paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
65dded18-adec-4e79-9d92-c4b690980214 | storydall-e-adapting-pretrained-text-to-image | 2209.06192 | null | https://arxiv.org/abs/2209.06192v1 | https://arxiv.org/pdf/2209.06192v1.pdf | StoryDALL-E: Adapting Pretrained Text-to-Image Transformers for Story Continuation | Recent advances in text-to-image synthesis have led to large pretrained transformers with excellent capabilities to generate visualizations from a given text. However, these models are ill-suited for specialized tasks like story visualization, which requires an agent to produce a sequence of images given a correspondin... | ['Mohit Bansal', 'Darryl Hannan', 'Adyasha Maharana'] | 2022-09-13 | null | null | null | null | ['story-continuation', 'story-visualization'] | ['computer-vision', 'computer-vision'] | [ 6.43804491e-01 4.53760743e-01 2.30747491e-01 -1.25467792e-01
-5.94174922e-01 -8.74235988e-01 1.16436136e+00 -5.22129297e-01
2.71666348e-02 7.88553178e-01 5.21758795e-01 -2.39862263e-01
3.67788255e-01 -8.70754957e-01 -1.09613216e+00 -6.37553632e-01
3.63142610e-01 4.07402366e-01 1.29892929e-02 -3.03120166... | [11.203125, 0.5130406022071838] |
0465f2a0-b4ba-41ff-878d-56edd7b1c7fd | mdqe-mining-discriminative-query-embeddings | 2303.14395 | null | https://arxiv.org/abs/2303.14395v1 | https://arxiv.org/pdf/2303.14395v1.pdf | MDQE: Mining Discriminative Query Embeddings to Segment Occluded Instances on Challenging Videos | While impressive progress has been achieved, video instance segmentation (VIS) methods with per-clip input often fail on challenging videos with occluded objects and crowded scenes. This is mainly because instance queries in these methods cannot encode well the discriminative embeddings of instances, making the query-b... | ['Lei Zhang', 'Wangmeng Xiang', 'Shuai Li', 'Minghan Li'] | 2023-03-25 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_MDQE_Mining_Discriminative_Query_Embeddings_To_Segment_Occluded_Instances_on_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_MDQE_Mining_Discriminative_Query_Embeddings_To_Segment_Occluded_Instances_on_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-instance-segmentation'] | ['computer-vision'] | [-5.91557734e-02 -1.21258937e-01 -4.97054011e-01 -2.95960307e-01
-1.03095436e+00 -5.92036545e-01 2.21600667e-01 -1.35634005e-01
-5.20504415e-01 5.18397629e-01 1.28296930e-02 1.82532683e-01
-6.28860444e-02 -4.57520515e-01 -9.61998820e-01 -4.82874364e-01
-2.82354027e-01 3.43649119e-01 8.12089145e-01 9.01558772... | [9.2901029586792, 0.06659847497940063] |
75d6c0c6-4a6d-41f1-a967-cd98240b7661 | a-fully-automated-and-explainable-algorithm | 2307.03757 | null | https://arxiv.org/abs/2307.03757v1 | https://arxiv.org/pdf/2307.03757v1.pdf | A Fully Automated and Explainable Algorithm for the Prediction of Malignant Transformation in Oral Epithelial Dysplasia | Oral epithelial dysplasia (OED) is a premalignant histopathological diagnosis given to lesions of the oral cavity. Its grading suffers from significant inter-/intra- observer variability, and does not reliably predict malignancy progression, potentially leading to suboptimal treatment decisions. To address this, we dev... | ['Nasir M Rajpoot', 'Syed Ali Khurram', 'Hisham Mehanna', 'Paul Nankivell', 'Jill Brooks', 'Jacqueline James', 'Stephanie G Craig', 'Kris D McCombe', 'Shan E Ahmed Raza', 'Fayyaz Minhas', 'Mostafa Jahanifar', 'Hanya Mahmood', 'Raja Muhammad Saad Bashir', 'Adam J Shephard'] | 2023-07-06 | null | null | null | null | ['whole-slide-images'] | ['computer-vision'] | [ 3.75115305e-01 5.83862960e-01 -2.80092180e-01 -9.84163806e-02
-1.02965057e+00 -3.27733010e-01 4.76376384e-01 6.78787351e-01
-6.73720837e-01 4.00424063e-01 2.84046471e-01 -7.56727159e-01
-3.49271655e-01 -7.71734416e-01 -5.40965982e-02 -1.11210287e+00
-5.83946109e-02 9.66505647e-01 1.94872156e-01 4.13210131... | [15.197936058044434, -3.1076037883758545] |
381246e3-581b-4c5e-a914-71fb579c6281 | reconstructing-a-large-scale-3d-face-dataset | 2010.08391 | null | https://arxiv.org/abs/2010.08391v2 | https://arxiv.org/pdf/2010.08391v2.pdf | Reconstructing A Large Scale 3D Face Dataset for Deep 3D Face Identification | Deep learning methods have brought many breakthroughs to computer vision, especially in 2D face recognition. However, the bottleneck of deep learning based 3D face recognition is that it is difficult to collect millions of 3D faces, whether for industry or academia. In view of this situation, there are many methods to ... | ['Huibin Li', 'Zihui Zhang', 'Cuican Yu'] | 2020-10-16 | null | null | null | null | ['face-reconstruction'] | ['computer-vision'] | [-3.17571610e-01 -6.25044107e-02 1.88652039e-01 -5.27586460e-01
-4.58969414e-01 -3.68910760e-01 3.89196873e-01 -1.06090593e+00
3.52178607e-03 1.71425417e-01 -2.51159370e-01 -3.42837095e-01
1.61384940e-01 -9.06984627e-01 -6.75128579e-01 -8.55526447e-01
4.80783135e-02 6.31526411e-01 -5.51192701e-01 -9.27947164... | [13.265971183776855, 0.48118194937705994] |
7c3189f0-7fed-4710-9ec9-ea83c0b45ad5 | 3d-facial-imperfection-regeneration-deep | 2303.14381 | null | https://arxiv.org/abs/2303.14381v1 | https://arxiv.org/pdf/2303.14381v1.pdf | 3D Facial Imperfection Regeneration: Deep learning approach and 3D printing prototypes | This study explores the potential of a fully convolutional mesh autoencoder model for regenerating 3D nature faces with the presence of imperfect areas. We utilize deep learning approaches in graph processing and analysis to investigate the capabilities model in recreating a filling part for facial scars. Our approach ... | ['H. Nguyen-Xuan', 'Li-Wei Chou', 'Thanh Q. Nguyen', 'Duong Q. Nguyen', 'Thinh D. Le', 'Phuong D. Nguyen'] | 2023-03-25 | null | null | null | null | ['face-reconstruction'] | ['computer-vision'] | [-1.01981036e-01 4.07773882e-01 -2.18906021e-03 4.24282029e-02
-1.11035906e-01 9.86846834e-02 -1.40572395e-02 -4.88532335e-01
2.18923539e-01 6.75484598e-01 4.97204661e-01 -2.18066871e-01
-1.08664386e-01 -1.19154382e+00 -6.77768290e-01 -6.14051104e-01
-5.40701253e-03 2.50850260e-01 -5.45010924e-01 -4.78652596... | [12.803813934326172, -0.024428170174360275] |
070d61d6-4789-4959-8521-10e6cbb9fedb | global-optimality-of-elman-type-rnn-in-the | 2303.06726 | null | https://arxiv.org/abs/2303.06726v1 | https://arxiv.org/pdf/2303.06726v1.pdf | Global Optimality of Elman-type RNN in the Mean-Field Regime | We analyze Elman-type Recurrent Reural Networks (RNNs) and their training in the mean-field regime. Specifically, we show convergence of gradient descent training dynamics of the RNN to the corresponding mean-field formulation in the large width limit. We also show that the fixed points of the limiting infinite-width d... | ['Sayan Mukherjee', 'Jianfeng Lu', 'Andrea Agazzi'] | 2023-03-12 | null | null | null | null | ['type'] | ['speech'] | [-1.64005741e-01 2.26733550e-01 -1.51033342e-01 -5.73904738e-02
-3.19398463e-01 -4.86229241e-01 3.79084200e-01 -4.75989282e-01
-6.39284849e-01 7.11781025e-01 5.79281271e-01 -3.31900328e-01
-6.89864814e-01 -6.18348181e-01 -8.35608602e-01 -9.86831486e-01
-1.23031046e-02 3.19529980e-01 -9.76107642e-02 -6.08804405... | [7.883634567260742, 3.5065433979034424] |
2d799a83-b7ed-44eb-88eb-7dd13e57df99 | not-all-poisons-are-created-equal-robust | 2210.09671 | null | https://arxiv.org/abs/2210.09671v1 | https://arxiv.org/pdf/2210.09671v1.pdf | Not All Poisons are Created Equal: Robust Training against Data Poisoning | Data poisoning causes misclassification of test time target examples by injecting maliciously crafted samples in the training data. Existing defenses are often effective only against a specific type of targeted attack, significantly degrade the generalization performance, or are prohibitive for standard deep learning p... | ['Baharan Mirzasoleiman', 'Tian Yu Liu', 'Yu Yang'] | 2022-10-18 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [-4.15958352e-02 -2.46432930e-01 -3.10647070e-01 -1.11436017e-01
-8.80721152e-01 -9.57104921e-01 3.09487224e-01 2.82387108e-01
-6.00887060e-01 5.56027830e-01 -4.05959040e-01 -3.82172316e-01
1.34592401e-02 -9.94345307e-01 -1.12916410e+00 -1.00238907e+00
-3.46107036e-01 6.79805100e-01 6.89208150e-01 -7.46568991... | [5.789267539978027, 7.627106666564941] |
016c05fa-7bb2-464a-923a-6d791bceb248 | addressing-the-challenges-of-open-world | 2303.14930 | null | https://arxiv.org/abs/2303.14930v1 | https://arxiv.org/pdf/2303.14930v1.pdf | Addressing the Challenges of Open-World Object Detection | We address the challenging problem of open world object detection (OWOD), where object detectors must identify objects from known classes while also identifying and continually learning to detect novel objects. Prior work has resulted in detectors that have a relatively low ability to detect novel objects, and a high l... | ['Niko Sünderhauf', 'Dimity Miller', 'Feras Dayoub', 'David Pershouse'] | 2023-03-27 | null | null | null | null | ['open-world-object-detection'] | ['computer-vision'] | [ 1.75117910e-01 1.43065199e-01 3.71353142e-02 -1.04004517e-01
-9.60972428e-01 -8.68759453e-01 6.90136254e-01 5.21716297e-01
-6.74951077e-01 5.94635546e-01 -2.79379040e-01 3.28494497e-02
-7.86916092e-02 -8.33510220e-01 -7.71267831e-01 -2.74352908e-01
-4.93711323e-01 7.14093387e-01 1.18164968e+00 1.87547401... | [9.444011688232422, 1.5577353239059448] |
bc24486f-b32d-40a5-8f5b-6e3138a99371 | distributed-submodular-cover-succinctly | null | null | http://papers.nips.cc/paper/5752-distributed-submodular-cover-succinctly-summarizing-massive-data | http://papers.nips.cc/paper/5752-distributed-submodular-cover-succinctly-summarizing-massive-data.pdf | Distributed Submodular Cover: Succinctly Summarizing Massive Data | How can one find a subset, ideally as small as possible, that well represents a massive dataset? I.e., its corresponding utility, measured according to a suitable utility function, should be comparable to that of the whole dataset. In this paper, we formalize this challenge as a submodular cover problem. Here, the util... | ['Ashwinkumar Badanidiyuru', 'Baharan Mirzasoleiman', 'Andreas Krause', 'Amin Karbasi'] | 2015-12-01 | null | null | null | neurips-2015-12 | ['data-summarization'] | ['miscellaneous'] | [ 4.85122316e-02 2.84255922e-01 -1.71269253e-01 -2.04254106e-01
-1.03482616e+00 -8.79406393e-01 -2.80301332e-01 7.65591800e-01
-6.10830113e-02 9.95378077e-01 -4.83938940e-02 7.80477673e-02
-5.57640254e-01 -1.22015262e+00 -1.00997949e+00 -7.80596495e-01
-3.54284823e-01 9.03266609e-01 1.16337694e-01 -1.95243239... | [6.620212554931641, 4.928375244140625] |
5e764ee8-3268-474f-b1fd-d4d1274a514d | decoupling-recognition-from-detection-single | 2207.07253 | null | https://arxiv.org/abs/2207.07253v4 | https://arxiv.org/pdf/2207.07253v4.pdf | Single Shot Self-Reliant Scene Text Spotter by Decoupled yet Collaborative Detection and Recognition | Typical text spotters follow the two-stage spotting paradigm which detects the boundary for a text instance first and then performs text recognition within the detected regions. Despite the remarkable progress of such spotting paradigm, an important limitation is that the performance of text recognition depends heavily... | ['Wenjie Pei', 'Chengquan Zhang', 'Guangming Lu', 'Pengyuan Lyu', 'Jingjing Wu'] | 2022-07-15 | null | null | null | null | ['text-spotting'] | ['computer-vision'] | [ 6.34270787e-01 -4.33166325e-01 -8.17476362e-02 -9.79012698e-02
-9.04128373e-01 -2.55938917e-01 7.32123017e-01 1.34071797e-01
-3.47313493e-01 1.81955442e-01 -1.24476478e-01 8.98127034e-02
8.17150623e-02 -7.30539322e-01 -4.92057174e-01 -8.92310262e-01
5.61471522e-01 6.13201082e-01 6.22967005e-01 1.69562772... | [11.997896194458008, 2.242135763168335] |
3eb371ed-3f57-4325-a3e1-8f124c86339b | very-deep-convolutional-neural-networks-for-1 | 1610.00277 | null | http://arxiv.org/abs/1610.00277v1 | http://arxiv.org/pdf/1610.00277v1.pdf | Very Deep Convolutional Neural Networks for Robust Speech Recognition | This paper describes the extension and optimization of our previous work on
very deep convolutional neural networks (CNNs) for effective recognition of
noisy speech in the Aurora 4 task. The appropriate number of convolutional
layers, the sizes of the filters, pooling operations and input feature maps are
all modified:... | ['Philip C. Woodland', 'Yanmin Qian'] | 2016-10-02 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 1.54055983e-01 7.38214925e-02 2.36397326e-01 -6.81568503e-01
-1.36431623e+00 -3.24308127e-01 4.58638191e-01 -4.36309487e-01
-9.28684413e-01 4.45698202e-01 3.78086239e-01 -4.19491619e-01
2.87916716e-02 -2.53198832e-01 -5.78160882e-01 -8.32828879e-01
3.90294045e-02 -1.39524162e-01 3.07731777e-01 -3.25099289... | [14.594536781311035, 6.177769660949707] |
a596c215-31a4-4293-8070-db6fe3212b47 | integration-of-physics-based-and-data-driven | 2206.05508 | null | https://arxiv.org/abs/2206.05508v2 | https://arxiv.org/pdf/2206.05508v2.pdf | Integration of Physics-Based and Data-Driven Models for Hyperspectral Image Unmixing | Spectral unmixing is one of the most important quantitative analysis tasks in hyperspectral data processing. Conventional physics-based models are characterized by clear interpretation. However they may not be suitable for analyzing scenes with unknown complex physical characteristics. Data-driven methods have develope... | ['Susanto Rahardja', 'Cédric Richard', 'Xiuheng Wang', 'Min Zhao', 'Jie Chen'] | 2022-06-11 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 3.54341835e-01 -6.83561862e-01 -1.12715609e-01 -3.15829158e-01
-2.89370656e-01 -2.08135724e-01 5.29738486e-01 5.21967933e-02
-2.00007960e-01 8.01378191e-01 -3.45553644e-02 -2.21836254e-01
-8.24278831e-01 -8.54554892e-01 -2.81935424e-01 -1.16523492e+00
2.85686493e-01 3.19965929e-01 -4.15652156e-01 -4.15956557... | [10.12356185913086, -2.0513389110565186] |
bafd08f0-105a-462f-9d0f-267a404896e0 | feature-concatenation-multi-view-subspace | 1901.10657 | null | https://arxiv.org/abs/1901.10657v6 | https://arxiv.org/pdf/1901.10657v6.pdf | Feature Concatenation Multi-view Subspace Clustering | Multi-view clustering is a learning paradigm based on multi-view data. Since statistic properties of different views are diverse, even incompatible, few approaches implement multi-view clustering based on the concatenated features straightforward. However, feature concatenation is a natural way to combine multi-view da... | ['Shanmin Pang', 'Qinghai Zheng', 'Jun Wang', 'Jihua Zhu', 'Zhongyu Li', 'Yaochen Li'] | 2019-01-30 | null | null | null | null | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-3.12518060e-01 -6.86112761e-01 4.80457321e-02 -2.99483567e-01
-7.98753738e-01 -4.76644218e-01 3.54931355e-01 -2.81334430e-01
-1.20981224e-01 3.06515068e-01 3.51099342e-01 4.25129622e-01
-5.10624409e-01 -4.63013768e-01 -1.54266298e-01 -1.27654684e+00
1.56882852e-01 4.22037542e-02 -2.46178538e-01 4.72515449... | [8.2442045211792, 4.623725414276123] |
1c74e2bb-46e0-4798-bbdd-f0dbfaacd973 | variational-autoencoders-for-semi-supervised | 1603.02514 | null | http://arxiv.org/abs/1603.02514v3 | http://arxiv.org/pdf/1603.02514v3.pdf | Variational Autoencoders for Semi-supervised Text Classification | Although semi-supervised variational autoencoder (SemiVAE) works in image
classification task, it fails in text classification task if using vanilla LSTM
as its decoder. From a perspective of reinforcement learning, it is verified
that the decoder's capability to distinguish between different categorical
labels is esse... | ['Ying Tan', 'Weidi Xu', 'Haoze Sun', 'Chao Deng'] | 2016-03-08 | null | null | null | null | ['semi-supervised-text-classification-1'] | ['natural-language-processing'] | [ 1.26078740e-01 2.19911024e-01 -5.05207717e-01 -3.47336054e-01
-8.18906903e-01 -2.54482597e-01 7.24234521e-01 -1.01762548e-01
-7.01864183e-01 7.83244073e-01 -8.51821005e-02 -2.81240761e-01
2.78152406e-01 -5.16439021e-01 -6.07740879e-01 -9.70312119e-01
6.12837613e-01 5.16296089e-01 5.52469902e-02 1.22329049... | [9.50025463104248, 2.9934909343719482] |
f66b8c94-98a0-4c89-9473-125e678a0576 | unsupervised-deformable-ultrasound-image | 2306.13329 | null | https://arxiv.org/abs/2306.13329v1 | https://arxiv.org/pdf/2306.13329v1.pdf | Unsupervised Deformable Ultrasound Image Registration and Its Application for Vessel Segmentation | This paper presents a deep-learning model for deformable registration of ultrasound images at online rates, which we call U-RAFT. As its name suggests, U-RAFT is based on RAFT, a convolutional neural network for estimating optical flow. U-RAFT, however, can be trained in an unsupervised manner and can generate syntheti... | ['Howie Choset', 'John Galeotti', 'Ananya Bal', 'Andrew L. Orekhov', 'FNU Abhimanyu'] | 2023-06-23 | null | null | null | null | ['optical-flow-estimation', 'image-registration'] | ['computer-vision', 'computer-vision'] | [ 2.45464802e-01 5.76733589e-01 2.62690037e-01 -3.05318892e-01
-6.89639151e-01 -5.87037683e-01 -6.54754192e-02 -5.44697978e-02
-2.16026664e-01 4.84640092e-01 1.09734572e-01 -3.15171868e-01
1.00476280e-01 -7.72695780e-01 -8.85067344e-01 -4.22628105e-01
-4.74406391e-01 5.25725842e-01 4.34974700e-01 -1.86918467... | [14.13929271697998, -2.403764247894287] |
6747d81c-4b69-463d-b861-25196046c5ec | elc-ois-ellipsoidal-clustering-for-open-world | 2303.04351 | null | https://arxiv.org/abs/2303.04351v1 | https://arxiv.org/pdf/2303.04351v1.pdf | ElC-OIS: Ellipsoidal Clustering for Open-World Instance Segmentation on LiDAR Data | Open-world Instance Segmentation (OIS) is a challenging task that aims to accurately segment every object instance appearing in the current observation, regardless of whether these instances have been labeled in the training set. This is important for safety-critical applications such as robust autonomous navigation. I... | ['Xieyuanli Chen', 'Zhiqiang Zheng', 'Huimin Lu', 'Qinghua Yu', 'Kaihong Huang', 'Wenbang Deng'] | 2023-03-08 | null | null | null | null | ['panoptic-segmentation'] | ['computer-vision'] | [ 2.14018404e-01 -1.55691030e-02 -2.91291270e-02 -3.86348993e-01
-8.50055993e-01 -5.94868898e-01 3.72032881e-01 9.13331136e-02
-3.48938942e-01 6.34580970e-01 -8.73854041e-01 -1.73693538e-01
-2.66577452e-01 -1.01741731e+00 -7.63296008e-01 -6.65827155e-01
9.81920213e-02 1.11538756e+00 7.62121320e-01 1.56506926... | [8.087016105651855, -2.7031478881835938] |
68caa69a-4873-4c9c-868b-0dd8e940934a | memory-constrained-policy-optimization-1 | 2204.09315 | null | https://arxiv.org/abs/2204.09315v2 | https://arxiv.org/pdf/2204.09315v2.pdf | Learning to Constrain Policy Optimization with Virtual Trust Region | We introduce a constrained optimization method for policy gradient reinforcement learning, which uses a virtual trust region to regulate each policy update. In addition to using the proximity of one single old policy as the normal trust region, we propose forming a second trust region through another virtual policy rep... | ['Svetha Venkatesh', 'Sunil Gupta', 'Kien Do', 'Dung Nguyen', 'Majid Abdolshah', 'Thommen Karimpanal George', 'Hung Le'] | 2022-04-20 | memory-constrained-policy-optimization | https://openreview.net/forum?id=7yuU9VeIpde | https://openreview.net/pdf?id=7yuU9VeIpde | null | ['policy-gradient-methods'] | ['methodology'] | [-4.08447623e-01 1.42525241e-03 -7.04285264e-01 -7.14038610e-02
-2.18348950e-01 -5.06807625e-01 6.00102365e-01 5.37903085e-02
-1.00643229e+00 1.42767346e+00 2.30531409e-01 -2.70649850e-01
-6.86834380e-02 -7.69878805e-01 -9.09921944e-01 -7.33694136e-01
-2.77628571e-01 2.77228624e-01 3.62111926e-01 -5.88937700... | [4.102975368499756, 2.109081983566284] |
cb538d90-56be-457c-a57a-fa413cfc7e19 | polar-transformer-networks | 1709.01889 | null | http://arxiv.org/abs/1709.01889v3 | http://arxiv.org/pdf/1709.01889v3.pdf | Polar Transformer Networks | Convolutional neural networks (CNNs) are inherently equivariant to
translation. Efforts to embed other forms of equivariance have concentrated
solely on rotation. We expand the notion of equivariance in CNNs through the
Polar Transformer Network (PTN). PTN combines ideas from the Spatial
Transformer Network (STN) and c... | ['Christine Allen-Blanchette', 'Xiaowei Zhou', 'Kostas Daniilidis', 'Carlos Esteves'] | 2017-09-06 | polar-transformer-networks-1 | https://openreview.net/forum?id=HktRlUlAZ | https://openreview.net/pdf?id=HktRlUlAZ | iclr-2018-1 | ['rotated-mnist'] | ['computer-vision'] | [ 2.39836127e-02 1.97950497e-01 8.72689039e-02 -3.10151398e-01
-3.19053888e-01 -9.94093597e-01 1.19207060e+00 -6.89461946e-01
-4.64386493e-01 3.38155240e-01 4.07984942e-01 -4.72827047e-01
1.26313224e-01 -5.50413251e-01 -1.08196759e+00 -5.42963147e-01
-5.08739613e-02 4.20331150e-01 -4.54074852e-02 -7.13217974... | [8.918286323547363, 2.3460941314697266] |
faf54ff8-fd88-4a4e-8a81-4b4e18a205c4 | contrastive-attention-networks-for | 2306.07998 | null | https://arxiv.org/abs/2306.07998v1 | https://arxiv.org/pdf/2306.07998v1.pdf | Contrastive Attention Networks for Attribution of Early Modern Print | In this paper, we develop machine learning techniques to identify unknown printers in early modern (c.~1500--1800) English printed books. Specifically, we focus on matching uniquely damaged character type-imprints in anonymously printed books to works with known printers in order to provide evidence of their origins. U... | ['Taylor Berg-Kirkpatrick', "Max G'Sell", 'Christopher N. Warren', 'Samuel V. Lemley', 'Elizaveta Pertseva', 'Kishore PV Reddy', 'Kartik Goyal', 'Nikolai Vogler'] | 2023-06-12 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [ 2.41305858e-01 -1.78811595e-01 2.37905189e-01 -1.13447897e-01
-1.04985011e+00 -9.10438776e-01 1.02810311e+00 3.09092969e-01
-2.96790838e-01 5.25186241e-01 3.26816380e-01 4.87455055e-02
-2.99396813e-01 -7.64652133e-01 -9.07080829e-01 -1.71426550e-01
2.87176192e-01 7.57471204e-01 5.45678586e-02 -1.96497262... | [10.188698768615723, 10.238249778747559] |
75cee51c-bff7-43a5-ad20-57ca7a42b47f | scaling-up-learning-with-gait-prop | 2102.11598 | null | https://arxiv.org/abs/2102.11598v3 | https://arxiv.org/pdf/2102.11598v3.pdf | Gradient-adjusted Incremental Target Propagation Provides Effective Credit Assignment in Deep Neural Networks | Many of the recent advances in the field of artificial intelligence have been fueled by the highly successful backpropagation of error (BP) algorithm, which efficiently solves the credit assignment problem in artificial neural networks. However, it is unlikely that BP is implemented in its usual form within biological ... | ['Marcel van Gerven', 'Luca Ambrogioni', 'Nasir Ahmad', 'Sander Dalm'] | 2021-02-23 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 5.29337466e-01 2.74550736e-01 2.95340508e-01 -3.13793123e-01
4.84409370e-02 -3.60254645e-02 5.40626049e-01 1.88614398e-01
-7.63025939e-01 1.13565373e+00 -1.38716400e-01 -1.92498609e-01
-2.34866351e-01 -7.99603820e-01 -9.04284954e-01 -8.21313560e-01
-1.47238597e-01 6.15156054e-01 6.16842985e-01 -1.45799324... | [8.201138496398926, 2.886044979095459] |
f0aabdf7-e8dc-43eb-b9c6-fbfb9b555526 | graphganfed-a-federated-generative-framework | 2304.05498 | null | https://arxiv.org/abs/2304.05498v1 | https://arxiv.org/pdf/2304.05498v1.pdf | GraphGANFed: A Federated Generative Framework for Graph-Structured Molecules Towards Efficient Drug Discovery | Recent advances in deep learning have accelerated its use in various applications, such as cellular image analysis and molecular discovery. In molecular discovery, a generative adversarial network (GAN), which comprises a discriminator to distinguish generated molecules from existing molecules and a generator to genera... | ['Xiang Sun', 'Wuji Liu', 'Jingjing Yao', 'Daniel Manu'] | 2023-04-11 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 2.48352721e-01 -6.63894974e-03 -1.96100518e-01 -7.14369342e-02
-5.53200245e-01 -8.22445393e-01 3.97130907e-01 1.04799289e-02
-1.14184767e-01 1.17106164e+00 -1.12477034e-01 -3.78888458e-01
1.36448890e-01 -1.22758806e+00 -8.54414940e-01 -1.15929317e+00
-3.49109024e-02 3.77914831e-02 -1.21350616e-01 -1.67671159... | [5.802748680114746, 7.233469009399414] |
1d07ec95-cb17-4d3b-9378-224399118f72 | hybrid-neural-diffeomorphic-flow-for-shape | 2307.01957 | null | https://arxiv.org/abs/2307.01957v1 | https://arxiv.org/pdf/2307.01957v1.pdf | Hybrid Neural Diffeomorphic Flow for Shape Representation and Generation via Triplane | Deep Implicit Functions (DIFs) have gained popularity in 3D computer vision due to their compactness and continuous representation capabilities. However, addressing dense correspondences and semantic relationships across DIF-encoded shapes remains a critical challenge, limiting their applications in texture transfer an... | ['Xiaohui Xie', 'Shanlin Sun', 'Kun Han'] | 2023-07-04 | null | null | null | null | ['3d-shape-generation', '3d-shape-representation'] | ['computer-vision', 'computer-vision'] | [ 2.39666952e-05 2.75140256e-01 -3.07051279e-02 -4.27341282e-01
-4.94374216e-01 -8.53889048e-01 7.40318358e-01 -2.68922076e-02
2.23550558e-01 6.32308364e-01 5.13409734e-01 -1.39296100e-01
-3.26241106e-01 -1.04258418e+00 -6.96031928e-01 -7.70224273e-01
-6.58843592e-02 7.48377323e-01 -6.20829239e-02 -2.67076313... | [8.770262718200684, -3.6708285808563232] |
1b0d0afb-1f9d-4161-9a90-7c5bffbf7edd | naive-regularizers-for-low-resource-neural | null | null | https://aclanthology.org/R19-1013 | https://aclanthology.org/R19-1013.pdf | Naive Regularizers for Low-Resource Neural Machine Translation | Neural machine translation models have little inductive bias, which can be a disadvantage in low-resource scenarios. Neural models have to be trained on large amounts of data and have been shown to perform poorly when only limited data is available. We show that using naive regularization methods, based on sentence len... | ['Anders S{\\o}gaard', 'Ana Valeria Gonzalez', 'Marcel Bollmann', 'Meriem Beloucif'] | 2019-09-01 | null | null | null | ranlp-2019-9 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 2.47303471e-01 -7.37603903e-02 -6.01773858e-01 -4.78188843e-01
-1.48405659e+00 -7.07465470e-01 5.93287170e-01 1.15182042e-01
-1.01064515e+00 1.30483866e+00 4.21582639e-01 -7.92612731e-01
5.36808908e-01 -4.77305174e-01 -9.83003855e-01 -4.09075767e-01
2.83935636e-01 6.00987911e-01 -3.83430213e-01 -4.10749316... | [11.561464309692383, 10.255462646484375] |
80b253ad-5bc4-47a1-b8c7-9da4f8f423b9 | unsupervised-multiple-object-tracking-with-a | 2202.09315 | null | https://arxiv.org/abs/2202.09315v2 | https://arxiv.org/pdf/2202.09315v2.pdf | Unsupervised Multiple-Object Tracking with a Dynamical Variational Autoencoder | In this paper, we present an unsupervised probabilistic model and associated estimation algorithm for multi-object tracking (MOT) based on a dynamical variational autoencoder (DVAE), called DVAE-UMOT. The DVAE is a latent-variable deep generative model that can be seen as an extension of the variational autoencoder for... | ['Xavier Alameda-Pineda', 'Laurent Girin', 'Xiaoyu Lin'] | 2022-02-18 | null | null | null | null | ['temporal-sequences'] | ['reasoning'] | [-3.82428318e-01 8.91941041e-02 3.11443973e-02 -1.17953405e-01
-4.68335658e-01 -3.51776809e-01 9.55410302e-01 -4.18094814e-01
-2.84149736e-01 6.45905674e-01 -6.05658032e-02 2.15461269e-01
-2.34914988e-01 -6.24449849e-01 -9.53520119e-01 -9.37833846e-01
9.09628421e-02 1.22537184e+00 5.05555868e-01 3.54151458... | [7.360686302185059, -0.05284833535552025] |
dc3d2dab-2bf0-4908-90e6-21f0a1ee9846 | an-effective-discourse-parser-that-uses-rich | null | null | https://aclanthology.org/N09-1064/ | https://aclanthology.org/N09-1064.pdf | An effective Discourse Parser that uses Rich Linguistic Information | This paper presents a first-order logic learning approach to determine rhetorical relations between discourse segments. Beyond linguistic cues and lexical information, our approach exploits compositional semantics and segment discourse structure data. We report a statistically significant improvement in classifying rel... | ['Barbara Di Eugenio', 'Rajen Subba'] | 2009-05-31 | null | null | null | proceedings-of-human-language-technologies | ['discourse-parsing'] | ['natural-language-processing'] | [ 5.00743687e-01 1.03167593e+00 -1.19805777e+00 -7.06124663e-01
-1.13941991e+00 -9.40444589e-01 1.02308881e+00 9.73042011e-01
-1.70625627e-01 1.05473936e+00 8.73224914e-01 -1.15824378e+00
-3.43378335e-02 -1.09407270e+00 -5.68119109e-01 -4.42684330e-02
-1.98583037e-01 4.79680270e-01 6.73829973e-01 -5.74288428... | [10.746925354003906, 9.337820053100586] |
b3db8e10-b2b9-45e8-a6f2-f0eb7dec1b5d | communication-efficient-federated-bilevel | 2302.06701 | null | https://arxiv.org/abs/2302.06701v1 | https://arxiv.org/pdf/2302.06701v1.pdf | Communication-Efficient Federated Bilevel Optimization with Local and Global Lower Level Problems | Bilevel Optimization has witnessed notable progress recently with new emerging efficient algorithms, yet it is underexplored in the Federated Learning setting. It is unclear how the challenges of Federated Learning affect the convergence of bilevel algorithms. In this work, we study Federated Bilevel Optimization probl... | ['Heng Huang', 'Feihu Huang', 'Junyi Li'] | 2023-02-13 | null | null | null | null | ['bilevel-optimization'] | ['methodology'] | [-6.02899075e-01 -1.88158117e-02 -3.17083180e-01 -1.13131151e-01
-1.04668152e+00 -6.05516016e-01 1.63218945e-01 2.91833222e-01
-5.50268292e-01 8.96237552e-01 1.26169715e-02 -5.96159339e-01
-5.67745805e-01 -8.56168270e-01 -1.11538756e+00 -8.26817334e-01
-7.28244126e-01 5.94636500e-01 -3.34467322e-01 3.15872729... | [6.214059829711914, 5.049936294555664] |
b5ca2866-cc37-4777-ae9d-bd0a63a36f36 | towards-stroke-patients-upper-limb-automatic | 2212.05062 | null | https://arxiv.org/abs/2212.05062v1 | https://arxiv.org/pdf/2212.05062v1.pdf | Towards Stroke Patients' Upper-limb Automatic Motor Assessment Using Smartwatches | Assessing the physical condition in rehabilitation scenarios is a challenging problem, since it involves Human Activity Recognition (HAR) and kinematic analysis methods. In addition, the difficulties increase in unconstrained rehabilitation scenarios, which are much closer to the real use cases. In particular, our aim ... | ['Miguel A. Ferrer', 'Josep Lladós', 'Cristina Carmona-Duarte', 'Alicia Fornés', 'Jialuo Chen', 'Asma Bensalah'] | 2022-12-09 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [ 1.71914622e-01 -5.22587076e-02 -5.26613832e-01 6.59580603e-02
-6.84657753e-01 -4.70682889e-01 5.26942551e-01 -1.32428110e-01
-8.03673565e-01 6.91697836e-01 1.00953710e+00 -1.60957500e-01
-3.20142567e-01 -4.43209976e-01 2.91128606e-02 -3.67308855e-01
-4.49084550e-01 3.75616014e-01 4.52239215e-01 -2.79679358... | [7.07755184173584, 0.2852584719657898] |
e7a8a28b-0bcf-4095-a974-ff154e1b4518 | strong-but-simple-baseline-with-dual | 2012.05010 | null | https://arxiv.org/abs/2012.05010v2 | https://arxiv.org/pdf/2012.05010v2.pdf | Strong but Simple Baseline with Dual-Granularity Triplet Loss for Visible-Thermal Person Re-Identification | In this letter, we propose a conceptually simple and effective dual-granularity triplet loss for visible-thermal person re-identification (VT-ReID). In general, ReID models are always trained with the sample-based triplet loss and identification loss from the fine granularity level. It is possible when a center-based l... | ['Xichuan Zhou', 'Dong Li', 'Xiaoheng Tan', 'Yanxia Chai', 'Haijun Liu'] | 2020-12-09 | null | null | null | null | ['cross-view-person-re-identification'] | ['computer-vision'] | [-2.26425260e-01 -3.47249746e-01 -2.03072131e-01 -6.54953599e-01
-9.19640958e-01 -4.23894733e-01 7.19269156e-01 2.74909079e-01
-6.73775673e-01 7.56156325e-01 3.16833228e-01 -1.03662789e-01
5.20582460e-02 -7.04697967e-01 -7.01567769e-01 -7.31553376e-01
1.51695192e-01 3.83051157e-01 1.32799670e-01 -6.90837651... | [14.72497272491455, 0.9865638017654419] |
5204a529-86b4-485c-b6a9-70a550f8732e | empirical-asset-pricing-via-ensemble-gaussian | 2212.01048 | null | https://arxiv.org/abs/2212.01048v1 | https://arxiv.org/pdf/2212.01048v1.pdf | Empirical Asset Pricing via Ensemble Gaussian Process Regression | We introduce an ensemble learning method based on Gaussian Process Regression (GPR) for predicting conditional expected stock returns given stock-level and macro-economic information. Our ensemble learning approach significantly reduces the computational complexity inherent in GPR inference and lends itself to general ... | ['Puneet Pasricha', 'Damir Filipović'] | 2022-12-02 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [-6.86206460e-01 -6.45916238e-02 -2.98439503e-01 -3.08769971e-01
-1.07828128e+00 -9.36455548e-01 7.77931511e-01 2.43099779e-02
-3.39841604e-01 9.42950070e-01 2.22871974e-01 -9.86639023e-01
-5.38528204e-01 -1.04889357e+00 -4.86973226e-01 -5.44492900e-01
-4.31984603e-01 3.65146756e-01 -2.01501966e-01 3.10738266... | [4.842289447784424, 4.071173667907715] |
27c2f982-2e2d-4584-a8d1-6f369db329e8 | gini-regularized-optimal-transport-with-an | 1712.02512 | null | http://arxiv.org/abs/1712.02512v1 | http://arxiv.org/pdf/1712.02512v1.pdf | Gini-regularized Optimal Transport with an Application to Spatio-Temporal Forecasting | Rapidly growing product lines and services require a finer-granularity
forecast that considers geographic locales. However the open question remains,
how to assess the quality of a spatio-temporal forecast? In this manuscript we
introduce a metric to evaluate spatio-temporal forecasts. This metric is based
on an Opti- ... | ['Leo Razoumov', 'Lin Su', 'Yuyang Wang', 'Lucas Roberts'] | 2017-12-07 | null | null | null | null | ['spatio-temporal-forecasting'] | ['time-series'] | [-3.73132795e-01 -6.13769926e-02 1.28193215e-01 -2.19761655e-01
-6.82168067e-01 -3.50606501e-01 6.90033674e-01 2.26524234e-01
-3.03726673e-01 1.00649226e+00 4.49103564e-02 -3.56731176e-01
-4.97028738e-01 -9.81025517e-01 -4.10243750e-01 -1.06246781e+00
-3.29265058e-01 7.86618114e-01 2.28294834e-01 -5.45561671... | [6.427553176879883, 3.8280978202819824] |
ac676919-d1f3-455e-b8ae-21a6b89b8f6d | mixup-of-feature-maps-in-a-hidden-layer-for | 1906.09739 | null | https://arxiv.org/abs/1906.09739v1 | https://arxiv.org/pdf/1906.09739v1.pdf | Mixup of Feature Maps in a Hidden Layer for Training of Convolutional Neural Network | The deep Convolutional Neural Network (CNN) became very popular as a fundamental technique for image classification and objects recognition. To improve the recognition accuracy for the more complex tasks, deeper networks have being introduced. However, the recognition accuracy of the trained deep CNN drastically decrea... | ['Takio Kurita', 'Hideki Oki'] | 2019-06-24 | null | null | null | null | ['image-morphing'] | ['computer-vision'] | [ 2.23792166e-01 -1.29428580e-01 1.72965080e-02 -3.90823066e-01
-3.93296778e-02 2.83268951e-02 5.74189246e-01 -1.39876842e-01
-7.20156610e-01 5.55097997e-01 -6.14877157e-02 2.52219498e-01
2.62824774e-01 -1.10474575e+00 -7.63797879e-01 -7.61900842e-01
3.53509694e-01 2.04988346e-01 1.61754847e-01 -1.39871895... | [9.318387031555176, 2.184021234512329] |
a9e499fa-c9ab-4b9a-8cde-f99848e296fe | point-cloud-registration-driven-robust | 2209.06395 | null | https://arxiv.org/abs/2209.06395v2 | https://arxiv.org/pdf/2209.06395v2.pdf | Point Cloud Registration-Driven Robust Feature Matching for 3D Siamese Object Tracking | Learning robust feature matching between the template and search area is crucial for 3D Siamese tracking. The core of Siamese feature matching is how to assign high feature similarity on the corresponding points between the template and search area for precise object localization. In this paper, we propose a novel poin... | ['Jian Yang', 'Jin Xie', 'Guangyu Li', 'Le Hui', 'Kaihao Lan', 'Haobo Jiang'] | 2022-09-14 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [-2.50743181e-01 -6.00757539e-01 -2.12327346e-01 -2.48906657e-01
-1.10953176e+00 -4.81694013e-01 3.82371068e-01 -7.07554072e-03
-4.11483705e-01 6.68270588e-02 -2.89716776e-02 4.10851985e-01
-4.92524505e-01 -4.35086787e-01 -6.75875187e-01 -8.53549778e-01
1.13464125e-01 5.82050383e-01 5.42704403e-01 7.25183785... | [6.649143218994141, -2.393994092941284] |
2ace200a-6950-44e3-96eb-3279850487a5 | spatial-context-aware-deep-neural-network-for | 2111.12296 | null | https://arxiv.org/abs/2111.12296v2 | https://arxiv.org/pdf/2111.12296v2.pdf | Spatial-context-aware deep neural network for multi-class image classification | Multi-label image classification is a fundamental but challenging task in computer vision. Over the past few decades, solutions exploring relationships between semantic labels have made great progress. However, the underlying spatial-contextual information of labels is under-exploited. To tackle this problem, a spatial... | ['Jiang Liu', 'Yitian Zhao', 'Jianfeng Ren', 'Qian Zhang', 'Jialu Zhang'] | 2021-11-24 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 4.51647282e-01 -3.80788028e-01 -2.42415965e-01 -7.45253623e-01
-7.55008042e-01 -3.64149898e-01 5.80910921e-01 4.44347948e-01
-7.50358462e-01 6.54023051e-01 -2.98868358e-01 -4.90040258e-02
-2.50114679e-01 -4.51030850e-01 -4.34667706e-01 -7.12024570e-01
3.91673476e-01 2.33961239e-01 3.54027450e-01 1.24938995... | [9.799433708190918, 3.9811649322509766] |
36f4a2ee-0af6-4e96-9653-9bdd7ed94917 | strategies-in-transfer-learning-for-low | 2306.12040 | null | https://arxiv.org/abs/2306.12040v1 | https://arxiv.org/pdf/2306.12040v1.pdf | Strategies in Transfer Learning for Low-Resource Speech Synthesis: Phone Mapping, Features Input, and Source Language Selection | We compare using a PHOIBLE-based phone mapping method and using phonological features input in transfer learning for TTS in low-resource languages. We use diverse source languages (English, Finnish, Hindi, Japanese, and Russian) and target languages (Bulgarian, Georgian, Kazakh, Swahili, Urdu, and Uzbek) to test the la... | ['Esther Klabbers', 'Jelske Dijkstra', 'Matt Coler', 'Phat Do'] | 2023-06-21 | null | null | null | null | ['transfer-learning', 'speech-synthesis'] | ['miscellaneous', 'speech'] | [ 7.60086924e-02 -2.87714392e-01 -1.82082936e-01 -3.16053659e-01
-1.12195683e+00 -8.15972269e-01 8.18760037e-01 -4.79607321e-02
-8.56651723e-01 1.13946772e+00 4.14449841e-01 -7.54497647e-01
-1.21766627e-01 -5.90377748e-01 -4.31869596e-01 -5.80080628e-01
2.39212587e-01 6.76847816e-01 4.36585486e-01 -3.03456813... | [14.297798156738281, 6.962036609649658] |
b5bcca5d-f565-436a-affd-c2f7a29c8f9c | sdcl-self-distillation-contrastive-learning | 2210.17168 | null | https://arxiv.org/abs/2210.17168v4 | https://arxiv.org/pdf/2210.17168v4.pdf | SDCL: Self-Distillation Contrastive Learning for Chinese Spell Checking | Due to the ambiguity of homophones, Chinese Spell Checking (CSC) has widespread applications. Existing systems typically utilize BERT for text encoding. However, CSC requires the model to account for both phonetic and graphemic information. To adapt BERT to the CSC task, we propose a token-level self-distillation contr... | ['Yu Sun', 'Xipeng Qiu', 'Hang Yan', 'Xiaotian Zhang'] | 2022-10-31 | null | null | null | null | ['chinese-spell-checking'] | ['natural-language-processing'] | [ 3.60847622e-01 -2.01669782e-01 -2.79172450e-01 -4.83742923e-01
-1.20444214e+00 -4.78046119e-01 3.85876179e-01 2.04693750e-01
-6.70483053e-01 6.15159035e-01 6.41229987e-01 -4.72896725e-01
7.79162586e-01 -3.73827070e-01 -9.05107796e-01 -4.39207047e-01
2.82776803e-01 9.79487300e-02 2.90621370e-01 -6.86158910... | [10.939059257507324, 10.649880409240723] |
b31c3b1b-c3e0-48ed-b7c4-cf968bd6c719 | continuous-offline-handwriting-recognition | 2112.13328 | null | https://arxiv.org/abs/2112.13328v1 | https://arxiv.org/pdf/2112.13328v1.pdf | Continuous Offline Handwriting Recognition using Deep Learning Models | Handwritten text recognition is an open problem of great interest in the area of automatic document image analysis. The transcription of handwritten content present in digitized documents is significant in analyzing historical archives or digitizing information from handwritten documents, forms, and communications. In ... | ['Jorge Sueiras'] | 2021-12-26 | null | null | null | null | ['handwriting-recognition'] | ['computer-vision'] | [ 4.48313653e-01 -5.13673842e-01 2.27000162e-01 -3.15408230e-01
-2.67618388e-01 -5.90994418e-01 6.92241669e-01 6.34492338e-02
-5.73292613e-01 6.54122531e-01 -5.15730307e-02 -2.84819335e-01
-2.54316092e-01 -6.72517061e-01 -4.67507631e-01 -7.17038155e-01
1.38905153e-01 5.71061432e-01 4.62381281e-02 -2.61022776... | [11.813530921936035, 2.627074956893921] |
2aec6988-e224-4640-bbf9-c48bced70268 | cascaded-information-enhancement-and-cross | 2302.08670 | null | https://arxiv.org/abs/2302.08670v1 | https://arxiv.org/pdf/2302.08670v1.pdf | Cascaded information enhancement and cross-modal attention feature fusion for multispectral pedestrian detection | Multispectral pedestrian detection is a technology designed to detect and locate pedestrians in Color and Thermal images, which has been widely used in automatic driving, video surveillance, etc. So far most available multispectral pedestrian detection algorithms only achieved limited success in pedestrian detection be... | ['Kaizheng Wang', 'Kaixiong Xu', 'Yang Yang'] | 2023-02-17 | null | null | null | null | ['pedestrian-detection'] | ['computer-vision'] | [ 1.77903362e-02 -7.61095583e-01 3.48215133e-01 -8.52511972e-02
-6.24500155e-01 -2.43021145e-01 5.25438964e-01 4.42904793e-02
-8.28053355e-01 5.45568705e-01 1.47799775e-01 -2.33241194e-03
2.25207523e-01 -7.61234462e-01 -3.50964189e-01 -1.22691107e+00
3.56911510e-01 -3.81250501e-01 7.17489541e-01 -1.86539739... | [9.823103904724121, -1.396195650100708] |
f6d2b75d-4a1c-47ab-9c94-79f61e9ad5d4 | 3d-object-proposals-for-accurate-object-class | null | null | http://papers.nips.cc/paper/5644-3d-object-proposals-for-accurate-object-class-detection | http://papers.nips.cc/paper/5644-3d-object-proposals-for-accurate-object-class-detection.pdf | 3D Object Proposals for Accurate Object Class Detection | The goal of this paper is to generate high-quality 3D object proposals in the context of autonomous driving. Our method exploits stereo imagery to place proposals in the form of 3D bounding boxes. We formulate the problem as minimizing an energy function encoding object size priors, ground plane as well as several de... | ['Andrew G. Berneshawi', 'Yukun Zhu', 'Sanja Fidler', 'Raquel Urtasun', 'Xiaozhi Chen', 'Huimin Ma', 'Kaustav Kundu'] | 2015-12-01 | null | null | null | neurips-2015-12 | ['vehicle-pose-estimation'] | ['computer-vision'] | [-1.35932192e-01 4.00937855e-01 -9.11635011e-02 -7.61670887e-01
-7.95628130e-01 -4.76793557e-01 8.31497431e-01 -8.66176113e-02
-6.73732519e-01 4.72716302e-01 4.34159413e-02 -9.72784013e-02
1.67263880e-01 -6.89833879e-01 -9.80493844e-01 -1.91306621e-01
2.48924103e-02 1.02117753e+00 7.58783102e-01 -3.97603959... | [7.775477886199951, -2.6327199935913086] |
71039fed-706e-479e-8b6a-fb7a866495e0 | beyond-deep-residual-learning-for-image | 1611.06345 | null | http://arxiv.org/abs/1611.06345v4 | http://arxiv.org/pdf/1611.06345v4.pdf | Beyond Deep Residual Learning for Image Restoration: Persistent Homology-Guided Manifold Simplification | The latest deep learning approaches perform better than the state-of-the-art
signal processing approaches in various image restoration tasks. However, if an
image contains many patterns and structures, the performance of these CNNs is
still inferior. To address this issue, here we propose a novel feature space
deep res... | ['Woong Bae', 'Jong Chul Ye', 'Jaejun Yoo'] | 2016-11-19 | null | null | null | null | ['color-image-denoising'] | ['computer-vision'] | [-8.53460953e-02 -2.21216559e-01 2.02053964e-01 -5.38537279e-02
-9.15789127e-01 -1.55918866e-01 5.58755815e-01 -4.41816002e-01
-1.40856847e-01 6.83192015e-01 5.40306509e-01 6.12962507e-02
-3.40048134e-01 -5.14886796e-01 -8.21115196e-01 -6.82467282e-01
-1.49174288e-01 2.91743316e-02 1.89963743e-01 -3.46901655... | [11.165032386779785, -2.2095212936401367] |
69f62ef7-dfaf-49da-ac10-af221a56493d | counterfactual-supervision-based-information | 2208.07798 | null | https://arxiv.org/abs/2208.07798v3 | https://arxiv.org/pdf/2208.07798v3.pdf | Counterfactual Supervision-based Information Bottleneck for Out-of-Distribution Generalization | Learning invariant (causal) features for out-of-distribution (OOD) generalization has attracted extensive attention recently, and among the proposals invariant risk minimization (IRM) is a notable solution. In spite of its theoretical promise for linear regression, the challenges of using IRM in linear classification p... | ['Kui Jia', 'Bin Deng'] | 2022-08-16 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 3.25886935e-01 4.45435584e-01 -6.13642752e-01 -3.54293376e-01
-1.00155902e+00 -2.84529507e-01 5.50758302e-01 -3.30756754e-02
-2.41629481e-01 9.94253874e-01 5.32746091e-02 -7.00560093e-01
-8.28859329e-01 -5.94888628e-01 -1.05891371e+00 -6.64875984e-01
-4.06760544e-01 2.23694444e-01 -6.19785301e-02 8.58810544... | [8.68509292602539, 4.1954827308654785] |
50222f16-eb9d-4b19-ab7f-3c223e945008 | complex-word-identification-based-on | null | null | https://aclanthology.org/W18-0521 | https://aclanthology.org/W18-0521.pdf | Complex Word Identification Based on Frequency in a Learner Corpus | We introduce the TMU systems for the Complex Word Identification (CWI) Shared Task 2018. TMU systems use random forest classifiers and regressors whose features are the number of characters, the number of words, and the frequency of target words in various corpora. Our simple systems performed best on 5 tracks out of 1... | ['Tomoyuki Kajiwara', 'Mamoru Komachi'] | 2018-06-01 | null | null | null | ws-2018-6 | ['complex-word-identification'] | ['natural-language-processing'] | [ 2.62460351e-01 -8.66836235e-02 -5.30178845e-01 -3.02138887e-02
-7.56861150e-01 -7.05517113e-01 9.92858350e-01 2.00236395e-01
-1.04970253e+00 5.84021628e-01 4.48823601e-01 -8.23201835e-01
2.30769455e-01 -4.69434738e-01 -5.10472357e-01 -1.72797307e-01
-6.47813827e-02 5.13149798e-01 1.72825679e-01 -1.50398076... | [10.499053001403809, 10.491033554077148] |
5325e099-2b2a-49a0-b30b-271c5e5105ea | multimodal-emotion-recognition-model-using | 1911.12918 | null | https://arxiv.org/abs/1911.12918v2 | https://arxiv.org/pdf/1911.12918v2.pdf | Multimodal Affective States Recognition Based on Multiscale CNNs and Biologically Inspired Decision Fusion Model | There has been an encouraging progress in the affective states recognition models based on the single-modality signals as electroencephalogram (EEG) signals or peripheral physiological signals in recent years. However, multimodal physiological signals-based affective states recognition methods have not been thoroughly ... | ['Yuxuan Zhao', 'Xinyan Cao', 'Jinlong Lin', 'Xixin Cao', 'Dunshan Yu'] | 2019-11-29 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 2.14237764e-01 -3.17613214e-01 3.52956206e-01 -5.83519936e-01
-4.76866215e-01 -1.49940342e-01 3.27495813e-01 1.97791755e-01
-4.82249856e-01 1.02332807e+00 1.83509201e-01 4.56948608e-01
-1.05055466e-01 -6.87749088e-01 -3.10105771e-01 -1.00011969e+00
-7.58377789e-03 -3.52587909e-01 -3.62691849e-01 -1.19615734... | [13.177459716796875, 3.442643165588379] |
987695fd-fc34-4567-a420-4fb202ca7eaa | knowledge-driven-event-embedding-for-stock | null | null | https://aclanthology.org/C16-1201 | https://aclanthology.org/C16-1201.pdf | Knowledge-Driven Event Embedding for Stock Prediction | Representing structured events as vectors in continuous space offers a new way for defining dense features for natural language processing (NLP) applications. Prior work has proposed effective methods to learn event representations that can capture syntactic and semantic information over text corpus, demonstrating thei... | ['Yue Zhang', 'Ting Liu', 'Junwen Duan', 'Xiao Ding'] | 2016-12-01 | knowledge-driven-event-embedding-for-stock-1 | https://aclanthology.org/C16-1201 | https://aclanthology.org/C16-1201.pdf | coling-2016-12 | ['stock-market-prediction', 'stock-prediction'] | ['time-series', 'time-series'] | [-3.67306679e-01 1.90933011e-02 -5.64279914e-01 -5.37703872e-01
-2.06183895e-01 -7.17804313e-01 9.06877160e-01 8.74118209e-01
-4.76301551e-01 6.77261114e-01 8.83366942e-01 -2.13059619e-01
-9.15689915e-02 -1.57645619e+00 -5.59152246e-01 -2.90152192e-01
-4.97879803e-01 2.42504373e-01 2.79840350e-01 -1.94361433... | [8.728378295898438, 8.268895149230957] |
345500b0-6a99-4cbd-965b-318f3c4e5485 | non-autoregressive-text-generation-with-pre | 2102.08220 | null | https://arxiv.org/abs/2102.08220v1 | https://arxiv.org/pdf/2102.08220v1.pdf | Non-Autoregressive Text Generation with Pre-trained Language Models | Non-autoregressive generation (NAG) has recently attracted great attention due to its fast inference speed. However, the generation quality of existing NAG models still lags behind their autoregressive counterparts. In this work, we show that BERT can be employed as the backbone of a NAG model to greatly improve perfor... | ['Nigel Collier', 'Piji Li', 'Simon Baker', 'David Vandyke', 'Yan Wang', 'Deng Cai', 'Yixuan Su'] | 2021-02-16 | null | https://aclanthology.org/2021.eacl-main.18 | https://aclanthology.org/2021.eacl-main.18.pdf | eacl-2021-2 | ['sentence-compression'] | ['natural-language-processing'] | [ 3.19907099e-01 4.56270464e-02 -2.60289788e-01 -2.74596781e-01
-1.17079914e+00 -3.08662236e-01 6.88713312e-01 -1.71430126e-01
-6.65747747e-02 8.70904565e-01 7.62742102e-01 -6.56783164e-01
3.00243348e-01 -6.17287636e-01 -8.30648541e-01 -6.05921268e-01
2.79390007e-01 3.93145114e-01 -9.26966965e-02 -2.58257896... | [11.986348152160645, 9.18514347076416] |
3466cdc9-b0e2-4e1f-82b7-58ab5324ace9 | randgan-randomized-generative-adversarial | 2010.06418 | null | https://arxiv.org/abs/2010.06418v1 | https://arxiv.org/pdf/2010.06418v1.pdf | RANDGAN: Randomized Generative Adversarial Network for Detection of COVID-19 in Chest X-ray | COVID-19 spread across the globe at an immense rate has left healthcare systems incapacitated to diagnose and test patients at the needed rate. Studies have shown promising results for detection of COVID-19 from viral bacterial pneumonia in chest X-rays. Automation of COVID-19 testing using medical images can speed up ... | ['Farzad Khalvati', 'Patrik Rogalla', 'Saman Motamed'] | 2020-10-06 | null | null | null | null | ['covid-19-image-segmentation'] | ['computer-vision'] | [ 5.11651993e-01 -1.25129461e-01 3.47770810e-01 -7.09297359e-02
-6.69421494e-01 -8.93968225e-01 2.23047242e-01 -2.32373141e-02
-5.05868256e-01 8.18697512e-01 -2.92758197e-01 -8.63812268e-01
-5.64561691e-03 -9.07683253e-01 -8.75617266e-01 -8.91214907e-01
8.89889970e-02 1.21342742e+00 -1.67987663e-02 2.86429495... | [15.498934745788574, -1.792251467704773] |
5a728658-5fb0-455e-9043-1668f99cab40 | generating-clarifying-questions-for-query | 2201.09974 | null | https://arxiv.org/abs/2201.09974v1 | https://arxiv.org/pdf/2201.09974v1.pdf | Generating Clarifying Questions for Query Refinement in Source Code Search | In source code search, a common information-seeking strategy involves providing a short initial query with a broad meaning, and then iteratively refining the query using terms gleaned from the results of subsequent searches. This strategy requires programmers to spend time reading search results that are irrelevant to ... | ['Collin McMillan', 'Zachary Eberhart'] | 2022-01-24 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [ 3.61235470e-01 2.43932068e-01 -1.71405509e-01 -2.27728888e-01
-1.11773252e+00 -1.07611120e+00 3.56661081e-01 7.66984463e-01
-3.03217947e-01 5.44567466e-01 2.87125677e-01 -8.51670682e-01
-1.27561033e-01 -3.48027498e-01 -3.86104465e-01 4.62799698e-01
4.85154480e-01 9.43462253e-02 4.71917301e-01 -3.04864764... | [7.617844581604004, 7.998866558074951] |
ec7bf9cb-7a5a-49f2-b37c-726cf09c438d | understanding-the-impact-of-edge-cases-from | 2204.12402 | null | https://arxiv.org/abs/2204.12402v1 | https://arxiv.org/pdf/2204.12402v1.pdf | Understanding the Impact of Edge Cases from Occluded Pedestrians for ML Systems | Machine learning (ML)-enabled approaches are considered a substantial support technique of detection and classification of obstacles of traffic participants in self-driving vehicles. Major breakthroughs have been demonstrated the past few years, even covering complete end-to-end data processing chain from sensory input... | ['Stig Ursing', 'Christian Berger', 'Jens Henriksson'] | 2022-04-26 | null | null | null | null | ['body-detection'] | ['computer-vision'] | [-9.39485580e-02 3.48217815e-01 -3.65445465e-01 -4.79903579e-01
-3.30721796e-01 -1.30259171e-01 4.78775084e-01 -4.63964455e-02
-7.01019168e-01 5.03168344e-01 -4.86097746e-02 -4.34291989e-01
2.23020330e-01 -5.77589571e-01 -9.16936100e-01 -6.29075170e-01
-8.10331199e-03 1.99824199e-01 7.28458345e-01 -4.33202714... | [8.082756996154785, -0.7932628393173218] |
b012cd99-ce73-470b-bbb7-b37144dd9265 | transfering-hierarchical-structure-with-dual | 2201.11981 | null | https://arxiv.org/abs/2201.11981v2 | https://arxiv.org/pdf/2201.11981v2.pdf | Transfering Hierarchical Structure with Dual Meta Imitation Learning | Hierarchical Imitation Learning (HIL) is an effective way for robots to learn sub-skills from long-horizon unsegmented demonstrations. However, the learned hierarchical structure lacks the mechanism to transfer across multi-tasks or to new tasks, which makes them have to learn from scratch when facing a new situation. ... | ['Feng Chen', 'Yizhou Jiang', 'Chongkai Gao'] | 2022-01-28 | null | null | null | null | ['few-shot-imitation-learning'] | ['methodology'] | [-4.59090434e-02 1.33197710e-01 -7.87836388e-02 -1.21683612e-01
-3.85638624e-01 -1.81863248e-01 5.38371444e-01 -4.21344280e-01
-6.01245344e-01 9.39098120e-01 -5.71388640e-02 1.81498230e-01
-2.38963634e-01 -5.84131777e-01 -1.17241490e+00 -7.59116173e-01
-3.06245595e-01 5.49506426e-01 6.40694022e-01 -3.35756928... | [4.353198051452637, 1.1291435956954956] |
3346b7c2-98c7-4ac7-9227-d444ec567830 | d2a-a-dataset-built-for-ai-based | 2102.07995 | null | https://arxiv.org/abs/2102.07995v1 | https://arxiv.org/pdf/2102.07995v1.pdf | D2A: A Dataset Built for AI-Based Vulnerability Detection Methods Using Differential Analysis | Static analysis tools are widely used for vulnerability detection as they understand programs with complex behavior and millions of lines of code. Despite their popularity, static analysis tools are known to generate an excess of false positives. The recent ability of Machine Learning models to understand programming l... | ['Zhong Su', 'Alessandro Morari', 'Jim Laredo', 'Bo Yang', 'Edward Epstein', 'Luca Buratti', 'Burn Lewis', 'Saurabh Pujar', 'Yunhui Zheng'] | 2021-02-16 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [-1.05842628e-01 -7.01267570e-02 -2.94800580e-01 -2.25087389e-01
-1.04832017e+00 -9.87077832e-01 -6.28674924e-02 7.05988705e-01
3.03373247e-01 4.34567630e-01 -2.02559501e-01 -7.58622408e-01
1.02569237e-01 -8.37305188e-01 -8.30392003e-01 4.63980203e-03
-3.71319890e-01 -1.12892069e-01 7.91576862e-01 -9.91945788... | [7.3006696701049805, 7.754489421844482] |
b3950f8c-5b25-4f18-9159-7f506dade823 | zero-shot-dialogue-relation-extraction-by | 2306.06141 | null | https://arxiv.org/abs/2306.06141v1 | https://arxiv.org/pdf/2306.06141v1.pdf | Zero-Shot Dialogue Relation Extraction by Relating Explainable Triggers and Relation Names | Developing dialogue relation extraction (DRE) systems often requires a large amount of labeled data, which can be costly and time-consuming to annotate. In order to improve scalability and support diverse, unseen relation extraction, this paper proposes a method for leveraging the ability to capture triggers and relate... | ['Yun-Nung Chen', 'Ze-Song Xu'] | 2023-06-09 | null | null | null | null | ['relation-extraction'] | ['natural-language-processing'] | [ 9.19970199e-02 7.93719828e-01 -2.13184819e-01 -2.87692904e-01
-8.97722900e-01 -9.00010288e-01 7.82414198e-01 3.36827368e-01
-1.33853674e-01 1.09950519e+00 4.40691233e-01 -2.80943096e-01
5.68395816e-02 -8.56904328e-01 5.43236434e-02 -6.16040686e-03
-5.53536117e-02 7.05611408e-01 3.90795439e-01 -7.46289909... | [12.442692756652832, 8.072003364562988] |
a7732f94-b2d6-4f38-9cef-a44d802dd350 | multi-scale-cross-form-pyramid-network-for | 1904.11309 | null | https://arxiv.org/abs/1904.11309v3 | https://arxiv.org/pdf/1904.11309v3.pdf | Multi-scale Cross-form Pyramid Network for Stereo Matching | Stereo matching plays an indispensable part in autonomous driving, robotics and 3D scene reconstruction. We propose a novel deep learning architecture, which called CFP-Net, a Cross-Form Pyramid stereo matching network for regressing disparity from a rectified pair of stereo images. The network consists of three module... | ['Yuchao Dai', 'Zhibo Rao', 'Bo Li', 'Mingyi He', 'Zhidong Zhu'] | 2019-04-25 | null | null | null | null | ['3d-feature-matching', 'stereo-matching', '3d-scene-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.44214809e-01 -4.10558701e-01 2.10869327e-01 -4.40365613e-01
-4.61945176e-01 -7.41025433e-02 5.91544569e-01 -2.67341495e-01
-7.26397038e-01 3.69532555e-01 2.13148475e-01 -2.02258050e-01
-8.06827918e-02 -1.03426027e+00 -9.48703885e-01 -4.61027026e-01
1.54739037e-01 2.19561785e-01 7.41323054e-01 -4.92929041... | [8.870176315307617, -2.280872106552124] |
9ab657f6-d383-44f8-9f75-d5a01a0ead94 | context-aware-change-detection-with-semi | 2306.08935 | null | https://arxiv.org/abs/2306.08935v1 | https://arxiv.org/pdf/2306.08935v1.pdf | Context-Aware Change Detection With Semi-Supervised Learning | Change detection using earth observation data plays a vital role in quantifying the impact of disasters in affected areas. While data sources like Sentinel-2 provide rich optical information, they are often hindered by cloud cover, limiting their usage in disaster scenarios. However, leveraging pre-disaster optical dat... | ['Yifang Ban', 'Andrea Nascetti', 'Ritu Yadav'] | 2023-06-15 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 2.14513808e-01 -1.69414714e-01 1.18126422e-01 -8.13147575e-02
-4.29077774e-01 -4.49772894e-01 6.14922106e-01 8.62261176e-01
-6.44398153e-01 8.65304410e-01 5.34014642e-01 -7.20689833e-01
-2.83676505e-01 -1.54656684e+00 -2.33959183e-01 -6.29630625e-01
-5.26922703e-01 1.35674074e-01 3.53963137e-01 -7.34611332... | [9.422934532165527, -1.3801205158233643] |
d6e298e4-0110-4bcd-9cac-f033b800d165 | reinforced-pedestrian-attribute-recognition | 2205.14042 | null | https://arxiv.org/abs/2205.14042v1 | https://arxiv.org/pdf/2205.14042v1.pdf | Reinforced Pedestrian Attribute Recognition with Group Optimization Reward | Pedestrian Attribute Recognition (PAR) is a challenging task in intelligent video surveillance. Two key challenges in PAR include complex alignment relations between images and attributes, and imbalanced data distribution. Existing approaches usually formulate PAR as a recognition task. Different from them, this paper ... | ['Shengjia Li', 'Yaodong Wang', 'Zhenfei Hu', 'Zhong Ji'] | 2022-05-21 | null | null | null | null | ['pedestrian-attribute-recognition'] | ['computer-vision'] | [ 2.73795933e-01 -7.54303634e-02 -3.25474560e-01 -7.00590909e-01
-6.82740808e-01 -1.53070658e-01 5.77297688e-01 -7.56218955e-02
-4.36339438e-01 6.43935084e-01 1.29993871e-01 -1.27858192e-01
-3.93083766e-02 -7.46308208e-01 -6.34082139e-01 -9.03922141e-01
1.25447705e-01 4.49820369e-01 1.70835570e-01 3.72624174... | [14.315349578857422, 1.032454013824463] |
e3f393f8-d2e6-47a1-85a5-8643d4070fee | past-visions-of-artificial-futures-one | 1806.01322 | null | http://arxiv.org/abs/1806.01322v1 | http://arxiv.org/pdf/1806.01322v1.pdf | Past Visions of Artificial Futures: One Hundred and Fifty Years under the Spectre of Evolving Machines | The influence of Artificial Intelligence (AI) and Artificial Life (ALife)
technologies upon society, and their potential to fundamentally shape the
future evolution of humankind, are topics very much at the forefront of current
scientific, governmental and public debate. While these might seem like very
modern concerns... | ['Tim Taylor', 'Alan Dorin'] | 2018-06-04 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 3.69583130e-01 2.80448794e-01 9.65661705e-02 -7.41870254e-02
5.75531065e-01 -7.66507745e-01 1.20649374e+00 1.57604188e-01
-7.35137463e-01 7.85883546e-01 3.40918839e-01 -7.52910376e-01
-1.89401016e-01 -6.54518902e-01 -4.16960657e-01 -6.07780218e-01
7.12592602e-02 2.44400263e-01 -4.79248837e-02 -8.28160644... | [5.630260944366455, 4.216994285583496] |
e69e0432-5676-4366-9a1f-844dfdf8ec15 | provably-efficient-causal-model-based | 2202.06545 | null | https://arxiv.org/abs/2202.06545v3 | https://arxiv.org/pdf/2202.06545v3.pdf | Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization | In the sequential decision making setting, an agent aims to achieve systematic generalization over a large, possibly infinite, set of environments. Such environments are modeled as discrete Markov decision processes with both states and actions represented through a feature vector. The underlying structure of the envir... | ['Marcello Restelli', 'Michael Bronstein', 'Juan Felipe Calderon', 'Emanuele Rossi', 'Riccardo De Santi', 'Mirco Mutti'] | 2022-02-14 | null | null | null | null | ['systematic-generalization'] | ['reasoning'] | [ 3.69893104e-01 5.26687980e-01 -2.33176574e-01 -1.86500654e-01
-5.84367216e-01 -6.53052866e-01 7.85553873e-01 2.33647808e-01
-4.74191129e-01 8.38637054e-01 1.13693036e-01 -3.91039222e-01
-4.57258821e-01 -1.02749836e+00 -8.09391916e-01 -8.84408057e-01
-4.79589760e-01 9.62031364e-01 2.65277565e-01 -1.74064040... | [4.383595943450928, 2.1636734008789062] |
d93cbf71-b886-48d5-b360-d6f983efb6cb | boosting-adversarial-robustness-using-feature | 2306.06462 | null | https://arxiv.org/abs/2306.06462v1 | https://arxiv.org/pdf/2306.06462v1.pdf | Boosting Adversarial Robustness using Feature Level Stochastic Smoothing | Advances in adversarial defenses have led to a significant improvement in the robustness of Deep Neural Networks. However, the robust accuracy of present state-ofthe-art defenses is far from the requirements in critical applications such as robotics and autonomous navigation systems. Further, in practical use cases, ne... | ['R. Venkatesh Babu', 'Gaurang Sriramanan', 'Samyak Jain', 'Sravanti Addepalli'] | 2023-06-10 | null | null | null | null | ['adversarial-robustness', 'autonomous-navigation'] | ['adversarial', 'computer-vision'] | [ 2.52094746e-01 2.08128452e-01 -1.04706939e-02 -3.48270684e-01
-4.94798779e-01 -7.38835096e-01 7.63928056e-01 -8.16243738e-02
-4.39057589e-01 9.83516455e-01 -1.83715284e-01 -6.03851199e-01
-1.86211437e-01 -8.50815833e-01 -5.95421255e-01 -9.41048205e-01
-1.17726855e-01 -1.37619510e-01 6.63087189e-01 -3.05730194... | [5.511204242706299, 7.826510906219482] |
7ae2ce5a-6750-49e5-ad36-91eedea2d4c5 | power-norm-based-lifelong-learning-for | null | null | https://openreview.net/forum?id=4TMv3kdrhuc | https://openreview.net/pdf?id=4TMv3kdrhuc | Power Norm Based Lifelong Learning for Paraphrase Generations | Seq2seq language generation models are trained with multiple domains in a continue learning manner, where the data from each domain being observed in an online fashion. However, continual learning studies usually suffer a lot from catastrophic forgetting, a persistent challenge for lifelong learning. To handle this pro... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 1.26168966e-01 -1.15222774e-01 -3.29204142e-01 -1.71243489e-01
-7.55989313e-01 -4.71981943e-01 6.91238046e-01 -1.31534249e-01
-3.99642497e-01 1.24820387e+00 4.90725487e-01 -2.78297246e-01
1.01028532e-01 -9.31373596e-01 -8.53941917e-01 -4.43424851e-01
3.66771996e-01 3.79256368e-01 2.26299867e-01 -3.44748974... | [9.921573638916016, 3.544647693634033] |
09e54e3a-55f9-40a4-abdb-7589fb1c7b17 | translatotron-2-robust-direct-speech-to | 2107.08661 | null | https://arxiv.org/abs/2107.08661v5 | https://arxiv.org/pdf/2107.08661v5.pdf | Translatotron 2: High-quality direct speech-to-speech translation with voice preservation | We present Translatotron 2, a neural direct speech-to-speech translation model that can be trained end-to-end. Translatotron 2 consists of a speech encoder, a linguistic decoder, an acoustic synthesizer, and a single attention module that connects them together. Experimental results on three datasets consistently show ... | ['Roi Pomerantz', 'Tal Remez', 'Michelle Tadmor Ramanovich', 'Ye Jia'] | 2021-07-19 | translatotron-2-robust-direct-speech-to-1 | https://openreview.net/forum?id=HTfUrAxjPkR | https://openreview.net/pdf?id=HTfUrAxjPkR | null | ['speech-to-speech-translation', 'voice-cloning'] | ['speech', 'speech'] | [ 2.14878023e-01 4.22264516e-01 -7.33680800e-02 -2.78704941e-01
-1.40705156e+00 -7.25182414e-01 4.81392592e-01 -3.47312123e-01
-2.07706064e-01 5.54353118e-01 4.61721450e-01 -6.99731410e-01
7.49220967e-01 -3.32588464e-01 -9.63499367e-01 -3.70270312e-01
4.61788595e-01 3.15665603e-01 -3.71399224e-02 -3.85656729... | [14.741936683654785, 6.8988752365112305] |
1afe0f0a-1e98-49e1-893c-abbc1dc1133f | visual-question-answering-from-another-1 | 2212.01639 | null | https://arxiv.org/abs/2212.01639v1 | https://arxiv.org/pdf/2212.01639v1.pdf | Visual Question Answering From Another Perspective: CLEVR Mental Rotation Tests | Different types of mental rotation tests have been used extensively in psychology to understand human visual reasoning and perception. Understanding what an object or visual scene would look like from another viewpoint is a challenging problem that is made even harder if it must be performed from a single image. We exp... | ['Christopher Pal', 'Derek Nowrouzezahrai', 'Sina Honari', 'Florian Golemo', 'Martin Weiss', 'Christopher Beckham'] | 2022-12-03 | visual-question-answering-from-another | https://openreview.net/forum?id=aYbCpFNnHdh | https://openreview.net/pdf?id=aYbCpFNnHdh | null | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [ 3.81581217e-01 1.87815472e-01 2.99387246e-01 -3.35698217e-01
-2.67810941e-01 -9.58768129e-01 9.03402805e-01 -1.20258048e-01
-5.15586078e-01 2.37516686e-01 1.33707091e-01 -5.99358916e-01
-9.53210443e-02 -6.13960862e-01 -8.57540607e-01 -3.89948696e-01
1.58880979e-01 4.13307309e-01 4.23557572e-02 -7.62694925... | [10.535155296325684, 2.08100962638855] |
21f529d8-6994-47e1-be67-bd605a31cb0f | jailbreaking-chatgpt-via-prompt-engineering | 2305.13860 | null | https://arxiv.org/abs/2305.13860v1 | https://arxiv.org/pdf/2305.13860v1.pdf | Jailbreaking ChatGPT via Prompt Engineering: An Empirical Study | Large Language Models (LLMs), like ChatGPT, have demonstrated vast potential but also introduce challenges related to content constraints and potential misuse. Our study investigates three key research questions: (1) the number of different prompt types that can jailbreak LLMs, (2) the effectiveness of jailbreak prompt... | ['Yang Liu', 'Tianwei Zhang', 'Lida Zhao', 'Ying Zhang', 'Yaowen Zheng', 'Yuekang Li', 'Zhengzi Xu', 'Gelei Deng', 'Yi Liu'] | 2023-05-23 | null | null | null | null | ['prompt-engineering'] | ['natural-language-processing'] | [-1.96018979e-01 -1.60652861e-01 -3.26029509e-01 -4.92914207e-02
-9.65204954e-01 -1.16536069e+00 7.46352375e-01 3.69675338e-01
-3.15246373e-01 3.61360729e-01 5.60616314e-01 -1.15725327e+00
-5.68597376e-01 -2.53126889e-01 -2.96515167e-01 8.17794502e-02
3.30745041e-01 3.19726259e-01 3.17094624e-01 -4.74246740... | [11.158808708190918, 8.391949653625488] |
0f3fdee5-0005-46c9-99f5-09379f6cdc20 | pandaset-advanced-sensor-suite-dataset-for | 2112.12610 | null | https://arxiv.org/abs/2112.12610v1 | https://arxiv.org/pdf/2112.12610v1.pdf | PandaSet: Advanced Sensor Suite Dataset for Autonomous Driving | The accelerating development of autonomous driving technology has placed greater demands on obtaining large amounts of high-quality data. Representative, labeled, real world data serves as the fuel for training deep learning networks, critical for improving self-driving perception algorithms. In this paper, we introduc... | ['Diange Yang', 'Yunlong Wang', 'Kun Jiang', 'Kai Sun', 'Jian Wu', 'Zesong Li', 'Judy Jiao', 'Xiaolin Chai', 'Zishuo Zhang', 'Steven Hao', 'Zhenlei Shao', 'Pengchuan Xiao'] | 2021-12-23 | null | null | null | null | ['point-cloud-segmentation'] | ['computer-vision'] | [-2.07556188e-02 -3.20151567e-01 -3.86469901e-01 -1.08063316e+00
-9.15324032e-01 -8.79906654e-01 3.79446685e-01 8.45851377e-03
-6.05339348e-01 2.94410378e-01 -6.27168536e-01 -5.15552402e-01
1.86267152e-01 -7.80468524e-01 -1.09498775e+00 -2.99300551e-01
1.87072277e-01 7.72876441e-01 5.12287378e-01 -1.74127426... | [7.828461647033691, -2.368272066116333] |
b9d7c0c7-fa00-4ea7-80dc-4b3684b65d81 | transfer-learning-for-material-classification | 1609.06188 | null | http://arxiv.org/abs/1609.06188v1 | http://arxiv.org/pdf/1609.06188v1.pdf | Transfer Learning for Material Classification using Convolutional Networks | Material classification in natural settings is a challenge due to complex
interplay of geometry, reflectance properties, and illumination. Previous work
on material classification relies strongly on hand-engineered features of
visual samples. In this work we use a Convolutional Neural Network (convnet)
that learns desc... | ['Hendrik P. A. Lensch', 'Patrick Wieschollek'] | 2016-09-20 | null | null | null | null | ['material-classification', 'material-recognition'] | ['computer-vision', 'computer-vision'] | [ 5.50848007e-01 -3.56997728e-01 1.86068892e-01 -3.57000560e-01
-4.56313312e-01 -4.38014388e-01 7.62527466e-01 8.54668915e-02
-1.53230906e-01 7.23475814e-01 -2.48417482e-02 6.46995986e-03
-2.19942406e-01 -1.17814779e+00 -8.41660798e-01 -8.58593822e-01
8.11852589e-02 1.95582047e-01 2.81164646e-01 -3.06811601... | [10.199088096618652, -0.16858245432376862] |
14571e2c-9c75-40e4-b29d-581e37d72cf2 | why-out-of-distribution-detection-in-cnns | 2110.07043 | null | https://arxiv.org/abs/2110.07043v1 | https://arxiv.org/pdf/2110.07043v1.pdf | Why Out-of-distribution Detection in CNNs Does Not Like Mahalanobis -- and What to Use Instead | Convolutional neural networks applied for real-world classification tasks need to recognize inputs that are far or out-of-distribution (OoD) with respect to the known or training data. To achieve this, many methods estimate class-conditional posterior probabilities and use confidence scores obtained from the posterior ... | ['Henryk Maciejewski', 'Tomasz Walkowiak', 'Kamil Szyc'] | 2021-10-13 | null | null | null | null | ['2048'] | ['playing-games'] | [-6.20090902e-01 -9.94482338e-02 2.54212886e-01 -6.42985940e-01
-7.57323027e-01 -2.49015555e-01 4.80491847e-01 6.18039668e-02
-8.98868740e-01 1.00199425e+00 -1.81641072e-01 -2.68748343e-01
-3.25311571e-01 -7.86510646e-01 -9.77152586e-01 -6.32429242e-01
-1.95582956e-01 5.82969368e-01 3.50535780e-01 4.54434574... | [7.57492733001709, 3.6338205337524414] |
d5179d35-b78a-4845-8e18-a4f88ad7420b | denoising-time-series-data-using-asymmetric | null | null | https://doi.org/10.1007/978-3-319-93040-4_23 | https://www.researchgate.net/publication/325808737_Denoising_Time_Series_Data_Using_Asymmetric_Generative_Adversarial_Networks | Denoising Time Series Data Using Asymmetric Generative Adversarial Networks | Denoising data is a preprocessing step for several time series mining algorithms. This step is especially important if the noise in data originates from diverse sources. Consequently, it is commonly used in biomedical applications that use Electroencephalography (EEG) data. In EEG data noise can occur due to ocular, mu... | ['Sunil Gandhi', 'David Hairston', 'Tinoosh Mohsenin', 'Tim Oates'] | 2018-06-17 | null | null | null | advances-in-knowledge-discovery-and-data | ['eeg-denoising'] | ['methodology'] | [ 1.77882969e-01 -1.97471499e-01 7.94336319e-01 -4.03981298e-01
-3.82063746e-01 -5.62568605e-01 3.85618269e-01 2.36758329e-02
-4.91652012e-01 9.19202805e-01 1.39152646e-01 -8.83641243e-02
-1.59397274e-01 -6.01544738e-01 -7.02873349e-01 -7.87503898e-01
-2.58448511e-01 1.43047441e-02 -2.19108686e-01 -2.51769871... | [13.179211616516113, 3.4496243000030518] |
6233c1ba-668b-4359-9af0-867ae1ab14b8 | question-answer-sentence-graph-for-joint | 2203.03549 | null | https://arxiv.org/abs/2203.03549v2 | https://arxiv.org/pdf/2203.03549v2.pdf | Question-Answer Sentence Graph for Joint Modeling Answer Selection | This research studies graph-based approaches for Answer Sentence Selection (AS2), an essential component for retrieval-based Question Answering (QA) systems. During offline learning, our model constructs a small-scale relevant training graph per question in an unsupervised manner, and integrates with Graph Neural Netwo... | ['Yizhou Sun', 'Alessandro Moschitti', 'Thuy Vu', 'Roshni G. Iyer'] | 2022-02-16 | null | null | null | null | ['answer-selection'] | ['natural-language-processing'] | [ 3.22068334e-01 5.56601286e-01 -8.09606835e-02 -5.11925101e-01
-1.40350628e+00 -6.08518541e-01 2.81872123e-01 9.88378227e-01
-2.90736794e-01 2.52705485e-01 3.02194089e-01 -7.07156122e-01
-2.05057904e-01 -1.24236321e+00 -5.72613835e-01 2.43694812e-01
-2.02763863e-02 1.03483188e+00 8.28317046e-01 -6.62514687... | [10.967705726623535, 8.011178970336914] |
6f3e3820-5b7f-4aee-99a5-48faa1fb78f9 | on-contrastive-learning-of-semantic | 2305.03843 | null | https://arxiv.org/abs/2305.03843v1 | https://arxiv.org/pdf/2305.03843v1.pdf | On Contrastive Learning of Semantic Similarity forCode to Code Search | This paper introduces a novel code-to-code search technique that enhances the performance of Large Language Models (LLMs) by including both static and dynamic features as well as utilizing both similar and dissimilar examples during training. We present the first-ever code search method that encodes dynamic runtime inf... | ['Gail Kaiser', 'Saikat Chakraborty', 'Anthony Saieva'] | 2023-05-05 | null | null | null | null | ['code-search', 'code-search', 'semantic-textual-similarity', 'semantic-similarity'] | ['computer-code', 'computer-vision', 'natural-language-processing', 'natural-language-processing'] | [-1.17453650e-01 -4.44397748e-01 -6.15238667e-01 -1.32773623e-01
-1.26099825e+00 -8.90054286e-01 8.07053447e-01 1.97791994e-01
-5.03146529e-01 1.93390131e-01 -9.00125876e-02 -9.20859694e-01
-2.77298521e-02 -3.54973465e-01 -8.58911633e-01 1.11451536e-01
-1.16020240e-01 2.79749811e-01 4.51753259e-01 -2.69685745... | [7.625427722930908, 7.989203929901123] |
dbfeda6b-8ded-4c11-88b8-fb544c9c5551 | learning-to-learn-better-for-video-object | 2212.02112 | null | https://arxiv.org/abs/2212.02112v1 | https://arxiv.org/pdf/2212.02112v1.pdf | Learning to Learn Better for Video Object Segmentation | Recently, the joint learning framework (JOINT) integrates matching based transductive reasoning and online inductive learning to achieve accurate and robust semi-supervised video object segmentation (SVOS). However, using the mask embedding as the label to guide the generation of target features in the two branches may... | ['DaCheng Tao', 'Lefei Zhang', 'Jing Zhang', 'Meng Lan'] | 2022-12-05 | null | null | null | null | ['semi-supervised-video-object-segmentation', 'video-object-segmentation'] | ['computer-vision', 'computer-vision'] | [ 1.99715897e-01 2.12505013e-01 -5.62887371e-01 -4.33946520e-01
-1.01507926e+00 -3.02270532e-01 4.41927612e-01 -3.94086093e-02
-2.93769777e-01 5.41868985e-01 -3.74939787e-04 3.59777249e-02
3.76730934e-02 -7.77395308e-01 -8.34156871e-01 -1.12092388e+00
2.53664792e-01 2.62642890e-01 6.81976020e-01 1.45772070... | [9.31706428527832, 1.3343416452407837] |
37654268-ad1c-45e7-9fd4-dbbd58a63f82 | exploration-of-algorithmic-trading-strategies | 2110.14936 | null | https://arxiv.org/abs/2110.14936v1 | https://arxiv.org/pdf/2110.14936v1.pdf | Exploration of Algorithmic Trading Strategies for the Bitcoin Market | Bitcoin is firmly becoming a mainstream asset in our global society. Its highly volatile nature has traders and speculators flooding into the market to take advantage of its significant price swings in the hope of making money. This work brings an algorithmic trading approach to the Bitcoin market to exploit the variab... | ['Tomas Ward', 'Eoin Brophy', 'Nathan Crone'] | 2021-10-28 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-6.87798321e-01 1.56427249e-01 -6.07727706e-01 -1.10056162e-01
-2.43689984e-01 -9.55777884e-01 9.37244713e-01 -9.50688720e-02
-1.79376453e-01 6.98147595e-01 1.51109695e-01 -8.15025985e-01
-1.79556504e-01 -8.44290197e-01 -2.97747105e-01 -4.44814116e-01
-2.77940869e-01 7.00646698e-01 1.54769579e-02 -3.01633567... | [4.651387691497803, 4.163681983947754] |
5b039777-e807-4d6e-b778-d2d4bbe1fd11 | learning-vector-quantized-shape-code-for | 2012.00985 | null | https://arxiv.org/abs/2012.00985v1 | https://arxiv.org/pdf/2012.00985v1.pdf | Learning Vector Quantized Shape Code for Amodal Blastomere Instance Segmentation | Blastomere instance segmentation is important for analyzing embryos' abnormality. To measure the accurate shapes and sizes of blastomeres, their amodal segmentation is necessary. Amodal instance segmentation aims to recover the complete silhouette of an object even when the object is not fully visible. For each detecte... | ['Hanspeter Pfister', 'Daniel Needleman', 'Dalit Ben-Yosef', 'James Tompkin', 'Helen Yang', 'Brian Leahy', 'Xingxuan Zhang', 'Donglai Wei', 'Won-Dong Jang'] | 2020-12-02 | null | null | null | null | ['amodal-instance-segmentation'] | ['computer-vision'] | [ 3.00719649e-01 7.35787868e-01 1.57791212e-01 -4.04378027e-01
-6.18120432e-01 -9.08957839e-01 2.75189131e-01 1.67712897e-01
-5.35343550e-02 3.15037102e-01 -2.22499639e-01 -2.76498795e-01
2.28642449e-01 -9.78533745e-01 -1.00684202e+00 -8.10815752e-01
2.88667560e-01 9.92935538e-01 1.00935996e-01 7.34145492... | [14.610157012939453, -2.923241138458252] |
7efa701d-348e-4edf-af31-8a2853ba019f | exemplar-based-generative-facial-editing | 2006.00472 | null | https://arxiv.org/abs/2006.00472v1 | https://arxiv.org/pdf/2006.00472v1.pdf | Exemplar-based Generative Facial Editing | Image synthesis has witnessed substantial progress due to the increasing power of generative model. This paper we propose a novel generative approach for exemplar based facial editing in the form of the region inpainting. Our method first masks the facial editing region to eliminates the pixel constraints of the origin... | ['Zuowei Zhou', 'Yi Liu', 'Jingtao Guo', 'Zhenzhen Qian'] | 2020-05-31 | null | null | null | null | ['facial-editing'] | ['computer-vision'] | [ 6.64101243e-01 6.11747384e-01 -6.98608384e-02 -5.38061500e-01
-4.33575809e-01 -4.05567855e-01 5.76166451e-01 -8.62367988e-01
1.15803868e-01 9.98679519e-01 3.05130035e-01 4.42666262e-01
2.91176178e-02 -6.33902013e-01 -8.02795172e-01 -7.54847765e-01
5.56770504e-01 1.78263441e-01 -6.52524769e-01 -8.60701054... | [12.524622917175293, -0.255819708108902] |
143c6d6c-7c95-413d-83ac-151824d4b473 | a-web-based-mpox-skin-lesion-detection-system | 2306.14169 | null | https://arxiv.org/abs/2306.14169v1 | https://arxiv.org/pdf/2306.14169v1.pdf | A Web-based Mpox Skin Lesion Detection System Using State-of-the-art Deep Learning Models Considering Racial Diversity | The recent 'Mpox' outbreak, formerly known as 'Monkeypox', has become a significant public health concern and has spread to over 110 countries globally. The challenge of clinically diagnosing mpox early on is due, in part, to its similarity to other types of rashes. Computer-aided screening tools have been proven valua... | ['Taufiq Hasan', 'Anzirun Nahar Asma', 'Nawsabah Noor', 'S. M. Sakeef Sani', 'Joydip Paul', 'Tasnim Jahan', 'Md. Tazuddin Ahmed', 'Shams Nafisa Ali'] | 2023-06-25 | null | null | null | null | ['transfer-learning'] | ['miscellaneous'] | [ 4.17862982e-01 -4.48211551e-01 -2.53757518e-02 -1.03477046e-01
-6.03113592e-01 -5.28286636e-01 3.63986641e-01 3.59457582e-01
-5.38775325e-01 8.40371907e-01 -3.74760419e-01 -6.09150708e-01
-9.47737545e-02 -7.99717426e-01 -4.30039823e-01 -6.48678899e-01
-3.84042025e-01 2.86995828e-01 -3.53336730e-03 6.25068769... | [15.685322761535645, -2.986222982406616] |
febe4afc-126f-4857-a07d-a055fde9ef02 | real-world-video-super-resolution-a-benchmark | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Yang_Real-World_Video_Super-Resolution_A_Benchmark_Dataset_and_a_Decomposition_Based_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Yang_Real-World_Video_Super-Resolution_A_Benchmark_Dataset_and_a_Decomposition_Based_ICCV_2021_paper.pdf | Real-World Video Super-Resolution: A Benchmark Dataset and a Decomposition Based Learning Scheme | Video super-resolution (VSR) aims to improve the spatial resolution of low-resolution (LR) videos. Existing VSR methods are mostly trained and evaluated on synthetic datasets, where the LR videos are uniformly downsampled from their high-resolution (HR) counterparts by some simple operators (e.g., bicubic downsampl... | ['Lei Zhang', 'Hui Zeng', 'Wangmeng Xiang', 'Xi Yang'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['video-super-resolution'] | ['computer-vision'] | [ 4.24831063e-01 -6.31959558e-01 -2.26377591e-01 -5.89400865e-02
-9.51933384e-01 -4.51025754e-01 2.61206657e-01 -9.24971640e-01
-1.61176305e-02 9.28056717e-01 2.22943008e-01 2.79985368e-02
2.95625657e-01 -6.17476940e-01 -8.31210017e-01 -7.30167270e-01
1.21080160e-01 -3.70623767e-01 3.78912359e-01 -3.76576036... | [11.088287353515625, -2.083588123321533] |
97e3aadf-7ca9-4378-a6d1-74c847c5de15 | causal-intervention-for-subject-deconfounded | 2204.07935 | null | https://arxiv.org/abs/2204.07935v1 | https://arxiv.org/pdf/2204.07935v1.pdf | Causal Intervention for Subject-Deconfounded Facial Action Unit Recognition | Subject-invariant facial action unit (AU) recognition remains challenging for the reason that the data distribution varies among subjects. In this paper, we propose a causal inference framework for subject-invariant facial action unit recognition. To illustrate the causal effect existing in AU recognition task, we form... | ['Yun Liang', 'Yizhou Wang', 'Tao Wang', 'Diqi Chen', 'Yingjie Chen'] | 2022-04-17 | null | null | null | null | ['facial-action-unit-detection'] | ['computer-vision'] | [ 6.51277065e-01 2.63195187e-01 -5.31759083e-01 -5.63712239e-01
-2.88501054e-01 -1.57915289e-03 6.73576772e-01 -8.09044361e-01
2.57155806e-01 7.21115112e-01 8.44102681e-01 4.70441654e-02
-2.43675008e-01 -5.86059213e-01 -9.45791602e-01 -1.04533851e+00
-1.60189211e-01 -3.11950177e-01 -3.21773261e-01 3.51877868... | [13.59408187866211, 1.4924367666244507] |
6da968b2-5757-48e8-84d2-499583b32237 | stable-deep-mri-reconstruction-using | 2210.13834 | null | https://arxiv.org/abs/2210.13834v3 | https://arxiv.org/pdf/2210.13834v3.pdf | Stable Deep MRI Reconstruction using Generative Priors | Data-driven approaches recently achieved remarkable success in magnetic resonance imaging (MRI) reconstruction, but integration into clinical routine remains challenging due to a lack of generalizability and interpretability. In this paper, we address these challenges in a unified framework based on generative image pr... | ['Thomas Pock', 'Florian Knoll', 'Martin Zach'] | 2022-10-25 | null | null | null | null | ['mri-reconstruction'] | ['computer-vision'] | [ 3.97059947e-01 2.23497346e-01 9.07162502e-02 -4.21592802e-01
-1.33164442e+00 -3.37854505e-01 6.13005817e-01 8.69270191e-02
-6.37423396e-01 8.58180225e-01 2.99202472e-01 -2.17586849e-02
-3.72273207e-01 -5.21848381e-01 -9.32215333e-01 -1.15344179e+00
-1.04642130e-01 6.71970606e-01 1.79320052e-01 1.25599027... | [13.498917579650879, -2.3579514026641846] |
e9783db5-f5ef-45c8-94b5-b3af06032929 | aspect-category-opinion-sentiment-quadruple | null | null | https://aclanthology.org/2021.acl-long.29 | https://aclanthology.org/2021.acl-long.29.pdf | Aspect-Category-Opinion-Sentiment Quadruple Extraction with Implicit Aspects and Opinions | Product reviews contain a large number of implicit aspects and implicit opinions. However, most of the existing studies in aspect-based sentiment analysis ignored this problem. In this work, we introduce a new task, named Aspect-Category-Opinion-Sentiment (ACOS) Quadruple Extraction, with the goal to extract all aspect... | ['Jianfei Yu', 'Rui Xia', 'Hongjie Cai'] | 2021-08-01 | null | null | null | acl-2021-5 | ['aspect-category-opinion-sentiment-quadruple'] | ['natural-language-processing'] | [-3.11481375e-02 9.39663500e-02 -4.36104119e-01 -8.96245062e-01
-9.65649068e-01 -8.29766631e-01 5.07955015e-01 3.97013307e-01
-2.61496395e-01 6.62575305e-01 3.07295293e-01 -3.27033997e-01
5.31074226e-01 -6.40172005e-01 -4.95872796e-01 -5.01592278e-01
4.10436541e-01 1.67286783e-01 -2.42893938e-02 -5.92306435... | [11.4527006149292, 6.667998790740967] |
446c711d-c4e3-4e50-b668-3bdcf44d374e | qr-mix-distributional-value-function | 2009.04197 | null | https://arxiv.org/abs/2009.04197v5 | https://arxiv.org/pdf/2009.04197v5.pdf | QR-MIX: Distributional Value Function Factorisation for Cooperative Multi-Agent Reinforcement Learning | In Cooperative Multi-Agent Reinforcement Learning (MARL) and under the setting of Centralized Training with Decentralized Execution (CTDE), agents observe and interact with their environment locally and independently. With local observation and random sampling, the randomness in rewards and observations leads to random... | ['Siyue Hu', 'Shih-wei Liao', 'Jian Hu', 'Haibin Wu', 'Seth Austin Harding'] | 2020-09-09 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-6.79233789e-01 7.83959925e-02 -4.01862383e-01 -2.15785772e-01
-7.12680519e-01 -6.01422131e-01 5.49931824e-01 1.85071036e-01
-7.72503853e-01 1.35388267e+00 1.18941426e-01 7.16949180e-02
-5.89617908e-01 -9.54869330e-01 -7.87061632e-01 -8.76360118e-01
-6.53334141e-01 8.17434311e-01 5.73513508e-02 -3.66426408... | [3.8020694255828857, 2.1220955848693848] |
07b57547-0d10-4121-88fa-7d8642024cc1 | contextual-outpainting-with-object-level | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Li_Contextual_Outpainting_With_Object-Level_Contrastive_Learning_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Li_Contextual_Outpainting_With_Object-Level_Contrastive_Learning_CVPR_2022_paper.pdf | Contextual Outpainting With Object-Level Contrastive Learning | We study the problem of contextual outpainting, which aims to hallucinate the missing background contents based on the remaining foreground contents. Existing image outpainting methods focus on completing object shapes or extending existing scenery textures, neglecting the semantically meaningful relationship betwe... | ['Zhiwei Xiong', 'Chang Chen', 'Jiacheng Li'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['image-outpainting'] | ['computer-vision'] | [ 6.46071613e-01 1.25026003e-01 4.66401055e-02 -1.60331488e-01
-7.55886376e-01 -4.73526835e-01 5.39765537e-01 -3.91553640e-01
3.83571059e-01 8.40818703e-01 3.30790609e-01 9.76922363e-02
3.87775838e-01 -8.48277628e-01 -1.28436601e+00 -8.56495261e-01
5.55163145e-01 2.06127167e-01 1.96224675e-01 -1.43015981... | [11.411153793334961, -0.9594154953956604] |
f1e722c0-117b-44bd-be14-e7c5dbc68ad1 | when-deep-learning-met-code-search | 1905.03813 | null | https://arxiv.org/abs/1905.03813v4 | https://arxiv.org/pdf/1905.03813v4.pdf | When Deep Learning Met Code Search | There have been multiple recent proposals on using deep neural networks for code search using natural language. Common across these proposals is the idea of $\mathit{embedding}$ code and natural language queries, into real vectors and then using vector distance to approximate semantic correlation between code and the q... | ['Jose Cambronero', 'Hongyu Li', 'Satish Chandra', 'Koushik Sen', 'Seohyun Kim'] | 2019-05-09 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [ 2.84008328e-02 -7.91822970e-02 -2.30769038e-01 -5.22033334e-01
-6.27644122e-01 -7.22752869e-01 4.92493570e-01 3.87202889e-01
-6.27963185e-01 2.97878146e-01 1.14436276e-01 -7.59233475e-01
-3.63467008e-01 -7.88338721e-01 -6.11917615e-01 -2.77020156e-01
-9.55921710e-02 4.70142365e-01 2.98743576e-01 -1.94730207... | [7.58123254776001, 7.949474334716797] |
1729fee7-0154-41e4-9f3e-229fab323cfb | discovering-causality-for-efficient | 2306.11846 | null | https://arxiv.org/abs/2306.11846v1 | https://arxiv.org/pdf/2306.11846v1.pdf | Discovering Causality for Efficient Cooperation in Multi-Agent Environments | In cooperative Multi-Agent Reinforcement Learning (MARL) agents are required to learn behaviours as a team to achieve a common goal. However, while learning a task, some agents may end up learning sub-optimal policies, not contributing to the objective of the team. Such agents are called lazy agents due to their non-co... | ['Corentin Artaud', 'Varuna De Silva', 'Rafael Pina'] | 2023-06-20 | null | null | null | null | ['causal-discovery', 'multi-agent-reinforcement-learning'] | ['knowledge-base', 'methodology'] | [-1.63217038e-01 2.74676949e-01 1.43232923e-02 -6.01377860e-02
-4.60168600e-01 -5.63131273e-01 5.60189307e-01 4.59896117e-01
-6.08786583e-01 1.10080814e+00 -4.48634773e-02 -7.16421828e-02
-6.43742383e-01 -6.76031768e-01 -7.25681067e-01 -8.88492882e-01
-6.05949223e-01 8.06421280e-01 3.39072675e-01 -2.50492960... | [3.7839460372924805, 1.985613226890564] |
c44008ef-f298-43bc-827a-5fc2aef36b56 | multi-class-skin-cancer-classification | 2303.07520 | null | https://arxiv.org/abs/2303.07520v1 | https://arxiv.org/pdf/2303.07520v1.pdf | Multi-class Skin Cancer Classification Architecture Based on Deep Convolutional Neural Network | Skin cancer detection is challenging since different types of skin lesions share high similarities. This paper proposes a computer-based deep learning approach that will accurately identify different kinds of skin lesions. Deep learning approaches can detect skin cancer very accurately since the models learn each pixel... | ['Alfredo Cuzzocrea', 'Sweta Sneha', 'Hossain Shahriar', 'Mst Shapna Akter'] | 2023-03-13 | null | null | null | null | ['skin-cancer-classification'] | ['medical'] | [-6.81311935e-02 -1.72357827e-01 -2.37314492e-01 1.09406687e-01
-3.51264834e-01 -2.21002653e-01 3.88875037e-01 -2.28742715e-02
-4.27295953e-01 4.96340662e-01 -2.28028268e-01 -3.88894171e-01
1.06382363e-01 -1.13876152e+00 -1.91092432e-01 -9.13581491e-01
2.24936992e-01 -7.63628483e-02 4.18786794e-01 -7.90731460... | [15.680010795593262, -2.9975733757019043] |
b1a66dc2-1caa-491c-9ac7-410da84df7e8 | mere-account-mein-kitna-balance-hai-on | 2010.16411 | null | https://arxiv.org/abs/2010.16411v1 | https://arxiv.org/pdf/2010.16411v1.pdf | Mere account mein kitna balance hai? -- On building voice enabled Banking Services for Multilingual Communities | Tremendous progress in speech and language processing has brought language technologies closer to daily human life. Voice technology has the potential to act as a horizontal enabling layer across all aspects of digitization. It is especially beneficial to rural communities in scenarios like a pandemic. In this work we ... | ['Alan W Black', 'Sai Krishna Rallabandi', 'Akshat Gupta'] | 2020-10-09 | null | null | null | null | ['intent-recognition'] | ['natural-language-processing'] | [ 6.27254881e-03 -5.67547759e-05 -2.63772532e-02 -3.61981839e-01
-7.92468131e-01 -5.55991113e-01 7.73128390e-01 1.23153172e-01
-3.58983785e-01 5.04879594e-01 9.25407827e-01 -9.07501161e-01
3.03056985e-01 -4.49441731e-01 5.68946153e-02 -4.16760057e-01
-1.56180011e-02 5.50117254e-01 7.38004893e-02 -5.62937021... | [14.264182090759277, 6.998344421386719] |
5722964f-ccda-4c9b-aafb-5f439eb06a1d | safe-exploration-for-constrained | 2112.00885 | null | https://arxiv.org/abs/2112.00885v3 | https://arxiv.org/pdf/2112.00885v3.pdf | DOPE: Doubly Optimistic and Pessimistic Exploration for Safe Reinforcement Learning | Safe reinforcement learning is extremely challenging--not only must the agent explore an unknown environment, it must do so while ensuring no safety constraint violations. We formulate this safe reinforcement learning (RL) problem using the framework of a finite-horizon Constrained Markov Decision Process (CMDP) with a... | ['Jean-Francois Chamberland', 'Srinivas Shakkottai', 'Dileep Kalathil', 'Aria HasanzadeZonuzy', 'Archana Bura'] | 2021-12-01 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 1.26349172e-02 7.12742150e-01 -3.01227003e-01 -9.26995650e-02
-8.69778216e-01 -5.40622711e-01 3.03691000e-01 3.43397111e-01
-8.79422307e-01 1.24175608e+00 -2.54740179e-01 -6.23758912e-01
-4.21197772e-01 -8.74039471e-01 -8.31784785e-01 -7.85395741e-01
-5.75561106e-01 4.93907064e-01 1.69705451e-01 -1.36827901... | [4.386439800262451, 2.7627532482147217] |
eac9eb16-25c5-4fb3-a54d-4140f3429e4a | knowledge-preserving-incremental-social-event | 2101.08747 | null | https://arxiv.org/abs/2101.08747v2 | https://arxiv.org/pdf/2101.08747v2.pdf | Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNs | Social events provide valuable insights into group social behaviors and public concerns and therefore have many applications in fields such as product recommendation and crisis management. The complexity and streaming nature of social messages make it appealing to address social event detection in an incremental learni... | ['Philip S. Yu', 'JianXin Li', 'Yingtong Dou', 'Jia Wu', 'Hao Peng', 'Yuwei Cao'] | 2021-01-21 | null | null | null | null | ['twitter-event-detection'] | ['natural-language-processing'] | [ 1.74530089e-01 2.59294331e-01 -6.12272441e-01 -3.11523587e-01
-2.11470023e-01 -3.09946060e-01 2.17249170e-01 7.91110575e-01
-3.11471522e-01 5.10509968e-01 3.45075727e-01 2.00515930e-02
-3.79855752e-01 -1.17316556e+00 -6.31268978e-01 -4.19569999e-01
-6.10065997e-01 2.37546340e-01 4.90621209e-01 -3.60072255... | [7.246924877166748, 6.043272495269775] |
36ac86f7-f1b0-4fee-acbb-3077f965ef60 | snomed2vec-random-walk-and-poincare | 1907.08650 | null | https://arxiv.org/abs/1907.08650v1 | https://arxiv.org/pdf/1907.08650v1.pdf | Snomed2Vec: Random Walk and Poincaré Embeddings of a Clinical Knowledge Base for Healthcare Analytics | Representation learning methods that transform encoded data (e.g., diagnosis and drug codes) into continuous vector spaces (i.e., vector embeddings) are critical for the application of deep learning in healthcare. Initial work in this area explored the use of variants of the word2vec algorithm to learn embeddings for m... | ['Khushbu Agarwal', 'Sutanay Choudhury', 'Tome Eftimov', 'Suzanne Tamang', 'Robert Rallo', 'Raghavendra Addanki'] | 2019-07-19 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [ 8.73031020e-02 7.47793913e-01 -5.93818426e-01 -2.48728484e-01
-5.02218425e-01 -1.97657377e-01 3.01752120e-01 1.33334422e+00
-4.87901211e-01 2.87120283e-01 9.26881135e-01 -6.74833953e-01
-5.82342803e-01 -9.15987432e-01 -8.62242207e-02 -4.11155671e-01
-6.55848682e-01 6.92941129e-01 -2.77554750e-01 -2.61494994... | [7.946043968200684, 7.092226982116699] |
9e9a89c7-84cd-4613-8de5-485d24df1de7 | monoedge-monocular-3d-object-detection-using | 2301.01802 | null | https://arxiv.org/abs/2301.01802v1 | https://arxiv.org/pdf/2301.01802v1.pdf | MonoEdge: Monocular 3D Object Detection Using Local Perspectives | We propose a novel approach for monocular 3D object detection by leveraging local perspective effects of each object. While the global perspective effect shown as size and position variations has been exploited for monocular 3D detection extensively, the local perspectives has long been overlooked. We design a local pe... | ['Huei Peng', 'Panqu Wang', 'Lingting Ge', 'Minghan Zhu'] | 2023-01-04 | null | null | null | null | ['monocular-3d-object-detection'] | ['computer-vision'] | [-1.9017529e-01 -2.4681997e-01 -1.9507676e-01 -2.8234300e-01
-2.3403086e-01 -8.2718563e-01 8.3303660e-01 -2.6675674e-01
-2.8685254e-01 1.1312797e-01 1.2641288e-01 -1.7757913e-01
3.2860044e-01 -4.8564255e-01 -8.2864624e-01 -7.9237783e-01
3.2827774e-01 2.6631802e-01 6.1307502e-01 2.8182185e-01
4.8209450e-01... | [7.846647262573242, -2.551821231842041] |
89ddf788-b025-4c60-a38c-01c9226476d6 | part-based-deep-hashing-for-large-scale | 1705.02145 | null | http://arxiv.org/abs/1705.02145v1 | http://arxiv.org/pdf/1705.02145v1.pdf | Part-based Deep Hashing for Large-scale Person Re-identification | Large-scale is a trend in person re-identification (re-id). It is important
that real-time search be performed in a large gallery. While previous methods
mostly focus on discriminative learning, this paper makes the attempt in
integrating deep learning and hashing into one framework to evaluate the
efficiency and accur... | ['Liang Zheng', 'Xiangwei Kong', 'Haiyan Fu', 'Qi Tian', 'Fuqing Zhu'] | 2017-05-05 | null | null | null | null | ['large-scale-person-re-identification'] | ['computer-vision'] | [-5.40857315e-01 -6.18778169e-01 2.88219471e-02 -3.52982014e-01
-8.86123180e-01 -3.40885460e-01 6.17028594e-01 3.03626835e-01
-7.61458039e-01 6.96151555e-01 4.20129716e-01 3.43921274e-01
3.32580775e-01 -8.23731065e-01 -7.13500321e-01 -9.48600054e-01
6.44598231e-02 6.42606676e-01 1.47640690e-01 7.41886571... | [14.737329483032227, 0.905683696269989] |
a4630821-ceb2-4581-adc3-2da9a1d9940e | cg-nerf-conditional-generative-neural | 2112.03517 | null | https://arxiv.org/abs/2112.03517v1 | https://arxiv.org/pdf/2112.03517v1.pdf | CG-NeRF: Conditional Generative Neural Radiance Fields | While recent NeRF-based generative models achieve the generation of diverse 3D-aware images, these approaches have limitations when generating images that contain user-specified characteristics. In this paper, we propose a novel model, referred to as the conditional generative neural radiance fields (CG-NeRF), which ca... | ['Jaegul Choo', 'Soyoung Yang', 'Sanghun Jung', 'Gyumin Shim', 'Kyungmin Jo'] | 2021-12-07 | null | null | null | null | ['3d-aware-image-synthesis'] | ['computer-vision'] | [ 2.87487477e-01 -2.64731526e-01 1.61822811e-01 -3.33430648e-01
-6.60650849e-01 -5.56544065e-01 7.84532309e-01 -6.43088877e-01
3.93257499e-01 7.94178188e-01 2.84630090e-01 4.05317508e-02
-9.00199562e-02 -8.59542847e-01 -7.06397951e-01 -9.84550834e-01
6.25331223e-01 2.40008160e-01 -3.58814932e-02 -2.37714425... | [11.543147087097168, -0.5757175087928772] |
82742bcd-f009-42eb-816d-8e3c245b9738 | box-of-lies-multimodal-deception-detection-in | null | null | https://aclanthology.org/N19-1175 | https://aclanthology.org/N19-1175.pdf | Box of Lies: Multimodal Deception Detection in Dialogues | Deception often takes place during everyday conversations, yet conversational dialogues remain largely unexplored by current work on automatic deception detection. In this paper, we address the task of detecting multimodal deceptive cues during conversational dialogues. We introduce a multimodal dataset containing dece... | ["Ver{\\'o}nica P{\\'e}rez-Rosas", 'Felix Soldner', 'Rada Mihalcea'] | 2019-06-01 | null | null | null | naacl-2019-6 | ['deception-detection'] | ['miscellaneous'] | [ 1.10845000e-01 4.27297205e-02 1.06340267e-01 -7.35876024e-01
-1.00873280e+00 -1.01661050e+00 9.99748170e-01 -1.95448995e-01
-4.49762404e-01 9.37912941e-01 3.17467034e-01 -5.51304184e-02
5.28325379e-01 1.30909145e-01 -1.46936983e-01 -6.82711184e-01
1.58716068e-01 3.49478573e-01 -3.13584656e-01 -3.26173753... | [8.234585762023926, 10.382684707641602] |
3be66e0c-b2de-4ea3-94fe-752fb5d0f244 | ground-plane-based-absolute-scale-estimation | 1903.00912 | null | http://arxiv.org/abs/1903.00912v1 | http://arxiv.org/pdf/1903.00912v1.pdf | Ground Plane based Absolute Scale Estimation for Monocular Visual Odometry | Recovering the absolute metric scale from a monocular camera is a challenging
but highly desirable problem for monocular camera-based systems. By using
different kinds of cues, various approaches have been proposed for scale
estimation, such as camera height, object size etc. In this paper, firstly, we
summarize differ... | ['Yuchao Dai', 'Dingfu Zhou', 'Hongdong Li'] | 2019-03-03 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-1.25958771e-01 -5.13658881e-01 -1.18241541e-01 -3.53657871e-01
-1.74465418e-01 -4.19357300e-01 4.21579659e-01 -2.49495089e-01
-4.11559224e-01 6.08336151e-01 6.40743747e-02 2.04994172e-01
-1.68197360e-02 -7.12776542e-01 -5.69250703e-01 -4.98048156e-01
5.65795600e-01 1.58107415e-01 5.92737615e-01 5.10789678... | [7.84186315536499, -2.1521358489990234] |
8d7cff07-248a-4dc7-ad91-f2187b8287c3 | improving-the-results-of-string-kernels-in | 1808.08409 | null | http://arxiv.org/abs/1808.08409v2 | http://arxiv.org/pdf/1808.08409v2.pdf | Improving the results of string kernels in sentiment analysis and Arabic dialect identification by adapting them to your test set | Recently, string kernels have obtained state-of-the-art results in various
text classification tasks such as Arabic dialect identification or native
language identification. In this paper, we apply two simple yet effective
transductive learning approaches to further improve the results of string
kernels. The first appr... | ['Andrei M. Butnaru', 'Radu Tudor Ionescu'] | 2018-08-25 | improving-the-results-of-string-kernels-in-1 | https://aclanthology.org/D18-1135 | https://aclanthology.org/D18-1135.pdf | emnlp-2018-10 | ['native-language-identification'] | ['natural-language-processing'] | [ 3.44874352e-01 9.93540883e-02 -1.44188687e-01 -6.90486073e-01
-7.24858046e-01 -8.90672207e-01 6.26497269e-01 3.84861678e-01
-4.95513469e-01 6.74383521e-01 -5.01820147e-02 -3.71353954e-01
1.89618036e-01 -9.08626020e-01 -4.29688096e-01 -9.06301320e-01
1.33486718e-01 7.86535382e-01 4.70895380e-01 -4.35934365... | [10.217606544494629, 10.52568531036377] |
2fbaac9a-ec46-4e4a-8a59-17fa4564b82d | local-aggressive-adversarial-attacks-on-3d | 2105.09090 | null | https://arxiv.org/abs/2105.09090v2 | https://arxiv.org/pdf/2105.09090v2.pdf | Local Aggressive Adversarial Attacks on 3D Point Cloud | Deep neural networks are found to be prone to adversarial examples which could deliberately fool the model to make mistakes. Recently, a few of works expand this task from 2D image to 3D point cloud by using global point cloud optimization. However, the perturbations of global point are not effective for misleading the... | ['Mingjie Wang', 'Zhiyu Chen', 'Feng Chen', 'Yiming Sun'] | 2021-05-19 | null | null | null | null | ['image-to-3d'] | ['computer-vision'] | [-4.97837700e-02 2.82612324e-01 2.12686941e-01 6.51536733e-02
-6.53047204e-01 -8.76210570e-01 5.49830794e-01 -2.34985456e-01
-1.03752136e-01 5.63069820e-01 -2.27780566e-01 1.19751245e-02
6.47501845e-04 -1.00024414e+00 -1.04493284e+00 -1.01341617e+00
-1.14338644e-01 4.61987913e-01 1.52549595e-01 -4.86479670... | [7.712672710418701, -4.456973075866699] |
2ddf514f-f3df-423e-9916-214635342945 | finbert-lstm-deep-learning-based-stock-price | 2211.07392 | null | https://arxiv.org/abs/2211.07392v1 | https://arxiv.org/pdf/2211.07392v1.pdf | FinBERT-LSTM: Deep Learning based stock price prediction using News Sentiment Analysis | Economy is severely dependent on the stock market. An uptrend usually corresponds to prosperity while a downtrend correlates to recession. Predicting the stock market has thus been a centre of research and experiment for a long time. Being able to predict short term movements in the market enables investors to reap gre... | ['Shayan Halder'] | 2022-11-11 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-7.59972334e-01 -3.48489404e-01 -3.66888374e-01 -1.09438539e-01
-1.86350241e-01 -5.42202890e-01 8.90280902e-01 -2.08744127e-02
-4.73096788e-01 1.04845381e+00 5.26766419e-01 -6.19107306e-01
1.14013478e-01 -1.15075111e+00 -5.35878539e-01 -3.21277797e-01
-8.99863020e-02 2.12678149e-01 1.43922687e-01 -7.26236880... | [4.449534893035889, 4.275467395782471] |
930efa30-4e32-4227-82cc-148da3c2d8a5 | positive-unlabeled-classification-under-class-1 | 2107.05045 | null | https://arxiv.org/abs/2107.05045v2 | https://arxiv.org/pdf/2107.05045v2.pdf | Positive-Unlabeled Classification under Class-Prior Shift: A Prior-invariant Approach Based on Density Ratio Estimation | Learning from positive and unlabeled (PU) data is an important problem in various applications. Most of the recent approaches for PU classification assume that the class-prior (the ratio of positive samples) in the training unlabeled dataset is identical to that of the test data, which does not hold in many practical c... | ['Masashi Sugiyama', 'Shota Nakajima'] | 2021-07-11 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [ 2.51406848e-01 -1.16512559e-01 -6.35722637e-01 -3.36393297e-01
-5.66683173e-01 -4.58961487e-01 3.17668736e-01 3.68990928e-01
-3.66034001e-01 1.19579077e+00 -4.61927295e-01 -4.13576722e-01
1.28661931e-01 -1.08898163e+00 -6.99585438e-01 -8.74893546e-01
6.07237995e-01 4.03083473e-01 5.35693049e-01 2.21051186... | [9.120147705078125, 3.9715349674224854] |
6733282f-0f70-41d4-94c3-bf8ae389db40 | attention-modulation-for-zero-shot-cross | null | null | https://aclanthology.org/2022.codi-1.11 | https://aclanthology.org/2022.codi-1.11.pdf | Attention Modulation for Zero-Shot Cross-Domain Dialogue State Tracking | Dialog state tracking (DST) is a core step for task-oriented dialogue systems aiming to track the user’s current goal during a dialogue. Recently a special focus has been put on applying existing DST models to new domains, in other words performing zero-shot cross-domain transfer. While recent state-of-the-art models l... | ['Sophie Rosset', 'Guillaume Bernard', 'Olivier Galibert', 'Mathilde Veron'] | null | null | null | null | coling-codi-crac-2022-10 | ['dialogue-state-tracking', 'task-oriented-dialogue-systems'] | ['natural-language-processing', 'natural-language-processing'] | [-7.35963061e-02 4.39404100e-01 -9.99078006e-02 -4.40279305e-01
-5.12097180e-01 -2.80331850e-01 1.20619500e+00 2.03868672e-01
-7.01838672e-01 8.84747028e-01 5.14413238e-01 -2.14506686e-01
1.12230383e-01 -5.62022567e-01 8.38238671e-02 -1.89934358e-01
2.66695172e-01 9.81846452e-01 7.43159831e-01 -9.15359378... | [12.75997257232666, 7.919741630554199] |
62e3b811-2b7f-4a3d-86dc-cce62bf9f464 | feature-selection-and-classification-of | null | null | https://doi.org/10.1109/LGRS.2007.905116 | https://doi.org/10.1109/LGRS.2007.905116 | Feature Selection and Classification of Hyperspectral Images With Support Vector Machines | Hyperspectral images consist of large number of bands which require sophisticated analysis to extract. One approach to reduce computational cost, information representation, and accelerate knowledge discovery is to eliminate bands that do not add value to the classification and analysis method which is being applied. I... | ['George Fann', 'Rick Archibald'] | 2007-10-15 | null | null | null | ieee-geoscience-and-remote-sensing-letters-11 | ['classification-of-hyperspectral-images', 'few-shot-image-classification'] | ['computer-vision', 'computer-vision'] | [ 9.15382922e-01 -4.40902114e-01 -6.11449825e-03 -5.62388301e-01
-3.07317317e-01 -6.13509059e-01 3.31647545e-01 9.22847092e-02
-1.99264571e-01 8.95349741e-01 -4.54447120e-01 -4.94485885e-01
-5.73100746e-01 -1.05242705e+00 -6.35252297e-02 -9.34287250e-01
-7.71918222e-02 8.26788843e-02 4.97077927e-02 -1.78095013... | [9.710464477539062, -1.7328022718429565] |
58f5223d-f34c-4391-9bd4-1a768ce0b5c5 | a-joint-model-for-definition-extraction-with | 1911.01678 | null | https://arxiv.org/abs/1911.01678v4 | https://arxiv.org/pdf/1911.01678v4.pdf | A Joint Model for Definition Extraction with Syntactic Connection and Semantic Consistency | Definition Extraction (DE) is one of the well-known topics in Information Extraction that aims to identify terms and their corresponding definitions in unstructured texts. This task can be formalized either as a sentence classification task (i.e., containing term-definition pairs or not) or a sequential labeling task (... | ['Franck Dernoncourt', 'Thien Huu Nguyen', 'Dejing Dou', 'Amir Pouran Ben Veyseh'] | 2019-11-05 | null | null | null | null | ['definition-extraction'] | ['natural-language-processing'] | [ 4.80418533e-01 2.49485090e-01 -2.88842559e-01 -4.04927611e-01
-3.13125372e-01 -7.26887882e-01 7.76807487e-01 5.96356988e-01
-1.86948568e-01 3.26186121e-01 2.99183965e-01 -5.38630068e-01
-3.32277119e-01 -9.66285050e-01 -3.40798676e-01 -6.27985656e-01
1.68126509e-01 3.22172642e-01 1.20822256e-02 -2.33649641... | [10.029236793518066, 8.674064636230469] |
5f42555b-d093-4480-9de4-209f8d7bdbcf | temporal-knowledge-base-completion-new | 2005.05035 | null | https://arxiv.org/abs/2005.05035v2 | https://arxiv.org/pdf/2005.05035v2.pdf | Temporal Knowledge Base Completion: New Algorithms and Evaluation Protocols | Temporal knowledge bases associate relational (s,r,o) triples with a set of times (or a single time instant) when the relation is valid. While time-agnostic KB completion (KBC) has witnessed significant research, temporal KB completion (TKBC) is in its early days. In this paper, we consider predicting missing entities ... | ['Sushant Rathi', 'Prachi Jain', 'Mausam', 'Soumen Chakrabarti'] | 2020-05-02 | null | https://aclanthology.org/2020.emnlp-main.305 | https://aclanthology.org/2020.emnlp-main.305.pdf | emnlp-2020-11 | ['knowledge-base-completion', 'temporal-knowledge-graph-completion', 'knowledge-base-completion', 'temporal-information-extraction'] | ['graphs', 'knowledge-base', 'knowledge-base', 'natural-language-processing'] | [-3.08942646e-01 4.47075933e-01 -8.12554717e-01 -4.34248149e-01
-6.44782305e-01 -5.90454876e-01 8.14081192e-01 6.48680031e-01
-1.29994780e-01 1.12383699e+00 2.39231765e-01 -4.52208072e-01
-6.18442059e-01 -8.95781457e-01 -6.58982038e-01 -3.85037549e-02
-7.13327706e-01 1.09134710e+00 8.38761747e-01 -2.36594245... | [8.565873146057129, 7.932240962982178] |
d7a914c7-363a-465c-9d13-c572afdfbad4 | is-my-automatic-audio-captioning-system-so | 2211.08983 | null | https://arxiv.org/abs/2211.08983v1 | https://arxiv.org/pdf/2211.08983v1.pdf | Is my automatic audio captioning system so bad? spider-max: a metric to consider several caption candidates | Automatic Audio Captioning (AAC) is the task that aims to describe an audio signal using natural language. AAC systems take as input an audio signal and output a free-form text sentence, called a caption. Evaluating such systems is not trivial, since there are many ways to express the same idea. For this reason, severa... | ['Julien Pinquier', 'Thomas Pellegrini', 'Etienne Labbé'] | 2022-11-14 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 4.96383607e-01 2.45645687e-01 3.46524656e-01 -3.03796947e-01
-1.27753627e+00 -7.81964302e-01 7.81360984e-01 4.84344900e-01
-3.88782203e-01 9.68663573e-01 4.75868255e-01 2.65709236e-02
-6.74988925e-02 -2.79303074e-01 -5.28871834e-01 -4.97970223e-01
1.37790442e-01 6.85443103e-01 3.39428812e-01 -2.34650105... | [15.248779296875, 4.850766181945801] |
d9179acd-067c-4d2d-96ea-56c10d4fc9b7 | let-s-be-explicit-about-that-distant | 2106.03192 | null | https://arxiv.org/abs/2106.03192v1 | https://arxiv.org/pdf/2106.03192v1.pdf | Let's be explicit about that: Distant supervision for implicit discourse relation classification via connective prediction | In implicit discourse relation classification, we want to predict the relation between adjacent sentences in the absence of any overt discourse connectives. This is challenging even for humans, leading to shortage of annotated data, a fact that makes the task even more difficult for supervised machine learning approach... | ['Robert Östling', 'Murathan Kurfali'] | 2021-06-06 | null | null | null | null | ['implicit-discourse-relation-classification', 'implicit-relations'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.53308648e-01 1.08369005e+00 -4.23715919e-01 -3.21216077e-01
-4.85674828e-01 -4.66995806e-01 1.32629025e+00 6.42296791e-01
-3.30041945e-01 1.08764970e+00 4.79484439e-01 -5.69258392e-01
-4.85038087e-02 -6.40081346e-01 -2.25158691e-01 -5.09796977e-01
-2.13873666e-02 6.51094913e-01 4.11449254e-01 -6.85494959... | [10.782835960388184, 9.323203086853027] |
22535b5c-ab23-4c92-b3ed-26c0be996e95 | neural-sheaf-diffusion-a-topological | 2202.04579 | null | https://arxiv.org/abs/2202.04579v4 | https://arxiv.org/pdf/2202.04579v4.pdf | Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs | Cellular sheaves equip graphs with a "geometrical" structure by assigning vector spaces and linear maps to nodes and edges. Graph Neural Networks (GNNs) implicitly assume a graph with a trivial underlying sheaf. This choice is reflected in the structure of the graph Laplacian operator, the properties of the associated ... | ['Michael M. Bronstein', 'Pietro Liò', 'Benjamin Paul Chamberlain', 'Francesco Di Giovanni', 'Cristian Bodnar'] | 2022-02-09 | null | null | null | null | ['node-classification-on-non-homophilic'] | ['graphs'] | [-1.22302420e-01 6.20591283e-01 -8.08666497e-02 2.62172848e-01
4.79275823e-01 -8.22032690e-01 8.59805286e-01 6.42668828e-02
-1.13701008e-01 4.31375772e-01 6.67405576e-02 -2.41890088e-01
-4.92836922e-01 -1.08758903e+00 -7.94594884e-01 -1.14681709e+00
-5.70286930e-01 5.99564493e-01 3.09257805e-01 -5.21672487... | [6.849144458770752, 5.953017711639404] |
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