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47f95bb5-3d3b-4439-a043-992b2ef39eab | towards-high-fidelity-single-view-holistic | 2207.08656 | null | https://arxiv.org/abs/2207.08656v2 | https://arxiv.org/pdf/2207.08656v2.pdf | Towards High-Fidelity Single-view Holistic Reconstruction of Indoor Scenes | We present a new framework to reconstruct holistic 3D indoor scenes including both room background and indoor objects from single-view images. Existing methods can only produce 3D shapes of indoor objects with limited geometry quality because of the heavy occlusion of indoor scenes. To solve this, we propose an instanc... | ['Xiaoguang Han', 'Shuguang Cui', 'GuanYing Chen', 'Yujian Zheng', 'Haolin Liu'] | 2022-07-18 | null | null | null | null | ['object-reconstruction'] | ['computer-vision'] | [ 2.47269586e-01 -3.13175544e-02 3.73541385e-01 -5.17273307e-01
-6.53047144e-01 -6.85357988e-01 4.35614139e-01 -3.42447519e-01
3.03867757e-01 6.57948196e-01 2.26050213e-01 -2.34027877e-01
1.75002292e-01 -9.61597502e-01 -1.00553524e+00 -6.17701948e-01
4.89668638e-01 4.75968063e-01 2.74407834e-01 5.01487255... | [8.810627937316895, -3.018479585647583] |
1897c957-9f6c-4aad-9fab-f08ec3ca8296 | temporal-perceiver-a-general-architecture-for | 2203.00307 | null | https://arxiv.org/abs/2203.00307v2 | https://arxiv.org/pdf/2203.00307v2.pdf | Temporal Perceiver: A General Architecture for Arbitrary Boundary Detection | Generic Boundary Detection (GBD) aims at locating the general boundaries that divide videos into semantically coherent and taxonomy-free units, and could serve as an important pre-processing step for long-form video understanding. Previous works often separately handle these different types of generic boundaries with s... | ['LiMin Wang', 'Gangshan Wu', 'Yuhong Wang', 'Jing Tan'] | 2022-03-01 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 7.63687771e-03 -2.62917936e-01 -2.13559479e-01 -2.55264193e-01
-7.86551058e-01 -5.92206240e-01 5.50183058e-01 -1.13762267e-01
-3.32747966e-01 1.41965359e-01 2.47738346e-01 -3.28214578e-02
4.43119258e-02 -6.13248348e-01 -9.68186855e-01 -4.72101480e-01
-4.40525055e-01 -5.10734655e-02 3.94438237e-01 -1.56321704... | [9.248205184936523, 0.5174456834793091] |
2dedddb6-3d02-42dd-962d-82fcb75e4437 | deepsetnet-predicting-sets-with-deep-neural | 1611.08998 | null | http://arxiv.org/abs/1611.08998v5 | http://arxiv.org/pdf/1611.08998v5.pdf | DeepSetNet: Predicting Sets with Deep Neural Networks | This paper addresses the task of set prediction using deep learning. This is
important because the output of many computer vision tasks, including image
tagging and object detection, are naturally expressed as sets of entities
rather than vectors. As opposed to a vector, the size of a set is not fixed in
advance, and i... | ['Vijay Kumar B G', 'S. Hamid Rezatofighi', 'Anton Milan', 'Anthony Dick', 'Ehsan Abbasnejad', 'Ian Reid'] | 2016-11-28 | deepsetnet-predicting-sets-with-deep-neural-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Rezatofighi_DeepSetNet_Predicting_Sets_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Rezatofighi_DeepSetNet_Predicting_Sets_ICCV_2017_paper.pdf | iccv-2017-10 | ['object-counting'] | ['computer-vision'] | [ 3.77976477e-01 3.24754417e-02 -1.80276394e-01 -6.97107196e-01
-3.40750843e-01 -7.15815604e-01 6.21622264e-01 5.25574863e-01
-7.65614331e-01 6.40514135e-01 -1.32778287e-01 -1.55158758e-01
-1.32551551e-01 -1.00248885e+00 -1.00034022e+00 -4.79710430e-01
-1.56922907e-01 7.70694971e-01 2.60674536e-01 1.44445568... | [9.380012512207031, 2.3904314041137695] |
a72ec42a-41a8-4b6d-a925-ddbd3c2e45d8 | time-series-classification-for-detecting | 2304.11265 | null | https://arxiv.org/abs/2304.11265v1 | https://arxiv.org/pdf/2304.11265v1.pdf | Time Series Classification for Detecting Parkinson's Disease from Wrist Motions | Parkinson's disease (PD) is a neurodegenerative disease with frequently changing motor symptoms where continuous symptom monitoring enables more targeted treatment. Classical time series classification (TSC) and deep learning techniques have limited performance for PD symptom monitoring using wearable accelerometer dat... | ['Sandra Hirche', 'Satoshi Endo', 'Neha Das', 'Cedric Donié'] | 2023-04-21 | null | null | null | null | ['time-series-classification'] | ['time-series'] | [-2.61897117e-01 -2.95464218e-01 -5.08103251e-01 -1.19249215e-02
-8.08050215e-01 -6.97055161e-02 4.03886378e-01 -2.77612567e-01
-6.63031340e-01 7.78432906e-01 4.60963219e-01 -9.00447890e-02
-6.66085362e-01 -4.59892780e-01 -1.90585345e-01 -7.59742022e-01
-4.66828644e-01 9.02024567e-01 4.58718389e-01 -3.49673569... | [7.125436305999756, 0.34610024094581604] |
349aafd4-053d-4093-bcee-f02341286d55 | minimalist-and-high-quality-panoramic-imaging | 2306.12992 | null | https://arxiv.org/abs/2306.12992v1 | https://arxiv.org/pdf/2306.12992v1.pdf | Minimalist and High-Quality Panoramic Imaging with PSF-aware Transformers | High-quality panoramic images with a Field of View (FoV) of 360-degree are essential for contemporary panoramic computer vision tasks. However, conventional imaging systems come with sophisticated lens designs and heavy optical components. This disqualifies their usage in many mobile and wearable applications where thi... | ['Kaiwei Wang', 'Lei Sun', 'Hao Shi', 'Zhonghua Yi', 'Kailun Yang', 'Yao Gao', 'Shaohua Gao', 'Qi Jiang'] | 2023-06-22 | null | null | null | null | ['super-resolution'] | ['computer-vision'] | [ 4.28445071e-01 -2.96893299e-01 2.61243761e-01 -2.52042830e-01
-4.51628089e-01 -3.23501468e-01 3.84922206e-01 -7.84088075e-01
-2.37550586e-01 2.94932455e-01 1.26776829e-01 -3.25375229e-01
-2.34369308e-01 -6.28712356e-01 -8.76924634e-01 -7.56890476e-01
2.02522054e-01 -1.64334103e-01 5.44222891e-01 -2.39063725... | [10.727479934692383, -2.542196035385132] |
f4ea8d21-d928-4ec2-8e24-c495d445ce1c | dwelling-type-classification-for-disaster | 2211.11636 | null | https://arxiv.org/abs/2211.11636v1 | https://arxiv.org/pdf/2211.11636v1.pdf | Dwelling Type Classification for Disaster Risk Assessment Using Satellite Imagery | Vulnerability and risk assessment of neighborhoods is essential for effective disaster preparedness. Existing traditional systems, due to dependency on time-consuming and cost-intensive field surveying, do not provide a scalable way to decipher warnings and assess the precise extent of the risk at a hyper-local level. ... | ['Juan Lavista Ferres', 'Rahul Dodhia', 'Sumedh Ranjan Ghatage', 'Sundeep Reddy Mallu', 'Anshu Sharma', 'Tina Sederholm', 'Md Nasir'] | 2022-11-16 | null | null | null | null | ['type'] | ['speech'] | [ 1.97415665e-01 -1.54961543e-02 7.97189996e-02 -2.95986146e-01
-7.31927633e-01 -4.52763647e-01 2.83984184e-01 9.31590557e-01
-5.65576077e-01 5.96966505e-01 7.36830413e-01 -1.05523539e+00
-2.10974798e-01 -1.56027949e+00 -2.14691952e-01 -5.66904187e-01
-2.10698307e-01 1.65732741e-01 1.59643382e-01 -5.16570449... | [9.444388389587402, -1.3393168449401855] |
064daa1b-1866-4fc3-8e2e-47ce59809497 | colonoscopy-coverage-revisited-identifying | 2305.10026 | null | https://arxiv.org/abs/2305.10026v1 | https://arxiv.org/pdf/2305.10026v1.pdf | Colonoscopy Coverage Revisited: Identifying Scanning Gaps in Real-Time | Colonoscopy is the most widely used medical technique for preventing Colorectal Cancer, by detecting and removing polyps before they become malignant. Recent studies show that around one quarter of the existing polyps are routinely missed. While some of these do appear in the endoscopist's field of view, others are mis... | ['E. Rivlin', 'M. Elad', 'R. Goldenberg', 'I. Kligvasser', 'G. Leifman'] | 2023-05-17 | null | null | null | null | ['3d-reconstruction', 'specificity'] | ['computer-vision', 'natural-language-processing'] | [ 3.14490885e-01 3.85492712e-01 -1.42206065e-03 3.14073563e-01
-5.89893818e-01 -9.01601315e-01 1.39694393e-01 8.51968884e-01
-4.10666704e-01 4.52487767e-01 -1.10366426e-01 -6.91648364e-01
6.40697777e-02 -6.84241295e-01 -8.22766304e-01 -6.78493142e-01
-3.04565877e-01 1.86129153e-01 7.98343301e-01 9.09367502... | [14.024516105651855, -3.092751979827881] |
ef3c98d7-bf9c-4769-8903-39dc06e164a5 | harnessing-mixed-offline-reinforcement | 2306.13085 | null | https://arxiv.org/abs/2306.13085v1 | https://arxiv.org/pdf/2306.13085v1.pdf | Harnessing Mixed Offline Reinforcement Learning Datasets via Trajectory Weighting | Most offline reinforcement learning (RL) algorithms return a target policy maximizing a trade-off between (1) the expected performance gain over the behavior policy that collected the dataset, and (2) the risk stemming from the out-of-distribution-ness of the induced state-action occupancy. It follows that the performa... | ['Romain Laroche', 'Rémi Tachet des Combes', 'Pulkit Agrawal', 'Zhang-Wei Hong'] | 2023-06-22 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [-1.04792669e-01 1.94283575e-01 -4.68938053e-01 4.97831404e-02
-9.15366948e-01 -8.84654343e-01 6.79623246e-01 2.44284913e-01
-7.71606147e-01 1.05265284e+00 1.64132893e-01 -5.39405942e-01
-3.73176634e-01 -9.19783413e-01 -9.18621063e-01 -9.18551624e-01
-4.02262628e-01 5.31342924e-01 2.90418088e-01 -1.08571835... | [4.132997035980225, 2.3799033164978027] |
aaa6dafa-a06d-44f0-904a-322eb503eb4e | compiling-a-highly-accurate-bilingual-lexicon | null | null | https://aclanthology.org/2022.gwll-1.6 | https://aclanthology.org/2022.gwll-1.6.pdf | Compiling a Highly Accurate Bilingual Lexicon by Combining Different Approaches | Bilingual lexicons can be generated automatically using a wide variety of approaches. We perform a rigorous manual evaluation of four different methods: word alignments on different types of bilingual data, pivoting, machine translation and cross-lingual word embeddings. We investigate how the different setups perform ... | ['Andy Way', 'Hrafn Loftsson', 'Finnur Ingimundarson', 'Luke O’Brien', 'Steinþór Steingrímsson'] | null | null | null | null | gwll-lrec-2022-6 | ['cross-lingual-word-embeddings'] | ['natural-language-processing'] | [-4.63998318e-02 4.50885780e-02 -3.08094472e-01 -4.09004152e-01
-1.38694870e+00 -1.18666875e+00 1.07225215e+00 3.50167155e-01
-8.25388610e-01 1.06524205e+00 5.79764187e-01 -4.86052126e-01
9.14643332e-02 -4.89304394e-01 -6.67376161e-01 -3.78783733e-01
3.44004095e-01 1.26980603e+00 1.61169425e-01 -5.69300830... | [11.112781524658203, 10.133016586303711] |
3a3a0495-d0ad-4cf0-9c5a-d7e6ebca0fdb | pagp-a-physics-assisted-gaussian-process | 2204.02583 | null | https://arxiv.org/abs/2204.02583v1 | https://arxiv.org/pdf/2204.02583v1.pdf | PAGP: A physics-assisted Gaussian process framework with active learning for forward and inverse problems of partial differential equations | In this work, a Gaussian process regression(GPR) model incorporated with given physical information in partial differential equations(PDEs) is developed: physics-assisted Gaussian processes(PAGP). The targets of this model can be divided into two types of problem: finding solutions or discovering unknown coefficients o... | ['Guang Lin', 'Shiqi Zhang', 'Jiahao Zhang'] | 2022-04-06 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [-1.98840424e-01 -1.65953469e-02 4.25126493e-01 9.57410634e-02
-8.72662067e-01 -2.17296958e-01 4.66853440e-01 1.28685802e-01
-2.99603552e-01 1.09093714e+00 -5.06452858e-01 8.54051858e-02
-6.23666704e-01 -8.08295488e-01 -6.02304816e-01 -1.29216921e+00
-1.48133785e-01 7.32449293e-01 3.02628309e-01 1.32465169... | [6.609189033508301, 3.545553207397461] |
e27d25cb-f12d-4b23-97ae-990eb664da85 | dramatic-conversation-disentanglement | 2305.16648 | null | https://arxiv.org/abs/2305.16648v1 | https://arxiv.org/pdf/2305.16648v1.pdf | Dramatic Conversation Disentanglement | We present a new dataset for studying conversation disentanglement in movies and TV series. While previous work has focused on conversation disentanglement in IRC chatroom dialogues, movies and TV shows provide a space for studying complex pragmatic patterns of floor and topic change in face-to-face multi-party interac... | ['David Bamman', 'Danica Chen', 'Kent K. Chang'] | 2023-05-26 | null | null | null | null | ['disentanglement', 'conversation-disentanglement'] | ['methodology', 'natural-language-processing'] | [ 5.08551002e-02 6.34391844e-01 -3.01981926e-01 -4.40305203e-01
-6.02729917e-01 -1.09080815e+00 1.41783452e+00 4.89373095e-02
-1.95383444e-01 6.54956102e-01 1.37345481e+00 -3.31497401e-01
7.14886412e-02 -3.50809962e-01 -4.60649282e-02 -4.40544277e-01
2.92467419e-02 5.02836049e-01 -9.93605033e-02 -6.54911816... | [12.519224166870117, 8.052574157714844] |
f27c3849-2565-4673-bba6-c628af4e89b2 | does-william-shakespeare-really-write-hamlet | 1705.03202 | null | http://arxiv.org/abs/1705.03202v2 | http://arxiv.org/pdf/1705.03202v2.pdf | Does William Shakespeare REALLY Write Hamlet? Knowledge Representation Learning with Confidence | Knowledge graphs (KGs), which could provide essential relational information
between entities, have been widely utilized in various knowledge-driven
applications. Since the overall human knowledge is innumerable that still grows
explosively and changes frequently, knowledge construction and update
inevitably involve au... | ['Ruobing Xie', 'Fen Lin', 'Leyu Lin', 'Zhiyuan Liu'] | 2017-05-09 | null | null | null | null | ['triple-classification'] | ['graphs'] | [-2.33861819e-01 1.07605480e-01 -4.65334535e-01 -2.10519537e-01
-3.93602908e-01 -4.20948178e-01 1.87005490e-01 5.57713509e-01
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-4.97656584e-01 -1.30296707e+00 -7.86638618e-01 -3.84323359e-01
5.68677671e-02 3.92287105e-01 4.67257887e-01 -2.57706434... | [8.784164428710938, 7.895570278167725] |
e0d5ac48-4787-467b-ba01-ee38f0c19230 | counterfactual-explanation-based-on-gradual | 2008.01897 | null | https://arxiv.org/abs/2008.01897v2 | https://arxiv.org/pdf/2008.01897v2.pdf | Counterfactual Explanation Based on Gradual Construction for Deep Networks | To understand the black-box characteristics of deep networks, counterfactual explanation that deduces not only the important features of an input space but also how those features should be modified to classify input as a target class has gained an increasing interest. The patterns that deep networks have learned from ... | ['Hee-Dong Kim', 'Hong-Gyu Jung', 'Seong-Whan Lee', 'Dong-Ok Won', 'Sin-Han Kang'] | 2020-08-05 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 4.52143610e-01 3.96112740e-01 -4.06702816e-01 -8.95577908e-01
-7.04878345e-02 -5.41016042e-01 6.28595114e-01 4.83261501e-05
-3.65716487e-01 9.04012084e-01 1.49024889e-01 -4.40562218e-01
-5.06804764e-01 -9.67757106e-01 -9.43858624e-01 -9.13105071e-01
2.13769123e-01 4.07292783e-01 -1.61865935e-01 1.63810596... | [8.727127075195312, 5.611660480499268] |
e4e45793-2591-4292-a5df-1a660c2e74b9 | facedirector-continuous-control-of-facial | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Malleson_FaceDirector_Continuous_Control_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Malleson_FaceDirector_Continuous_Control_ICCV_2015_paper.pdf | FaceDirector: Continuous Control of Facial Performance in Video | We present a method to continuously blend between multiple facial performances of an actor, which can contain different facial expressions or emotional states. As an example, given sad and angry video takes of a scene, our method empowers the movie director to specify arbitrary weighted combinations and smooth transiti... | ['Alexander Sorkine-Hornung', 'Jean-Charles Bazin', 'Charles Malleson', 'Adrian Hilton', 'Thabo Beeler', 'Oliver Wang', 'Derek Bradley'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['audio-visual-synchronization', 'audio-visual-synchronization'] | ['audio', 'computer-vision'] | [ 2.62287498e-01 -8.19579735e-02 7.15274587e-02 -3.25817436e-01
-5.42919219e-01 -8.12979758e-01 5.33073783e-01 -5.95303774e-02
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-1.67703498e-02 -2.03698725e-01 -1.38493665e-02 -3.75029027... | [12.994586944580078, -0.44305336475372314] |
11d47bd4-cbe5-4548-8089-8ea5171be8fe | lightx3ecg-a-lightweight-and-explainable-deep | 2207.12381 | null | https://arxiv.org/abs/2207.12381v1 | https://arxiv.org/pdf/2207.12381v1.pdf | LightX3ECG: A Lightweight and eXplainable Deep Learning System for 3-lead Electrocardiogram Classification | Cardiovascular diseases (CVDs) are a group of heart and blood vessel disorders that is one of the most serious dangers to human health, and the number of such patients is still growing. Early and accurate detection plays a key role in successful treatment and intervention. Electrocardiogram (ECG) is the gold standard f... | ['Cuong D. Do', 'Tien N. Thanh', 'Tu A. Nguyen', 'Thao BT. Nguyen', 'Hieu H. Pham', 'Khiem H. Le'] | 2022-07-25 | null | null | null | null | ['ecg-classification'] | ['medical'] | [-3.05045489e-02 -6.62842989e-01 -2.78265029e-01 -2.82719672e-01
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-2.13394180e-01 3.23235989e-01 1.46811903e-01 1.08933359... | [14.258171081542969, 3.207213878631592] |
8eb3796a-4960-4097-b10b-9813acdd11b6 | you-might-think-about-slightly-revising-the-2 | 2306.14911 | null | https://arxiv.org/abs/2306.14911v1 | https://arxiv.org/pdf/2306.14911v1.pdf | "You might think about slightly revising the title": identifying hedges in peer-tutoring interactions | Hedges play an important role in the management of conversational interaction. In peer tutoring, they are notably used by tutors in dyads (pairs of interlocutors) experiencing low rapport to tone down the impact of instructions and negative feedback. Pursuing the objective of building a tutoring agent that manages rapp... | ['Justine Cassell', 'Chloé Clavel', 'Yann Raphalen'] | 2023-06-18 | null | null | null | null | ['management'] | ['miscellaneous'] | [ 2.36151926e-02 9.73810077e-01 -1.72002703e-01 -3.27668011e-01
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-3.86599869e-01 -6.22218490e-01 -4.67974275e-01 -2.94754386e-01
5.25788814e-02 4.41648334e-01 1.60254434e-01 -6.87888026... | [12.220253944396973, 8.051685333251953] |
c4c61969-2a3a-40e7-a793-6939353d1f3e | egocentric-affordance-detection-with-the-one | 1906.05794 | null | https://arxiv.org/abs/1906.05794v1 | https://arxiv.org/pdf/1906.05794v1.pdf | Egocentric affordance detection with the one-shot geometry-driven Interaction Tensor | In this abstract we describe recent [4,7] and latest work on the determination of affordances in visually perceived 3D scenes. Our method builds on the hypothesis that geometry on its own provides enough information to enable the detection of significant interaction possibilities in the environment. The motivation behi... | ['Walterio Mayol-Cuevas', 'Eduardo Ruiz'] | 2019-06-13 | null | null | null | null | ['affordance-detection'] | ['computer-vision'] | [ 2.76973993e-01 2.35229999e-01 4.55605000e-01 -4.21359062e-01
5.94170019e-03 -6.51723981e-01 7.27510750e-01 2.64129937e-01
-3.85100812e-01 4.63613570e-01 1.85520351e-01 -3.72023553e-01
-2.98922598e-01 -5.01063764e-01 -5.38913488e-01 -2.99366683e-01
-6.06360316e-01 4.47366416e-01 4.66346741e-01 -5.84717512... | [4.937552452087402, 0.3827999234199524] |
a3f16881-f82c-4a4d-869b-d6520ec9460b | unlearnable-clusters-towards-label-agnostic | 2301.01217 | null | https://arxiv.org/abs/2301.01217v4 | https://arxiv.org/pdf/2301.01217v4.pdf | Unlearnable Clusters: Towards Label-agnostic Unlearnable Examples | There is a growing interest in developing unlearnable examples (UEs) against visual privacy leaks on the Internet. UEs are training samples added with invisible but unlearnable noise, which have been found can prevent unauthorized training of machine learning models. UEs typically are generated via a bilevel optimizati... | ['Yu-Gang Jiang', 'Changsheng Xu', 'YaoWei Wang', 'Jitao Sang', 'Qi Yi', 'Xingjun Ma', 'Jiaming Zhang'] | 2022-12-31 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Unlearnable_Clusters_Towards_Label-Agnostic_Unlearnable_Examples_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Unlearnable_Clusters_Towards_Label-Agnostic_Unlearnable_Examples_CVPR_2023_paper.pdf | cvpr-2023-1 | ['data-poisoning'] | ['adversarial'] | [ 2.94497669e-01 1.03745861e-02 -1.05080284e-01 5.04246578e-02
-8.53276134e-01 -1.33974683e+00 5.19499660e-01 -2.44464546e-01
-2.73352146e-01 7.85823643e-01 -1.37270287e-01 -5.26950657e-01
1.16251811e-01 -5.83918214e-01 -1.11930919e+00 -7.69528270e-01
2.46294037e-01 -5.41659258e-02 -4.12732549e-02 2.26957217... | [5.750309467315674, 7.46855354309082] |
f28ac9ed-4039-458d-947b-605ccf8ad9c2 | improving-text-independent-speaker | 2109.09674 | null | https://arxiv.org/abs/2109.09674v1 | https://arxiv.org/pdf/2109.09674v1.pdf | Improving Text-Independent Speaker Verification with Auxiliary Speakers Using Graph | The paper presents a novel approach to refining similarity scores between input utterances for robust speaker verification. Given the embeddings from a pair of input utterances, a graph model is designed to incorporate additional information from a group of embeddings representing the so-called auxiliary speakers. The ... | ['Tan Lee', 'Si-Ioi Ng', 'Jingyu Li'] | 2021-09-20 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [ 1.72018945e-01 6.09403789e-01 2.04082757e-01 -7.48205781e-01
-4.24696922e-01 -2.53563136e-01 6.53990388e-01 4.85047460e-01
-1.82430744e-01 7.66585469e-02 4.17860389e-01 -1.99467272e-01
-5.39650172e-02 -6.12668633e-01 -2.59509385e-01 -6.74818277e-01
-1.26978800e-01 5.95714033e-01 2.00755857e-02 -2.43728980... | [14.312861442565918, 6.19175386428833] |
92f30401-5319-497c-a5d7-c08c9c5bcd5a | subject-independent-brain-computer-interfaces | 2301.07894 | null | https://arxiv.org/abs/2301.07894v1 | https://arxiv.org/pdf/2301.07894v1.pdf | Subject-Independent Brain-Computer Interfaces with Open-Set Subject Recognition | A brain-computer interface (BCI) can't be effectively used since electroencephalography (EEG) varies between and within subjects. BCI systems require calibration steps to adjust the model to subject-specific data. It is widely acknowledged that this is a major obstacle to the development of BCIs. To address this issue,... | ['Geun-Deok Jang', 'Dong-Young Kim', 'Dong-Kyun Han'] | 2023-01-19 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 3.88340443e-01 2.43577808e-02 2.76717991e-01 -8.29718828e-01
-5.30866265e-01 -5.31621218e-01 2.90781111e-01 -4.64310616e-01
-3.50245446e-01 1.16340756e+00 7.00219721e-02 1.28044456e-01
-2.44898573e-01 -4.05143917e-01 -4.95724291e-01 -4.95932192e-01
-1.44210393e-02 4.43515062e-01 5.71127571e-02 -3.40194821... | [13.128351211547852, 3.4553775787353516] |
bcf3e15e-a4f6-4dfb-9809-8e464c1c97c5 | entanglement-as-a-method-to-reduce | 2302.05898 | null | https://arxiv.org/abs/2302.05898v1 | https://arxiv.org/pdf/2302.05898v1.pdf | Entanglement as a Method to Reduce Uncertainty | In physics, entanglement 'reduces' the entropy of an entity, because the (von Neumann) entropy of, e.g., a composite bipartite entity in a pure entangled state is systematically lower than the entropy of the component sub-entities. We show here that this 'genuinely non-classical reduction of entropy as a result of comp... | ['Sandro Sozzo', 'Suzette Geriente', 'Lester Beltran', 'Jonito Aerts Argëlles', 'Diederik Aerts'] | 2023-02-12 | null | null | null | null | ['culture'] | ['speech'] | [ 1.45739734e-01 7.42112398e-01 4.38712120e-01 4.71890345e-03
2.09228784e-01 -8.33496034e-01 8.95489573e-01 2.67329097e-01
-5.79384208e-01 1.04195452e+00 3.81195277e-01 -2.94835746e-01
-2.42817774e-01 -1.18689084e+00 -5.09482563e-01 -1.07836437e+00
-2.70186216e-01 4.32501435e-01 1.83281735e-01 -4.62003231... | [5.646490573883057, 4.915651321411133] |
067e6e15-29d9-4cb4-8712-5b621e9feb3a | rawgment-noise-accounted-raw-augmentation | 2210.16046 | null | https://arxiv.org/abs/2210.16046v2 | https://arxiv.org/pdf/2210.16046v2.pdf | Rawgment: Noise-Accounted RAW Augmentation Enables Recognition in a Wide Variety of Environments | Image recognition models that work in challenging environments (e.g., extremely dark, blurry, or high dynamic range conditions) must be useful. However, creating training datasets for such environments is expensive and hard due to the difficulties of data collection and annotation. It is desirable if we could get a rob... | ['Takeshi Ohashi', 'Atsushi Irie', 'Junji Otsuka', 'Masakazu Yoshimura'] | 2022-10-28 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yoshimura_Rawgment_Noise-Accounted_RAW_Augmentation_Enables_Recognition_in_a_Wide_Variety_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yoshimura_Rawgment_Noise-Accounted_RAW_Augmentation_Enables_Recognition_in_a_Wide_Variety_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-augmentation'] | ['computer-vision'] | [ 7.74935126e-01 -3.40574443e-01 4.94967520e-01 -4.40835238e-01
-4.04619098e-01 -6.20088518e-01 3.38856220e-01 -3.02128702e-01
-5.65730512e-01 6.20458484e-01 -2.89277732e-01 -2.49304205e-01
-8.72458369e-02 -5.24212658e-01 -8.57208490e-01 -8.17960560e-01
3.42518985e-01 -1.44311875e-01 1.03218071e-01 7.09875673... | [10.520033836364746, -2.497134208679199] |
a7bf988e-5e2f-48c8-b8c7-56153bb46d40 | detectorguard-provably-securing-object | 2102.02956 | null | https://arxiv.org/abs/2102.02956v3 | https://arxiv.org/pdf/2102.02956v3.pdf | DetectorGuard: Provably Securing Object Detectors against Localized Patch Hiding Attacks | State-of-the-art object detectors are vulnerable to localized patch hiding attacks, where an adversary introduces a small adversarial patch to make detectors miss the detection of salient objects. The patch attacker can carry out a physical-world attack by printing and attaching an adversarial patch to the victim objec... | ['Prateek Mittal', 'Chong Xiang'] | 2021-02-05 | null | null | null | null | ['robust-object-detection'] | ['computer-vision'] | [ 5.15494645e-01 1.59871295e-01 -5.83240413e-04 6.14411496e-02
-1.18522429e+00 -1.32912576e+00 3.81490171e-01 1.85480177e-01
-1.76102355e-01 8.54072496e-02 -4.34970081e-01 -3.26206923e-01
2.49823630e-01 -6.47698283e-01 -1.42557716e+00 -9.04059887e-01
-2.18786523e-01 -1.39268890e-01 7.31929302e-01 8.34948849... | [5.552002906799316, 7.920490741729736] |
67707be0-31c1-4644-932d-d0017ce1c27b | 3d-point-cloud-classification-and | 1711.08241 | null | http://arxiv.org/abs/1711.08241v1 | http://arxiv.org/pdf/1711.08241v1.pdf | 3D Point Cloud Classification and Segmentation using 3D Modified Fisher Vector Representation for Convolutional Neural Networks | The point cloud is gaining prominence as a method for representing 3D shapes,
but its irregular format poses a challenge for deep learning methods. The
common solution of transforming the data into a 3D voxel grid introduces its
own challenges, mainly large memory size. In this paper we propose a novel 3D
point cloud r... | ['Michael Lindenbaum', 'Yizhak Ben-Shabat', 'Anath Fischer'] | 2017-11-22 | null | null | null | null | ['3d-part-segmentation'] | ['computer-vision'] | [-4.46591794e-01 -4.20589477e-01 9.68359262e-02 -2.52868503e-01
-6.76373959e-01 -4.71890539e-01 5.57354212e-01 1.68242186e-01
-2.66221613e-01 2.47784048e-01 -3.80466461e-01 -3.97041887e-01
1.29578769e-01 -1.04869008e+00 -9.92845774e-01 -4.22655761e-01
-1.74725503e-01 6.95870101e-01 3.50090832e-01 -1.17385417... | [7.957791328430176, -3.612375497817993] |
c3e03246-6ee3-4719-9d9d-700fc43f28ab | an-accurate-non-accelerometer-based-ppg | 2106.11512 | null | https://arxiv.org/abs/2106.11512v1 | https://arxiv.org/pdf/2106.11512v1.pdf | An Accurate Non-accelerometer-based PPG Motion Artifact Removal Technique using CycleGAN | A photoplethysmography (PPG) is an uncomplicated and inexpensive optical technique widely used in the healthcare domain to extract valuable health-related information, e.g., heart rate variability, blood pressure, and respiration rate. PPG signals can easily be collected continuously and remotely using portable wearabl... | ['Fadi Kurdahi', 'Amir M. Rahmani', 'Hadi Khodabandeh', 'Seyed Amir Hossein Aqajari', 'Amir Hosein Afandizadeh Zargari'] | 2021-06-22 | null | null | null | null | ['photoplethysmography-ppg', 'heart-rate-variability'] | ['medical', 'medical'] | [ 3.94199699e-01 -2.29192488e-02 2.68584579e-01 8.03093910e-02
-4.59005356e-01 -3.29101712e-01 -2.04067245e-01 -2.31408879e-01
-2.59575993e-01 8.84518266e-01 2.70038396e-01 -1.01343147e-01
2.12457702e-01 -4.63495463e-01 -4.30449516e-01 -8.36850584e-01
1.50442824e-01 -3.66344601e-01 -3.28959413e-02 2.89457500... | [13.942517280578613, 2.9940614700317383] |
1a4620a3-956d-4ab6-8342-6aa35b05ff14 | direction-of-arrival-estimation-of-noisy | 2102.09853 | null | https://arxiv.org/abs/2102.09853v3 | https://arxiv.org/pdf/2102.09853v3.pdf | Direction of Arrival Estimation of Noisy Speech Using Convolutional Recurrent Neural Networks with Higher-Order Ambisonics Signals | Training convolutional recurrent neural networks on first-order Ambisonics signals is a well-known approach when estimating the direction of arrival for speech/sound signals. In this work, we investigate whether increasing the order of Ambisonics up to the fourth order further improves the estimation performance of con... | ['Jürgen Peissig', 'Stephan Preihs', 'Robert Hupke', 'Nils Poschadel'] | 2021-02-19 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [-1.09238297e-01 -4.05234784e-01 7.31500804e-01 -2.14329302e-01
-1.00308764e+00 -3.46111387e-01 6.01366460e-01 7.96011388e-02
-5.12958646e-01 5.33058941e-01 5.37971199e-01 -5.20411789e-01
-3.52813572e-01 -5.30515075e-01 -4.21831429e-01 -9.40088928e-01
-5.34808874e-01 -4.38851677e-02 -1.06363349e-01 -3.29674512... | [15.10775089263916, 5.769680976867676] |
6ce40275-a633-461d-a76a-64b4ec1ccee7 | classification-with-costly-features-using | 1711.07364 | null | http://arxiv.org/abs/1711.07364v2 | http://arxiv.org/pdf/1711.07364v2.pdf | Classification with Costly Features using Deep Reinforcement Learning | We study a classification problem where each feature can be acquired for a
cost and the goal is to optimize a trade-off between the expected
classification error and the feature cost. We revisit a former approach that
has framed the problem as a sequential decision-making problem and solved it by
Q-learning with a line... | ['Viliam Lisý', 'Tomáš Pevný', 'Jaromír Janisch'] | 2017-11-20 | null | null | null | null | ['classification-with-costly-features'] | ['miscellaneous'] | [ 4.69239026e-01 4.15825397e-01 -3.88918400e-01 -6.07986271e-01
-8.68574500e-01 -4.53897119e-01 6.54573560e-01 2.72193581e-01
-7.67412901e-01 1.11769092e+00 -2.87408173e-01 -2.02953845e-01
-6.73634410e-01 -7.65537441e-01 -5.87111354e-01 -7.57825315e-01
-3.20752829e-01 7.56422520e-01 1.11671582e-01 -9.03788060... | [4.292187213897705, 2.164916515350342] |
947832eb-560b-44a0-aa66-bb125d26a0a7 | the-lambada-dataset-word-prediction-requiring | 1606.06031 | null | http://arxiv.org/abs/1606.06031v1 | http://arxiv.org/pdf/1606.06031v1.pdf | The LAMBADA dataset: Word prediction requiring a broad discourse context | We introduce LAMBADA, a dataset to evaluate the capabilities of computational
models for text understanding by means of a word prediction task. LAMBADA is a
collection of narrative passages sharing the characteristic that human subjects
are able to guess their last word if they are exposed to the whole passage, but
not... | ['Raquel Fernández', 'Quan Ngoc Pham', 'Germán Kruszewski', 'Sandro Pezzelle', 'Raffaella Bernardi', 'Angeliki Lazaridou', 'Gemma Boleda', 'Denis Paperno', 'Marco Baroni'] | 2016-06-20 | the-lambada-dataset-word-prediction-requiring-1 | https://aclanthology.org/P16-1144 | https://aclanthology.org/P16-1144.pdf | acl-2016-8 | ['lambada'] | ['natural-language-processing'] | [ 1.09598242e-01 1.92139164e-01 -4.31144863e-01 -1.31957754e-01
-7.51060367e-01 -9.43857789e-01 1.32516742e+00 8.28574002e-01
-5.95382214e-01 6.60748243e-01 7.45606244e-01 -5.38340747e-01
1.52659684e-01 -9.52543557e-01 -4.31210220e-01 -5.76790646e-02
2.23279186e-02 5.62163472e-01 5.31098902e-01 -7.47011185... | [11.106837272644043, 8.879469871520996] |
c08c3ae6-01a7-4d75-85f6-a095b0d85f2c | ufal-corpipe-at-crac-2022-effectivity-of | 2209.07278 | null | https://arxiv.org/abs/2209.07278v1 | https://arxiv.org/pdf/2209.07278v1.pdf | ÚFAL CorPipe at CRAC 2022: Effectivity of Multilingual Models for Coreference Resolution | We describe the winning submission to the CRAC 2022 Shared Task on Multilingual Coreference Resolution. Our system first solves mention detection and then coreference linking on the retrieved spans with an antecedent-maximization approach, and both tasks are fine-tuned jointly with shared Transformer weights. We report... | ['Jana Straková', 'Milan Straka'] | 2022-09-15 | null | https://aclanthology.org/2022.crac-mcr.4 | https://aclanthology.org/2022.crac-mcr.4.pdf | crac-acl-2022-10 | ['coreference-resolution'] | ['natural-language-processing'] | [-4.00325507e-01 4.06187057e-01 -6.17242157e-01 -3.25468004e-01
-1.59578645e+00 -8.88849795e-01 7.22129643e-01 -3.00979242e-02
-6.34608746e-01 1.18626773e+00 8.98179293e-01 -3.11677575e-01
-2.14202836e-01 -3.02859664e-01 -7.61291504e-01 -1.10811420e-01
-9.97675210e-02 1.24995577e+00 -5.07569611e-02 -6.62014842... | [9.31447696685791, 9.582733154296875] |
f9bb6dfc-38b5-478e-b1ca-9b7e886a4dd5 | dote-rethinking-predictive-wan-traffic | 2303.00735 | null | https://arxiv.org/abs/2303.00735v2 | https://arxiv.org/pdf/2303.00735v2.pdf | A Deep Learning Perspective on Network Routing | Routing is, arguably, the most fundamental task in computer networking, and the most extensively studied one. A key challenge for routing in real-world environments is the need to contend with uncertainty about future traffic demands. We present a new approach to routing under demand uncertainty: tackling this challeng... | ['Aviv Tamar', 'Michael Schapira', 'Ishai Menache', 'Srikanth Kandula', 'Chaim Hoch', 'Felipe Vieira Frujeri', 'Yarin Perry'] | 2023-03-01 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [-1.09814825e-02 -6.31595179e-02 -6.97965801e-01 -4.38733339e-01
-7.09606290e-01 -6.74652338e-01 2.35569272e-02 -3.76754463e-01
-1.55817091e-01 1.43886566e+00 -1.52255818e-01 -1.15241015e+00
-6.89331532e-01 -6.85785353e-01 -6.12978041e-01 -4.90514308e-01
-8.20370436e-01 1.22155666e+00 1.81289792e-01 -1.69774845... | [5.6350321769714355, 1.7051268815994263] |
4115c9b7-941e-47ce-acd8-54a3f1ceac83 | pixcue-joint-uncertainty-estimation-and-image | 2303.00111 | null | https://arxiv.org/abs/2303.00111v2 | https://arxiv.org/pdf/2303.00111v2.pdf | PixCUE: Joint Uncertainty Estimation and Image Reconstruction in MRI using Deep Pixel Classification | Deep learning (DL) models are capable of successfully exploiting latent representations in MR data and have become state-of-the-art for accelerated MRI reconstruction. However, undersampling the measurements in k-space as well as the over- or under-parameterized and non-transparent nature of DL make these models expose... | ['Zhaolin Chen', 'Gary Egan', 'Kamlesh Pawar', 'Mevan Ekanayake'] | 2023-02-28 | null | null | null | null | ['mri-reconstruction'] | ['computer-vision'] | [ 2.93523848e-01 -4.98206429e-02 2.36668438e-01 -3.89556587e-01
-1.43529439e+00 -3.11972171e-01 5.56981027e-01 1.09489024e-01
-6.26290143e-01 1.08064950e+00 1.06080964e-01 -1.01947144e-01
-1.79872841e-01 -7.04638481e-01 -1.04121220e+00 -1.05368316e+00
-1.50197089e-01 2.14916468e-01 -2.09637657e-02 5.97651124... | [13.531387329101562, -2.341578483581543] |
3cc38e25-0d0a-46d6-b49f-f79d689bc74c | an-image-processing-based-object-counting | 1802.05911 | null | http://arxiv.org/abs/1802.05911v1 | http://arxiv.org/pdf/1802.05911v1.pdf | An Image Processing based Object Counting Approach for Machine Vision Application | Machine vision applications are low cost and high precision measurement
systems which are frequently used in production lines. With these systems that
provide contactless control and measurement, production facilities are able to
reach high production numbers without errors. Machine vision operations such as
product co... | ['Erhan Akin', 'Alisan Sarimaden', 'Mehmet Karakose', 'Mehmet Baygin'] | 2018-02-16 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [ 3.48815709e-01 -5.04737556e-01 4.92920205e-02 -5.93783110e-02
2.18787834e-01 -5.58964610e-01 4.19386417e-01 5.06329656e-01
-5.80290556e-01 3.07161450e-01 -8.97869408e-01 -2.49881044e-01
1.62906677e-01 -9.39843237e-01 -2.31656492e-01 -4.73042995e-01
5.13265491e-01 6.79531097e-01 1.97904497e-01 7.73830665... | [9.317381858825684, -1.5608540773391724] |
18ffe972-723e-4339-bc27-8c1b246a86e0 | what-evidence-does-deep-learning-model-use-to | 1811.01051 | null | http://arxiv.org/abs/1811.01051v3 | http://arxiv.org/pdf/1811.01051v3.pdf | What evidence does deep learning model use to classify Skin Lesions? | Melanoma is a type of skin cancer with the most rapidly increasing incidence.
Early detection of melanoma using dermoscopy images significantly increases
patients' survival rate. However, accurately classifying skin lesions by eye,
especially in the early stage of melanoma, is extremely challenging for the
dermatologis... | ['Hongda Jiang', 'Xiaoxiao Li', 'Eric Z. Chen', 'Junyan Wu'] | 2018-11-02 | null | null | null | null | ['melanoma-diagnosis'] | ['computer-vision'] | [ 4.95832324e-01 -1.26640886e-01 -4.89855111e-01 -1.09638326e-01
-5.25993407e-01 -1.93289340e-01 1.88164234e-01 3.44307214e-01
-3.53298426e-01 8.00805509e-01 -1.55102581e-01 -3.34450066e-01
-1.73961341e-01 -7.16318607e-01 -1.86813623e-01 -1.17153347e+00
9.26649868e-02 -1.62379727e-01 -1.24580618e-02 -3.50365788... | [15.600715637207031, -3.0209829807281494] |
42ddec2b-1a30-492e-a686-edff3f53bee4 | discovering-multiple-algorithm-configurations | 2303.07434 | null | https://arxiv.org/abs/2303.07434v1 | https://arxiv.org/pdf/2303.07434v1.pdf | Discovering Multiple Algorithm Configurations | Many practitioners in robotics regularly depend on classic, hand-designed algorithms. Often the performance of these algorithms is tuned across a dataset of annotated examples which represent typical deployment conditions. Automatic tuning of these settings is traditionally known as algorithm configuration. In this wor... | ['Martial Hebert', 'Leonid Keselman'] | 2023-03-13 | null | null | null | null | ['motion-planning'] | ['robots'] | [ 3.08536679e-01 -6.88131303e-02 -5.36458731e-01 -1.17340133e-01
-9.79073167e-01 -1.12706041e+00 6.25157475e-01 -4.55210768e-02
-3.21335077e-01 7.23722398e-01 -4.60288301e-03 -4.48239982e-01
-8.97135973e-01 -2.97190815e-01 -8.28957498e-01 -6.08177781e-01
-3.91212553e-01 1.11941850e+00 1.80558115e-01 5.17055281... | [4.348991394042969, 1.9167594909667969] |
e17a1279-d45b-46b7-832b-d737374a907d | harmonizing-pathological-and-normal-pixels | 2203.15347 | null | https://arxiv.org/abs/2203.15347v1 | https://arxiv.org/pdf/2203.15347v1.pdf | Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis | Synthesizing a subject-specific pathology-free image from a pathological image is valuable for algorithm development and clinical practice. In recent years, several approaches based on the Generative Adversarial Network (GAN) have achieved promising results in pseudo-healthy synthesis. However, the discriminator (i.e.,... | ['Yizhou Yu', 'Lin Yang', 'Guisheng Wang', 'Xinghao Ding', 'Yue Huang', 'LiyanSun', 'Yihong Zhuang', 'Xin Lin', 'Yunlong Zhang'] | 2022-03-29 | null | null | null | null | ['medical-image-enhancement'] | ['computer-vision'] | [ 5.19011974e-01 1.01193063e-01 -1.63240179e-01 -4.63782176e-02
-1.05423212e+00 -2.47402146e-01 1.94481596e-01 -3.82576138e-01
-1.70290709e-01 7.95733988e-01 -7.09299073e-02 -4.34016064e-03
2.84340799e-01 -7.17263579e-01 -4.41872448e-01 -1.10438502e+00
3.41702044e-01 1.93974942e-01 3.22387069e-01 -1.96573753... | [13.880086898803711, -2.1758463382720947] |
24e343be-ae02-4eec-9f68-7a3b3d567c01 | using-deep-convolutional-neural-networks-to-2 | null | null | https://www.medrxiv.org/content/10.1101/2022.10.03.22280640v3 | https://www.medrxiv.org/content/10.1101/2022.10.03.22280640v3.full.pdf | Using deep convolutional neural networks to predict patients age based on ECGs from an independent test cohort | Electrocardiography is one of the most frequently used methods to evaluate cardiovascular diseases. However, the last decade has shown that deep convolutional neural networks (CNN) can extract information from the electrocardiogram (ECG) that goes beyond traditional diagnostics, such as predicting a persons age. In thi... | ['Belal Tavashi', 'Bjørn-Jostein Singstad'] | 2022-10-06 | null | null | null | medrxiv-2022-10 | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [-5.30012026e-02 7.49881715e-02 4.01332736e-01 -4.09987122e-01
-4.40486461e-01 -3.67354572e-01 -3.65317389e-02 5.44975996e-01
-7.55567312e-01 8.89309585e-01 -2.09311798e-01 -4.92533088e-01
-2.64372498e-01 -9.41895843e-01 -3.60358834e-01 -7.35779285e-01
-6.07926369e-01 4.92917448e-01 -3.26155275e-01 1.07531538... | [14.321404457092285, 3.2962570190429688] |
d7bfd61e-b106-4290-98d4-c27166df2f4a | confound-leakage-confound-removal-in-machine | 2210.09232 | null | https://arxiv.org/abs/2210.09232v2 | https://arxiv.org/pdf/2210.09232v2.pdf | Confound-leakage: Confound Removal in Machine Learning Leads to Leakage | Machine learning (ML) approaches to data analysis are now widely adopted in many fields including epidemiology and medicine. To apply these approaches, confounds must first be removed as is commonly done by featurewise removal of their variance by linear regression before applying ML. Here, we show this common approach... | ['Kaustubh R. Patil', 'Simon B. Eickhoff', 'Holger Schwender', 'Susanne Weis', 'Georg G. von Polier', 'Bradley C. Love', 'Sami Hamdan'] | 2022-10-17 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 5.85585535e-01 1.19393930e-01 -4.83073413e-01 -5.67073405e-01
-8.36759984e-01 -5.50041974e-01 4.02177989e-01 6.04179084e-01
-4.72841144e-01 8.68515968e-01 5.12790084e-01 -9.57512736e-01
-2.92165726e-01 -2.74143875e-01 -8.49809587e-01 -3.55783761e-01
-1.56839296e-01 8.23225901e-02 -6.02824509e-01 4.61153507... | [8.028822898864746, 5.5230512619018555] |
d67e674b-2d94-4de0-a48a-2d9acb23fe70 | syntactic-and-semantic-driven-learning-for | 2103.03448 | null | https://arxiv.org/abs/2103.03448v1 | https://arxiv.org/pdf/2103.03448v1.pdf | Syntactic and Semantic-driven Learning for Open Information Extraction | One of the biggest bottlenecks in building accurate, high coverage neural open IE systems is the need for large labelled corpora. The diversity of open domain corpora and the variety of natural language expressions further exacerbate this problem. In this paper, we propose a syntactic and semantic-driven learning appro... | ['Hua Wu', 'Xinyan Xiao', 'Le Sun', 'Xianpei Han', 'Hongyu Lin', 'Yaojie Lu', 'Jialong Tang'] | 2021-03-05 | null | https://aclanthology.org/2020.findings-emnlp.69 | https://aclanthology.org/2020.findings-emnlp.69.pdf | findings-of-the-association-for-computational | ['open-information-extraction'] | ['natural-language-processing'] | [ 2.82005221e-01 6.61044300e-01 -4.88732040e-01 -6.15426481e-01
-8.42400908e-01 -6.86596990e-01 4.16080177e-01 -1.56754717e-01
-5.59913933e-01 1.00801468e+00 2.25816116e-01 -4.37153727e-01
-1.73048433e-02 -8.22894514e-01 -1.08101463e+00 -2.14957729e-01
2.20528007e-01 9.15022850e-01 2.50879526e-01 -2.23941430... | [10.517142295837402, 8.877142906188965] |
ace8b8eb-8864-4e9f-b6fa-54471c2ad104 | generative-multimodal-entity-linking | 2306.12725 | null | https://arxiv.org/abs/2306.12725v1 | https://arxiv.org/pdf/2306.12725v1.pdf | Generative Multimodal Entity Linking | Multimodal Entity Linking (MEL) is the task of mapping mentions with multimodal contexts to the referent entities from a knowledge base (e.g., Wikipedia). Prior MEL methods mainly focus on designing complex multimodal interaction mechanisms and require fine-tuning all model parameters, which can be prohibitively costly... | ['Min Zhang', 'Baotian Hu', 'Zhenran Xu', 'Senbao Shi'] | 2023-06-22 | null | null | null | null | ['entity-linking'] | ['natural-language-processing'] | [-1.14196643e-01 4.49430674e-01 -6.44900948e-02 -5.66348247e-02
-1.23858476e+00 -8.60281229e-01 9.32426572e-01 -5.32775279e-03
-7.57205427e-01 7.15194404e-01 1.85612559e-01 -1.15521990e-01
2.55606651e-01 -5.48728287e-01 -1.10555518e+00 -3.26511681e-01
4.04295512e-02 7.60112286e-01 2.76365746e-02 -4.04937565... | [10.882445335388184, 1.6278167963027954] |
baa52932-660c-4894-9cac-04b34baca1fb | smae-few-shot-learning-for-hdr-deghosting | 2304.06914 | null | https://arxiv.org/abs/2304.06914v1 | https://arxiv.org/pdf/2304.06914v1.pdf | SMAE: Few-shot Learning for HDR Deghosting with Saturation-Aware Masked Autoencoders | Generating a high-quality High Dynamic Range (HDR) image from dynamic scenes has recently been extensively studied by exploiting Deep Neural Networks (DNNs). Most DNNs-based methods require a large amount of training data with ground truth, requiring tedious and time-consuming work. Few-shot HDR imaging aims to generat... | ['Yanning Zhang', 'Luc van Gool', 'Jinqiu Sun', 'Yu Zhu', 'Hao Tang', 'Weiye Chen', 'Song Zhang', 'Qingsen Yan'] | 2023-04-14 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yan_SMAE_Few-Shot_Learning_for_HDR_Deghosting_With_Saturation-Aware_Masked_Autoencoders_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yan_SMAE_Few-Shot_Learning_for_HDR_Deghosting_With_Saturation-Aware_Masked_Autoencoders_CVPR_2023_paper.pdf | cvpr-2023-1 | ['pseudo-label'] | ['miscellaneous'] | [ 4.86068308e-01 -3.23636122e-02 -1.40643362e-02 -3.65224123e-01
-5.54814875e-01 -2.10856035e-01 3.91544431e-01 -4.60111171e-01
-2.60859847e-01 8.08622718e-01 6.08818121e-02 7.78080449e-02
3.00491787e-02 -9.08817589e-01 -7.56402850e-01 -1.09207916e+00
4.11317378e-01 3.25941741e-01 3.55865359e-01 -2.89431095... | [10.860905647277832, -2.214545965194702] |
2abd0637-83de-485a-95fc-daa92794ec16 | relational-space-time-query-in-long-form | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yang_Relational_Space-Time_Query_in_Long-Form_Videos_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yang_Relational_Space-Time_Query_in_Long-Form_Videos_CVPR_2023_paper.pdf | Relational Space-Time Query in Long-Form Videos | Egocentric videos are often available in the form of uninterrupted, uncurated long videos capturing the camera wearers' daily life activities.Understanding these videos requires models to be able to reason about activities, objects, and their interactions. However, current video benchmarks study these problems inde... | ['Du Tran', 'Lorenzo Torresani', 'Raghav Goyal', 'Matt Feiszli', 'Fu-Jen Chu', 'Xitong Yang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['video-understanding'] | ['computer-vision'] | [ 3.94864790e-02 -7.68987983e-02 -4.81866807e-01 -5.27640998e-01
-6.05718672e-01 -7.48979628e-01 5.87832868e-01 -4.91787314e-01
-1.32095441e-01 1.78761795e-01 7.24576354e-01 7.14024678e-02
-1.15037397e-01 -3.89654726e-01 -9.75977004e-01 -9.93411243e-03
-1.14697061e-01 3.69337946e-01 2.34522954e-01 4.09095213... | [9.899053573608398, 0.8202782869338989] |
8a3987f2-f95c-434a-8671-3ec768d1155d | understanding-satirical-articles-using-common | null | null | https://aclanthology.org/Q16-1038 | https://aclanthology.org/Q16-1038.pdf | Understanding Satirical Articles Using Common-Sense | Automatic satire detection is a subtle text classification task, for machines and at times, even for humans. In this paper we argue that satire detection should be approached using common-sense inferences, rather than traditional text classification methods. We present a highly structured latent variable model capturin... | ['Xiao Zhang', 'Dan Goldwasser'] | 2016-01-01 | null | null | null | tacl-2016-1 | ['satire-detection'] | ['natural-language-processing'] | [ 2.09537238e-01 1.81647107e-01 -6.14457548e-01 -3.82279903e-01
-3.08286071e-01 -7.91608810e-01 8.16361666e-01 7.10730612e-01
-4.36612934e-01 7.21396565e-01 6.48817718e-01 -5.61007679e-01
7.62112290e-02 -5.96129000e-01 1.56333774e-01 -4.76712942e-01
1.98153690e-01 7.53505170e-01 1.33578360e-01 -7.18257204... | [8.91259479522705, 10.025059700012207] |
58e0c618-fed0-43d7-baec-5c2d58230697 | analyzing-the-influence-of-dataset | 2103.03700 | null | https://arxiv.org/abs/2103.03700v1 | https://arxiv.org/pdf/2103.03700v1.pdf | Analyzing the Influence of Dataset Composition for Emotion Recognition | Recognizing emotions from text in multimodal architectures has yielded promising results, surpassing video and audio modalities under certain circumstances. However, the method by which multimodal data is collected can be significant for recognizing emotional features in language. In this paper, we address the influenc... | ['S. Wermter', 'C. Weber', 'S. Magg', 'A. Sutherland'] | 2021-03-05 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [-1.30513132e-01 -3.25302243e-01 -2.25544333e-01 -5.97487271e-01
-5.60771167e-01 -6.48922741e-01 5.90203822e-01 1.55694604e-01
-6.30103528e-01 6.41412914e-01 5.35532892e-01 2.50835270e-02
-1.59471527e-01 -2.85753876e-01 -1.67288527e-01 -4.90495682e-01
-1.38239339e-02 8.57062936e-02 -8.34953487e-01 -3.50887090... | [13.201798439025879, 5.544878005981445] |
ab08cc4b-a155-4b6e-a02d-fc194dcdd0d9 | hybrid-fusion-based-interpretable-multimodal | 2208.11450 | null | https://arxiv.org/abs/2208.11450v1 | https://arxiv.org/pdf/2208.11450v1.pdf | Hybrid Fusion Based Interpretable Multimodal Emotion Recognition with Insufficient Labelled Data | This paper proposes a multimodal emotion recognition system, VIsual Spoken Textual Additive Net (VISTA Net), to classify the emotions reflected by a multimodal input containing image, speech, and text into discrete classes. A new interpretability technique, K-Average Additive exPlanation (KAAP), has also been developed... | ['Balasubramanian Raman', 'Sarthak Malik', 'Puneet Kumar'] | 2022-08-24 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 3.15090775e-01 5.98260108e-03 4.58077453e-02 -6.14467144e-01
-6.19985223e-01 -3.09799075e-01 8.46081138e-01 1.27925158e-01
-3.53701651e-01 4.00664985e-01 4.17948216e-01 8.09665993e-02
-2.62572709e-02 -1.35630235e-01 -9.77655873e-02 -7.32905924e-01
2.44895771e-01 7.83932880e-02 -5.27267754e-01 -1.27209112... | [13.250375747680664, 5.147392272949219] |
80705e98-acd9-4e2f-8dd6-f15f22de40f1 | identifying-the-causes-of-pyrocumulonimbus | 2211.08883 | null | https://arxiv.org/abs/2211.08883v3 | https://arxiv.org/pdf/2211.08883v3.pdf | Identifying the Causes of Pyrocumulonimbus (PyroCb) | A first causal discovery analysis from observational data of pyroCb (storm clouds generated from extreme wildfires) is presented. Invariant Causal Prediction was used to develop tools to understand the causal drivers of pyroCb formation. This includes a conditional independence test for testing $Y$ conditionally indepe... | ['Nis Meinert', 'Paula Harder', 'Duncan Watson-Parris', 'Kara D. Lamb', 'Daniel Okoh', 'Ashwin Braude', 'Kenza Tazi', 'Emiliano Díaz Salas-Porras'] | 2022-11-16 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 7.68308109e-03 -1.08586989e-01 -1.01165533e-01 -2.14647740e-01
-2.85088599e-01 -3.15465599e-01 5.80369115e-01 1.38682172e-01
4.48519830e-04 1.48682201e+00 2.90848404e-01 -9.46757615e-01
-5.92726290e-01 -1.47345901e+00 -5.85835755e-01 -6.62103951e-01
-1.06848824e+00 1.47184297e-01 -5.93771040e-02 -8.55185688... | [6.513112545013428, 3.2179112434387207] |
4129856a-e770-4297-b4e6-aebe212aa64e | study-on-sparse-representation-based | 1502.06073 | null | http://arxiv.org/abs/1502.06073v2 | http://arxiv.org/pdf/1502.06073v2.pdf | Study on Sparse Representation based Classification for Biometric Verification | In this paper, we propose a multimodal verification system integrating face
and ear based on sparse representation based classification (SRC). The face and
ear query samples are first encoded separately to derive sparsity-based match
scores, and which are then combined with sum-rule fusion for verification.
Apart from ... | ['Zengxi Huang', 'Yiguang Liu', 'Xiaoming Wang', 'Jinrong Hu'] | 2015-02-21 | null | null | null | null | ['sparse-representation-based-classification'] | ['computer-vision'] | [ 4.09897029e-01 -3.54726702e-01 -1.18333228e-01 -4.42382276e-01
-1.09956896e+00 -2.97338039e-01 2.27985144e-01 1.02256157e-01
-1.93400979e-02 7.09578574e-01 2.78306991e-01 1.17392860e-01
-3.01874965e-01 -3.85205954e-01 -9.62300971e-02 -1.05768204e+00
2.30108425e-01 -1.03911266e-01 -4.08619642e-01 -1.37424067... | [12.821490287780762, 0.4919026494026184] |
ad5a6678-368f-49b5-adf4-93fb4970ffec | climax-an-exploration-of-classifier-based | 2307.00680 | null | https://arxiv.org/abs/2307.00680v1 | https://arxiv.org/pdf/2307.00680v1.pdf | CLIMAX: An exploration of Classifier-Based Contrastive Explanations | Explainable AI is an evolving area that deals with understanding the decision making of machine learning models so that these models are more transparent, accountable, and understandable for humans. In particular, post-hoc model-agnostic interpretable AI techniques explain the decisions of a black-box ML model for a si... | ['Ranjitha Prasad', 'Praharsh Nanavati'] | 2023-07-02 | null | null | null | null | ['decision-making'] | ['reasoning'] | [ 5.42777359e-01 8.15041542e-01 -4.64670986e-01 -5.53544402e-01
-6.43318713e-01 -5.45850158e-01 1.14039147e+00 2.52830714e-01
8.66914093e-02 8.54342282e-01 1.43018559e-01 -4.64686036e-01
-5.52214205e-01 -6.22960091e-01 -9.13807809e-01 -6.83372378e-01
3.10613692e-01 9.94377315e-01 -1.49722517e-01 1.82594761... | [8.833276748657227, 5.699516773223877] |
32d57297-6be8-4e1c-b2fd-563e11c73e39 | learning-networks-from-random-walk-based-node | 1801.07386 | null | http://arxiv.org/abs/1801.07386v1 | http://arxiv.org/pdf/1801.07386v1.pdf | Learning Networks from Random Walk-Based Node Similarities | Digital presence in the world of online social media entails significant
privacy risks. In this work we consider a privacy threat to a social network in
which an attacker has access to a subset of random walk-based node
similarities, such as effective resistances (i.e., commute times) or
personalized PageRank scores. U... | ['Cameron Musco', 'Jeremy G. Hoskins', 'Charalampos E. Tsourakakis', 'Christopher Musco'] | 2018-01-23 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [ 9.26794037e-02 5.56099534e-01 -2.94534802e-01 -6.16094135e-02
-5.50719082e-01 -9.71746385e-01 2.18641371e-01 6.29550993e-01
-3.07509124e-01 5.15612364e-01 -7.10883662e-02 -4.80424315e-01
-5.43318033e-01 -1.24338889e+00 -7.30688751e-01 -5.24810731e-01
-7.28061676e-01 5.13803899e-01 7.17831329e-02 -1.23002872... | [6.775928497314453, 5.600727081298828] |
21d3e58c-312a-41ff-97f7-c5bf366835c8 | adversarial-audio-synthesis | 1802.04208 | null | http://arxiv.org/abs/1802.04208v3 | http://arxiv.org/pdf/1802.04208v3.pdf | Adversarial Audio Synthesis | Audio signals are sampled at high temporal resolutions, and learning to
synthesize audio requires capturing structure across a range of timescales.
Generative adversarial networks (GANs) have seen wide success at generating
images that are both locally and globally coherent, but they have seen little
application to aud... | ['Chris Donahue', 'Miller Puckette', 'Julian McAuley'] | 2018-02-12 | adversarial-audio-synthesis-1 | https://openreview.net/forum?id=ByMVTsR5KQ | https://openreview.net/pdf?id=ByMVTsR5KQ | iclr-2019-5 | ['audio-generation'] | ['audio'] | [ 5.75289965e-01 4.93267864e-01 3.10206592e-01 5.65681420e-02
-1.45416760e+00 -9.55822408e-01 8.73904526e-01 -7.88560152e-01
5.40755510e-01 9.37590063e-01 6.54901862e-01 3.67744304e-02
3.82607549e-01 -9.89979565e-01 -8.83057475e-01 -7.54710853e-01
-2.46717334e-01 2.65302867e-01 -2.20642820e-01 -2.69761622... | [15.583807945251465, 5.996086597442627] |
4a5b1230-b322-41fc-9a6f-8b3bfffe3117 | a-batch-noise-contrastive-estimation-approach | 1708.05997 | null | http://arxiv.org/abs/1708.05997v2 | http://arxiv.org/pdf/1708.05997v2.pdf | A Batch Noise Contrastive Estimation Approach for Training Large Vocabulary Language Models | Training large vocabulary Neural Network Language Models (NNLMs) is a
difficult task due to the explicit requirement of the output layer
normalization, which typically involves the evaluation of the full softmax
function over the complete vocabulary. This paper proposes a Batch Noise
Contrastive Estimation (B-NCE) appr... | ['Dietrich Klakow', 'Youssef Oualil'] | 2017-08-20 | null | null | null | null | ['text-compression'] | ['natural-language-processing'] | [ 2.59999037e-01 -2.14089662e-01 3.67279857e-01 -5.02926707e-01
-7.92143822e-01 -4.66788262e-02 6.31503463e-01 1.84563190e-01
-1.22013879e+00 4.95068699e-01 1.15470499e-01 -5.57821810e-01
3.85691345e-01 -5.87183475e-01 -7.06979156e-01 -8.58628452e-01
3.25592399e-01 4.67918187e-01 1.06905118e-01 -1.28068760... | [14.021909713745117, 6.506819248199463] |
30c88ba2-1810-4510-a0b8-6a496bd48cdb | concept-based-explanations-to-test-for-false | 2307.01900 | null | https://arxiv.org/abs/2307.01900v1 | https://arxiv.org/pdf/2307.01900v1.pdf | Concept-Based Explanations to Test for False Causal Relationships Learned by Abusive Language Classifiers | Classifiers tend to learn a false causal relationship between an over-represented concept and a label, which can result in over-reliance on the concept and compromised classification accuracy. It is imperative to have methods in place that can compare different models and identify over-reliances on specific concepts. W... | ['Esma Balkir', 'Kathleen C. Fraser', 'Svetlana Kiritchenko', 'Isar Nejadgholi'] | 2023-07-04 | null | null | null | null | ['abusive-language'] | ['natural-language-processing'] | [ 2.74235815e-01 2.30151281e-01 -3.89859825e-01 -8.70105326e-01
-3.74286324e-01 -7.13577986e-01 7.50404418e-01 8.13454866e-01
-4.73377377e-01 6.96826100e-01 1.67653501e-01 -5.53953052e-01
-1.49208769e-01 -6.15709484e-01 -5.09446859e-01 -5.70916414e-01
-4.18735482e-03 1.65178195e-01 -1.84028104e-01 4.36097346... | [8.739704132080078, 5.422821998596191] |
76f1ef95-87ce-4fe0-a84c-05ae111f8e17 | rethinking-self-attention-an-interpretable | 1911.03875 | null | https://arxiv.org/abs/1911.03875v3 | https://arxiv.org/pdf/1911.03875v3.pdf | Rethinking Self-Attention: Towards Interpretability in Neural Parsing | Attention mechanisms have improved the performance of NLP tasks while allowing models to remain explainable. Self-attention is currently widely used, however interpretability is difficult due to the numerous attention distributions. Recent work has shown that model representations can benefit from label-specific inform... | ['Quan Tran', 'Franck Dernoncourt', 'Walter Chang', 'Trung Bui', 'Khalil Mrini', 'Ndapa Nakashole'] | 2019-11-10 | null | https://aclanthology.org/2020.findings-emnlp.65 | https://aclanthology.org/2020.findings-emnlp.65.pdf | findings-of-the-association-for-computational | ['constituency-parsing'] | ['natural-language-processing'] | [ 6.24397323e-02 9.32606697e-01 -2.32009038e-01 -9.42456007e-01
-1.09703422e+00 -5.04788101e-01 2.57963359e-01 2.03922838e-01
-2.90781587e-01 8.01978350e-01 6.83769405e-01 -6.02976024e-01
4.04946834e-01 -5.60634077e-01 -8.04382443e-01 -1.48935392e-01
1.90151438e-01 7.38579988e-01 -2.27970108e-02 -2.00008959... | [10.4799165725708, 9.377573013305664] |
06ec0b5e-5bbd-4b6b-aca3-c851924d0f5d | does-long-term-series-forecasting-need | 2306.05035 | null | https://arxiv.org/abs/2306.05035v2 | https://arxiv.org/pdf/2306.05035v2.pdf | Does Long-Term Series Forecasting Need Complex Attention and Extra Long Inputs? | As Transformer-based models have achieved impressive performance on various time series tasks, Long-Term Series Forecasting (LTSF) tasks have also received extensive attention in recent years. However, due to the inherent computational complexity and long sequences demanding of Transformer-based methods, its applicatio... | ['Minggao Zhang', 'Dongyang Li', 'Xiaoyan Ma', 'Dongfeng Yuan', 'Haixia Zhang', 'Daojun Liang'] | 2023-06-08 | null | null | null | null | ['hyperparameter-optimization', 'bayesian-optimization'] | ['methodology', 'methodology'] | [-6.22112602e-02 -4.58196431e-01 2.87359431e-02 -3.15954298e-01
-6.26142204e-01 -2.60666072e-01 3.31315935e-01 4.99660848e-03
-2.16145217e-01 5.08335233e-01 -8.05729628e-02 -4.17977870e-01
-1.89993754e-01 -8.01463068e-01 -5.83322406e-01 -1.10607827e+00
-7.96252936e-02 2.88712591e-01 3.23442489e-01 -1.24878094... | [7.064192295074463, 2.9019718170166016] |
16e17fb2-cfa2-44c9-9128-f97769d29023 | pcb-randnet-rethinking-random-sampling-for | 2209.13797 | null | https://arxiv.org/abs/2209.13797v1 | https://arxiv.org/pdf/2209.13797v1.pdf | PCB-RandNet: Rethinking Random Sampling for LIDAR Semantic Segmentation in Autonomous Driving Scene | Fast and efficient semantic segmentation of large-scale LiDAR point clouds is a fundamental problem in autonomous driving. To achieve this goal, the existing point-based methods mainly choose to adopt Random Sampling strategy to process large-scale point clouds. However, our quantative and qualitative studies have foun... | ['GuoQiang Xiao', 'Dehong He', 'Hang Jiang', 'XianFeng Han', 'Huixian Cheng'] | 2022-09-28 | null | null | null | null | ['lidar-semantic-segmentation'] | ['computer-vision'] | [-1.50442541e-01 -3.21953923e-01 -3.38691324e-01 -5.39609730e-01
-5.58843613e-01 -3.03265035e-01 3.63975048e-01 8.82714912e-02
-5.23667037e-01 5.70761859e-01 -3.58371168e-01 -1.82587802e-01
-1.30536169e-01 -1.22671664e+00 -7.18756735e-01 -6.78778708e-01
4.86947566e-01 8.28593850e-01 9.11748230e-01 -1.02949515... | [8.060661315917969, -2.7728488445281982] |
896a35a2-7ed3-4f91-baae-b7dddee2e574 | gcpg-a-general-framework-for-controllable | null | null | https://openreview.net/forum?id=3YX-sCVoGl | https://openreview.net/pdf?id=3YX-sCVoGl | GCPG: A General Framework for Controllable Paraphrase Generation | Controllable paraphrase generation (CPG) incorporates various external conditions to obtain desirable paraphrases. However, existing works only highlight a special condition under two indispensable aspects of CPG (i.e., lexically and syntactically CPG) individually, lacking a unified circumstance to explore and analyze... | ['Anonymous'] | 2021-10-16 | null | null | null | acl-arr-october-2021-10 | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 3.46883178e-01 -3.75582308e-01 -2.08086759e-01 -3.09107423e-01
-7.03200698e-01 -6.19414508e-01 6.75742567e-01 -8.95074382e-02
-1.25891501e-02 8.33609760e-01 6.39264822e-01 -2.96830416e-01
-1.39482722e-01 -8.09589446e-01 -8.24502051e-01 -6.00591779e-01
6.04578555e-01 1.01843707e-01 4.64397743e-02 -5.64112604... | [11.692728042602539, 9.364376068115234] |
b7e565e8-af33-4e48-b717-607b4b6c7a72 | fine-tuning-large-language-models-for | null | null | https://link.springer.com/chapter/10.1007/978-3-031-36021-3_15 | https://link.springer.com/chapter/10.1007/978-3-031-36021-3_15 | Fine-Tuning Large Language Models for Answering Programming Questions with Code Snippets | We study the ability of pretrained large language models (LLM) to answer questions from online question answering fora such as Stack Overflow. We consider question-answer pairs where the main part of the answer consists of source code. On two benchmark datasets—CoNaLa and a newly collected dataset based on Stack Overfl... | ['Artem Aliev', 'Sergey Nikolenko', 'Maxim Omelchenko', 'Sergey Kovalchuk', 'Vadim Lomshakov'] | 2023-06-26 | null | null | null | iccs-international-conference-on | ['code-generation', 'program-synthesis', 'text-to-code-generation', 'question-answering', 'prompt-engineering'] | ['computer-code', 'computer-code', 'computer-code', 'natural-language-processing', 'natural-language-processing'] | [-3.35070372e-01 2.40087450e-01 2.73513943e-01 -3.38514477e-01
-1.49247754e+00 -9.23607290e-01 8.38330016e-02 2.51964748e-01
-3.22351217e-01 7.42178932e-02 2.02074081e-01 -1.00905418e+00
-6.62430227e-02 -7.58003473e-01 -9.57511187e-01 2.64656961e-01
2.47138157e-01 4.35866624e-01 4.87361729e-01 -4.23027098... | [11.286859512329102, 8.011332511901855] |
dbf98535-4f36-40f3-a08b-7f89f3b3db0e | expnet-landmark-free-deep-3d-facial | 1802.00542 | null | http://arxiv.org/abs/1802.00542v1 | http://arxiv.org/pdf/1802.00542v1.pdf | ExpNet: Landmark-Free, Deep, 3D Facial Expressions | We describe a deep learning based method for estimating 3D facial expression
coefficients. Unlike previous work, our process does not relay on facial
landmark detection methods as a proxy step. Recent methods have shown that a
CNN can be trained to regress accurate and discriminative 3D morphable model
(3DMM) represent... | ['Iacopo Masi', 'Feng-Ju Chang', 'Ram Nevatia', 'Gerard Medioni', 'Anh Tuan Tran', 'Tal Hassner'] | 2018-02-02 | null | null | null | null | ['3d-facial-expression-recognition'] | ['computer-vision'] | [-5.06476685e-02 1.06636554e-01 9.68520865e-02 -7.83613265e-01
-7.34578550e-01 -4.78314072e-01 4.96523976e-01 -3.67520422e-01
-5.05111933e-01 3.29105020e-01 -1.83030710e-01 2.28619084e-01
2.96214163e-01 -5.72614849e-01 -5.77381849e-01 -5.55169523e-01
-3.77114624e-01 2.05569044e-01 -3.77974898e-01 -2.83532649... | [13.470171928405762, 1.331436038017273] |
cfee0317-33c5-46a9-9d5e-50440e27d482 | dagformer-directed-acyclic-graph-transformer | 2210.13148 | null | https://arxiv.org/abs/2210.13148v5 | https://arxiv.org/pdf/2210.13148v5.pdf | Transformers over Directed Acyclic Graphs | Transformer models have recently gained popularity in graph representation learning as they have the potential to learn complex relationships beyond the ones captured by regular graph neural networks. The main research question is how to inject the structural bias of graphs into the transformer architecture, and severa... | ['Lei Shi', 'Veronika Thost', 'Yuankai Luo'] | 2022-10-24 | null | null | null | null | ['graph-property-prediction'] | ['graphs'] | [ 3.20953399e-01 5.66360712e-01 -2.82433212e-01 -8.69564414e-02
-1.22322112e-01 -7.44716465e-01 6.20778382e-01 7.29642332e-01
-2.49283239e-02 5.41588545e-01 3.25275064e-01 -8.81671965e-01
-5.17883658e-01 -1.17586231e+00 -8.54822814e-01 -5.88679552e-01
-4.71841305e-01 8.54107141e-01 4.16750014e-01 -3.59954029... | [6.926620960235596, 6.22547721862793] |
3d09fcfb-df22-42a1-99e5-41fafd59aede | togethernet-bridging-image-restoration-and | 2209.01373 | null | https://arxiv.org/abs/2209.01373v1 | https://arxiv.org/pdf/2209.01373v1.pdf | TogetherNet: Bridging Image Restoration and Object Detection Together via Dynamic Enhancement Learning | Adverse weather conditions such as haze, rain, and snow often impair the quality of captured images, causing detection networks trained on normal images to generalize poorly in these scenarios. In this paper, we raise an intriguing question - if the combination of image restoration and object detection, can boost the p... | ['Mingqiang Wei', 'Fu Lee Wang', 'Haoran Xie', 'Lina Gong', 'Kaiwen Zhang', 'Xuefeng Yan', 'Yongzhen Wang'] | 2022-09-03 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 3.84922117e-01 -4.30898696e-01 1.41533598e-01 -1.88059047e-01
-5.01121938e-01 -5.21620214e-01 5.50723970e-01 -8.10798630e-02
-4.03468817e-01 4.58413482e-01 -3.63311879e-02 -2.27434799e-01
4.35019098e-02 -8.28976452e-01 -8.28477144e-01 -9.96251345e-01
2.72969659e-02 -3.49905610e-01 6.26391649e-01 -3.65087360... | [10.767985343933105, -3.000232219696045] |
8b5dc85d-1549-4f8a-b145-8a474abf5bfd | robust-unstructured-knowledge-access-in | 2211.03990 | null | https://arxiv.org/abs/2211.03990v1 | https://arxiv.org/pdf/2211.03990v1.pdf | Robust Unstructured Knowledge Access in Conversational Dialogue with ASR Errors | Performance of spoken language understanding (SLU) can be degraded with automatic speech recognition (ASR) errors. We propose a novel approach to improve SLU robustness by randomly corrupting clean training text with an ASR error simulator, followed by self-correcting the errors and minimizing the target classification... | ['Shuhan Yuan', 'Tinglong Liao', 'Zecheng Wang', 'Jiakai Zou', 'Jiacheng Xu', 'Yik-Cheung Tam'] | 2022-11-08 | null | null | null | null | ['spoken-language-understanding', 'spoken-language-understanding'] | ['natural-language-processing', 'speech'] | [ 1.26767471e-01 2.01239392e-01 2.45967880e-01 -2.32434288e-01
-1.54473305e+00 -9.00015116e-01 4.69929159e-01 -1.35405406e-01
-4.89383966e-01 7.58332610e-01 3.17749262e-01 -4.16241646e-01
3.29528451e-02 -2.70587951e-01 -7.99771011e-01 -4.52910990e-01
2.05048963e-01 5.24855494e-01 2.40248963e-01 -4.46905583... | [14.373149871826172, 6.902366638183594] |
d1b0b772-fe87-4187-baaf-95cca99ed464 | bert-meets-ctc-new-formulation-of-end-to-end | 2210.16663 | null | https://arxiv.org/abs/2210.16663v2 | https://arxiv.org/pdf/2210.16663v2.pdf | BERT Meets CTC: New Formulation of End-to-End Speech Recognition with Pre-trained Masked Language Model | This paper presents BERT-CTC, a novel formulation of end-to-end speech recognition that adapts BERT for connectionist temporal classification (CTC). Our formulation relaxes the conditional independence assumptions used in conventional CTC and incorporates linguistic knowledge through the explicit output dependency obta... | ['Shinji Watanabe', 'Tetsunori Kobayashi', 'Tetsuji Ogawa', 'Siddhant Arora', 'Brian Yan', 'Yosuke Higuchi'] | 2022-10-29 | null | null | null | null | ['spoken-language-understanding', 'spoken-language-understanding'] | ['natural-language-processing', 'speech'] | [ 2.31472328e-01 3.74216408e-01 -2.82805830e-01 -8.06395948e-01
-9.63283658e-01 -5.51384628e-01 7.11607814e-01 -2.49244735e-01
-4.10594583e-01 4.27852064e-01 7.76384294e-01 -5.34426987e-01
1.57160237e-01 -2.12243140e-01 -4.38797444e-01 -5.99819005e-01
-1.35600790e-01 5.82290053e-01 1.02840446e-01 -1.05555855... | [14.270590782165527, 6.815913677215576] |
72209d75-aa3d-43c3-8073-579a1d9ce9d7 | ascm-an-answer-space-clustered-prompting | null | null | https://aclanthology.org/2022.findings-acl.193 | https://aclanthology.org/2022.findings-acl.193.pdf | ASCM: An Answer Space Clustered Prompting Method without Answer Engineering | Prompt-based learning, which exploits knowledge from pre-trained language models by providing textual prompts and designing appropriate answer-category mapping methods, has achieved impressive successes on few-shot text classification and natural language inference (NLI). Because of the diverse linguistic expression, t... | ['Azmat Anwar', 'Rui Dong', 'Lei Wang', 'Bo Ma', 'Zhou Xi', 'Yating Yang', 'Zhen Wang'] | null | null | null | null | findings-acl-2022-5 | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 2.42119655e-01 2.03431193e-02 -6.90969646e-01 -5.77623427e-01
-9.38027978e-01 -4.71283704e-01 8.88527453e-01 5.58966994e-01
-6.16618574e-01 5.41490734e-01 4.78767604e-01 -3.94860893e-01
-2.48363808e-01 -6.96492374e-01 -4.16670144e-02 -6.66319281e-02
5.18152356e-01 6.91726089e-01 5.69968164e-01 -4.91523564... | [10.853470802307129, 7.731945991516113] |
5a83c446-4148-44b3-9c1d-f7ec8be47997 | aerial-spectral-super-resolution-using | 1712.08690 | null | http://arxiv.org/abs/1712.08690v1 | http://arxiv.org/pdf/1712.08690v1.pdf | Aerial Spectral Super-Resolution using Conditional Adversarial Networks | Inferring spectral signatures from ground based natural images has acquired a
lot of interest in applied deep learning. In contrast to the spectra of ground
based images, aerial spectral images have low spatial resolution and suffer
from higher noise interference. In this paper, we train a conditional
adversarial netwo... | ['Matthew Hoffman', 'Emmett Ientilucci', 'Nilay Mokashi', 'Christopher Kanan', 'Aneesh Rangnekar'] | 2017-12-23 | null | null | null | null | ['spectral-super-resolution'] | ['computer-vision'] | [ 1.21089280e+00 -1.99687198e-01 3.28899652e-01 -1.81648612e-01
-7.41344988e-01 -1.06747055e+00 2.77541727e-01 -5.78153551e-01
-3.34352642e-01 1.07699907e+00 -4.18029606e-01 -4.60223168e-01
-3.73715580e-01 -1.37894797e+00 -9.08573091e-01 -8.88506174e-01
-3.17240477e-01 -6.30684718e-02 -1.52211979e-01 -2.26316810... | [10.166072845458984, -1.9993056058883667] |
bc857548-3bf6-4b0a-a379-ae5a597c8bbc | determinant-free-fermionic-wave-function | 2108.08631 | null | https://arxiv.org/abs/2108.08631v2 | https://arxiv.org/pdf/2108.08631v2.pdf | Determinant-free fermionic wave function using feed-forward neural networks | We propose a general framework for finding the ground state of many-body fermionic systems by using feed-forward neural networks. The anticommutation relation for fermions is usually implemented to a variational wave function by the Slater determinant (or Pfaffian), which is a computational bottleneck because of the nu... | ['Yukitoshi Motome', 'Yasuyuki Kato', 'Koji Inui'] | 2021-08-19 | null | null | null | null | ['variational-monte-carlo'] | ['miscellaneous'] | [ 7.00738877e-02 -3.16584557e-01 1.10318279e-02 -2.60166675e-01
-4.50527161e-01 -2.32384712e-01 5.89363575e-01 -1.26015216e-01
-7.49720156e-01 1.13724053e+00 -1.65050134e-01 -3.77484232e-01
-1.59537226e-01 -1.13042498e+00 -6.84595227e-01 -1.19293487e+00
-2.03916863e-01 5.32164037e-01 2.13550311e-02 -5.28219283... | [5.504789352416992, 5.0378899574279785] |
014c7024-0603-4ca4-9aa2-9fed9cad6751 | identifying-computer-translated-paragraphs | 1812.10896 | null | http://arxiv.org/abs/1812.10896v1 | http://arxiv.org/pdf/1812.10896v1.pdf | Identifying Computer-Translated Paragraphs using Coherence Features | We have developed a method for extracting the coherence features from a
paragraph by matching similar words in its sentences. We conducted an
experiment with a parallel German corpus containing 2000 human-created and 2000
machine-translated paragraphs. The result showed that our method achieved the
best performance (ac... | ['Junichi Yamagishi', 'Ngoc-Dung T. Tieu', 'Hoang-Quoc Nguyen-Son', 'Isao Echizen', 'Huy H. Nguyen'] | 2018-12-28 | null | https://aclanthology.org/Y18-1056 | https://aclanthology.org/Y18-1056.pdf | paclic-2018-12 | ['paper-generation'] | ['natural-language-processing'] | [ 1.72507875e-02 1.52699426e-02 -1.98390171e-01 -2.49181199e-03
-1.35997975e+00 -5.99063277e-01 1.11217833e+00 -2.20995769e-01
-4.40124810e-01 1.39511752e+00 6.05775774e-01 -2.67319471e-01
3.18194151e-01 -6.98364675e-01 -1.59372196e-01 -3.77589405e-01
1.85675412e-01 7.40401566e-01 1.28761664e-01 -3.79281998... | [11.508429527282715, 10.196490287780762] |
44633150-9d7d-4e5c-a8b4-d8723a874254 | transformers-on-sarcasm-detection-with | null | null | https://aclanthology.org/2020.figlang-1.13 | https://aclanthology.org/2020.figlang-1.13.pdf | Transformers on Sarcasm Detection with Context | Sarcasm Detection with Context, a shared task of Second Workshop on Figurative Language Processing (co-located with ACL 2020), is study of effect of context on Sarcasm detection in conversations of Social media. We present different techniques and models, mostly based on transformer for Sarcasm Detection with Context. ... | [] | 2020-07-01 | null | null | null | acl-2020-7 | ['sentence-pair-classification'] | ['natural-language-processing'] | [-2.20494613e-01 2.68594861e-01 3.19292247e-01 -5.13154566e-01
-6.89878345e-01 -3.22479904e-01 8.63950491e-01 3.84588420e-01
-3.27085167e-01 4.85893130e-01 1.12207508e+00 -2.11380899e-01
4.41143960e-01 -4.11426604e-01 8.61011520e-02 -1.65649071e-01
2.22278833e-01 5.06507576e-01 -3.49002667e-02 -8.99377167... | [9.092945098876953, 10.72872257232666] |
60772674-787a-4c1c-9f22-c691e6558dba | leveraging-alignment-and-phonology-for-low | null | null | https://aclanthology.org/2020.icon-main.51 | https://aclanthology.org/2020.icon-main.51.pdf | Leveraging Alignment and Phonology for low-resource Indic to English Neural Machine Transliteration | In this paper we present a novel transliteration technique based on Orthographic Syllable(OS) segmentation for low-resource Indian languages (ILs). Given that alignment has produced promising results in Statistical Machine Transliteration systems and phonology plays an important role in transliteration, we introduce a ... | ['Arjun Atreya', 'Pushpak Bhattacharya', 'Manthan Mehta', 'Parth Patel'] | null | null | null | null | icon-2020-12 | ['transliteration'] | ['natural-language-processing'] | [ 3.57875288e-01 -2.16686606e-01 -4.29851651e-01 -3.84208828e-01
-7.53756166e-01 -5.71292639e-01 4.19441044e-01 7.23353252e-02
-9.08277690e-01 7.23766804e-01 4.78089362e-01 -1.15287673e+00
4.48778123e-01 -5.54162264e-01 -6.48695052e-01 -8.15768912e-02
4.53985363e-01 9.93296504e-01 7.39620905e-03 -3.09705466... | [11.397391319274902, 10.359014511108398] |
da66f291-232b-4c6d-92fc-2b90a0f702a4 | practical-license-plate-recognition-in | 1910.04324 | null | https://arxiv.org/abs/1910.04324v1 | https://arxiv.org/pdf/1910.04324v1.pdf | Practical License Plate Recognition in Unconstrained Surveillance Systems with Adversarial Super-Resolution | Although most current license plate (LP) recognition applications have been significantly advanced, they are still limited to ideal environments where training data are carefully annotated with constrained scenes. In this paper, we propose a novel license plate recognition method to handle unconstrained real world traf... | ['Yoojin Hong', 'Younkwan Lee', 'Moongu Jeon', 'Jiwon Jun'] | 2019-10-10 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [ 5.72716296e-01 -4.94121462e-01 3.74610685e-02 -2.07131490e-01
-9.31845844e-01 -7.12365866e-01 5.34238219e-01 -9.90757227e-01
-2.35801712e-01 7.84651458e-01 -3.46628070e-01 -2.63450414e-01
4.93415207e-01 -6.60367906e-01 -1.00463736e+00 -6.11138463e-01
5.30882716e-01 2.42362976e-01 9.04354692e-01 -2.69461758... | [9.869264602661133, -4.878361701965332] |
991f8802-b311-48f5-9f5c-d104f494c77b | weakly-supervised-convolutional-lstm-approach | 1812.01366 | null | http://arxiv.org/abs/1812.01366v2 | http://arxiv.org/pdf/1812.01366v2.pdf | Weakly Supervised Convolutional LSTM Approach for Tool Tracking in Laparoscopic Videos | Purpose: Real-time surgical tool tracking is a core component of the future
intelligent operating room (OR), because it is highly instrumental to analyze
and understand the surgical activities. Current methods for surgical tool
tracking in videos need to be trained on data in which the spatial positions of
the tools ar... | ['Jacques Marescaux', 'Didier Mutter', 'Nicolas Padoy', 'Chinedu Innocent Nwoye'] | 2018-12-04 | null | null | null | null | ['instrument-recognition', 'video-object-tracking', 'surgical-tool-detection'] | ['audio', 'computer-vision', 'computer-vision'] | [ 2.24335104e-01 3.96833494e-02 -5.73708653e-01 1.01554126e-01
-6.66594267e-01 -7.86972404e-01 2.94517815e-01 -1.04592830e-01
-6.01758361e-01 -3.93992253e-02 3.87946493e-03 -3.99650455e-01
-4.96009625e-02 7.90310502e-02 -9.06515956e-01 -6.08267069e-01
-2.14294165e-01 -1.70732036e-01 3.32574964e-01 4.74309511... | [14.04632568359375, -3.319890022277832] |
87b7dea2-ffe3-4302-97ab-ac3b9bc96ce4 | improving-performance-of-federated-learning | 2112.06194 | null | https://arxiv.org/abs/2112.06194v2 | https://arxiv.org/pdf/2112.06194v2.pdf | Improving Performance of Federated Learning based Medical Image Analysis in Non-IID Settings using Image Augmentation | Federated Learning (FL) is a suitable solution for making use of sensitive data belonging to patients, people, companies, or industries that are obligatory to work under rigid privacy constraints. FL mainly or partially supports data privacy and security issues and provides an alternative to model problems facilitating... | ['Seref Sagiroglu', 'Murat Akin', 'Alper Emin Cetinkaya'] | 2021-12-12 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 8.90789777e-02 1.21252723e-01 -3.96704406e-01 -6.47681653e-01
-6.28012776e-01 -5.71361899e-01 1.98649645e-01 1.37476236e-01
-3.34400088e-01 9.83084679e-01 1.28566816e-01 -5.26350260e-01
-4.65697318e-01 -5.51709592e-01 -4.07872826e-01 -9.87873375e-01
1.22233145e-01 4.54097927e-01 -7.38074556e-02 1.46718651... | [6.079319477081299, 6.512689113616943] |
307ac3ce-e73e-4926-a8d4-52ca760cb311 | caching-historical-embeddings-in | 2211.14155 | null | https://arxiv.org/abs/2211.14155v1 | https://arxiv.org/pdf/2211.14155v1.pdf | Caching Historical Embeddings in Conversational Search | Rapid response, namely low latency, is fundamental in search applications; it is particularly so in interactive search sessions, such as those encountered in conversational settings. An observation with a potential to reduce latency asserts that conversational queries exhibit a temporal locality in the lists of documen... | ['Nicola Tonellotto', 'Raffaele Perego', 'Franco Maria Nardini', 'Cristina Ioana Muntean', 'Ida Mele', 'Ophir Frieder'] | 2022-11-25 | null | null | null | null | ['document-embedding', 'conversational-search'] | ['methodology', 'natural-language-processing'] | [-2.88041115e-01 -2.23635495e-01 -3.93285602e-01 -3.59426796e-01
-1.09257603e+00 -5.93300641e-01 9.69078481e-01 4.89254951e-01
-7.45353460e-01 2.51045763e-01 9.75330770e-01 -2.46854439e-01
-4.00798738e-01 -9.00963604e-01 -3.89210582e-01 -1.95680216e-01
-3.16761076e-01 1.09434581e+00 5.72889507e-01 -3.80330771... | [11.659317970275879, 7.652909278869629] |
22a85dbf-ffa7-4834-86e9-151843e3ff74 | on-semidefinite-relaxations-for-the-block | 1406.5647 | null | http://arxiv.org/abs/1406.5647v3 | http://arxiv.org/pdf/1406.5647v3.pdf | On semidefinite relaxations for the block model | The stochastic block model (SBM) is a popular tool for community detection in
networks, but fitting it by maximum likelihood (MLE) involves a computationally
infeasible optimization problem. We propose a new semidefinite programming
(SDP) solution to the problem of fitting the SBM, derived as a relaxation of
the MLE. W... | ['Arash A. Amini', 'Elizaveta Levina'] | 2014-06-21 | null | null | null | null | ['graphon-estimation'] | ['graphs'] | [ 2.51366645e-01 2.28731036e-01 -2.96090990e-01 1.37169927e-01
-5.72835982e-01 -8.69874656e-01 2.65003145e-01 1.90921277e-01
-9.78867039e-02 7.92790055e-01 7.09125698e-02 -3.66835237e-01
-6.49460614e-01 -7.96464145e-01 -9.87746894e-01 -9.73183274e-01
-6.16981387e-01 7.91889966e-01 2.68125355e-01 -2.57480085... | [6.897597312927246, 5.079958438873291] |
31295121-1fc7-4a05-8158-bcbba7fcd3cd | context-guided-triple-matching-for-multiple | 2109.12996 | null | https://arxiv.org/abs/2109.12996v1 | https://arxiv.org/pdf/2109.12996v1.pdf | Context-guided Triple Matching for Multiple Choice Question Answering | The task of multiple choice question answering (MCQA) refers to identifying a suitable answer from multiple candidates, by estimating the matching score among the triple of the passage, question and answer. Despite the general research interest in this regard, existing methods decouple the process into several pair-wis... | ['Wanqing Li', 'Jie Yang', 'Junping Liu', 'Xinrong Hu', 'Junlong Ma', 'Xun Yao'] | 2021-09-27 | null | null | null | null | ['multiple-choice-qa'] | ['natural-language-processing'] | [ 1.28667757e-01 -1.63727209e-01 1.25534832e-01 -4.07481730e-01
-1.41173089e+00 -4.74799693e-01 5.94913423e-01 5.05758584e-01
-5.50906539e-01 6.20106876e-01 2.38103867e-01 -2.07587391e-01
-3.50239128e-01 -5.65789998e-01 -4.91464078e-01 -5.30627728e-01
6.16899490e-01 4.10795897e-01 6.98666513e-01 -2.14036837... | [11.3480806350708, 8.034926414489746] |
dd0ddb2d-3510-411f-9b58-e5a5b3f15713 | how-bayesian-should-bayesian-optimisation-be | 2105.00894 | null | https://arxiv.org/abs/2105.00894v1 | https://arxiv.org/pdf/2105.00894v1.pdf | How Bayesian Should Bayesian Optimisation Be? | Bayesian optimisation (BO) uses probabilistic surrogate models - usually Gaussian processes (GPs) - for the optimisation of expensive black-box functions. At each BO iteration, the GP hyperparameters are fit to previously-evaluated data by maximising the marginal likelihood. However, this fails to account for uncertain... | ['Jonathan Fieldsend', 'Richard Everson', 'George De Ath'] | 2021-05-03 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 9.98665616e-02 2.05250010e-01 4.91421252e-01 -1.18053630e-01
-8.34732473e-01 -3.59596074e-01 9.28119540e-01 2.32770741e-01
-6.23114109e-01 8.66622567e-01 1.53657317e-01 -4.91261005e-01
-7.88236618e-01 -6.99570954e-01 -5.14055669e-01 -1.27752101e+00
-6.47441298e-03 7.65236795e-01 4.10469830e-01 1.43009812... | [6.394711971282959, 3.7407796382904053] |
78b83287-6d1f-4e37-9354-448267212c3b | rankpose-learning-generalised-feature-with | 2005.10984 | null | https://arxiv.org/abs/2005.10984v1 | https://arxiv.org/pdf/2005.10984v1.pdf | RankPose: Learning Generalised Feature with Rank Supervision for Head Pose Estimation | We address the challenging problem of RGB image-based head pose estimation. We first reformulate head pose representation learning to constrain it to a bounded space. Head pose represented as vector projection or vector angles shows helpful to improving performance. Further, a ranking loss combined with MSE regression ... | ['Donggen Dai', 'Zhuojun Chen', 'Wangkit Wong'] | 2020-05-22 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [-2.77806491e-01 3.37415546e-01 -1.79175824e-01 -1.00032449e+00
-1.28502309e+00 -2.38880560e-01 5.18911421e-01 -2.12655097e-01
-6.96334720e-01 7.91719973e-01 8.07538092e-01 1.99103385e-01
-1.36387050e-02 -2.94890851e-01 -7.65858769e-01 -6.90730214e-01
-3.55399370e-01 4.85194355e-01 -1.08652472e-01 -1.72512367... | [13.662213325500488, 0.29411616921424866] |
def9b274-8354-4acf-9379-574b6624cc08 | simulation-toolkit-for-digital-material | null | null | https://www.sciencedirect.com/science/article/abs/pii/S0927025623000150 | https://www.sciencedirect.com/science/article/pii/S0927025623000150/pdfft?isDTMRedir=true&download=true | Simulation toolkit for digital material characterization of large image-based microstructures | In this paper, an efficient image-based simulation toolkit for material characterization is presented, which is scalable to work from personal computers to workstations. The effective thermal conductivity, elasticity, and permeability are evaluated employing a computational homogenization framework based on the Finite ... | ['André M.B. Pereira', 'Ricardo Leiderman', 'Federico Semeraro', 'Victor W. Sapucaia', 'Rafael S. Vianna', 'Pedro C.F. Lopes'] | 2023-01-18 | null | null | null | computational-materials-science-2023-1 | ['physical-simulations'] | ['miscellaneous'] | [ 1.38709811e-03 -3.56475621e-01 6.19669199e-01 3.05274665e-01
-3.18294346e-01 -1.07723989e-01 3.73267949e-01 6.20740578e-02
-5.23278236e-01 7.37502038e-01 -3.26972425e-01 -4.35071200e-01
-2.30006069e-01 -1.17761183e+00 -4.12907451e-01 -1.09067667e+00
4.25573774e-02 6.42653942e-01 3.98922235e-01 -8.17824900... | [6.406203269958496, 3.2507855892181396] |
fb5ea43f-82f7-4ea3-a9bf-f0df0d5f3f46 | joint-learning-of-interpretation-and | 2005.11638 | null | https://arxiv.org/abs/2005.11638v1 | https://arxiv.org/pdf/2005.11638v1.pdf | Joint learning of interpretation and distillation | The extra trust brought by the model interpretation has made it an indispensable part of machine learning systems. But to explain a distilled model's prediction, one may either work with the student model itself, or turn to its teacher model. This leads to a more fundamental question: if a distilled model should give a... | ['Shenghong Li', 'Fucai Luo', 'Zhicong Yan', 'Jinchao Huang', 'Guofu Li'] | 2020-05-24 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [ 2.46483132e-01 1.13313675e+00 -6.04540527e-01 -7.63258576e-01
-2.12175325e-01 -3.28994125e-01 6.95622921e-01 5.79674765e-02
2.49061435e-01 8.76540840e-01 1.54532820e-01 -1.25722528e+00
-1.57452817e-03 -7.77931511e-01 -5.80001175e-01 -7.11808681e-01
3.29085678e-01 9.08966243e-01 2.82343388e-01 -2.96496361... | [9.02055835723877, 6.1263885498046875] |
bef96a1f-a9c5-4c8b-8efa-71ab02f4d2de | learning-dynamics-via-graph-neural-networks | 2106.03772 | null | https://arxiv.org/abs/2106.03772v1 | https://arxiv.org/pdf/2106.03772v1.pdf | Learning Dynamics via Graph Neural Networks for Human Pose Estimation and Tracking | Multi-person pose estimation and tracking serve as crucial steps for video understanding. Most state-of-the-art approaches rely on first estimating poses in each frame and only then implementing data association and refinement. Despite the promising results achieved, such a strategy is inevitably prone to missed detect... | ['Gang Hua', 'Xinchao Wang', 'Chunluan Zhou', 'Haoxiang Li', 'Zhou Ren', 'Yiding Yang'] | 2021-06-07 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Yang_Learning_Dynamics_via_Graph_Neural_Networks_for_Human_Pose_Estimation_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Yang_Learning_Dynamics_via_Graph_Neural_Networks_for_Human_Pose_Estimation_CVPR_2021_paper.pdf | cvpr-2021-1 | ['multi-person-pose-estimation-and-tracking'] | ['computer-vision'] | [ 7.07102716e-02 -1.74498141e-01 3.61989774e-02 -2.93722861e-02
-5.71020782e-01 -3.62232983e-01 4.66987342e-01 1.27071915e-02
-5.19808412e-01 7.45142639e-01 1.30091935e-01 3.98264438e-01
-3.70137095e-02 -5.11073053e-01 -8.96167397e-01 -5.73066652e-01
-1.00627027e-01 7.97849715e-01 4.63261127e-01 -1.45118877... | [7.013377666473389, -0.9597166776657104] |
8e4dafa7-c3ea-4723-b982-766d28d943de | weighted-sampling-for-masked-language | 2302.14225 | null | https://arxiv.org/abs/2302.14225v2 | https://arxiv.org/pdf/2302.14225v2.pdf | Weighted Sampling for Masked Language Modeling | Masked Language Modeling (MLM) is widely used to pretrain language models. The standard random masking strategy in MLM causes the pre-trained language models (PLMs) to be biased toward high-frequency tokens. Representation learning of rare tokens is poor and PLMs have limited performance on downstream tasks. To allevia... | ['Wei Wang', 'Yuxin Jiang', 'Kongzhang Hao', 'Xin Cao', 'Chong Deng', 'Wen Wang', 'Qian Chen', 'Linhan Zhang'] | 2023-02-28 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings', 'semantic-textual-similarity'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [ 1.39528349e-01 5.81818447e-02 -6.24443054e-01 -4.68127251e-01
-1.09771454e+00 -4.72140014e-01 6.86889946e-01 4.48065251e-01
-8.71515989e-01 4.93368864e-01 4.86697614e-01 -4.55742091e-01
3.75157773e-01 -6.64481640e-01 -6.87452674e-01 -4.22375470e-01
-5.73644787e-02 2.95948446e-01 4.02922720e-01 -1.91473275... | [10.877740859985352, 8.680668830871582] |
77dabe9f-7ca5-4d19-8971-209c9a5a603f | deep-unsupervised-multi-view-detection-of | 1807.09715 | null | http://arxiv.org/abs/1807.09715v1 | http://arxiv.org/pdf/1807.09715v1.pdf | Deep Unsupervised Multi-View Detection of Video Game Stream Highlights | We consider the problem of automatic highlight-detection in video game
streams. Currently, the vast majority of highlight-detection systems for games
are triggered by the occurrence of hard-coded game events (e.g., score change,
end-game), while most advanced tools and techniques are based on detection of
highlights vi... | ['Charles Ringer', 'Mihalis A. Nicolaou'] | 2018-07-25 | null | null | null | null | ['highlight-detection'] | ['computer-vision'] | [ 4.20712709e-01 -6.69418797e-02 3.77372891e-01 -5.14246859e-02
-7.59936690e-01 -7.44212866e-01 4.90816563e-01 8.33149552e-01
-3.56926888e-01 1.79836348e-01 4.55032259e-01 2.14481890e-01
2.81321436e-01 -8.43356371e-01 -4.06008631e-01 -5.38530409e-01
-5.32630146e-01 -2.62834907e-01 5.35374165e-01 -6.11034811... | [10.148282051086426, 0.47637873888015747] |
38e6e74f-4cfc-45f0-8410-bfbf80eefbff | label-embedding-by-johnson-lindenstrauss | 2305.19470 | null | https://arxiv.org/abs/2305.19470v2 | https://arxiv.org/pdf/2305.19470v2.pdf | Label Embedding by Johnson-Lindenstrauss Matrices | We present a simple and scalable framework for extreme multiclass classification based on Johnson-Lindenstrauss matrices (JLMs). Using the columns of a JLM to embed the labels, a $C$-class classification problem is transformed into a regression problem with $\cO(\log C)$ output dimension. We derive an excess risk bound... | ['Clayton Scott', 'Jianxin Zhang'] | 2023-05-31 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [ 1.23136066e-01 -2.57135555e-02 -4.48899567e-01 -7.27443278e-01
-1.00523961e+00 -4.59564567e-01 -1.03316225e-01 6.93191886e-02
-3.40402424e-01 7.14415789e-01 -4.61144745e-01 -6.65932000e-01
-4.26584870e-01 -5.85921884e-01 -5.33226311e-01 -8.15917373e-01
-4.08844620e-01 4.23393697e-01 -1.75693631e-01 1.45298824... | [8.015353202819824, 4.209837436676025] |
65820ce6-b331-454d-9870-957aa66be29a | kinematic-3d-object-detection-in-monocular | 2007.09548 | null | https://arxiv.org/abs/2007.09548v1 | https://arxiv.org/pdf/2007.09548v1.pdf | Kinematic 3D Object Detection in Monocular Video | Perceiving the physical world in 3D is fundamental for self-driving applications. Although temporal motion is an invaluable resource to human vision for detection, tracking, and depth perception, such features have not been thoroughly utilized in modern 3D object detectors. In this work, we propose a novel method for m... | ['Gerard Pons-Moll', 'Xiaoming Liu', 'Garrick Brazil', 'Bernt Schiele'] | 2020-07-19 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4241_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123680137.pdf | eccv-2020-8 | ['vehicle-pose-estimation'] | ['computer-vision'] | [-3.83739650e-01 -6.83985531e-01 -1.43933237e-01 -2.80613810e-01
-3.55788589e-01 -8.86569440e-01 6.03441596e-01 -2.83052415e-01
-4.98835444e-01 6.51636049e-02 -1.28035069e-01 -3.18774134e-01
2.63791829e-01 -1.36369154e-01 -7.81663597e-01 -4.61702704e-01
-6.46737516e-02 -9.51956399e-03 8.29197347e-01 1.04332089... | [7.971288681030273, -2.448915719985962] |
606bc448-37a9-426c-b033-ceb810ff172c | point-cloud-segmentation-using-sparse | 2112.00289 | null | https://arxiv.org/abs/2112.00289v2 | https://arxiv.org/pdf/2112.00289v2.pdf | Point Cloud Segmentation Using Sparse Temporal Local Attention | Point clouds are a key modality used for perception in autonomous vehicles, providing the means for a robust geometric understanding of the surrounding environment. However despite the sensor outputs from autonomous vehicles being naturally temporal in nature, there is still limited exploration of exploiting point clou... | ['Sridha Sridharan', 'Clinton Fookes', 'Peyman Moghadam', 'Joshua Knights'] | 2021-12-01 | null | null | null | null | ['point-cloud-segmentation'] | ['computer-vision'] | [ 1.99087262e-01 5.51916547e-02 -1.85270116e-01 -6.63495421e-01
-1.02967000e+00 -6.77104294e-01 9.05339479e-01 -1.60983298e-02
-5.31459153e-01 2.09284380e-01 -9.40501988e-02 -1.88078880e-01
2.85031080e-01 -7.72985101e-01 -1.17126524e+00 -5.60141861e-01
-1.18837237e-01 5.78461826e-01 6.94059968e-01 -2.40786374... | [7.988485813140869, -2.4000415802001953] |
9c66101d-6b6b-4efe-a35b-40fcbd3cf0e4 | cpm-a-large-scale-generative-chinese-pre | 2012.00413 | null | https://arxiv.org/abs/2012.00413v1 | https://arxiv.org/pdf/2012.00413v1.pdf | CPM: A Large-scale Generative Chinese Pre-trained Language Model | Pre-trained Language Models (PLMs) have proven to be beneficial for various downstream NLP tasks. Recently, GPT-3, with 175 billion parameters and 570GB training data, drew a lot of attention due to the capacity of few-shot (even zero-shot) learning. However, applying GPT-3 to address Chinese NLP tasks is still challen... | ['Maosong Sun', 'Xiaoyan Zhu', 'Juanzi Li', 'Jie Tang', 'Wentao Han', 'Minlie Huang', 'Zhiyuan Liu', 'Zhenbo Sun', 'Daixuan Li', 'Shengqi Chen', 'Huanqi Cao', 'Guoyang Zeng', 'Yanan Zheng', 'Xiaozhi Wang', 'Fanchao Qi', 'Jian Guan', 'Haozhe Ji', 'Yusheng Su', 'Yujia Qin', 'Deming Ye', 'Yuxian Gu', 'Pei Ke', 'Hao Zhou',... | 2020-12-01 | null | null | null | null | ['cloze-test'] | ['natural-language-processing'] | [-1.84326544e-01 1.46640018e-01 -3.51672053e-01 -1.71471685e-01
-1.29889107e+00 -3.90963227e-01 6.13807678e-01 -2.35006899e-01
-3.48255247e-01 9.47582483e-01 5.49156725e-01 -6.76885962e-01
4.91365463e-01 -7.60539770e-01 -4.01570052e-01 -5.85605741e-01
2.05248281e-01 7.25779653e-01 -8.30328390e-02 -3.41198146... | [11.513409614562988, 9.0104398727417] |
22c6b52a-2585-4a72-92df-669cfc428464 | learning-dynamic-relationships-for-3d-human | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Cui_Learning_Dynamic_Relationships_for_3D_Human_Motion_Prediction_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Cui_Learning_Dynamic_Relationships_for_3D_Human_Motion_Prediction_CVPR_2020_paper.pdf | Learning Dynamic Relationships for 3D Human Motion Prediction | 3D human motion prediction, i.e., forecasting future sequences from given historical poses, is a fundamental task for action analysis, human-computer interaction, machine intelligence. Recently, the state-of-the-art method assumes that the whole human motion sequence involves a fully-connected graph formed by links bet... | [' Fei Yang', ' Huaijiang Sun', 'Qiongjie Cui'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['action-analysis'] | ['computer-vision'] | [-2.97258906e-02 1.78107724e-01 -3.12753379e-01 5.35863861e-02
-1.11434139e-01 -3.78771752e-01 5.50481617e-01 -2.76425332e-01
-1.89568713e-01 6.57798052e-01 5.05626023e-01 2.04899372e-03
8.41633976e-02 -7.09853947e-01 -8.56180787e-01 -6.81542575e-01
-2.23283380e-01 3.75718534e-01 5.35518587e-01 -3.71768326... | [7.4229302406311035, -0.21662938594818115] |
089aae1e-8ff6-4de8-a712-da8c018b3585 | h-denseunet-hybrid-densely-connected-unet-for | 1709.07330 | null | http://arxiv.org/abs/1709.07330v3 | http://arxiv.org/pdf/1709.07330v3.pdf | H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes | Liver cancer is one of the leading causes of cancer death. To assist doctors
in hepatocellular carcinoma diagnosis and treatment planning, an accurate and
automatic liver and tumor segmentation method is highly demanded in the
clinical practice. Recently, fully convolutional neural networks (FCNs),
including 2D and 3D ... | ['Chi-Wing Fu', 'Xiaojuan Qi', 'Qi Dou', 'Hao Chen', 'Pheng Ann Heng', 'Xiaomeng Li'] | 2017-09-21 | null | null | null | null | ['liver-segmentation', 'automatic-liver-and-tumor-segmentation'] | ['medical', 'medical'] | [-3.03047299e-01 -7.17777982e-02 -2.98653185e-01 -5.62212586e-01
-8.35702717e-01 -3.25555027e-01 5.51587343e-01 1.59122229e-01
-2.13576779e-01 2.82360852e-01 4.51553285e-01 -4.51369971e-01
1.12417586e-01 -8.50128055e-01 -4.21452403e-01 -9.30775583e-01
-3.09679180e-01 5.33751428e-01 8.54573324e-02 1.75257176... | [14.681790351867676, -2.531067371368408] |
85d40717-f7c0-442a-8112-1f0a71e9210d | masked-autoencoders-as-the-unified-learners | 2208.00231 | null | https://arxiv.org/abs/2208.00231v1 | https://arxiv.org/pdf/2208.00231v1.pdf | Masked Autoencoders As The Unified Learners For Pre-Trained Sentence Representation | Despite the progresses on pre-trained language models, there is a lack of unified frameworks for pre-trained sentence representation. As such, it calls for different pre-training methods for specific scenarios, and the pre-trained models are likely to be limited by their universality and representation quality. In this... | ['Samuel Yang', 'Alexander Liu'] | 2022-07-30 | null | null | null | null | ['natural-questions'] | ['miscellaneous'] | [ 3.55103612e-01 -1.64509922e-01 -1.73600152e-01 -3.15258056e-01
-1.04385602e+00 -3.12645495e-01 8.01574588e-01 3.54524910e-01
-6.00670516e-01 6.30947948e-01 4.90765959e-01 -2.94616729e-01
-2.61128634e-01 -9.21179414e-01 -3.30003411e-01 -4.49038804e-01
9.75565240e-02 3.35654527e-01 2.85472989e-01 -9.11478162... | [10.969178199768066, 8.468058586120605] |
b2ffbe97-29c8-4586-b3be-e6de61cf111b | balancing-exploration-and-exploitation | 2306.01683 | null | https://arxiv.org/abs/2306.01683v1 | https://arxiv.org/pdf/2306.01683v1.pdf | Balancing Exploration and Exploitation: Disentangled $β$-CVAE in De Novo Drug Design | Deep generative models have recently emerged as a promising de novo drug design method. In this respect, deep generative conditional variational autoencoder (CVAE) models are a powerful approach for generating novel molecules with desired drug-like properties. However, molecular graph-based models with disentanglement ... | ['Bingquan Shen', 'De Tao Irwin Chin', 'Guang Jun Nicholas Ang'] | 2023-06-02 | null | null | null | null | ['disentanglement'] | ['methodology'] | [ 5.13309166e-02 1.51678681e-01 -1.99411258e-01 1.51847214e-01
-4.84974384e-01 -5.98980963e-01 6.07249916e-01 4.79645431e-01
-3.01952809e-01 1.26625752e+00 -5.75949587e-02 -4.39849466e-01
-2.22880259e-01 -1.01849473e+00 -8.51002693e-01 -1.20611477e+00
-3.65825921e-01 2.75498480e-01 -2.97770679e-01 -2.69537330... | [5.012134075164795, 5.684840202331543] |
37c8e5d4-b713-409c-bb6d-b357627e7a4b | pd-morl-preference-driven-multi-objective | 2208.07914 | null | https://arxiv.org/abs/2208.07914v3 | https://arxiv.org/pdf/2208.07914v3.pdf | PD-MORL: Preference-Driven Multi-Objective Reinforcement Learning Algorithm | Multi-objective reinforcement learning (MORL) approaches have emerged to tackle many real-world problems with multiple conflicting objectives by maximizing a joint objective function weighted by a preference vector. These approaches find fixed customized policies corresponding to preference vectors specified during tra... | ['Umit Y. Ogras', 'Suat Gumussoy', 'Toygun Basaklar'] | 2022-08-16 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [ 3.32557023e-01 -2.01323479e-01 -6.53261304e-01 -1.26431718e-01
-9.94523346e-01 -6.82947576e-01 5.51901385e-03 2.01413170e-01
-6.36056900e-01 1.26019144e+00 -7.79902115e-02 -1.47328064e-01
-9.06100333e-01 -4.64695096e-01 -7.49820471e-01 -8.65392804e-01
-3.31183672e-02 7.74540365e-01 6.40942752e-02 -1.63204014... | [4.253900527954102, 2.359938144683838] |
621e34d8-c232-4f36-9b94-9285f2fbdd93 | segment-anything-model-for-medical-image | 2304.10517 | null | https://arxiv.org/abs/2304.10517v3 | https://arxiv.org/pdf/2304.10517v3.pdf | Segment Anything Model for Medical Image Analysis: an Experimental Study | Training segmentation models for medical images continues to be challenging due to the limited availability of data annotations. Segment Anything Model (SAM) is a foundation model that is intended to segment user-defined objects of interest in an interactive manner. While the performance on natural images is impressive... | ['Yixin Zhang', 'Nicholas Konz', 'Jichen Yang', 'Hanxue Gu', 'Haoyu Dong', 'Maciej A. Mazurowski'] | 2023-04-20 | null | null | null | null | ['zero-shot-segmentation', 'interactive-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.57246727e-01 1.92577973e-01 -3.46619099e-01 -4.92416561e-01
-1.30845022e+00 -6.77771270e-01 3.70697290e-01 3.99286449e-01
-5.94866037e-01 4.38060552e-01 -2.56556012e-02 -7.14234710e-01
-1.84223726e-01 -9.44819227e-02 -3.03291559e-01 -6.41106129e-01
-1.17130809e-01 8.29577386e-01 6.61500812e-01 -1.32629514... | [14.708962440490723, -2.3083293437957764] |
4c4a83f8-df10-4c33-8d78-52f46ad4f36e | state-wise-constrained-policy-optimization | 2306.12594 | null | https://arxiv.org/abs/2306.12594v2 | https://arxiv.org/pdf/2306.12594v2.pdf | State-wise Constrained Policy Optimization | Reinforcement Learning (RL) algorithms have shown tremendous success in simulation environments, but their application to real-world problems faces significant challenges, with safety being a major concern. In particular, enforcing state-wise constraints is essential for many challenging tasks such as autonomous drivin... | ['Changliu Liu', 'Tianhao Wei', 'Yifan Sun', 'Rui Chen', 'WeiYe Zhao'] | 2023-06-21 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 1.32508725e-01 1.12046063e-01 -6.33283615e-01 -6.08402416e-02
-4.59608734e-01 -3.27296138e-01 4.36334342e-01 7.81823620e-02
-7.63303459e-01 1.20416927e+00 -2.10271850e-01 -5.55729270e-01
-4.46066350e-01 -5.23994923e-01 -8.41948986e-01 -8.51991296e-01
-5.84940374e-01 4.68705505e-01 3.64034623e-01 -4.72845823... | [4.584259510040283, 2.018918991088867] |
416f43ec-7ada-456f-9f69-372fac1896e7 | trec-cast-2019-the-conversational-assistance | 2003.13624 | null | https://arxiv.org/abs/2003.13624v1 | https://arxiv.org/pdf/2003.13624v1.pdf | TREC CAsT 2019: The Conversational Assistance Track Overview | The Conversational Assistance Track (CAsT) is a new track for TREC 2019 to facilitate Conversational Information Seeking (CIS) research and to create a large-scale reusable test collection for conversational search systems. The document corpus is 38,426,252 passages from the TREC Complex Answer Retrieval (CAR) and Micr... | ['Jamie Callan', 'Chenyan Xiong', 'Jeffrey Dalton'] | 2020-03-30 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 3.00061822e-01 4.68644798e-01 7.90875852e-02 -2.86241889e-01
-1.56750357e+00 -7.53440022e-01 1.28734529e+00 1.54118612e-01
-6.43956721e-01 9.21972990e-01 9.71244156e-01 -7.64202774e-01
-4.03059930e-01 -1.73235267e-01 -4.74988185e-02 1.30634019e-02
4.06405441e-02 1.13017845e+00 3.34467262e-01 -9.80424881... | [12.123334884643555, 7.823229789733887] |
c2c4edf3-0cbc-407a-955a-1d929eb10b4c | disaster-anomaly-detector-via-deeper-fcdds | 2306.02517 | null | https://arxiv.org/abs/2306.02517v2 | https://arxiv.org/pdf/2306.02517v2.pdf | Disaster Anomaly Detector via Deeper FCDDs for Explainable Initial Responses | Extreme natural disasters can have devastating effects on both urban and rural areas. In any disaster event, an initial response is the key to rescue within 72 hours and prompt recovery. During the initial stage of disaster response, it is important to quickly assess the damage over a wide area and identify priority ar... | ['Junichiro Fujii', 'Masahiro Okano', 'Takato Yasuno'] | 2023-06-05 | null | null | null | null | ['scene-understanding'] | ['computer-vision'] | [-3.17550749e-02 -2.05024704e-03 2.77975649e-01 -2.71621048e-01
-1.17252059e-01 -2.16565326e-01 3.84168983e-01 9.17237759e-01
-3.42666626e-01 5.94305873e-01 5.26899576e-01 -5.32753646e-01
-2.26759613e-01 -1.27243292e+00 -4.06955212e-01 -6.68993831e-01
-6.81063831e-01 3.50764513e-01 -1.18728586e-01 -7.85961270... | [9.5518217086792, -1.3130160570144653] |
2ac0ba19-30bc-4d74-a9ff-40cd590b25a5 | virtuously-safe-reinforcement-learning | 1805.11447 | null | http://arxiv.org/abs/1805.11447v1 | http://arxiv.org/pdf/1805.11447v1.pdf | Virtuously Safe Reinforcement Learning | We show that when a third party, the adversary, steps into the two-party
setting (agent and operator) of safely interruptible reinforcement learning, a
trade-off has to be made between the probability of following the optimal
policy in the limit, and the probability of escaping a dangerous situation
created by the adve... | ['Alexandre Maurer', 'Rachid Guerraoui', 'Henrik Aslund', 'El Mahdi El Mhamdi'] | 2018-05-29 | null | null | null | null | ['safe-exploration'] | ['robots'] | [-3.38540152e-02 7.19392538e-01 -1.05744891e-01 1.65432602e-01
-5.26167870e-01 -1.16618705e+00 5.42429626e-01 6.28397986e-02
-9.72127080e-01 1.02259672e+00 -4.70533706e-02 -6.62482381e-01
-2.84274459e-01 -8.22101712e-01 -7.30655789e-01 -1.00089061e+00
-4.92991596e-01 5.37001729e-01 1.77618146e-01 -2.84137547... | [4.315341949462891, 2.1090633869171143] |
8ed7ec62-71f8-4cbf-81b8-23b0db191ec4 | caulking-the-leakage-effect-in-meeg-source | 1810.00786 | null | http://arxiv.org/abs/1810.00786v1 | http://arxiv.org/pdf/1810.00786v1.pdf | Caulking the Leakage Effect in MEEG Source Connectivity Analysis | Simplistic estimation of neural connectivity in MEEG sensor space is
impossible due to volume conduction. The only viable alternative is to carry
out connectivity estimation in source space. Among the neuroscience community
this is claimed to be impossible or misleading due to Leakage: linear mixing of
the reconstructe... | ['Pedro A. Valdes-Sosa', 'Maria Luisa Bringas-Vega', 'Jorge Bosch-Bayard', 'Pedro A. Valdes-Hernandez', 'Eduardo Martinez-Montes', 'Eduardo Gonzalez-Moreira', 'Deirel Paz-Linares'] | 2018-09-28 | null | null | null | null | ['connectivity-estimation'] | ['graphs'] | [ 6.25788271e-02 2.12887883e-01 6.58801556e-01 6.46976801e-03
-4.39792186e-01 -5.62000215e-01 5.24089456e-01 7.35445768e-02
-5.80843866e-01 9.17689919e-01 4.57154736e-02 5.20853139e-02
-6.59021318e-01 -6.73651040e-01 -7.96536863e-01 -8.10783207e-01
-4.05167699e-01 9.94318351e-02 6.69604167e-02 -3.35545726... | [7.034594535827637, 3.9103424549102783] |
a1fa1eb5-d712-4551-a94b-1aa3629e17ee | multi-granularity-semantic-aware-graph-model | 2205.02132 | null | https://arxiv.org/abs/2205.02132v2 | https://arxiv.org/pdf/2205.02132v2.pdf | Multi-Granularity Semantic Aware Graph Model for Reducing Position Bias in Emotion-Cause Pair Extraction | The Emotion-Cause Pair Extraction (ECPE) task aims to extract emotions and causes as pairs from documents. We observe that the relative distance distribution of emotions and causes is extremely imbalanced in the typical ECPE dataset. Existing methods have set a fixed size window to capture relations between neighboring... | ['Songlin Hu', 'Wei Zhou', 'Lingwei Wei', 'Qianwen Ma', 'Yinan Bao'] | 2022-05-04 | null | null | null | null | ['emotion-cause-pair-extraction'] | ['natural-language-processing'] | [-5.96718118e-03 1.32139036e-02 -4.08854693e-01 -7.13291168e-01
-6.71205461e-01 -4.87472296e-01 5.35215616e-01 5.63380301e-01
-1.35590598e-01 6.32173657e-01 5.72975934e-01 1.89055026e-01
-5.96498668e-01 -1.00380325e+00 -3.88057947e-01 -5.04194617e-01
2.42151134e-02 2.66559094e-01 1.51772559e-01 -4.27255154... | [12.625280380249023, 6.2133049964904785] |
00fbd2a3-6ac7-448f-9620-e6de0ad85a9f | modular-adaptation-for-cross-domain-few-shot | 2104.00619 | null | https://arxiv.org/abs/2104.00619v1 | https://arxiv.org/pdf/2104.00619v1.pdf | Modular Adaptation for Cross-Domain Few-Shot Learning | Adapting pre-trained representations has become the go-to recipe for learning new downstream tasks with limited examples. While literature has demonstrated great successes via representation learning, in this work, we show that substantial performance improvement of downstream tasks can also be achieved by appropriate ... | ['Yi Yao', 'Ajay Divakaran', 'Nikoletta Basiou', 'Giedrius Buracas', 'Yunye Gong', 'Meng Ye', 'Xiao Lin'] | 2021-04-01 | null | null | null | null | ['cross-domain-few-shot', 'cross-domain-few-shot-learning'] | ['computer-vision', 'computer-vision'] | [ 2.46386662e-01 -6.61527216e-02 -2.78767407e-01 -5.02390325e-01
-6.63007677e-01 -5.04277229e-01 8.69739056e-01 -1.97793320e-01
-7.31139362e-01 6.97504818e-01 5.33651650e-01 -5.78890443e-02
-1.06667569e-02 -7.34404445e-01 -6.62654042e-01 -5.52522063e-01
1.33568704e-01 4.00493860e-01 6.46089494e-01 -7.22556233... | [9.938730239868164, 2.9196720123291016] |
2238f64d-5245-40ce-8c38-ec1e099c3389 | slow-motion-matters-a-slow-motion-enhanced | 2211.11324 | null | https://arxiv.org/abs/2211.11324v1 | https://arxiv.org/pdf/2211.11324v1.pdf | Slow Motion Matters: A Slow Motion Enhanced Network for Weakly Supervised Temporal Action Localization | Weakly supervised temporal action localization (WTAL) aims to localize actions in untrimmed videos with only weak supervision information (e.g. video-level labels). Most existing models handle all input videos with a fixed temporal scale. However, such models are not sensitive to actions whose pace of the movements is ... | ['Dong Xu', 'Qian Yu', 'Rui Su', 'Weiqi Sun'] | 2022-11-21 | null | null | null | null | ['weakly-supervised-temporal-action', 'action-localization'] | ['computer-vision', 'computer-vision'] | [ 4.11300540e-01 -2.53110796e-01 -9.34883475e-01 -1.15245335e-01
-3.26528281e-01 -3.57838959e-01 5.45852542e-01 -4.25647497e-01
-3.22445601e-01 4.37946618e-01 5.20698130e-01 6.91611245e-02
-5.47905453e-02 -3.19657594e-01 -6.26872718e-01 -8.96965742e-01
-4.68743503e-01 -3.32171917e-01 7.44404078e-01 6.02468150... | [8.444452285766602, 0.6317089796066284] |
4c1f777d-8989-4b30-af6d-2b8d6ee7e948 | faithfulness-in-natural-language-generation-a | 2203.05227 | null | https://arxiv.org/abs/2203.05227v1 | https://arxiv.org/pdf/2203.05227v1.pdf | Faithfulness in Natural Language Generation: A Systematic Survey of Analysis, Evaluation and Optimization Methods | Natural Language Generation (NLG) has made great progress in recent years due to the development of deep learning techniques such as pre-trained language models. This advancement has resulted in more fluent, coherent and even properties controllable (e.g. stylistic, sentiment, length etc.) generation, naturally leading... | ['Hua Wu', 'Xinyan Xiao', 'Jiachen Liu', 'Moye Chen', 'Wenhao Wu', 'Wei Li'] | 2022-03-10 | null | null | null | null | ['data-to-text-generation'] | ['natural-language-processing'] | [ 2.84072220e-01 4.59135711e-01 -2.80167282e-01 -3.29083920e-01
-8.03224802e-01 -4.32337582e-01 1.08949304e+00 3.59662503e-01
2.38266364e-02 1.23609865e+00 8.64886343e-01 2.06816673e-01
2.00500488e-01 -7.28811681e-01 -2.63966918e-01 -5.55925369e-01
2.62863964e-01 6.39020264e-01 -3.98765862e-01 -7.12597787... | [11.984149932861328, 9.187590599060059] |
32b10fd1-eb17-4ceb-90da-8aa6c4cf6cf7 | i-vise-interactive-video-surveillance-as-an | 2003.04169 | null | https://arxiv.org/abs/2003.04169v1 | https://arxiv.org/pdf/2003.04169v1.pdf | I-ViSE: Interactive Video Surveillance as an Edge Service using Unsupervised Feature Queries | Situation AWareness (SAW) is essential for many mission critical applications. However, SAW is very challenging when trying to immediately identify objects of interest or zoom in on suspicious activities from thousands of video frames. This work aims at developing a queryable system to instantly select interesting cont... | ['Erik Blasch', 'Yu Chen', 'Seyed Yahya Nikouei', 'Alexander Aved'] | 2020-03-09 | null | null | null | null | ['scene-recognition'] | ['computer-vision'] | [ 1.69441119e-01 -2.54610270e-01 8.87450799e-02 -6.15664124e-01
-9.90516245e-02 -2.05377400e-01 4.00514632e-01 6.74296692e-02
-1.71013981e-01 4.99542236e-01 -1.79880887e-01 -2.40669101e-02
-2.38823384e-01 -8.45555842e-01 -2.23098099e-01 -7.22961307e-01
-1.08194083e-01 1.02309436e-01 6.16181195e-01 -2.85231799... | [8.405241966247559, -1.0639246702194214] |
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