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cdb4f94b-8d73-4180-995d-7bfeb0b0891c | svvad-personal-voice-activity-detection-for | 2305.19581 | null | https://arxiv.org/abs/2305.19581v1 | https://arxiv.org/pdf/2305.19581v1.pdf | SVVAD: Personal Voice Activity Detection for Speaker Verification | Voice activity detection (VAD) improves the performance of speaker verification (SV) by preserving speech segments and attenuating the effects of non-speech. However, this scheme is not ideal: (1) it fails in noisy environments or multi-speaker conversations; (2) it is trained based on inaccurate non-SV sensitive label... | ['Jing Xiao', 'Junqing Peng', 'Jianzong Wang', 'Zuheng Kang'] | 2023-05-31 | null | null | null | null | ['action-detection', 'activity-detection', 'speaker-verification'] | ['computer-vision', 'computer-vision', 'speech'] | [ 2.08457544e-01 2.30083335e-02 -2.20826447e-01 -4.17854726e-01
-1.16194725e+00 -6.44595444e-01 5.41660905e-01 2.98094023e-02
-2.50398606e-01 5.27838111e-01 6.65917337e-01 -5.06762862e-01
3.94893140e-01 -9.78100672e-02 -2.23657355e-01 -7.65984178e-01
4.86198217e-01 8.01162645e-02 1.92767650e-01 -2.50879675... | [14.52081298828125, 6.120588779449463] |
d8737fe7-b4f9-4855-911b-c19abf4fa2f1 | hyrr-hybrid-infused-reranking-for-passage | 2212.10528 | null | https://arxiv.org/abs/2212.10528v1 | https://arxiv.org/pdf/2212.10528v1.pdf | HYRR: Hybrid Infused Reranking for Passage Retrieval | We present Hybrid Infused Reranking for Passages Retrieval (HYRR), a framework for training rerankers based on a hybrid of BM25 and neural retrieval models. Retrievers based on hybrid models have been shown to outperform both BM25 and neural models alone. Our approach exploits this improved performance when training a ... | ['Jianmo Ni', 'Ji Ma', 'Keith Hall', 'Jing Lu'] | 2022-12-20 | null | null | null | null | ['passage-retrieval'] | ['natural-language-processing'] | [ 3.64415310e-02 -2.49187857e-01 -4.45877165e-01 1.00186564e-01
-1.65086424e+00 -3.87797832e-01 1.05583525e+00 3.47660601e-01
-8.76720965e-01 6.36701167e-01 9.99563277e-01 -4.42962125e-02
-6.02350414e-01 -5.59291363e-01 -5.05823851e-01 -1.43798932e-01
-1.42538458e-01 9.07689512e-01 4.64239180e-01 -9.13915813... | [11.508254051208496, 7.660204887390137] |
de7811e8-8a8f-42b0-b6aa-6d62ca1e2a4f | efficient-semantic-segmentation-by-altering | 2303.07224 | null | https://arxiv.org/abs/2303.07224v1 | https://arxiv.org/pdf/2303.07224v1.pdf | Efficient Semantic Segmentation by Altering Resolutions for Compressed Videos | Video semantic segmentation (VSS) is a computationally expensive task due to the per-frame prediction for videos of high frame rates. In recent work, compact models or adaptive network strategies have been proposed for efficient VSS. However, they did not consider a crucial factor that affects the computational cost fr... | ['Yong-Jin Liu', 'Jiangtao Wen', 'Yuxing Han', 'Jisheng Li', 'Yanghao Li', 'Yuze He', 'Yubin Hu'] | 2023-03-13 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Hu_Efficient_Semantic_Segmentation_by_Altering_Resolutions_for_Compressed_Videos_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Hu_Efficient_Semantic_Segmentation_by_Altering_Resolutions_for_Compressed_Videos_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-semantic-segmentation'] | ['computer-vision'] | [ 2.40776494e-01 -2.36835569e-01 -2.25678369e-01 -3.00631523e-01
-7.28464127e-01 -7.71787390e-02 2.45517641e-01 -2.33222961e-01
-6.45441532e-01 4.07246888e-01 6.44731671e-02 -7.07562715e-02
8.94284397e-02 -9.03673053e-01 -7.01001704e-01 -6.35394692e-01
-8.27436298e-02 -1.38552904e-01 9.51827645e-01 -1.76322177... | [9.194637298583984, -0.21430885791778564] |
50f6529f-f745-4e83-9408-03c9221240b0 | seq3-differentiable-sequence-to-sequence-to-1 | null | null | https://aclanthology.org/N19-1071 | https://aclanthology.org/N19-1071.pdf | SEQ\^3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression | Neural sequence-to-sequence models are currently the dominant approach in several natural language processing tasks, but require large parallel corpora. We present a sequence-to-sequence-to-sequence autoencoder (SEQ{\^{}}3), consisting of two chained encoder-decoder pairs, with words used as a sequence of discrete late... | ['ros', 'Ioannis Konstas', 'Christos Baziotis', 'Ion Androutsopoulos', 'Alex Potamianos'] | 2019-06-01 | null | null | null | naacl-2019-6 | ['sentence-compression', 'unsupervised-abstractive-sentence-compression'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.07201004e-01 4.00399119e-01 -2.02720493e-01 -4.54121739e-01
-6.93961322e-01 -2.24380523e-01 6.41021609e-01 4.49896157e-01
-9.28304017e-01 8.85230660e-01 6.24348104e-01 -1.79668888e-01
2.14432448e-01 -7.87032545e-01 -1.02875876e+00 -7.39081860e-01
1.06001318e-01 6.61072671e-01 -2.84169555e-01 -1.02692164... | [12.151823997497559, 9.282356262207031] |
f8c7e8d4-da84-43c3-8e00-b10fe9292784 | few-shot-class-incremental-pill-recognition | 2304.11959 | null | https://arxiv.org/abs/2304.11959v1 | https://arxiv.org/pdf/2304.11959v1.pdf | Few-shot Class-incremental Pill Recognition | The automatic pill recognition system is of great significance in improving the efficiency of the hospital, helping people with visual impairment, and avoiding cross-infection. However, most existing pill recognition systems based on deep learning can merely perform pill classification on the learned pill categories wi... | ['Dewen Hu', 'Kai Gao', 'Li Liu', 'Jinghua Zhang'] | 2023-04-24 | null | null | null | null | ['class-incremental-learning', 'few-shot-class-incremental-learning', 'incremental-learning'] | ['computer-vision', 'methodology', 'methodology'] | [ 4.03098583e-01 -8.81449506e-02 -6.06073380e-01 -5.23532271e-01
-6.48562193e-01 -8.15125629e-02 2.94288337e-01 2.25534141e-01
-1.76222133e-03 5.12643874e-01 -8.65187645e-02 -4.99405600e-02
-1.58644736e-01 -1.07415152e+00 -5.04059792e-01 -8.47264409e-01
3.81571978e-01 3.52251053e-01 -4.67209779e-02 7.56773278... | [15.031317710876465, -2.5583224296569824] |
a12a06de-9f9a-4a98-a77e-03243a092f67 | explaining-legal-concepts-with-augmented | 2306.09525 | null | https://arxiv.org/abs/2306.09525v2 | https://arxiv.org/pdf/2306.09525v2.pdf | Explaining Legal Concepts with Augmented Large Language Models (GPT-4) | Interpreting the meaning of legal open-textured terms is a key task of legal professionals. An important source for this interpretation is how the term was applied in previous court cases. In this paper, we evaluate the performance of GPT-4 in generating factually accurate, clear and relevant explanations of terms in l... | ['Huihui Xu', 'Hannes Westermann', 'Morgan A. Gray', 'Kevin D. Ashley', 'Jaromir Savelka'] | 2023-06-15 | null | null | null | null | ['information-retrieval'] | ['natural-language-processing'] | [ 4.57728535e-01 8.84617329e-01 -5.56923449e-03 -4.37184036e-01
-1.15340328e+00 -6.99789584e-01 7.69442499e-01 3.68134290e-01
-4.36665453e-02 7.66586781e-01 9.01154935e-01 -9.75642681e-01
-5.34636974e-01 -4.89938200e-01 -5.26321530e-01 -1.81102663e-01
4.80710953e-01 5.01932502e-01 1.07831098e-02 -6.04418337... | [10.18641185760498, 7.95232629776001] |
f1a19372-11be-4446-a60a-efa9aee5c3e1 | reference-conditioned-super-resolution-by | 1804.03360 | null | http://arxiv.org/abs/1804.03360v1 | http://arxiv.org/pdf/1804.03360v1.pdf | Reference-Conditioned Super-Resolution by Neural Texture Transfer | With the recent advancement in deep learning, we have witnessed a great
progress in single image super-resolution. However, due to the significant
information loss of the image downscaling process, it has become extremely
challenging to further advance the state-of-the-art, especially for large
upscaling factors. This ... | ['Zhifei Zhang', 'Zhaowen Wang', 'Zhe Lin', 'Hairong Qi'] | 2018-04-10 | null | null | null | null | ['image-stylization', 'reference-based-super-resolution'] | ['computer-vision', 'computer-vision'] | [ 7.63979673e-01 -1.16396144e-01 5.03537320e-02 -2.16434240e-01
-1.17872739e+00 -3.73278558e-02 5.56877553e-01 -7.79888988e-01
4.84277084e-02 9.22114611e-01 5.21177948e-01 4.73378211e-01
-8.07465389e-02 -9.66525733e-01 -8.06263447e-01 -9.44088399e-01
4.38531429e-01 2.19774153e-02 2.87039220e-01 -6.55932605... | [10.969175338745117, -2.084467649459839] |
d8916b6a-4bab-43f0-a82a-4353dac2f9f4 | towards-unsupervised-learning-of-temporal | 1401.6427 | null | http://arxiv.org/abs/1401.6427v1 | http://arxiv.org/pdf/1401.6427v1.pdf | Towards Unsupervised Learning of Temporal Relations between Events | Automatic extraction of temporal relations between event pairs is an
important task for several natural language processing applications such as
Question Answering, Information Extraction, and Summarization. Since most
existing methods are supervised and require large corpora, which for many
languages do not exist, we ... | ['Gholamreza Ghassem-Sani', 'Seyed Abolghasem Mirroshandel'] | 2014-01-23 | null | null | null | null | ['temporal-relation-extraction'] | ['natural-language-processing'] | [ 3.09211642e-01 4.96230036e-01 -3.19050103e-01 -4.05483574e-01
-8.10460806e-01 -4.87183452e-01 8.84678960e-01 8.70910287e-01
-5.08085847e-01 9.43406880e-01 2.26742730e-01 -3.80532384e-01
-2.26347283e-01 -8.01106036e-01 -4.03976053e-01 -6.69048071e-01
-2.53146917e-01 5.60293972e-01 7.05763161e-01 -2.46725023... | [9.094813346862793, 9.183455467224121] |
b93d616c-a15f-43de-8e18-f06c00c2c8d6 | shot-by-shot-movie-version-comparison | null | null | https://videoprocessing.ai/other/film-comparison.html | https://storage.videoprocessing.ml/compression/video-beta/video-diff/article.pdf | Shot-by-Shot Movie Version Comparison | Some movies are released in two or more versions. One of the frequent causes is shrinking the movie for theatrical showcase with subsequent release of both the theatrical (shrunk) cut and the director’s (original, extended) cut. Automatic editing difference detection is complicated by potential changes to color gamut, ... | ['Dmitriy Vatolin', 'Ivan Molodetskikh'] | 2018-12-01 | null | null | null | world-of-technique-of-cinema-2018-12 | ['video-alignment'] | ['computer-vision'] | [ 4.69466150e-01 -2.91386098e-01 2.82267034e-01 -2.18823239e-01
-2.11352393e-01 -1.25759304e+00 5.35266697e-01 3.20836246e-01
-1.43295795e-01 6.52387381e-01 2.23416299e-01 -9.63345692e-02
-2.19716012e-01 -4.11411673e-01 -5.46505868e-01 -7.38589019e-02
-1.02017172e-01 1.30051866e-01 5.25816977e-01 -2.00504556... | [11.040456771850586, -0.9231205582618713] |
603aa6fc-6d2f-4397-ac45-ca9757154282 | emotion-controllable-generalized-talking-face | 2205.01155 | null | https://arxiv.org/abs/2205.01155v1 | https://arxiv.org/pdf/2205.01155v1.pdf | Emotion-Controllable Generalized Talking Face Generation | Despite the significant progress in recent years, very few of the AI-based talking face generation methods attempt to render natural emotions. Moreover, the scope of the methods is majorly limited to the characteristics of the training dataset, hence they fail to generalize to arbitrary unseen faces. In this paper, we ... | ['Brojeshwar Bhowmick', 'Ravindra Yadav', 'Sandika Biswas', 'Sanjana Sinha'] | 2022-05-02 | null | null | null | null | ['texture-synthesis', 'talking-face-generation'] | ['computer-vision', 'computer-vision'] | [ 1.99742347e-01 4.36865628e-01 2.71178633e-01 -5.69929004e-01
-2.48128489e-01 -4.05058384e-01 6.57716870e-01 -7.75722802e-01
1.88097030e-01 6.42452538e-01 1.78066209e-01 2.16712609e-01
1.90960079e-01 -9.61603701e-01 -5.36582947e-01 -8.24217558e-01
1.93823636e-01 4.06677783e-01 -7.17230067e-02 -5.61520159... | [12.82950496673584, -0.22038288414478302] |
e980e299-c213-4497-a788-cc72aab71009 | learning-spatial-and-spatio-temporal-pixel | 2101.10760 | null | https://arxiv.org/abs/2101.10760v1 | https://arxiv.org/pdf/2101.10760v1.pdf | Learning Spatial and Spatio-Temporal Pixel Aggregations for Image and Video Denoising | Existing denoising methods typically restore clear results by aggregating pixels from the noisy input. Instead of relying on hand-crafted aggregation schemes, we propose to explicitly learn this process with deep neural networks. We present a spatial pixel aggregation network and learn the pixel sampling and averaging ... | ['Ming-Hsuan Yang', 'Wenxiu Sun', 'Muchen Li', 'Xiangyu Xu'] | 2021-01-26 | null | null | null | null | ['video-denoising'] | ['computer-vision'] | [ 5.14974773e-01 -4.89549786e-01 4.37415212e-01 -4.06864703e-01
-1.07812512e+00 -3.15659493e-01 5.42444825e-01 -3.40823650e-01
-5.51327467e-01 6.38232887e-01 2.23714262e-01 5.17077409e-02
-7.43944272e-02 -7.98597693e-01 -9.32360709e-01 -1.09172523e+00
-6.43267436e-03 -3.23188156e-01 8.57802704e-02 -1.68388456... | [11.386716842651367, -2.2272017002105713] |
1c80b0ac-5111-41b1-a0bd-7db8f44d50be | knowledge-distillation-for-small-footprint | 1608.00892 | null | http://arxiv.org/abs/1608.00892v3 | http://arxiv.org/pdf/1608.00892v3.pdf | Knowledge Distillation for Small-footprint Highway Networks | Deep learning has significantly advanced state-of-the-art of speech
recognition in the past few years. However, compared to conventional Gaussian
mixture acoustic models, neural network models are usually much larger, and are
therefore not very deployable in embedded devices. Previously, we investigated
a compact highw... | ['Steve Renals', 'Liang Lu', 'Michelle Guo'] | 2016-08-02 | null | null | null | null | ['acoustic-modelling'] | ['speech'] | [ 2.29465559e-01 2.35042065e-01 2.23604605e-01 -4.86815035e-01
-7.21460402e-01 -2.46614695e-01 3.08532357e-01 -4.60961968e-01
-7.98440754e-01 3.49058032e-01 -1.02402873e-01 -9.40168977e-01
1.64681524e-01 -4.46134001e-01 -7.73979545e-01 -7.77729273e-01
2.83353508e-01 5.38255334e-01 1.76740587e-01 1.09432034... | [14.425528526306152, 6.42282772064209] |
22c6a55c-1620-45ec-a862-6a025f754dd5 | what-s-in-a-name-beyond-class-indices-for | 2304.02364 | null | https://arxiv.org/abs/2304.02364v1 | https://arxiv.org/pdf/2304.02364v1.pdf | What's in a Name? Beyond Class Indices for Image Recognition | Existing machine learning models demonstrate excellent performance in image object recognition after training on a large-scale dataset under full supervision. However, these models only learn to map an image to a predefined class index, without revealing the actual semantic meaning of the object in the image. In contra... | ['Xuhui Jia', 'Jie Li', 'Sagar Vaze', 'Yandong Li', 'Kai Han'] | 2023-04-05 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [ 4.28088248e-01 -7.45988190e-02 -5.08916140e-01 -6.19700313e-01
-6.75161839e-01 -8.36911798e-01 1.01337147e+00 -1.19545072e-01
-7.57022917e-01 3.41870993e-01 2.09957850e-03 -6.11479245e-02
-6.47118688e-02 -7.08031893e-01 -6.87396705e-01 -5.63289404e-01
3.03185970e-01 9.44679976e-01 3.36572826e-01 3.40575397... | [9.922109603881836, 1.9385303258895874] |
9a6db18b-1432-43cc-aeba-a9d51809f27c | sjtu-nict-s-supervised-and-unsupervised | 2010.05122 | null | https://arxiv.org/abs/2010.05122v1 | https://arxiv.org/pdf/2010.05122v1.pdf | SJTU-NICT's Supervised and Unsupervised Neural Machine Translation Systems for the WMT20 News Translation Task | In this paper, we introduced our joint team SJTU-NICT 's participation in the WMT 2020 machine translation shared task. In this shared task, we participated in four translation directions of three language pairs: English-Chinese, English-Polish on supervised machine translation track, German-Upper Sorbian on low-resour... | ['Eiichiro Sumita', 'Masao Utiyama', 'Kehai Chen', 'Rui Wang', 'Hai Zhao', 'Zuchao Li'] | 2020-10-11 | null | null | null | null | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 4.28322643e-01 -7.61061907e-02 -5.00126123e-01 -4.81320769e-01
-1.41777658e+00 -6.33336484e-01 8.60600591e-01 -4.23834115e-01
-7.89848328e-01 1.27360523e+00 5.28490782e-01 -9.74864244e-01
4.96752374e-02 -2.40283191e-01 -4.79447961e-01 -2.92431593e-01
7.27022171e-01 1.55821335e+00 -3.22941452e-01 -5.39137244... | [11.604384422302246, 10.426523208618164] |
5300c967-8278-484c-9dc7-0c967eb683e4 | situational-perception-guided-image-matting | 2204.09276 | null | https://arxiv.org/abs/2204.09276v3 | https://arxiv.org/pdf/2204.09276v3.pdf | Situational Perception Guided Image Matting | Most automatic matting methods try to separate the salient foreground from the background. However, the insufficient quantity and subjective bias of the current existing matting datasets make it difficult to fully explore the semantic association between object-to-object and object-to-environment in a given image. In t... | ['Yong Tang', 'Yandong Guo', 'Cheng Lu', 'Ziwen Li', 'Han Huang', 'Jiake Xie', 'Bo Xu'] | 2022-04-20 | null | null | null | null | ['image-matting'] | ['computer-vision'] | [ 4.69857097e-01 -3.93142328e-02 -1.13812454e-01 -5.75518847e-01
-4.51963276e-01 -1.91540092e-01 5.12128234e-01 -1.58639029e-01
-1.19937167e-01 3.79074097e-01 2.40467086e-01 -1.40099600e-03
1.15322366e-01 -6.04106128e-01 -9.71694291e-01 -6.49353445e-01
5.91688335e-01 2.71273069e-02 8.30379605e-01 -1.99907467... | [10.429675102233887, -0.7281911373138428] |
44819952-4bc5-46dd-8dc2-bebdb6376c9e | a-case-study-on-designing-evaluations-of-ml | 2302.07444 | null | https://arxiv.org/abs/2302.07444v2 | https://arxiv.org/pdf/2302.07444v2.pdf | A Case Study on Designing Evaluations of ML Explanations with Simulated User Studies | When conducting user studies to ascertain the usefulness of model explanations in aiding human decision-making, it is important to use real-world use cases, data, and users. However, this process can be resource-intensive, allowing only a limited number of explanation methods to be evaluated. Simulated user evaluations... | ['Pedro Saleiro', 'Sérgio Jesus', 'Valerie Chen', 'Ada Martin'] | 2023-02-15 | null | null | null | null | ['fraud-detection'] | ['miscellaneous'] | [-2.33611967e-02 3.57869595e-01 -5.79149842e-01 -6.05843604e-01
-4.17222917e-01 -4.55959916e-01 6.82202101e-01 5.67411542e-01
-6.01168036e-01 5.84926128e-01 1.88951269e-01 -8.49504471e-01
4.03693877e-02 -6.93032503e-01 -5.10831118e-01 1.14650868e-01
1.06959462e-01 6.01978183e-01 1.89391244e-02 -2.31080428... | [8.828702926635742, 5.888394355773926] |
1d6e8f8d-52ab-43d5-8cf7-0c2f8dae122f | towards-reliable-image-outpainting-learning | 2204.05543 | null | https://arxiv.org/abs/2204.05543v2 | https://arxiv.org/pdf/2204.05543v2.pdf | Towards Reliable Image Outpainting: Learning Structure-Aware Multimodal Fusion with Depth Guidance | Image outpainting technology generates visually plausible content regardless of authenticity, making it unreliable to be applied in practice. Thus, we propose a reliable image outpainting task, introducing the sparse depth from LiDARs to extrapolate authentic RGB scenes. The large field view of LiDARs allows it to serv... | ['Yao Zhao', 'Chunyu Lin', 'Kang Liao', 'Lei Zhang'] | 2022-04-12 | null | null | null | null | ['image-outpainting'] | ['computer-vision'] | [ 4.12956417e-01 7.49244317e-02 -2.88553387e-01 -3.29087108e-01
-9.91040111e-01 -3.86475265e-01 4.93130624e-01 -2.70419002e-01
-1.23236805e-01 6.96114302e-01 2.35646829e-01 2.31486876e-02
-3.99285443e-02 -7.68083632e-01 -7.92290270e-01 -7.39971638e-01
3.68625879e-01 7.11451620e-02 8.19295421e-02 -2.14659229... | [11.188953399658203, -1.2971534729003906] |
c039a525-13df-4a43-95c9-c249f939cc63 | how-the-move-acceptance-hyper-heuristic-copes | 2304.10414 | null | https://arxiv.org/abs/2304.10414v1 | https://arxiv.org/pdf/2304.10414v1.pdf | How the Move Acceptance Hyper-Heuristic Copes With Local Optima: Drastic Differences Between Jumps and Cliffs | In recent work, Lissovoi, Oliveto, and Warwicker (Artificial Intelligence (2023)) proved that the Move Acceptance Hyper-Heuristic (MAHH) leaves the local optimum of the multimodal cliff benchmark with remarkable efficiency. With its $O(n^3)$ runtime, for almost all cliff widths $d,$ the MAHH massively outperforms the $... | ['Aurélien Stumpf', 'Johannes Lutzeyer', 'Arthur Dremaux', 'Benjamin Doerr'] | 2023-04-20 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 3.27818394e-01 1.70695037e-01 5.63087650e-02 5.61162494e-02
-9.74468350e-01 -7.14659512e-01 -7.30175525e-02 2.81101465e-01
-9.46415365e-01 9.32968616e-01 -8.64060223e-01 -7.02104867e-01
-6.28268540e-01 -9.37697768e-01 -7.76075363e-01 -1.24663734e+00
-6.89186215e-01 6.46868050e-01 1.12769440e-01 -5.23709416... | [6.338179111480713, 4.543881893157959] |
940f5aa1-5798-45ab-b9b8-f09ec5602a00 | semantic-positive-pairs-for-enhancing | 2306.16122 | null | https://arxiv.org/abs/2306.16122v1 | https://arxiv.org/pdf/2306.16122v1.pdf | Semantic Positive Pairs for Enhancing Contrastive Instance Discrimination | Self-supervised learning algorithms based on instance discrimination effectively prevent representation collapse and produce promising results in representation learning. However, the process of attracting positive pairs (i.e., two views of the same instance) in the embedding space and repelling all other instances (i.... | ['Mingjun Zhong', 'Georgios Leontidis', 'Mohammad Alkhalefi'] | 2023-06-28 | null | null | null | null | ['self-supervised-learning'] | ['computer-vision'] | [ 4.61191446e-01 5.10709360e-02 -3.54282439e-01 -3.31490099e-01
-5.91776311e-01 -5.53825796e-01 7.38137484e-01 2.12062627e-01
-6.56412482e-01 7.60616958e-01 2.01604329e-02 -6.97210878e-02
-1.98758971e-02 -8.04849088e-01 -7.09195197e-01 -8.20534706e-01
-1.90884054e-01 2.94482946e-01 2.52792150e-01 -1.06350714... | [9.6100435256958, 2.7150485515594482] |
6e1cdf32-9b3e-4203-9aff-aa62e2c92bad | prediction-of-amino-acid-side-chain | 1707.08381 | null | http://arxiv.org/abs/1707.08381v1 | http://arxiv.org/pdf/1707.08381v1.pdf | Prediction of amino acid side chain conformation using a deep neural network | A deep neural network based architecture was constructed to predict amino
acid side chain conformation with unprecedented accuracy. Amino acid side chain
conformation prediction is essential for protein homology modeling and protein
design. Current widely-adopted methods use physics-based energy functions to
evaluate s... | ['Günter Blobel', 'Shengwen Peng', 'Zhenyu Zhou', 'Suocheng Tan', 'Junqiu Wu', 'Qilin Dong', 'Jun Ma', 'Jie Fan', 'Xiangyan Sun', 'Ke Liu'] | 2017-07-26 | null | null | null | null | ['protein-design'] | ['medical'] | [ 2.79712439e-01 6.11002408e-02 -5.51555902e-02 -5.43675482e-01
-7.03070998e-01 -4.64392185e-01 1.72324385e-02 5.03529370e-01
-4.94571239e-01 1.33332312e+00 -4.47965302e-02 -9.02976155e-01
1.50150597e-01 -4.61468637e-01 -1.26400054e+00 -1.11438119e+00
-1.78662106e-01 8.67999375e-01 -2.16270193e-01 -1.72582820... | [4.759714603424072, 5.583127975463867] |
5ce2b220-c648-4926-89a6-f3cf69c551ad | supervised-machine-learning-models-for | null | null | https://link.springer.com/article/10.1007/s42979-020-00394-7 | https://rdcu.be/cSbRX | Supervised Machine Learning Models for Prediction of COVID-19 Infection using Epidemiology Dataset | COVID-19 or 2019-nCoV is no longer pandemic but rather endemic, with more than 651,247 people around world having lost their lives after contracting the disease. Currently, there is no specific treatment or cure for COVID-19, and thus living with the disease and its symptoms is inevitable. This reality has placed a mas... | ['Chinmay Chakraborty & I. A. Mohammed', 'Abdulkadir Ahmad', 'Sani Sharif Usman', 'Ebrahem A. Algehyne', 'L. J. Muhammad'] | 2020-11-27 | null | null | null | springer-nature-singapore-pte-ltd-2020-2020 | ['epidemiology'] | ['medical'] | [-2.35935241e-01 -2.69089013e-01 -1.59069136e-01 -7.96783641e-02
2.50933707e-01 -5.00917733e-01 3.64942700e-01 4.52214003e-01
-6.37089849e-01 1.05085433e+00 5.02057513e-03 -5.25694609e-01
-1.92623168e-01 -7.41196930e-01 -1.44283503e-01 -7.30963767e-01
-2.58823514e-01 1.04072380e+00 1.35987597e-02 -2.17040613... | [5.535154342651367, 4.955984115600586] |
4fca2fe9-9113-44e3-823a-c0d10d58e62c | faspell-a-fast-adaptable-simple-powerful | null | null | https://aclanthology.org/D19-5522 | https://aclanthology.org/D19-5522.pdf | FASPell: A Fast, Adaptable, Simple, Powerful Chinese Spell Checker Based On DAE-Decoder Paradigm | We propose a Chinese spell checker {--} FASPell based on a new paradigm which consists of a denoising autoencoder (DAE) and a decoder. In comparison with previous state-of-the-art models, the new paradigm allows our spell checker to be Faster in computation, readily Adaptable to both simplified and traditional Chinese ... | ['Xianguo Yu', 'Junhui Liu', 'Yuzhong Hong', 'Nan Liu', 'Neng He'] | 2019-11-01 | null | null | null | ws-2019-11 | ['chinese-spell-checking'] | ['natural-language-processing'] | [ 1.93943352e-01 -1.38871655e-01 5.25090754e-01 -1.02234662e-01
-6.33539319e-01 -4.55296606e-01 3.77500534e-01 2.45222241e-01
-7.50290513e-01 7.76699066e-01 1.78938568e-01 -5.48096180e-01
2.44234696e-01 -7.58946896e-01 -5.75820446e-01 -5.19799173e-01
2.82367408e-01 2.56201625e-01 3.65074694e-01 -5.56877971... | [10.883604049682617, 10.676987648010254] |
fd1229ea-64e8-49c4-b41a-9599bb3b7fff | language-independent-approach-for | null | null | https://aclanthology.org/2022.coling-1.470 | https://aclanthology.org/2022.coling-1.470.pdf | Language-Independent Approach for Morphological Disambiguation | This paper presents a language-independent approach for morphological disambiguation which has been regarded as an extension of POS tagging, jointly predicting complex morphological tags. In the proposed approach, all words, roots, POS and morpheme tags are embedded into vectors, and contexts representations from surfa... | ['Rustam Mussabayev', 'Gulmira Tolegen', 'Alymzhan Toleu'] | null | null | null | null | coling-2022-10 | ['morphological-analysis', 'morphological-disambiguation'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.55884994e-02 -3.78395170e-01 -8.74814689e-02 -2.96285540e-01
-5.63895583e-01 -9.82888579e-01 4.28990066e-01 5.95530570e-01
-1.10998237e+00 5.43063164e-01 4.51452404e-01 -5.51582932e-01
2.56217241e-01 -6.24612033e-01 -2.08757341e-01 -5.59455216e-01
-2.92888343e-01 1.99547321e-01 2.86112159e-01 -2.27647364... | [10.311624526977539, 10.169166564941406] |
a781205a-0441-4863-b3d6-7e7684938633 | passnet-learning-pass-probability-surfaces | null | null | https://openreview.net/forum?id=r1xxKJBKvr | https://openreview.net/pdf?id=r1xxKJBKvr | PassNet: Learning pass probability surfaces from single-location labels. An architecture for visually-interpretable soccer analytics | We propose a fully convolutional network architecture that is able to estimate a full surface of pass probabilities from single-location labels derived from high frequency spatio-temporal data of professional soccer matches. The network is able to perform remarkably well from low-level inputs by learning a feature hier... | ['Luke Bornn', 'Javier Fernández'] | 2019-09-25 | null | null | null | null | ['sports-analytics'] | ['computer-vision'] | [ 1.11406036e-01 1.30205050e-01 -1.80334777e-01 -5.85932851e-01
-9.68041420e-01 -4.23051804e-01 6.40763521e-01 7.39888430e-01
-6.03881121e-01 5.72482646e-01 5.32979965e-01 4.85332608e-02
-3.09102267e-01 -1.17870390e+00 -1.10620058e+00 -4.45422113e-01
-4.74131972e-01 6.45984173e-01 6.71596944e-01 -2.83965617... | [7.950044631958008, 0.19564521312713623] |
b8138870-3637-4c76-8211-985815120658 | evaluating-the-adversarial-robustness-of-a | null | null | https://openreview.net/forum?id=HyhSFQ1hOgV | https://openreview.net/pdf?id=HyhSFQ1hOgV | Evaluating the Adversarial Robustness of a Foveated Texture Transform Module in a CNN | Biologically inspired mechanisms such as foveation and multiple fixation points have previously been shown to help alleviate adversarial examples (Reddy et al., 2020). By mimicking the effects of visual crowding present in human vision, foveated, texture-based computations may provide another route for increasing adver... | ['Arturo Deza', 'Andrzej Banburski', 'Jonathan M Gant'] | 2021-10-12 | null | null | null | neurips-workshop-svrhm-2021-12 | ['texture-synthesis', 'foveation'] | ['computer-vision', 'computer-vision'] | [ 2.54168600e-01 2.16525659e-01 5.58036268e-01 2.96105556e-02
-2.61933744e-01 -7.22744286e-01 9.53346908e-01 -2.01399878e-01
-5.18005431e-01 6.51768029e-01 8.36524516e-02 -3.16690505e-01
-8.57566148e-02 -9.10391390e-01 -1.11902058e+00 -8.79879117e-01
-1.39186069e-01 -4.02422905e-01 1.82239652e-01 -2.12488726... | [5.5259599685668945, 7.896059989929199] |
91220fe5-a82e-49e4-bbe9-32d63902739c | face-anti-spoofing-with-human-material | 2007.02157 | null | https://arxiv.org/abs/2007.02157v1 | https://arxiv.org/pdf/2007.02157v1.pdf | Face Anti-Spoofing with Human Material Perception | Face anti-spoofing (FAS) plays a vital role in securing the face recognition systems from presentation attacks. Most existing FAS methods capture various cues (e.g., texture, depth and reflection) to distinguish the live faces from the spoofing faces. All these cues are based on the discrepancy among physical materials... | ['Xiaobai Li', 'Jingang Shi', 'Xuesong Niu', 'Zitong Yu', 'Guoying Zhao'] | 2020-07-04 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/318_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123520545.pdf | eccv-2020-8 | ['material-recognition'] | ['computer-vision'] | [ 3.85467857e-01 -3.61215115e-01 -5.40276542e-02 -1.73987731e-01
-3.56213123e-01 -3.38777155e-01 5.01708329e-01 -2.92198300e-01
1.43512383e-01 3.39985937e-01 6.82094023e-02 6.29406795e-02
-5.14671132e-02 -8.54636312e-01 -6.28264010e-01 -1.07580888e+00
-1.06414281e-01 -3.45057398e-01 2.33034611e-01 -2.78603166... | [12.983641624450684, 1.1238634586334229] |
81dcfd7d-32ba-4367-8227-ad37ebaabd9e | neustip-a-novel-neuro-symbolic-model-for-link | 2305.11301 | null | https://arxiv.org/abs/2305.11301v1 | https://arxiv.org/pdf/2305.11301v1.pdf | NeuSTIP: A Novel Neuro-Symbolic Model for Link and Time Prediction in Temporal Knowledge Graphs | While Knowledge Graph Completion (KGC) on static facts is a matured field, Temporal Knowledge Graph Completion (TKGC), that incorporates validity time into static facts is still in its nascent stage. The KGC methods fall into multiple categories including embedding-based, rule-based, GNN-based, pretrained Language Mode... | ['Mausam', 'Garima Gaur', 'Navdeep Kaur', 'Ishaan Singh'] | 2023-05-15 | null | null | null | null | ['link-prediction', 'knowledge-graph-completion', 'temporal-knowledge-graph-completion'] | ['graphs', 'knowledge-base', 'knowledge-base'] | [-4.13956881e-01 4.28108513e-01 -9.48586106e-01 -3.47137064e-01
-1.39619589e-01 -4.05910254e-01 6.82421744e-01 5.74460924e-01
-1.12660415e-01 7.70923078e-01 3.03436041e-01 -5.32577753e-01
-8.39542747e-01 -1.18150175e+00 -6.74678326e-01 -1.68815404e-02
-6.85754299e-01 7.22241819e-01 7.84188688e-01 -3.08897853... | [8.552845001220703, 7.916233539581299] |
941db61d-76c9-48c3-8237-057173deac77 | gmlight-lighting-estimation-via-geometric | 2102.10244 | null | https://arxiv.org/abs/2102.10244v2 | https://arxiv.org/pdf/2102.10244v2.pdf | GMLight: Lighting Estimation via Geometric Distribution Approximation | Inferring the scene illumination from a single image is an essential yet challenging task in computer vision and computer graphics. Existing works estimate lighting by regressing representative illumination parameters or generating illumination maps directly. However, these methods often suffer from poor accuracy and g... | ['WenBo Hu', 'Xuansong Xie', 'Feiying Ma', 'Ling Shao', 'Shijian Lu', 'Changgong Zhang', 'Rongliang Wu', 'Yingchen Yu', 'Fangneng Zhan'] | 2021-02-20 | null | null | null | null | ['lighting-estimation'] | ['computer-vision'] | [ 2.11773500e-01 -4.65847313e-01 2.42646247e-01 -6.30376041e-01
-5.47538996e-01 -4.27476853e-01 3.25965106e-01 -7.34229863e-01
-8.68873578e-03 5.44069767e-01 1.05357051e-01 -1.97560459e-01
2.03714713e-01 -8.02963495e-01 -7.32713103e-01 -1.02356899e+00
6.26437426e-01 1.01860892e-03 -1.80416107e-01 -9.64020640... | [9.837608337402344, -2.876744508743286] |
f9507ac8-d2b9-43f8-8685-e7e4083109ab | zero-shot-temporal-relation-extraction-with | 2304.05454 | null | https://arxiv.org/abs/2304.05454v1 | https://arxiv.org/pdf/2304.05454v1.pdf | Zero-shot Temporal Relation Extraction with ChatGPT | The goal of temporal relation extraction is to infer the temporal relation between two events in the document. Supervised models are dominant in this task. In this work, we investigate ChatGPT's ability on zero-shot temporal relation extraction. We designed three different prompt techniques to break down the task and e... | ['Sophia Ananiadou', 'Qianqian Xie', 'Chenhan Yuan'] | 2023-04-11 | null | null | null | null | ['temporal-relation-extraction'] | ['natural-language-processing'] | [-5.41763008e-02 4.38038200e-01 -5.61912954e-01 -4.58903342e-01
-6.74871743e-01 -6.48117006e-01 9.54453409e-01 2.76948392e-01
-1.22349732e-01 8.05884480e-01 1.62626296e-01 -6.42645538e-01
-3.83087784e-01 -5.46819866e-01 -5.86732812e-02 -2.47594729e-01
-5.04227936e-01 6.86186731e-01 8.94492447e-01 -3.21349114... | [9.05298900604248, 9.229907035827637] |
7ba2647b-36ef-44b9-bfbd-e4ae5efd9a19 | state-of-the-art-of-audio-and-video-based | 2207.01487 | null | https://arxiv.org/abs/2207.01487v2 | https://arxiv.org/pdf/2207.01487v2.pdf | State of the Art of Audio- and Video-Based Solutions for AAL | The report illustrates the state of the art of the most successful AAL applications and functions based on audio and video data, namely (i) lifelogging and self-monitoring, (ii) remote monitoring of vital signs, (iii) emotional state recognition, (iv) food intake monitoring, activity and behaviour recognition, (v) acti... | ['Andrej Zgank', 'Hilda Tellioglu', 'Lacramioara Stoicu-Tivadar', 'Anna Sigridur Islind', 'Maria Jose Santofimia', 'Albert Ali Salah', 'Susanna Spinsante', 'Mara Pudane', 'Angelica Poli', 'Peter Pocta', 'Sintija Petrovica', 'Galidiya Petrova', 'Rodrigo Rodriguez Peerez', 'Zada Pajalic', 'Sophie Noiret', 'Wiktor Mucha',... | 2022-06-26 | null | null | null | null | ['gesture-recognition'] | ['computer-vision'] | [ 1.66280791e-01 -2.43873104e-01 -3.83884877e-01 -8.19737241e-02
-4.91592199e-01 -2.51205117e-01 1.95781365e-01 4.15913492e-01
-5.94410837e-01 1.15299737e+00 6.25280201e-01 -3.19250166e-01
-4.84578907e-01 -3.80544037e-01 -6.26721308e-02 -6.80228055e-01
-6.90309167e-01 1.43841967e-01 2.04545617e-01 -2.20755383... | [7.15816593170166, 0.47466179728507996] |
124e0f1a-f131-45ee-9b34-e68b50c6d753 | lpat-learning-to-predict-adaptive-threshold | 1910.11285 | null | https://arxiv.org/abs/1910.11285v3 | https://arxiv.org/pdf/1910.11285v3.pdf | Towards Train-Test Consistency for Semi-supervised Temporal Action Localization | Recently, Weakly-supervised Temporal Action Localization (WTAL) has been densely studied but there is still a large gap between weakly-supervised models and fully-supervised models. It is practical and intuitive to annotate temporal boundaries of a few examples and utilize them to help WTAL models better detect actions... | ['Shih-Fu Chang', 'Xudong Lin', 'Zheng Shou'] | 2019-10-24 | null | null | null | null | ['weakly-supervised-temporal-action'] | ['computer-vision'] | [ 4.15259272e-01 -2.25992817e-02 -1.01281428e+00 -4.50161815e-01
-9.51075077e-01 -4.55393851e-01 3.40739816e-01 -2.14738891e-01
-4.95801806e-01 5.95669270e-01 2.31793091e-01 -7.41630271e-02
2.81602740e-01 -1.80798769e-01 -8.15827191e-01 -6.17459357e-01
-3.64292055e-01 1.67119011e-01 9.76744652e-01 3.79319310... | [8.495060920715332, 0.6340492367744446] |
96d3e9ce-06fc-442e-80ec-be1948ae3c80 | safe-exploration-for-interactive-machine | 1910.13726 | null | https://arxiv.org/abs/1910.13726v1 | https://arxiv.org/pdf/1910.13726v1.pdf | Safe Exploration for Interactive Machine Learning | In Interactive Machine Learning (IML), we iteratively make decisions and obtain noisy observations of an unknown function. While IML methods, e.g., Bayesian optimization and active learning, have been successful in applications, on real-world systems they must provably avoid unsafe decisions. To this end, safe IML algo... | ['Andreas Krause', 'Matteo Turchetta', 'Felix Berkenkamp'] | 2019-10-30 | safe-exploration-for-interactive-machine-1 | http://papers.nips.cc/paper/8555-safe-exploration-for-interactive-machine-learning | http://papers.nips.cc/paper/8555-safe-exploration-for-interactive-machine-learning.pdf | neurips-2019-12 | ['safe-exploration'] | ['robots'] | [ 1.74959928e-01 5.65760851e-01 -3.15002680e-01 -2.57545501e-01
-1.02096844e+00 -7.12729216e-01 6.46166205e-01 4.89296675e-01
-6.46607101e-01 7.93297350e-01 -2.00476438e-01 -7.44174600e-01
-3.63347590e-01 -8.14708829e-01 -8.89270544e-01 -1.02235258e+00
-9.91776213e-02 7.68093348e-01 2.17923924e-01 2.04836294... | [4.717435836791992, 2.7731192111968994] |
4b772ac4-e6d4-41f7-8e15-a3e2ee9b6e00 | self-supervised-metric-learning-in-multi-view | 2106.07138 | null | https://arxiv.org/abs/2106.07138v4 | https://arxiv.org/pdf/2106.07138v4.pdf | Self-Supervised Metric Learning in Multi-View Data: A Downstream Task Perspective | Self-supervised metric learning has been a successful approach for learning a distance from an unlabeled dataset. The resulting distance is broadly useful for improving various distance-based downstream tasks, even when no information from downstream tasks is utilized in the metric learning stage. To gain insights into... | ['Shulei Wang'] | 2021-06-14 | null | null | null | null | ['hypothesis-testing', 'hypothesis-testing'] | ['methodology', 'miscellaneous'] | [ 5.94001040e-02 -5.30033186e-03 -5.11057496e-01 -9.37445521e-01
-1.19131160e+00 -5.83764374e-01 2.22397432e-01 3.17318499e-01
-5.24209917e-01 6.61713243e-01 1.46252483e-01 -3.17224801e-01
-7.56062508e-01 -6.82743430e-01 -2.92841017e-01 -9.10330415e-01
1.00839220e-01 1.69701815e-01 -9.12353620e-02 1.10288919... | [9.343716621398926, 3.36238956451416] |
74a6e75e-5e46-46e7-852e-dfdd30d247b1 | what-s-in-a-name-answer-equivalence-for-open | 2109.05289 | null | https://arxiv.org/abs/2109.05289v1 | https://arxiv.org/pdf/2109.05289v1.pdf | What's in a Name? Answer Equivalence For Open-Domain Question Answering | A flaw in QA evaluation is that annotations often only provide one gold answer. Thus, model predictions semantically equivalent to the answer but superficially different are considered incorrect. This work explores mining alias entities from knowledge bases and using them as additional gold answers (i.e., equivalent an... | ['Jordan Boyd-Graber', 'Chen Zhao', 'Chenglei Si'] | 2021-09-11 | null | null | null | null | ['triviaqa'] | ['miscellaneous'] | [-3.46905701e-02 7.93131948e-01 -2.15584233e-01 -5.83120763e-01
-1.29710531e+00 -9.57583368e-01 6.28557801e-01 6.81313097e-01
-6.92767024e-01 1.08788371e+00 5.77692986e-01 -5.86110890e-01
-4.39106897e-02 -1.01665783e+00 -8.43515813e-01 2.25368723e-01
4.30508673e-01 9.62983668e-01 9.21763778e-01 -6.01195753... | [11.009427070617676, 7.946611404418945] |
a9965368-3196-4200-8c27-c4b450feb06a | deepmedix-a-deep-learning-driven-resource | 2307.00324 | null | https://arxiv.org/abs/2307.00324v1 | https://arxiv.org/pdf/2307.00324v1.pdf | DeepMediX: A Deep Learning-Driven Resource-Efficient Medical Diagnosis Across the Spectrum | In the rapidly evolving landscape of medical imaging diagnostics, achieving high accuracy while preserving computational efficiency remains a formidable challenge. This work presents \texttt{DeepMediX}, a groundbreaking, resource-efficient model that significantly addresses this challenge. Built on top of the MobileNet... | ['Balasubramanian Raman', 'Uppala Vivek Narayan', 'Pradeep Singh', 'Kishore Babu Nampalle'] | 2023-07-01 | null | null | null | null | ['medical-diagnosis'] | ['medical'] | [ 3.22493136e-01 2.13735804e-01 -4.63947505e-01 -5.23265481e-01
-7.74360538e-01 -5.64223766e-01 2.37959310e-01 4.33356225e-01
-6.20926321e-01 5.32013535e-01 1.41688600e-01 -7.20394135e-01
-5.64626336e-01 -4.53940421e-01 -3.22552860e-01 -6.36286557e-01
-2.52337813e-01 4.67840105e-01 -2.22992837e-01 3.68219078... | [6.185377597808838, 6.501511096954346] |
ad477963-1145-4bbe-9273-8147afdf03b6 | ariadne-s-thread-using-text-prompts-to | 2307.03942 | null | https://arxiv.org/abs/2307.03942v1 | https://arxiv.org/pdf/2307.03942v1.pdf | Ariadne's Thread:Using Text Prompts to Improve Segmentation of Infected Areas from Chest X-ray images | Segmentation of the infected areas of the lung is essential for quantifying the severity of lung disease like pulmonary infections. Existing medical image segmentation methods are almost uni-modal methods based on image. However, these image-only methods tend to produce inaccurate results unless trained with large amou... | ['Ming Wu', 'Kaixin Chen', 'Kongming Liang', 'Mengqiu Xu', 'Yi Zhong'] | 2023-07-08 | null | null | null | null | ['semantic-segmentation', 'image-segmentation', 'medical-image-segmentation'] | ['computer-vision', 'computer-vision', 'medical'] | [ 2.71175861e-01 -1.45587862e-01 -2.05933884e-01 -3.89986746e-02
-1.06467950e+00 -6.13503098e-01 4.12305772e-01 5.42078959e-03
-4.32313144e-01 4.32823777e-01 1.12175524e-01 -4.72700238e-01
-6.76812753e-02 -6.26607776e-01 -2.48205990e-01 -6.50025249e-01
3.88471365e-01 9.49536085e-01 5.99851191e-01 3.52474838... | [14.928387641906738, -2.1022121906280518] |
d3453e1c-7a72-4a1c-83b3-bdc4c4ed3c35 | general-board-game-playing-for-education-and | 1907.06508 | null | https://arxiv.org/abs/1907.06508v1 | https://arxiv.org/pdf/1907.06508v1.pdf | General Board Game Playing for Education and Research in Generic AI Game Learning | We present a new general board game (GBG) playing and learning framework. GBG defines the common interfaces for board games, game states and their AI agents. It allows one to run competitions of different agents on different games. It standardizes those parts of board game playing and learning that otherwise would be t... | ['Wolfgang Konen'] | 2019-07-11 | null | null | null | null | ['board-games'] | ['playing-games'] | [-4.87195700e-01 2.15518758e-01 1.44231811e-01 1.74052790e-01
-6.04700327e-01 -8.01361322e-01 3.70460242e-01 1.43377051e-01
-3.72330874e-01 8.33744884e-01 -3.13777417e-01 -6.25220656e-01
-4.69327271e-01 -1.15456414e+00 -3.50914687e-01 -4.86807853e-01
-4.68867987e-01 8.42639625e-01 7.77558148e-01 -1.07018173... | [3.4020113945007324, 1.4697850942611694] |
8022ecc5-dba4-42a7-9124-5fabba89eb95 | uit-e10dot3-at-semeval-2021-task-5-toxic | 2104.07376 | null | https://arxiv.org/abs/2104.07376v1 | https://arxiv.org/pdf/2104.07376v1.pdf | UIT-E10dot3 at SemEval-2021 Task 5: Toxic Spans Detection with Named Entity Recognition and Question-Answering Approaches | The increment of toxic comments on online space is causing tremendous effects on other vulnerable users. For this reason, considerable efforts are made to deal with this, and SemEval-2021 Task 5: Toxic Spans Detection is one of those. This task asks competitors to extract spans that have toxicity from the given texts, ... | ['Kiet Van Nguyen', 'Luan Thanh Nguyen', 'Phu Gia Hoang'] | 2021-04-15 | null | https://aclanthology.org/2021.semeval-1.125 | https://aclanthology.org/2021.semeval-1.125.pdf | semeval-2021 | ['toxic-spans-detection'] | ['natural-language-processing'] | [-6.34856284e-01 -6.98440000e-02 -1.29597768e-01 -5.64130656e-02
-9.41307306e-01 -7.27333307e-01 2.37971857e-01 9.08077136e-02
-5.10155916e-01 1.01449525e+00 6.57257557e-01 -3.65992844e-01
1.64699644e-01 -7.51157522e-01 -3.40416610e-01 -1.27036711e-02
-1.16470516e-01 1.25355303e-01 7.52096713e-01 -4.37969387... | [8.972380638122559, 10.623212814331055] |
1918c7b4-b1a9-4c7f-bc81-87f643464bb0 | assamese-word-sense-disambiguation-using | null | null | https://aclanthology.org/2020.icon-main.40 | https://aclanthology.org/2020.icon-main.40.pdf | Assamese Word Sense Disambiguation using Genetic Algorithm | Word sense disambiguation (WSD) is a problem to determine a word according to a context in which it occurs. There are plenty amount of works done in WSD for some languages such as English, but research work on Assamese WSD remains limited. It is a more exigent task because Assamese has an intrinsic complexity in its wr... | ['Shikhar Kr. Sarma', 'Nomi Baruah', 'Arjun Gogoi'] | null | null | null | null | icon-2020-12 | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 3.47364873e-01 -2.84285188e-01 -3.63279819e-01 -6.61504790e-02
6.20648116e-02 -6.57960653e-01 4.31326032e-01 6.31105721e-01
-7.95393169e-01 9.96846020e-01 5.04443824e-01 -2.54210293e-01
-6.02499902e-01 -9.01968241e-01 3.60427409e-01 -4.10170585e-01
3.55689317e-01 4.94122624e-01 4.72984999e-01 -8.96989286... | [10.142845153808594, 9.16417407989502] |
f5272e1b-742c-4883-91f0-1f5b0612a82e | shallow-discourse-parsing-for-open | null | null | https://aclanthology.org/2022.codi-1.9 | https://aclanthology.org/2022.codi-1.9.pdf | Shallow Discourse Parsing for Open Information Extraction and Text Simplification | We present a discourse-aware text simplification (TS) approach that recursively splits and rephrases complex English sentences into a semantic hierarchy of simplified sentences. Using a set of linguistically principled transformation patterns, sentences are converted into a hierarchical representation in the form of co... | ['Siegfried Handschuh', 'André Freitas', 'Christina Niklaus'] | null | null | null | null | coling-codi-crac-2022-10 | ['discourse-parsing', 'open-information-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.18728375e-01 1.28146553e+00 -1.85712427e-02 -2.83808410e-01
-7.76679337e-01 -6.58323467e-01 9.12731051e-01 9.39399004e-01
-2.05448225e-01 7.84715116e-01 9.26761568e-01 -2.80252784e-01
-2.28636712e-01 -1.01463330e+00 -4.48613673e-01 -2.26516952e-03
4.75362420e-01 5.24100959e-01 5.64778566e-01 -6.49485826... | [10.310153007507324, 9.180363655090332] |
81e56231-18da-4ad9-835b-3730af880309 | sipos-a-benchmark-dataset-for-sindhi-part-of | null | null | https://aclanthology.org/2021.ranlp-srw.4 | https://aclanthology.org/2021.ranlp-srw.4.pdf | SiPOS: A Benchmark Dataset for Sindhi Part-of-Speech Tagging | In this paper, we introduce the SiPOS dataset for part-of-speech tagging in the low-resource Sindhi language with quality baselines. The dataset consists of more than 293K tokens annotated with sixteen universal part-of-speech categories. Two experienced native annotators annotated the SiPOS using the Doccano text anno... | ['Jay Kumar', 'Zenglin Xu', 'Wazir Ali'] | null | null | null | null | ranlp-2021-9 | ['text-annotation'] | ['natural-language-processing'] | [-8.45661163e-02 1.35305479e-01 -3.84271055e-01 -4.79557484e-01
-1.38815939e+00 -6.26143277e-01 2.90366113e-01 6.28666952e-02
-9.66632307e-01 9.16889369e-01 5.65230906e-01 -3.74984980e-01
4.75119054e-01 -5.52535176e-01 -5.58032215e-01 -6.14115834e-01
3.38704996e-02 6.81413531e-01 1.02950998e-01 -3.29875685... | [9.96670150756836, 9.818185806274414] |
55951237-3f8f-4735-85fe-be25aa192810 | encyclopedic-vqa-visual-questions-about | 2306.09224 | null | https://arxiv.org/abs/2306.09224v1 | https://arxiv.org/pdf/2306.09224v1.pdf | Encyclopedic VQA: Visual questions about detailed properties of fine-grained categories | We propose Encyclopedic-VQA, a large scale visual question answering (VQA) dataset featuring visual questions about detailed properties of fine-grained categories and instances. It contains 221k unique question+answer pairs each matched with (up to) 5 images, resulting in a total of 1M VQA samples. Moreover, our datase... | ['Vittorio Ferrari', 'André Araujo', 'Fei Sha', 'Howard Zhou', 'Felipe Cadar', 'Arushi Goel', 'Lluis Castrejon', 'Jasper Uijlings', 'Thomas Mensink'] | 2023-06-15 | null | null | null | null | ['visual-question-answering-1', 'question-answering'] | ['computer-vision', 'natural-language-processing'] | [-2.93750733e-01 1.19734459e-01 -3.68251681e-01 -6.60000741e-02
-1.49221063e+00 -1.09811664e+00 6.50077403e-01 2.72973403e-02
-4.78569537e-01 4.06520635e-01 3.59156519e-01 -4.28641856e-01
1.34531707e-01 -7.02318192e-01 -1.16503286e+00 -1.61963299e-01
2.24050358e-01 8.21591496e-01 6.28733635e-01 -4.00602728... | [10.90605640411377, 1.6614762544631958] |
57337739-ad7c-4302-9eff-7ac7f3373638 | diagonal-rnns-in-symbolic-music-modeling | 1704.05420 | null | http://arxiv.org/abs/1704.05420v2 | http://arxiv.org/pdf/1704.05420v2.pdf | Diagonal RNNs in Symbolic Music Modeling | In this paper, we propose a new Recurrent Neural Network (RNN) architecture.
The novelty is simple: We use diagonal recurrent matrices instead of full. This
results in better test likelihood and faster convergence compared to regular
full RNNs in most of our experiments. We show the benefits of using diagonal
recurrent... | ['Y. Cem Subakan', 'Paris Smaragdis'] | 2017-04-18 | null | null | null | null | ['music-modeling'] | ['music'] | [ 1.73200876e-01 -2.13792861e-01 7.25933462e-02 1.17948808e-01
-5.28256536e-01 -3.41358691e-01 5.41878939e-01 -7.84576833e-01
-4.43416357e-01 9.77261901e-01 3.97630513e-01 -5.15840530e-01
-1.86323151e-01 -5.25123954e-01 -7.11428642e-01 -5.46105385e-01
-1.97116122e-01 2.13972971e-01 -1.97023496e-01 -4.56527710... | [10.89930534362793, 6.365229606628418] |
ad6b51e7-e490-4572-af69-0d3c8e548df3 | unsupervised-feature-learning-via-non-1 | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Wu_Unsupervised_Feature_Learning_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Wu_Unsupervised_Feature_Learning_CVPR_2018_paper.pdf | Unsupervised Feature Learning via Non-Parametric Instance Discrimination | Neural net classifiers trained on data with annotated class labels can also capture apparent visual similarity among categories without being directed to do so. We study whether this observation can be extended beyond the conventional domain of supervised learning: Can we learn a good feature representation that captur... | ['Zhirong Wu', 'Yuanjun Xiong', 'Stella X. Yu', 'Dahua Lin'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['self-supervised-image-classification'] | ['computer-vision'] | [ 3.89905959e-01 -9.87294540e-02 -3.79210174e-01 -7.51062453e-01
-8.94310892e-01 -6.91935182e-01 8.88562202e-01 1.36386886e-01
-6.96517467e-01 4.43651110e-01 -6.02381788e-02 -1.50817066e-01
-3.52189064e-01 -6.61251724e-01 -9.44071949e-01 -6.72760010e-01
-9.67580676e-02 5.12059093e-01 3.85597646e-01 2.30234355... | [9.54239273071289, 2.563886880874634] |
e782d971-f359-4bb8-a570-e8ae8c309e07 | mixed-modality-representation-learning-and | 2210.05197 | null | https://arxiv.org/abs/2210.05197v1 | https://arxiv.org/pdf/2210.05197v1.pdf | Mixed-modality Representation Learning and Pre-training for Joint Table-and-Text Retrieval in OpenQA | Retrieving evidences from tabular and textual resources is essential for open-domain question answering (OpenQA), which provides more comprehensive information. However, training an effective dense table-text retriever is difficult due to the challenges of table-text discrepancy and data sparsity problem. To address th... | ['Nan Duan', 'Daxin Jiang', 'Ming Gong', 'Qian Liu', 'Wanjun Zhong', 'JunJie Huang'] | 2022-10-11 | null | null | null | null | ['open-domain-question-answering'] | ['natural-language-processing'] | [-9.42301750e-02 -1.32895872e-01 -2.66201079e-01 -2.40509152e-01
-2.09758282e+00 -5.18965602e-01 4.22974676e-01 1.15639992e-01
-3.16538215e-01 8.47158968e-01 4.36524630e-01 -2.17221990e-01
-3.08278084e-01 -6.12868071e-01 -8.15806210e-01 -6.15454912e-01
4.91503388e-01 7.89548695e-01 1.50370941e-01 -7.49266148... | [11.345511436462402, 7.745286464691162] |
856a367a-a558-4283-9986-87d9886a9775 | optimistic-bounds-for-multi-output-learning | null | null | https://proceedings.icml.cc/static/paper_files/icml/2020/5808-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/5808-Paper.pdf | Optimistic bounds for multi-output learning | We investigate the challenge of multi-output learning, where the goal is to learn a vector-valued function based on a supervised data set. This includes a range of important problems in Machine Learning including multi-target regression, multi-class classification and multi-label classification. We begin our analysis b... | ['Ata Kaban', 'Henry Reeve'] | null | null | https://proceedings.icml.cc/static/paper_files/icml/2020/5808-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/5808-Paper.pdf | icml-2020-1 | ['multi-target-regression'] | ['miscellaneous'] | [ 2.10648358e-01 -2.66562682e-02 -5.17844260e-01 -7.00091362e-01
-1.45243394e+00 -8.11015368e-01 1.38340980e-01 6.24776661e-01
-5.48511326e-01 8.49714518e-01 -2.01669350e-01 -3.75134379e-01
-2.42577448e-01 -5.06524742e-01 -8.68975699e-01 -1.01167774e+00
-8.35537240e-02 2.47426882e-01 1.17053322e-01 -2.03059435... | [8.267651557922363, 4.124420642852783] |
cc578dfb-bc35-4d88-8e90-ce8678a0a172 | fsid-fully-synthetic-image-denoising-via | 2212.03961 | null | https://arxiv.org/abs/2212.03961v1 | https://arxiv.org/pdf/2212.03961v1.pdf | FSID: Fully Synthetic Image Denoising via Procedural Scene Generation | For low-level computer vision and image processing ML tasks, training on large datasets is critical for generalization. However, the standard practice of relying on real-world images primarily from the Internet comes with image quality, scalability, and privacy issues, especially in commercial contexts. To address this... | ['Rakesh Ranjan', 'Bo Zhu', 'Xiaoyu Xiang', 'Seonghyeon Nam', 'Beibei Du', 'Gyeongmin Choe'] | 2022-12-07 | null | null | null | null | ['scene-generation', 'synthetic-data-generation', 'synthetic-data-generation'] | ['computer-vision', 'medical', 'miscellaneous'] | [ 5.36712885e-01 -2.35772222e-01 6.67842388e-01 -4.48642582e-01
-1.25322783e+00 -5.92011571e-01 6.48266673e-01 -1.39998853e-01
-6.98536336e-01 3.91082793e-01 4.57232818e-02 4.11254317e-02
4.37754035e-01 -6.69639409e-01 -9.94872570e-01 -5.02405286e-01
3.82508278e-01 6.09747358e-02 6.62031621e-02 -1.51019618... | [10.959970474243164, -2.3829073905944824] |
9c9a1b7d-3c43-46ae-b407-15c7c8876aa9 | transfer-learning-for-music-classification | 1703.09179 | null | http://arxiv.org/abs/1703.09179v4 | http://arxiv.org/pdf/1703.09179v4.pdf | Transfer learning for music classification and regression tasks | In this paper, we present a transfer learning approach for music
classification and regression tasks. We propose to use a pre-trained convnet
feature, a concatenated feature vector using the activations of feature maps of
multiple layers in a trained convolutional network. We show how this convnet
feature can serve as ... | ['György Fazekas', 'Kyunghyun Cho', 'Keunwoo Choi', 'Mark Sandler'] | 2017-03-27 | null | null | null | null | ['music-classification'] | ['music'] | [ 2.01751381e-01 -4.23172742e-01 -9.10537541e-02 -2.67224282e-01
-6.82114959e-01 -5.16095757e-01 6.44652843e-01 -4.49478254e-03
-5.69373310e-01 5.50752401e-01 4.54483032e-01 3.31941843e-01
-3.85760248e-01 -5.89688003e-01 -4.39003140e-01 -5.41775167e-01
-3.47342551e-01 4.04444262e-02 -7.66596571e-02 -9.86414850... | [15.752266883850098, 5.223875522613525] |
13f31827-197f-4d13-8137-16e3f7ac8654 | look-more-but-care-less-in-video-recognition | 2211.09992 | null | https://arxiv.org/abs/2211.09992v1 | https://arxiv.org/pdf/2211.09992v1.pdf | Look More but Care Less in Video Recognition | Existing action recognition methods typically sample a few frames to represent each video to avoid the enormous computation, which often limits the recognition performance. To tackle this problem, we propose Ample and Focal Network (AFNet), which is composed of two branches to utilize more frames but with less computat... | ['Yun Fu', 'Yi Xu', 'Huan Wang', 'Yue Bai', 'Yitian Zhang'] | 2022-11-18 | null | null | null | null | ['video-recognition'] | ['computer-vision'] | [ 1.61774054e-01 -2.42890596e-01 -3.03657979e-01 -3.39261293e-01
-5.30695543e-02 -1.28465295e-01 2.44095191e-01 -3.65982890e-01
-4.61692929e-01 4.86501098e-01 1.28253937e-01 6.92892745e-02
-1.01038702e-01 -8.17674279e-01 -3.47174168e-01 -8.13338161e-01
9.17224884e-02 -3.49013060e-01 7.46089339e-01 1.14202216... | [9.207528114318848, -0.0316602848470211] |
f567ea52-c1a7-4909-a3da-f5a27461029e | that-s-bad-blind-anomaly-detection-by | 2307.03243 | null | https://arxiv.org/abs/2307.03243v1 | https://arxiv.org/pdf/2307.03243v1.pdf | That's BAD: Blind Anomaly Detection by Implicit Local Feature Clustering | Recent studies on visual anomaly detection (AD) of industrial objects/textures have achieved quite good performance. They consider an unsupervised setting, specifically the one-class setting, in which we assume the availability of a set of normal (\textit{i.e.}, anomaly-free) images for training. In this paper, we cons... | ['Takayuki Okatani', 'Masanori Suganuma', 'Jie Zhang'] | 2023-07-06 | null | null | null | null | ['anomaly-detection', 'clustering', 'outlier-detection'] | ['methodology', 'methodology', 'methodology'] | [ 5.56323588e-01 -4.78509739e-02 3.61305952e-01 -8.15689191e-02
-4.54590410e-01 -4.73456532e-01 6.27139270e-01 4.47223663e-01
-1.06496304e-01 1.82505190e-01 -5.08584380e-01 -4.19399261e-01
-2.90053338e-02 -5.97889185e-01 -9.87787068e-01 -1.08297884e+00
2.72856522e-02 2.83182979e-01 3.42284828e-01 1.01757124... | [7.6765546798706055, 2.2155163288116455] |
f6e00d50-ab17-44ff-bfae-1163c93e5d32 | rethinking-diversified-and-discriminative | 1805.03508 | null | http://arxiv.org/abs/1805.03508v1 | http://arxiv.org/pdf/1805.03508v1.pdf | Rethinking Diversified and Discriminative Proposal Generation for Visual Grounding | Visual grounding aims to localize an object in an image referred to by a
textual query phrase. Various visual grounding approaches have been proposed,
and the problem can be modularized into a general framework: proposal
generation, multi-modal feature representation, and proposal ranking. Of these
three modules, most ... | ['DaCheng Tao', 'Zhou Zhao', 'Qi Tian', 'Zhou Yu', 'Jun Yu', 'Chenchao Xiang'] | 2018-05-09 | null | null | null | null | ['phrase-grounding'] | ['natural-language-processing'] | [ 2.02859472e-02 3.40318948e-01 -4.52929974e-01 -1.50982201e-01
-1.03340542e+00 -5.26399255e-01 8.00585210e-01 -9.39478725e-03
-2.29393080e-01 4.50409412e-01 4.03212070e-01 3.25054526e-02
1.21719152e-01 -6.18643820e-01 -6.84171557e-01 -4.86470163e-01
2.03574479e-01 3.61394167e-01 7.09815443e-01 -3.44810486... | [10.404012680053711, 1.4689068794250488] |
5c9b0fcd-2b68-4834-a5f8-9dd8c3f8b79e | adic-anomaly-detection-integrated-circuit-in | 2008.09442 | null | https://arxiv.org/abs/2008.09442v1 | https://arxiv.org/pdf/2008.09442v1.pdf | ADIC: Anomaly Detection Integrated Circuit in 65nm CMOS utilizing Approximate Computing | In this paper, we present a low-power anomaly detection integrated circuit (ADIC) based on a one-class classifier (OCC) neural network. The ADIC achieves low-power operation through a combination of (a) careful choice of algorithm for online learning and (b) approximate computing techniques to lower average energy. In ... | ['Sumon Kumar Bose', 'Pradeep Kumar Gopalakrishnan', 'Mohendra Roy', 'Bapi Kar', 'Arindam Basu'] | 2020-08-21 | null | null | null | null | ['one-class-classifier'] | ['methodology'] | [ 1.13638110e-01 4.76514809e-02 -3.45487356e-01 -4.30556059e-01
-2.00277314e-01 -3.60615551e-01 1.16263792e-01 4.05718744e-01
-8.51628840e-01 8.38195324e-01 -7.77833939e-01 -4.23886806e-01
1.25864679e-02 -7.55513847e-01 -7.95131743e-01 -7.50529051e-01
9.56909657e-02 8.84445682e-02 4.15324211e-01 1.79283589... | [8.294916152954102, 2.5768611431121826] |
6dc464b9-38a1-442e-8f48-64f455d65ec9 | query2box-reasoning-over-knowledge-graphs-in-1 | 2002.05969 | null | https://arxiv.org/abs/2002.05969v2 | https://arxiv.org/pdf/2002.05969v2.pdf | Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings | Answering complex logical queries on large-scale incomplete knowledge graphs (KGs) is a fundamental yet challenging task. Recently, a promising approach to this problem has been to embed KG entities as well as the query into a vector space such that entities that answer the query are embedded close to the query. Howeve... | ['Jure Leskovec', 'Weihua Hu', 'Hongyu Ren'] | 2020-02-14 | null | https://openreview.net/forum?id=BJgr4kSFDS | https://openreview.net/pdf?id=BJgr4kSFDS | iclr-2020-1 | ['complex-query-answering'] | ['knowledge-base'] | [-1.33245662e-01 5.01561165e-01 -2.23889142e-01 -2.33123481e-01
-5.53918064e-01 -8.74810338e-01 -1.00200661e-01 5.66128910e-01
-1.80908963e-01 6.06620491e-01 -1.35006681e-01 -5.93684316e-01
-3.79727125e-01 -1.69018590e+00 -1.19136989e+00 -7.58801401e-02
-5.49800634e-01 8.88801873e-01 6.38813853e-01 -5.08125007... | [9.031747817993164, 7.578350067138672] |
21f624f9-8cea-4a8a-be75-f1e91aca3dbe | stock-price-prediction-using-cnn-and-lstm | 2010.13891 | null | https://arxiv.org/abs/2010.13891v1 | https://arxiv.org/pdf/2010.13891v1.pdf | Stock Price Prediction Using CNN and LSTM-Based Deep Learning Models | Designing robust and accurate predictive models for stock price prediction has been an active area of research for a long time. While on one side, the supporters of the efficient market hypothesis claim that it is impossible to forecast stock prices accurately, many researchers believe otherwise. There exist propositio... | ['Jaydip Sen', 'Sidra Mehtab'] | 2020-10-22 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-4.71927196e-01 -1.78800151e-01 -2.34702811e-01 -3.71952713e-01
-3.39037240e-01 -5.44756949e-01 6.53764129e-01 -1.52881863e-02
-2.84995437e-01 8.59168172e-01 1.53862119e-01 -7.52714932e-01
-1.32537514e-01 -1.17754745e+00 -6.87412739e-01 -6.06381595e-01
-3.94055367e-01 4.77214545e-01 3.48388143e-02 -4.39108461... | [4.471744060516357, 4.2193217277526855] |
61f51e01-5dc4-4be9-bb58-aaf94abc1a73 | synonym-replacement-based-on-a-study-of-basic | null | null | https://aclanthology.org/2021.nodalida-main.26 | https://aclanthology.org/2021.nodalida-main.26.pdf | Synonym Replacement based on a Study of Basic-level Nouns in Swedish Texts of Different Complexity | Basic-level terms have been described as the most important to human categorisation. They are the earliest emerging words in children’s language acquisition, and seem to be more frequently occurring in language in general. In this article, we explored the use of basic-level nouns in texts of different complexity, and h... | ['Arne Jönsson', 'Evelina Rennes'] | null | null | null | null | nodalida-2021-5 | ['lexical-simplification'] | ['natural-language-processing'] | [ 1.03868254e-01 3.41649562e-01 -1.45135015e-01 -3.58670861e-01
1.14204451e-01 -3.87340546e-01 5.89296162e-01 9.28845763e-01
-1.04933810e+00 5.11742949e-01 5.89899838e-01 -5.28019369e-01
-5.97837865e-01 -5.34990191e-01 -1.45083264e-01 -2.46385619e-01
3.47326666e-01 6.07222497e-01 3.86822462e-01 -5.10052621... | [10.68879222869873, 10.22141170501709] |
d29914da-e121-4b54-aab2-8f252fcb92e2 | wider-and-higher-intensive-integration-and | 2210.06919 | null | https://arxiv.org/abs/2210.06919v1 | https://arxiv.org/pdf/2210.06919v1.pdf | Wider and Higher: Intensive Integration and Global Foreground Perception for Image Matting | This paper reviews recent deep-learning-based matting research and conceives our wider and higher motivation for image matting. Many approaches achieve alpha mattes with complex encoders to extract robust semantics, then resort to the U-net-like decoder to concatenate or fuse encoder features. However, image matting is... | ['Xin Yang', 'Qiang Zhang', 'Dongsheng Zhou', 'Yuxin Wang', 'Yuhao Liu', 'Ziqi Wei', 'Yu Qiao'] | 2022-10-13 | null | null | null | null | ['image-matting'] | ['computer-vision'] | [ 6.58974946e-01 8.14330727e-02 -2.07348049e-01 -3.91057193e-01
-5.78087151e-01 2.67067924e-02 4.66766894e-01 -2.80258417e-01
-1.47557318e-01 5.46580672e-01 2.94222653e-01 -2.52824724e-01
3.68946135e-01 -1.02559316e+00 -1.28939795e+00 -7.07500994e-01
2.57514626e-01 -2.44850875e-03 4.22887444e-01 -1.05963156... | [10.63437557220459, -0.9048005938529968] |
3b8a5c60-61c6-4aa0-9967-c43fd8eb49e8 | bridging-images-and-videos-a-simple-learning | 2212.10147 | null | https://arxiv.org/abs/2212.10147v1 | https://arxiv.org/pdf/2212.10147v1.pdf | Bridging Images and Videos: A Simple Learning Framework for Large Vocabulary Video Object Detection | Scaling object taxonomies is one of the important steps toward a robust real-world deployment of recognition systems. We have faced remarkable progress in images since the introduction of the LVIS benchmark. To continue this success in videos, a new video benchmark, TAO, was recently presented. Given the recent encoura... | ['Joon-Young Lee', 'In So Kweon', 'Seoung Wug Oh', 'KwanYong Park', 'Sanghyun Woo'] | 2022-12-20 | null | null | null | null | ['video-object-detection'] | ['computer-vision'] | [ 3.78023013e-02 -4.32323098e-01 -4.16088760e-01 -8.05231705e-02
-7.14932561e-01 -7.14622200e-01 6.09316051e-01 -4.45143968e-01
-3.76803070e-01 5.20727873e-01 7.52189308e-02 5.46677224e-02
2.14829966e-01 -1.06208839e-01 -1.02223396e+00 -7.35655487e-01
-2.17320487e-01 4.38659102e-01 7.82310545e-01 -4.33518104... | [6.320186138153076, -2.0639238357543945] |
259bb9ff-170a-43a2-a0d8-c78dc01b640d | slicematch-geometry-guided-aggregation-for | 2211.14651 | null | https://arxiv.org/abs/2211.14651v3 | https://arxiv.org/pdf/2211.14651v3.pdf | SliceMatch: Geometry-guided Aggregation for Cross-View Pose Estimation | This work addresses cross-view camera pose estimation, i.e., determining the 3-Degrees-of-Freedom camera pose of a given ground-level image w.r.t. an aerial image of the local area. We propose SliceMatch, which consists of ground and aerial feature extractors, feature aggregators, and a pose predictor. The feature extr... | ['Ted Lentsch', 'Julian F. P. Kooij', 'Holger Caesar', 'Zimin Xia'] | 2022-11-26 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Lentsch_SliceMatch_Geometry-Guided_Aggregation_for_Cross-View_Pose_Estimation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Lentsch_SliceMatch_Geometry-Guided_Aggregation_for_Cross-View_Pose_Estimation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['visual-localization'] | ['computer-vision'] | [ 1.73376486e-01 -2.58589447e-01 -1.10081926e-01 -8.00467432e-02
-1.00153327e+00 -1.12279987e+00 5.06707907e-01 -3.27212140e-02
-4.61860001e-01 -7.73672312e-02 3.71709391e-02 2.71754801e-01
-1.14295147e-01 -8.12781096e-01 -7.20200777e-01 -6.69363141e-01
-9.27639976e-02 1.94004849e-01 3.13305616e-01 -4.95890528... | [7.634697914123535, -2.249546527862549] |
71855a5e-9711-42c4-9982-3ecf81c3cba3 | conaclip-exploring-distillation-of-fully | 2305.17652 | null | https://arxiv.org/abs/2305.17652v1 | https://arxiv.org/pdf/2305.17652v1.pdf | ConaCLIP: Exploring Distillation of Fully-Connected Knowledge Interaction Graph for Lightweight Text-Image Retrieval | Large-scale pre-trained text-image models with dual-encoder architectures (such as CLIP) are typically adopted for various vision-language applications, including text-image retrieval. However,these models are still less practical on edge devices or for real-time situations, due to the substantial indexing and inferenc... | ['Lianwen Jin', 'Jun Huang', 'Xiaodan Wang', 'Chengyu Wang', 'Jiapeng Wang'] | 2023-05-28 | null | null | null | null | ['model-compression'] | ['methodology'] | [ 3.68473530e-01 -1.63188860e-01 -5.61067760e-01 -7.66711310e-02
-7.51528800e-01 -1.44739211e-01 7.95077801e-01 -1.71744198e-01
-5.34083247e-01 3.79871488e-01 6.89182356e-02 -5.31752527e-01
-1.44615084e-01 -4.70240891e-01 -7.68825412e-01 -5.88992834e-01
3.12565714e-01 5.37832558e-01 1.06274724e-01 1.17970824... | [10.24763011932373, 0.9905239939689636] |
6df25ddc-407f-4cd8-b7fc-2c9243bee122 | generating-virtual-on-body-accelerometer-data | 2305.03187 | null | https://arxiv.org/abs/2305.03187v1 | https://arxiv.org/pdf/2305.03187v1.pdf | Generating Virtual On-body Accelerometer Data from Virtual Textual Descriptions for Human Activity Recognition | The development of robust, generalized models in human activity recognition (HAR) has been hindered by the scarcity of large-scale, labeled data sets. Recent work has shown that virtual IMU data extracted from videos using computer vision techniques can lead to substantial performance improvements when training HAR mod... | ['Thomas Plötz', 'Hyeokhyen Kwon', 'Zikang Leng'] | 2023-05-04 | null | null | null | null | ['motion-synthesis', 'human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'computer-vision', 'time-series'] | [ 3.89644861e-01 2.50441842e-02 -5.69823943e-02 2.05604844e-02
-1.00983191e+00 -5.32600999e-01 1.19045079e+00 -4.51445997e-01
-6.23983562e-01 8.42701674e-01 6.52633131e-01 7.71073550e-02
4.21396703e-01 -4.44553018e-01 -8.88354778e-01 -2.02225491e-01
1.27113327e-01 6.29534602e-01 3.53039652e-01 -1.83875978... | [7.385823726654053, -0.06998332589864731] |
659e1308-1ec5-4495-a565-9d5428c64d01 | hyperspectral-remote-sensing-image | 2106.14804 | null | https://arxiv.org/abs/2106.14804v1 | https://arxiv.org/pdf/2106.14804v1.pdf | Hyperspectral Remote Sensing Image Classification Based on Multi-scale Cross Graphic Convolution | The mining and utilization of features directly affect the classification performance of models used in the classification and recognition of hyperspectral remote sensing images. Traditional models usually conduct feature mining from a single perspective, with the features mined being limited and the internal relations... | ['Guojin Liu', 'Tianchong Qiu', 'Zhihan Chen', 'Yin Li', 'Yunsong Zhao'] | 2021-06-28 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 5.57988226e-01 -2.95850039e-01 2.16464221e-01 -4.07141566e-01
1.66707281e-02 -2.07651809e-01 4.80157882e-02 -2.44347826e-01
-1.76158398e-01 4.73596543e-01 -2.78482586e-01 -2.84809560e-01
-7.30612576e-01 -1.29246616e+00 -7.30928779e-02 -1.01450074e+00
-1.92683280e-01 -8.88425410e-02 -3.11936438e-01 -2.60887444... | [9.89140796661377, -1.631320834159851] |
90aa012f-1832-4bd6-9d81-c306b9f72846 | simulating-time-to-event-prediction-with | 2103.02583 | null | https://arxiv.org/abs/2103.02583v1 | https://arxiv.org/pdf/2103.02583v1.pdf | Simulating time to event prediction with spatiotemporal echocardiography deep learning | Integrating methods for time-to-event prediction with diagnostic imaging modalities is of considerable interest, as accurate estimates of survival requires accounting for censoring of individuals within the observation period. New methods for time-to-event prediction have been developed by extending the cox-proportiona... | ['William Hiesinger', 'Curtis P. Langlotz', 'John P. Cunningham', 'Jeffrey Teuteberg', 'Michelle C. Li', 'Kate M. Callon', 'Cayley Bowles', 'Patpilai Kasinpila', 'Robyn Fong', 'Nicolas Quach', 'Rohan Shad'] | 2021-03-03 | null | null | null | null | ['time-to-event-prediction'] | ['time-series'] | [-8.98325220e-02 -1.51029825e-01 -1.91213623e-01 -6.37478173e-01
-8.72470319e-01 -4.17983681e-01 1.90281663e-02 -2.63186339e-02
-2.65970528e-01 1.04384005e+00 3.83757710e-01 -6.38289750e-01
-3.85216445e-01 -6.31487012e-01 -3.71798903e-01 -4.38443542e-01
-1.20549679e+00 7.18581259e-01 -3.01899850e-01 2.52830267... | [7.936946392059326, 5.794567108154297] |
94f25e2c-f8f2-4991-b921-3b3db22c9575 | learning-to-generate-novel-scientific | 2305.14259 | null | https://arxiv.org/abs/2305.14259v1 | https://arxiv.org/pdf/2305.14259v1.pdf | Learning to Generate Novel Scientific Directions with Contextualized Literature-based Discovery | Literature-Based Discovery (LBD) aims to discover new scientific knowledge by mining papers and generating hypotheses. Standard LBD is limited to predicting pairwise relations between discrete concepts (e.g., drug-disease links). LBD also ignores critical contexts like experimental settings (e.g., a specific patient po... | ['Tom Hope', 'Heng Ji', 'Doug Downey', 'Qingyun Wang'] | 2023-05-23 | null | null | null | null | ['contextualized-literature-based-discovery'] | ['natural-language-processing'] | [ 2.80033678e-01 5.31042516e-01 -1.02884817e+00 -7.54962564e-02
-6.34327829e-01 -8.55812967e-01 8.04814398e-01 7.85995901e-01
5.79018779e-02 1.42608333e+00 3.26646149e-01 -9.15085495e-01
-5.07940948e-01 -9.60862756e-01 -1.03394520e+00 -2.18086705e-01
-2.14908436e-01 7.15943992e-01 -1.26882106e-01 2.09677309... | [8.71737003326416, 8.334962844848633] |
9572e8bb-3e8c-4e4b-bef2-bdf153dbdf58 | spectral-algorithms-optimally-recover | 2203.11847 | null | https://arxiv.org/abs/2203.11847v2 | https://arxiv.org/pdf/2203.11847v2.pdf | Spectral Algorithms Optimally Recover Planted Sub-structures | Spectral algorithms are an important building block in machine learning and graph algorithms. We are interested in studying when such algorithms can be applied directly to provide optimal solutions to inference tasks. Previous works by Abbe, Fan, Wang and Zhong (2020) and by Dhara, Gaudio, Mossel and Sandon (2022) show... | ['Colin Sandon', 'Elchanan Mossel', 'Julia Gaudio', 'Souvik Dhara'] | 2022-03-22 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 3.99901807e-01 3.34820181e-01 -2.61363924e-01 1.41974598e-01
-5.64385533e-01 -6.63732648e-01 4.73521143e-01 6.52551427e-02
2.52570957e-01 1.07262731e+00 5.56047596e-02 -7.25842535e-01
-8.44845712e-01 -7.62866795e-01 -6.21757627e-01 -8.08828890e-01
-8.06801796e-01 5.94452739e-01 2.84295529e-01 -1.37880826... | [6.87750768661499, 5.143777370452881] |
95cf1dd5-a535-40d3-bf37-564a9ef37495 | predicting-gene-expression-from-network | 2005.03961 | null | https://arxiv.org/abs/2005.03961v2 | https://arxiv.org/pdf/2005.03961v2.pdf | A Graph Feature Auto-Encoder for the Prediction of Unobserved Node Features on Biological Networks | Motivation: Molecular interaction networks summarize complex biological processes as graphs, whose structure is informative of biological function at multiple scales. Simultaneously, omics technologies measure the variation or activity of genes, proteins, or metabolites across individuals or experimental conditions. In... | ['Ramin Hasibi', 'Tom Michoel'] | 2020-05-08 | null | null | null | null | ['graph-reconstruction'] | ['graphs'] | [ 3.92306983e-01 2.67145634e-01 -3.60473841e-01 -2.50196844e-01
-2.43440289e-02 -6.02813303e-01 3.78685802e-01 7.37229645e-01
6.92137107e-02 7.33334124e-01 2.78112441e-01 -2.66608953e-01
-3.24930072e-01 -9.93843734e-01 -1.08526731e+00 -8.46740484e-01
-3.60118032e-01 5.85920632e-01 -3.94749463e-01 5.46440631... | [6.058343410491943, 5.806270599365234] |
ec89ab90-1414-4458-ab90-6490820c92d9 | on-the-role-of-the-zero-conditional-mean | 2211.09502 | null | https://arxiv.org/abs/2211.09502v1 | https://arxiv.org/pdf/2211.09502v1.pdf | On the Role of the Zero Conditional Mean Assumption for Causal Inference in Linear Models | Many econometrics textbooks imply that under mean independence of the regressors and the error term, the OLS parameters have a causal interpretation. We show that even when this assumption is satisfied, OLS might identify a pseudo-parameter that does not have a causal interpretation. Even assuming that the linear model... | ['Joeri Smits', 'Giovanni Mellace', 'Michael C. Knaus', 'Federico Crudu'] | 2022-11-17 | null | null | null | null | ['econometrics'] | ['miscellaneous'] | [-7.21060187e-02 2.74708539e-01 -9.02688861e-01 -4.51271802e-01
-3.48571032e-01 -6.53054297e-01 6.03956401e-01 -1.71479076e-01
-7.31172934e-02 9.61807549e-01 6.34507120e-01 -1.19825816e+00
-4.24131125e-01 -7.63829291e-01 -1.02906442e+00 -4.46306586e-01
5.38083613e-02 1.06555112e-01 -3.86877120e-01 3.43956798... | [7.993809223175049, 5.247262477874756] |
3ba9eda1-83c7-4a7e-a1be-d715234afeab | speech-watermarking-a-solution-for | 2203.02275 | null | https://arxiv.org/abs/2203.02275v2 | https://arxiv.org/pdf/2203.02275v2.pdf | Speech watermarking: an approach for the forensic analysis of digital telephonic recordings | In this article, the authors discuss the problem of forensic authentication of digital audio recordings. Although forensic audio has been addressed in several articles, the existing approaches are focused on analog magnetic recordings, which are less prevalent because of the large amount of digital recorders available ... | ['Martin Hagmueller', 'Jose Juan Lucena-Molina', 'Marcos Faundez-Zanuy'] | 2022-02-23 | null | null | null | null | ['speaker-identification'] | ['speech'] | [ 4.31571573e-01 -7.37333670e-02 -3.58848572e-02 2.59724140e-01
-4.64213312e-01 -5.88135958e-01 2.86256760e-01 6.04589581e-01
-6.10082448e-01 8.23488593e-01 -3.23003978e-01 -4.25054580e-01
-2.04731867e-01 -6.41574919e-01 -3.19291025e-01 -7.02436447e-01
7.10593760e-02 -3.39403632e-03 4.28305775e-01 9.07663442... | [14.989420890808105, 5.558487892150879] |
316ceff0-eb24-436f-9f62-ca4ba80e542b | signal-reconstruction-from-quantized-noisy | 2201.03114 | null | https://arxiv.org/abs/2201.03114v1 | https://arxiv.org/pdf/2201.03114v1.pdf | Signal Reconstruction from Quantized Noisy Samples of the Discrete Fourier Transform | In this paper, we present two variations of an algorithm for signal reconstruction from one-bit or two-bit noisy observations of the discrete Fourier transform (DFT). The one-bit observations of the DFT correspond to the sign of its real part, whereas, the two-bit observations of the DFT correspond to the signs of both... | ['Animesh Kumar', 'Mohak Goyal'] | 2022-01-09 | null | null | null | null | ['ms-ssim'] | ['computer-vision'] | [ 7.65590787e-01 -5.11063710e-02 -4.40880917e-02 1.09517880e-01
-6.03260100e-01 -3.10547113e-01 2.06156462e-01 -1.13591716e-01
-5.16756356e-01 6.17682040e-01 -8.12165365e-02 -2.84557581e-01
-1.06086373e-01 -3.68088126e-01 -7.09633827e-01 -9.42634106e-01
-4.73495305e-01 -6.01771235e-01 -3.12707108e-03 -2.78338119... | [6.695490837097168, 1.4945605993270874] |
3fcd049c-9f76-44cb-b3a1-4334c3ecd2f7 | from-plane-crashes-to-algorithmic-harm | 2210.03535 | null | https://arxiv.org/abs/2210.03535v1 | https://arxiv.org/pdf/2210.03535v1.pdf | From plane crashes to algorithmic harm: applicability of safety engineering frameworks for responsible ML | Inappropriate design and deployment of machine learning (ML) systems leads to negative downstream social and ethical impact -- described here as social and ethical risks -- for users, society and the environment. Despite the growing need to regulate ML systems, current processes for assessing and mitigating risks are d... | ['Negar Rostamzadeh', 'AJung Moon', 'Joshua Kroll', 'Edgar Jatho', 'Andrew Smart', 'Renee Shelby', 'Shalaleh Rismani'] | 2022-10-06 | null | null | null | null | ['culture'] | ['speech'] | [ 2.64042735e-01 5.85730433e-01 -3.58684547e-02 7.63720693e-03
-5.41604519e-01 -7.68020868e-01 3.08964789e-01 4.52255905e-01
-9.05914605e-02 1.43183336e-01 5.19641459e-01 -1.05834150e+00
-3.72515798e-01 -4.99262750e-01 -4.87294286e-01 -4.28112298e-01
5.42526007e-01 -3.70064408e-01 -3.42617840e-01 3.66871618... | [9.109076499938965, 6.37045431137085] |
ae6b19fe-e1fb-4ed8-92a3-a8d3ac72f18a | the-impact-of-random-models-on-clustering | 1701.06508 | null | http://arxiv.org/abs/1701.06508v2 | http://arxiv.org/pdf/1701.06508v2.pdf | The Impact of Random Models on Clustering Similarity | Clustering is a central approach for unsupervised learning. After clustering
is applied, the most fundamental analysis is to quantitatively compare
clusterings. Such comparisons are crucial for the evaluation of clustering
methods as well as other tasks such as consensus clustering. It is often argued
that, in order to... | ['Alexander J. Gates', 'Yong-Yeol Ahn'] | 2017-01-23 | null | null | null | null | ['clustering-ensemble'] | ['graphs'] | [ 2.60063767e-01 -3.34577531e-01 1.77837178e-01 -4.25108254e-01
-5.51712990e-01 -7.70355701e-01 8.43595326e-01 6.31693184e-01
-6.63082361e-01 4.75497931e-01 1.00601554e-01 -1.86202034e-01
-6.07613504e-01 -6.93704069e-01 -3.14872652e-01 -1.30098021e+00
-4.89271693e-02 6.09007895e-01 1.95944563e-01 1.27456933... | [7.631160736083984, 4.546947479248047] |
e8a37646-acfa-47a7-b384-6354c9d9acc9 | provably-efficient-primal-dual-reinforcement | 2201.11965 | null | https://arxiv.org/abs/2201.11965v4 | https://arxiv.org/pdf/2201.11965v4.pdf | Provably Efficient Primal-Dual Reinforcement Learning for CMDPs with Non-stationary Objectives and Constraints | We consider primal-dual-based reinforcement learning (RL) in episodic constrained Markov decision processes (CMDPs) with non-stationary objectives and constraints, which plays a central role in ensuring the safety of RL in time-varying environments. In this problem, the reward/utility functions and the state transition... | ['Javad Lavaei', 'Yuhao Ding'] | 2022-01-28 | null | null | null | null | ['safe-exploration'] | ['robots'] | [-9.14154053e-02 2.25331381e-01 -4.74435270e-01 1.11810505e-01
-7.24149764e-01 -7.86543906e-01 1.59942836e-01 2.02557534e-01
-7.93397605e-01 1.46916926e+00 -4.47229475e-01 -5.30400753e-01
-9.09663081e-01 -6.60365462e-01 -6.54781282e-01 -1.15452361e+00
-6.05604053e-01 7.43597209e-01 -1.45498291e-01 9.65057581... | [4.390807628631592, 2.7648656368255615] |
9b648528-7d13-4536-b818-07ff2cd9d2ae | effective-learning-based-illuminant | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Cheng_Effective_Learning-Based_Illuminant_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Cheng_Effective_Learning-Based_Illuminant_2015_CVPR_paper.pdf | Effective Learning-Based Illuminant Estimation Using Simple Features | Illumination estimation is the process of determining the chromaticity of the illumination in an imaged scene in order to remove undesirable color casts through white-balancing. While computational color constancy is a well-studied topic in computer vision, it remains challenging due to the ill-posed nature of the pr... | ['Brian Price', 'Scott Cohen', 'Michael S. Brown', 'Dongliang Cheng'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['color-constancy'] | ['computer-vision'] | [ 4.39366579e-01 -7.85363913e-01 2.17191368e-01 -4.00304675e-01
-6.12176955e-01 -5.97762525e-01 4.69719619e-01 -1.19499527e-01
-3.73884588e-01 7.37233639e-01 -5.55161476e-01 -2.86361665e-01
1.69477481e-02 -4.16122854e-01 -3.67580682e-01 -1.09683156e+00
5.86861968e-02 2.80923605e-01 4.75422949e-01 -2.40310431... | [10.372610092163086, -2.5443942546844482] |
74ba9318-e01e-49c2-9d67-9bd363e4349b | multimodal-multi-user-surface-recognition | 2303.04930 | null | https://arxiv.org/abs/2303.04930v1 | https://arxiv.org/pdf/2303.04930v1.pdf | Multimodal Multi-User Surface Recognition with the Kernel Two-Sample Test | Machine learning and deep learning have been used extensively to classify physical surfaces through images and time-series contact data. However, these methods rely on human expertise and entail the time-consuming processes of data and parameter tuning. To overcome these challenges, we propose an easily implemented fra... | ['Katherine J. Kuchenbecker', 'Sebastian Trimpe', 'Friedrich Solowjow', 'Behnam Khojasteh'] | 2023-03-08 | null | null | null | null | ['hypothesis-testing', 'hypothesis-testing'] | ['methodology', 'miscellaneous'] | [ 4.70985711e-01 -1.63441330e-01 2.18871593e-01 -2.14106664e-01
-1.29299974e+00 -6.00076556e-01 5.03168821e-01 2.95476198e-01
-3.93381834e-01 3.05099458e-01 -1.94181100e-01 -2.07003281e-01
-3.39451909e-01 -5.88928998e-01 -8.33759248e-01 -6.19491339e-01
-3.37539792e-01 2.04865292e-01 3.56060922e-01 -2.88345695... | [10.019116401672363, -0.043466828763484955] |
de2b2a10-9c25-4b97-94f3-647cd5afa7a5 | ho-3d-a-multi-user-multi-object-dataset-for | 1907.01481 | null | https://arxiv.org/abs/1907.01481v6 | https://arxiv.org/pdf/1907.01481v6.pdf | HOnnotate: A method for 3D Annotation of Hand and Object Poses | We propose a method for annotating images of a hand manipulating an object with the 3D poses of both the hand and the object, together with a dataset created using this method. Our motivation is the current lack of annotated real images for this problem, as estimating the 3D poses is challenging, mostly because of the ... | ['Vincent Lepetit', 'Shreyas Hampali', 'Markus Oberweger', 'Mahdi Rad'] | 2019-07-02 | honnotate-a-method-for-3d-annotation-of-hand | http://openaccess.thecvf.com/content_CVPR_2020/html/Hampali_HOnnotate_A_Method_for_3D_Annotation_of_Hand_and_Object_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Hampali_HOnnotate_A_Method_for_3D_Annotation_of_Hand_and_Object_CVPR_2020_paper.pdf | cvpr-2020-6 | ['hand-object-pose'] | ['computer-vision'] | [ 1.09895386e-01 -9.20047015e-02 1.90455779e-01 -1.63291410e-01
-4.99729425e-01 -8.07064235e-01 2.74913698e-01 -5.39140940e-01
-3.45633626e-01 2.92231351e-01 1.64509520e-01 3.33504558e-01
1.72343850e-01 4.88882810e-02 -7.26106942e-01 -4.69010562e-01
2.80925259e-03 1.07758272e+00 4.14826095e-01 1.65311068... | [6.547422885894775, -1.0084484815597534] |
f1289b35-17cc-4bae-bfbc-16f31fdd3279 | convergence-of-first-order-algorithms-for | 2301.06806 | null | https://arxiv.org/abs/2301.06806v1 | https://arxiv.org/pdf/2301.06806v1.pdf | Convergence of First-Order Algorithms for Meta-Learning with Moreau Envelopes | In this work, we consider the problem of minimizing the sum of Moreau envelopes of given functions, which has previously appeared in the context of meta-learning and personalized federated learning. In contrast to the existing theory that requires running subsolvers until a certain precision is reached, we only assume ... | ['Peter Richtárik', 'Slavomír Hanzely', 'Konstantin Mishchenko'] | 2023-01-17 | null | null | null | null | ['personalized-federated-learning'] | ['methodology'] | [-5.91685921e-02 3.31418753e-01 -3.23330432e-01 -7.70170242e-02
-1.00597131e+00 -6.15200520e-01 3.67610753e-01 3.80377710e-01
-3.39995861e-01 6.88470960e-01 7.47649968e-02 -3.47398221e-01
-5.67510366e-01 -8.12806189e-01 -1.18912554e+00 -8.49529386e-01
-2.59194642e-01 3.02014619e-01 -1.66035458e-01 -5.38548052... | [6.343122482299805, 4.737835884094238] |
245b073e-13cd-4d16-8e9f-ca1c6667a1d9 | light-yolov5-a-lightweight-algorithm-for | 2208.13422 | null | https://arxiv.org/abs/2208.13422v3 | https://arxiv.org/pdf/2208.13422v3.pdf | Light-YOLOv5: A Lightweight Algorithm for Improved YOLOv5 in Complex Fire Scenarios | Fire-detection technology is of great importance for successful fire-prevention measures. Image-based fire detection is one effective method. At present, object-detection algorithms are deficient in performing detection speed and accuracy tasks when they are applied in complex fire scenarios. In this study, a lightweig... | ['Fei Zhong', 'Bo Li', 'Hao Xu'] | 2022-08-29 | null | null | null | null | ['fire-detection'] | ['time-series'] | [ 1.74173653e-01 -8.64803433e-01 1.60973668e-01 2.66429543e-01
1.45038873e-01 -1.35350272e-01 3.14747214e-01 -3.34713012e-01
-6.11130774e-01 3.63422751e-01 -3.50982159e-01 -6.43182397e-02
-2.34094262e-02 -1.23375225e+00 -2.87380010e-01 -1.09204876e+00
1.31833121e-01 -3.14777732e-01 8.43661427e-01 -1.38181582... | [9.234251976013184, -1.024664044380188] |
f200b630-71fb-41e0-b6c4-022b0f9b625c | hunor-a-hungarianrussian-parallel-corpus | null | null | https://aclanthology.org/L12-1106 | https://aclanthology.org/L12-1106.pdf | HunOr: A Hungarian---Russian Parallel Corpus | In this paper, we present HunOr, the first multi-domain Hungarian―Russian parallel corpus. Some of the corpus texts have been manually aligned and split into sentences, besides, named entities also have been annotated while the other parts are automatically aligned at the sentence level and they are POS-tagged as wel... | ["Istv{\\'a}n Nagy T.", "Martina Katalin Szab{\\'o}", 'Veronika Vincze'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [-1.98922023e-01 3.66531521e-01 -3.20674390e-01 -4.18019086e-01
-6.05007470e-01 -7.25631177e-01 6.00540459e-01 4.93454397e-01
-7.44504809e-01 1.27176285e+00 6.08430982e-01 -5.52221179e-01
1.66960619e-02 -7.15657055e-01 -3.02659392e-01 -1.31292492e-01
4.94464755e-01 1.08438158e+00 4.08165812e-01 -6.08831286... | [10.42579174041748, 10.108892440795898] |
5c5071cb-4c39-4e2c-9fbc-d2aa1aa18bc9 | mvp-robust-multi-view-practice-for-driving | 2207.02042 | null | https://arxiv.org/abs/2207.02042v1 | https://arxiv.org/pdf/2207.02042v1.pdf | MVP: Robust Multi-View Practice for Driving Action Localization | Distracted driving causes thousands of deaths per year, and how to apply deep-learning methods to prevent these tragedies has become a crucial problem. In Track3 of the 6th AI City Challenge, researchers provide a high-quality video dataset with densely action annotations. Due to the small data scale and unclear action... | ['Yangguang Li', 'Haisheng Su', 'Kaibin Tian', 'Kunchang Li', 'Jingjie Shang'] | 2022-07-05 | null | null | null | null | ['action-localization'] | ['computer-vision'] | [-2.52831578e-02 -4.33002412e-01 -4.81732696e-01 -3.15596402e-01
-1.06517375e+00 -4.55591857e-01 5.60521603e-01 -5.73333442e-01
-5.31363845e-01 5.67005157e-01 6.57343268e-01 1.78477317e-01
1.95362553e-01 -5.00640810e-01 -7.25132406e-01 -8.21240366e-01
1.22186812e-02 8.28183964e-02 7.59060383e-01 -1.05881961... | [8.200764656066895, 0.35050448775291443] |
f8aa58e2-c502-443f-8b45-14515a772691 | natural-language-processing-of-clinical-notes | 1908.05780 | null | https://arxiv.org/abs/1908.05780v1 | https://arxiv.org/pdf/1908.05780v1.pdf | Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review | Of the 2652 articles considered, 106 met the inclusion criteria. Review of the included papers resulted in identification of 43 chronic diseases, which were then further classified into 10 disease categories using ICD-10. The majority of studies focused on diseases of the circulatory system (n=38) while endocrine and m... | ['Joel T. Dudley', 'Venet Osmani', 'Alberto Lavelli', 'Fabio Rinaldi', 'Seyedmostafa Sheikhalishahi', 'Riccardo Miotto'] | 2019-08-15 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [-5.09817479e-03 2.16723368e-01 -6.35469675e-01 -1.39984041e-01
-6.37940109e-01 -5.11355042e-01 2.92342752e-01 1.03539252e+00
-5.29580772e-01 8.25559199e-01 7.27965474e-01 -5.51534772e-01
-4.45110559e-01 -6.77567661e-01 -7.80600533e-02 -5.70571780e-01
-3.34062368e-01 6.16655886e-01 -2.26433486e-01 1.79538906... | [8.403791427612305, 8.549577713012695] |
3acc3379-381f-4a39-bf35-8957bc3e051c | depth-map-generation-using-pixel-matching-in | 1902.03471 | null | https://arxiv.org/abs/1902.03471v3 | https://arxiv.org/pdf/1902.03471v3.pdf | Depth-Map Generation using Pixel Matching in Stereoscopic Pair of Images | Modern day multimedia content generation and dissemination is moving towards the presentation of more and more `realistic' scenarios. The switch from 2-dimensional (2D) to 3-dimensional (3D) has been a major driving force in that direction. Over the recent past, a large number of approaches have been proposed for creat... | ['Mohd. Samar Ansari', 'Asra Aslam'] | 2019-02-09 | null | null | null | null | ['3d-depth-estimation'] | ['computer-vision'] | [ 6.66876256e-01 -1.40202120e-01 1.12996943e-01 -4.44040358e-01
-8.11172605e-01 -4.16860580e-01 7.61550784e-01 1.80223510e-01
-3.38129371e-01 6.90541565e-01 2.80923307e-01 1.87508501e-02
-1.65614970e-02 -8.91207337e-01 -3.52882862e-01 -7.27356136e-01
-1.30745918e-01 1.68716431e-01 8.03995073e-01 -1.48706481... | [9.219204902648926, -2.536027431488037] |
6c0c9002-1959-4086-915d-5c5ab702066f | parkinsons-disease-emg-data-augmentation-and | null | null | https://doi.org/10.3390/s20092605 | https://doi.org/10.3390/s20092605 | Parkinson’s Disease EMG Data Augmentation and Simulation with DCGANs and Style Transfer | This paper proposes two new data augmentation approaches based on Deep Convolutional Generative Adversarial Networks (DCGANs) and Style Transfer for augmenting Parkinson’s Disease (PD) electromyography (EMG) signals. The experimental results indicate that the proposed models can adapt to different frequencies and ampli... | ['Esther Luna Colombini', 'Rafael Anicet Zanini'] | 2020-05-03 | null | null | null | null | ['electromyography-emg'] | ['medical'] | [ 2.78045624e-01 3.88631135e-01 -1.77574709e-01 -2.41512526e-02
-5.88639855e-01 -1.75143331e-01 3.33866626e-01 -1.12401271e+00
-3.28023165e-01 1.02417278e+00 5.54337680e-01 -1.95778102e-01
-8.63714069e-02 -5.98433375e-01 -4.55363542e-01 -4.98677462e-01
-5.08566022e-01 6.93968415e-01 -6.19749464e-02 -3.33674371... | [6.911042213439941, 0.19230982661247253] |
cbb6d316-0d3e-48bc-95fc-030bed8a3ab7 | exploiting-category-names-for-few-shot | 2211.16594 | null | https://arxiv.org/abs/2211.16594v3 | https://arxiv.org/pdf/2211.16594v3.pdf | Exploiting Category Names for Few-Shot Classification with Vision-Language Models | Vision-language foundation models pretrained on large-scale data provide a powerful tool for many visual understanding tasks. Notably, many vision-language models build two encoders (visual and textual) that can map two modalities into the same embedding space. As a result, the learned representations achieve good zero... | ['Ming-Hsuan Yang', 'Shengyang Dai', 'Jiahui Yu', 'Liangliang Cao', 'ZiRui Wang', 'Taihong Xiao'] | 2022-11-29 | null | null | null | null | ['few-shot-image-classification'] | ['computer-vision'] | [-7.83912838e-02 -7.81067312e-02 -5.40942073e-01 -4.64060843e-01
-6.96804106e-01 -2.78677404e-01 1.05473650e+00 -3.42872851e-02
-5.00607610e-01 3.55730712e-01 2.68905073e-01 -1.22913651e-01
4.65175301e-01 -7.50044405e-01 -7.94848442e-01 -4.83669400e-01
3.23854268e-01 2.29510918e-01 4.58241075e-01 -2.06135437... | [10.019664764404297, 2.221747875213623] |
9b7bc68f-8570-46dc-a0c5-464f6976a253 | sentence-extraction-based-machine-reading | 2105.09043 | null | https://arxiv.org/abs/2105.09043v2 | https://arxiv.org/pdf/2105.09043v2.pdf | Sentence Extraction-Based Machine Reading Comprehension for Vietnamese | The development of natural language processing (NLP) in general and machine reading comprehension in particular has attracted the great attention of the research community. In recent years, there are a few datasets for machine reading comprehension tasks in Vietnamese with large sizes, such as UIT-ViQuAD and UIT-ViNews... | ['Ngan Luu-Thuy Nguyen', 'Anh Gia-Tuan Nguyen', 'Kiet Van Nguyen', 'Tin Van Huynh', 'Nhat Duy Nguyen', 'Phong Nguyen-Thuan Do'] | 2021-05-19 | null | null | null | null | ['vietnamese-datasets'] | ['natural-language-processing'] | [ 2.24584699e-01 2.86694676e-01 2.50813901e-01 -3.14925015e-01
-1.35486782e+00 -6.80198610e-01 3.05873603e-01 6.48034215e-01
-9.02115345e-01 7.89338887e-01 6.12250805e-01 -7.27202237e-01
4.74267304e-02 -9.91998911e-01 -7.34787643e-01 -2.07569197e-01
4.65045005e-01 5.60938895e-01 2.54175663e-01 -7.94412374... | [11.390488624572754, 8.260286331176758] |
e6e46d45-0eb4-4177-8e7b-fd9eaf3639c0 | objects-matter-learning-object-relation-graph | 2205.13280 | null | https://arxiv.org/abs/2205.13280v1 | https://arxiv.org/pdf/2205.13280v1.pdf | Objects Matter: Learning Object Relation Graph for Robust Camera Relocalization | Visual relocalization aims to estimate the pose of a camera from one or more images. In recent years deep learning based pose regression methods have attracted many attentions. They feature predicting the absolute poses without relying on any prior built maps or stored images, making the relocalization very efficient. ... | ['Xinglu Wang', 'Zhiyu Xiang', 'Chengyu Qiao'] | 2022-05-26 | null | null | null | null | ['camera-relocalization'] | ['computer-vision'] | [-2.94432282e-01 -3.04287493e-01 -1.93303823e-01 -5.61908007e-01
-2.88189530e-01 -4.15002495e-01 4.25374597e-01 -4.97911964e-03
-4.02826518e-01 5.42459369e-01 1.22507557e-01 3.50915641e-01
-2.79416114e-01 -5.90032697e-01 -9.43685234e-01 -4.85274941e-01
2.42017299e-01 3.70755315e-01 5.71841061e-01 -1.03289947... | [7.788843154907227, -2.267019033432007] |
d568154c-f05c-4eb4-8783-56947a28942a | scalably-learning-quantum-many-body | 2209.14328 | null | https://arxiv.org/abs/2209.14328v1 | https://arxiv.org/pdf/2209.14328v1.pdf | Scalably learning quantum many-body Hamiltonians from dynamical data | The physics of a closed quantum mechanical system is governed by its Hamiltonian. However, in most practical situations, this Hamiltonian is not precisely known, and ultimately all there is are data obtained from measurements on the system. In this work, we introduce a highly scalable, data-driven approach to learning ... | ['Jens Eisert', 'Ryan Sweke', 'Dominik Hangleiter', 'Ingo Roth', 'Augustine Kshetrimayum', 'Frederik Wilde'] | 2022-09-28 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [ 5.44309877e-02 -3.15352887e-01 3.78130600e-02 -1.60464585e-01
-4.52360928e-01 -7.28531301e-01 6.49263501e-01 2.13890433e-01
-6.59910679e-01 9.16189075e-01 -8.76097158e-02 -3.99501055e-01
-2.94603825e-01 -8.13394845e-01 -4.87546414e-01 -1.02097797e+00
-2.82305062e-01 7.62392700e-01 -5.11104846e-03 -5.19412398... | [5.645634651184082, 4.899710655212402] |
75d949f4-fc7b-4c1a-a9cb-5a4cbb452f15 | casia-face-africa-a-large-scale-african-face | 2105.03632 | null | https://arxiv.org/abs/2105.03632v2 | https://arxiv.org/pdf/2105.03632v2.pdf | CASIA-Face-Africa: A Large-scale African Face Image Database | Face recognition is a popular and well-studied area with wide applications in our society. However, racial bias had been proven to be inherent in most State Of The Art (SOTA) face recognition systems. Many investigative studies on face recognition algorithms have reported higher false positive rates of African subjects... | ['Zhenan Sun', 'Kunbo Zhang', 'Caiyong Wang', 'Yunlong Wang', 'Jawad Muhammad'] | 2021-05-08 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [ 9.16804224e-02 -5.11421025e-01 -2.34117299e-01 -8.50807846e-01
-3.45633537e-01 -2.95664012e-01 4.67184901e-01 -4.29807991e-01
-3.11022818e-01 5.58289945e-01 -2.10455909e-01 4.99068573e-02
-5.13486750e-02 -6.73118591e-01 -1.00290671e-01 -1.11810768e+00
-6.03510849e-02 4.07635838e-01 -5.65590799e-01 -4.92329635... | [13.259722709655762, 0.8940135836601257] |
afaa2aa1-d59d-490c-b9c1-d4348a233aeb | robust-a-optimal-experimental-design-for | 2305.03855 | null | https://arxiv.org/abs/2305.03855v1 | https://arxiv.org/pdf/2305.03855v1.pdf | Robust A-Optimal Experimental Design for Bayesian Inverse Problems | Optimal design of experiments for Bayesian inverse problems has recently gained wide popularity and attracted much attention, especially in the computational science and Bayesian inversion communities. An optimal design maximizes a predefined utility function that is formulated in terms of the elements of an inverse pr... | ['Todd Munson', 'Sven Leyffer', 'Ahmed Attia'] | 2023-05-05 | null | null | null | null | ['experimental-design'] | ['methodology'] | [ 5.02632022e-01 2.51630723e-01 1.96536735e-01 -3.55172038e-01
-8.21456075e-01 -4.42750663e-01 1.30446956e-01 -1.50441661e-01
-3.15592527e-01 8.67137730e-01 1.26974776e-01 -2.55200028e-01
-1.40295148e+00 -3.98280323e-01 -7.17953622e-01 -1.20903254e+00
1.87128082e-01 6.61340415e-01 -2.73679316e-01 1.10297039... | [6.531422138214111, 3.516587257385254] |
45bed365-9faf-4a02-adf5-43032359d0bb | target-sound-extraction-with-variable-cross | 2303.08372 | null | https://arxiv.org/abs/2303.08372v1 | https://arxiv.org/pdf/2303.08372v1.pdf | Target Sound Extraction with Variable Cross-modality Clues | Automatic target sound extraction (TSE) is a machine learning approach to mimic the human auditory perception capability of attending to a sound source of interest from a mixture of sources. It often uses a model conditioned on a fixed form of target sound clues, such as a sound class label, which limits the ways in wh... | ['Michael Zeng', 'Yanmin Qian', 'Shujie Liu', 'Takuya Yoshioka', 'Dongmei Wang', 'Zhuo Chen', 'Yao Qian', 'Chenda Li'] | 2023-03-15 | null | null | null | null | ['target-sound-extraction'] | ['audio'] | [ 3.01564693e-01 -4.31990713e-01 2.54975736e-01 -2.05603153e-01
-1.44922614e+00 -7.90642142e-01 4.97392595e-01 1.79350656e-02
-3.67118657e-01 2.87154645e-01 3.99092108e-01 -5.42375520e-02
8.13556537e-02 -4.57120836e-01 -6.65286362e-01 -4.23720419e-01
5.30408248e-02 1.48437396e-01 9.10226166e-01 9.30583999... | [15.202513694763184, 5.299941062927246] |
9a6567b2-58bf-4d2c-bd70-9955c2b2d90a | a-dynamic-evolutionary-framework-for-timeline | 1905.05550 | null | https://arxiv.org/abs/1905.05550v2 | https://arxiv.org/pdf/1905.05550v2.pdf | A Dynamic Evolutionary Framework for Timeline Generation based on Distributed Representations | Given the collection of timestamped web documents related to the evolving topic, timeline summarization (TS) highlights its most important events in the form of relevant summaries to represent the development of a topic over time. Most of the previous work focuses on fully-observable ranking models and depends on hand-... | ['Guo-Hua Wang', 'Jing Nie', 'Dongyun Liang'] | 2019-05-14 | null | null | null | null | ['timeline-summarization'] | ['natural-language-processing'] | [ 2.26727828e-01 2.24160269e-01 -3.57869089e-01 -2.51036495e-01
-1.04214430e+00 -6.15378261e-01 1.28440166e+00 6.30673707e-01
1.02583796e-01 7.00185895e-01 1.00918484e+00 2.47577235e-01
-4.36628550e-01 -8.46853912e-01 -6.17753744e-01 -4.90667015e-01
-5.05687118e-01 5.62185585e-01 5.22431016e-01 -1.63232997... | [12.616626739501953, 9.498787879943848] |
265e067f-ecb1-4c97-a27e-74bbe1b498a4 | are-the-best-multilingual-document-embeddings | 2304.14796 | null | https://arxiv.org/abs/2304.14796v1 | https://arxiv.org/pdf/2304.14796v1.pdf | Are the Best Multilingual Document Embeddings simply Based on Sentence Embeddings? | Dense vector representations for textual data are crucial in modern NLP. Word embeddings and sentence embeddings estimated from raw texts are key in achieving state-of-the-art results in various tasks requiring semantic understanding. However, obtaining embeddings at the document level is challenging due to computation... | ['Cristina Espana-Bonet', 'Josef van Genabith', 'Sonal Sannigrahi'] | 2023-04-28 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [ 2.23824903e-01 -1.16808854e-01 -2.97516078e-01 -6.40667021e-01
-1.10268712e+00 -6.38445973e-01 7.98946738e-01 5.26925564e-01
-8.82998228e-01 7.11475790e-01 4.86544311e-01 -3.57133120e-01
4.88629155e-02 -6.19269252e-01 -5.37179887e-01 -3.45521867e-01
1.82812363e-01 5.67790926e-01 1.35620369e-03 -4.70927566... | [10.667266845703125, 8.791193962097168] |
f50b0103-9425-45f4-8c88-199121714387 | pyramidal-fisher-motion-for-multiview-gait | 1403.6950 | null | http://arxiv.org/abs/1403.6950v1 | http://arxiv.org/pdf/1403.6950v1.pdf | Pyramidal Fisher Motion for Multiview Gait Recognition | The goal of this paper is to identify individuals by analyzing their gait.
Instead of using binary silhouettes as input data (as done in many previous
works) we propose and evaluate the use of motion descriptors based on densely
sampled short-term trajectories. We take advantage of state-of-the-art people
detectors to ... | ['R. Medina-Carnicer', 'M. J. Marin-Jimenez', 'F. M. Castro'] | 2014-03-27 | null | null | null | null | ['multiview-gait-recognition'] | ['computer-vision'] | [-2.08214372e-01 -6.07702374e-01 1.36338118e-02 -6.90869987e-02
-3.66758436e-01 -4.54638898e-01 7.67936647e-01 3.30312133e-01
-7.50854850e-01 4.53391671e-01 3.69554684e-02 5.29026568e-01
-1.15205839e-01 -8.05395246e-01 -1.70487925e-01 -8.46460760e-01
-4.15107369e-01 4.97840405e-01 5.96796095e-01 -2.45119214... | [14.20322322845459, 1.5042697191238403] |
d14e1feb-43ff-4d44-9198-30dce350fd8e | conspiracy-machines-the-role-of-social-bots | 2012.09536 | null | https://arxiv.org/abs/2012.09536v1 | https://arxiv.org/pdf/2012.09536v1.pdf | Conspiracy Machines -- The Role of Social Bots during the COVID-19 Infodemic | The omnipresent COVID-19 pandemic gave rise to a parallel spreading of misinformation, also referred to as an Infodemic. Consequently, social media have become targets for the application of social bots, that is, algorithms that mimic human behaviour. Their ability to exert influence on social media can be exploited by... | ['Eric Hochstrate', 'Milad Mirbabaie', 'Felix Brünker', 'Julian Marx'] | 2020-12-17 | null | null | null | null | ['rumour-detection'] | ['natural-language-processing'] | [-5.74162789e-02 5.23198843e-01 -1.72734007e-01 6.19995594e-01
3.04933548e-01 -7.13563919e-01 1.04958391e+00 4.12625730e-01
-4.16680932e-01 6.62402570e-01 5.09331107e-01 -5.63576162e-01
4.11010593e-01 -8.83647561e-01 -6.44637719e-02 -5.04366994e-01
-1.86678357e-02 2.06797510e-01 2.35543936e-01 -5.99090576... | [8.38931655883789, 10.027200698852539] |
959ccdae-740f-4a81-9936-64f4e38d00bd | camera-ppg-waveforms-at-the-forehead | 2306.09879 | null | https://arxiv.org/abs/2306.09879v1 | https://arxiv.org/pdf/2306.09879v1.pdf | Camera PPG waveforms at the forehead | In order to obtain insights into the feasibility of replacing ECG-guided triggering in magnetic resonance imaging (MRI) by a system based on video photoplethysmography (PPG), PPG and ECG data were collected from volunteers in an MRI scanner. PPG waveforms obtained using remote camera PPG directed at the forehead are st... | ['C. Possanzini', 'R. Springorum', 'J. Sénégas', 'M. Padalko', 'B. Balmaekers', 'J. H. Wülbern', 'H. H. Der Sarkissian', 'A. C. den Brinker'] | 2023-06-16 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [ 1.76857561e-01 -1.36822954e-01 3.41781050e-01 -3.46478313e-01
-5.26578605e-01 -7.14244604e-01 3.10720921e-01 1.42466977e-01
-3.71571869e-01 5.00592113e-01 2.08184481e-01 -1.50222644e-01
-3.59386981e-01 -3.39841507e-02 -1.92225367e-01 -8.36158395e-01
-7.59748280e-01 3.30693841e-01 2.77403742e-01 1.44016147... | [14.050289154052734, 3.0480542182922363] |
6f9cc6da-db04-4e0d-855b-173c36af2c37 | a-novel-deep-learning-model-for-hotel-demand | 2203.04383 | null | https://arxiv.org/abs/2203.04383v1 | https://arxiv.org/pdf/2203.04383v1.pdf | A Novel Deep Learning Model for Hotel Demand and Revenue Prediction amid COVID-19 | The COVID-19 pandemic has significantly impacted the tourism and hospitality sector. Public policies such as travel restrictions and stay-at-home orders had significantly affected tourist activities and service businesses' operations and profitability. To this end, it is essential to develop an interpretable forecast m... | ['Zhishan Guo', 'Arthur Huang', 'Ashkan Farhangi'] | 2022-03-08 | null | null | null | null | ['covid-19-modelling', 'time-series-prediction'] | ['time-series', 'time-series'] | [-7.25549400e-01 -2.26638079e-01 -4.11036581e-01 -4.62909669e-01
-3.52329165e-01 -5.30823350e-01 8.16679239e-01 9.46863219e-02
-1.13586538e-01 4.54270691e-01 9.11567748e-01 -9.01610732e-01
-2.09689304e-01 -8.60694051e-01 -3.81063849e-01 -5.55875003e-01
-4.83777612e-01 5.13440430e-01 -6.94002271e-01 -6.02850258... | [6.649960517883301, 2.841557741165161] |
51832699-2bbe-49e3-89ce-047ee131b5c9 | skelevision-towards-adversarial-resiliency-of | 2204.00734 | null | https://arxiv.org/abs/2204.00734v1 | https://arxiv.org/pdf/2204.00734v1.pdf | SkeleVision: Towards Adversarial Resiliency of Person Tracking with Multi-Task Learning | Person tracking using computer vision techniques has wide ranging applications such as autonomous driving, home security and sports analytics. However, the growing threat of adversarial attacks raises serious concerns regarding the security and reliability of such techniques. In this work, we study the impact of multi-... | ['Duen Horng Chau', 'Sheng-Yun Peng', 'Nilaksh Das'] | 2022-04-02 | null | null | null | null | ['sports-analytics'] | ['computer-vision'] | [ 5.77684119e-02 -1.11360647e-01 1.41221136e-01 2.42116123e-01
-6.07252777e-01 -9.16009784e-01 5.73300838e-01 -1.91019908e-01
-5.25966883e-01 6.36446655e-01 1.94765832e-02 -3.25971514e-01
-3.03794146e-02 -3.03899825e-01 -9.31383729e-01 -4.92013097e-01
-6.33296132e-01 1.53930530e-01 3.93052667e-01 -1.67283416... | [5.413630962371826, 7.971582412719727] |
e2e20542-2395-44f1-b55b-d95d819a9817 | banditq-no-regret-learning-with-guaranteed | 2304.05219 | null | https://arxiv.org/abs/2304.05219v2 | https://arxiv.org/pdf/2304.05219v2.pdf | $\texttt{BanditQ}:$ Fair Multi-Armed Bandits with Guaranteed Rewards per Arm | Classic no-regret online prediction algorithms, including variants of the Upper Confidence Bound ($\texttt{UCB}$) algorithm, $\texttt{Hedge}$, and $\texttt{EXP3}$, are inherently unfair by design. The unfairness stems from their very objective of playing the most rewarding arm as many times as possible while ignoring t... | ['Abhishek Sinha'] | 2023-04-11 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 7.53932819e-02 3.64547342e-01 -4.63205963e-01 -5.44526756e-01
-1.00080824e+00 -8.03297520e-01 -3.37054044e-01 8.32670927e-02
-5.79035282e-01 1.25140190e+00 -3.27590704e-01 -8.61560881e-01
-9.32965755e-01 -7.32167423e-01 -9.83006775e-01 -9.32662904e-01
-3.68377388e-01 5.01034677e-01 -9.13851783e-02 -2.45713890... | [4.544561862945557, 3.3394603729248047] |
d1201467-c14d-438a-8838-686341d56dd2 | multi-perspective-context-aggregation-for | 1808.06289 | null | http://arxiv.org/abs/1808.06289v1 | http://arxiv.org/pdf/1808.06289v1.pdf | Multi-Perspective Context Aggregation for Semi-supervised Cloze-style Reading Comprehension | Cloze-style reading comprehension has been a popular task for measuring the
progress of natural language understanding in recent years. In this paper, we
design a novel multi-perspective framework, which can be seen as the joint
training of heterogeneous experts and aggregate context information from
different perspect... | ['Liang Wang', 'Jingming Liu', 'Ruoyu Jia', 'Meng Sun', 'Kewei Shen', 'Wei Zhao', 'Sujian Li'] | 2018-08-20 | multi-perspective-context-aggregation-for-2 | https://aclanthology.org/C18-1073 | https://aclanthology.org/C18-1073.pdf | coling-2018-8 | ['cloze-test'] | ['natural-language-processing'] | [ 1.51676625e-01 1.27555177e-01 -1.85331121e-01 -6.31345332e-01
-1.45239329e+00 -6.69281662e-01 3.79620731e-01 1.05252601e-01
-3.72344732e-01 5.82516253e-01 4.55355525e-01 -3.78948510e-01
1.29878521e-01 -6.76003873e-01 -7.25864768e-01 -4.38550234e-01
7.87344515e-01 5.34353435e-01 3.89780432e-01 -1.24738686... | [11.207940101623535, 8.127957344055176] |
8f87960f-1feb-4092-8300-a4bc9be63a1a | entity-and-evidence-guided-document-level | null | null | https://aclanthology.org/2021.repl4nlp-1.30 | https://aclanthology.org/2021.repl4nlp-1.30.pdf | Entity and Evidence Guided Document-Level Relation Extraction | Document-level relation extraction is a challenging task, requiring reasoning over multiple sentences to predict a set of relations in a document. In this paper, we propose a novel framework E2GRE (Entity and Evidence Guided Relation Extraction) that jointly extracts relations and the underlying evidence sentences by u... | ['Jing Huang', 'Tengyu Ma', 'Guangtao Wang', 'Peng Qi', 'Kevin Huang'] | null | null | null | null | acl-repl4nlp-2021-8 | ['document-level-relation-extraction'] | ['natural-language-processing'] | [ 1.15395844e-01 9.10093606e-01 -5.78905523e-01 -4.53310519e-01
-8.47466111e-01 -3.52773070e-01 5.62286317e-01 5.77595472e-01
-5.31999230e-01 1.09491301e+00 5.33402681e-01 -3.75671476e-01
-2.53326058e-01 -8.51601601e-01 -1.10798323e+00 -6.84717000e-02
-2.43829206e-01 3.50261658e-01 3.06664437e-01 8.12430084... | [9.303592681884766, 8.596776008605957] |
c9162ea7-6e53-4d3c-9ce2-4ad0969a5569 | joint-deep-learning-for-car-detection | 1412.7854 | null | http://arxiv.org/abs/1412.7854v2 | http://arxiv.org/pdf/1412.7854v2.pdf | Joint Deep Learning for Car Detection | Traditional object recognition approaches apply feature extraction, part
deformation handling, occlusion handling and classification sequentially while
they are independent from each other. Ouyang and Wang proposed a model for
jointly learning of all of the mentioned processes using one deep neural
network. We utilized... | ['Seyedshams Feyzabadi'] | 2014-12-25 | null | null | null | null | ['occlusion-handling'] | ['computer-vision'] | [-4.00174893e-02 -1.50624961e-01 1.41955882e-01 -2.47887388e-01
-3.93464208e-01 -4.27817553e-01 5.77461839e-01 -2.22647965e-01
-4.73264515e-01 2.93084770e-01 -4.44872320e-01 -2.07864150e-01
1.59087643e-01 -8.07800829e-01 -7.94671297e-01 -7.78640032e-01
1.91158969e-02 5.20568252e-01 9.27984178e-01 -5.86861782... | [8.553661346435547, -0.5197449922561646] |
17efc550-0b09-4805-8232-635456f3eef5 | primal-dual-algorithms-for-non-negative | 1412.1788 | null | http://arxiv.org/abs/1412.1788v1 | http://arxiv.org/pdf/1412.1788v1.pdf | Primal-Dual Algorithms for Non-negative Matrix Factorization with the Kullback-Leibler Divergence | Non-negative matrix factorization (NMF) approximates a given matrix as a
product of two non-negative matrices. Multiplicative algorithms deliver
reliable results, but they show slow convergence for high-dimensional data and
may be stuck away from local minima. Gradient descent methods have better
behavior, but only app... | ['Francis Bach', 'Felipe Yanez'] | 2014-12-04 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 1.18252791e-01 -7.85052553e-02 -3.29790682e-01 -1.38990387e-01
-1.18985093e+00 -6.62702262e-01 3.40281814e-01 -1.18201472e-01
-3.51780802e-01 9.03620064e-01 7.68949166e-02 -4.21995133e-01
-5.05966127e-01 -5.37493169e-01 -6.11637414e-01 -9.28762376e-01
-3.24677169e-01 5.70441544e-01 -2.71097749e-01 -2.47538656... | [7.171154975891113, 4.498510360717773] |
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