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46eb8c4e-7ae2-4ced-a979-261c02ce1ac6 | on-permutation-invariant-training-for-speech | 2102.04945 | null | https://arxiv.org/abs/2102.04945v2 | https://arxiv.org/pdf/2102.04945v2.pdf | On permutation invariant training for speech source separation | We study permutation invariant training (PIT), which targets at the permutation ambiguity problem for speaker independent source separation models. We extend two state-of-the-art PIT strategies. First, we look at the two-stage speaker separation and tracking algorithm based on frame level PIT (tPIT) and clustering, whi... | ['Jordi Pons', 'Xiaoyu Liu'] | 2021-02-09 | null | null | null | null | ['speaker-separation'] | ['speech'] | [ 5.00045240e-01 3.06223333e-02 7.70888925e-02 -4.11262989e-01
-1.35619032e+00 -6.03457570e-01 5.32787919e-01 -3.35339963e-01
-1.72234863e-01 4.26060289e-01 3.72399569e-01 -9.06505883e-02
-5.65747619e-01 2.94437017e-02 -5.49696505e-01 -9.90135968e-01
-3.68364990e-01 4.36554283e-01 1.98429704e-01 -1.21394731... | [14.946768760681152, 5.842853546142578] |
f79386a9-093e-422d-bf74-601f3ce86b28 | a-baseline-for-3d-multi-object-tracking | 1907.03961 | null | https://arxiv.org/abs/1907.03961v5 | https://arxiv.org/pdf/1907.03961v5.pdf | 3D Multi-Object Tracking: A Baseline and New Evaluation Metrics | 3D multi-object tracking (MOT) is an essential component for many applications such as autonomous driving and assistive robotics. Recent work on 3D MOT focuses on developing accurate systems giving less attention to practical considerations such as computational cost and system complexity. In contrast, this work propos... | ['David Held', 'Kris Kitani', 'Xinshuo Weng', 'Jianren Wang'] | 2019-07-09 | null | null | null | null | ['3d-multi-object-tracking'] | ['computer-vision'] | [-2.06449002e-01 -4.53120947e-01 7.45083094e-02 -9.73043442e-02
-4.35575426e-01 -4.38394099e-01 5.97962022e-01 -6.29823059e-02
-7.03365266e-01 6.47580862e-01 -8.19060862e-01 -5.73642790e-01
-9.14565171e-04 -8.94669235e-01 -6.45410419e-01 -6.68526590e-01
-1.32625014e-01 9.88287687e-01 8.73063862e-01 -4.49592263... | [6.7227678298950195, -2.2536988258361816] |
c5e4a1ff-07d5-427f-8834-f98b13abde29 | modeling-multi-hop-question-answering-as | null | null | https://openreview.net/forum?id=C1XEENowywW | https://openreview.net/pdf?id=C1XEENowywW | Modeling Multi-hop Question Answering as Single Sequence Prediction | Fusion-in-decoder (Fid) (Izacard and Grave, 2020) is a generative question answering (QA) model that leverages passage retrieval with a pre-trained transformer and pushed the state of the art on single-hop QA. However, the complexity of multi-hop QA hinders the effectiveness of the generative QA approach. In this work,... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['multi-hop-question-answering', 'generative-question-answering'] | ['knowledge-base', 'natural-language-processing'] | [-1.64659947e-01 5.72726130e-01 1.65149987e-01 -2.08409280e-01
-1.69665611e+00 -7.76507556e-01 8.46621513e-01 6.48983046e-02
8.79535675e-02 9.70142126e-01 9.47264612e-01 -6.37941539e-01
-1.31609932e-01 -9.90723372e-01 -8.25522006e-01 -1.47434762e-02
5.05673826e-01 8.50523591e-01 5.63930631e-01 -8.59613180... | [11.126029968261719, 7.951905727386475] |
03abe157-d310-4482-bc54-7117233b0c32 | improving-deep-policy-gradients-with-value | 2302.10145 | null | https://arxiv.org/abs/2302.10145v1 | https://arxiv.org/pdf/2302.10145v1.pdf | Improving Deep Policy Gradients with Value Function Search | Deep Policy Gradient (PG) algorithms employ value networks to drive the learning of parameterized policies and reduce the variance of the gradient estimates. However, value function approximation gets stuck in local optima and struggles to fit the actual return, limiting the variance reduction efficacy and leading poli... | ['Christopher Amato', 'Enrico Marchesini'] | 2023-02-20 | null | null | null | null | ['value-prediction', 'continuous-control'] | ['computer-code', 'playing-games'] | [-1.21398583e-01 8.36726874e-02 -5.80691397e-01 -1.10546030e-01
-7.78080940e-01 -6.34180069e-01 6.40938342e-01 2.72043705e-01
-6.95477307e-01 1.02727592e+00 1.55549422e-01 -4.71455842e-01
-2.49965951e-01 -7.66337156e-01 -9.92716551e-01 -7.24456787e-01
-2.67131120e-01 3.35355192e-01 1.42562285e-01 -2.76299030... | [4.088036060333252, 2.255612850189209] |
b4bb5ee5-1cdf-4d1b-9eee-991b16399ceb | optimal-proposal-learning-for-deployable-end | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Song_Optimal_Proposal_Learning_for_Deployable_End-to-End_Pedestrian_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Song_Optimal_Proposal_Learning_for_Deployable_End-to-End_Pedestrian_Detection_CVPR_2023_paper.pdf | Optimal Proposal Learning for Deployable End-to-End Pedestrian Detection | End-to-end pedestrian detection focuses on training a pedestrian detection model via discarding the Non-Maximum Suppression (NMS) post-processing. Though a few methods have been explored, most of them still suffer from longer training time and more complex deployment, which cannot be deployed in the actual industri... | ['Honggang Zhang', 'Xuansong Xie', 'Yifeng Geng', 'Biao Wang', 'Jun-Yan He', 'Pengyu Li', 'Binghui Chen', 'Xiaolin Song'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['pedestrian-detection'] | ['computer-vision'] | [ 3.64874192e-02 -1.61854684e-01 2.58663237e-01 -4.21563625e-01
-5.72485864e-01 -1.16591163e-01 2.90184587e-01 4.92519476e-02
-6.45321131e-01 6.69696033e-01 -2.85130262e-01 -1.72161609e-01
6.21835291e-01 -7.45541930e-01 -6.60746038e-01 -7.29376912e-01
2.82258093e-01 2.13263884e-01 1.04318714e+00 -1.79196727... | [8.068449020385742, -0.5954602360725403] |
ec181da1-ef20-4dc5-82e0-8dca8b065749 | multilingual-controllable-transformer-based | 2307.02120 | null | https://arxiv.org/abs/2307.02120v1 | https://arxiv.org/pdf/2307.02120v1.pdf | Multilingual Controllable Transformer-Based Lexical Simplification | Text is by far the most ubiquitous source of knowledge and information and should be made easily accessible to as many people as possible; however, texts often contain complex words that hinder reading comprehension and accessibility. Therefore, suggesting simpler alternatives for complex words without compromising mea... | ['Horacio Saggion', 'Kim Cheng SHEANG'] | 2023-07-05 | null | null | null | null | ['lexical-simplification', 'reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.48154892e-04 3.22729230e-01 -2.03705013e-01 -7.31729120e-02
-1.19224513e+00 -5.18261671e-01 7.68132687e-01 6.24613166e-01
-9.91315544e-01 1.00418377e+00 7.15243876e-01 -5.77489793e-01
4.65893447e-02 -4.94884968e-01 -7.13864386e-01 -1.24001160e-01
5.52212775e-01 8.13092113e-01 3.09255123e-01 -9.94067967... | [10.951156616210938, 10.388480186462402] |
15bfd45a-0202-4848-aba7-3e0481d646b3 | radon-features-and-barcodes-for-medical-image | 1604.04675 | null | http://arxiv.org/abs/1604.04675v1 | http://arxiv.org/pdf/1604.04675v1.pdf | Radon Features and Barcodes for Medical Image Retrieval via SVM | For more than two decades, research has been performed on content-based image
retrieval (CBIR). By combining Radon projections and the support vector
machines (SVM), a content-based medical image retrieval method is presented in
this work. The proposed approach employs the normalized Radon projections with
correspondin... | ['H. R. Tizhoosh', 'Shujin Zhu'] | 2016-04-16 | null | null | null | null | ['medical-image-retrieval', 'medical-image-retrieval'] | ['computer-vision', 'medical'] | [ 6.19009316e-01 -1.22656502e-01 -5.18508434e-01 -5.01526892e-01
-1.27479231e+00 -3.19248676e-01 5.73780715e-01 6.57354653e-01
-4.96083707e-01 2.93690473e-01 1.33813322e-01 -3.66640896e-01
-4.79079157e-01 -1.02992892e+00 -1.65951490e-01 -9.01499212e-01
1.50403157e-01 3.63504261e-01 5.04504502e-01 2.17182979... | [14.275508880615234, -1.4590497016906738] |
9d034f30-a19d-4277-b60a-d41c58626d6f | learning-metric-graphs-for-neuron | 1902.00100 | null | http://arxiv.org/abs/1902.00100v1 | http://arxiv.org/pdf/1902.00100v1.pdf | Learning Metric Graphs for Neuron Segmentation In Electron Microscopy Images | In the deep metric learning approach to image segmentation, a convolutional
net densely generates feature vectors at the pixels of an image. Pairs of
feature vectors are trained to be similar or different, depending on whether
the corresponding pixels belong to same or different ground truth segments. To
segment a new ... | ['Kyle Luther', 'H. Sebastian Seung'] | 2019-01-31 | null | null | null | null | ['electron-microscopy-image-segmentation'] | ['computer-vision'] | [ 5.45667470e-01 4.59533095e-01 2.75626928e-01 -5.53657234e-01
-6.29116535e-01 -7.71818459e-01 3.80972177e-01 4.12730068e-01
-6.23965740e-01 5.93642116e-01 -2.75824457e-01 -1.81370199e-01
-4.30605710e-01 -7.11166859e-01 -8.11826944e-01 -9.68491673e-01
-1.09736212e-01 8.08348298e-01 4.96485680e-01 1.30287081... | [14.213846206665039, -2.9566657543182373] |
c4aa67dc-8c39-4d40-92b9-d6ec10a3c536 | span-based-discontinuous-constituency-parsing | 2003.13785 | null | https://arxiv.org/abs/2003.13785v1 | https://arxiv.org/pdf/2003.13785v1.pdf | Span-based discontinuous constituency parsing: a family of exact chart-based algorithms with time complexities from O(n^6) down to O(n^3) | We introduce a novel chart-based algorithm for span-based parsing of discontinuous constituency trees of block degree two, including ill-nested structures. In particular, we show that we can build variants of our parser with smaller search spaces and time complexities ranging from $\mathcal O(n^6)$ down to $\mathcal O(... | ['Caio Corro'] | 2020-03-30 | null | null | null | null | ['constituency-parsing'] | ['natural-language-processing'] | [ 9.57491025e-02 5.65935552e-01 -6.78616017e-02 -4.24628288e-01
-1.44722474e+00 -1.02842629e+00 -8.22282806e-02 6.17404759e-01
-7.01374590e-01 7.33729839e-01 3.10447097e-01 -1.20938694e+00
2.15935871e-01 -9.89370346e-01 -5.58674276e-01 -2.63041735e-01
-5.87189615e-01 4.89965320e-01 4.85251576e-01 -6.17621422... | [10.332148551940918, 9.74126148223877] |
7ee459b7-d20b-422c-ba13-4815caef1dba | towards-tractable-mathematical-reasoning | 2111.05364 | null | https://arxiv.org/abs/2111.05364v1 | https://arxiv.org/pdf/2111.05364v1.pdf | Towards Tractable Mathematical Reasoning: Challenges, Strategies, and Opportunities for Solving Math Word Problems | Mathematical reasoning would be one of the next frontiers for artificial intelligence to make significant progress. The ongoing surge to solve math word problems (MWPs) and hence achieve better mathematical reasoning ability would continue to be a key line of research in the coming time. We inspect non-neural and neura... | ['Aditi Avasthi', 'Manas Gaur', 'Prashant Kikani', 'Amit Sheth', 'Keyur Faldu'] | 2021-10-29 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [ 3.28995585e-01 5.08028746e-01 6.09544925e-02 -3.58415544e-01
-3.68180156e-01 -8.91616106e-01 2.91868418e-01 8.26208964e-02
2.22061053e-02 7.78950155e-01 2.00500816e-01 -6.82661831e-01
-7.05874205e-01 -1.48353803e+00 -6.23200893e-01 -1.64368257e-01
3.28979790e-01 5.07137537e-01 -2.74699569e-01 -5.54547429... | [9.444352149963379, 7.250920295715332] |
00c6616e-3a26-46e2-a0f3-c8a53613dc21 | dytanvo-joint-refinement-of-visual-odometry | 2209.08430 | null | https://arxiv.org/abs/2209.08430v4 | https://arxiv.org/pdf/2209.08430v4.pdf | DytanVO: Joint Refinement of Visual Odometry and Motion Segmentation in Dynamic Environments | Learning-based visual odometry (VO) algorithms achieve remarkable performance on common static scenes, benefiting from high-capacity models and massive annotated data, but tend to fail in dynamic, populated environments. Semantic segmentation is largely used to discard dynamic associations before estimating camera moti... | ['Sebastian Scherer', 'Wenshan Wang', 'Yilin Cai', 'Shihao Shen'] | 2022-09-17 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [-1.70300752e-01 -1.26174614e-01 -3.60114813e-01 -2.35697299e-01
-5.74829698e-01 -7.54547119e-01 3.94802868e-01 -2.74787158e-01
-5.00582278e-01 4.90239888e-01 1.33775756e-01 -1.19099922e-01
2.78905630e-01 -2.54202753e-01 -7.62160122e-01 -5.52395642e-01
-1.58445776e-01 9.55570221e-01 8.66671443e-01 -8.97831321... | [8.10881519317627, -2.1210713386535645] |
4c3489b8-8dab-46af-980d-dece0ebda42a | recursive-generalization-transformer-for | 2303.06373 | null | https://arxiv.org/abs/2303.06373v2 | https://arxiv.org/pdf/2303.06373v2.pdf | Recursive Generalization Transformer for Image Super-Resolution | Transformer architectures have exhibited remarkable performance in image super-resolution (SR). Since the quadratic computational complexity of the self-attention (SA) in Transformer, existing methods tend to adopt SA in a local region to reduce overheads. However, the local design restricts the global context exploita... | ['Xiaokang Yang', 'Linghe Kong', 'Jinjin Gu', 'Yulun Zhang', 'Zheng Chen'] | 2023-03-11 | null | null | null | null | ['image-super-resolution'] | ['computer-vision'] | [ 1.64740697e-01 -3.24497521e-01 -6.76443428e-02 -2.61791974e-01
-8.65807891e-01 4.74715009e-02 3.52424055e-01 -1.99564472e-01
-1.17582813e-01 3.81109267e-01 4.95515436e-01 4.94640172e-02
-3.43760103e-01 -1.00676477e+00 -5.25783837e-01 -8.24835718e-01
1.25695020e-01 -2.79512703e-01 5.05897105e-01 -3.15931082... | [10.889854431152344, -1.8447080850601196] |
dc5e007e-0c9d-4f56-9899-f78694b03b71 | ts-sep-joint-diarization-and-separation | 2303.03849 | null | https://arxiv.org/abs/2303.03849v2 | https://arxiv.org/pdf/2303.03849v2.pdf | TS-SEP: Joint Diarization and Separation Conditioned on Estimated Speaker Embeddings | Since diarization and source separation of meeting data are closely related tasks, we here propose an approach to perform the two objectives jointly. It builds upon the target-speaker voice activity detection (TS-VAD) diarization approach, which assumes that initial speaker embeddings are available. We replace the fina... | ['Jonathan Le Roux', 'Reinhold Haeb-Umbach', 'Gordon Wichern', 'Aswin Shanmugam Subramanian', 'Christoph Boeddeker'] | 2023-03-07 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [ 4.83866304e-01 1.93097144e-01 1.13049164e-01 -3.93860281e-01
-1.66435087e+00 -6.42445922e-01 7.67630577e-01 3.79319638e-02
-4.07535255e-01 3.13951850e-01 6.83959544e-01 -2.79258072e-01
2.53823735e-02 -1.54151976e-01 -2.51695693e-01 -8.74795437e-01
2.78155133e-02 3.16838883e-02 5.08791767e-02 1.10514835... | [14.703490257263184, 6.096660614013672] |
fb4aada6-ca87-44e4-a35f-b83726bf7d95 | fedvmr-a-new-federated-learning-method-for | 2210.15977 | null | https://arxiv.org/abs/2210.15977v1 | https://arxiv.org/pdf/2210.15977v1.pdf | FedVMR: A New Federated Learning method for Video Moment Retrieval | Despite the great success achieved, existing video moment retrieval (VMR) methods are developed under the assumption that data are centralizedly stored. However, in real-world applications, due to the inherent nature of data generation and privacy concerns, data are often distributed on different silos, bringing huge c... | ['Xin-Shun Xu', 'Meng Liu', 'Peng-Fei Zhang', 'Zhen-Duo Chen', 'Xin Luo', 'Yan Wang'] | 2022-10-28 | null | null | null | null | ['moment-retrieval'] | ['computer-vision'] | [-1.41081676e-01 -5.06082714e-01 -4.77281749e-01 -2.38316640e-01
-6.32140636e-01 -5.32515466e-01 7.44631827e-01 5.25481738e-02
-3.20162654e-01 7.40517855e-01 -1.28611242e-02 -3.77684414e-01
-3.03058326e-01 -6.69203401e-01 -4.69709426e-01 -8.32315803e-01
-2.56943077e-01 1.31646529e-01 3.29904035e-02 -8.67686868... | [5.862910270690918, 6.3329596519470215] |
e632393c-9f50-4a06-a365-8923bde37731 | clustering-ensemble-meets-low-rank-tensor | 2012.08916 | null | https://arxiv.org/abs/2012.08916v1 | https://arxiv.org/pdf/2012.08916v1.pdf | Clustering Ensemble Meets Low-rank Tensor Approximation | This paper explores the problem of clustering ensemble, which aims to combine multiple base clusterings to produce better performance than that of the individual one. The existing clustering ensemble methods generally construct a co-association matrix, which indicates the pairwise similarity between samples, as the wei... | ['Qingfu Zhang', 'Junhui Hou', 'Hui Liu', 'Yuheng Jia'] | 2020-12-16 | null | null | null | null | ['clustering-ensemble'] | ['graphs'] | [-1.42611772e-01 -5.24680376e-01 3.45012136e-02 -1.83195323e-01
-5.62434494e-01 -5.49993753e-01 1.73951462e-01 -5.20504937e-02
-8.91461894e-02 2.79367417e-01 1.62771925e-01 8.22633803e-02
-6.50270760e-01 -4.36006844e-01 -1.97106466e-01 -1.25893652e+00
-2.56481707e-01 5.14478803e-01 -1.75827593e-02 -4.81847078... | [7.960501194000244, 4.654808521270752] |
56853ae2-bf5f-4381-b172-af0923de2c2f | sass-data-and-methods-for-subject-aware | 2303.14589 | null | https://arxiv.org/abs/2303.14589v1 | https://arxiv.org/pdf/2303.14589v1.pdf | SASS: Data and Methods for Subject Aware Sentence Simplification | Sentence simplification tends to focus on the generic simplification of sentences by making them more readable and easier to understand. This paper provides a dataset aimed at training models that perform subject aware sentence simplifications rather than simplifying sentences as a whole. We also test models on that da... | ['Anand Tyagi', 'Luke Martin', 'Brad Windsor'] | 2023-03-26 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 3.10082406e-01 7.15831578e-01 2.30174419e-02 -7.99994707e-01
-9.24262822e-01 -4.36237693e-01 6.81864262e-01 5.62887967e-01
-6.04815185e-01 8.70765507e-01 1.11382830e+00 -1.95755899e-01
1.38338938e-01 -5.44313431e-01 -4.72302616e-01 4.14799750e-02
2.75959104e-01 6.98130667e-01 -3.81586224e-01 -7.13779747... | [11.313506126403809, 10.124588012695312] |
b1d8c5c3-c554-48d3-8fc8-9aa01db0ad0c | adapting-marbert-for-improved-arabic-dialect | 2103.01065 | null | https://arxiv.org/abs/2103.01065v1 | https://arxiv.org/pdf/2103.01065v1.pdf | Adapting MARBERT for Improved Arabic Dialect Identification: Submission to the NADI 2021 Shared Task | In this paper, we tackle the Nuanced Arabic Dialect Identification (NADI) shared task (Abdul-Mageed et al., 2021) and demonstrate state-of-the-art results on all of its four subtasks. Tasks are to identify the geographic origin of short Dialectal (DA) and Modern Standard Arabic (MSA) utterances at the levels of both co... | ['Khaled Essam', 'Muhammad ElNokrashy', 'Mohamed Gabr', 'Badr AlKhamissi'] | 2021-03-01 | null | https://aclanthology.org/2021.wanlp-1.29 | https://aclanthology.org/2021.wanlp-1.29.pdf | eacl-wanlp-2021-4 | ['dialect-identification'] | ['natural-language-processing'] | [-4.47990477e-01 -9.54215899e-02 1.21107750e-01 -6.15185022e-01
-1.28753495e+00 -8.58378232e-01 9.64122713e-01 -2.74466842e-01
-3.70598942e-01 6.27757370e-01 2.66724885e-01 -5.16732931e-01
9.47997421e-02 -3.80365491e-01 -2.27198154e-01 -5.65619648e-01
-3.28767896e-01 8.32207620e-01 -2.86240242e-02 -1.02825928... | [10.17410659790039, 10.77118968963623] |
71000dac-8334-4b05-a03a-b9287aded1ef | an-efficient-probabilistically-sound | cs/9905007 | null | https://arxiv.org/abs/cs/9905007v1 | https://arxiv.org/pdf/cs/9905007v1.pdf | An Efficient, Probabilistically Sound Algorithm for Segmentation and Word Discovery | This paper presents a model-based, unsupervised algorithm for recovering word boundaries in a natural-language text from which they have been deleted. The algorithm is derived from a probability model of the source that generated the text. The fundamental structure of the model is specified abstractly so that the detai... | ['Michael R. Brent'] | 1999-05-12 | null | null | null | null | ['language-acquisition'] | ['natural-language-processing'] | [ 6.20467603e-01 3.29657465e-01 -2.57842124e-01 -5.07807016e-01
-4.96908873e-01 -5.25240898e-01 2.90937603e-01 2.64244646e-01
-4.27680939e-01 7.19312012e-01 2.88790107e-01 -6.15276754e-01
-2.25923851e-01 -7.38954425e-01 -6.03510737e-01 -5.67759514e-01
2.20243663e-01 9.82416868e-01 2.59455711e-01 -4.29840311... | [10.50473403930664, 9.693554878234863] |
d098a2ac-b140-4d13-bea0-00ca21069f2b | when-geometric-deep-learning-meets-pretrained | 2212.03447 | null | https://arxiv.org/abs/2212.03447v1 | https://arxiv.org/pdf/2212.03447v1.pdf | When Geometric Deep Learning Meets Pretrained Protein Language Models | Geometric deep learning has recently achieved great success in non-Euclidean domains, and learning on 3D structures of large biomolecules is emerging as a distinct research area. However, its efficacy is largely constrained due to the limited quantity of structural data. Meanwhile, protein language models trained on su... | ['Jinbo Xu', 'Dragomir Radev', 'Yu Tao', 'Fang Wu'] | 2022-12-07 | null | null | null | null | ['protein-interface-prediction'] | ['miscellaneous'] | [ 1.65736869e-01 1.95540085e-01 -2.93095678e-01 -4.57215965e-01
-9.00107145e-01 -5.84409535e-01 4.53243971e-01 5.67765594e-01
-4.39064533e-01 6.97561145e-01 1.71512589e-01 -5.62422395e-01
4.93479483e-02 -5.28502285e-01 -1.08149230e+00 -8.24955761e-01
-1.88597456e-01 7.96084881e-01 1.25869825e-01 -3.28154892... | [4.9024858474731445, 5.697126865386963] |
24f29e20-5e06-4d30-a7db-8bd6becefcac | hsolo-homography-from-a-single-affine-aware | 2009.05004 | null | https://arxiv.org/abs/2009.05004v1 | https://arxiv.org/pdf/2009.05004v1.pdf | HSolo: Homography from a single affine aware correspondence | The performance of existing robust homography estimation algorithms is highly dependent on the inlier rate of feature point correspondences. In this paper, we present a novel procedure for homography estimation that is particularly well suited for inlier-poor domains. By utilizing the scale and rotation byproducts crea... | ['Tony Perkins', 'Cara Monical', 'Antonio Gonzales'] | 2020-09-10 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [-1.06869027e-01 -3.79813403e-01 -2.11188048e-01 -3.45606320e-02
-1.05632675e+00 -8.18460226e-01 5.62623799e-01 8.05007070e-02
-2.25899890e-01 7.47923017e-01 1.36510059e-01 3.93890530e-01
-3.13589685e-02 -6.80076838e-01 -6.26181066e-01 -3.42107445e-01
2.45146349e-01 6.56007528e-01 2.40540192e-01 -2.89631605... | [7.8930134773254395, -2.3348515033721924] |
925c6212-c1b9-480e-9639-20b969021eaa | polysemous-visual-semantic-embedding-for-1 | 1906.04402 | null | https://arxiv.org/abs/1906.04402v2 | https://arxiv.org/pdf/1906.04402v2.pdf | Polysemous Visual-Semantic Embedding for Cross-Modal Retrieval | Visual-semantic embedding aims to find a shared latent space where related visual and textual instances are close to each other. Most current methods learn injective embedding functions that map an instance to a single point in the shared space. Unfortunately, injective embedding cannot effectively handle polysemous in... | ['Yale Song', 'Mohammad Soleymani'] | 2019-06-11 | polysemous-visual-semantic-embedding-for | http://openaccess.thecvf.com/content_CVPR_2019/html/Song_Polysemous_Visual-Semantic_Embedding_for_Cross-Modal_Retrieval_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Song_Polysemous_Visual-Semantic_Embedding_for_Cross-Modal_Retrieval_CVPR_2019_paper.pdf | cvpr-2019-6 | ['video-text-retrieval'] | ['computer-vision'] | [ 1.93350241e-01 -3.61131430e-01 -4.67729837e-01 -3.65638554e-01
-1.12967479e+00 -5.68141639e-01 8.57762814e-01 1.01193063e-01
-4.02699560e-01 3.87752950e-01 4.93884474e-01 6.76600412e-02
-3.78572762e-01 -4.10209805e-01 -6.56957746e-01 -6.07568800e-01
3.79656963e-02 4.18548822e-01 -1.80013478e-01 -1.09711565... | [10.486713409423828, 1.1839659214019775] |
0f7ea0b4-82c5-4d02-9ea8-8b9431a9a82f | vlg-net-video-language-graph-matching-network | 2011.10132 | null | https://arxiv.org/abs/2011.10132v2 | https://arxiv.org/pdf/2011.10132v2.pdf | VLG-Net: Video-Language Graph Matching Network for Video Grounding | Grounding language queries in videos aims at identifying the time interval (or moment) semantically relevant to a language query. The solution to this challenging task demands understanding videos' and queries' semantic content and the fine-grained reasoning about their multi-modal interactions. Our key idea is to reca... | ['Sisi Qu', 'Mengmeng Xu', 'Bernard Ghanem', 'Jesper Tegner', 'Mattia Soldan'] | 2020-11-19 | null | null | null | null | ['video-grounding', 'moment-retrieval', 'natural-language-moment-retrieval'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.28614132e-02 -1.98261559e-01 -5.64745009e-01 -3.23614955e-01
-8.35728228e-01 -5.92883170e-01 9.04709518e-01 5.63077390e-01
-2.46772185e-01 3.53111066e-02 8.28556836e-01 1.46418393e-01
-3.14907357e-02 -5.83926558e-01 -7.84585059e-01 -1.75863892e-01
-4.24639463e-01 7.84928817e-03 3.28890920e-01 -1.18999235... | [10.058099746704102, 0.8284958004951477] |
67658ba9-d0bd-49b7-86f9-48d3fa797e6f | deepportraitdrawing-generating-human-body | 2205.02070 | null | https://arxiv.org/abs/2205.02070v2 | https://arxiv.org/pdf/2205.02070v2.pdf | DeepPortraitDrawing: Generating Human Body Images from Freehand Sketches | Researchers have explored various ways to generate realistic images from freehand sketches, e.g., for objects and human faces. However, how to generate realistic human body images from sketches is still a challenging problem. It is, first because of the sensitivity to human shapes, second because of the complexity of h... | ['Shi-Min Hu', 'Song-Hai Zhang', 'Ariel Shamir', 'Hongbo Fu', 'Chen Wang', 'Xian Wu'] | 2022-05-04 | null | null | null | null | ['sketch-to-image-translation'] | ['computer-vision'] | [ 2.89758652e-01 2.17760861e-01 1.73720002e-01 -4.63457853e-01
-2.61785775e-01 -5.45198321e-01 6.49187744e-01 -8.35880280e-01
1.39866814e-01 5.50265670e-01 2.35545039e-01 1.83303297e-01
1.66939840e-01 -1.03919673e+00 -7.11854279e-01 -3.39009970e-01
5.14055431e-01 6.22505069e-01 1.41572997e-01 -4.24259841... | [12.041792869567871, -0.4376649260520935] |
e7b0e463-7b74-4634-ba25-13ea0daaf5f7 | smug-towards-robust-mri-reconstruction-by | 2303.12735 | null | https://arxiv.org/abs/2303.12735v1 | https://arxiv.org/pdf/2303.12735v1.pdf | SMUG: Towards robust MRI reconstruction by smoothed unrolling | Although deep learning (DL) has gained much popularity for accelerated magnetic resonance imaging (MRI), recent studies have shown that DL-based MRI reconstruction models could be oversensitive to tiny input perturbations (that are called 'adversarial perturbations'), which cause unstable, low-quality reconstructed ima... | ['Sijia Liu', 'Saiprasad Ravishankar', 'Yuguang Yao', 'Shijun Liang', 'Jinghan Jia', 'Hui Li'] | 2023-03-14 | null | null | null | null | ['adversarial-defense', 'unrolling', 'mri-reconstruction'] | ['adversarial', 'computer-vision', 'computer-vision'] | [ 3.05906951e-01 9.04006511e-02 2.20350191e-01 -1.51821837e-01
-1.17983556e+00 -5.16344905e-01 3.42454076e-01 -1.64872959e-01
-4.18578446e-01 4.97307748e-01 2.47019351e-01 -5.32933414e-01
1.36378957e-02 -5.37553668e-01 -9.03157830e-01 -1.05852365e+00
-2.62004852e-01 -1.55758828e-01 3.35955888e-01 -2.53580183... | [13.53173828125, -2.2964131832122803] |
0d4e1569-17f9-4b7c-aec1-aaa9145c0686 | candid-correspondence-alignment-for-deep | 2306.09887 | null | https://arxiv.org/abs/2306.09887v1 | https://arxiv.org/pdf/2306.09887v1.pdf | CANDID: Correspondence AligNment for Deep-burst Image Denoising | With the advent of mobile phone photography and point-and-shoot cameras, deep-burst imaging is widely used for a number of photographic effects such as depth of field, super-resolution, motion deblurring, and image denoising. In this work, we propose to solve the problem of deep-burst image denoising by including an op... | ['Hendrik PA Lensch', 'Raphael Braun', 'Arijit Mallick'] | 2023-06-16 | null | null | null | null | ['deblurring', 'optical-flow-estimation', 'image-denoising', 'super-resolution'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 3.33774596e-01 -3.34762841e-01 3.13249052e-01 -2.36105040e-01
-5.50036609e-01 -3.56005698e-01 4.48920429e-01 -2.56223440e-01
-7.51337409e-01 5.83707631e-01 3.32904607e-01 2.19360948e-01
1.22655623e-01 -5.45820355e-01 -6.04208231e-01 -7.66778529e-01
3.78980726e-01 -1.07810885e-01 5.46001017e-01 -1.06487028... | [10.892706871032715, -1.7559303045272827] |
8bff86b6-8687-45ae-86f6-b988597b2bac | clustering-noisy-signals-with-structured | 1510.05214 | null | http://arxiv.org/abs/1510.05214v1 | http://arxiv.org/pdf/1510.05214v1.pdf | Clustering Noisy Signals with Structured Sparsity Using Time-Frequency Representation | We propose a simple and efficient time-series clustering framework
particularly suited for low Signal-to-Noise Ratio (SNR), by simultaneous
smoothing and dimensionality reduction aimed at preserving clustering
information. We extend the sparse K-means algorithm by incorporating structured
sparsity, and use it to exploi... | ['Or Zuk', 'Tom Hope', 'Avishai Wagner'] | 2015-10-18 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [ 1.83441445e-01 -3.50297421e-01 1.05117977e-01 -2.83859074e-01
-5.50479591e-01 -4.66865331e-01 5.30432045e-01 -1.13431722e-01
-2.10780427e-01 1.96671620e-01 5.85494757e-01 3.09803963e-01
-5.95060229e-01 -6.35610044e-01 -2.05876067e-01 -1.20777404e+00
-8.32170725e-01 -1.73226833e-01 2.54602879e-02 1.17359748... | [11.712676048278809, -2.2940471172332764] |
6948850e-a293-43b2-976e-720bff32730c | food-recommendation-framework-existing | 1905.06269 | null | https://arxiv.org/abs/1905.06269v2 | https://arxiv.org/pdf/1905.06269v2.pdf | Food Recommendation: Framework, Existing Solutions and Challenges | A growing proportion of the global population is becoming overweight or obese, leading to various diseases (e.g., diabetes, ischemic heart disease and even cancer) due to unhealthy eating patterns, such as increased intake of food with high energy and high fat. Food recommendation is of paramount importance to alleviat... | ['Weiqing Min', 'Ramesh Jain', 'Shuqiang Jiang'] | 2019-05-15 | null | null | null | null | ['food-recommendation'] | ['miscellaneous'] | [ 1.50858283e-01 -1.57510489e-01 -1.03297913e+00 -2.39711151e-01
2.06857264e-01 -3.42466354e-01 -1.86719507e-01 9.17141557e-01
-3.69575806e-02 2.55562305e-01 6.71373546e-01 -1.56816438e-01
-1.05605111e-01 -1.18903410e+00 -2.54516155e-01 -5.53100586e-01
-8.51973891e-02 -2.47113422e-01 1.39320329e-01 -3.24510306... | [11.545733451843262, 4.458858966827393] |
c5ab727f-1cf3-4468-9081-b0724039ebbc | on-the-model-based-stochastic-value-gradient | 2008.12775 | null | https://arxiv.org/abs/2008.12775v3 | https://arxiv.org/pdf/2008.12775v3.pdf | On the model-based stochastic value gradient for continuous reinforcement learning | For over a decade, model-based reinforcement learning has been seen as a way to leverage control-based domain knowledge to improve the sample-efficiency of reinforcement learning agents. While model-based agents are conceptually appealing, their policies tend to lag behind those of model-free agents in terms of final r... | ['Samuel Stanton', 'Brandon Amos', 'Andrew Gordon Wilson', 'Denis Yarats'] | 2020-08-28 | null | null | null | null | ['humanoid-control'] | ['robots'] | [-2.52048165e-01 8.41019154e-02 -5.75237513e-01 4.46353927e-02
-7.63931632e-01 -5.77991068e-01 8.69332790e-01 1.81065693e-01
-1.00622118e+00 1.10383105e+00 2.29792580e-01 -3.41025770e-01
-3.73281926e-01 -5.00165701e-01 -6.05776131e-01 -6.31077051e-01
-1.84871882e-01 8.72350574e-01 1.69002295e-01 -5.35035372... | [4.1407952308654785, 1.9398424625396729] |
6cc7fc78-4b7d-4c8b-928d-e24fb51f6273 | destruction-and-construction-learning-for | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Chen_Destruction_and_Construction_Learning_for_Fine-Grained_Image_Recognition_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Chen_Destruction_and_Construction_Learning_for_Fine-Grained_Image_Recognition_CVPR_2019_paper.pdf | Destruction and Construction Learning for Fine-Grained Image Recognition | Delicate feature representation about object parts plays a critical role in fine-grained recognition. For example, experts can even distinguish fine-grained objects relying only on object parts according to professional knowledge. In this paper, we propose a novel "Destruction and Construction Learning" (DCL) method to... | [' Tao Mei', ' Wei Zhang', ' Yalong Bai', 'Yue Chen'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['fine-grained-image-recognition'] | ['computer-vision'] | [ 1.80695012e-01 -1.53372750e-01 -2.05712304e-01 -3.95446002e-01
-5.82763672e-01 -7.63059378e-01 3.61615747e-01 -4.88462932e-02
-2.38560945e-01 4.79916811e-01 -1.28471985e-01 -1.87436327e-01
-1.76160961e-01 -9.41546500e-01 -9.27461505e-01 -9.29192305e-01
4.03418243e-01 1.88436538e-01 2.16800809e-01 9.31586623... | [9.641033172607422, 2.0254297256469727] |
dc09f51f-53d0-4a86-a4b6-0dfa2a7afa85 | silk-simple-learned-keypoints | 2304.06194 | null | https://arxiv.org/abs/2304.06194v1 | https://arxiv.org/pdf/2304.06194v1.pdf | SiLK -- Simple Learned Keypoints | Keypoint detection & descriptors are foundational tech-nologies for computer vision tasks like image matching, 3D reconstruction and visual odometry. Hand-engineered methods like Harris corners, SIFT, and HOG descriptors have been used for decades; more recently, there has been a trend to introduce learning in an attem... | ['Matt Feiszli', 'Weiyao Wang', 'Pierre Gleize'] | 2023-04-12 | null | null | null | null | ['point-cloud-registration', 'keypoint-detection', '3d-reconstruction', 'homography-estimation', 'visual-odometry'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'robots'] | [-2.81349599e-01 -3.79646361e-01 -5.43622851e-01 -1.30859286e-01
-5.74685395e-01 -6.55162096e-01 7.81183720e-01 7.56767858e-03
-4.24838632e-01 -2.02876702e-02 -1.13030791e-01 -1.58621550e-01
-2.06498861e-01 -3.83806825e-01 -1.01703393e+00 -4.58242506e-01
-2.08262682e-01 4.15896773e-01 4.90218639e-01 -3.12995374... | [7.863284587860107, -2.171964168548584] |
cfbee086-7bf5-4231-bb1b-153c7fd07fc4 | sim-to-real-6d-object-pose-estimation-via | 2204.07049 | null | https://arxiv.org/abs/2204.07049v2 | https://arxiv.org/pdf/2204.07049v2.pdf | Sim-to-Real 6D Object Pose Estimation via Iterative Self-training for Robotic Bin Picking | In this paper, we propose an iterative self-training framework for sim-to-real 6D object pose estimation to facilitate cost-effective robotic grasping. Given a bin-picking scenario, we establish a photo-realistic simulator to synthesize abundant virtual data, and use this to train an initial pose estimation network. Th... | ['Qi Dou', 'Pieter Abbeel', 'Yun-hui Liu', 'Yichuan Li', 'Stephen James', 'Rui Cao', 'Kai Chen'] | 2022-04-14 | null | null | null | null | ['6d-pose-estimation', 'robotic-grasping'] | ['computer-vision', 'robots'] | [ 2.97433615e-01 2.18708158e-01 -8.54764059e-02 -4.43180591e-01
-1.03700376e+00 -7.55012512e-01 2.49648303e-01 -8.28837529e-02
-5.10083616e-01 5.79980016e-01 -2.85569340e-01 -1.44217968e-01
5.64765707e-02 -6.53933525e-01 -1.22173059e+00 -6.48253262e-01
-2.02194974e-01 1.13573527e+00 4.02369261e-01 -2.62503803... | [5.826952934265137, -0.8953045606613159] |
c56aa890-74fd-4740-9c5c-9a669b45b4b6 | carfi-rider-localization-using-wi-fi-csi | 2301.01592 | null | https://arxiv.org/abs/2301.01592v1 | https://arxiv.org/pdf/2301.01592v1.pdf | CarFi: Rider Localization Using Wi-Fi CSI | With the rise of hailing services, people are increasingly relying on shared mobility (e.g., Uber, Lyft) drivers to pick up for transportation. However, such drivers and riders have difficulties finding each other in urban areas as GPS signals get blocked by skyscrapers, in crowded environments (e.g., in stadiums, airp... | ['Shahriar Nirjon', 'Shan Lin', 'Mahathir Monjur', 'Shiwei Fang', 'Hongkai Chen', 'Sirajum Munir'] | 2022-12-21 | null | null | null | null | ['blocking'] | ['natural-language-processing'] | [-5.10172904e-01 -3.50745201e-01 -2.68551767e-01 5.82925044e-02
-4.35426205e-01 -6.24203622e-01 2.48003095e-01 -5.82604110e-01
-3.98234636e-01 8.35439503e-01 -2.02136219e-01 -7.69248426e-01
-1.99251100e-02 -1.13068831e+00 -4.33920920e-01 -7.43586004e-01
-6.70718178e-02 2.33345315e-01 6.22434795e-01 -3.70674044... | [6.243051052093506, 1.031459093093872] |
5b60858e-b74b-47d1-8354-feabb721f7df | a-tale-of-two-latent-flows-learning-latent | 2301.09300 | null | https://arxiv.org/abs/2301.09300v1 | https://arxiv.org/pdf/2301.09300v1.pdf | A Tale of Two Latent Flows: Learning Latent Space Normalizing Flow with Short-run Langevin Flow for Approximate Inference | We study a normalizing flow in the latent space of a top-down generator model, in which the normalizing flow model plays the role of the informative prior model of the generator. We propose to jointly learn the latent space normalizing flow prior model and the top-down generator model by a Markov chain Monte Carlo (MCM... | ['Ping Li', 'Dingcheng Li', 'Yifei Xu', 'Yaxuan Zhu', 'Jianwen Xie'] | 2023-01-23 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 5.47129452e-01 3.09571803e-01 -1.29593164e-01 -4.50733453e-02
-7.87185431e-01 -2.69713163e-01 9.16185260e-01 -4.86527920e-01
-2.61029869e-01 6.95919514e-01 3.33890885e-01 -1.58154830e-01
-2.64100969e-01 -7.37003207e-01 -8.40425968e-01 -1.04357457e+00
1.36203170e-01 7.95768261e-01 -3.20499510e-01 2.86048353... | [7.039623260498047, 3.7757680416107178] |
c3e9bf11-cc7b-4d38-bb60-fc9b35de6af9 | contrastive-masked-autoencoders-for-self | 2211.11210 | null | https://arxiv.org/abs/2211.11210v2 | https://arxiv.org/pdf/2211.11210v2.pdf | Contrastive Masked Autoencoders for Self-Supervised Video Hashing | Self-Supervised Video Hashing (SSVH) models learn to generate short binary representations for videos without ground-truth supervision, facilitating large-scale video retrieval efficiency and attracting increasing research attention. The success of SSVH lies in the understanding of video content and the ability to capt... | ['Shutao Xia', 'Ziyun Zeng', 'Bin Chen', 'Jinpeng Wang', 'Yuting Wang'] | 2022-11-21 | null | null | null | null | ['video-similarity'] | ['computer-vision'] | [-3.93178985e-02 -1.67614207e-01 -5.83103359e-01 -3.96621615e-01
-8.15991879e-01 -3.70923042e-01 4.04213399e-01 -2.17505526e-02
-2.81392336e-01 4.70708400e-01 3.62868518e-01 1.09073393e-01
2.69321859e-01 -6.38306379e-01 -9.22209024e-01 -7.66830444e-01
-1.28406852e-01 3.24256755e-02 3.27638507e-01 5.73581643... | [10.063794136047363, 0.7104232907295227] |
7cd18a96-2733-470e-859c-85587eb5ea4a | motion-aware-transformer-for-occluded-person | 2202.04243 | null | https://arxiv.org/abs/2202.04243v2 | https://arxiv.org/pdf/2202.04243v2.pdf | Motion-Aware Transformer For Occluded Person Re-identification | Recently, occluded person re-identification(Re-ID) remains a challenging task that people are frequently obscured by other people or obstacles, especially in a crowd massing situation. In this paper, we propose a self-supervised deep learning method to improve the location performance for human parts through occluded p... | ['Xiai Chen', 'Wei Hong', 'Zhekun Lv', 'Hongye Liu', 'Mi Zhou'] | 2022-02-09 | null | null | null | null | ['human-part-segmentation'] | ['computer-vision'] | [-1.68565989e-01 -1.84960216e-01 -1.91225275e-01 -1.97322711e-01
-5.29328704e-01 -2.61669546e-01 2.79537976e-01 -4.30639237e-01
-4.16632503e-01 6.01296306e-01 5.49200177e-01 5.80773771e-01
3.82041126e-01 -5.31779110e-01 -4.73093629e-01 -6.60755575e-01
2.94937819e-01 5.43955743e-01 3.94534290e-01 -9.71745774... | [14.595158576965332, 0.8008497953414917] |
03ee7b78-9030-4c90-86ed-4388ffe19da6 | a-comprehensive-survey-on-deep-graph | 2304.05055 | null | https://arxiv.org/abs/2304.05055v2 | https://arxiv.org/pdf/2304.05055v2.pdf | A Comprehensive Survey on Deep Graph Representation Learning | Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied in a range of fields, including machine learning and data mining. Classic graph embedding methods follow the basic idea that... | ['Ming Zhang', 'Xiao Luo', 'Yusheng Zhao', 'Jingyang Yuan', 'Junwei Yang', 'Zhiping Xiao', 'Fang Sun', 'Jianhao Shen', 'Yifang Qin', 'Ziyue Qiao', 'Qingqing Long', 'Zequn Liu', 'Yiyang Gu', 'Zheng Fang', 'Wei Ju'] | 2023-04-11 | null | null | null | null | ['graph-embedding'] | ['graphs'] | [ 3.33481096e-02 3.46254498e-01 -5.74732840e-01 -9.44072530e-02
2.15134807e-02 -2.09341139e-01 4.74188685e-01 4.74703759e-01
2.49434467e-02 3.48325014e-01 2.79079229e-01 -2.77454555e-01
-3.36396515e-01 -1.07585835e+00 -3.93022269e-01 -8.06295991e-01
-4.65157241e-01 4.41874951e-01 -1.04657233e-01 -3.13373774... | [7.103440761566162, 6.280629634857178] |
db8ed22f-6b37-46e3-80b8-5309f5f84859 | gensyn-a-multi-stage-framework-for-generating | 2212.05975 | null | https://arxiv.org/abs/2212.05975v1 | https://arxiv.org/pdf/2212.05975v1.pdf | GenSyn: A Multi-stage Framework for Generating Synthetic Microdata using Macro Data Sources | Individual-level data (microdata) that characterizes a population, is essential for studying many real-world problems. However, acquiring such data is not straightforward due to cost and privacy constraints, and access is often limited to aggregated data (macro data) sources. In this study, we examine synthetic data ge... | ['Huzefa Rangwala', 'Sanmay Das', 'Siddhartha Sikdar', 'Angeela Acharya'] | 2022-12-08 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [-6.31648228e-02 8.06341022e-02 -2.36303672e-01 -4.08593684e-01
-1.07366025e+00 -4.98011053e-01 6.54480875e-01 6.52722895e-01
-2.10210890e-01 1.52043808e+00 4.72245783e-01 -1.10475667e-01
-3.57085735e-01 -1.40632010e+00 -7.68760920e-01 -5.56101382e-01
-9.22873691e-02 6.07787013e-01 -2.02370688e-01 -1.10887345... | [6.653600692749023, 4.241783618927002] |
b7c62b56-77a5-4419-81cc-5cb9219a23af | histred-a-historical-document-level-relation | 2307.04285 | null | https://arxiv.org/abs/2307.04285v1 | https://arxiv.org/pdf/2307.04285v1.pdf | HistRED: A Historical Document-Level Relation Extraction Dataset | Despite the extensive applications of relation extraction (RE) tasks in various domains, little has been explored in the historical context, which contains promising data across hundreds and thousands of years. To promote the historical RE research, we present HistRED constructed from Yeonhaengnok. Yeonhaengnok is a co... | ['Jaegul Choo', 'Youngwoo Cho', 'Minseok Choi', 'Soyoung Yang'] | 2023-07-10 | null | null | null | null | ['document-level-relation-extraction', 'relation-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [-3.91856909e-01 -2.50187796e-02 -7.09446549e-01 -2.77691036e-01
-8.68900537e-01 -8.49966049e-01 8.86013329e-01 1.38701499e-01
-3.19335580e-01 1.00642335e+00 7.29076028e-01 -4.74021256e-01
1.11624740e-01 -7.50686407e-01 -5.43386459e-01 -1.09138474e-01
1.41333088e-01 2.84502894e-01 -2.49113724e-01 -4.01129514... | [9.689759254455566, 8.982097625732422] |
befcfa6c-ce9c-435f-9078-dc42b7a37b2c | verifying-results-of-the-ibm-qiskit-quantum | 2009.02376 | null | https://arxiv.org/abs/2009.02376v1 | https://arxiv.org/pdf/2009.02376v1.pdf | Verifying Results of the IBM Qiskit Quantum Circuit Compilation Flow | Realizing a conceptual quantum algorithm on an actual physical device necessitates the algorithm's quantum circuit description to undergo certain transformations in order to adhere to all constraints imposed by the hardware. In this regard, the individual high-level circuit components are first synthesized to the suppo... | ['Lukas Burgholzer', 'Robert Wille', 'Rudy Raymond'] | 2020-09-04 | null | null | null | null | ['quantum-circuit-equivalence-checking'] | ['methodology'] | [ 3.66421252e-01 -6.86850958e-03 1.18607014e-01 -7.72029832e-02
-7.64716387e-01 -9.58685637e-01 3.28668654e-01 4.17072147e-01
-5.02301678e-02 7.89619327e-01 -6.91168427e-01 -9.17846084e-01
3.22076119e-02 -1.23989081e+00 -9.39067483e-01 -6.69691443e-01
7.33799636e-02 3.79445672e-01 1.48091182e-01 -4.77990627... | [5.590461730957031, 4.942792892456055] |
f0763fa8-1cc0-465b-95ea-d6359940126f | unsupervised-multi-stream-highlight-detection | 1910.06189 | null | https://arxiv.org/abs/1910.06189v2 | https://arxiv.org/pdf/1910.06189v2.pdf | Unsupervised Multi-stream Highlight detection for the Game "Honor of Kings" | With the increasing popularity of E-sport live, Highlight Flashback has been a critical functionality of live platforms, which aggregates the overall exciting fighting scenes in a few seconds. In this paper, we introduce a novel training strategy without any additional annotation to automatically generate highlights fo... | ['Hui Zhan', 'Wentao Yao', 'Li Wang', 'Chengwei Zhu', 'Zixun Sun'] | 2019-10-14 | null | null | null | null | ['highlight-detection'] | ['computer-vision'] | [ 1.09003946e-01 -2.88639128e-01 5.16597666e-02 -9.78842005e-02
-1.02003908e+00 -6.72945678e-01 5.11256516e-01 8.00438747e-02
-4.80448604e-01 6.05654120e-01 3.12881291e-01 2.71483302e-01
-2.06610173e-01 -4.07741815e-01 -6.33546650e-01 -4.04304445e-01
-5.42406738e-01 -4.72021163e-01 7.34603286e-01 -1.29208177... | [10.095919609069824, 0.417460173368454] |
24567745-c8a9-4e20-b7ad-02b630a5a8e8 | a-combined-pca-mlp-network-for-early-breast | 2206.09128 | null | https://arxiv.org/abs/2206.09128v1 | https://arxiv.org/pdf/2206.09128v1.pdf | A Combined PCA-MLP Network for Early Breast Cancer Detection | Breast cancer is the second most responsible for all cancer types and has been the cause of numerous deaths over the years, especially among women. Any improvisation of the existing diagnosis system for the detection of cancer can contribute to minimizing the death ratio. Moreover, cancer detection at an early stage ha... | ['Md. Saif Hassan Onim', 'Arunima Dey Pooja', 'Md. Wahiduzzaman Khan Arnob'] | 2022-06-18 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 2.04168633e-01 3.34376365e-01 -5.52311361e-01 -2.51225561e-01
-3.62913370e-01 3.39071393e-01 5.54635584e-01 5.87134659e-01
-5.28499365e-01 5.77577055e-01 -4.44111042e-02 -4.07448202e-01
-1.89982399e-01 -1.02888501e+00 -1.62904829e-01 -9.33996499e-01
-5.87823875e-02 4.28554416e-01 -1.21617518e-01 6.70826882... | [15.287921905517578, -2.771505355834961] |
49edcabf-2108-4eab-95d3-fee606786e5d | livable-exploring-long-tailed-classification | 2306.06935 | null | https://arxiv.org/abs/2306.06935v1 | https://arxiv.org/pdf/2306.06935v1.pdf | LIVABLE: Exploring Long-Tailed Classification of Software Vulnerability Types | Prior studies generally focus on software vulnerability detection and have demonstrated the effectiveness of Graph Neural Network (GNN)-based approaches for the task. Considering the various types of software vulnerabilities and the associated different degrees of severity, it is also beneficial to determine the type o... | ['Qing Liao', 'Ge Li', 'Haoyu Wang', 'Feng Luo', 'Cuiyun Gao', 'Xin-Cheng Wen'] | 2023-06-12 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [-8.45731869e-02 -1.25581309e-01 -1.22201130e-01 -1.30015954e-01
-1.65705413e-01 -5.70526481e-01 7.55349249e-02 4.21454728e-01
9.46014712e-04 3.18203807e-01 -3.32009681e-02 -7.07381546e-01
-9.74289700e-02 -1.20537925e+00 -2.78293550e-01 -5.59281766e-01
-2.62955904e-01 -1.20463803e-01 6.38095140e-01 -4.36289608... | [7.075276851654053, 7.759525775909424] |
d64c43c8-970e-4ae3-ab9c-5e0751e01334 | a-preliminary-exploration-of-gans-for | null | null | https://aclanthology.org/2020.emnlp-main.645 | https://aclanthology.org/2020.emnlp-main.645.pdf | A Preliminary Exploration of GANs for Keyphrase Generation | We introduce a new keyphrase generation approach using Generative Adversarial Networks (GANs). For a given document, the generator produces a sequence of keyphrases, and the discriminator distinguishes between human-curated and machine-generated keyphrases. We evaluated this approach on standard benchmark datasets. We ... | ['Amanda Stent', 'Rajiv Ratn Shah', 'Rakesh Gosangi', 'Debanjan Mahata', 'Haimin Zhang', 'Avinash Swaminathan'] | null | null | null | null | emnlp-2020-11 | ['keyphrase-generation'] | ['natural-language-processing'] | [ 3.64698410e-01 2.88919985e-01 -6.94450513e-02 2.70620346e-01
-1.13181365e+00 -1.10857105e+00 1.29882264e+00 2.14202821e-01
-2.90190816e-01 9.34913635e-01 4.42635089e-01 -5.35554409e-01
2.87899256e-01 -1.22082102e+00 -9.70035195e-01 -5.91389954e-01
1.28702149e-01 5.37714720e-01 1.08560458e-01 -5.02211511... | [12.288890838623047, 8.899931907653809] |
00e7716c-47c1-422e-8d23-d44b282c700d | crepe-open-domain-question-answering-with | 2211.17257 | null | https://arxiv.org/abs/2211.17257v1 | https://arxiv.org/pdf/2211.17257v1.pdf | CREPE: Open-Domain Question Answering with False Presuppositions | Information seeking users often pose questions with false presuppositions, especially when asking about unfamiliar topics. Most existing question answering (QA) datasets, in contrast, assume all questions have well defined answers. We introduce CREPE, a QA dataset containing a natural distribution of presupposition fai... | ['Hannaneh Hajishirzi', 'Luke Zettlemoyer', 'Sewon Min', 'Xinyan Velocity Yu'] | 2022-11-30 | null | null | null | null | ['open-domain-question-answering'] | ['natural-language-processing'] | [ 2.38678068e-01 7.48195410e-01 9.86488238e-02 -3.40660930e-01
-1.69116330e+00 -1.01369011e+00 6.88206255e-01 4.22993839e-01
-2.72635877e-01 8.67002726e-01 6.38895750e-01 -8.47880661e-01
-3.98727626e-01 -7.11979330e-01 -8.97162437e-01 1.09041654e-01
4.52532113e-01 9.19386566e-01 8.88984263e-01 -8.96165609... | [11.175873756408691, 8.001626014709473] |
a94809cb-aa0d-456b-8cf0-910a8fb0b8ec | fast-and-accurate-capitalization-and | 1908.02404 | null | https://arxiv.org/abs/1908.02404v1 | https://arxiv.org/pdf/1908.02404v1.pdf | Fast and Accurate Capitalization and Punctuation for Automatic Speech Recognition Using Transformer and Chunk Merging | In recent years, studies on automatic speech recognition (ASR) have shown outstanding results that reach human parity on short speech segments. However, there are still difficulties in standardizing the output of ASR such as capitalization and punctuation restoration for long-speech transcription. The problems obstruct... | ['The-Loc Nguyen', 'Hien Nguyen', 'Binh Nguyen', 'Vu Bao Hung Nguyen', 'Quoc Truong Do', 'Luong Chi Mai', 'Pham Ngoc Phuong'] | 2019-08-07 | null | null | null | null | ['punctuation-restoration'] | ['natural-language-processing'] | [ 4.61777538e-01 2.93844491e-01 1.68091938e-01 -4.21313852e-01
-8.35745394e-01 -4.50447112e-01 3.06929708e-01 3.96387070e-01
-4.97948378e-01 6.31495833e-01 5.49686968e-01 -7.99953401e-01
3.20339918e-01 -3.39881063e-01 -4.57777888e-01 -3.31860185e-01
4.55176443e-01 4.33956116e-01 4.62282240e-01 -3.53082448... | [14.24354076385498, 7.102929592132568] |
938a2c09-9123-4d3d-9350-e83d4859f630 | towards-automated-survey-variable-search-and | 2209.06804 | null | https://arxiv.org/abs/2209.06804v1 | https://arxiv.org/pdf/2209.06804v1.pdf | Towards Automated Survey Variable Search and Summarization in Social Science Publications | Nowadays there is a growing trend in many scientific disciplines to support researchers by providing enhanced information access through linking of publications and underlying datasets, so as to support research with infrastructure to enhance reproducibility and reusability of research results. In this research note, w... | ['Andrea Zielinski', 'Benjamin Zapilko', 'Simone Paolo Ponzetto', 'Philipp Mayr', 'Henning Kroll', 'Kai Eckert', 'Tornike Tsereteli', 'Sotaro Takeshita', 'Yavuz Selim Kartal'] | 2022-09-14 | null | null | null | null | ['variable-detection'] | ['natural-language-processing'] | [ 8.70786384e-02 2.29178861e-01 -5.01346052e-01 -2.11387873e-01
-5.79649389e-01 -9.40368950e-01 7.66930342e-01 8.47562611e-01
-3.73931229e-01 7.41537154e-01 6.97162211e-01 -7.07609832e-01
-6.68914497e-01 -4.76413637e-01 4.36477624e-02 -1.67096406e-02
4.66682613e-01 5.69609344e-01 -1.32838622e-01 -3.54706608... | [9.649813652038574, 8.287088394165039] |
4e3ffd9a-1d5b-4fe9-8817-1bd38a22a810 | lissnas-locality-based-iterative-search-space | 2307.03110 | null | https://arxiv.org/abs/2307.03110v1 | https://arxiv.org/pdf/2307.03110v1.pdf | LISSNAS: Locality-based Iterative Search Space Shrinkage for Neural Architecture Search | Search spaces hallmark the advancement of Neural Architecture Search (NAS). Large and complex search spaces with versatile building operators and structures provide more opportunities to brew promising architectures, yet pose severe challenges on efficient exploration and exploitation. Subsequently, several search spac... | ['Yiran Chen', 'Tunhou Zhang', 'Arjun Sridhar', 'Bhavna Gopal'] | 2023-07-06 | null | null | null | null | ['efficient-exploration', 'architecture-search'] | ['methodology', 'methodology'] | [ 1.72284693e-02 -7.89479762e-02 -5.42606711e-01 -3.38191211e-01
-8.50115359e-01 -5.94088614e-01 3.22447181e-01 -3.32588553e-01
-6.77236259e-01 6.78171217e-01 4.39759083e-02 -4.75239456e-01
-8.31568897e-01 -3.77859354e-01 -5.01219928e-01 -7.16899514e-01
-2.54563570e-01 5.56718588e-01 3.48699689e-01 -2.02319205... | [8.603718757629395, 3.2441904544830322] |
59a759df-0845-4167-9606-1cda36efe08d | meta-learning-a-real-time-tabular-automl | 2207.01848 | null | https://arxiv.org/abs/2207.01848v5 | https://arxiv.org/pdf/2207.01848v5.pdf | TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second | We present TabPFN, a trained Transformer that can do supervised classification for small tabular datasets in less than a second, needs no hyperparameter tuning and is competitive with state-of-the-art classification methods. TabPFN is fully entailed in the weights of our network, which accepts training and test samples... | ['Frank Hutter', 'Katharina Eggensperger', 'Samuel Müller', 'Noah Hollmann'] | 2022-07-05 | null | null | null | null | ['classification'] | ['methodology'] | [ 1.10182464e-01 6.46446705e-01 -5.44403493e-01 -7.47459888e-01
-9.93990123e-01 -4.11031246e-01 8.08482826e-01 6.94233179e-02
5.11054769e-02 1.33691895e+00 1.28233120e-01 -8.15103233e-01
-3.46465737e-01 -9.30943549e-01 -1.15856183e+00 -5.33232570e-01
-3.01869363e-01 1.18508363e+00 1.67517066e-01 2.36775890... | [8.725162506103516, 6.400200366973877] |
2516e737-55fb-43f8-b50f-7a6f7fb94bcb | cap-vstnet-content-affinity-preserved | 2303.17867 | null | https://arxiv.org/abs/2303.17867v1 | https://arxiv.org/pdf/2303.17867v1.pdf | CAP-VSTNet: Content Affinity Preserved Versatile Style Transfer | Content affinity loss including feature and pixel affinity is a main problem which leads to artifacts in photorealistic and video style transfer. This paper proposes a new framework named CAP-VSTNet, which consists of a new reversible residual network and an unbiased linear transform module, for versatile style transfe... | ['Changqing Zou', 'Chengying Gao', 'Linfeng Wen'] | 2023-03-31 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wen_CAP-VSTNet_Content_Affinity_Preserved_Versatile_Style_Transfer_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wen_CAP-VSTNet_Content_Affinity_Preserved_Versatile_Style_Transfer_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-style-transfer', 'image-matting'] | ['computer-vision', 'computer-vision'] | [ 3.47079664e-01 -6.87142164e-02 -2.05548942e-01 1.21339438e-02
-3.31823677e-01 -3.79288942e-01 3.45497787e-01 -8.81250739e-01
-1.80964857e-01 1.18102717e+00 2.90452510e-01 1.34169877e-01
2.46909663e-01 -6.95972860e-01 -7.78948188e-01 -7.81593502e-01
7.04504073e-01 8.23588148e-02 1.83827147e-01 -3.40654701... | [11.537464141845703, -0.7198633551597595] |
27f403f5-1478-4d81-b210-be92b6a2cffa | embedding-based-entity-alignment-using | null | null | https://ieeexplore.ieee.org/document/9194492/authors | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9194492 | Embedding-Based Entity Alignment Using Relation Structural Similarity | Entity alignment aims to find entities in different knowledge graphs that semantically represent the same real-world entity. Recently, embedding-based entity alignment methods, which represent knowledge graphs as low-dimensional embeddings and perform entity alignments by measuring the similarity between entity embeddi... | ['Yanhui Peng; Jing Zhang; Cangqi Zhou; Jian Xu'] | 2022-09-11 | null | null | null | 2020-ieee-international-conference-on-4 | ['knowledge-graph-embedding', 'entity-alignment', 'entity-embeddings', 'entity-alignment'] | ['graphs', 'knowledge-base', 'methodology', 'natural-language-processing'] | [-4.69553828e-01 3.48412037e-01 -3.46772194e-01 -1.67528898e-01
-1.47961095e-01 -3.65587294e-01 5.31052947e-01 9.04622912e-01
-4.02263165e-01 4.24670726e-01 3.25990975e-01 1.45352468e-01
-6.40055954e-01 -1.28328216e+00 -3.27437997e-01 -6.05630040e-01
-1.47904783e-01 6.18928850e-01 3.45776618e-01 -2.75223494... | [8.840375900268555, 7.816086769104004] |
609ac1a7-5443-4cab-97d2-122130a51b0b | lexical-simplification-benchmarks-for-english | 2209.05301 | null | https://arxiv.org/abs/2209.05301v1 | https://arxiv.org/pdf/2209.05301v1.pdf | Lexical Simplification Benchmarks for English, Portuguese, and Spanish | Even in highly-developed countries, as many as 15-30\% of the population can only understand texts written using a basic vocabulary. Their understanding of everyday texts is limited, which prevents them from taking an active role in society and making informed decisions regarding healthcare, legal representation, or de... | ['Horacio Saggion', 'Marcos Zampieri', 'Kai North', 'Matthew Shardlow', 'Daniel Ferres', 'Sanja Stajner'] | 2022-09-12 | null | null | null | null | ['lexical-simplification'] | ['natural-language-processing'] | [ 8.29776004e-02 2.53227085e-01 -3.14288467e-01 -3.10521901e-01
-4.70187783e-01 -2.87018538e-01 6.29879117e-01 6.36287808e-01
-1.04612732e+00 8.24268699e-01 4.55914617e-01 -3.55522096e-01
6.29626438e-02 -8.02464664e-01 -1.50160015e-01 -2.12400749e-01
6.03434861e-01 7.36326039e-01 -4.44255918e-02 -7.77355909... | [10.91717529296875, 10.415300369262695] |
df85f62b-32d1-42de-9f7c-f84db28072cc | unsupervised-3d-object-learning-through | 2302.11622 | null | https://arxiv.org/abs/2302.11622v1 | https://arxiv.org/pdf/2302.11622v1.pdf | Unsupervised 3D Object Learning through Neuron Activity aware Plasticity | We present an unsupervised deep learning model for 3D object classification. Conventional Hebbian learning, a well-known unsupervised model, suffers from loss of local features leading to reduced performance for tasks with complex geometric objects. We present a deep network with a novel Neuron Activity Aware (NeAW) He... | ['Saibal Mukhopadhyay', 'Biswadeep Chakraborty', 'Beomseok Kang'] | 2023-02-22 | null | null | null | null | ['3d-object-classification'] | ['computer-vision'] | [-1.11078009e-01 9.37733874e-02 -2.66566038e-01 -3.75846177e-01
5.59849560e-01 -4.98393416e-01 8.34835947e-01 1.21556394e-01
-5.73274374e-01 6.27960980e-01 1.40909076e-01 -2.02008843e-01
-5.42090416e-01 -8.38483274e-01 -8.64931464e-01 -1.17630649e+00
-2.21740618e-01 8.13848376e-01 6.92508280e-01 -3.65010314... | [9.703516960144043, 2.35276198387146] |
7dd4f7b8-7995-48d9-89f6-a68233904528 | cross-modality-multi-atlas-segmentation-using-1 | 2202.02000 | null | https://arxiv.org/abs/2202.02000v3 | https://arxiv.org/pdf/2202.02000v3.pdf | Cross-Modality Multi-Atlas Segmentation via Deep Registration and Label Fusion | Multi-atlas segmentation (MAS) is a promising framework for medical image segmentation. Generally, MAS methods register multiple atlases, i.e., medical images with corresponding labels, to a target image; and the transformed atlas labels can be combined to generate target segmentation via label fusion schemes. Many con... | ['Liqin Huang', 'Xiahai Zhuang', 'Lei LI', 'Wangbin Ding'] | 2022-02-04 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [ 1.03262044e-01 -1.20662510e-01 -8.69152546e-02 -5.69616616e-01
-9.91837025e-01 -4.37855512e-01 3.44896257e-01 1.43832088e-01
-5.51523924e-01 3.27222854e-01 2.42938638e-01 2.11329833e-01
8.67726952e-02 -7.67748535e-01 -3.14600438e-01 -1.06216729e+00
2.88967639e-01 4.68381882e-01 4.11519140e-01 -1.57531053... | [14.306962966918945, -2.51255202293396] |
3b15d10c-774b-4b95-b79b-618d24a6c8b5 | cross-lingual-lexical-sememe-prediction | null | null | https://aclanthology.org/D18-1033 | https://aclanthology.org/D18-1033.pdf | Cross-lingual Lexical Sememe Prediction | Sememes are defined as the minimum semantic units of human languages. As important knowledge sources, sememe-based linguistic knowledge bases have been widely used in many NLP tasks. However, most languages still do not have sememe-based linguistic knowledge bases. Thus we present a task of cross-lingual lexical sememe... | ['Ruobing Xie', 'Yankai Lin', 'Fanchao Qi', 'Maosong Sun', 'Hao Zhu', 'Zhiyuan Liu'] | 2018-10-01 | null | null | null | emnlp-2018-10 | ['multilingual-word-embeddings', 'learning-word-embeddings'] | ['methodology', 'methodology'] | [-3.78050655e-01 -2.73048580e-01 -6.52619481e-01 -3.86173636e-01
-6.57738149e-01 -5.56538105e-01 4.84367073e-01 2.45631188e-01
-5.85137188e-01 8.52396488e-01 3.98038447e-01 -4.44612414e-01
2.90966369e-02 -8.87589455e-01 -5.60603917e-01 -1.76168337e-01
3.21003109e-01 6.04338825e-01 4.62745540e-02 -3.58407617... | [10.701483726501465, 9.63391399383545] |
39a3276a-85e7-40ff-9f99-6721ed69b8ac | atca-an-arc-trajectory-based-model-with | 2208.00856 | null | https://arxiv.org/abs/2208.00856v1 | https://arxiv.org/pdf/2208.00856v1.pdf | ATCA: an Arc Trajectory Based Model with Curvature Attention for Video Frame Interpolation | Video frame interpolation is a classic and challenging low-level computer vision task. Recently, deep learning based methods have achieved impressive results, and it has been proven that optical flow based methods can synthesize frames with higher quality. However, most flow-based methods assume a line trajectory with ... | ['Jie Yang', 'Lingtong Kong', 'Jinfeng Liu'] | 2022-08-01 | null | null | null | null | ['video-frame-interpolation'] | ['computer-vision'] | [-2.68398315e-01 -3.73780549e-01 -4.02353346e-01 -1.31740168e-01
-3.53536189e-01 -1.94390729e-01 6.05850756e-01 -2.45567724e-01
-3.76014292e-01 8.07100058e-01 7.82020018e-02 -3.04060668e-01
2.81612813e-01 -7.91255414e-01 -8.89462113e-01 -4.73103404e-01
-2.63219960e-02 5.66332228e-02 6.43465161e-01 2.75008380... | [10.610309600830078, -1.3519113063812256] |
53a80f8e-688a-4f97-b05d-823b51aa6257 | learning-multi-subset-of-classes-for-fine | null | null | https://dl.acm.org/doi/abs/10.1145/3552484.3555754 | https://dl.acm.org/doi/abs/10.1145/3552484.3555754 | Learning Multi-Subset of Classes for Fine-Grained Food Recognition | Food image recognition is a complex computer vision task, because of the large number of fine-grained food classes. Fine-grained recognition tasks focus on learning subtle discriminative details to distinguish similar classes. In this paper, we introduce a new method to improve the classification of classes that are mo... | ['Petia Radeva', 'Marc Bolaños', 'Bhalaji Nagarajan', 'Javier Ródenas'] | 2022-10-10 | null | null | null | 30th-acm-international-conference-on | ['food-recognition', 'fine-grained-image-classification'] | ['computer-vision', 'computer-vision'] | [ 4.02397782e-01 -3.00770015e-01 -1.07689977e-01 -6.52880847e-01
-4.77966458e-01 -6.37262404e-01 4.36540216e-01 6.05169773e-01
-5.24557650e-01 3.26704264e-01 1.05185047e-01 3.00427765e-01
-3.84460129e-02 -6.70228720e-01 -9.01885033e-01 -9.74715233e-01
-5.27502336e-02 2.63240606e-01 3.81678492e-01 -5.97227216... | [11.528765678405762, 4.35015869140625] |
cf8f530e-3553-4ddf-a9a9-a08d9b2b9420 | on-a-two-truths-phenomenon-in-spectral-graph | 1808.07801 | null | http://arxiv.org/abs/1808.07801v3 | http://arxiv.org/pdf/1808.07801v3.pdf | On a 'Two Truths' Phenomenon in Spectral Graph Clustering | Clustering is concerned with coherently grouping observations without any
explicit concept of true groupings. Spectral graph clustering - clustering the
vertices of a graph based on their spectral embedding - is commonly approached
via K-means (or, more generally, Gaussian mixture model) clustering composed
with either... | ['Youngser Park', 'Eric Bridgeford', 'Minh Tang', 'Joshua Cape', 'John M. Conroy', 'Vince Lyzinski', 'Joshua T. Vogelstein', 'Carey E. Priebe', 'Avanti Athreya'] | 2018-08-23 | null | null | null | null | ['spectral-graph-clustering'] | ['graphs'] | [-1.34926111e-01 2.80992121e-01 -6.67999759e-02 -9.40446183e-02
4.67075892e-02 -6.78365648e-01 5.87790132e-01 1.94807962e-01
-1.31817207e-01 1.39428318e-01 5.72652698e-01 -2.38767743e-01
-7.00503647e-01 -4.36371118e-01 6.44332124e-03 -1.07499981e+00
-6.55635178e-01 6.61461473e-01 5.88804297e-02 8.08519050... | [7.115475654602051, 5.202324867248535] |
1ad3b85c-d6a5-4e49-8431-385e1fdf633c | gravl-bert-graphical-visual-linguistic | null | null | https://aclanthology.org/2022.coling-1.22 | https://aclanthology.org/2022.coling-1.22.pdf | GRAVL-BERT: Graphical Visual-Linguistic Representations for Multimodal Coreference Resolution | Learning from multimodal data has become a popular research topic in recent years. Multimodal coreference resolution (MCR) is an important task in this area. MCR involves resolving the references across different modalities, e.g., text and images, which is a crucial capability for building next-generation conversationa... | ['Mohit Bansal', 'Tagyoung Chung', 'Chien-Wei Lin', 'Arijit Biswas', 'Shuyang Gao', 'Jiun-Yu Kao', 'Sanchit Agarwal', 'Arpit Gupta', 'Danfeng Guo'] | null | null | null | null | coling-2022-10 | ['coreference-resolution'] | ['natural-language-processing'] | [ 1.99104935e-01 1.57126456e-01 -2.79317647e-01 -1.74597278e-01
-1.05201137e+00 -5.54523885e-01 9.94995475e-01 8.79194662e-02
-2.82667071e-01 6.37161136e-01 6.35459065e-01 -1.81707978e-01
2.97473609e-01 -3.66488934e-01 -4.23756093e-01 -5.11172116e-01
2.49466330e-01 7.64277518e-01 4.67726052e-01 -6.26119912... | [10.88027286529541, 1.4546972513198853] |
acda3f09-5f28-4be5-ab5a-ae22c41e5f0a | comparison-of-multiple-features-and-modeling | 1707.04373 | null | http://arxiv.org/abs/1707.04373v2 | http://arxiv.org/pdf/1707.04373v2.pdf | Comparison of Multiple Features and Modeling Methods for Text-dependent Speaker Verification | Text-dependent speaker verification is becoming popular in the speaker
recognition society. However, the conventional i-vector framework which has
been successful for speaker identification and other similar tasks works
relatively poorly in this task. Researchers have proposed several new methods
to improve performance... | ['Yi Liu', 'Zhuzi Chen', 'Michael T. Johnson', 'Liang He', 'Yao Tian', 'Jia Liu'] | 2017-07-14 | null | null | null | null | ['text-independent-speaker-verification', 'text-dependent-speaker-verification'] | ['speech', 'speech'] | [-1.24170281e-01 -5.82793355e-01 -1.68638974e-01 -6.13694906e-01
-1.17015672e+00 -4.87753063e-01 4.57442433e-01 -2.17675045e-01
-5.50628185e-01 5.60340703e-01 2.60438263e-01 -8.20852280e-01
2.37483665e-01 -3.89585481e-03 -1.99377090e-01 -8.90664577e-01
3.04558963e-01 2.11217239e-01 1.49797320e-01 -3.03455651... | [14.343689918518066, 6.196404457092285] |
a04db6a2-fe8f-4508-9325-4352a0a282c0 | active-learning-for-transition-state | 2108.04698 | null | https://arxiv.org/abs/2108.04698v2 | https://arxiv.org/pdf/2108.04698v2.pdf | Active Learning for Saddle Point Calculation | The saddle point (SP) calculation is a grand challenge for computationally intensive energy function in computational chemistry area, where the saddle point may represent the transition state (TS). The traditional methods need to evaluate the gradients of the energy function at a very large number of locations. To redu... | ['Xiang Zhou', 'Hongqiao Wang', 'Shuting Gu'] | 2021-08-10 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 1.32061794e-01 -2.52305437e-02 -7.14472532e-02 1.09402779e-02
-1.45700216e+00 -5.07360518e-01 4.20757473e-01 4.95400339e-01
-6.98335588e-01 8.76923025e-01 -2.18484655e-01 -4.05555338e-01
-7.37355277e-02 -6.34108543e-01 -8.96927357e-01 -1.47639310e+00
-1.33441299e-01 5.03555715e-01 3.20024192e-01 1.22215319... | [5.339611530303955, 5.050850868225098] |
36c77a97-8248-4725-9019-e6dcad798fb7 | predicting-tasks-in-goal-oriented-spoken | null | null | https://aclanthology.org/W13-4038 | https://aclanthology.org/W13-4038.pdf | Predicting Tasks in Goal-Oriented Spoken Dialog Systems using Semantic Knowledge Bases | null | ['er', 'Aasish Pappu', 'Alex Rudnicky'] | 2013-08-01 | null | null | null | ws-2013-8 | ['goal-oriented-dialog'] | ['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... | [-7.172306537628174, 3.7500662803649902] |
1227d887-dcc2-4960-9918-d33253b7bfab | a-framework-of-meta-functional-learning-for | 2203.14840 | null | https://arxiv.org/abs/2203.14840v1 | https://arxiv.org/pdf/2203.14840v1.pdf | A Framework of Meta Functional Learning for Regularising Knowledge Transfer | Machine learning classifiers' capability is largely dependent on the scale of available training data and limited by the model overfitting in data-scarce learning tasks. To address this problem, this work proposes a novel framework of Meta Functional Learning (MFL) by meta-learning a generalisable functional model from... | ['Shaogang Gong', 'Yanwei Fu', 'Pan Li'] | 2022-03-28 | null | null | null | null | ['cross-domain-few-shot', 'cross-domain-few-shot-learning'] | ['computer-vision', 'computer-vision'] | [ 5.58744848e-01 1.57340795e-01 -4.04209644e-01 -4.14234042e-01
-6.78740382e-01 -2.12750807e-01 8.72311175e-01 2.16354772e-01
-6.05284512e-01 1.05158961e+00 1.21946938e-01 2.04180539e-01
-7.08037376e-01 -1.00573540e+00 -8.30596685e-01 -6.61089599e-01
6.88328058e-04 2.21263900e-01 5.97702682e-01 -3.44263583... | [10.014633178710938, 3.1050620079040527] |
7aabe2ff-bfbc-4da9-8ab4-b6f4d5189221 | interpretable-machine-learning-of-amino-acid | 2303.15228 | null | https://arxiv.org/abs/2303.15228v1 | https://arxiv.org/pdf/2303.15228v1.pdf | Interpretable machine learning of amino acid patterns in proteins: a statistical ensemble approach | Explainable and interpretable unsupervised machine learning helps understand the underlying structure of data. We introduce an ensemble analysis of machine learning models to consolidate their interpretation. Its application shows that restricted Boltzmann machines compress consistently into a few bits the information ... | ['Marco Baiesi', 'Enzo Orlandini', 'Anna Braghetto'] | 2023-03-27 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [ 1.95131168e-01 4.03559685e-01 -3.22281033e-01 -3.80966574e-01
1.00434318e-01 -5.09573042e-01 4.87122476e-01 5.29858232e-01
-5.80742717e-01 1.15057790e+00 4.27003354e-01 -6.33347988e-01
-4.93349470e-02 -5.76160371e-01 -5.99417806e-01 -1.31843042e+00
-5.81494272e-01 5.43662369e-01 7.93733373e-02 -4.59910393... | [4.781103134155273, 5.347103595733643] |
1ebbaae3-fc42-490b-b7dd-bc43ee54435a | deep-learning-of-physical-laws-from-scarce | 2005.03448 | null | https://arxiv.org/abs/2005.03448v3 | https://arxiv.org/pdf/2005.03448v3.pdf | Physics-informed learning of governing equations from scarce data | Harnessing data to discover the underlying governing laws or equations that describe the behavior of complex physical systems can significantly advance our modeling, simulation and understanding of such systems in various science and engineering disciplines. This work introduces a novel physics-informed deep learning f... | ['Hao Sun', 'Zhao Chen', 'Yang Liu'] | 2020-05-05 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [-2.40390494e-01 -5.10275364e-01 3.65271658e-01 1.94885954e-01
-4.79567111e-01 -7.79424906e-01 6.11292779e-01 1.23313896e-01
9.11795422e-02 9.81759727e-01 7.96493888e-02 -4.20594364e-01
-5.47362924e-01 -4.44554657e-01 -5.43879449e-01 -1.06333649e+00
-3.82711411e-01 2.63752222e-01 -2.99038559e-01 -3.86869788... | [6.548013210296631, 3.4209301471710205] |
7a5a8ab2-f385-4a70-8739-51b4fed6d30e | time-space-transformers-for-video-panoptic | 2210.03546 | null | https://arxiv.org/abs/2210.03546v1 | https://arxiv.org/pdf/2210.03546v1.pdf | Time-Space Transformers for Video Panoptic Segmentation | We propose a novel solution for the task of video panoptic segmentation, that simultaneously predicts pixel-level semantic and instance segmentation and generates clip-level instance tracks. Our network, named VPS-Transformer, with a hybrid architecture based on the state-of-the-art panoptic segmentation network Panopt... | ['Sergiu Nedevschi', 'Andra Petrovai'] | 2022-10-07 | null | null | null | null | ['panoptic-segmentation'] | ['computer-vision'] | [ 5.76777533e-02 -2.33556554e-01 -2.03105539e-01 -3.03996801e-01
-7.46763170e-01 -4.62036461e-01 5.06250024e-01 -1.69899777e-01
-1.48739204e-01 3.16748500e-01 6.48721084e-02 -2.04692572e-01
2.89425626e-02 -1.09900129e+00 -8.46260905e-01 -7.15138972e-01
-3.24691921e-01 3.70419979e-01 9.71487761e-01 2.46209744... | [9.355246543884277, -0.009507892653346062] |
e6ba8826-ab8e-4026-baba-f56f0bc01af4 | enhancing-low-light-images-using-infrared | 2307.04122 | null | https://arxiv.org/abs/2307.04122v1 | https://arxiv.org/pdf/2307.04122v1.pdf | Enhancing Low-Light Images Using Infrared-Encoded Images | Low-light image enhancement task is essential yet challenging as it is ill-posed intrinsically. Previous arts mainly focus on the low-light images captured in the visible spectrum using pixel-wise loss, which limits the capacity of recovering the brightness, contrast, and texture details due to the small number of inco... | ['Bihan Wen', 'Alex C. Kot', 'Wenhan Yang', 'Renjie Wan', 'YuFei Wang', 'Shulin Tian'] | 2023-07-09 | null | null | null | null | ['image-enhancement', 'low-light-image-enhancement'] | ['computer-vision', 'computer-vision'] | [ 7.48106360e-01 -3.88841838e-01 8.49744231e-02 -2.01205462e-02
-4.82276499e-01 -4.20211375e-01 2.32477590e-01 -3.33799988e-01
-4.88389403e-01 6.79781258e-01 1.23088390e-01 -2.08302382e-02
9.58261415e-02 -8.04536164e-01 -5.51656246e-01 -1.16028380e+00
3.62416923e-01 -6.96372747e-01 2.68607914e-01 2.68900413... | [10.746853828430176, -2.5354561805725098] |
464a905c-c586-4780-9fc8-3af8217d658d | forensic-dental-age-estimation-using-modified | 2208.09799 | null | https://arxiv.org/abs/2208.09799v1 | https://arxiv.org/pdf/2208.09799v1.pdf | Forensic Dental Age Estimation Using Modified Deep Learning Neural Network | Dental age is one of the most reliable methods to identify an individual's age. By using dental panoramic radiography (DPR) images, physicians and pathologists in forensic sciences try to establish the chronological age of individuals with no valid legal records or registered patients. The current methods in practice d... | ['Yahya Dogan', 'Musa Atas', 'Cuneyt Ozdemir', 'Isa Atas'] | 2022-08-21 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [-9.82192010e-02 2.14421690e-01 8.21372196e-02 -2.28893578e-01
-5.95921814e-01 1.64346084e-01 -3.60263675e-03 2.71563560e-01
-1.05360210e+00 7.50945032e-01 -1.58112749e-01 -3.81367952e-01
-3.31702858e-01 -1.02201366e+00 -3.21822524e-01 -7.71721303e-01
-2.37063035e-01 6.54871225e-01 4.94562425e-02 2.61542320... | [14.154430389404297, -1.773532509803772] |
d13717c8-cd0d-4920-8dc5-79bafcf8e681 | a-hybrid-event-detection-approach-for-non | 1903.09180 | null | http://arxiv.org/abs/1903.09180v1 | http://arxiv.org/pdf/1903.09180v1.pdf | A Hybrid Event Detection Approach for Non-Intrusive Load Monitoring | Non-Intrusive Load Monitoring (NILM) is a practical method to provide
appliance-level electricity consumption information. Event detection, as an
important part of event-based NILM methods, has a direct impact on the accuracy
of the ultimate load disaggregation results in the entire NILM framework. This
paper presents ... | [] | 2019-03-21 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 1.25649467e-01 -3.25152040e-01 8.99389908e-02 -3.15897524e-01
-6.41681910e-01 -4.51423258e-01 6.76680565e-01 6.86658204e-01
-2.90139187e-02 9.64455128e-01 6.27724528e-02 -2.13617146e-01
-3.56422037e-01 -1.03091240e+00 -2.89279427e-02 -8.70502532e-01
-2.44925484e-01 3.03544521e-01 2.24726632e-01 1.69627413... | [6.0070013999938965, 2.583630084991455] |
ad2dee00-524f-4499-8e0b-52828a673250 | preserving-privacy-in-domain-transfer-of | 2306.06503 | null | https://arxiv.org/abs/2306.06503v1 | https://arxiv.org/pdf/2306.06503v1.pdf | Preserving privacy in domain transfer of medical AI models comes at no performance costs: The integral role of differential privacy | Developing robust and effective artificial intelligence (AI) models in medicine requires access to large amounts of patient data. The use of AI models solely trained on large multi-institutional datasets can help with this, yet the imperative to ensure data privacy remains, particularly as membership inference risks br... | ['Daniel Truhn', 'Georgios Kaissis', 'Sven Nebelung', 'Christiane Kuhl', 'Peter Isfort', 'Marwin Saehn', 'Teresa Nolte', 'Mahshad Lotfinia', 'Soroosh Tayebi Arasteh'] | 2023-06-10 | null | null | null | null | ['image-classification-with-dp', 'medical-diagnosis', 'domain-generalization', 'specificity'] | ['computer-vision', 'medical', 'methodology', 'natural-language-processing'] | [ 2.51400799e-01 6.01667166e-01 -1.37468085e-01 -4.67466474e-01
-8.94248903e-01 -6.36622310e-01 3.01675677e-01 5.00337005e-01
-7.36984789e-01 6.72338903e-01 2.53318012e-01 -8.38386476e-01
-3.37319434e-01 -6.71455026e-01 -6.87660933e-01 -4.91057843e-01
8.77509266e-02 6.83754206e-01 -1.77353099e-01 5.57769716... | [6.343343734741211, 6.671206951141357] |
a27b229e-dd94-4355-abe6-b91f7e2f6835 | dinf-dynamic-instance-noise-filter-for | 2301.05565 | null | https://arxiv.org/abs/2301.05565v1 | https://arxiv.org/pdf/2301.05565v1.pdf | DINF: Dynamic Instance Noise Filter for Occluded Pedestrian Detection | Occlusion issue is the biggest challenge in pedestrian detection. RCNN-based detectors extract instance features by cropping rectangle regions of interest in the feature maps. However, the visible pixels of the occluded objects are limited, making the rectangle instance feature mixed with a lot of instance-irrelevant n... | ['Xiao Jiajie', 'Luo Haibo', 'He Miao', 'Li Xiang'] | 2023-01-13 | null | null | null | null | ['pedestrian-detection'] | ['computer-vision'] | [-1.02863483e-01 -2.54579097e-01 2.72314548e-01 -4.07357365e-01
-3.90674263e-01 -1.58399791e-01 2.38388166e-01 -1.18903987e-01
-7.69685686e-01 5.50726414e-01 4.77002263e-02 2.19936427e-02
3.38183165e-01 -9.98299837e-01 -6.00330830e-01 -9.93261993e-01
4.35615666e-02 -3.36953551e-01 6.81051791e-01 -1.68608099... | [8.089515686035156, -0.5832346081733704] |
57d4c742-fc60-4105-b390-d24808849a48 | learning-canonical-3d-object-representation | 2108.04628 | null | https://arxiv.org/abs/2108.04628v1 | https://arxiv.org/pdf/2108.04628v1.pdf | Learning Canonical 3D Object Representation for Fine-Grained Recognition | We propose a novel framework for fine-grained object recognition that learns to recover object variation in 3D space from a single image, trained on an image collection without using any ground-truth 3D annotation. We accomplish this by representing an object as a composition of 3D shape and its appearance, while elimi... | ['Kwanghoon Sohn', 'Ig-Jae Kim', 'Minsu Kim', 'Seungryong Kim', 'Sunghun Joung'] | 2021-08-10 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Joung_Learning_Canonical_3D_Object_Representation_for_Fine-Grained_Recognition_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Joung_Learning_Canonical_3D_Object_Representation_for_Fine-Grained_Recognition_ICCV_2021_paper.pdf | iccv-2021-1 | ['fine-grained-image-recognition'] | ['computer-vision'] | [ 2.00510353e-01 -1.75922349e-01 1.24075646e-02 -6.67520285e-01
-8.08867931e-01 -1.06722319e+00 8.27157855e-01 -4.29054260e-01
6.91830122e-04 -6.43302873e-02 -9.17767659e-02 -3.42422873e-02
2.32840836e-01 -8.20300996e-01 -1.30599427e+00 -6.20713711e-01
5.04013896e-01 7.57351518e-01 8.85683969e-02 -2.26001777... | [8.38293170928955, -3.231828451156616] |
29cd10d2-f2f5-4289-8a01-dc3a690f4520 | jazznet-a-dataset-of-fundamental-piano | 2302.08632 | null | https://arxiv.org/abs/2302.08632v1 | https://arxiv.org/pdf/2302.08632v1.pdf | jazznet: A Dataset of Fundamental Piano Patterns for Music Audio Machine Learning Research | This paper introduces the jazznet Dataset, a dataset of fundamental jazz piano music patterns for developing machine learning (ML) algorithms in music information retrieval (MIR). The dataset contains 162520 labeled piano patterns, including chords, arpeggios, scales, and chord progressions with their inversions, resul... | ['Tosiron Adegbija'] | 2023-02-17 | null | null | null | null | ['music-information-retrieval'] | ['music'] | [ 1.24431349e-01 -2.87968993e-01 -1.42698869e-01 2.16412187e-01
-6.36550963e-01 -9.51837003e-01 3.60288203e-01 -4.54452395e-01
1.76793635e-01 3.40894729e-01 4.97761905e-01 -1.29276574e-01
-3.79852384e-01 -8.38160455e-01 -3.82483482e-01 -4.63116288e-01
-8.93337652e-02 4.51060623e-01 -7.40330592e-02 -5.40873230... | [16.013181686401367, 5.4868950843811035] |
35aeea29-4f73-42f6-aead-475db1507eeb | sage-ndvi-a-stereotype-breaking-evaluation | 2306.06288 | null | https://arxiv.org/abs/2306.06288v1 | https://arxiv.org/pdf/2306.06288v1.pdf | SAGE-NDVI: A Stereotype-Breaking Evaluation Metric for Remote Sensing Image Dehazing Using Satellite-to-Ground NDVI Knowledge | Image dehazing is a meaningful low-level computer vision task and can be applied to a variety of contexts. In our industrial deployment scenario based on remote sensing (RS) images, the quality of image dehazing directly affects the grade of our crop identification and growth monitoring products. However, the widely us... | ['Jui-Hsin Lai', 'Jun Yu', 'Mei Han', 'Yibing Wei', 'Andy Wong', 'Mingye Zhu', 'Zhicheng Yang', 'Zepeng Liu'] | 2023-06-09 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 8.20701480e-01 -3.31546336e-01 1.99952256e-02 6.16409332e-02
-3.05332810e-01 -7.08876967e-01 5.41940689e-01 5.63473642e-01
-1.11134529e-01 4.34274226e-01 -2.58713871e-01 -4.65988398e-01
-2.24695042e-01 -1.16154206e+00 -5.75611234e-01 -8.31185520e-01
-1.84287325e-01 -7.61632442e-01 3.26354682e-01 -5.38815081... | [10.922576904296875, -2.965663194656372] |
ca5f551b-fc79-490c-b015-a8eef066fb11 | multilingual-training-for-software | 2112.02043 | null | https://arxiv.org/abs/2112.02043v4 | https://arxiv.org/pdf/2112.02043v4.pdf | Multilingual training for Software Engineering | Well-trained machine-learning models, which leverage large amounts of open-source software data, have now become an interesting approach to automating many software engineering tasks. Several SE tasks have all been subject to this approach, with performance gradually improving over the past several years with better mo... | ['Premkumar Devanbu', 'Toufique Ahmed'] | 2021-12-03 | null | null | null | null | ['type-prediction'] | ['computer-code'] | [-1.70521975e-01 -1.17792428e-01 -4.47557360e-01 -3.18251133e-01
-8.29949081e-01 -9.46841359e-01 5.28631330e-01 4.60633695e-01
-3.84818584e-01 6.54827714e-01 4.99509275e-01 -6.36478066e-01
3.47856246e-02 -4.65642095e-01 -7.33020067e-01 -3.06885183e-01
1.48443893e-01 7.53702670e-02 -7.07035512e-02 -5.91542006... | [7.694273471832275, 7.906487464904785] |
30034962-c245-4a85-a9dc-d489f874e9fe | language-free-compositional-action-generation | 2307.03538 | null | https://arxiv.org/abs/2307.03538v1 | https://arxiv.org/pdf/2307.03538v1.pdf | Language-free Compositional Action Generation via Decoupling Refinement | Composing simple elements into complex concepts is crucial yet challenging, especially for 3D action generation. Existing methods largely rely on extensive neural language annotations to discern composable latent semantics, a process that is often costly and labor-intensive. In this study, we introduce a novel framewor... | ['Ser-Nam Lim', 'Guangrun Wang', 'Yansong Tang', 'Guangyi Chen', 'Xiao Liu'] | 2023-07-07 | null | null | null | null | ['action-generation'] | ['computer-vision'] | [ 6.74763918e-01 2.78035581e-01 1.22629806e-01 -2.18358666e-01
-9.24517095e-01 -6.04802072e-01 1.01472771e+00 -4.26077783e-01
-1.90121643e-02 4.69596356e-01 5.86380839e-01 2.43656840e-02
3.52264762e-01 -8.45959604e-01 -8.94501567e-01 -5.99559307e-01
3.70022655e-01 2.06367195e-01 7.79409632e-02 -1.32097930... | [11.461967468261719, -0.4728357791900635] |
b3be3669-3479-45ed-9912-d02554543959 | the-construction-of-a-chinese-collocational | null | null | https://aclanthology.org/C16-1307 | https://aclanthology.org/C16-1307.pdf | The Construction of a Chinese Collocational Knowledge Resource and Its Application for Second Language Acquisition | The appropriate use of collocations is a challenge for second language acquisition. However, high quality and easily accessible Chinese collocation resources are not available for both teachers and students. This paper presents the design and construction of a large scale resource of Chinese collocational knowledge, an... | ['Kuang-hua Chen', 'Jiayong Chen', 'Renfen Hu'] | 2016-12-01 | the-construction-of-a-chinese-collocational-1 | https://aclanthology.org/C16-1307 | https://aclanthology.org/C16-1307.pdf | coling-2016-12 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-7.11593151e-01 -3.40970278e-01 5.50702959e-02 -2.13302523e-02
-9.38365638e-01 -7.03432202e-01 -3.38556796e-01 6.12958491e-01
-6.74015224e-01 1.22377849e+00 2.20869556e-01 -6.35764301e-01
9.38358754e-02 -6.62627280e-01 -4.51742530e-01 -2.36417070e-01
1.28499061e-01 3.35419834e-01 2.37808794e-01 -6.06189787... | [11.017912864685059, 10.765830993652344] |
62507329-a2b9-4901-993e-0a1fd717aefa | enhanced-multimodal-representation-learning-1 | 2306.07646 | null | https://arxiv.org/abs/2306.07646v1 | https://arxiv.org/pdf/2306.07646v1.pdf | Enhanced Multimodal Representation Learning with Cross-modal KD | This paper explores the tasks of leveraging auxiliary modalities which are only available at training to enhance multimodal representation learning through cross-modal Knowledge Distillation (KD). The widely adopted mutual information maximization-based objective leads to a short-cut solution of the weak teacher, i.e.,... | ['Ya zhang', 'Yu Wang', 'Linyu Xing', 'Mengxi Chen'] | 2023-06-13 | enhanced-multimodal-representation-learning | http://openaccess.thecvf.com//content/CVPR2023/html/Chen_Enhanced_Multimodal_Representation_Learning_With_Cross-Modal_KD_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chen_Enhanced_Multimodal_Representation_Learning_With_Cross-Modal_KD_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-recognition', 'video-retrieval', 'emotion-classification', 'emotion-classification'] | ['computer-vision', 'computer-vision', 'computer-vision', 'natural-language-processing'] | [ 4.50376898e-01 3.57288122e-01 -2.20174178e-01 -2.68517643e-01
-1.00956714e+00 -3.40607613e-01 5.47210872e-01 1.01047106e-01
-6.11755729e-01 7.05182910e-01 -1.69581342e-02 -1.61661580e-01
-2.13252261e-01 -4.35481966e-01 -8.05344820e-01 -1.21707547e+00
2.27031842e-01 -3.35373916e-02 -2.67863065e-01 -5.39667793... | [13.088095664978027, 5.067083835601807] |
330b0ed4-7b2b-48d3-8dc3-077005fe231d | knowlege-graph-embedding-by-flexible | 1505.05253 | null | http://arxiv.org/abs/1505.05253v2 | http://arxiv.org/pdf/1505.05253v2.pdf | Knowlege Graph Embedding by Flexible Translation | Knowledge graph embedding refers to projecting entities and relations in
knowledge graph into continuous vector spaces. State-of-the-art methods, such
as TransE, TransH, and TransR build embeddings by treating relation as
translation from head entity to tail entity. However, previous models can not
deal with reflexive/... | ['Minlie Huang', 'Mantong Zhou', 'Jun Feng', 'Xiaoyan Zhu', 'Yu Hao'] | 2015-05-20 | null | null | null | null | ['triple-classification'] | ['graphs'] | [-4.04905677e-01 1.47151709e-01 -6.02949679e-01 -1.88798830e-01
-1.65877983e-01 -5.33178747e-01 7.96258628e-01 1.15590796e-01
-2.32805565e-01 7.12154925e-01 4.30629462e-01 -4.75614548e-01
-3.57209295e-01 -1.07940674e+00 -7.05203712e-01 -2.28291377e-01
-3.01475793e-01 7.11185932e-01 3.09442759e-01 -4.94283170... | [8.752547264099121, 7.87985897064209] |
b2b709e1-c581-4a8e-8052-af42404edc19 | self-supervised-contrastive-learning-for-eeg | 2109.07839 | null | https://arxiv.org/abs/2109.07839v1 | https://arxiv.org/pdf/2109.07839v1.pdf | Self-supervised Contrastive Learning for EEG-based Sleep Staging | EEG signals are usually simple to obtain but expensive to label. Although supervised learning has been widely used in the field of EEG signal analysis, its generalization performance is limited by the amount of annotated data. Self-supervised learning (SSL), as a popular learning paradigm in computer vision (CV) and na... | ['Zhiyong Yuan', 'Bo Du', 'Jianhui Zhao', 'Xue Jiang'] | 2021-09-16 | null | null | null | null | ['sleep-staging', 'eeg-based-sleep-staging'] | ['medical', 'time-series'] | [ 2.92074531e-01 -1.48720637e-01 -2.36874819e-01 -7.00700521e-01
-3.66578341e-01 -2.75408745e-01 1.59281820e-01 -2.64838953e-02
-6.81387365e-01 1.01100588e+00 -1.44716026e-02 8.26244205e-02
-1.62298828e-01 -4.27738070e-01 -3.41840982e-01 -9.61188018e-01
1.72803216e-02 1.17359154e-01 -5.52663393e-02 -1.36875913... | [13.18875789642334, 3.4714975357055664] |
17dfbe77-6af9-4289-965e-846779d885d7 | segmentation-of-drilled-holes-in-texture | null | null | https://www.mdpi.com/1424-8220/21/11/3633 | https://www.mdpi.com/1424-8220/21/11/3633 | Segmentation of Drilled Holes in Texture Wooden Furniture Panels Using Deep Neural Network | Drilling operations are an essential part of furniture from MDF laminated boards required for product assembly. Faults in the process might introduce adverse effects to the furniture. Inspection of the drilling quality can be challenging due to a big variety of board surface textures, dust, or woodchips in the manufact... | ['Tadas Surgailis', 'Arūnas Lipnickas', 'Rytis Augustauskas'] | 2021-05-23 | null | null | null | mdpi-sensors-2021-5 | ['unet-segmentation', '2d-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 5.35731137e-01 8.96398947e-02 2.07524911e-01 -1.92236498e-01
-3.07346076e-01 -4.46447760e-01 9.84025151e-02 -1.29579101e-02
5.72016314e-02 1.85696498e-01 -2.44310781e-01 -3.98120843e-02
-4.54002708e-01 -7.78426051e-01 -7.44182467e-01 -5.92188478e-01
2.09363043e-01 3.57317209e-01 3.62276256e-01 -1.76900163... | [7.490850448608398, 1.7877473831176758] |
cf3523ff-0aa1-4fea-881f-ef3d4ba69611 | hsmd-an-object-motion-detection-algorithm | 2109.04119 | null | https://arxiv.org/abs/2109.04119v1 | https://arxiv.org/pdf/2109.04119v1.pdf | HSMD: An object motion detection algorithm using a Hybrid Spiking Neural Network Architecture | The detection of moving objects is a trivial task performed by vertebrate retinas, yet a complex computer vision task. Object-motion-sensitive ganglion cells (OMS-GC) are specialised cells in the retina that sense moving objects. OMS-GC take as input continuous signals and produce spike patterns as output, that are tra... | ['T. M. McGinnity', 'Joao Filipe Ferreira', 'Andreas Oikonomou', 'Pedro Machado'] | 2021-09-09 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 6.09377563e-01 -7.15169013e-01 7.36899257e-01 1.61658615e-01
-1.15957167e-02 -5.33497691e-01 8.06115448e-01 -3.05262983e-01
-1.09746277e+00 7.39106417e-01 -2.74173230e-01 -7.76387081e-02
2.72983342e-01 -3.04939300e-01 -7.12572157e-01 -1.14041877e+00
4.85201068e-02 -2.88606972e-01 1.43502295e+00 -2.06713095... | [8.671516418457031, -1.110706090927124] |
a20c3442-d763-4a73-b7d3-87f56633ea83 | 2012-11655 | 2012.11655 | null | https://arxiv.org/abs/2012.11655v3 | https://arxiv.org/pdf/2012.11655v3.pdf | Learning Dynamic Network Using a Reuse Gate Function in Semi-supervised Video Object Segmentation | Current state-of-the-art approaches for Semi-supervised Video Object Segmentation (Semi-VOS) propagates information from previous frames to generate segmentation mask for the current frame. This results in high-quality segmentation across challenging scenarios such as changes in appearance and occlusion. But it also le... | ['Nojun Kwak', 'Ganesh Venkatesh', 'Seohyeong Jeong', 'Jayeon Yoo', 'Hyojin Park'] | 2020-12-21 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Park_Learning_Dynamic_Network_Using_a_Reuse_Gate_Function_in_Semi-Supervised_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Park_Learning_Dynamic_Network_Using_a_Reuse_Gate_Function_in_Semi-Supervised_CVPR_2021_paper.pdf | cvpr-2021-1 | ['one-shot-visual-object-segmentation'] | ['computer-vision'] | [ 1.67325616e-01 -1.06482007e-01 -3.71542573e-01 -4.43183988e-01
-4.76354748e-01 -4.54257488e-01 1.35232329e-01 -1.90790296e-01
-4.34559852e-01 6.52372956e-01 -1.95212752e-01 -8.03496987e-02
2.24812999e-01 -4.51848060e-01 -7.04063594e-01 -6.17357135e-01
1.58940945e-02 3.05076361e-01 9.94283795e-01 1.85538054... | [9.18741226196289, -0.1111103817820549] |
36bac4ff-13e3-4acc-bc37-f1e738835059 | parallel-chinese-english-entities-relations | null | null | https://aclanthology.org/L16-1589 | https://aclanthology.org/L16-1589.pdf | Parallel Chinese-English Entities, Relations and Events Corpora | This paper introduces the parallel Chinese-English Entities, Relations and Events (ERE) corpora developed by Linguistic Data Consortium under the DARPA Deep Exploration and Filtering of Text (DEFT) Program. Original Chinese newswire and discussion forum documents are annotated for two versions of the ERE task. The text... | ['Justin Mott', 'Zhiyi Song', 'Ann Bies', 'Stephanie Strassel'] | 2016-05-01 | parallel-chinese-english-entities-relations-1 | https://aclanthology.org/L16-1589 | https://aclanthology.org/L16-1589.pdf | lrec-2016-5 | ['knowledge-base-population'] | ['natural-language-processing'] | [ 1.55420229e-01 5.67603409e-01 -2.99366057e-01 -8.11920285e-01
-1.31566226e+00 -8.03005099e-01 8.37202907e-01 2.27251932e-01
-1.18579412e+00 1.03207755e+00 1.16680741e+00 -4.99729902e-01
6.68780953e-02 -5.55260718e-01 -3.61090958e-01 5.66584058e-02
2.32408762e-01 1.16388905e+00 1.54287577e-01 -4.62969929... | [9.618907928466797, 9.37723159790039] |
e4f7a41f-b15a-4e2d-b4d9-0a5bfd24b87a | on-the-anatomy-of-latent-variable-generative | null | null | https://openreview.net/forum?id=29mx8VNszc | https://openreview.net/pdf?id=29mx8VNszc | On the Anatomy of Latent-variable Generative Models for Conditional Text Generation | Conditional text generation is a non-trivial task, which is until now predominantly performed with latent-variable generative models. In this work, we intend to explore several choices that are shown to affect the two essential aspects of model performance: expressivity and controllability. We propose to experiment wit... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['conditional-text-generation'] | ['natural-language-processing'] | [ 4.90492046e-01 3.17928702e-01 -9.02973861e-02 6.64265677e-02
-6.09457374e-01 -5.77613592e-01 1.19943368e+00 -3.51717085e-01
-3.71085145e-02 1.01272237e+00 2.95151651e-01 -4.03465003e-01
-1.95922047e-01 -7.47901499e-01 -4.19601172e-01 -8.15696716e-01
2.98953921e-01 5.53913593e-01 -1.31957397e-01 -2.03665793... | [6.993789196014404, 3.8926377296447754] |
4a0caed8-b961-4796-aba6-54c4b13f2709 | a-storytelling-robot-managing-persuasive-and | 2107.12845 | null | https://arxiv.org/abs/2107.12845v2 | https://arxiv.org/pdf/2107.12845v2.pdf | A Storytelling Robot managing Persuasive and Ethical Stances via ACT-R: an Exploratory Study | We present a storytelling robot, controlled via the ACT-R cognitive architecture, able to adopt different persuasive techniques and ethical stances while conversing about some topics concerning COVID-19. The main contribution of the paper consists in the proposal of a needs-driven model that guides and evaluates, durin... | ['Antonio Lieto', 'Manuel Gentile', 'Giuseppe Città', 'Agnese Augello'] | 2021-07-27 | null | null | null | null | ['dialogue-management'] | ['natural-language-processing'] | [-2.39372849e-02 1.18320537e+00 1.50784656e-01 -6.02869749e-01
9.45187733e-02 -6.87109530e-01 1.33414447e+00 4.35979784e-01
-4.06272352e-01 1.04285097e+00 9.93893564e-01 -6.06565475e-01
-3.49436492e-01 -6.91630960e-01 -1.55376896e-01 -1.75625935e-01
1.66855142e-01 7.92826355e-01 1.68722019e-01 -1.03993261... | [12.896756172180176, 7.926074028015137] |
a63f81a5-23e5-4e38-98cc-49ffe0c40da3 | p-3-lm-probabilistically-permuted-prophet | 2210.12339 | null | https://arxiv.org/abs/2210.12339v1 | https://arxiv.org/pdf/2210.12339v1.pdf | P$^3$LM: Probabilistically Permuted Prophet Language Modeling for Generative Pre-Training | Conventional autoregressive left-to-right (L2R) sequence generation faces two issues during decoding: limited to unidirectional target sequence modeling, and constrained on strong local dependencies. To address the aforementioned problem, we propose P$^3$LM, a probabilistically permuted prophet language model, which st... | ['Xiaodong He', 'Youzheng Wu', 'Jing Zhao', 'Yeyun Gong', 'Jiangyong Ying', 'Yifan Wang', 'Junwei Bao'] | 2022-10-22 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [ 6.95378482e-01 6.91827357e-01 1.45598436e-02 -3.19618046e-01
-1.37790537e+00 -6.15814924e-01 8.33330393e-01 -3.01694125e-01
-6.09208681e-02 1.17178357e+00 8.69383276e-01 -7.10096478e-01
4.24142092e-01 -8.26610386e-01 -9.83113289e-01 -5.70903182e-01
1.67287767e-01 8.57332289e-01 -2.37660870e-01 -6.51842117... | [11.9098482131958, 8.958085060119629] |
515d3961-2d7b-45db-9796-d0bfd73d65c0 | federated-self-learning-with-weak-supervision | 2306.12015 | null | https://arxiv.org/abs/2306.12015v1 | https://arxiv.org/pdf/2306.12015v1.pdf | Federated Self-Learning with Weak Supervision for Speech Recognition | Automatic speech recognition (ASR) models with low-footprint are increasingly being deployed on edge devices for conversational agents, which enhances privacy. We study the problem of federated continual incremental learning for recurrent neural network-transducer (RNN-T) ASR models in the privacy-enhancing scheme of l... | ['Jasha Droppo', 'Ariya Rastrow', 'Anirudh Raju', 'Anit Kumar Sahu', 'Gautam Tiwari', 'Gopinath Chennupati', 'Milind Rao'] | 2023-06-21 | null | null | null | null | ['incremental-learning', 'self-learning', 'automatic-speech-recognition'] | ['methodology', 'natural-language-processing', 'speech'] | [ 5.04389107e-01 6.21408463e-01 -2.24910393e-01 -4.00520653e-01
-1.42111492e+00 -4.49147969e-01 7.11089075e-01 -1.26827732e-01
-4.95032430e-01 7.43745744e-01 6.31967783e-01 -5.80775142e-01
2.84590095e-01 2.18515247e-02 -9.00956213e-01 -4.45961028e-01
-6.71592308e-03 4.77618366e-01 -1.58902749e-01 -9.70297828... | [14.264229774475098, 6.493922233581543] |
8b5a6209-7b94-4484-8c34-4d664f14f450 | convnets-with-smooth-adaptive-activation | null | null | http://proceedings.mlr.press/v54/hou17a.html | http://proceedings.mlr.press/v54/hou17a/hou17a.pdf | ConvNets with Smooth Adaptive Activation Functions for Regression | Within Neural Networks (NN), the parameters of Adaptive Activation Functions (AAF) control the shapes of activation functions.
These parameters are trained along with other parameters in the NN. AAFs have improved performance of Convolutional Neural Networks (CNN) in multiple classification tasks. In this paper, we pr... | ['Le Hou ; Dimitris Samaras ; Tahsin M. Kurc ; Yi Gao ; Joel H. Saltz'] | 2017-01-01 | null | null | null | null | ['age-and-gender-classification'] | ['computer-vision'] | [ 5.32506965e-03 3.39553982e-01 -1.12784050e-01 -7.71228433e-01
-2.09300548e-01 -3.44569236e-01 9.03652236e-02 -3.29783469e-01
-7.45730519e-01 5.33949375e-01 -2.12086588e-01 -1.17934957e-01
8.08634683e-02 -6.71686590e-01 -1.15525770e+00 -6.32423222e-01
1.53665856e-01 5.31065799e-02 3.44602108e-01 -2.01142877... | [8.927109718322754, 2.491614580154419] |
ae219131-ebe1-48f8-adf8-98a662c07225 | physics-informed-machine-learning-techniques | 2205.07838 | null | https://arxiv.org/abs/2205.07838v1 | https://arxiv.org/pdf/2205.07838v1.pdf | Physics-informed machine learning techniques for edge plasma turbulence modelling in computational theory and experiment | Edge plasma turbulence is critical to the performance of magnetic confinement fusion devices. Towards better understanding edge turbulence in both theory and experiment, a custom-built physics-informed deep learning framework constrained by partial differential equations is developed to accurately learn turbulent field... | ['Abhilash Mathews'] | 2022-05-16 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [-2.14269966e-01 -3.90377581e-01 3.85192961e-01 -1.43754676e-01
7.72990212e-02 -4.42915186e-02 9.42158043e-01 -3.73937696e-01
-5.87073326e-01 1.01328623e+00 -7.08062649e-02 -7.57476330e-01
-3.03018540e-01 -6.90882683e-01 -1.84339076e-01 -1.13328111e+00
-2.27473587e-01 1.14539540e+00 -3.91805053e-01 -5.99531770... | [6.45668888092041, 3.506392478942871] |
94c7508c-5f9f-42c3-b2ac-bff39ec5df1a | a-novel-metric-for-evaluating-semantics | 2110.01176 | null | https://arxiv.org/abs/2110.01176v3 | https://arxiv.org/pdf/2110.01176v3.pdf | Contextualized Semantic Distance between Highly Overlapped Texts | Overlapping frequently occurs in paired texts in natural language processing tasks like text editing and semantic similarity evaluation. Better evaluation of the semantic distance between the overlapped sentences benefits the language system's understanding and guides the generation. Since conventional semantic metrics... | ['Hai Zhao', 'Zuchao Li', 'Letian Peng'] | 2021-10-04 | null | null | null | null | ['predicate-detection', 'sentence-compression', 'text-compression'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 4.96994525e-01 4.43156287e-02 -1.16065763e-01 -5.48340976e-01
-5.05825162e-01 -3.32449526e-01 7.17219710e-01 6.25309885e-01
-4.75510329e-01 5.69461823e-01 6.25670254e-01 -3.14685106e-01
-1.71358109e-01 -7.89132833e-01 -3.54888052e-01 -4.26759183e-01
1.22010849e-01 5.67854047e-01 3.22825491e-01 -6.17049634... | [10.975997924804688, 8.962203025817871] |
6e0704e9-5b7f-4d61-8a88-211bf12f4f98 | accurate-and-efficient-event-based-semantic | 2304.11857 | null | https://arxiv.org/abs/2304.11857v2 | https://arxiv.org/pdf/2304.11857v2.pdf | Accurate and Efficient Event-based Semantic Segmentation Using Adaptive Spiking Encoder-Decoder Network | Leveraging the low-power, event-driven computation and the inherent temporal dynamics, spiking neural networks (SNNs) are potentially ideal solutions for processing dynamic and asynchronous signals from event-based sensors. However, due to the challenges in training and the restrictions in architectural design, there a... | ['Ran Cheng', 'Jiangxing Liao', 'Qinghai Guo', 'Jie Cheng', 'Hu Zhang', 'Kaiwei Che', 'Luziwei Leng', 'Rui Zhang'] | 2023-04-24 | null | null | null | null | ['event-based-vision'] | ['computer-vision'] | [ 8.36948037e-01 -1.82326198e-01 2.80268788e-01 -1.66353613e-01
-5.86891294e-01 -2.94696569e-01 4.48579699e-01 3.50666702e-01
-8.79116178e-01 9.08277929e-01 -1.59635589e-01 1.02799706e-01
-1.10431470e-01 -6.96671724e-01 -9.90717947e-01 -9.32062626e-01
-3.50387484e-01 1.95791826e-01 7.52633750e-01 3.99173656... | [8.217541694641113, 2.4153876304626465] |
f7b230c9-9f72-4bf2-8d23-debc234039b6 | recent-progress-in-the-cuhk-dysarthric-speech | 2201.05845 | null | https://arxiv.org/abs/2201.05845v2 | https://arxiv.org/pdf/2201.05845v2.pdf | Recent Progress in the CUHK Dysarthric Speech Recognition System | Despite the rapid progress of automatic speech recognition (ASR) technologies in the past few decades, recognition of disordered speech remains a highly challenging task to date. Disordered speech presents a wide spectrum of challenges to current data intensive deep neural networks (DNNs) based ASR technologies that pr... | ['Helen Meng', 'Xunying Liu', 'Jianwei Yu', 'Mingyu Cui', 'Xurong Xie', 'Shoukang Hu', 'Mengzhe Geng', 'Shansong Liu'] | 2022-01-15 | null | null | null | null | ['audio-visual-speech-recognition'] | ['speech'] | [ 1.20214663e-01 7.40375966e-02 5.62137067e-01 -2.30896518e-01
-1.18131936e+00 -2.81878918e-01 6.03296280e-01 -6.11496210e-01
-5.36919653e-01 3.87547851e-01 4.69845384e-01 -4.06677365e-01
7.00757205e-02 3.35267670e-02 -2.09870979e-01 -7.40853131e-01
1.27561748e-01 6.56097651e-01 4.05863151e-02 -5.13340771... | [14.571558952331543, 6.438146114349365] |
e68406f9-2000-4496-a444-c83eae974ddd | how-to-train-your-event-camera-neural-network | 2003.09078 | null | https://arxiv.org/abs/2003.09078v5 | https://arxiv.org/pdf/2003.09078v5.pdf | Reducing the Sim-to-Real Gap for Event Cameras | Event cameras are paradigm-shifting novel sensors that report asynchronous, per-pixel brightness changes called 'events' with unparalleled low latency. This makes them ideal for high speed, high dynamic range scenes where conventional cameras would fail. Recent work has demonstrated impressive results using Convolution... | ['Tom Drummond', 'Nick Barnes', 'Timo Stoffregen', 'Davide Scaramuzza', 'Cedric Scheerlinck', 'Robert Mahony', 'Lindsay Kleeman'] | 2020-03-20 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/5855_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123720528.pdf | eccv-2020-8 | ['video-reconstruction'] | ['computer-vision'] | [ 2.36186236e-01 -5.90812743e-01 1.39064565e-01 -4.90271837e-01
-4.75210518e-01 -2.92065531e-01 6.38917565e-01 -4.20953274e-01
-7.23172545e-01 6.98656857e-01 4.81441647e-01 -5.09668849e-02
2.62601614e-01 -6.31422698e-01 -1.23186982e+00 -3.67528379e-01
-2.97014922e-01 -2.49264821e-01 6.94656134e-01 2.42060162... | [8.639253616333008, -1.190571904182434] |
bf63f9ea-ff1c-4c5c-968c-a153ce25387f | bootstrapped-representations-in-reinforcement | 2306.10171 | null | https://arxiv.org/abs/2306.10171v1 | https://arxiv.org/pdf/2306.10171v1.pdf | Bootstrapped Representations in Reinforcement Learning | In reinforcement learning (RL), state representations are key to dealing with large or continuous state spaces. While one of the promises of deep learning algorithms is to automatically construct features well-tuned for the task they try to solve, such a representation might not emerge from end-to-end training of deep ... | ['Will Dabney', 'Marc G. Bellemare', 'Rishabh Agarwal', 'Anna Harutyunyan', 'Mark Rowland', 'Stephen Tu', 'Charline Le Lan'] | 2023-06-16 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [ 3.25901471e-02 8.58919322e-02 -4.62387145e-01 -8.80143791e-02
-8.54348004e-01 -8.20665121e-01 8.75327110e-01 1.26795173e-01
-7.64944673e-01 1.30361974e+00 2.96281189e-01 -6.39530003e-01
-3.13502014e-01 -5.04462242e-01 -7.01455355e-01 -8.17843258e-01
-4.24275428e-01 5.37501156e-01 5.53281941e-02 -5.49155295... | [4.07340145111084, 1.8553258180618286] |
766055f3-aaec-4608-8112-c7679ba7a564 | diffusion-models-and-semi-supervised-learners | 2302.10586 | null | https://arxiv.org/abs/2302.10586v2 | https://arxiv.org/pdf/2302.10586v2.pdf | Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labels | In an effort to further advance semi-supervised generative and classification tasks, we propose a simple yet effective training strategy called dual pseudo training (DPT), built upon strong semi-supervised learners and diffusion models. DPT operates in three stages: training a classifier on partially labeled data to pr... | ['Jun Zhu', 'Chongxuan Li', 'Jiacheng Sun', 'Fan Bao', 'Yong Zhong', 'Zebin You'] | 2023-02-21 | null | null | null | null | ['semi-supervised-image-classification', 'conditional-image-generation'] | ['computer-vision', 'computer-vision'] | [ 4.72117871e-01 4.67758983e-01 -4.17479455e-01 -3.86637568e-01
-1.06036079e+00 -5.28553724e-01 1.07918572e+00 -3.14412832e-01
-3.78743440e-01 9.25513804e-01 -9.93963610e-03 -4.26097363e-01
3.19718510e-01 -7.64059246e-01 -7.79485106e-01 -7.43404567e-01
1.56957090e-01 7.48578966e-01 -1.39646605e-01 1.30511567... | [11.466998100280762, -0.1411079466342926] |
ffd4e97e-cecd-4757-a9ab-9e2f74ed7240 | correction-of-errors-in-preference-ratings | 2306.03866 | null | https://arxiv.org/abs/2306.03866v1 | https://arxiv.org/pdf/2306.03866v1.pdf | Correction of Errors in Preference Ratings from Automated Metrics for Text Generation | A major challenge in the field of Text Generation is evaluation: Human evaluations are cost-intensive, and automated metrics often display considerable disagreement with human judgments. In this paper, we propose a statistical model of Text Generation evaluation that accounts for the error-proneness of automated metric... | ['Mark Cieliebak', 'Don Tuggener', 'Pius von Däniken', 'Jan Deriu'] | 2023-06-06 | null | null | null | null | ['text-summarization'] | ['natural-language-processing'] | [ 4.00586545e-01 6.11111641e-01 1.31920844e-01 -4.56690311e-01
-1.19099057e+00 -9.35090780e-01 1.10136759e+00 4.75691408e-01
-4.38315630e-01 1.14110243e+00 4.53559756e-01 -3.15896869e-01
1.27480999e-01 -4.57986027e-01 -2.04094440e-01 -2.89012522e-01
4.25789773e-01 8.24190319e-01 9.31144208e-02 -3.76226693... | [11.810128211975098, 8.964162826538086] |
05f80221-150e-439f-8874-47da56f57bd4 | towards-evaluating-gaussian-blurring-in | 2002.00140 | null | https://arxiv.org/abs/2002.00140v2 | https://arxiv.org/pdf/2002.00140v2.pdf | Towards Evaluating Gaussian Blurring in Perceptual Hashing as a Facial Image Filter | With the growth in social media, there is a huge amount of images of faces available on the internet. Often, people use other people's pictures on their own profile. Perceptual hashing is often used to detect whether two images are identical. Therefore, it can be used to detect whether people are misusing others' pictu... | ['Yigit Alparslan', 'Mannika Kshettry', 'Ken Alparslan', 'Louis Kratz'] | 2020-02-01 | null | null | null | null | ['image-cropping', 'text-annotation'] | ['computer-vision', 'natural-language-processing'] | [ 3.55337530e-01 -1.62956506e-01 3.02783459e-01 -4.59082007e-01
-1.34722441e-01 -8.59174550e-01 4.35739756e-01 3.42972517e-01
-4.76420879e-01 3.87171298e-01 -4.52371947e-02 -1.81258291e-01
5.08151650e-01 -9.40184116e-01 -6.11793280e-01 -6.75022960e-01
3.99617366e-02 -1.48674399e-01 6.43231392e-01 -3.19562219... | [12.675508499145508, 1.0716829299926758] |
0f6e7690-24f6-4107-9d70-13ea9c69af8e | a-close-up-comparison-of-the | 1907.11505 | null | https://arxiv.org/abs/1907.11505v1 | https://arxiv.org/pdf/1907.11505v1.pdf | A close-up comparison of the misclassification error distance and the adjusted Rand index for external clustering evaluation | The misclassification error distance and the adjusted Rand index are two of the most commonly used criteria to evaluate the performance of clustering algorithms. This paper provides an in-depth comparison of the two criteria, aimed to better understand exactly what they measure, their properties and their differences. ... | ['José E. Chacón'] | 2019-07-26 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [-4.78232503e-02 -2.30610490e-01 -3.52894247e-01 -5.19673705e-01
-1.70656800e-01 -4.87314016e-01 4.97509629e-01 3.91689211e-01
-4.43550646e-01 7.97758162e-01 -1.45752832e-01 -4.77694452e-01
-9.35072899e-01 -7.01594472e-01 8.93505216e-02 -9.49677110e-01
-3.49085093e-01 9.14561808e-01 6.59202933e-02 1.08038180... | [7.6774516105651855, 4.494951248168945] |
2f49128b-2e3c-4874-937d-d003183957eb | deep-hierarchical-planning-from-pixels | 2206.04114 | null | https://arxiv.org/abs/2206.04114v1 | https://arxiv.org/pdf/2206.04114v1.pdf | Deep Hierarchical Planning from Pixels | Intelligent agents need to select long sequences of actions to solve complex tasks. While humans easily break down tasks into subgoals and reach them through millions of muscle commands, current artificial intelligence is limited to tasks with horizons of a few hundred decisions, despite large compute budgets. Research... | ['Pieter Abbeel', 'Ian Fischer', 'Kuang-Huei Lee', 'Danijar Hafner'] | 2022-06-08 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [ 5.07107470e-03 3.50325972e-01 -1.83415160e-01 -4.93297242e-02
-4.36004400e-01 -6.55689299e-01 5.32699883e-01 -2.25265175e-01
-4.53648031e-01 1.09385049e+00 3.07504207e-01 -1.63850754e-01
-3.09203237e-01 -6.68793082e-01 -5.48431158e-01 -9.27071571e-01
-4.97638017e-01 7.57844925e-01 1.39157414e-01 -2.59568781... | [4.159060001373291, 1.3018606901168823] |
a65c7198-625a-4787-b958-98de98eab7e9 | targeted-attention-attack-on-deep-learning | 2010.04331 | null | https://arxiv.org/abs/2010.04331v3 | https://arxiv.org/pdf/2010.04331v3.pdf | Targeted Physical-World Attention Attack on Deep Learning Models in Road Sign Recognition | Real world traffic sign recognition is an important step towards building autonomous vehicles, most of which highly dependent on Deep Neural Networks (DNNs). Recent studies demonstrated that DNNs are surprisingly susceptible to adversarial examples. Many attack methods have been proposed to understand and generate adve... | ['DaCheng Tao', 'Wei Liu', 'Shengli Zhang', 'Weifeng Liu', 'Xinghao Yang'] | 2020-10-09 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 3.83557007e-02 -1.66192994e-01 -5.97767904e-02 -1.54488087e-01
-7.76044548e-01 -5.03318191e-01 6.22544885e-01 -7.15883791e-01
-3.93096179e-01 6.80872679e-01 8.23553354e-02 -4.89251941e-01
1.56045035e-01 -8.86932969e-01 -9.10369933e-01 -7.88501501e-01
1.33290276e-01 1.16804965e-01 6.99028313e-01 -5.40388286... | [5.39818000793457, 7.8810954093933105] |
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