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69243588-f55e-4cfd-8c89-dd0db34a4ab8 | unpaired-deep-image-dehazing-using | 2203.07677 | null | https://arxiv.org/abs/2203.07677v2 | https://arxiv.org/pdf/2203.07677v2.pdf | Unpaired Deep Image Dehazing Using Contrastive Disentanglement Learning | We offer a practical unpaired learning based image dehazing network from an unpaired set of clear and hazy images. This paper provides a new perspective to treat image dehazing as a two-class separated factor disentanglement task, i.e, the task-relevant factor of clear image reconstruction and the task-irrelevant facto... | ['Longgang Dai', 'Pengpeng Li', 'Caihua Kong', 'Yufeng Huang', 'Yufeng Li', 'Zhuoran Zheng', 'Zhentao Fan', 'Xiang Chen'] | 2022-03-15 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 5.96846104e-01 2.26393670e-01 1.50598213e-01 -8.24348852e-02
-8.59320283e-01 -4.56338912e-01 7.11042821e-01 -1.00155294e+00
-1.83900431e-01 7.89831698e-01 4.06788200e-01 -6.54368624e-02
-9.50277224e-02 -7.83977032e-01 -7.64296889e-01 -1.44039953e+00
2.05196202e-01 7.36931935e-02 -3.67976308e-01 -3.52752745... | [10.9655122756958, -3.1074671745300293] |
1c176413-2249-4092-bafc-be1bf4f80663 | e-3-pose-energy-efficient-edge-assisted-multi | 2301.09015 | null | https://arxiv.org/abs/2301.09015v1 | https://arxiv.org/pdf/2301.09015v1.pdf | E$^3$Pose: Energy-Efficient Edge-assisted Multi-camera System for Multi-human 3D Pose Estimation | Multi-human 3D pose estimation plays a key role in establishing a seamless connection between the real world and the virtual world. Recent efforts adopted a two-stage framework that first builds 2D pose estimations in multiple camera views from different perspectives and then synthesizes them into 3D poses. However, th... | ['Jie Xu', 'Letian Zhang'] | 2023-01-21 | null | null | null | null | ['3d-pose-estimation'] | ['computer-vision'] | [ 1.62884295e-02 -3.77006412e-01 -9.99380350e-02 1.93773080e-02
-5.44707596e-01 -4.90258723e-01 -6.57982156e-02 -3.08007389e-01
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-8.23922157e-02 2.51179457e-01 1.48982808e-01 1.32290512... | [7.203241348266602, -1.0757694244384766] |
df0cd4c0-a3b0-4ad8-b5e9-92d04b038102 | a-learning-based-method-for-online-adjustment | 2008.06262 | null | https://arxiv.org/abs/2008.06262v1 | https://arxiv.org/pdf/2008.06262v1.pdf | A Learning-based Method for Online Adjustment of C-arm Cone-Beam CT Source Trajectories for Artifact Avoidance | During spinal fusion surgery, screws are placed close to critical nerves suggesting the need for highly accurate screw placement. Verifying screw placement on high-quality tomographic imaging is essential. C-arm Cone-beam CT (CBCT) provides intraoperative 3D tomographic imaging which would allow for immediate verificat... | ['Jan-Nico Zäch', 'Russell Taylor', 'Cong Gao', 'Mathias Unberath', 'Nassir Navab', 'Mareike Thies', 'Andreas Maier'] | 2020-08-14 | null | null | null | null | ['tomographic-reconstructions'] | ['medical'] | [ 1.43230513e-01 1.64668068e-01 -1.22378498e-01 -2.62551606e-01
-7.40865469e-01 -3.97996485e-01 2.53192514e-01 4.96480435e-01
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-9.67312828e-02 1.03796220e+00 1.92862377e-01 -2.59050969... | [13.689618110656738, -2.779433488845825] |
5c787bae-95fd-4f84-9bb5-f17e56b9056f | invariance-to-quantile-selection-in | 2212.14262 | null | https://arxiv.org/abs/2212.14262v1 | https://arxiv.org/pdf/2212.14262v1.pdf | Invariance to Quantile Selection in Distributional Continuous Control | In recent years distributional reinforcement learning has produced many state of the art results. Increasingly sample efficient Distributional algorithms for the discrete action domain have been developed over time that vary primarily in the way they parameterize their approximations of value distributions, and how the... | ['Ioannis Iossifidis', 'Tobias Glasmachers', 'Muhammad Saif-ur-Rehman', 'Felix Grün'] | 2022-12-29 | null | null | null | null | ['distributional-reinforcement-learning', 'continuous-control'] | ['methodology', 'playing-games'] | [-1.49886802e-01 4.51802909e-02 -3.43794107e-01 -1.12601966e-01
-8.36065114e-01 -8.04392159e-01 1.12551379e+00 1.47073880e-01
-8.92155707e-01 1.15600538e+00 6.29323781e-01 -3.94307196e-01
-4.51354086e-01 -7.51453519e-01 -3.99036258e-01 -8.22683394e-01
-2.21806094e-02 9.14514303e-01 2.06986368e-01 -4.54039186... | [4.057559013366699, 2.5656254291534424] |
24971cd6-71d0-4f6c-918c-f778e387f236 | federated-non-negative-matrix-factorization | 2205.13300 | null | https://arxiv.org/abs/2205.13300v1 | https://arxiv.org/pdf/2205.13300v1.pdf | Federated Non-negative Matrix Factorization for Short Texts Topic Modeling with Mutual Information | Non-negative matrix factorization (NMF) based topic modeling is widely used in natural language processing (NLP) to uncover hidden topics of short text documents. Usually, training a high-quality topic model requires large amount of textual data. In many real-world scenarios, customer textual data should be private and... | ['Jing Xiao', 'Qinliang Su', 'Ruiyi Zhang', 'Jianzong Wang', 'Shijing Si'] | 2022-05-26 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [-3.33238095e-01 8.54370371e-03 -5.18965065e-01 -6.56316876e-01
-8.92889977e-01 -2.63871878e-01 6.66144192e-01 -5.83633445e-02
-4.18201312e-02 4.62991506e-01 4.86129731e-01 -2.20234901e-01
-2.00611740e-01 -8.44393253e-01 -3.88871223e-01 -8.90082479e-01
1.35685191e-01 8.35016131e-01 -1.43254012e-01 1.85899615... | [10.388615608215332, 6.929931163787842] |
44d20490-5c12-45f4-9d54-2c331db6bdc4 | the-effects-of-data-size-on-automated-essay | 2108.13275 | null | https://arxiv.org/abs/2108.13275v1 | https://arxiv.org/pdf/2108.13275v1.pdf | The effects of data size on Automated Essay Scoring engines | We study the effects of data size and quality on the performance on Automated Essay Scoring (AES) engines that are designed in accordance with three different paradigms; A frequency and hand-crafted feature-based model, a recurrent neural network model, and a pretrained transformer-based language model that is fine-tun... | ['Paul van Wamelen', 'Amy Harris', 'Milan Patel', 'Susan Lottridge', 'Amir Jafari', 'Christopher Ormerod'] | 2021-08-30 | null | null | null | null | ['automated-essay-scoring'] | ['natural-language-processing'] | [ 3.79843600e-02 -2.81908125e-01 -1.51107639e-01 -3.61102402e-01
-4.29016113e-01 -6.52729332e-01 5.80365956e-01 2.34145969e-01
-6.90786123e-01 4.63421017e-01 4.07122344e-01 -7.87775099e-01
-3.91089916e-01 -8.97437632e-01 -3.16620439e-01 -1.24330834e-01
4.18278515e-01 2.95397222e-01 -1.69743113e-02 -5.53103805... | [11.348877906799316, 9.306035041809082] |
4cdf3af8-c8fd-4c71-ae38-0153b3d4b870 | a-study-of-the-effect-of-resolving-negation | 1907.03871 | null | https://arxiv.org/abs/1907.03871v1 | https://arxiv.org/pdf/1907.03871v1.pdf | A Study of the Effect of Resolving Negation and Sentiment Analysis in Recognizing Text Entailment for Arabic | Recognizing the entailment relation showed that its influence to extract the semantic inferences in wide-ranging natural language processing domains (text summarization, question answering, etc.) and enhanced the results of their output. For Arabic language, few attempts concerns with Arabic entailment problem. This pa... | ['Fatima T. AL-Khawaldeh'] | 2019-07-05 | null | null | null | null | ['negation-detection'] | ['natural-language-processing'] | [ 5.07474124e-01 5.11471093e-01 1.72811523e-01 -6.14307284e-01
-1.47990555e-01 -1.01678669e+00 8.72839630e-01 8.84124637e-01
-3.23890686e-01 1.12170577e+00 3.44191402e-01 -7.58217812e-01
-2.06822082e-01 -9.74009693e-01 -6.09523118e-01 -2.29817659e-01
2.26162240e-01 5.40494442e-01 1.04363821e-01 -9.62375104... | [11.05753231048584, 6.929227828979492] |
7eb86337-92ff-4fb2-afce-e210f94b92e5 | unsupervised-learning-for-computational | 1612.08425 | null | http://arxiv.org/abs/1612.08425v2 | http://arxiv.org/pdf/1612.08425v2.pdf | Unsupervised Learning for Computational Phenotyping | With large volumes of health care data comes the research area of
computational phenotyping, making use of techniques such as machine learning to
describe illnesses and other clinical concepts from the data itself. The
"traditional" approach of using supervised learning relies on a domain expert,
and has two main limit... | ['Chris Hodapp'] | 2016-12-26 | null | null | null | null | ['computational-phenotyping'] | ['medical'] | [-1.51034847e-01 5.92141994e-04 -2.47425497e-01 -4.49807197e-01
-1.96266577e-01 -6.49090230e-01 7.62162879e-02 7.91775763e-01
4.14665043e-02 6.85098708e-01 2.13100448e-01 -7.81436682e-01
-5.04528880e-01 -7.03681111e-01 -1.60665169e-01 -7.54290283e-01
-3.53859782e-01 8.66054416e-01 -1.47282392e-01 1.13266118... | [7.956182956695557, 6.127477645874023] |
e2ddaec7-a974-4eff-9709-07c1a48b53ea | danzero-mastering-guandan-game-with | 2210.17087 | null | https://arxiv.org/abs/2210.17087v1 | https://arxiv.org/pdf/2210.17087v1.pdf | DanZero: Mastering GuanDan Game with Reinforcement Learning | Card game AI has always been a hot topic in the research of artificial intelligence. In recent years, complex card games such as Mahjong, DouDizhu and Texas Hold'em have been solved and the corresponding AI programs have reached the level of human experts. In this paper, we are devoted to developing an AI program for a... | ['Houqiang Li', 'Wengang Zhou', 'Youpeng Zhao', 'Jian Zhao', 'Yudong Lu'] | 2022-10-31 | null | null | null | null | ['card-games'] | ['playing-games'] | [-5.14950037e-01 -2.46170074e-01 2.73845568e-02 1.81284919e-01
-2.14994073e-01 -5.85611761e-01 5.69198072e-01 -4.55276668e-01
-4.79145229e-01 9.49545026e-01 -2.43672878e-01 -2.78397292e-01
4.45021354e-02 -1.19309330e+00 -4.04502153e-01 -6.75890625e-01
-1.26815215e-01 1.00665677e+00 5.88644266e-01 -6.53248250... | [3.5773870944976807, 1.5423684120178223] |
2e568929-62c6-44cc-91f0-08baa9696f4b | protein-design-with-guided-discrete-diffusion | 2305.20009 | null | https://arxiv.org/abs/2305.20009v1 | https://arxiv.org/pdf/2305.20009v1.pdf | Protein Design with Guided Discrete Diffusion | A popular approach to protein design is to combine a generative model with a discriminative model for conditional sampling. The generative model samples plausible sequences while the discriminative model guides a search for sequences with high fitness. Given its broad success in conditional sampling, classifier-guided ... | ['Andrew Gordon Wilson', 'Kyunghyun Cho', 'Arvind Rajpal', 'Julien Lafrance-Vanasse', 'Isidro Hotzel', 'Tim G. J. Rudner', 'Nathan C. Frey', 'Samuel Stanton', 'Nate Gruver'] | 2023-05-31 | null | null | null | null | ['protein-design', 'bayesian-optimization'] | ['medical', 'methodology'] | [ 5.84533453e-01 2.02667713e-01 -2.87679315e-01 -3.21824670e-01
-5.34714997e-01 -4.26378280e-01 3.42705369e-01 -7.46714249e-02
-3.59606504e-01 9.93968010e-01 3.19148362e-01 -2.67306447e-01
-2.31232911e-01 -4.64384556e-01 -9.16002989e-01 -8.69751453e-01
1.68891564e-01 4.49312121e-01 1.80449076e-02 -2.34785318... | [4.857175350189209, 5.490725040435791] |
eb96f737-7494-4656-8193-a5b886c259df | reinforcement-learning-with-latent-flow-1 | 2101.01857 | null | https://arxiv.org/abs/2101.01857v1 | https://arxiv.org/pdf/2101.01857v1.pdf | Reinforcement Learning with Latent Flow | Temporal information is essential to learning effective policies with Reinforcement Learning (RL). However, current state-of-the-art RL algorithms either assume that such information is given as part of the state space or, when learning from pixels, use the simple heuristic of frame-stacking to implicitly capture tempo... | ['Michael Laskin', 'Pieter Abbeel', 'Yang Gao', 'Aravind Rajeswaran', 'Aravind Srinivas', 'Xiaofei Wang', 'Wenling Shang'] | 2021-01-06 | reinforcement-learning-with-latent-flow | http://proceedings.neurips.cc/paper/2021/hash/ba3c5fe1d6d6708b5bffaeb6942b7e04-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/ba3c5fe1d6d6708b5bffaeb6942b7e04-Paper.pdf | neurips-2021-12 | ['montezumas-revenge'] | ['playing-games'] | [ 1.79575637e-01 -3.67130518e-01 -6.59902513e-01 1.68566436e-01
-7.00225413e-01 -4.79990751e-01 1.08049011e+00 -3.95608991e-01
-7.08285630e-01 9.24143851e-01 2.48981580e-01 -4.55686271e-01
-2.62352854e-01 -5.33325434e-01 -9.83362257e-01 -1.00393116e+00
-5.57973564e-01 4.69976246e-01 2.96856582e-01 -5.95820069... | [4.2708024978637695, 1.5357977151870728] |
783905cc-305a-4da5-a3d9-ed7bef34851d | yolo-pose-enhancing-yolo-for-multi-person | 2204.06806 | null | https://arxiv.org/abs/2204.06806v1 | https://arxiv.org/pdf/2204.06806v1.pdf | YOLO-Pose: Enhancing YOLO for Multi Person Pose Estimation Using Object Keypoint Similarity Loss | We introduce YOLO-pose, a novel heatmap-free approach for joint detection, and 2D multi-person pose estimation in an image based on the popular YOLO object detection framework. Existing heatmap based two-stage approaches are sub-optimal as they are not end-to-end trainable and training relies on a surrogate L1 loss tha... | ['Deepak Poddar', 'Manu Mathew', 'Soyeb Nagori', 'Debapriya Maji'] | 2022-04-14 | null | null | null | null | ['multi-person-pose-estimation'] | ['computer-vision'] | [-4.36491191e-01 -1.65004600e-02 1.02928340e-01 -3.50881994e-01
-1.20295191e+00 -5.26827157e-01 3.37133050e-01 2.24893540e-02
-6.53548002e-01 5.63913405e-01 -2.01112822e-01 3.22358966e-01
6.18430227e-02 -4.28124726e-01 -9.98805225e-01 -5.33595562e-01
-2.17243582e-01 8.56887817e-01 6.02904379e-01 7.89528340... | [7.358706951141357, -0.706452488899231] |
e18534f8-8edc-4811-b611-a988a0c95e6f | writeahead2-mining-lexical-grammar-patterns | null | null | https://aclanthology.org/N15-3022 | https://aclanthology.org/N15-3022.pdf | WriteAhead2: Mining Lexical Grammar Patterns for Assisted Writing | null | ['Jason Chang', 'Jim Chang'] | 2015-06-01 | null | null | null | naacl-2015-6 | ['grammatical-error-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... | [-7.393991470336914, 3.7183563709259033] |
2496f5db-9831-4d63-889b-aa8158aee312 | pedestrian-alignment-network-for-large-scale | 1707.00408 | null | http://arxiv.org/abs/1707.00408v1 | http://arxiv.org/pdf/1707.00408v1.pdf | Pedestrian Alignment Network for Large-scale Person Re-identification | Person re-identification (person re-ID) is mostly viewed as an image
retrieval problem. This task aims to search a query person in a large image
pool. In practice, person re-ID usually adopts automatic detectors to obtain
cropped pedestrian images. However, this process suffers from two types of
detector errors: excess... | ['Yi Yang', 'Zhedong Zheng', 'Liang Zheng'] | 2017-07-03 | null | null | null | null | ['large-scale-person-re-identification'] | ['computer-vision'] | [-7.20716342e-02 -3.79308164e-01 3.53633389e-02 -2.54932612e-01
-4.23025191e-01 -3.99921685e-01 6.47062719e-01 1.24722384e-02
-9.72436488e-01 4.86338228e-01 2.57085502e-01 4.41402286e-01
5.47780871e-01 -6.17005646e-01 -6.70947015e-01 -6.52907789e-01
2.89619058e-01 3.26818496e-01 2.63404757e-01 1.72410011... | [14.753361701965332, 0.853361964225769] |
e3198613-194d-47f0-83e3-6e148c8279ce | variational-deep-logic-network-for-joint | null | null | https://aclanthology.org/2021.cl-4.26 | https://aclanthology.org/2021.cl-4.26.pdf | Variational Deep Logic Network for Joint Inference of Entities and Relations | Abstract Currently, deep learning models have been widely adopted and achieved promising results on various application domains. Despite their intriguing performance, most deep learning models function as black boxes, lacking explicit reasoning capabilities and explanations, which are usually essential for complex prob... | ['Sinno Jialin Pan', 'Wenya Wang'] | null | null | null | null | cl-acl-2021-12 | ['relational-reasoning'] | ['natural-language-processing'] | [-1.04544401e-01 4.88342285e-01 -4.36091721e-01 -6.45434141e-01
-3.18322957e-01 -2.67428547e-01 7.69708753e-01 2.14042410e-01
-2.26562604e-01 6.97879732e-01 2.12953001e-01 -2.12113336e-01
-3.62484485e-01 -1.16620219e+00 -8.40731204e-01 -5.52229166e-01
4.98126745e-02 5.55934012e-01 1.07442982e-01 -2.10839987... | [9.3324613571167, 8.458518028259277] |
bad925de-4f43-4e05-a874-dd780bf9af79 | histogram-layers-for-texture-analysis | 2001.00215 | null | https://arxiv.org/abs/2001.00215v9 | https://arxiv.org/pdf/2001.00215v9.pdf | Histogram Layers for Texture Analysis | We present a histogram layer for artificial neural networks (ANNs). An essential aspect of texture analysis is the extraction of features that describe the distribution of values in local spatial regions. The proposed histogram layer directly computes the spatial distribution of features for texture analysis and parame... | ['Weihuang Xu', 'Joshua Peeples', 'Alina Zare'] | 2020-01-01 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 2.69016236e-01 -1.37021571e-01 1.19201116e-01 -6.43471718e-01
-6.74763143e-01 2.98269279e-02 5.89678168e-01 3.51248235e-01
-2.99216509e-01 5.69921732e-01 -3.18273567e-02 -1.74949199e-01
-3.66838962e-01 -1.61436903e+00 -7.87484765e-01 -1.07463789e+00
-5.23640811e-01 4.38309759e-01 3.39030385e-01 -5.00434339... | [10.18730640411377, -0.1943131685256958] |
4fc3a0a3-c499-4633-b54d-aa196cbf351a | the-creative-frontier-of-generative-ai | 2306.03601 | null | https://arxiv.org/abs/2306.03601v1 | https://arxiv.org/pdf/2306.03601v1.pdf | The Creative Frontier of Generative AI: Managing the Novelty-Usefulness Tradeoff | In this paper, drawing inspiration from the human creativity literature, we explore the optimal balance between novelty and usefulness in generative Artificial Intelligence (AI) systems. We posit that overemphasizing either aspect can lead to limitations such as hallucinations and memorization. Hallucinations, characte... | ['Hannah Chang', 'Anirban Mukherjee'] | 2023-06-06 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 3.43059123e-01 2.02381492e-01 -8.15302804e-02 -1.18173763e-01
-6.45636916e-02 -6.13096714e-01 8.05751324e-01 1.30629718e-01
-2.52279073e-01 7.98347890e-01 3.79120797e-01 6.51438609e-02
-2.59628892e-01 -8.35811675e-01 -1.74840346e-01 -1.61490604e-01
4.49575603e-01 4.19127047e-01 -4.90199357e-01 -1.40877336... | [11.61330795288086, 8.773098945617676] |
d2ec65a1-5848-4e7c-83f4-334548873816 | exploring-the-robustness-of-distributional | 2109.08776 | null | https://arxiv.org/abs/2109.08776v5 | https://arxiv.org/pdf/2109.08776v5.pdf | Exploring the Training Robustness of Distributional Reinforcement Learning against Noisy State Observations | In real scenarios, state observations that an agent observes may contain measurement errors or adversarial noises, misleading the agent to take suboptimal actions or even collapse while training. In this paper, we study the training robustness of distributional Reinforcement Learning (RL), a class of state-of-the-art m... | ['Linglong Kong', 'Shangling Jui', 'Yingnan Zhao', 'Ke Sun'] | 2021-09-17 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-1.13799244e-01 1.98324054e-01 4.99991849e-02 -1.72151521e-01
-1.12356341e+00 -8.29790056e-01 6.95321977e-01 2.61836872e-02
-7.36723959e-01 9.03546214e-01 1.80815496e-02 -6.54373169e-01
-2.72805214e-01 -5.17065585e-01 -8.74357045e-01 -1.31539547e+00
-3.50354910e-01 3.20689499e-01 -1.79062948e-01 -6.57113269... | [4.264456748962402, 2.47283935546875] |
cec9bc2c-21c2-4693-98db-dd5215946059 | morphosyntactic-tagging-with-a-meta-bilstm | 1805.08237 | null | http://arxiv.org/abs/1805.08237v1 | http://arxiv.org/pdf/1805.08237v1.pdf | Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings | The rise of neural networks, and particularly recurrent neural networks, has
produced significant advances in part-of-speech tagging accuracy. One
characteristic common among these models is the presence of rich initial word
encodings. These encodings typically are composed of a recurrent
character-based representation... | ['Ryan Mcdonald', 'Joshua Maynez', 'Goncalo Simoes', 'Emily Pitler', 'Daniel Andor', 'Bernd Bohnet'] | 2018-05-21 | morphosyntactic-tagging-with-a-meta-bilstm-1 | https://aclanthology.org/P18-1246 | https://aclanthology.org/P18-1246.pdf | acl-2018-7 | ['morphological-tagging'] | ['natural-language-processing'] | [ 2.80513495e-01 6.85590580e-02 -5.43794692e-01 -4.63098139e-01
-6.05174541e-01 -5.25140643e-01 4.90289271e-01 5.28795481e-01
-7.26588786e-01 5.56646287e-01 6.00830138e-01 -4.91094321e-01
2.21625865e-01 -7.52161145e-01 -3.77480477e-01 -5.53869545e-01
-2.72655308e-01 3.74173403e-01 3.69053662e-01 -4.66537178... | [10.389246940612793, 9.792105674743652] |
3dc8779c-efe7-4581-b51f-55d2517ab150 | language-is-not-all-you-need-aligning | 2302.14045 | null | https://arxiv.org/abs/2302.14045v2 | https://arxiv.org/pdf/2302.14045v2.pdf | Language Is Not All You Need: Aligning Perception with Language Models | A big convergence of language, multimodal perception, action, and world modeling is a key step toward artificial general intelligence. In this work, we introduce Kosmos-1, a Multimodal Large Language Model (MLLM) that can perceive general modalities, learn in context (i.e., few-shot), and follow instructions (i.e., zer... | ['Barun Patra', 'Furu Wei', 'Xia Song', 'Subhojit Som', 'Vishrav Chaudhary', 'Johan Bjorck', 'Zewen Chi', 'Kriti Aggarwal', 'Qiang Liu', 'Owais Khan Mohammed', 'Lei Cui', 'Tengchao Lv', 'Shuming Ma', 'Saksham Singhal', 'Yaru Hao', 'Wenhui Wang', 'Li Dong', 'Shaohan Huang'] | 2023-02-27 | null | null | null | null | ['optical-character-recognition'] | ['computer-vision'] | [ 2.78568953e-01 4.41486314e-02 -4.28969860e-02 -4.55615193e-01
-9.44582164e-01 -6.98065817e-01 8.75155747e-01 1.85288116e-03
-4.95555103e-01 3.72796863e-01 3.68210644e-01 -3.91413063e-01
2.69750893e-01 -3.39663446e-01 -9.86699402e-01 -2.99341470e-01
2.86347717e-01 6.26768351e-01 -1.49718374e-01 -3.35640788... | [10.939318656921387, 1.4934486150741577] |
ca595ac3-98e5-48e9-b026-101ea5708dbf | fh-gan-face-hallucination-and-recognition | 1905.06537 | null | https://arxiv.org/abs/1905.06537v1 | https://arxiv.org/pdf/1905.06537v1.pdf | FH-GAN: Face Hallucination and Recognition using Generative Adversarial Network | There are many factors affecting visual face recognition, such as low resolution images, aging, illumination and pose variance, etc. One of the most important problem is low resolution face images which can result in bad performance on face recognition. Most of the general face recognition algorithms usually assume a s... | ['Usman Ali', 'Te Qi', 'Hongtao Lu', 'Bayram Bayramli'] | 2019-05-16 | null | null | null | null | ['face-hallucination'] | ['computer-vision'] | [ 2.77487218e-01 -8.42992887e-02 2.19763502e-01 -3.15536171e-01
-5.28813422e-01 -5.80764338e-02 5.33686459e-01 -1.26455200e+00
2.07111970e-01 8.26418042e-01 3.12590390e-01 3.96699250e-01
1.41587481e-01 -9.75349545e-01 -7.43521333e-01 -6.78379655e-01
4.27346349e-01 1.77767023e-01 -4.09299582e-01 -1.77128986... | [12.826318740844727, -0.022560568526387215] |
3b16cf67-4243-4b67-83b0-e8154cb71ca7 | mitoem-dataset-large-scale-3d-mitochondria | null | null | https://donglaiw.github.io/paper/2020_miccai_mitoEM.pdf | https://donglaiw.github.io/paper/2020_miccai_mitoEM.pdf | MitoEM Dataset: Large-scale 3D Mitochondria Instance Segmentation from EM Images | Electron microscopy (EM) allows the identification of intracellular organelles such as mitochondria, providing insights for clinical and scientific studies. However, public mitochondria segmentation datasets only contain hundreds of instances with simple shapes. It is unclear if existing methods achieving human-level a... | ['Hanspeter Pfister', 'Jeff Lichtman', 'Ignacio Arganda-Carreras', 'Xueying Wang', 'Won-Dong Jang', 'Aarush Gupta', 'Xin Huang', 'Wenjie Yin', 'Xingyu Liu', 'Nils Wendt', 'Daniel Franco-Barranco', 'Zudi Lin', 'Donglai Wei'] | 2020-10-04 | null | null | null | medical-image-computing-and-computer-assisted-2 | ['3d-instance-segmentation-1'] | ['computer-vision'] | [-1.28767326e-01 -1.32839799e-01 -9.76710096e-02 -1.19962774e-01
-8.49227726e-01 -8.77035499e-01 2.22281277e-01 3.77203822e-01
-3.75895351e-01 1.20703149e+00 -2.44768247e-01 -2.99495757e-01
1.43646896e-01 -3.57845902e-01 -6.93873644e-01 -8.24733019e-01
-9.03753862e-02 9.25805449e-01 2.44439676e-01 4.79066759... | [14.301351547241211, -3.132519245147705] |
542a6ae6-e0f5-4251-8219-8448e7ca2eee | firerisk-a-remote-sensing-dataset-for-fire | 2303.07035 | null | https://arxiv.org/abs/2303.07035v1 | https://arxiv.org/pdf/2303.07035v1.pdf | FireRisk: A Remote Sensing Dataset for Fire Risk Assessment with Benchmarks Using Supervised and Self-supervised Learning | In recent decades, wildfires, as widespread and extremely destructive natural disasters, have caused tremendous property losses and fatalities, as well as extensive damage to forest ecosystems. Many fire risk assessment projects have been proposed to prevent wildfires, but GIS-based methods are inherently challenging t... | ['Michael Kirley', 'Xinye Wanyan', 'Sachith Seneviratne', 'Shuchang Shen'] | 2023-03-13 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 5.10122359e-01 -2.54964381e-01 -1.55120715e-01 -1.57138258e-01
-2.35235959e-01 -3.34121823e-01 7.82701492e-01 5.39402403e-02
-4.98004317e-01 9.00133908e-01 5.31675696e-01 -7.68105626e-01
-3.01770478e-01 -1.76931703e+00 -3.20283204e-01 -6.87013149e-01
-5.39209545e-01 5.74929686e-03 -2.54415840e-01 -6.37792051... | [9.421952247619629, -1.4454344511032104] |
6a5ae693-5498-46e6-ac87-6a62384861b9 | anifacegan-animatable-3d-aware-face-image | 2210.06465 | null | https://arxiv.org/abs/2210.06465v1 | https://arxiv.org/pdf/2210.06465v1.pdf | AniFaceGAN: Animatable 3D-Aware Face Image Generation for Video Avatars | Although 2D generative models have made great progress in face image generation and animation, they often suffer from undesirable artifacts such as 3D inconsistency when rendering images from different camera viewpoints. This prevents them from synthesizing video animations indistinguishable from real ones. Recently, 3... | ['Xin Tong', 'Qifeng Chen', 'Fangyun Wei', 'Jiaolong Yang', 'Yu Deng', 'Yue Wu'] | 2022-10-12 | null | null | null | null | ['face-model'] | ['computer-vision'] | [ 4.22178954e-02 2.76446998e-01 -1.21162966e-01 -2.84840852e-01
-6.06844246e-01 -8.61805320e-01 6.78889513e-01 -1.10265577e+00
4.06322062e-01 5.72780371e-01 2.22183838e-01 -7.52272876e-03
4.43101436e-01 -7.48300612e-01 -8.45295727e-01 -9.18436170e-01
3.32045972e-01 3.86561185e-01 -4.32684869e-01 -2.20825627... | [12.612902641296387, -0.29772523045539856] |
bbedec63-2f48-44c6-862b-0c6ecbe219f6 | improving-multi-task-generalization-ability | 2204.02725 | null | https://arxiv.org/abs/2204.02725v2 | https://arxiv.org/pdf/2204.02725v2.pdf | Match-Prompt: Improving Multi-task Generalization Ability for Neural Text Matching via Prompt Learning | Text matching is a fundamental technique in both information retrieval and natural language processing. Text matching tasks share the same paradigm that determines the relationship between two given texts. The relationships vary from task to task, e.g.~relevance in document retrieval, semantic alignment in paraphrase i... | ['Xueqi Cheng', 'HuaWei Shen', 'Liang Pang', 'Shicheng Xu'] | 2022-04-06 | null | null | null | null | ['paraphrase-identification'] | ['natural-language-processing'] | [ 5.35444677e-01 -6.01331949e-01 -4.36898917e-01 -7.45028079e-01
-9.56340730e-01 -5.81529319e-01 7.19112039e-01 1.65537655e-01
-5.34100711e-01 1.46822125e-01 1.71113551e-01 -1.48926437e-01
-3.78467917e-01 -5.17262638e-01 -5.71020961e-01 -1.12932086e-01
6.46289170e-01 6.52904034e-01 3.99912983e-01 -5.07870734... | [11.104310989379883, 8.188011169433594] |
6d51e4a7-b29e-4406-ac46-5d9ada777b20 | graph-tensor-networks-an-intuitive-framework | 2303.13565 | null | https://arxiv.org/abs/2303.13565v1 | https://arxiv.org/pdf/2303.13565v1.pdf | Graph Tensor Networks: An Intuitive Framework for Designing Large-Scale Neural Learning Systems on Multiple Domains | Despite the omnipresence of tensors and tensor operations in modern deep learning, the use of tensor mathematics to formally design and describe neural networks is still under-explored within the deep learning community. To this end, we introduce the Graph Tensor Network (GTN) framework, an intuitive yet rigorous graph... | ['Danilo P. Mandic', 'Kriton Konstantinidis', 'Yao Lei Xu'] | 2023-03-23 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [-2.71045178e-01 -2.12995317e-02 -3.46946530e-02 -4.24754888e-01
2.83026189e-01 -5.09544611e-01 8.12631130e-01 -8.21205005e-02
-1.53009564e-01 4.13480401e-01 -3.14486288e-02 -4.90280509e-01
-5.34250140e-01 -7.72610128e-01 -4.17286366e-01 -7.72755742e-01
-8.16370904e-01 1.28668517e-01 7.46523356e-03 -3.92655909... | [6.249281883239746, 5.105773448944092] |
2172bd73-a050-4ccd-bcc9-f65c8ec9c07a | huber-additive-models-for-non-stationary-time | null | null | https://openreview.net/forum?id=9kpuB2bgnim | https://openreview.net/pdf?id=9kpuB2bgnim | Huber Additive Models for Non-stationary Time Series Analysis | Sparse additive models have shown promising flexibility and interpretability in processing time series data. However, existing methods usually assume the time series data to be stationary and the innovation is sampled from a Gaussian distribution. Both assumptions are too stringent for heavy-tailed and non-stationary t... | ['DaCheng Tao', 'Hong Chen', 'Fengxiang He', 'Xianrui Zhong', 'Yingjie Wang'] | 2021-09-29 | null | null | null | iclr-2022-4 | ['additive-models'] | ['methodology'] | [ 1.47760227e-01 -2.90585130e-01 -9.93027911e-02 -3.41066897e-01
-6.97308302e-01 -4.60362166e-01 5.44422865e-01 1.38583541e-01
-2.53030807e-01 8.45541060e-01 9.38496217e-02 -3.26005876e-01
-5.74936330e-01 -5.83629251e-01 -7.01000035e-01 -1.14607036e+00
-3.05371493e-01 3.71954560e-01 4.24680579e-03 2.10512683... | [6.9487409591674805, 3.7449095249176025] |
1fb288b4-fe6f-438d-af0e-4d504592b003 | forest-parameter-prediction-by-multiobjective | 2306.11103 | null | https://arxiv.org/abs/2306.11103v1 | https://arxiv.org/pdf/2306.11103v1.pdf | Forest Parameter Prediction by Multiobjective Deep Learning of Regression Models Trained with Pseudo-Target Imputation | In prediction of forest parameters with data from remote sensing (RS), regression models have traditionally been trained on a small sample of ground reference data. This paper proposes to impute this sample of true prediction targets with data from an existing RS-based prediction map that we consider as pseudo-targets.... | ['Lennart Noordermeer', 'Terje Gobakken', 'Erik Næsset', 'Michael Kampffmeyer', 'Stian N. Anfinsen', 'Sara Björk'] | 2023-06-19 | null | null | null | null | ['imputation', 'parameter-prediction', 'imputation', 'imputation'] | ['computer-vision', 'miscellaneous', 'miscellaneous', 'time-series'] | [ 9.33659554e-01 1.30383343e-01 -5.16826153e-01 -6.78099394e-01
-9.66686010e-01 -4.56501007e-01 7.69474089e-01 -1.00789264e-01
-4.77303594e-01 1.48444438e+00 1.96858287e-01 -6.45240128e-01
-3.45915586e-01 -1.11738229e+00 -7.78028905e-01 -7.52127528e-01
-3.78975987e-01 6.31227314e-01 -2.83573478e-01 -4.30663675... | [9.474952697753906, -1.5252399444580078] |
617502e6-1e62-4838-91c8-10b1264d9947 | generative-zero-shot-network-quantization | 2101.08430 | null | https://arxiv.org/abs/2101.08430v1 | https://arxiv.org/pdf/2101.08430v1.pdf | Generative Zero-shot Network Quantization | Convolutional neural networks are able to learn realistic image priors from numerous training samples in low-level image generation and restoration. We show that, for high-level image recognition tasks, we can further reconstruct "realistic" images of each category by leveraging intrinsic Batch Normalization (BN) stati... | ['Jian Cheng', 'Peisong Wang', 'Qinghao Hu', 'Xiangyu He'] | 2021-01-21 | null | null | null | null | ['data-free-quantization', 'data-free-quantization'] | ['computer-vision', 'methodology'] | [ 6.91923976e-01 4.22637314e-01 -2.33481199e-01 -5.26390016e-01
-1.14267159e+00 -2.24468082e-01 8.06950986e-01 -3.15554738e-01
-4.89570260e-01 8.22659552e-01 2.81882256e-01 4.41938788e-02
2.68899411e-01 -9.05853271e-01 -1.14803481e+00 -8.95494759e-01
5.38288295e-01 2.04498947e-01 -3.68049324e-01 2.57553142... | [11.6868896484375, -0.31729620695114136] |
1764ea06-ee09-488d-90ba-67ecbfcbaa31 | unifying-semi-supervised-and-robust-learning | null | null | https://openreview.net/forum?id=r1gp1jRN_4 | https://openreview.net/pdf?id=r1gp1jRN_4 | Unifying semi-supervised and robust learning by mixup | Supervised deep learning methods require cleanly labeled large-scale datasets, but collecting such data is difficult and sometimes impossible. There exist two popular frameworks to alleviate this problem: semi-supervised learning and robust learning to label noise. Although these frameworks relax the restriction of sup... | ['Hideki Nakayama', 'Ryuichiro Hataya'] | 2019-03-24 | null | null | null | iclr-workshop-lld-2019 | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 1.14393331e-01 1.84905544e-01 -3.81443232e-01 -6.64234936e-01
-1.33922100e+00 -7.13066697e-01 3.56033027e-01 8.70280564e-02
-4.26658422e-01 9.71511662e-01 2.13592261e-01 -4.92247082e-02
-1.58738092e-01 -8.17593753e-01 -9.93760943e-01 -1.08342075e+00
4.28322583e-01 3.13162774e-01 -3.78736466e-01 1.95941925... | [9.41451358795166, 3.92673397064209] |
e65ae6d9-cbcb-4faf-aff8-55a74e3e8351 | geometric-multi-model-fitting-with-a-convex | 1706.01553 | null | http://arxiv.org/abs/1706.01553v1 | http://arxiv.org/pdf/1706.01553v1.pdf | Geometric Multi-Model Fitting with a Convex Relaxation Algorithm | We propose a novel method to fit and segment multi-structural data via convex
relaxation. Unlike greedy methods --which maximise the number of inliers-- this
approach efficiently searches for a soft assignment of points to models by
minimising the energy of the overall classification. Our approach is similar to
state-o... | ['Pedro Pinies', 'Lina M. Paz', 'Paul Amayo', 'Paul Newman'] | 2017-06-05 | geometric-multi-model-fitting-with-a-convex-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Amayo_Geometric_Multi-Model_Fitting_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Amayo_Geometric_Multi-Model_Fitting_CVPR_2018_paper.pdf | cvpr-2018-6 | ['homography-estimation'] | ['computer-vision'] | [ 3.83536726e-01 1.30873412e-01 2.06633657e-02 -2.63831735e-01
-9.69045758e-01 -6.36670470e-01 3.08268607e-01 6.64491504e-02
-3.39698374e-01 4.46905822e-01 -1.30981624e-01 -5.28385229e-02
-3.52811754e-01 -6.67148590e-01 -7.35819221e-01 -6.49387479e-01
1.23786584e-01 7.19443619e-01 2.96462387e-01 -3.74025591... | [7.974825859069824, -2.4483113288879395] |
98b3f410-9319-4d9e-94f0-293657aa8981 | align-deep-features-for-oriented-object | 2008.09397 | null | https://arxiv.org/abs/2008.09397v3 | https://arxiv.org/pdf/2008.09397v3.pdf | Align Deep Features for Oriented Object Detection | The past decade has witnessed significant progress on detecting objects in aerial images that are often distributed with large scale variations and arbitrary orientations. However most of existing methods rely on heuristically defined anchors with different scales, angles and aspect ratios and usually suffer from sever... | ['Gui-Song Xia', 'Jiaming Han', 'Jian Ding', 'Jie Li'] | 2020-08-21 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [ 8.85572284e-02 -4.95997965e-01 1.23971507e-01 -4.25111145e-01
-3.70553285e-01 -6.62967622e-01 2.94681549e-01 -1.52119562e-01
-3.50917548e-01 1.64089128e-01 -1.75856695e-01 -2.08372399e-02
-2.05440685e-01 -8.94802868e-01 -5.21633863e-01 -7.76493251e-01
-1.16397776e-01 -1.04253300e-01 7.14408159e-01 -3.04965019... | [8.728741645812988, -0.8277407884597778] |
bb572170-b1a1-4c93-ad4d-2bc0db4c6469 | supporting-medical-relation-extraction-via | 2208.13472 | null | https://arxiv.org/abs/2208.13472v1 | https://arxiv.org/pdf/2208.13472v1.pdf | Supporting Medical Relation Extraction via Causality-Pruned Semantic Dependency Forest | Medical Relation Extraction (MRE) task aims to extract relations between entities in medical texts. Traditional relation extraction methods achieve impressive success by exploring the syntactic information, e.g., dependency tree. However, the quality of the 1-best dependency tree for medical texts produced by an out-of... | ['Xiaohui Hu', 'Chengbo Jiao', 'Zheng Lian', 'Jiangmeng Li', 'Yifan Jin'] | 2022-08-29 | null | https://aclanthology.org/2022.coling-1.216 | https://aclanthology.org/2022.coling-1.216.pdf | coling-2022-10 | ['medical-relation-extraction'] | ['medical'] | [ 3.98500413e-01 7.91806936e-01 -4.94768590e-01 -5.18838286e-01
-4.94384497e-01 -4.00381684e-02 2.29088545e-01 3.28477234e-01
-7.06448480e-02 8.00391555e-01 6.63449645e-01 -6.73755348e-01
-1.51455864e-01 -8.54054272e-01 -4.72789496e-01 -4.65197891e-01
-1.58265859e-01 3.20869565e-01 4.73380461e-02 -1.44832149... | [8.7108736038208, 8.796212196350098] |
34deccb2-d67f-4902-a86d-7d566db2001c | vus-at-iwslt-2021-a-finetuned-pipeline-for | null | null | https://aclanthology.org/2021.iwslt-1.12 | https://aclanthology.org/2021.iwslt-1.12.pdf | VUS at IWSLT 2021: A Finetuned Pipeline for Offline Speech Translation | In this technical report, we describe the fine-tuned ASR-MT pipeline used for the IWSLT shared task. We remove less useful speech samples by checking WER with an ASR model, and further train a wav2vec and Transformers-based ASR module based on the filtered data. In addition, we cleanse the errata that can interfere wit... | ['Won Ik Cho', 'Jihyung Moon', 'Jungyoon Choi', 'Minji Jung', 'Youngki Moon', 'Yong Rae Jo'] | null | null | null | null | acl-iwslt-2021-8 | ['boundary-detection'] | ['computer-vision'] | [ 4.09184724e-01 4.15218323e-01 1.95779294e-01 -5.04088104e-01
-1.46866918e+00 -7.28894055e-01 4.40772653e-01 -1.07069127e-01
-6.14327192e-01 8.21854711e-01 5.75543523e-01 -9.34730172e-01
4.42919850e-01 -3.71724963e-01 -6.41389668e-01 -3.69870156e-01
4.99862492e-01 5.17205596e-01 1.51971459e-01 -4.48704392... | [14.403006553649902, 7.163525581359863] |
4dc56ef9-aeae-4f12-aa4e-321e0250580d | pareto-secure-machine-learning-psml | 2307.01292 | null | https://arxiv.org/abs/2307.01292v1 | https://arxiv.org/pdf/2307.01292v1.pdf | Pareto-Secure Machine Learning (PSML): Fingerprinting and Securing Inference Serving Systems | With the emergence of large foundational models, model-serving systems are becoming popular. In such a system, users send the queries to the server and specify the desired performance metrics (e.g., accuracy, latency, etc.). The server maintains a set of models (model zoo) in the back-end and serves the queries based o... | ['Alexey Tumanov', 'Sibin Mohan', 'Tianhao Wang', 'Somesh Jha', 'Shahab Nikkhoo', 'Prahlad Jasti', 'Manav Agrawal', 'Jui-Tse Hung', 'Debopam Sanyal'] | 2023-07-03 | null | null | null | null | ['model-extraction', 'model-extraction'] | ['adversarial', 'methodology'] | [ 6.96242750e-02 -4.55491364e-01 -1.74467042e-01 -1.35244682e-01
-9.58500087e-01 -1.07433915e+00 -2.23238934e-02 6.03196137e-02
-4.76335287e-01 6.81492537e-02 -6.66000247e-01 -1.00763929e+00
2.60390760e-03 -1.01528656e+00 -7.25955904e-01 -2.76764840e-01
-4.45931494e-01 3.48980814e-01 5.47542214e-01 1.03579260... | [5.683661460876465, 7.309680938720703] |
18865bc2-e009-4e47-95bc-1e9498e10710 | on-the-development-of-a-large-scale-corpus | null | null | http://www.ep.liu.se/ecp/article.asp?issue=155&article=012&volume= | http://www.ep.liu.se/ecp/155/012/ecp18155012.pdf | On the Development of a Large Scale Corpus for Native Language Identification | Native Language Identification (NLI) is the task of identifying an author’s native language from their writings in a second language. In this paper, we introduce a new corpus (italki), which is larger than the current corpora. It can be used for training machine learning based systems for classifying and identifying th... | ['Sardar Jaf', 'Thomas Hudson'] | 2018-12-10 | null | null | null | tlt17-2018-12 | ['native-language-identification'] | ['natural-language-processing'] | [-1.79406390e-01 1.48680145e-02 -2.46486798e-01 -8.59851465e-02
-8.96091342e-01 -1.16406500e+00 1.05123103e+00 -6.97766244e-02
-6.79078758e-01 6.44079506e-01 3.14324111e-01 -6.71932697e-01
5.75047061e-02 -2.58637220e-01 -4.81041402e-01 -1.20940380e-01
3.12720567e-01 6.85544133e-01 -9.12510008e-02 -1.69570353... | [10.409684181213379, 10.505586624145508] |
6e56d016-4664-4329-add3-94f8cd2af680 | learning-object-centric-neural-scattering | 2303.06138 | null | https://arxiv.org/abs/2303.06138v3 | https://arxiv.org/pdf/2303.06138v3.pdf | Learning Object-Centric Neural Scattering Functions for Free-Viewpoint Relighting and Scene Composition | Photorealistic object appearance modeling from 2D images is a constant topic in vision and graphics. While neural implicit methods (such as Neural Radiance Fields) have shown high-fidelity view synthesis results, they cannot relight the captured objects. More recent neural inverse rendering approaches have enabled obje... | ['Jiajun Wu', 'Thomas Funkhouser', 'Ruohan Gao', 'Eric Ryan Chan', 'Yen-Yu Chang', 'Alireza Fathi', 'Michelle Guo', 'Hong-Xing Yu'] | 2023-03-10 | null | null | null | null | ['inverse-rendering'] | ['computer-vision'] | [ 5.30815244e-01 1.55173510e-01 4.03744072e-01 -3.91613722e-01
-1.37457564e-01 -4.46131110e-01 7.10214794e-01 -5.81913471e-01
2.95416653e-01 5.94328105e-01 2.81607267e-03 -2.27506474e-01
2.99244493e-01 -9.23825622e-01 -1.04219508e+00 -7.21284866e-01
3.87656510e-01 6.73302412e-01 1.55043483e-01 -4.24869098... | [9.60857105255127, -3.1497154235839844] |
3cb049bb-c1a1-4e6f-83e7-565b6ad8ce1e | hierarchical-automatic-power-plane-generation | 2210.16314 | null | https://arxiv.org/abs/2210.16314v2 | https://arxiv.org/pdf/2210.16314v2.pdf | Hierarchical Automatic Power Plane Generation with Genetic Optimization and Multilayer Perceptron | We present an automatic multilayer power plane generation method to accelerate the design of printed circuit boards (PCB). In PCB design, while automatic solvers have been developed to predict important indicators such as the IR-drop, power integrity, and signal integrity, the generation of the power plane itself still... | ['Levent Burak Kara', 'Mirko Spasojevic', 'Taylor Hogan', 'Elias Fallon', 'Devika Shanbhag', 'Xuliang Dong', 'Vinay Patil', 'Haiguang Liao'] | 2022-10-28 | null | null | null | null | ['contour-detection'] | ['computer-vision'] | [ 4.40455317e-01 1.30042121e-01 3.09781302e-02 -6.81430846e-03
-7.60488987e-01 -6.99748755e-01 -8.47864449e-02 2.81536549e-01
2.92487472e-01 6.59603536e-01 -4.53602701e-01 -3.45845848e-01
-9.42949533e-01 -8.64650130e-01 -6.32769525e-01 -6.55947983e-01
-3.96681577e-01 5.42353213e-01 3.14518601e-01 -1.74611986... | [5.87297248840332, 3.409100294113159] |
77d59747-c91e-4a7d-8339-f9984267c35e | few-shot-camouflaged-animal-detection-and | 2304.07444 | null | https://arxiv.org/abs/2304.07444v1 | https://arxiv.org/pdf/2304.07444v1.pdf | Few-shot Camouflaged Animal Detection and Segmentation | Camouflaged object detection and segmentation is a new and challenging research topic in computer vision. There is a serious issue of lacking data of camouflaged objects such as camouflaged animals in natural scenes. In this paper, we address the problem of few-shot learning for camouflaged object detection and segment... | ['Tam V. Nguyen', 'Minh-Triet Tran', 'Thanh-Toan Do', 'Thanh Duc Ngo', 'Vinh-Tiep Nguyen', 'Nhat-Duy Nguyen', 'Anh-Khoa Nguyen Vu', 'Thanh-Danh Nguyen'] | 2023-04-15 | null | null | null | null | ['camouflaged-object-segmentation'] | ['computer-vision'] | [ 3.56210321e-01 -4.60475832e-01 -1.69961333e-01 -3.94979212e-03
-5.58212876e-01 -4.31297392e-01 2.65335172e-01 -2.72041142e-01
-5.21216393e-01 7.48875201e-01 -4.36935484e-01 -1.79923289e-02
2.47900784e-01 -4.90954995e-01 -7.62348652e-01 -8.30349267e-01
2.47796580e-01 1.01045616e-01 8.22684765e-01 1.12962209... | [9.663321495056152, -0.18220816552639008] |
608be0ca-19a2-4ae8-a9c8-b8f141b1942b | relight-my-nerf-a-dataset-for-novel-view | 2304.10448 | null | https://arxiv.org/abs/2304.10448v1 | https://arxiv.org/pdf/2304.10448v1.pdf | ReLight My NeRF: A Dataset for Novel View Synthesis and Relighting of Real World Objects | In this paper, we focus on the problem of rendering novel views from a Neural Radiance Field (NeRF) under unobserved light conditions. To this end, we introduce a novel dataset, dubbed ReNe (Relighting NeRF), framing real world objects under one-light-at-time (OLAT) conditions, annotated with accurate ground-truth came... | ['Samuele Salti', 'Luigi Di Stefano', 'Daniele De Gregorio', 'Riccardo Spezialetti', 'Riccardo De Matteo', 'Marco Toschi'] | 2023-04-20 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Toschi_ReLight_My_NeRF_A_Dataset_for_Novel_View_Synthesis_and_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Toschi_ReLight_My_NeRF_A_Dataset_for_Novel_View_Synthesis_and_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-relighting'] | ['computer-vision'] | [ 2.45858133e-01 -2.10758656e-01 4.53823566e-01 -3.63018721e-01
-4.48564798e-01 -1.01866817e+00 7.52369046e-01 -7.23789811e-01
-7.58528337e-02 3.67783964e-01 2.22552996e-02 -1.32988229e-01
1.55245051e-01 -6.04940891e-01 -1.25855148e+00 -5.91290355e-01
1.67233273e-01 1.72076225e-01 -1.09620295e-01 -2.69656122... | [9.423449516296387, -3.0414936542510986] |
f61e8958-4cc7-4f18-ac38-823766399f48 | measuring-and-reducing-non-multifact | 2005.00789 | null | https://arxiv.org/abs/2005.00789v3 | https://arxiv.org/pdf/2005.00789v3.pdf | Is Multihop QA in DiRe Condition? Measuring and Reducing Disconnected Reasoning | Has there been real progress in multi-hop question-answering? Models often exploit dataset artifacts to produce correct answers, without connecting information across multiple supporting facts. This limits our ability to measure true progress and defeats the purpose of building multi-hop QA datasets. We make three cont... | ['Tushar Khot', 'Ashish Sabharwal', 'Niranjan Balasubramanian', 'Harsh Trivedi'] | 2020-05-02 | null | https://aclanthology.org/2020.emnlp-main.712 | https://aclanthology.org/2020.emnlp-main.712.pdf | emnlp-2020-11 | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 1.70371860e-01 9.95478094e-01 -5.37929386e-02 -3.15634638e-01
-1.61815727e+00 -1.08419168e+00 5.21899402e-01 2.86955267e-01
-4.35586393e-01 9.84475434e-01 7.30844557e-01 -8.68486345e-01
-2.36434132e-01 -9.82410908e-01 -1.11076224e+00 -1.67072222e-01
5.52523851e-01 8.19956899e-01 3.59623283e-01 -8.19251955... | [11.003938674926758, 7.969555377960205] |
ffb5d188-9bab-4e12-8659-d356f79f545b | on-robust-inference-in-time-series-regression | 2203.04080 | null | https://arxiv.org/abs/2203.04080v1 | https://arxiv.org/pdf/2203.04080v1.pdf | On Robust Inference in Time Series Regression | Least squares regression with heteroskedasticity and autocorrelation consistent (HAC) standard errors has proved very useful in cross section environments. However, several major difficulties, which are generally overlooked, must be confronted when transferring the HAC estimation technology to time series environments.... | ['Kun Ho Kim', 'George Kapetanios', 'Francis X. Diebold', 'Richard T. Baillie'] | 2022-03-08 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [-3.55818152e-01 -4.16468889e-01 -5.09540081e-01 -2.15500921e-01
-7.30068624e-01 -4.88688588e-01 5.11094511e-01 -3.15021098e-01
-4.31466252e-01 1.03291357e+00 1.29747331e-01 -8.53446901e-01
-4.86717671e-01 -4.90406781e-01 -4.60596204e-01 -5.77661335e-01
-1.32166892e-01 -1.37586966e-01 -3.07109237e-01 2.31051311... | [6.435882568359375, 4.207743167877197] |
ab62031a-49ff-4bb6-ad74-89866d6cb797 | label-flipping-data-poisoning-attack-against | 2208.08433 | null | https://arxiv.org/abs/2208.08433v1 | https://arxiv.org/pdf/2208.08433v1.pdf | Label Flipping Data Poisoning Attack Against Wearable Human Activity Recognition System | Human Activity Recognition (HAR) is a problem of interpreting sensor data to human movement using an efficient machine learning (ML) approach. The HAR systems rely on data from untrusted users, making them susceptible to data poisoning attacks. In a poisoning attack, attackers manipulate the sensor readings to contamin... | ['Tauhidul Alam', 'Diane A. Igoche', 'Peter Y. Wu', 'Ahmed Imteaj', 'Abdur R. Shahid'] | 2022-08-17 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 8.20889294e-01 3.76228020e-02 -2.39022061e-01 -9.29075927e-02
-2.89344072e-01 -8.75925899e-01 4.47687447e-01 3.61435562e-01
-5.58165312e-01 8.13414931e-01 -9.67097282e-02 -6.73384428e-01
-1.23176403e-01 -9.25605953e-01 -8.19569468e-01 -8.70689213e-01
-2.11775810e-01 -7.61416703e-02 1.41180545e-01 2.26240873... | [5.586164474487305, 7.1304402351379395] |
1e7c2be8-ff60-473d-8c71-d765895ab143 | correlated-time-series-self-supervised | 2306.06994 | null | https://arxiv.org/abs/2306.06994v2 | https://arxiv.org/pdf/2306.06994v2.pdf | Correlated Time Series Self-Supervised Representation Learning via Spatiotemporal Bootstrapping | Correlated time series analysis plays an important role in many real-world industries. Learning an efficient representation of this large-scale data for further downstream tasks is necessary but challenging. In this paper, we propose a time-step-level representation learning framework for individual instances via boots... | ['Fugee Tsung', 'Rui Zhao', 'Ziyue Li', 'Lei Bai', 'Luxuan Wang'] | 2023-06-12 | null | null | null | null | ['correlated-time-series-forecasting', 'time-series'] | ['time-series', 'time-series'] | [-1.10569783e-01 -2.13216990e-01 -1.91179439e-01 -4.86845434e-01
-1.09437037e+00 -4.86464798e-01 5.33422053e-01 9.98360217e-02
1.22919768e-01 7.64333248e-01 2.26654395e-01 -4.43992525e-01
-4.26973671e-01 -8.51504564e-01 -6.36945367e-01 -7.07137048e-01
-4.47848499e-01 1.87462851e-01 -1.88027665e-01 -3.02697182... | [6.9184675216674805, 2.8826940059661865] |
905f1a6f-cd3f-4d21-abc1-2371bd8e5631 | 3d-pick-mix-object-part-blending-in-joint | 1811.01068 | null | http://arxiv.org/abs/1811.01068v1 | http://arxiv.org/pdf/1811.01068v1.pdf | 3D Pick & Mix: Object Part Blending in Joint Shape and Image Manifolds | We present 3D Pick & Mix, a new 3D shape retrieval system that provides users
with a new level of freedom to explore 3D shape and Internet image collections
by introducing the ability to reason about objects at the level of their
constituent parts. While classic retrieval systems can only formulate simple
searches such... | ['Adrian Penate-Sanchez', 'Lourdes Agapito'] | 2018-11-02 | null | null | null | null | ['3d-shape-retrieval'] | ['computer-vision'] | [-1.93911850e-01 -4.55758758e-02 7.01445192e-02 -5.71288802e-02
-6.98947728e-01 -9.61626291e-01 4.46495175e-01 3.57202739e-01
3.07360172e-01 -4.12992500e-02 -6.60550641e-03 -5.31536818e-01
-7.64737010e-01 -6.61832750e-01 -1.78013548e-01 -4.35135543e-01
4.35101315e-02 8.89183700e-01 4.93182987e-01 -5.69029808... | [8.459378242492676, -3.505995273590088] |
95db263a-4975-4c97-843e-57ebdb82da6f | spatial-temporal-transformer-for-3d-point | 2110.09783 | null | https://arxiv.org/abs/2110.09783v1 | https://arxiv.org/pdf/2110.09783v1.pdf | Spatial-Temporal Transformer for 3D Point Cloud Sequences | Effective learning of spatial-temporal information within a point cloud sequence is highly important for many down-stream tasks such as 4D semantic segmentation and 3D action recognition. In this paper, we propose a novel framework named Point Spatial-Temporal Transformer (PST2) to learn spatial-temporal representation... | ['Yulan Guo', 'Qiuhong Ke', 'TingTing Xie', 'Hao liu', 'Yimin Wei'] | 2021-10-19 | null | null | null | null | ['3d-human-action-recognition'] | ['computer-vision'] | [ 2.71814913e-01 -3.13378602e-01 -1.19868621e-01 -3.42575938e-01
-7.27830529e-01 -2.90293157e-01 6.66810751e-01 2.26905923e-02
-2.09444731e-01 1.50788561e-01 2.88817972e-01 -6.18683826e-03
-7.46882409e-02 -6.54568911e-01 -8.78615022e-01 -5.74226618e-01
-1.51237562e-01 1.19715165e-02 8.87511075e-01 -9.62119102... | [8.36065673828125, 0.11850260943174362] |
8cb09920-a68e-4b03-ae05-947415cbe9e2 | zero-shot-audio-source-separation-through | 2112.07891 | null | https://arxiv.org/abs/2112.07891v4 | https://arxiv.org/pdf/2112.07891v4.pdf | Zero-shot Audio Source Separation through Query-based Learning from Weakly-labeled Data | Deep learning techniques for separating audio into different sound sources face several challenges. Standard architectures require training separate models for different types of audio sources. Although some universal separators employ a single model to target multiple sources, they have difficulty generalizing to unse... | ['Taylor Berg-Kirkpatrick', 'Shlomo Dubnov', 'Zejun Ma', 'Bilei Zhu', 'Xingjian Du', 'Ke Chen'] | 2021-12-15 | null | null | null | null | ['audio-tagging', 'audio-source-separation'] | ['audio', 'audio'] | [ 2.53462821e-01 -2.55333722e-01 -1.41432017e-01 -2.47255608e-01
-1.67728031e+00 -8.02611411e-01 1.88258499e-01 1.93927661e-01
-2.32568562e-01 1.50452957e-01 4.33466852e-01 -6.19398020e-02
3.35064456e-02 -3.86198312e-01 -5.69656551e-01 -5.74218392e-01
-1.82428971e-01 2.47501120e-01 5.20881295e-01 7.54333735... | [15.254385948181152, 5.393296718597412] |
e582c57b-148b-4592-8399-7744a36f4e8e | robust-cross-modal-knowledge-distillation-for | 2304.07775 | null | https://arxiv.org/abs/2304.07775v2 | https://arxiv.org/pdf/2304.07775v2.pdf | Robust Cross-Modal Knowledge Distillation for Unconstrained Videos | Cross-modal distillation has been widely used to transfer knowledge across different modalities, enriching the representation of the target unimodal one. Recent studies highly relate the temporal synchronization between vision and sound to the semantic consistency for cross-modal distillation. However, such semantic co... | ['Di Hu', 'Dejing Dou', 'Haoyi Xiong', 'Andong Deng', 'Xingjian Li', 'Wenke Xia'] | 2023-04-16 | null | null | null | null | ['audio-tagging', 'video-retrieval', 'action-recognition-in-videos'] | ['audio', 'computer-vision', 'computer-vision'] | [ 5.75138740e-02 -1.40928850e-01 1.18688624e-02 -3.01840812e-01
-9.81073797e-01 -6.52354300e-01 7.24814236e-01 -4.62414883e-03
-4.03862655e-01 5.34669936e-01 2.71025687e-01 7.53334016e-02
-2.33377382e-01 -3.95771980e-01 -7.53320754e-01 -8.34054589e-01
7.31497258e-02 -5.02088666e-03 4.22723502e-01 -8.56731739... | [10.118688583374023, 0.8346592783927917] |
66e51a8b-32fd-492d-9d70-0617ce56ac3e | intkb-a-verifiable-interactive-framework-for | null | null | https://aclanthology.org/2020.coling-main.490 | https://aclanthology.org/2020.coling-main.490.pdf | IntKB: A Verifiable Interactive Framework for Knowledge Base Completion | Knowledge bases (KBs) are essential for many downstream NLP tasks, yet their prime shortcoming is that they are often incomplete. State-of-the-art frameworks for KB completion often lack sufficient accuracy to work fully automated without human supervision. As a remedy, we propose : a novel interactive framework for KB... | ['Dennis Diefenbach', 'Stefan Feuerriegel', 'Guo Kunpeng', 'Bernhard Kratzwald'] | 2020-12-01 | null | null | null | coling-2020-8 | ['knowledge-base-completion', 'knowledge-base-completion'] | ['graphs', 'knowledge-base'] | [ 1.30467638e-01 6.65531993e-01 -2.00890437e-01 -1.75434709e-01
-1.00947320e+00 -6.46606445e-01 4.94199693e-01 4.99118596e-01
-4.13102746e-01 1.07602704e+00 -1.39932679e-02 -6.24765158e-01
-6.88102795e-03 -9.72923875e-01 -8.19746673e-01 -1.27334118e-01
2.53337562e-01 1.03616655e+00 6.44381106e-01 -5.30381083... | [9.718048095703125, 8.410362243652344] |
25b7dd26-61cd-4829-8f13-da927f571c39 | a-comprehensive-review-of-3d-convolutional | 2306.09418 | null | https://arxiv.org/abs/2306.09418v1 | https://arxiv.org/pdf/2306.09418v1.pdf | A comprehensive review of 3D convolutional neural network-based classification techniques of diseased and defective crops using non-UAV-based hyperspectral images | Hyperspectral imaging (HSI) is a non-destructive and contactless technology that provides valuable information about the structure and composition of an object. It can capture detailed information about the chemical and physical properties of agricultural crops. Due to its wide spectral range, compared with multispectr... | ['Christopher J. Henry', 'Christopher P. Bidinosti', 'Michael A. Beck', 'Nooshin Noshiri'] | 2023-06-15 | null | null | null | null | ['classification-of-hyperspectral-images'] | ['computer-vision'] | [ 5.35503328e-01 -5.57013571e-01 -1.13356441e-01 3.45107280e-02
-2.99997211e-01 -8.62510443e-01 9.48462728e-03 5.29093802e-01
-1.67970080e-02 3.98008794e-01 -4.16464984e-01 -6.45566046e-01
-3.48419368e-01 -1.14966893e+00 -2.52767295e-01 -1.13014317e+00
-2.38518000e-01 -1.50761204e-02 -1.70186296e-01 -5.35624027... | [9.258797645568848, -1.5756549835205078] |
71463e99-06e2-4fb1-bfb4-7d99c3050521 | local-optimization-achieves-global-optimality | 2305.04819 | null | https://arxiv.org/abs/2305.04819v1 | https://arxiv.org/pdf/2305.04819v1.pdf | Local Optimization Achieves Global Optimality in Multi-Agent Reinforcement Learning | Policy optimization methods with function approximation are widely used in multi-agent reinforcement learning. However, it remains elusive how to design such algorithms with statistical guarantees. Leveraging a multi-agent performance difference lemma that characterizes the landscape of multi-agent policy optimization,... | ['Jason D. Lee', 'Zhaoran Wang', 'Zhuoran Yang', 'Yulai Zhao'] | 2023-05-08 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [-4.34725106e-01 3.31267387e-01 -7.14294016e-01 3.86796057e-01
-9.36508358e-01 -7.66647160e-01 2.77801812e-01 4.07701313e-01
-9.44486320e-01 1.31346142e+00 6.40931800e-02 -4.59710568e-01
-4.68071401e-01 -4.50515717e-01 -7.47362614e-01 -9.13990259e-01
-4.08608675e-01 7.84913957e-01 -2.84634158e-02 -3.66109848... | [4.213704586029053, 2.6356866359710693] |
4c8489bb-a9a8-4368-a4c1-02efc6b61c52 | connect-the-dots-in-situ-4d-seismic | 2105.11622 | null | https://arxiv.org/abs/2105.11622v2 | https://arxiv.org/pdf/2105.11622v2.pdf | Connect the Dots: In Situ 4D Seismic Monitoring of CO2 Storage with Spatio-temporal CNNs | 4D seismic imaging has been widely used in CO$_2$ sequestration projects to monitor the fluid flow in the volumetric subsurface region that is not sampled by wells. Ideally, real-time monitoring and near-future forecasting would provide site operators with great insights to understand the dynamics of the subsurface res... | ['Youzuo Lin', 'Neill Symons', 'Brendt Wohlberg', 'Xitong Zhang', 'Shihang Feng'] | 2021-05-25 | null | null | null | null | ['seismic-imaging'] | ['miscellaneous'] | [-7.57551193e-02 -2.40344197e-01 3.49896699e-01 -1.35258555e-01
-8.09463382e-01 -3.36593479e-01 2.94084221e-01 3.43808010e-02
-3.87966573e-01 5.58130801e-01 2.94737101e-01 -4.78919476e-01
-2.16510177e-01 -1.22019708e+00 -7.62801051e-01 -7.27762222e-01
-7.06000388e-01 2.89639324e-01 2.03408554e-01 -1.50782049... | [6.864328861236572, 2.6064493656158447] |
2ae9b4d1-6ba0-4dd4-b8f8-f982a4f70b00 | model-compression-for-dnn-based-text | 2210.17326 | null | https://arxiv.org/abs/2210.17326v4 | https://arxiv.org/pdf/2210.17326v4.pdf | Model Compression for DNN-Based Text-Independent Speaker Verification Using Weight Quantization | DNN-based models achieve significant performance in the speaker verification (SV) task with substantial computation costs. Model compression can be applied to reduce the model size for lower resource consumption. The present study exploits weight quantization to compress two widely-used SV models, ECAPA-TDNN and ResNet... | ['Tan Lee', 'Jiong Wang', 'Zhaoyang Zhang', 'Wei Liu', 'Jingyu Li'] | 2022-10-31 | null | null | null | null | ['text-independent-speaker-verification', 'speaker-verification'] | ['speech', 'speech'] | [ 5.97367026e-02 2.28300959e-01 -2.95096666e-01 -4.94999886e-01
-7.82089889e-01 -2.28900779e-02 3.09651613e-01 -6.49018586e-02
-6.04488313e-01 3.92998844e-01 5.38369596e-01 -3.54486614e-01
-2.95818243e-02 -4.68129337e-01 -3.43531072e-01 -5.62718213e-01
8.34906623e-02 1.50897458e-01 1.70055643e-01 -3.76200825... | [14.27401351928711, 6.234599590301514] |
1bf78881-40a1-4841-9b68-c9541ddc77b2 | pacific-towards-proactive-conversational | 2210.08817 | null | https://arxiv.org/abs/2210.08817v2 | https://arxiv.org/pdf/2210.08817v2.pdf | PACIFIC: Towards Proactive Conversational Question Answering over Tabular and Textual Data in Finance | To facilitate conversational question answering (CQA) over hybrid contexts in finance, we present a new dataset, named PACIFIC. Compared with existing CQA datasets, PACIFIC exhibits three key features: (i) proactivity, (ii) numerical reasoning, and (iii) hybrid context of tables and text. A new task is defined accordin... | ['Tat-Seng Chua', 'Wai Lam', 'Wenxuan Zhang', 'Wenqiang Lei', 'Yang Deng'] | 2022-10-17 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [-1.41807329e-02 6.73688948e-02 4.79202658e-01 -5.74685633e-01
-1.74272668e+00 -8.61067712e-01 5.33066630e-01 3.34683396e-02
-1.79740608e-01 8.75510454e-01 7.20869899e-01 -5.98044157e-01
-3.21925543e-02 -9.18113768e-01 -5.19605815e-01 -5.12923658e-01
2.97376066e-01 7.60154784e-01 -6.56486228e-02 -8.94879043... | [11.793158531188965, 8.013343811035156] |
c8fe341f-f1f9-4c08-99e9-31952348abc1 | unsupervised-paraphrasing-consistency | null | null | https://aclanthology.org/2021.emnlp-main.430 | https://aclanthology.org/2021.emnlp-main.430.pdf | Unsupervised Paraphrasing Consistency Training for Low Resource Named Entity Recognition | Unsupervised consistency training is a way of semi-supervised learning that encourages consistency in model predictions between the original and augmented data. For Named Entity Recognition (NER), existing approaches augment the input sequence with token replacement, assuming annotations on the replaced positions uncha... | ['Ricardo Henao', 'Rui Wang'] | null | null | null | null | emnlp-2021-11 | ['low-resource-named-entity-recognition'] | ['natural-language-processing'] | [ 4.09049988e-01 5.05742610e-01 -5.09389639e-01 -9.28973794e-01
-5.99863589e-01 -6.73080087e-01 5.77391863e-01 3.27791274e-01
-5.62049568e-01 1.03608716e+00 3.68265152e-01 -2.33863726e-01
5.09303391e-01 -4.32628274e-01 -8.00787628e-01 -2.79455125e-01
4.15418565e-01 5.02109289e-01 -2.18148921e-02 1.12823077... | [9.780606269836426, 9.48792552947998] |
06f2a90b-c6fc-4960-a408-c4cd0e86cab1 | downstream-task-performance-of-bert-models | null | null | https://aclanthology.org/2022.lrec-1.451 | https://aclanthology.org/2022.lrec-1.451.pdf | Downstream Task Performance of BERT Models Pre-Trained Using Automatically De-Identified Clinical Data | Automatic de-identification is a cost-effective and straightforward way of removing large amounts of personally identifiable information from large and sensitive corpora. However, these systems also introduce errors into datasets due to their imperfect precision. These corruptions of the data may negatively impact the ... | ['Hercules Dalianis', 'Aron Henriksson', 'Anastasios Lamproudis', 'Thomas Vakili'] | null | null | null | null | lrec-2022-6 | ['de-identification'] | ['natural-language-processing'] | [ 5.92965662e-01 6.04745865e-01 7.98621699e-02 -5.62128305e-01
-1.36021161e+00 -8.00186872e-01 3.28159183e-01 7.13197768e-01
-1.05465722e+00 1.17737091e+00 5.98304272e-01 -2.87586629e-01
-2.31928110e-01 -3.25151533e-01 -3.22380066e-01 -6.37878418e-01
4.61292505e-01 7.77830124e-01 7.53465071e-02 -5.23088947... | [6.810202121734619, 7.039198875427246] |
42a3dcef-3e6d-4c1f-b247-5a29d12d374b | deformable-filter-convolution-for-point-cloud | 1907.13079 | null | https://arxiv.org/abs/1907.13079v1 | https://arxiv.org/pdf/1907.13079v1.pdf | Deformable Filter Convolution for Point Cloud Reasoning | Point clouds are the native output of many real-world 3D sensors. To borrow the success of 2D convolutional network architectures, a majority of popular 3D perception models voxelize the points, which can result in a loss of local geometric details that cannot be recovered. In this paper, we propose a novel learnable c... | ['Yuwen Xiong', 'Raquel Urtasun', 'Mengye Ren', 'Kelvin Wong', 'Renjie Liao'] | 2019-07-30 | null | null | null | null | ['lidar-semantic-segmentation'] | ['computer-vision'] | [ 2.19020978e-01 2.92405009e-01 3.98609862e-02 -5.77310979e-01
-5.15159428e-01 -7.55410612e-01 4.15811151e-01 2.53366619e-01
-2.40905926e-01 -4.69059721e-02 -2.75452733e-01 -3.57393563e-01
2.31258810e-01 -1.33582878e+00 -1.27245581e+00 -7.02642649e-02
7.25466525e-03 8.66263092e-01 7.96885252e-01 -5.15211485... | [8.060582160949707, -3.4418880939483643] |
0abd70e2-399d-475a-bf8c-7df894578250 | models-genesis-generic-autodidactic-models | 1908.06912 | null | https://arxiv.org/abs/1908.06912v1 | https://arxiv.org/pdf/1908.06912v1.pdf | Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis | Transfer learning from natural image to medical image has established as one of the most practical paradigms in deep learning for medical image analysis. However, to fit this paradigm, 3D imaging tasks in the most prominent imaging modalities (e.g., CT and MRI) have to be reformulated and solved in 2D, losing rich 3D a... | ['Jianming Liang', 'Michael B. Gotway', 'Zongwei Zhou', 'Ruibin Feng', 'Nima Tajbakhsh', 'Vatsal Sodha', 'Md Mahfuzur Rahman Siddiquee'] | 2019-08-19 | null | null | null | null | ['pulmonary-embolism-detection', 'lung-nodule-detection', 'lung-nodule-segmentation', 'liver-segmentation'] | ['medical', 'medical', 'medical', 'medical'] | [ 2.44251683e-01 5.92648804e-01 -2.22161382e-01 -5.43655038e-01
-9.20903206e-01 -4.21108633e-01 3.80557179e-01 -1.09530970e-01
-4.99514729e-01 5.93593121e-01 1.54084742e-01 -3.42666179e-01
-8.57265741e-02 -6.16788089e-01 -8.59376371e-01 -7.03901470e-01
-2.77635425e-01 6.78588271e-01 2.33954847e-01 -1.74431562... | [14.636885643005371, -2.189363718032837] |
54d4abf7-6d32-435e-b44a-20f89b2263c0 | deep-learning-techniques-for-humor-detection | null | null | https://aclanthology.org/W19-1307 | https://aclanthology.org/W19-1307.pdf | Deep Learning Techniques for Humor Detection in Hindi-English Code-Mixed Tweets | We propose bilingual word embeddings based on word2vec and fastText models (CBOW and Skip-gram) to address the problem of Humor detection in Hindi-English code-mixed tweets in combination with deep learning architectures. We focus on deep learning approaches which are not widely used on code-mixed data and analyzed the... | ['Suraj Tripathi', 'Radhika Mamidi', 'Koushik Reddy Sane', 'Sushmitha Reddy Sane'] | 2019-06-01 | null | null | null | ws-2019-6 | ['humor-detection'] | ['natural-language-processing'] | [-6.71017408e-01 -2.87008822e-01 -1.37820810e-01 -7.54044801e-02
-5.68721592e-01 -7.26318965e-03 6.93678796e-01 8.15507397e-02
-7.61819899e-01 6.13449752e-01 7.27787197e-01 -8.79657388e-01
5.04060268e-01 -9.00460720e-01 -4.64737356e-01 -1.33746132e-01
-1.38866846e-02 1.95197850e-01 -6.85308054e-02 -8.81647587... | [8.920578956604004, 10.898447036743164] |
3f9719c4-a0d3-42ee-a848-5c70075506f8 | talecrafter-interactive-story-visualization | 2305.18247 | null | https://arxiv.org/abs/2305.18247v2 | https://arxiv.org/pdf/2305.18247v2.pdf | TaleCrafter: Interactive Story Visualization with Multiple Characters | Accurate Story visualization requires several necessary elements, such as identity consistency across frames, the alignment between plain text and visual content, and a reasonable layout of objects in images. Most previous works endeavor to meet these requirements by fitting a text-to-image (T2I) model on a set of vide... | ['Yingqing He', 'Yujiu Yang', 'Ying Shan', 'Xintao Wang', 'Yong Zhang', 'Longyue Wang', 'Haoxin Chen', 'Menghan Xia', 'Xiaodong Cun', 'Youxin Pang', 'Yuan Gong'] | 2023-05-29 | null | null | null | null | ['story-visualization'] | ['computer-vision'] | [ 1.89905301e-01 -1.58247367e-01 2.02842221e-01 -3.12748432e-01
-2.58192897e-01 -9.23542917e-01 7.94679701e-01 -2.38247234e-02
1.39536679e-01 3.46243858e-01 2.12529331e-01 -2.38015264e-01
7.49032646e-02 -6.38403118e-01 -7.02138066e-01 -1.57839134e-01
1.56214118e-01 2.10338652e-01 5.09739041e-01 -2.30280697... | [11.234031677246094, 0.0020187068730592728] |
794d4877-51b0-449f-80ce-715486efe9f5 | a-specifically-designed-machine-learning | 2006.09067 | null | https://arxiv.org/abs/2006.09067v1 | https://arxiv.org/pdf/2006.09067v1.pdf | A specifically designed machine learning algorithm for GNSS position time series prediction and its applications in outlier and anomaly detection and earthquake prediction | We present a simple yet efficient supervised machine learning algorithm that is designed for the GNSS position time series prediction. This algorithm has four steps. First, the mean value of the time series is subtracted from it. Second, the trends in the time series are removed. Third, wavelets are used to separate th... | ['M. Kiani'] | 2020-06-16 | null | null | null | null | ['earthquake-prediction'] | ['computer-vision'] | [-2.04227850e-01 -2.28918165e-01 2.33320400e-01 -6.76762983e-02
-4.20918077e-01 -1.99474975e-01 2.77950972e-01 3.79001617e-01
-4.47536081e-01 5.92321813e-01 -5.55760190e-02 -4.68629122e-01
-3.77641261e-01 -7.36138701e-01 -3.98915082e-01 -1.01581097e+00
-6.92797065e-01 5.12419567e-02 2.17619449e-01 -4.83328223... | [6.6946940422058105, 2.974480152130127] |
7f20e1cf-50bc-4407-9ffb-fbc01fdefb48 | an-empirical-study-on-leveraging-position | 2109.01238 | null | https://arxiv.org/abs/2109.01238v1 | https://arxiv.org/pdf/2109.01238v1.pdf | An Empirical Study on Leveraging Position Embeddings for Target-oriented Opinion Words Extraction | Target-oriented opinion words extraction (TOWE) (Fan et al., 2019b) is a new subtask of target-oriented sentiment analysis that aims to extract opinion words for a given aspect in text. Current state-of-the-art methods leverage position embeddings to capture the relative position of a word to the target. However, the p... | ['Nikolaos Aletras', 'Kai Sun', 'Samuel Mensah'] | 2021-09-02 | null | https://aclanthology.org/2021.emnlp-main.722 | https://aclanthology.org/2021.emnlp-main.722.pdf | emnlp-2021-11 | ['target-oriented-opinion-words-extraction'] | ['natural-language-processing'] | [ 3.12010676e-01 2.69050181e-01 -3.13557863e-01 -4.47474062e-01
-7.41965890e-01 -5.64091206e-01 8.02681446e-01 5.00864863e-01
-5.38455784e-01 1.80302888e-01 6.92052841e-01 -6.42155588e-01
3.42752695e-01 -9.37215567e-01 -5.22894800e-01 -3.78927737e-01
-7.76802450e-02 1.34784445e-01 8.11150074e-02 -8.00257444... | [11.41247844696045, 6.7529616355896] |
02ac4345-4cbc-4c0d-b824-f5b47e6dc8fa | streaming-classification-of-variable-stars | 1912.02235 | null | https://arxiv.org/abs/1912.02235v1 | https://arxiv.org/pdf/1912.02235v1.pdf | Streaming Classification of Variable Stars | In the last years, automatic classification of variable stars has received substantial attention. Using machine learning techniques for this task has proven to be quite useful. Typically, machine learning classifiers used for this task require to have a fixed training set, and the training process is performed offline.... | ['Lukas Zorich', 'Karim Pichara', 'Pavlos Protopapas'] | 2019-12-04 | null | null | null | null | ['classification-of-variable-stars'] | ['miscellaneous'] | [-8.08837786e-02 -5.70223629e-01 -9.27091092e-02 -5.24660349e-01
-3.16010058e-01 -8.22968066e-01 7.72195280e-01 3.44774812e-01
-4.49873000e-01 6.64589167e-01 -6.77581429e-01 -5.06613553e-01
-2.16511741e-01 -9.73710299e-01 -4.47406679e-01 -7.89297760e-01
-1.54261336e-01 9.72558975e-01 8.52914929e-01 -1.23224914... | [7.6394500732421875, 3.1035611629486084] |
8b66468b-85b6-4c45-b9dd-d27c70fb17d3 | segmentation-guided-domain-adaptation-for | 2210.09213 | null | https://arxiv.org/abs/2210.09213v2 | https://arxiv.org/pdf/2210.09213v2.pdf | Segmentation-guided Domain Adaptation for Efficient Depth Completion | Complete depth information and efficient estimators have become vital ingredients in scene understanding for automated driving tasks. A major problem for LiDAR-based depth completion is the inefficient utilization of convolutions due to the lack of coherent information as provided by the sparse nature of uncorrelated L... | ['Stefan Rudolph', 'Anselm Haselhoff', 'Martin Sunkel', 'Fabian Märkert'] | 2022-10-14 | null | null | null | null | ['depth-completion'] | ['computer-vision'] | [ 1.50508881e-01 -1.09609552e-01 1.32748768e-01 -5.86107790e-01
-6.53437912e-01 -3.93745780e-01 4.77233142e-01 1.09827369e-01
-8.43992829e-01 9.18723643e-01 -1.42973185e-01 -2.42238835e-01
-2.57960320e-01 -9.83450055e-01 -6.87749028e-01 -4.85929281e-01
1.74760282e-01 7.42072046e-01 4.13451999e-01 -1.14247486... | [8.366994857788086, -2.5291032791137695] |
3d3d5c20-bc04-40c3-b94d-4e0594d45fe3 | imprecise-label-learning-a-unified-framework | 2305.12715 | null | https://arxiv.org/abs/2305.12715v2 | https://arxiv.org/pdf/2305.12715v2.pdf | Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations | In this paper, we introduce the imprecise label learning (ILL) framework, a unified approach to handle various imprecise label configurations, which are commonplace challenges in machine learning tasks. ILL leverages an expectation-maximization (EM) algorithm for the maximum likelihood estimation (MLE) of the imprecise... | ['Bhiksha Raj', 'Rita Singh', 'Masashi Sugiyama', 'Xing Xie', 'Yidong Wang', 'Ran Tao', 'Jindong Wang', 'Ankit Shah', 'Hao Chen'] | 2023-05-22 | null | null | null | null | ['learning-with-noisy-labels', 'partial-label-learning', 'learning-with-noisy-labels'] | ['computer-vision', 'methodology', 'natural-language-processing'] | [ 3.05458248e-01 1.86349779e-01 -5.72003126e-01 -1.05802655e+00
-1.33192849e+00 -9.52579260e-01 6.35780215e-01 2.52326012e-01
-3.83475959e-01 7.75706172e-01 -3.72121274e-01 -2.92568892e-01
-3.59405249e-01 -3.99399936e-01 -7.47521520e-01 -4.90788549e-01
4.64277267e-02 6.95752740e-01 -1.73955351e-01 2.34973267... | [9.329264640808105, 4.193579196929932] |
7989e37a-0875-406e-8ed5-e57a5241c2ce | class-conditional-embeddings-for-music-source | 1811.03076 | null | http://arxiv.org/abs/1811.03076v1 | http://arxiv.org/pdf/1811.03076v1.pdf | Class-conditional embeddings for music source separation | Isolating individual instruments in a musical mixture has a myriad of
potential applications, and seems imminently achievable given the levels of
performance reached by recent deep learning methods. While most musical source
separation techniques learn an independent model for each instrument, we
propose using a common... | ['Shrikant Venkataramani', 'Prem Seetharaman', 'Jonathan Le Roux', 'Gordon Wichern'] | 2018-11-07 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 6.07177727e-02 8.65306109e-02 -7.20615685e-03 -2.85421684e-02
-1.10233271e+00 -9.67332304e-01 5.67511082e-01 -5.15087619e-02
-1.80143267e-01 1.47443205e-01 4.78316873e-01 -4.40558866e-02
-4.69275892e-01 -2.76545525e-01 -4.51445073e-01 -6.99623466e-01
-1.36679292e-01 6.21112823e-01 -2.03928664e-01 -7.27668330... | [15.469719886779785, 5.577300548553467] |
22d0963f-a997-4ab6-86e1-e87d44aff133 | using-connectome-features-to-constrain-echo | 2206.02094 | null | https://arxiv.org/abs/2206.02094v2 | https://arxiv.org/pdf/2206.02094v2.pdf | Using Connectome Features to Constrain Echo State Networks | We report an improvement to the conventional Echo State Network (ESN) across three benchmark chaotic time-series prediction tasks using fruit fly connectome data alone. We also investigate the impact of key connectome-derived structural features on prediction performance -- uniquely bridging neurobiological structure a... | ['Mark Daley', 'Jacob Morra'] | 2022-06-05 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [ 1.65009111e-01 8.75730533e-03 1.14400804e-01 6.05557784e-02
4.00593907e-01 -6.44949555e-01 6.50737941e-01 -6.84386236e-04
-4.55724955e-01 7.94461966e-01 3.28922458e-03 -4.31939662e-01
-6.19822025e-01 -5.76715708e-01 -4.60109919e-01 -7.81475246e-01
-1.18676150e+00 6.62578583e-01 4.31446046e-01 -4.10349607... | [6.784858703613281, 3.595660448074341] |
9b7d4c76-2d3d-4c4e-b819-a7432191603b | acoustic-non-line-of-sight-imaging | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Lindell_Acoustic_Non-Line-Of-Sight_Imaging_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Lindell_Acoustic_Non-Line-Of-Sight_Imaging_CVPR_2019_paper.pdf | Acoustic Non-Line-Of-Sight Imaging | Non-line-of-sight (NLOS) imaging enables unprecedented capabilities in a wide range of applications, including robotic and machine vision, remote sensing, autonomous vehicle navigation, and medical imaging. Recent approaches to solving this challenging problem employ optical time-of-flight imaging systems with highly s... | [' Vladlen Koltun', ' Gordon Wetzstein', 'David B. Lindell'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['seismic-imaging'] | ['miscellaneous'] | [ 5.69972754e-01 -2.39468992e-01 4.26148355e-01 -1.76359683e-01
-5.89938462e-01 -5.79808891e-01 2.93096632e-01 -4.23449099e-01
-8.44341218e-01 5.41395187e-01 -1.59262389e-01 -2.21154600e-01
-2.69045621e-01 -6.36574447e-01 -2.89269239e-01 -8.68988514e-01
7.47283250e-02 7.20017910e-01 4.38632280e-01 1.55231088... | [9.846494674682617, -2.7764647006988525] |
9d39155f-4474-4270-8c67-e6478292bd44 | less-is-more-rethinking-state-of-the-art | 2209.00243 | null | https://arxiv.org/abs/2209.00243v1 | https://arxiv.org/pdf/2209.00243v1.pdf | Less is More: Rethinking State-of-the-art Continual Relation Extraction Models with a Frustratingly Easy but Effective Approach | Continual relation extraction (CRE) requires the model to continually learn new relations from class-incremental data streams. In this paper, we propose a Frustratingly easy but Effective Approach (FEA) method with two learning stages for CRE: 1) Fast Adaption (FA) warms up the model with only new data. 2) Balanced Tun... | ['Zhifang Sui', 'Yunbo Cao', 'Binghuai Lin', 'Rundong Gao', 'Tianyu Liu', 'YiFan Song', 'Peiyi Wang'] | 2022-09-01 | null | null | null | null | ['continual-relation-extraction'] | ['natural-language-processing'] | [ 1.52742773e-01 5.57956517e-01 -3.66574883e-01 -6.40383184e-01
-5.18110931e-01 -3.63173157e-01 7.13241339e-01 2.07217231e-01
-4.17384267e-01 6.95070863e-01 2.25373805e-01 -5.52441120e-01
-2.86299456e-02 -9.55095351e-01 -7.08218455e-01 -3.36489141e-01
-9.88758728e-02 5.75014055e-01 4.80807483e-01 -3.40463340... | [9.226712226867676, 8.579191207885742] |
80c73f44-e17c-45b5-b914-98753ac58b30 | leveraging-language-foundation-models-for | 2209.05479 | null | https://arxiv.org/abs/2209.05479v2 | https://arxiv.org/pdf/2209.05479v2.pdf | Leveraging Language Foundation Models for Human Mobility Forecasting | In this paper, we propose a novel pipeline that leverages language foundation models for temporal sequential pattern mining, such as for human mobility forecasting tasks. For example, in the task of predicting Place-of-Interest (POI) customer flows, typically the number of visits is extracted from historical logs, and ... | ['Bhanu Prakash Voutharoja', 'Flora D. Salim', 'Hao Xue'] | 2022-09-11 | null | null | null | null | ['sequential-pattern-mining', 'temporal-sequences'] | ['natural-language-processing', 'reasoning'] | [ 1.60598144e-01 -2.42622510e-01 -7.30481982e-01 -7.70030081e-01
-3.67167234e-01 -2.56758898e-01 5.89832723e-01 2.54560500e-01
-5.26222169e-01 6.36357367e-01 7.73629606e-01 -7.42940068e-01
7.73041174e-02 -1.02828074e+00 -6.62570119e-01 -1.64203331e-01
-4.68025416e-01 4.33920443e-01 2.20381111e-01 -5.56582868... | [6.581500053405762, 2.0792582035064697] |
2cf4a2aa-346c-413a-83a6-7e4d83369dae | going-for-goal-a-resource-for-grounded | 2211.04534 | null | https://arxiv.org/abs/2211.04534v1 | https://arxiv.org/pdf/2211.04534v1.pdf | Going for GOAL: A Resource for Grounded Football Commentaries | Recent video+language datasets cover domains where the interaction is highly structured, such as instructional videos, or where the interaction is scripted, such as TV shows. Both of these properties can lead to spurious cues to be exploited by models rather than learning to ground language. In this paper, we present G... | ['Verena Rieser', 'Ioannis Konstas', 'Lu Yu', 'Malvina Nikandrou', 'Shubham Agarwal', 'Andrea Vanzo', 'Emanuele Bastianelli', 'José Lopes', 'Alessandro Suglia'] | 2022-11-08 | null | null | null | null | ['moment-retrieval'] | ['computer-vision'] | [ 5.28248474e-02 1.48914903e-02 -4.77521241e-01 -2.47296527e-01
-1.36459005e+00 -8.31129432e-01 8.38041127e-01 1.08557984e-01
-4.60107803e-01 7.70855784e-01 7.94588923e-01 -3.86308789e-01
5.24731040e-01 -3.67028922e-01 -1.05969179e+00 -3.89795691e-01
5.47911157e-04 1.31836012e-01 4.83114839e-01 -3.87098670... | [10.447897911071777, 0.7943729162216187] |
80edd125-e491-4817-97d7-be415cab028d | encore-pre-training-entity-encoders-using | 2305.12924 | null | https://arxiv.org/abs/2305.12924v1 | https://arxiv.org/pdf/2305.12924v1.pdf | EnCore: Pre-Training Entity Encoders using Coreference Chains | Entity typing is the task of assigning semantic types to the entities that are mentioned in a text. Since obtaining sufficient amounts of manual annotations is expensive, current state-of-the-art methods are typically trained on automatically labelled datasets, e.g. by exploiting links between Wikipedia pages. In this ... | ['Steven Schockaert', 'Frank Mtumbuka'] | 2023-05-22 | null | null | null | null | ['entity-embeddings', 'entity-typing'] | ['methodology', 'natural-language-processing'] | [-1.30549103e-01 5.09713113e-01 -3.57422113e-01 -3.38827193e-01
-6.13981366e-01 -7.66982496e-01 6.81106806e-01 6.32548809e-01
-1.04504597e+00 8.99195790e-01 2.64091730e-01 4.64984775e-02
3.04092020e-02 -9.29636478e-01 -1.15823793e+00 -1.81272522e-01
-5.87441958e-02 7.45949209e-01 4.56838727e-01 -2.40992501... | [9.507608413696289, 8.883102416992188] |
2e4d0590-b34d-4303-b777-d06efe920dae | clustered-object-detection-in-aerial-images | 1904.08008 | null | https://arxiv.org/abs/1904.08008v3 | https://arxiv.org/pdf/1904.08008v3.pdf | Clustered Object Detection in Aerial Images | Detecting objects in aerial images is challenging for at least two reasons: (1) target objects like pedestrians are very small in pixels, making them hardly distinguished from surrounding background; and (2) targets are in general sparsely and non-uniformly distributed, making the detection very inefficient. In this pa... | ['Heng Fan', 'Haibin Ling', 'Fan Yang', 'Peng Chu', 'Erik Blasch'] | 2019-04-16 | clustered-object-detection-in-aerial-images-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Yang_Clustered_Object_Detection_in_Aerial_Images_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Yang_Clustered_Object_Detection_in_Aerial_Images_ICCV_2019_paper.pdf | iccv-2019-10 | ['object-detection-in-aerial-images'] | ['computer-vision'] | [-3.24830301e-02 -3.10818285e-01 2.47658789e-02 -1.04701832e-01
-3.60943526e-01 -6.65352881e-01 2.82759815e-01 2.20075861e-01
-5.47588348e-01 2.99839497e-01 -4.04856801e-01 2.68345326e-02
1.50022343e-01 -9.28370953e-01 -4.51540500e-01 -8.47903609e-01
-1.75496235e-01 2.84711689e-01 1.12450242e+00 1.90792143... | [8.682943344116211, -0.7771471738815308] |
47aaa4c5-8bba-4a99-9334-a1a88578a7f3 | pruning-pre-trained-language-models-with | 2305.12394 | null | https://arxiv.org/abs/2305.12394v1 | https://arxiv.org/pdf/2305.12394v1.pdf | Pruning Pre-trained Language Models with Principled Importance and Self-regularization | Iterative pruning is one of the most effective compression methods for pre-trained language models. We discovered that finding the optimal pruning decision is an equality-constrained 0-1 Integer Linear Programming problem. The solution to this optimization problem leads to a principled importance criterion which we use... | ['Kenny Q. Zhu', 'Siyu Ren'] | 2023-05-21 | null | null | null | null | ['data-to-text-generation'] | ['natural-language-processing'] | [ 4.34971690e-01 3.20014834e-01 -8.78338277e-01 -4.46855485e-01
-8.58606160e-01 -1.16278157e-01 2.08807111e-01 2.88314253e-01
-3.17748070e-01 6.39618039e-01 2.51261175e-01 -7.30153978e-01
-3.18013996e-01 -6.88736022e-01 -6.76899433e-01 -4.11342867e-02
-4.98188548e-02 8.41175258e-01 1.63760394e-01 -1.89600974... | [8.735090255737305, 3.6623895168304443] |
b1e7af04-dbea-4c8e-979c-b1d4949ab547 | editing-implicit-assumptions-in-text-to-image | 2303.08084 | null | https://arxiv.org/abs/2303.08084v1 | https://arxiv.org/pdf/2303.08084v1.pdf | Editing Implicit Assumptions in Text-to-Image Diffusion Models | Text-to-image diffusion models often make implicit assumptions about the world when generating images. While some assumptions are useful (e.g., the sky is blue), they can also be outdated, incorrect, or reflective of social biases present in the training data. Thus, there is a need to control these assumptions without ... | ['Yonatan Belinkov', 'Bahjat Kawar', 'Hadas Orgad'] | 2023-03-14 | null | null | null | null | ['model-editing'] | ['natural-language-processing'] | [ 5.65496624e-01 3.75506401e-01 2.66758204e-02 -6.49940014e-01
-3.29205960e-01 -7.37865746e-01 1.12241793e+00 3.09093148e-01
-4.96450871e-01 6.00577295e-01 1.45616889e-01 -2.95004457e-01
2.91134208e-01 -7.92563081e-01 -1.03119552e+00 -4.69663918e-01
4.47448939e-01 7.67791867e-01 2.03929003e-02 -2.70123422... | [11.409403800964355, -0.22310779988765717] |
d5d42635-4621-48b4-b5e0-3b6017b1b538 | mutually-guided-few-shot-learning-for | 2306.13310 | null | https://arxiv.org/abs/2306.13310v1 | https://arxiv.org/pdf/2306.13310v1.pdf | Mutually Guided Few-shot Learning for Relational Triple Extraction | Knowledge graphs (KGs), containing many entity-relation-entity triples, provide rich information for downstream applications. Although extracting triples from unstructured texts has been widely explored, most of them require a large number of labeled instances. The performance will drop dramatically when only few label... | ['Lianghua He', 'Chen Ma', 'Bowei He', 'Shuai Jiang', 'Chengmei Yang'] | 2023-06-23 | null | null | null | null | ['cross-domain-few-shot', 'knowledge-graphs', 'few-shot-learning', 'relation-classification'] | ['computer-vision', 'knowledge-base', 'methodology', 'natural-language-processing'] | [-2.17579335e-01 5.88099718e-01 -6.18991673e-01 -4.22419697e-01
-1.17617309e+00 -2.14790717e-01 6.16059721e-01 5.49455464e-01
-1.90304533e-01 9.40634906e-01 1.73647225e-01 -4.64733131e-02
8.63745958e-02 -1.21789300e+00 -7.95380473e-01 -3.56586516e-01
1.09676741e-01 6.95504069e-01 6.28023148e-01 -3.94374251... | [9.289665222167969, 8.601180076599121] |
53b97897-4f97-478b-9d5d-32b14e2212b5 | neural-temporality-adaptation-for-document | null | null | https://aclanthology.org/P19-1403 | https://aclanthology.org/P19-1403.pdf | Neural Temporality Adaptation for Document Classification: Diachronic Word Embeddings and Domain Adaptation Models | Language usage can change across periods of time, but document classifiers models are usually trained and tested on corpora spanning multiple years without considering temporal variations. This paper describes two complementary ways to adapt classifiers to shifts across time. First, we show that diachronic word embeddi... | ['Xiaolei Huang', 'Michael J. Paul'] | 2019-07-01 | null | null | null | acl-2019-7 | ['diachronic-word-embeddings'] | ['natural-language-processing'] | [-3.47436070e-02 -5.06069601e-01 -7.96253562e-01 -5.74060857e-01
-1.08227804e-01 -6.99108899e-01 1.10876620e+00 3.14140081e-01
-9.07558978e-01 8.91421676e-01 4.61262167e-01 -4.88776326e-01
-1.40776336e-02 -9.13312316e-01 -2.37030819e-01 -3.18556756e-01
-1.99158132e-01 1.96210384e-01 2.33628988e-01 -3.91939998... | [10.22545337677002, 8.871541976928711] |
85bc89a9-98d5-43ce-8571-5b62822335f7 | asymmetric-co-teaching-for-unsupervised-cross | 1912.01349 | null | https://arxiv.org/abs/1912.01349v1 | https://arxiv.org/pdf/1912.01349v1.pdf | Asymmetric Co-Teaching for Unsupervised Cross Domain Person Re-Identification | Person re-identification (re-ID), is a challenging task due to the high variance within identity samples and imaging conditions. Although recent advances in deep learning have achieved remarkable accuracy in settled scenes, i.e., source domain, few works can generalize well on the unseen target domain. One popular solu... | ['Fengxiang Yang', 'Zhun Zhong', 'Xiaowei Guo', 'Shaozi Li', 'Ke Li', 'Hao Cheng', 'Zhiming Luo', 'Xing Sun', 'Rongrong Ji', 'Feiyue Huang'] | 2019-12-03 | null | null | null | null | ['miscellaneous'] | ['miscellaneous'] | [-8.58121272e-03 -2.86172986e-01 -7.59637579e-02 -5.49987257e-01
-4.68881994e-01 -3.57103080e-01 3.85368824e-01 -1.81872621e-01
-6.02211237e-01 8.27651560e-01 2.76957117e-02 2.98835009e-01
2.69073565e-02 -6.04107082e-01 -5.11509836e-01 -1.01417100e+00
3.29829663e-01 6.70095980e-01 -9.53596532e-02 2.79214591... | [14.825742721557617, 1.0947003364562988] |
2ada8d9f-c685-40fe-9a00-49ff90d6341d | emergent-a-novel-data-set-for-stance | null | null | https://aclanthology.org/N16-1138 | https://aclanthology.org/N16-1138.pdf | Emergent: a novel data-set for stance classification | null | ['William Ferreira', 'Andreas Vlachos'] | 2016-06-01 | null | null | null | naacl-2016-6 | ['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... | [-7.380704879760742, 3.715984344482422] |
089f4fda-295e-4eac-82da-e9ad2cb857d5 | predicting-future-shanghai-stock-market-price | 1609.05394 | null | http://arxiv.org/abs/1609.05394v1 | http://arxiv.org/pdf/1609.05394v1.pdf | Predicting Future Shanghai Stock Market Price using ANN in the Period 21-Sep-2016 to 11-Oct-2016 | Predicting the prices of stocks at any stock market remains a quest for many
investors and researchers. Those who trade at the stock market tend to use
technical, fundamental or time series analysis in their predictions. These
methods usually guide on trends and not the exact likely prices. It is for this
reason that A... | ['Barack Wamkaya Wanjawa'] | 2016-09-17 | null | null | null | null | ['stock-prediction'] | ['time-series'] | [-8.28666031e-01 -1.97872102e-01 -3.52864116e-01 -2.64197350e-01
1.38049975e-01 -4.74032789e-01 4.82748657e-01 1.27577305e-01
-4.73658592e-01 1.03714907e+00 9.65775996e-02 -8.61545801e-01
-1.09654471e-01 -9.65600491e-01 -3.14082623e-01 -2.80641407e-01
-1.52145445e-01 3.56928855e-01 2.61971891e-01 -6.52319252... | [4.506277084350586, 4.207151412963867] |
b3e58a20-9c1e-4474-9cfb-a63a0918dd1d | semi-supervised-graph-embedding-approach-to | 1610.04351 | null | http://arxiv.org/abs/1610.04351v1 | http://arxiv.org/pdf/1610.04351v1.pdf | Semi-supervised Graph Embedding Approach to Dynamic Link Prediction | We propose a simple discrete time semi-supervised graph embedding approach to
link prediction in dynamic networks. The learned embedding reflects information
from both the temporal and cross-sectional network structures, which is
performed by defining the loss function as a weighted sum of the supervised
loss from past... | ['Ryohei Hisano'] | 2016-10-14 | null | null | null | null | ['dynamic-link-prediction'] | ['graphs'] | [-3.36149037e-01 4.98435259e-01 -9.26980317e-01 -7.47012943e-02
2.41298839e-01 -4.35164809e-01 9.26780999e-01 4.70616549e-01
1.09851979e-01 8.56382728e-01 4.09312189e-01 -1.52636573e-01
-8.80290866e-01 -1.42830133e+00 -5.62306821e-01 -4.83898997e-01
-1.24561894e+00 1.07188559e+00 5.95548511e-01 -4.52698588... | [7.274959087371826, 6.250614166259766] |
53dd6957-94ad-468b-9671-2704c00da9c6 | dialogue-act-recognition-via-crf-attentive | 1711.05568 | null | http://arxiv.org/abs/1711.05568v1 | http://arxiv.org/pdf/1711.05568v1.pdf | Dialogue Act Recognition via CRF-Attentive Structured Network | Dialogue Act Recognition (DAR) is a challenging problem in dialogue
interpretation, which aims to attach semantic labels to utterances and
characterize the speaker's intention. Currently, many existing approaches
formulate the DAR problem ranging from multi-classification to structured
prediction, which suffer from han... | ['Rongqin Yang', 'Zhou Zhao', 'Zheqian Chen', 'Deng Cai', 'Xiaofei He'] | 2017-11-15 | dialogue-act-recognition-via-crf-attentive-1 | https://dl.acm.org/doi/10.1145/3209978.3209997 | https://dl.acm.org/doi/pdf/10.1145/3209978.3209997 | sigir-2018-7 | ['dialogue-act-classification', 'dialogue-interpretation'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.20396298e-01 7.65046358e-01 -1.81070156e-02 -1.07550657e+00
-7.06288159e-01 -2.68397927e-01 8.60761404e-01 -1.33078814e-01
-4.32327330e-01 1.00225449e+00 8.96081686e-01 -2.08381489e-01
3.87996286e-01 -2.40379900e-01 1.51131619e-02 -4.65160757e-01
2.45924264e-01 1.00238919e+00 1.50283903e-01 -5.32777071... | [12.67773723602295, 7.6604485511779785] |
41bc46ce-ee73-4ab8-bccc-d18eddbba1b6 | fairness-constraint-in-structural | 2202.08977 | null | https://arxiv.org/abs/2202.08977v1 | https://arxiv.org/pdf/2202.08977v1.pdf | Fairness constraint in Structural Econometrics and Application to fair estimation using Instrumental Variables | A supervised machine learning algorithm determines a model from a learning sample that will be used to predict new observations. To this end, it aggregates individual characteristics of the observations of the learning sample. But this information aggregation does not consider any potential selection on unobservables a... | ['Jean-Michel Loubes', 'Jean-Pierre Florens', 'Samuele Centorrino'] | 2022-02-16 | null | null | null | null | ['econometrics'] | ['miscellaneous'] | [ 9.94748846e-02 7.11871803e-01 -8.03778291e-01 -6.23915255e-01
-5.30781209e-01 -4.40115005e-01 3.68100584e-01 1.62039608e-01
-7.35375345e-01 9.99711573e-01 5.40584326e-01 -7.09800720e-01
-5.64354420e-01 -7.35978484e-01 -4.07318175e-01 -7.14071810e-01
3.20218891e-01 3.76241624e-01 -8.36230695e-01 3.22633445... | [8.71043872833252, 5.317661285400391] |
c464e0fe-4d3d-4bdb-921a-3e87d4768726 | cola-coarse-label-pre-training-for-3d | 2202.06884 | null | https://arxiv.org/abs/2202.06884v3 | https://arxiv.org/pdf/2202.06884v3.pdf | COLA: COarse LAbel pre-training for 3D semantic segmentation of sparse LiDAR datasets | Transfer learning is a proven technique in 2D computer vision to leverage the large amount of data available and achieve high performance with datasets limited in size due to the cost of acquisition or annotation. In 3D, annotation is known to be a costly task; nevertheless, pre-training methods have only recently been... | ['François Goulette', 'Jean-Emmanuel Deschaud', 'Jules Sanchez'] | 2022-02-14 | null | null | null | null | ['real-time-3d-semantic-segmentation', 'unsupervised-pre-training'] | ['computer-vision', 'methodology'] | [ 4.30129528e-01 3.39043528e-01 -1.50193721e-01 -5.76732814e-01
-8.45695019e-01 -6.29064143e-01 6.68969870e-01 3.22778672e-01
-7.73470283e-01 6.12418056e-01 -3.53159398e-01 -3.53872329e-01
-1.69393476e-02 -6.79457963e-01 -7.77416229e-01 -4.52039361e-01
-6.85351901e-03 8.80654991e-01 5.43102384e-01 6.92670001... | [8.177294731140137, -2.6768925189971924] |
6a051859-8357-4592-bdee-fb83b5f0fb0a | hybrid-active-learning-via-deep-clustering | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Rana_Hybrid_Active_Learning_via_Deep_Clustering_for_Video_Action_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Rana_Hybrid_Active_Learning_via_Deep_Clustering_for_Video_Action_Detection_CVPR_2023_paper.pdf | Hybrid Active Learning via Deep Clustering for Video Action Detection | In this work, we focus on reducing the annotation cost for video action detection which requires costly frame-wise dense annotations. We study a novel hybrid active learning (AL) strategy which performs efficient labeling using both intra-sample and inter-sample selection. The intra-sample selection leads to labeli... | ['Yogesh S. Rawat', 'Aayush J. Rana'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['deep-clustering', 'deep-clustering'] | ['miscellaneous', 'natural-language-processing'] | [ 3.68946552e-01 5.71937561e-02 -4.95324403e-01 -5.22144318e-01
-1.58989120e+00 -4.28845525e-01 3.53645533e-01 3.51435065e-01
-8.26103508e-01 8.12077343e-01 9.82521251e-02 1.78159177e-01
1.14943281e-01 -3.08725923e-01 -6.37068272e-01 -8.62548530e-01
1.46967232e-01 3.32220465e-01 6.94157004e-01 5.51177621... | [8.483945846557617, 0.5685277581214905] |
3e5d3ca3-eff0-4c0d-89a8-cf0a19e08329 | comparing-approaches-for-automatic-question | null | null | https://aclanthology.org/S17-1013 | https://aclanthology.org/S17-1013.pdf | Comparing Approaches for Automatic Question Identification | Collecting spontaneous speech corpora that are open-ended, yet topically constrained, is increasingly popular for research in spoken dialogue systems and speaker state, inter alia. Typically, these corpora are labeled by human annotators, either in the lab or through crowd-sourcing; however, this is cumbersome and time... | ['Kara Schechtman', 'Sarah Ita Levitan', 'Angel Maredia', 'Julia Hirschberg'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['cross-corpus'] | ['computer-vision'] | [-2.27850080e-01 3.28150541e-01 -9.28088427e-02 -6.43493176e-01
-1.46248996e+00 -1.17227221e+00 6.51658118e-01 -1.60425305e-01
-5.63755274e-01 1.02269971e+00 5.83831131e-01 -1.90130875e-01
5.66440105e-01 1.84779659e-01 -5.67996651e-02 -3.12106252e-01
-6.36140034e-02 6.33473456e-01 1.53922334e-01 -3.55545908... | [12.91567325592041, 7.829317569732666] |
9f93db06-6e1d-4054-a5af-8dfb77b383b6 | scaf-skip-connections-in-auto-encoder-for | null | null | https://link.springer.com/chapter/10.1007/978-3-031-06427-2_36 | https://hal.archives-ouvertes.fr/hal-03687091/file/SCAF_submitted%20%282%29.pdf | SCAF: Skip-Connections in Auto-encoder for Face alignment with few annotated data | Supervised face alignment methods need large amounts of training data to achieve good performance in terms of accuracy and generalization. However face alignment datasets rarely exceed a few thousand samples making these methods prone to overfitting on the specific training dataset. Semi-supervised methods like TS3 or ... | ['Bertrand Coüasnon', 'Yann Ricquebourg', 'Christian Raymond', 'Philippe-Henri Gosselin', 'Martin Dornier'] | 2022-05-15 | null | null | null | iciap-2022-5 | ['face-alignment'] | ['computer-vision'] | [ 2.00269714e-01 4.02507275e-01 -2.46854365e-01 -1.00589395e+00
-6.75761640e-01 -2.11075798e-01 6.91756368e-01 -1.39759108e-01
-3.66991758e-01 6.22739792e-01 6.75319582e-02 2.85225123e-01
-2.14198306e-02 -6.24389291e-01 -7.52417266e-01 -5.60390353e-01
-1.04800031e-01 8.33187103e-01 -1.01064831e-01 -4.49136645... | [13.485594749450684, 0.3555440604686737] |
ae26c3cf-e099-4c13-98bc-d73e289c4be5 | modeling-spatio-temporal-human-track | 1806.11008 | null | http://arxiv.org/abs/1806.11008v1 | http://arxiv.org/pdf/1806.11008v1.pdf | Modeling Spatio-Temporal Human Track Structure for Action Localization | This paper addresses spatio-temporal localization of human actions in video.
In order to localize actions in time, we propose a recurrent localization
network (RecLNet) designed to model the temporal structure of actions on the
level of person tracks. Our model is trained to simultaneously recognize and
localize action... | ['Ivan Laptev', 'Guilhem Chéron', 'Anton Osokin', 'Cordelia Schmid'] | 2018-06-28 | null | null | null | null | ['spatio-temporal-action-localization'] | ['computer-vision'] | [ 9.21792090e-02 -4.97302234e-01 -2.78370976e-01 -2.83285026e-02
-5.22329450e-01 -3.58372808e-01 7.73592174e-01 -1.35681719e-01
-6.71476841e-01 5.15460789e-01 8.38236570e-01 3.98717999e-01
1.67114735e-01 -4.16729510e-01 -7.04289317e-01 -3.72526407e-01
-5.03258884e-01 5.24467565e-02 4.86319095e-01 8.03460702... | [8.240006446838379, 0.42355313897132874] |
e7fa143e-fe10-44b6-a78d-afe565039584 | a-machine-learning-approach-to-the-prediction | 2305.18406 | null | https://arxiv.org/abs/2305.18406v1 | https://arxiv.org/pdf/2305.18406v1.pdf | A machine learning approach to the prediction of heat-transfer coefficients in micro-channels | The accurate prediction of the two-phase heat transfer coefficient (HTC) as a function of working fluids, channel geometries and process conditions is key to the optimal design and operation of compact heat exchangers. Advances in artificial intelligence research have recently boosted the application of machine learnin... | ['Omar K. Matar', 'Tassos G. Karayiannis', 'Luca Magri', 'Francesco Coletti', 'Tullio Traverso'] | 2023-05-28 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [-5.08341305e-02 -2.52151698e-01 -1.17909983e-02 -3.82706910e-01
-4.80685264e-01 -2.14380085e-01 4.50546831e-01 4.69586968e-01
-2.70795912e-01 8.67844224e-01 -2.32137129e-01 -5.86164355e-01
-3.37565899e-01 -6.50797009e-01 -4.73285139e-01 -8.81322980e-01
5.56637347e-02 5.15960038e-01 3.63532938e-02 2.71756023... | [6.262622356414795, 3.3700647354125977] |
eaefa01d-e581-4837-8a0f-dab78304881d | idd-a-dataset-for-exploring-problems-of | 1811.10200 | null | http://arxiv.org/abs/1811.10200v1 | http://arxiv.org/pdf/1811.10200v1.pdf | IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments | While several datasets for autonomous navigation have become available in
recent years, they tend to focus on structured driving environments. This
usually corresponds to well-delineated infrastructure such as lanes, a small
number of well-defined categories for traffic participants, low variation in
object or backgrou... | ['C. V. Jawahar', 'Anbumani Subramanian', 'Anoop Namboodiri', 'Manmohan Chandraker', 'Girish Varma'] | 2018-11-26 | null | null | null | null | ['road-scene-understanding'] | ['computer-vision'] | [ 2.61790812e-01 1.38055652e-01 -4.74132389e-01 -7.05562711e-01
-5.66636920e-01 -5.80812454e-01 8.74458134e-01 -9.73640010e-03
-4.94006515e-01 7.24893034e-01 9.89613608e-02 -4.03887451e-01
-2.94951677e-01 -1.00518274e+00 -4.40808058e-01 -5.35856128e-01
-8.63743946e-02 8.38612378e-01 9.30756152e-01 -5.41392744... | [8.238816261291504, -1.633687973022461] |
247fc165-f402-41c6-a31b-5c87741354d9 | sgloc-scene-geometry-encoding-for-outdoor | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_SGLoc_Scene_Geometry_Encoding_for_Outdoor_LiDAR_Localization_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_SGLoc_Scene_Geometry_Encoding_for_Outdoor_LiDAR_Localization_CVPR_2023_paper.pdf | SGLoc: Scene Geometry Encoding for Outdoor LiDAR Localization | LiDAR-based absolute pose regression estimates the global pose through a deep network in an end-to-end manner, achieving impressive results in learning-based localization. However, the accuracy of existing methods still has room to improve due to the difficulty of effectively encoding the scene geometry and the uns... | ['Chenglu Wen', 'Siqi Shen', 'Guosheng Hu', 'Cheng Wang', 'Shangshu Yu', 'Wen Li'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['outdoor-localization'] | ['robots'] | [-2.22525164e-01 -3.32054019e-01 -2.00232379e-02 -7.77434766e-01
-1.42771220e+00 -5.20992398e-01 3.94179881e-01 1.28045946e-01
-5.37833810e-01 6.16502941e-01 -1.36131287e-01 2.38794778e-02
-1.79624587e-01 -9.03573096e-01 -1.07948649e+00 -6.69800520e-01
6.91999123e-02 6.76219583e-01 5.36777265e-02 -6.42749816... | [7.535606861114502, -2.2347075939178467] |
c1752d34-b0b4-4e9c-bbd9-9eb53eb5f2ef | the-future-of-artificial-intelligence-ai-and | 2305.02327 | null | https://arxiv.org/abs/2305.02327v1 | https://arxiv.org/pdf/2305.02327v1.pdf | The Future of Artificial Intelligence (AI) and Machine Learning (ML) in Landscape Design: A Case Study in Coastal Virginia, USA | There have been theory-based endeavours that directly engage with AI and ML in the landscape discipline. By presenting a case that uses machine learning techniques to predict variables in a coastal environment, this paper provides empirical evidence of the forthcoming cybernetic environment, in which designers are conc... | ['Ben Bowes', 'Zihao Zhang'] | 2023-05-03 | null | null | null | null | ['ethics'] | ['miscellaneous'] | [ 1.11185655e-01 4.06562120e-01 5.29809557e-02 1.81831628e-01
5.38265049e-01 -6.12134635e-01 1.02045631e+00 -1.18915848e-01
-4.65742201e-01 5.57265997e-01 8.10283720e-01 -7.48015523e-01
-4.47129220e-01 -7.55807579e-01 -2.76563138e-01 -3.55507016e-01
-8.23134854e-02 -9.65043083e-02 -3.21699589e-01 -1.11103940... | [9.035406112670898, 6.235238075256348] |
2283f811-ee8e-4ef0-88d4-03dfd81faff0 | improving-pixel-level-contrastive-learning-by | 2211.10177 | null | https://arxiv.org/abs/2211.10177v1 | https://arxiv.org/pdf/2211.10177v1.pdf | Improving Pixel-Level Contrastive Learning by Leveraging Exogenous Depth Information | Self-supervised representation learning based on Contrastive Learning (CL) has been the subject of much attention in recent years. This is due to the excellent results obtained on a variety of subsequent tasks (in particular classification), without requiring a large amount of labeled samples. However, most reference C... | ['Gabriele Facciolo', 'Adrien Courtois', 'Axel Davy', 'Josselin Kherroubi', 'Kristina Prokopetc', 'Ahmed Ben Saad'] | 2022-11-18 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 6.52629077e-01 -5.90770058e-02 -3.25545929e-02 -4.58666980e-01
-8.08734655e-01 -6.78953588e-01 4.91801858e-01 3.71391386e-01
-6.29660785e-01 7.01632440e-01 -2.93785810e-01 -9.84200761e-02
-2.73255557e-01 -1.11173093e+00 -9.09744322e-01 -1.02812445e+00
1.01570234e-01 2.58085042e-01 5.36530674e-01 -2.38964438... | [9.45419979095459, -0.8937613368034363] |
defc6a22-a018-4499-8f91-d15d25c49dff | comprehensive-event-representations-using | 2303.04794 | null | https://arxiv.org/abs/2303.04794v1 | https://arxiv.org/pdf/2303.04794v1.pdf | Comprehensive Event Representations using Event Knowledge Graphs and Natural Language Processing | Recent work has utilised knowledge-aware approaches to natural language understanding, question answering, recommendation systems, and other tasks. These approaches rely on well-constructed and large-scale knowledge graphs that can be useful for many downstream applications and empower knowledge-aware models with commo... | ['Tin Kuculo'] | 2023-03-08 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [ 3.91546845e-01 7.48007655e-01 -3.00431818e-01 -3.97049993e-01
-4.50664818e-01 -5.89015305e-01 1.15779972e+00 1.20434380e+00
-2.68991530e-01 6.91121638e-01 9.32893634e-01 -4.57603335e-01
-7.34061360e-01 -1.29902363e+00 -4.42015231e-01 2.91021645e-01
-3.09877366e-01 4.58271295e-01 4.20455545e-01 -6.52976453... | [9.421257019042969, 8.38774585723877] |
ed6aff3e-ca31-4d4f-b0db-d69c7c499c09 | data-dependent-gaussian-prior-objective-for | null | null | https://openreview.net/forum?id=S1efxTVYDr | https://openreview.net/pdf?id=S1efxTVYDr | Data-dependent Gaussian Prior Objective for Language Generation | For typical sequence prediction problems such as language generation, maximum likelihood estimation (MLE) has commonly been adopted as it encourages the predicted sequence most consistent with the ground-truth sequence to have the highest probability of occurring. However, MLE focuses on once-to-all matching between th... | ['Kehai Chen', 'Zuchao Li', 'Rui Wang', 'Masso Utiyama', 'Zhuosheng Zhang', 'Eiichiro Sumita', 'Hai Zhao'] | 2020-05-01 | null | null | null | iclr-2020-1 | ['l2-regularization', 'unsupervised-machine-translation'] | ['methodology', 'natural-language-processing'] | [ 7.08054483e-01 5.25208235e-01 -2.07955867e-01 -3.58538270e-01
-9.49521124e-01 -3.86562824e-01 8.49219441e-01 3.15516710e-01
-4.95676666e-01 1.17287958e+00 4.83346134e-01 -2.36836657e-01
2.94132441e-01 -5.42477489e-01 -8.97730470e-01 -7.20943809e-01
3.53694469e-01 4.94040281e-01 5.44773787e-02 -1.32679194... | [11.881521224975586, 9.23363208770752] |
8c61eda7-e05c-4b7e-9581-170dca587bfe | why-deep-surgical-models-fail-revisiting | 2209.08647 | null | https://arxiv.org/abs/2209.08647v2 | https://arxiv.org/pdf/2209.08647v2.pdf | Why Deep Surgical Models Fail?: Revisiting Surgical Action Triplet Recognition through the Lens of Robustness | Surgical action triplet recognition provides a better understanding of the surgical scene. This task is of high relevance as it provides the surgeon with context-aware support and safety. The current go-to strategy for improving performance is the development of new network mechanisms. However, the performance of curre... | ['Angelica I. Aviles-Rivero', 'Carola-Bibiane Schönlieb', 'Yueming Jin', 'Shujun Wang', 'Lihao Liu', 'Yanqi Cheng'] | 2022-09-18 | null | null | null | null | ['action-triplet-recognition'] | ['computer-vision'] | [ 4.64291900e-01 4.49532628e-01 -3.54353368e-01 -2.51801878e-01
-7.82896161e-01 -4.95491564e-01 3.22946370e-01 2.68328935e-01
-4.15262908e-01 6.56102002e-01 5.33863127e-01 -5.86820722e-01
-6.95409536e-01 -3.56920063e-01 -7.72297204e-01 -8.85856211e-01
-2.41125748e-01 8.59109536e-02 -4.79164980e-02 -4.78764981... | [14.145244598388672, -3.2947945594787598] |
6113852f-b008-44c1-a6de-39a00bb749b9 | diversified-patch-based-style-transfer-with | 2101.06381 | null | https://arxiv.org/abs/2101.06381v2 | https://arxiv.org/pdf/2101.06381v2.pdf | DivSwapper: Towards Diversified Patch-based Arbitrary Style Transfer | Gram-based and patch-based approaches are two important research lines of style transfer. Recent diversified Gram-based methods have been able to produce multiple and diverse stylized outputs for the same content and style images. However, as another widespread research interest, the diversity of patch-based methods re... | ['Dongming Lu', 'Wei Xing', 'Ailin Li', 'Zhiwen Zuo', 'Haibo Chen', 'Lei Zhao', 'Zhizhong Wang'] | 2021-01-16 | null | null | null | null | ['patch-matching'] | ['computer-vision'] | [ 1.10729359e-01 -2.65368581e-01 -3.24709378e-02 -1.87895983e-01
-5.40828466e-01 -7.48845875e-01 3.55385065e-01 -4.51473325e-01
1.67048335e-01 9.50314581e-01 1.70386180e-01 -1.13012329e-01
7.11911470e-02 -8.72320712e-01 -6.68188274e-01 -6.40306294e-01
4.34232652e-01 2.38579839e-01 2.28692934e-01 -5.93805075... | [11.62535285949707, -0.45740777254104614] |
3e0ad605-c77d-455a-9048-2f95b500c987 | a-corpus-based-approach-for-spanish-chinese | null | null | https://aclanthology.org/W16-4913 | https://aclanthology.org/W16-4913.pdf | A Corpus-based Approach for Spanish-Chinese Language Learning | Due to the huge population that speaks Spanish and Chinese, these languages occupy an important position in the language learning studies. Although there are some automatic translation systems that benefit the learning of both languages, there is enough space to create resources in order to help language learners. As a... | ['Shuyuan Cao', 'Mikel Iruskieta', 'Iria da Cunha'] | 2016-12-01 | null | null | null | ws-2016-12 | ['discourse-segmentation'] | ['natural-language-processing'] | [-1.88460082e-01 1.64490212e-02 -5.72949409e-01 -1.98867798e-01
-8.88660312e-01 -8.32519710e-01 6.53039634e-01 3.55917603e-01
-5.82671344e-01 1.09483695e+00 1.70623735e-01 -1.00880659e+00
2.65467584e-01 -8.03077102e-01 -2.67294109e-01 -4.10668761e-01
2.70986676e-01 6.24459863e-01 5.03831804e-01 -6.14597023... | [10.514493942260742, 10.066756248474121] |
72602498-1e4c-4f74-8f38-f4253af80fc9 | understanding-user-resistance-strategies-in | null | null | https://aclanthology.org/2020.findings-emnlp.431 | https://aclanthology.org/2020.findings-emnlp.431.pdf | Understanding User Resistance Strategies in Persuasive Conversations | Persuasive dialog systems have various usages, such as donation persuasion and physical exercise persuasion. Previous persuasive dialog systems research mostly focused on analyzing the persuader{'}s strategies and paid little attention to the persuadee (user). However, understanding and addressing users{'} resistance s... | ['Zhou Yu', 'Chen Li', 'Weiyan Shi', 'Youzhi Tian'] | 2020-11-01 | null | null | null | findings-of-the-association-for-computational | ['persuasion-strategies'] | ['computer-vision'] | [ 4.42493021e-01 8.03294480e-01 -5.53512812e-01 -6.62204862e-01
-2.40262643e-01 -4.33516681e-01 8.00099134e-01 2.39334583e-01
-3.19204986e-01 8.90874267e-01 9.44858134e-01 -7.93220460e-01
-3.54658097e-01 -6.59709275e-01 2.77706087e-01 -1.84770286e-01
8.12098801e-01 1.53197289e-01 4.24359962e-02 -1.08637583... | [12.821542739868164, 7.874197006225586] |
ec4ec31c-666f-42c6-8cf8-d057500b3b36 | lexicon-injected-semantic-parsing-for-task | 2211.14508 | null | https://arxiv.org/abs/2211.14508v1 | https://arxiv.org/pdf/2211.14508v1.pdf | Lexicon-injected Semantic Parsing for Task-Oriented Dialog | Recently, semantic parsing using hierarchical representations for dialog systems has captured substantial attention. Task-Oriented Parse (TOP), a tree representation with intents and slots as labels of nested tree nodes, has been proposed for parsing user utterances. Previous TOP parsing methods are limited on tackling... | ['Qun Liu', 'Xin Jiang', 'Zhiyong Wu', 'Baojun Wang', 'Yasheng Wang', 'Wenlin Dai', 'Xiaojun Meng'] | 2022-11-26 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [ 1.86031953e-01 5.32453001e-01 -1.51380524e-01 -7.26481676e-01
-7.54284024e-01 -8.56580853e-01 2.78803200e-01 2.12922439e-01
-3.29885393e-01 7.81762719e-01 5.78766167e-01 -3.65228474e-01
1.74980566e-01 -8.45224023e-01 -1.29263535e-01 -2.26719514e-01
1.64964899e-01 9.03484762e-01 8.62741709e-01 -7.60486126... | [12.624560356140137, 7.509890079498291] |
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