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e3856e5c-aa11-4b1f-868b-2535d0fbb4e7 | self-supervised-image-prior-learning-with-gmm | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Liu_Self-Supervised_Image_Prior_Learning_With_GMM_From_a_Single_Noisy_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Liu_Self-Supervised_Image_Prior_Learning_With_GMM_From_a_Single_Noisy_ICCV_2021_paper.pdf | Self-Supervised Image Prior Learning With GMM From a Single Noisy Image | The lack of clean images undermines the practicability of supervised image prior learning methods, of which the training schemes require a large number of clean images. To free image prior learning from the image collection burden, a novel Self-Supervised learning method for Gaussian Mixture Model (SS-GMM) is propo... | ['Shan Tan', 'Jiangbo Lu', 'Xuan Liu', 'Haosen Liu'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['noise-estimation'] | ['medical'] | [ 1.78934753e-01 -1.12917364e-01 1.34007186e-01 -2.47022435e-01
-8.65307629e-01 -1.94185928e-01 3.66721660e-01 -2.10603803e-01
-4.43888366e-01 4.99754369e-01 -4.72734533e-02 -1.23824455e-01
-5.43207228e-02 -7.48135507e-01 -7.30307341e-01 -1.26910853e+00
1.67067498e-01 -3.00578177e-01 -6.58556074e-02 3.72990891... | [11.443758010864258, -2.4103171825408936] |
44b2baaf-13cb-40f8-b0da-eb50587f0e64 | interpretability-and-causal-discovery-of-the | 2212.10718 | null | https://arxiv.org/abs/2212.10718v1 | https://arxiv.org/pdf/2212.10718v1.pdf | Interpretability and causal discovery of the machine learning models to predict the production of CBM wells after hydraulic fracturing | Machine learning approaches are widely studied in the production prediction of CBM wells after hydraulic fracturing, but merely used in practice due to the low generalization ability and the lack of interpretability. A novel methodology is proposed in this article to discover the latent causality from observed data, wh... | ['Zhaozhong Yang', 'Xiaogang Li', 'Liangjie Gou', 'Guoquan Wen', 'Chao Min'] | 2022-12-21 | null | null | null | null | ['causal-discovery', 'interpretable-machine-learning'] | ['knowledge-base', 'methodology'] | [ 1.55693501e-01 2.03056723e-01 -3.54876876e-01 -2.80320887e-02
3.29049736e-01 -1.56778173e-04 7.25084424e-01 4.24218297e-01
7.61584193e-02 8.28686595e-01 3.43929142e-01 -5.83960593e-01
-9.27548349e-01 -1.10664392e+00 -5.86380899e-01 -9.44643557e-01
-3.18730533e-01 1.89049706e-01 -1.40905365e-01 -2.66453385... | [7.860784530639648, 5.240818977355957] |
12298351-ea54-4c37-a8e9-82d04bc93b06 | zju-reler-submission-for-epic-kitchen-1 | 2307.02508 | null | https://arxiv.org/abs/2307.02508v2 | https://arxiv.org/pdf/2307.02508v2.pdf | ZJU ReLER Submission for EPIC-KITCHEN Challenge 2023: TREK-150 Single Object Tracking | The Associating Objects with Transformers (AOT) framework has exhibited exceptional performance in a wide range of complex scenarios for video object tracking and segmentation. In this study, we convert the bounding boxes to masks in reference frames with the help of the Segment Anything Model (SAM) and Alpha-Refine, a... | ['Yueting Zhuang', 'Yi Yang', 'Zongxin Yang', 'Jiahao Li', 'Yuanyou Xu'] | 2023-07-05 | null | null | null | null | ['object-tracking', 'video-object-tracking', 'video-object-segmentation', 'video-semantic-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 3.20198059e-01 -3.97283584e-01 -1.31468982e-01 -1.30053759e-01
-3.99090499e-01 -8.15402925e-01 5.22420824e-01 -3.22672665e-01
-3.84960234e-01 1.70890361e-01 -7.51624331e-02 -1.79241404e-01
6.05571456e-02 -3.36482406e-01 -6.94359720e-01 -2.63172567e-01
-1.87828809e-01 2.86088377e-01 1.20345449e+00 -3.75660695... | [9.034890174865723, -0.18746280670166016] |
98101bb7-53c4-4bd5-9ec0-07984a873ab0 | fpaenet-pneumonia-detection-network-based-on | 2011.08706 | null | http://arxiv.org/abs/2011.08706v1 | http://arxiv.org/pdf/2011.08706v1.pdf | FPAENet: Pneumonia Detection Network Based on Feature Pyramid Attention Enhancement | Automatic pneumonia Detection based on deep learning has increasing clinical
value. Although the existing Feature Pyramid Network (FPN) and its variants
have already achieved some great successes, their detection accuracies for
pneumonia lesions in medical images are still unsatisfactory. In this paper, we
propose a pn... | [] | 2020-11-16 | null | null | null | null | ['pneumonia-detection'] | ['medical'] | [ 1.43651426e-01 -4.42387998e-01 3.16016600e-02 -1.99290603e-01
-6.76140010e-01 9.70815495e-02 2.61101335e-01 -6.82564452e-02
-5.43175936e-01 4.41844225e-01 5.27443886e-01 1.07914641e-01
-9.92114469e-02 -7.90530503e-01 -2.58140236e-01 -8.13239157e-01
1.13701344e-01 2.69343078e-01 7.34151721e-01 1.73632830... | [15.508631706237793, -1.780225396156311] |
7488423d-b36b-4901-b533-0742cc442324 | oair-object-aware-image-retargeting-using-pso | 2209.04804 | null | https://arxiv.org/abs/2209.04804v1 | https://arxiv.org/pdf/2209.04804v1.pdf | OAIR: Object-Aware Image Retargeting Using PSO and Aesthetic Quality Assessment | Image retargeting aims at altering an image size while preserving important content and minimizing noticeable distortions. However, previous image retargeting methods create outputs that suffer from artifacts and distortions. Besides, most previous works attempt to retarget the background and foreground of the input im... | ['Shadrokh Samavi', 'Shahram Shirani', 'Nader Karimi', 'Mohammad Hossein Givkashi', 'Mohammad Reza Naderi'] | 2022-09-11 | null | null | null | null | ['image-retargeting'] | ['computer-vision'] | [ 7.10438490e-01 -4.03157435e-02 1.64494455e-01 1.78819716e-01
-1.60561100e-01 -3.33838701e-01 2.34045878e-01 -1.36848986e-02
-3.49557579e-01 7.34764695e-01 8.31464306e-04 2.26202458e-01
-6.10442180e-03 -1.03743315e+00 -4.17003810e-01 -1.01847649e+00
5.79484642e-01 -7.93319475e-03 7.92962432e-01 -1.61583275... | [11.128072738647461, -1.1647045612335205] |
ad270add-4c5d-41ab-acf0-43a75261d78d | event-based-camera-pose-tracking-using-a | 1510.01972 | null | http://arxiv.org/abs/1510.01972v1 | http://arxiv.org/pdf/1510.01972v1.pdf | Event-based Camera Pose Tracking using a Generative Event Model | Event-based vision sensors mimic the operation of biological retina and they
represent a major paradigm shift from traditional cameras. Instead of providing
frames of intensity measurements synchronously, at artificially chosen rates,
event-based cameras provide information on brightness changes asynchronously,
when th... | ['Guillermo Gallego', 'Davide Scaramuzza', 'Christian Forster', 'Elias Mueggler'] | 2015-10-07 | null | null | null | null | ['camera-localization', 'event-based-vision'] | ['computer-vision', 'computer-vision'] | [ 5.17982602e-01 -2.17605621e-01 5.27250230e-01 -2.71475285e-01
-2.60024726e-01 -5.26825666e-01 6.79182589e-01 1.78729936e-01
-9.71035063e-01 6.16417944e-01 -4.37905751e-02 3.15006286e-01
-1.63554996e-01 -6.61823630e-01 -9.34262514e-01 -1.02375984e+00
1.42096400e-01 6.48535192e-02 6.93706214e-01 3.75316441... | [8.62703800201416, -1.4056122303009033] |
273e41da-15e9-4902-91ff-76c508b7dc7d | specific-differential-entropy-rate-estimation | 1606.02615 | null | http://arxiv.org/abs/1606.02615v1 | http://arxiv.org/pdf/1606.02615v1.pdf | Specific Differential Entropy Rate Estimation for Continuous-Valued Time Series | We introduce a method for quantifying the inherent unpredictability of a
continuous-valued time series via an extension of the differential Shannon
entropy rate. Our extension, the specific entropy rate, quantifies the amount
of predictive uncertainty associated with a specific state, rather than
averaged over all stat... | ['David Darmon'] | 2016-06-08 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 3.79933476e-01 2.08534360e-01 -1.11512631e-01 -5.04725993e-01
-4.75792050e-01 -5.25461018e-01 4.25811261e-01 7.19937623e-01
-3.88216585e-01 1.10732806e+00 6.17083125e-02 -1.00932814e-01
-2.98112929e-01 -5.09904861e-01 -2.82723367e-01 -5.90275645e-01
-9.29774404e-01 -7.61767710e-03 -1.24921761e-01 1.60592809... | [6.917368412017822, 3.767368793487549] |
d34ba617-cfec-4f52-966e-99516487726c | self-supervision-and-spatial-sequential | 2110.10734 | null | https://arxiv.org/abs/2110.10734v1 | https://arxiv.org/pdf/2110.10734v1.pdf | Self-Supervision and Spatial-Sequential Attention Based Loss for Multi-Person Pose Estimation | Bottom-up based multi-person pose estimation approaches use heatmaps with auxiliary predictions to estimate joint positions and belonging at one time. Recently, various combinations between auxiliary predictions and heatmaps have been proposed for higher performance, these predictions are supervised by the correspondin... | ['Takeshi Ikenaga', 'Songlin Du', 'Dingli Luo', 'Haiyang Liu'] | 2021-10-20 | null | null | null | null | ['multi-person-pose-estimation'] | ['computer-vision'] | [-6.17479272e-02 5.58166504e-01 -1.93714112e-01 -5.99237919e-01
-1.03970730e+00 3.39367166e-02 4.40240532e-01 -3.48918200e-01
-3.90473843e-01 9.18810129e-01 5.44781804e-01 5.53699851e-01
5.54253273e-02 -4.76202339e-01 -1.06722045e+00 -5.32615006e-01
1.23588637e-01 6.49909496e-01 5.36614358e-01 -2.02551082... | [7.141907691955566, -0.7956111431121826] |
f64e6644-4dd3-4b7f-9727-6bdce530c464 | transitional-adaptation-of-pretrained-models | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Yu_Transitional_Adaptation_of_Pretrained_Models_for_Visual_Storytelling_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Yu_Transitional_Adaptation_of_Pretrained_Models_for_Visual_Storytelling_CVPR_2021_paper.pdf | Transitional Adaptation of Pretrained Models for Visual Storytelling | Previous models for vision-to-language generation tasks usually pretrain a visual encoder and a language generator in the respective domains and jointly finetune them with the target task. However, this direct transfer practice may suffer from the discord between visual specificity and language fluency since they a... | ['Gunhee Kim', 'Jongseok Kim', 'Heeseung Yun', 'Jiwan Chung', 'Youngjae Yu'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['visual-storytelling'] | ['natural-language-processing'] | [ 4.30006385e-01 2.64416367e-01 -6.05357736e-02 -3.66828054e-01
-9.22480345e-01 -6.66957974e-01 1.11870384e+00 -9.47468132e-02
-5.36033332e-01 7.15784788e-01 4.77364033e-01 -3.81830633e-01
5.36712170e-01 -4.25362080e-01 -1.17578888e+00 -4.43638086e-01
4.58041817e-01 6.12128675e-01 1.43365040e-01 -1.86391383... | [11.057526588439941, 1.1951984167099] |
5b5ef402-cbc5-44ca-b969-2d85e59a2029 | interactive-visual-hull-refinement-for | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Zuo_Interactive_Visual_Hull_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Zuo_Interactive_Visual_Hull_ICCV_2015_paper.pdf | Interactive Visual Hull Refinement for Specular and Transparent Object Surface Reconstruction | In this paper we present a method of using standard multi-view images for 3D surface reconstruction of non-Lambertian objects. We extend the original visual hull concept to incorporate 3D cues presented by internal occluding contours, i.e., occluding contours that are inside the object's silhouettes. We discovered that... | ['Ruigang Yang', 'Xinxin Zuo', 'Sen Wang', 'Chao Du', 'Jiangbin Zheng'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['transparent-objects', 'contour-detection'] | ['computer-vision', 'computer-vision'] | [ 2.14870855e-01 1.86649725e-01 4.44304854e-01 -1.25086591e-01
-3.62006515e-01 -7.29664385e-01 2.45460868e-01 7.93537945e-02
1.31513461e-01 2.17274219e-01 -5.64548634e-02 6.87377229e-02
2.41553113e-01 -6.56738400e-01 -6.81410491e-01 -4.69036162e-01
-1.01593556e-02 6.86149716e-01 8.64192843e-01 -2.84422457... | [9.368170738220215, -2.999990224838257] |
ec598ff5-69ad-448c-bb4c-cf8878c12338 | conservative-safety-critics-for-exploration-1 | 2010.14497 | null | https://arxiv.org/abs/2010.14497v2 | https://arxiv.org/pdf/2010.14497v2.pdf | Conservative Safety Critics for Exploration | Safe exploration presents a major challenge in reinforcement learning (RL): when active data collection requires deploying partially trained policies, we must ensure that these policies avoid catastrophically unsafe regions, while still enabling trial and error learning. In this paper, we target the problem of safe exp... | ['Animesh Garg', 'Florian Shkurti', 'Sergey Levine', 'Nicholas Rhinehart', 'Aviral Kumar', 'Homanga Bharadhwaj'] | 2020-10-27 | conservative-safety-critics-for-exploration | https://openreview.net/forum?id=iaO86DUuKi | https://openreview.net/pdf?id=iaO86DUuKi | iclr-2021-1 | ['safe-exploration'] | ['robots'] | [ 3.91625836e-02 4.49319303e-01 -4.20849353e-01 3.19859125e-02
-1.18523371e+00 -7.15758145e-01 2.54505038e-01 1.94773540e-01
-8.69310141e-01 1.21613932e+00 -2.09772304e-01 -4.45691317e-01
-2.60537863e-01 -5.51332653e-01 -1.24284160e+00 -8.30889404e-01
-6.25532210e-01 1.89876392e-01 2.07789987e-01 -1.32258147... | [4.524734973907471, 2.1077752113342285] |
554c64cd-c67e-4773-b9ec-88c098e047bb | multi-task-balanced-and-recalibrated-network | 2109.02418 | null | https://arxiv.org/abs/2109.02418v3 | https://arxiv.org/pdf/2109.02418v3.pdf | Multitask Balanced and Recalibrated Network for Medical Code Prediction | Human coders assign standardized medical codes to clinical documents generated during patients' hospitalization, which is error-prone and labor-intensive. Automated medical coding approaches have been developed using machine learning methods such as deep neural networks. Nevertheless, automated medical coding is still ... | ['Pekka Marttinen', 'Erik Cambria', 'Shaoxiong Ji', 'Wei Sun'] | 2021-09-06 | null | null | null | null | ['medical-code-prediction'] | ['medical'] | [ 2.46354178e-01 -8.02672878e-02 -1.36687815e-01 -5.20578682e-01
-9.88344193e-01 -9.64602157e-02 -3.09055895e-01 5.78182220e-01
-3.46463561e-01 4.72398579e-01 4.18606162e-01 -3.03689122e-01
-3.72826755e-01 -5.69517255e-01 -3.95152539e-01 -4.81231421e-01
1.03868760e-01 5.92971802e-01 -1.33687377e-01 6.42968044... | [7.981778621673584, 6.801783084869385] |
554080a5-affc-4b56-b5c1-08a17619d9e7 | mumu-cooperative-multitask-learning-based | null | null | https://www.researchgate.net/publication/358345510_MuMu_Cooperative_Multitask_Learning-based_Guided_Multimodal_Fusion | https://www.researchgate.net/publication/358345510_MuMu_Cooperative_Multitask_Learning-based_Guided_Multimodal_Fusion | MuMu: Cooperative Multitask Learning-based Guided Multimodal Fusion | Multimodal sensors (visual, non-visual, and wearable) can provide complementary information to develop robust perception systems for recognizing activities accurately. However, it is challenging to extract robust multimodal representations due to the heterogeneous characteristics of data from multimodal sensors and dis... | ['Tariq Iqbal', 'Md Mofijul Islam'] | 2022-02-22 | null | null | null | aaai-2022-2 | ['multimodal-activity-recognition'] | ['computer-vision'] | [ 5.04462302e-01 -4.46187884e-01 -1.06017210e-01 -8.08488280e-02
-1.39246416e+00 -5.12827456e-01 5.71663618e-01 2.28134781e-01
-2.28147298e-01 7.00974286e-01 7.32203364e-01 3.48723263e-01
-1.85679451e-01 -1.37904853e-01 -6.10093057e-01 -8.90846908e-01
-2.50474304e-01 -1.45161122e-01 -6.99869916e-02 -1.15877971... | [13.120906829833984, 4.958114147186279] |
df9590ee-112d-4ec5-ac26-97b4a2aedaac | state-of-the-art-vietnamese-word-segmentation | 1906.07662 | null | https://arxiv.org/abs/1906.07662v1 | https://arxiv.org/pdf/1906.07662v1.pdf | State-of-the-Art Vietnamese Word Segmentation | Word segmentation is the first step of any tasks in Vietnamese language processing. This paper reviews stateof-the-art approaches and systems for word segmentation in Vietnamese. To have an overview of all stages from building corpora to developing toolkits, we discuss building the corpus stage, approaches applied to s... | ['Rachsuda Jiamthapthaksin', 'Song Nguyen Duc Cong', 'Quoc Hung Ngo'] | 2019-06-18 | null | null | null | null | ['vietnamese-word-segmentation'] | ['natural-language-processing'] | [-2.34797657e-01 1.58080637e-01 -3.95173281e-01 -5.06910861e-01
-7.73507178e-01 -7.88186491e-01 2.43133962e-01 1.10817976e-01
-1.03899860e+00 7.67478406e-01 1.68466434e-01 -6.82982922e-01
5.00943780e-01 -6.48005545e-01 9.50535983e-02 -5.26139081e-01
1.36765301e-01 8.71390343e-01 2.82419980e-01 -7.57944167... | [10.354610443115234, 10.108430862426758] |
f96c6754-b1e4-4731-b6c0-59038b956fe9 | sta-vpr-spatio-temporal-alignment-for-visual | 2103.1358 | null | https://arxiv.org/abs/2103.13580v2 | https://arxiv.org/pdf/2103.13580v2.pdf | STA-VPR: Spatio-temporal Alignment for Visual Place Recognition | Recently, the methods based on Convolutional Neural Networks (CNNs) have gained popularity in the field of visual place recognition (VPR). In particular, the features from the middle layers of CNNs are more robust to drastic appearance changes than handcrafted features and high-layer features. Unfortunately, the holist... | ['Dezhen Song', 'Xiang-Dong Zhou', 'Baifan Chen', 'Feng Lu'] | 2021-03-25 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [-6.13028966e-02 -9.33478475e-01 -3.30966294e-01 -3.21608633e-01
-5.05345583e-01 -4.57164586e-01 6.54881835e-01 1.03140669e-02
-6.91580713e-01 3.59743595e-01 -1.23440633e-02 -1.43265545e-01
-4.25096937e-02 -8.70843112e-01 -6.97596014e-01 -7.65762687e-01
-5.74355870e-02 -3.25701267e-01 5.24845302e-01 -4.06756133... | [7.926788806915283, -1.7216229438781738] |
9c81a341-07c4-4edf-bcc4-50653479416b | argus-context-based-detection-of-stealthy-iot | 2302.07589 | null | https://arxiv.org/abs/2302.07589v2 | https://arxiv.org/pdf/2302.07589v2.pdf | ARGUS: Context-Based Detection of Stealthy IoT Infiltration Attacks | IoT application domains, device diversity and connectivity are rapidly growing. IoT devices control various functions in smart homes and buildings, smart cities, and smart factories, making these devices an attractive target for attackers. On the other hand, the large variability of different application scenarios and ... | ['Ahmad-Reza Sadeghi', 'Hossein Fereidooni', 'Markus Miettinen', 'Reham Mohamed', 'Marco Chilese', 'Phillip Rieger'] | 2023-02-15 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [ 2.98530042e-01 -2.61427581e-01 -1.83231965e-01 8.94238353e-02
-1.49852052e-01 -7.29018688e-01 5.70718467e-01 2.11680532e-01
-2.41314545e-01 4.11199749e-01 -1.72150224e-01 -8.13542128e-01
6.20428957e-02 -9.59734678e-01 -5.32895982e-01 -8.85352731e-01
-4.11825068e-02 2.75771886e-01 5.35580993e-01 2.64426202... | [5.18396520614624, 7.180993556976318] |
f2e2f2a5-6d00-4dfd-8e3e-c21d41169936 | visual-speech-enhancement | 1711.08789 | null | http://arxiv.org/abs/1711.08789v3 | http://arxiv.org/pdf/1711.08789v3.pdf | Visual Speech Enhancement | When video is shot in noisy environment, the voice of a speaker seen in the
video can be enhanced using the visible mouth movements, reducing background
noise. While most existing methods use audio-only inputs, improved performance
is obtained with our visual speech enhancement, based on an audio-visual neural
network.... | ['Shmuel Peleg', 'Asaph Shamir', 'Aviv Gabbay'] | 2017-11-23 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 2.58146673e-01 8.20429400e-02 -3.53334755e-01 2.45250762e-02
-6.65105224e-01 -2.81022221e-01 3.31892610e-01 -1.39209583e-01
-4.21809524e-01 6.41907096e-01 6.86348677e-01 -2.23646075e-01
5.34883857e-01 -2.28148967e-01 -6.97205126e-01 -6.88400686e-01
3.28777522e-01 -2.45255560e-01 2.24017143e-01 5.98114245... | [14.441160202026367, 5.166386604309082] |
cc5799f3-de3c-4013-bf03-c1e43694206f | revisiting-table-detection-datasets-for | 2305.04833 | null | https://arxiv.org/abs/2305.04833v1 | https://arxiv.org/pdf/2305.04833v1.pdf | Revisiting Table Detection Datasets for Visually Rich Documents | Table Detection has become a fundamental task for visually rich document understanding with the surging number of electronic documents. There have been some open datasets widely used in many studies. However, popular available datasets have some inherent limitations, including the noisy and inconsistent samples, and th... | ['Ala Abu Alkheir', 'Burak Kantarci', 'Murat Simsek', 'Bin Xiao'] | 2023-05-04 | null | null | null | null | ['table-detection'] | ['miscellaneous'] | [-1.42950684e-01 -2.14648530e-01 -3.54331344e-01 -1.29257396e-01
-7.55045891e-01 -8.48466873e-01 6.25365496e-01 -3.81846018e-02
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2.20069155e-01 4.05897915e-01 4.36080515e-01 1.12703284... | [11.448568344116211, 2.369306802749634] |
a134834c-09e8-4abb-9bf3-f6e071bc037d | conditional-online-learning-for-keyword | 2305.13332 | null | https://arxiv.org/abs/2305.13332v1 | https://arxiv.org/pdf/2305.13332v1.pdf | Conditional Online Learning for Keyword Spotting | Modern approaches for keyword spotting rely on training deep neural networks on large static datasets with i.i.d. distributions. However, the resulting models tend to underperform when presented with changing data regimes in real-life applications. This work investigates a simple but effective online continual learning... | ['Bruno Iwami', 'Michel Meneses'] | 2023-05-19 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 3.40568461e-02 -1.74541980e-01 -3.40375543e-01 2.48624682e-02
-9.41066861e-01 -2.13352427e-01 1.29984453e-01 1.87431842e-01
-5.83938777e-01 7.11718440e-01 9.35956463e-02 -5.90405643e-01
9.72507894e-02 -3.84959489e-01 -1.17017710e+00 -4.46720898e-01
-2.90299803e-01 3.84138227e-01 3.77614230e-01 -7.39406981... | [9.952963829040527, 3.6173899173736572] |
d812de91-ec09-4ff3-a404-6b11ba493be2 | noise-pollution-in-hospital-readmission | 2005.01259 | null | https://arxiv.org/abs/2005.01259v2 | https://arxiv.org/pdf/2005.01259v2.pdf | Noise Pollution in Hospital Readmission Prediction: Long Document Classification with Reinforcement Learning | This paper presents a reinforcement learning approach to extract noise in long clinical documents for the task of readmission prediction after kidney transplant. We face the challenges of developing robust models on a small dataset where each document may consist of over 10K tokens with full of noise including tabular ... | ['Rachel E. Patzer', 'Julien Hogan', 'Liyan Xu', 'Jinho D. Choi'] | 2020-05-04 | noise-pollution-in-hospital-readmission-1 | https://aclanthology.org/2020.bionlp-1.10 | https://aclanthology.org/2020.bionlp-1.10.pdf | ws-2020-7 | ['readmission-prediction'] | ['medical'] | [ 1.86590642e-01 2.85221934e-01 -1.23764731e-01 -4.17178601e-01
-1.71607494e+00 -2.33340621e-01 2.84561604e-01 6.19238675e-01
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-1.90171033e-01 -6.45220399e-01 -6.75257683e-01 -4.96811241e-01
-2.74327934e-01 4.90449011e-01 -3.46703827e-01 4.48830891... | [8.443737030029297, 8.506046295166016] |
39cf39be-6375-469b-bc6b-3c55a6ee9e27 | denoising-of-mr-images-with-rician-noise | null | null | https://www.sciencedirect.com/science/article/abs/pii/S0730725X18306751 | https://drive.google.com/file/d/1XRzZNc4tqJnaK0sYzLHdEiNJ7B8ZokHh/view?usp=sharing | Denoising of MR images with Rician noise using a wider neural network and noise range division | Magnetic resonance (MR) images denoising is important in medical image analysis. Denoising methods based on deep learning have shown great promise and outperform all of the other conventional methods. However, deep- learning methods are limited by the number of training samples. In this article, using a small sample si... | ['C', 'Wei Wanga', 'Minghe Maoa', 'Hao Lua', '⁎', 'Ning Caoa', 'B', 'Xuexiao Youa'] | 2019-06-17 | null | null | null | magnetic-resonance-imaging-2019-6 | ['denoising'] | ['computer-vision'] | [ 1.05293445e-01 -2.66558319e-01 1.27307668e-01 -3.13859195e-01
-8.54818940e-01 7.58004487e-02 1.94132537e-01 1.17204972e-01
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-3.28277409e-01 2.03482866e-01 3.78725946e-01 -2.97660381... | [13.391668319702148, -2.4828169345855713] |
fe12f35b-5155-4b5b-99e3-fb9e4bc1e681 | hub-at-semeval-2021-task-5-toxic-span | null | null | https://aclanthology.org/2021.semeval-1.122 | https://aclanthology.org/2021.semeval-1.122.pdf | hub at SemEval-2021 Task 5: Toxic Span Detection Based on Word-Level Classification | This article introduces the system description of the hub team, which explains the related work and experimental results of our team{'}s participation in SemEval 2021 Task 5: Toxic Spans Detection. The data for this shared task comes from some posts on the Internet. The task goal is to identify the toxic content contai... | ['Xiaobing Zhou', 'Yang Bai', 'Bo Huang'] | 2021-08-01 | null | null | null | semeval-2021 | ['toxic-spans-detection'] | ['natural-language-processing'] | [-1.97367072e-01 -1.15503848e-01 -4.99254055e-02 -1.25489891e-01
-1.13548362e+00 -5.38141191e-01 4.41526502e-01 2.79029876e-01
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4.57785763e-02 -5.31682134e-01 -5.12876451e-01 -5.79274952e-01
1.36020094e-01 5.93162775e-01 3.30226928e-01 -1.64008066... | [8.950429916381836, 10.611958503723145] |
7ef4ae0b-f7fa-4fd4-b59e-5608f3d66edb | trufor-leveraging-all-round-clues-for | 2212.10957 | null | https://arxiv.org/abs/2212.10957v3 | https://arxiv.org/pdf/2212.10957v3.pdf | TruFor: Leveraging all-round clues for trustworthy image forgery detection and localization | In this paper we present TruFor, a forensic framework that can be applied to a large variety of image manipulation methods, from classic cheapfakes to more recent manipulations based on deep learning. We rely on the extraction of both high-level and low-level traces through a transformer-based fusion architecture that ... | ['Luisa Verdoliva', 'Nicholas Dufour', 'Avneesh Sud', 'Davide Cozzolino', 'Fabrizio Guillaro'] | 2022-12-21 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Guillaro_TruFor_Leveraging_All-Round_Clues_for_Trustworthy_Image_Forgery_Detection_and_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Guillaro_TruFor_Leveraging_All-Round_Clues_for_Trustworthy_Image_Forgery_Detection_and_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-manipulation-detection', 'image-manipulation'] | ['computer-vision', 'computer-vision'] | [ 3.68072718e-01 -3.16734135e-01 2.31089607e-01 -1.97208777e-01
-1.02497828e+00 -7.19985783e-01 6.07695162e-01 1.49233639e-01
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-1.69414803e-01 -8.67550373e-01 -1.02700341e+00 -8.14166903e-01
-3.00217837e-01 1.61895573e-01 5.26791632e-01 1.07868733... | [12.361230850219727, 0.992313802242279] |
c2118417-ec35-4ac3-a7d6-7608f4254e78 | retinex-image-enhancement-based-on-sequential | 2210.05436 | null | https://arxiv.org/abs/2210.05436v2 | https://arxiv.org/pdf/2210.05436v2.pdf | Retinex Image Enhancement Based on Sequential Decomposition With a Plug-and-Play Framework | The Retinex model is one of the most representative and effective methods for low-light image enhancement. However, the Retinex model does not explicitly tackle the noise problem, and shows unsatisfactory enhancing results. In recent years, due to the excellent performance, deep learning models have been widely used in... | ['Tieyong Zeng', 'Feng-Lei Fan', 'Ying Yang', 'Wenna Wu', 'Tingting Wu'] | 2022-10-11 | null | null | null | null | ['low-light-image-enhancement'] | ['computer-vision'] | [ 2.32480183e-01 -6.31545722e-01 2.62558639e-01 -2.26841837e-01
-3.96087021e-01 3.73762171e-03 4.01970923e-01 -2.07126558e-01
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-1.18401319e-01 -9.99002278e-01 -5.67174077e-01 -1.15991414e+00
3.72303635e-01 -4.30816352e-01 8.80114734e-02 -4.86078858... | [10.882299423217773, -2.4920921325683594] |
581398dd-52ee-47f7-8d49-8bb5d74273ad | closure-assessing-systematic-generalization | 1912.05783 | null | https://arxiv.org/abs/1912.05783v2 | https://arxiv.org/pdf/1912.05783v2.pdf | CLOSURE: Assessing Systematic Generalization of CLEVR Models | The CLEVR dataset of natural-looking questions about 3D-rendered scenes has recently received much attention from the research community. A number of models have been proposed for this task, many of which achieved very high accuracies of around 97-99%. In this work, we study how systematic the generalization of such mo... | ['Yoshua Bengio', "Timothy J. O'Donnell", 'Harm de Vries', 'Shikhar Murty', 'Philippe Beaudoin', 'Dzmitry Bahdanau', 'Aaron Courville'] | 2019-12-12 | null | null | null | null | ['systematic-generalization'] | ['reasoning'] | [ 7.63688385e-02 1.52195901e-01 -1.32648587e-01 -7.86467135e-01
-5.56529343e-01 -5.84182560e-01 4.83565271e-01 1.33387968e-01
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1.32515669e-01 4.32905614e-01 4.50964421e-01 -4.49332029... | [9.652390480041504, 7.025389671325684] |
f1919a61-f5e5-40e8-9d74-1f6970d6db1d | heterogeneous-graph-learning-for-acoustic | 2303.02665 | null | https://arxiv.org/abs/2303.02665v2 | https://arxiv.org/pdf/2303.02665v2.pdf | Heterogeneous Graph Learning for Acoustic Event Classification | Heterogeneous graphs provide a compact, efficient, and scalable way to model data involving multiple disparate modalities. This makes modeling audiovisual data using heterogeneous graphs an attractive option. However, graph structure does not appear naturally in audiovisual data. Graphs for audiovisual data are constru... | ['Tanaya Guha', 'Krishna Somandepalli', 'Mona Ahmadian', 'Amir Shirian'] | 2023-03-05 | null | null | null | null | ['graph-construction'] | ['graphs'] | [-1.72180369e-01 1.19539060e-01 -3.67726117e-01 -2.88518798e-02
-7.48375893e-01 -6.81329131e-01 3.60950977e-01 2.09120229e-01
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-3.43142241e-01 -8.06468904e-01 -6.76983058e-01 -5.03528416e-01
-4.25231785e-01 4.92712140e-01 4.60653365e-01 -1.47766469... | [8.568031311035156, 7.5033159255981445] |
8f33d013-ed7d-432b-804d-ac965d85c8cd | mixed-vine-copulas-as-joint-models-of-spike | null | null | http://papers.nips.cc/paper/6069-mixed-vine-copulas-as-joint-models-of-spike-counts-and-local-field-potentials | http://papers.nips.cc/paper/6069-mixed-vine-copulas-as-joint-models-of-spike-counts-and-local-field-potentials.pdf | Mixed vine copulas as joint models of spike counts and local field potentials | Concurrent measurements of neural activity at multiple scales, sometimes performed with multimodal techniques, become increasingly important for studying brain function. However, statistical methods for their concurrent analysis are currently lacking. Here we introduce such techniques in a framework based on vine copul... | ['Arno Onken', 'Stefano Panzeri'] | 2016-12-01 | null | null | null | neurips-2016-12 | ['mutual-information-estimation'] | ['methodology'] | [ 3.76247525e-01 -6.30222023e-01 5.65274537e-01 -3.53833675e-01
-9.69448328e-01 -6.26909494e-01 7.50018239e-01 2.95174211e-01
-9.08420026e-01 1.34504497e+00 -2.02378884e-01 8.92939419e-02
-2.45467529e-01 -4.62446749e-01 -8.07126760e-01 -1.03495240e+00
-4.61731791e-01 3.15025955e-01 1.40939265e-01 2.95661181... | [6.9732160568237305, 3.821150541305542] |
6503bfc5-992e-48f0-8dd6-5b7c78918bf6 | gpr-net-multi-view-layout-estimation-via-a | 2210.11419 | null | https://arxiv.org/abs/2210.11419v2 | https://arxiv.org/pdf/2210.11419v2.pdf | GPR-Net: Multi-view Layout Estimation via a Geometry-aware Panorama Registration Network | Reconstructing 3D layouts from multiple $360^{\circ}$ panoramas has received increasing attention recently as estimating a complete layout of a large-scale and complex room from a single panorama is very difficult. The state-of-the-art method, called PSMNet, introduces the first learning-based framework that jointly es... | ['Hung-Kuo Chu', 'Peter Wonka', 'Chi-Han Peng', 'Jheng-Wei Su'] | 2022-10-20 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 4.41380084e-01 -2.65630126e-01 2.39636883e-01 -4.39797133e-01
-1.24360609e+00 -8.29674542e-01 6.27949655e-01 -1.11633927e-01
-1.49106666e-01 7.28523061e-02 2.04450592e-01 -6.46221312e-03
-3.21063250e-01 -1.05651069e+00 -1.13502693e+00 -6.10092461e-01
3.31288218e-01 6.64029062e-01 8.87364298e-02 -3.02581549... | [8.415629386901855, -2.591055154800415] |
120974fb-dd53-4962-b655-aa6c068c6e5d | memory-like-adaptive-modeling-multi-agent | 2212.07646 | null | https://arxiv.org/abs/2212.07646v2 | https://arxiv.org/pdf/2212.07646v2.pdf | Adaptive Multi-Agent Continuous Learning System | We propose an adaptive multi-agent clustering recognition system that can be self-supervised driven, based on a temporal sequences continuous learning mechanism with adaptability. The system is designed to use some different functional agents to build up a connection structure to improve adaptability to cope with envir... | ['Zhitang Song', 'Weibang Dai', 'Shunfen Li', 'Xiaogang Chen', 'Wen-Chi Yang', 'Longfei Liang', 'Aximu Yuemaier', 'Xingyu Qian'] | 2022-12-15 | null | null | null | null | ['temporal-sequences'] | ['reasoning'] | [-1.73560843e-01 -4.36293691e-01 -2.33798951e-01 -2.31379882e-01
3.30405414e-01 -4.53195512e-01 5.46965420e-01 -1.31986678e-01
-3.82721245e-01 5.66531003e-01 -1.35587053e-02 2.01263860e-01
-2.70867229e-01 -7.40624189e-01 -3.03849876e-01 -1.00480998e+00
-4.99181002e-01 8.28114450e-01 4.68262941e-01 -4.60632354... | [3.8036203384399414, 1.9462400674819946] |
b1def502-6170-40eb-acb1-dda2bda88cb8 | mtstereo-2-0-improved-accuracy-of-stereo | 2006.15373 | null | https://arxiv.org/abs/2006.15373v1 | https://arxiv.org/pdf/2006.15373v1.pdf | MTStereo 2.0: improved accuracy of stereo depth estimation withMax-trees | Efficient yet accurate extraction of depth from stereo image pairs is required by systems with low power resources, such as robotics and embedded systems. State-of-the-art stereo matching methods based on convolutional neural networks require intensive computations on GPUs and are difficult to deploy on embedded system... | ['Nicolai Petkov', 'Rafael Brandt', 'Nicola Strisciuglio'] | 2020-06-27 | null | null | null | null | ['stereo-depth-estimation'] | ['computer-vision'] | [ 1.21862767e-02 -2.07267910e-01 4.53138947e-02 -4.32901621e-01
-3.48393440e-01 -3.38213474e-01 4.08883482e-01 3.51413339e-01
-8.77592802e-01 3.51209939e-01 -3.20028931e-01 -2.95226514e-01
2.34891504e-01 -1.25356209e+00 -7.77005494e-01 -3.53886276e-01
5.49464785e-02 4.13216621e-01 8.88074815e-01 -5.25831640... | [8.740099906921387, -2.29551100730896] |
969a60f8-9f41-4c11-9c8e-21d6021907b5 | cab-empathetic-dialogue-generation-with | 2302.01935 | null | https://arxiv.org/abs/2302.01935v2 | https://arxiv.org/pdf/2302.01935v2.pdf | CAB: Empathetic Dialogue Generation with Cognition, Affection and Behavior | Empathy is an important characteristic to be considered when building a more intelligent and humanized dialogue agent. However, existing methods did not fully comprehend empathy as a complex process involving three aspects: cognition, affection and behavior. In this paper, we propose CAB, a novel framework that takes a... | ['Zikun Wang', 'Xuejiao Zhang', 'Rui Zhou', 'Donghong Han', 'Pan Gao'] | 2023-02-03 | null | null | null | null | ['dialogue-generation', 'dialogue-generation'] | ['natural-language-processing', 'speech'] | [-3.63313138e-01 2.19827816e-01 -9.94093642e-02 -6.77883685e-01
7.19296262e-02 -3.19271207e-01 5.77686727e-01 1.08649679e-01
-2.08115876e-01 7.01636016e-01 7.73417830e-01 2.74232149e-01
6.65142983e-02 -6.65823579e-01 1.62540637e-02 -4.33369279e-01
6.45493209e-01 3.18172544e-01 -3.78832936e-01 -7.34403253... | [13.138728141784668, 7.58414363861084] |
74d0de05-4650-4019-b594-80e83e615c99 | vulnerability-analysis-of-face-morphing | 2012.05344 | null | https://arxiv.org/abs/2012.05344v1 | https://arxiv.org/pdf/2012.05344v1.pdf | Vulnerability Analysis of Face Morphing Attacks from Landmarks and Generative Adversarial Networks | Morphing attacks is a threat to biometric systems where the biometric reference in an identity document can be altered. This form of attack presents an important issue in applications relying on identity documents such as border security or access control. Research in face morphing attack detection is developing rapidl... | ['Sébastien Marcel', 'Laurent Colbois', 'Pavel Korshunov', 'Eklavya Sarkar'] | 2020-12-09 | null | null | null | null | ['image-morphing'] | ['computer-vision'] | [ 8.16900730e-02 9.17701870e-02 3.62612993e-01 -4.40283209e-01
-1.17077932e-01 -9.61748779e-01 7.72335827e-01 -8.07360291e-01
-2.73624901e-03 7.19795942e-01 -3.20499003e-01 -4.06883061e-01
2.16280267e-01 -1.06383598e+00 -7.01038837e-01 -6.00338340e-01
-6.50247186e-02 8.71306509e-02 -2.89155066e-01 -4.72335517... | [12.883724212646484, 1.0500816106796265] |
1b8342c0-26be-48ed-837e-fd4c5b949b6d | autonomous-apex-detection-and-micro | null | null | https://ijnaa.semnan.ac.ir/article_4707.html | https://ijnaa.semnan.ac.ir/article_4707.html | Autonomous Apex Detection and Micro-Expression Recognition using Proposed Diagonal Planes | Micro-expression as the main way of non-verbal communication occurs quickly and subtle in highrisk situations. Since it cannot be misleading, it discloses the real human aim. Nonetheless, feature extraction is an arduous task due to its two particular features. To resolve this problem in this paper, we propose Local Bi... | ['Seyed Omid Shahdi', 'Mahmood Mohassel Feghhi', 'Vida Esmaeili'] | 2020-04-01 | null | null | null | int-j-nonlinear-anal-appl-2020-4 | ['motion-magnification', 'micro-expression-recognition'] | ['computer-vision', 'computer-vision'] | [-4.47361954e-02 -3.31461221e-01 -3.52138817e-01 -8.88572037e-02
-1.68736339e-01 -5.20025015e-01 4.03281391e-01 -2.25526780e-01
-6.72162473e-01 8.51624429e-01 4.73785609e-01 1.10573553e-01
2.47342572e-01 -4.10094321e-01 -2.50786208e-02 -9.40464675e-01
9.07500163e-02 -1.33902162e-01 5.18110543e-02 -1.04491577... | [13.637578010559082, 1.814102053642273] |
c1c2e7ad-d8bc-49ad-9752-7a7fa9253ce3 | dece-decision-explorer-with-counterfactual | 2008.08353 | null | https://arxiv.org/abs/2008.08353v1 | https://arxiv.org/pdf/2008.08353v1.pdf | DECE: Decision Explorer with Counterfactual Explanations for Machine Learning Models | With machine learning models being increasingly applied to various decision-making scenarios, people have spent growing efforts to make machine learning models more transparent and explainable. Among various explanation techniques, counterfactual explanations have the advantages of being human-friendly and actionable -... | ['Huamin Qu', 'Yao Ming', 'Furui Cheng'] | 2020-08-19 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [-3.79637517e-02 7.79848218e-01 -5.81174552e-01 -7.89073110e-01
-9.54530463e-02 -4.89596009e-01 6.76815152e-01 3.01292896e-01
1.63849682e-01 7.71319211e-01 4.43186879e-01 -1.21682334e+00
-2.51839429e-01 -5.32763898e-01 -2.34436259e-01 -7.15842023e-02
-3.09603214e-01 3.58212590e-01 -1.78111717e-01 5.28930984... | [8.732924461364746, 5.766793727874756] |
803c5846-ffcf-44ff-a784-3afdef61b2d7 | real-time-variational-method-for-learning | 2305.11278 | null | https://arxiv.org/abs/2305.11278v1 | https://arxiv.org/pdf/2305.11278v1.pdf | Real-Time Variational Method for Learning Neural Trajectory and its Dynamics | Latent variable models have become instrumental in computational neuroscience for reasoning about neural computation. This has fostered the development of powerful offline algorithms for extracting latent neural trajectories from neural recordings. However, despite the potential of real time alternatives to give immedi... | ['Il Memming Park', 'Yuan Zhao', 'Matthew Dowling'] | 2023-05-18 | null | null | null | null | ['experimental-design'] | ['methodology'] | [ 1.00444622e-01 5.91293164e-03 -6.11432865e-02 -1.52520791e-01
-8.65980864e-01 -8.33736181e-01 8.52930486e-01 -1.70904636e-01
-6.51127636e-01 8.58759701e-01 1.52696948e-02 -4.08308089e-01
-2.49039158e-01 -2.47945264e-02 -8.30417991e-01 -1.07968307e+00
-7.03908354e-02 3.22193891e-01 5.92174903e-02 5.66931069... | [6.833313941955566, 3.8062477111816406] |
ee559c70-a3a9-4049-a1a7-4b8c4ead980f | geracao-de-expressoes-de-referencia-usando | null | null | https://aclanthology.org/W13-4810 | https://aclanthology.org/W13-4810.pdf | Gera\cc\~ao de Express\~oes de Refer\^encia usando Rela\cc\~oes Espaciais (Referring Expression Generation Using Spatial Relations) [in Portuguese] | null | ["r{\\'e}", 'Diego dos Santos Silva', 'Iv Paraboni'] | 2013-01-01 | null | null | null | ws-2013-1 | ['referring-expression-generation'] | ['computer-vision'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.2464680671691895, 3.7301340103149414] |
1f0e3734-d5ba-4fe2-99b8-ec1cb17f7446 | matrix-completion-with-sparse-noisy-rows | 2204.0153 | null | https://arxiv.org/abs/2204.01530v2 | https://arxiv.org/pdf/2204.01530v2.pdf | Matrix Completion with Sparse Noisy Rows | Exact matrix completion and low rank matrix estimation problems has been studied in different underlying conditions. In this work we study exact low-rank completion under non-degenerate noise model. Non-degenerate random noise model has been previously studied by many researchers under given condition that the noise is... | ['Jafar Jafarov'] | 2022-04-01 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 5.04468560e-01 9.13723782e-02 1.69947445e-01 6.56313449e-02
-7.73602962e-01 -8.27449441e-01 2.45571524e-01 -4.00874138e-01
-1.24873109e-01 7.62628853e-01 6.02960944e-01 -1.15903176e-01
-2.44187862e-01 -5.02325058e-01 -9.26348746e-01 -9.53223109e-01
-2.00321361e-01 5.55076599e-01 -1.49639592e-01 -3.27409089... | [6.990589618682861, 4.656816482543945] |
34a019a2-de0b-4fb2-865c-692daba40103 | type-i-tobit-bayesian-additive-regression | 2211.07506 | null | https://arxiv.org/abs/2211.07506v2 | https://arxiv.org/pdf/2211.07506v2.pdf | Type I Tobit Bayesian Additive Regression Trees for Censored Outcome Regression | This paper introduces Type I Tobit Bayesian Additive Regression Trees (TOBART-1). Simulation results and applications to real data sets demonstrate that TOBART-1 produces more accurate predictions than competing methods. TOBART-1 provides accurate posterior intervals for the conditional expectation and other quantities... | ["Eoghan O'Neill"] | 2022-11-14 | null | null | null | null | ['type'] | ['speech'] | [ 2.24223044e-02 -4.16975990e-02 -3.09234262e-01 -9.84690607e-01
-1.30840540e+00 1.42057955e-01 5.04110634e-01 -6.00200612e-03
-1.72925949e-01 1.25435698e+00 -1.18195780e-01 -5.54291785e-01
-4.24166203e-01 -9.42238808e-01 -5.04725039e-01 -7.24522173e-01
-1.84729651e-01 1.13767040e+00 2.51613706e-01 4.18715328... | [7.0502119064331055, 4.14346170425415] |
abfbd0bc-eb83-469c-8c30-0923126751a0 | looking-for-the-devil-in-the-details-learning | 1903.0615 | null | https://arxiv.org/abs/1903.06150v2 | https://arxiv.org/pdf/1903.06150v2.pdf | Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-grained Image Recognition | Learning subtle yet discriminative features (e.g., beak and eyes for a bird) plays a significant role in fine-grained image recognition. Existing attention-based approaches localize and amplify significant parts to learn fine-grained details, which often suffer from a limited number of parts and heavy computational cos... | ['Zheng-Jun Zha', 'Jiebo Luo', 'Jianlong Fu', 'Heliang Zheng'] | 2019-03-14 | looking-for-the-devil-in-the-details-learning-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Zheng_Looking_for_the_Devil_in_the_Details_Learning_Trilinear_Attention_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Zheng_Looking_for_the_Devil_in_the_Details_Learning_Trilinear_Attention_CVPR_2019_paper.pdf | cvpr-2019-6 | ['fine-grained-image-recognition'] | ['computer-vision'] | [-1.18092276e-01 -2.68529296e-01 -1.55409932e-01 -4.10695553e-01
-8.52902174e-01 -3.50628704e-01 6.27530575e-01 -2.89893411e-02
-2.15622991e-01 6.48380458e-01 3.89689475e-01 1.85947284e-01
-2.39969313e-01 -7.05794275e-01 -1.06340361e+00 -7.03029156e-01
-9.07598138e-02 3.25706482e-01 3.49335283e-01 7.87715311... | [9.552122116088867, 1.9688518047332764] |
cb0b5fa6-06e2-4d50-96ec-3f7a4f9709a6 | sml-a-new-semantic-embedding-alignment | 2103.09635 | null | https://arxiv.org/abs/2103.09635v3 | https://arxiv.org/pdf/2103.09635v3.pdf | SILT: Efficient transformer training for inter-lingual inference | The ability of transformers to perform precision tasks such as question answering, Natural Language Inference (NLI) or summarising, have enabled them to be ranked as one of the best paradigm to address Natural Language Processing (NLP) tasks. NLI is one of the best scenarios to test these architectures, due to the know... | ['David Camacho', 'Alejandro Martín', 'Javier Huertas-Tato'] | 2021-03-17 | null | null | null | null | ['cross-lingual-natural-language-inference'] | ['natural-language-processing'] | [ 7.85454959e-02 8.53241161e-02 2.47204360e-02 -3.73755634e-01
-1.02155113e+00 -9.65962112e-01 1.02182209e+00 2.58555412e-01
-6.28949523e-01 5.64996183e-01 3.91244113e-01 -6.56366110e-01
-1.18660353e-01 -5.75668156e-01 -8.09389353e-01 -2.66524673e-01
-8.63874331e-02 7.79712141e-01 -4.23775353e-02 -3.92983645... | [11.0145263671875, 9.64376449584961] |
78f2d790-fa90-4daa-91c7-a1b6e8bb7f02 | sumhis-extractive-summarization-exploiting | null | null | https://openreview.net/forum?id=ZaM7EsLb5X | https://openreview.net/pdf?id=ZaM7EsLb5X | SumHiS: Extractive Summarization Exploiting Hidden Sctructure | Extractive summarization is a task of highlighting the most important parts of the text. We introduce a new approach to extractive summarization task using hidden clustering structure of the text. Experimental results on CNN/DailyMail demonstrate that our approach generates more accurate summaries than both extractive ... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['extractive-summarization'] | ['natural-language-processing'] | [ 2.67682135e-01 7.23915696e-01 -1.19319558e-01 -9.21986848e-02
-9.96720850e-01 -6.04552329e-01 8.78521860e-01 6.56288207e-01
-4.32388812e-01 9.40797508e-01 1.31469321e+00 -4.23282618e-03
4.96432930e-02 -4.51528907e-01 -4.80459720e-01 -3.88655216e-01
-7.44413584e-02 2.89275348e-01 1.31921515e-01 -3.00502837... | [12.534834861755371, 9.511123657226562] |
9b200548-5ba8-48b1-b8ba-587f0d0aa4a8 | mimic-iii-a-freely-accessible-critical-care | null | null | https://www.nature.com/articles/sdata201635 | https://www.nature.com/articles/sdata201635.pdf | MIMIC-III, a freely accessible critical care database | MIMIC-III (‘Medical Information Mart for Intensive Care’) is a large, single-center database comprising information relating to patients admitted to critical care units at a large tertiary care hospital. Data includes vital signs, medications, laboratory measurements, observations and notes charted by care providers, f... | ['Roger G. Mark', 'Leo Anthony Celi', 'Peter Szolovits', 'Benjamin Moody', 'Mohammad Ghassemi', 'Mengling Feng', 'Li-wei H. Lehman', 'Lu Shen', 'Tom J. Pollard', 'Alistair E.W. Johnson'] | 2016-05-24 | null | null | null | nature-2016-5 | ['data-integration', 'blood-pressure-estimation', 'multi-label-classification-of-biomedical', 'medical-code-prediction', 'length-of-stay-prediction', 'multi-label-text-classification', 'multi-label-text-classification'] | ['knowledge-base', 'medical', 'medical', 'medical', 'medical', 'methodology', 'natural-language-processing'] | [-2.32092943e-02 -1.37576789e-01 -3.42169493e-01 -1.08117834e-01
-7.93724418e-01 -6.85387015e-01 -1.64362583e-02 1.33524299e+00
-5.65262139e-01 6.16145909e-01 6.36585593e-01 -9.23818767e-01
-4.51531202e-01 -6.07563376e-01 -3.08998466e-01 -3.10101956e-01
-1.03980586e-01 6.14879251e-01 -5.61722368e-02 3.66651058... | [7.982906341552734, 6.23223876953125] |
ca53e290-f126-4b59-a182-780b6e052caf | contrastive-representation-learning-for-gaze | 2210.13404 | null | https://arxiv.org/abs/2210.13404v1 | https://arxiv.org/pdf/2210.13404v1.pdf | Contrastive Representation Learning for Gaze Estimation | Self-supervised learning (SSL) has become prevalent for learning representations in computer vision. Notably, SSL exploits contrastive learning to encourage visual representations to be invariant under various image transformations. The task of gaze estimation, on the other hand, demands not just invariance to various ... | ['Roberto Manduchi', 'Swati Jindal'] | 2022-10-24 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [ 1.75249875e-01 -1.29886851e-01 -4.62657809e-01 -4.59699154e-01
-5.10592997e-01 -2.80020624e-01 6.61697209e-01 -2.94761598e-01
-2.64978677e-01 4.21009839e-01 2.28260934e-01 3.13299708e-02
6.09279312e-02 -1.12572894e-01 -7.08547294e-01 -5.09980321e-01
3.80278081e-01 -1.94975317e-01 -5.38129499e-03 -3.71504813... | [14.09802532196045, 0.04113779217004776] |
c6b1c266-55aa-4087-a1dc-4a94315609db | rsgt-relational-structure-guided-temporal | null | null | https://aclanthology.org/2022.coling-1.174 | https://aclanthology.org/2022.coling-1.174.pdf | RSGT: Relational Structure Guided Temporal Relation Extraction | Temporal relation extraction aims to extract temporal relations between event pairs, which is crucial for natural language understanding. Few efforts have been devoted to capturing the global features. In this paper, we propose RSGT: Relational Structure Guided Temporal Relation Extraction to extract the relational str... | ['Yong Dou', 'Xiaodong Wang', 'Hongkui Tu', 'Shenpo Dong', 'Jie zhou'] | null | null | null | null | coling-2022-10 | ['temporal-relation-extraction', 'temporal-relation-classification'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.72538802e-01 2.62540102e-01 -5.65600634e-01 -7.38715529e-01
-4.86759692e-01 -2.48737514e-01 5.66903472e-01 3.80968094e-01
-3.25999200e-01 5.34997284e-01 5.06751835e-01 -1.84892192e-01
-1.80510834e-01 -9.82911587e-01 -6.22322500e-01 -3.04512471e-01
-4.14627552e-01 1.20714575e-01 4.49917465e-01 -3.16253334... | [9.088274955749512, 9.072221755981445] |
291e550d-03a2-45df-ab1b-a471a2617a0b | better-distractions-transformer-based | 2010.09598 | null | https://arxiv.org/abs/2010.09598v1 | https://arxiv.org/pdf/2010.09598v1.pdf | Better Distractions: Transformer-based Distractor Generation and Multiple Choice Question Filtering | For the field of education, being able to generate semantically correct and educationally relevant multiple choice questions (MCQs) could have a large impact. While question generation itself is an active research topic, generating distractors (the incorrect multiple choice options) receives much less attention. A miss... | ['Tessa Verhoef', 'Suzan Verberne', 'Jeroen Offerijns'] | 2020-10-19 | null | null | null | null | ['distractor-generation'] | ['natural-language-processing'] | [ 7.61085972e-02 5.22900403e-01 2.96618015e-01 -1.60531700e-01
-1.25669682e+00 -7.69588649e-01 7.97688425e-01 3.57047558e-01
-4.42419380e-01 9.24044013e-01 6.15349829e-01 -7.24701047e-01
-1.39600784e-01 -9.92531955e-01 -7.85018742e-01 -6.38178289e-02
4.82846469e-01 5.64855993e-01 5.72158277e-01 -6.15379751... | [11.4575777053833, 8.11384391784668] |
0d66a95d-65b1-4794-b218-4bd070251cac | sips-unsupervised-succinct-interest-points | 1805.01358 | null | https://arxiv.org/abs/1805.01358v2 | https://arxiv.org/pdf/1805.01358v2.pdf | SIPs: Succinct Interest Points from Unsupervised Inlierness Probability Learning | A wide range of computer vision algorithms rely on identifying sparse interest points in images and establishing correspondences between them. However, only a subset of the initially identified interest points results in true correspondences (inliers). In this paper, we seek a detector that finds the minimum number of ... | ['Davide Scaramuzza', 'Konstantinos G. Derpanis', 'Titus Cieslewski'] | 2018-05-03 | null | null | null | null | ['interest-point-detection'] | ['computer-vision'] | [-6.93139136e-02 -1.57508582e-01 -1.99737936e-01 -2.24793494e-01
-9.84408081e-01 -4.46747303e-01 5.50549746e-01 2.62714088e-01
-3.51126522e-01 4.30564791e-01 -4.06099670e-02 5.35365893e-03
7.80056342e-02 -6.88162804e-01 -9.00238693e-01 -5.09966671e-01
6.87415227e-02 3.49006683e-01 3.99138778e-01 -2.87524704... | [8.020519256591797, -2.0609095096588135] |
9f34944e-5212-42cc-9cc6-cac5906db8a0 | peace-cross-platform-hate-speech-detection-a | 2306.08804 | null | https://arxiv.org/abs/2306.08804v1 | https://arxiv.org/pdf/2306.08804v1.pdf | PEACE: Cross-Platform Hate Speech Detection- A Causality-guided Framework | Hate speech detection refers to the task of detecting hateful content that aims at denigrating an individual or a group based on their religion, gender, sexual orientation, or other characteristics. Due to the different policies of the platforms, different groups of people express hate in different ways. Furthermore, d... | ['Huan Liu', 'Aman Chadha', 'Raha Moraffah', 'Tharindu Kumarage', 'Paras Sheth'] | 2023-06-15 | null | null | null | null | ['hate-speech-detection'] | ['natural-language-processing'] | [-2.17141896e-01 -1.47957548e-01 -4.62043077e-01 -1.70266837e-01
-2.39418358e-01 -8.61596525e-01 1.01239812e+00 4.66970086e-01
-6.73125088e-02 4.74596918e-01 6.23964071e-01 -1.40520021e-01
1.66237473e-01 -5.72391033e-01 -4.26640034e-01 -5.26192546e-01
-9.98227671e-02 -1.25767186e-01 -8.67805108e-02 -5.90661764... | [8.75023365020752, 10.560490608215332] |
6e36864b-a3db-4823-be74-a8672020ae72 | rethinking-so-3-equivariance-with-bilinear | 2303.11288 | null | https://arxiv.org/abs/2303.11288v1 | https://arxiv.org/pdf/2303.11288v1.pdf | Rethinking SO(3)-equivariance with Bilinear Tensor Networks | Many datasets in scientific and engineering applications are comprised of objects which have specific geometric structure. A common example is data which inhabits a representation of the group SO$(3)$ of 3D rotations: scalars, vectors, tensors, \textit{etc}. One way for a neural network to exploit prior knowledge of th... | ['Ema Smith', 'Zhelun Li', 'Chase Shimmin'] | 2023-03-20 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [ 1.74042717e-01 -6.87752888e-02 7.53218085e-02 -7.54442155e-01
-3.26950669e-01 -6.45686626e-01 7.34880805e-01 2.59529091e-02
-5.55225253e-01 3.44358355e-01 4.58466150e-02 -6.87011778e-01
-2.86634296e-01 -7.86104679e-01 -9.35811520e-01 -7.20815361e-01
-2.33803093e-01 6.82527781e-01 -7.83949047e-02 -4.49106634... | [7.908650875091553, 4.391617298126221] |
ad5f6aaf-90cd-4b70-86e6-d5b3ee01ec26 | wavenet-based-low-rate-speech-coding | 1712.0112 | null | http://arxiv.org/abs/1712.01120v1 | http://arxiv.org/pdf/1712.01120v1.pdf | Wavenet based low rate speech coding | Traditional parametric coding of speech facilitates low rate but provides
poor reconstruction quality because of the inadequacy of the model used. We
describe how a WaveNet generative speech model can be used to generate high
quality speech from the bit stream of a standard parametric coder operating at
2.4 kb/s. We co... | ['Thomas C. Walters', 'W. Bastiaan Kleijn', 'Jan Skoglund', 'Alejandro Luebs', 'Quan Wang', 'Florian Stimberg', 'Felicia S. C. Lim'] | 2017-12-01 | null | null | null | null | ['bandwidth-extension', 'bandwidth-extension'] | ['audio', 'speech'] | [ 2.08029911e-01 2.53618121e-01 2.28916436e-01 -1.12540670e-01
-7.89054453e-01 -4.09853011e-01 3.38969350e-01 -2.64458597e-01
-2.00080827e-01 5.21186411e-01 3.64778638e-01 -5.32361686e-01
1.47727683e-01 -3.61215115e-01 -4.81099159e-01 -7.35942423e-01
-1.26176059e-01 3.84392887e-01 2.67271310e-01 1.56640783... | [15.169451713562012, 6.092424392700195] |
a8b85e25-d319-4859-b3bd-4100b308a385 | textual-echo-cancellation | 2008.06006 | null | https://arxiv.org/abs/2008.06006v4 | https://arxiv.org/pdf/2008.06006v4.pdf | Textual Echo Cancellation | In this paper, we propose Textual Echo Cancellation (TEC) - a framework for cancelling the text-to-speech (TTS) playback echo from overlapping speech recordings. Such a system can largely improve speech recognition performance and user experience for intelligent devices such as smart speakers, as the user can talk to t... | ['Ye Jia', 'Shaojin Ding', 'Ke Hu', 'Quan Wang'] | 2020-08-13 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 6.10677242e-01 -2.59178281e-01 3.15518171e-01 -1.28934816e-01
-1.05883801e+00 -4.83896941e-01 1.12976685e-01 -2.61947900e-01
-3.16991299e-01 6.07904308e-02 4.95433390e-01 -5.57476938e-01
2.83233672e-01 -4.86837476e-02 -5.90751588e-01 -5.49755096e-01
1.77141085e-01 -2.45182931e-01 5.52075922e-01 -2.34830350... | [14.89628791809082, 5.996911525726318] |
2d0fead3-68f4-4352-9ca8-1adbcffe9578 | crts-a-type-system-for-representing-clinical | 1609.01592 | null | http://arxiv.org/abs/1609.01592v1 | http://arxiv.org/pdf/1609.01592v1.pdf | CRTS: A type system for representing clinical recommendations | Background: Clinical guidelines and recommendations are the driving wheels of
the evidence-based medicine (EBM) paradigm, but these are available primarily
as unstructured text and are generally highly heterogeneous in nature. This
significantly reduces the dissemination and automatic application of these
recommendatio... | ['Siddhartha R. Jonnalagadda', 'Ravi P Garg', 'Kalpana Raja'] | 2016-09-06 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [ 9.81338769e-02 2.73899317e-01 -9.91227746e-01 -2.38573011e-02
-7.24503219e-01 -4.28485990e-01 1.85291320e-01 8.87229860e-01
-4.02956158e-01 9.35528696e-01 8.17631185e-01 -8.81801605e-01
-1.00166547e+00 -5.41568100e-01 1.82957202e-02 -2.45590478e-01
2.84187943e-01 6.44306898e-01 1.92748949e-01 -1.69687256... | [8.5403413772583, 8.614315032958984] |
71da0d83-e6b6-40d3-b8c0-eb9dc357d0a1 | causalapm-generalizable-literal | 2305.02865 | null | https://arxiv.org/abs/2305.02865v1 | https://arxiv.org/pdf/2305.02865v1.pdf | CausalAPM: Generalizable Literal Disentanglement for NLU Debiasing | Dataset bias, i.e., the over-reliance on dataset-specific literal heuristics, is getting increasing attention for its detrimental effect on the generalization ability of NLU models. Existing works focus on eliminating dataset bias by down-weighting problematic data in the training process, which induce the omission of ... | ['Xuanjing Huang', 'Qi Zhang', 'Junjie Shan', 'Shihan Dou', 'Songyang Gao'] | 2023-05-04 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [ 0.28159782 0.20540154 -0.8782854 -0.6681401 -0.35455146 -0.51895094
0.5099345 0.191865 0.00738191 1.0862665 0.5439985 -0.25912628
-0.51611817 -1.0755879 -0.7528511 -0.597463 0.28506595 0.18738417
-0.25564125 0.05679366 0.4444724 0.13551092 -1.3600471 0.543014
1.3285899 0.93443245 -0.31... | [9.688224792480469, 7.948047637939453] |
b48defff-34fc-4ea5-a0ec-f447e0a203bc | salsa-spatial-cue-augmented-log-spectrogram | 2110.00275 | null | https://arxiv.org/abs/2110.00275v3 | https://arxiv.org/pdf/2110.00275v3.pdf | SALSA: Spatial Cue-Augmented Log-Spectrogram Features for Polyphonic Sound Event Localization and Detection | Sound event localization and detection (SELD) consists of two subtasks, which are sound event detection and direction-of-arrival estimation. While sound event detection mainly relies on time-frequency patterns to distinguish different sound classes, direction-of-arrival estimation uses amplitude and/or phase difference... | ['Woon-Seng Gan', 'Douglas L. Jones', 'Ngoc Khanh Nguyen', 'Karn N. Watcharasupat', 'Thi Ngoc Tho Nguyen'] | 2021-10-01 | null | null | null | null | ['direction-of-arrival-estimation', 'sound-event-localization-and-detection'] | ['audio', 'audio'] | [ 1.47532851e-01 -8.54816437e-01 5.70904613e-01 3.55881490e-02
-1.20533729e+00 -7.82006502e-01 2.77977377e-01 5.23216426e-01
-4.15029079e-01 3.32618713e-01 2.97930390e-01 -1.30870581e-01
-6.23212099e-01 -5.84255159e-01 -3.85522664e-01 -8.78718674e-01
-3.63588363e-01 -3.47114354e-01 4.69723523e-01 2.04011977... | [15.197555541992188, 5.3846845626831055] |
2010658b-22f0-43d6-b514-b7c459a30153 | incorporating-transformer-designs-into | 2303.14324 | null | https://arxiv.org/abs/2303.14324v1 | https://arxiv.org/pdf/2303.14324v1.pdf | Incorporating Transformer Designs into Convolutions for Lightweight Image Super-Resolution | In recent years, the use of large convolutional kernels has become popular in designing convolutional neural networks due to their ability to capture long-range dependencies and provide large receptive fields. However, the increase in kernel size also leads to a quadratic growth in the number of parameters, resulting i... | ['Xianming Liu', 'Yuanchao Bai', 'Junjun Jiang', 'Gang Wu'] | 2023-03-25 | null | null | null | null | ['image-super-resolution'] | ['computer-vision'] | [ 3.47178094e-02 -3.00964504e-01 -4.67221178e-02 -5.05640864e-01
-6.38607562e-01 -2.84966350e-01 2.63985664e-01 -3.11786115e-01
-6.09142363e-01 2.56944805e-01 3.68058443e-01 -2.57955551e-01
-3.07093118e-03 -7.65252829e-01 -7.15905607e-01 -3.84998739e-01
-1.09182624e-02 -4.89772648e-01 7.09244311e-01 -2.44175419... | [9.064985275268555, 1.980484127998352] |
0f522584-78b2-4ea5-9bb2-5389a7a1f0f9 | convolutional-neural-networks-for-no | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Kang_Convolutional_Neural_Networks_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Kang_Convolutional_Neural_Networks_2014_CVPR_paper.pdf | Convolutional Neural Networks for No-Reference Image Quality Assessment | In this work we describe a Convolutional Neural Network (CNN) to accurately predict image quality without a reference image. Taking image patches as input, the CNN works in the spatial domain without using hand-crafted features that are employed by most previous methods. The network consists of one convolutional layer ... | ['David Doermann', 'Yi Li', 'Le Kang', 'Peng Ye'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [ 1.23146974e-01 -2.10113376e-01 -2.07875729e-01 -4.38500047e-01
-5.76483488e-01 -1.05795339e-01 2.86508888e-01 -2.31740419e-02
-4.65671748e-01 5.06969512e-01 -1.09715961e-01 -5.56071363e-02
-8.14463943e-03 -1.03113663e+00 -7.90056467e-01 -5.98227620e-01
-1.13764353e-01 -3.33953172e-01 4.91813213e-01 -6.37227297... | [11.593491554260254, -1.6424884796142578] |
56a8aa58-6acd-47ad-b245-db284ba544f6 | modular-representation-underlies-systematic | 2004.14623 | null | https://arxiv.org/abs/2004.14623v4 | https://arxiv.org/pdf/2004.14623v4.pdf | Neural Natural Language Inference Models Partially Embed Theories of Lexical Entailment and Negation | We address whether neural models for Natural Language Inference (NLI) can learn the compositional interactions between lexical entailment and negation, using four methods: the behavioral evaluation methods of (1) challenge test sets and (2) systematic generalization tasks, and the structural evaluation methods of (3) p... | ['Christopher Potts', 'Kyle Richardson', 'Atticus Geiger'] | 2020-04-30 | null | https://aclanthology.org/2020.blackboxnlp-1.16 | https://aclanthology.org/2020.blackboxnlp-1.16.pdf | emnlp-blackboxnlp-2020-11 | ['systematic-generalization'] | ['reasoning'] | [ 2.60880500e-01 3.94012600e-01 -5.01263916e-01 -5.22573948e-01
-4.02072191e-01 -7.28649378e-01 1.05483079e+00 -1.79402460e-03
-4.44692969e-01 7.39718258e-01 7.58035064e-01 -9.67690110e-01
-3.79995465e-01 -8.67765546e-01 -1.18930316e+00 -1.70225143e-01
-2.57188320e-01 6.18616879e-01 1.08575299e-01 -3.72775614... | [9.819010734558105, 7.729911804199219] |
c28ec404-34d9-47ad-8eeb-d5d8b89a339c | hyper-laplacian-regularized-concept | 2304.11435 | null | https://arxiv.org/abs/2304.11435v1 | https://arxiv.org/pdf/2304.11435v1.pdf | Hyper-Laplacian Regularized Concept Factorization in Low-rank Tensor Space for Multi-view Clustering | Tensor-oriented multi-view subspace clustering has achieved significant strides in assessing high-order correlations and improving clustering analysis of multi-view data. Nevertheless, most of existing investigations are typically hampered by the two flaws. First, self-representation based tensor subspace learning usua... | ['Zhoumin Lu', 'Zhiling Cai', 'Lele Fu', 'Zixiao Yu'] | 2023-04-22 | null | null | null | null | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-3.79965633e-01 -6.61943972e-01 -2.03386873e-01 1.25909358e-01
-2.74065435e-01 -6.91590190e-01 2.25160927e-01 -3.30687791e-01
1.50417238e-02 4.63787802e-02 7.32116938e-01 -6.78819641e-02
-7.48634458e-01 -2.96906650e-01 -8.28884542e-02 -1.16521859e+00
-1.18145347e-01 1.42885551e-01 -2.60967195e-01 -1.11960046... | [8.212246894836426, 4.617711544036865] |
c6f9e525-7df1-400a-a26a-67147bfcbc5a | foundations-of-coupled-nonlinear | 1509.0888 | null | http://arxiv.org/abs/1509.08880v2 | http://arxiv.org/pdf/1509.08880v2.pdf | Foundations of Coupled Nonlinear Dimensionality Reduction | In this paper we introduce and analyze the learning scenario of \emph{coupled
nonlinear dimensionality reduction}, which combines two major steps of machine
learning pipeline: projection onto a manifold and subsequent supervised
learning. First, we present new generalization bounds for this scenario and,
second, we int... | ['Mehryar Mohri', 'Dmitry Storcheus', 'Afshin Rostamizadeh'] | 2015-09-29 | null | null | null | null | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [ 1.42421752e-01 4.48508352e-01 1.76971465e-01 -1.50057256e-01
-7.57782996e-01 -6.02500856e-01 2.77414113e-01 9.48276464e-03
-6.01623774e-01 3.50399256e-01 -2.56995469e-01 -4.24730688e-01
-7.54198432e-01 -4.97411370e-01 -6.78439915e-01 -1.08257413e+00
-4.60329503e-01 3.47228706e-01 -1.02198087e-01 -4.42943759... | [7.592872142791748, 4.1789445877075195] |
ed52e195-d755-4a1f-aa25-16c6efd5aa3b | vggin-net-deep-transfer-network-for | null | null | https://ieeexplore.ieee.org/document/9744541 | https://drive.google.com/file/d/1catVKX8IgJX_aTLfgS72JUCmTsNBXE3m/view?usp=sharing | VGGIN-Net: Deep Transfer Network for Imbalanced Breast Cancer Dataset | In this paper, we have presented a novel deep neural network architecture involving transfer learning approach, formed
by freezing and concatenating all the layers till block4 pool layer of VGG16 pre-trained model (at the lower level) with the layers
of a randomly initialized naïve Inception block module (at the hi... | ['Seba Susan', 'Manisha Saini'] | 2022-03-29 | null | null | null | ieee-acm-transactions-on-computational-1 | ['breast-cancer-detection', 'breast-cancer-detection', 'breast-cancer-histology-image-classification'] | ['knowledge-base', 'medical', 'medical'] | [ 1.61907852e-01 4.56431419e-01 -4.54710759e-02 -6.57635927e-01
-3.81124139e-01 1.65398151e-01 3.51266265e-01 4.08946574e-02
-6.18700504e-01 8.69206905e-01 2.03952640e-02 -3.56673002e-01
-2.12728560e-01 -8.79458129e-01 -9.75778520e-01 -7.53778160e-01
-5.83036505e-02 3.64284366e-01 2.94379264e-01 -3.24817210... | [14.930514335632324, -2.58400297164917] |
c57f590e-eda3-4df9-8280-a0648d92dba1 | reasoning-chain-based-adversarial-attack-for | 2112.09658 | null | https://arxiv.org/abs/2112.09658v1 | https://arxiv.org/pdf/2112.09658v1.pdf | Reasoning Chain Based Adversarial Attack for Multi-hop Question Answering | Recent years have witnessed impressive advances in challenging multi-hop QA tasks. However, these QA models may fail when faced with some disturbance in the input text and their interpretability for conducting multi-hop reasoning remains uncertain. Previous adversarial attack works usually edit the whole question sente... | ['Zhongyu Wei', 'Qin Chen', 'Siyuan Wang', 'Jiayu Ding'] | 2021-12-17 | null | null | null | null | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 1.12473853e-01 7.70504296e-01 2.05086455e-01 -3.34609866e-01
-1.08282316e+00 -1.14128256e+00 4.37208295e-01 2.31196389e-01
7.09736208e-03 7.59303570e-01 3.79657388e-01 -6.82442069e-01
-8.51837993e-02 -1.23971879e+00 -1.01601982e+00 -1.91107437e-01
2.66809314e-01 7.54263580e-01 7.56231666e-01 -7.87061930... | [11.01381778717041, 7.976478099822998] |
e5a51767-490d-48c0-b238-42126ed57df5 | temporal-consistency-learning-of-inter-frames | 2211.01639 | null | https://arxiv.org/abs/2211.01639v1 | https://arxiv.org/pdf/2211.01639v1.pdf | Temporal Consistency Learning of inter-frames for Video Super-Resolution | Video super-resolution (VSR) is a task that aims to reconstruct high-resolution (HR) frames from the low-resolution (LR) reference frame and multiple neighboring frames. The vital operation is to utilize the relative misaligned frames for the current frame reconstruction and preserve the consistency of the results. Exi... | ['Yao Zhao', 'Chunyu Lin', 'Chao Yao', 'Shuo Jin', 'Meiqin Liu'] | 2022-11-03 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 1.97402481e-02 -5.98908484e-01 -2.66765058e-01 -3.86092752e-01
-7.54652500e-01 9.00220498e-02 3.16168785e-01 -3.61746311e-01
-3.04325730e-01 6.10438287e-01 5.58400095e-01 4.82322484e-01
-2.63549864e-01 -4.49811608e-01 -5.17910898e-01 -7.70638824e-01
-1.87717681e-03 -4.90946025e-01 5.16712487e-01 -3.46020222... | [11.059795379638672, -1.845155119895935] |
1b733a5d-7797-41f8-be5a-f4a08ddb36b9 | pose-aware-instance-segmentation-framework | 2002.02143 | null | https://arxiv.org/abs/2002.02143v1 | https://arxiv.org/pdf/2002.02143v1.pdf | Pose-Aware Instance Segmentation Framework from Cone Beam CT Images for Tooth Segmentation | Individual tooth segmentation from cone beam computed tomography (CBCT) images is an essential prerequisite for an anatomical understanding of orthodontic structures in several applications, such as tooth reformation planning and implant guide simulations. However, the presence of severe metal artifacts in CBCT images ... | ['Yeong-Gil Shin', 'Sanguk Park', 'Jingyu Lee', 'Jeongjin Lee', 'Minyoung Chung', 'Minkyung Lee', 'Jusang Lee', 'Jioh Hong'] | 2020-02-06 | null | null | null | null | ['image-cropping'] | ['computer-vision'] | [ 7.30852604e-01 6.67938769e-01 -1.34529859e-01 -4.68439639e-01
-9.22170997e-01 1.17349967e-01 2.58137584e-01 3.98400247e-01
-5.92215538e-01 2.74131209e-01 -3.27640206e-01 -2.78449625e-01
-1.15365870e-01 -7.42233038e-01 -8.02047014e-01 -7.67618597e-01
2.00554103e-01 6.62693918e-01 4.22996372e-01 -3.25501012... | [13.796040534973145, -2.2657124996185303] |
aa6bd19d-87c4-4136-ace1-64edfdca59e8 | pad-program-aided-distillation-specializes | 2305.13888 | null | https://arxiv.org/abs/2305.13888v1 | https://arxiv.org/pdf/2305.13888v1.pdf | PaD: Program-aided Distillation Specializes Large Models in Reasoning | While Large Language Models (LLMs) excel in several natural language processing tasks, their size and inaccessibility present challenges for extensive practical application. Previous studies acquire specialized skills through distillation on LLMs, which result in trading generic abilities, called model specialization. ... | ['BoWen Zhou', 'Xingwei Long', 'Kaiyan Zhang', 'Biqing Qi', 'Xuekai Zhu'] | 2023-05-23 | null | null | null | null | ['gsm8k'] | ['natural-language-processing'] | [ 8.47106799e-02 5.28758824e-01 -3.52437437e-01 -2.97183126e-01
-4.95516658e-01 -3.61153007e-01 4.37243223e-01 2.51780748e-01
-3.61522734e-01 4.05381650e-01 7.68049881e-02 -8.82202446e-01
1.42238364e-01 -1.01688528e+00 -8.63964319e-01 -5.50205186e-02
1.28699616e-01 4.62307274e-01 3.72649699e-01 -4.88786697... | [9.685480117797852, 7.4244608879089355] |
4ccf1fd5-a7f0-46d1-918b-3279454b3045 | multi-2oie-multilingual-open-information | 2009.08128 | null | https://arxiv.org/abs/2009.08128v2 | https://arxiv.org/pdf/2009.08128v2.pdf | Multi$^2$OIE: Multilingual Open Information Extraction Based on Multi-Head Attention with BERT | In this paper, we propose Multi$^2$OIE, which performs open information extraction (open IE) by combining BERT with multi-head attention. Our model is a sequence-labeling system with an efficient and effective argument extraction method. We use a query, key, and value setting inspired by the Multimodal Transformer to r... | ['Yukyung Lee', 'Youngbin Ro', 'Pilsung Kang'] | 2020-09-17 | null | null | null | null | ['open-information-extraction'] | ['natural-language-processing'] | [ 1.35268671e-02 2.12883130e-01 -6.06281698e-01 2.77121142e-02
-1.40955245e+00 -9.91197646e-01 7.07503319e-01 3.41855437e-01
-1.04265320e+00 1.12875974e+00 3.06728274e-01 -8.55263710e-01
6.76371902e-02 -7.75799274e-01 -1.14846003e+00 5.19656278e-02
2.21193373e-01 7.59777546e-01 1.35552004e-01 -5.08128941... | [9.8978853225708, 8.984013557434082] |
f70fadc3-81af-4687-a371-5c09abf7bdd7 | logo-2k-a-large-scale-logo-dataset-for | 1911.07924 | null | https://arxiv.org/abs/1911.07924v1 | https://arxiv.org/pdf/1911.07924v1.pdf | Logo-2K+: A Large-Scale Logo Dataset for Scalable Logo Classification | Logo classification has gained increasing attention for its various applications, such as copyright infringement detection, product recommendation and contextual advertising. Compared with other types of object images, the real-world logo images have larger variety in logo appearance and more complexity in their backgr... | ['Yuanjie Zheng', 'Weiqing Min', 'Sujuan Hou', 'Jing Wang', 'Shengnan Ma', 'Shuqiang Jiang', 'Haishuai Wang'] | 2019-11-11 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [ 3.00911367e-01 -2.79796600e-01 -8.69742393e-01 -2.74113268e-01
-6.74004793e-01 -7.40164876e-01 3.63710046e-01 9.84050427e-03
1.14717111e-01 1.14349343e-01 -2.25977041e-02 -3.40504885e-01
-3.57258134e-02 -8.61922622e-01 -8.05800736e-01 -4.12832260e-01
-2.69664466e-01 3.96414220e-01 4.09429431e-01 1.07195534... | [9.357660293579102, 1.3925336599349976] |
8542c2c9-3823-4608-8696-d42d61611d41 | from-2d-images-to-3d-model-weakly-supervised | 2204.03842 | null | https://arxiv.org/abs/2204.03842v2 | https://arxiv.org/pdf/2204.03842v2.pdf | From 2D Images to 3D Model:Weakly Supervised Multi-View Face Reconstruction with Deep Fusion | We consider the problem of Multi-view 3D Face Reconstruction (MVR) with weakly supervised learning that leverages a limited number of 2D face images (e.g. 3) to generate a high-quality 3D face model with very light annotation. Despite their encouraging performance, present MVR methods simply concatenate multi-view imag... | ['Kaizhu Huang', 'Xi Yang', 'Yuyao Yan', 'Jianan Ye', 'Chaolong Yang', 'Weiguang Zhao'] | 2022-04-08 | null | null | null | null | ['3d-face-reconstruction', 'face-model', 'face-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 9.60031617e-03 2.32691139e-01 -1.29170224e-01 -5.50045788e-01
-1.13515615e+00 -4.70421642e-01 4.24447387e-01 -6.43296421e-01
4.85466467e-03 1.84401259e-01 2.48244464e-01 3.03588342e-02
2.12328866e-01 -6.09223008e-01 -9.23072517e-01 -4.80073899e-01
3.20493668e-01 3.52714866e-01 -3.53563040e-01 -9.23913568... | [13.205309867858887, 0.17605504393577576] |
f37d8e44-1ebe-4505-be71-99839f41f783 | introduction-to-medical-imaging-informatics | 2306.00421 | null | https://arxiv.org/abs/2306.00421v3 | https://arxiv.org/pdf/2306.00421v3.pdf | Introduction to Medical Imaging Informatics | Medical imaging informatics is a rapidly growing field that combines the principles of medical imaging and informatics to improve the acquisition, management, and interpretation of medical images. This chapter introduces the basic concepts of medical imaging informatics, including image processing, feature engineering,... | ['Sajedul Talukder', 'Md Jahangir Alam', 'Md. Mahim Anjum Haque', 'MD Abdullah Al Nasim', 'Riadul Islam', 'Ruksat Hossain', 'Md. Zihad Bin Jahangir'] | 2023-06-01 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [ 5.09056687e-01 -6.89144358e-02 -4.36827302e-01 -5.08290708e-01
-4.86905873e-01 1.30839691e-01 -5.55600487e-02 5.64837158e-01
-4.27365810e-01 9.35456604e-02 2.93316960e-01 -3.06154937e-01
-2.12395683e-01 -6.77294731e-01 -1.03018552e-01 -8.03845644e-01
-5.06181836e-01 5.65271795e-01 -2.90614486e-01 3.75032932... | [14.800044059753418, -2.4887442588806152] |
ebcb11d0-fa2b-4d51-a800-1046752f1013 | a-novel-model-based-heuristic-for-energy | 1712.03719 | null | http://arxiv.org/abs/1712.03719v2 | http://arxiv.org/pdf/1712.03719v2.pdf | A novel model-based heuristic for energy optimal motion planning for automated driving | Predictive motion planning is the key to achieve energy-efficient driving,
which is one of the main benefits of automated driving. Researchers have been
studying the planning of velocity trajectories, a simpler form of motion
planning, for over a decade now and many different methods are available.
Dynamic programming ... | ['Zlatan Ajanovic', 'Michael Stolz', 'Martin Horn'] | 2017-12-11 | null | null | null | null | ['optimal-motion-planning'] | ['robots'] | [-1.34824291e-01 1.66472763e-01 -4.15356278e-01 -3.89952436e-02
-1.28264889e-01 -6.26258552e-01 5.41271746e-01 2.33425736e-01
-5.74079931e-01 9.42397833e-01 -2.61648536e-01 -5.50299764e-01
-7.22253859e-01 -9.49404776e-01 -3.64747077e-01 -8.04402053e-01
-1.08054966e-01 6.89893246e-01 4.29546952e-01 -4.89530981... | [5.248668193817139, 1.7563602924346924] |
43010faa-f5b5-4e8f-acad-a3ba5d52c3e1 | realistic-bokeh-effect-rendering-on-mobile | 2211.06769 | null | https://arxiv.org/abs/2211.06769v1 | https://arxiv.org/pdf/2211.06769v1.pdf | Realistic Bokeh Effect Rendering on Mobile GPUs, Mobile AI & AIM 2022 challenge: Report | As mobile cameras with compact optics are unable to produce a strong bokeh effect, lots of interest is now devoted to deep learning-based solutions for this task. In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based bokeh effect rendering approach that can run on modern smartphone GPU... | ['Lei Lei', 'Xiaotao Wang', 'Yanan Li', 'Huixin Ma', 'Mingyang Qian', 'Munchurl Kim', 'Byeongjun Kwon', 'Hyebin Cho', 'Huaijin Chen', 'Lei Fei', 'Brian Lee', 'Guangjing Yan', 'Ziping Wang', 'Pan Mu', 'Wentao Tong', 'Haotian Qian', 'Minsu Kwon', 'Hongbin Wang', 'Zhe Ma', 'Gaocheng Yu', 'Feng Zhang', 'Jin Zhang', 'Radu T... | 2022-11-07 | null | null | null | null | ['bokeh-effect-rendering'] | ['computer-vision'] | [ 2.73172557e-02 -4.27223712e-01 4.13947046e-01 -3.86179388e-01
-8.57526779e-01 -1.91877216e-01 5.25215447e-01 -5.62060237e-01
-6.37644708e-01 1.94955468e-01 1.10448323e-01 -1.84738338e-01
9.35040042e-02 -2.49960691e-01 -7.82201827e-01 -3.06823462e-01
-3.93472030e-04 2.49296814e-01 2.34980091e-01 -2.59037942... | [10.240823745727539, -2.2500922679901123] |
4c6a154b-d96c-482b-870e-c6658d0cf9c9 | language-model-based-chinese-handwriting | null | null | https://aclanthology.org/2022.rocling-1.1 | https://aclanthology.org/2022.rocling-1.1.pdf | Language Model Based Chinese Handwriting Address Recognition | Chinese handwritten address recognition of consignment note is an important challenge of smart logistics automation. Chinese handwritten characters detection and recognition is the key technology for this application. Since the writing mode of handwritten characters is more complex and diverse than printed characters, ... | ['Yun-Wei Hung', 'Yung-Ping Tien', 'Chieh-Jen Wang'] | null | null | null | null | rocling-2022-11 | ['handwriting-recognition'] | ['computer-vision'] | [ 3.27809528e-02 -8.70279849e-01 -4.50698398e-02 5.47644794e-02
1.30739033e-01 -8.84355962e-01 4.32841569e-01 -3.09422761e-01
-3.69086981e-01 5.58156192e-01 -2.31469404e-02 -3.05678278e-01
-1.07134217e-02 -5.77811778e-01 3.17468010e-02 -7.82791078e-01
7.11623013e-01 3.54556203e-01 1.38415486e-01 -7.04187751... | [11.834693908691406, 2.611419439315796] |
8476d1f9-ffd7-4c63-b8fb-5a163d670e0a | human-face-recognition-from-part-of-a-facial | 2203.05601 | null | https://arxiv.org/abs/2203.05601v1 | https://arxiv.org/pdf/2203.05601v1.pdf | Human Face Recognition from Part of a Facial Image based on Image Stitching | Most of the current techniques for face recognition require the presence of a full face of the person to be recognized, and this situation is difficult to achieve in practice, the required person may appear with a part of his face, which requires prediction of the part that did not appear. Most of the current forecasti... | ['Ahmed I. Taloba', 'Rasha M. Abd El-Aziz', 'Alanazi Rayan', 'Rami Ayedi', 'Osama R. Shahin'] | 2022-03-10 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 4.62735504e-01 -1.65218070e-01 1.96625084e-01 -2.69169152e-01
2.73296863e-01 -1.03329688e-01 6.12152994e-01 -5.84616959e-01
-2.24363729e-01 4.74066019e-01 -1.22202627e-01 1.39082581e-01
-2.10365027e-01 -7.54042864e-01 -2.86576450e-01 -9.39169824e-01
2.58590728e-01 4.00980920e-01 -5.32189310e-02 -2.45927721... | [13.042181015014648, 0.575559675693512] |
e5748aa8-d477-425c-b0a6-61c0c576caab | shifting-attention-to-relevance-towards-the | 2307.01379 | null | https://arxiv.org/abs/2307.01379v1 | https://arxiv.org/pdf/2307.01379v1.pdf | Shifting Attention to Relevance: Towards the Uncertainty Estimation of Large Language Models | Although Large Language Models (LLMs) have shown great potential in Natural Language Generation, it is still challenging to characterize the uncertainty of model generations, i.e., when users could trust model outputs. Our research is derived from the heuristic facts that tokens are created unequally in reflecting the ... | ['Kaidi Xu', 'Bhavya Kailkhura', 'Renjing Xu', 'Alex Zavalny', 'Chenan Wang', 'Shiqi Wang', 'Hao Cheng', 'Jinhao Duan'] | 2023-07-03 | null | null | null | null | ['text-generation', 'question-answering'] | ['natural-language-processing', 'natural-language-processing'] | [-1.92127571e-01 4.06224638e-01 -3.45130831e-01 -6.20319784e-01
-1.03158259e+00 -5.38512349e-01 6.22556210e-01 8.54360238e-02
-3.19463491e-01 1.10805357e+00 6.36167645e-01 -3.85838658e-01
-1.21052507e-02 -9.33803201e-01 -9.46083724e-01 -3.76359135e-01
3.68348897e-01 4.36668962e-01 -2.51396179e-01 -2.84301341... | [11.7300443649292, 8.881454467773438] |
bb29d5d8-591e-4a92-ae06-7b0076a4dee0 | seeing-is-not-always-believing-a-quantitative | 2304.13023 | null | https://arxiv.org/abs/2304.13023v2 | https://arxiv.org/pdf/2304.13023v2.pdf | Seeing is not always believing: Benchmarking Human and Model Perception of AI-Generated Images | Photos serve as a way for humans to record what they experience in their daily lives, and they are often regarded as trustworthy sources of information. However, there is a growing concern that the advancement of artificial intelligence (AI) technology may produce fake photos, which can create confusion and diminish tr... | ['Wanli Ouyang', 'Chengyue Wu', 'Jingjing Qu', 'Xihui Liu', 'Lei Bai', 'Di Huang', 'Zeyu Lu'] | 2023-04-25 | null | null | null | null | ['fake-image-detection'] | ['computer-vision'] | [ 3.92554849e-02 3.40546757e-01 -1.04393288e-02 -7.46179819e-02
-4.98078614e-01 -6.51513040e-01 8.76338542e-01 -7.47391656e-02
-4.07417893e-01 5.51589489e-01 -6.95915520e-02 -1.97458759e-01
6.43030822e-01 -6.34722054e-01 -9.77890849e-01 -4.27379191e-01
1.57313883e-01 1.79044604e-01 1.57340050e-01 -2.06032440... | [12.391343116760254, 1.1448798179626465] |
6ce6028d-4d38-4283-94b7-2ddadb65019c | construe-a-software-solution-for-the | 2003.07596 | null | https://arxiv.org/abs/2003.07596v1 | https://arxiv.org/pdf/2003.07596v1.pdf | Construe: a software solution for the explanation-based interpretation of time series | This paper presents a software implementation of a general framework for time series interpretation based on abductive reasoning. The software provides a data model and a set of algorithms to make inference to the best explanation of a time series, resulting in a description in multiple abstraction levels of the proces... | ['Paulo Felix', 'Tomas Teijeiro'] | 2020-03-17 | null | null | null | null | ['heartbeat-classification', 'atrial-fibrillation-detection'] | ['medical', 'medical'] | [ 9.52279568e-02 5.89652181e-01 -1.89245552e-01 -4.87526715e-01
1.35909215e-01 -4.20466125e-01 2.69075722e-01 4.04008448e-01
1.88745007e-01 5.68351030e-01 -1.03249967e-01 -8.40905428e-01
-7.82878816e-01 -8.28018069e-01 -8.20996761e-02 -2.61375129e-01
-5.85583150e-01 6.15659654e-01 -4.86131310e-02 -4.26292032... | [14.122015953063965, 3.151890993118286] |
00efecff-91f2-46b3-ac6c-6a23a0597fcb | deep-graph-level-clustering-using-pseudo | 2302.02369 | null | https://arxiv.org/abs/2302.02369v1 | https://arxiv.org/pdf/2302.02369v1.pdf | Deep Graph-Level Clustering Using Pseudo-Label-Guided Mutual Information Maximization Network | In this work, we study the problem of partitioning a set of graphs into different groups such that the graphs in the same group are similar while the graphs in different groups are dissimilar. This problem was rarely studied previously, although there have been a lot of work on node clustering and graph classification.... | ['Jicong Fan', 'Wenzhong Guo', 'Yi Han', 'Jinyu Cai'] | 2023-02-05 | null | null | null | null | ['graph-classification'] | ['graphs'] | [-1.40072092e-01 7.37889856e-02 -1.57040909e-01 -2.65007526e-01
-2.48207852e-01 -5.92962384e-01 4.12611276e-01 6.68072283e-01
8.73074979e-02 -6.95886165e-02 2.92744711e-02 -1.38374776e-01
-3.58768255e-01 -9.73418117e-01 -3.41056466e-01 -9.08141017e-01
-3.95820230e-01 4.19020772e-01 2.73516059e-01 1.16926327... | [7.22797155380249, 6.094588756561279] |
3198ed7a-3133-4979-883f-09a79d11898d | knod-domain-knowledge-distilled-tree-decoder | 2302.01857 | null | https://arxiv.org/abs/2302.01857v3 | https://arxiv.org/pdf/2302.01857v3.pdf | KNOD: Domain Knowledge Distilled Tree Decoder for Automated Program Repair | Automated Program Repair (APR) improves software reliability by generating patches for a buggy program automatically. Recent APR techniques leverage deep learning (DL) to build models to learn to generate patches from existing patches and code corpora. While promising, DL-based APR techniques suffer from the abundant s... | ['Xiangyu Zhang', 'Dan Goldwasser', 'Lin Tan', 'Yiling Lou', 'Thibaud Lutellier', 'Nan Jiang'] | 2023-02-03 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [-3.46993655e-02 3.09469044e-01 -5.81007659e-01 -2.10462272e-01
-1.11529028e+00 -6.25720382e-01 -1.16122719e-02 2.69589484e-01
4.99283671e-01 5.33273041e-01 2.62535233e-02 -7.65120506e-01
2.81678200e-01 -9.02535677e-01 -1.27004969e+00 -1.11774415e-01
5.23440540e-03 2.76131816e-02 4.79586750e-01 -2.09712818... | [7.58788537979126, 7.7371673583984375] |
b8fab4f2-b697-4e1c-93be-b65da433e86a | streamyolo-real-time-object-detection-for | 2207.10433 | null | https://arxiv.org/abs/2207.10433v1 | https://arxiv.org/pdf/2207.10433v1.pdf | StreamYOLO: Real-time Object Detection for Streaming Perception | The perceptive models of autonomous driving require fast inference within a low latency for safety. While existing works ignore the inevitable environmental changes after processing, streaming perception jointly evaluates the latency and accuracy into a single metric for video online perception, guiding the previous wo... | ['Jian Sun', 'Xiaoping Li', 'Zeming Li', 'Songtao Liu', 'Jinrong Yang'] | 2022-07-21 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [-1.78600419e-02 -2.74268717e-01 -1.25803158e-01 -4.37392384e-01
-3.30287755e-01 -3.29140455e-01 6.97851241e-01 1.38142243e-01
-7.01963425e-01 1.74266428e-01 1.42004550e-01 -3.71960461e-01
2.06873333e-03 -9.22771037e-01 -8.67861211e-01 -7.02260733e-01
-4.70859706e-01 -1.02358066e-01 1.11586773e+00 -3.38727593... | [8.389498710632324, -0.8053340911865234] |
3c735cd2-5d02-4245-a576-ef8b146834cd | iterative-scale-up-expansioniou-and-deep | 2306.13074 | null | https://arxiv.org/abs/2306.13074v1 | https://arxiv.org/pdf/2306.13074v1.pdf | Iterative Scale-Up ExpansionIoU and Deep Features Association for Multi-Object Tracking in Sports | Multi-object tracking algorithms have made significant advancements due to the recent developments in object detection. However, most existing methods primarily focus on tracking pedestrians or vehicles, which exhibit relatively simple and regular motion patterns. Consequently, there is a scarcity of algorithms that ad... | ['Chung-I Huang', 'Jenq-Neng Hwang', 'Cheng-Yen Yang', 'Hsiang-Wei Huang'] | 2023-06-22 | null | null | null | null | ['object-tracking', 'multi-object-tracking'] | ['computer-vision', 'computer-vision'] | [-3.28925490e-01 -9.13742125e-01 -2.38559112e-01 2.50698060e-01
-3.95987988e-01 -5.76924086e-01 3.71109247e-01 3.27238925e-02
-6.23357415e-01 4.50898111e-01 -4.03898448e-01 -9.28853527e-02
6.75122961e-02 -4.81960744e-01 -6.32614255e-01 -7.66489148e-01
-1.51277054e-02 3.83679897e-01 1.08550334e+00 1.75224002... | [6.458896160125732, -2.044389247894287] |
e1aa2bef-4801-4d53-bb60-a8490578afc3 | sentence-compression-via-dc-programming | 1902.07248 | null | http://arxiv.org/abs/1902.07248v1 | http://arxiv.org/pdf/1902.07248v1.pdf | Sentence Compression via DC Programming Approach | Sentence compression is an important problem in natural language processing.
In this paper, we firstly establish a new sentence compression model based on
the probability model and the parse tree model. Our sentence compression model
is equivalent to an integer linear program (ILP) which can both guarantee the
syntax c... | ['Xi-Wei Hu', 'Yi-Shuai Niu', 'Faouzi Mohamed Benammour', 'Yu You', 'Hu Zhang'] | 2019-02-13 | null | null | null | null | ['sentence-compression'] | ['natural-language-processing'] | [ 5.13760924e-01 2.42413267e-01 -2.95406997e-01 -4.85650867e-01
-8.42604578e-01 -3.17360938e-01 -1.46930620e-01 6.45445466e-01
-3.70024174e-01 6.73950374e-01 5.04499555e-01 -5.94734788e-01
-4.51236874e-01 -1.06431460e+00 -7.36918151e-01 -3.59551221e-01
1.12787656e-01 2.86534548e-01 -2.76376009e-02 -1.82447672... | [12.223577499389648, 9.2434720993042] |
79772f71-6fce-46ef-b1d9-5890a72dd979 | graph-less-collaborative-filtering | 2303.08537 | null | https://arxiv.org/abs/2303.08537v3 | https://arxiv.org/pdf/2303.08537v3.pdf | Graph-less Collaborative Filtering | Graph neural networks (GNNs) have shown the power in representation learning over graph-structured user-item interaction data for collaborative filtering (CF) task. However, with their inherently recursive message propagation among neighboring nodes, existing GNN-based CF models may generate indistinguishable and inacc... | ['Yong Xu', 'Jiao Shi', 'Chao Huang', 'Lianghao Xia'] | 2023-03-15 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [ 5.09830341e-02 6.92790374e-02 -5.68219908e-02 -2.15056092e-01
-3.32374662e-01 -4.85256970e-01 4.22950447e-01 1.56051010e-01
-1.40473142e-01 4.93308336e-01 7.86068559e-01 -4.62403715e-01
-2.85427094e-01 -9.97146845e-01 -6.44781828e-01 -3.92837197e-01
-3.48339468e-01 6.21900819e-02 1.59093272e-02 -1.97391942... | [10.197134971618652, 5.609814167022705] |
44567f82-9f45-4ec7-ac60-dc4e16871d4b | aggregating-multiple-types-of-complex-data-in | 1805.05617 | null | http://arxiv.org/abs/1805.05617v1 | http://arxiv.org/pdf/1805.05617v1.pdf | Aggregating multiple types of complex data in stock market prediction: A model-independent framework | The increasing richness in volume, and especially types of data in the
financial domain provides unprecedented opportunities to understand the stock
market more comprehensively and makes the price prediction more accurate than
before. However, they also bring challenges to classic statistic approaches
since those model... | [] | 2018-05-15 | null | null | null | null | ['stock-market-prediction'] | ['time-series'] | [-7.76928544e-01 -2.23706633e-01 -5.06929338e-01 -2.09077656e-01
-8.42802227e-02 -8.27863634e-01 9.46965098e-01 3.08708906e-01
-2.37253323e-01 6.93232179e-01 1.21171065e-01 -5.21428108e-01
-3.43483835e-01 -1.27774823e+00 -2.83743769e-01 -7.25485384e-01
-4.51462746e-01 3.72795194e-01 -4.35265228e-02 -6.63821816... | [4.5758585929870605, 4.202176570892334] |
19e05d8e-befd-4bd2-9c03-4aa4dfb3731e | few-shot-geometry-aware-keypoint-localization | 2303.17216 | null | https://arxiv.org/abs/2303.17216v1 | https://arxiv.org/pdf/2303.17216v1.pdf | Few-shot Geometry-Aware Keypoint Localization | Supervised keypoint localization methods rely on large manually labeled image datasets, where objects can deform, articulate, or occlude. However, creating such large keypoint labels is time-consuming and costly, and is often error-prone due to inconsistent labeling. Thus, we desire an approach that can learn keypoint ... | ['Pablo Garrido', 'Helge Rhodin', 'David Ferman', 'Gaurav Bharaj', 'Xingzhe He'] | 2023-03-30 | null | http://openaccess.thecvf.com//content/CVPR2023/html/He_Few-Shot_Geometry-Aware_Keypoint_Localization_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/He_Few-Shot_Geometry-Aware_Keypoint_Localization_CVPR_2023_paper.pdf | cvpr-2023-1 | ['object-localization'] | ['computer-vision'] | [-2.80012190e-01 -6.32905364e-02 -4.61812913e-01 -4.78909105e-01
-1.05918241e+00 -5.37186563e-01 5.50109863e-01 -8.04653019e-02
-1.14123464e-01 5.16909957e-01 -9.19429958e-03 1.28904790e-01
3.42390612e-02 -4.71235514e-01 -8.01318705e-01 -6.66748047e-01
1.61158174e-01 5.74704111e-01 2.05376387e-01 1.17890321... | [8.0037841796875, -2.672381639480591] |
aaaccae4-9f79-4308-8889-55b651ce6946 | thompson-sampling-under-bernoulli-rewards | 2307.00863 | null | https://arxiv.org/abs/2307.00863v1 | https://arxiv.org/pdf/2307.00863v1.pdf | Thompson Sampling under Bernoulli Rewards with Local Differential Privacy | This paper investigates the problem of regret minimization for multi-armed bandit (MAB) problems with local differential privacy (LDP) guarantee. Given a fixed privacy budget $\epsilon$, we consider three privatizing mechanisms under Bernoulli scenario: linear, quadratic and exponential mechanisms. Under each mechanism... | ['Ming Li', 'Tianchi Zhao', 'Bo Jiang'] | 2023-07-03 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [-2.34241694e-01 4.89292443e-02 -6.39139473e-01 -7.20250547e-01
-1.18307853e+00 -1.06613111e+00 -8.80346373e-02 -1.96392640e-01
-5.60764730e-01 1.51018751e+00 1.57698885e-01 -6.88481271e-01
-6.32630646e-01 -7.73723364e-01 -7.77945101e-01 -9.90719795e-01
2.14023024e-01 3.17904413e-01 -6.89794004e-01 2.54044890... | [4.567505836486816, 3.429560661315918] |
b064e6be-d581-4ea1-8649-abad5c180db4 | few-shot-multi-domain-knowledge-rearming-for | 2306.07685 | null | https://arxiv.org/abs/2306.07685v2 | https://arxiv.org/pdf/2306.07685v2.pdf | Few-shot Multi-domain Knowledge Rearming for Context-aware Defence against Advanced Persistent Threats | Advanced persistent threats (APTs) have novel features such as multi-stage penetration, highly-tailored intention, and evasive tactics. APTs defense requires fusing multi-dimensional Cyber threat intelligence data to identify attack intentions and conducts efficient knowledge discovery strategies by data-driven machine... | ['Yuchen Liu', 'Wenqi Wei', 'YuanYuan Zhao', 'Gaolei Li'] | 2023-06-13 | null | null | null | null | ['meta-learning'] | ['methodology'] | [ 1.34853600e-02 -3.42593610e-01 -6.72979414e-01 -1.64840728e-01
-5.20907700e-01 -7.91260481e-01 4.01175737e-01 -3.80376428e-02
-1.78537101e-01 3.32172066e-01 -2.86637783e-01 -7.95807123e-01
-6.27930939e-01 -1.01908922e+00 -1.47954017e-01 -2.95870692e-01
-2.09322885e-01 5.89396119e-01 5.94599664e-01 -4.39242840... | [5.292325019836426, 7.194939136505127] |
90ea26ab-f076-4fb4-b123-6cd140b513d9 | stereonet-guided-hierarchical-refinement-for | 1807.08865 | null | http://arxiv.org/abs/1807.08865v1 | http://arxiv.org/pdf/1807.08865v1.pdf | StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction | This paper presents StereoNet, the first end-to-end deep architecture for
real-time stereo matching that runs at 60 fps on an NVidia Titan X, producing
high-quality, edge-preserved, quantization-free disparity maps. A key insight
of this paper is that the network achieves a sub-pixel matching precision than
is a magnit... | ['Julien Valentin', 'Adarsh Kowdle', 'Christoph Rhemann', 'Sean Fanello', 'Sameh Khamis', 'Shahram Izadi'] | 2018-07-24 | stereonet-guided-hierarchical-refinement-for-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Sameh_Khamis_StereoNet_Guided_Hierarchical_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Sameh_Khamis_StereoNet_Guided_Hierarchical_ECCV_2018_paper.pdf | eccv-2018-9 | ['stereo-depth-estimation'] | ['computer-vision'] | [ 2.64315337e-01 -1.73506036e-01 2.77346633e-02 -4.35482532e-01
-8.40791762e-01 -1.91095397e-01 4.07386273e-01 -2.15702370e-01
-6.36854291e-01 5.88185430e-01 2.53728300e-01 -8.88694599e-02
2.75551498e-01 -9.82013524e-01 -8.96572709e-01 -3.18044484e-01
-1.26266539e-01 1.72369897e-01 5.38384676e-01 -3.19994837... | [8.934012413024902, -2.2933223247528076] |
7dd67799-bf3f-4530-90bf-a12e1a68c359 | monocular-3d-human-pose-estimation-by-1 | 1904.01324 | null | https://arxiv.org/abs/1904.01324v2 | https://arxiv.org/pdf/1904.01324v2.pdf | Monocular 3D Human Pose Estimation by Generation and Ordinal Ranking | Monocular 3D human-pose estimation from static images is a challenging problem, due to the curse of dimensionality and the ill-posed nature of lifting 2D-to-3D. In this paper, we propose a Deep Conditional Variational Autoencoder based model that synthesizes diverse anatomically plausible 3D-pose samples conditioned on... | ['Prashast Bindal', 'Arjun Jain', 'Saurabh Sharma', 'Abhishek Sharma', 'Pavan Teja Varigonda'] | 2019-04-02 | monocular-3d-human-pose-estimation-by-2 | http://openaccess.thecvf.com/content_ICCV_2019/html/Sharma_Monocular_3D_Human_Pose_Estimation_by_Generation_and_Ordinal_Ranking_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Sharma_Monocular_3D_Human_Pose_Estimation_by_Generation_and_Ordinal_Ranking_ICCV_2019_paper.pdf | iccv-2019-10 | ['multi-hypotheses-3d-human-pose-estimation', 'monocular-3d-human-pose-estimation', 'image-to-3d'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-2.86122747e-02 1.68911442e-01 -2.34074414e-01 -3.26784998e-01
-1.41888988e+00 -4.94664013e-01 2.69509047e-01 -4.01885450e-01
-3.54494333e-01 6.43820822e-01 5.90558946e-01 1.00401938e-01
-2.91185416e-02 -3.80002379e-01 -1.08605075e+00 -5.11140287e-01
-6.02520406e-02 9.83424306e-01 1.15785599e-01 1.88392550... | [7.000046730041504, -0.9800901412963867] |
be3d9186-74c4-485b-9a1e-79ae1e6cb467 | softpoolnet-shape-descriptor-for-point-cloud | 2008.07358 | null | https://arxiv.org/abs/2008.07358v1 | https://arxiv.org/pdf/2008.07358v1.pdf | SoftPoolNet: Shape Descriptor for Point Cloud Completion and Classification | Point clouds are often the default choice for many applications as they exhibit more flexibility and efficiency than volumetric data. Nevertheless, their unorganized nature -- points are stored in an unordered way -- makes them less suited to be processed by deep learning pipelines. In this paper, we propose a method f... | ['Federico Tombari', 'David Joseph Tan', 'Yida Wang', 'Nassir Navab'] | 2020-08-17 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/5457_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480069.pdf | eccv-2020-8 | ['point-cloud-completion'] | ['computer-vision'] | [-1.14483394e-01 1.45019263e-01 3.18708122e-01 -6.77942216e-01
-4.89951819e-01 -3.12465549e-01 7.42799163e-01 2.64743984e-01
-5.44929147e-01 3.33495975e-01 -8.04885924e-02 -5.66388704e-02
-8.20118934e-02 -1.02948928e+00 -1.14560151e+00 -5.03953397e-01
-1.14640325e-01 6.29473984e-01 5.02133183e-02 -6.13530315... | [8.13775634765625, -3.63703989982605] |
cdf81121-e0cc-46e2-a429-5055769aa3ea | associating-frailty-and-dynamic-dysregulation | 2303.13591 | null | https://arxiv.org/abs/2303.13591v1 | https://arxiv.org/pdf/2303.13591v1.pdf | Associating Frailty and Dynamic Dysregulation between Motor and Cardiac Autonomic Systems | Frailty is a geriatric syndrome associated with the lack of physiological reserve and consequent adverse outcomes (therapy complications and death) in older adults. Recent research has shown associations between heart rate (HR) dynamics (HR changes during physical activity) with frailty. The goal of the present study w... | ['Nima Toosizadeh', 'Kaveh Laksari', 'Patricio Arrué'] | 2023-03-23 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [-3.33474278e-01 -4.52486351e-02 -6.18686318e-01 1.17816404e-01
-1.32254571e-01 -3.93807292e-01 3.87723409e-02 -1.72257811e-01
-8.38472664e-01 1.20237660e+00 4.39505130e-01 -6.34190023e-01
-3.78900349e-01 -5.90064943e-01 -1.47429377e-01 -2.16110468e-01
-8.94224942e-01 -2.94812359e-02 -3.79416704e-01 -1.09573498... | [14.070024490356445, 3.0756723880767822] |
18ddbcf5-1eaf-4f4d-b393-91af8c8b5f20 | f-pabee-flexible-patience-based-early-exiting | 2305.11916 | null | https://arxiv.org/abs/2305.11916v1 | https://arxiv.org/pdf/2305.11916v1.pdf | F-PABEE: Flexible-patience-based Early Exiting for Single-label and Multi-label text Classification Tasks | Computational complexity and overthinking problems have become the bottlenecks for pre-training language models (PLMs) with millions or even trillions of parameters. A Flexible-Patience-Based Early Exiting method (F-PABEE) has been proposed to alleviate the problems mentioned above for single-label classification (SLC)... | ['Congrui Yin', 'Jiasheng Gao', 'Wei Zhu', 'Xiangxiang Gao'] | 2023-05-21 | null | null | null | null | ['multi-label-text-classification', 'multi-label-text-classification'] | ['methodology', 'natural-language-processing'] | [-2.51049876e-01 -1.36784911e-01 -5.03339887e-01 -6.13193750e-01
-9.14858580e-01 -4.67362970e-01 5.51115632e-01 2.52282262e-01
-7.46906877e-01 9.26842749e-01 -4.08971369e-01 -5.75809240e-01
-3.86263609e-01 -3.78314823e-01 -5.26127756e-01 -4.82743204e-01
-1.63157642e-01 1.18890738e+00 6.27074659e-01 -8.34258571... | [9.562752723693848, 4.480677127838135] |
db6a8f08-9499-44bc-820c-08faedd12f85 | adaptivepose-a-powerful-single-stage-network | 2210.04014 | null | https://arxiv.org/abs/2210.04014v1 | https://arxiv.org/pdf/2210.04014v1.pdf | AdaptivePose++: A Powerful Single-Stage Network for Multi-Person Pose Regression | Multi-person pose estimation generally follows top-down and bottom-up paradigms. Both of them use an extra stage ($\boldsymbol{e.g.,}$ human detection in top-down paradigm or grouping process in bottom-up paradigm) to build the relationship between the human instance and corresponding keypoints, thus leading to the hig... | ['Jian Zhao', 'Shuicheng Yan', 'Mei Song', 'Lei Jin', 'Kai Su', 'Dongdong Yu', 'Xiaojuan Wang', 'Yabo Xiao'] | 2022-10-08 | null | null | null | null | ['3d-multi-person-pose-estimation', 'multi-person-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-2.14700088e-01 -2.20564201e-01 2.81053185e-01 -3.58270735e-01
-6.51076078e-01 -2.09660843e-01 2.82541126e-01 -5.81020489e-03
-6.38951957e-01 3.51761520e-01 1.02639809e-01 2.58224726e-01
5.36361076e-02 -6.73119962e-01 -7.24296331e-01 -4.37550455e-01
8.05994943e-02 7.12289631e-01 4.63966310e-01 -3.52905512... | [7.150362968444824, -0.7932339906692505] |
7b47c5a0-9151-452c-8c26-a6b8a48639b5 | r5-rule-discovery-with-reinforced-and-1 | 2205.06454 | null | https://arxiv.org/abs/2205.06454v1 | https://arxiv.org/pdf/2205.06454v1.pdf | R5: Rule Discovery with Reinforced and Recurrent Relational Reasoning | Systematicity, i.e., the ability to recombine known parts and rules to form new sequences while reasoning over relational data, is critical to machine intelligence. A model with strong systematicity is able to train on small-scale tasks and generalize to large-scale tasks. In this paper, we propose R5, a relational rea... | ['Di Niu', 'Shangling Jui', 'Keith G. Mills', 'Bang Liu', 'Shengyao Lu'] | 2022-05-13 | r5-rule-discovery-with-reinforced-and | https://openreview.net/forum?id=2eXhNpHeW6E | https://openreview.net/pdf?id=2eXhNpHeW6E | iclr-2022-4 | ['relational-reasoning'] | ['natural-language-processing'] | [ 4.47823942e-01 1.00482476e+00 -8.27009320e-01 -5.61155736e-01
-3.85420382e-01 -2.96581656e-01 7.08563924e-01 2.26102963e-01
2.40367040e-01 7.60158122e-01 2.99157202e-01 -8.51941884e-01
-4.77700502e-01 -1.34133613e+00 -1.18943012e+00 -4.68343385e-02
-3.28080624e-01 8.90930772e-01 4.22738552e-01 -2.59782672... | [8.96353816986084, 7.7183756828308105] |
2e330c63-7779-4d51-abda-b8a99c8d1d76 | learning-for-online-mixed-integer-model | 2303.12152 | null | https://arxiv.org/abs/2303.12152v2 | https://arxiv.org/pdf/2303.12152v2.pdf | Learning for Online Mixed-Integer Model Predictive Control with Parametric Optimality Certificates | We propose a supervised learning framework for computing solutions of multi-parametric Mixed Integer Linear Programs (MILPs) that arise in Model Predictive Control. Our approach also quantifies sub-optimality for the computed solutions. Inspired by Branch-and-Bound techniques, the key idea is to train a Neural Network/... | ['Francesco Borrelli', 'Luigi Glielmo', 'Siddharth H. Nair', 'Luigi Russo'] | 2023-03-21 | null | null | null | null | ['motion-planning'] | ['robots'] | [ 2.62070835e-01 4.21422303e-01 -1.08886349e+00 -3.66168432e-02
-1.07943749e+00 -8.30508530e-01 1.04954772e-01 2.98444599e-01
1.53880000e-01 1.28586638e+00 3.21242190e-03 -5.08150637e-01
-7.48404503e-01 -1.05944169e+00 -1.05323088e+00 -5.72915196e-01
-4.63937283e-01 1.19790173e+00 -1.47761047e-01 2.00708166... | [5.111069679260254, 2.8800208568573] |
f09a2aeb-78bf-41a4-8309-7c5ff64194ec | eider-evidence-enhanced-document-level-1 | null | null | https://openreview.net/forum?id=Z9arKXstUo5 | https://openreview.net/pdf?id=Z9arKXstUo5 | EIDER: Evidence-enhanced Document-level Relation Extraction | Document-level relation extraction (DocRE) aims at extracting the semantic relations among entity pairs in a document. In DocRE, a subset of the sentences in a document, called the evidence sentences, might be sufficient for predicting the relation between a specific entity pair. To make better use of the evidence sent... | ['Anonymous'] | 2021-07-17 | null | https://openreview.net/forum?id=_5lTEMDR2e1 | https://openreview.net/pdf?id=_5lTEMDR2e1 | acl-arr-november-2021-11 | ['document-level-relation-extraction'] | ['natural-language-processing'] | [-3.42896767e-02 3.70317757e-01 -3.88784558e-01 -2.98720837e-01
-1.02132106e+00 -2.11527050e-01 6.30828261e-01 6.94098473e-01
-5.52894831e-01 1.00340867e+00 2.45483115e-01 -1.63851917e-01
-4.03818101e-01 -9.16771710e-01 -6.64723516e-01 -2.31803745e-01
6.73219329e-03 4.38923568e-01 5.84089577e-01 -2.11790368... | [9.309957504272461, 8.628804206848145] |
79b39fb1-2e54-4f45-8acd-3e3a30920c9a | leveraging-bev-representation-for-360-degree | 2305.13814 | null | https://arxiv.org/abs/2305.13814v1 | https://arxiv.org/pdf/2305.13814v1.pdf | Leveraging BEV Representation for 360-degree Visual Place Recognition | This paper investigates the advantages of using Bird's Eye View (BEV) representation in 360-degree visual place recognition (VPR). We propose a novel network architecture that utilizes the BEV representation in feature extraction, feature aggregation, and vision-LiDAR fusion, which bridges visual cues and spatial aware... | ['Yue Wang', 'Rong Xiong', 'Xiaqing Ding', 'Sha Lu', 'Yanmei Jiao', 'Xuecheng Xu'] | 2023-05-23 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [-2.29154881e-02 -3.55068922e-01 -1.60843775e-01 -4.76410747e-01
-3.60257536e-01 -5.76421082e-01 7.01980889e-01 -1.81803122e-01
-4.91464466e-01 5.24163723e-01 5.91315795e-03 -1.16474099e-01
-2.65543252e-01 -6.34384274e-01 -7.50719666e-01 -4.25067127e-01
1.68041795e-01 -1.42748281e-01 1.42230302e-01 -3.42112601... | [7.669142723083496, -2.0735208988189697] |
c03f84a3-325c-47f1-b6cb-294444a2e966 | fast-bayesian-inference-with-batch-bayesian | 2206.04734 | null | https://arxiv.org/abs/2206.04734v4 | https://arxiv.org/pdf/2206.04734v4.pdf | Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination | Calculation of Bayesian posteriors and model evidences typically requires numerical integration. Bayesian quadrature (BQ), a surrogate-model-based approach to numerical integration, is capable of superb sample efficiency, but its lack of parallelisation has hindered its practical applications. In this work, we propose ... | ['Michael A. Osborne', 'Harald Oberhauser', 'Martin Jørgensen', 'Satoshi Hayakawa', 'Masaki Adachi'] | 2022-06-09 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [-1.04568817e-01 -1.62196532e-01 -2.29953721e-01 -2.88881093e-01
-1.50530624e+00 -4.34620112e-01 8.10517550e-01 2.58763671e-01
-3.47003728e-01 1.34390604e+00 -3.80845517e-01 -6.69416547e-01
-3.94300342e-01 -8.81318510e-01 -7.21576095e-01 -7.10987151e-01
-1.64061617e-02 9.70471144e-01 3.94192874e-01 2.84080982... | [6.861993789672852, 4.055766582489014] |
438f9179-b739-4a15-86e5-3cf101d6ba50 | kernel-dependence-regularizers-and-gaussian | 1911.04322 | null | https://arxiv.org/abs/1911.04322v1 | https://arxiv.org/pdf/1911.04322v1.pdf | Kernel Dependence Regularizers and Gaussian Processes with Applications to Algorithmic Fairness | Current adoption of machine learning in industrial, societal and economical activities has raised concerns about the fairness, equity and ethics of automated decisions. Predictive models are often developed using biased datasets and thus retain or even exacerbate biases in their decisions and recommendations. Removing ... | ['Gustau Camps-Valls', 'Adrian Perez-Suay', 'Dino Sejdinovic', 'Zhu Li'] | 2019-11-11 | null | null | null | null | ['crime-prediction'] | ['miscellaneous'] | [ 2.91243225e-01 4.24139112e-01 -4.38953280e-01 -7.75081098e-01
-4.46485609e-01 -2.89118260e-01 5.90447247e-01 2.93444067e-01
-6.36585355e-01 1.07199323e+00 9.84465331e-02 -2.47148663e-01
-5.24480402e-01 -8.60795796e-01 -3.56820375e-01 -9.95315731e-01
2.50288039e-01 3.25673431e-01 -3.89434308e-01 5.37172779... | [8.6744966506958, 5.149975299835205] |
e6476e81-a132-4db0-bd55-7284b1048c52 | differentiable-data-augmentation-with-kornia | 2011.09832 | null | https://arxiv.org/abs/2011.09832v1 | https://arxiv.org/pdf/2011.09832v1.pdf | Differentiable Data Augmentation with Kornia | In this paper we present a review of the Kornia differentiable data augmentation (DDA) module for both for spatial (2D) and volumetric (3D) tensors. This module leverages differentiable computer vision solutions from Kornia, with an aim of integrating data augmentation (DA) pipelines and strategies to existing PyTorch ... | ['Anguelos Nicolaou', 'Francesc Moreno', 'Dmytro Mishkin', 'Edgar Riba', 'Jian Shi'] | 2020-11-19 | null | null | null | null | ['image-smoothing'] | ['computer-vision'] | [-3.76663923e-01 9.14489105e-02 7.77546838e-02 -1.38939813e-01
-3.14798385e-01 -3.94295216e-01 6.89453006e-01 -1.44579023e-01
-2.66297996e-01 4.23476905e-01 2.77449667e-01 -3.37655902e-01
-4.08493400e-01 -4.96564239e-01 -4.07568961e-01 -7.36088753e-01
-4.20215964e-01 6.06384158e-01 -3.51324528e-01 -4.15682942... | [8.91197395324707, -2.052309274673462] |
43026d08-6fe9-4d1a-a51b-6780b3fac0c9 | learning-to-reconstruct-missing-data-from | 2205.13479 | null | https://arxiv.org/abs/2205.13479v2 | https://arxiv.org/pdf/2205.13479v2.pdf | Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations | Modeling multivariate time series as temporal signals over a (possibly dynamic) graph is an effective representational framework that allows for developing models for time series analysis. In fact, discrete sequences of graphs can be processed by autoregressive graph neural networks to recursively learn representations... | ['Cesare Alippi', 'Andrea Cini', 'Ivan Marisca'] | 2022-05-26 | null | null | null | null | ['multivariate-time-series-imputation', 'traffic-data-imputation'] | ['time-series', 'time-series'] | [ 3.56752604e-01 2.70198971e-01 -1.36650234e-01 -2.06369609e-01
-6.43601179e-01 -2.82584310e-01 4.81036514e-01 3.11325252e-01
2.08943233e-01 6.73363388e-01 4.08835500e-01 -7.18750730e-02
-3.99259776e-01 -8.69584858e-01 -1.28550148e+00 -6.89459026e-01
-4.95544136e-01 3.97414833e-01 -3.23118925e-01 5.61385565... | [6.820616245269775, 2.9247491359710693] |
9380eac1-600d-4894-acc4-b5c40b7c5a57 | task-embedded-control-networks-for-few-shot | 1810.03237 | null | http://arxiv.org/abs/1810.03237v1 | http://arxiv.org/pdf/1810.03237v1.pdf | Task-Embedded Control Networks for Few-Shot Imitation Learning | Much like humans, robots should have the ability to leverage knowledge from
previously learned tasks in order to learn new tasks quickly in new and
unfamiliar environments. Despite this, most robot learning approaches have
focused on learning a single task, from scratch, with a limited notion of
generalisation, and no ... | ['Andrew J. Davison', 'Stephen James', 'Michael Bloesch'] | 2018-10-08 | null | null | null | null | ['few-shot-imitation-learning'] | ['methodology'] | [ 3.55674446e-01 2.84688413e-01 2.64545023e-01 -1.67440370e-01
-2.33317226e-01 -5.39960444e-01 5.80402195e-01 7.53043219e-02
-6.87882423e-01 9.54947650e-01 -1.95926443e-01 -4.98882979e-02
-3.69090766e-01 -5.69751978e-01 -7.87126780e-01 -6.06883168e-01
-4.60782915e-01 6.37928486e-01 5.48861027e-01 -4.13430303... | [4.431717395782471, 0.981383204460144] |
671ae52b-ce54-4a9a-841a-6515e0033453 | arabic-dialect-identification-an-arabic-bert | null | null | https://aclanthology.org/2020.wanlp-1.28 | https://aclanthology.org/2020.wanlp-1.28.pdf | Arabic dialect identification: An Arabic-BERT model with data augmentation and ensembling strategy | This paper presents the ArabicProcessors team’s deep learning system designed for the NADI 2020 Subtask 1 (country-level dialect identification) and Subtask 2 (province-level dialect identification). We used Arabic-Bert in combination with data augmentation and ensembling methods. Unlabeled data provided by task organi... | ['Imade Benelallam', 'Kamel Gaanoun'] | null | null | null | null | coling-wanlp-2020-12 | ['dialect-identification'] | ['natural-language-processing'] | [-4.06983018e-01 2.13024870e-01 6.29590228e-02 -6.72840416e-01
-9.26003933e-01 -9.94822085e-01 1.22348106e+00 2.67892368e-02
-7.37634420e-01 1.00721204e+00 3.09092194e-01 -5.72567463e-01
1.56760067e-01 -7.45912969e-01 -3.94029766e-01 -3.36445302e-01
-3.65832001e-01 1.21012008e+00 -4.44495492e-02 -8.21734786... | [10.16657829284668, 10.783455848693848] |
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