the query frame, is a key component in semi-supervised video object segmentation. If surjective matching is adopted, background distractors can easily occur and degrade the performance. Bijective matching mechanisms try to prevent th... | ['Sangyoun Lee', 'Chaewon Park', 'Minhyeok Lee', 'Seunghoon Lee', 'MyeongAh Cho', 'Woo Jin Kim', 'Suhwan Cho'] | 2022-09-04 | null | null | null | null | ['semi-supervised-video-object-segmentation'] | ['computer-vision'] | [ 0.4289758 0.07187465 -0.5807999 -0.39339277 -0.8473154 -0.50261086
0.528147 -0.06335836 -0.60399777 0.58947873 -0.08850454 -0.01882804
0.21327367 -0.6287555 -0.9672189 -0.65958136 0.07787567 0.12231856
0.81202894 0.2519071 0.33928034 0.24879882 -1.6237096 0.21430178
0.82571524 1.2713555 0.... | [9.180779457092285, -0.11028552055358887] |
bdd2397e-f8f5-4a9d-9376-c2ee1c6ecbbc | continuously-index-domain-adaptation | null | null | https://icml.cc/virtual/2020/poster/5986 | http://wanghao.in/paper/ICML20_CIDA.pdf | Continuously Index Domain Adaptation | Existing domain adaptation focuses on transferring knowledge between domains with categorical indices (e.g., between datasets A and B). However, many tasks involve continuously indexed domains. For example, in medical applications, one often needs to transfer disease analysis and prediction across patients of different... | ['Hao Wang', 'Dina Katabi', 'Hao He'] | 2020-07-01 | null | null | null | icml-2020-7 | ['continuously-indexed-domain-adaptation'] | ['methodology'] | [ 6.76463425e-01 7.48980232e-03 -4.38361198e-01 -4.46925193e-01
-8.09865892e-01 -6.87347770e-01 3.89945060e-01 2.20011026e-01
-3.27262431e-01 1.21299338e+00 1.49736419e-01 -1.82799697e-01
-1.26878634e-01 -9.19099748e-01 -8.48575592e-01 -5.93791127e-01
-2.53831856e-02 8.89527082e-01 1.76017776e-01 -2.67085999... | [10.35343074798584, 3.112499713897705] |
e8c2d446-70ff-4bfa-b1a1-8075325b6020 | extracting-pico-elements-from-rct-abstracts | 1901.08351 | null | http://arxiv.org/abs/1901.08351v1 | http://arxiv.org/pdf/1901.08351v1.pdf | Extracting PICO elements from RCT abstracts using 1-2gram analysis and multitask classification | The core of evidence-based medicine is to read and analyze numerous papers in
the medical literature on a specific clinical problem and summarize the
authoritative answers to that problem. Currently, to formulate a clear and
focused clinical problem, the popular PICO framework is usually adopted, in
which each clinical... | ['Wu Jinfa', 'Xia Yuan', 'Shi Qinwen', 'Li Ke', 'Li Shilei', 'Liao xiaoli'] | 2019-01-24 | null | null | null | null | ['pico'] | ['natural-language-processing'] | [ 2.53252298e-01 -2.66075313e-01 -7.25478947e-01 -3.45003068e-01
-7.33608961e-01 9.48846992e-03 4.58268136e-01 7.91121840e-01
-2.73960471e-01 1.00295281e+00 9.58747864e-02 -4.10260201e-01
-6.88659489e-01 -3.06145966e-01 -3.13429207e-01 -8.13376188e-01
-1.54996822e-02 4.91788596e-01 8.52569938e-02 1.53702021... | [8.370064735412598, 8.572443008422852] |
f09934c4-940a-45b3-a0fd-0399ce540392 | iterative-residual-image-deconvolution | 1804.06042 | null | http://arxiv.org/abs/1804.06042v2 | http://arxiv.org/pdf/1804.06042v2.pdf | Iterative Residual Image Deconvolution | Image deblurring, a.k.a. image deconvolution, recovers a clear image from
pixel superposition caused by blur degradation. Few deep convolutional neural
networks (CNN) succeed in addressing this task. In this paper, we first
demonstrate that the minimum-mean-square-error (MMSE) solution to image
deblurring can be intere... | ['Qian Yin', 'Zijian Hu', 'Li Si-Yao', 'Junfeng Li', 'Furong Zhao', 'Dongwei Ren'] | 2018-04-17 | null | null | null | null | ['image-deconvolution'] | ['computer-vision'] | [ 5.42672992e-01 -7.02522993e-02 3.08571070e-01 -1.71160400e-01
-4.08595353e-01 -2.99798429e-01 3.67307812e-01 -7.20317066e-01
-1.65132564e-02 7.37113535e-01 4.88448769e-01 -2.56977439e-01
5.58549091e-02 -2.01461062e-01 -7.68042147e-01 -8.37789893e-01
5.61195388e-02 -7.41154909e-01 3.81240994e-02 5.34059666... | [11.482413291931152, -2.5796139240264893] |
0305cfcf-7329-4af4-b2c6-89457d12bb77 | olenet-at-semeval-2019-task-9-bert-based | null | null | https://aclanthology.org/S19-2216 | https://aclanthology.org/S19-2216.pdf | OleNet at SemEval-2019 Task 9: BERT based Multi-Perspective Models for Suggestion Mining | This paper describes our system partici- pated in Task 9 of SemEval-2019: the task is focused on suggestion mining and it aims to classify given sentences into sug- gestion and non-suggestion classes in do- main specific and cross domain training setting respectively. We propose a multi- perspective architecture for le... | ['Yu Sun', 'Jiaxiang Liu', 'Shuohuan Wang'] | 2019-06-01 | null | null | null | semeval-2019-6 | ['suggestion-mining'] | ['natural-language-processing'] | [ 3.17056745e-01 6.22518182e-01 9.46706682e-02 -6.25374675e-01
-8.22911263e-01 -1.96430117e-01 6.12774432e-01 2.21375391e-01
-5.28634369e-01 7.37122476e-01 6.58961415e-01 -6.60012484e-01
1.35243028e-01 -7.70328104e-01 -8.84187162e-01 -2.50381291e-01
-1.04670592e-01 5.49164176e-01 -1.05129667e-02 -5.65153956... | [10.946457862854004, 7.663675785064697] |
80c3a9d2-415b-46f9-887f-b895c6f6629d | embodied-navigation-at-the-art-gallery | 2204.09069 | null | https://arxiv.org/abs/2204.09069v1 | https://arxiv.org/pdf/2204.09069v1.pdf | Embodied Navigation at the Art Gallery | Embodied agents, trained to explore and navigate indoor photorealistic environments, have achieved impressive results on standard datasets and benchmarks. So far, experiments and evaluations have involved domestic and working scenes like offices, flats, and houses. In this paper, we build and release a new 3D space wit... | ['Rita Cucchiara', 'Lorenzo Baraldi', 'Marcella Cornia', 'Silvia Cascianelli', 'Federico Landi', 'Roberto Bigazzi'] | 2022-04-19 | null | null | null | null | ['pointgoal-navigation'] | ['robots'] | [-3.59464675e-01 -6.08621025e-03 4.53066766e-01 -1.53228238e-01
-1.40656590e-01 -7.56439626e-01 8.44998062e-01 -2.83221733e-02
-6.99046612e-01 7.56187499e-01 5.05124032e-01 -2.72300243e-01
1.62248854e-02 -9.74193811e-01 -6.86285436e-01 -6.37874365e-01
-5.02774179e-01 6.49386764e-01 4.52241361e-01 -7.53281593... | [4.703334331512451, 0.5293849110603333] |
14b819a6-6c7c-46eb-8e52-8f3b37c38590 | rethinking-video-salient-object-ranking | 2203.17257 | null | https://arxiv.org/abs/2203.17257v1 | https://arxiv.org/pdf/2203.17257v1.pdf | Rethinking Video Salient Object Ranking | Salient Object Ranking (SOR) involves ranking the degree of saliency of multiple salient objects in an input image. Most recently, a method is proposed for ranking salient objects in an input video based on a predicted fixation map. It relies solely on the density of the fixations within the salient objects to infer th... | ['Rynson W. H. Lau', 'Huankang Guan', 'Jiaying Lin'] | 2022-03-31 | null | null | null | null | ['saliency-ranking'] | ['computer-vision'] | [ 2.38460019e-01 -2.04954341e-01 -3.06121379e-01 -3.89200777e-01
-5.02918780e-01 -1.66157648e-01 2.87756413e-01 2.77761638e-01
-2.10795745e-01 4.79680598e-01 4.96137440e-01 3.22611004e-01
-9.98964533e-02 -3.72969300e-01 -7.58063197e-01 -4.42390561e-01
-1.98842585e-01 -1.48133770e-01 1.13051200e+00 -3.78376603... | [9.762688636779785, -0.16618628799915314] |
981dded1-04bf-4ec3-bbff-fccc93981485 | stop-hop-early-classification-of-irregular | 2208.09795 | null | https://arxiv.org/abs/2208.09795v1 | https://arxiv.org/pdf/2208.09795v1.pdf | Stop&Hop: Early Classification of Irregular Time Series | Early classification algorithms help users react faster to their machine learning model's predictions. Early warning systems in hospitals, for example, let clinicians improve their patients' outcomes by accurately predicting infections. While early classification systems are advancing rapidly, a major gap remains: exis... | ['Elke Rundensteiner', 'Xiangnan Kong', 'Jidapa Thadajarassiri', 'Walter Gerych', 'Thomas Hartvigsen'] | 2022-08-21 | null | null | null | null | ['classification', 'irregular-time-series'] | ['methodology', 'time-series'] | [ 2.28330940e-01 -1.29703656e-01 -4.94728625e-01 -2.75483608e-01
-7.54234672e-01 -3.65855485e-01 3.52777034e-01 6.89377904e-01
-3.43671024e-01 4.94853050e-01 1.93036363e-01 -8.13150465e-01
-3.65473807e-01 -6.04821086e-01 -3.00909519e-01 -5.62214851e-01
-7.35310137e-01 4.24150318e-01 1.00547686e-01 -1.80177093... | [7.284955024719238, 3.259550094604492] |
59acb3f8-9e38-435c-974f-cd51d825c1eb | parameter-estimation-in-ill-conditioned-low | 2208.04471 | null | https://arxiv.org/abs/2208.04471v1 | https://arxiv.org/pdf/2208.04471v1.pdf | Parameter Estimation in Ill-conditioned Low-inertia Power Systems | This paper examines model parameter estimation in dynamic power systems whose governing electro-mechanical equations are ill-conditioned or singular. This ill-conditioning is because of converter-interfaced power systems generators' zero or small inertia contribution. Consequently, the overall system inertia decreases,... | ['Oliver Kosut', 'Lalitha Sankar', 'Rajasekhar Anguluri'] | 2022-08-09 | null | null | null | null | ['connectivity-estimation'] | ['graphs'] | [ 2.61446591e-02 1.55440360e-01 -2.64904916e-01 5.59034765e-01
1.17895072e-02 -9.96858954e-01 3.01171869e-01 -3.54297966e-01
2.40692094e-01 8.32979620e-01 -3.15432906e-01 -3.18484902e-01
-7.96353877e-01 -4.63861406e-01 -1.65189564e-01 -9.60635602e-01
-3.64525139e-01 4.12743390e-01 -1.61162585e-01 -4.38039184... | [5.5233235359191895, 2.6724047660827637] |
ae585a73-9a94-49e2-b849-1c09c5d6cfc6 | multi-source-semantic-graph-based-multimodal | 2306.16650 | null | https://arxiv.org/abs/2306.16650v1 | https://arxiv.org/pdf/2306.16650v1.pdf | Multi-source Semantic Graph-based Multimodal Sarcasm Explanation Generation | Multimodal Sarcasm Explanation (MuSE) is a new yet challenging task, which aims to generate a natural language sentence for a multimodal social post (an image as well as its caption) to explain why it contains sarcasm. Although the existing pioneer study has achieved great success with the BART backbone, it overlooks t... | ['Liqiang Nie', 'Mengzhao Jia', 'Kun Ouyang', 'Xuemeng Song', 'Liqiang Jing'] | 2023-06-29 | null | null | null | null | ['explanation-generation'] | ['natural-language-processing'] | [ 1.72273785e-01 4.90535438e-01 -7.52818882e-02 -3.04274291e-01
-5.53536415e-01 -3.06876510e-01 4.75480974e-01 3.24202418e-01
-7.72335678e-02 3.86371404e-01 6.55838251e-01 6.19159788e-02
1.17855296e-01 -4.35437381e-01 -5.68442047e-01 -4.59193587e-01
9.52634156e-01 3.68856937e-01 6.68612644e-02 -4.39090222... | [10.576823234558105, 1.2118016481399536] |
8b1443d0-c589-4ccb-9564-2f4b45b513b1 | a-neural-ode-interpretation-of-transformer | 2212.06011 | null | https://arxiv.org/abs/2212.06011v1 | https://arxiv.org/pdf/2212.06011v1.pdf | A Neural ODE Interpretation of Transformer Layers | Transformer layers, which use an alternating pattern of multi-head attention and multi-layer perceptron (MLP) layers, provide an effective tool for a variety of machine learning problems. As the transformer layers use residual connections to avoid the problem of vanishing gradients, they can be viewed as the numerical ... | ['Biswadip Dey', 'Amit Chakraborty', 'Tongtao Zhang', 'Yaofeng Desmond Zhong'] | 2022-12-12 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [-1.22085243e-01 3.31241250e-01 2.02096373e-01 -2.35436112e-02
-1.51019841e-01 1.26443654e-02 4.49258655e-01 -1.06172472e-01
-4.81140435e-01 5.89899719e-01 -1.33521587e-01 -4.59453851e-01
1.70133427e-01 -6.51376963e-01 -8.81221771e-01 -8.08155000e-01
2.59757787e-01 1.02636375e-01 4.42984372e-01 -2.81638533... | [7.44025182723999, 3.4817287921905518] |
fe58dc0d-2beb-4bf8-a283-4cde673b9071 | tracking-emerges-by-looking-around-static | 2008.01295 | null | https://arxiv.org/abs/2008.01295v1 | https://arxiv.org/pdf/2008.01295v1.pdf | Tracking Emerges by Looking Around Static Scenes, with Neural 3D Mapping | We hypothesize that an agent that can look around in static scenes can learn rich visual representations applicable to 3D object tracking in complex dynamic scenes. We are motivated in this pursuit by the fact that the physical world itself is mostly static, and multiview correspondence labels are relatively cheap to c... | ['Paul Schydlo', 'Adam W. Harley', 'Shrinidhi K. Lakshmikanth', 'Katerina Fragkiadaki'] | 2020-08-04 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/5494_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123710596.pdf | eccv-2020-8 | ['3d-object-tracking'] | ['computer-vision'] | [-2.40777388e-01 3.32302190e-02 -2.75881052e-01 -1.44056335e-01
-6.18221045e-01 -9.86528397e-01 7.91135669e-01 -1.14693835e-01
-2.53515154e-01 2.80114532e-01 -2.17740580e-01 1.08234577e-01
7.66045302e-02 -4.03214514e-01 -1.27899647e+00 -5.25341392e-01
-3.25941950e-01 7.70782471e-01 2.51986802e-01 4.81225252... | [7.021667003631592, -2.343470573425293] |
d769eff8-a6b1-4cae-af7a-cbc472fc72ff | diffface-diffusion-based-face-swapping-with | 2212.13344 | null | https://arxiv.org/abs/2212.13344v1 | https://arxiv.org/pdf/2212.13344v1.pdf | DiffFace: Diffusion-based Face Swapping with Facial Guidance | In this paper, we propose a diffusion-based face swapping framework for the first time, called DiffFace, composed of training ID conditional DDPM, sampling with facial guidance, and a target-preserving blending. In specific, in the training process, the ID conditional DDPM is trained to generate face images with the de... | ['Kwanghee Lee', 'Seungryong Kim', 'Kychul Lee', 'Jisu Nam', 'Junyoung Seo', 'Seokju Cho', 'Yunho Kim', 'Kihong Kim'] | 2022-12-27 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [ 2.35709175e-01 3.33390623e-01 -7.62785450e-02 -2.76662171e-01
-3.81161481e-01 -3.33108842e-01 5.05251586e-01 -6.60684884e-01
2.38532886e-01 6.35094762e-01 -1.41462818e-01 1.04143001e-01
1.08300015e-01 -9.02206898e-01 -7.74565041e-01 -9.74551678e-01
5.02245009e-01 4.19479996e-01 -6.66859522e-02 -2.02056512... | [12.72463321685791, -0.07322370260953903] |
06b73885-30b7-4837-b46e-6a3c7a5b4c3c | on-context-distribution-shift-in-task | 2304.00354 | null | https://arxiv.org/abs/2304.00354v2 | https://arxiv.org/pdf/2304.00354v2.pdf | On Context Distribution Shift in Task Representation Learning for Offline Meta RL | Offline Meta Reinforcement Learning (OMRL) aims to learn transferable knowledge from offline datasets to enhance the learning process for new target tasks. Context-based Reinforcement Learning (RL) adopts a context encoder to expediently adapt the agent to new tasks by inferring the task representation, and then adjust... | ['Bin Liu', 'ZiHao Zhou', 'Chenyang Zhao'] | 2023-04-01 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [ 3.07738096e-01 -6.02553459e-03 -3.94774467e-01 -4.01647627e-01
-8.24987829e-01 -6.50767148e-01 6.45927548e-01 -6.80517331e-02
-4.54369903e-01 1.08523476e+00 -6.58972487e-02 -4.88290608e-01
-1.04412615e-01 -5.00527620e-01 -9.83645737e-01 -4.60250050e-01
-1.66111335e-01 3.58372003e-01 -1.37336284e-01 -2.01002717... | [4.1221208572387695, 1.9004857540130615] |
8eb384da-f52e-4ebc-b40a-b4dbc3320425 | a-fast-and-robust-camera-imu-online | 2305.08247 | null | https://arxiv.org/abs/2305.08247v1 | https://arxiv.org/pdf/2305.08247v1.pdf | A Fast and Robust Camera-IMU Online Calibration Method For Localization System | Autonomous driving has spurred the development of sensor fusion techniques, which combine data from multiple sensors to improve system performance. In particular, localization system based on sensor fusion , such as Visual Simultaneous Localization and Mapping (VSLAM), is an important component in environment perceptio... | ['Jian Zhao', 'Bing Zhu', 'Pengxiang Meng', 'Xiaowen Tao'] | 2023-05-14 | null | null | null | null | ['simultaneous-localization-and-mapping'] | ['computer-vision'] | [-3.51116002e-01 -7.48355210e-01 -8.36604908e-02 -1.97537005e-01
-2.69236296e-01 -6.16415083e-01 4.94472474e-01 2.81680422e-03
-5.70581973e-01 4.75843310e-01 -2.77251571e-01 -5.49795777e-02
1.00115418e-01 -4.62187916e-01 -7.97032475e-01 -6.96493447e-01
4.99497473e-01 4.94178608e-02 3.56035858e-01 -3.55835706... | [7.514829158782959, -1.9747815132141113] |
6b959daa-c391-4e4e-98a2-a9e34556eaff | one-shot-detail-retouching-with-patch-space | 2210.01217 | null | https://arxiv.org/abs/2210.01217v3 | https://arxiv.org/pdf/2210.01217v3.pdf | One-shot Detail Retouching with Patch Space Neural Transformation Blending | Photo retouching is a difficult task for novice users as it requires expert knowledge and advanced tools. Photographers often spend a great deal of time generating high-quality retouched photos with intricate details. In this paper, we introduce a one-shot learning based technique to automatically retouch details of an... | ['Cengiz Oztireli', 'Fazilet Gokbudak'] | 2022-10-03 | null | null | null | null | ['photo-retouching', 'one-shot-learning'] | ['computer-vision', 'methodology'] | [ 6.12753570e-01 1.15409821e-01 2.73091167e-01 -2.20443070e-01
-7.62823761e-01 -6.82081342e-01 7.21663952e-01 7.91955516e-02
-1.30187526e-01 6.42385900e-01 1.26270562e-01 3.33646595e-01
-8.42706934e-02 -7.47018933e-01 -1.07480228e+00 -3.40106726e-01
3.45499426e-01 -4.23500910e-02 5.08176625e-01 -2.03039721... | [11.443848609924316, -0.6696160435676575] |
0d14c6e1-59dd-4a42-b168-c7b8b0b8e035 | neural-cell-video-synthesis-via-optical-flow | 2212.03250 | null | https://arxiv.org/abs/2212.03250v1 | https://arxiv.org/pdf/2212.03250v1.pdf | Neural Cell Video Synthesis via Optical-Flow Diffusion | The biomedical imaging world is notorious for working with small amounts of data, frustrating state-of-the-art efforts in the computer vision and deep learning worlds. With large datasets, it is easier to make progress we have seen from the natural image distribution. It is the same with microscopy videos of neuron cel... | ['Min Zou', 'Nathaniel Harris', 'Khoa Luu', 'Manuel Serna-Aguilera'] | 2022-12-06 | null | null | null | null | ['video-generation', 'culture'] | ['computer-vision', 'speech'] | [ 1.86078370e-01 -1.01035431e-01 2.34983936e-01 -3.90955210e-02
-3.23100209e-01 -5.47197282e-01 4.14345711e-01 -3.02813381e-01
-6.57171369e-01 1.02717364e+00 1.54818594e-01 -2.01355904e-01
7.69191459e-02 -5.92575133e-01 -7.47751236e-01 -1.04250598e+00
1.55688161e-02 1.32496044e-01 2.99039096e-01 4.35796380... | [10.785906791687012, -1.3900699615478516] |
41b9b7b3-4798-42a4-94d6-b2de3ebf7403 | adding-syntactic-annotations-to-flickr30k | null | null | https://aclanthology.org/L18-1716 | https://aclanthology.org/L18-1716.pdf | Adding Syntactic Annotations to Flickr30k Entities Corpus for Multimodal Ambiguous Prepositional-Phrase Attachment Resolution | null | ['Frederic Bechet', 'Alexis Nasr', 'Sebastien Delecraz', 'Benoit Favre'] | 2018-05-01 | adding-syntactic-annotations-to-flickr30k-1 | https://aclanthology.org/L18-1716 | https://aclanthology.org/L18-1716.pdf | lrec-2018-5 | ['prepositional-phrase-attachment'] | ['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... | [-7.288529396057129, 3.716902494430542] |
0c748a81-74d0-43b0-9739-aee4ef6912e4 | pediatric-automatic-sleep-staging-a | 2108.10211 | null | https://arxiv.org/abs/2108.10211v3 | https://arxiv.org/pdf/2108.10211v3.pdf | Pediatric Automatic Sleep Staging: A comparative study of state-of-the-art deep learning methods | Background: Despite the tremendous progress recently made towards automatic sleep staging in adults, it is currently unknown if the most advanced algorithms generalize to the pediatric population, which displays distinctive characteristics in overnight polysomnography (PSG). Methods: To answer the question, in this wor... | ['Mathias Baumert', 'Alfred Mertins', 'Huy Phan'] | 2021-08-23 | null | null | null | null | ['sleep-staging'] | ['medical'] | [ 2.87145879e-02 2.28305086e-01 -3.98628652e-01 -5.30524850e-01
-3.01868498e-01 -3.02854627e-01 -2.57436067e-01 3.66926134e-01
-4.90798235e-01 9.03008401e-01 1.61873624e-01 -1.53859228e-01
-4.25781250e-01 -2.86897212e-01 -5.97711317e-02 -8.53074968e-01
-2.08330780e-01 7.80992568e-01 8.94202739e-02 5.17503768... | [13.506965637207031, 3.516704797744751] |
71f808e6-77fb-4a7d-b486-4d54d939e5cc | a-secure-federated-learning-framework-for | 2209.14547 | null | https://arxiv.org/abs/2209.14547v2 | https://arxiv.org/pdf/2209.14547v2.pdf | A Secure Federated Learning Framework for Residential Short Term Load Forecasting | Smart meter measurements, though critical for accurate demand forecasting, face several drawbacks including consumers' privacy, data breach issues, to name a few. Recent literature has explored Federated Learning (FL) as a promising privacy-preserving machine learning alternative which enables collaborative learning of... | ['Robin Doss', 'Abdun Naser Mahmood', 'Shama Naz Islam', 'Nasser Hosseinzadeh', 'Adnan Anwar', 'Muhammad Akbar Husnoo'] | 2022-09-29 | null | null | null | null | ['load-forecasting'] | ['miscellaneous'] | [ 9.91260037e-02 -2.61595491e-02 -1.18317641e-01 -5.71634948e-01
-1.00352013e+00 -1.21809804e+00 7.34423697e-01 1.72289491e-01
-8.42075348e-02 6.14245057e-01 2.34335303e-01 -6.80787683e-01
7.14084879e-02 -8.14413846e-01 -8.23508799e-01 -1.24417925e+00
-3.13913167e-01 -7.96058103e-02 -2.37347811e-01 1.25392139... | [5.816794395446777, 6.668407917022705] |
45ca7e0b-b05c-44e3-bc0d-5e7ab44de8c1 | unaligned-supervision-for-automatic-music | 2204.13668 | null | https://arxiv.org/abs/2204.13668v1 | https://arxiv.org/pdf/2204.13668v1.pdf | Unaligned Supervision For Automatic Music Transcription in The Wild | Multi-instrument Automatic Music Transcription (AMT), or the decoding of a musical recording into semantic musical content, is one of the holy grails of Music Information Retrieval. Current AMT approaches are restricted to piano and (some) guitar recordings, due to difficult data collection. In order to overcome data c... | ['Amit H. Bermano', 'Ben Maman'] | 2022-04-28 | null | null | null | null | ['music-transcription', 'music-information-retrieval'] | ['music', 'music'] | [ 6.73657954e-01 -8.28615203e-02 -8.83254111e-02 -2.22394243e-01
-1.59992826e+00 -1.26037109e+00 3.24187130e-01 -2.71434337e-01
-2.25434467e-01 4.79516983e-01 3.64507616e-01 1.47292182e-01
-3.17548454e-01 -2.00246111e-01 -7.18109548e-01 -4.43986058e-01
8.98724943e-02 7.68888652e-01 -2.88503468e-01 -1.45148620... | [15.830445289611816, 5.348630428314209] |
c7c247f7-06a9-44c7-80f5-afaeb60f8190 | gait-recognition-in-the-wild-a-benchmark-1 | 2205.02692 | null | https://arxiv.org/abs/2205.02692v1 | https://arxiv.org/pdf/2205.02692v1.pdf | Gait Recognition in the Wild: A Benchmark | Gait benchmarks empower the research community to train and evaluate high-performance gait recognition systems. Even though growing efforts have been devoted to cross-view recognition, academia is restricted by current existing databases captured in the controlled environment. In this paper, we contribute a new benchma... | ['Jie zhou', 'Jiwen Lu', 'Dalong Du', 'Guan Huang', 'Jiankang Deng', 'JunJie Huang', 'Tian Yang', 'Xianda Guo', 'Zheng Zhu'] | 2022-05-05 | gait-recognition-in-the-wild-a-benchmark | http://openaccess.thecvf.com//content/ICCV2021/html/Zhu_Gait_Recognition_in_the_Wild_A_Benchmark_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zhu_Gait_Recognition_in_the_Wild_A_Benchmark_ICCV_2021_paper.pdf | iccv-2021-1 | ['gait-recognition-in-the-wild', 'gait-recognition'] | ['computer-vision', 'computer-vision'] | [-1.29667297e-01 -7.84648359e-01 -2.47289777e-01 -1.27485722e-01
-5.91086924e-01 -3.31334531e-01 1.86231777e-01 -7.08893359e-01
-2.32647792e-01 4.45668101e-01 1.07362986e-01 2.93703556e-01
4.33581352e-01 -5.74780345e-01 -3.72414291e-01 -1.07694244e+00
-4.37225431e-01 3.98965716e-01 3.42039227e-01 -3.38346899... | [14.303915023803711, 1.4065386056900024] |
19cccf5b-1581-4200-886c-ec7dfda984e2 | o2d2-out-of-distribution-detector-to-capture | 2106.15825 | null | https://arxiv.org/abs/2106.15825v3 | https://arxiv.org/pdf/2106.15825v3.pdf | O2D2: Out-Of-Distribution Detector to Capture Undecidable Trials in Authorship Verification | The PAN 2021 authorship verification (AV) challenge is part of a three-year strategy, moving from a cross-topic/closed-set AV task to a cross-topic/open-set AV task over a collection of fanfiction texts. In this work, we present a novel hybrid neural-probabilistic framework that is designed to tackle the challenges of ... | ['Dorothea Kolossa', 'Robert M. Nickel', 'Benedikt Boenninghoff'] | 2021-06-30 | null | null | null | null | ['authorship-verification'] | ['natural-language-processing'] | [ 1.16791494e-01 1.17284860e-02 8.33898708e-02 -5.03414989e-01
-1.30373871e+00 -7.55222976e-01 1.11343014e+00 1.99040085e-01
-5.39088607e-01 6.95941031e-01 2.33780518e-01 -1.48708194e-01
9.43811331e-03 -3.20437223e-01 -6.78481340e-01 -2.11512119e-01
4.46610063e-01 7.04065323e-01 3.28104943e-01 6.22772202... | [9.590338706970215, 10.562538146972656] |
07fa7dc9-4ff8-4d4f-8cc4-ddc953f76b34 | data-provenance-inference-in-machine-learning | 2211.13416 | null | https://arxiv.org/abs/2211.13416v2 | https://arxiv.org/pdf/2211.13416v2.pdf | Data Origin Inference in Machine Learning | It is a growing direction to utilize unintended memorization in ML models to benefit real-world applications, with recent efforts like user auditing, dataset ownership inference and forgotten data measurement. Standing on the point of ML model development, we introduce a process named data origin inference, to assist M... | ['Xiang-Yang Li', 'Mingxue Xu'] | 2022-11-24 | null | null | null | null | ['inference-attack', 'memorization'] | ['adversarial', 'natural-language-processing'] | [ 6.80166110e-02 1.32880434e-01 -4.53649044e-01 -5.19540966e-01
-4.04789925e-01 -4.48662728e-01 5.32428563e-01 2.98185587e-01
-2.93556303e-01 8.79957080e-01 -2.15295032e-01 -8.89911234e-01
-1.08857766e-01 -9.28646982e-01 -1.10282135e+00 -1.62085012e-01
1.00017786e-01 2.94916123e-01 1.14074640e-01 2.02596992... | [9.14093017578125, 7.596006393432617] |
fe05dbea-600d-4606-bf5c-74fa6a713dcf | spoofing-and-anti-spoofing-with-wax-figure | 1910.05457 | null | https://arxiv.org/abs/1910.05457v1 | https://arxiv.org/pdf/1910.05457v1.pdf | Spoofing and Anti-Spoofing with Wax Figure Faces | We have witnessed rapid advances in both face presentation attack models and presentation attack detection (PAD) in recent years. Compared to widely studied 2D face presentation attacks (e.g. printed photos and video replays), 3D face presentation attacks are more challenging because face recognition systems (FRS) is m... | ['Zhengquan Xu', 'Shan Jia', 'Xin Li', 'Chuanbo Hu'] | 2019-10-12 | null | null | null | null | ['small-data'] | ['computer-vision'] | [ 4.62574422e-01 -3.36075932e-01 1.81942448e-01 -3.39425765e-02
-4.70882177e-01 -6.71925426e-01 7.33520687e-01 -3.71922404e-01
4.25797999e-02 3.43826562e-01 -9.63614732e-02 -1.81831002e-01
-7.45694637e-02 -4.77444321e-01 -3.16842079e-01 -8.04096758e-01
-3.52863044e-01 -7.45600834e-02 1.85535431e-01 -3.67965639... | [13.073365211486816, 1.1427404880523682] |
ed40940e-3ab3-4d27-a20a-db72388bbbf1 | extremely-low-resource-machine-translation | 2105.13065 | null | https://arxiv.org/abs/2105.13065v1 | https://arxiv.org/pdf/2105.13065v1.pdf | Extremely low-resource machine translation for closely related languages | An effective method to improve extremely low-resource neural machine translation is multilingual training, which can be improved by leveraging monolingual data to create synthetic bilingual corpora using the back-translation method. This work focuses on closely related languages from the Uralic language family: from Es... | ['Mark Fišel', 'Andre Tättar', 'Maali Tars'] | 2021-05-27 | null | https://aclanthology.org/2021.nodalida-main.5 | https://aclanthology.org/2021.nodalida-main.5.pdf | nodalida-2021-5 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [-5.05985022e-02 -1.38718069e-01 -5.76075017e-01 -4.35130626e-01
-1.49846649e+00 -8.76796603e-01 8.27221096e-01 -6.18405461e-01
-6.71180248e-01 1.59952426e+00 3.34214061e-01 -9.30931568e-01
4.69629496e-01 -6.61628783e-01 -1.03622484e+00 -2.79374897e-01
3.33855689e-01 1.08466315e+00 -3.30067426e-01 -9.27097201... | [11.588528633117676, 10.331844329833984] |
55f6c0d9-7ea3-447e-a9c0-de8c6c58ea8f | thailmcut-unsupervised-pretraining-for-thai | null | null | https://aclanthology.org/2020.lrec-1.858 | https://aclanthology.org/2020.lrec-1.858.pdf | ThaiLMCut: Unsupervised Pretraining for Thai Word Segmentation | We propose ThaiLMCut, a semi-supervised approach for Thai word segmentation which utilizes a bi-directional character language model (LM) as a way to leverage useful linguistic knowledge from unlabeled data. After the language model is trained on substantial unlabeled corpora, the weights of its embedding and recurrent... | ['Hinrich Sch{\\"u}tze', 'Michael Matuschek', 'Liliana Mamani Sanchez', 'Ivan Bilan', 'Suteera Seeha', 'Johannes Huber'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['thai-word-tokenization'] | ['natural-language-processing'] | [ 2.31034055e-01 4.77729261e-01 -5.69801807e-01 -3.35303307e-01
-1.30889785e+00 -8.05529594e-01 3.01150560e-01 3.53483036e-02
-8.21769118e-01 7.55835176e-01 1.43959522e-01 -4.83388275e-01
4.45900738e-01 -4.61462498e-01 -6.26609743e-01 -5.77743411e-01
2.68614113e-01 6.83015406e-01 4.20849442e-01 -6.44989237... | [10.043135643005371, 10.07657241821289] |
7241e7b3-8379-499a-917d-e4ea3f68bdba | mucic-lt-edi-acl2022-hope-speech-detection | null | null | https://aclanthology.org/2022.ltedi-1.20 | https://aclanthology.org/2022.ltedi-1.20.pdf | MUCIC@LT-EDI-ACL2022: Hope Speech Detection using Data Re-Sampling and 1D Conv-LSTM | Spreading positive vibes or hope content on social media may help many people to get motivated in their life. To address Hope Speech detection in YouTube comments, this paper presents the description of the models submitted by our team - MUCIC, to the Hope Speech Detection for Equality, Diversity, and Inclusion (HopeED... | ['Grigori Sidorov', 'Hosahalli Shashirekha', 'Fazlourrahman Balouchzahi', 'Anusha Gowda'] | null | null | null | null | ltedi-acl-2022-5 | ['hope-speech-detection'] | ['natural-language-processing'] | [-4.77632761e-01 4.05036807e-01 -7.34310269e-01 -9.21041444e-02
-1.04180288e+00 -3.38908046e-01 7.77896225e-01 5.89685738e-01
-3.12313855e-01 8.74180734e-01 1.00447845e+00 -2.39330411e-01
1.68852538e-01 -3.35611165e-01 -9.61985886e-02 -2.27163523e-01
3.64595443e-01 2.48859569e-01 -2.00674012e-01 -4.40235168... | [8.999302864074707, 10.696272850036621] |
591a2516-b9e9-4aaf-ac59-266331cba605 | zero-shot-cross-lingual-conversational-2 | 2204.04914 | null | https://arxiv.org/abs/2204.04914v1 | https://arxiv.org/pdf/2204.04914v1.pdf | Zero-shot Cross-lingual Conversational Semantic Role Labeling | While conversational semantic role labeling (CSRL) has shown its usefulness on Chinese conversational tasks, it is still under-explored in non-Chinese languages due to the lack of multilingual CSRL annotations for the parser training. To avoid expensive data collection and error-propagation of translation-based methods... | ['Linqi Song', 'Lianwei Wu', 'Shuqi Liu', 'Kun Xu', 'Haochen Tan', 'Han Wu'] | 2022-04-11 | zero-shot-cross-lingual-conversational | https://aclanthology.org/2022.findings-naacl.20 | https://aclanthology.org/2022.findings-naacl.20.pdf | findings-naacl-2022-7 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 2.04873249e-01 4.83855039e-01 -2.03680068e-01 -7.63065219e-01
-1.39719164e+00 -7.94474661e-01 7.49720871e-01 -6.58068880e-02
-4.87486959e-01 1.13259041e+00 1.07405174e+00 -4.77405697e-01
3.41649562e-01 -4.11942899e-01 -2.66603738e-01 -3.32825691e-01
9.35517848e-02 7.04733491e-01 2.04697848e-04 -8.70679617... | [12.37765884399414, 8.095559120178223] |
5c3f6686-fa5a-4a7d-a434-e779828a50b4 | hagnn-hybrid-aggregation-for-heterogeneous | 2307.01636 | null | https://arxiv.org/abs/2307.01636v1 | https://arxiv.org/pdf/2307.01636v1.pdf | HAGNN: Hybrid Aggregation for Heterogeneous Graph Neural Networks | Heterogeneous graph neural networks (GNNs) have been successful in handling heterogeneous graphs. In existing heterogeneous GNNs, meta-path plays an essential role. However, recent work pointed out that simple homogeneous graph model without meta-path can also achieve comparable results, which calls into question the n... | ['Yihua Huang', 'Chunfeng Yuan', 'Hongyang Chen', 'Zhennan Zhu', 'Guanghui Zhu'] | 2023-07-04 | null | null | null | null | ['node-classification', 'link-prediction'] | ['graphs', 'graphs'] | [-3.35733265e-01 4.55525398e-01 -5.24347126e-01 -3.47372927e-02
-2.23296538e-01 -1.62639037e-01 4.28171515e-01 3.91662121e-01
-3.49989161e-03 7.13393331e-01 2.28864267e-01 -1.26437306e-01
-3.39182019e-01 -1.66281891e+00 -3.62577826e-01 -6.77182019e-01
-2.36816660e-01 5.13636291e-01 6.61963522e-01 -3.67153823... | [7.3465118408203125, 6.357824802398682] |
d72ae7fc-4504-4a54-9cf8-2ac1cb209c60 | automatic-lung-cancer-prediction-from-chest-x | 1808.10858 | null | http://arxiv.org/abs/1808.10858v1 | http://arxiv.org/pdf/1808.10858v1.pdf | Automatic Lung Cancer Prediction from Chest X-ray Images Using Deep Learning Approach | Since, cancer is curable when diagnosed at an early stage, lung cancer
screening plays an important role in preventive care. Although both low dose
computed tomography (LDCT) and computed tomography (CT) scans provide more
medical information than normal chest x-rays, there is very limited access to
these technologies ... | ['Arjaree Thirach', 'Sanparith Marukatat', 'Worawate Ausawalaithong', 'Theerawit Wilaiprasitporn'] | 2018-08-31 | null | null | null | null | ['lung-cancer-diagnosis'] | ['medical'] | [ 8.39288533e-02 1.47197232e-01 -5.88310182e-01 -1.78572968e-01
-7.85152793e-01 -1.69287529e-03 1.63339242e-01 -5.62419221e-02
-4.26188231e-01 7.22461462e-01 -1.70013040e-01 -9.94813681e-01
-8.15560371e-02 -1.34942830e+00 -5.53540289e-01 -6.21843755e-01
-3.81508432e-02 5.31971633e-01 2.03540489e-01 2.61655211... | [15.349769592285156, -2.1978328227996826] |
a27de734-febd-413b-b86f-f725c66d2552 | efficient-large-scale-multi-modal | 1802.02892 | null | http://arxiv.org/abs/1802.02892v1 | http://arxiv.org/pdf/1802.02892v1.pdf | Efficient Large-Scale Multi-Modal Classification | While the incipient internet was largely text-based, the modern digital world
is becoming increasingly multi-modal. Here, we examine multi-modal
classification where one modality is discrete, e.g. text, and the other is
continuous, e.g. visual representations transferred from a convolutional neural
network. In particul... | ['T. Mikolov', 'A. Joulin', 'E. Grave', 'D. Kiela'] | 2018-02-06 | null | null | null | null | ['multi-modal-classification'] | ['miscellaneous'] | [ 2.36235067e-01 -3.44231606e-01 -1.72812829e-03 -3.24625313e-01
-1.17085361e+00 -7.88361847e-01 9.50917900e-01 6.32477045e-01
-3.99197251e-01 6.73432887e-01 2.90854931e-01 -3.59007180e-01
-3.14828128e-01 -7.64654577e-01 -4.13921177e-01 -4.40998852e-01
2.58216739e-01 5.51347017e-01 -2.06543818e-01 -6.36718050... | [13.135440826416016, 5.071769714355469] |
2cb312f5-ce14-46c8-be92-cb92908295b6 | impact-of-a-dct-driven-loss-in-attention | 2205.01997 | null | https://arxiv.org/abs/2205.01997v2 | https://arxiv.org/pdf/2205.01997v2.pdf | Attention-based Knowledge Distillation in Multi-attention Tasks: The Impact of a DCT-driven Loss | Knowledge Distillation (KD) is a strategy for the definition of a set of transferability gangways to improve the efficiency of Convolutional Neural Networks. Feature-based Knowledge Distillation is a subfield of KD that relies on intermediate network representations, either unaltered or depth-reduced via maximum activa... | ['Juan C. SanMiguel', 'Jesús Bescós', 'Marcos Escudero-Viñolo', 'Alejandro López-Cifuentes'] | 2022-05-04 | null | null | null | null | ['scene-recognition'] | ['computer-vision'] | [ 3.59808385e-01 2.39089459e-01 4.48947586e-02 -3.22557837e-01
-2.90003955e-01 -6.17836177e-01 8.90683293e-01 2.04845607e-01
-8.40291142e-01 8.56804311e-01 -2.27543443e-01 -1.97201550e-01
-7.00701296e-01 -1.06289995e+00 -1.01247084e+00 -9.86969829e-01
1.49151832e-01 1.11188255e-01 3.91649634e-01 -2.73689747... | [9.675445556640625, 2.1573946475982666] |
7cbf9dc4-f319-4c28-8645-d8ad89c79b50 | bert-4ever-evahan-2022-ancient-chinese-word | null | null | https://aclanthology.org/2022.lt4hala-1.22 | https://aclanthology.org/2022.lt4hala-1.22.pdf | BERT 4EVER@EvaHan 2022: Ancient Chinese Word Segmentation and Part-of-Speech Tagging Based on Adversarial Learning and Continual Pre-training | With the development of artificial intelligence (AI) and digital humanities, ancient Chinese resources and language technology have also developed and grown, which have become an increasingly important part to the study of historiography and traditional Chinese culture. In order to promote the research on automatic ana... | ['Ruoyao Ding', 'Yingwen Fu', 'Ziyu Yang', 'Hailin Zhang'] | null | null | null | null | lt4hala-lrec-2022-6 | ['chinese-word-segmentation', 'culture'] | ['natural-language-processing', 'speech'] | [ 2.12282464e-01 -2.79985100e-01 5.38331829e-02 -3.84699076e-01
-6.21375561e-01 -7.12593317e-01 4.30976868e-01 -3.11321050e-01
-9.37171042e-01 5.97511590e-01 3.27496022e-01 -6.72344804e-01
7.54211187e-01 -5.26303947e-01 -1.69517085e-01 -6.57673359e-01
4.64003533e-02 4.50815052e-01 2.54923999e-01 -1.26464829... | [9.984850883483887, 10.137078285217285] |
fd1c1146-7200-48fa-aedc-3772aa49d2f7 | effect-of-lossy-compression-algorithms-on | 2302.12593 | null | https://arxiv.org/abs/2302.12593v1 | https://arxiv.org/pdf/2302.12593v1.pdf | Effect of Lossy Compression Algorithms on Face Image Quality and Recognition | Lossy face image compression can degrade the image quality and the utility for the purpose of face recognition. This work investigates the effect of lossy image compression on a state-of-the-art face recognition model, and on multiple face image quality assessment models. The analysis is conducted over a range of speci... | ['Christoph Busch', 'Juan Tapia', 'Christian Rathgeb', 'Sebastian Schachner', 'Torsten Schlett'] | 2023-02-24 | null | null | null | null | ['face-image-quality', 'image-quality-assessment', 'face-image-quality-assessment'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 5.12064159e-01 -3.30808878e-01 -8.95958766e-02 -4.58255082e-01
-7.46746004e-01 -1.14459746e-01 6.61120296e-01 -1.91513434e-01
-5.03385246e-01 5.26644111e-01 1.57146171e-01 -1.50798738e-01
-3.68889153e-01 -6.32141352e-01 -3.89129072e-01 -6.14853740e-01
-2.35236526e-01 1.02934122e-01 -1.87031478e-01 -1.67889092... | [13.062054634094238, 1.0140457153320312] |
ed441225-54b8-46d1-82ba-47234e3b0f67 | machine-made-media-monitoring-the | 2305.09820 | null | https://arxiv.org/abs/2305.09820v1 | https://arxiv.org/pdf/2305.09820v1.pdf | Machine-Made Media: Monitoring the Mobilization of Machine-Generated Articles on Misinformation and Mainstream News Websites | With the increasing popularity of generative large language models (LLMs) like ChatGPT, an increasing number of news websites have begun utilizing them to generate articles. However, not only can these language models produce factually inaccurate articles on reputable websites but disreputable news sites can utilize th... | ['Zakir Durumeric', 'Hans W. A. Hanley'] | 2023-05-16 | null | null | null | null | ['misinformation'] | ['miscellaneous'] | [-4.67159957e-01 5.54789126e-01 -2.98002571e-01 1.86473131e-01
-1.36138701e+00 -9.13607299e-01 1.26428294e+00 1.94311872e-01
-2.96438247e-01 9.15278316e-01 6.92908704e-01 -4.50388461e-01
5.26193261e-01 -1.08720243e+00 -1.01933539e+00 -1.50509194e-01
1.51991442e-01 6.24981046e-01 3.67825270e-01 -3.18895340... | [8.351356506347656, 10.123703002929688] |
d6fe86db-b30d-4435-93aa-73e27e21a947 | alignsts-speech-to-singing-conversion-via | 2305.04476 | null | https://arxiv.org/abs/2305.04476v4 | https://arxiv.org/pdf/2305.04476v4.pdf | AlignSTS: Speech-to-Singing Conversion via Cross-Modal Alignment | The speech-to-singing (STS) voice conversion task aims to generate singing samples corresponding to speech recordings while facing a major challenge: the alignment between the target (singing) pitch contour and the source (speech) content is difficult to learn in a text-free situation. This paper proposes AlignSTS, an ... | ['Zhou Zhao', 'Jinglin Liu', 'Lichao Zhang', 'Rongjie Huang', 'RuiQi Li'] | 2023-05-08 | null | null | null | null | ['voice-conversion', 'voice-conversion'] | ['audio', 'speech'] | [ 2.9452914e-01 -8.7695979e-03 -1.5612048e-01 -3.0916380e-02
-1.2084107e+00 -6.6580737e-01 3.2133305e-01 -4.0986004e-01
2.0740791e-01 2.9443026e-01 8.8002300e-01 2.0797420e-01
2.1444412e-01 -3.5416022e-01 -4.5196438e-01 -6.8984640e-01
3.2571629e-01 3.6295041e-02 -1.5047891e-01 -4.3377748e-01
1.1439169e-02... | [15.56523609161377, 5.963982105255127] |
20da8a9e-ac5c-4397-a13d-ad3e3e87723d | learning-implicit-fields-for-generative-shape | 1812.02822 | null | https://arxiv.org/abs/1812.02822v5 | https://arxiv.org/pdf/1812.02822v5.pdf | Learning Implicit Fields for Generative Shape Modeling | We advocate the use of implicit fields for learning generative models of shapes and introduce an implicit field decoder, called IM-NET, for shape generation, aimed at improving the visual quality of the generated shapes. An implicit field assigns a value to each point in 3D space, so that a shape can be extracted as an... | ['Hao Zhang', 'Zhiqin Chen'] | 2018-12-06 | learning-implicit-fields-for-generative-shape-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Chen_Learning_Implicit_Fields_for_Generative_Shape_Modeling_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Chen_Learning_Implicit_Fields_for_Generative_Shape_Modeling_CVPR_2019_paper.pdf | cvpr-2019-6 | ['single-view-3d-reconstruction', '3d-shape-representation'] | ['computer-vision', 'computer-vision'] | [ 3.51187825e-01 4.46506619e-01 -3.34780174e-03 -3.88017029e-01
-7.61417985e-01 -6.53287530e-01 7.28459597e-01 2.16345955e-02
3.64082873e-01 4.70282674e-01 3.41084540e-01 -2.46386632e-01
4.12483633e-01 -1.31389654e+00 -1.02542925e+00 -4.69545335e-01
2.85380751e-01 5.89636683e-01 -2.46259347e-01 2.11000577... | [8.907564163208008, -3.611281633377075] |
3ea73718-3882-43e3-be60-8bbe1b692bfa | better-combine-them-together-integrating | null | null | https://aclanthology.org/2021.findings-acl.49 | https://aclanthology.org/2021.findings-acl.49.pdf | Better Combine Them Together! Integrating Syntactic Constituency and Dependency Representations for Semantic Role Labeling | null | ['Donghong Ji', 'Fei Li', 'Yafeng Ren', 'Shengqiong Wu', 'Hao Fei'] | null | null | null | null | findings-acl-2021-8 | ['semantic-role-labeling', 'semantic-role-labeling-predicted-predicates'] | ['natural-language-processing', 'natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.452225685119629, 3.6311428546905518] |
0d259593-cec8-4a20-b680-7d18308afaf4 | the-blessings-of-unlabeled-background-in | 2103.13183 | null | https://arxiv.org/abs/2103.13183v2 | https://arxiv.org/pdf/2103.13183v2.pdf | The Blessings of Unlabeled Background in Untrimmed Videos | Weakly-supervised Temporal Action Localization (WTAL) aims to detect the action segments with only video-level action labels in training. The key challenge is how to distinguish the action of interest segments from the background, which is unlabelled even on the video-level. While previous works treat the background as... | ['Hanwang Zhang', 'Jianqiang Huang', 'Bing Deng', 'Zhenfang Chen', 'Jingyuan Chen', 'YuAn Liu'] | 2021-03-24 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Liu_The_Blessings_of_Unlabeled_Background_in_Untrimmed_Videos_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Liu_The_Blessings_of_Unlabeled_Background_in_Untrimmed_Videos_CVPR_2021_paper.pdf | cvpr-2021-1 | ['weakly-supervised-temporal-action'] | ['computer-vision'] | [ 6.42201126e-01 3.59342024e-02 -6.58348501e-01 1.78099558e-01
-8.07461262e-01 -3.06316733e-01 5.83712041e-01 -4.80364949e-01
-2.43570119e-01 5.51443934e-01 4.83904481e-01 -4.89911251e-02
1.14295110e-01 -1.31311208e-01 -1.01978588e+00 -1.07742858e+00
-1.16810143e-01 -1.31217510e-01 5.55025339e-01 4.94981587... | [8.539482116699219, 0.6872782111167908] |
b47ebc21-7273-4af0-85c8-c2d8f772732a | loop-closure-detection-using-local-3d-deep | 2111.00440 | null | https://arxiv.org/abs/2111.00440v2 | https://arxiv.org/pdf/2111.00440v2.pdf | Loop closure detection using local 3D deep descriptors | We present a simple yet effective method to address loop closure detection in simultaneous localisation and mapping using local 3D deep descriptors (L3Ds). L3Ds are emerging compact representations of patches extracted from point clouds that are learnt from data using a deep learning algorithm. We propose a novel overl... | ['Yi Wan', 'Qi Qin', 'Fabio Poiesi', 'Yiming Wang', 'Youjie Zhou'] | 2021-10-31 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [-1.75247520e-01 -1.54407486e-01 -8.27841833e-02 -3.06392282e-01
-1.00385034e+00 -5.73190928e-01 8.49818468e-01 6.16460085e-01
-5.14494181e-01 4.43389267e-01 -1.64886087e-01 -2.05860317e-01
-1.48007661e-01 -8.30138743e-01 -1.11226964e+00 -8.39670449e-02
-4.08105135e-01 7.56092489e-01 3.56756389e-01 -1.38529599... | [7.442224502563477, -2.2054550647735596] |
09e03723-a0e7-465a-8247-e565e458deeb | face-alignment-in-the-wild-a-survey | 1608.04188 | null | http://arxiv.org/abs/1608.04188v1 | http://arxiv.org/pdf/1608.04188v1.pdf | Face Alignment In-the-Wild: A Survey | Over the last two decades, face alignment or localizing fiducial facial
points has received increasing attention owing to its comprehensive
applications in automatic face analysis. However, such a task has proven
extremely challenging in unconstrained environments due to many confounding
factors, such as pose, occlusio... | ['Xin Jin', 'Xiaoyang Tan'] | 2016-08-15 | null | null | null | null | ['robust-face-alignment'] | ['computer-vision'] | [ 7.29047582e-02 -9.95912179e-02 -3.53368431e-01 -6.81196034e-01
-5.01139224e-01 -5.50278544e-01 4.70623851e-01 -5.16263008e-01
-5.39362617e-02 5.43235362e-01 1.28209945e-02 1.42095864e-01
8.94656107e-02 -2.26642061e-02 -1.88327149e-01 -8.00489068e-01
-6.63660392e-02 2.71416932e-01 -5.12394845e-01 -5.72935566... | [13.357195854187012, 0.4080319106578827] |
c606b457-c924-40ed-b061-38092335d8e0 | online-obstructive-sleep-apnea-detection | 2110.00660 | null | https://arxiv.org/abs/2110.00660v2 | https://arxiv.org/pdf/2110.00660v2.pdf | Automatic Home-based Screening of Obstructive Sleep Apnea Using Single Channel Electrocardiogram and SPO2 Signals | Obstructive sleep apnea (OSA) is one of the most widespread respiratory diseases today. Complete or relative breathing cessations due to upper airway subsidence during sleep is OSA. It has confirmed potential influence on Covid-19 hospitalization and mortality, and is strongly associated with major comorbidities of sev... | ['Hosna Ghandeharioun'] | 2021-10-01 | null | null | null | null | ['sleep-apnea-detection'] | ['medical'] | [ 5.53040430e-02 -2.06204355e-01 -6.61611557e-03 -1.70191273e-01
-1.84478872e-02 -3.72662634e-01 -2.20645607e-01 1.89354181e-01
-4.89016682e-01 1.14727044e+00 5.89634757e-03 -3.09379995e-01
-6.09434843e-01 -4.95957047e-01 5.30581236e-01 -7.26243019e-01
-1.41511515e-01 7.53056467e-01 2.18890309e-01 1.14633562... | [13.674422264099121, 3.3322713375091553] |
8ab96aa0-db5c-4ab3-9ec3-e08d1059d856 | refining-generative-process-with | 2211.17091 | null | https://arxiv.org/abs/2211.17091v4 | https://arxiv.org/pdf/2211.17091v4.pdf | Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models | The proposed method, Discriminator Guidance, aims to improve sample generation of pre-trained diffusion models. The approach introduces a discriminator that gives explicit supervision to a denoising sample path whether it is realistic or not. Unlike GANs, our approach does not require joint training of score and discri... | ['Il-Chul Moon', 'Wanmo Kang', 'Se Jung Kwon', 'Yeongmin Kim', 'Dongjun Kim'] | 2022-11-28 | null | null | null | null | ['conditional-image-generation'] | ['computer-vision'] | [ 2.32536286e-01 2.52108067e-01 -2.96818227e-01 -4.67472553e-01
-1.09492803e+00 -4.93340552e-01 6.30256832e-01 -2.79116541e-01
-5.22434056e-01 8.19269836e-01 6.47545829e-02 -6.43447563e-02
2.06317872e-01 -7.84408987e-01 -4.63333696e-01 -1.00521290e+00
6.59398288e-02 4.75728780e-01 1.78166673e-01 1.18176481... | [11.52497386932373, -0.15977443754673004] |
775ab5cf-e51d-474b-b8fc-d8f245aa2cea | improving-generalization-in-language-model | 2305.17378 | null | https://arxiv.org/abs/2305.17378v1 | https://arxiv.org/pdf/2305.17378v1.pdf | Improving Generalization in Language Model-Based Text-to-SQL Semantic Parsing: Two Simple Semantic Boundary-Based Techniques | Compositional and domain generalization present significant challenges in semantic parsing, even for state-of-the-art semantic parsers based on pre-trained language models (LMs). In this study, we empirically investigate improving an LM's generalization in semantic parsing with two simple techniques: at the token level... | ['Ziyu Yao', 'Yilun Zhou', 'Bailin Wang', 'Daking Rai'] | 2023-05-27 | null | null | null | null | ['text-to-sql', 'domain-generalization', 'semantic-parsing'] | ['computer-code', 'methodology', 'natural-language-processing'] | [ 4.78852391e-01 5.50050676e-01 -2.39724338e-01 -5.01297951e-01
-7.78806090e-01 -9.62188303e-01 3.37970555e-01 4.65609938e-01
-2.31535167e-01 4.26522702e-01 1.05731241e-01 -7.37165272e-01
4.30547446e-01 -9.96663988e-01 -8.94661009e-01 -3.06553021e-02
1.91155389e-01 2.82274723e-01 6.73355937e-01 -1.56438068... | [10.478086471557617, 9.205924987792969] |
5092d14d-d925-405d-8d0e-23a138346578 | emotion-and-sentiment-guided-paraphrasing | 2306.05556 | null | https://arxiv.org/abs/2306.05556v1 | https://arxiv.org/pdf/2306.05556v1.pdf | Emotion and Sentiment Guided Paraphrasing | Paraphrase generation, a.k.a. paraphrasing, is a common and important task in natural language processing. Emotional paraphrasing, which changes the emotion embodied in a piece of text while preserving its meaning, has many potential applications, including moderating online dialogues and preventing cyberbullying. We i... | ['Ameeta Agrawal', 'Justin J. Xie'] | 2023-06-08 | null | null | null | null | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 1.36227876e-01 -1.16221420e-01 -3.42671663e-01 -6.80661321e-01
-8.00845087e-01 -9.02331948e-01 6.06232882e-01 1.90227076e-01
3.17024104e-02 8.03753257e-01 1.04395092e+00 1.52417079e-01
3.01005274e-01 -5.58177769e-01 -6.39581978e-01 -2.80976057e-01
7.73121357e-01 1.50048003e-01 -7.61401355e-01 -6.53420448... | [11.627874374389648, 9.340893745422363] |
42173a78-5012-459d-8893-3105449cf046 | time-series-analysis-of-big-data-for | 1907.13016 | null | https://arxiv.org/abs/1907.13016v1 | https://arxiv.org/pdf/1907.13016v1.pdf | Time Series Analysis of Big Data for Electricity Price and Demand to Find Cyber-Attacks part 2: Decomposition Analysis | In this paper, in following of the first part (which ADF tests using ACI evaluation) has conducted, Time Series (TSs) are analyzed using decomposition analysis. In fact, TSs are composed of four components including trend (long term behavior or progression of series), cyclic component (non-periodic fluctuation behavior... | ['Mohammad Rajabdorri', 'Mohsen Rakhshandehroo'] | 2019-07-30 | null | null | null | null | ['cyber-attack-detection'] | ['miscellaneous'] | [-5.47016263e-02 -6.63745344e-01 2.72344381e-01 1.76411256e-01
5.01353033e-02 -9.58500564e-01 6.13275290e-01 2.63177663e-01
1.02421261e-01 9.40873444e-01 2.67559737e-01 -7.25558221e-01
-1.72626331e-01 -7.62311459e-01 -1.93830639e-01 -8.06337953e-01
-2.51229316e-01 -7.19245747e-02 3.45130742e-01 -3.19411933... | [6.9873785972595215, 3.262935161590576] |
cdc3c8a8-af82-4298-a9b2-2f3d9bfdeb47 | no-shifted-augmentations-nsa-strong-baselines | null | null | https://openreview.net/forum?id=7VH_ZMpwZXa | https://openreview.net/pdf?id=7VH_ZMpwZXa | No Shifted Augmentations (NSA): strong baselines for self-supervised Anomaly Detection | Unsupervised Anomaly detection (AD) requires building a notion of normalcy, distinguishing in-distribution (ID) and out-of-distribution (OOD) data, using only available ID samples. Recently, large gains were made on this task for the domain of natural images using self-supervised contrastive feature learning as a first... | ['Unmesh Kurup', 'Tom Bishop', 'Mohamed Yousef'] | 2021-09-29 | null | null | null | null | ['self-supervised-anomaly-detection', 'supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [ 1.26131281e-01 1.85039595e-01 1.98908269e-01 -3.61310124e-01
-5.17510414e-01 -5.25614917e-01 8.21987033e-01 2.49505103e-01
-3.95646542e-01 3.87440383e-01 -1.91253781e-01 -1.59058377e-01
-3.51898611e-01 -5.60686052e-01 -5.91084421e-01 -1.03692496e+00
-4.47333157e-01 6.34225905e-01 2.25516573e-01 -6.41107932... | [7.7504072189331055, 2.440551996231079] |
a50a970e-879b-4677-9491-2fb7174e7c79 | going-deeper-into-action-recognition-a-survey | 1605.04988 | null | http://arxiv.org/abs/1605.04988v2 | http://arxiv.org/pdf/1605.04988v2.pdf | Going Deeper into Action Recognition: A Survey | Understanding human actions in visual data is tied to advances in
complementary research areas including object recognition, human dynamics,
domain adaptation and semantic segmentation. Over the last decade, human action
analysis evolved from earlier schemes that are often limited to controlled
environments to nowadays... | ['Mehrtash Harandi', 'Fatih Porikli', 'Samitha Herath'] | 2016-05-16 | null | null | null | null | ['action-analysis', 'human-dynamics'] | ['computer-vision', 'computer-vision'] | [ 6.44713640e-01 7.20716417e-02 -4.09890831e-01 -2.51521975e-01
-8.90635848e-02 -4.53133345e-01 8.33797693e-01 7.13298768e-02
-5.52154958e-01 5.69203198e-01 3.59410703e-01 1.33388881e-02
-2.04771057e-01 -5.38591743e-01 -2.54741907e-01 -5.83504438e-01
-2.96887249e-01 1.22144699e-01 4.13692474e-01 -2.76059389... | [8.263738632202148, 0.4836721122264862] |
114c7b83-5b12-4ff1-b0ba-377ebb682cf4 | adacoach-a-virtual-coach-for-training | 2204.12935 | null | https://arxiv.org/abs/2204.12935v1 | https://arxiv.org/pdf/2204.12935v1.pdf | AdaCoach: A Virtual Coach for Training Customer Service Agents | With the development of online business, customer service agents gradually play a crucial role as an interface between the companies and their customers. Most companies spend a lot of time and effort on hiring and training customer service agents. To this end, we propose AdaCoach: A Virtual Coach for Training Customer ... | ['Biao Fan', 'Xuelian Li', 'Zujie Wen', 'Dan Liu', 'Haozhou Huang', 'Minghui Yang', 'Shuai Zhu', 'Shuang Peng'] | 2022-04-27 | null | null | null | null | ['dialogue-evaluation'] | ['natural-language-processing'] | [-4.44827855e-01 4.73530799e-01 1.53434038e-01 -6.00304186e-01
-2.26479873e-01 -7.36802042e-01 6.49661481e-01 2.91966349e-01
-5.68506777e-01 5.73312044e-01 -3.92227829e-01 -5.21697581e-01
1.03434317e-01 -8.93217623e-01 4.62233573e-02 -4.62751329e-01
3.13046634e-01 1.77349591e+00 2.48899326e-01 -8.40567112... | [12.905326843261719, 7.977902889251709] |
b57e8d8d-d8a7-497d-952b-53a80c91298f | movement-tracking-by-optical-flow-assisted | 2006.13856 | null | https://arxiv.org/abs/2006.13856v1 | https://arxiv.org/pdf/2006.13856v1.pdf | Movement Tracking by Optical Flow Assisted Inertial Navigation | Robust and accurate six degree-of-freedom tracking on portable devices remains a challenging problem, especially on small hand-held devices such as smartphones. For improved robustness and accuracy, complementary movement information from an IMU and a camera is often fused. Conventional visual-inertial methods fuse inf... | ['Arno Solin', 'Lassi Meronen', 'William J. Wilkinson'] | 2020-06-24 | null | null | null | null | ['probabilistic-deep-learning'] | ['computer-vision'] | [-1.01135366e-01 -5.80810905e-01 -3.50376725e-01 2.62045622e-01
-3.17595392e-01 -6.13297701e-01 3.61410141e-01 -4.77655858e-01
-3.81127149e-01 8.98913920e-01 2.73508400e-01 -6.26879707e-02
2.72364020e-02 -2.76869446e-01 -6.88392818e-01 -4.92945820e-01
1.93701684e-02 4.77242135e-02 -5.97477928e-02 1.34239152... | [7.869621753692627, -2.0535430908203125] |
ce8df833-ffee-472b-873b-7ebf571a49ac | sign-scalable-inception-graph-neural-networks | 2004.11198 | null | https://arxiv.org/abs/2004.11198v3 | https://arxiv.org/pdf/2004.11198v3.pdf | SIGN: Scalable Inception Graph Neural Networks | Graph representation learning has recently been applied to a broad spectrum of problems ranging from computer graphics and chemistry to high energy physics and social media. The popularity of graph neural networks has sparked interest, both in academia and in industry, in developing methods that scale to very large gra... | ['Davide Eynard', 'Federico Monti', 'Fabrizio Frasca', 'Ben Chamberlain', 'Michael Bronstein', 'Emanuele Rossi'] | 2020-04-23 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [ 2.75004338e-02 2.72191525e-01 -3.31592351e-01 -2.37581015e-01
-3.13642889e-01 -5.27242184e-01 6.23033881e-01 4.22999024e-01
-4.46347594e-01 6.89680278e-01 6.82987347e-02 -6.77028954e-01
-1.29614621e-01 -1.41742861e+00 -9.03778076e-01 -4.86192256e-01
-5.58013320e-01 6.68610275e-01 3.67677271e-01 -1.72068805... | [6.953188419342041, 6.139644145965576] |
5ef3cd4b-5e44-444a-867d-6a159515cefe | stochastic-mpc-for-energy-hubs-using-data | 2304.12438 | null | https://arxiv.org/abs/2304.12438v1 | https://arxiv.org/pdf/2304.12438v1.pdf | Stochastic MPC for energy hubs using data driven demand forecasting | Energy hubs convert and distribute energy resources by combining different energy inputs through multiple conversion and storage components. The optimal operation of the energy hub exploits its flexibility to increase the energy efficiency and reduce the operational costs. However, uncertainties in the demand present c... | ['John Lygeros', 'Philipp Heer', 'Jonas Mehr', 'Varsha Behrunani', 'Francesco Micheli'] | 2023-04-24 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [-3.53804827e-01 -1.07788727e-01 1.39556363e-01 -8.00883584e-03
-3.82662565e-01 -7.39936352e-01 5.08488238e-01 1.07348226e-01
3.66193593e-01 1.11417234e+00 1.48052517e-02 -9.15420800e-02
-5.33023119e-01 -1.16862357e+00 -3.32837254e-01 -1.08883607e+00
2.22717181e-01 6.46732867e-01 -4.04138446e-01 1.19269595... | [5.6777729988098145, 2.523050546646118] |
1960aa36-86ca-4504-a81b-dd1741f258f7 | a-spatial-compositional-model-scm-for-linear | 1509.09243 | null | http://arxiv.org/abs/1509.09243v1 | http://arxiv.org/pdf/1509.09243v1.pdf | A spatial compositional model (SCM) for linear unmixing and endmember uncertainty estimation | The normal compositional model (NCM) has been extensively used in
hyperspectral unmixing. However, most of the previous research has focused on
estimation of endmembers and/or their variability. Also, little work has
employed spatial information in NCM. In this paper, we show that NCM can be
used for calculating the un... | ['Yuan Zhou', 'Anand Rangarajan', 'Paul Gader'] | 2015-09-30 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 5.40059745e-01 -4.78307188e-01 -2.32797787e-02 -1.74794406e-01
-3.43218744e-01 -4.84304488e-01 8.24318349e-01 7.85349160e-02
-1.12215057e-01 1.00093746e+00 1.83204964e-01 -3.82184625e-01
-1.55700058e-01 -9.12129521e-01 -5.22058308e-01 -1.19401371e+00
-1.01423357e-02 4.02701706e-01 1.08317472e-01 1.82652175... | [10.027214050292969, -2.0305604934692383] |
097f4cbe-b9d6-4e92-8a13-4952374968da | data-level-recombination-and-lightweight | 2009.05102 | null | https://arxiv.org/abs/2009.05102v1 | https://arxiv.org/pdf/2009.05102v1.pdf | Data-Level Recombination and Lightweight Fusion Scheme for RGB-D Salient Object Detection | Existing RGB-D salient object detection methods treat depth information as an independent component to complement its RGB part, and widely follow the bi-stream parallel network architecture. To selectively fuse the CNNs features extracted from both RGB and depth as a final result, the state-of-the-art (SOTA) bi-stream ... | ['Xuehao Wang', 'Shuai Li', 'Chenglizhao Chen', 'Hong Qin', 'Aimin Hao', 'Yuming Fang'] | 2020-08-07 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 3.91427368e-01 1.40434101e-01 -6.85448349e-02 -3.07824999e-01
-7.01382935e-01 -9.46642682e-02 4.11856383e-01 -4.52273376e-02
-3.10895383e-01 4.22960669e-01 1.46989033e-01 1.26655838e-02
-3.37889232e-02 -9.18882608e-01 -5.74470758e-01 -1.08958042e+00
3.93271118e-01 -6.25577495e-02 8.58695328e-01 -3.66991878... | [9.635773658752441, -0.8099986910820007] |
89219f7b-bbc1-4ff2-bda1-2467837abe7c | pixel-sampling-for-style-preserving-face-pose | 2106.07310 | null | https://arxiv.org/abs/2106.07310v1 | https://arxiv.org/pdf/2106.07310v1.pdf | Pixel Sampling for Style Preserving Face Pose Editing | The existing auto-encoder based face pose editing methods primarily focus on modeling the identity preserving ability during pose synthesis, but are less able to preserve the image style properly, which refers to the color, brightness, saturation, etc. In this paper, we take advantage of the well-known frontal/profile ... | ['Liming Chen', 'Yunhong Wang', 'Zehua Fu', 'Hongyu Yang', 'Di Huang', 'Xiangnan Yin'] | 2021-06-14 | null | null | null | null | ['facial-inpainting'] | ['computer-vision'] | [ 2.76942909e-01 2.76944488e-01 -2.24819966e-03 -3.83528471e-01
-1.50489807e-01 -6.11248314e-01 5.11256158e-01 -4.12781894e-01
-2.52519101e-01 6.12321377e-01 2.36300886e-01 3.31176460e-01
1.01913914e-01 -7.51762211e-01 -6.78484857e-01 -8.39513302e-01
6.46673501e-01 -7.82020390e-02 -2.43693098e-01 -1.93466216... | [12.722853660583496, -0.16818831861019135] |
d3b5758e-f720-4ada-b6fd-04d5f0193709 | long-short-temporal-co-teaching-for-weakly | 2303.18044 | null | https://arxiv.org/abs/2303.18044v2 | https://arxiv.org/pdf/2303.18044v2.pdf | Long-Short Temporal Co-Teaching for Weakly Supervised Video Anomaly Detection | Weakly supervised video anomaly detection (WS-VAD) is a challenging problem that aims to learn VAD models only with video-level annotations. In this work, we propose a Long-Short Temporal Co-teaching (LSTC) method to address the WS-VAD problem. It constructs two tubelet-based spatio-temporal transformer networks to lea... | ['Xiaojin Gong', 'Shengyang Sun'] | 2023-03-31 | null | null | null | null | ['video-anomaly-detection', 'multiple-instance-learning'] | ['computer-vision', 'methodology'] | [ 1.94367409e-01 -1.75909296e-01 -1.41018257e-01 -4.19155985e-01
-8.98316264e-01 -3.11691761e-01 5.67275584e-01 1.40854731e-01
-3.90781373e-01 2.67890453e-01 -4.06590961e-02 -3.41656446e-01
7.41145983e-02 -4.08889711e-01 -1.00600088e+00 -6.53456807e-01
-5.20421088e-01 3.70038509e-01 4.50919449e-01 1.04282811... | [7.853674411773682, 1.5900557041168213] |
ac2206af-da7c-4f0d-9752-8af068f0a0d8 | capnet-continuous-approximation-projection | 1811.11731 | null | http://arxiv.org/abs/1811.11731v1 | http://arxiv.org/pdf/1811.11731v1.pdf | CAPNet: Continuous Approximation Projection For 3D Point Cloud Reconstruction Using 2D Supervision | Knowledge of 3D properties of objects is a necessity in order to build
effective computer vision systems. However, lack of large scale 3D datasets can
be a major constraint for data-driven approaches in learning such properties.
We consider the task of single image 3D point cloud reconstruction, and aim to
utilize mult... | ['Priyanka Mandikal', 'R. Venkatesh Babu', 'Mayank Agarwal', 'Navaneet K L'] | 2018-11-28 | null | null | null | null | ['3d-point-cloud-reconstruction', 'point-cloud-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 3.05824697e-01 3.02274805e-02 1.67427853e-01 -3.14272434e-01
-8.24858665e-01 -3.71415585e-01 5.80407679e-01 -4.07009214e-01
-6.66060075e-02 3.86213690e-01 4.80328463e-02 -2.12329820e-01
-4.85728420e-02 -6.26850545e-01 -1.14581847e+00 -6.24463260e-01
4.02951866e-01 7.86091268e-01 5.25447249e-01 1.09088518... | [8.377989768981934, -3.1927459239959717] |
0068244c-e16d-4222-9ab5-f5d0b33c23a5 | an-comparative-analysis-of-different-pitch | 2301.13383 | null | https://arxiv.org/abs/2301.13383v1 | https://arxiv.org/pdf/2301.13383v1.pdf | An Comparative Analysis of Different Pitch and Metrical Grid Encoding Methods in the Task of Sequential Music Generation | Pitch and meter are two fundamental music features for symbolic music generation tasks, where researchers usually choose different encoding methods depending on specific goals. However, the advantages and drawbacks of different encoding methods have not been frequently discussed. This paper presents a integrated analys... | ['George Fazekas', 'Shengchen Li', 'Yuqiang Li'] | 2023-01-31 | null | null | null | null | ['music-generation', 'feature-engineering', 'music-generation'] | ['audio', 'methodology', 'music'] | [ 2.66958088e-01 -7.29047805e-02 -2.65307836e-02 1.40951037e-01
-6.65025473e-01 -5.78979075e-01 5.41484296e-01 2.85126597e-01
-5.00486195e-01 7.58687794e-01 2.74987221e-01 -6.78891018e-02
-6.42300069e-01 -6.21793151e-01 -1.63572565e-01 -8.02390873e-01
-2.83195257e-01 2.04412282e-01 7.19440207e-02 -3.67637843... | [15.79648208618164, 5.337507247924805] |
cf3e836e-c7c3-42fc-b548-7ef145102261 | word-segmentation-as-unsupervised | null | null | https://aclanthology.org/2022.acl-long.283 | https://aclanthology.org/2022.acl-long.283.pdf | Word Segmentation as Unsupervised Constituency Parsing | Word identification from continuous input is typically viewed as a segmentation task. Experiments with human adults suggest that familiarity with syntactic structures in their native language also influences word identification in artificial languages; however, the relation between syntactic processing and word identif... | ['Raquel Alhama'] | null | null | null | null | acl-2022-5 | ['constituency-parsing'] | ['natural-language-processing'] | [ 2.53571033e-01 4.28760886e-01 -6.13841936e-02 -5.51815450e-01
3.51987518e-02 -8.31193209e-01 4.67404753e-01 4.98412997e-01
-1.02112925e+00 9.43921655e-02 2.64250308e-01 -5.47707617e-01
2.76414454e-01 -7.43081748e-01 -2.73074061e-01 -3.88457119e-01
2.18314677e-01 6.39028370e-01 3.96051705e-02 -1.22037999... | [10.451141357421875, 9.54215145111084] |
55a027a9-3fcb-4aa5-8f6d-1524edeb79e2 | bbw-matching-csv-to-wikidata-via-meta-lookup | null | null | https://drive.google.com/file/d/1b5sdsJfhXGGXP-xNj3xVu3ULH5tV4Nap/view | https://drive.google.com/file/d/1b5sdsJfhXGGXP-xNj3xVu3ULH5tV4Nap/view | bbw: Matching CSV to Wikidata via Meta-lookup | We present our publicly available semantic annotator bbw (boosted by wiki) tested at the second Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab2020). It annotates a raw CSV-table using the entities, types and properties in Wikidata. Our key ideas are meta-lookup over the SearX metasearch API ... | ['Irene Schumm', 'Jörg Mechnich', 'Lars Oberländer', 'Jan Kamlah', 'Philipp Zumstein', 'Renat Shigapov'] | 2021-03-01 | null | null | null | null | ['table-annotation', 'table-annotation'] | ['knowledge-base', 'natural-language-processing'] | [-4.89224374e-01 8.55371773e-01 -2.87462294e-01 -1.78865999e-01
-5.92085600e-01 -9.80829954e-01 7.58741617e-01 8.25802207e-01
-4.78357226e-01 9.20814157e-01 6.59589231e-01 -1.27375662e-01
-7.77622521e-01 -1.08256674e+00 -7.83331335e-01 1.83574095e-01
-2.06069313e-02 9.85021710e-01 7.92155325e-01 -5.73507905... | [9.268011093139648, 8.009982109069824] |
b23b0216-97a8-4816-a6b2-5e8d07e02d35 | modelling-technical-and-biological-effects-in | 2209.06716 | null | https://arxiv.org/abs/2209.06716v2 | https://arxiv.org/pdf/2209.06716v2.pdf | Modelling Technical and Biological Effects in scRNA-seq data with Scalable GPLVMs | Single-cell RNA-seq datasets are growing in size and complexity, enabling the study of cellular composition changes in various biological/clinical contexts. Scalable dimensionality reduction techniques are in need to disentangle biological variation in them, while accounting for technical and biological confounders. In... | ['Neil D. Lawrence', 'Sarah A. Teichmann', 'Shaista Madad', 'Rik G. H. Lindeboom', 'Dinithi Sumanaweera', 'Natsuhiko Kumasaka', 'Emma Dann', 'Aditya Ravuri', 'Vidhi Lalchand'] | 2022-09-14 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [ 3.96023780e-01 -3.70032728e-01 -8.81800354e-02 -2.21455559e-01
-9.10043955e-01 -8.16483736e-01 5.99431753e-01 1.88975096e-01
-1.64590001e-01 8.97391498e-01 6.13352060e-01 -3.72796599e-03
-4.64704096e-01 -6.07617736e-01 -2.35407382e-01 -1.16838408e+00
-5.12268692e-02 1.05528247e+00 -4.94995415e-01 3.84028673... | [6.720701694488525, 5.210686206817627] |
c7253f9b-cb71-49fd-a4b7-8cb27726a471 | maximum-bayes-smatch-ensemble-distillation | 2112.07790 | null | https://arxiv.org/abs/2112.07790v2 | https://arxiv.org/pdf/2112.07790v2.pdf | Maximum Bayes Smatch Ensemble Distillation for AMR Parsing | AMR parsing has experienced an unprecendented increase in performance in the last three years, due to a mixture of effects including architecture improvements and transfer learning. Self-learning techniques have also played a role in pushing performance forward. However, for most recent high performant parsers, the eff... | ['Salim Roukos', 'Radu Florian', 'Tahira Naseem', 'Thanh Lam Hoang', 'Ramon Fernandez Astudillo', 'Young-suk Lee'] | 2021-12-14 | null | https://aclanthology.org/2022.naacl-main.393 | https://aclanthology.org/2022.naacl-main.393.pdf | naacl-2022-7 | ['self-learning'] | ['natural-language-processing'] | [ 1.35095537e-01 5.20705044e-01 5.94736896e-02 -5.39343059e-01
-1.38769853e+00 -6.12332582e-01 5.77203989e-01 1.85292691e-01
-7.37950623e-01 7.66685307e-01 2.08377406e-01 -5.47586083e-01
2.93532401e-01 -5.16167164e-01 -7.92317390e-01 -4.64032888e-01
-1.81280170e-02 7.74942458e-01 1.83913305e-01 -5.79400480... | [10.420746803283691, 9.716361999511719] |
509881a4-d6ef-41c3-bf92-3e601ec23aaf | translating-sumo-k-to-higher-order-set-theory | 2305.07903 | null | https://arxiv.org/abs/2305.07903v1 | https://arxiv.org/pdf/2305.07903v1.pdf | Translating SUMO-K to Higher-Order Set Theory | We describe a translation from a fragment of SUMO (SUMO-K) into higher-order set theory. The translation provides a formal semantics for portions of SUMO which are beyond first-order and which have previously only had an informal interpretation. It also for the first time embeds a large common-sense ontology into a ver... | ['Josef Urban', 'Adam Pease', 'Chad Brown'] | 2023-05-13 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving', 'common-sense-reasoning'] | ['miscellaneous', 'reasoning', 'reasoning'] | [ 4.01051313e-01 9.45941508e-01 1.12103611e-01 -1.94296107e-01
-4.81960744e-01 -1.07777786e+00 6.30464792e-01 2.60128289e-01
7.06399605e-02 9.78206336e-01 -1.05826944e-01 -1.35765553e+00
-5.18853903e-01 -1.15249360e+00 -7.37651348e-01 3.06178451e-01
-3.36361289e-01 7.62757242e-01 1.17191374e+00 -8.51166725... | [8.786149024963379, 6.848349571228027] |
7cbe1e6a-8755-4fe8-bdaf-81f919e08ce8 | dual-alignment-unsupervised-domain-adaptation | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Hao_Dual_Alignment_Unsupervised_Domain_Adaptation_for_Video-Text_Retrieval_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Hao_Dual_Alignment_Unsupervised_Domain_Adaptation_for_Video-Text_Retrieval_CVPR_2023_paper.pdf | Dual Alignment Unsupervised Domain Adaptation for Video-Text Retrieval | Video-text retrieval is an emerging stream in both computer vision and natural language processing communities, which aims to find relevant videos given text queries. In this paper, we study the notoriously challenging task, i.e., Unsupervised Domain Adaptation Video-text Retrieval (UDAVR), wherein training and tes... | ['Bo Li', 'Fei Zhu', 'Dayan Wu', 'Wanqian Zhang', 'Xiaoshuai Hao'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['video-text-retrieval', 'unsupervised-domain-adaptation'] | ['computer-vision', 'methodology'] | [ 2.88656533e-01 -6.66761816e-01 -2.14027733e-01 -2.87801147e-01
-9.61365581e-01 -6.57487273e-01 7.31969059e-01 -1.27731696e-01
-5.49004316e-01 4.23521727e-01 4.95145231e-01 2.44369656e-01
-2.97774434e-01 -3.41435432e-01 -4.86149192e-01 -8.45999777e-01
3.25901777e-01 2.70538568e-01 1.88242257e-01 -1.68680564... | [10.344731330871582, 0.9001376628875732] |
22324de4-9203-4081-a4ce-d3b8c0108ad4 | neural-network-acceptability-judgments | 1805.12471 | null | https://arxiv.org/abs/1805.12471v3 | https://arxiv.org/pdf/1805.12471v3.pdf | Neural Network Acceptability Judgments | This paper investigates the ability of artificial neural networks to judge the grammatical acceptability of a sentence, with the goal of testing their linguistic competence. We introduce the Corpus of Linguistic Acceptability (CoLA), a set of 10,657 English sentences labeled as grammatical or ungrammatical from publish... | ['Samuel R. Bowman', 'Alex Warstadt', 'Amanpreet Singh'] | 2018-05-31 | neural-network-acceptability-judgments-1 | https://aclanthology.org/Q19-1040 | https://aclanthology.org/Q19-1040.pdf | tacl-2019-3 | ['linguistic-acceptability'] | ['natural-language-processing'] | [ 5.48201986e-02 6.30315721e-01 1.55929387e-01 -9.69226718e-01
-7.63793826e-01 -8.13434124e-01 4.15726155e-01 4.95362073e-01
-5.60040593e-01 3.92399758e-01 4.30105150e-01 -9.99631166e-01
6.28598407e-02 -8.50969791e-01 -7.31998920e-01 -3.00856587e-02
-1.91273857e-02 4.25545633e-01 -3.93862963e-01 -5.64609826... | [10.683989524841309, 9.429229736328125] |
547f71d2-a0ca-4fa9-84f1-92745f396f5c | improving-few-shot-text-classification-via | 1908.08788 | null | https://arxiv.org/abs/1908.08788v2 | https://arxiv.org/pdf/1908.08788v2.pdf | When Low Resource NLP Meets Unsupervised Language Model: Meta-pretraining Then Meta-learning for Few-shot Text Classification | Text classification tends to be difficult when data are deficient or when it is required to adapt to unseen classes. In such challenging scenarios, recent studies have often used meta-learning to simulate the few-shot task, thus negating implicit common linguistic features across tasks. This paper addresses such proble... | ['Ningyu Zhang', 'Shumin Deng', 'Jiaoyan Chen', 'Huajun Chen', 'Zhanlin Sun'] | 2019-08-22 | null | null | null | null | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 1.99835747e-01 -2.35559884e-02 -1.58661664e-01 -5.75558066e-01
-7.38827705e-01 -2.56409377e-01 8.81341934e-01 4.68126595e-01
-7.41224349e-01 7.35622048e-01 4.37944829e-02 -1.74922809e-01
-8.30973387e-02 -6.74339950e-01 -4.41913664e-01 -5.86744964e-01
2.58589387e-01 6.38134181e-01 2.08518803e-01 -4.61394668... | [10.706286430358887, 7.73544454574585] |
78f9bcb0-4e17-486b-b4f4-6e529cea7c1c | opt-one-shot-pose-controllable-talking-head | 2302.08197 | null | https://arxiv.org/abs/2302.08197v1 | https://arxiv.org/pdf/2302.08197v1.pdf | OPT: One-shot Pose-Controllable Talking Head Generation | One-shot talking head generation produces lip-sync talking heads based on arbitrary audio and one source face. To guarantee the naturalness and realness, recent methods propose to achieve free pose control instead of simply editing mouth areas. However, existing methods do not preserve accurate identity of source face ... | ['Jizhong Han', 'Jiao Dai', 'Cai Yu', 'Yesheng Chai', 'Xiaomeng Fu', 'Xi Wang', 'Jin Liu'] | 2023-02-16 | null | null | null | null | ['talking-head-generation'] | ['computer-vision'] | [ 3.68286371e-02 2.92317808e-01 -4.47214842e-02 -4.33001190e-01
-9.62044358e-01 -3.38843733e-01 3.28766257e-01 -8.13345432e-01
1.62371174e-01 5.64294934e-01 6.68274939e-01 6.29722476e-01
1.68249503e-01 -3.13029796e-01 -5.41930139e-01 -9.98924971e-01
3.63027513e-01 -1.37334302e-01 -2.71782547e-01 -1.84167176... | [13.229835510253906, -0.4219840168952942] |
36bad896-db02-4af1-9991-0b741e15b7bf | on-the-automatic-generation-of-medical | 1711.08195 | null | http://arxiv.org/abs/1711.08195v3 | http://arxiv.org/pdf/1711.08195v3.pdf | On the Automatic Generation of Medical Imaging Reports | Medical imaging is widely used in clinical practice for diagnosis and
treatment. Report-writing can be error-prone for unexperienced physicians, and
time- consuming and tedious for experienced physicians. To address these
issues, we study the automatic generation of medical imaging reports. This task
presents several c... | ['Baoyu Jing', 'Eric Xing', 'Pengtao Xie'] | 2017-11-22 | on-the-automatic-generation-of-medical-1 | https://aclanthology.org/P18-1240 | https://aclanthology.org/P18-1240.pdf | acl-2018-7 | ['medical-report-generation'] | ['medical'] | [ 4.16167915e-01 1.17010176e-01 -1.79550517e-02 -3.60071599e-01
-1.42245877e+00 -3.63544613e-01 2.50068665e-01 4.83368576e-01
-2.69622579e-02 9.27508116e-01 6.19133294e-01 -3.30254972e-01
1.10008776e-01 -7.45390058e-01 -5.51891744e-01 -6.69001460e-01
-1.52931675e-01 3.16227853e-01 6.81092292e-02 5.60855031... | [15.044610023498535, -1.4010933637619019] |
64d90caf-6803-457d-9201-5fb09bddc675 | cluster-guided-contrastive-graph-clustering | 2301.01098 | null | https://arxiv.org/abs/2301.01098v1 | https://arxiv.org/pdf/2301.01098v1.pdf | Cluster-guided Contrastive Graph Clustering Network | Benefiting from the intrinsic supervision information exploitation capability, contrastive learning has achieved promising performance in the field of deep graph clustering recently. However, we observe that two drawbacks of the positive and negative sample construction mechanisms limit the performance of existing algo... | ['En Zhu', 'Liming Fang', 'Xinwang Liu', 'Qun Zheng', 'Wenxuan Tu', 'Siwei Wang', 'Sihang Zhou', 'Yue Liu', 'Xihong Yang'] | 2023-01-03 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [-1.25671715e-01 1.48573726e-01 -3.58745068e-01 -4.58260089e-01
-5.32804787e-01 -2.98861474e-01 3.90684992e-01 6.61275387e-02
-1.54914185e-01 4.92605478e-01 1.16604648e-03 3.24818105e-01
-4.34986353e-01 -7.87553728e-01 -6.01493955e-01 -1.13473415e+00
-1.95228800e-01 6.20691299e-01 1.26690879e-01 2.00961664... | [7.458194255828857, 5.923952102661133] |
3ee271cb-a9cd-42b6-8e17-9da686e90abd | numerical-smoothing-with-hierarchical | 2111.01874 | null | https://arxiv.org/abs/2111.01874v2 | https://arxiv.org/pdf/2111.01874v2.pdf | Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing | When approximating the expectations of a functional of a solution to a stochastic differential equation, the numerical performance of deterministic quadrature methods, such as sparse grid quadrature and quasi-Monte Carlo (QMC) methods, may critically depend on the regularity of the integrand. To overcome this issue and... | ['Raúl Tempone', 'Chiheb Ben Hammouda', 'Christian Bayer'] | 2021-11-02 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [-1.94275439e-01 -1.70636430e-01 4.68838036e-01 3.76945108e-01
-9.01982784e-01 -2.64448613e-01 4.82603997e-01 1.74996823e-01
-5.29301763e-01 1.38944018e+00 -1.64503574e-01 -2.62376785e-01
-2.51969039e-01 -1.06346941e+00 -4.43738788e-01 -1.15983689e+00
-3.03841149e-03 5.09505868e-01 9.41208377e-02 -2.88209915... | [6.371648788452148, 3.7724499702453613] |
36af8e3c-f1da-487d-b9ab-098d62afa69d | gait-based-human-identification-through | 2110.09286 | null | https://arxiv.org/abs/2110.09286v1 | https://arxiv.org/pdf/2110.09286v1.pdf | Gait-based Human Identification through Minimum Gait-phases and Sensors | Human identification is one of the most common and critical tasks for condition monitoring, human-machine interaction, and providing assistive services in smart environments. Recently, human gait has gained new attention as a biometric for identification to achieve contactless identification from a distance robust to p... | ['Jinwook Kim', 'Kyung-Ryoul Mun', 'Mina Park', 'Dawoon Jung', 'Muhammad Zeeshan Arshad'] | 2021-10-15 | null | null | null | null | ['gait-identification'] | ['computer-vision'] | [ 2.72139579e-01 -2.57470727e-01 -2.20690235e-01 1.16853928e-02
-1.11124828e-01 -1.50593206e-01 1.51066720e-01 3.95540923e-01
-6.31236613e-01 6.84223711e-01 -3.85428071e-01 1.02808371e-01
-2.17830002e-01 -6.95285499e-01 -7.03798309e-02 -7.39587069e-01
-1.24866597e-01 4.04830545e-01 2.16712698e-01 -1.88891307... | [14.048177719116211, 1.4934331178665161] |
a780c432-5cd6-4baf-8b74-1f04d3644766 | dixit-interactive-visual-storytelling-via | 1903.02230 | null | https://arxiv.org/abs/1903.02230v3 | https://arxiv.org/pdf/1903.02230v3.pdf | Dixit: Interactive Visual Storytelling via Term Manipulation | In this paper, we introduce Dixit, an interactive visual storytelling system that the user interacts with iteratively to compose a short story for a photo sequence. The user initiates the process by uploading a sequence of photos. Dixit first extracts text terms from each photo which describe the objects (e.g., boy, bi... | ['Lun-Wei Ku', "Ting-Hao 'Kenneth' Huang", 'Hsin-Yu Lin', 'Zi-Yuan Chen', 'Yu-Hua Chen', 'Chao-Chun Hsu'] | 2019-03-06 | null | null | null | null | ['visual-storytelling'] | ['natural-language-processing'] | [ 6.24183714e-01 3.66969109e-01 -4.75633033e-02 -2.83157200e-01
-2.88778454e-01 -6.72225952e-01 1.03057051e+00 1.34870917e-01
8.65406319e-02 6.95135832e-01 5.49057364e-01 -1.20021693e-01
3.36204380e-01 -7.75930464e-01 -7.96425223e-01 -6.19322598e-01
3.11773151e-01 4.56062317e-01 4.75807637e-02 -1.11844771... | [11.188536643981934, 0.7256268858909607] |
4bded800-ee49-427c-8edb-34dd698e9263 | assessing-robustness-of-text-classification | 2010.02004 | null | https://arxiv.org/abs/2010.02004v2 | https://arxiv.org/pdf/2010.02004v2.pdf | Assessing Robustness of Text Classification through Maximal Safe Radius Computation | Neural network NLP models are vulnerable to small modifications of the input that maintain the original meaning but result in a different prediction. In this paper, we focus on robustness of text classification against word substitutions, aiming to provide guarantees that the model prediction does not change if a word ... | ['Marta Kwiatkowska', 'Anthony Hartshorn', 'Benjie Wang', 'Luca Laurenti', 'Min Wu', 'Emanuele La Malfa'] | 2020-10-01 | null | https://aclanthology.org/2020.findings-emnlp.266 | https://aclanthology.org/2020.findings-emnlp.266.pdf | findings-of-the-association-for-computational | ['news-classification'] | ['natural-language-processing'] | [ 2.06275403e-01 1.82276070e-01 3.06426808e-02 -5.14320612e-01
-3.45239520e-01 -8.41129184e-01 7.08742082e-01 7.89713502e-01
-9.25495446e-01 5.08555055e-01 2.54605234e-01 -5.88842213e-01
-1.22050464e-01 -7.56212950e-01 -8.90924096e-01 -5.54271817e-01
-4.88618053e-02 1.04613669e-01 2.21137658e-01 -1.65828243... | [10.302454948425293, 8.317198753356934] |
af9d3253-65c3-4301-9c24-e5dbfc597bb7 | deepscalper-a-risk-aware-deep-reinforcement | 2201.09058 | null | https://arxiv.org/abs/2201.09058v3 | https://arxiv.org/pdf/2201.09058v3.pdf | DeepScalper: A Risk-Aware Reinforcement Learning Framework to Capture Fleeting Intraday Trading Opportunities | Reinforcement learning (RL) techniques have shown great success in many challenging quantitative trading tasks, such as portfolio management and algorithmic trading. Especially, intraday trading is one of the most profitable and risky tasks because of the intraday behaviors of the financial market that reflect billions... | ['Bo An', 'Jian Li', 'Wanqi Xue', 'Rundong Wang', 'Junlei Zhu', 'Xu He', 'Shuo Sun'] | 2021-12-15 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-6.27973557e-01 -1.85119584e-01 -4.12206054e-01 -1.29897982e-01
-9.10519361e-01 -6.58610761e-01 7.09765255e-01 -1.64472058e-01
-4.72466648e-01 5.81101835e-01 2.52122015e-01 -6.75977767e-01
-1.52291864e-01 -9.26220894e-01 -6.76568568e-01 -3.60223800e-01
-5.53948462e-01 6.99612856e-01 -1.66193619e-01 -3.88585180... | [4.433291912078857, 3.9462928771972656] |
a7349d5a-6b7e-4c55-95bf-ba2711b36a55 | human-s-role-in-the-loop | 2204.14192 | null | https://arxiv.org/abs/2204.14192v1 | https://arxiv.org/pdf/2204.14192v1.pdf | Human's Role in-the-Loop | Data integration has been recently challenged by the need to handle large volumes of data, arriving at high velocity from a variety of sources, which demonstrate varying levels of veracity. This challenging setting, often referred to as big data, renders many of the existing techniques, especially those that are human-... | ['Roee Shraga', 'Avigdor Gal'] | 2022-04-27 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [-2.07310185e-01 2.27310330e-01 -2.06970200e-01 -2.87283897e-01
-3.62038523e-01 -3.83532315e-01 7.53759623e-01 3.34504724e-01
-4.71826136e-01 2.80928195e-01 3.87873411e-01 -3.74243706e-01
-4.23346043e-01 -1.14259863e+00 -3.20988357e-01 -3.98090750e-01
4.86673683e-01 9.90466595e-01 -2.43178368e-01 -4.96076763... | [9.050870895385742, 6.4189133644104] |
bebb04ba-3d9b-4523-9948-3d54d3a0d10e | machine-learning-predictions-for-local | 2204.05967 | null | https://arxiv.org/abs/2204.05967v1 | https://arxiv.org/pdf/2204.05967v1.pdf | Machine learning predictions for local electronic properties of disordered correlated electron systems | We present a scalable machine learning (ML) model to predict local electronic properties such as on-site electron number and double occupation for disordered correlated electron systems. Our approach is based on the locality principle, or the nearsightedness nature, of many-electron systems, which means local electroni... | ['Gia-Wei Chern', 'Ting-Kuo Lee', 'Puhan Zhang', 'Sheng Zhang', 'Yi-Hsuan Liu'] | 2022-04-12 | null | null | null | null | ['variational-monte-carlo'] | ['miscellaneous'] | [ 7.72939622e-03 -7.49129117e-01 -3.26899320e-01 -4.35792834e-01
-9.77672100e-01 -1.77783966e-02 8.03467274e-01 1.37692079e-01
-5.41523814e-01 1.06899929e+00 1.42996818e-01 -2.33666673e-01
-1.26505181e-01 -8.03302228e-01 -6.29740000e-01 -1.37959456e+00
-3.24328214e-01 7.90718019e-01 8.07685703e-02 -2.82430291... | [5.417576789855957, 5.098823547363281] |
d64be40c-05d3-42a4-84e4-f9c5181ad2b3 | social-norms-grounded-machine-ethics-in | null | null | https://aclanthology.org/2022.coling-1.114 | https://aclanthology.org/2022.coling-1.114.pdf | Social Norms-Grounded Machine Ethics in Complex Narrative Situation | Ethical judgment aims to determine if a person in a narrative situation acts under people’s social norms under a culture, so it is crucial to understand actions in narratives and achieve machine ethics. Recent works depend on data-driven methods to directly judge the ethics of complex real-world narratives but face two... | ['Daxin Jiang', 'Xiubo Geng', 'Tao Shen'] | null | null | null | null | coling-2022-10 | ['culture'] | ['speech'] | [ 4.98708546e-01 5.08678257e-01 -9.25524831e-02 -5.00684023e-01
-7.63410255e-02 -3.95427883e-01 7.78699815e-01 1.27759829e-01
-5.35332680e-01 6.85961723e-01 9.67990220e-01 3.01148109e-02
-2.75030434e-01 -8.14034760e-01 -1.71263143e-01 -1.92222670e-01
5.39055884e-01 3.74682814e-01 -1.79757625e-01 -7.20896184... | [11.634629249572754, 8.17578411102295] |
caab5d72-0ecc-4ef6-a044-9714d83bff45 | using-ontologies-to-improve-performance-in | null | null | https://openreview.net/forum?id=r1g1LoAcFm | https://openreview.net/pdf?id=r1g1LoAcFm | Using Ontologies To Improve Performance In Massively Multi-label Prediction | Massively multi-label prediction/classification problems arise in environments like health-care or biology where it is useful to make very precise predictions. One challenge with massively multi-label problems is that there is often a long-tailed frequency distribution for the labels, resulting in few positive examples... | ['Peter J. Liu', 'Ethan Steinberg'] | 2019-05-01 | null | null | null | iclr-2019-5 | ['protein-function-prediction'] | ['medical'] | [ 4.46421802e-01 1.97224200e-01 -4.01219964e-01 -8.32439721e-01
-8.64505649e-01 -4.58464980e-01 9.33018848e-02 6.23979688e-01
-5.06154954e-01 1.05649042e+00 1.02402316e-02 -4.98466730e-01
-3.88877124e-01 -5.96672654e-01 -6.25812590e-01 -8.30229819e-01
-3.72927822e-02 9.10432756e-01 1.95429295e-01 2.77167946... | [9.452596664428711, 4.387707710266113] |
3805a216-c29f-47dc-91d1-10fd1b0128d1 | reler-zju-submission-to-the-ego4d-moment | 2211.09558 | null | https://arxiv.org/abs/2211.09558v1 | https://arxiv.org/pdf/2211.09558v1.pdf | ReLER@ZJU Submission to the Ego4D Moment Queries Challenge 2022 | In this report, we present the ReLER@ZJU1 submission to the Ego4D Moment Queries Challenge in ECCV 2022. In this task, the goal is to retrieve and localize all instances of possible activities in egocentric videos. Ego4D dataset is challenging for the temporal action localization task as the temporal duration of the vi... | ['Yi Yang', 'Xiaohan Wang', 'Jiayi Shao'] | 2022-11-17 | null | null | null | null | ['action-localization'] | ['computer-vision'] | [-1.30297497e-01 -1.40325457e-01 -5.26167214e-01 -3.18701893e-01
-7.53178537e-01 -5.03740847e-01 5.02914131e-01 -3.38498980e-01
-2.93547690e-01 5.18665254e-01 7.17932522e-01 2.98109204e-01
2.34835520e-02 -3.27980816e-01 -8.16187918e-01 -4.78104591e-01
-3.36646855e-01 1.39821053e-01 4.07680273e-01 2.80031443... | [8.362823486328125, 0.40327778458595276] |
1570b094-97ac-43da-a41b-d78c882dad92 | temporal-superpixels-based-on-proximity | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Lee_Temporal_Superpixels_Based_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Lee_Temporal_Superpixels_Based_ICCV_2017_paper.pdf | Temporal Superpixels Based on Proximity-Weighted Patch Matching | A temporal superpixel algorithm based on proximity-weighted patch matching (TS-PPM) is proposed in this work. We develop the proximity-weighted patch matching (PPM), which estimates the motion vector of a superpixel robustly, by considering the patch matching distances of neighboring superpixels as well as the target s... | ['Chang-Su Kim', 'Won-Dong Jang', 'Se-Ho Lee'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['patch-matching'] | ['computer-vision'] | [ 3.66423100e-01 -1.97667748e-01 -3.07531655e-01 -2.60948002e-01
-6.25308037e-01 -3.60663503e-01 3.28040048e-02 5.18832766e-02
-3.50697815e-01 6.19920373e-01 -7.92417899e-02 2.14621946e-01
1.59147426e-01 -6.67417347e-01 -4.30317461e-01 -8.46541166e-01
1.73253179e-01 -2.98693366e-02 1.26919961e+00 2.34149843... | [9.328707695007324, -0.4342607259750366] |
98b37f92-c809-4058-80a0-a9c5ba06c4d2 | improving-few-shot-relation-classification-by | null | null | https://aclanthology.org/2022.findings-naacl.34 | https://aclanthology.org/2022.findings-naacl.34.pdf | Improving Few-Shot Relation Classification by Prototypical Representation Learning with Definition Text | Few-shot relation classification is difficult because the few instances available may not represent well the relation patterns. Some existing approaches explored extra information such as relation definition, in addition to the instances, to learn a better relation representation. However, the encoding of the extra inf... | ['Dongsheng Li', 'Jian-Yun Nie', 'Yuyang Zhang', 'Li Zhenzhen'] | null | null | null | null | findings-naacl-2022-7 | ['few-shot-relation-classification', 'relation-classification', 'few-shot-relation-classification'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [ 3.16574454e-01 6.62963390e-01 -6.38940394e-01 -5.75710297e-01
-4.65451747e-01 -2.11540926e-02 5.75466990e-01 5.16135812e-01
-1.76322848e-01 1.05421734e+00 5.60790859e-02 -4.61923778e-02
-1.93090037e-01 -1.21542037e+00 -6.53538465e-01 -3.97386640e-01
8.27977899e-03 7.10236967e-01 2.40403429e-01 -1.87904030... | [9.26545524597168, 8.539090156555176] |
55b36de1-169c-4256-9e41-15c6c013d37f | unified-perspective-on-probability-divergence | 2201.13127 | null | https://arxiv.org/abs/2201.13127v1 | https://arxiv.org/pdf/2201.13127v1.pdf | Unified Perspective on Probability Divergence via Maximum Likelihood Density Ratio Estimation: Bridging KL-Divergence and Integral Probability Metrics | This paper provides a unified perspective for the Kullback-Leibler (KL)-divergence and the integral probability metrics (IPMs) from the perspective of maximum likelihood density-ratio estimation (DRE). Both the KL-divergence and the IPMs are widely used in various fields in applications such as generative modeling. How... | ['Kentaro Minami', 'Masaaki Imaizumi', 'Masahiro Kato'] | 2022-01-31 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [ 6.25112280e-02 -7.73188546e-02 -2.08722636e-01 -4.50021505e-01
-9.12725329e-01 -5.44587255e-01 4.82505143e-01 -2.58971393e-01
-1.95317611e-01 1.03389716e+00 -1.83265954e-01 -4.03145492e-01
-5.21332264e-01 -7.65855551e-01 -6.09618247e-01 -7.59999812e-01
-1.70771778e-01 2.38434672e-02 2.25914806e-01 1.23612173... | [7.231180667877197, 3.9956088066101074] |
fe7484b8-d0dd-46e3-9802-a3b96d4a2aeb | the-surprising-effectiveness-of-visual | 2108.11550 | null | https://arxiv.org/abs/2108.11550v1 | https://arxiv.org/pdf/2108.11550v1.pdf | The Surprising Effectiveness of Visual Odometry Techniques for Embodied PointGoal Navigation | It is fundamental for personal robots to reliably navigate to a specified goal. To study this task, PointGoal navigation has been introduced in simulated Embodied AI environments. Recent advances solve this PointGoal navigation task with near-perfect accuracy (99.6% success) in photo-realistically simulated environment... | ['Alexander Schwing', 'Dhruv Batra', 'Harsh Agrawal', 'Xiaoming Zhao'] | 2021-08-26 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zhao_The_Surprising_Effectiveness_of_Visual_Odometry_Techniques_for_Embodied_PointGoal_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zhao_The_Surprising_Effectiveness_of_Visual_Odometry_Techniques_for_Embodied_PointGoal_ICCV_2021_paper.pdf | iccv-2021-1 | ['pointgoal-navigation'] | ['robots'] | [-9.15528983e-02 2.41174057e-01 3.13330591e-01 -1.43111318e-01
-4.52879161e-01 -7.87559390e-01 6.74335539e-01 2.02644482e-01
-9.41852152e-01 8.78233969e-01 -2.31031686e-01 -2.52823383e-01
-7.08975568e-02 -6.71637833e-01 -1.03956330e+00 -5.69951177e-01
-5.82109749e-01 7.22699761e-01 3.01319659e-01 -7.55686879... | [4.679556369781494, 0.6644973158836365] |
932a39d9-0f2d-432b-a83c-f53e1a4911ad | griddehazenet-an-enhanced-multi-scale-network | 2103.13998 | null | https://arxiv.org/abs/2103.13998v2 | https://arxiv.org/pdf/2103.13998v2.pdf | GridDehazeNet+: An Enhanced Multi-Scale Network with Intra-Task Knowledge Transfer for Single Image Dehazing | We propose an enhanced multi-scale network, dubbed GridDehazeNet+, for single image dehazing. The proposed dehazing method does not rely on the Atmosphere Scattering Model (ASM), and an explanation as to why it is not necessarily performing the dimension reduction offered by this model is provided. GridDehazeNet+ consi... | ['Jun Chen', 'Zijun Wu', 'Zhihao Shi', 'Xiaohong Liu'] | 2021-03-25 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 3.53626072e-01 -2.13835254e-01 6.23302341e-01 -3.80826622e-01
-7.65231371e-01 -1.52293682e-01 3.83709371e-01 -1.95784450e-01
-5.23810029e-01 8.13967645e-01 1.42676011e-01 1.59158930e-02
-1.56150147e-01 -1.14170039e+00 -9.16664362e-01 -1.06276321e+00
1.50931418e-01 8.53034258e-02 5.70419908e-01 -4.44039345... | [10.937148094177246, -3.0611257553100586] |
005eb98b-ed3d-4bb2-9edd-ea35f74c6d3d | robustification-of-multilingual-language | 2210.04782 | null | https://arxiv.org/abs/2210.04782v2 | https://arxiv.org/pdf/2210.04782v2.pdf | Robustification of Multilingual Language Models to Real-world Noise in Crosslingual Zero-shot Settings with Robust Contrastive Pretraining | Advances in neural modeling have achieved state-of-the-art (SOTA) results on public natural language processing (NLP) benchmarks, at times surpassing human performance. However, there is a gap between public benchmarks and real-world applications where noise, such as typographical or grammatical mistakes, is abundant a... | ['He He', 'Saab Mansour', 'Jason Krone', 'Sailik Sengupta', 'Asa Cooper Stickland'] | 2022-10-10 | null | null | null | null | ['pretrained-multilingual-language-models'] | ['natural-language-processing'] | [ 9.20017287e-02 -3.59656751e-01 2.62393001e-02 -4.96287614e-01
-1.55435264e+00 -8.45823109e-01 6.16904199e-01 3.33575577e-01
-1.06838262e+00 8.71652782e-01 4.44677353e-01 -4.86099333e-01
3.79768163e-01 -4.79398310e-01 -1.09672880e+00 -4.33111638e-01
1.68844715e-01 3.65950763e-01 -1.41896009e-01 -4.54823941... | [10.728277206420898, 9.808436393737793] |
4baff1a4-3d77-449b-b1c5-3abbac54bebc | ftnet-feature-transverse-network-for-thermal | null | null | https://ieeexplore.ieee.org/abstract/document/9585453 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9585453 | FTNet: Feature Transverse Network for Thermal Image Semantic Segmentation | Thermal imaging is a process of using infrared radiation and thermal energy to collect information about objects. It is superior to visible imaging for its ability to operate in darkness and tolerate illumination variations. In addition, it has potential to penetrate smoke, aerosol, dust, and mist, which are critical i... | ['Srijith Rajeev and Sos S Agaian', 'Shreyas Kamath K.M', 'Karen Panetta'] | 2021-10-26 | null | null | null | ieee-access-2021-10 | ['scene-segmentation', 'thermal-image-segmentation'] | ['computer-vision', 'computer-vision'] | [ 5.40685117e-01 -5.84087133e-01 1.64441764e-01 -2.81342208e-01
-3.65724683e-01 -5.09451330e-01 1.27909318e-01 -4.54885542e-01
-2.11940467e-01 3.61845285e-01 -3.85170579e-01 -3.53840560e-01
7.97425807e-02 -7.60960460e-01 -3.55273634e-01 -1.11658919e+00
4.00384367e-01 -3.28278951e-02 3.95241857e-01 3.38218659... | [10.25008487701416, -2.323323965072632] |
241ae138-e0ef-4069-ba20-8cf8aa1cc12e | gp-guided-mppi-for-efficient-navigation-in | 2307.04019 | null | https://arxiv.org/abs/2307.04019v1 | https://arxiv.org/pdf/2307.04019v1.pdf | GP-guided MPPI for Efficient Navigation in Complex Unknown Cluttered Environments | Robotic navigation in unknown, cluttered environments with limited sensing capabilities poses significant challenges in robotics. Local trajectory optimization methods, such as Model Predictive Path Intergal (MPPI), are a promising solution to this challenge. However, global guidance is required to ensure effective nav... | ['Lantao Liu', 'Mahmoud Ali', 'Ihab S. Mohamed'] | 2023-07-08 | null | null | null | null | ['autonomous-navigation'] | ['computer-vision'] | [ 1.54626500e-02 9.02425796e-02 -3.05083580e-03 1.19607776e-01
-6.39877796e-01 -5.54953158e-01 3.96946400e-01 1.81891933e-01
-4.84015584e-01 6.26430750e-01 -1.64460376e-01 -3.90753567e-01
-5.84213912e-01 -7.97532678e-01 -6.61162138e-01 -1.02389061e+00
-2.72860199e-01 5.50151765e-01 1.94190294e-01 -2.81801283... | [4.871448993682861, 1.1969724893569946] |
c62c3fbc-c448-4e47-8898-a73b2427d453 | multi-cell-multi-task-convolutional-neural | 1808.10564 | null | http://arxiv.org/abs/1808.10564v2 | http://arxiv.org/pdf/1808.10564v2.pdf | Multi-Cell Multi-Task Convolutional Neural Networks for Diabetic Retinopathy Grading | Diabetic Retinopathy (DR) is a non-negligible eye disease among patients with
Diabetes Mellitus, and automatic retinal image analysis algorithm for the DR
screening is in high demand. Considering the resolution of retinal image is
very high, where small pathological tissues can be detected only with large
resolution im... | ['Zaiwang Gu', 'Kang Zhou', 'Wen Liu', 'Jiang Liu', 'Shenghua Gao', 'Weixin Luo', 'Jun Cheng'] | 2018-08-31 | null | null | null | null | ['diabetic-retinopathy-grading'] | ['medical'] | [ 4.47528362e-02 -3.63779753e-01 -1.52865335e-01 -3.12863231e-01
-7.17521489e-01 3.67372151e-04 -5.66773936e-02 -3.10244948e-01
-3.63275826e-01 8.44162345e-01 7.54875690e-02 -2.22274482e-01
-2.13612810e-01 -6.44499302e-01 -4.18276429e-01 -9.40791965e-01
3.00117671e-01 2.28305057e-01 4.02776033e-01 -2.06704214... | [15.810193061828613, -3.9761431217193604] |
70e16d69-afdb-497f-8c40-f507ffaa82b3 | a-self-supervised-approach-to-reconstruction | 2211.00002 | null | https://arxiv.org/abs/2211.00002v1 | https://arxiv.org/pdf/2211.00002v1.pdf | A Self-Supervised Approach to Reconstruction in Sparse X-Ray Computed Tomography | Computed tomography has propelled scientific advances in fields from biology to materials science. This technology allows for the elucidation of 3-dimensional internal structure by the attenuation of x-rays through an object at different rotations relative to the beam. By imaging 2-dimensional projections, a 3-dimensio... | ['Vidya Ganapati', 'Juliane Mueller', 'Talita Perciano', 'Vincent Dumont', 'Judith Weng Zhu', 'Minh Nguyen', 'Rey Mendoza'] | 2022-10-30 | null | null | null | null | ['probabilistic-deep-learning'] | ['computer-vision'] | [ 7.09748864e-02 4.81744222e-02 1.82111114e-01 -2.83568650e-01
-7.07076609e-01 -3.06837440e-01 2.15202093e-01 -2.27843657e-01
-4.10248160e-01 6.63036525e-01 2.52500344e-02 -1.79622546e-01
-2.57448368e-02 -1.09109116e+00 -1.07842577e+00 -9.86963093e-01
2.10808009e-01 1.12425709e+00 7.00525865e-02 3.69474888... | [13.127585411071777, -2.7547712326049805] |
4f743b7a-f9c4-4e64-a3e2-94a6187ecb3d | on-device-learning-a-neural-network-based | 2203.01077 | null | https://arxiv.org/abs/2203.01077v2 | https://arxiv.org/pdf/2203.01077v2.pdf | On-Device Learning: A Neural Network Based Field-Trainable Edge AI | In real-world edge AI applications, their accuracy is often affected by various environmental factors, such as noises, location/calibration of sensors, and time-related changes. This article introduces a neural network based on-device learning approach to address this issue without going deep. Our approach is quite dif... | ['Masaaki Kondo', 'Mineto Tsukada', 'Hiroki Matsutani'] | 2022-03-02 | null | null | null | null | ['pico'] | ['natural-language-processing'] | [ 6.28431141e-02 -1.75458163e-01 -7.54569918e-02 -1.10936955e-01
2.70122886e-01 -7.14990571e-02 -2.57604152e-01 3.29118706e-02
-4.78664577e-01 6.42224669e-01 -5.55758417e-01 -2.42351294e-01
-3.14957798e-01 -8.92371118e-01 -8.35545182e-01 -4.32681412e-01
-1.76422730e-01 -7.82167539e-02 3.15827429e-01 -1.44953087... | [8.047245979309082, 2.631040573120117] |
ac76eb4b-5e0f-45d7-b873-752ff17b72de | a-novel-decentralized-algorithm-for | 2305.04124 | null | https://arxiv.org/abs/2305.04124v1 | https://arxiv.org/pdf/2305.04124v1.pdf | A Novel Decentralized Algorithm for Coordinating the Optimal Power and Traffic Flows with EVs based on Variable Inner Loop Selection | The electric power distribution network (PDN) and the transportation network (TN) are generally operated/coordinated by different entities. However, they are coupled with each other due to electric vehicle charging stations (EVCSs). This paper proposes to coordinate the operation of the two systems via a fully decentra... | ['QiFeng Li', 'Santosh Sharma'] | 2023-05-06 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-3.69097888e-01 3.16401601e-01 -2.28514612e-01 -2.43885756e-01
-3.77759129e-01 -7.52745092e-01 1.35506064e-01 1.25275284e-01
-2.08759919e-01 1.08863854e+00 -2.60341287e-01 -2.68516064e-01
-4.70905453e-01 -8.85063529e-01 -4.10946399e-01 -1.20105135e+00
-3.75900388e-01 7.12603748e-01 -3.73364657e-01 -1.37458518... | [6.169833660125732, 4.915868759155273] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.