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65d4d63b-8276-4339-9214-5be0ea9b1b32 | one-2-3-45-any-single-image-to-3d-mesh-in-45 | 2306.16928 | null | https://arxiv.org/abs/2306.16928v1 | https://arxiv.org/pdf/2306.16928v1.pdf | One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization | Single image 3D reconstruction is an important but challenging task that requires extensive knowledge of our natural world. Many existing methods solve this problem by optimizing a neural radiance field under the guidance of 2D diffusion models but suffer from lengthy optimization time, 3D inconsistency results, and po... | ['Hao Su', 'Zexiang Xu', 'Mukund Varma T', 'Linghao Chen', 'Haian Jin', 'Chao Xu', 'Minghua Liu'] | 2023-06-29 | null | null | null | null | ['3d-reconstruction', 'image-to-3d', 'text-to-3d'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.21338347e-01 5.96313141e-02 2.04362229e-01 -2.42065743e-01
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5.31630039e-01 6.91239119e-01 2.51726508e-01 -2.63650090... | [9.177760124206543, -3.1653449535369873] |
c6b52c4e-6058-46bc-b476-9b92b6aa7e86 | tgrnet-a-table-graph-reconstruction-network | 2106.10598 | null | https://arxiv.org/abs/2106.10598v3 | https://arxiv.org/pdf/2106.10598v3.pdf | TGRNet: A Table Graph Reconstruction Network for Table Structure Recognition | A table arranging data in rows and columns is a very effective data structure, which has been widely used in business and scientific research. Considering large-scale tabular data in online and offline documents, automatic table recognition has attracted increasing attention from the document analysis community. Though... | ['Qingyong Li', 'DaCheng Tao', 'Wen Wang', 'Baosheng Yu', 'Wenyuan Xue'] | 2021-06-20 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Xue_TGRNet_A_Table_Graph_Reconstruction_Network_for_Table_Structure_Recognition_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Xue_TGRNet_A_Table_Graph_Reconstruction_Network_for_Table_Structure_Recognition_ICCV_2021_paper.pdf | iccv-2021-1 | ['table-recognition', 'cell-detection', 'graph-reconstruction'] | ['computer-vision', 'computer-vision', 'graphs'] | [ 9.47787240e-02 -8.19656551e-02 -4.76309717e-01 -3.11311692e-01
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2a4b9e90-28f3-4284-969a-17ef1c997ed9 | 3d-human-pose-estimation-for-free-form | 2110.08314 | null | https://arxiv.org/abs/2110.08314v1 | https://arxiv.org/pdf/2110.08314v1.pdf | 3D Human Pose Estimation for Free-form Activity Using WiFi Signals | WiFi human sensing has become increasingly attractive in enabling emerging human-computer interaction applications. The corresponding technique has gradually evolved from the classification of multiple activity types to more fine-grained tracking of 3D human poses. However, existing WiFi-based 3D human pose tracking is... | ['Jie Yang', 'Yili Ren'] | 2021-10-15 | null | null | null | null | ['3d-human-pose-tracking'] | ['computer-vision'] | [ 1.42914951e-01 -2.07946748e-01 -1.35335207e-01 9.87147987e-02
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-4.04059947e-01 3.58391732e-01 2.79519677e-01 4.71173748... | [6.787161827087402, 0.4580027759075165] |
d9f433f5-d932-4af6-bd8a-06a18722c15c | robust-design-of-deep-neural-networks-against | 1911.04636 | null | https://arxiv.org/abs/1911.04636v1 | https://arxiv.org/pdf/1911.04636v1.pdf | Robust Design of Deep Neural Networks against Adversarial Attacks based on Lyapunov Theory | Deep neural networks (DNNs) are vulnerable to subtle adversarial perturbations applied to the input. These adversarial perturbations, though imperceptible, can easily mislead the DNN. In this work, we take a control theoretic approach to the problem of robustness in DNNs. We treat each individual layer of the DNN as a ... | ['Arash Rahnama', 'Andre T. Nguyen', 'Edward Raff'] | 2019-11-12 | robust-design-of-deep-neural-networks-against-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Rahnama_Robust_Design_of_Deep_Neural_Networks_Against_Adversarial_Attacks_Based_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Rahnama_Robust_Design_of_Deep_Neural_Networks_Against_Adversarial_Attacks_Based_CVPR_2020_paper.pdf | cvpr-2020-6 | ['robust-design'] | ['miscellaneous'] | [ 1.91881180e-01 3.84757429e-01 3.59346330e-01 6.40499145e-02
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-3.34627718e-01 -3.39617014e-01 2.43453115e-01 -4.43658859... | [5.540449142456055, 7.873496055603027] |
f43b5ee1-a07e-4655-92ab-a8dac2cff93a | autonomous-driving-in-reality-with | 1801.05299 | null | http://arxiv.org/abs/1801.05299v2 | http://arxiv.org/pdf/1801.05299v2.pdf | Autonomous Driving in Reality with Reinforcement Learning and Image Translation | Supervised learning is widely used in training autonomous driving vehicle.
However, it is trained with large amount of supervised labeled data.
Reinforcement learning can be trained without abundant labeled data, but we
cannot train it in reality because it would involve many unpredictable
accidents. Nevertheless, trai... | ['Bingyu Kong', 'Bowen Tan', 'Nayun Xu'] | 2018-01-13 | null | null | null | null | ['carracing-v0'] | ['playing-games'] | [-3.85526925e-01 1.39354095e-01 -1.96780130e-01 -6.02240264e-01
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1.22345686e-01 7.18630731e-01 7.42810428e-01 -6.71996236... | [5.142045497894287, 1.2050009965896606] |
fba08ba2-dde8-47b3-b97f-65f48eef31c4 | difficulty-aware-meta-learning-for-rare | 1907.00354 | null | https://arxiv.org/abs/1907.00354v2 | https://arxiv.org/pdf/1907.00354v2.pdf | Difficulty-aware Meta-learning for Rare Disease Diagnosis | Rare diseases have extremely low-data regimes, unlike common diseases with large amount of available labeled data. Hence, to train a neural network to classify rare diseases with a few per-class data samples is very challenging, and so far, catches very little attention. In this paper, we present a difficulty-aware met... | ['Pheng-Ann Heng', 'Chi-Wing Fu', 'Xiaomeng Li', 'Lequan Yu', 'Lei Xing', 'Yueming Jin'] | 2019-06-30 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 4.90636945e-01 1.87145844e-01 -5.03033638e-01 -3.05249274e-01
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2.16690093e-01 5.63459933e-01 -6.42803591e-03 4.12458703... | [15.410158157348633, -2.7290728092193604] |
79a0b260-6aa7-4694-9313-e2a4a2cb12b3 | xmem-long-term-video-object-segmentation-with | 2207.07115 | null | https://arxiv.org/abs/2207.07115v2 | https://arxiv.org/pdf/2207.07115v2.pdf | XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model | We present XMem, a video object segmentation architecture for long videos with unified feature memory stores inspired by the Atkinson-Shiffrin memory model. Prior work on video object segmentation typically only uses one type of feature memory. For videos longer than a minute, a single feature memory model tightly link... | ['Alexander G. Schwing', 'Ho Kei Cheng'] | 2022-07-14 | null | null | null | null | ['semi-supervised-video-object-segmentation', '2d-human-pose-estimation', '3d-absolute-human-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 7.63447359e-02 -2.32936159e-01 -4.40419316e-01 -1.97971240e-01
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-5.59855215e-02 1.97082818e-01 8.04176092e-01 3.33893806... | [9.036210060119629, 0.3133019506931305] |
245604a9-b398-4823-8c58-71f5fe589868 | drafting-in-collectible-card-games-via | null | null | https://ieeexplore.ieee.org/document/9291616 | https://www.sbgames.org/proceedings2020/ComputacaoFull/209690.pdf | Drafting in Collectible Card Games via Reinforcement Learning | Collectible card games are played by tens of millions of players worldwide. Their intricate rules and diverse cards make them much harder than traditional card games. To win, players must be proficient in two interdependent tasks: deck building and battling. In this paper, we present a deep reinforcement learning appro... | ['Luiz Chaimowicz', 'Anderson Rocha Tavares', 'Ronaldo Vieira'] | 2020-11-07 | null | null | null | null | ['card-games'] | ['playing-games'] | [-3.45820069e-01 -1.35066926e-01 1.23591714e-01 2.54106969e-01
-6.85767651e-01 -1.10821986e+00 3.26588452e-01 -2.57171303e-01
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-1.89624041e-01 1.24473822e+00 2.67051190e-01 -8.59771967... | [3.4754638671875, 1.4887306690216064] |
ae6ad664-3a9d-4425-a1e5-2b1ff786ef17 | scelmo-source-code-embeddings-from-language-1 | 2004.13214 | null | https://arxiv.org/abs/2004.13214v1 | https://arxiv.org/pdf/2004.13214v1.pdf | SCELMo: Source Code Embeddings from Language Models | Continuous embeddings of tokens in computer programs have been used to support a variety of software development tools, including readability, code search, and program repair. Contextual embeddings are common in natural language processing but have not been previously applied in software engineering. We introduce a new... | ['Rafael - Michael Karampatsis', 'Charles Sutton'] | 2020-04-28 | null | https://openreview.net/forum?id=ryxnJlSKvr | https://openreview.net/pdf?id=ryxnJlSKvr | null | ['program-repair', 'code-search', 'code-search', 'program-repair'] | ['computer-code', 'computer-code', 'computer-vision', 'reasoning'] | [-1.49023458e-01 7.60878175e-02 -3.66755992e-01 -1.53853595e-01
-4.18645561e-01 -4.00359064e-01 2.99091518e-01 8.63458037e-01
-1.99983716e-01 -1.53442353e-01 3.05999666e-01 -7.81181097e-01
1.01766713e-01 -8.18215311e-01 -5.56376994e-01 -3.92722758e-03
-3.31488252e-01 -2.57934004e-01 3.42592895e-01 -3.79911304... | [7.546055793762207, 7.828531265258789] |
eba6ab80-46cc-43d9-870f-a1c924564303 | abstractive-text-image-summarization-using | null | null | https://aclanthology.org/D18-1438 | https://aclanthology.org/D18-1438.pdf | Abstractive Text-Image Summarization Using Multi-Modal Attentional Hierarchical RNN | Rapid growth of multi-modal documents on the Internet makes multi-modal summarization research necessary. Most previous research summarizes texts or images separately. Recent neural summarization research shows the strength of the Encoder-Decoder model in text summarization. This paper proposes an abstractive text-imag... | ['Hai Zhuge', 'Jingqiang Chen'] | 2018-10-01 | null | null | null | emnlp-2018-10 | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 6.87422097e-01 3.43064904e-01 -2.71210760e-01 -3.67079616e-01
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5.41522443e-01 2.73176730e-01 1.59619108e-01 1.65155977... | [12.595327377319336, 9.452679634094238] |
16b3f846-56e2-4601-83d4-a16a97eeb6f1 | concentration-of-polynomial-random-matrices | 2209.02655 | null | https://arxiv.org/abs/2209.02655v2 | https://arxiv.org/pdf/2209.02655v2.pdf | Concentration of polynomial random matrices via Efron-Stein inequalities | Analyzing concentration of large random matrices is a common task in a wide variety of fields. Given independent random variables, many tools are available to analyze random matrices whose entries are linear in the variables, e.g. the matrix-Bernstein inequality. However, in many applications, we need to analyze random... | ['Madhur Tulsiani', 'Goutham Rajendran'] | 2022-09-06 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [ 8.49786922e-02 1.46017641e-01 -5.69600519e-03 4.89956051e-01
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-6.10651970e-01 -8.26312959e-01 -9.44577038e-01 -1.12196529e+00
-3.86055976e-01 3.40349704e-01 1.16701582e-02 -3.63599807... | [6.933527946472168, 5.025960922241211] |
1acee791-f999-4e0f-8397-620d44a2c725 | learning-theorem-proving-components | 2107.10034 | null | https://arxiv.org/abs/2107.10034v1 | https://arxiv.org/pdf/2107.10034v1.pdf | Learning Theorem Proving Components | Saturation-style automated theorem provers (ATPs) based on the given clause procedure are today the strongest general reasoners for classical first-order logic. The clause selection heuristics in such systems are, however, often evaluating clauses in isolation, ignoring other clauses. This has changed recently by equip... | ['Josef Urban', 'Miroslav Olšák', 'Jan Jakubův', 'Karel Chvalovský'] | 2021-07-21 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 4.74244088e-01 9.35137868e-01 -5.60825884e-01 -2.77929276e-01
-4.43780214e-01 -7.18078315e-01 6.05499625e-01 3.31242472e-01
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-4.09021467e-01 1.11738527e+00 5.10369956e-01 -3.79243016... | [8.880488395690918, 7.032257556915283] |
b0403ed9-1c6e-4f69-be51-190b5a14189d | squeeze-excitation-embedded-attention-unet | 2305.07850 | null | https://arxiv.org/abs/2305.07850v1 | https://arxiv.org/pdf/2305.07850v1.pdf | Squeeze Excitation Embedded Attention UNet for Brain Tumor Segmentation | Deep Learning based techniques have gained significance over the past few years in the field of medicine. They are used in various applications such as classifying medical images, segmentation and identification. The existing architectures such as UNet, Attention UNet and Attention Residual UNet are already currently e... | ['Sathiya Narayanan', 'Lalitha G', 'John Rohit Ernest', 'Gaurav Prasanna'] | 2023-05-13 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation'] | ['computer-vision', 'medical'] | [ 2.97740698e-01 2.77610630e-01 9.33648199e-02 -2.31561691e-01
-3.93501073e-01 4.67162244e-02 3.46686780e-01 3.66141737e-01
-4.37385291e-01 8.11936140e-01 3.41734111e-01 -8.14059526e-02
-4.22998190e-01 -5.73629081e-01 -4.13940996e-01 -6.69720054e-01
-2.85446532e-02 2.85995275e-01 5.15355289e-01 -1.71811178... | [14.90655517578125, -2.606045722961426] |
65871c02-ea86-4683-a077-9556e2ddcd47 | sharing-cultural-heritage-the-clavius-on-the | null | null | https://aclanthology.org/L14-1317 | https://aclanthology.org/L14-1317.pdf | Sharing Cultural Heritage: the Clavius on the Web Project | In the last few years the amount of manuscripts digitized and made available on the Web has been constantly increasing. However, there is still a considarable lack of results concerning both the explicitation of their content and the tools developed to make it available. The objective of the Clavius on the Web project ... | ['Matteo Abrate', 'Lorenzo Mancini', 'Andrea Marchetti', 'Irene Pedretti', 'Damiana Luzzi', 'Silvia Piccini', 'Emiliano Giovannetti', 'Angelo Mario Del Grosso', 'Angelica Lo Duca'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['morphological-tagging'] | ['natural-language-processing'] | [-1.73977956e-01 3.36982459e-01 2.11406848e-03 -5.41644134e-02
-2.46857762e-01 -8.15898955e-01 1.05277038e+00 9.60828781e-01
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-1.51774725e-02 6.03370368e-01 4.46116894e-01 -4.44982260... | [9.372252464294434, 8.52665901184082] |
8f8ba9f0-a790-46f4-99d2-220f50474da2 | learning-with-fantasy-semantic-aware-virtual | 2304.00426 | null | https://arxiv.org/abs/2304.00426v1 | https://arxiv.org/pdf/2304.00426v1.pdf | Learning with Fantasy: Semantic-Aware Virtual Contrastive Constraint for Few-Shot Class-Incremental Learning | Few-shot class-incremental learning (FSCIL) aims at learning to classify new classes continually from limited samples without forgetting the old classes. The mainstream framework tackling FSCIL is first to adopt the cross-entropy (CE) loss for training at the base session, then freeze the feature extractor to adapt to ... | ['Yonghong Tian', 'Li Yuan', 'Peixi Peng', 'Yujun Shi', 'Yifan Zhao', 'Zeyin Song'] | 2023-04-02 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Song_Learning_With_Fantasy_Semantic-Aware_Virtual_Contrastive_Constraint_for_Few-Shot_Class-Incremental_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Song_Learning_With_Fantasy_Semantic-Aware_Virtual_Contrastive_Constraint_for_Few-Shot_Class-Incremental_CVPR_2023_paper.pdf | cvpr-2023-1 | ['class-incremental-learning', 'few-shot-class-incremental-learning'] | ['computer-vision', 'methodology'] | [ 4.39537942e-01 -2.65409816e-02 -2.56109118e-01 -3.43444765e-01
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5.05768359e-02 2.69320428e-01 5.16830444e-01 -4.69105422... | [9.858951568603516, 3.2672348022460938] |
df590546-b5fe-4dc3-b926-851556e69202 | prospect-expanded-conditioning-for-the | 2305.16225 | null | https://arxiv.org/abs/2305.16225v2 | https://arxiv.org/pdf/2305.16225v2.pdf | ProSpect: Expanded Conditioning for the Personalization of Attribute-aware Image Generation | Personalizing generative models offers a way to guide image generation with user-provided references. Current personalization methods can invert an object or concept into the textual conditioning space and compose new natural sentences for text-to-image diffusion models. However, representing and editing specific visua... | ['Changsheng Xu', 'Oliver Deussen', 'Tong-Yee Lee', 'Chongyang Ma', 'Haibin Huang', 'Nisha Huang', 'Fan Tang', 'WeiMing Dong', 'Yuxin Zhang'] | 2023-05-25 | null | null | null | null | ['disentanglement'] | ['methodology'] | [ 7.87798047e-01 -1.09163038e-02 -6.86786184e-03 -3.16731513e-01
-3.50480586e-01 -8.81954670e-01 1.13445306e+00 -1.82736490e-03
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5.11005819e-01 3.82733911e-01 -2.57728010e-01 -4.19900030... | [11.453754425048828, -0.28231334686279297] |
52e51671-82f1-4852-8ddd-81a2a73ce71b | building-spatio-temporal-transformers-for | 2206.04785 | null | https://arxiv.org/abs/2206.04785v1 | https://arxiv.org/pdf/2206.04785v1.pdf | Building Spatio-temporal Transformers for Egocentric 3D Pose Estimation | Egocentric 3D human pose estimation (HPE) from images is challenging due to severe self-occlusions and strong distortion introduced by the fish-eye view from the head mounted camera. Although existing works use intermediate heatmap-based representations to counter distortion with some success, addressing self-occlusion... | ['Paul Fieguth', 'Sirisha Rambhatla', 'Norikatsu Sumi', 'Saad Hossain', 'Kimathi Kaai', 'JinMan Park'] | 2022-06-09 | null | null | null | null | ['3d-pose-estimation'] | ['computer-vision'] | [-1.05561383e-01 3.02738816e-01 1.47488505e-01 -5.79912961e-01
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2.58949190e-01 -3.04340988e-01 -9.38710630e-01 -2.00447232e-01
-5.84520102e-02 4.85224009e-01 1.32925749e-01 -1.63515925... | [6.991176128387451, -0.9615811109542847] |
87c7f5d2-b6f1-4fb7-abd1-6a6db777c072 | magsac-marginalizing-sample-consensus | 1803.07469 | null | https://arxiv.org/abs/1803.07469v2 | https://arxiv.org/pdf/1803.07469v2.pdf | MAGSAC: marginalizing sample consensus | A method called, sigma-consensus, is proposed to eliminate the need for a user-defined inlier-outlier threshold in RANSAC. Instead of estimating the noise sigma, it is marginalized over a range of noise scales. The optimized model is obtained by weighted least-squares fitting where the weights come from the marginaliza... | ['Jiri Matas', 'Daniel Barath', 'Jana Noskova'] | 2018-03-20 | magsac-marginalizing-sample-consensus-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Barath_MAGSAC_Marginalizing_Sample_Consensus_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Barath_MAGSAC_Marginalizing_Sample_Consensus_CVPR_2019_paper.pdf | cvpr-2019-6 | ['homography-estimation'] | ['computer-vision'] | [-1.00266024e-01 -3.76817495e-01 3.81157190e-01 -3.29755038e-01
-1.06992447e+00 -5.96385598e-01 5.18650115e-01 1.57638535e-01
-4.53719735e-01 5.40577531e-01 -1.41870910e-02 -2.10771672e-02
-3.25409174e-01 -3.62181038e-01 -6.90398097e-01 -8.79177868e-01
3.36912721e-01 5.06848514e-01 4.23139125e-01 4.99051474... | [7.966612815856934, -2.35746169090271] |
e6b33e2d-88af-44bf-9ce3-5d56342152c9 | ced-color-event-camera-dataset | 1904.10772 | null | http://arxiv.org/abs/1904.10772v1 | http://arxiv.org/pdf/1904.10772v1.pdf | CED: Color Event Camera Dataset | Event cameras are novel, bio-inspired visual sensors, whose pixels output
asynchronous and independent timestamped spikes at local intensity changes,
called 'events'. Event cameras offer advantages over conventional frame-based
cameras in terms of latency, high dynamic range (HDR) and temporal resolution.
Until recentl... | ['Cedric Scheerlinck', 'Nick Barnes', 'Timo Stoffregen', 'Henri Rebecq', 'Davide Scaramuzza', 'Robert Mahony'] | 2019-04-24 | null | null | null | null | ['event-based-vision'] | ['computer-vision'] | [ 4.85359997e-01 -9.10475135e-01 6.23567164e-01 -3.22351366e-01
-3.49353164e-01 -5.48230171e-01 6.34653151e-01 2.46136174e-01
-6.27728522e-01 5.79969168e-01 -1.24690577e-01 2.04591565e-02
3.11271906e-01 -6.44803584e-01 -7.93271244e-01 -6.54489458e-01
-1.52480945e-01 -1.72577515e-01 9.62488413e-01 2.27527022... | [8.644552230834961, -1.2860462665557861] |
389b955d-6dc0-48f2-858a-643f988b07d8 | a-novel-bistatic-joint-radar-communication | 2006.16591 | null | https://arxiv.org/abs/2006.16591v1 | https://arxiv.org/pdf/2006.16591v1.pdf | A Novel Bistatic Joint Radar-Communication System in Multi-path Environments | Radar detection and communication can be operated simultaneously in joint radar-communication (JRC) system. In this paper, we propose a bistatic JRC system which is applicable in multi-path environments. Basing on a novel joint waveform, a joint detection process is designed for both target detection and channel estima... | ['Jiazhi Ma', 'Jialei Liu', 'Longfei Shi', 'Yuan Quan'] | 2020-06-30 | null | null | null | null | ['joint-radar-communication'] | ['robots'] | [ 5.61837494e-01 -3.72276992e-01 3.01599264e-01 7.32233524e-02
-5.04018545e-01 -3.09364915e-01 3.44811916e-01 1.64825364e-03
-7.04717457e-01 8.14972639e-01 -4.39426184e-01 -6.26487315e-01
-3.43443185e-01 -7.95469999e-01 -2.69361027e-02 -1.00772572e+00
-3.33054572e-01 -2.49673292e-01 3.58666897e-01 -2.16455385... | [6.405921936035156, 1.2413874864578247] |
0a7baa2b-295a-4442-807d-36bd53ba45b6 | marginalized-latent-semantic-encoder-for-zero | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Ding_Marginalized_Latent_Semantic_Encoder_for_Zero-Shot_Learning_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Ding_Marginalized_Latent_Semantic_Encoder_for_Zero-Shot_Learning_CVPR_2019_paper.pdf | Marginalized Latent Semantic Encoder for Zero-Shot Learning | Zero-shot learning has been well explored to precisely identify new unobserved classes through a visual-semantic function obtained from the existing objects. However, there exist two challenging obstacles: one is that the human-annotated semantics are insufficient to fully describe the visual samples; the other is the ... | [' Hongfu Liu', 'Zhengming Ding'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['graph-reconstruction'] | ['graphs'] | [ 2.98988968e-01 2.32365415e-01 -5.19448221e-01 -4.53659117e-01
-4.82756913e-01 -2.21317843e-01 5.66173851e-01 1.20910734e-01
7.54293203e-02 5.43395162e-01 4.62258458e-01 4.13478971e-01
-1.77778855e-01 -7.92239785e-01 -6.97875559e-01 -6.94659948e-01
2.73342013e-01 1.40415832e-01 5.69416106e-01 3.05771604... | [9.964936256408691, 2.3463027477264404] |
d5ea921f-b772-4c22-aeab-1aa7a5540cb0 | sparse-text-generation | 2004.02644 | null | https://arxiv.org/abs/2004.02644v3 | https://arxiv.org/pdf/2004.02644v3.pdf | Sparse Text Generation | Current state-of-the-art text generators build on powerful language models such as GPT-2, achieving impressive performance. However, to avoid degenerate text, they require sampling from a modified softmax, via temperature parameters or ad-hoc truncation techniques, as in top-$k$ or nucleus sampling. This creates a mism... | ['André F. T. Martins', 'Zita Marinho', 'Pedro Henrique Martins'] | 2020-04-06 | null | https://aclanthology.org/2020.emnlp-main.348 | https://aclanthology.org/2020.emnlp-main.348.pdf | emnlp-2020-11 | ['story-completion'] | ['natural-language-processing'] | [ 2.11761743e-01 4.15787488e-01 -1.77808911e-01 -3.95722508e-01
-8.69447172e-01 -3.10054809e-01 9.14465606e-01 -4.17549312e-02
-2.19148085e-01 1.21759069e+00 7.47574687e-01 -1.25012591e-01
1.74628288e-01 -7.83770800e-01 -3.72113466e-01 -4.30259883e-01
8.72315615e-02 6.42824411e-01 -2.51809478e-01 -4.32822824... | [11.735664367675781, 9.097115516662598] |
5c08feff-dc9c-4b69-995c-172036cd2254 | matching-web-tables-to-dbpedia-a-feature | null | null | https://www.semanticscholar.org/paper/Matching-Web-Tables-To-DBpedia-A-Feature-Utility-Ritze-Bizer/74c2c4dc375515a2dc6e3d73993c3ad2d77b0757 | https://openproceedings.org/2017/conf/edbt/paper-148.pdf | Matching web tables to DBpedia-A feature utility study | Relational HTML tables on the Web contain data describing a multitude of entities and covering a wide range of topics. Thus, web tables are very useful for filling missing values in cross-domain knowledge bases such as DBpedia, YAGO, or the Google Knowledge Graph. Before web table data can be used to fill missing value... | ['Christian Bizer', 'Dominique Ritze'] | 2017-03-01 | null | null | null | edbt-2017-3 | ['table-annotation', 'row-annotation', 'table-annotation', 'table-type-detection', 'columns-property-annotation'] | ['knowledge-base', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-2.65961945e-01 2.87913978e-01 -4.87457573e-01 -2.31867850e-01
-1.06496990e+00 -8.43001902e-01 7.51895428e-01 1.08664322e+00
-3.79326232e-02 1.03049827e+00 2.27937758e-01 1.53454449e-02
-7.87230015e-01 -1.49788058e+00 -6.67442143e-01 -3.74910906e-02
1.21167213e-01 9.59321678e-01 8.51795018e-01 -5.27765453... | [9.262965202331543, 8.03534984588623] |
6bef4209-a407-4554-96e5-b90886855c3f | sentiment-uncertainty-and-spam-in-twitter | 1509.07612 | null | http://arxiv.org/abs/1509.07612v1 | http://arxiv.org/pdf/1509.07612v1.pdf | Sentiment Uncertainty and Spam in Twitter Streams and Its Implications for General Purpose Realtime Sentiment Analysis | State of the art benchmarks for Twitter Sentiment Analysis do not consider
the fact that for more than half of the tweets from the public stream a
distinct sentiment cannot be chosen. This paper provides a new perspective on
Twitter Sentiment Analysis by highlighting the necessity of explicitly
incorporating uncertaint... | ['Nils Haldenwang', 'Oliver Vornberger'] | 2015-09-25 | null | null | null | null | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-1.87254861e-01 1.32083178e-01 -5.98883927e-01 -7.75792241e-01
-8.18276644e-01 -7.67765522e-01 8.35201800e-01 8.27401042e-01
-6.91959143e-01 8.23134243e-01 3.66900802e-01 -1.52668655e-01
2.58240923e-02 -9.47086155e-01 -3.00965786e-01 -5.65994084e-01
2.43954107e-01 4.71188605e-01 -1.99633360e-01 -7.17485785... | [11.084087371826172, 6.9160990715026855] |
abbf03b7-cb88-476e-9b41-aec9e574e9e2 | integrative-imaging-informatics-for-cancer | 2210.03151 | null | https://arxiv.org/abs/2210.03151v1 | https://arxiv.org/pdf/2210.03151v1.pdf | Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-oncology (I3CR-WANO) | Efforts to utilize growing volumes of clinical imaging data to generate tumor evaluations continue to require significant manual data wrangling owing to the data heterogeneity. Here, we propose an artificial intelligence-based solution for the aggregation and processing of multisequence neuro-oncology MRI data to extra... | ['Daniel S. Marcus', 'Aristeidis Sotiras', 'Caroline Chung', 'Sherry Thorpe', 'Yuzhuo Su', 'Michael Adams', 'John Wood', 'Pamela Lamontagne', 'Matthew Kelsey', 'Divya Yadav', 'Isabelle Hren', 'Mahati Mokkarala', 'Mina Mousa', 'Syed Amaan Abidi', 'Satrajit Chakrabarty'] | 2022-10-06 | null | null | null | null | ['tumor-segmentation'] | ['computer-vision'] | [ 2.75263369e-01 1.95496500e-01 4.30874005e-02 -3.98465723e-01
-1.17901015e+00 -5.89611590e-01 2.59307742e-01 4.97900277e-01
-9.41093087e-01 6.74476326e-01 1.34109780e-01 -7.61915982e-01
-4.88241971e-01 -5.22676229e-01 -1.99902222e-01 -1.03668940e+00
-2.84579515e-01 9.09874380e-01 6.86361790e-02 1.24023370... | [14.61888599395752, -2.49133038520813] |
e3fffbcd-7822-42d0-9ab8-cef54ac0ba52 | balanced-coarsening-for-multilevel-hypergraph | 2106.07501 | null | https://arxiv.org/abs/2106.07501v1 | https://arxiv.org/pdf/2106.07501v1.pdf | Balanced Coarsening for Multilevel Hypergraph Partitioning via Wasserstein Discrepancy | We propose a balanced coarsening scheme for multilevel hypergraph partitioning. In addition, an initial partitioning algorithm is designed to improve the quality of k-way hypergraph partitioning. By assigning vertex weights through the LPT algorithm, we generate a prior hypergraph under a relaxed balance constraint. Wi... | ['Xu Liu', 'Licheng Jiao', 'Jiaxuan Zhao', 'Zhicheng Guo'] | 2021-06-14 | null | null | null | null | ['hypergraph-partitioning'] | ['graphs'] | [ 4.18211371e-02 3.18600833e-01 -3.04825723e-01 -6.29133284e-02
-5.40057898e-01 -4.17602718e-01 3.23953748e-01 1.36613041e-01
-2.25113437e-01 7.05071032e-01 7.39384666e-02 -1.22796990e-01
-5.34037292e-01 -1.50749958e+00 -5.57761788e-01 -8.73811424e-01
1.33921653e-01 6.81739867e-01 2.20083386e-01 -3.48966988... | [7.158837795257568, 5.1531476974487305] |
f85d64ac-a969-41f5-bcce-84f8348aaef3 | exploration-by-self-supervised-exploitation | 2302.11563 | null | https://arxiv.org/abs/2302.11563v2 | https://arxiv.org/pdf/2302.11563v2.pdf | Exploration by self-supervised exploitation | Reinforcement learning can solve decision-making problems and train an agent to behave in an environment according to a predesigned reward function. However, such an approach becomes very problematic if the reward is too sparse and the agent does not come across the reward during the environmental exploration. The solu... | ['Igor Farkaš', 'Michal Chovanec', 'Matej Pecháč'] | 2023-02-22 | null | null | null | null | ['atari-games'] | ['playing-games'] | [ 2.10527763e-01 4.15176690e-01 -3.44679177e-01 -2.51777202e-01
-1.13588035e-01 -2.18786627e-01 6.46348238e-01 3.45132172e-01
-1.03048635e+00 1.17794049e+00 -6.78627044e-02 -9.31521878e-02
-1.85253397e-01 -9.53273714e-01 -6.31312430e-01 -8.18115234e-01
-4.58074480e-01 5.87220788e-01 2.30257332e-01 -5.68210781... | [4.078431129455566, 1.712270975112915] |
f8083e68-2f56-49db-84be-62e5193b84b2 | multi-frame-super-resolution-from-noisy-data | 2103.13778 | null | https://arxiv.org/abs/2103.13778v1 | https://arxiv.org/pdf/2103.13778v1.pdf | Multi-frame Super-resolution from Noisy Data | Obtaining high resolution images from low resolution data with clipped noise is algorithmically challenging due to the ill-posed nature of the problem. So far such problems have hardly been tackled, and the few existing approaches use simplistic regularisers. We show the usefulness of two adaptive regularisers based on... | ['Joachim Weickert', 'Kireeti Bodduna'] | 2021-03-25 | null | null | null | null | ['multi-frame-super-resolution'] | ['computer-vision'] | [ 6.68360829e-01 -3.40151303e-02 6.54825568e-01 1.41847774e-01
-8.83464098e-01 -5.14488280e-01 9.38234925e-01 -3.04390699e-01
-6.49695933e-01 9.67749357e-01 6.26401067e-01 1.12199858e-01
-6.82885051e-01 -6.10397041e-01 -3.11738253e-01 -1.17733753e+00
-3.77895236e-02 2.99832672e-01 5.95307767e-01 -4.76957709... | [11.535502433776855, -2.408971071243286] |
65e36d76-5e6a-49a9-ba13-86c58c3d75da | training-augmentation-with-adversarial | 1806.02782 | null | http://arxiv.org/abs/1806.02782v2 | http://arxiv.org/pdf/1806.02782v2.pdf | Training Augmentation with Adversarial Examples for Robust Speech Recognition | This paper explores the use of adversarial examples in training speech
recognition systems to increase robustness of deep neural network acoustic
models. During training, the fast gradient sign method is used to generate
adversarial examples augmenting the original training data. Different from
conventional data augmen... | ['Mei-Yuh Hwang', 'Mari Ostendorf', 'Ching-Feng Yeh', 'Sining Sun', 'Lei Xie'] | 2018-06-07 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 5.08589387e-01 4.45296168e-01 3.27781349e-01 -3.79070073e-01
-1.29462683e+00 -7.02095211e-01 7.83250868e-01 -3.70826840e-01
-7.35605717e-01 5.29178500e-01 2.44335309e-01 -5.82955897e-01
4.22325701e-01 -4.50551242e-01 -9.04718041e-01 -6.51989162e-01
-3.04825485e-01 1.69603363e-01 4.46410757e-03 -2.59287089... | [14.83792495727539, 6.242227077484131] |
e39ff1ce-39fe-40f6-a242-e48a3e4fdd94 | investigating-non-local-features-for-neural-1 | null | null | https://openreview.net/forum?id=n-pZduaeJvB | https://openreview.net/pdf?id=n-pZduaeJvB | Investigating Non-local Features for Neural Constituency Parsing | Thanks to the strong representation power of neural encoders, neural chart-based parsers have achieved highly competitive performance by using local features. Recently, it has been shown that non-local features in CRF structures lead to improvements. In this paper, we investigate injecting non-local features into the t... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['constituency-parsing'] | ['natural-language-processing'] | [-1.63937047e-01 3.55398268e-01 -3.13681394e-01 -8.73985410e-01
-1.37101364e+00 -5.51717937e-01 6.06244802e-01 2.82182604e-01
-5.54750562e-01 8.23350430e-01 5.62522292e-01 -5.76263189e-01
-1.04213215e-01 -8.42450857e-01 -7.66918480e-01 -4.11293834e-01
-2.19596863e-01 5.78990936e-01 2.76629031e-01 -3.63471776... | [10.403670310974121, 9.730461120605469] |
5ddf8185-5b9e-4da8-9d34-21393bf40843 | sister-help-data-augmentation-for-frame | 2109.07725 | null | https://arxiv.org/abs/2109.07725v1 | https://arxiv.org/pdf/2109.07725v1.pdf | Sister Help: Data Augmentation for Frame-Semantic Role Labeling | While FrameNet is widely regarded as a rich resource of semantics in natural language processing, a major criticism concerns its lack of coverage and the relative paucity of its labeled data compared to other commonly used lexical resources such as PropBank and VerbNet. This paper reports on a pilot study to address th... | ['Swabha Swayamdipta', 'Miriam R. L. Petruck', 'Ayush Pancholy'] | 2021-09-16 | null | https://aclanthology.org/2021.law-1.8 | https://aclanthology.org/2021.law-1.8.pdf | emnlp-law-dmr-2021-11 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 5.80411613e-01 8.60525906e-01 -6.57805800e-01 -6.87205315e-01
-7.78897285e-01 -7.67109990e-01 7.41229951e-01 7.14422286e-01
-6.34652138e-01 1.12082207e+00 1.04344606e+00 -4.23205733e-01
1.64576009e-01 -6.62992299e-01 -5.57493091e-01 -6.65713288e-03
3.74287337e-01 6.27526522e-01 5.82106948e-01 -7.09859967... | [10.235014915466309, 9.335504531860352] |
3bc98bf5-6e7a-4786-93be-70d0d8fe2508 | improving-multilingual-neural-machine-1 | null | null | https://aclanthology.org/2020.loresmt-1.8 | https://aclanthology.org/2020.loresmt-1.8.pdf | Improving Multilingual Neural Machine Translation For Low-Resource Languages: French, English - Vietnamese | Prior works have demonstrated that a low-resource language pair can benefit from multilingual machine translation (MT) systems, which rely on many language pairs’ joint training. This paper proposes two simple strategies to address the rare word issue in multilingual MT systems for two low-resource language pairs: Fren... | ['Le-Minh Nguyen', 'Khac-Quy Dinh', 'Thanh-Le Ha', 'Phuong-Thai Nguyen', 'Thi-Vinh Ngo'] | null | null | null | null | loresmt-aacl-2020-12 | ['word-similarity'] | ['natural-language-processing'] | [-2.89359957e-01 -2.68765062e-01 -5.13957739e-01 -2.61783779e-01
-1.35167491e+00 -8.29554439e-01 8.84016216e-01 -1.33452058e-01
-7.62126684e-01 1.27889371e+00 2.18829751e-01 -6.34687304e-01
5.99539876e-01 -5.11030972e-01 -8.88656437e-01 -4.80305642e-01
2.14053676e-01 6.28162086e-01 -2.02424914e-01 -6.96177840... | [11.508719444274902, 10.308104515075684] |
249f47e1-39b9-41a3-b85c-1fa22b1cd7fd | a-high-frequency-focused-network-for | 2303.11701 | null | https://arxiv.org/abs/2303.11701v1 | https://arxiv.org/pdf/2303.11701v1.pdf | A High-Frequency Focused Network for Lightweight Single Image Super-Resolution | Lightweight neural networks for single-image super-resolution (SISR) tasks have made substantial breakthroughs in recent years. Compared to low-frequency information, high-frequency detail is much more difficult to reconstruct. Most SISR models allocate equal computational resources for low-frequency and high-frequency... | ['Yudong Zhang', 'Junsheng Zhou', 'Yanhui Gu', 'Zhichao Zheng', 'Yi Chen', 'Xiaotian Weng'] | 2023-03-21 | null | null | null | null | ['image-super-resolution'] | ['computer-vision'] | [ 4.10507947e-01 -3.57966006e-01 -9.96416509e-02 -2.87596405e-01
-8.73519123e-01 1.31628588e-01 2.25391895e-01 -3.18858176e-01
-2.11003959e-01 7.89547622e-01 5.43344676e-01 4.27658200e-01
-4.33231711e-01 -9.88349020e-01 -7.26028621e-01 -7.30407774e-01
-5.54538555e-02 -4.28952038e-01 3.98179203e-01 -3.05355906... | [10.985795974731445, -2.0335311889648438] |
95c7d4f5-a7a1-4aaf-9e60-23e84cd37581 | multi-dialectal-representation-learning-of | 2307.01209 | null | https://arxiv.org/abs/2307.01209v1 | https://arxiv.org/pdf/2307.01209v1.pdf | Multi-Dialectal Representation Learning of Sinitic Phonology | Machine learning techniques have shown their competence for representing and reasoning in symbolic systems such as language and phonology. In Sinitic Historical Phonology, notable tasks that could benefit from machine learning include the comparison of dialects and reconstruction of proto-languages systems. Motivated b... | ['Zhibai Jia'] | 2023-06-30 | null | null | null | null | ['representation-learning'] | ['methodology'] | [ 1.92617290e-02 2.82520831e-01 -2.69869089e-01 -3.27983439e-01
-3.63901079e-01 -7.17690647e-01 8.29757333e-01 2.75321424e-01
-1.45475611e-01 2.43464231e-01 5.64525902e-01 -6.50271952e-01
-3.03167820e-01 -7.47943640e-01 -5.33732116e-01 -6.37363017e-01
2.44079176e-02 9.61704075e-01 2.66390413e-01 -2.87162691... | [10.705199241638184, 9.812982559204102] |
5b054dda-ac11-4bc7-90f4-cddcb17398cb | a-survey-on-applications-of-artificial | 2007.02202 | null | https://arxiv.org/abs/2007.02202v2 | https://arxiv.org/pdf/2007.02202v2.pdf | A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19 | The COVID-19 pandemic caused by the SARS-CoV-2 virus has spread rapidly worldwide, leading to a global outbreak. Most governments, enterprises, and scientific research institutions are participating in the COVID-19 struggle to curb the spread of the pandemic. As a powerful tool against COVID-19, artificial intelligence... | ['Keqin Li', 'Zhaolei Zhang', 'Philip S. Yu', 'Kenli Li', 'Jianguo Chen'] | 2020-07-04 | null | null | null | null | ['virology'] | ['miscellaneous'] | [ 1.99282423e-01 -5.87141216e-01 1.35580167e-01 2.02564508e-01
1.45347998e-01 -4.96194154e-01 4.40794416e-02 3.74135226e-01
-3.60236645e-01 7.47151017e-01 -1.39159456e-01 -1.59698129e-01
-1.35610774e-01 -7.04183757e-01 -5.55237643e-02 -1.00184524e+00
-3.68530452e-01 1.09527779e+00 -3.75272721e-01 -6.32437348... | [5.553374290466309, 4.921543598175049] |
acdec856-e3fe-4bf2-8358-e87aa9f301e9 | approximated-prompt-tuning-for-vision | 2306.15706 | null | https://arxiv.org/abs/2306.15706v1 | https://arxiv.org/pdf/2306.15706v1.pdf | Approximated Prompt Tuning for Vision-Language Pre-trained Models | Prompt tuning is a parameter-efficient way to deploy large-scale pre-trained models to downstream tasks by adding task-specific tokens. In terms of vision-language pre-trained (VLP) models, prompt tuning often requires a large number of learnable tokens to bridge the gap between the pre-training and downstream tasks, w... | ['Rongrong Ji', 'Guannan Jiang', 'Annan Shu', 'Pingyang Dai', 'Yiyi Zhou', 'Shubin Huang', 'Qiong Wu'] | 2023-06-27 | null | null | null | null | ['transfer-learning'] | ['miscellaneous'] | [-9.37697757e-03 -1.78771362e-01 -2.58493960e-01 -1.49107084e-01
-1.10585213e+00 -4.19837356e-01 6.29931808e-01 -1.21480212e-01
-5.64218938e-01 6.57000780e-01 4.81251329e-02 -4.75670636e-01
8.82291347e-02 -5.80723882e-01 -8.92869234e-01 -8.47685933e-01
3.12762141e-01 2.82494098e-01 3.45413983e-01 -5.56475334... | [10.195931434631348, 1.9513461589813232] |
4a73442d-84bb-4edc-a456-8c5857152fcd | gcdh-lt-edi-eacl2021-xlm-roberta-for-hope | null | null | https://aclanthology.org/2021.ltedi-1.19 | https://aclanthology.org/2021.ltedi-1.19.pdf | GCDH@LT-EDI-EACL2021: XLM-RoBERTa for Hope Speech Detection in English, Malayalam, and Tamil | This paper describes approaches to identify Hope Speech in short, informal texts in English, Malayalam and Tamil using different machine learning techniques. We demonstrate that even very simple baseline algorithms perform reasonably well on this task if provided with enough training data. However, our best performing ... | ['Aravind Krishnan', 'Franziska Pannach', 'Stefan Ziehe'] | null | null | null | null | eacl-ltedi-2021-4 | ['hope-speech-detection'] | ['natural-language-processing'] | [ 8.04418400e-02 1.58821680e-02 -4.21884298e-01 -5.02954900e-01
-1.58796728e+00 -8.58055532e-01 9.66274381e-01 -1.52377129e-01
-6.30756557e-01 1.06718874e+00 4.83124763e-01 -8.36914718e-01
1.72355801e-01 -3.50292414e-01 -4.45139706e-01 -3.88120443e-01
-1.01033516e-01 7.97714591e-01 9.97130051e-02 -4.66995984... | [10.770959854125977, 10.173806190490723] |
0ac53112-e098-4a60-9de0-eb4e50eee927 | mixedteacher-knowledge-distillation-for-fast | 2306.09859 | null | https://arxiv.org/abs/2306.09859v1 | https://arxiv.org/pdf/2306.09859v1.pdf | MixedTeacher : Knowledge Distillation for fast inference textural anomaly detection | For a very long time, unsupervised learning for anomaly detection has been at the heart of image processing research and a stepping stone for high performance industrial automation process. With the emergence of CNN, several methods have been proposed such as Autoencoders, GAN, deep feature extraction, etc. In this pap... | ['Mahmoud Soua', 'Hichem Snoussi', 'Simon Thomine'] | 2023-06-16 | null | null | null | null | ['anomaly-detection'] | ['methodology'] | [ 2.80880511e-01 1.92592904e-01 2.88241953e-01 -2.56696075e-01
1.58140466e-01 1.89310551e-01 3.45740080e-01 1.24511793e-01
-2.26501018e-01 3.90449703e-01 -3.48398626e-01 -5.17900363e-02
-2.15326697e-01 -1.08934951e+00 -4.07808989e-01 -1.01576030e+00
2.20545560e-01 4.31152582e-01 4.06734824e-01 -6.05039895... | [7.544058799743652, 2.0813941955566406] |
65bdba68-bc54-40f5-957a-ad244e5564d8 | consistency-regularization-for-domain | 2208.11084 | null | https://arxiv.org/abs/2208.11084v1 | https://arxiv.org/pdf/2208.11084v1.pdf | Consistency Regularization for Domain Adaptation | Collection of real world annotations for training semantic segmentation models is an expensive process. Unsupervised domain adaptation (UDA) tries to solve this problem by studying how more accessible data such as synthetic data can be used to train and adapt models to real world images without requiring their annotati... | ['Basura Fernando', 'Kian Boon Koh'] | 2022-08-23 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [ 4.32942420e-01 5.97272515e-01 -9.49949846e-02 -6.53881669e-01
-8.19149911e-01 -3.51702809e-01 6.22230530e-01 1.22220561e-01
-8.10720861e-01 8.61790121e-01 -3.82619947e-01 -1.10353015e-01
6.32083416e-02 -7.99512804e-01 -1.11601865e+00 -4.48382199e-01
2.12933645e-01 8.92416060e-01 7.57201374e-01 -1.88894480... | [9.698201179504395, 1.357782244682312] |
401d1b45-2cd1-4a79-9997-9bf4549f1c22 | graformer-graph-convolution-transformer-for | 2109.08364 | null | https://arxiv.org/abs/2109.08364v1 | https://arxiv.org/pdf/2109.08364v1.pdf | GraFormer: Graph Convolution Transformer for 3D Pose Estimation | Exploiting relations among 2D joints plays a crucial role yet remains semi-developed in 2D-to-3D pose estimation. To alleviate this issue, we propose GraFormer, a novel transformer architecture combined with graph convolution for 3D pose estimation. The proposed GraFormer comprises two repeatedly stacked core modules, ... | ['Weiqiang Wang', 'Jianbin Jiao', 'Qixiang Ye', 'Yunjie Tian', 'Weixi Zhao'] | 2021-09-17 | null | null | null | null | ['3d-pose-estimation', 'implicit-relations'] | ['computer-vision', 'natural-language-processing'] | [-5.61059415e-01 5.04318953e-01 8.90137702e-02 -2.21375078e-01
-1.11181386e-01 -3.55680943e-01 6.16844952e-01 -3.78820390e-01
-3.60558063e-01 3.52562010e-01 4.82722282e-01 -8.43532979e-02
-1.71038210e-02 -6.57063425e-01 -1.04071105e+00 -5.65694988e-01
-2.03394473e-01 5.25137007e-01 3.11080158e-01 -3.87965977... | [7.0786848068237305, -0.6368225812911987] |
19b23bc7-3285-4f3e-9fd8-48d427ef1853 | automated-static-camera-calibration-with | 2304.10814 | null | https://arxiv.org/abs/2304.10814v1 | https://arxiv.org/pdf/2304.10814v1.pdf | Automated Static Camera Calibration with Intelligent Vehicles | Connected and cooperative driving requires precise calibration of the roadside infrastructure for having a reliable perception system. To solve this requirement in an automated manner, we present a robust extrinsic calibration method for automated geo-referenced camera calibration. Our method requires a calibration veh... | ['Vasileios Belagiannis', 'Michael Buchholz', 'Jan Strohbeck', 'Adrian Holzbock', 'Alexander Tsaregorodtsev'] | 2023-04-21 | null | null | null | null | ['camera-calibration'] | ['computer-vision'] | [ 5.58315739e-02 1.11371271e-01 3.26393917e-02 -4.20416921e-01
-6.43079340e-01 -5.60935915e-01 4.83559221e-01 -7.49685839e-02
-5.69476664e-01 6.87039852e-01 -4.98360008e-01 -5.50135255e-01
1.71840519e-01 -1.00930905e+00 -1.03343427e+00 -4.02405649e-01
2.20677957e-01 3.48660469e-01 6.70522928e-01 -3.14439029... | [7.639147758483887, -2.077221632003784] |
5d4141fe-552f-4b6c-aae3-46aa7a5e26ba | notes-on-using-determinantal-point-processes | 1410.6975 | null | http://arxiv.org/abs/1410.6975v1 | http://arxiv.org/pdf/1410.6975v1.pdf | Notes on using Determinantal Point Processes for Clustering with Applications to Text Clustering | In this paper, we compare three initialization schemes for the KMEANS
clustering algorithm: 1) random initialization (KMEANSRAND), 2) KMEANS++, and
3) KMEANSD++. Both KMEANSRAND and KMEANS++ have a major that the value of k
needs to be set by the user of the algorithms. (Kang 2013) recently proposed a
novel use of dete... | ['Krzysztof Choromanski', 'Apoorv Agarwal', 'Anna Choromanska'] | 2014-10-26 | null | null | null | null | ['text-clustering'] | ['natural-language-processing'] | [-3.81410271e-01 -1.21825196e-01 -2.39861701e-02 9.94424298e-02
-4.45829421e-01 -8.80513728e-01 7.97996163e-01 4.43153113e-01
-6.10886157e-01 6.00430906e-01 -2.27031391e-02 -2.28524834e-01
-7.17303574e-01 -6.80152297e-01 -5.35650373e-01 -8.62430751e-01
-5.89218140e-02 8.25326085e-01 8.55718374e-01 -2.28278413... | [7.543861389160156, 4.600857257843018] |
20ef2c32-a426-4112-8e18-671a1b42caed | reservoir-computing-via-quantum-recurrent | 2211.02612 | null | https://arxiv.org/abs/2211.02612v1 | https://arxiv.org/pdf/2211.02612v1.pdf | Reservoir Computing via Quantum Recurrent Neural Networks | Recent developments in quantum computing and machine learning have propelled the interdisciplinary study of quantum machine learning. Sequential modeling is an important task with high scientific and commercial value. Existing VQC or QNN-based methods require significant computational resources to perform the gradient-... | ['Charlee Stefanski', 'Vladimir Rastunkov', 'Amol Deshmukh', 'Daniel Fry', 'Samuel Yen-Chi Chen'] | 2022-11-04 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [ 2.61640966e-01 -1.73290864e-01 1.54605195e-01 -2.68786307e-02
-6.60747170e-01 -2.49712557e-01 5.57574511e-01 4.47913408e-02
-8.47884834e-01 7.13314891e-01 -5.05325198e-01 -6.37979448e-01
8.51563886e-02 -1.19269621e+00 -7.46152997e-01 -1.06605399e+00
1.45583928e-01 3.59366417e-01 1.48734167e-01 -8.42172325... | [5.509880542755127, 5.019260883331299] |
f2b28f89-3b2e-4093-b8a7-c1b5f1e36951 | cxr-net-an-encoder-decoder-encoder-multitask | 2110.10813 | null | https://arxiv.org/abs/2110.10813v1 | https://arxiv.org/pdf/2110.10813v1.pdf | CXR-Net: An Encoder-Decoder-Encoder Multitask Deep Neural Network for Explainable and Accurate Diagnosis of COVID-19 pneumonia with Chest X-ray Images | Accurate and rapid detection of COVID-19 pneumonia is crucial for optimal patient treatment. Chest X-Ray (CXR) is the first line imaging test for COVID-19 pneumonia diagnosis as it is fast, cheap and easily accessible. Inspired by the success of deep learning (DL) in computer vision, many DL-models have been proposed t... | ['Stephen White', 'Haoming Chen', 'Ascanio Tridente', 'Symeon Lechareas', 'Nina Dempsey', 'Lianghao Han', 'Tam Sobeih', 'Liangxiu Han', 'Xin Zhang'] | 2021-10-20 | null | null | null | null | ['pneumonia-detection'] | ['medical'] | [ 9.65529084e-02 -2.38176703e-01 -1.75164431e-01 1.97115578e-02
-6.47241592e-01 -3.51790160e-01 3.14500481e-01 1.48325235e-01
-1.18572325e-01 7.55287647e-01 1.26455814e-01 -6.71443164e-01
-1.49033055e-01 -3.57276231e-01 -4.22819614e-01 -8.12644422e-01
1.43931001e-01 5.98354638e-01 2.28080153e-01 4.89068598... | [15.526358604431152, -1.7574409246444702] |
d98dcfa4-8a00-45bb-a8de-c51d29c88411 | accurate-molecular-orbital-based-machine | 2204.09831 | null | https://arxiv.org/abs/2204.09831v1 | https://arxiv.org/pdf/2204.09831v1.pdf | Accurate Molecular-Orbital-Based Machine Learning Energies via Unsupervised Clustering of Chemical Space | We introduce an unsupervised clustering algorithm to improve training efficiency and accuracy in predicting energies using molecular-orbital-based machine learning (MOB-ML). This work determines clusters via the Gaussian mixture model (GMM) in an entirely automatic manner and simplifies an earlier supervised clustering... | ['Thomas F. Miller III', 'Jiace Sun', 'Lixue Cheng'] | 2022-04-21 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 2.38997154e-02 -2.05514506e-01 -1.44032389e-01 -1.69007853e-01
-9.71507132e-01 -2.45357513e-01 4.67727512e-01 5.18779993e-01
-4.32996631e-01 8.24429929e-01 -3.82994860e-01 -5.59160948e-01
-1.52381212e-01 -7.55873203e-01 -6.60688221e-01 -1.41822433e+00
-3.42384607e-01 7.30198503e-01 -1.28902480e-01 1.86446562... | [5.172074794769287, 5.378032684326172] |
918931a2-ca26-4895-b8a1-2cbf8042e9f8 | increasing-robustness-for-cross-domain | null | null | https://aclanthology.org/2022.wnut-1.20 | https://aclanthology.org/2022.wnut-1.20.pdf | Increasing Robustness for Cross-domain Dialogue Act Classification on Social Media Data | Automatically detecting the intent of an utterance is important for various downstream natural language processing tasks. This task is also called Dialogue Act Classification (DAC) and was primarily researched on spoken one-to-one conversations. The rise of social media has made this an interesting data source to explo... | ['Rob van der Goot', 'Nikolaj Wallenius', 'Marcus Vielsted'] | null | null | null | null | coling-wnut-2022-10 | ['lexical-normalization', 'dialogue-act-classification'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.79797158e-01 2.35826537e-01 -1.87346339e-01 -5.82482934e-01
-7.90730715e-01 -7.28512228e-01 1.21527898e+00 2.44093880e-01
-5.52401543e-01 7.54869401e-01 9.76136088e-01 -2.70379931e-01
9.34070051e-02 -3.22308034e-01 9.68846157e-02 -4.43912506e-01
8.45632330e-02 5.63655138e-01 2.48357594e-01 -7.16011047... | [12.744385719299316, 7.834784984588623] |
5e4bf20c-e6b2-4842-bc7a-39ddcede32c1 | an-unsupervised-learning-model-for-deformable | 1802.02604 | null | http://arxiv.org/abs/1802.02604v3 | http://arxiv.org/pdf/1802.02604v3.pdf | An Unsupervised Learning Model for Deformable Medical Image Registration | We present a fast learning-based algorithm for deformable, pairwise 3D
medical image registration. Current registration methods optimize an objective
function independently for each pair of images, which can be time-consuming for
large data. We define registration as a parametric function, and optimize its
parameters g... | ['John Guttag', 'Mert R. Sabuncu', 'Guha Balakrishnan', 'Adrian V. Dalca', 'Amy Zhao'] | 2018-02-07 | an-unsupervised-learning-model-for-deformable-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Balakrishnan_An_Unsupervised_Learning_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Balakrishnan_An_Unsupervised_Learning_CVPR_2018_paper.pdf | cvpr-2018-6 | ['deformable-medical-image-registration'] | ['medical'] | [ 2.11862803e-01 1.38701051e-01 -2.12309748e-01 -7.45149136e-01
-1.31365550e+00 -5.89409053e-01 2.64141470e-01 5.93970418e-01
-8.02852154e-01 2.21562430e-01 1.46771103e-01 -1.37731299e-01
-6.58854656e-03 -7.95708656e-01 -6.23063803e-01 -6.59897208e-01
-3.59702349e-01 9.14529324e-01 2.14507312e-01 5.34753576... | [13.957432746887207, -2.587369918823242] |
1bb3a292-c76b-4887-bda3-f388460058a0 | interpreting-forecasted-vital-signs-using-n | 2306.14016 | null | https://arxiv.org/abs/2306.14016v1 | https://arxiv.org/pdf/2306.14016v1.pdf | Interpreting Forecasted Vital Signs Using N-BEATS in Sepsis Patients | Detecting and predicting septic shock early is crucial for the best possible outcome for patients. Accurately forecasting the vital signs of patients with sepsis provides valuable insights to clinicians for timely interventions, such as administering stabilizing drugs or optimizing infusion strategies. Our research exa... | ['Jang Yong Kim', 'Yonghwan Kim', 'San Lee', 'Choongmin Kim', 'Marium Hassan', 'Naveen Thangavelu', 'Anubhav Bhatti'] | 2023-06-24 | null | null | null | null | ['dynamic-time-warping'] | ['time-series'] | [-8.99564996e-02 -4.10841197e-01 3.22254866e-01 -2.23454520e-01
-2.34823719e-01 -4.14862901e-01 1.70708016e-01 5.24810314e-01
-3.93832803e-01 6.57234967e-01 3.41400355e-01 -7.69078732e-01
-3.87519360e-01 -3.38093609e-01 -4.58276629e-01 -8.63665938e-01
-6.99638128e-01 5.16338944e-01 -2.15135351e-01 -6.73955157... | [8.012691497802734, 6.164812088012695] |
51db38be-a270-4439-9373-d53119ed134b | collaborative-recognition-of-feasible-region | 2103.00947 | null | https://arxiv.org/abs/2103.00947v2 | https://arxiv.org/pdf/2103.00947v2.pdf | Collaborative Recognition of Feasible Region with Aerial and Ground Robots through DPCN | Ground robots always get collision in that only if they get close to the obstacles, can they sense the danger and take actions, which is usually too late to avoid the crash, causing severe damage to the robots. To address this issue, we present collaboration of aerial and ground robots in recognition of feasible region... | ['Rong Xiong', 'Yue Wang', 'Zexi Chen', 'Zheyuan Huang', 'Yunshuang Li'] | 2021-03-01 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [-2.16942281e-02 -1.91859342e-02 1.97355561e-02 -2.15848088e-01
-2.23864600e-01 -5.96024454e-01 -2.14997586e-02 -2.23115370e-01
-4.16225702e-01 4.87839043e-01 -6.54198885e-01 -2.09362134e-01
-4.86372650e-01 -1.31330168e+00 -5.27223170e-01 -7.59357750e-01
3.02012824e-02 8.26003373e-01 6.30321562e-01 -6.12829566... | [7.944855690002441, -2.0048234462738037] |
df302808-2dcc-41dd-92fe-5b7c58fef2a4 | xraysyn-realistic-view-synthesis-from-a | 2012.02407 | null | https://arxiv.org/abs/2012.02407v2 | https://arxiv.org/pdf/2012.02407v2.pdf | XraySyn: Realistic View Synthesis From a Single Radiograph Through CT Priors | A radiograph visualizes the internal anatomy of a patient through the use of X-ray, which projects 3D information onto a 2D plane. Hence, radiograph analysis naturally requires physicians to relate the prior about 3D human anatomy to 2D radiographs. Synthesizing novel radiographic views in a small range can assist phys... | ['Rama Chellappa', 'Shaohua Kevin Zhou', 'Jiebo Luo', 'Gina Wong', 'Haofu Liao', 'Cheng Peng'] | 2020-12-04 | null | null | null | null | ['3d-aware-image-synthesis', 'bone-suppression-from-dual-energy-chest-x'] | ['computer-vision', 'medical'] | [ 5.12824237e-01 6.75221741e-01 -2.52393782e-02 -3.46769392e-01
-1.17995727e+00 -4.40503150e-01 3.04049641e-01 -2.48064980e-01
-7.56385550e-03 3.97628725e-01 1.62943721e-01 -6.67933822e-01
-3.45368646e-02 -7.89911807e-01 -7.62805820e-01 -2.73445606e-01
-7.27566332e-02 7.24922299e-01 1.11147344e-01 -9.52957645... | [13.491292953491211, -2.630314588546753] |
3e8947e4-81af-4ec5-be06-4b600c92203a | classbases-at-case-2022-multilingual-protest | 2301.06617 | null | https://arxiv.org/abs/2301.06617v1 | https://arxiv.org/pdf/2301.06617v1.pdf | ClassBases at CASE-2022 Multilingual Protest Event Detection Tasks: Multilingual Protest News Detection and Automatically Replicating Manually Created Event Datasets | In this report, we describe our ClassBases submissions to a shared task on multilingual protest event detection. For the multilingual protest news detection, we participated in subtask-1, subtask-2, and subtask-4, which are document classification, sentence classification, and token classification. In subtask-1, we com... | ['Peratham Wiriyathammabhum'] | 2023-01-16 | null | null | null | null | ['document-classification', 'sentence-classification'] | ['natural-language-processing', 'natural-language-processing'] | [-2.19370008e-01 7.79372454e-02 -1.68433979e-01 -3.66951972e-01
-1.61252773e+00 -1.03116333e+00 8.12278152e-01 6.18445635e-01
-7.09026992e-01 1.03212059e+00 5.20702064e-01 -2.99913824e-01
2.57468849e-01 -5.87834239e-01 -8.03181589e-01 -2.15425819e-01
-2.26069670e-02 8.29643488e-01 4.61349308e-01 -8.10063601... | [9.052780151367188, 9.737550735473633] |
a9f8e153-3731-4388-8c9b-bcd1f75a305f | discourse-aware-neural-rewards-for-coherent | 1805.03766 | null | http://arxiv.org/abs/1805.03766v1 | http://arxiv.org/pdf/1805.03766v1.pdf | Discourse-Aware Neural Rewards for Coherent Text Generation | In this paper, we investigate the use of discourse-aware rewards with
reinforcement learning to guide a model to generate long, coherent text. In
particular, we propose to learn neural rewards to model cross-sentence ordering
as a means to approximate desired discourse structure. Empirical results
demonstrate that a ge... | ['Po-Sen Huang', 'Xiaodong He', 'Yejin Choi', 'Jianfeng Gao', 'Asli Celikyilmaz', 'Antoine Bosselut'] | 2018-05-10 | discourse-aware-neural-rewards-for-coherent-1 | https://aclanthology.org/N18-1016 | https://aclanthology.org/N18-1016.pdf | naacl-2018-6 | ['sentence-ordering'] | ['natural-language-processing'] | [ 2.56074548e-01 9.72067356e-01 -3.41673255e-01 -5.29465556e-01
-8.48288774e-01 -3.46170992e-01 9.69791532e-01 8.92983526e-02
-2.35677972e-01 1.27534461e+00 1.07088816e+00 -1.25896409e-01
1.58007234e-01 -8.70226741e-01 -7.27383375e-01 -7.42312381e-03
2.99116652e-02 6.16804719e-01 -2.76416868e-01 -5.50428331... | [11.985013961791992, 9.134705543518066] |
49b3fe0a-6f81-42ca-b384-657ce66e9e5f | privacy-preserving-chaotic-extreme-learning | 2208.02587 | null | https://arxiv.org/abs/2208.02587v1 | https://arxiv.org/pdf/2208.02587v1.pdf | Privacy-Preserving Chaotic Extreme Learning Machine with Fully Homomorphic Encryption | The Machine Learning and Deep Learning Models require a lot of data for the training process, and in some scenarios, there might be some sensitive data, such as customer information involved, which the organizations might be hesitant to outsource for model building. Some of the privacy-preserving techniques such as Dif... | ['Vadlamani Ravi', 'Syed Imtiaz Ahamed'] | 2022-08-04 | null | null | null | null | ['machine-learning', 'machine-learning'] | ['methodology', 'miscellaneous'] | [-3.78635526e-01 -1.08029768e-01 1.99662879e-01 -7.39496052e-01
-1.65311038e-01 -8.03904176e-01 4.16549593e-01 2.29199290e-01
-7.08506823e-01 6.52922034e-01 -2.67774731e-01 -4.01850551e-01
4.31003757e-02 -1.24569428e+00 -5.92280209e-01 -1.01303327e+00
-9.16331857e-02 4.72487211e-01 -4.47902739e-01 -2.23366380... | [5.881494045257568, 6.908942699432373] |
362a2212-ad00-4051-a9a7-86f774c3f1b0 | leveraging-algorithmic-fairness-to-mitigate | 2211.10209 | null | https://arxiv.org/abs/2211.10209v1 | https://arxiv.org/pdf/2211.10209v1.pdf | Leveraging Algorithmic Fairness to Mitigate Blackbox Attribute Inference Attacks | Machine learning (ML) models have been deployed for high-stakes applications, e.g., healthcare and criminal justice. Prior work has shown that ML models are vulnerable to attribute inference attacks where an adversary, with some background knowledge, trains an ML attack model to infer sensitive attributes by exploiting... | ['Antoine Boutet', 'Vasisht Duddu', 'Jan Aalmoes'] | 2022-11-18 | null | null | null | null | ['inference-attack'] | ['adversarial'] | [ 7.34087288e-01 5.04885375e-01 -5.37158012e-01 -8.72617960e-01
-6.11564100e-01 -7.98545122e-01 4.50203657e-01 3.48872125e-01
-6.41347706e-01 9.67342973e-01 -1.44055590e-01 -6.10147893e-01
-2.59240836e-01 -1.04737961e+00 -7.65542388e-01 -6.81630552e-01
1.57228708e-01 4.27217513e-01 -1.56173244e-01 7.11190654... | [5.893559455871582, 7.196859836578369] |
33491721-5d3d-4b36-b83d-8f66db2e6dd4 | semi-supervised-seizure-prediction-with | 1806.08235 | null | http://arxiv.org/abs/1806.08235v1 | http://arxiv.org/pdf/1806.08235v1.pdf | Semi-supervised Seizure Prediction with Generative Adversarial Networks | In this article, we propose an approach that can make use of not only labeled
EEG signals but also the unlabeled ones which is more accessible. We also
suggest the use of data fusion to further improve the seizure prediction
accuracy. Data fusion in our vision includes EEG signals, cardiogram signals,
body temperature ... | ['Mohammad Reza Bonyadi', 'Nhan Duy Truong', 'Levin Kuhlmann', 'Omid Kavehei'] | 2018-06-20 | null | null | null | null | ['seizure-prediction'] | ['medical'] | [ 4.96504962e-01 1.92339301e-01 3.83630455e-01 -5.40680587e-01
-8.52764368e-01 -4.81405318e-01 6.43742383e-02 1.78791165e-01
-4.98269051e-01 1.04024649e+00 -1.20575413e-01 -7.00315088e-02
-1.80743716e-03 -6.23511314e-01 -4.38897640e-01 -9.80066180e-01
-3.90079558e-01 1.15084752e-01 -2.04165369e-01 1.22138411... | [13.225115776062012, 3.5145857334136963] |
9327237a-1ac5-40a9-b264-0ce36d14fcee | lisac-fsdm-usmba-at-semeval-2021-task-5 | null | null | https://aclanthology.org/2021.semeval-1.116 | https://aclanthology.org/2021.semeval-1.116.pdf | LISAC FSDM USMBA at SemEval-2021 Task 5: Tackling Toxic Spans Detection Challenge with Supervised SpanBERT-based Model and Unsupervised LIME-based Model | Toxic spans detection is an emerging challenge that aims to find toxic spans within a toxic text. In this paper, we describe our solutions to tackle toxic spans detection. The first solution, which follows a supervised approach, is based on SpanBERT model. This latter is intended to better embed and predict spans of te... | ['Hamza Alami', 'Ahmed Alami', 'Abdessamad Benlahbib'] | 2021-08-01 | null | null | null | semeval-2021 | ['toxic-spans-detection'] | ['natural-language-processing'] | [ 3.07882488e-01 2.99020201e-01 -1.98490039e-01 1.54507011e-01
-7.25265145e-01 -5.18162072e-01 6.09500706e-01 5.08488774e-01
1.04142062e-01 7.54873991e-01 3.67700577e-01 -3.41215611e-01
-3.90520781e-01 -5.46162844e-01 -5.63032210e-01 -3.17739785e-01
-1.00353695e-01 2.90762782e-01 3.02335560e-01 -1.78556353... | [8.953656196594238, 10.60356330871582] |
b6aae83b-5c14-4061-b44e-c9edf716b861 | bico-net-regress-globally-match-locally-for | 2205.03536 | null | https://arxiv.org/abs/2205.03536v1 | https://arxiv.org/pdf/2205.03536v1.pdf | BiCo-Net: Regress Globally, Match Locally for Robust 6D Pose Estimation | The challenges of learning a robust 6D pose function lie in 1) severe occlusion and 2) systematic noises in depth images. Inspired by the success of point-pair features, the goal of this paper is to recover the 6D pose of an object instance segmented from RGB-D images by locally matching pairs of oriented points betwee... | ['Kui Jia', 'Ke Chen', 'Yichen Zhang', 'Zelin Xu'] | 2022-05-07 | null | null | null | null | ['6d-pose-estimation-1'] | ['computer-vision'] | [ 0.15228197 0.16492835 -0.04087397 -0.40674803 -1.128731 -0.5151114
0.54876083 -0.08223451 -0.0771189 0.19352074 -0.10916135 0.07679987
-0.08334923 -0.6704463 -1.1222172 -0.41281176 0.16246551 0.9160985
0.26574832 -0.09635844 0.34860072 0.9462047 -1.5997688 -0.04151009
0.7549059 1.1507797 0.14... | [7.614170551300049, -2.7513527870178223] |
02fc5b0d-9725-4657-997c-6bcab9ebc360 | gmsf-global-matching-scene-flow | 2305.17432 | null | https://arxiv.org/abs/2305.17432v1 | https://arxiv.org/pdf/2305.17432v1.pdf | GMSF: Global Matching Scene Flow | We tackle the task of scene flow estimation from point clouds. Given a source and a target point cloud, the objective is to estimate a translation from each point in the source point cloud to the target, resulting in a 3D motion vector field. Previous dominant scene flow estimation methods require complicated coarse-to... | ['Michael Felsberg', 'Maria Magnusson', 'Per-Erik Forssén', 'Bastian Wandt', 'Johan Edstedt', 'Yushan Zhang'] | 2023-05-27 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [-1.48080643e-02 -7.97455609e-01 -4.27023694e-02 -2.62332976e-01
-7.12725580e-01 -4.68255162e-01 5.41861892e-01 2.58036125e-02
-2.99445778e-01 3.90128583e-01 2.19476987e-02 2.92255934e-02
9.67874378e-02 -7.90130734e-01 -6.27730012e-01 -4.29470479e-01
6.73362538e-02 3.41201901e-01 6.45416141e-01 -3.33272249... | [8.571270942687988, -2.0073301792144775] |
885aa2a1-59a3-427b-9cad-886ffa1fe3db | speech-drives-templates-co-speech-gesture | 2108.08020 | null | https://arxiv.org/abs/2108.08020v2 | https://arxiv.org/pdf/2108.08020v2.pdf | Speech Drives Templates: Co-Speech Gesture Synthesis with Learned Templates | Co-speech gesture generation is to synthesize a gesture sequence that not only looks real but also matches with the input speech audio. Our method generates the movements of a complete upper body, including arms, hands, and the head. Although recent data-driven methods achieve great success, challenges still exist, suc... | ['YiHao Zhi', 'Shenghua Gao', 'Wen Liu', 'Zhi Tu', 'Shenhan Qian'] | 2021-08-18 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Qian_Speech_Drives_Templates_Co-Speech_Gesture_Synthesis_With_Learned_Templates_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Qian_Speech_Drives_Templates_Co-Speech_Gesture_Synthesis_With_Learned_Templates_ICCV_2021_paper.pdf | iccv-2021-1 | ['gesture-generation'] | ['robots'] | [ 1.45732954e-01 -1.24835178e-01 -1.11358993e-01 -2.83202171e-01
-8.05855513e-01 -4.29784954e-01 6.25471115e-01 -7.80533552e-01
1.37761503e-01 2.54222840e-01 6.50149107e-01 1.65636674e-01
1.36085212e-01 -1.22023195e-01 -4.44956034e-01 -8.85179102e-01
3.63883197e-01 3.61237191e-02 1.64431743e-02 -1.66854933... | [5.734460353851318, -0.1974591761827469] |
2a26c658-7d02-4900-8562-37e7d8a5adca | deep-demosaicing-for-polarimetric-filter | 2211.13732 | null | https://arxiv.org/abs/2211.13732v1 | https://arxiv.org/pdf/2211.13732v1.pdf | Deep Demosaicing for Polarimetric Filter Array Cameras | Polarisation Filter Array (PFA) cameras allow the analysis of light polarisation state in a simple and cost-effective manner. Such filter arrays work as the Bayer pattern for colour cameras, sharing similar advantages and drawbacks. Among the others, the raw image must be demosaiced considering the local variations of ... | ['Andrea Torsello', 'Tehreem Fatima', 'Filippo Bergamasco', 'Mara Pistellato'] | 2022-11-24 | null | null | null | null | ['demosaicking'] | ['computer-vision'] | [ 5.69543779e-01 -2.48745471e-01 5.71737647e-01 -3.49204242e-01
-2.50322580e-01 -6.46074891e-01 7.10185587e-01 -4.14308667e-01
-6.87245786e-01 4.93916452e-01 -1.47097170e-01 -1.15633383e-01
-1.14406094e-01 -8.98976505e-01 -9.14925575e-01 -1.28565514e+00
2.06733555e-01 1.85798764e-01 1.91137806e-01 -1.62625477... | [10.163440704345703, -2.6095499992370605] |
8f5fe3e5-036c-4960-9e66-540720586223 | gibert-enhancing-bert-with-linguistic | null | null | https://aclanthology.org/2021.findings-emnlp.200 | https://aclanthology.org/2021.findings-emnlp.200.pdf | GiBERT: Enhancing BERT with Linguistic Information using a Lightweight Gated Injection Method | Large pre-trained language models such as BERT have been the driving force behind recent improvements across many NLP tasks. However, BERT is only trained to predict missing words – either through masking or next sentence prediction – and has no knowledge of lexical, syntactic or semantic information beyond what it pic... | ['Maria Liakata', 'Marek Rei', 'Nicole Peinelt'] | null | null | null | null | findings-emnlp-2021-11 | ['unsupervised-pre-training'] | ['methodology'] | [ 1.08298056e-01 3.64646912e-01 -6.02489054e-01 -4.08037156e-01
-6.10775769e-01 -5.77212691e-01 6.20170712e-01 6.36939645e-01
-8.07558239e-01 5.93367040e-01 1.09423137e+00 -4.72567737e-01
-1.86004490e-01 -7.06828713e-01 -4.96057063e-01 -7.52356127e-02
-6.78507537e-02 5.48857808e-01 -8.09290633e-02 -6.58839524... | [10.550459861755371, 8.776674270629883] |
ce400647-6cf3-4be2-a3e7-f62072e1ae3c | idol-indicator-oriented-logic-pre-training | 2306.15273 | null | https://arxiv.org/abs/2306.15273v1 | https://arxiv.org/pdf/2306.15273v1.pdf | IDOL: Indicator-oriented Logic Pre-training for Logical Reasoning | In the field of machine reading comprehension (MRC), existing systems have surpassed the average performance of human beings in many tasks like SQuAD. However, there is still a long way to go when it comes to logical reasoning. Although some methods for it have been put forward, they either are designed in a quite comp... | ['Shijin Wang', 'Yiming Cui', 'Ziqing Yang', 'Zihang Xu'] | 2023-06-27 | null | null | null | null | ['reading-comprehension', 'machine-reading-comprehension', 'logical-reasoning'] | ['natural-language-processing', 'natural-language-processing', 'reasoning'] | [-1.37632862e-01 5.85755527e-01 5.57600521e-03 -5.67345321e-01
-6.74215496e-01 -4.84191418e-01 4.73036408e-01 5.42318106e-01
-3.21136355e-01 7.40281522e-01 2.06023544e-01 -1.06604075e+00
-4.34520483e-01 -1.13989556e+00 -9.68100429e-01 -1.35504967e-02
1.43970355e-01 8.61568272e-01 5.93320251e-01 -7.41036892... | [9.667505264282227, 7.400908946990967] |
96a5fb0a-7b93-418d-bb53-509ef17f3417 | no-reference-image-quality-assessment-metric | 1901.05811 | null | https://arxiv.org/abs/1901.05811v2 | https://arxiv.org/pdf/1901.05811v2.pdf | No reference image quality assessment metric based on regional mutual information among images | With the inclusion of camera in daily life, an automatic no reference image quality evaluation index is required for automatic classification of images. The present manuscripts proposes a new No Reference Regional Mutual Information based technique for evaluating the quality of an image. We use regional mutual informat... | ['Rahul Upadhyay', 'Vinay Kumar', 'Vivek Singh Bawa'] | 2019-01-17 | null | null | null | null | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [ 1.54147729e-01 -3.25307399e-01 -7.68915266e-02 -4.45580989e-01
-8.27553689e-01 -1.52021304e-01 5.98905325e-01 2.43339002e-01
-8.32947075e-01 6.48523867e-01 4.77687083e-02 3.76670361e-01
-4.30476606e-01 -7.15890467e-01 -2.40927324e-01 -5.68483591e-01
4.55073714e-02 9.47353989e-02 3.83769572e-01 -8.83008987... | [11.7461519241333, -1.9543507099151611] |
e6807ede-fe6c-4457-bfb4-2622a84838de | i-know-what-you-do-not-know-knowledge-graph | 2208.09828 | null | https://arxiv.org/abs/2208.09828v3 | https://arxiv.org/pdf/2208.09828v3.pdf | I Know What You Do Not Know: Knowledge Graph Embedding via Co-distillation Learning | Knowledge graph (KG) embedding seeks to learn vector representations for entities and relations. Conventional models reason over graph structures, but they suffer from the issues of graph incompleteness and long-tail entities. Recent studies have used pre-trained language models to learn embeddings based on the textual... | ['Guangyao Li', 'Zequn Sun', 'Wei Hu', 'Yang Liu'] | 2022-08-21 | null | null | null | null | ['knowledge-graph-embedding'] | ['graphs'] | [-2.74302483e-01 8.17340374e-01 -6.34006560e-01 -2.16957882e-01
-2.05624685e-01 -5.81506729e-01 8.19972992e-01 5.04637301e-01
-2.75922090e-01 3.66059273e-01 6.25286222e-01 -4.85892713e-01
4.62805703e-02 -1.26587939e+00 -7.08252668e-01 -3.61322999e-01
-2.22704634e-01 5.77105284e-01 7.45886713e-02 -2.47938812... | [8.862527847290039, 7.920527935028076] |
b741a17d-4b24-46bd-9f45-2772c8951a0c | end-to-end-multi-person-pose-estimation-with | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Shi_End-to-End_Multi-Person_Pose_Estimation_With_Transformers_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Shi_End-to-End_Multi-Person_Pose_Estimation_With_Transformers_CVPR_2022_paper.pdf | End-to-End Multi-Person Pose Estimation With Transformers | Current methods of multi-person pose estimation typically treat the localization and association of body joints separately. In this paper, we propose the first fully end-to-end multi-person Pose Estimation framework with TRansformers, termed PETR. Our method views pose estimation as a hierarchical set prediction pr... | ['Wenming Tan', 'Ye Ren', 'Liangqi Li', 'Xing Wei', 'Dahu Shi'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['multi-person-pose-estimation'] | ['computer-vision'] | [-2.48320460e-01 -2.31092097e-03 -1.50714386e-02 -3.32573473e-01
-1.06441987e+00 -3.46923083e-01 5.30562282e-01 -1.07851863e-01
-6.39697373e-01 4.24306393e-01 5.13999045e-01 4.81149226e-01
8.00744966e-02 -2.90034831e-01 -6.67155266e-01 -3.80213529e-01
4.78075445e-02 7.89022446e-01 2.49776319e-01 -2.43918255... | [7.155218124389648, -0.7307378053665161] |
5f812ce6-fbe4-4ac4-b735-a962b054f956 | evaluating-open-domain-dialogues-in-latent | 2305.16967 | null | https://arxiv.org/abs/2305.16967v3 | https://arxiv.org/pdf/2305.16967v3.pdf | Evaluating Open-Domain Dialogues in Latent Space with Next Sentence Prediction and Mutual Information | The long-standing one-to-many issue of the open-domain dialogues poses significant challenges for automatic evaluation methods, i.e., there may be multiple suitable responses which differ in semantics for a given conversational context. To tackle this challenge, we propose a novel learning-based automatic evaluation me... | ['Xiaohui Cui', 'Aline Villavicencio', 'Wenge Rong', 'Chenghua Lin', 'Bohao Yang', 'Kun Zhao'] | 2023-05-26 | null | null | null | null | ['semantic-textual-similarity', 'semantic-similarity'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.24787778e-01 1.23106919e-01 2.75306314e-01 -7.37133563e-01
-9.87002492e-01 -5.81570446e-01 7.30069935e-01 -5.83786378e-03
-4.97290611e-01 9.44709659e-01 8.49766195e-01 1.02733709e-02
3.23752388e-02 -5.99442005e-01 -1.82760105e-01 -4.12031382e-01
4.76036847e-01 9.27730918e-01 3.69973779e-02 -7.78667867... | [12.751179695129395, 8.128955841064453] |
3877de66-1ee0-4e8d-9dbf-86e28eb94f16 | global-aggregation-then-local-distribution | 2107.13154 | null | https://arxiv.org/abs/2107.13154v1 | https://arxiv.org/pdf/2107.13154v1.pdf | Global Aggregation then Local Distribution for Scene Parsing | Modelling long-range contextual relationships is critical for pixel-wise prediction tasks such as semantic segmentation. However, convolutional neural networks (CNNs) are inherently limited to model such dependencies due to the naive structure in its building modules (\eg, local convolution kernel). While recent global... | ['Tao Xiang', 'Xiatian Zhu', 'Yunhai Tong', 'Kuiyuan Yang', 'Guangliang Cheng', 'Li Zhang', 'Xiangtai Li'] | 2021-07-28 | null | null | null | null | ['scene-parsing'] | ['computer-vision'] | [ 7.15174302e-02 2.90725768e-01 -1.80181593e-01 -7.37947762e-01
-3.52320462e-01 -4.90094870e-01 4.60301280e-01 1.19765930e-01
-5.01962662e-01 4.85594004e-01 4.46818806e-02 -3.45318705e-01
9.66911465e-02 -9.41061795e-01 -8.25037837e-01 -6.64926827e-01
7.11571798e-02 3.50880831e-01 7.32790649e-01 -2.01288238... | [9.54516315460205, 0.3476068377494812] |
0c41aba4-658a-491d-bcb2-cb040e7c17aa | pose2seg-detection-free-human-instance | 1803.10683 | null | http://arxiv.org/abs/1803.10683v3 | http://arxiv.org/pdf/1803.10683v3.pdf | Pose2Seg: Detection Free Human Instance Segmentation | The standard approach to image instance segmentation is to perform the object
detection first, and then segment the object from the detection bounding-box.
More recently, deep learning methods like Mask R-CNN perform them jointly.
However, little research takes into account the uniqueness of the "human"
category, which... | ['Rui-Long Li', 'Shi-Min Hu', 'Hao-Zhi Huang', 'Song-Hai Zhang', 'Zixi Cai', 'Han Xi', 'Xin Dong', 'Paul L. Rosin', 'Dingcheng Yang'] | 2018-03-28 | pose2seg-detection-free-human-instance-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Zhang_Pose2Seg_Detection_Free_Human_Instance_Segmentation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_Pose2Seg_Detection_Free_Human_Instance_Segmentation_CVPR_2019_paper.pdf | cvpr-2019-6 | ['human-instance-segmentation', '2d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [ 2.20675245e-02 2.58387625e-01 -2.76258111e-01 -2.52207071e-01
-4.43192959e-01 -1.05393074e-01 3.98161232e-01 -6.66066483e-02
-5.58012605e-01 6.51967168e-01 -2.20617518e-01 3.05216610e-01
3.32295507e-01 -7.09715843e-01 -7.29691625e-01 -5.67085326e-01
2.57679015e-01 8.83821607e-01 4.82607484e-01 -5.70091978... | [8.157857894897461, -0.38176777958869934] |
094ad3c8-edfa-4525-a78c-3beb505cab19 | hypershot-few-shot-learning-by-kernel | 2203.11378 | null | https://arxiv.org/abs/2203.11378v1 | https://arxiv.org/pdf/2203.11378v1.pdf | HyperShot: Few-Shot Learning by Kernel HyperNetworks | Few-shot models aim at making predictions using a minimal number of labeled examples from a given task. The main challenge in this area is the one-shot setting where only one element represents each class. We propose HyperShot - the fusion of kernels and hypernetwork paradigm. Compared to reference approaches that appl... | ['Przemysław Spurek', 'Jacek Tabor', 'Maciej Zięba', 'Konrad Karanowski', 'Marcin Przewięźlikowski', 'Marcin Sendera'] | 2022-03-21 | null | null | null | null | ['few-shot-image-classification'] | ['computer-vision'] | [ 2.25642025e-01 4.43456769e-01 -2.44460702e-01 -4.27416354e-01
-1.48533300e-01 -3.59159529e-01 8.41726899e-01 4.36389863e-01
-7.69145846e-01 5.89067400e-01 1.72490422e-02 1.21486083e-01
-5.65608859e-01 -1.00314212e+00 -5.27449310e-01 -7.41130054e-01
5.65171577e-02 6.07192516e-01 4.96306449e-01 -3.70834798... | [9.924230575561523, 3.0354278087615967] |
3de9e9f6-5155-44c9-b99e-755116305bc3 | topological-parallax-a-geometric | 2306.11835 | null | https://arxiv.org/abs/2306.11835v1 | https://arxiv.org/pdf/2306.11835v1.pdf | Topological Parallax: A Geometric Specification for Deep Perception Models | For safety and robustness of AI systems, we introduce topological parallax as a theoretical and computational tool that compares a trained model to a reference dataset to determine whether they have similar multiscale geometric structure. Our proofs and examples show that this geometric similarity between dataset and m... | ['Paul Bendich', 'Nirav Patel', 'Gabrielle Angeloro', 'Michael J. Catanzaro', 'Abraham D. Smith'] | 2023-06-20 | null | null | null | null | ['topological-data-analysis'] | ['graphs'] | [-6.19088784e-02 -3.50176692e-02 6.56331033e-02 2.37952154e-02
-1.64852500e-01 -9.65055406e-01 1.02656281e+00 2.89986610e-01
-1.39842361e-01 6.43384874e-01 9.11502913e-02 -3.92082900e-01
-3.94109786e-01 -9.84846354e-01 -1.17285025e+00 -1.03069139e+00
-6.67380095e-01 4.04533654e-01 3.21262002e-01 -4.03843671... | [7.829907417297363, 3.9096426963806152] |
627acde1-1edb-4792-ba11-a97cc2e588ce | towards-robust-multivariate-time-series | 2207.09572 | null | https://arxiv.org/abs/2207.09572v3 | https://arxiv.org/pdf/2207.09572v3.pdf | Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms | This work studies the threats of adversarial attack on multivariate probabilistic forecasting models and viable defense mechanisms. Our studies discover a new attack pattern that negatively impact the forecasting of a target time series via making strategic, sparse (imperceptible) modifications to the past observations... | ['Jun Huan', 'Hilaf Hasson', 'Trong Nghia Hoang', 'Youngsuk Park', 'Linbo Liu'] | 2022-07-19 | null | null | null | null | ['univariate-time-series-forecasting'] | ['time-series'] | [ 3.60968053e-01 5.97173311e-02 1.61283642e-01 -2.45558426e-01
-7.20335126e-01 -1.29904366e+00 1.05388391e+00 -2.18776539e-01
1.16644941e-01 6.04916096e-01 2.30958968e-01 -7.86495090e-01
-1.06847540e-01 -7.79831052e-01 -8.20096314e-01 -9.43806350e-01
-7.42611468e-01 1.59152180e-01 1.23941422e-01 -3.88269335... | [5.721859455108643, 7.790887355804443] |
7f1e5bc8-6c3b-4f49-aca1-9adfb69b73e7 | fast-inference-in-capsule-networks-using | 1904.07304 | null | http://arxiv.org/abs/1904.07304v1 | http://arxiv.org/pdf/1904.07304v1.pdf | Fast Inference in Capsule Networks Using Accumulated Routing Coefficients | We present a method for fast inference in Capsule Networks (CapsNets) by
taking advantage of a key insight regarding the routing coefficients that link
capsules between adjacent network layers. Since the routing coefficients are
responsible for assigning object parts to wholes, and an object whole generally
contains si... | ['Ishan Patel', 'K. P. Unnikrishnan', 'Zhen Zhao', 'Gursharan Sandhu', 'Ashley Kleinhans'] | 2019-04-15 | null | null | null | null | ['rotated-mnist'] | ['computer-vision'] | [ 7.46301860e-02 7.81018212e-02 -1.69369519e-01 -6.40467286e-01
-2.41544604e-01 -6.49916708e-01 3.08749229e-01 2.14056611e-01
-4.48127776e-01 7.22294986e-01 -2.33901560e-01 -1.68542504e-01
-2.66929120e-01 -8.88329029e-01 -8.93338501e-01 -7.42353141e-01
-3.05947691e-01 6.76434398e-01 2.80551910e-01 2.10501835... | [8.79638671875, 2.950023889541626] |
f18049c8-e7cb-471e-91d7-3a0953b5c024 | stock-prediction-a-method-based-on-extraction | 1707.07585 | null | http://arxiv.org/abs/1707.07585v1 | http://arxiv.org/pdf/1707.07585v1.pdf | Stock Prediction: a method based on extraction of news features and recurrent neural networks | This paper proposed a method for stock prediction. In terms of feature
extraction, we extract the features of stock-related news besides stock prices.
We first select some seed words based on experience which are the symbols of
good news and bad news. Then we propose an optimization method and calculate
the positive po... | ['Hongfei Yan', 'Weizheng Chen', 'Zeya Zhang'] | 2017-07-19 | null | null | null | null | ['stock-prediction'] | ['time-series'] | [-5.00064135e-01 -4.99565691e-01 -7.13138878e-01 -1.91326827e-01
-1.25890553e-01 -3.46889883e-01 5.84789276e-01 -3.24817970e-02
-5.69364846e-01 9.73451316e-01 7.26012409e-01 -2.91310489e-01
1.78376019e-01 -1.19516945e+00 -4.10576731e-01 -4.36669528e-01
-1.41753197e-01 4.71259393e-02 3.82909387e-01 -5.42344332... | [4.411773681640625, 4.286801815032959] |
9cc502f1-b344-4b74-9a18-57da5dcc0011 | distribution-network-fault-prediction | 2306.12724 | null | https://arxiv.org/abs/2306.12724v1 | https://arxiv.org/pdf/2306.12724v1.pdf | Distribution Network Fault Prediction Utilising Protection Relay Disturbance Recordings And Machine Learning | As society becomes increasingly reliant on electricity, the reliability requirements for electricity supply continue to rise. In response, transmission/distribution system operators (T/DSOs) must improve their networks and operational practices to reduce the number of interruptions and enhance their fault localization,... | ['Ari Salo', 'Anna Kulmala', 'Henry Niveri', 'Petri Hovila', 'Viktor Olsson', 'Karl Bäckström', 'Ebrahim Balouji'] | 2023-06-22 | null | null | null | null | ['fault-localization'] | ['computer-code'] | [-5.68640567e-02 -1.06587879e-01 -1.95131600e-01 -2.91394651e-01
-5.59447519e-02 -6.93847775e-01 1.25916749e-01 4.80612904e-01
3.89973342e-01 7.92954743e-01 1.45697528e-02 -5.68463743e-01
-7.50238121e-01 -9.67529893e-01 2.30846837e-01 -8.11385095e-01
-1.37526412e-02 8.07270229e-01 -4.07395512e-01 -7.00522400... | [6.05373477935791, 2.535360813140869] |
d96d7025-8bc2-44e9-8dd5-9238229ef208 | context-dependent-embedding-utterance | 2304.08216 | null | https://arxiv.org/abs/2304.08216v2 | https://arxiv.org/pdf/2304.08216v2.pdf | Context-Dependent Embedding Utterance Representations for Emotion Recognition in Conversations | Emotion Recognition in Conversations (ERC) has been gaining increasing importance as conversational agents become more and more common. Recognizing emotions is key for effective communication, being a crucial component in the development of effective and empathetic conversational agents. Knowledge and understanding of ... | ['Joao Paulo Carvalho', 'Isabel Dias', 'Helena Moniz', 'Patrícia Pereira'] | 2023-04-17 | null | null | null | null | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 3.03514320e-02 2.78134406e-01 2.60074914e-01 -6.83883786e-01
-3.50564450e-01 -3.98463964e-01 9.45315957e-01 4.29025531e-01
-7.69620240e-01 4.82858390e-01 6.60346091e-01 -7.40387887e-02
2.81609297e-01 -5.90998888e-01 -2.06408277e-01 -6.58255279e-01
4.62697237e-04 3.66383553e-01 -2.74480075e-01 -4.40441221... | [12.985427856445312, 6.252177715301514] |
99af6a4b-9cc0-4ec4-94dd-13293013b3ef | on-breast-cancer-detection-an-application-of | 1711.07831 | null | http://arxiv.org/abs/1711.07831v4 | http://arxiv.org/pdf/1711.07831v4.pdf | On Breast Cancer Detection: An Application of Machine Learning Algorithms on the Wisconsin Diagnostic Dataset | This paper presents a comparison of six machine learning (ML) algorithms:
GRU-SVM (Agarap, 2017), Linear Regression, Multilayer Perceptron (MLP), Nearest
Neighbor (NN) search, Softmax Regression, and Support Vector Machine (SVM) on
the Wisconsin Diagnostic Breast Cancer (WDBC) dataset (Wolberg, Street, &
Mangasarian, 1... | ['Abien Fred Agarap'] | 2017-11-20 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 2.79276371e-01 1.62023768e-01 -5.73784828e-01 -5.25931954e-01
-3.04780841e-01 -7.65214041e-02 7.08143950e-01 6.68954074e-01
-4.69180554e-01 1.03181624e+00 -1.41894430e-01 -7.16083944e-01
-6.35825157e-01 -6.88449681e-01 -9.19878185e-02 -7.92554200e-01
-1.48966566e-01 5.07566988e-01 3.25691819e-01 9.50474143... | [8.379202842712402, 4.807256698608398] |
1e093b2d-39e8-4e9c-b735-39f57a7759c3 | minimize-exposure-bias-of-seq2seq-models-in | 2009.07503 | null | https://arxiv.org/abs/2009.07503v2 | https://arxiv.org/pdf/2009.07503v2.pdf | Minimize Exposure Bias of Seq2Seq Models in Joint Entity and Relation Extraction | Joint entity and relation extraction aims to extract relation triplets from plain text directly. Prior work leverages Sequence-to-Sequence (Seq2Seq) models for triplet sequence generation. However, Seq2Seq enforces an unnecessary order on the unordered triplets and involves a large decoding length associated with error... | ['Ranran Haoran Zhang', 'Daisuke Kawahara', 'Sadao Kurohashi', 'Heng Ji', 'Daojian Zeng', 'Qianying Liu', 'Fei Cheng', 'Aysa Xuemo Fan'] | 2020-09-16 | null | https://aclanthology.org/2020.findings-emnlp.23 | https://aclanthology.org/2020.findings-emnlp.23.pdf | findings-of-the-association-for-computational | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [ 5.94119489e-01 1.35006636e-01 -2.92877525e-01 -5.28636277e-01
-9.78845060e-01 -8.60836208e-01 2.48824686e-01 1.06154561e-01
-3.01294059e-01 9.81493235e-01 3.42109352e-01 -6.21664405e-01
1.57661468e-01 -7.43493199e-01 -7.77795374e-01 -3.67361099e-01
1.34556711e-01 4.00481552e-01 3.07000820e-02 -1.45189658... | [9.449974060058594, 8.704893112182617] |
f455cc67-e299-4885-8ccb-5de35310682b | distribution-based-emotion-recognition-in | 2211.04834 | null | https://arxiv.org/abs/2211.04834v1 | https://arxiv.org/pdf/2211.04834v1.pdf | Distribution-based Emotion Recognition in Conversation | Automatic emotion recognition in conversation (ERC) is crucial for emotion-aware conversational artificial intelligence. This paper proposes a distribution-based framework that formulates ERC as a sequence-to-sequence problem for emotion distribution estimation. The inherent ambiguity of emotions and the subjectivity o... | ['Philip C. Woodland', 'Chao Zhang', 'Wen Wu'] | 2022-11-09 | null | null | null | null | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [-1.43030817e-02 2.39889234e-01 2.00658366e-01 -1.07073665e+00
-8.46517622e-01 -4.27285224e-01 6.02442980e-01 -1.11607432e-01
-3.66909236e-01 1.00375998e+00 5.64636648e-01 1.67800814e-01
1.70417681e-01 -2.67590016e-01 -1.27646446e-01 -8.23279977e-01
1.46154180e-01 6.26881897e-01 -4.43169564e-01 -6.93753660... | [13.060138702392578, 6.0675578117370605] |
c2e8973d-ceda-4b85-84f5-95e1c7864dea | mappsent-a-textual-mapping-approach-for | null | null | https://aclanthology.org/R17-1040 | https://aclanthology.org/R17-1040.pdf | MappSent: a Textual Mapping Approach for Question-to-Question Similarity | Since the advent of word embedding methods, the representation of longer pieces of texts such as sentences and paragraphs is gaining more and more interest, especially for textual similarity tasks. Mikolov et al. (2013) have demonstrated that words and phrases exhibit linear structures that allow to meaningfully combin... | ['Hern', 'Amir Hazem', 'Nicolas ez', 'Basma El Amel Boussaha'] | 2017-09-01 | null | null | null | ranlp-2017-9 | ['question-similarity'] | ['natural-language-processing'] | [ 1.83199927e-01 1.04610890e-01 -1.99381202e-01 -2.49499083e-01
-7.34547496e-01 -5.69898605e-01 1.10008085e+00 8.08562577e-01
-7.53678381e-01 2.81610578e-01 8.22609127e-01 -4.34322417e-01
-2.79906601e-01 -6.02660358e-01 -5.29876530e-01 -3.88482660e-01
8.59553590e-02 4.00108665e-01 -1.22601114e-01 -4.67591792... | [10.76860523223877, 8.739768981933594] |
1d749fd1-28a8-4c5e-9cb5-66ee8ded04d7 | addressing-class-imbalance-in-grammatical | null | null | https://aclanthology.org/W15-5902 | https://aclanthology.org/W15-5902.pdf | Addressing Class Imbalance in Grammatical Error Detection with Evaluation Metric Optimization | null | ['Anoop Kunchukuttan', 'Pushpak Bhattacharyya'] | 2015-12-01 | null | null | null | ws-2015-12 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.378777027130127, 3.6888372898101807] |
67e3e0fa-0d1c-409d-89bf-f348384b938c | learning-to-learn-unlearned-feature-for-brain | 2305.08878 | null | https://arxiv.org/abs/2305.08878v1 | https://arxiv.org/pdf/2305.08878v1.pdf | Learning to Learn Unlearned Feature for Brain Tumor Segmentation | We propose a fine-tuning algorithm for brain tumor segmentation that needs only a few data samples and helps networks not to forget the original tasks. Our approach is based on active learning and meta-learning. One of the difficulties in medical image segmentation is the lack of datasets with proper annotations, becau... | ['Seunghong Choi', 'Jungwoo Lee', 'Seokhyeon Ha', 'Yeongmo Kim', 'Seungyub Han'] | 2023-05-13 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation'] | ['computer-vision', 'medical'] | [ 3.27943474e-01 5.32518983e-01 -5.48635602e-01 -4.44578290e-01
-8.71096075e-01 -6.20809235e-02 1.50572047e-01 1.70121267e-01
-6.84905708e-01 9.19246554e-01 5.45822047e-02 -2.17434332e-01
-3.93258393e-01 -7.19231784e-01 -2.65082061e-01 -1.06906104e+00
-1.66971050e-02 8.86575758e-01 6.36416912e-01 1.55849308... | [14.774016380310059, -2.1725192070007324] |
b9620532-6c05-476b-852f-3c22771bb23c | federated-survival-forests | 2302.02807 | null | https://arxiv.org/abs/2302.02807v1 | https://arxiv.org/pdf/2302.02807v1.pdf | Federated Survival Forests | Survival analysis is a subfield of statistics concerned with modeling the occurrence time of a particular event of interest for a population. Survival analysis found widespread applications in healthcare, engineering, and social sciences. However, real-world applications involve survival datasets that are distributed, ... | ['Matteo Matteucci', 'Alberto Archetti'] | 2023-02-06 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [-1.62328959e-01 -1.51453659e-01 -3.98276359e-01 -5.77803791e-01
-7.85040915e-01 -3.39903802e-01 1.48262069e-01 1.89732090e-01
-3.92956972e-01 1.27678514e+00 1.78021252e-01 -5.80685794e-01
-3.50832462e-01 -9.87449229e-01 -5.68471014e-01 -1.04656959e+00
-5.96377671e-01 3.48506480e-01 -5.43231308e-01 2.61559457... | [6.15330171585083, 6.463613986968994] |
75e7942a-060a-4c7f-ac41-edb9f59fbf8c | 3d-fully-convolutional-networks-for | 1612.03925 | null | http://arxiv.org/abs/1612.03925v2 | http://arxiv.org/pdf/1612.03925v2.pdf | 3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study | This study investigates a 3D and fully convolutional neural network (CNN) for
subcortical brain structure segmentation in MRI. 3D CNN architectures have been
generally avoided due to their computational and memory requirements during
inference. We address the problem via small kernels, allowing deeper
architectures. We... | ['J. Dolz', 'I. Ben Ayed', 'C. Desrosiers'] | 2016-12-12 | null | null | null | null | ['3d-medical-imaging-segmentation'] | ['medical'] | [-3.89946327e-02 2.49853581e-01 -2.03438923e-02 -7.06202030e-01
-8.00133467e-01 -4.04525906e-01 4.10815328e-01 3.78068030e-01
-9.02726710e-01 5.35538256e-01 2.79897809e-01 -2.65255600e-01
7.09707290e-02 -6.12591505e-01 -5.75481832e-01 -3.03768694e-01
-4.99965757e-01 8.79724622e-01 4.28861290e-01 1.18797682... | [14.216487884521484, -2.3363473415374756] |
0892c8d3-37d6-41f0-a442-4d041ae6919c | on-the-distribution-of-penultimate | 2107.01900 | null | https://arxiv.org/abs/2107.01900v2 | https://arxiv.org/pdf/2107.01900v2.pdf | On The Distribution of Penultimate Activations of Classification Networks | This paper studies probability distributions of penultimate activations of classification networks. We show that, when a classification network is trained with the cross-entropy loss, its final classification layer forms a Generative-Discriminative pair with a generative classifier based on a specific distribution of p... | ['Suha Kwak', 'Yoonho Lee', 'Minkyo Seo'] | 2021-07-05 | null | null | null | null | ['conditional-image-generation'] | ['computer-vision'] | [ 4.73929137e-01 7.69121826e-01 -9.56593528e-02 -2.10997209e-01
-3.93744200e-01 -8.55324030e-01 9.97032166e-01 -4.99680936e-01
-2.20135048e-01 1.00039721e+00 -5.68235107e-02 -1.82207048e-01
-3.34623158e-02 -1.18655527e+00 -1.20978081e+00 -1.12318504e+00
3.07853639e-01 6.47728205e-01 -1.21144563e-01 -2.15729978... | [11.532443046569824, -0.09879951924085617] |
673ffe0c-a890-42e1-8bb8-072039bf2ea5 | using-cameras-for-precise-measurement-of-two | 1904.13187 | null | https://arxiv.org/abs/1904.13187v2 | https://arxiv.org/pdf/1904.13187v2.pdf | Using cameras for precise measurement of two-dimensional plant features: CASS | Images are used frequently in plant phenotyping to capture measurements. This chapter offers a repeatable method for capturing two-dimensional measurements of plant parts in field or laboratory settings using a variety of camera styles (cellular phone, DSLR), with the addition of a printed calibration pattern. The meth... | ['Germán A Holguín', 'Amy Tabb', 'Rachel Naegele'] | 2019-04-30 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [ 5.72951436e-01 -4.76809591e-01 4.42830324e-02 -1.30123839e-01
-2.35989527e-03 -1.30308795e+00 -5.79023100e-02 2.31496468e-01
2.53372699e-01 2.18792543e-01 -4.14021403e-01 -7.84136713e-01
-1.62799418e-01 -9.02177215e-01 -5.37426174e-01 -5.57390213e-01
3.39433581e-01 3.25642318e-01 5.10220230e-01 1.11021370... | [9.110320091247559, -1.6418259143829346] |
87fc594b-30e9-4211-b65e-8b07eb53e182 | enhancing-adversarial-robustness-via-score | 2307.04333 | null | https://arxiv.org/abs/2307.04333v1 | https://arxiv.org/pdf/2307.04333v1.pdf | Enhancing Adversarial Robustness via Score-Based Optimization | Adversarial attacks have the potential to mislead deep neural network classifiers by introducing slight perturbations. Developing algorithms that can mitigate the effects of these attacks is crucial for ensuring the safe use of artificial intelligence. Recent studies have suggested that score-based diffusion models are... | ['Zhihua Zhang', 'Weijian Luo', 'Boya Zhang'] | 2023-07-10 | null | null | null | null | ['adversarial-defense', 'adversarial-robustness'] | ['adversarial', 'adversarial'] | [ 5.45172542e-02 -1.58317685e-01 2.56226033e-01 -1.70418307e-01
-8.00081968e-01 -1.19241822e+00 8.44596446e-01 -4.07221258e-01
-4.52043027e-01 7.50772536e-01 -1.38356775e-01 -6.29858077e-01
-1.56767562e-01 -9.22733307e-01 -7.30524361e-01 -9.96523798e-01
-1.36408418e-01 6.19110540e-02 2.21621767e-01 -3.01318169... | [5.6455183029174805, 7.8426666259765625] |
f87594bd-ee5f-4120-9e75-b73135cb16f2 | face-hallucination-with-finishing-touches | 2002.03308 | null | https://arxiv.org/abs/2002.03308v2 | https://arxiv.org/pdf/2002.03308v2.pdf | Face Hallucination with Finishing Touches | Obtaining a high-quality frontal face image from a low-resolution (LR) non-frontal face image is primarily important for many facial analysis applications. However, mainstreams either focus on super-resolving near-frontal LR faces or frontalizing non-frontal high-resolution (HR) faces. It is desirable to perform both t... | ['Yang Zhang', 'Xin Yu', 'Xiaobo Lu', 'Jun Li', 'Ping Liu', 'Ivor W. Tsang'] | 2020-02-09 | null | null | null | null | ['face-hallucination'] | ['computer-vision'] | [ 1.22230746e-01 4.56511438e-01 1.81480706e-01 -4.50906634e-01
-5.63079596e-01 -4.13475245e-01 2.21644357e-01 -1.17015815e+00
3.72492403e-01 6.06478035e-01 2.17274740e-01 3.13470900e-01
2.37912253e-01 -8.90348077e-01 -5.39610565e-01 -8.78805697e-01
2.55645186e-01 5.15751280e-02 -3.04360777e-01 -3.57426822... | [12.802275657653809, -0.0675317719578743] |
ef573a5a-dc0c-42ef-82c4-292296b27c4a | fantasia3d-disentangling-geometry-and | 2303.13873 | null | https://arxiv.org/abs/2303.13873v2 | https://arxiv.org/pdf/2303.13873v2.pdf | Fantasia3D: Disentangling Geometry and Appearance for High-quality Text-to-3D Content Creation | Automatic 3D content creation has achieved rapid progress recently due to the availability of pre-trained, large language models and image diffusion models, forming the emerging topic of text-to-3D content creation. Existing text-to-3D methods commonly use implicit scene representations, which couple the geometry and a... | ['Kui Jia', 'Ningxin Jiao', 'Yongwei Chen', 'Rui Chen'] | 2023-03-24 | null | null | null | null | ['text-to-3d'] | ['computer-vision'] | [ 1.88463539e-01 -1.12290859e-01 3.47277313e-01 1.53455082e-02
-4.40659881e-01 -4.69124019e-01 8.86952460e-01 -2.12599114e-01
3.08698446e-01 3.44728351e-01 2.99440444e-01 -2.58641899e-01
2.24650204e-01 -1.09370530e+00 -6.19147897e-01 -6.86172724e-01
2.38855928e-01 3.92931581e-01 -1.77723411e-02 -3.30858886... | [9.319794654846191, -3.219099760055542] |
f211ee68-c671-4b3c-bb8e-d0e40567faab | curriculum-deepsdf | 2003.08593 | null | https://arxiv.org/abs/2003.08593v3 | https://arxiv.org/pdf/2003.08593v3.pdf | Curriculum DeepSDF | When learning to sketch, beginners start with simple and flexible shapes, and then gradually strive for more complex and accurate ones in the subsequent training sessions. In this paper, we design a "shape curriculum" for learning continuous Signed Distance Function (SDF) on shapes, namely Curriculum DeepSDF. Inspired ... | ['Haidong Zhu', 'Yueqi Duan', 'Li Yi', 'Leonidas J. Guibas', 'Ram Nevatia', 'He Wang'] | 2020-03-19 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/441_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123530052.pdf | eccv-2020-8 | ['3d-shape-representation'] | ['computer-vision'] | [-6.01759441e-02 1.76595494e-01 1.16049752e-01 -4.96969610e-01
-3.97628099e-01 -6.40476406e-01 4.45510030e-01 9.27488506e-02
-1.11908630e-01 4.06264901e-01 1.47799477e-02 -2.31440365e-01
-3.34425896e-01 -1.13138700e+00 -7.23040283e-01 -4.49226439e-01
1.14270985e-01 7.26576388e-01 2.15761527e-01 -2.76649654... | [8.494535446166992, -3.6755483150482178] |
555578ab-94f6-482c-9d1e-9916c8854dda | fusing-visual-appearance-and-geometry-for | 2302.11458 | null | https://arxiv.org/abs/2302.11458v1 | https://arxiv.org/pdf/2302.11458v1.pdf | Fusing Visual Appearance and Geometry for Multi-modality 6DoF Object Tracking | In many applications of advanced robotic manipulation, six degrees of freedom (6DoF) object pose estimates are continuously required. In this work, we develop a multi-modality tracker that fuses information from visual appearance and geometry to estimate object poses. The algorithm extends our previous method ICG, whic... | ['Rudolph Triebel', 'Dongheui Lee', 'Florian Steidle', 'Anne E. Reichert', 'Mariam Elsayed', 'Manuel Stoiber'] | 2023-02-22 | null | null | null | null | ['6d-pose-estimation-using-rgbd', '6d-pose-estimation-1', '3d-object-tracking'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.83672830e-01 -4.13687497e-01 -1.13886446e-01 1.71010643e-02
-4.63260204e-01 -6.63486540e-01 4.95994717e-01 8.49451274e-02
-1.62845343e-01 2.66846538e-01 -3.67853373e-01 3.27486813e-01
-7.53546804e-02 -3.77044469e-01 -7.22447455e-01 -6.02490127e-01
9.92190391e-02 4.85259593e-01 5.69980681e-01 -2.08213665... | [6.981172561645508, -2.2159149646759033] |
4ca6e930-114b-49d2-ba03-24393132d301 | investigating-poor-performance-regions-of | 2306.12507 | null | https://arxiv.org/abs/2306.12507v1 | https://arxiv.org/pdf/2306.12507v1.pdf | Investigating Poor Performance Regions of Black Boxes: LIME-based Exploration in Sepsis Detection | Interpreting machine learning models remains a challenge, hindering their adoption in clinical settings. This paper proposes leveraging Local Interpretable Model-Agnostic Explanations (LIME) to provide interpretable descriptions of black box classification models in high-stakes sepsis detection. By analyzing misclassif... | ['Jang Yong Kim', 'Yonghwan Kim', 'Choongmin Kim', 'San Lee', 'Surajsinh Parmar', 'Mozhgan Salimiparsa'] | 2023-06-21 | null | null | null | null | ['decision-making'] | ['reasoning'] | [ 6.75852656e-01 5.04632711e-01 -4.24129009e-01 -3.84436607e-01
-4.29993331e-01 -5.22381663e-01 9.62862000e-02 1.09402764e+00
-2.53325284e-01 6.31674230e-01 7.04943240e-01 -1.12177384e+00
-6.28231585e-01 -2.25346133e-01 -3.71764392e-01 -7.25551128e-01
-2.33246103e-01 3.76712859e-01 -6.73844218e-01 3.30297410... | [8.347339630126953, 5.868264675140381] |
6480672c-6ad3-4862-93e8-43b76a78ede4 | attention-based-convolutional-recurrent | 1907.02230 | null | https://arxiv.org/abs/1907.02230v1 | https://arxiv.org/pdf/1907.02230v1.pdf | Attention based Convolutional Recurrent Neural Network for Environmental Sound Classification | Environmental sound classification (ESC) is a challenging problem due to the complexity of sounds. The ESC performance is heavily dependent on the effectiveness of representative features extracted from the environmental sounds. However, ESC often suffers from the semantically irrelevant frames and silent frames. In or... | ['Shugong Xu', 'Shunqing Zhang', 'Tianhao Qiao', 'Zhichao Zhang', 'Shan Cao'] | 2019-07-04 | null | null | null | null | ['environmental-sound-classification', 'sound-classification'] | ['audio', 'audio'] | [ 1.97929561e-01 -8.38716626e-01 4.51542288e-01 -2.42205858e-01
-9.15859401e-01 -3.29923965e-02 2.41925836e-01 -1.76330283e-01
-4.91785944e-01 4.39035803e-01 4.58918154e-01 2.17889979e-01
-1.24080777e-01 -4.72762108e-01 -4.24828440e-01 -8.51569057e-01
-7.77492970e-02 -7.84221053e-01 4.53416884e-01 6.96533695... | [15.187357902526855, 5.23673152923584] |
f69f11b2-4a7b-4455-a7a6-6485472effc0 | pmc-vqa-visual-instruction-tuning-for-medical | 2305.10415 | null | https://arxiv.org/abs/2305.10415v5 | https://arxiv.org/pdf/2305.10415v5.pdf | PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering | In this paper, we focus on the problem of Medical Visual Question Answering (MedVQA), which is crucial in efficiently interpreting medical images with vital clinic-relevant information. Firstly, we reframe the problem of MedVQA as a generation task that naturally follows the human-machine interaction, we propose a gene... | ['Weidi Xie', 'Yanfeng Wang', 'Ya zhang', 'Weixiong Lin', 'Ziheng Zhao', 'Chaoyi Wu', 'Xiaoman Zhang'] | 2023-05-17 | null | null | null | null | ['generative-visual-question-answering'] | ['computer-vision'] | [ 3.99808586e-01 5.57560563e-01 1.70667857e-01 -3.23536605e-01
-1.26874566e+00 -5.36325753e-01 5.37206888e-01 7.03632385e-02
-2.96627551e-01 5.56602240e-01 3.55874509e-01 -7.79548228e-01
2.49202728e-01 -5.99095881e-01 -7.81255841e-01 -4.64617193e-01
3.55493367e-01 6.54206336e-01 1.45994276e-01 -1.85589403... | [11.022500038146973, 1.5739049911499023] |
a39ad28f-c45c-4560-a4c0-d02bb5c8f6f4 | low-rank-approximation-for-general-tensor | 2207.07417 | null | https://arxiv.org/abs/2207.07417v2 | https://arxiv.org/pdf/2207.07417v2.pdf | Near-Linear Time and Fixed-Parameter Tractable Algorithms for Tensor Decompositions | We study low rank approximation of tensors, focusing on the tensor train and Tucker decompositions, as well as approximations with tree tensor networks and more general tensor networks. For tensor train decomposition, we give a bicriteria $(1 + \eps)$-approximation algorithm with a small bicriteria rank and $O(q \cdot ... | ['Ziyu Zhang', 'David P. Woodruff', 'Arvind V. Mahankali'] | 2022-07-15 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [-2.03622341e-01 2.01268703e-01 1.50721282e-01 1.67320624e-01
-7.50668824e-01 -1.08367658e+00 -1.02915883e-01 -2.42375687e-01
-3.61524761e-01 2.87605613e-01 -5.68341687e-02 -9.51647341e-01
-7.40934968e-01 -9.68266428e-01 -8.50893617e-01 -8.99988592e-01
-1.06897449e+00 8.24640870e-01 -1.02809090e-02 -4.47853655... | [6.403107166290283, 4.865062236785889] |
d722a54d-a8bf-4924-a088-cbb41128cd1a | nonlinear-acoustic-echo-cancellation-with | 2106.13754 | null | https://arxiv.org/abs/2106.13754v1 | https://arxiv.org/pdf/2106.13754v1.pdf | Nonlinear Acoustic Echo Cancellation with Deep Learning | We propose a nonlinear acoustic echo cancellation system, which aims to model the echo path from the far-end signal to the near-end microphone in two parts. Inspired by the physical behavior of modern hands-free devices, we first introduce a novel neural network architecture that is specifically designed to model the n... | ['Baruch Berdugo', 'Israel Cohen', 'Amir Ivry'] | 2021-06-25 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 1.42495260e-01 -7.77800232e-02 4.88089383e-01 -2.94963956e-01
-4.57496345e-01 -5.36671102e-01 1.72130764e-01 -3.96815747e-01
-7.66333520e-01 6.00273162e-02 6.25714660e-02 -5.61472714e-01
1.07873395e-01 -4.27542120e-01 -9.25857425e-01 -5.14553070e-01
-4.03656274e-01 4.50152420e-02 2.30900884e-01 -1.80823967... | [15.04574966430664, 5.948282718658447] |
34017115-81c9-4b39-a5aa-969c2e7626e3 | krf-keypoint-refinement-with-fusion-network | 2210.03437 | null | https://arxiv.org/abs/2210.03437v1 | https://arxiv.org/pdf/2210.03437v1.pdf | KRF: Keypoint Refinement with Fusion Network for 6D Pose Estimation | Existing refinement methods gradually lose their ability to further improve pose estimation methods' accuracy. In this paper, we propose a new refinement pipeline, Keypoint Refinement with Fusion Network (KRF), for 6D pose estimation, especially for objects with serious occlusion. The pipeline consists of two steps. It... | ['Yong-Jin Liu', 'Long Zeng', 'Yu-Ping Wang', 'Yiheng Han', 'Irvin Haozhe Zhan'] | 2022-10-07 | null | null | null | null | ['6d-pose-estimation-1'] | ['computer-vision'] | [-2.10301787e-01 -3.23056340e-01 -1.73722953e-01 -7.23035261e-02
-7.49886215e-01 -3.34757000e-01 2.88290918e-01 4.98686619e-02
-2.79420733e-01 3.82760912e-01 -2.39177719e-01 1.60925508e-01
4.64290120e-02 -8.17835629e-01 -6.73998058e-01 -4.53561813e-01
7.18437135e-02 6.71643436e-01 7.17720747e-01 -1.62468657... | [7.6440749168396, -2.7640693187713623] |
5c324067-332a-4a6d-85b4-61e1d65ad3a8 | a-retrieve-and-rewrite-initialization-method | null | null | https://aclanthology.org/2020.acl-main.320 | https://aclanthology.org/2020.acl-main.320.pdf | A Retrieve-and-Rewrite Initialization Method for Unsupervised Machine Translation | The commonly used framework for unsupervised machine translation builds initial translation models of both translation directions, and then performs iterative back-translation to jointly boost their translation performance. The initialization stage is very important since bad initialization may wrongly squeeze the sear... | ['Shuai Ma', 'Yu Wu', 'Ming Zhou', 'Shuo Ren', 'Shujie Liu'] | 2020-07-01 | null | null | null | acl-2020-6 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 2.64333159e-01 -1.42312706e-01 -3.70607316e-01 -4.70072806e-01
-1.37223315e+00 -9.01145816e-01 5.85676074e-01 -2.64085770e-01
-2.70478904e-01 8.16864908e-01 3.94121706e-01 -6.22383714e-01
4.52737749e-01 -5.36803842e-01 -7.25643516e-01 -5.80872297e-01
7.74311781e-01 1.06000698e+00 -9.44383964e-02 -4.09246385... | [11.693782806396484, 10.301527976989746] |
2f1dbe3d-87be-4c0d-9245-6972ad1e995c | sequence-to-set-semantic-tagging-for-complex | null | null | https://aclanthology.org/2020.bionlp-1.2 | https://aclanthology.org/2020.bionlp-1.2.pdf | Sequence-to-Set Semantic Tagging for Complex Query Reformulation and Automated Text Categorization in Biomedical IR using Self-Attention | Novel contexts, comprising a set of terms referring to one or more concepts, may often arise in complex querying scenarios such as in evidence-based medicine (EBM) involving biomedical literature. These may not explicitly refer to entities or canonical concept forms occurring in a fact-based knowledge source, e.g. the ... | ['Eric Fosler-Lussier', 'Juanxi Li', 'Simon Lin', 'Manirupa Das', 'Yungui Huang', 'Steve Rust', 'Rajiv Ramnath'] | 2020-07-01 | null | null | null | ws-2020-7 | ['text-categorization'] | ['natural-language-processing'] | [ 7.58252621e-01 1.37132689e-01 -3.81013006e-01 -1.86357036e-01
-1.17819834e+00 -8.63889456e-01 8.71884942e-01 9.68585491e-01
-8.80546808e-01 8.87226224e-01 4.53191668e-01 -4.11185384e-01
-6.19197190e-01 -4.12912369e-01 -7.28358150e-01 -5.19492686e-01
-6.90471679e-02 1.04429030e+00 1.60998598e-01 -1.07667528... | [8.68999195098877, 8.622978210449219] |
1bc3695b-c376-4c24-aace-4f564baea06d | global-constraints-with-prompting-for-zero | 2302.04459 | null | https://arxiv.org/abs/2302.04459v1 | https://arxiv.org/pdf/2302.04459v1.pdf | Global Constraints with Prompting for Zero-Shot Event Argument Classification | Determining the role of event arguments is a crucial subtask of event extraction. Most previous supervised models leverage costly annotations, which is not practical for open-domain applications. In this work, we propose to use global constraints with prompting to effectively tackles event argument classification witho... | ['Yangqiu Song', 'Hongming Zhang', 'Zizheng Lin'] | 2023-02-09 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [ 2.89885193e-01 2.24830538e-01 -6.42715514e-01 -4.42141771e-01
-1.17940760e+00 -8.41337800e-01 6.80431783e-01 8.37393880e-01
-7.27174222e-01 8.39673102e-01 4.49204117e-01 -2.69783884e-01
-3.18404064e-02 -6.93538487e-01 -6.34380162e-01 -9.61368829e-02
3.47040920e-03 4.70645368e-01 7.37633049e-01 -4.93093692... | [9.121862411499023, 9.184117317199707] |
9d3f20b8-541a-46fe-96d2-ce294a2c761f | toward-forgetting-sensitive-referring | 2007.08672 | null | https://arxiv.org/abs/2007.08672v1 | https://arxiv.org/pdf/2007.08672v1.pdf | Toward Forgetting-Sensitive Referring Expression Generationfor Integrated Robot Architectures | To engage in human-like dialogue, robots require the ability to describe the objects, locations, and people in their environment, a capability known as "Referring Expression Generation." As speakers repeatedly refer to similar objects, they tend to re-use properties from previous descriptions, in part to help the liste... | ['Kellyn Larson', 'Will Culpepper', 'Torin Johnson', 'Tom Williams'] | 2020-07-16 | null | null | null | null | ['referring-expression-generation'] | ['computer-vision'] | [ 9.78473667e-03 4.99977142e-01 2.73731709e-01 -3.39369625e-01
-2.03154296e-01 -4.51842517e-01 5.84318817e-01 2.59961873e-01
-4.24010932e-01 7.99622416e-01 4.68434364e-01 -8.24450850e-02
-1.50979042e-01 -8.73543143e-01 -3.45712274e-01 -3.17034751e-01
-2.46445388e-02 7.56156623e-01 1.34861162e-02 -3.85245949... | [4.383352756500244, 1.070912480354309] |
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