paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
72535554-217f-41fa-ac01-24f3b89c1224 | learning-selective-mutual-attention-and | 2010.05537 | null | https://arxiv.org/abs/2010.05537v1 | https://arxiv.org/pdf/2010.05537v1.pdf | Learning Selective Mutual Attention and Contrast for RGB-D Saliency Detection | How to effectively fuse cross-modal information is the key problem for RGB-D salient object detection. Early fusion and the result fusion schemes fuse RGB and depth information at the input and output stages, respectively, hence incur the problem of distribution gap or information loss. Many models use the feature fusi... | ['Junwei Han', 'Ling Shao', 'Ni Zhang', 'Nian Liu'] | 2020-10-12 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 1.06031619e-01 -1.28098115e-01 1.48521513e-01 -4.38916057e-01
-8.76287401e-01 -6.73871115e-02 3.89899433e-01 2.35726878e-01
-5.70521116e-01 4.13351923e-01 2.53224075e-01 1.93063214e-01
-1.48763180e-01 -7.38598585e-01 -7.30174661e-01 -8.74706209e-01
4.10776824e-01 -3.31243753e-01 7.31968522e-01 -1.36799216... | [9.697681427001953, -0.8423434495925903] |
3d07e5ce-20ca-4049-9974-6af811104c0f | consrec-learning-consensus-behind | 2302.03555 | null | https://arxiv.org/abs/2302.03555v1 | https://arxiv.org/pdf/2302.03555v1.pdf | ConsRec: Learning Consensus Behind Interactions for Group Recommendation | Since group activities have become very common in daily life, there is an urgent demand for generating recommendations for a group of users, referred to as group recommendation task. Existing group recommendation methods usually infer groups' preferences via aggregating diverse members' interests. Actually, groups' ult... | ['Philip S. Yu', 'Yangyong Zhu', 'Jiawei Zhang', 'Yizhu Jiao', 'Yao Zhang', 'Yun Xiong', 'Xixi Wu'] | 2023-02-07 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [ 2.25187764e-02 -1.67247802e-02 -4.69513714e-01 -6.31256342e-01
-5.62948883e-01 -3.53621781e-01 5.17449617e-01 2.30590984e-01
3.96122366e-01 6.69194639e-01 9.08500016e-01 9.81217250e-02
-3.66949618e-01 -1.11898327e+00 -5.45387328e-01 -6.67444289e-01
1.63395107e-01 3.82590353e-01 -2.43357599e-01 -4.22179699... | [10.18775749206543, 5.60822057723999] |
6d7dcf96-fece-4968-ae84-455cef0bed5b | thompson-sampling-achieves-tilde-o-sqrt-t | 2206.08520 | null | https://arxiv.org/abs/2206.08520v1 | https://arxiv.org/pdf/2206.08520v1.pdf | Thompson Sampling Achieves $\tilde O(\sqrt{T})$ Regret in Linear Quadratic Control | Thompson Sampling (TS) is an efficient method for decision-making under uncertainty, where an action is sampled from a carefully prescribed distribution which is updated based on the observed data. In this work, we study the problem of adaptive control of stabilizable linear-quadratic regulators (LQRs) using TS, where ... | ['Babak Hassibi', 'Anima Anandkumar', 'Kamyar Azizzadenesheli', 'Sahin Lale', 'Taylan Kargin'] | 2022-06-17 | null | null | null | null | ['decision-making-under-uncertainty', 'thompson-sampling', 'decision-making-under-uncertainty'] | ['medical', 'methodology', 'reasoning'] | [ 2.01998949e-01 2.59794891e-01 -3.06583047e-01 3.66502881e-01
-1.23302948e+00 -7.99100995e-01 2.44514436e-01 6.07792772e-02
-3.48330349e-01 1.00043678e+00 -2.04285771e-01 -6.48359478e-01
-6.45346045e-01 -4.84221846e-01 -1.02969313e+00 -1.07229042e+00
-1.51707754e-01 4.51566964e-01 -1.47925839e-01 -1.35315552... | [4.537545204162598, 2.619454860687256] |
ed7c645f-5b42-4692-9dd9-ec7f61c8a280 | open-data-for-moroccan-license-plates-for-ocr | 2104.08244 | null | https://arxiv.org/abs/2104.08244v1 | https://arxiv.org/pdf/2104.08244v1.pdf | Open data for Moroccan license plates for OCR applications : data collection, labeling, and model construction | Significant number of researches have been developed recently around intelligent system for traffic management, especially, OCR based license plate recognition, as it is considered as a main step for any automatic traffic management system. Good quality data sets are increasingly needed and produced by the research com... | ['Ikram Chairi', 'Saad Benjelloun', 'Mohamed El Fakir', 'Abdelkrim Alahyane'] | 2021-04-16 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [ 1.72899678e-01 -3.41959685e-01 -6.51096255e-02 -2.63882399e-01
-4.15632367e-01 -6.37820303e-01 5.39714515e-01 1.52267575e-01
-6.32462919e-01 6.86343908e-01 -2.40837529e-01 -2.58614063e-01
1.26509011e-01 -8.42581689e-01 -6.26392007e-01 -5.44449270e-01
1.18218139e-01 9.74753618e-01 5.99291205e-01 -4.37857360... | [9.819555282592773, -4.969911575317383] |
688a37b4-30d8-4aef-8b3c-4d199481967b | a-critical-reassessment-of-evolutionary | 1407.1993 | null | http://arxiv.org/abs/1407.1993v1 | http://arxiv.org/pdf/1407.1993v1.pdf | A Critical Reassessment of Evolutionary Algorithms on the cryptanalysis of the simplified data encryption standard algorithm | In this paper we analyze the cryptanalysis of the simplified data encryption
standard algorithm using meta-heuristics and in particular genetic algorithms.
The classic fitness function when using such an algorithm is to compare n-gram
statistics of a the decrypted message with those of the target message. We show
that ... | ['Cyril Fonlupt', 'Fabien Teytaud'] | 2014-07-08 | null | null | null | null | ['cryptanalysis'] | ['miscellaneous'] | [ 4.43290323e-01 1.38343245e-01 2.08336800e-01 -8.97926744e-03
-7.27898628e-02 -6.37415588e-01 3.45368326e-01 6.31125271e-01
-8.64071548e-01 5.88543177e-01 -2.19699830e-01 -7.40682542e-01
-5.14815331e-01 -1.19536102e+00 -5.03967822e-01 -1.12851024e+00
-2.63175368e-01 4.25575376e-01 -6.09900337e-03 -5.63367605... | [5.772855281829834, 4.64322566986084] |
f8b64185-3e05-48b5-b196-a263096337c1 | human-body-shape-classification-based-on-a | 2305.18480 | null | https://arxiv.org/abs/2305.18480v1 | https://arxiv.org/pdf/2305.18480v1.pdf | Human Body Shape Classification Based on a Single Image | There is high demand for online fashion recommender systems that incorporate the needs of the consumer's body shape. As such, we present a methodology to classify human body shape from a single image. This is achieved through the use of instance segmentation and keypoint estimation models, trained only on open-source b... | ['Alberto de Santos', 'Dario Dotti', 'Filipa Peleja', 'Cameron Trotter'] | 2023-05-29 | null | null | null | null | ['virtual-try-on'] | ['computer-vision'] | [ 2.34731033e-01 1.14333056e-01 -3.47202197e-02 -3.80355239e-01
-5.01837015e-01 -6.14814579e-01 5.35185516e-01 5.56639969e-01
-3.72042835e-01 1.54068500e-01 -1.26228463e-02 -6.69527501e-02
-1.32707775e-01 -8.37284744e-01 -6.33841395e-01 -3.11663240e-01
3.02439053e-02 8.40922713e-01 2.11924478e-01 -4.92908299... | [6.999751567840576, -1.1585099697113037] |
da425478-1ee1-44b8-9a42-91f59fd54d53 | entertaining-and-opinionated-but-too | 1908.04832 | null | https://arxiv.org/abs/1908.04832v1 | https://arxiv.org/pdf/1908.04832v1.pdf | Entertaining and Opinionated but Too Controlling: A Large-Scale User Study of an Open Domain Alexa Prize System | Conversational systems typically focus on functional tasks such as scheduling appointments or creating todo lists. Instead we design and evaluate SlugBot (SB), one of 8 semifinalists in the 2018 AlexaPrize, whose goal is to support casual open-domain social inter-action. This novel application requires both broad topic... | ['Steve Whittaker', 'Kevin K. Bowden', 'Nicholas Santer', 'Marilyn Walker', 'Juraj Juraska', 'Wen Cui', 'Brian Schwarzmann', 'Vrindavan Harrison', 'Jiaqi Wu'] | 2019-08-13 | null | null | null | null | ['topic-coverage'] | ['natural-language-processing'] | [-3.82137388e-01 8.28372359e-01 1.25385225e-01 -3.56497794e-01
-7.30043650e-01 -8.73167217e-01 7.37901151e-01 9.29556713e-02
-1.50337592e-01 9.49640274e-01 7.37259805e-01 -5.10741651e-01
5.27241416e-02 -4.69857097e-01 -1.40455529e-01 1.18350595e-01
1.84798419e-01 8.04467738e-01 1.10677585e-01 -8.34284484... | [12.455456733703613, 7.83193302154541] |
704b9ae6-62e5-4957-a955-5632bf5c47d2 | improved-projection-free-online-continuous | 2305.18442 | null | https://arxiv.org/abs/2305.18442v1 | https://arxiv.org/pdf/2305.18442v1.pdf | Improved Projection-free Online Continuous Submodular Maximization | We investigate the problem of online learning with monotone and continuous DR-submodular reward functions, which has received great attention recently. To efficiently handle this problem, especially in the case with complicated decision sets, previous studies have proposed an efficient projection-free algorithm called ... | ['Mingli Song', 'Chang Yao', 'Yuanyu Wan', 'Yucheng Liao'] | 2023-05-29 | null | null | null | null | ['blocking'] | ['natural-language-processing'] | [-1.21835180e-01 9.21300985e-03 -1.90573037e-01 -4.16821748e-01
-1.20801485e+00 -7.03250587e-01 -2.65624732e-01 2.40693033e-01
-9.13066566e-01 9.84026194e-01 -1.92754149e-01 -8.31285357e-01
-5.15688896e-01 -9.27820861e-01 -9.05258179e-01 -7.33268023e-01
-3.45628679e-01 4.65487748e-01 7.50959590e-02 -2.83531934... | [4.888996601104736, 3.62335205078125] |
68107ab9-fce7-4783-aa0d-bad4baf320f0 | backflipping-with-miniature-quadcopters-by | 2209.14652 | null | https://arxiv.org/abs/2209.14652v2 | https://arxiv.org/pdf/2209.14652v2.pdf | Backflipping with Miniature Quadcopters by Gaussian Process Based Control and Planning | The paper proposes two control methods for performing a backflip maneuver with miniature quadcopters. First, an existing feedforward control approach is improved by finding the optimal sequence of motion primitives via Bayesian optimization, using a surrogate Gaussian Process model. To evaluate the cost function, the f... | ['Roland Tóth', 'Tamás Péni', 'Péter Antal'] | 2022-09-29 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [ 1.02065645e-01 2.15111867e-01 -5.96506409e-02 2.45790422e-01
-4.19207990e-01 -6.03609622e-01 6.80554569e-01 -1.05050169e-01
-5.52583575e-01 1.01106989e+00 -3.80710572e-01 -2.88352638e-01
-6.74116135e-01 -5.18966019e-01 -6.72942758e-01 -8.93146038e-01
-8.42934474e-02 6.13486469e-01 2.28269100e-01 -2.39751801... | [5.186638832092285, 2.269819974899292] |
c8d6f087-08dc-4073-b7f6-b9f9476ce24d | pali-nlp-at-semeval-2022-task-4 | 2203.04616 | null | https://arxiv.org/abs/2203.04616v2 | https://arxiv.org/pdf/2203.04616v2.pdf | PALI-NLP at SemEval-2022 Task 4: Discriminative Fine-tuning of Transformers for Patronizing and Condescending Language Detection | Patronizing and condescending language (PCL) has a large harmful impact and is difficult to detect, both for human judges and existing NLP systems. At SemEval-2022 Task 4, we propose a novel Transformer-based model and its ensembles to accurately understand such language context for PCL detection. To facilitate compreh... | ['Xiaofeng Shi', 'Yang Mo', 'Lianxin Jiang', 'Meizhi Jin', 'Mengfei Yuan', 'Xiyang Du', 'Mengyuan Zhou', 'Dou Hu'] | 2022-03-09 | pali-nlp-at-semeval-2022-task-4-1 | https://aclanthology.org/2022.semeval-1.43 | https://aclanthology.org/2022.semeval-1.43.pdf | semeval-naacl-2022-7 | ['semeval-2022-task-4-1-binary-pcl-detection', 'semeval-2022-task-4-1-binary-pcl-detection', 'semeval-2022-task-4-2-multi-label-pcl', 'semeval-2022-task-4-1-binary-pcl-detection'] | ['miscellaneous', 'music', 'natural-language-processing', 'natural-language-processing'] | [-9.89113301e-02 -4.08370495e-01 -4.75562394e-01 -3.85587126e-01
-1.11709893e+00 -9.31722224e-01 7.21621394e-01 1.82666883e-01
-5.21227479e-01 6.09334946e-01 4.66865361e-01 -5.38853943e-01
-2.15333812e-02 -3.84836681e-02 -3.53604674e-01 -2.22443298e-01
2.75712851e-02 5.24853170e-01 2.48915076e-01 -3.84885013... | [10.183706283569336, 10.248018264770508] |
f79e4cbf-870c-43cd-94ae-f706b19fd776 | transweather-transformer-based-restoration-of | 2111.14813 | null | https://arxiv.org/abs/2111.14813v2 | https://arxiv.org/pdf/2111.14813v2.pdf | TransWeather: Transformer-based Restoration of Images Degraded by Adverse Weather Conditions | Removing adverse weather conditions like rain, fog, and snow from images is an important problem in many applications. Most methods proposed in the literature have been designed to deal with just removing one type of degradation. Recently, a CNN-based method using neural architecture search (All-in-One) was proposed to... | ['Vishal M. Patel', 'Rajeev Yasarla', 'Jeya Maria Jose Valanarasu'] | 2021-11-29 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Valanarasu_TransWeather_Transformer-Based_Restoration_of_Images_Degraded_by_Adverse_Weather_Conditions_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Valanarasu_TransWeather_Transformer-Based_Restoration_of_Images_Degraded_by_Adverse_Weather_Conditions_CVPR_2022_paper.pdf | cvpr-2022-1 | ['single-image-desnowing', 'single-image-deraining'] | ['computer-vision', 'computer-vision'] | [-5.04270829e-02 -4.36469942e-01 5.10377407e-01 -5.39753139e-01
-5.73266625e-01 -4.82041299e-01 6.26964569e-02 -2.31520653e-01
-1.92541018e-01 4.90960389e-01 3.26132119e-01 -7.29074776e-02
9.28729475e-02 -6.40805900e-01 -8.32533538e-01 -8.59275341e-01
1.71455383e-01 -1.03737466e-01 2.69371271e-01 -5.66918910... | [10.995413780212402, -3.052257537841797] |
57869c0e-84b9-47f1-92c2-823e94cc1a24 | a-novel-application-for-real-time-arrhythmia | 2305.16727 | null | https://arxiv.org/abs/2305.16727v1 | https://arxiv.org/pdf/2305.16727v1.pdf | A novel application for real-time arrhythmia detection using YOLOv8 | In recent years, there has been an increasing need to reduce healthcare costs in remote monitoring of cardiovascular health. Detecting and classifying cardiac arrhythmia is critical to diagnosing patients with cardiac abnormalities. This paper shows that complex systems such as electrocardiograms (ECG) can be applicabl... | ['B. Shen', 'Z. T. Woon', 'T. Jason', 'R. B. A. Mustaffa', 'X. C. Lee', 'H. Chan', 'A. K. Goil', 'G. J. N. Ang'] | 2023-05-26 | null | null | null | null | ['arrhythmia-detection'] | ['medical'] | [ 2.38067042e-02 -4.47111070e-01 3.68095785e-01 -1.98866367e-01
-6.69916034e-01 -5.75878918e-01 -5.50370872e-01 3.48526955e-01
-9.77686122e-02 5.47167182e-01 -4.83999133e-01 -7.63074756e-01
-5.75178042e-02 -5.50937235e-01 6.52961060e-02 -3.13148260e-01
-6.16646647e-01 2.35182732e-01 -3.35828662e-02 2.21411884... | [14.236138343811035, 3.2486419677734375] |
d9865d27-160a-4f65-9eca-f8f235010f22 | futures-quantitative-investment-with | 2303.16532 | null | https://arxiv.org/abs/2303.16532v1 | https://arxiv.org/pdf/2303.16532v1.pdf | Futures Quantitative Investment with Heterogeneous Continual Graph Neural Network | It is a challenging problem to predict trends of futures prices with traditional econometric models as one needs to consider not only futures' historical data but also correlations among different futures. Spatial-temporal graph neural networks (STGNNs) have great advantages in dealing with such kind of spatial-tempora... | ['Bin Liu', 'Lu Wei', 'YiXuan Wang', 'Min Hu', 'Zhizhong Tan'] | 2023-03-29 | null | null | null | null | ['change-point-detection'] | ['time-series'] | [-3.32647860e-01 -3.34170491e-01 -1.31235108e-01 -3.57555360e-01
-2.35461920e-01 -4.20949847e-01 5.95482051e-01 7.60706142e-02
-3.29582393e-01 7.35271215e-01 2.84909457e-01 -5.79486310e-01
-4.85334158e-01 -1.29045343e+00 -5.70659518e-01 -6.24876678e-01
-6.51496649e-01 2.86504120e-01 2.01682284e-01 -3.98689687... | [4.443220138549805, 4.245934009552002] |
58fbb027-d671-42c1-8a0f-5c728e59ca6c | on-formal-feature-attribution-and-its | 2307.03380 | null | https://arxiv.org/abs/2307.03380v1 | https://arxiv.org/pdf/2307.03380v1.pdf | On Formal Feature Attribution and Its Approximation | Recent years have witnessed the widespread use of artificial intelligence (AI) algorithms and machine learning (ML) models. Despite their tremendous success, a number of vital problems like ML model brittleness, their fairness, and the lack of interpretability warrant the need for the active developments in explainable... | ['Peter J. Stuckey', 'Alexey Ignatiev', 'Jinqiang Yu'] | 2023-07-07 | null | null | null | null | ['explainable-artificial-intelligence', 'fairness', 'feature-importance', 'fairness'] | ['computer-vision', 'computer-vision', 'methodology', 'miscellaneous'] | [ 4.10679221e-01 5.85524738e-01 -4.35270160e-01 -3.74095023e-01
-2.50739783e-01 -3.46915871e-01 7.46164858e-01 3.16916592e-02
1.80258796e-01 1.02237570e+00 -2.46930242e-01 -5.27910471e-01
-8.65346789e-01 -5.91714919e-01 -4.70447361e-01 -4.28689629e-01
-1.31938219e-01 5.52091599e-01 -3.26356202e-01 7.95658119... | [8.654051780700684, 5.5081000328063965] |
135774ff-abea-46c1-b7cc-5d10ded6e821 | dynamic-facial-expression-generation-on | 1907.10087 | null | https://arxiv.org/abs/1907.10087v2 | https://arxiv.org/pdf/1907.10087v2.pdf | Dynamic Facial Expression Generation on Hilbert Hypersphere with Conditional Wasserstein Generative Adversarial Nets | In this work, we propose a novel approach for generating videos of the six basic facial expressions given a neutral face image. We propose to exploit the face geometry by modeling the facial landmarks motion as curves encoded as points on a hypersphere. By proposing a conditional version of manifold-valued Wasserstein ... | ['Mohamed Daoudi', 'Lahoucine Ballihi', 'Naima Otberdout', 'Stefano Berretti', 'Anis Kacem'] | 2019-07-23 | null | null | null | null | ['facial-expression-generation'] | ['computer-vision'] | [ 2.57145554e-01 3.93348485e-01 1.89034760e-01 -4.42631751e-01
-5.42198837e-01 -5.28595448e-01 7.06867695e-01 -1.02914917e+00
-1.96512155e-02 8.56059790e-01 2.78142150e-02 2.00866416e-01
2.89433867e-01 -7.71958232e-01 -9.94110882e-01 -9.48175669e-01
-7.60607272e-02 3.66243035e-01 -5.78494489e-01 -4.12751555... | [12.787934303283691, -0.15392489731311798] |
8ce05976-d4d0-47c0-840c-b9b63dd48799 | coastal-aquaculture-extraction-using-gf-3 | null | null | https://www.mdpi.com/2072-4292/15/9/2246 | https://www.mdpi.com/2072-4292/15/9/2246 | Coastal Aquaculture Extraction Using GF-3 Fully Polarimetric SAR Imagery: A Framework Integrating UNet++ with Marker-Controlled Watershed Segmentation | Coastal aquaculture monitoring is vital for sustainable offshore aquaculture management.
However, the dense distribution and various sizes of aquacultures make it challenging to accurately
extract the boundaries of aquaculture ponds. In this study, we develop a novel combined framework
that integrates UNet++ with a ... | ['Jia Xu and Jiacheng Xiong', 'Mahdi Motagh', 'Peng Yang', 'Xiufeng He', 'Juanjuan Yu'] | 2023-04-19 | null | null | null | remote-sensing-2023-4 | ['culture'] | ['speech'] | [ 3.22779387e-01 -1.38336167e-01 7.41916656e-01 5.57721108e-02
-3.64843994e-01 -5.61071992e-01 1.75859526e-01 1.07935220e-01
-3.92331064e-01 6.74177766e-01 -1.21952511e-01 4.08420525e-02
-4.80516583e-01 -9.02582526e-01 -4.12907243e-01 -1.41895127e+00
-5.60549140e-01 1.09283276e-01 2.87926972e-01 -2.02958837... | [9.785746574401855, -1.9613559246063232] |
badef1f4-892f-4b15-970b-8804fe8289a2 | formal-specifications-from-natural-language | 2206.01962 | null | https://arxiv.org/abs/2206.01962v2 | https://arxiv.org/pdf/2206.01962v2.pdf | Formal Specifications from Natural Language | We study the generalization abilities of language models when translating natural language into formal specifications with complex semantics. In particular, we fine-tune language models on three datasets consisting of English sentences and their corresponding formal representation: 1) regular expressions (regex), frequ... | ['Bernd Finkbeiner', 'Julian Siber', 'Niklas Metzger', 'Julia J. Tillman', 'Frederik Schmitt', 'Christopher Hahn'] | 2022-06-04 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 1.18752301e-01 2.00942203e-01 -7.90165901e-01 -3.11276317e-01
-7.55627573e-01 -8.85166705e-01 6.31423414e-01 1.00006603e-01
1.40253767e-01 7.18784332e-01 -2.40442678e-01 -1.17566371e+00
5.20406514e-02 -9.17977512e-01 -1.04419959e+00 1.61055773e-01
-4.13566798e-01 3.79437327e-01 5.03146231e-01 -3.35786104... | [8.91202163696289, 7.189895153045654] |
2971a1c4-4625-43d2-911c-e617ef6928fb | iperceive-applying-common-sense-reasoning-to-1 | 2011.07735 | null | https://arxiv.org/abs/2011.07735v1 | https://arxiv.org/pdf/2011.07735v1.pdf | iPerceive: Applying Common-Sense Reasoning to Multi-Modal Dense Video Captioning and Video Question Answering | Most prior art in visual understanding relies solely on analyzing the "what" (e.g., event recognition) and "where" (e.g., event localization), which in some cases, fails to describe correct contextual relationships between events or leads to incorrect underlying visual attention. Part of what defines us as human and fu... | ['Navpreet Kaloty', 'Gurneet Arora', 'Aman Chadha'] | 2020-11-16 | iperceive-applying-common-sense-reasoning-to | https://arxiv.org/abs/2011.07735 | https://arxiv.org/pdf/2011.07735 | null | ['dense-video-captioning'] | ['computer-vision'] | [ 3.33118081e-01 -1.54634237e-01 1.83748808e-02 -4.38865036e-01
-7.18639970e-01 -8.63380373e-01 8.58231187e-01 3.25401008e-01
-1.29982606e-01 6.33759797e-01 5.53786337e-01 -5.66761672e-01
8.83074179e-02 -6.24497831e-01 -1.23387790e+00 -3.38833094e-01
7.31586218e-02 -2.70916019e-02 9.94426683e-02 -6.44817576... | [10.398954391479492, 1.0367120504379272] |
64adc95a-7981-40ba-a5b7-3a5ee578e5e2 | occlusion-guided-self-supervised-scene-flow | 2104.04724 | null | https://arxiv.org/abs/2104.04724v2 | https://arxiv.org/pdf/2104.04724v2.pdf | Occlusion Guided Self-supervised Scene Flow Estimation on 3D Point Clouds | Understanding the flow in 3D space of sparsely sampled points between two consecutive time frames is the core stone of modern geometric-driven systems such as VR/AR, Robotics, and Autonomous driving. The lack of real, non-simulated, labeled data for this task emphasizes the importance of self- or un-supervised deep arc... | ['Dan Raviv', 'Bojun Ouyang'] | 2021-04-10 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [-2.90995598e-01 -2.03775629e-01 -1.41119033e-01 -6.67753458e-01
-5.63152581e-02 -4.53911424e-01 5.67005754e-01 -1.90950096e-01
-1.93225026e-01 6.19824648e-01 3.62921625e-01 -1.97969928e-01
1.03281528e-01 -6.82583988e-01 -6.44138455e-01 -2.62347192e-01
-5.78385592e-01 5.63202620e-01 5.01538754e-01 -3.33278090... | [8.579141616821289, -2.0303046703338623] |
b3e86567-fc6b-4408-9f04-e1ff5accd861 | minimizing-age-of-information-for-mobile-edge | 2307.01366 | null | https://arxiv.org/abs/2307.01366v1 | https://arxiv.org/pdf/2307.01366v1.pdf | Minimizing Age of Information for Mobile Edge Computing Systems: A Nested Index Approach | Exploiting the computational heterogeneity of mobile devices and edge nodes, mobile edge computation (MEC) provides an efficient approach to achieving real-time applications that are sensitive to information freshness, by offloading tasks from mobile devices to edge nodes. We use the metric Age-of-Information (AoI) to ... | ['Jun Wang', 'Meng Zhang', 'Ning Yang', 'Shuo Chen'] | 2023-07-03 | null | null | null | null | ['edge-computing'] | ['time-series'] | [ 9.81577579e-03 -1.09011188e-01 -8.76636326e-01 2.14018196e-01
-9.83450472e-01 -6.89320445e-01 -5.53393969e-03 -1.99019313e-01
-2.63113230e-01 8.04020643e-01 -7.62639344e-02 -9.25957024e-01
-5.72485030e-01 -5.03396809e-01 -7.80204594e-01 -8.40939999e-01
-1.39213935e-01 6.43926203e-01 1.05429098e-01 3.79999638... | [4.920729160308838, 3.4003143310546875] |
2f6daa9c-d015-448a-8087-4a813715accf | algorithms-for-generating-ordered-solutions | 1401.5852 | null | http://arxiv.org/abs/1401.5852v1 | http://arxiv.org/pdf/1401.5852v1.pdf | Algorithms for Generating Ordered Solutions for Explicit AND/OR Structures | We present algorithms for generating alternative solutions for explicit
acyclic AND/OR structures in non-decreasing order of cost. The proposed
algorithms use a best first search technique and report the solutions using an
implicit representation ordered by cost. In this paper, we present two versions
of the search alg... | ['Pallab Dasgupta', 'Amit Sharma', 'P. P. Chakrabarti', 'Priyankar Ghosh'] | 2014-01-23 | null | null | null | null | ['service-composition'] | ['miscellaneous'] | [ 6.42332613e-01 1.80321902e-01 1.39246002e-01 -1.22822285e-01
-5.15624583e-01 -7.12605596e-01 1.77307844e-01 2.38848448e-01
-1.67915374e-01 1.28495622e+00 1.15236402e-01 -4.39457655e-01
-7.37237751e-01 -8.98179352e-01 -5.77671349e-01 -7.40820706e-01
-3.05870086e-01 9.71374810e-01 5.84188402e-01 -3.97481084... | [5.930912017822266, 4.4034013748168945] |
cf6aed0f-a1cb-4211-aa8e-c096772392c9 | entropic-descent-archetypal-analysis-for | 2209.11002 | null | https://arxiv.org/abs/2209.11002v2 | https://arxiv.org/pdf/2209.11002v2.pdf | Entropic Descent Archetypal Analysis for Blind Hyperspectral Unmixing | In this paper, we introduce a new algorithm based on archetypal analysis for blind hyperspectral unmixing, assuming linear mixing of endmembers. Archetypal analysis is a natural formulation for this task. This method does not require the presence of pure pixels (i.e., pixels containing a single material) but instead re... | ['Julien Mairal', 'Jocelyn Chanussot', 'Behnood Rasti', 'Gedeon Muhawenayo', 'Alexandre Zouaoui'] | 2022-09-22 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 2.80458510e-01 -6.40727341e-01 3.20190564e-02 1.43874988e-01
-9.10289049e-01 -8.63098681e-01 6.95832789e-01 -7.40045384e-02
-1.02989741e-01 6.16008401e-01 2.06038237e-01 -5.13334334e-01
-2.90638059e-01 -7.68010318e-01 -8.13243926e-01 -1.24438334e+00
7.17109442e-02 4.15376872e-01 -4.37990725e-01 -6.75801337... | [10.088667869567871, -2.0214505195617676] |
b311f6c2-79f3-4b53-b319-0eef34dfff8a | few-shot-3d-lidar-semantic-segmentation-for | 2302.08785 | null | https://arxiv.org/abs/2302.08785v2 | https://arxiv.org/pdf/2302.08785v2.pdf | Few-shot 3D LiDAR Semantic Segmentation for Autonomous Driving | In autonomous driving, the novel objects and lack of annotations challenge the traditional 3D LiDAR semantic segmentation based on deep learning. Few-shot learning is a feasible way to solve these issues. However, currently few-shot semantic segmentation methods focus on camera data, and most of them only predict the n... | ['Yu Hu', 'Junbao Zhou', 'Jilin Mei'] | 2023-02-17 | null | null | null | null | ['generalized-few-shot-semantic-segmentation', 'lidar-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 1.74530432e-01 9.80223194e-02 -3.91966373e-01 -7.22207308e-01
-9.34558690e-01 -2.58108288e-01 3.78251404e-01 -1.45118520e-01
-4.29957807e-01 5.17543018e-01 -5.91651618e-01 -1.26797959e-01
-2.10278165e-02 -9.30572152e-01 -8.05970490e-01 -5.25972128e-01
5.04648447e-01 5.08365393e-01 8.69129837e-01 -6.61110729... | [8.151728630065918, -2.8242969512939453] |
a6070d56-74ba-4ceb-9141-265ac8570a89 | conditional-image-to-video-generation-with | 2303.13744 | null | https://arxiv.org/abs/2303.13744v1 | https://arxiv.org/pdf/2303.13744v1.pdf | Conditional Image-to-Video Generation with Latent Flow Diffusion Models | Conditional image-to-video (cI2V) generation aims to synthesize a new plausible video starting from an image (e.g., a person's face) and a condition (e.g., an action class label like smile). The key challenge of the cI2V task lies in the simultaneous generation of realistic spatial appearance and temporal dynamics corr... | ['Martin Renqiang Min', 'Sharon X. Huang', 'Kai Li', 'Changhao Shi', 'Haomiao Ni'] | 2023-03-24 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ni_Conditional_Image-to-Video_Generation_With_Latent_Flow_Diffusion_Models_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ni_Conditional_Image-to-Video_Generation_With_Latent_Flow_Diffusion_Models_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-generation', 'image-to-video'] | ['computer-vision', 'computer-vision'] | [ 4.20923293e-01 -4.46356786e-03 -2.87623793e-01 -7.45055731e-03
-4.35940027e-01 -4.11860347e-01 7.67243624e-01 -6.90134108e-01
8.37500691e-02 6.72987521e-01 4.74067479e-01 -1.36330754e-01
3.38149488e-01 -8.06135595e-01 -7.29329646e-01 -7.84872830e-01
2.64246970e-01 8.46501142e-02 8.29268172e-02 2.49525398... | [10.833025932312012, -0.7546895146369934] |
5314b4f3-9d40-49d7-9660-510581871552 | mfrfnn-multi-functional-recurrent-fuzzy | null | null | https://www.sciencedirect.com/science/article/pii/S0925231222010074 | https://www.sciencedirect.com/science/article/pii/S0925231222010074 | MFRFNN: Multi-Functional Recurrent Fuzzy Neural Network for Chaotic Time Series Prediction | Chaotic time series prediction, a challenging research topic in dynamic system modeling, has drawn great attention from researchers around the world. In recent years extensive researches have been done on developing chaotic time series prediction methods, and various models have been proposed. Among them, recurrent fuz... | ['Mohammad Mehdi Ebadzadeh', 'Hamid Nasiri'] | 2022-08-01 | null | null | null | neurocomputing-2022-8 | ['time-series-prediction', 'stock-price-prediction'] | ['time-series', 'time-series'] | [-3.80400181e-01 -8.62452090e-01 1.16377302e-01 -2.35633366e-02
4.75528538e-01 -2.14757264e-01 2.93255597e-01 -1.76634207e-01
-3.85243446e-01 7.08283246e-01 -2.92116612e-01 -2.21715540e-01
-4.80175614e-01 -1.11039317e+00 -7.04265237e-02 -1.01919961e+00
-9.42816511e-02 1.89739034e-01 3.06900471e-01 -6.53662860... | [5.308457374572754, 3.67041015625] |
576176ed-7733-4796-965d-b7b7c836aa6d | crossmatch-improving-semi-supervised-object | null | null | https://openreview.net/forum?id=rFUwBW8qgIZ | https://openreview.net/pdf?id=rFUwBW8qgIZ | CrossMatch: Improving Semi-Supervised Object Detection via Multi-Scale Consistency | We present a novel method, CrossMatch, for semi-supervised object detection. Inspired by the fact that teacher/student pseudo-labeling approaches result in a weak and sparse gradient signal due to the difficulty of confidence-thresholding, CrossMatch leverages \textit{multi-scale feature extraction} in object detectio... | ['Zsolt Kira', 'Chih-Yao Ma', 'Yen-Cheng Liu', 'Zhuoran Yu'] | 2021-09-29 | null | null | null | null | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 1.27067327e-01 1.70528144e-01 -2.53969401e-01 -5.68897486e-01
-1.01775408e+00 -5.26758254e-01 4.56067860e-01 2.03408614e-01
-5.69774270e-01 5.69922090e-01 -4.47206378e-01 1.78111479e-01
1.07976474e-01 -3.54315400e-01 -9.06083763e-01 -5.65002561e-01
1.48018971e-01 2.97215849e-01 7.72886395e-01 1.62920639... | [9.17115306854248, 1.286679983139038] |
6ca801a0-2cf0-4a85-830e-b225f1cfaa20 | learning-semantic-aware-disentangled | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Sun_Learning_Semantic-Aware_Disentangled_Representation_for_Flexible_3D_Human_Body_Editing_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Sun_Learning_Semantic-Aware_Disentangled_Representation_for_Flexible_3D_Human_Body_Editing_CVPR_2023_paper.pdf | Learning Semantic-Aware Disentangled Representation for Flexible 3D Human Body Editing | 3D human body representation learning has received increasing attention in recent years. However, existing works cannot flexibly, controllably and accurately represent human bodies, limited by coarse semantics and unsatisfactory representation capability, particularly in the absence of supervised data. In this pape... | ['Kun Li', 'Jingyu Yang', 'Yu-Kun Lai', 'Jinsong Zhang', 'Xiongzheng Li', 'Qiao Feng', 'Xiaokun Sun'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['style-transfer'] | ['computer-vision'] | [ 3.41053009e-01 1.13500558e-01 -3.06776375e-01 -3.42874616e-01
-2.50704378e-01 -3.68438631e-01 4.69914973e-01 -1.53871372e-01
1.49178118e-01 4.73603785e-01 5.88196397e-01 3.71875554e-01
-2.45599642e-01 -9.52309966e-01 -6.44356608e-01 -6.01897418e-01
2.85280406e-01 5.24552643e-01 6.80192113e-02 -2.55973428... | [7.252405643463135, -1.3864279985427856] |
75d6f1cc-cc05-4b68-901d-bc1f840cef31 | 3d-gated-recurrent-fusion-for-semantic-scene | 2002.07269 | null | https://arxiv.org/abs/2002.07269v1 | https://arxiv.org/pdf/2002.07269v1.pdf | 3D Gated Recurrent Fusion for Semantic Scene Completion | This paper tackles the problem of data fusion in the semantic scene completion (SSC) task, which can simultaneously deal with semantic labeling and scene completion. RGB images contain texture details of the object(s) which are vital for semantic scene understanding. Meanwhile, depth images capture geometric clues of h... | ['Cesar Cadena', 'Chunxia Zhao', 'Yu Liu', 'Xia Yuan', 'Qingsen Yan', 'Jie Li', 'Ian Reid'] | 2020-02-17 | null | null | null | null | ['3d-semantic-scene-completion'] | ['computer-vision'] | [ 5.53866386e-01 -4.18977290e-02 1.58239052e-01 -4.36768353e-01
-8.11844468e-01 -3.06289315e-01 3.99674296e-01 8.77600983e-02
-2.21201137e-01 2.73842484e-01 2.99748689e-01 -2.38818564e-02
-7.53443763e-02 -8.02150011e-01 -5.33308446e-01 -9.25284386e-01
4.68152016e-01 4.75673797e-03 3.43055815e-01 -2.74428219... | [9.452998161315918, -1.0894358158111572] |
e8e41042-fa8f-4bd9-879e-8a875661e5a1 | fast-segment-anything | 2306.12156 | null | https://arxiv.org/abs/2306.12156v1 | https://arxiv.org/pdf/2306.12156v1.pdf | Fast Segment Anything | The recently proposed segment anything model (SAM) has made a significant influence in many computer vision tasks. It is becoming a foundation step for many high-level tasks, like image segmentation, image caption, and image editing. However, its huge computation costs prevent it from wider applications in industry sce... | ['Jinqiao Wang', 'Ming Tang', 'Min Li', 'Tao Yu', 'Yinglong Du', 'Yongqi An', 'Wenchao Ding', 'Xu Zhao'] | 2023-06-21 | null | null | null | null | ['visual-prompting', 'object-proposal-generation', 'edge-detection', 'instance-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 6.23255730e-01 1.94847420e-01 -2.86693186e-01 -4.54136282e-01
-9.27940011e-01 -5.43604195e-01 5.28633893e-01 -1.18944518e-01
-5.51821172e-01 4.25570995e-01 -5.06914973e-01 -6.09661877e-01
4.00186658e-01 -7.21355855e-01 -9.10271943e-01 -6.47647083e-01
3.53000969e-01 4.38268423e-01 6.09734535e-01 -5.56270331... | [9.65816879272461, -0.11627183109521866] |
ecd93807-0b2d-488a-ad7c-282d3d49d080 | image-patch-matching-using-convolutional | 1710.11359 | null | http://arxiv.org/abs/1710.11359v1 | http://arxiv.org/pdf/1710.11359v1.pdf | Image Patch Matching Using Convolutional Descriptors with Euclidean Distance | In this work we propose a neural network based image descriptor suitable for
image patch matching, which is an important task in many computer vision
applications. Our approach is influenced by recent success of deep
convolutional neural networks (CNNs) in object detection and classification
tasks. We develop a model w... | ['Esa Rahtu', 'Juho Kannala', 'Iaroslav Melekhov'] | 2017-10-31 | null | null | null | null | ['patch-matching'] | ['computer-vision'] | [ 1.13791548e-01 -3.36529851e-01 -1.03366464e-01 -4.93948013e-01
-3.00811797e-01 -3.77922416e-01 8.68271172e-01 4.78667140e-01
-9.25155163e-01 2.74674147e-01 6.63823336e-02 -4.96062823e-02
-3.21884364e-01 -9.78025794e-01 -6.64682508e-01 -6.41184390e-01
-2.05382317e-01 1.31550014e-01 4.54374194e-01 -3.19628924... | [10.568707466125488, 0.29635104537010193] |
c9d0a56b-2ca8-468e-8713-0b2e587696a7 | selfie-video-stabilization | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Jiyang_Yu_Selfie_Video_Stabilization_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Jiyang_Yu_Selfie_Video_Stabilization_ECCV_2018_paper.pdf | Selfie Video Stabilization | We propose a novel algorithm for stabilizing selfie videos. Our goal is to automatically generate stabilized video that has optimal smooth motion in the sense of both foreground and background. The key insight is that non-rigid foreground motion in selfie videos can be analyzed using a 3D face model, and background mot... | ['Ravi Ramamoorthi', 'Jiyang Yu'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['video-stabilization'] | ['computer-vision'] | [-1.83085315e-02 3.73298163e-03 -1.55953482e-01 1.33758456e-01
-2.08634049e-01 -5.82671106e-01 2.02461675e-01 -6.16690457e-01
2.51748748e-02 5.39781928e-01 3.33537221e-01 3.93155128e-01
3.21768999e-01 -3.86768848e-01 -1.00341523e+00 -9.69017684e-01
8.72358009e-02 -3.71248275e-01 5.97910702e-01 -1.73984379... | [10.625807762145996, -1.403538703918457] |
a334dc63-5812-4c77-b895-be1579c165a7 | autoattention-automatic-field-pair-selection | 2210.15154 | null | https://arxiv.org/abs/2210.15154v1 | https://arxiv.org/pdf/2210.15154v1.pdf | AutoAttention: Automatic Field Pair Selection for Attention in User Behavior Modeling | In Click-through rate (CTR) prediction models, a user's interest is usually represented as a fixed-length vector based on her history behaviors. Recently, several methods are proposed to learn an attentive weight for each user behavior and conduct weighted sum pooling. However, these methods only manually select severa... | ['Jie Jiang', 'Dapeng Liu', 'Guihai Chen', 'Qi Luo', 'Junwei Pan', 'Xiaofeng Gao', 'Zuowu Zheng'] | 2022-10-27 | null | null | null | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [ 2.84752369e-01 -4.64583278e-01 -3.11640203e-01 -6.01056159e-01
-6.07717574e-01 -3.06524694e-01 2.21924976e-01 4.80529308e-01
-8.44063640e-01 5.18468380e-01 2.54604876e-01 -6.97560236e-02
-2.18739390e-01 -8.36615443e-01 -6.24213815e-01 -4.89413232e-01
-3.63611765e-02 8.54493380e-02 7.72591472e-01 -2.51143813... | [10.154664039611816, 5.473358631134033] |
5a4e6f68-2723-4008-8aeb-ce34e85668c0 | idea-increasing-text-diversity-via-online | 2207.05333 | null | https://arxiv.org/abs/2207.05333v2 | https://arxiv.org/pdf/2207.05333v2.pdf | IDEA: Increasing Text Diversity via Online Multi-Label Recognition for Vision-Language Pre-training | Vision-Language Pre-training (VLP) with large-scale image-text pairs has demonstrated superior performance in various fields. However, the image-text pairs co-occurrent on the Internet typically lack explicit alignment information, which is suboptimal for VLP. Existing methods proposed to adopt an off-the-shelf object ... | ['Xiaobo Zhang', 'Yandong Guo', 'Yaqian Li', 'Yuejie Zhang', 'Rui Feng', 'RuiWei Zhao', 'Weiwei Tian', 'Ying Cheng', 'Youcai Zhang', 'Xinyu Huang'] | 2022-07-12 | null | null | null | null | ['multi-label-learning'] | ['methodology'] | [ 2.45742217e-01 -2.69212276e-01 -5.31978011e-01 -4.05795962e-01
-1.35365808e+00 -7.17108488e-01 4.44436491e-01 8.54639560e-02
-7.24379778e-01 2.53503829e-01 -3.17693241e-02 -3.91160287e-02
1.94002643e-01 -2.80579478e-01 -8.38497519e-01 -7.45662510e-01
6.30975366e-01 6.43670321e-01 2.90328622e-01 1.61273763... | [10.46064567565918, 1.7122724056243896] |
32a60976-649e-40da-983c-54822ac86ee2 | graph-based-label-propagation-for-semi | 2106.08207 | null | https://arxiv.org/abs/2106.08207v1 | https://arxiv.org/pdf/2106.08207v1.pdf | Graph-based Label Propagation for Semi-Supervised Speaker Identification | Speaker identification in the household scenario (e.g., for smart speakers) is typically based on only a few enrollment utterances but a much larger set of unlabeled data, suggesting semisupervised learning to improve speaker profiles. We propose a graph-based semi-supervised learning approach for speaker identificatio... | ['Andreas Stolcke', 'Venkatesh Ravichandran', 'Long Chen'] | 2021-06-15 | null | null | null | null | ['speaker-identification'] | ['speech'] | [ 3.63376290e-01 5.77513635e-01 -3.48997921e-01 -1.04415500e+00
-1.05027306e+00 -6.74626589e-01 5.32776356e-01 2.15884000e-01
-1.07623175e-01 2.60697961e-01 3.69613469e-01 -9.40312147e-02
7.35039115e-02 -3.35567653e-01 -3.24650228e-01 -6.50646865e-01
-1.32131845e-01 8.60179961e-01 -1.90318063e-01 4.66358587... | [14.331735610961914, 6.139033317565918] |
f9c60b5b-134c-4a84-8b0a-a4b6ec977fab | distribution-matching-for-heterogeneous-multi | 2105.03790 | null | https://arxiv.org/abs/2105.03790v1 | https://arxiv.org/pdf/2105.03790v1.pdf | Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Study | Multi-Task Learning has emerged as a methodology in which multiple tasks are jointly learned by a shared learning algorithm, such as a DNN. MTL is based on the assumption that the tasks under consideration are related; therefore it exploits shared knowledge for improving performance on each individual task. Tasks are g... | ['Stefanos Zafeiriou', 'Viktoriia Sharmanska', 'Dimitrios Kollias'] | 2021-05-08 | null | null | null | null | ['continuous-affect-estimation', 'action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 3.35980773e-01 1.18209578e-01 -1.88535228e-01 -4.53008324e-01
-7.21217692e-01 -3.43373924e-01 7.49799788e-01 -1.00064287e-02
-3.55853289e-01 7.41508782e-01 1.21229902e-01 3.75925839e-01
-2.77533621e-01 -3.34656030e-01 -5.82204521e-01 -8.70646358e-01
3.73506323e-02 5.32134652e-01 -1.07470147e-01 -9.61664170... | [13.60357666015625, 1.6783208847045898] |
c719db4d-6887-4ce9-852b-4362a866f9dd | scalable-regularization-of-scene-graph | 2209.02749 | null | https://arxiv.org/abs/2209.02749v1 | https://arxiv.org/pdf/2209.02749v1.pdf | Scalable Regularization of Scene Graph Generation Models using Symbolic Theories | Several techniques have recently aimed to improve the performance of deep learning models for Scene Graph Generation (SGG) by incorporating background knowledge. State-of-the-art techniques can be divided into two families: one where the background knowledge is incorporated into the model in a subsymbolic fashion, and ... | ['Efthymia Tsamoura', 'Davide Buffelli'] | 2022-09-06 | null | null | null | null | ['scene-graph-generation'] | ['computer-vision'] | [ 5.73200405e-01 5.30678272e-01 -9.40312669e-02 -1.30586773e-01
-4.85406607e-01 -3.59782308e-01 7.49466181e-01 7.26974756e-02
-2.06547782e-01 6.51305258e-01 -2.04880908e-02 -6.33412123e-01
1.04336731e-01 -1.18572855e+00 -1.10881722e+00 -4.92304653e-01
1.83047056e-01 4.84045118e-01 7.85327733e-01 -2.30978489... | [10.344991683959961, 1.4433581829071045] |
649990b9-697d-4ba5-a68c-5bb4e59c0a36 | deep-learning-on-graphs-for-natural-language | null | null | https://aclanthology.org/2021.naacl-tutorials.3 | https://aclanthology.org/2021.naacl-tutorials.3.pdf | Deep Learning on Graphs for Natural Language Processing | Due to its great power in modeling non-Euclidean data like graphs or manifolds, deep learning on graph techniques (i.e., Graph Neural Networks (GNNs)) have opened a new door to solving challenging graph-related NLP problems. There has seen a surge of interests in applying deep learning on graph techniques to NLP, and h... | ['Yunyao Li', 'Heng Ji', 'Yu Chen', 'Lingfei Wu'] | 2021-06-01 | null | null | null | naacl-2021-4 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 3.15770626e-01 6.46602392e-01 -1.35017812e-01 -3.12055886e-01
-6.32690251e-01 -9.48439538e-01 3.69035929e-01 5.68265259e-01
3.68257500e-02 7.55400956e-01 3.18435162e-01 -8.19126725e-01
1.08761996e-01 -1.34846735e+00 -6.53038919e-01 -3.42446595e-01
-3.05935383e-01 7.82518208e-01 -1.88860506e-01 -4.69197541... | [10.161809921264648, 8.287087440490723] |
35cd8abd-8769-4f20-adfb-db0ceb78a143 | depth-structure-preserving-scene-image | 1706.00212 | null | http://arxiv.org/abs/1706.00212v2 | http://arxiv.org/pdf/1706.00212v2.pdf | Depth Structure Preserving Scene Image Generation | Key to automatically generate natural scene images is to properly arrange
among various spatial elements, especially in the depth direction. To this end,
we introduce a novel depth structure preserving scene image generation network
(DSP-GAN), which favors a hierarchical and heterogeneous architecture, for the
purpose ... | ['Bingbing Ni', 'Yichao Yan', 'Jingwei Xu', 'Xiaokang Yang', 'Wendong Zhang'] | 2017-06-01 | null | null | null | null | ['scene-generation'] | ['computer-vision'] | [ 6.97237313e-01 4.78087723e-01 4.89242345e-01 -2.50565290e-01
-5.21801054e-01 -6.12849772e-01 9.89455163e-01 -4.43177164e-01
-2.46757008e-02 4.86481100e-01 3.87654990e-01 -5.40669151e-02
2.11240768e-01 -1.39108872e+00 -8.80951345e-01 -6.72270715e-01
4.51736599e-01 4.39746946e-01 2.13915572e-01 -2.72735476... | [11.454706192016602, -0.4202173352241516] |
f489ea81-9b7b-4f1b-82a1-d2558892e2e0 | translating-a-math-word-problem-to-an | 1811.05632 | null | http://arxiv.org/abs/1811.05632v2 | http://arxiv.org/pdf/1811.05632v2.pdf | Translating a Math Word Problem to an Expression Tree | Sequence-to-sequence (SEQ2SEQ) models have been successfully applied to
automatic math word problem solving. Despite its simplicity, a drawback still
remains: a math word problem can be correctly solved by more than one
equations. This non-deterministic transduction harms the performance of maximum
likelihood estimatio... | ['Dongxiang Zhang', 'Xiaojiang Liu', 'Deng Cai', 'Yan Wang', 'Lei Wang'] | 2018-11-14 | null | null | null | null | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [ 2.88303494e-01 -3.03975642e-01 1.16205327e-01 -4.14614379e-01
-5.48822880e-01 -7.34965920e-01 -7.77340680e-03 1.29110053e-01
-3.33427191e-01 8.69960546e-01 1.49781415e-02 -2.44935364e-01
-4.64736253e-01 -9.24393356e-01 -3.73732209e-01 -4.80943322e-01
5.83343983e-01 3.30007255e-01 -5.80136776e-02 -6.23628259... | [9.762303352355957, 7.441067695617676] |
a830dec3-8c6c-462e-8287-0731dd1f530c | astock-a-new-dataset-and-automated-stock | 2206.06606 | null | https://arxiv.org/abs/2206.06606v1 | https://arxiv.org/pdf/2206.06606v1.pdf | Astock: A New Dataset and Automated Stock Trading based on Stock-specific News Analyzing Model | Natural Language Processing(NLP) demonstrates a great potential to support financial decision-making by analyzing the text from social media or news outlets. In this work, we build a platform to study the NLP-aided stock auto-trading algorithms systematically. In contrast to the previous work, our platform is character... | ['Javen Qinfeng Shi', 'Ehsan Abbasnejad', 'YuHao Lin', 'Lingqiao Liu', 'Haiyao Cao', 'Jinan Zou'] | 2022-06-14 | null | null | null | null | ['text-based-stock-prediction', 'news-classification', 'semantic-role-labeling', 'stock-trend-prediction', 'stock-market-prediction', 'stock-price-prediction', 'stock-prediction'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'time-series', 'time-series', 'time-series', 'time-series'] | [-6.17304385e-01 -2.70674974e-01 -5.60716331e-01 -4.93611068e-01
-9.97539401e-01 -9.92974639e-01 8.75741243e-01 2.19812408e-01
-3.74809951e-01 6.83499098e-01 6.78560495e-01 -1.21235639e-01
7.28001669e-02 -1.15729570e+00 -8.43543470e-01 -4.22278970e-01
-1.16122756e-02 3.51155698e-01 3.47517371e-01 -3.13463211... | [4.420980453491211, 4.2673540115356445] |
b0940aa8-36ce-4d23-8191-c956e8e215ed | pku-mmd-a-large-scale-benchmark-for | 1703.07475 | null | http://arxiv.org/abs/1703.07475v2 | http://arxiv.org/pdf/1703.07475v2.pdf | PKU-MMD: A Large Scale Benchmark for Continuous Multi-Modal Human Action Understanding | Despite the fact that many 3D human activity benchmarks being proposed, most
existing action datasets focus on the action recognition tasks for the
segmented videos. There is a lack of standard large-scale benchmarks,
especially for current popular data-hungry deep learning based methods. In this
paper, we introduce a ... | ['Yueyu Hu', 'Sijie Song', 'Jiaying Liu', 'Chunhui Liu', 'Yanghao Li'] | 2017-03-22 | null | null | null | null | ['action-understanding'] | ['computer-vision'] | [ 3.95357758e-01 -4.09378886e-01 -4.61441696e-01 -1.66153312e-01
-7.90095031e-01 -3.32542300e-01 4.46443677e-01 -4.39943820e-01
-4.59887981e-01 7.37017572e-01 8.62204909e-01 4.72811937e-01
2.64652193e-01 -4.34010655e-01 -6.07457161e-01 -8.54034841e-01
-1.49651706e-01 3.41408402e-01 6.93755686e-01 1.45471260... | [7.851931571960449, 0.4126207232475281] |
6e708809-4f48-4230-b249-0243b0d2483d | sample-hardness-based-gradient-loss-for-long | 2208.03779 | null | https://arxiv.org/abs/2208.03779v1 | https://arxiv.org/pdf/2208.03779v1.pdf | Sample hardness based gradient loss for long-tailed cervical cell detection | Due to the difficulty of cancer samples collection and annotation, cervical cancer datasets usually exhibit a long-tailed data distribution. When training a detector to detect the cancer cells in a WSI (Whole Slice Image) image captured from the TCT (Thinprep Cytology Test) specimen, head categories (e.g. normal cells ... | ['Huisi Wu', 'Linlin Shen', 'Junliang Chen', 'Xiangbo Gao', 'Xuechen Li', 'Minmin Liu'] | 2022-08-07 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 1.38970241e-01 1.18941315e-01 -6.66332245e-01 -3.41555536e-01
-9.60828006e-01 -4.90068078e-01 4.38427359e-01 5.08576334e-01
-4.91992861e-01 5.39651513e-01 -2.64419407e-01 -4.29169089e-01
-1.83271822e-02 -7.34370530e-01 -5.45513451e-01 -1.36308885e+00
-2.08681170e-03 7.03517854e-01 5.73256254e-01 6.71733767... | [14.952096939086914, -3.0722150802612305] |
9c0acb36-bdd2-4203-8857-3952e23f7c29 | leveraging-neo4j-and-deep-learning-for | 2304.00192 | null | https://arxiv.org/abs/2304.00192v1 | https://arxiv.org/pdf/2304.00192v1.pdf | Leveraging Neo4j and deep learning for traffic congestion simulation & optimization | Traffic congestion has been a major challenge in many urban road networks. Extensive research studies have been conducted to highlight traffic-related congestion and address the issue using data-driven approaches. Currently, most traffic congestion analyses are done using simulation software that offers limited insight... | ['Syed Adnan Yusuf', 'Riad Souissi', 'Arshad Ali Khan', 'Shyam Pratap Singh'] | 2023-04-01 | null | null | null | null | ['traffic-prediction'] | ['time-series'] | [-3.21464986e-01 -7.14289099e-02 -2.41007864e-01 -2.82832861e-01
2.04670057e-02 3.77436206e-02 4.25551564e-01 9.13538504e-03
-3.29859406e-01 1.11747336e+00 1.29249990e-01 -1.25690126e+00
-5.31033218e-01 -1.49418986e+00 -4.07472104e-01 -4.42298427e-02
-4.49733406e-01 6.32749736e-01 4.20465887e-01 -6.98027492... | [6.264317989349365, 1.8464857339859009] |
2d91c3cc-8471-4647-bc5c-8e5e63550237 | lifelong-pretraining-continually-adapting | 2110.08534 | null | https://arxiv.org/abs/2110.08534v3 | https://arxiv.org/pdf/2110.08534v3.pdf | Lifelong Pretraining: Continually Adapting Language Models to Emerging Corpora | Pretrained language models (PTLMs) are typically learned over a large, static corpus and further fine-tuned for various downstream tasks. However, when deployed in the real world, a PTLM-based model must deal with data distributions that deviate from what the PTLM was initially trained on. In this paper, we study a lif... | ['Xiang Ren', 'Andrew Arnold', 'Xiaokai Wei', 'Shang-Wen Li', 'Wei Xiao', 'Henghui Zhu', 'Dejiao Zhang', 'Xisen Jin'] | 2021-10-16 | null | https://aclanthology.org/2022.naacl-main.351 | https://aclanthology.org/2022.naacl-main.351.pdf | naacl-2022-7 | ['continual-pretraining'] | ['methodology'] | [-3.73068207e-04 -1.77059665e-01 -3.74222964e-01 -4.71313298e-01
-7.83650160e-01 -8.05614114e-01 7.05604851e-01 3.88339043e-01
-9.40619290e-01 9.04852331e-01 3.31078827e-01 -5.46840727e-01
-6.81069195e-02 -7.03350782e-01 -7.36479461e-01 -2.20984966e-01
-2.46492445e-01 1.01342893e+00 4.36465651e-01 -3.34237427... | [10.624861717224121, 8.299921035766602] |
a3879dfa-7a0f-4b65-b8dc-baba9f8d1920 | social-fabric-tubelet-compositions-for-video | 2108.08363 | null | https://arxiv.org/abs/2108.08363v1 | https://arxiv.org/pdf/2108.08363v1.pdf | Social Fabric: Tubelet Compositions for Video Relation Detection | This paper strives to classify and detect the relationship between object tubelets appearing within a video as a <subject-predicate-object> triplet. Where existing works treat object proposals or tubelets as single entities and model their relations a posteriori, we propose to classify and detect predicates for pairs o... | ['Cees G. M. Snoek', 'Pascal Mettes', 'Zenglin Shi', 'Shuo Chen'] | 2021-08-18 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Chen_Social_Fabric_Tubelet_Compositions_for_Video_Relation_Detection_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Chen_Social_Fabric_Tubelet_Compositions_for_Video_Relation_Detection_ICCV_2021_paper.pdf | iccv-2021-1 | ['video-visual-relation-detection'] | ['computer-vision'] | [ 9.37156472e-03 2.52465516e-01 -3.04765433e-01 -6.09494865e-01
-2.83096075e-01 -5.07123232e-01 7.97888637e-01 4.98712242e-01
-1.86840966e-01 2.54352093e-01 2.64407843e-01 -5.15032299e-02
-3.10295105e-01 -8.71457338e-01 -1.04438281e+00 -3.10710549e-01
-6.08081698e-01 8.21911573e-01 8.80417228e-01 8.68893117... | [9.272546768188477, 0.7221463918685913] |
529196d2-6f41-471d-a87f-001fdf3761ce | analysis-by-synthesis-3d-object-recognition | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Hejrati_Analysis_by_Synthesis_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Hejrati_Analysis_by_Synthesis_2014_CVPR_paper.pdf | Analysis by Synthesis: 3D Object Recognition by Object Reconstruction | We introduce a new approach for recognizing and reconstructing 3D objects in images. Our approach is based on an analysis by synthesis strategy. A forward synthesis model constructs possible geometric interpretations of the world, and then selects the interpretation that best agrees with the measured visual evidence. T... | ['Deva Ramanan', 'Mohsen Hejrati'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['3d-object-recognition'] | ['computer-vision'] | [ 2.69812495e-01 5.33160046e-02 -1.11645930e-01 -4.81279850e-01
-7.09566534e-01 -6.44641936e-01 9.50547159e-01 -2.33669013e-01
-3.74992639e-02 1.48768872e-01 1.36763221e-02 -2.92878300e-01
9.78959054e-02 -7.42652118e-01 -1.04192400e+00 -5.06080151e-01
2.91771621e-01 9.68289971e-01 6.30118966e-01 1.22198164... | [7.78505802154541, -2.699373245239258] |
ef2b1a9f-7048-41f8-82b1-7c5f8597fb04 | ssn-nlp-mlrg-at-semeval-2022-task-4-ensemble | null | null | https://aclanthology.org/2022.semeval-1.53 | https://aclanthology.org/2022.semeval-1.53.pdf | SSN_NLP_MLRG at SemEval-2022 Task 4: Ensemble Learning strategies to detect Patronizing and Condescending Language | In this paper, we describe our efforts at SemEval 2022 Shared Task 4 on Patronizing and Condescending Language (PCL) Detection. This is the first shared task to detect PCL which is to identify and categorize PCL language towards vulnerable communities. The shared task consists of two subtasks: Patronizing and Condescen... | ['Thenmozhi Durairaj', 'Kalaivani Adaikkan'] | null | null | null | null | semeval-naacl-2022-7 | ['multi-label-text-classification', 'multi-label-text-classification'] | ['methodology', 'natural-language-processing'] | [-1.60875707e-03 -3.00519675e-01 -2.28492007e-01 7.15406612e-02
-1.26075494e+00 -6.98357046e-01 9.07742083e-01 4.74947870e-01
-4.85617012e-01 6.52189553e-01 1.02933623e-01 -4.81580406e-01
1.09776512e-01 -4.27623302e-01 5.87473027e-02 -6.97374403e-01
-1.03270687e-01 4.39223439e-01 4.16542262e-01 1.24942526... | [8.830322265625, 10.613480567932129] |
3b4dcb82-dc6f-4399-8837-b86178669aeb | openslu-a-unified-modularized-and-extensible | 2305.10231 | null | https://arxiv.org/abs/2305.10231v1 | https://arxiv.org/pdf/2305.10231v1.pdf | OpenSLU: A Unified, Modularized, and Extensible Toolkit for Spoken Language Understanding | Spoken Language Understanding (SLU) is one of the core components of a task-oriented dialogue system, which aims to extract the semantic meaning of user queries (e.g., intents and slots). In this work, we introduce OpenSLU, an open-source toolkit to provide a unified, modularized, and extensible toolkit for spoken lang... | ['Wanxiang Che', 'Yunlong Feng', 'Xiao Xu', 'Qiguang Chen', 'Libo Qin'] | 2023-05-17 | null | null | null | null | ['spoken-language-understanding', 'spoken-language-understanding'] | ['natural-language-processing', 'speech'] | [-2.96841294e-01 1.95148230e-01 -1.86623797e-01 -7.28698313e-01
-8.55004191e-01 -7.86411524e-01 4.83350366e-01 -1.03585251e-01
-1.16115980e-01 4.81279820e-01 4.86191541e-01 -5.16707122e-01
2.26343110e-01 -6.64984107e-01 -3.32887650e-01 -3.30188982e-02
8.91183466e-02 5.05725384e-01 1.99738936e-03 -4.12315995... | [12.613245964050293, 7.481632232666016] |
36188a54-7609-491e-bc1c-b0762e990e21 | learning-node-representations-from-noisy | 2012.02434 | null | https://arxiv.org/abs/2012.02434v1 | https://arxiv.org/pdf/2012.02434v1.pdf | Learning Node Representations from Noisy Graph Structures | Learning low-dimensional representations on graphs has proved to be effective in various downstream tasks. However, noises prevail in real-world networks, which compromise networks to a large extent in that edges in networks propagate noises through the whole network instead of only the node itself. While existing meth... | ['Chuan Shi', 'Guojie Song', 'Weiyu Zhang', 'Qingqing Long', 'Ziyao Li', 'Junshan Wang'] | 2020-12-04 | null | null | null | null | ['graph-reconstruction'] | ['graphs'] | [ 1.27511024e-01 2.54997760e-01 9.20735598e-02 -6.05115928e-02
-3.31962347e-01 -6.48314238e-01 5.01378238e-01 -5.45365065e-02
8.13933685e-02 6.55147731e-01 1.40229106e-01 -2.03032091e-01
-1.56476989e-01 -1.25688827e+00 -5.59803426e-01 -1.02577829e+00
1.99787065e-01 3.92101496e-01 3.93762533e-03 -3.38504642... | [7.168384075164795, 6.257957458496094] |
ba3f53a3-567c-4776-9d10-6d7c919c4dae | interpreting-and-generating-gestures-with | null | null | https://openreview.net/forum?id=bAVVYLysfJ | https://openreview.net/pdf?id=bAVVYLysfJ | Interpreting and Generating Gestures with Embodied Human Computer Interactions | In this paper, we discuss the role that gesture plays for an embodied intelligent virtual agent (IVA) in the context of multimodal task-oriented dialogues with a human. We have developed a
simulation platform, VoxWorld, for modeling and building {\it Embodied Human-Computer Interactions (EHCI)}, where communication ... | ['Anonymous'] | 2020-09-18 | null | null | null | acm-iva-workshop-genea-2020-10 | ['gesture-generation'] | ['robots'] | [ 2.86585569e-01 3.82701188e-01 3.38319331e-01 -2.23974034e-01
8.67507160e-02 -9.10997093e-01 1.30457366e+00 -8.71079788e-02
-4.71184105e-01 5.43677270e-01 5.96824288e-01 -3.63465637e-01
-1.63455196e-02 -4.66339886e-01 -5.17320335e-02 -6.75500035e-01
-4.34099399e-02 5.46779096e-01 -2.89209783e-02 -4.88105297... | [5.237761974334717, 0.3524947762489319] |
3aa75331-8b15-4d72-9322-56cc6c43c760 | detection-and-annotation-of-plant-organs-from | 2007.13106 | null | https://arxiv.org/abs/2007.13106v2 | https://arxiv.org/pdf/2007.13106v2.pdf | Detection and Annotation of Plant Organs from Digitized Herbarium Scans using Deep Learning | As herbarium specimens are increasingly becoming digitized and accessible in online repositories, advanced computer vision techniques are being used to extract information from them. The presence of certain plant organs on herbarium sheets is useful information in various scientific contexts and automatic recognition o... | ['Claus Weiland', 'Marco Schmidt', 'Thomas Hickler', 'Stefan Dressler', 'Sohaib Younis', 'Bernhard Seeger'] | 2020-07-26 | null | null | null | null | ['organ-detection'] | ['medical'] | [ 9.61589813e-02 1.31200448e-01 -1.45569906e-01 -9.04035196e-03
-1.28842667e-01 -1.13940442e+00 4.25146043e-01 5.71831703e-01
-2.55495757e-01 5.08340955e-01 -3.48188043e-01 -3.09241116e-01
6.08687252e-02 -9.81466830e-01 -3.13049912e-01 -3.38911414e-01
-4.12658632e-01 6.39780045e-01 1.86403632e-01 4.35101129... | [9.154156684875488, -1.5132694244384766] |
cd27ac1b-1ae9-45c5-adf1-a24d2bc2f7ac | 190600884 | 1906.00884 | null | https://arxiv.org/abs/1906.00884v2 | https://arxiv.org/pdf/1906.00884v2.pdf | Fashion Editing with Adversarial Parsing Learning | Interactive fashion image manipulation, which enables users to edit images with sketches and color strokes, is an interesting research problem with great application value. Existing works often treat it as a general inpainting task and do not fully leverage the semantic structural information in fashion images. Moreove... | ['Jian Yin', 'Ziqi Zhang', 'Yixuan Zhang', 'Xiaohui Shen', 'Xiaodan Liang', 'Haoye Dong', 'Zhenyu Xie', 'Xujie Zhang', 'Bowen Wu'] | 2019-06-03 | fashion-editing-with-adversarial-parsing | http://openaccess.thecvf.com/content_CVPR_2020/html/Dong_Fashion_Editing_With_Adversarial_Parsing_Learning_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Dong_Fashion_Editing_With_Adversarial_Parsing_Learning_CVPR_2020_paper.pdf | cvpr-2020-6 | ['human-parsing'] | ['computer-vision'] | [ 5.71965456e-01 4.12780009e-02 -5.64660393e-02 -4.12354439e-01
-4.46035832e-01 -5.68718731e-01 5.00865877e-01 -7.17607856e-01
-1.10543452e-01 5.22987425e-01 1.88020840e-01 -3.41692977e-02
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88bf5170-9fc7-4bea-a229-2bbbe792ed20 | vilio-state-of-the-art-visio-linguistic | 2012.07788 | null | https://arxiv.org/abs/2012.07788v1 | https://arxiv.org/pdf/2012.07788v1.pdf | Vilio: State-of-the-art Visio-Linguistic Models applied to Hateful Memes | This work presents Vilio, an implementation of state-of-the-art visio-linguistic models and their application to the Hateful Memes Dataset. The implemented models have been fitted into a uniform code-base and altered to yield better performance. The goal of Vilio is to provide a user-friendly starting point for any vis... | ['Niklas Muennighoff'] | 2020-12-14 | null | null | null | null | ['meme-classification'] | ['natural-language-processing'] | [-7.44177163e-01 -7.27899894e-02 -2.92677313e-01 8.67312625e-02
-2.91379929e-01 -6.56536579e-01 8.69508684e-01 2.80322462e-01
-5.61577737e-01 4.56271738e-01 4.53917980e-01 -3.97603922e-02
4.78910953e-01 -2.68387467e-01 -2.66638368e-01 -1.04684241e-01
6.97042570e-02 2.42700249e-01 3.07477918e-02 -7.73126423... | [8.711427688598633, 10.569079399108887] |
8517e516-1acd-43ed-a2a2-cb36fb10681f | textgail-generative-adversarial-imitation | 2004.13796 | null | https://arxiv.org/abs/2004.13796v4 | https://arxiv.org/pdf/2004.13796v4.pdf | TextGAIL: Generative Adversarial Imitation Learning for Text Generation | Generative Adversarial Networks (GANs) for text generation have recently received many criticisms, as they perform worse than their MLE counterparts. We suspect previous text GANs' inferior performance is due to the lack of a reliable guiding signal in their discriminators. To address this problem, we propose a generat... | ['Lei LI', 'Qingyang Wu', 'Zhou Yu'] | 2020-04-07 | null | null | null | null | ['conditional-text-generation'] | ['natural-language-processing'] | [ 2.22925439e-01 3.21383476e-01 -1.20986335e-01 -1.30872354e-01
-1.34442592e+00 -6.70526147e-01 1.23798513e+00 -5.58139920e-01
-1.31647110e-01 1.29545462e+00 5.51365674e-01 -3.71040344e-01
5.12812257e-01 -6.86654031e-01 -7.29892015e-01 -7.11045921e-01
3.70861799e-01 6.39141798e-01 -4.03803051e-01 -5.12214422... | [11.82753849029541, 9.290367126464844] |
53b96ef7-3a91-46f3-8dee-6dc71aae839a | academic-expert-finding-via-k-mathcal-p-core | null | null | https://openreview.net/forum?id=BY5V3w4bWRU | https://openreview.net/pdf?id=BY5V3w4bWRU | Academic Expert Finding via $(k,\mathcal{P})$-Core based Embedding over Heterogeneous Graphs | Finding relevant experts in specified areas is often crucial for a wide range of applications in both academia and industry. Given a user input query and a large amount of academic knowledge (e.g., academic papers), expert finding aims to find and rank the experts who are most relevant to the given query, from the acad... | ['Anonymous'] | 2021-07-26 | null | null | null | null | ['document-embedding'] | ['methodology'] | [-3.27247530e-01 -6.68661892e-02 -2.64092475e-01 2.02578995e-02
-4.18940037e-01 -9.27965760e-01 2.55292505e-01 7.05466211e-01
-1.17172189e-01 4.55248326e-01 5.28760813e-02 -3.95121187e-01
-1.08318102e+00 -1.24015725e+00 -4.42298323e-01 -3.96078825e-01
-2.17479616e-01 4.92419541e-01 2.55292565e-01 7.40425475... | [9.168649673461914, 7.889381408691406] |
284f5fd6-535e-469c-a79b-51a96a107c54 | distinguishing-representational-geometries | 2211.15053 | null | https://arxiv.org/abs/2211.15053v1 | https://arxiv.org/pdf/2211.15053v1.pdf | Distinguishing representational geometries with controversial stimuli: Bayesian experimental design and its application to face dissimilarity judgments | Comparing representations of complex stimuli in neural network layers to human brain representations or behavioral judgments can guide model development. However, even qualitatively distinct neural network models often predict similar representational geometries of typical stimulus sets. We propose a Bayesian experimen... | ['Nikolaus Kriegeskorte', 'Heiko H. Schütt', 'Wenxuan Guo', 'Tal Golan'] | 2022-11-28 | null | null | null | null | ['face-model'] | ['computer-vision'] | [ 7.74750412e-01 1.19157895e-01 1.16604775e-01 -7.96190441e-01
-1.46386251e-01 -5.94556689e-01 9.18775141e-01 -3.04840207e-01
-2.26204872e-01 3.34740691e-02 1.43767267e-01 -5.54837167e-01
-2.46674895e-01 -4.36788797e-01 -5.01209199e-01 -2.85283357e-01
2.30100960e-01 5.29619932e-01 -2.59655923e-01 9.82716829... | [12.712453842163086, 1.2876708507537842] |
78027481-b7df-40e4-a77e-edb75c2a4f9a | learning-treatment-plan-representations-for | 2206.02912 | null | https://arxiv.org/abs/2206.02912v2 | https://arxiv.org/pdf/2206.02912v2.pdf | Learning Image Representations for Content Based Image Retrieval of Radiotherapy Treatment Plans | Objective: Knowledge based planning (KBP) typically involves training an end-to-end deep learning model to predict dose distributions. However, training end-to-end methods may be associated with practical limitations due to the limited size of medical datasets that are often used. To address these limitations, we propo... | ['Lei Xing', 'Yong Yang', 'Joseph B. Schulz', 'Jen-Yeu Wang', 'Piotr Dubrowski', 'Yusuke Nomura', 'Md Tauhidul Islam', 'Oscar Pastor-Serrano', 'Varun Vasudevan', 'Charles Huang'] | 2022-06-06 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [ 1.84487239e-01 2.73090184e-01 -3.90194833e-01 -3.97985637e-01
-1.54380739e+00 -3.23365390e-01 4.35000956e-01 6.39094532e-01
-5.91550350e-01 6.72287583e-01 1.00444770e+00 -6.89848214e-02
-9.26314056e-01 -9.06580448e-01 -4.87417012e-01 -7.32645333e-01
-8.93926397e-02 1.05504918e+00 -2.21474338e-02 -1.38118654... | [14.572431564331055, -1.9103533029556274] |
e1641d97-fb4e-404e-8b06-62c4998966d0 | pseudo-labels-regularization-for-imbalanced | 2303.03946 | null | https://arxiv.org/abs/2303.03946v1 | https://arxiv.org/pdf/2303.03946v1.pdf | Pseudo Labels Regularization for Imbalanced Partial-Label Learning | Partial-label learning (PLL) is an important branch of weakly supervised learning where the single ground truth resides in a set of candidate labels, while the research rarely considers the label imbalance. A recent study for imbalanced partial-Label learning proposed that the combinatorial challenge of partial-label l... | ['Zheng Lian', 'Mingyu Xu'] | 2023-03-06 | null | null | null | null | ['partial-label-learning'] | ['methodology'] | [ 1.22149296e-01 3.49554271e-01 -9.43864942e-01 -6.06362760e-01
-1.04978549e+00 -4.10365969e-01 3.34563315e-01 3.38053674e-01
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-8.98868591e-03 -5.41583836e-01 -7.83591330e-01 -1.00357747e+00
2.20182046e-01 7.68290222e-01 1.33388177e-01 2.08365276... | [9.377470016479492, 4.038986682891846] |
d61d4adf-6373-45e9-a116-ccda026958cf | relational-embeddings-for-language | 2210.05715 | null | https://arxiv.org/abs/2210.05715v1 | https://arxiv.org/pdf/2210.05715v1.pdf | Relational Embeddings for Language Independent Stance Detection | The large majority of the research performed on stance detection has been focused on developing more or less sophisticated text classification systems, even when many benchmarks are based on social network data such as Twitter. This paper aims to take on the stance detection task by placing the emphasis not so much on ... | ['Rodrigo Agerri', 'Joseba Fernandez de Landa'] | 2022-10-11 | null | null | null | null | ['stance-detection'] | ['natural-language-processing'] | [-1.35841966e-02 2.20152229e-01 -8.02136123e-01 -3.27619433e-01
-5.23827791e-01 -5.88648856e-01 1.35894835e+00 6.87955618e-01
-6.37968779e-01 4.88830745e-01 8.91417623e-01 -3.36340517e-01
3.33488882e-01 -8.56763482e-01 -8.81284103e-02 -2.55422235e-01
3.11381035e-02 5.63419819e-01 3.03085655e-01 -6.94945216... | [9.068441390991211, 9.934353828430176] |
ebfc4dbd-0b72-4c10-8002-c3552ae9ac46 | restoring-ancient-text-using-deep-learning-a | 1910.06262 | null | https://arxiv.org/abs/1910.06262v1 | https://arxiv.org/pdf/1910.06262v1.pdf | Restoring ancient text using deep learning: a case study on Greek epigraphy | Ancient history relies on disciplines such as epigraphy, the study of ancient inscribed texts, for evidence of the recorded past. However, these texts, "inscriptions", are often damaged over the centuries, and illegible parts of the text must be restored by specialists, known as epigraphists. This work presents Pythia,... | ['Jonathan Prag', 'Yannis Assael', 'Thea Sommerschield'] | 2019-10-14 | restoring-ancient-text-using-deep-learning-a-1 | https://aclanthology.org/D19-1668 | https://aclanthology.org/D19-1668.pdf | ijcnlp-2019-11 | ['ancient-tex-restoration'] | ['miscellaneous'] | [ 4.25866455e-01 3.92201692e-01 4.87329029e-02 -1.03685103e-01
-7.44498730e-01 -6.11471236e-01 6.21059299e-01 -1.89239562e-01
-5.14559984e-01 6.27843738e-01 8.33987772e-01 -2.30018869e-01
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3.45424205e-01 9.16428745e-01 -7.23298937e-02 -5.34474373... | [10.69898509979248, 10.24173641204834] |
5167bd4d-3b5f-46fd-975c-c1a6cba57b0d | policy-based-inference-in-trick-taking-card | 1905.10911 | null | https://arxiv.org/abs/1905.10911v1 | https://arxiv.org/pdf/1905.10911v1.pdf | Policy Based Inference in Trick-Taking Card Games | Trick-taking card games feature a large amount of private information that slowly gets revealed through a long sequence of actions. This makes the number of histories exponentially large in the action sequence length, as well as creating extremely large information sets. As a result, these games become too large to sol... | ['Douglas Rebstock', 'Christopher Solinas', 'Nathan R. Sturtevant', 'Michael Buro'] | 2019-05-27 | null | null | null | null | ['card-games'] | ['playing-games'] | [ 3.46821696e-02 1.14048421e-01 -3.56997907e-01 2.54030228e-01
-8.64671767e-01 -1.03406930e+00 8.53227317e-01 -2.62870073e-01
-6.27523959e-01 1.03933668e+00 7.41433427e-02 -8.27404737e-01
-1.91090643e-01 -1.06032097e+00 -5.55850625e-01 -6.08969986e-01
-1.00430749e-01 1.27515745e+00 8.01831126e-01 -2.70049781... | [3.6431021690368652, 1.6872738599777222] |
22ea8c8c-73bb-47ba-80fa-4d412e3cb5ae | warping-residual-based-image-stitching-for | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Lee_Warping_Residual_Based_Image_Stitching_for_Large_Parallax_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Lee_Warping_Residual_Based_Image_Stitching_for_Large_Parallax_CVPR_2020_paper.pdf | Warping Residual Based Image Stitching for Large Parallax | Image stitching techniques align two images captured at different viewing positions onto a single wider image. When the captured 3D scene is not planar and the camera baseline is large, two images exhibit parallax where the relative positions of scene structures are quite different from each view. The existing image st... | [' Jae-Young Sim', 'Kyu-Yul Lee'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['image-stitching'] | ['computer-vision'] | [ 4.74469244e-01 -3.95111263e-01 2.91571952e-02 2.15477228e-01
-2.25698382e-01 -9.09297287e-01 4.12492603e-01 -3.84016871e-01
-1.13388225e-01 2.86209702e-01 1.43424496e-01 2.06048265e-01
2.04936918e-02 -2.53381371e-01 -6.75448895e-01 -9.77742732e-01
3.39339584e-01 3.05815816e-01 5.18471003e-01 -2.07436718... | [9.312482833862305, -2.3786137104034424] |
fbdcca12-9dfa-4b99-bc53-47bb8a3700b5 | attentive-task-agnostic-meta-learning-for-few | null | null | https://openreview.net/forum?id=SyxMWh09KX | https://openreview.net/pdf?id=SyxMWh09KX | Attentive Task-Agnostic Meta-Learning for Few-Shot Text Classification | Current deep learning based text classification methods are limited by their ability to achieve fast learning and generalization when the data is scarce. We address this problem by integrating a meta-learning procedure that uses the knowledge learned across many tasks as an inductive bias towards better natural languag... | ['Stan Matwin', 'Nicolas Chapados', 'Xiang Jiang', 'Mohammad Havaei', 'Andrew Jesson', 'Thomas Vincent', 'Hassan Chouaib', 'Gabriel Chartrand'] | null | null | null | null | iclr-2019-5 | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 2.33053520e-01 8.36401060e-02 -5.55302501e-01 -6.15044951e-01
-7.61548936e-01 -1.44043401e-01 9.64952767e-01 3.74319613e-01
-7.00339198e-01 7.82767177e-01 4.18308884e-01 -1.87482879e-01
-1.35230035e-01 -6.45096123e-01 -4.91448671e-01 -5.69182217e-01
4.73030508e-01 6.16353393e-01 -1.45271406e-01 -3.73698026... | [10.738923072814941, 7.673578262329102] |
8eaa93e6-0bcf-418f-bc35-d499bf335181 | spade-semi-supervised-anomaly-detection-under | 2212.00173 | null | https://arxiv.org/abs/2212.00173v1 | https://arxiv.org/pdf/2212.00173v1.pdf | SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch | Semi-supervised anomaly detection is a common problem, as often the datasets containing anomalies are partially labeled. We propose a canonical framework: Semi-supervised Pseudo-labeler Anomaly Detection with Ensembling (SPADE) that isn't limited by the assumption that labeled and unlabeled data come from the same dist... | ['Tomas Pfister', 'Sercan O. Arik', 'Chun-Liang Li', 'Kihyuk Sohn', 'Jinsung Yoon'] | 2022-11-30 | null | null | null | null | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [-4.43635583e-02 -8.64899065e-03 8.34970102e-02 -6.36394143e-01
-9.34560597e-01 -7.11449087e-01 4.63696539e-01 5.36617935e-01
-1.86065003e-01 5.34485579e-01 -4.19190347e-01 -2.64310241e-01
1.68502048e-01 -4.11084294e-01 -6.24603927e-01 -8.24999094e-01
-1.39555722e-01 7.70121753e-01 2.49050349e-01 -6.52798489... | [7.670196533203125, 2.3947951793670654] |
9f9da09d-d751-4034-ad6d-dc107fdcc189 | explainable-empirical-risk-minimization | 2009.01492 | null | https://arxiv.org/abs/2009.01492v3 | https://arxiv.org/pdf/2009.01492v3.pdf | Explainable Empirical Risk Minimization | The successful application of machine learning (ML) methods becomes increasingly dependent on their interpretability or explainability. Designing explainable ML systems is instrumental to ensuring transparency of automated decision-making that targets humans. The explainability of ML methods is also an essential ingred... | ['A. Jung', 'L. Dogruel', 'A. Odnoblyudova', 'G. Karakasidis', 'L. Zhang'] | 2020-09-03 | null | null | null | null | ['high-school-mathematics'] | ['reasoning'] | [ 2.42079005e-01 7.55710781e-01 -3.27712864e-01 -7.14257061e-01
-2.74701536e-01 -4.84231293e-01 4.66944695e-01 2.46652812e-01
-9.41322520e-02 7.39482164e-01 -1.75700605e-01 -8.58637154e-01
-3.54544699e-01 -6.04136050e-01 -5.94889104e-01 -2.92032629e-01
-6.35909438e-02 2.94865251e-01 -3.67743194e-01 1.86360732... | [8.742087364196777, 5.650977611541748] |
8a5faeb2-4c29-430a-a928-081698690be3 | neural-prior-stochastic-block-model | 2303.09995 | null | https://arxiv.org/abs/2303.09995v1 | https://arxiv.org/pdf/2303.09995v1.pdf | Neural-prior stochastic block model | The stochastic block model (SBM) is widely studied as a benchmark for graph clustering aka community detection. In practice, graph data often come with node attributes that bear additional information about the communities. Previous works modeled such data by considering that the node attributes are generated from the ... | ['L. Zdeborovà', 'O. Duranthon'] | 2023-03-17 | null | null | null | null | ['graph-clustering', 'stochastic-block-model', 'community-detection'] | ['graphs', 'graphs', 'graphs'] | [ 1.63368449e-01 1.71520770e-01 5.50405495e-02 -2.73648232e-01
-1.38608828e-01 -3.00599128e-01 8.64889622e-01 7.03951836e-01
-2.43183136e-01 3.85658771e-01 1.00261927e-01 -1.54431820e-01
-4.00456250e-01 -9.86487985e-01 -9.35406387e-01 -1.08991742e+00
-6.11795068e-01 9.08624828e-01 1.64859518e-01 -7.44625404... | [7.009875297546387, 5.461490154266357] |
7ec9a0ae-c84f-43cd-a1ea-76097a3c3082 | multi-paradigm-analysis-of-thai-capital | null | null | https://www.researchgate.net/publication/368721017_Multi-Paradigm_Analysis_of_Thai_Capital_Market_Linkages_BivariateVine_Copulas_Granger_Causality_Network_Centrality_and_Graph_Neural_NetworkGraph_Embedding_Approaches | https://www.researchgate.net/publication/368721017_Multi-Paradigm_Analysis_of_Thai_Capital_Market_Linkages_BivariateVine_Copulas_Granger_Causality_Network_Centrality_and_Graph_Neural_NetworkGraph_Embedding_Approaches#fullTextFileContent | Multi-Paradigm Analysis of Thai Capital Market Linkages: Bivariate/Vine Copulas, Granger Causality, Network Centrality, and Graph Neural Network/Graph Embedding Approaches | Analytically thorough understanding of causal, probabilistic, and informational linkages amongst modern, highly-interconnected capital markets is fundamental to the promotion of capital-market innovation, efficiency, and resilience; whereupon innovative, efficient, and resilient capital markets are fundamental to the s... | ['Isariyaporn Sukcharoenchaikul', 'Puvarith Veerabulyarith', 'Pitikorn Khlaisamniang', 'Kongkan Kalakan', 'Poomjai Nacaskul'] | 2023-02-23 | null | null | null | www-researchgate-net-2023-2 | ['econometrics'] | ['miscellaneous'] | [-4.87208277e-01 -1.76079378e-01 -2.62731999e-01 4.66440320e-01
-2.28158429e-01 -9.57342088e-01 7.84573674e-01 3.52075726e-01
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-1.02381051e+00 -1.29583061e+00 -3.65549386e-01 -4.49676603e-01
-7.48588502e-01 4.65911090e-01 -3.62671107e-01 -4.20080453... | [4.8455915451049805, 4.146173477172852] |
b46c7719-405a-4065-9baf-e3926a8e4063 | fleet-prognosis-with-physics-informed | 1901.05512 | null | http://arxiv.org/abs/1901.05512v1 | http://arxiv.org/pdf/1901.05512v1.pdf | Fleet Prognosis with Physics-informed Recurrent Neural Networks | Services and warranties of large fleets of engineering assets is a very
profitable business. The success of companies in that area is often related to
predictive maintenance driven by advanced analytics. Therefore, accurate
modeling, as a way to understand how the complex interactions between operating
conditions and c... | ['Felipe A. C. Viana', 'Renato Giorgiani Nascimento'] | 2019-01-16 | null | null | null | null | ['physics-informed-machine-learning', 'graph-regression', 'graph-to-sequence'] | ['graphs', 'graphs', 'natural-language-processing'] | [-1.02159627e-01 -2.80801058e-01 1.82810128e-01 -2.75583501e-04
-7.67687634e-02 -3.23432162e-02 -5.62643632e-03 1.98380306e-01
1.50342241e-01 6.57110691e-01 -3.39686990e-01 -3.20566505e-01
-8.03677857e-01 -8.15267444e-01 -7.48282313e-01 -1.06432736e+00
-5.32574773e-01 7.89927363e-01 2.50005163e-03 -6.73857749... | [6.770388603210449, 2.4520628452301025] |
d69fbb44-c46a-4945-aa89-4eee32beab45 | lightface-a-hybrid-deep-face-recognition | null | null | https://ieeexplore.ieee.org/document/9259802 | https://ieeexplore.ieee.org/document/9259802 | LightFace: A Hybrid Deep Face Recognition Framework | Face recognition constitutes a relatively a popular area which has emerged from the rulers of the social media to top universities in the world. Those frontiers and rule makers recently designed deep learning based custom face recognition models. A modern face recognition pipeline consists of four common stages: detect... | ['Alper Ozpinar', 'Sefik Ilkin Serengil'] | 2020-11-23 | null | null | null | 2020-innovations-in-intelligent-systems-and | ['face-swapping'] | ['computer-vision'] | [ 9.24668014e-02 8.28406028e-03 -6.51157554e-03 -7.93727934e-01
-5.01140067e-03 -1.03303961e-01 9.61779296e-01 -6.97335124e-01
-3.20298404e-01 2.52143204e-01 -1.67330608e-01 2.55018212e-02
-2.61599034e-01 -7.18998253e-01 -3.44365776e-01 -5.67422152e-01
1.70588382e-02 5.32330990e-01 -2.49397755e-01 -1.16480283... | [13.294685363769531, 0.9072215557098389] |
ca92a998-0d59-40a5-a1e4-fb974c779ee6 | ecgnet-learning-where-to-attend-for-detection-1 | 1812.07422 | null | http://arxiv.org/abs/1812.07422v2 | http://arxiv.org/pdf/1812.07422v2.pdf | ECGNET: Learning where to attend for detection of atrial fibrillation with deep visual attention | The complexity of the patterns associated with Atrial Fibrillation (AF) and
the high level of noise affecting these patterns have significantly limited the
current signal processing and shallow machine learning approaches to get
accurate AF detection results. Deep neural networks have shown to be very
powerful to learn... | [] | 2019-02-15 | ecgnet-learning-where-to-attend-for-detection | https://arxiv.org/abs/1812.07422 | https://arxiv.org/pdf/1812.07422 | arxiv181207422-2018-12 | ['atrial-fibrillation-detection'] | ['medical'] | [ 3.42710376e-01 -2.97622114e-01 2.21139446e-01 -3.79788399e-01
-4.64141428e-01 -5.78197241e-01 -1.10538043e-01 3.91493708e-01
-2.84639180e-01 7.70845234e-01 -1.51238842e-02 -6.05891407e-01
-5.02231002e-01 -7.08874226e-01 -3.83114636e-01 -7.12011218e-01
-8.36606562e-01 2.34285191e-01 -3.59827280e-01 1.84317380... | [14.276095390319824, 3.251603841781616] |
9c58b35e-f5d1-482d-a350-a822da60850e | clinical-note-owns-its-hierarchy-multi-level | 2305.09756 | null | https://arxiv.org/abs/2305.09756v1 | https://arxiv.org/pdf/2305.09756v1.pdf | Clinical Note Owns its Hierarchy: Multi-Level Hypergraph Neural Networks for Patient-Level Representation Learning | Leveraging knowledge from electronic health records (EHRs) to predict a patient's condition is essential to the effective delivery of appropriate care. Clinical notes of patient EHRs contain valuable information from healthcare professionals, but have been underused due to their difficult contents and complex hierarchi... | ['Sun Kim', 'Yinhua Piao', 'Nayeon Kim'] | 2023-05-16 | null | null | null | null | ['mortality-prediction'] | ['medical'] | [ 5.97786456e-02 4.31931406e-01 -4.05761510e-01 -2.04785481e-01
-4.31478828e-01 -1.40445337e-01 -2.58504212e-01 1.01249182e+00
2.63979025e-02 6.10248923e-01 8.60101223e-01 -3.74145657e-01
-4.92670655e-01 -9.37771142e-01 -2.36390829e-01 -5.79739809e-01
-3.61367226e-01 8.08908701e-01 1.49833541e-02 -2.49047697... | [7.886765956878662, 6.693014144897461] |
3874891f-3094-4555-a062-d7a6f7a8a9e1 | model-rubik-s-cube-twisting-resolution-depth | 2010.14819 | null | https://arxiv.org/abs/2010.14819v2 | https://arxiv.org/pdf/2010.14819v2.pdf | Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets | To obtain excellent deep neural architectures, a series of techniques are carefully designed in EfficientNets. The giant formula for simultaneously enlarging the resolution, depth and width provides us a Rubik's cube for neural networks. So that we can find networks with high efficiency and excellent performance by twi... | ['Tong Zhang', 'Chunjing Xu', 'Wei zhang', 'Qiulin Zhang', 'Yunhe Wang', 'Kai Han'] | 2020-10-28 | null | null | null | null | ['rubik-s-cube'] | ['graphs'] | [-5.32871962e-01 6.56127557e-02 -1.66666508e-01 -2.73870111e-01
1.29990160e-01 -4.13504064e-01 -3.55142541e-02 -5.86361468e-01
-7.68682361e-01 7.30714619e-01 -2.93907672e-01 -5.95598757e-01
-5.06908484e-02 -9.95791137e-01 -8.73832166e-01 -6.62852347e-01
-3.00994199e-02 5.86833656e-02 5.12879014e-01 -4.35701966... | [8.64639663696289, 3.0054728984832764] |
2a0dc300-aa7b-476e-86c4-11f535814e1e | structured-q-learning-for-antibody-design | 2209.04698 | null | https://arxiv.org/abs/2209.04698v2 | https://arxiv.org/pdf/2209.04698v2.pdf | Structured Q-learning For Antibody Design | Optimizing combinatorial structures is core to many real-world problems, such as those encountered in life sciences. For example, one of the crucial steps involved in antibody design is to find an arrangement of amino acids in a protein sequence that improves its binding with a pathogen. Combinatorial optimization of a... | ['Haitham Bou Ammar', 'Jan Peters', 'Jun Wang', 'Liu Furui', 'Asif Khan', 'Aivar Sootla', 'Philip John Gorinski', 'Alexander I. Cowen-Rivers'] | 2022-09-10 | null | null | null | null | ['molecular-docking'] | ['medical'] | [ 3.52131993e-01 -2.68093497e-01 -1.85768500e-01 -2.45414019e-01
-7.30879009e-01 -7.84531057e-01 -1.30645141e-01 6.08816445e-01
-8.03378224e-01 1.56878591e+00 -1.30165726e-01 -8.26920748e-01
-1.59921944e-01 -3.77342939e-01 -1.05884683e+00 -6.44635499e-01
-3.20421845e-01 8.43842447e-01 -1.60615683e-01 -2.74446756... | [4.7496161460876465, 5.602956771850586] |
23213d83-7655-42d8-b666-853cdc44acb5 | graphtts-graph-to-sequence-modelling-in | 2003.01924 | null | https://arxiv.org/abs/2003.01924v1 | https://arxiv.org/pdf/2003.01924v1.pdf | GraphTTS: graph-to-sequence modelling in neural text-to-speech | This paper leverages the graph-to-sequence method in neural text-to-speech (GraphTTS), which maps the graph embedding of the input sequence to spectrograms. The graphical inputs consist of node and edge representations constructed from input texts. The encoding of these graphical inputs incorporates syntax information ... | ['Jing Xiao', 'Zhen Zeng', 'Huayi Peng', 'Aolan Sun', 'Jianzong Wang', 'Ning Cheng'] | 2020-03-04 | null | null | null | null | ['graph-to-sequence'] | ['natural-language-processing'] | [ 4.91036981e-01 5.60519814e-01 -2.24449132e-02 -2.54299760e-01
-4.54066485e-01 -5.68836629e-01 1.37187913e-01 -1.03054242e-02
-7.80892447e-02 4.45486337e-01 5.35531580e-01 -6.14454746e-01
5.04060030e-01 -6.85137868e-01 -7.38265097e-01 -3.33452195e-01
1.47584528e-01 -1.01409592e-01 1.76336884e-01 -4.74509180... | [14.846321105957031, 6.715612888336182] |
421362cf-6ab7-4060-bcf5-817b83c25641 | unsupervised-deep-shape-descriptor-with-point | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Shi_Unsupervised_Deep_Shape_Descriptor_With_Point_Distribution_Learning_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Shi_Unsupervised_Deep_Shape_Descriptor_With_Point_Distribution_Learning_CVPR_2020_paper.pdf | Unsupervised Deep Shape Descriptor With Point Distribution Learning | Deep learning models have achieved great success in supervised shape descriptor learning for 3D shape retrieval, classification, and correspondence. However, the unsupervised shape descriptor calculated via deep learning is less studied than that of supervised ones due to the design challenges of unsupervised neural ne... | [' Yi Fang', ' Shuaihang Yuan', ' Mengchen Xu', 'Yi Shi'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['3d-shape-retrieval'] | ['computer-vision'] | [-4.35950637e-01 -3.23868990e-01 -4.76155467e-02 -5.92809677e-01
-7.15202034e-01 -5.91514170e-01 7.75764048e-01 1.88993901e-01
-2.68918484e-01 5.66672459e-02 -5.95089421e-02 6.52286783e-02
-5.16855717e-01 -8.74385476e-01 -6.26053452e-01 -9.82970774e-01
6.90565780e-02 1.11126900e+00 -1.26424849e-01 1.67340696... | [8.109295845031738, -3.808340549468994] |
5830ca68-c269-4f06-bc1b-71b9672d8d03 | learning-saliency-propagation-for-semi | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_Learning_Saliency_Propagation_for_Semi-Supervised_Instance_Segmentation_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhou_Learning_Saliency_Propagation_for_Semi-Supervised_Instance_Segmentation_CVPR_2020_paper.pdf | Learning Saliency Propagation for Semi-Supervised Instance Segmentation | Instance segmentation is a challenging task for both modeling and annotation. Due to the high annotation cost, modeling becomes more difficult because of the limited amount of supervision. We aim to improve the accuracy of the existing instance segmentation models by utilizing a large amount of detection supervision. W... | [' Fisher Yu', ' Trevor Darrell', ' Jianbin Jiao', ' Xin Wang', 'Yanzhao Zhou'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['semi-supervised-instance-segmentation'] | ['computer-vision'] | [ 3.78110528e-01 5.60022473e-01 -4.02075022e-01 -6.22290909e-01
-9.17076588e-01 -5.11794865e-01 4.78045195e-01 1.70587137e-01
-6.34784937e-01 5.75797737e-01 -2.93151945e-01 -3.04598734e-02
4.06475276e-01 -4.54954118e-01 -1.08822072e+00 -4.22647148e-01
2.31222883e-01 6.65852010e-01 7.65122116e-01 2.33948693... | [9.491838455200195, 0.5145073533058167] |
42940781-1826-4cac-899b-d7c2f0c23007 | 190412640 | 1904.12640 | null | http://arxiv.org/abs/1904.12640v2 | http://arxiv.org/pdf/1904.12640v2.pdf | TextCohesion: Detecting Text for Arbitrary Shapes | In this paper, we propose a pixel-wise method named TextCohesion for scene
text detection, which splits a text instance into five key components: a Text
Skeleton and four Directional Pixel Regions. These components are easier to
handle than the entire text instance. A confidence scoring mechanism is
designed to filter ... | ['Jici Xing', 'Hong Zhou', 'Weijia Wu'] | 2019-04-22 | null | null | null | null | ['curved-text-detection'] | ['computer-vision'] | [ 4.51645732e-01 -3.68200302e-01 -3.17113608e-01 -6.63699880e-02
-7.32592344e-01 -2.09487960e-01 6.34477854e-01 4.95291241e-02
-4.32482630e-01 3.64667147e-01 -4.25402932e-02 -1.67097762e-01
6.59277797e-01 -6.21260941e-01 -5.08343577e-01 -5.63061893e-01
5.88032663e-01 3.34086180e-01 1.25953138e+00 1.65322870... | [12.084009170532227, 2.3121538162231445] |
7fd7b31e-1bf3-4191-994f-10b0b0ddbd8f | center-focusing-network-for-real-time-lidar | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_Center_Focusing_Network_for_Real-Time_LiDAR_Panoptic_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_Center_Focusing_Network_for_Real-Time_LiDAR_Panoptic_Segmentation_CVPR_2023_paper.pdf | Center Focusing Network for Real-Time LiDAR Panoptic Segmentation | LiDAR panoptic segmentation facilitates an autonomous vehicle to comprehensively understand the surrounding objects and scenes and is required to run in real time. The recent proposal-free methods accelerate the algorithm, but their effectiveness and efficiency are still limited owing to the difficulty of modeling ... | ['BaoCai Yin', 'Yongli Hu', 'Boyue Wang', 'Gang Zhang', 'Xiaoyan Li'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['panoptic-segmentation'] | ['computer-vision'] | [-1.01747975e-01 -4.45302367e-01 -3.82852376e-01 -4.10427332e-01
-4.26388592e-01 -7.65263736e-01 4.27715838e-01 1.22247070e-01
-2.37466514e-01 2.66240150e-01 -4.76029307e-01 -3.14309806e-01
3.57197113e-02 -1.05879748e+00 -6.56774461e-01 -6.45456791e-01
-9.95876342e-02 8.73470068e-01 7.02866077e-01 1.27346739... | [7.9972405433654785, -2.9015884399414062] |
0229c8d9-3563-48a1-86a6-48f4a3f04998 | robustfusion-robust-volumetric-performance | 2104.14837 | null | https://arxiv.org/abs/2104.14837v1 | https://arxiv.org/pdf/2104.14837v1.pdf | RobustFusion: Robust Volumetric Performance Reconstruction under Human-object Interactions from Monocular RGBD Stream | High-quality 4D reconstruction of human performance with complex interactions to various objects is essential in real-world scenarios, which enables numerous immersive VR/AR applications. However, recent advances still fail to provide reliable performance reconstruction, suffering from challenging interaction patterns ... | ['Lu Fang', 'Shuxue Quan', 'Fan Deng', 'Zhong Li', 'Dawei Zhong', 'Lan Xu', 'Zhuo Su'] | 2021-04-30 | null | null | null | null | ['human-parsing'] | ['computer-vision'] | [ 2.54975390e-02 -2.03024745e-01 1.92511424e-01 -2.38038450e-01
-2.98617274e-01 -4.10247713e-01 2.03719497e-01 -6.24340363e-02
-3.18669856e-01 3.98262858e-01 1.40280500e-01 1.17850214e-01
-3.40246558e-01 -4.71543401e-01 -5.57817817e-01 -5.25127292e-01
1.30584449e-01 5.68103194e-01 6.48431599e-01 -2.40994737... | [7.133054256439209, -1.2114146947860718] |
6f9c29f8-c41d-448f-a707-a70ac41b530c | inverting-adversarially-robust-networks-for | 2106.06927 | null | https://arxiv.org/abs/2106.06927v5 | https://arxiv.org/pdf/2106.06927v5.pdf | Inverting Adversarially Robust Networks for Image Synthesis | Despite unconditional feature inversion being the foundation of many image synthesis applications, training an inverter demands a high computational budget, large decoding capacity and imposing conditions such as autoregressive priors. To address these limitations, we propose the use of adversarially robust representat... | ['Anh Nguyen', 'Minh N. Do', 'Raymond A. Yeh', 'Renan A. Rojas-Gomez'] | 2021-06-13 | null | null | null | null | ['deep-feature-inversion'] | ['computer-vision'] | [ 6.24973834e-01 2.86749572e-01 2.58539338e-02 -2.77460963e-01
-6.86127663e-01 -7.25353897e-01 7.86278486e-01 -5.80461264e-01
-2.22370222e-01 6.78750098e-01 1.82001621e-01 -2.24130541e-01
1.47714645e-01 -8.40171218e-01 -1.09258330e+00 -5.12129724e-01
2.17613176e-01 9.44568142e-02 -2.44030699e-01 -3.35711509... | [11.60236930847168, -0.43197551369667053] |
8b4fa6d1-53d7-4baa-982d-9ca93f58cda9 | predictive-coding-for-animation-based-video | 2307.04187 | null | https://arxiv.org/abs/2307.04187v1 | https://arxiv.org/pdf/2307.04187v1.pdf | Predictive Coding For Animation-Based Video Compression | We address the problem of efficiently compressing video for conferencing-type applications. We build on recent approaches based on image animation, which can achieve good reconstruction quality at very low bitrate by representing face motions with a compact set of sparse keypoints. However, these methods encode video i... | ['Giuseppe Valenzise', 'Stéphane Lathuilière', 'Goluck Konuko'] | 2023-07-09 | null | null | null | null | ['video-compression', 'image-animation'] | ['computer-vision', 'computer-vision'] | [ 3.57771903e-01 2.13793516e-01 -3.09196085e-01 -1.12057276e-01
-6.73288047e-01 -5.29691204e-02 5.86006284e-01 -3.06086034e-01
-1.63866475e-01 6.80499434e-01 3.68676066e-01 -1.25164017e-01
3.23828489e-01 -3.94762009e-01 -7.19264150e-01 -7.63953507e-01
-1.62103102e-01 1.97829083e-02 1.96726590e-01 3.97219732... | [11.403833389282227, -1.5897479057312012] |
0fe76a2c-17ee-44d7-9ae6-8bdbe6f6449b | device-robust-acoustic-scene-classification-1 | 2305.07499 | null | https://arxiv.org/abs/2305.07499v2 | https://arxiv.org/pdf/2305.07499v2.pdf | Device-Robust Acoustic Scene Classification via Impulse Response Augmentation | The ability to generalize to a wide range of recording devices is a crucial performance factor for audio classification models. The characteristics of different types of microphones introduce distributional shifts in the digitized audio signals due to their varying frequency responses. If this domain shift is not taken... | ['Gerhard Widmer', 'Khaled Koutini', 'Florian Schmid', 'Tobias Morocutti'] | 2023-05-12 | null | null | null | null | ['acoustic-scene-classification', 'audio-classification', 'scene-classification'] | ['audio', 'audio', 'computer-vision'] | [ 4.80019242e-01 -3.03611010e-01 5.08482099e-01 -2.50057817e-01
-7.87874997e-01 -9.37391102e-01 1.87541813e-01 4.43074554e-02
-2.78600514e-01 3.67729753e-01 1.34327114e-01 -2.20926687e-01
2.19138786e-02 -4.30849254e-01 -9.34140265e-01 -6.44371629e-01
7.07491906e-03 9.27018672e-02 1.52261406e-01 -1.09582119... | [15.14627742767334, 5.438260078430176] |
4ced243b-45c7-4470-a9e4-7387d0b8fc13 | sentence-embedder-guided-utterance-encoder | 2305.12301 | null | https://arxiv.org/abs/2305.12301v1 | https://arxiv.org/pdf/2305.12301v1.pdf | Sentence Embedder Guided Utterance Encoder (SEGUE) for Spoken Language Understanding | The pre-trained speech encoder wav2vec 2.0 performs very well on various spoken language understanding (SLU) tasks. However, on many tasks, it trails behind text encoders with textual input. To improve the understanding capability of SLU encoders, various studies have used knowledge distillation to transfer knowledge f... | ['Soujanya Poria', 'Navonil Majumder', 'Yi Xuan Tan'] | 2023-05-20 | null | null | null | null | ['spoken-language-understanding', 'spoken-language-understanding'] | ['natural-language-processing', 'speech'] | [ 1.91878363e-01 5.24168491e-01 -3.28770326e-03 -3.87781322e-01
-9.89184618e-01 -5.41939974e-01 7.46003389e-01 1.24363333e-01
-5.99161446e-01 7.67742991e-01 7.91078806e-01 -5.30615389e-01
4.50948417e-01 -7.33120322e-01 -8.69285882e-01 -2.74369597e-01
5.88071942e-02 4.68400776e-01 7.74967298e-02 -5.20586312... | [13.98713207244873, 6.94209623336792] |
dee8a47e-d88c-4f86-960f-2fbed7ab3235 | explainable-artificial-intelligence-and-1 | 2211.10595 | null | https://arxiv.org/abs/2211.10595v1 | https://arxiv.org/pdf/2211.10595v1.pdf | Explainable Artificial Intelligence and Causal Inference based ATM Fraud Detection | Gaining the trust of customers and providing them empathy are very critical in the financial domain. Frequent occurrence of fraudulent activities affects these two factors. Hence, financial organizations and banks must take utmost care to mitigate them. Among them, ATM fraudulent transaction is a common problem faced b... | ['Laveti Ramesh Naidu', 'Abhay Anand Mane', 'Vadlamani Ravi', 'Yelleti Vivek'] | 2022-11-19 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [ 9.73714516e-02 -1.86849222e-01 -1.60976619e-01 -2.71606326e-01
-7.80533850e-02 -3.15025598e-01 4.59289640e-01 2.03490973e-01
-1.37966380e-01 1.29488158e+00 1.78182140e-01 -7.13703513e-01
-3.30198973e-01 -9.65289176e-01 -5.45418501e-01 -6.23506308e-01
7.31516257e-02 3.36508274e-01 -2.02362537e-01 -1.48875564... | [8.110737800598145, 4.943126201629639] |
be1df11f-4ab0-4466-a5db-c43e315b5293 | a-maximal-inequality-for-local-empirical | 2307.01328 | null | https://arxiv.org/abs/2307.01328v1 | https://arxiv.org/pdf/2307.01328v1.pdf | A maximal inequality for local empirical processes under weak dependence | We introduce a maximal inequality for a local empirical process under strongly mixing data. Local empirical processes are defined as the (local) averages $\frac{1}{nh}\sum_{i=1}^n \mathbf{1}\{x - h \leq X_i \leq x+h\}f(Z_i)$, where $f$ belongs to a class of functions, $x \in \mathbb{R}$ and $h > 0$ is a bandwidth. Our ... | ['Cristine Pinto', 'Luis Alvarez'] | 2023-07-03 | null | null | null | null | ['density-estimation'] | ['methodology'] | [-5.62479161e-02 2.62199193e-01 -1.53177738e-01 1.94127616e-02
-1.30627549e+00 -5.60625017e-01 -4.04089838e-02 1.82652041e-01
-6.08224452e-01 1.24752235e+00 -5.00851274e-01 -3.67940068e-01
-5.11072457e-01 -1.09882510e+00 -8.78790736e-01 -1.11689389e+00
-6.42332673e-01 5.39291859e-01 1.46632537e-01 1.91245191... | [6.768400192260742, 4.3069939613342285] |
ebd2b5ee-3e16-4354-9554-db8dbc10df1e | closed-loop-acas-xu-nncs-is-unsafe-quantized | 2201.06626 | null | https://arxiv.org/abs/2201.06626v3 | https://arxiv.org/pdf/2201.06626v3.pdf | Neural Network Compression of ACAS Xu Early Prototype is Unsafe: Closed-Loop Verification through Quantized State Backreachability | ACAS Xu is an air-to-air collision avoidance system designed for unmanned aircraft that issues horizontal turn advisories to avoid an intruder aircraft. Due the use of a large lookup table in the design, a neural network compression of the policy was proposed. Analysis of this system has spurred a significant body of r... | ['Hoang-Dung Tran', 'Stanley Bak'] | 2022-01-17 | null | null | null | null | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 4.44248289e-01 6.67737544e-01 -3.48617554e-01 -6.27140328e-02
1.88452408e-01 -8.76950204e-01 3.40427667e-01 -8.26501548e-02
-1.68544278e-01 8.14756691e-01 -5.88070273e-01 -1.46825385e+00
-2.90233135e-01 -7.11905360e-01 -8.32416534e-01 -4.78986621e-01
-4.43612993e-01 1.01548359e-01 2.56561339e-01 -6.03690982... | [4.911627769470215, 2.2981042861938477] |
3f7a1a35-9c83-4349-a149-1c0af5985416 | hearing-lips-in-noise-universal-viseme | 2306.10563 | null | https://arxiv.org/abs/2306.10563v1 | https://arxiv.org/pdf/2306.10563v1.pdf | Hearing Lips in Noise: Universal Viseme-Phoneme Mapping and Transfer for Robust Audio-Visual Speech Recognition | Audio-visual speech recognition (AVSR) provides a promising solution to ameliorate the noise-robustness of audio-only speech recognition with visual information. However, most existing efforts still focus on audio modality to improve robustness considering its dominance in AVSR task, with noise adaptation techniques su... | ['Eng Siong Chng', 'Qiushi Zhu', 'Chengwei Qin', 'Chen Chen', 'Ruizhe Li', 'Yuchen Hu'] | 2023-06-18 | null | null | null | null | ['visual-speech-recognition', 'audio-visual-speech-recognition'] | ['speech', 'speech'] | [ 4.54087704e-01 -3.99641931e-01 3.11514586e-01 -6.62736148e-02
-1.25114274e+00 -4.61834371e-01 5.62711954e-01 -3.99469227e-01
-4.68801171e-01 5.41932523e-01 5.01603067e-01 -2.69108146e-01
2.64373392e-01 -3.59856188e-01 -7.79694796e-01 -9.96343315e-01
5.95842063e-01 -3.45191598e-01 2.73010910e-01 -3.27047259... | [14.443132400512695, 5.3191237449646] |
42f245d1-ac9f-4dca-b46c-56f039076342 | action-sensitivity-learning-for-temporal | 2305.15701 | null | https://arxiv.org/abs/2305.15701v1 | https://arxiv.org/pdf/2305.15701v1.pdf | Action Sensitivity Learning for Temporal Action Localization | Temporal action localization (TAL), which involves recognizing and locating action instances, is a challenging task in video understanding. Most existing approaches directly predict action classes and regress offsets to boundaries, while overlooking the discrepant importance of each frame. In this paper, we propose an ... | ['Yi Yang', 'Jiang Yang', 'Junjun Zheng', 'Ruijie Quan', 'Xiaohan Wang', 'Jiayi Shao'] | 2023-05-25 | null | null | null | null | ['video-understanding', 'action-localization', 'action-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.69423217e-01 -3.41136307e-01 -4.92599219e-01 -3.59506547e-01
-7.53483653e-01 -3.92873853e-01 6.26678169e-01 -2.19759017e-01
-5.06178021e-01 6.71685040e-01 5.29707372e-01 2.56927758e-01
1.30845644e-02 -4.24641877e-01 -6.42228425e-01 -9.35242116e-01
5.61561100e-02 -4.33670543e-02 7.15413272e-01 5.01963794... | [8.375280380249023, 0.586197018623352] |
661f70e9-492f-4fee-98f1-f17170313d9b | modeling-hierarchical-reasoning-chains-by-1 | 2306.12069 | null | https://arxiv.org/abs/2306.12069v1 | https://arxiv.org/pdf/2306.12069v1.pdf | Modeling Hierarchical Reasoning Chains by Linking Discourse Units and Key Phrases for Reading Comprehension | Machine reading comprehension (MRC) poses new challenges over logical reasoning, which aims to understand the implicit logical relations entailed in the given contexts and perform inference over them. Due to the complexity of logic, logical relations exist at different granularity levels. However, most existing methods... | ['Hai Zhao', 'Zhuosheng Zhang', 'Jialin Chen'] | 2023-06-21 | modeling-hierarchical-reasoning-chains-by | https://aclanthology.org/2022.coling-1.126 | https://aclanthology.org/2022.coling-1.126.pdf | coling-2022-10 | ['natural-language-inference', 'relation-extraction', 'reading-comprehension', 'machine-reading-comprehension', 'logical-reasoning'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'reasoning'] | [ 1.46657348e-01 6.93214536e-01 -2.42919743e-01 -5.16765535e-01
9.36626047e-02 -5.29565573e-01 7.36759603e-01 6.20857358e-01
3.23581904e-01 8.66384745e-01 6.33881450e-01 -8.72784615e-01
-6.61876500e-01 -1.56241488e+00 -6.31759644e-01 -4.02744533e-03
-4.58043590e-02 3.54649723e-01 6.07238770e-01 -6.98575258... | [9.483741760253906, 7.6108245849609375] |
05d4740c-ea58-430f-9db0-13805196a8b4 | coupled-attention-networks-for-multivariate | 2306.07114 | null | https://arxiv.org/abs/2306.07114v1 | https://arxiv.org/pdf/2306.07114v1.pdf | Coupled Attention Networks for Multivariate Time Series Anomaly Detection | Multivariate time series anomaly detection (MTAD) plays a vital role in a wide variety of real-world application domains. Over the past few years, MTAD has attracted rapidly increasing attention from both academia and industry. Many deep learning and graph learning models have been developed for effective anomaly detec... | ['Linlin You', 'Mujie Liu', 'Mingliang Hou', 'Shuo Yu', 'Xin Chen', 'Feng Xia'] | 2023-06-12 | null | null | null | null | ['graph-attention', 'anomaly-detection', 'time-series-anomaly-detection'] | ['graphs', 'methodology', 'time-series'] | [ 1.04838423e-02 -3.05204123e-01 8.55720937e-02 -2.25792438e-01
-8.03450271e-02 -4.31163423e-02 4.20570523e-01 6.16922975e-01
-9.13133472e-03 1.68869406e-01 1.95765600e-01 -4.03120786e-01
-1.54996052e-01 -7.72347927e-01 -6.43380463e-01 -5.78963935e-01
-7.45700121e-01 1.04138426e-01 1.65995926e-01 -2.68303990... | [7.187770843505859, 2.720886707305908] |
0fa4e8f3-ed7f-4006-9446-c13b36c0c31a | domain-adaptive-deep-network-compression | 1709.01041 | null | http://arxiv.org/abs/1709.01041v2 | http://arxiv.org/pdf/1709.01041v2.pdf | Domain-adaptive deep network compression | Deep Neural Networks trained on large datasets can be easily transferred to
new domains with far fewer labeled examples by a process called fine-tuning.
This has the advantage that representations learned in the large source domain
can be exploited on smaller target domains. However, networks designed to be
optimal for... | ['Jose M. Alvarez', 'Joost Van de Weijer', 'Marc Masana', 'Andrew D. Bagdanov', 'Luis Herranz'] | 2017-09-04 | domain-adaptive-deep-network-compression-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Masana_Domain-Adaptive_Deep_Network_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Masana_Domain-Adaptive_Deep_Network_ICCV_2017_paper.pdf | iccv-2017-10 | ['low-rank-compression'] | ['computer-code'] | [ 5.36753118e-01 2.62512326e-01 -2.60877818e-01 -4.50783104e-01
-7.04019129e-01 -4.29442406e-01 5.03971875e-01 -4.36033458e-02
-8.58316183e-01 8.96194696e-01 3.71753365e-01 -1.22101814e-01
-3.40512544e-01 -6.88872159e-01 -1.09072912e+00 -5.90886295e-01
9.12382379e-02 6.90589666e-01 1.57442033e-01 4.04046103... | [8.610246658325195, 3.21467661857605] |
2429d414-f696-4256-a545-24b35728df03 | biff-bi-level-future-fusion-with-polyline | 2306.14161 | null | https://arxiv.org/abs/2306.14161v1 | https://arxiv.org/pdf/2306.14161v1.pdf | BiFF: Bi-level Future Fusion with Polyline-based Coordinate for Interactive Trajectory Prediction | Predicting future trajectories of surrounding agents is essential for safety-critical autonomous driving. Most existing work focuses on predicting marginal trajectories for each agent independently. However, it has rarely been explored in predicting joint trajectories for interactive agents. In this work, we propose Bi... | ['Shaojie Shen', 'Di Luan', 'Yiyao Zhu'] | 2023-06-25 | null | null | null | null | ['trajectory-prediction'] | ['computer-vision'] | [-4.09473449e-01 3.11829168e-02 -4.00568157e-01 -4.46591318e-01
-5.28906703e-01 -1.18359223e-01 9.51498270e-01 -5.05397581e-02
-2.55393505e-01 6.46945477e-01 5.66740870e-01 -1.81158319e-01
-1.43342149e-02 -9.40254033e-01 -7.44593024e-01 -5.45857370e-01
-5.09904027e-01 3.60017896e-01 9.80773687e-01 -3.69511724... | [5.896537780761719, 0.811883807182312] |
de4d02c5-354b-4d5c-8bab-9d1427401498 | sesame-street-to-mount-sinai-bert-constrained | null | null | https://aclanthology.org/2021.wnut-1.52 | https://aclanthology.org/2021.wnut-1.52.pdf | Sesame Street to Mount Sinai: BERT-constrained character-level Moses models for multilingual lexical normalization | This paper describes the HEL-LJU submissions to the MultiLexNorm shared task on multilingual lexical normalization. Our system is based on a BERT token classification preprocessing step, where for each token the type of the necessary transformation is predicted (none, uppercase, lowercase, capitalize, modify), and a ch... | ['Nikola Ljubešić', 'Yves Scherrer'] | null | null | null | null | wnut-acl-2021-11 | ['lexical-normalization'] | ['natural-language-processing'] | [ 4.70604599e-02 1.21645972e-01 -3.85253161e-01 -5.80414712e-01
-9.07487571e-01 -7.63131976e-01 6.72108531e-01 6.80589080e-01
-9.29291666e-01 1.22566652e+00 4.89860296e-01 -5.35546720e-01
1.66093692e-01 -2.86364049e-01 -5.54337382e-01 -1.54926270e-01
5.65561652e-01 8.68598163e-01 -1.39783267e-02 -5.73881209... | [10.338214874267578, 10.022643089294434] |
e34bb439-0767-4a93-878b-989455402376 | multimodal-representation-for-neural-code | 2107.00992 | null | https://arxiv.org/abs/2107.00992v3 | https://arxiv.org/pdf/2107.00992v3.pdf | Multimodal Representation for Neural Code Search | Semantic code search is about finding semantically relevant code snippets for a given natural language query. In the state-of-the-art approaches, the semantic similarity between code and query is quantified as the distance of their representation in the shared vector space. In this paper, to improve the vector space, w... | ['Martin Monperrus', 'Zimin Chen', 'Jian Gu'] | 2021-07-02 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [-2.96865672e-01 -3.97476584e-01 -4.30135310e-01 -3.45281184e-01
-8.25908422e-01 -7.05489635e-01 2.08741590e-01 5.57257891e-01
-1.44920692e-01 -9.24223885e-02 5.22004724e-01 -4.82855916e-01
-2.11156532e-01 -5.69572747e-01 -2.89422095e-01 -6.04749881e-02
-9.22503471e-02 5.07439002e-02 4.89386886e-01 -2.97929555... | [7.502721309661865, 8.068973541259766] |
2bb5940c-fd5f-4843-83c8-e163824581e2 | hybrid-parallel-imaging-and-compressed | 2209.08807 | null | https://arxiv.org/abs/2209.08807v2 | https://arxiv.org/pdf/2209.08807v2.pdf | A Deep Learning Approach for Parallel Imaging and Compressed Sensing MRI Reconstruction | Parallel imaging accelerates MRI data acquisition by acquiring additional sensitivity information with an array of receiver coils, resulting in fewer phase encoding steps. Because of fewer data requirements than parallel imaging, compressed sensing magnetic resonance imaging (CS-MRI) has gained popularity in the field ... | ['Md. Kamrul Hasan', 'Farhan Sadik'] | 2022-09-19 | null | null | null | null | ['de-aliasing', 'mri-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 8.29619586e-01 1.66978285e-01 9.97092351e-02 -3.64503592e-01
-8.90815735e-01 -7.02428892e-02 1.41567856e-01 -1.33156449e-01
-6.27017140e-01 5.66149652e-01 2.36825064e-01 -3.22629511e-02
-7.96789527e-02 -8.46726835e-01 -6.90052867e-01 -8.91776264e-01
-1.22336663e-01 1.96979910e-01 1.12678826e-01 5.40622137... | [13.54054069519043, -2.407158374786377] |
9f46f42b-f38d-4ebd-aad7-b507f200e76e | pixel-pair-occlusion-relationship-map-p2orm | 2007.12088 | null | https://arxiv.org/abs/2007.12088v1 | https://arxiv.org/pdf/2007.12088v1.pdf | Pixel-Pair Occlusion Relationship Map(P2ORM): Formulation, Inference & Application | We formalize concepts around geometric occlusion in 2D images (i.e., ignoring semantics), and propose a novel unified formulation of both occlusion boundaries and occlusion orientations via a pixel-pair occlusion relation. The former provides a way to generate large-scale accurate occlusion datasets while, based on the... | ['Xuchong Qiu', 'Renaud Marlet', 'Chaohui Wang', 'Yang Xiao'] | 2020-07-23 | null | null | null | null | ['occlusion-estimation'] | ['computer-vision'] | [ 1.54215232e-01 1.85138751e-02 -2.80926883e-01 -2.69229084e-01
-6.11679733e-01 -3.57860893e-01 5.68834603e-01 -2.77384847e-01
-9.33506414e-02 7.42070436e-01 1.26011223e-01 -6.82926029e-02
2.31039822e-01 -7.98090279e-01 -6.93125844e-01 -6.82973981e-01
4.12996560e-01 3.35500389e-01 4.87062216e-01 4.90587279... | [8.899261474609375, -2.5814945697784424] |
3d985e4c-07a0-4a25-bd24-86a07614eff3 | coder-an-efficient-framework-for-improving | 2112.08766 | null | https://arxiv.org/abs/2112.08766v3 | https://arxiv.org/pdf/2112.08766v3.pdf | CODER: An efficient framework for improving retrieval through COntextual Document Embedding Reranking | Contrastive learning has been the dominant approach to training dense retrieval models. In this work, we investigate the impact of ranking context - an often overlooked aspect of learning dense retrieval models. In particular, we examine the effect of its constituent parts: jointly scoring a large number of negatives p... | ['Carsten Eickhoff', 'Daniel Cohen', 'Navid Rekabsaz', 'George Zerveas'] | 2021-12-16 | null | null | null | null | ['document-embedding'] | ['methodology'] | [-5.27187064e-02 -5.23966074e-01 -3.56926680e-01 -9.99946147e-02
-1.49702859e+00 -5.95316827e-01 8.95655692e-01 5.63969433e-01
-9.65626895e-01 5.44757068e-01 3.68759722e-01 -3.47834826e-01
-3.21991801e-01 -6.89960539e-01 -7.95053720e-01 -4.77305084e-01
-3.56895953e-01 7.67251194e-01 4.18544263e-01 -5.66996157... | [11.485593795776367, 7.567935943603516] |
0fc16826-4ccd-48f3-a6ab-2628ac76cf67 | eigenlanes-data-driven-lane-descriptors-for | 2203.15302 | null | https://arxiv.org/abs/2203.15302v1 | https://arxiv.org/pdf/2203.15302v1.pdf | Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes | A novel algorithm to detect road lanes in the eigenlane space is proposed in this paper. First, we introduce the notion of eigenlanes, which are data-driven descriptors for structurally diverse lanes, including curved, as well as straight, lanes. To obtain eigenlanes, we perform the best rank-M approximation of a lane ... | ['Chang-Su Kim', 'Heeyeon Kwon', 'Seong-Gyun Jeong', 'Wonhui Park', 'Dongkwon Jin'] | 2022-03-29 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Jin_Eigenlanes_Data-Driven_Lane_Descriptors_for_Structurally_Diverse_Lanes_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Jin_Eigenlanes_Data-Driven_Lane_Descriptors_for_Structurally_Diverse_Lanes_CVPR_2022_paper.pdf | cvpr-2022-1 | ['lane-detection'] | ['computer-vision'] | [-1.64182261e-01 -1.26506373e-01 -3.01556766e-01 -2.81243205e-01
-7.12699711e-01 -8.49943995e-01 3.16195667e-01 -5.02628624e-01
5.28522767e-02 3.67053837e-01 4.53722298e-01 -6.42573535e-01
-2.53358424e-01 -8.61336350e-01 -4.84704375e-01 -6.08262718e-01
-3.09274077e-01 7.43459016e-02 5.05677998e-01 -1.99111849... | [8.073652267456055, -1.5633418560028076] |
41f49e2d-1a97-4db6-aad6-2dd24a7fec4d | end-to-end-chinese-speaker-identification | null | null | https://aclanthology.org/2022.naacl-main.165 | https://aclanthology.org/2022.naacl-main.165.pdf | End-to-End Chinese Speaker Identification | Speaker identification (SI) in texts aims to identify the speaker(s) for each utterance in texts. Previous studies divide SI into several sub-tasks (e.g., quote extraction, named entity recognition, gender identification, and coreference resolution). However, we are still far from solving these sub-tasks, making SI sys... | ['Dong Yu', 'Ben Zhou', 'Dian Yu'] | null | null | null | null | naacl-2022-7 | ['coreference-resolution', 'machine-reading-comprehension', 'speaker-identification'] | ['natural-language-processing', 'natural-language-processing', 'speech'] | [ 3.77525598e-01 3.47841680e-01 -2.16476917e-01 -5.12564778e-01
-1.69090271e+00 -7.53233731e-01 4.51628566e-01 -9.11722928e-02
-5.20791054e-01 6.27357423e-01 6.51787698e-01 -2.70242184e-01
4.65608925e-01 -1.58507839e-01 -6.84168696e-01 -5.16433656e-01
2.16434523e-01 8.56214106e-01 2.67014474e-01 -2.60173172... | [14.102513313293457, 6.897889137268066] |
7458ecf7-223b-473b-a35f-03b15d69ff06 | deep-attentive-ranking-networks-for-learning | 2001.00056 | null | https://arxiv.org/abs/2001.00056v1 | https://arxiv.org/pdf/2001.00056v1.pdf | Deep Attentive Ranking Networks for Learning to Order Sentences | We present an attention-based ranking framework for learning to order sentences given a paragraph. Our framework is built on a bidirectional sentence encoder and a self-attention based transformer network to obtain an input order invariant representation of paragraphs. Moreover, it allows seamless training using a vari... | ['Dhanajit Brahma', 'Pawan Kumar', 'Piyush Rai', 'Harish Karnick'] | 2019-12-31 | deep-attentive-ranking-networks-for-learning-1 | null | null | aaai-2020-2019-12 | ['sentence-ordering'] | ['natural-language-processing'] | [ 2.02049598e-01 1.70159656e-05 -5.07638335e-01 -9.26970661e-01
-1.02559519e+00 -6.16494536e-01 8.70897770e-01 5.59335589e-01
-5.31563520e-01 7.60244012e-01 8.50616932e-01 -3.89281780e-01
-2.86487639e-01 -6.90705061e-01 -1.00444233e+00 -2.76914597e-01
-1.24084175e-01 4.97255504e-01 1.06719188e-01 -4.14164424... | [11.190410614013672, 8.77800464630127] |
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