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b1e41cbe-2193-49c3-b0c1-ca3e40469022 | answer-me-multi-task-open-vocabulary-visual | 2205.00949 | null | https://arxiv.org/abs/2205.00949v2 | https://arxiv.org/pdf/2205.00949v2.pdf | Answer-Me: Multi-Task Open-Vocabulary Visual Question Answering | We present Answer-Me, a task-aware multi-task framework which unifies a variety of question answering tasks, such as, visual question answering, visual entailment, visual reasoning. In contrast to previous works using contrastive or generative captioning training, we propose a novel and simple recipe to pre-train a vis... | ['Anelia Angelova', 'Fred Bertsch', 'Mohammad Saffar', 'Weicheng Kuo', 'Wei Li', 'AJ Piergiovanni'] | 2022-05-02 | null | null | null | null | ['visual-entailment'] | ['reasoning'] | [ 7.46440738e-02 -3.67168710e-02 1.15748584e-01 -3.13152164e-01
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7.10850835e-01 7.11191297e-01 3.48155916e-01 -2.87988126... | [10.851680755615234, 1.6480261087417603] |
4f89ba03-4126-4308-a72d-820f05c6c338 | weakly-supervised-3d-human-pose-and-shape | 2003.10350 | null | https://arxiv.org/abs/2003.10350v2 | https://arxiv.org/pdf/2003.10350v2.pdf | Weakly Supervised 3D Human Pose and Shape Reconstruction with Normalizing Flows | Monocular 3D human pose and shape estimation is challenging due to the many degrees of freedom of the human body and thedifficulty to acquire training data for large-scale supervised learning in complex visual scenes. In this paper we present practical semi-supervised and self-supervised models that support training an... | ['Rahul Sukthankar', 'Eduard Gabriel Bazavan', 'Hongyi Xu', 'Bill Freeman', 'Cristian Sminchisescu', 'Andrei Zanfir'] | 2020-03-23 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/6296_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123510460.pdf | eccv-2020-8 | ['3d-human-pose-and-shape-estimation'] | ['computer-vision'] | [-1.51105821e-01 6.39638007e-02 -7.34867036e-01 -2.15583548e-01
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29bfb591-0346-4784-9774-62c177c29461 | dwa-differential-wavelet-amplifier-for-image-1 | 2307.04593 | null | https://arxiv.org/abs/2307.04593v1 | https://arxiv.org/pdf/2307.04593v1.pdf | DWA: Differential Wavelet Amplifier for Image Super-Resolution | This work introduces Differential Wavelet Amplifier (DWA), a drop-in module for wavelet-based image Super-Resolution (SR). DWA invigorates an approach recently receiving less attention, namely Discrete Wavelet Transformation (DWT). DWT enables an efficient image representation for SR and reduces the spatial area of its... | ['Andreas Dengel', 'Sebastian Palacio', 'Federico Raue', 'Stanislav Frolov', 'Brian B. Moser'] | 2023-07-10 | null | null | null | null | ['image-super-resolution', 'super-resolution'] | ['computer-vision', 'computer-vision'] | [ 8.65807116e-01 1.42765855e-02 -9.38807428e-02 -3.87037918e-02
-9.21370447e-01 -2.80926198e-01 4.24999028e-01 -2.44153842e-01
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9.04784352e-02 -6.09167695e-01 5.11947513e-01 -5.18868744... | [11.106490135192871, -1.93467378616333] |
ea09e366-8ae1-4764-9704-94ffce7dd53f | landmine-detection-using-autoencoders-on | 1810.01316 | null | http://arxiv.org/abs/1810.01316v1 | http://arxiv.org/pdf/1810.01316v1.pdf | Landmine Detection Using Autoencoders on Multi-polarization GPR Volumetric Data | Buried landmines and unexploded remnants of war are a constant threat for the
population of many countries that have been hit by wars in the past years. The
huge amount of human lives lost due to this phenomenon has been a strong
motivation for the research community toward the development of safe and robust
techniques... | ['Federico Lombardi', 'Paolo Bestagini', 'Francesco Picetti', 'Maurizio Lualdi', 'Stefano Tubaro'] | 2018-10-02 | null | null | null | null | ['landmine'] | ['computer-vision'] | [ 3.48507673e-01 2.17001345e-02 5.89979589e-01 -3.73676568e-01
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e4436145-11f9-4000-ae5e-bc69d967dfc7 | unsupervised-dependency-graph-network | null | null | https://openreview.net/forum?id=yYJhaF4-dZ9 | https://openreview.net/pdf?id=yYJhaF4-dZ9 | Unsupervised Dependency Graph Network | Recent work has identified properties of pretrained self-attention models that mirror those of dependency parse structures. In particular, some self-attention heads correspond well to individual dependency types. Inspired by these developments, we propose a new competitive mechanism that encourages these attention head... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['unsupervised-dependency-parsing'] | ['natural-language-processing'] | [-3.92604381e-01 4.93705481e-01 -2.53278583e-01 -7.63647079e-01
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04636ee4-6e51-4eff-8070-71fde2767c1d | owq-lessons-learned-from-activation-outliers | 2306.02272 | null | https://arxiv.org/abs/2306.02272v2 | https://arxiv.org/pdf/2306.02272v2.pdf | OWQ: Lessons learned from activation outliers for weight quantization in large language models | Large language models (LLMs) with hundreds of billions of parameters show impressive results across various language tasks using simple prompt tuning and few-shot examples, without the need for task-specific fine-tuning. However, their enormous size requires multiple server-grade GPUs even for inference, creating a sig... | ['Eunhyeok Park', 'HyungJun Kim', 'Taesu Kim', 'Jungyu Jin', 'Changhun Lee'] | 2023-06-04 | null | null | null | null | ['quantization'] | ['methodology'] | [-3.62069234e-02 -3.80671442e-01 -3.93543273e-01 -3.80566955e-01
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ad9a24ee-10fa-492d-9f67-c3cb174f0325 | question-generation-based-on-grammar | null | null | https://aclanthology.org/2022.coling-1.562 | https://aclanthology.org/2022.coling-1.562.pdf | Question Generation Based on Grammar Knowledge and Fine-grained Classification | Question generation is the task of automatically generating questions based on given context and answers, and there are problems that the types of questions and answers do not match. In minority languages such as Tibetan, since the grammar rules are complex and the training data is small, the related research on questi... | ['Xiaobing Zhao', 'Zhengcuo Dan', 'Sisi Liu', 'Yuan Sun'] | null | null | null | null | coling-2022-10 | ['question-generation'] | ['natural-language-processing'] | [-1.79579318e-01 3.31778288e-01 1.01653649e-03 -4.23036575e-01
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4.98155087e-01 6.28482461e-01 5.68062663e-01 -9.27315891... | [11.511557579040527, 8.160409927368164] |
8e4f9de1-ff24-41f3-99a7-d817bad57c2c | prediction-of-cytochrome-p450-mediated | 1811.09366 | null | http://arxiv.org/abs/1811.09366v1 | http://arxiv.org/pdf/1811.09366v1.pdf | Prediction of Cytochrome P450-Mediated Metabolism Using a Combination of QSAR Derived Reactivity and Induced Fit Docking | Prediction of metabolism in cytochrome P450s remains to be a crucial yet
challenging topic in discovering and designing drugs, agrochemicals and
nutritional supplements. The problem is challenging because the rate of P450
metabolism depends upon both the intrinsic chemical reactivity of the site and
the protein-ligand ... | [] | 2018-11-23 | null | null | null | null | ['molecular-docking'] | ['medical'] | [ 1.90550685e-01 4.34312038e-02 -2.41684079e-01 4.59074713e-02
-6.31291866e-01 -7.89409041e-01 2.50331819e-01 5.27123213e-01
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-1.84222832e-01 4.26616460e-01 3.95279795e-01 -3.20455432... | [4.813024997711182, 5.361324787139893] |
fe36f5ae-3657-4945-b804-f6576ff64213 | point-transformer | 2011.00931 | null | https://arxiv.org/abs/2011.00931v2 | https://arxiv.org/pdf/2011.00931v2.pdf | Point Transformer | In this work, we present Point Transformer, a deep neural network that operates directly on unordered and unstructured point sets. We design Point Transformer to extract local and global features and relate both representations by introducing the local-global attention mechanism, which aims to capture spatial point rel... | ['Klaus Dietmayer', 'Vasileios Belagiannis', 'Nico Engel'] | 2020-11-02 | null | null | null | null | ['3d-object-classification', '3d-part-segmentation'] | ['computer-vision', 'computer-vision'] | [ 5.29533476e-02 -1.37557313e-01 -2.49710232e-01 -5.26995242e-01
-5.56234598e-01 -6.71730161e-01 5.78156412e-01 8.41238275e-02
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-3.37568641e-01 -1.10711753e+00 -1.13735378e+00 -3.20153624e-01
4.27342989e-02 6.06835008e-01 4.50905770e-01 -1.30239531... | [7.928971290588379, -3.580961227416992] |
49c1a602-b444-460e-9b85-781cf688c4c6 | scaling-distributed-training-of-flood-filling | 1905.06236 | null | https://arxiv.org/abs/1905.06236v4 | https://arxiv.org/pdf/1905.06236v4.pdf | Scaling Distributed Training of Flood-Filling Networks on HPC Infrastructure for Brain Mapping | Mapping all the neurons in the brain requires automatic reconstruction of entire cells from volume electron microscopy data. The flood-filling network (FFN) architecture has demonstrated leading performance for segmenting structures from this data. However, the training of the network is computationally expensive. In o... | ['Peter Littlewood', 'Narayanan Kasthuri', 'Samuel Flender', 'Murat Keceli', 'Wushi Dong', 'Tom Uram', 'Rafael Vescovi', 'Hanyu Li', 'Elise Jennings', 'Corey Adams', 'Venkatram Vishwanath', 'Nicola Ferrier'] | 2019-05-13 | null | null | null | null | ['2048'] | ['playing-games'] | [-1.49588943e-01 6.00894392e-02 4.22446221e-01 -6.01378798e-01
-5.81874788e-01 -3.53684992e-01 2.64465362e-01 8.17121863e-02
-9.73735988e-01 1.13341784e+00 -2.95769721e-01 -5.28629899e-01
4.19756994e-02 -7.84824014e-01 -8.95283937e-01 -7.24464655e-01
-1.65770262e-01 1.06205583e+00 6.17045343e-01 3.45433205... | [14.255128860473633, -3.1185030937194824] |
45f807e2-066f-4c30-a6ca-273fab7a8340 | refin-a-refinement-approach-for-video-frame | null | null | https://openreview.net/forum?id=4_cgHrh0BpN | https://openreview.net/pdf?id=4_cgHrh0BpN | ReFIn: A Refinement Approach for Video Frame Interpolation | Video Frame Interpolation is an important video enhancement problem which aims to generate one or multiple frames between consecutive frames in video. Optical flow-based frame interpolation approaches estimate intermediate optical flow from interpolated frame to input frames and warped frames are fused to generate inte... | ['Anurag Mittal', 'Saikat Dutta'] | 2021-10-19 | null | null | null | neurips-workshop-deep-invers-2021-12 | ['video-enhancement'] | ['computer-vision'] | [ 1.51959524e-01 -1.80806786e-01 -3.91387828e-02 -2.62542754e-01
-3.16087902e-01 -2.08367795e-01 3.79086435e-01 -3.64097685e-01
-3.40606481e-01 1.13374615e+00 2.57071793e-01 -1.27572939e-01
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-1.77993804e-01 -2.02323854e-01 7.08852232e-01 -1.61768109... | [10.713141441345215, -1.4371494054794312] |
ec676819-bdc6-4f5d-add9-6b194d6783e5 | a-one-covariate-at-a-time-method-for | 2204.12023 | null | https://arxiv.org/abs/2204.12023v1 | https://arxiv.org/pdf/2204.12023v1.pdf | A One-Covariate-at-a-Time Method for Nonparametric Additive Models | This paper proposes a one-covariate-at-a-time multiple testing (OCMT) approach to choose significant variables in high-dimensional nonparametric additive regression models. Similarly to Chudik, Kapetanios and Pesaran (2018), we consider the statistical significance of individual nonparametric additive components one at... | ['Qiankun Zhou', 'Yonghui Zhang', 'Thomas Tao Yang', 'Liangjun Su'] | 2022-04-26 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 1.35182485e-01 -1.66027144e-01 -4.19135690e-01 -4.75666434e-01
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-3.90700191e-01 3.31292123e-01 -2.13796631e-01 3.70303363... | [7.685286998748779, 4.957160949707031] |
efed939f-9d25-4573-9b9d-3c8dc8c6d28c | studying-the-impact-of-filling-information | null | null | https://aclanthology.org/2020.inlg-1.6 | https://aclanthology.org/2020.inlg-1.6.pdf | Studying the Impact of Filling Information Gaps on the Output Quality of Neural Data-to-Text | It is unfair to expect neural data-to-text to produce high quality output when there are gaps between system input data and information contained in the training text. Thomson et al. (2020) identify and narrow information gaps in Rotowire, a popular data-to-text dataset. In this paper, we describe a study which finds t... | ['Somayajulu Sripada', 'Zhijie Zhao', 'Craig Thomson'] | null | null | null | null | inlg-acl-2020-12 | ['data-to-text-generation'] | ['natural-language-processing'] | [ 4.49013151e-02 1.56854033e-01 -4.05379564e-01 -5.90988040e-01
-6.02240682e-01 -6.50380552e-01 6.66358531e-01 6.38894737e-01
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-3.97395432e-01 -8.25532913e-01 -5.51641583e-01 2.89684325e-01
5.26768565e-01 4.78997469e-01 -1.78617761e-01 -3.40426207... | [11.7523193359375, 9.150795936584473] |
e38ad972-d61c-4353-9778-dba266c67819 | deltanet-conditional-medical-report | null | null | https://aclanthology.org/2022.coling-1.261 | https://aclanthology.org/2022.coling-1.261.pdf | DeltaNet: Conditional Medical Report Generation for COVID-19 Diagnosis | Fast screening and diagnosis are critical in COVID-19 patient treatment. In addition to the gold standard RT-PCR, radiological imaging like X-ray and CT also works as an important means in patient screening and follow-up. However, due to the excessive number of patients, writing reports becomes a heavy burden for radio... | ['Li Xiao', 'S. Kevin Zhou', 'Yefeng Zheng', 'Xingwang Wu', 'Yangtian Yan', 'Shen Ge', 'Zhaopeng Qiu', 'Shuxin Yang', 'Xian Wu'] | null | null | null | null | coling-2022-10 | ['covid-19-detection', 'medical-report-generation'] | ['medical', 'medical'] | [ 4.11981940e-01 1.66315794e-01 -2.17563361e-01 -2.25250915e-01
-1.22989321e+00 -3.74015629e-01 3.59660536e-01 5.14628053e-01
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2.39656165e-01 7.14476109e-01 3.50439250e-01 3.02735567... | [15.05380916595459, -1.3825308084487915] |
16dc8c0b-45cd-43cc-a3f9-7dbbc2d0cec3 | graph-transformer-for-graph-to-sequence | 1911.07470 | null | https://arxiv.org/abs/1911.07470v2 | https://arxiv.org/pdf/1911.07470v2.pdf | Graph Transformer for Graph-to-Sequence Learning | The dominant graph-to-sequence transduction models employ graph neural networks for graph representation learning, where the structural information is reflected by the receptive field of neurons. Unlike graph neural networks that restrict the information exchange between immediate neighborhood, we propose a new model, ... | ['Deng Cai', 'Wai Lam'] | 2019-11-18 | null | null | null | null | ['graph-to-sequence'] | ['natural-language-processing'] | [ 5.69161534e-01 6.25374615e-01 -3.09545547e-01 -4.37120013e-02
-7.13780761e-01 -5.77198744e-01 8.87752295e-01 3.62875879e-01
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7.54368082e-02 6.74873114e-01 -1.37293741e-01 -7.67074764... | [10.275025367736816, 8.365957260131836] |
2b5b01a6-1a03-4566-8e94-f3714899124b | robust-cross-view-gait-identification-with | 1811.10493 | null | https://arxiv.org/abs/1811.10493v3 | https://arxiv.org/pdf/1811.10493v3.pdf | Robust Cross-View Gait Recognition with Evidence: A Discriminant Gait GAN (DiGGAN) Approach | Gait as a biometric trait has attracted much attention in many security and privacy applications such as identity recognition and authentication, during the last few decades. Because of its nature as a long-distance biometric trait, gait can be easily collected and used to identify individuals non-intrusively through C... | ['Yan Gao', 'Yu Guan', 'Thomas Ploetz', 'BingZhang Hu', 'Nicholas Lane', 'Yang Long'] | 2018-11-26 | null | null | null | null | ['gait-identification'] | ['computer-vision'] | [ 4.25832011e-02 -6.03010595e-01 -1.62689552e-01 -1.19988203e-01
-1.57847837e-01 -5.32267392e-01 3.50258619e-01 -4.53266293e-01
-1.25690416e-01 7.38333523e-01 4.04805019e-02 1.00989550e-01
-5.11906072e-02 -8.49812508e-01 -2.28968576e-01 -1.01699519e+00
-1.50533214e-01 1.57636534e-02 -3.07101551e-02 -2.94088960... | [14.241421699523926, 1.4074974060058594] |
01fba422-96e8-4c79-9356-23357585a138 | a-topological-view-of-rule-learning-in | 2110.02510 | null | https://arxiv.org/abs/2110.02510v3 | https://arxiv.org/pdf/2110.02510v3.pdf | Cycle Representation Learning for Inductive Relation Prediction | In recent years, algebraic topology and its modern development, the theory of persistent homology, has shown great potential in graph representation learning. In this paper, based on the mathematics of algebraic topology, we propose a novel solution for inductive relation prediction, an important learning task for know... | ['Chao Chen', 'Zhi Tang', 'Liangcai Gao', 'Tengfei Ma', 'Zuoyu Yan'] | 2021-10-06 | null | null | null | null | ['inductive-relation-prediction'] | ['graphs'] | [ 8.42515081e-02 2.18388006e-01 -4.99652147e-01 9.20001939e-02
1.71212003e-01 -6.83562934e-01 4.52863485e-01 2.22555578e-01
1.01268806e-01 3.11795831e-01 5.32536209e-02 -8.36722553e-01
-3.63672048e-01 -1.48523724e+00 -9.33112383e-01 -3.70025069e-01
-5.71664035e-01 4.02854741e-01 2.75854439e-01 -3.22417945... | [8.64362621307373, 7.701328277587891] |
aaac113d-cfc3-4e02-9973-9a5956c97bbf | 190807654 | 1908.07654 | null | https://arxiv.org/abs/1908.07654v2 | https://arxiv.org/pdf/1908.07654v2.pdf | FusionNet: Incorporating Shape and Texture for Abnormality Detection in 3D Abdominal CT Scans | Automatic abnormality detection in abdominal CT scans can help doctors improve the accuracy and efficiency in diagnosis. In this paper we aim at detecting pancreatic ductal adenocarcinoma (PDAC), the most common pancreatic cancer. Taking the fact that the existence of tumor can affect both the shape and the texture of ... | ['Fengze Liu', 'Yuyin Zhou', 'Elliot Fishman', 'Alan Yuille'] | 2019-08-21 | null | null | null | null | ['3d-classification'] | ['computer-vision'] | [ 9.96664613e-02 6.61010249e-03 -2.86191255e-01 -3.07103604e-01
-5.68189204e-01 -5.96323073e-01 1.74472839e-01 3.97569478e-01
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-4.64430116e-02 -8.08056116e-01 -3.62006456e-01 -1.08297265e+00
-7.48867840e-02 6.03541315e-01 5.08627057e-01 4.54041988... | [14.750021934509277, -2.6617166996002197] |
1ac14fd6-30f4-457f-8473-b3cbb63b8c56 | image-morphing-with-perceptual-constraints | 2004.14071 | null | https://arxiv.org/abs/2004.14071v1 | https://arxiv.org/pdf/2004.14071v1.pdf | Image Morphing with Perceptual Constraints and STN Alignment | In image morphing, a sequence of plausible frames are synthesized and composited together to form a smooth transformation between given instances. Intermediates must remain faithful to the input, stand on their own as members of the set, and maintain a well-paced visual transition from one to the next. In this paper, w... | ['Daniel Cohen-Or', 'Noa Fish', 'Lilach Perry', 'Connelly Barnes', 'Richard Zhang', 'Eli Shechtman'] | 2020-04-29 | null | null | null | null | ['image-morphing'] | ['computer-vision'] | [ 5.45209587e-01 7.10295677e-01 -3.68348323e-03 -3.44366401e-01
-6.37789190e-01 -7.94905484e-01 1.04539108e+00 -3.61672014e-01
1.49325170e-02 6.78950131e-01 2.50539660e-01 1.92498162e-01
1.78586274e-01 -1.07484031e+00 -1.26982641e+00 -6.41251445e-01
1.88960046e-01 5.00326216e-01 2.21595570e-01 -3.84680897... | [11.631582260131836, -0.6190052032470703] |
f64d572c-0b95-4538-bc08-638369d8a354 | deepfake-mnist-a-deepfake-facial-animation | 2108.07949 | null | https://arxiv.org/abs/2108.07949v1 | https://arxiv.org/pdf/2108.07949v1.pdf | DeepFake MNIST+: A DeepFake Facial Animation Dataset | The DeepFakes, which are the facial manipulation techniques, is the emerging threat to digital society. Various DeepFake detection methods and datasets are proposed for detecting such data, especially for face-swapping. However, recent researches less consider facial animation, which is also important in the DeepFake a... | ['Chang Xu', 'Pei Du', 'Bo Du', 'Xueyu Wang', 'Jiajun Huang'] | 2021-08-18 | null | null | null | null | ['image-animation'] | ['computer-vision'] | [ 2.21161142e-01 -1.94136843e-01 -7.13079944e-02 -9.42540616e-02
-6.37444779e-02 -6.47368610e-01 7.10243940e-01 -7.70192742e-01
-6.35505766e-02 2.33924687e-01 4.13844138e-02 -2.02890515e-01
1.63819820e-01 -6.46953106e-01 -3.65498841e-01 -8.76413167e-01
-1.60509482e-01 -1.75427064e-01 2.13058740e-01 -3.56323421... | [12.887554168701172, 1.1060163974761963] |
c60e0362-beb6-4324-9b31-1af18a9ad4a3 | pats-patch-area-transportation-with | 2303.07700 | null | https://arxiv.org/abs/2303.07700v2 | https://arxiv.org/pdf/2303.07700v2.pdf | PATS: Patch Area Transportation with Subdivision for Local Feature Matching | Local feature matching aims at establishing sparse correspondences between a pair of images. Recently, detector-free methods present generally better performance but are not satisfactory in image pairs with large scale differences. In this paper, we propose Patch Area Transportation with Subdivision (PATS) to tackle th... | ['Guofeng Zhang', 'Zhaopeng Cui', 'Hujun Bao', 'Hongsheng Li', 'Zhaoyang Huang', 'Yijin Li', 'Junjie Ni'] | 2023-03-14 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ni_PATS_Patch_Area_Transportation_With_Subdivision_for_Local_Feature_Matching_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ni_PATS_Patch_Area_Transportation_With_Subdivision_for_Local_Feature_Matching_CVPR_2023_paper.pdf | cvpr-2023-1 | ['visual-localization', 'graph-matching'] | ['computer-vision', 'graphs'] | [-4.25661132e-02 -2.74819940e-01 -2.13297531e-01 -6.12601358e-03
-7.60476589e-01 -7.26047516e-01 2.88344681e-01 2.43382663e-01
-1.54624164e-01 3.96253139e-01 -6.65898994e-02 -4.48876731e-02
-1.10590179e-02 -9.14292097e-01 -7.11433589e-01 -5.24949789e-01
1.21829472e-01 3.57973099e-01 5.38229287e-01 -6.03433065... | [8.532251358032227, -2.2230277061462402] |
753821ca-2e3b-4e28-b9ca-c74a1c1873ca | image-denoising-by-gaussian-patch-mixture | 2011.10290 | null | https://arxiv.org/abs/2011.10290v1 | https://arxiv.org/pdf/2011.10290v1.pdf | Image Denoising by Gaussian Patch Mixture Model and Low Rank Patches | Non-local self-similarity based low rank algorithms are the state-of-the-art methods for image denoising. In this paper, a new method is proposed by solving two issues: how to improve similar patches matching accuracy and build an appropriate low rank matrix approximation model for Gaussian noise. For the first issue, ... | ['Michael Kwok-Po Ng', 'Qiyu Jin', 'Chen Luo', 'Shuping Wang', 'Jing Guo'] | 2020-11-20 | null | null | null | null | ['patch-matching'] | ['computer-vision'] | [-8.04650411e-02 -5.95183313e-01 3.65297288e-01 1.24965884e-01
-8.29867482e-01 -5.14513664e-02 -3.71276848e-02 2.71265917e-02
-1.99252620e-01 3.78979385e-01 1.97803557e-01 4.28507656e-01
-5.07489860e-01 -8.79655659e-01 -7.12558448e-01 -1.09951639e+00
-1.15092075e-03 -8.73222873e-02 5.90329945e-01 -4.10134405... | [11.326695442199707, -2.4338443279266357] |
3bc7f9a1-8e60-4aec-a60b-eb4b87617961 | question-relevance-in-vqa-identifying-non | 1606.06622 | null | http://arxiv.org/abs/1606.06622v3 | http://arxiv.org/pdf/1606.06622v3.pdf | Question Relevance in VQA: Identifying Non-Visual And False-Premise Questions | Visual Question Answering (VQA) is the task of answering natural-language
questions about images. We introduce the novel problem of determining the
relevance of questions to images in VQA. Current VQA models do not reason about
whether a question is even related to the given image (e.g. What is the capital
of Argentina... | ['Arijit Ray', 'Mohit Bansal', 'Dhruv Batra', 'Gordon Christie', 'Devi Parikh'] | 2016-06-21 | question-relevance-in-vqa-identifying-non-1 | https://aclanthology.org/D16-1090 | https://aclanthology.org/D16-1090.pdf | emnlp-2016-11 | ['question-similarity'] | ['natural-language-processing'] | [ 2.50341952e-01 5.30913174e-01 1.79358989e-01 -6.16838813e-01
-1.13240421e+00 -7.69247055e-01 7.71501124e-01 4.55376357e-01
-4.17231441e-01 6.08626604e-01 5.44038355e-01 -7.23566055e-01
1.31051019e-01 -5.96339047e-01 -8.39747190e-01 -1.10604048e-01
5.85974574e-01 7.21639276e-01 3.44879895e-01 -3.53279591... | [10.965899467468262, 1.6846423149108887] |
294f98b5-12fe-45e6-8f1b-094daa605f61 | nonlinear-supervised-dimensionality-reduction | 1710.07120 | null | http://arxiv.org/abs/1710.07120v2 | http://arxiv.org/pdf/1710.07120v2.pdf | Nonlinear Supervised Dimensionality Reduction via Smooth Regular Embeddings | The recovery of the intrinsic geometric structures of data collections is an
important problem in data analysis. Supervised extensions of several manifold
learning approaches have been proposed in the recent years. Meanwhile, existing
methods primarily focus on the embedding of the training data, and the
generalization... | ['Elif Vural', 'Cem Ornek'] | 2017-10-19 | null | null | null | null | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [-9.04375017e-02 1.10893875e-01 -3.98362517e-01 -4.27675933e-01
-4.59623218e-01 -3.85408610e-01 4.21425194e-01 2.45736584e-01
-2.16824085e-01 4.89684016e-01 -5.50838187e-02 1.05552711e-01
-5.70155144e-01 -7.20134258e-01 -4.73605096e-01 -9.51680541e-01
-2.00707048e-01 1.87917829e-01 -6.29789829e-02 5.51127791... | [7.930380344390869, 4.131680488586426] |
edc1f7d3-dda5-4f90-b82a-6826ab75b009 | learnable-hollow-kernels-for-anatomical | 2007.05103 | null | https://arxiv.org/abs/2007.05103v2 | https://arxiv.org/pdf/2007.05103v2.pdf | LORCK: Learnable Object-Resembling Convolution Kernels | Segmentation of certain hollow organs, such as the bladder, is especially hard to automate due to their complex geometry, vague intensity gradients in the soft tissues, and a tedious manual process of the data annotation routine. Yet, accurate localization of the walls and the cancer regions in the radiologic images of... | ['Dmitry V. Dylov', 'Oleg Rogov', 'Denis Larionov', 'Olga Shegai', 'Elizaveta Lazareva'] | 2020-07-09 | null | null | null | null | ['bladder-segmentation'] | ['medical'] | [-9.30245128e-03 3.15932900e-01 -2.76924103e-01 -3.40279102e-01
-5.16125262e-01 -6.70921803e-01 3.84300053e-01 2.86908805e-01
-6.52933776e-01 4.42789406e-01 -2.01100521e-02 -5.50566435e-01
-1.22588217e-01 -6.01291656e-01 -6.13743305e-01 -9.29686844e-01
-2.05229148e-01 3.15679193e-01 4.36785966e-01 3.76278795... | [14.651639938354492, -2.582350730895996] |
7565fc6e-a122-49a3-8ffc-0c0959586277 | dual-attention-model-for-aspect-level | 2303.07689 | null | https://arxiv.org/abs/2303.07689v1 | https://arxiv.org/pdf/2303.07689v1.pdf | Dual-Attention Model for Aspect-Level Sentiment Classification | I propose a novel dual-attention model(DAM) for aspect-level sentiment classification. Many methods have been proposed, such as support vector machines for artificial design features, long short-term memory networks based on attention mechanisms, and graph neural networks based on dependency parsing. While these method... | ['Mengfei Ye'] | 2023-03-14 | null | null | null | null | ['dependency-parsing'] | ['natural-language-processing'] | [-3.46143275e-01 -2.52146013e-02 -4.97656018e-01 -5.49584746e-01
-2.61206597e-01 -1.85807794e-01 3.25825304e-01 3.91295969e-01
-2.17389539e-01 4.65677470e-01 4.75530475e-01 -6.06077015e-01
9.95808318e-02 -9.15523410e-01 -5.45280933e-01 -3.06619525e-01
-3.42742838e-02 2.55458534e-01 3.81634645e-02 -4.49662298... | [11.416118621826172, 6.719272136688232] |
dec49dbc-ad65-4e49-abc5-79f3b0b88160 | hawkes-processes-for-continuous-time-sequence | null | null | https://aclanthology.org/P16-2064 | https://aclanthology.org/P16-2064.pdf | Hawkes Processes for Continuous Time Sequence Classification: an Application to Rumour Stance Classification in Twitter | null | ['Arkaitz Zubiaga', 'Kalina Bontcheva', 'P. K. Srijith', 'Michal Lukasik', 'Trevor Cohn', 'Duy Vu'] | 2016-08-01 | null | null | null | acl-2016-8 | ['rumour-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.236628532409668, 3.856640338897705] |
08fef9ea-f782-4912-a651-13b18db34e2c | multi-scale-multi-modal-micro-expression | 2301.02969 | null | https://arxiv.org/abs/2301.02969v2 | https://arxiv.org/pdf/2301.02969v2.pdf | Multi-scale multi-modal micro-expression recognition algorithm based on transformer | A micro-expression is a spontaneous unconscious facial muscle movement that can reveal the true emotions people attempt to hide. Although manual methods have made good progress and deep learning is gaining prominence. Due to the short duration of micro-expression and different scales of expressed in facial regions, exi... | ['Pan Wang', 'Lin Wang', 'Chun Qi', 'Jie Li', 'Fengping Wang'] | 2023-01-08 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [-2.62098797e-02 -3.30552906e-01 -2.18798980e-01 -4.62501109e-01
-8.72653663e-01 7.47974589e-02 2.93681681e-01 -6.89008474e-01
-2.86578953e-01 4.52447653e-01 2.85451144e-01 6.92580879e-01
-4.51210439e-02 -4.45160329e-01 -4.66771394e-01 -1.19935489e+00
-1.09432101e-01 -1.02142349e-01 -1.42385930e-01 -3.58299226... | [13.646313667297363, 1.6899478435516357] |
8a48a1e6-0b26-4870-b81f-d7c2f9ad8d71 | foreground-guidance-and-multi-layer-feature | 2210.13053 | null | https://arxiv.org/abs/2210.13053v1 | https://arxiv.org/pdf/2210.13053v1.pdf | Foreground Guidance and Multi-Layer Feature Fusion for Unsupervised Object Discovery with Transformers | Unsupervised object discovery (UOD) has recently shown encouraging progress with the adoption of pre-trained Transformer features. However, current methods based on Transformers mainly focus on designing the localization head (e.g., seed selection-expansion and normalized cut) and overlook the importance of improving T... | ['Yongtao Wang', 'Zengyu Yang', 'Zhiwei Lin'] | 2022-10-24 | null | null | null | null | ['object-discovery'] | ['computer-vision'] | [ 1.67327777e-01 -2.06738457e-01 2.06698161e-02 -3.15177947e-01
-7.09071219e-01 -3.00636709e-01 3.27702075e-01 1.15846144e-02
-6.72023967e-02 2.17316121e-01 9.33326315e-03 1.76009834e-01
-1.35271624e-01 -6.34585559e-01 -4.74431723e-01 -9.41289008e-01
1.71634108e-01 1.72757372e-01 8.88373315e-01 1.71266615... | [9.279473304748535, 0.9578142166137695] |
3db59908-f3ba-4717-b0b9-1935a88c01f4 | towards-measuring-ethicality-of-an | 2303.03929 | null | https://arxiv.org/abs/2303.03929v1 | https://arxiv.org/pdf/2303.03929v1.pdf | Towards Measuring Ethicality of an Intelligent Assistive System | Artificial intelligence (AI) based assistive systems, so called intelligent assistive technology (IAT) are becoming increasingly ubiquitous by each day. IAT helps people in improving their quality of life by providing intelligent assistance based on the provided data. A few examples of such IATs include self-driving ca... | ['Thomas Kirste', 'Sebastian Bader', 'J. -C. Põder', 'M. Salman Shaukat'] | 2023-02-28 | null | null | null | null | ['self-driving-cars'] | ['computer-vision'] | [-1.47320643e-01 7.69330740e-01 5.75582147e-01 -2.87206054e-01
4.56923366e-01 -1.99106589e-01 6.44691110e-01 4.01105694e-02
-1.09005892e+00 1.28195965e+00 3.50667715e-01 -4.42723453e-01
-3.49071890e-01 -5.73626161e-01 -2.04036742e-01 -2.57678419e-01
1.34386774e-02 4.95675832e-01 1.31440625e-01 -3.71461123... | [4.97033166885376, 0.9773358106613159] |
18439422-10b3-4577-8396-fd6391cb01bb | less-is-more-data-efficient-complex-question | 2010.15881 | null | https://arxiv.org/abs/2010.15881v1 | https://arxiv.org/pdf/2010.15881v1.pdf | Less is More: Data-Efficient Complex Question Answering over Knowledge Bases | Question answering is an effective method for obtaining information from knowledge bases (KB). In this paper, we propose the Neural-Symbolic Complex Question Answering (NS-CQA) model, a data-efficient reinforcement learning framework for complex question answering by using only a modest number of training samples. Our ... | ['Daiqing Qi', 'Jingyao Zhang', 'Wei Wu', 'Guilin Qi', 'Yuan-Fang Li', 'Yuncheng Hua'] | 2020-10-29 | null | null | null | null | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 1.72527619e-02 1.23653881e-01 -2.58588523e-01 -3.34724247e-01
-1.06511116e+00 -6.50652409e-01 3.49406719e-01 1.95417747e-01
-5.42175531e-01 5.17019212e-01 5.28822513e-03 -5.87350309e-01
-1.84742454e-02 -1.17168891e+00 -1.07097650e+00 -2.81267613e-01
2.54439026e-01 3.66827965e-01 5.45426965e-01 -3.72936726... | [10.898393630981445, 7.85999870300293] |
68ea3923-a996-4e4a-9d40-70f37b9e443a | adversarial-intrinsic-motivation-for | 2105.13345 | null | https://arxiv.org/abs/2105.13345v3 | https://arxiv.org/pdf/2105.13345v3.pdf | Adversarial Intrinsic Motivation for Reinforcement Learning | Learning with an objective to minimize the mismatch with a reference distribution has been shown to be useful for generative modeling and imitation learning. In this paper, we investigate whether one such objective, the Wasserstein-1 distance between a policy's state visitation distribution and a target distribution, c... | ['Peter Stone', 'Scott Niekum', 'Mauricio Tec', 'Ishan Durugkar'] | 2021-05-27 | null | http://proceedings.neurips.cc/paper/2021/hash/486c0401c56bf7ec2daa9eba58907da9-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/486c0401c56bf7ec2daa9eba58907da9-Paper.pdf | neurips-2021-12 | ['multi-goal-reinforcement-learning'] | ['methodology'] | [ 6.07844489e-03 5.69045246e-01 -1.50466204e-01 -3.33826840e-02
-8.51373613e-01 -4.04839844e-01 7.78603494e-01 5.91141172e-02
-8.97620738e-01 1.09286654e+00 -9.09310952e-02 -2.14839339e-01
-3.03678423e-01 -7.21494496e-01 -1.00339067e+00 -1.16918528e+00
-3.46857876e-01 6.91363871e-01 -1.01094976e-01 -8.07036534... | [4.099189758300781, 2.1504199504852295] |
1913b7ab-b1b0-4089-b6b5-cb44ae0b37a3 | adapting-sequence-to-sequence-models-for-text | 1904.06100 | null | http://arxiv.org/abs/1904.06100v1 | http://arxiv.org/pdf/1904.06100v1.pdf | Adapting Sequence to Sequence models for Text Normalization in Social Media | Social media offer an abundant source of valuable raw data, however informal
writing can quickly become a bottleneck for many natural language processing
(NLP) tasks. Off-the-shelf tools are usually trained on formal text and cannot
explicitly handle noise found in short online posts. Moreover, the variety of
frequentl... | ['ChengXiang Zhai', 'Kabir Manghnani', 'Ismini Lourentzou'] | 2019-04-12 | null | null | null | null | ['lexical-normalization'] | ['natural-language-processing'] | [ 6.47834361e-01 3.04202107e-03 5.66052534e-02 -4.07235503e-01
-8.24269295e-01 -6.54321909e-01 4.85190511e-01 8.35024416e-01
-9.12236094e-01 5.21054685e-01 5.71309090e-01 -3.85156155e-01
3.81450593e-01 -6.61037385e-01 -7.00611830e-01 -4.73076701e-02
6.74955606e-01 3.75800580e-01 -8.62802863e-02 -8.00111294... | [10.826698303222656, 9.993329048156738] |
8d1ba5ae-e2b5-44bd-a5de-6b1d54a42043 | improving-knowledge-extraction-from-llms-for | 2306.06770 | null | https://arxiv.org/abs/2306.06770v2 | https://arxiv.org/pdf/2306.06770v2.pdf | Improving Knowledge Extraction from LLMs for Robotic Task Learning through Agent Analysis | Large language models (LLMs) offer significant promise as a knowledge source for robotic task learning. Prompt engineering has been shown to be effective for eliciting knowledge from an LLM but alone is insufficient for acquiring relevant, situationally grounded knowledge for an embodied robotic agent learning novel ta... | ['Peter Lindes', 'Robert E. Wray', 'James R. Kirk'] | 2023-06-11 | null | null | null | null | ['one-shot-learning', 'prompt-engineering'] | ['methodology', 'natural-language-processing'] | [ 1.51812568e-01 6.82487428e-01 -5.12178093e-02 -3.45931143e-01
-8.87516260e-01 -8.82117808e-01 5.87999046e-01 3.19908768e-01
-5.84153235e-01 6.23897374e-01 2.67653167e-01 -2.91488439e-01
-4.50113922e-01 -1.92643553e-01 -5.39905012e-01 -1.71366557e-01
1.13328114e-01 6.76835716e-01 2.28267044e-01 -3.49644929... | [4.376928329467773, 0.9198378920555115] |
36f0ca61-a5f0-45ca-a93e-2d1a6e154ae3 | sca-streaming-cross-attention-alignment-for | 2211.00589 | null | https://arxiv.org/abs/2211.00589v1 | https://arxiv.org/pdf/2211.00589v1.pdf | SCA: Streaming Cross-attention Alignment for Echo Cancellation | End-to-End deep learning has shown promising results for speech enhancement tasks, such as noise suppression, dereverberation, and speech separation. However, most state-of-the-art methods for echo cancellation are either classical DSP-based or hybrid DSP-ML algorithms. Components such as the delay estimator and adapti... | ['Xin Lei', 'Sriram Srinivasan', 'Kaustubh Kalgaonkar', 'Yun Li', 'Yangyang Shi', 'Yang Liu'] | 2022-11-01 | null | null | null | null | ['speech-separation'] | ['speech'] | [ 7.16779232e-02 -2.89246529e-01 6.39680505e-01 -3.01398933e-01
-9.61672246e-01 -5.23351192e-01 3.99354815e-01 -3.02884430e-01
-5.14913559e-01 2.67298281e-01 5.91228187e-01 -4.34688807e-01
-7.28926212e-02 1.90366641e-01 -5.16185284e-01 -6.07579172e-01
-7.59717301e-02 -1.93178296e-01 1.53951868e-01 -3.89399260... | [15.01501178741455, 5.968545913696289] |
6766042c-9da3-4022-84c4-c5eee2f2b529 | unsupervised-learning-of-discourse-aware-text | null | null | https://aclanthology.org/P19-2053 | https://aclanthology.org/P19-2053.pdf | Unsupervised Learning of Discourse-Aware Text Representation for Essay Scoring | Existing document embedding approaches mainly focus on capturing sequences of words in documents. However, some document classification and regression tasks such as essay scoring need to consider discourse structure of documents. Although some prior approaches consider this issue and utilize discourse structure of text... | ['Paul Reisert', 'Naoya Inoue', 'Kentaro Inui', 'Hiroki Ouchi', 'Farjana Sultana Mim'] | 2019-07-01 | null | null | null | acl-2019-7 | ['document-embedding'] | ['methodology'] | [ 1.60511546e-02 3.85589540e-01 -5.98233879e-01 -4.99633461e-01
-5.47942102e-01 -6.33903980e-01 9.12293077e-01 8.14006627e-01
-4.04826730e-01 6.09667122e-01 8.90217066e-01 -4.89158064e-01
-2.21201386e-02 -8.92555296e-01 -2.20135916e-02 -3.48521382e-01
3.04269016e-01 2.16286957e-01 1.37754709e-01 -3.63242686... | [11.015473365783691, 9.332802772521973] |
d1e38421-2131-4393-8233-41d5a9d1a847 | using-drug-descriptions-and-molecular | null | null | https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btaa907/5938075#209442351 | https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaa907/34012017/btaa907.pdf | Using Drug Descriptions and Molecular Structures for Drug-Drug Interaction Extraction from Literature | Motivation
Neural methods to extract drug-drug interactions (DDIs) from literature require a large number of annotations. In this study, we propose a novel method to effectively utilize external drug database information as well as information from large-scale plain text for DDI extraction. Specifically, we focus on d... | ['Yutaka Sasaki', 'Makoto Miwa', 'Masaki Asada'] | 2020-10-24 | null | null | null | null | ['drug-drug-interaction-extraction'] | ['natural-language-processing'] | [ 5.66507764e-02 -4.38922763e-01 -7.42134511e-01 -2.13353381e-01
-8.60202968e-01 -5.51910102e-01 4.71151859e-01 5.16021132e-01
-2.96697050e-01 1.28496528e+00 2.71922857e-01 -3.92507255e-01
-2.81251073e-01 -7.00002134e-01 -7.49774396e-01 -8.22337985e-01
1.69526618e-02 4.74612117e-01 -1.00837816e-02 -7.53899589... | [8.301756858825684, 8.61764144897461] |
65f80e28-1141-4cbe-ad95-ffa7023bdbb4 | good-exploring-geometric-cues-for-detecting | 2212.11720 | null | https://arxiv.org/abs/2212.11720v3 | https://arxiv.org/pdf/2212.11720v3.pdf | GOOD: Exploring Geometric Cues for Detecting Objects in an Open World | We address the task of open-world class-agnostic object detection, i.e., detecting every object in an image by learning from a limited number of base object classes. State-of-the-art RGB-based models suffer from overfitting the training classes and often fail at detecting novel-looking objects. This is because RGB-base... | ['Dan Zhang', 'Andreas Geiger', 'Haiwen Huang'] | 2022-12-22 | null | null | null | null | ['class-agnostic-object-detection', 'open-world-object-detection'] | ['computer-vision', 'computer-vision'] | [ 1.32795021e-01 1.63616553e-01 6.16609640e-02 -4.58677351e-01
-9.09461617e-01 -6.91687644e-01 4.89794850e-01 1.93907797e-01
-6.09124660e-01 3.09012681e-01 -2.45573968e-01 2.14469969e-01
3.50087017e-01 -6.87942922e-01 -1.09523523e+00 -5.27419567e-01
1.82013556e-01 7.10528016e-01 9.56332862e-01 2.15967391... | [9.432851791381836, 1.3351812362670898] |
75218828-c85a-444d-a5e8-194ea9386095 | synthetic-yet-natural-properties-of-wordnet | null | null | https://aclanthology.org/2019.gwc-1.18 | https://aclanthology.org/2019.gwc-1.18.pdf | Synthetic, yet natural: Properties of WordNet random walk corpora and the impact of rare words on embedding performance | Creating word embeddings that reflect semantic relationships encoded in lexical knowledge resources is an open challenge. One approach is to use a random walk over a knowledge graph to generate a pseudo-corpus and use this corpus to train embeddings. However, the effect of the shape of the knowledge graph on the genera... | ['John Kelleher', 'Abhijit Mahalunkar', 'Alfredo Maldonado', 'Filip Klubička'] | null | null | null | null | gwc-2019-7 | ['word-similarity'] | ['natural-language-processing'] | [-1.67459637e-01 1.65201247e-01 -2.40975201e-01 -1.51106909e-01
-5.66205122e-02 -8.53349984e-01 8.20611775e-01 8.24678838e-01
-8.69964361e-01 4.51863497e-01 7.88008630e-01 -3.56597781e-01
-2.70291328e-01 -1.27945924e+00 -5.10412872e-01 -4.81762737e-01
-4.77497913e-02 3.62497658e-01 3.99593949e-01 -3.42901617... | [10.37275218963623, 8.920268058776855] |
d39b53ab-24e0-446a-8f05-33deb37ef2b4 | semantic-scene-completion-using-local-deep | 2011.09141 | null | https://arxiv.org/abs/2011.09141v3 | https://arxiv.org/pdf/2011.09141v3.pdf | Semantic Scene Completion using Local Deep Implicit Functions on LiDAR Data | Semantic scene completion is the task of jointly estimating 3D geometry and semantics of objects and surfaces within a given extent. This is a particularly challenging task on real-world data that is sparse and occluded. We propose a scene segmentation network based on local Deep Implicit Functions as a novel learning-... | ['Dariu M. Gavrila', 'Markus Enzweiler', 'David Emmerichs', 'Christoph B. Rist'] | 2020-11-18 | null | null | null | null | ['3d-semantic-scene-completion'] | ['computer-vision'] | [ 4.92064714e-01 1.05962642e-01 3.50638956e-01 -7.36910880e-01
-9.91362929e-01 -6.73791289e-01 5.69174170e-01 3.82639647e-01
-2.49589384e-01 3.62137914e-01 -9.97057036e-02 -3.57895158e-02
-3.54185253e-02 -1.19371092e+00 -1.17769444e+00 -2.85919249e-01
-1.19606871e-02 8.74422431e-01 3.86547059e-01 1.05109960... | [8.549749374389648, -2.9744179248809814] |
a290c4d0-1a2a-407e-b65c-5ef5ba578043 | findings-of-the-constraint-2022-shared-task | null | null | https://aclanthology.org/2022.constraint-1.1 | https://aclanthology.org/2022.constraint-1.1.pdf | Findings of the CONSTRAINT 2022 Shared Task on Detecting the Hero, the Villain, and the Victim in Memes | We present the findings of the shared task at the CONSTRAINT 2022 Workshop: Hero, Villain, and Victim: Dissecting harmful memes for Semantic role labeling of entities. The task aims to delve deeper into the domain of meme comprehension by deciphering the connotations behind the entities present in a meme. In more nuanc... | ['Tanmoy Chakraborty', 'Md. Shad Akhtar', 'Preslav Nakov', 'Himanshi Mathur', 'Atharva Kulkarni', 'Tharun Suresh', 'Shivam Sharma'] | null | null | null | null | constraint-acl-2022-5 | ['semantic-role-labeling'] | ['natural-language-processing'] | [-1.29487380e-01 4.79368985e-01 -1.39321819e-01 -2.21038297e-01
-4.49640214e-01 -1.14665246e+00 1.08768964e+00 6.30804420e-01
-4.26394641e-01 7.75461018e-01 1.06317031e+00 -2.49711141e-01
2.43482932e-01 -4.44023401e-01 -3.44512254e-01 -1.82535335e-01
3.15928221e-01 5.30799270e-01 -6.40890673e-02 -6.86541021... | [8.561338424682617, 10.665306091308594] |
059eb18d-780f-4591-a76c-8d3345e19582 | droneattention-sparse-weighted-temporal | 2212.03384 | null | https://arxiv.org/abs/2212.03384v1 | https://arxiv.org/pdf/2212.03384v1.pdf | DroneAttention: Sparse Weighted Temporal Attention for Drone-Camera Based Activity Recognition | Human activity recognition (HAR) using drone-mounted cameras has attracted considerable interest from the computer vision research community in recent years. A robust and efficient HAR system has a pivotal role in fields like video surveillance, crowd behavior analysis, sports analysis, and human-computer interaction. ... | ['Peter Corcoran', 'Hari Mohan Pandey', 'Heena Rathore', 'Kamlesh Tiwari', 'Esha Pahwa', 'Achleshwar Luthra', 'Santosh Kumar Yadav'] | 2022-12-07 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [ 2.13443846e-01 -4.82310683e-01 -2.57481076e-02 -6.06677197e-02
-2.46105865e-01 -1.90415651e-01 6.34375274e-01 -1.03671685e-01
-6.62551701e-01 6.47480607e-01 2.41737977e-01 1.95290864e-01
2.45524999e-02 -5.27521729e-01 -5.95921040e-01 -9.02037740e-01
-1.39187962e-01 -9.47190374e-02 4.83352095e-01 -2.15240389... | [8.084348678588867, 0.6136142015457153] |
97294a3a-6db0-4d30-a256-039b013fa1ac | distributed-dual-quaternion-based | 2203.06278 | null | https://arxiv.org/abs/2203.06278v1 | https://arxiv.org/pdf/2203.06278v1.pdf | Distributed Dual Quaternion Based Localization of Visual Sensor Networks | In this paper we consider the localization problem for a visual sensor network. Inspired by the alternate attitude and position distributed optimization framework discussed in [1], we propose an estimation scheme that exploits the unit dual quaternion algebra to describe the sensors pose. This representation is benefic... | ['Angelo Cenedese', 'Giulia Michieletto', 'Marco Fabris', 'Luca Varotto'] | 2022-03-11 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-7.72111043e-02 2.83582032e-01 -1.07513912e-01 5.00194095e-02
-2.37714816e-02 -6.50040209e-01 5.70480466e-01 5.53130865e-01
-8.55564415e-01 9.73909676e-01 -3.85831118e-01 3.18910391e-03
-3.28339040e-01 -5.88752270e-01 -6.49201989e-01 -7.99636602e-01
-3.94240506e-02 9.47267339e-02 -8.35828781e-02 -3.55702221... | [7.790817737579346, -2.2239720821380615] |
b75a0f71-5fe1-42ea-b771-20bd126ea21d | learn-an-effective-lip-reading-model-without | 2011.07557 | null | https://arxiv.org/abs/2011.07557v1 | https://arxiv.org/pdf/2011.07557v1.pdf | Learn an Effective Lip Reading Model without Pains | Lip reading, also known as visual speech recognition, aims to recognize the speech content from videos by analyzing the lip dynamics. There have been several appealing progress in recent years, benefiting much from the rapidly developed deep learning techniques and the recent large-scale lip-reading datasets. Most exis... | ['Xilin Chen', 'Shiguang Shan', 'Shuang Yang', 'Dalu Feng'] | 2020-11-15 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 8.25336352e-02 6.44045621e-02 -5.14200330e-01 -2.42605135e-01
-1.01741242e+00 -1.93124935e-01 6.18809283e-01 -3.76841962e-01
-3.71113569e-01 6.25709176e-01 4.60610360e-01 -2.32829556e-01
1.75361603e-01 -1.04360335e-01 -5.96202493e-01 -8.21728110e-01
1.66374803e-01 6.54703975e-02 4.09088880e-01 -2.10517691... | [14.309409141540527, 4.987491607666016] |
619c0352-7e88-4812-88e9-2fc09b44d334 | spotr-spatio-temporal-pose-transformers-for | 2303.06277 | null | https://arxiv.org/abs/2303.06277v1 | https://arxiv.org/pdf/2303.06277v1.pdf | SPOTR: Spatio-temporal Pose Transformers for Human Motion Prediction | 3D human motion prediction is a research area of high significance and a challenge in computer vision. It is useful for the design of many applications including robotics and autonomous driving. Traditionally, autogregressive models have been used to predict human motion. However, these models have high computation nee... | ['Misha Sra', 'Avinash Ajit Nargund'] | 2023-03-11 | null | null | null | null | ['motion-prediction'] | ['computer-vision'] | [ 9.96419564e-02 2.11513881e-02 -1.14056304e-01 -7.11235255e-02
-2.54617214e-01 -3.31069440e-01 1.02439630e+00 -4.61923450e-01
-4.49558914e-01 4.26254183e-01 5.91664910e-01 -1.35098353e-01
1.67701095e-01 -6.20393157e-01 -9.56808865e-01 -5.56332946e-01
-1.81359798e-01 3.84295493e-01 7.51369774e-01 -4.76393789... | [7.32735013961792, -0.1318986564874649] |
114bf613-3f71-4cc1-a627-e2b1c31e5e5f | dual-embeddings-and-metrics-for-relational | null | null | https://aclanthology.org/W17-6924 | https://aclanthology.org/W17-6924.pdf | Dual Embeddings and Metrics for Relational Similarity | null | ['D. Li', 'Douglas Summers-Stay', 'an'] | 2017-01-01 | null | null | null | ws-2017-1 | ['learning-word-embeddings'] | ['methodology'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.264651298522949, 3.6799516677856445] |
2a65aedd-f8c9-47ad-a516-80a4681ee7e6 | boosting-the-performance-of-transformer | 2306.00708 | null | https://arxiv.org/abs/2306.00708v1 | https://arxiv.org/pdf/2306.00708v1.pdf | Boosting the Performance of Transformer Architectures for Semantic Textual Similarity | Semantic textual similarity is the task of estimating the similarity between the meaning of two texts. In this paper, we fine-tune transformer architectures for semantic textual similarity on the Semantic Textual Similarity Benchmark by tuning the model partially and then end-to-end. We experiment with BERT, RoBERTa, a... | ['Vladimir Čeperić', 'Ivan Rep'] | 2023-06-01 | null | null | null | null | ['semantic-textual-similarity'] | ['natural-language-processing'] | [ 3.56486082e-01 6.86096102e-02 6.29852861e-02 -6.43259287e-01
-8.07567894e-01 -5.92183173e-01 9.28263009e-01 3.50455225e-01
-5.81568956e-01 3.53818476e-01 5.65512002e-01 -3.80716056e-01
-6.28167018e-02 -5.00758171e-01 -4.04516906e-01 -2.27193430e-01
2.88749546e-01 6.91875637e-01 2.65509814e-01 -3.83059919... | [11.142507553100586, 8.72928524017334] |
aaf5e2dc-9a9f-46df-8791-25d5d2b5faee | cross-modal-consensus-network-for-weakly | 2107.12589 | null | https://arxiv.org/abs/2107.12589v1 | https://arxiv.org/pdf/2107.12589v1.pdf | Cross-modal Consensus Network for Weakly Supervised Temporal Action Localization | Weakly supervised temporal action localization (WS-TAL) is a challenging task that aims to localize action instances in the given video with video-level categorical supervision. Both appearance and motion features are used in previous works, while they do not utilize them in a proper way but apply simple concatenation ... | ['Wei-Shi Zheng', 'Ying Shan', 'Dan Xu', 'Jia-Chang Feng', 'Fa-Ting Hong'] | 2021-07-27 | null | null | null | null | ['weakly-supervised-action-localization', 'weakly-supervised-temporal-action'] | ['computer-vision', 'computer-vision'] | [ 9.61031392e-02 -3.06748569e-01 -3.57109666e-01 -1.49206698e-01
-7.56777346e-01 -1.00992836e-01 5.73176324e-01 -3.24043512e-01
-4.91304606e-01 5.16006470e-01 5.78908980e-01 4.63277757e-01
-1.36786178e-01 -1.85215741e-01 -5.55735707e-01 -9.83547091e-01
7.71381184e-02 -1.12844683e-01 7.11732090e-01 -1.29991651... | [8.570191383361816, 0.7265576124191284] |
31571bca-6da8-4789-b034-6a7f9b0c5635 | one-shot-face-reenactment-on-megapixels | 2205.13368 | null | https://arxiv.org/abs/2205.13368v1 | https://arxiv.org/pdf/2205.13368v1.pdf | One-Shot Face Reenactment on Megapixels | The goal of face reenactment is to transfer a target expression and head pose to a source face while preserving the source identity. With the popularity of face-related applications, there has been much research on this topic. However, the results of existing methods are still limited to low-resolution and lack photore... | ['Nam Ik Cho', 'Hyung Il Koo', 'Geonsu Lee', 'Wonjun Kang'] | 2022-05-26 | null | null | null | null | ['talking-head-generation', 'face-reenactment', 'facial-editing', 'talking-face-generation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 2.69897610e-01 8.90315622e-02 1.23323947e-02 -5.31264067e-01
-3.54917139e-01 -2.73294985e-01 4.60593700e-01 -9.56728935e-01
7.36350566e-02 5.18578231e-01 2.86698699e-01 2.29178488e-01
1.53837889e-01 -5.03394604e-01 -4.96116251e-01 -7.24246323e-01
3.50179136e-01 4.28188927e-02 -6.69011474e-02 -3.75193000... | [12.841500282287598, -0.2555060386657715] |
97d3deba-1c2c-49e4-a868-9035f0050ae0 | column-type-annotation-using-chatgpt | 2306.00745 | null | https://arxiv.org/abs/2306.00745v1 | https://arxiv.org/pdf/2306.00745v1.pdf | Column Type Annotation using ChatGPT | Column type annotation is the task of annotating the columns of a relational table with the semantic type of the values contained in each column. Column type annotation is a crucial pre-processing step for data search and integration in the context of data lakes. State-of-the-art column type annotation methods either r... | ['Christian Bizer', 'Keti Korini'] | 2023-06-01 | null | null | null | null | ['table-annotation', 'table-annotation', 'column-type-annotation'] | ['knowledge-base', 'natural-language-processing', 'natural-language-processing'] | [-1.68585964e-02 5.01353085e-01 -2.49379218e-01 -3.59032452e-01
-1.07078099e+00 -9.30832565e-01 5.85124195e-01 9.07026112e-01
-6.83434784e-01 6.50585055e-01 1.43129468e-01 -1.98815495e-01
-7.88560659e-02 -7.60676086e-01 -7.40850270e-01 4.24760319e-02
1.38961837e-01 1.15026355e+00 6.60302401e-01 -4.94310468... | [9.650516510009766, 8.292823791503906] |
ac428919-7d57-484c-9a6c-983925bfb997 | plop-learning-without-forgetting-for | 2011.11390 | null | https://arxiv.org/abs/2011.11390v3 | https://arxiv.org/pdf/2011.11390v3.pdf | PLOP: Learning without Forgetting for Continual Semantic Segmentation | Deep learning approaches are nowadays ubiquitously used to tackle computer vision tasks such as semantic segmentation, requiring large datasets and substantial computational power. Continual learning for semantic segmentation (CSS) is an emerging trend that consists in updating an old model by sequentially adding new c... | ['Matthieu Cord', 'Arnaud Dapogny', 'Yifu Chen', 'Arthur Douillard'] | 2020-11-23 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Douillard_PLOP_Learning_Without_Forgetting_for_Continual_Semantic_Segmentation_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Douillard_PLOP_Learning_Without_Forgetting_for_Continual_Semantic_Segmentation_CVPR_2021_paper.pdf | cvpr-2021-1 | ['overlapped-100-5', 'overlapped-10-1', 'disjoint-15-5', 'disjoint-10-1', 'disjoint-15-1', 'overlapped-15-5', 'overlapped-100-10', 'overlapped-15-1', 'overlapped-50-50', 'overlapped-100-50', 'continual-semantic-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 5.67044199e-01 4.51396368e-02 1.14973485e-01 -3.75278622e-01
-5.35152972e-01 -4.71720129e-01 4.51146305e-01 5.38508952e-01
-8.29371095e-01 9.01234090e-01 -2.40897313e-01 2.58762062e-01
1.84870332e-01 -8.58377397e-01 -8.24389756e-01 -9.34611499e-01
3.09383541e-01 3.88392299e-01 1.06574047e+00 1.63986266... | [9.40975570678711, 1.94631028175354] |
b34db5bd-5486-47b0-8d15-2faec7cc9e31 | massive-online-crowdsourced-study-of | 1511.02919 | null | http://arxiv.org/abs/1511.02919v1 | http://arxiv.org/pdf/1511.02919v1.pdf | Massive Online Crowdsourced Study of Subjective and Objective Picture Quality | Most publicly available image quality databases have been created under
highly controlled conditions by introducing graded simulated distortions onto
high-quality photographs. However, images captured using typical real-world
mobile camera devices are usually afflicted by complex mixtures of multiple
distortions, which... | ['Alan C. Bovik', 'Deepti Ghadiyaram'] | 2015-11-09 | null | null | null | null | ['blind-image-quality-assessment'] | ['computer-vision'] | [ 8.25409740e-02 -5.97658753e-01 1.88630804e-01 -3.96951914e-01
-1.14636183e+00 -9.06085312e-01 4.59853113e-01 -1.24235883e-01
-6.51504338e-01 4.63021576e-01 3.77104700e-01 -2.31086850e-01
9.40838456e-02 -3.26397330e-01 -6.75492048e-01 -2.90498435e-01
7.20120221e-02 1.00677721e-01 2.67936975e-01 -1.45951286... | [11.870537757873535, -1.7967212200164795] |
2fc915c5-f84d-4980-a43f-ad4fc085c703 | the-effect-of-points-dispersion-on-the-k-nn-1 | 2302.13160 | null | https://arxiv.org/abs/2302.13160v1 | https://arxiv.org/pdf/2302.13160v1.pdf | The Effect of Points Dispersion on the $k$-nn Search in Random Projection Forests | Partitioning trees are efficient data structures for $k$-nearest neighbor search. Machine learning libraries commonly use a special type of partitioning trees called $k$d-trees to perform $k$-nn search. Unfortunately, $k$d-trees can be ineffective in high dimensions because they need more tree levels to decrease the ve... | ['Masahiro Takatsuka', 'Adel F. Ahmed', 'John Stavrakakis', 'Mashaan Alshammari'] | 2023-02-25 | null | null | null | null | ['instance-search', 'vector-quantization-k-means-problem'] | ['computer-vision', 'miscellaneous'] | [-4.22857493e-01 -4.66210842e-01 -4.71397400e-01 -4.21343505e-01
-4.77170140e-01 -4.58354384e-01 1.00486703e-01 2.23504409e-01
-3.41005832e-01 6.34282351e-01 2.59910464e-01 -3.66157055e-01
-4.99681026e-01 -1.40708518e+00 -2.84110069e-01 -7.37697542e-01
-4.57293428e-02 5.78800976e-01 5.86512208e-01 2.14817245... | [7.474233627319336, 4.689890384674072] |
e1842e24-b68a-4fb9-9560-c8310773372a | nima-neural-image-assessment | 1709.05424 | null | http://arxiv.org/abs/1709.05424v2 | http://arxiv.org/pdf/1709.05424v2.pdf | NIMA: Neural Image Assessment | Automatically learned quality assessment for images has recently become a hot
topic due to its usefulness in a wide variety of applications such as
evaluating image capture pipelines, storage techniques and sharing media.
Despite the subjective nature of this problem, most existing methods only
predict the mean opinion... | ['Peyman Milanfar', 'Hossein Talebi'] | 2017-09-15 | null | null | null | null | ['aesthetics-quality-assessment'] | ['computer-vision'] | [ 3.45041543e-01 -2.42658213e-01 2.17563435e-01 -7.05910683e-01
-6.67409241e-01 -5.77268124e-01 5.46373665e-01 4.04674977e-01
-6.78577960e-01 4.30556834e-01 1.26856431e-01 -1.17562756e-01
-9.55306590e-02 -6.66427851e-01 -5.80612183e-01 -5.09889543e-01
9.74110886e-02 2.61913568e-01 3.91209632e-01 -2.77869761... | [11.77366828918457, -1.8061224222183228] |
588341f8-9423-48ad-84ba-5b72eeaabe68 | feddef-robust-federated-learning-based | 2210.04052 | null | https://arxiv.org/abs/2210.04052v2 | https://arxiv.org/pdf/2210.04052v2.pdf | FedDef: Defense Against Gradient Leakage in Federated Learning-based Network Intrusion Detection Systems | Deep learning (DL) methods have been widely applied to anomaly-based network intrusion detection system (NIDS) to detect malicious traffic. To expand the usage scenarios of DL-based methods, the federated learning (FL) framework allows multiple users to train a global model on the basis of respecting individual data pr... | ['Xuewei Feng', 'Ke Xu', 'Qi Li', 'Yi Zhao', 'Jiahui Chen'] | 2022-10-08 | null | null | null | null | ['network-intrusion-detection'] | ['miscellaneous'] | [ 6.97273090e-02 -2.24507213e-01 -2.03888297e-01 -3.78625542e-01
-6.50621176e-01 -9.77499485e-01 6.27033651e-01 -2.64192730e-01
-1.24889478e-01 5.82166135e-01 -3.44476372e-01 -8.75961125e-01
-1.65644914e-01 -9.39996123e-01 -6.08630776e-01 -6.36764288e-01
-1.47126734e-01 3.15144777e-01 2.95687139e-01 -4.44538966... | [5.728770732879639, 7.211320877075195] |
1fef6c62-b606-4b2e-8314-c86634a7f2c6 | mini-model-adaptation-efficiently-extending | 2212.10503 | null | https://arxiv.org/abs/2212.10503v2 | https://arxiv.org/pdf/2212.10503v2.pdf | Mini-Model Adaptation: Efficiently Extending Pretrained Models to New Languages via Aligned Shallow Training | Prior work shows that it is possible to expand pretrained Masked Language Models (MLMs) to new languages by learning a new set of embeddings, while keeping the transformer body frozen. Despite learning a small subset of parameters, this approach is not compute-efficient, as training the new embeddings requires a full f... | ['Mikel Artetxe', 'Yihong Chen', 'Patrick Lewis', 'Kelly Marchisio'] | 2022-12-20 | null | null | null | null | ['cross-lingual-transfer'] | ['natural-language-processing'] | [-7.59407580e-02 3.44160110e-01 -2.30873913e-01 -6.92504704e-01
-1.28279543e+00 -7.27482677e-01 5.94628632e-01 -5.20555563e-02
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7.42043436e-01 -5.10827601e-01 -1.13932204e+00 -4.22033876e-01
-1.90073252e-02 8.85368705e-01 5.11338234e-01 -2.56525576... | [10.909208297729492, 9.489717483520508] |
f340fcd0-250a-4c51-b22a-6bb2e437ae7c | keystroke-patterns-as-prosody-in-digital | null | null | https://aclanthology.org/D14-1155 | https://aclanthology.org/D14-1155.pdf | Keystroke Patterns as Prosody in Digital Writings: A Case Study with Deceptive Reviews and Essays | null | ['Yejin Choi', 'Song Feng', 'Jun Seok Kang', 'Ritwik Banerjee'] | 2014-10-01 | null | null | null | emnlp-2014-10 | ['deception-detection'] | ['miscellaneous'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.314663410186768, 3.725912094116211] |
e08de16d-dcdd-4b00-aed9-3cdb208540aa | distance-based-authorship-verification-across | null | null | https://aclanthology.org/W19-5611 | https://aclanthology.org/W19-5611.pdf | Distance-Based Authorship Verification Across Modern Standard Arabic Genres | null | ['Hossam Ahmed'] | 2019-07-01 | null | null | null | ws-2019-7 | ['authorship-verification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.201930046081543, 3.826484203338623] |
b0404fab-7333-409d-aec3-c60d94261c05 | amortized-inference-for-gaussian-process | 2306.09819 | null | https://arxiv.org/abs/2306.09819v1 | https://arxiv.org/pdf/2306.09819v1.pdf | Amortized Inference for Gaussian Process Hyperparameters of Structured Kernels | Learning the kernel parameters for Gaussian processes is often the computational bottleneck in applications such as online learning, Bayesian optimization, or active learning. Amortizing parameter inference over different datasets is a promising approach to dramatically speed up training time. However, existing methods... | ['Christoph Zimmer', 'Mona Meister', 'Matthias Bitzer'] | 2023-06-16 | null | null | null | null | ['active-learning', 'gaussian-processes', 'bayesian-optimization', 'active-learning'] | ['methodology', 'methodology', 'methodology', 'natural-language-processing'] | [-2.14950189e-01 -2.71732062e-01 -5.06330058e-02 -5.98194003e-01
-8.30528855e-01 -8.35677266e-01 3.17135483e-01 3.92913401e-01
-8.54409456e-01 5.34029663e-01 -4.02226120e-01 -5.63767910e-01
-3.59692514e-01 -8.60497415e-01 -7.99798369e-01 -7.17889428e-01
-2.48652115e-01 7.61556447e-01 4.29976523e-01 5.31367362... | [7.398060321807861, 4.1565093994140625] |
cde73df9-12a5-4a41-9438-bfcc2c07892c | emotion-recognition-in-conversation-research | 1905.02947 | null | https://arxiv.org/abs/1905.02947v1 | https://arxiv.org/pdf/1905.02947v1.pdf | Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances | Emotion is intrinsic to humans and consequently emotion understanding is a key part of human-like artificial intelligence (AI). Emotion recognition in conversation (ERC) is becoming increasingly popular as a new research frontier in natural language processing (NLP) due to its ability to mine opinions from the plethora... | ['Eduard Hovy', 'Soujanya Poria', 'Navonil Majumder', 'Rada Mihalcea'] | 2019-05-08 | null | null | null | null | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 8.14253837e-02 2.37589628e-01 -1.30351156e-01 -5.16788363e-01
-2.58083761e-01 -3.59693766e-01 4.37046319e-01 6.13869727e-01
-4.06267852e-01 9.37083066e-01 4.82196957e-01 -1.41320571e-01
2.44305357e-02 -5.32203734e-01 3.90721597e-02 -4.52838182e-01
1.48900807e-01 2.01993376e-01 -3.77268583e-01 -5.57391763... | [12.953849792480469, 6.271485805511475] |
54a5ef81-0548-4e42-9b41-fca52060e502 | differentiable-patch-selection-for-image | 2104.03059 | null | https://arxiv.org/abs/2104.03059v1 | https://arxiv.org/pdf/2104.03059v1.pdf | Differentiable Patch Selection for Image Recognition | Neural Networks require large amounts of memory and compute to process high resolution images, even when only a small part of the image is actually informative for the task at hand. We propose a method based on a differentiable Top-K operator to select the most relevant parts of the input to efficiently process high re... | ['Thomas Unterthiner', 'Jakob Uszkoreit', 'Dirk Weissenborn', 'Alexey Dosovitskiy', 'Aravindh Mahendran', 'Jean-Baptiste Cordonnier'] | 2021-04-07 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Cordonnier_Differentiable_Patch_Selection_for_Image_Recognition_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Cordonnier_Differentiable_Patch_Selection_for_Image_Recognition_CVPR_2021_paper.pdf | cvpr-2021-1 | ['traffic-sign-recognition'] | ['computer-vision'] | [ 4.08953279e-01 2.62088507e-01 1.69355527e-01 -5.62351823e-01
-8.10197771e-01 -2.56988764e-01 4.88676637e-01 -1.30480289e-01
-5.73973835e-01 5.31160712e-01 3.03053148e-02 -9.60755348e-02
-3.35535020e-01 -1.04603243e+00 -9.82433796e-01 -5.71016550e-01
1.10150471e-01 8.56803775e-01 7.90237784e-01 -7.11055845... | [9.498353958129883, 0.7429116368293762] |
e372a9c6-a72f-4b92-9932-dd6c5e7e0355 | autorl-hyperparameter-landscapes | 2304.02396 | null | https://arxiv.org/abs/2304.02396v4 | https://arxiv.org/pdf/2304.02396v4.pdf | AutoRL Hyperparameter Landscapes | Although Reinforcement Learning (RL) has shown to be capable of producing impressive results, its use is limited by the impact of its hyperparameters on performance. This often makes it difficult to achieve good results in practice. Automated RL (AutoRL) addresses this difficulty, yet little is known about the dynamics... | ['Marius Lindauer', 'Alexander Dockhorn', 'Konrad Wienecke', 'Carolin Benjamins', 'Aditya Mohan'] | 2023-04-05 | null | null | null | null | ['automl', 'hyperparameter-optimization', 'open-question'] | ['methodology', 'methodology', 'natural-language-processing'] | [-9.85385403e-02 -1.65061355e-01 -3.00307363e-01 4.67934785e-03
-6.99669003e-01 -9.70779538e-01 4.70469564e-01 2.52366245e-01
-4.79013622e-01 9.67298210e-01 -7.20274262e-03 -3.47091526e-01
-7.04941094e-01 -7.13194966e-01 -5.92213273e-01 -8.97969186e-01
-2.39344358e-01 4.76704240e-01 1.05287768e-02 -5.73622823... | [4.349639415740967, 1.9754964113235474] |
9d47d90d-134b-41c3-b573-604be6c0dd0d | confidence-guided-adaptive-gate-and-dual | 2105.06714 | null | https://arxiv.org/abs/2105.06714v1 | https://arxiv.org/pdf/2105.06714v1.pdf | Confidence-guided Adaptive Gate and Dual Differential Enhancement for Video Salient Object Detection | Video salient object detection (VSOD) aims to locate and segment the most attractive object by exploiting both spatial cues and temporal cues hidden in video sequences. However, spatial and temporal cues are often unreliable in real-world scenarios, such as low-contrast foreground, fast motion, and multiple moving obje... | ['Huajun Zhou', 'Guangcong Wang', 'JianHuang Lai', 'Peijia Chen'] | 2021-05-14 | null | null | null | null | ['video-salient-object-detection'] | ['computer-vision'] | [ 1.73028946e-01 -4.61107969e-01 -2.55214721e-01 -2.36119345e-01
-4.61310506e-01 -3.01167428e-01 4.05285090e-01 1.03020087e-01
-4.97508138e-01 6.55964494e-01 8.88439938e-02 2.26808205e-01
-1.75817627e-02 -5.53062797e-01 -5.36460578e-01 -8.47504914e-01
-2.10197821e-01 -2.63430625e-01 1.05295038e+00 7.83682540... | [9.4002103805542, -0.4027370810508728] |
a3a4c45f-aa1b-43b6-9cf2-9e3b9e043798 | methods-for-sparse-and-low-rank-recovery | 1605.00507 | null | http://arxiv.org/abs/1605.00507v1 | http://arxiv.org/pdf/1605.00507v1.pdf | Methods for Sparse and Low-Rank Recovery under Simplex Constraints | The de-facto standard approach of promoting sparsity by means of
$\ell_1$-regularization becomes ineffective in the presence of simplex
constraints, i.e.,~the target is known to have non-negative entries summing up
to a given constant. The situation is analogous for the use of nuclear norm
regularization for low-rank r... | ['Syama Sundar Rangapuram', 'Martin Slawski', 'Ping Li'] | 2016-05-02 | null | null | null | null | ['quantum-state-tomography'] | ['medical'] | [ 5.19333661e-01 4.08176124e-01 -2.06744090e-01 -1.68245658e-01
-8.02309275e-01 -2.67483920e-01 8.83262232e-02 1.93251017e-02
-6.36076152e-01 1.03079271e+00 -9.13116243e-03 -3.72569889e-01
-4.91847992e-01 -6.41234636e-01 -6.01712525e-01 -1.16001821e+00
-9.93438438e-02 4.52721566e-01 -2.82154799e-01 -2.94697434... | [6.833868980407715, 4.6317596435546875] |
1f079489-52a2-4d48-b57b-067fd44f2fba | life-learning-individual-features-for | 2109.14844 | null | https://arxiv.org/abs/2109.14844v2 | https://arxiv.org/pdf/2109.14844v2.pdf | LIFE: Learning Individual Features for Multivariate Time Series Prediction with Missing Values | Multivariate time series (MTS) prediction is ubiquitous in real-world fields, but MTS data often contains missing values. In recent years, there has been an increasing interest in using end-to-end models to handle MTS with missing values. To generate features for prediction, existing methods either merge all input dime... | ['Zhi-Hua Zhou', 'Yuan Jiang', 'Shao-Qun Zhang', 'Zhao-Yu Zhang'] | 2021-09-30 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [ 1.08397059e-01 -4.70298231e-01 -1.92759529e-01 -4.91515726e-01
-8.30542624e-01 -1.16201349e-01 3.87682259e-01 1.38524905e-01
-1.63681600e-02 9.35649157e-01 2.10845679e-01 1.53988637e-02
-3.43213737e-01 -5.85876226e-01 -3.54284495e-01 -9.09949541e-01
-1.98567152e-01 2.19361886e-01 2.38366857e-01 -4.12270933... | [7.174736022949219, 2.85905385017395] |
18e698ea-ed1a-47ba-b47b-b7e1eb21ec1e | semantic-sensor-network-ontology-based | 2204.03059 | null | https://arxiv.org/abs/2204.03059v2 | https://arxiv.org/pdf/2204.03059v2.pdf | Semantic Sensor Network Ontology based Decision Support System for Forest Fire Management | The forests are significant assets for every country. When it gets destroyed, it may negatively impact the environment, and forest fire is one of the primary causes. Fire weather indices are widely used to measure fire danger and are used to issue bushfire warnings. It can also be used to predict the demand for emergen... | ['Sonali Agarwal', 'Kumar Abhishek', 'Navjot Singh', 'Ritesh Chandra'] | 2022-04-03 | null | null | null | null | ['fire-detection'] | ['time-series'] | [ 1.18583433e-01 -2.95702457e-01 -2.60916889e-01 -4.23530996e-01
6.97237492e-01 -5.01376927e-01 6.18897021e-01 5.21981478e-01
-3.06301028e-01 9.40509260e-01 3.05573702e-01 -2.86711663e-01
-6.97716594e-01 -1.90020251e+00 1.67379931e-01 -4.17213768e-01
-2.84400098e-02 2.28042066e-01 5.92229486e-01 -5.49835086... | [9.162602424621582, 7.683679580688477] |
e4c5d9b2-98b0-4d39-8ccf-db3f320e96b4 | from-synthetic-to-real-unsupervised-domain | 2103.14843 | null | https://arxiv.org/abs/2103.14843v1 | https://arxiv.org/pdf/2103.14843v1.pdf | From Synthetic to Real: Unsupervised Domain Adaptation for Animal Pose Estimation | Animal pose estimation is an important field that has received increasing attention in the recent years. The main challenge for this task is the lack of labeled data. Existing works circumvent this problem with pseudo labels generated from data of other easily accessible domains such as synthetic data. However, these p... | ['Gim Hee Lee', 'Chen Li'] | 2021-03-27 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Li_From_Synthetic_to_Real_Unsupervised_Domain_Adaptation_for_Animal_Pose_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Li_From_Synthetic_to_Real_Unsupervised_Domain_Adaptation_for_Animal_Pose_CVPR_2021_paper.pdf | cvpr-2021-1 | ['animal-pose-estimation'] | ['computer-vision'] | [ 1.92283809e-01 -6.29274398e-02 7.18712881e-02 -6.22212112e-01
-5.84661663e-01 -6.94433272e-01 4.94375408e-01 1.76618487e-01
-7.80393124e-01 1.00879002e+00 -2.10591376e-01 2.39210322e-01
8.32313001e-02 -6.43383265e-01 -1.00574374e+00 -6.13204122e-01
1.50782809e-01 8.36564541e-01 6.81081355e-01 -8.33879560... | [9.388365745544434, 1.30251145362854] |
868ef9e4-3399-4ebe-9bfc-aaf9f6ea47ce | cohs-cqg-context-and-history-selection-for | 2209.06652 | null | https://arxiv.org/abs/2209.06652v2 | https://arxiv.org/pdf/2209.06652v2.pdf | CoHS-CQG: Context and History Selection for Conversational Question Generation | Conversational question generation (CQG) serves as a vital task for machines to assist humans, such as interactive reading comprehension, through conversations. Compared to traditional single-turn question generation (SQG), CQG is more challenging in the sense that the generated question is required not only to be mean... | ['Ai Ti Aw', 'Shafiq Joty', 'Nancy F. Chen', 'Liangming Pan', 'Bowei Zou', 'Xuan Long Do'] | 2022-09-14 | null | https://aclanthology.org/2022.coling-1.48 | https://aclanthology.org/2022.coling-1.48.pdf | coling-2022-10 | ['question-generation'] | ['natural-language-processing'] | [ 3.33990365e-01 4.82245833e-01 2.25853890e-01 -4.32614744e-01
-8.67244840e-01 -7.15535164e-01 7.12683141e-01 2.88447648e-01
-1.30752474e-01 7.31669068e-01 7.76261628e-01 -7.28853643e-01
1.22060284e-01 -7.80214727e-01 -3.32624674e-01 -2.52770036e-01
4.12675053e-01 6.07687950e-01 4.46003407e-01 -7.37330139... | [11.873327255249023, 8.056998252868652] |
4c293cbe-2e9a-45e9-b4c6-2ac01b5d8464 | homophone-reveals-the-truth-a-reality-check | 2209.10791 | null | https://arxiv.org/abs/2209.10791v2 | https://arxiv.org/pdf/2209.10791v2.pdf | Homophone Reveals the Truth: A Reality Check for Speech2Vec | Generating spoken word embeddings that possess semantic information is a fascinating topic. Compared with text-based embeddings, they cover both phonetic and semantic characteristics, which can provide richer information and are potentially helpful for improving ASR and speech translation systems. In this paper, we rev... | ['Guangyu Chen'] | 2022-09-22 | null | null | null | null | ['word-similarity'] | ['natural-language-processing'] | [-6.43098876e-02 2.89742172e-01 -1.06678963e-01 -3.06893826e-01
-7.76424646e-01 -7.34103382e-01 8.46780241e-01 3.52484226e-01
-5.85422754e-01 4.14726049e-01 7.34738052e-01 -6.31357908e-01
1.05716966e-01 -4.80896413e-01 -4.52312380e-01 -5.97733080e-01
7.24538490e-02 4.56178516e-01 1.39180139e-01 -6.73320115... | [10.794832229614258, 8.683478355407715] |
3c2782e4-3a89-4ba5-b9d2-9d0f43344d65 | counting-dense-objects-in-remote-sensing | 2002.05928 | null | https://arxiv.org/abs/2002.05928v1 | https://arxiv.org/pdf/2002.05928v1.pdf | Counting dense objects in remote sensing images | Estimating accurate number of interested objects from a given image is a challenging yet important task. Significant efforts have been made to address this problem and achieve great progress, yet counting number of ground objects from remote sensing images is barely studied. In this paper, we are interested in counting... | ['Qingjie Liu', 'Yunhong Wang', 'Guangshuai Gao'] | 2020-02-14 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [ 2.31088638e-01 -5.30009508e-01 3.68401617e-01 -3.63743007e-01
-2.67869532e-01 -2.05511838e-01 6.12130761e-01 -1.06794640e-01
-7.68729925e-01 8.91040802e-01 1.16589241e-01 -2.15606704e-01
-6.80600330e-02 -1.31271863e+00 -5.98456144e-01 -6.06709898e-01
4.02632803e-02 3.21490943e-01 5.94017208e-01 2.31045112... | [8.531953811645508, -0.29251232743263245] |
d8da4b0e-d8fb-49cf-87e9-a9dea44a3001 | text-based-inference-of-moral-sentiment-1 | 2001.07209 | null | https://arxiv.org/abs/2001.07209v1 | https://arxiv.org/pdf/2001.07209v1.pdf | Text-based inference of moral sentiment change | We present a text-based framework for investigating moral sentiment change of the public via longitudinal corpora. Our framework is based on the premise that language use can inform people's moral perception toward right or wrong, and we build our methodology by exploring moral biases learned from diachronic word embed... | ['Renato Ferreira Pinto Jr.', 'Yang Xu', 'Jing Yi Xie', 'Graeme Hirst'] | 2020-01-20 | text-based-inference-of-moral-sentiment | https://aclanthology.org/D19-1472 | https://aclanthology.org/D19-1472.pdf | ijcnlp-2019-11 | ['diachronic-word-embeddings'] | ['natural-language-processing'] | [-1.82824537e-01 2.57783383e-01 -4.37569022e-01 -7.17419803e-01
2.25897282e-01 -4.94269222e-01 1.12526822e+00 5.42383671e-01
-1.04279101e+00 5.98625362e-01 1.20679057e+00 -4.73289788e-01
-8.25267360e-02 -8.76242399e-01 -1.30386025e-01 -5.05281448e-01
1.54916659e-01 2.44054511e-01 -6.64065182e-01 -9.06506598... | [9.329949378967285, 10.164267539978027] |
1e070dcd-70ec-48a5-8088-3cd5db736aab | deep-neural-network-for-musical-instrument | 2105.00933 | null | https://arxiv.org/abs/2105.00933v2 | https://arxiv.org/pdf/2105.00933v2.pdf | Deep Neural Network for Musical Instrument Recognition using MFCCs | The task of efficient automatic music classification is of vital importance and forms the basis for various advanced applications of AI in the musical domain. Musical instrument recognition is the task of instrument identification by virtue of its audio. This audio, also termed as the sound vibrations are leveraged by ... | ['Partha Pakray', 'Abdullah Faiz Ur Rahman Khilji', 'Saranga Kingkor Mahanta'] | 2021-05-03 | null | null | null | null | ['instrument-recognition', 'music-classification'] | ['audio', 'music'] | [ 3.24401915e-01 -4.42464739e-01 1.46108285e-01 1.62645280e-01
-6.66189075e-01 -9.38012719e-01 2.44741023e-01 2.14378517e-02
-3.25522095e-01 4.15974081e-01 9.70871449e-02 -9.95545983e-02
-4.37869549e-01 -3.10003310e-01 -1.81316495e-01 -4.55492526e-01
-3.81772012e-01 1.60432592e-01 -2.03076273e-01 -3.68473053... | [15.840409278869629, 5.262752056121826] |
a2c167ef-55eb-42ee-94a2-f4da5de30ca3 | upgpt-universal-diffusion-model-for-person | 2304.08870 | null | https://arxiv.org/abs/2304.08870v1 | https://arxiv.org/pdf/2304.08870v1.pdf | UPGPT: Universal Diffusion Model for Person Image Generation, Editing and Pose Transfer | Existing person image generative models can do either image generation or pose transfer but not both. We propose a unified diffusion model, UPGPT to provide a universal solution to perform all the person image tasks - generative, pose transfer, and editing. With fine-grained multimodality and disentanglement capabiliti... | ['Andrew Gilbert', 'Armin Mustafa', 'Soon Yau Cheong'] | 2023-04-18 | null | null | null | null | ['pose-transfer'] | ['computer-vision'] | [ 3.23501170e-01 4.03736383e-01 3.65613729e-01 -4.20456588e-01
-5.53966284e-01 -5.92370689e-01 9.47520494e-01 -5.70703030e-01
-2.52155930e-01 6.87108815e-01 -6.34607598e-02 2.65838325e-01
1.72159851e-01 -8.11062455e-01 -9.39580798e-01 -5.13402998e-01
4.33319002e-01 1.19552732e+00 7.06379637e-02 -3.49508345... | [11.941361427307129, -0.8137364387512207] |
3fd971a1-6b97-416f-9c71-120cb26d2552 | forward-modeling-for-partial-observation | 1812.00054 | null | http://arxiv.org/abs/1812.00054v1 | http://arxiv.org/pdf/1812.00054v1.pdf | Forward Modeling for Partial Observation Strategy Games - A StarCraft Defogger | We formulate the problem of defogging as state estimation and future state
prediction from previous, partial observations in the context of real-time
strategy games. We propose to employ encoder-decoder neural networks for this
task, and introduce proxy tasks and baselines for evaluation to assess their
ability of capt... | ['Jonas Gehring', 'Nicolas Usunier', 'Gabriel Synnaeve', 'Zeming Lin', 'Vegard Mella', 'Vasil Khalidov', 'Nicolas Carion', 'Dan Gant'] | 2018-11-30 | forward-modeling-for-partial-observation-2 | https://openreview.net/forum?id=B1nxTzbRZ | https://openreview.net/pdf?id=B1nxTzbRZ | iclr-2018-1 | ['real-time-strategy-games'] | ['playing-games'] | [-5.59211858e-02 3.60233244e-03 -4.30683941e-01 3.21012974e-01
-2.88924962e-01 -7.92348623e-01 9.84231889e-01 -4.08204645e-01
-6.20444417e-01 7.47561336e-01 5.44137061e-01 -6.01292491e-01
-5.54912947e-02 -6.95681155e-01 -4.28172559e-01 -3.79885994e-02
-5.46722770e-01 7.02548563e-01 7.35497832e-01 -1.12147045... | [3.667001485824585, 1.4796781539916992] |
93f7a7c5-ae67-431c-bd65-357754d463e8 | a-multiplicative-value-function-for-safe-and | 2303.04118 | null | https://arxiv.org/abs/2303.04118v1 | https://arxiv.org/pdf/2303.04118v1.pdf | A Multiplicative Value Function for Safe and Efficient Reinforcement Learning | An emerging field of sequential decision problems is safe Reinforcement Learning (RL), where the objective is to maximize the reward while obeying safety constraints. Being able to handle constraints is essential for deploying RL agents in real-world environments, where constraint violations can harm the agent and the ... | ['Luc van Gool', 'Fisher Yu', 'Alexander Liniger', 'Zhejun Zhang', 'Nick Bührer'] | 2023-03-07 | null | null | null | null | ['robot-navigation'] | ['robots'] | [ 8.90023783e-02 4.16711152e-01 -1.93099916e-01 -4.27714318e-01
-9.40456986e-01 -4.07631904e-01 4.85571951e-01 1.27062146e-02
-1.03279102e+00 1.13111567e+00 -1.32079929e-01 -3.78171176e-01
-1.90644443e-01 -6.34765208e-01 -8.17757666e-01 -7.23410428e-01
-4.56438094e-01 5.64616203e-01 1.04286119e-01 -3.55840772... | [4.468740940093994, 1.9916068315505981] |
c6c89078-9e4e-4a27-bf39-75a517c14227 | neural-arabic-text-diacritization-state-of-1 | 1911.03531 | null | https://arxiv.org/abs/1911.03531v1 | https://arxiv.org/pdf/1911.03531v1.pdf | Neural Arabic Text Diacritization: State of the Art Results and a Novel Approach for Machine Translation | In this work, we present several deep learning models for the automatic diacritization of Arabic text. Our models are built using two main approaches, viz. Feed-Forward Neural Network (FFNN) and Recurrent Neural Network (RNN), with several enhancements such as 100-hot encoding, embeddings, Conditional Random Field (CRF... | ['Mahmoud Al-Ayyoub', "Bara' Al-Jawarneh", 'Ibraheem Tuffaha', 'Ali Fadel'] | 2019-11-08 | neural-arabic-text-diacritization-state-of | https://aclanthology.org/D19-5229 | https://aclanthology.org/D19-5229.pdf | ws-2019-11 | ['arabic-text-diacritization'] | ['natural-language-processing'] | [ 2.00263426e-01 -2.34470926e-02 -6.16486706e-02 -4.66266990e-01
-6.83190703e-01 -5.05131483e-01 1.02691424e+00 8.35206360e-02
-6.23037875e-01 7.29799211e-01 5.12042880e-01 -8.05764914e-01
1.69903785e-01 -7.95385480e-01 -7.63030052e-01 -6.46008134e-01
1.27796745e-02 7.69273102e-01 -6.43778667e-02 -8.41140747... | [10.915863990783691, 10.31955623626709] |
ee2ea8fc-54b9-4a0a-8edd-76927ac25e2e | deepvo-towards-end-to-end-visual-odometry | 1709.08429 | null | http://arxiv.org/abs/1709.08429v1 | http://arxiv.org/pdf/1709.08429v1.pdf | DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networks | This paper studies monocular visual odometry (VO) problem. Most of existing
VO algorithms are developed under a standard pipeline including feature
extraction, feature matching, motion estimation, local optimisation, etc.
Although some of them have demonstrated superior performance, they usually need
to be carefully de... | ['Sen Wang', 'Ronald Clark', 'Hongkai Wen', 'Niki Trigoni'] | 2017-09-25 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-3.33055228e-01 -4.02092129e-01 -3.51549357e-01 -2.34727845e-01
-2.19598755e-01 -2.63979048e-01 5.48746943e-01 -7.39426970e-01
-3.96164984e-01 4.31047827e-01 7.45386854e-02 -7.55564049e-02
1.12693541e-01 -4.24668312e-01 -7.67745495e-01 -6.42435849e-01
-1.16328783e-01 4.69629407e-01 2.14849815e-01 -3.51320505... | [8.196529388427734, -2.1178882122039795] |
ee6c1615-4b90-4141-b32c-14b53ababff5 | decentralized-multi-agent-reinforcement-5 | 2306.12926 | null | https://arxiv.org/abs/2306.12926v1 | https://arxiv.org/pdf/2306.12926v1.pdf | Decentralized Multi-Agent Reinforcement Learning with Global State Prediction | Deep reinforcement learning (DRL) has seen remarkable success in the control of single robots. However, applying DRL to robot swarms presents significant challenges. A critical challenge is non-stationarity, which occurs when two or more robots update individual or shared policies concurrently, thereby engaging in an i... | ['Carlo Pinciroli', 'Apratim Mukherjee', 'Pranjal Paliwal', 'Joshua Bloom'] | 2023-06-22 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [-5.22910990e-02 9.98339131e-02 -5.38406298e-02 7.36397505e-02
-3.16525638e-01 -5.38163245e-01 6.72169626e-01 3.38866025e-01
-8.04880619e-01 1.16906548e+00 -3.75737041e-01 -1.95533231e-01
-4.37927842e-01 -7.52069771e-01 -1.04377353e+00 -1.29610503e+00
-7.59735465e-01 8.12647402e-01 4.34728980e-01 -3.98843497... | [4.014683246612549, 1.973343014717102] |
311fa6ee-d60d-4be4-a68d-e292bda210c2 | bayesian-inference-and-neural-estimation-of | 2305.17749 | null | https://arxiv.org/abs/2305.17749v1 | https://arxiv.org/pdf/2305.17749v1.pdf | Bayesian inference and neural estimation of acoustic wave propagation | In this work, we introduce a novel framework which combines physics and machine learning methods to analyse acoustic signals. Three methods are developed for this task: a Bayesian inference approach for inferring the spectral acoustics characteristics, a neural-physical model which equips a neural network with forward ... | ['Hong Ge', 'Yuhang He', 'Yongchao Huang'] | 2023-05-28 | null | null | null | null | ['room-impulse-response', 'bayesian-inference'] | ['audio', 'methodology'] | [ 3.26710671e-01 1.46958396e-01 6.70492530e-01 -5.54627895e-01
-7.02796161e-01 1.68168113e-01 4.75321054e-01 -1.14268521e-02
-4.88212645e-01 8.92286539e-01 6.06636629e-02 -4.57087755e-01
-7.32577801e-01 -8.06176901e-01 -3.77566814e-01 -1.02434063e+00
-2.21861795e-01 6.26446381e-02 3.22813869e-01 -1.47187188... | [15.209113121032715, 5.603954315185547] |
b7ea7a70-14b0-4213-9110-4b3bbbf4813c | visa-an-ambiguous-subtitles-dataset-for | 2201.08054 | null | https://arxiv.org/abs/2201.08054v3 | https://arxiv.org/pdf/2201.08054v3.pdf | VISA: An Ambiguous Subtitles Dataset for Visual Scene-Aware Machine Translation | Existing multimodal machine translation (MMT) datasets consist of images and video captions or general subtitles, which rarely contain linguistic ambiguity, making visual information not so effective to generate appropriate translations. We introduce VISA, a new dataset that consists of 40k Japanese-English parallel se... | ['Sadao Kurohashi', 'Chenhui Chu', 'Weiqi Gu', 'Shuichiro Shimizu', 'Yihang Li'] | 2022-01-20 | null | https://aclanthology.org/2022.lrec-1.725 | https://aclanthology.org/2022.lrec-1.725.pdf | lrec-2022-6 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 2.48498961e-01 -2.63339996e-01 -4.64393497e-01 -3.32804203e-01
-9.96111393e-01 -9.98470902e-01 6.36102855e-01 -2.18529388e-01
-1.39626727e-01 8.52473557e-01 4.71777081e-01 -5.11020005e-01
4.07653123e-01 -2.72162288e-01 -8.34864378e-01 -3.58291358e-01
3.44900638e-01 5.39431393e-01 -9.56233442e-02 -4.81218010... | [11.357680320739746, 1.5629624128341675] |
b0da5a26-2697-4f7d-886a-76a0d202c32e | linguistic-more-taking-a-further-step-toward | 2305.05140 | null | https://arxiv.org/abs/2305.05140v2 | https://arxiv.org/pdf/2305.05140v2.pdf | Linguistic More: Taking a Further Step toward Efficient and Accurate Scene Text Recognition | Vision model have gained increasing attention due to their simplicity and efficiency in Scene Text Recognition (STR) task. However, due to lacking the perception of linguistic knowledge and information, recent vision models suffer from two problems: (1) the pure vision-based query results in attention drift, which usua... | ['Yongdong Zhang', 'Jianjun Xu', 'Yuxin Wang', 'Hongtao Xie', 'Boqiang Zhang'] | 2023-05-09 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [-4.64244634e-02 -5.11770010e-01 -1.25002369e-01 -2.39153832e-01
-5.58406293e-01 -2.21640795e-01 5.60581803e-01 -1.63898379e-01
-5.35448909e-01 5.65238416e-01 1.41886428e-01 -1.45091951e-01
1.01840518e-01 -5.18843472e-01 -5.97826064e-01 -6.97940946e-01
8.02795708e-01 3.58952172e-02 1.28312707e-01 -2.12846115... | [11.7047119140625, 2.0527307987213135] |
349e3b6b-1523-426d-ab78-3eee9ae6d5c5 | portfolio-optimization-with-idiosyncratic-and | 2111.11286 | null | https://arxiv.org/abs/2111.11286v1 | https://arxiv.org/pdf/2111.11286v1.pdf | Portfolio optimization with idiosyncratic and systemic risks for financial networks | In this study, we propose a new multi-objective portfolio optimization with idiosyncratic and systemic risks for financial networks. The two risks are measured by the idiosyncratic variance and the network clustering coefficient derived from the asset correlation networks, respectively. We construct three types of fina... | ['Jihui Han', 'Chao Wang', 'Lin Chen', 'Longfeng Zhao', 'Yajie Yang'] | 2021-11-22 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-4.17349756e-01 3.85234095e-02 -2.14361817e-01 -1.70395654e-02
4.68227863e-02 -7.85860896e-01 4.41448718e-01 -1.49085268e-01
-8.70325416e-03 8.21701527e-01 3.06122690e-01 -9.13309529e-02
-1.41104043e+00 -1.37999582e+00 -1.20874099e-01 -5.70791185e-01
-5.49874783e-01 3.28124911e-01 -2.33910326e-02 -1.93511754... | [4.999718189239502, 4.041356086730957] |
7e573cc0-6b76-4f2a-ac47-971ffdebe93d | introduction-to-latent-variable-energy-based | 2306.02572 | null | https://arxiv.org/abs/2306.02572v1 | https://arxiv.org/pdf/2306.02572v1.pdf | Introduction to Latent Variable Energy-Based Models: A Path Towards Autonomous Machine Intelligence | Current automated systems have crucial limitations that need to be addressed before artificial intelligence can reach human-like levels and bring new technological revolutions. Among others, our societies still lack Level 5 self-driving cars, domestic robots, and virtual assistants that learn reliable world models, rea... | ['Yann Lecun', 'Anna Dawid'] | 2023-06-05 | null | null | null | null | ['self-driving-cars'] | ['computer-vision'] | [-3.54655176e-01 5.30509591e-01 -4.06413704e-01 -1.30021527e-01
6.23835362e-02 -2.25152835e-01 1.02391469e+00 -4.27768677e-01
-1.36058524e-01 9.23387647e-01 1.55637234e-01 -8.03636312e-02
-3.74750763e-01 -1.03375340e+00 -3.36402237e-01 -4.83780593e-01
-6.75562546e-02 8.03462267e-01 1.10239647e-01 -6.11536920... | [4.528379917144775, 1.167421579360962] |
fa4176c1-1c0e-4bd0-9855-e634b59b73e0 | considerations-for-meaningful-sign-language | 2211.15464 | null | https://arxiv.org/abs/2211.15464v1 | https://arxiv.org/pdf/2211.15464v1.pdf | Considerations for meaningful sign language machine translation based on glosses | Automatic sign language processing is gaining popularity in Natural Language Processing (NLP) research (Yin et al., 2021). In machine translation (MT) in particular, sign language translation based on glosses is a prominent approach. In this paper, we review recent works on neural gloss translation. We find that limita... | ['Sarah Ebling', 'Annette Rios', 'Amit Moryossef', 'Zifan Jiang', 'Mathias Müller'] | 2022-11-28 | null | null | null | null | ['sign-language-translation'] | ['computer-vision'] | [ 3.83391351e-01 -1.32179230e-01 -6.84592187e-01 -5.51244795e-01
-1.04617941e+00 -4.78794843e-01 6.85039520e-01 -4.30657506e-01
-6.97462618e-01 9.16486144e-01 9.30232167e-01 -3.08720887e-01
3.27309482e-02 -2.90432036e-01 -4.61946964e-01 -3.59775007e-01
5.16836941e-01 4.47664738e-01 -2.48248074e-02 -2.35726640... | [9.166788101196289, -6.493274211883545] |
b0f28c59-5816-49ce-9108-34e6e4063d5d | probabilistic-relations-for-modelling | 2303.09692 | null | https://arxiv.org/abs/2303.09692v2 | https://arxiv.org/pdf/2303.09692v2.pdf | Probabilistic relations for modelling epistemic and aleatoric uncertainty: semantics and automated reasoning with theorem proving | Probabilistic programming combines general computer programming, statistical inference, and formal semantics to help systems make decisions when facing uncertainty. Probabilistic programs are ubiquitous, including having a significant impact on machine intelligence. While many probabilistic algorithms have been used in... | ['Simon Foster', 'Jim Woodcock', 'Kangfeng Ye'] | 2023-03-16 | null | null | null | null | ['probabilistic-programming', 'automated-theorem-proving', 'automated-theorem-proving'] | ['methodology', 'miscellaneous', 'reasoning'] | [-3.97726241e-03 3.67831439e-01 7.99019635e-02 -4.74562943e-01
-4.41562772e-01 -6.63165271e-01 1.00967908e+00 3.88011456e-01
-3.29137623e-01 6.53899670e-01 -1.57026753e-01 -8.87669623e-01
-6.67750180e-01 -1.02035224e+00 -7.57882714e-01 -6.88899577e-01
-7.09766984e-01 7.53190339e-01 7.40364254e-01 -2.73506671... | [8.544563293457031, 6.615253448486328] |
3ce99edb-205f-40ea-be98-c1658e8b1fe7 | symmetry-detection-of-occluded-point-cloud | 2003.06520 | null | https://arxiv.org/abs/2003.06520v1 | https://arxiv.org/pdf/2003.06520v1.pdf | Symmetry Detection of Occluded Point Cloud Using Deep Learning | Symmetry detection has been a classical problem in computer graphics, many of which using traditional geometric methods. In recent years, however, we have witnessed the arising deep learning changed the landscape of computer graphics. In this paper, we aim to solve the symmetry detection of the occluded point cloud in ... | ['Hongyan Jiang', 'Zhelun Wu', 'Siyun He'] | 2020-03-14 | null | null | null | null | ['symmetry-detection', 'occluded-3d-object-symmetry-detection'] | ['computer-vision', 'computer-vision'] | [ 8.18227082e-02 -2.72021145e-02 1.77805975e-01 -2.89324701e-01
-3.03523451e-01 -1.39417350e-01 4.67838228e-01 -2.58086622e-01
-2.01060995e-01 1.96347445e-01 -1.67025864e-01 -4.42589611e-01
-7.13470206e-03 -6.98558629e-01 -9.51383948e-01 -3.91949594e-01
6.44865707e-02 3.02491933e-01 1.81938440e-01 -2.16734767... | [8.255020141601562, -2.4914276599884033] |
ca547966-37d5-4d85-aadb-49dd146ee073 | self-supervision-can-be-a-good-few-shot | 2207.09176 | null | https://arxiv.org/abs/2207.09176v1 | https://arxiv.org/pdf/2207.09176v1.pdf | Self-Supervision Can Be a Good Few-Shot Learner | Existing few-shot learning (FSL) methods rely on training with a large labeled dataset, which prevents them from leveraging abundant unlabeled data. From an information-theoretic perspective, we propose an effective unsupervised FSL method, learning representations with self-supervision. Following the InfoMax principle... | ['Xinmei Tian', 'Yajing Liu', 'Jianzhuang Liu', 'Liangjian Wen', 'Yuning Lu'] | 2022-07-19 | null | null | null | null | ['cross-domain-few-shot-learning', 'few-shot-image-classification', 'unsupervised-few-shot-learning', 'unsupervised-few-shot-image-classification'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 4.44294184e-01 3.67954522e-01 -7.35569119e-01 -7.93761671e-01
-6.89561963e-01 -2.13997573e-01 6.26802504e-01 1.92616671e-01
-3.57882291e-01 8.19432735e-01 2.27840289e-01 1.56791881e-01
-8.37915391e-02 -9.29210722e-01 -6.22512043e-01 -6.71215236e-01
1.01277977e-02 4.77309138e-01 1.13102607e-01 -2.60506757... | [9.956490516662598, 3.021212577819824] |
ed675361-283f-4d9b-b55f-44f77a960153 | asking-clarifying-questions-in-open-domain | 1907.06554 | null | https://arxiv.org/abs/1907.06554v1 | https://arxiv.org/pdf/1907.06554v1.pdf | Asking Clarifying Questions in Open-Domain Information-Seeking Conversations | Users often fail to formulate their complex information needs in a single query. As a consequence, they may need to scan multiple result pages or reformulate their queries, which may be a frustrating experience. Alternatively, systems can improve user satisfaction by proactively asking questions of the users to clarify... | ['Fabio Crestani', 'Mohammad Aliannejadi', 'W. Bruce Croft', 'Hamed Zamani'] | 2019-07-15 | null | null | null | null | ['question-selection'] | ['natural-language-processing'] | [-4.06910181e-02 1.38864145e-01 -3.83754708e-02 -4.69510645e-01
-1.56424785e+00 -1.12110293e+00 7.07625985e-01 2.94725627e-01
-6.77072525e-01 6.43847227e-01 4.71567720e-01 -4.42261279e-01
-1.57526005e-02 -3.03565472e-01 -4.23946202e-01 -1.33713828e-02
4.10097510e-01 7.49739885e-01 6.04327142e-01 -6.99892581... | [12.065069198608398, 7.849086284637451] |
d48bc21d-0af3-4ca2-85d8-4e055b91a903 | interactive-language-acquisition-with-one | 1805.00462 | null | http://arxiv.org/abs/1805.00462v1 | http://arxiv.org/pdf/1805.00462v1.pdf | Interactive Language Acquisition with One-shot Visual Concept Learning through a Conversational Game | Building intelligent agents that can communicate with and learn from humans
in natural language is of great value. Supervised language learning is limited
by the ability of capturing mainly the statistics of training data, and is
hardly adaptive to new scenarios or flexible for acquiring new knowledge
without inefficie... | ['Wei Xu', 'Haonan Yu', 'Haichao Zhang'] | 2018-04-26 | interactive-language-acquisition-with-one-1 | https://aclanthology.org/P18-1243 | https://aclanthology.org/P18-1243.pdf | acl-2018-7 | ['grounded-language-learning'] | ['natural-language-processing'] | [ 1.41508549e-01 6.63924932e-01 1.11887991e-01 4.53590080e-02
-2.59931028e-01 -5.94368160e-01 1.04865122e+00 4.79461625e-02
-7.25130439e-01 1.27594972e+00 -1.18544929e-01 6.42873496e-02
-2.18495056e-01 -8.79964113e-01 -7.43241489e-01 -7.06089914e-01
-2.64790773e-01 9.85229492e-01 3.96229237e-01 -6.78720057... | [4.092974662780762, 1.2989144325256348] |
5a403bec-f52d-4790-aac4-577b310dbd01 | adaptive-linear-span-network-for-object | 2011.03972 | null | https://arxiv.org/abs/2011.03972v1 | https://arxiv.org/pdf/2011.03972v1.pdf | Adaptive Linear Span Network for Object Skeleton Detection | Conventional networks for object skeleton detection are usually hand-crafted. Although effective, they require intensive priori knowledge to configure representative features for objects in different scale granularity.In this paper, we propose adaptive linear span network (AdaLSN), driven by neural architecture search ... | ['Qixiang Ye', 'Jianbin Jiao', 'Yunjie Tian', 'Chang Liu'] | 2020-11-08 | null | null | null | null | ['object-skeleton-detection'] | ['computer-vision'] | [ 2.47044042e-01 -9.68775749e-02 -2.45236903e-01 -2.09358141e-01
-7.51610756e-01 -2.34298483e-01 3.42933267e-01 -1.48648053e-01
-2.57887244e-01 4.19336706e-01 5.00360206e-02 -2.39144921e-01
-6.25652790e-01 -8.81385326e-01 -6.17222250e-01 -4.63264018e-01
1.86111089e-02 9.30514932e-02 4.95218724e-01 -2.37962261... | [9.241721153259277, -0.3051709234714508] |
5b96c3cb-d76f-418e-b130-2cfd7ddf8efe | representing-additive-gaussian-processes-by | 2305.00324 | null | https://arxiv.org/abs/2305.00324v1 | https://arxiv.org/pdf/2305.00324v1.pdf | Representing Additive Gaussian Processes by Sparse Matrices | Among generalized additive models, additive Mat\'ern Gaussian Processes (GPs) are one of the most popular for scalable high-dimensional problems. Thanks to their additive structure and stochastic differential equation representation, back-fitting-based algorithms can reduce the time complexity of computing the posterio... | ['Liang Ding', 'HaoYuan Chen', 'Lu Zou'] | 2023-04-29 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 4.08164822e-02 -2.17388034e-01 4.04620767e-01 -1.90656275e-01
-1.19015086e+00 -4.71759498e-01 6.48523495e-02 2.53791898e-01
-7.74300337e-01 7.01329231e-01 -2.92715281e-01 -3.26660097e-01
-5.48694253e-01 -8.59732628e-01 -9.11798716e-01 -9.85291958e-01
-5.22028923e-01 7.13044107e-01 2.30568945e-01 1.46528214... | [6.709073543548584, 4.24590539932251] |
1ad072ec-2332-4f3a-ad9d-05132f584015 | learning-roles-with-emergent-social-value | 2301.13812 | null | https://arxiv.org/abs/2301.13812v1 | https://arxiv.org/pdf/2301.13812v1.pdf | Learning Roles with Emergent Social Value Orientations | Social dilemmas can be considered situations where individual rationality leads to collective irrationality. The multi-agent reinforcement learning community has leveraged ideas from social science, such as social value orientations (SVO), to solve social dilemmas in complex cooperative tasks. In this paper, by first i... | ['Hongyuan Zha', 'Jingyi Lu', 'Bo Jin', 'Xiangfeng Wang', 'Wenhao Li'] | 2023-01-31 | null | null | null | null | ['role-embedding'] | ['graphs'] | [ 1.28213153e-03 8.16022754e-01 -1.60394654e-01 -9.14548784e-02
2.86546856e-01 -2.67630041e-01 5.16014874e-01 1.33230514e-03
-7.01080263e-01 1.01786482e+00 5.23419261e-01 4.74152006e-02
-7.85172462e-01 -6.74997866e-01 1.15396798e-01 -1.16908967e+00
-4.20124799e-01 3.64204496e-01 -4.35095161e-01 -9.66712177... | [3.7948689460754395, 2.2220165729522705] |
2c86dda6-d5b0-4ed0-a5f2-b896a8b758a5 | multiplication-fusion-of-sparse-and | 2001.07090 | null | https://arxiv.org/abs/2001.07090v1 | https://arxiv.org/pdf/2001.07090v1.pdf | Multiplication fusion of sparse and collaborative-competitive representation for image classification | Representation based classification methods have become a hot research topic during the past few years, and the two most prominent approaches are sparse representation based classification (SRC) and collaborative representation based classification (CRC). CRC reveals that it is the collaborative representation rather t... | ['He-Feng Yin', 'Xiao-Jun Wu', 'Zi-Qi Li', 'Jun Sun'] | 2020-01-20 | null | null | null | null | ['sparse-representation-based-classification'] | ['computer-vision'] | [ 2.57627100e-01 -4.25903738e-01 -3.80057484e-01 -2.32464880e-01
-7.37163007e-01 1.05552776e-02 2.75951087e-01 -3.47232670e-02
6.42806618e-03 4.61018562e-01 4.92648482e-01 5.14929891e-02
-3.61203969e-01 -5.68348467e-01 -6.46549389e-02 -1.07533193e+00
4.67279166e-01 -3.02157402e-01 -1.90077368e-02 -1.47775292... | [12.461137771606445, 0.41552504897117615] |
1b8a5eda-e47f-4b46-8247-b54f9df15500 | modality-influence-in-multimodal-machine | 2306.06476 | null | https://arxiv.org/abs/2306.06476v1 | https://arxiv.org/pdf/2306.06476v1.pdf | Modality Influence in Multimodal Machine Learning | Multimodal Machine Learning has emerged as a prominent research direction across various applications such as Sentiment Analysis, Emotion Recognition, Machine Translation, Hate Speech Recognition, and Movie Genre Classification. This approach has shown promising results by utilizing modern deep learning architectures. ... | ['Hadda Cherroun', 'Attia Nehar', 'Slimane Bellaouar', 'Abdelhamid Haouhat'] | 2023-06-10 | null | null | null | null | ['multimodal-sentiment-analysis', 'multimodal-emotion-recognition', 'genre-classification', 'sentiment-analysis', 'multimodal-sentiment-analysis', 'multimodal-emotion-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision', 'natural-language-processing', 'natural-language-processing', 'speech'] | [ 2.35078886e-01 -3.10241699e-01 -3.99011731e-01 -2.73824245e-01
-6.45163417e-01 -4.94726151e-01 9.32251513e-01 2.75848657e-01
-4.60219741e-01 4.57331955e-01 2.77067959e-01 -1.05806828e-01
-1.20702930e-01 -2.34226331e-01 -3.17736626e-01 -8.39531779e-01
1.62323698e-01 -6.09496869e-02 -4.96345729e-01 -2.86119491... | [12.987695693969727, 5.306820392608643] |
e01f83ef-7c2f-4c09-a599-089556f94fec | x-pool-cross-modal-language-video-attention | 2203.15086 | null | https://arxiv.org/abs/2203.15086v1 | https://arxiv.org/pdf/2203.15086v1.pdf | X-Pool: Cross-Modal Language-Video Attention for Text-Video Retrieval | In text-video retrieval, the objective is to learn a cross-modal similarity function between a text and a video that ranks relevant text-video pairs higher than irrelevant pairs. However, videos inherently express a much wider gamut of information than texts. Instead, texts often capture sub-regions of entire videos an... | ['Guangwei Yu', 'Animesh Garg', 'Maksims Volkovs', 'Keyvan Golestan', 'Junwei Ma', 'Noel Vouitsis', 'Satya Krishna Gorti'] | 2022-03-28 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Gorti_X-Pool_Cross-Modal_Language-Video_Attention_for_Text-Video_Retrieval_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Gorti_X-Pool_Cross-Modal_Language-Video_Attention_for_Text-Video_Retrieval_CVPR_2022_paper.pdf | cvpr-2022-1 | ['video-text-retrieval'] | ['computer-vision'] | [ 2.04359755e-01 -5.77418745e-01 -3.16563994e-01 -3.30010533e-01
-1.17068076e+00 -4.91496712e-01 6.85437799e-01 4.94074114e-02
-4.25327808e-01 3.17464918e-01 6.88845634e-01 1.63009554e-01
-3.04909591e-02 -4.10194844e-01 -8.54252279e-01 -6.30454600e-01
1.39038742e-01 4.29935679e-02 2.18033552e-01 -7.27316290... | [10.229463577270508, 0.8641865253448486] |
dc09c2d1-48d4-46e6-84ad-7e2b35424c95 | adventures-in-mathematical-reasoning | 2008.09067 | null | https://arxiv.org/abs/2008.09067v1 | https://arxiv.org/pdf/2008.09067v1.pdf | Adventures in Mathematical Reasoning | "Mathematics is not a careful march down a well-cleared highway, but a journey into a strange wilderness, where the explorers often get lost. Rigour should be a signal to the historian that the maps have been made, and the real explorers have gone elsewhere." W.S. Anglin, the Mathematical Intelligencer, 4 (4), 1982. | ['Toby Walsh'] | 2020-08-20 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [-3.43614876e-01 -2.97021475e-02 -2.26951048e-01 -3.02986681e-01
-3.12807709e-01 -5.45941949e-01 6.65661037e-01 -1.71368215e-02
-2.89317697e-01 1.04723048e+00 2.39425510e-01 -8.95665765e-01
-4.56913412e-01 -7.98526824e-01 -5.33119500e-01 -2.88447887e-01
-3.48033518e-01 5.97817004e-01 3.96847799e-02 -8.68429840... | [8.906855583190918, 6.264492034912109] |
bb23a7e3-c11b-47f5-b1f2-40176f7946f5 | rethinking-alignment-in-video-super | 2207.08494 | null | https://arxiv.org/abs/2207.08494v2 | https://arxiv.org/pdf/2207.08494v2.pdf | Rethinking Alignment in Video Super-Resolution Transformers | The alignment of adjacent frames is considered an essential operation in video super-resolution (VSR). Advanced VSR models, including the latest VSR Transformers, are generally equipped with well-designed alignment modules. However, the progress of the self-attention mechanism may violate this common sense. In this pap... | ['Chao Dong', 'Yujiu Yang', 'Xintao Wang', 'Liangbin Xie', 'Jinjin Gu', 'Shuwei Shi'] | 2022-07-18 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 1.92786977e-01 -2.15899020e-01 -3.22981447e-01 -1.65358275e-01
-4.52016681e-01 -3.72254074e-01 2.59215802e-01 -6.03750944e-01
-7.77713731e-02 5.18386066e-01 3.60672861e-01 -1.75609514e-01
-3.76811973e-03 -7.51250565e-01 -7.40621448e-01 -7.47876942e-01
2.32451454e-01 2.01397222e-02 5.29330552e-01 -6.10539079... | [11.064596176147461, -1.8610283136367798] |
5af0c09b-08e3-4ea3-a936-cede99c57c3f | the-bussgang-decomposition-of-non-linear | 2005.01597 | null | https://arxiv.org/abs/2005.01597v1 | https://arxiv.org/pdf/2005.01597v1.pdf | The Bussgang Decomposition of Non-Linear Systems: Basic Theory and MIMO Extensions | Many of the systems that appear in various signal processing applications are non-linear, for example, due to hardware impairments such as non-linear amplifiers and finite-resolution quantization. The Bussgang decomposition is a popular tool for analyzing the performance of systems that involve such non-linear componen... | ['Emil Björnson', 'Özlem Tuğfe Demir'] | 2020-05-04 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [ 2.79079080e-01 -3.44442934e-01 -2.10467577e-01 -1.75745517e-01
-5.50527334e-01 -8.96338105e-01 2.61090755e-01 -1.70173950e-03
-2.39577796e-02 5.53163886e-01 1.28494278e-01 -3.83400887e-01
-2.63278604e-01 -3.30794364e-01 -4.86929178e-01 -1.04626656e+00
-4.32869107e-01 -4.19549085e-02 1.58025231e-02 -2.97683120... | [15.396178245544434, 5.471036434173584] |
c019530b-4f76-43c4-bea1-6babec2bdb72 | document-level-relation-extraction-with-cross | 2303.03912 | null | https://arxiv.org/abs/2303.03912v1 | https://arxiv.org/pdf/2303.03912v1.pdf | Document-level Relation Extraction with Cross-sentence Reasoning Graph | Relation extraction (RE) has recently moved from the sentence-level to document-level, which requires aggregating document information and using entities and mentions for reasoning. Existing works put entity nodes and mention nodes with similar representations in a document-level graph, whose complex edges may incur re... | ['Fujun Hua', 'Ling Tian', 'Lizong Zhang', 'Zhao Kang', 'Hongfei Liu'] | 2023-03-07 | null | null | null | null | ['document-level-relation-extraction'] | ['natural-language-processing'] | [-1.59088597e-01 4.11001503e-01 -3.36171657e-01 -3.39562595e-01
-5.96459329e-01 -6.41961753e-01 4.54712570e-01 7.59634852e-01
-1.40762106e-01 6.52026832e-01 5.07213116e-01 -3.11448932e-01
-4.78509992e-01 -1.26709473e+00 -3.67724150e-01 2.94921417e-02
-5.77895567e-02 2.67516941e-01 4.90075380e-01 -4.78758425... | [9.226067543029785, 8.61160945892334] |
cfb5fe49-a5c6-4c57-9808-b8b057f0e937 | reduction-of-overfitting-in-diabetes | 1707.08386 | null | http://arxiv.org/abs/1707.08386v1 | http://arxiv.org/pdf/1707.08386v1.pdf | Reduction of Overfitting in Diabetes Prediction Using Deep Learning Neural Network | Augmented accuracy in prediction of diabetes will open up new frontiers in
health prognostics. Data overfitting is a performance-degrading issue in
diabetes prognosis. In this study, a prediction system for the disease of
diabetes is pre-sented where the issue of overfitting is minimized by using the
dropout method. De... | ['Jong-Myon Kim', 'Md. Rashedul Islam', 'Akm Ashiquzzaman', 'Abdul Kawsar Tushar'] | 2017-07-26 | null | null | null | null | ['diabetes-prediction'] | ['medical'] | [ 6.22537546e-02 3.22788298e-01 -4.27904189e-01 -8.01080406e-01
-4.60148811e-01 5.43003321e-01 3.92562337e-02 5.88805914e-01
-5.29927433e-01 1.17819786e+00 2.36096367e-01 -1.57993540e-01
-2.43814200e-01 -6.01175070e-01 -5.16791582e-01 -6.60814047e-01
-2.58475095e-01 6.66750669e-01 -2.57925779e-01 -4.30202082... | [8.02283763885498, 5.866252422332764] |
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