paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
c1b48d34-db75-4ef8-8f68-55bdf89537b7 | training-temporal-word-embeddings-with-a | 1906.02376 | null | https://arxiv.org/abs/1906.02376v1 | https://arxiv.org/pdf/1906.02376v1.pdf | Training Temporal Word Embeddings with a Compass | Temporal word embeddings have been proposed to support the analysis of word meaning shifts during time and to study the evolution of languages. Different approaches have been proposed to generate vector representations of words that embed their meaning during a specific time interval. However, the training process used... | ['Matteo Palmonari', 'Federico Bianchi', 'Valerio Di Carlo'] | 2019-06-05 | null | null | null | null | ['diachronic-word-embeddings'] | ['natural-language-processing'] | [-1.12959385e-01 -4.16720569e-01 -3.91689986e-01 -1.23226427e-01
-7.48300552e-02 -4.85712528e-01 1.03534615e+00 7.50089228e-01
-8.64277303e-01 4.03106481e-01 2.32484475e-01 -4.65814620e-01
-1.66088253e-01 -9.20221031e-01 -1.95524648e-01 -6.41565144e-01
1.22904032e-02 9.69318748e-02 2.78149515e-01 -3.31327379... | [10.413277626037598, 8.736639976501465] |
d92c7765-fbea-4851-8f80-3be09f2d7065 | automatic-counterfactual-augmentation-for | 2307.01214 | null | https://arxiv.org/abs/2307.01214v1 | https://arxiv.org/pdf/2307.01214v1.pdf | Automatic Counterfactual Augmentation for Robust Text Classification Based on Word-Group Search | Despite large-scale pre-trained language models have achieved striking results for text classificaion, recent work has raised concerns about the challenge of shortcut learning. In general, a keyword is regarded as a shortcut if it creates a superficial association with the label, resulting in a false prediction. Conver... | ['Hao Xu', 'Yingji Li', 'Fausto Giunchiglia', 'Rui Song'] | 2023-07-01 | null | null | null | null | ['fairness', 'fairness', 'text-classification'] | ['computer-vision', 'miscellaneous', 'natural-language-processing'] | [ 4.86564547e-01 1.88571706e-01 -8.20321441e-01 -4.84756798e-01
-3.85989130e-01 -2.93807983e-01 7.99506545e-01 4.90538925e-01
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6.78015500e-02 6.47421777e-02 -1.03734694e-01 -2.22211748... | [9.811394691467285, 7.798450946807861] |
9d8298fa-aeb7-46c7-9809-b07c081debed | compound-tokens-channel-fusion-for-vision | 2212.01447 | null | https://arxiv.org/abs/2212.01447v1 | https://arxiv.org/pdf/2212.01447v1.pdf | Compound Tokens: Channel Fusion for Vision-Language Representation Learning | We present an effective method for fusing visual-and-language representations for several question answering tasks including visual question answering and visual entailment. In contrast to prior works that concatenate unimodal representations or use only cross-attention, we compose multimodal representations via channe... | ['AJ Piergiovanni', 'Maxwell Mbabilla Aladago'] | 2022-12-02 | null | null | null | null | ['visual-entailment'] | ['reasoning'] | [ 2.81079486e-02 3.31377536e-02 1.20594010e-01 -3.19171727e-01
-1.51460195e+00 -6.36561036e-01 9.12127912e-01 2.23396510e-01
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4.86406714e-01 2.98168242e-01 -7.27085471e-02 -7.39697963... | [10.854939460754395, 1.6163209676742554] |
58361009-2784-4e90-996b-bf0218cf2813 | object-goal-visual-navigation-via-effective | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Du_Object-Goal_Visual_Navigation_via_Effective_Exploration_of_Relations_Among_Historical_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Du_Object-Goal_Visual_Navigation_via_Effective_Exploration_of_Relations_Among_Historical_CVPR_2023_paper.pdf | Object-Goal Visual Navigation via Effective Exploration of Relations Among Historical Navigation States | Object-goal visual navigation aims at steering an agent toward an object via a series of moving steps. Previous works mainly focus on learning informative visual representations for navigation, but overlook the impacts of navigation states on the effectiveness and efficiency of navigation. We observe that high rele... | ['Xin Yu', 'Zi Huang', 'Lincheng Li', 'Heming Du'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['visual-navigation'] | ['robots'] | [-1.67896748e-01 -1.84939399e-01 -1.85469180e-01 -3.44087034e-01
-2.10678264e-01 -2.83996701e-01 6.39404416e-01 -2.62842178e-01
-6.55763924e-01 5.36193013e-01 2.41677642e-01 -4.29971546e-01
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-1.62366390e-01 -5.41281234e-03 5.25063217e-01 -4.32345301... | [4.47498083114624, 0.5094690322875977] |
6a0fef9c-0efb-4b37-ae44-7045d0a36fec | command-driven-articulated-object | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chu_Command-Driven_Articulated_Object_Understanding_and_Manipulation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chu_Command-Driven_Articulated_Object_Understanding_and_Manipulation_CVPR_2023_paper.pdf | Command-Driven Articulated Object Understanding and Manipulation | We present Cart, a new approach towards articulated-object manipulations by human commands. Beyond the existing work that focuses on inferring articulation structures, we further support manipulating articulated shapes to align them subject to simple command templates. The key of Cart is to utilize the prediction o... | ['Jiaya Jia', 'Chi-Wing Fu', 'Xiaojuan Qi', 'Xiao Tan', 'Xiaoqing Ye', 'Zhengzhe Liu', 'Ruihang Chu'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['motion-prediction'] | ['computer-vision'] | [ 2.24078730e-01 7.28557184e-02 -4.98002052e-01 -3.92204374e-01
-1.76095009e-01 -8.62861514e-01 6.57485604e-01 -2.61885464e-01
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-1.63916424e-01 8.59150827e-01 3.99215877e-01 -3.71518344... | [5.01302433013916, 0.2791096568107605] |
a3489640-31ba-4b5b-aa98-b8972ba3f60c | protocon-pseudo-label-refinement-via-online | 2303.13556 | null | https://arxiv.org/abs/2303.13556v1 | https://arxiv.org/pdf/2303.13556v1.pdf | ProtoCon: Pseudo-label Refinement via Online Clustering and Prototypical Consistency for Efficient Semi-supervised Learning | Confidence-based pseudo-labeling is among the dominant approaches in semi-supervised learning (SSL). It relies on including high-confidence predictions made on unlabeled data as additional targets to train the model. We propose ProtoCon, a novel SSL method aimed at the less-explored label-scarce SSL where such methods ... | ['Gholamreza Haffari', 'Hamid Rezatofighi', 'Ehsan Abbasnejad', 'Munawar Hayat', 'Islam Nassar'] | 2023-03-22 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Nassar_ProtoCon_Pseudo-Label_Refinement_via_Online_Clustering_and_Prototypical_Consistency_for_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Nassar_ProtoCon_Pseudo-Label_Refinement_via_Online_Clustering_and_Prototypical_Consistency_for_CVPR_2023_paper.pdf | cvpr-2023-1 | ['online-clustering', 'pseudo-label'] | ['computer-vision', 'miscellaneous'] | [ 3.08234394e-01 4.70705539e-01 -3.70532900e-01 -6.78728640e-01
-9.41300273e-01 -4.77116823e-01 6.68220162e-01 3.80083412e-01
-7.98983932e-01 8.85214686e-01 -8.74521583e-02 -1.15590543e-01
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1.03200473e-01 6.28308237e-01 3.30602676e-01 1.90974027... | [9.477921485900879, 3.593045473098755] |
e851bc81-bc21-41b8-af7b-19ee90bc3cdb | lost-in-context-on-the-sense-wise-variance-of | 2208.09669 | null | https://arxiv.org/abs/2208.09669v1 | https://arxiv.org/pdf/2208.09669v1.pdf | Lost in Context? On the Sense-wise Variance of Contextualized Word Embeddings | Contextualized word embeddings in language models have given much advance to NLP. Intuitively, sentential information is integrated into the representation of words, which can help model polysemy. However, context sensitivity also leads to the variance of representations, which may break the semantic consistency for sy... | ['Yue Zhang', 'Yile Wang'] | 2022-08-20 | null | null | null | null | ['word-sense-disambiguation'] | ['natural-language-processing'] | [-1.45685613e-01 -4.05974269e-01 -4.53900367e-01 -5.80724120e-01
-3.50665301e-01 -8.05405021e-01 7.48884499e-01 6.72050476e-01
-9.02667105e-01 5.20955324e-01 9.45127904e-01 -2.78076351e-01
-1.15695573e-01 -8.73905420e-01 -1.70898497e-01 -6.09844863e-01
2.44420424e-01 4.23419215e-02 9.99913216e-02 -5.72101653... | [10.351179122924805, 8.924288749694824] |
8f3b5761-d43f-49b0-b952-38c1adcc2d3a | deep-learning-for-ps-weakly-dependent | 2302.00333 | null | https://arxiv.org/abs/2302.00333v1 | https://arxiv.org/pdf/2302.00333v1.pdf | Deep learning for $ψ$-weakly dependent processes | In this paper, we perform deep neural networks for learning $\psi$-weakly dependent processes. Such weak-dependence property includes a class of weak dependence conditions such as mixing, association,$\cdots$ and the setting considered here covers many commonly used situations such as: regression estimation, time serie... | ['Wade Modou', 'William Kengne'] | 2023-02-01 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [ 1.26094818e-01 1.46713346e-01 -5.74718237e-01 -4.89050269e-01
-6.74079657e-01 -1.81442704e-02 2.56653905e-01 1.45856366e-01
-5.65294027e-01 1.09748268e+00 -8.43532011e-02 -7.08958626e-01
-8.54379654e-01 -1.00513875e+00 -7.42847621e-01 -1.19489896e+00
-7.40937531e-01 4.04969007e-01 -5.71628630e-01 -3.59889492... | [7.100645542144775, 3.875985622406006] |
e3ec78ff-be8e-452b-bfc4-2df9a4a24e41 | context-aware-learning-using-transferable | 1803.00386 | null | http://arxiv.org/abs/1803.00386v2 | http://arxiv.org/pdf/1803.00386v2.pdf | Context-Aware Learning using Transferable Features for Classification of Breast Cancer Histology Images | Convolutional neural networks (CNNs) have been recently used for a variety of
histology image analysis. However, availability of a large dataset is a major
prerequisite for training a CNN which limits its use by the computational
pathology community. In previous studies, CNNs have demonstrated their
potential in terms ... | ['Ruqayya Awan', 'Navid Alemi Koohbanani', 'Muhammad Shaban', 'Nasir Rajpoot', 'Anna Lisowska'] | 2018-02-12 | null | null | null | null | ['classification-of-breast-cancer-histology'] | ['medical'] | [ 4.03166652e-01 -2.18748413e-02 -1.75332561e-01 -3.10742527e-01
-8.97852302e-01 -1.95565492e-01 5.06054997e-01 6.49772704e-01
-6.76463604e-01 6.87844634e-01 -1.34283006e-01 -3.74086797e-01
-2.20418021e-01 -9.09232795e-01 -5.33406734e-01 -1.04111290e+00
2.64399145e-02 5.96632920e-02 4.16627526e-01 -1.89029962... | [15.067785263061523, -2.8901560306549072] |
36f566a1-9e44-421e-87b2-ba2c15207f2f | collaborative-auto-encoding-for-blind-image | 2305.14684 | null | https://arxiv.org/abs/2305.14684v1 | https://arxiv.org/pdf/2305.14684v1.pdf | Collaborative Auto-encoding for Blind Image Quality Assessment | Blind image quality assessment (BIQA) is a challenging problem with important real-world applications. Recent efforts attempting to exploit powerful representations by deep neural networks (DNN) are hindered by the lack of subjectively annotated data. This paper presents a novel BIQA method which overcomes this fundame... | ['Guoping Qiu', 'Fei Zhou', 'Zehong Zhou'] | 2023-05-24 | null | null | null | null | ['blind-image-quality-assessment', 'image-quality-assessment'] | ['computer-vision', 'computer-vision'] | [-1.08907551e-01 -1.72177434e-01 1.95714071e-01 -3.33445549e-01
-8.65064919e-01 -4.68459755e-01 4.11972076e-01 -3.32266808e-01
-3.07865441e-01 5.09323895e-01 4.60568756e-01 -2.11708277e-01
5.61870895e-02 -7.36508608e-01 -5.98596573e-01 -8.48283172e-01
9.41193849e-02 -7.77214020e-02 -7.85510913e-02 -1.97947294... | [11.866552352905273, -1.8257113695144653] |
2d029c68-81e8-4e90-819d-9fc8000c456c | contrast-and-generation-make-bart-a-good | 2112.11202 | null | https://arxiv.org/abs/2112.11202v2 | https://arxiv.org/pdf/2112.11202v2.pdf | Contrast and Generation Make BART a Good Dialogue Emotion Recognizer | In dialogue systems, utterances with similar semantics may have distinctive emotions under different contexts. Therefore, modeling long-range contextual emotional relationships with speaker dependency plays a crucial part in dialogue emotion recognition. Meanwhile, distinguishing the different emotion categories is non... | ['Xipeng Qiu', 'Hang Yan', 'ShiMin Li'] | 2021-12-21 | null | null | null | null | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 1.02277227e-01 -6.40660450e-02 -3.87526527e-02 -9.31417465e-01
-4.77226496e-01 -3.02960575e-01 6.19999588e-01 2.58559622e-02
-3.94236892e-01 7.49974906e-01 5.98913014e-01 1.79504827e-01
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2.70030141e-01 1.65718243e-01 -3.67041767e-01 -6.16910458... | [13.01626968383789, 6.094055652618408] |
d6645a16-70e6-4043-9247-4e4a6f09b77f | data-poisoning-attacks-on-eeg-signal-based | 2302.04224 | null | https://arxiv.org/abs/2302.04224v1 | https://arxiv.org/pdf/2302.04224v1.pdf | Data Poisoning Attacks on EEG Signal-based Risk Assessment Systems | Industrial insider risk assessment using electroencephalogram (EEG) signals has consistently attracted a lot of research attention. However, EEG signal-based risk assessment systems, which could evaluate the emotional states of humans, have shown several vulnerabilities to data poison attacks. In this paper, from the a... | ['Chan Yeob Yeun', 'Ernesto Damiani', 'Sangyoung Yoon', 'Ahmed Y. Al Hammadi', 'Sani Umar', 'Zhibo Zhang'] | 2023-02-08 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 2.31045652e-02 -1.86241493e-01 4.68738198e-01 -3.30509245e-01
-3.16134959e-01 -9.51012969e-01 4.28663343e-01 5.73345542e-01
-5.70332468e-01 8.76013815e-01 -3.35138619e-01 -4.38857943e-01
-2.49584332e-01 -5.75067282e-01 -6.17500782e-01 -8.89659464e-01
-5.40052593e-01 -1.61441818e-01 -1.10586032e-01 1.15094922... | [13.266497611999512, 3.084388017654419] |
578fa9d9-a9b4-44a0-88a2-c06bd6da826b | rtfe-a-recursive-temporal-fact-embedding | 2009.14653 | null | https://arxiv.org/abs/2009.14653v4 | https://arxiv.org/pdf/2009.14653v4.pdf | RTFE: A Recursive Temporal Fact Embedding Framework for Temporal Knowledge Graph Completion | Static knowledge graph (SKG) embedding (SKGE) has been studied intensively in the past years. Recently, temporal knowledge graph (TKG) embedding (TKGE) has emerged. In this paper, we propose a Recursive Temporal Fact Embedding (RTFE) framework to transplant SKGE models to TKGs and to enhance the performance of existing... | ['yang jinrui', 'E Haihong', 'wang haotian', 'Xiaodong Lv', 'wenyu song', 'Meina Song', 'Youri Xu'] | 2020-09-30 | null | https://aclanthology.org/2021.naacl-main.451 | https://aclanthology.org/2021.naacl-main.451.pdf | naacl-2021-4 | ['temporal-knowledge-graph-completion'] | ['knowledge-base'] | [-4.16205436e-01 5.77534456e-03 -3.89860541e-01 -9.38573107e-02
-2.40497533e-02 -3.43914866e-01 7.69735873e-01 2.16157570e-01
-3.09297830e-01 5.01497447e-01 4.40689087e-01 -3.75908285e-01
-2.32840955e-01 -1.08243942e+00 -5.85415781e-01 -5.65315604e-01
-4.68407035e-01 2.11183056e-01 3.71767461e-01 3.48460674... | [8.548590660095215, 7.874508380889893] |
25c3354c-c72c-4cb5-b84d-dffbc170d2de | confidence-aware-active-feedback-for | 2110.12255 | null | https://arxiv.org/abs/2110.12255v3 | https://arxiv.org/pdf/2110.12255v3.pdf | Confidence-Aware Active Feedback for Interactive Instance Search | Online relevance feedback (RF) is widely utilized in instance search (INS) tasks to further refine imperfect ranking results, but it often has low interaction efficiency. The active learning (AL) technique addresses this problem by selecting valuable feedback candidates. However, mainstream AL methods require an initia... | ['Longxiang Jiang', 'Chao Liang', 'Yue Zhang'] | 2021-10-23 | null | null | null | null | ['instance-search'] | ['computer-vision'] | [ 1.27201483e-01 -1.90944746e-01 -3.28344822e-01 -3.06992710e-01
-1.19380426e+00 -4.57377791e-01 3.07279944e-01 9.81573015e-02
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-3.58652979e-01 -5.20907521e-01 -6.07915819e-01 -8.01145852e-01
3.17703420e-03 3.23774725e-01 3.86850595e-01 -2.14086443... | [10.058030128479004, 5.086414813995361] |
4e1b1844-2b4a-48b5-94c0-fa4c283ee5c5 | general-purpose-deep-point-cloud-feature | null | null | https://ieeexplore.ieee.org/abstract/document/8354322 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8354322 | General-Purpose Deep Point Cloud Feature Extractor | Depth sensors used in autonomous driving and gaming
systems often report back 3D point clouds. The lack of
structure from these sensors does not allow these systems
to take advantage of recent advances in convolutional neural networks which are dependent upon traditional filtering
and pooling operations. Analogous ... | ['Raymond Ptucha', 'Shagan Sah', 'Saloni Jain', 'Atir Petkar', 'Rohan Dhamdhere', 'Miguel Dominguez'] | 2018-03-12 | null | null | null | ieee-winter-conference-on-applications-of-2 | ['3d-object-classification'] | ['computer-vision'] | [-2.59920478e-01 1.75212279e-01 2.07756132e-01 -3.65342766e-01
-3.47624391e-01 -5.32252908e-01 6.54720008e-01 1.21635638e-01
-2.68689960e-01 -2.52548270e-02 -1.55027986e-01 -5.91455042e-01
1.13269776e-01 -1.10815573e+00 -8.93760800e-01 -2.56820112e-01
-4.84457970e-01 3.97226512e-01 6.70907378e-01 -4.28904653... | [7.943962097167969, -3.6384639739990234] |
413b14d7-4c95-4045-bfe5-7cb6e7542862 | benchmarking-adversarially-robust-quantum | 2211.12681 | null | https://arxiv.org/abs/2211.12681v1 | https://arxiv.org/pdf/2211.12681v1.pdf | Benchmarking Adversarially Robust Quantum Machine Learning at Scale | Machine learning (ML) methods such as artificial neural networks are rapidly becoming ubiquitous in modern science, technology and industry. Despite their accuracy and sophistication, neural networks can be easily fooled by carefully designed malicious inputs known as adversarial attacks. While such vulnerabilities rem... | ['Muhammad Usman', 'Lloyd C. L. Hollenberg', 'Martin Sevior', 'Christopher Leckie', 'Sarah M. Erfani', 'Maxwell T. West'] | 2022-11-23 | null | null | null | null | ['adversarial-attack-detection', 'adversarial-attack-detection'] | ['computer-vision', 'knowledge-base'] | [ 5.51924646e-01 5.44124186e-01 1.16997935e-01 4.28100303e-02
-8.19804728e-01 -1.01556349e+00 8.28988492e-01 -2.20820114e-01
-5.04082859e-01 7.75688171e-01 -6.00182950e-01 -5.64665794e-01
-3.93965133e-02 -1.04183769e+00 -1.01794469e+00 -1.25598311e+00
-7.48800561e-02 7.95938373e-02 6.56186566e-02 -5.49096227... | [5.56846284866333, 5.078665256500244] |
fe65f992-186c-4bd9-a590-fbb80e51c670 | the-hardness-of-reasoning-about-probabilities | 2305.09508 | null | https://arxiv.org/abs/2305.09508v1 | https://arxiv.org/pdf/2305.09508v1.pdf | The Hardness of Reasoning about Probabilities and Causality | We study formal languages which are capable of fully expressing quantitative probabilistic reasoning and do-calculus reasoning for causal effects, from a computational complexity perspective. We focus on satisfiability problems whose instance formulas allow expressing many tasks in probabilistic and causal inference. T... | ['Maciej Liśkiewicz', 'Markus Bläser', 'Benito van der Zander'] | 2023-05-16 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [ 1.41636819e-01 7.97868967e-01 -2.28340104e-01 -3.73047769e-01
-7.36826062e-01 -6.17006123e-01 8.02779615e-01 1.51292562e-01
-2.20863596e-01 1.14300823e+00 3.06317329e-01 -6.06540322e-01
-8.80433917e-01 -1.04823124e+00 -6.88707888e-01 -5.05167723e-01
-4.66945410e-01 6.12610221e-01 3.59067440e-01 -2.45867193... | [8.321263313293457, 6.07021427154541] |
c67c4bde-9e06-4826-a0f2-f66cc1934e53 | on-the-relation-between-syntactic-divergence | 2110.04644 | null | https://arxiv.org/abs/2110.04644v1 | https://arxiv.org/pdf/2110.04644v1.pdf | On the Relation between Syntactic Divergence and Zero-Shot Performance | We explore the link between the extent to which syntactic relations are preserved in translation and the ease of correctly constructing a parse tree in a zero-shot setting. While previous work suggests such a relation, it tends to focus on the macro level and not on the level of individual edges-a gap we aim to address... | ['Omri Abend', 'Taelin Karidi', 'Dmitry Nikolaev', 'Ofir Arviv'] | 2021-10-09 | null | https://aclanthology.org/2021.emnlp-main.394 | https://aclanthology.org/2021.emnlp-main.394.pdf | emnlp-2021-11 | ['cross-lingual-zero-shot-dependency-parsing'] | ['natural-language-processing'] | [ 5.30181043e-02 2.81113803e-01 -3.17001611e-01 -5.18217385e-01
-1.24079037e+00 -9.64551032e-01 5.10446012e-01 3.40566605e-01
-4.17854935e-01 7.10794210e-01 6.14159644e-01 -7.07132280e-01
1.38526455e-01 -8.08095276e-01 -7.16496408e-01 -2.04111323e-01
1.98610827e-01 2.86129892e-01 2.15752631e-01 -4.91582155... | [10.515064239501953, 9.79746150970459] |
7bdb29bf-e9fa-4dfe-b0a3-048f5e4e2f9a | movie-visual-model-based-policy-adaptation | 2307.00972 | null | https://arxiv.org/abs/2307.00972v1 | https://arxiv.org/pdf/2307.00972v1.pdf | MoVie: Visual Model-Based Policy Adaptation for View Generalization | Visual Reinforcement Learning (RL) agents trained on limited views face significant challenges in generalizing their learned abilities to unseen views. This inherent difficulty is known as the problem of $\textit{view generalization}$. In this work, we systematically categorize this fundamental problem into four distin... | ['Huazhe Xu', 'Yanjie Ze', 'Sizhe Yang'] | 2023-07-03 | null | null | null | null | ['reinforcement-learning-1'] | ['methodology'] | [-9.98728946e-02 -9.86717269e-02 -4.88123158e-03 -3.10558379e-01
-6.46362662e-01 -6.74294949e-01 4.84421611e-01 -3.02085280e-01
-6.57285929e-01 9.49655771e-01 -5.32187939e-01 -1.98916554e-01
-1.99892402e-01 -5.54484069e-01 -1.21492100e+00 -6.29306555e-01
-3.18237305e-01 7.64532238e-02 9.28939059e-02 -5.09443641... | [4.418013572692871, 0.8207255005836487] |
0d264bef-ca07-4c66-980b-f6254dfe5555 | deep-cg2real-synthetic-to-real-translation-1 | 2003.12649 | null | https://arxiv.org/abs/2003.12649v1 | https://arxiv.org/pdf/2003.12649v1.pdf | Deep CG2Real: Synthetic-to-Real Translation via Image Disentanglement | We present a method to improve the visual realism of low-quality, synthetic images, e.g. OpenGL renderings. Training an unpaired synthetic-to-real translation network in image space is severely under-constrained and produces visible artifacts. Instead, we propose a semi-supervised approach that operates on the disentan... | ['Vladimir Kim', 'Ravi Ramamoorthi', 'Kalyan Sunkavalli', 'Sai Bi', 'Eli Shechtman', 'Federico Perazzi'] | 2020-03-27 | deep-cg2real-synthetic-to-real-translation | http://openaccess.thecvf.com/content_ICCV_2019/html/Bi_Deep_CG2Real_Synthetic-to-Real_Translation_via_Image_Disentanglement_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Bi_Deep_CG2Real_Synthetic-to-Real_Translation_via_Image_Disentanglement_ICCV_2019_paper.pdf | iccv-2019-10 | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 7.77261615e-01 5.75559378e-01 3.60501260e-01 -6.87883139e-01
-7.59514272e-01 -5.28225362e-01 6.64845347e-01 -8.83674800e-01
1.25644296e-01 8.15125644e-01 1.96715921e-01 -1.78857058e-01
5.08733094e-01 -8.16434860e-01 -1.15647304e+00 -6.23689771e-01
2.03190014e-01 3.47118914e-01 -3.68900411e-02 -3.64575952... | [9.558294296264648, -3.102858304977417] |
c28142b6-49e7-4b2c-a0e3-b5263b703d2c | word-embedding-neural-networks-to-advance | 2212.11933 | null | https://arxiv.org/abs/2212.11933v1 | https://arxiv.org/pdf/2212.11933v1.pdf | Word Embedding Neural Networks to Advance Knee Osteoarthritis Research | Osteoarthritis (OA) is the most prevalent chronic joint disease worldwide, where knee OA takes more than 80% of commonly affected joints. Knee OA is not a curable disease yet, and it affects large columns of patients, making it costly to patients and healthcare systems. Etiology, diagnosis, and treatment of knee OA mig... | ['Ahmad P. Tafti', 'Johannes F. Plate', 'Hamid R. Arabnia', 'Hilal Maradit Kremers', 'Mehdi Assefi', 'Husam Ghazaleh', 'Soheyla Amirian'] | 2022-12-22 | null | null | null | null | ['keyword-extraction'] | ['natural-language-processing'] | [-2.78317332e-01 -1.10371493e-01 -8.03241313e-01 4.22857642e-01
-5.42806387e-01 -1.24741927e-01 4.20708358e-02 6.93885565e-01
-6.25724196e-01 8.07843626e-01 8.69942009e-01 -1.51758850e-01
-4.00194854e-01 -6.47136569e-01 3.97524349e-02 -4.54641938e-01
-1.82452992e-01 7.30864167e-01 -2.95106843e-02 -1.59956038... | [14.559945106506348, -1.7222821712493896] |
424cde99-e253-4e24-abe5-9660ab7a26c0 | anlirika-an-lstm-cnn-flow-twister-for-spoken | null | null | https://aclanthology.org/2021.sigtyp-1.14 | https://aclanthology.org/2021.sigtyp-1.14.pdf | Anlirika: An LSTM–CNN Flow Twister for Spoken Language Identification | The paper presents Anlirika’s submission to SIGTYP 2021 Shared Task on Robust Spoken Language Identification. The task aims at building a robust system that generalizes well across different domains and speakers. The training data is limited to a single domain only with predominantly single speaker per language while t... | ['Ekaterina Vylomova', 'Matthew Coleman', 'Siddharth Singh', 'Ritesh Kumar', 'Liam Whittle', 'Andreas Scherbakov'] | null | null | null | null | naacl-sigtyp-2021-6 | ['spoken-language-identification'] | ['speech'] | [-2.67144479e-02 6.31598234e-02 -1.44099174e-02 -8.86095047e-01
-1.24837017e+00 -8.13385308e-01 7.17792153e-01 -4.89138275e-01
-5.09423912e-01 6.60989165e-01 2.95740277e-01 -1.23310730e-01
5.13218522e-01 -5.54889068e-02 -4.35309350e-01 -3.25478971e-01
-1.25123531e-01 7.49972761e-01 1.49283861e-03 -5.45441449... | [14.182998657226562, 6.618321895599365] |
33fccbf3-0e21-4e9c-af9f-210b53213b95 | analyzing-the-mono-and-cross-lingual | 2205.11758 | null | https://arxiv.org/abs/2205.11758v2 | https://arxiv.org/pdf/2205.11758v2.pdf | Analyzing the Mono- and Cross-Lingual Pretraining Dynamics of Multilingual Language Models | The emergent cross-lingual transfer seen in multilingual pretrained models has sparked significant interest in studying their behavior. However, because these analyses have focused on fully trained multilingual models, little is known about the dynamics of the multilingual pretraining process. We investigate when these... | ['Luke Zettlemoyer', 'Hila Gonen', 'Terra Blevins'] | 2022-05-24 | null | null | null | null | ['xlm-r'] | ['natural-language-processing'] | [-2.62684345e-01 -9.24227536e-02 -6.71856329e-02 -2.79931903e-01
-9.77998018e-01 -1.10115492e+00 9.16585028e-01 2.97726721e-01
-7.00729191e-01 7.15755820e-01 2.93398201e-01 -5.95767736e-01
8.67352560e-02 -4.09628868e-01 -1.17064893e+00 -3.23449284e-01
-2.23511264e-01 5.43872237e-01 -6.01052567e-02 -4.81353909... | [10.875618934631348, 9.978885650634766] |
b26dca88-745c-4d1a-8d77-a6a6f1663d3d | few-labeled-atlases-are-necessary-for-deep | 1908.04466 | null | https://arxiv.org/abs/1908.04466v4 | https://arxiv.org/pdf/1908.04466v4.pdf | Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation | We tackle biomedical image segmentation in the scenario of only a few labeled brain MR images. This is an important and challenging task in medical applications, where manual annotations are time-consuming. Current multi-atlas based segmentation methods use image registration to warp segments from labeled images onto a... | ['Mert R. Sabuncu', 'Adrian V. Dalca', 'Hyeon Woo Lee'] | 2019-08-13 | null | null | null | null | ['deformable-medical-image-registration'] | ['medical'] | [ 5.65522194e-01 3.43681961e-01 -4.37987708e-02 -5.42683125e-01
-1.17876387e+00 -5.97433686e-01 5.63320935e-01 4.46646959e-01
-9.11669612e-01 7.57960439e-01 -2.61367381e-01 -7.75438361e-03
-1.15582876e-01 -4.15693939e-01 -5.94094694e-01 -8.67444336e-01
-2.47769840e-02 9.79252696e-01 4.40358996e-01 -1.32290259... | [14.352895736694336, -2.389650821685791] |
8d71eb5d-8ff9-43a2-a3c9-e2602aa718c0 | hybridsdf-combining-free-form-shapes-and | 2109.10767 | null | https://arxiv.org/abs/2109.10767v4 | https://arxiv.org/pdf/2109.10767v4.pdf | HybridSDF: Combining Deep Implicit Shapes and Geometric Primitives for 3D Shape Representation and Manipulation | Deep implicit surfaces excel at modeling generic shapes but do not always capture the regularities present in manufactured objects, which is something simple geometric primitives are particularly good at. In this paper, we propose a representation combining latent and explicit parameters that can be decoded into a set ... | ['Pierre Baqué', 'Pascal Fua', 'Jonathan Donier', 'Artem Lukoianov', 'Nicolas Talabot', 'Subeesh Vasu'] | 2021-09-22 | null | null | null | null | ['3d-shape-representation'] | ['computer-vision'] | [-1.82211936e-01 -4.01358008e-02 1.62753016e-01 -2.96188802e-01
-1.78895429e-01 -7.91385174e-01 7.83096194e-01 -6.03002869e-02
4.43150282e-01 3.58624578e-01 5.75500131e-02 -5.57890125e-02
-2.61907518e-01 -1.38630164e+00 -7.84879804e-01 -5.28209567e-01
1.24067381e-01 8.34751308e-01 -5.42331859e-02 -4.03760940... | [8.76565170288086, -3.635732889175415] |
a7162abf-3190-4dc5-9808-ad590f3691c2 | a-geometry-aware-deep-network-for-depth | 2304.10241 | null | https://arxiv.org/abs/2304.10241v1 | https://arxiv.org/pdf/2304.10241v1.pdf | A geometry-aware deep network for depth estimation in monocular endoscopy | Monocular depth estimation is critical for endoscopists to perform spatial perception and 3D navigation of surgical sites. However, most of the existing methods ignore the important geometric structural consistency, which inevitably leads to performance degradation and distortion of 3D reconstruction. To address this i... | ['Hao liu', 'Chengdong Wu', 'Zhuo Yang', 'Peng Wang', 'Tao Yang', 'Shuwei Shao', 'Yongming Yang'] | 2023-04-20 | null | null | null | null | ['3d-reconstruction', 'monocular-depth-estimation', 'anatomy'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [-2.30632469e-01 2.05570981e-01 -1.26697019e-01 -1.93728656e-02
-6.84847236e-01 -7.06138313e-01 4.33363207e-02 1.65315211e-01
-2.89272726e-01 4.09550130e-01 3.07894230e-01 -5.27245164e-01
-1.96593732e-01 -5.19360065e-01 -7.15480864e-01 -8.24715436e-01
-1.83455497e-01 -1.93797946e-01 2.07182214e-01 2.12303195... | [13.839280128479004, -3.10856294631958] |
66d0c4b6-ca26-4fab-8d37-73f5c2c7a08e | supervised-distributional-hypernym-discovery | null | null | https://aclanthology.org/D16-1041 | https://aclanthology.org/D16-1041.pdf | Supervised Distributional Hypernym Discovery via Domain Adaptation | null | ['Jose Camacho-Collados', 'Luis Espinosa-Anke', 'Claudio Delli Bovi', 'Horacio Saggion'] | 2016-11-01 | null | null | null | emnlp-2016-11 | ['hypernym-discovery'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.234970569610596, 3.7888295650482178] |
ef34b710-d700-4353-83e9-a0f9d95d72e8 | modeling-radiometric-uncertainty-for-vision | 1311.6887 | null | http://arxiv.org/abs/1311.6887v2 | http://arxiv.org/pdf/1311.6887v2.pdf | Modeling Radiometric Uncertainty for Vision with Tone-mapped Color Images | To produce images that are suitable for display, tone-mapping is widely used
in digital cameras to map linear color measurements into narrow gamuts with
limited dynamic range. This introduces non-linear distortion that must be
undone, through a radiometric calibration process, before computer vision
systems can analyze... | ['Kate Saenko', 'Trevor Darrell', 'Baochen Sun', 'Daniel Scharstein', 'Ying Xiong', 'Todd Zickler', 'Ayan Chakrabarti'] | 2013-11-27 | null | null | null | null | ['tone-mapping'] | ['computer-vision'] | [ 7.30030358e-01 -2.15956554e-01 7.95068964e-02 -7.40107417e-01
-8.86391282e-01 -8.83116782e-01 5.27933240e-01 -4.16290373e-01
-4.18132007e-01 6.54355288e-01 -5.07305264e-02 -5.23920834e-01
1.01472192e-01 -6.38044536e-01 -1.03557491e+00 -4.13186371e-01
3.37986767e-01 2.23646060e-01 5.41811943e-01 4.64317203... | [10.519660949707031, -2.546769142150879] |
5c7c9428-c220-449b-b66b-d06f66835fc9 | composer-compositional-learning-of-group | 2112.05892 | null | https://arxiv.org/abs/2112.05892v3 | https://arxiv.org/pdf/2112.05892v3.pdf | COMPOSER: Compositional Reasoning of Group Activity in Videos with Keypoint-Only Modality | Group Activity Recognition detects the activity collectively performed by a group of actors, which requires compositional reasoning of actors and objects. We approach the task by modeling the video as tokens that represent the multi-scale semantic concepts in the video. We propose COMPOSER, a Multiscale Transformer bas... | ['Hans Peter Graf', 'Mubbasir Kapadia', 'Ting Liu', 'Long Zhao', 'Farley Lai', 'Shijie Geng', 'Aviv Shamsian', 'Asim Kadav', 'Honglu Zhou'] | 2021-12-11 | null | null | null | null | ['group-activity-recognition', 'relational-reasoning'] | ['computer-vision', 'natural-language-processing'] | [ 1.55563086e-01 -5.48652448e-02 -3.60908866e-01 -3.21731478e-01
-9.25076008e-01 -7.85459161e-01 6.96300626e-01 7.88517669e-02
-1.68545514e-01 1.05710708e-01 9.24532175e-01 2.51106530e-01
1.20873928e-01 -5.05503058e-01 -1.01273561e+00 -4.39265370e-01
-3.44762839e-02 5.94111197e-02 4.92565855e-02 1.09661231... | [8.86916732788086, 0.6905648112297058] |
8b47e3e8-8790-468d-9f48-0c193e14e8df | online-hyperparameter-optimization-for-class | 2301.05032 | null | https://arxiv.org/abs/2301.05032v2 | https://arxiv.org/pdf/2301.05032v2.pdf | Online Hyperparameter Optimization for Class-Incremental Learning | Class-incremental learning (CIL) aims to train a classification model while the number of classes increases phase-by-phase. An inherent challenge of CIL is the stability-plasticity tradeoff, i.e., CIL models should keep stable to retain old knowledge and keep plastic to absorb new knowledge. However, none of the existi... | ['Qianru Sun', 'Bernt Schiele', 'YingYing Li', 'Yaoyao Liu'] | 2023-01-11 | null | null | null | null | ['class-incremental-learning'] | ['computer-vision'] | [-1.23536088e-01 3.80772278e-02 -8.83502364e-01 -2.99132675e-01
-8.97624195e-01 -4.64311302e-01 3.18999857e-01 -6.77810758e-02
-6.38874471e-01 7.85876215e-01 -2.53657222e-01 -4.30262268e-01
-3.13558161e-01 -5.72524607e-01 -1.12928629e+00 -8.45746517e-01
2.77449284e-03 5.29751599e-01 4.15141284e-01 9.37945992... | [9.337628364562988, 3.4648427963256836] |
82771040-31b9-469c-a8c4-8b20a8458cf5 | multivariate-time-series-regression-with | 2201.00818 | null | https://arxiv.org/abs/2201.00818v3 | https://arxiv.org/pdf/2201.00818v3.pdf | Graph Neural Networks for Multivariate Time Series Regression with Application to Seismic Data | Machine learning, with its advances in deep learning has shown great potential in analyzing time series. In many scenarios, however, additional information that can potentially improve the predictions is available. This is crucial for data that arise from e.g., sensor networks that contain information about sensor loca... | ['Martin Atzmueller', 'Alberto Michelini', 'Dario Jozinović', 'Jurgen van den Hoogen', 'Stefan Bloemheuvel'] | 2022-01-03 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [ 2.62197495e-01 1.39796525e-01 1.72559902e-01 -2.57356465e-01
-5.52481711e-01 -4.64397669e-01 4.04741794e-01 6.79409027e-01
-3.96331668e-01 5.86557865e-01 1.61751390e-01 -4.31633323e-01
-4.28852648e-01 -9.35030162e-01 -6.96512997e-01 -9.12098527e-01
-9.09600854e-01 1.98215425e-01 3.09498608e-01 -5.16093314... | [6.975058555603027, 2.82202410697937] |
21d40707-524b-4c11-8658-b50c28bbe2ce | gaussian-process-regression-with-1 | null | null | https://ieeexplore.ieee.org/abstract/document/9646444 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9646444 | Gaussian Process Regression With Interpretable Sample-Wise Feature Weights | Gaussian process regression (GPR) is a fundamental model used in machine learning (ML). Due to its accurate prediction with uncertainty and versatility in handling various data structures via kernels, GPR has been successfully used in various applications. However, in GPR, how the features of an input contribute to its... | ['Tomoharu Iwata', 'Yuya Yoshikawa'] | 2021-12-10 | null | null | null | ieee-transactions-on-neural-networks-and-4 | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 9.90181789e-02 2.93323845e-01 -8.84473845e-02 -3.25036466e-01
-6.32526934e-01 -3.33915241e-02 4.25745130e-01 3.22721452e-01
2.14578420e-01 7.57731855e-01 -1.37510180e-01 -2.25942016e-01
-4.30704534e-01 -6.57012820e-01 -6.35817230e-01 -1.15001667e+00
7.52580911e-02 6.18393421e-01 1.60214826e-01 4.63387698... | [6.738700866699219, 3.626147985458374] |
10bb2a29-0f13-4865-945c-9ff0dd7ed114 | sam-rl-sensing-aware-model-based | 2210.15185 | null | https://arxiv.org/abs/2210.15185v3 | https://arxiv.org/pdf/2210.15185v3.pdf | SAM-RL: Sensing-Aware Model-Based Reinforcement Learning via Differentiable Physics-Based Simulation and Rendering | Model-based reinforcement learning (MBRL) is recognized with the potential to be significantly more sample-efficient than model-free RL. How an accurate model can be developed automatically and efficiently from raw sensory inputs (such as images), especially for complex environments and tasks, is a challenging problem ... | ['Cewu Lu', 'Lin Shao', 'Shuang Zhao', 'Cheng Zhang', 'Yunhai Feng', 'Jun Lv'] | 2022-10-27 | null | null | null | null | ['deformable-object-manipulation'] | ['robots'] | [ 4.53909785e-02 -7.39167929e-02 -1.24806359e-01 -2.52122104e-01
-7.96588719e-01 -5.16331255e-01 4.59175736e-01 -1.94272593e-01
-3.27569395e-01 7.34836102e-01 -3.57085347e-01 -1.31774589e-01
-3.75431515e-02 -6.64865077e-01 -1.08537626e+00 -5.12999833e-01
6.16036057e-02 6.49845958e-01 2.45221525e-01 -2.66759247... | [4.678863525390625, 0.7682079076766968] |
56dc67e8-b798-4cad-856e-2f6ab691577d | phee-a-dataset-for-pharmacovigilance-event | 2210.12560 | null | https://arxiv.org/abs/2210.12560v1 | https://arxiv.org/pdf/2210.12560v1.pdf | PHEE: A Dataset for Pharmacovigilance Event Extraction from Text | The primary goal of drug safety researchers and regulators is to promptly identify adverse drug reactions. Doing so may in turn prevent or reduce the harm to patients and ultimately improve public health. Evaluating and monitoring drug safety (i.e., pharmacovigilance) involves analyzing an ever growing collection of sp... | ['Yulan He', 'Joseph Kim', 'Nigel Greene', 'Bino John', 'Byron C. Wallace', 'Gabriele Pergola', 'Jiazheng Li', 'Zhaoyue Sun'] | 2022-10-22 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [ 4.66016054e-01 4.60761115e-02 -7.88774014e-01 -3.61062855e-01
-1.12825668e+00 -8.82197261e-01 5.30356705e-01 1.45038021e+00
-3.09354603e-01 1.13936889e+00 5.37572563e-01 -5.37693501e-01
-2.15798751e-01 -6.19663358e-01 -6.87927127e-01 -3.84614885e-01
-2.05586061e-01 5.54068327e-01 -3.99765074e-01 3.68969202... | [8.384725570678711, 8.661361694335938] |
66bcdac4-f3fd-49dc-b9ab-f8ad121b9f7c | relationship-explainable-multi-objective | 1909.12268 | null | https://arxiv.org/abs/1909.12268v1 | https://arxiv.org/pdf/1909.12268v1.pdf | Relationship Explainable Multi-objective Reinforcement Learning with Semantic Explainability Generation | Solving multi-objective optimization problems is important in various applications where users are interested in obtaining optimal policies subject to multiple, yet often conflicting objectives. A typical approach to obtain optimal policies is to first construct a loss function that is based on the scalarization of ind... | ['Huixin Zhan', 'Yongcan Cao'] | 2019-09-26 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [ 1.47057772e-01 8.49606842e-02 -2.84915447e-01 -6.72932416e-02
-5.20747066e-01 -2.80403286e-01 1.75923824e-01 3.66901904e-01
-4.63502079e-01 9.53772068e-01 -4.30395082e-02 -6.00436702e-02
-8.54621649e-01 -5.59775352e-01 -7.77990818e-01 -6.74567401e-01
-1.42967507e-01 3.77263933e-01 -2.80638158e-01 -3.49458069... | [4.348354339599609, 2.420440673828125] |
f359f787-88c0-493e-b8fd-2b7f2d053839 | clevr-math-a-dataset-for-compositional | 2208.05358 | null | https://arxiv.org/abs/2208.05358v1 | https://arxiv.org/pdf/2208.05358v1.pdf | CLEVR-Math: A Dataset for Compositional Language, Visual and Mathematical Reasoning | We introduce CLEVR-Math, a multi-modal math word problems dataset consisting of simple math word problems involving addition/subtraction, represented partly by a textual description and partly by an image illustrating the scenario. The text describes actions performed on the scene that is depicted in the image. Since t... | ['Savitha Sam Abraham', 'Adam Dahlgren Lindström'] | 2022-08-10 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [ 2.91339844e-01 1.69767514e-01 3.71294111e-01 -1.98643655e-01
-3.94128174e-01 -1.02993298e+00 9.25201952e-01 4.45340693e-01
-2.57072747e-01 3.21721524e-01 1.37762010e-01 -6.85730159e-01
-2.39806324e-01 -1.06810057e+00 -9.09795880e-01 -2.69739926e-01
2.56790638e-01 8.19733024e-01 2.34660819e-01 -4.70693946... | [10.697480201721191, 2.0321624279022217] |
f9eeca90-0a61-420c-8fc1-c99d23a8bf73 | retrocomposer-discovering-novel-reactions-by | 2112.11225 | null | https://arxiv.org/abs/2112.11225v2 | https://arxiv.org/pdf/2112.11225v2.pdf | RetroComposer: Composing Templates for Template-Based Retrosynthesis Prediction | The main target of retrosynthesis is to recursively decompose desired molecules into available building blocks. Existing template-based retrosynthesis methods follow a template selection stereotype and suffer from limited training templates, which prevents them from discovering novel reactions. To overcome this limitat... | ['Junzhou Huang', 'Yang Yu', 'Chan Lu', 'Peilin Zhao', 'Chaochao Yan'] | 2021-12-20 | null | null | null | null | ['retrosynthesis'] | ['medical'] | [ 5.31578660e-01 1.18203694e-02 -8.27180743e-01 5.05159162e-02
-5.92542827e-01 -1.10865855e+00 8.10581744e-01 1.24692678e-01
-1.60667837e-01 1.21718228e+00 2.85695225e-01 -4.98171806e-01
2.34280050e-01 -7.56987274e-01 -5.41528106e-01 -7.04826117e-01
4.53116417e-01 2.48029351e-01 6.65440500e-01 -3.44739288... | [4.489120960235596, 6.1108222007751465] |
650b24de-2423-4e75-b17a-413490753df8 | diversification-quotients-based-on-var-and-es | 2301.03517 | null | https://arxiv.org/abs/2301.03517v3 | https://arxiv.org/pdf/2301.03517v3.pdf | Diversification quotients based on VaR and ES | The diversification quotient (DQ) is recently introduced for quantifying the degree of diversification of a stochastic portfolio model. It has an axiomatic foundation and can be defined through a parametric class of risk measures. Since the Value-at-Risk (VaR) and the Expected Shortfall (ES) are the most prominent risk... | ['Ruodu Wang', 'Liyuan Lin', 'Xia Han'] | 2023-01-09 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-2.96591967e-01 -3.62307690e-02 -2.34886378e-01 -2.42264971e-01
-2.61651218e-01 -7.37128973e-01 5.61422944e-01 8.87433439e-02
-2.63124764e-01 9.10679400e-01 1.16095200e-01 -5.33095479e-01
-7.46311903e-01 -1.24670422e+00 3.64364311e-02 -9.15957630e-01
-1.31523266e-01 3.00536752e-01 5.04025668e-02 -4.25433725... | [4.974542617797852, 3.9708774089813232] |
63de971d-eb72-462a-8c37-cae67eb59ac2 | autoregressive-3d-shape-generation-via | 2204.01955 | null | https://arxiv.org/abs/2204.01955v1 | https://arxiv.org/pdf/2204.01955v1.pdf | Autoregressive 3D Shape Generation via Canonical Mapping | With the capacity of modeling long-range dependencies in sequential data, transformers have shown remarkable performances in a variety of generative tasks such as image, audio, and text generation. Yet, taming them in generating less structured and voluminous data formats such as high-resolution point clouds have seldo... | ['Ming-Hsuan Yang', 'Min Sun', 'Sifei Liu', 'Xueting Li', 'An-Chieh Cheng'] | 2022-04-05 | null | null | null | null | ['point-cloud-reconstruction', '3d-shape-generation', 'point-cloud-generation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.36219841e-01 1.51781872e-01 1.81505814e-01 -1.38108447e-01
-1.06504476e+00 -7.54765332e-01 1.00374687e+00 2.83520147e-02
2.31687531e-01 6.15892470e-01 1.04594752e-01 -3.04641932e-01
1.28448442e-01 -1.01135015e+00 -1.10905731e+00 -7.42927074e-01
1.35913059e-01 8.78193498e-01 -1.20453350e-01 -2.45784447... | [8.894972801208496, -3.6304080486297607] |
8ca9ed0c-30ee-4ac7-9c69-e9de4404d5fa | surfacenet-adversarial-svbrdf-estimation-from | 2107.11298 | null | https://arxiv.org/abs/2107.11298v1 | https://arxiv.org/pdf/2107.11298v1.pdf | SurfaceNet: Adversarial SVBRDF Estimation from a Single Image | In this paper we present SurfaceNet, an approach for estimating spatially-varying bidirectional reflectance distribution function (SVBRDF) material properties from a single image. We pose the problem as an image translation task and propose a novel patch-based generative adversarial network (GAN) that is able to produc... | ['Concetto Spampinato', 'Simone Palazzo', 'Giuseppe Vecchio'] | 2021-07-23 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Vecchio_SurfaceNet_Adversarial_SVBRDF_Estimation_From_a_Single_Image_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Vecchio_SurfaceNet_Adversarial_SVBRDF_Estimation_From_a_Single_Image_ICCV_2021_paper.pdf | iccv-2021-1 | ['svbrdf-estimation'] | ['computer-vision'] | [ 8.10643017e-01 1.11561514e-01 3.29623431e-01 -1.48006678e-01
-9.88349736e-01 -4.63512599e-01 7.91848958e-01 -6.90965533e-01
8.73004496e-02 1.04562843e+00 -3.67627926e-02 -6.50316104e-02
2.28768602e-01 -1.12776721e+00 -1.16482365e+00 -9.53968585e-01
5.43588698e-01 4.96146113e-01 1.08506054e-01 -1.74344927... | [11.658092498779297, -0.7056453227996826] |
e016fb3f-05bc-48f9-bcdb-4a8f6fb10d7e | lesion-inspired-denoising-network-connecting | 2104.08845 | null | https://arxiv.org/abs/2104.08845v1 | https://arxiv.org/pdf/2104.08845v1.pdf | Lesion-Inspired Denoising Network: Connecting Medical Image Denoising and Lesion Detection | Deep learning has achieved notable performance in the denoising task of low-quality medical images and the detection task of lesions, respectively. However, existing low-quality medical image denoising approaches are disconnected from the detection task of lesions. Intuitively, the quality of denoised images will influ... | ['Xiaorong Pu', 'Jiayu Sun', 'Yazhou Ren', 'Kun Long', 'Kecheng Chen'] | 2021-04-18 | null | null | null | null | ['medical-image-denoising'] | ['computer-vision'] | [ 2.66094744e-01 4.49039694e-03 2.31365204e-01 -4.36709315e-01
-1.05264699e+00 -1.62898749e-01 4.16974455e-01 1.74772218e-01
-5.25144875e-01 3.93728435e-01 2.31583402e-01 -2.67553404e-02
-2.38566831e-01 -8.11565757e-01 -4.34844732e-01 -9.98927653e-01
1.52825922e-01 -2.50975430e-01 2.24157423e-01 -1.58631027... | [13.464948654174805, -2.461362838745117] |
49730753-9176-41d5-be4c-159b927fcbd5 | web-based-visualisation-of-head-pose-and | 1703.03949 | null | http://arxiv.org/abs/1703.03949v2 | http://arxiv.org/pdf/1703.03949v2.pdf | Web-based visualisation of head pose and facial expressions changes: monitoring human activity using depth data | Despite significant recent advances in the field of head pose estimation and
facial expression recognition, raising the cognitive level when analysing human
activity presents serious challenges to current concepts. Motivated by the need
of generating comprehensible visual representations from different sets of
data, we... | ['Grigorios Kalliatakis', 'Nikolaos Vidakis', 'Georgios Triantafyllidis'] | 2017-03-11 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [ 2.72967756e-01 2.02822223e-01 2.85589039e-01 -6.03974998e-01
-2.06257880e-01 -3.20133299e-01 5.97360194e-01 -1.62271574e-01
-4.71478254e-01 5.49357772e-01 2.69825250e-01 1.87617913e-01
8.89538378e-02 -3.57326061e-01 -4.03483063e-02 -6.17282569e-01
-2.36243784e-01 2.70516817e-02 -1.27698049e-01 -2.69435614... | [13.496696472167969, 2.2068915367126465] |
c7a8d0ef-da44-4467-b6c3-b6821b8ed16b | a-unified-objective-for-novel-class-discovery | 2108.08536 | null | https://arxiv.org/abs/2108.08536v4 | https://arxiv.org/pdf/2108.08536v4.pdf | A Unified Objective for Novel Class Discovery | In this paper, we study the problem of Novel Class Discovery (NCD). NCD aims at inferring novel object categories in an unlabeled set by leveraging from prior knowledge of a labeled set containing different, but related classes. Existing approaches tackle this problem by considering multiple objective functions, usuall... | ['Elisa Ricci', 'Moin Nabi', 'Zhun Zhong', 'Stéphane Lathuilière', 'Enver Sangineto', 'Enrico Fini'] | 2021-08-19 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Fini_A_Unified_Objective_for_Novel_Class_Discovery_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Fini_A_Unified_Objective_for_Novel_Class_Discovery_ICCV_2021_paper.pdf | iccv-2021-1 | ['novel-class-discovery', 'novel-class-discovery'] | ['computer-vision', 'methodology'] | [ 3.33966941e-01 3.05299819e-01 -3.70085865e-01 -6.61570907e-01
-9.90629971e-01 -6.13395751e-01 6.65937304e-01 3.34961444e-01
-4.34854925e-01 9.72906768e-01 -1.05296299e-01 6.17502369e-02
1.12241790e-01 -6.14357352e-01 -7.21841037e-01 -6.90469623e-01
9.27317813e-02 6.38960600e-01 1.45562366e-02 3.69807720... | [9.584932327270508, 3.1046884059906006] |
ea62985a-29ca-4155-bb0a-31c012b6aa52 | 1st-place-solutions-for-waymo-open-dataset | 2006.15506 | null | https://arxiv.org/abs/2006.15506v1 | https://arxiv.org/pdf/2006.15506v1.pdf | 1st Place Solutions for Waymo Open Dataset Challenges -- 2D and 3D Tracking | This technical report presents the online and real-time 2D and 3D multi-object tracking (MOT) algorithms that reached the 1st places on both Waymo Open Dataset 2D tracking and 3D tracking challenges. An efficient and pragmatic online tracking-by-detection framework named HorizonMOT is proposed for camera-based 2D track... | ['Yihan Hu', 'Zhuangzhuang Ding', 'Yu Wang', 'Sijia Chen', 'Runzhou Ge', 'Li Huang', 'Jie Liao'] | 2020-06-28 | null | null | null | null | ['3d-multi-object-tracking'] | ['computer-vision'] | [-6.03225946e-01 -4.28744227e-01 -6.44211173e-02 3.04103315e-01
-8.54732096e-01 -1.05883467e+00 6.42141402e-01 -3.06763332e-02
-4.42356139e-01 2.17632651e-01 -3.85476589e-01 -2.06545919e-01
-1.00650325e-01 -4.63806242e-01 -5.52370965e-01 -2.15367705e-01
-2.49976501e-01 8.95655870e-01 1.12316871e+00 -1.47424415... | [6.623039722442627, -2.218648672103882] |
b1a2380f-f11e-41a2-b977-befa08abb9b8 | solving-royal-game-of-ur-using-reinforcement | 2208.10669 | null | https://arxiv.org/abs/2208.10669v1 | https://arxiv.org/pdf/2208.10669v1.pdf | Solving Royal Game of Ur Using Reinforcement Learning | Reinforcement Learning has recently surfaced as a very powerful tool to solve complex problems in the domain of board games, wherein an agent is generally required to learn complex strategies and moves based on its own experiences and rewards received. While RL has outperformed existing state-of-the-art methods used fo... | ['Girik Malik', 'Sidharth Malhotra'] | 2022-08-23 | null | null | null | null | ['board-games'] | ['playing-games'] | [-1.54948846e-01 8.17067847e-02 -6.70076981e-02 3.05746406e-01
-7.80109644e-01 -7.86449790e-01 6.03150547e-01 -1.36592656e-01
-1.12070560e+00 1.37884855e+00 -5.91062345e-02 -5.48152745e-01
-4.37918812e-01 -7.11569786e-01 -5.15146494e-01 -7.89141238e-01
-5.96403956e-01 9.15718734e-01 4.71066117e-01 -8.81505132... | [3.572882890701294, 1.4830763339996338] |
32b9ba33-7b53-4549-a39c-370df4ee81ca | unique-class-group-based-multi-label | 2003.08751 | null | https://arxiv.org/abs/2003.08751v1 | https://arxiv.org/pdf/2003.08751v1.pdf | Unique Class Group Based Multi-Label Balancing Optimizer for Action Unit Detection | Balancing methods for single-label data cannot be applied to multi-label problems as they would also resample the samples with high occurrences. We propose to reformulate this problem as an optimization problem in order to balance multi-label data. We apply this balancing algorithm to training datasets for detecting is... | ['Jaspar Pahl', 'Dominik Seuss', 'Ines Rieger'] | 2020-03-05 | null | null | null | null | ['action-unit-detection'] | ['computer-vision'] | [ 5.19395411e-01 2.94407368e-01 -8.13550889e-01 -7.34018326e-01
-8.80665839e-01 -3.02445799e-01 2.73244113e-01 -1.80749446e-01
-4.27331150e-01 6.42971218e-01 3.22041184e-01 3.05585057e-01
1.77053764e-01 -1.83425203e-01 -2.07384422e-01 -6.29883051e-01
1.44972235e-01 4.42830741e-01 -5.06531417e-01 9.16037783... | [13.581450462341309, 1.8647009134292603] |
95501dab-70d1-4107-a332-a75c1e97fe20 | smoa-sparse-mixture-of-adapters-to-mitigate | 2302.14413 | null | https://arxiv.org/abs/2302.14413v1 | https://arxiv.org/pdf/2302.14413v1.pdf | SMoA: Sparse Mixture of Adapters to Mitigate Multiple Dataset Biases | Recent studies reveal that various biases exist in different NLP tasks, and over-reliance on biases results in models' poor generalization ability and low adversarial robustness. To mitigate datasets biases, previous works propose lots of debiasing techniques to tackle specific biases, which perform well on respective ... | ['Hua Wu', 'Jing Liu', 'Yan Chen', 'Jing Yan', 'Yanchen Liu'] | 2023-02-28 | null | null | null | null | ['paraphrase-identification'] | ['natural-language-processing'] | [ 1.25889048e-01 -1.98850468e-01 -4.61250573e-01 -5.03742814e-01
-5.75819194e-01 -8.36755931e-01 6.57496691e-01 -3.24017435e-01
-1.90824300e-01 9.77990568e-01 6.19711697e-01 -3.41856033e-01
-3.32808495e-02 -7.99555302e-01 -9.02983487e-01 -5.78870952e-01
8.07812214e-01 4.57816720e-01 -1.89329281e-01 -4.16814685... | [10.401604652404785, 7.7227935791015625] |
a85af9c5-c38e-43ef-a549-d3dc8ae90890 | multilingual-entity-and-relation-extraction | null | null | https://aclanthology.org/2021.eacl-main.166 | https://aclanthology.org/2021.eacl-main.166.pdf | Multilingual Entity and Relation Extraction Dataset and Model | We present a novel dataset and model for a multilingual setting to approach the task of Joint Entity and Relation Extraction. The SMiLER dataset consists of 1.1 M annotated sentences, representing 36 relations, and 14 languages. To the best of our knowledge, this is currently both the largest and the most comprehensive... | ['Piotr Andruszkiewicz', 'Micha{\\l} Sat{\\l}awa', 'Helena Skowronska', 'Klaudia Firl{\\k{a}}g', 'Alessandro Seganti'] | 2021-04-01 | null | null | null | eacl-2021-2 | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [-9.20058638e-02 4.76267815e-01 -6.41781330e-01 -3.07954341e-01
-9.41908121e-01 -8.60179484e-01 6.64727688e-01 4.80928063e-01
-7.00038850e-01 1.10897136e+00 8.24760273e-02 -4.69968766e-01
8.30033273e-02 -2.10620821e-01 -7.56882310e-01 -1.22066252e-01
-1.85102791e-01 1.09184468e+00 3.90972108e-01 -3.09342891... | [9.713955879211426, 9.153372764587402] |
d86c2c13-d64c-46f3-931a-0f5912f1e01f | a-color-temperature-based-high-speed | 2108.13656 | null | https://arxiv.org/abs/2108.13656v1 | https://arxiv.org/pdf/2108.13656v1.pdf | A color temperature-based high-speed decolorization: an empirical approach for tone mapping applications | Grayscale images are fundamental to many image processing applications like data compression, feature extraction, printing and tone mapping. However, some image information is lost when converting from color to grayscale. In this paper, we propose a light-weight and high-speed image decolorization method based on human... | ['Masayuki Ikebe', 'Yafei Ou', 'Prasoon Ambalathankandy'] | 2021-08-31 | null | null | null | null | ['tone-mapping'] | ['computer-vision'] | [ 4.49861526e-01 -7.53790319e-01 3.19400817e-01 -3.92772108e-02
-2.05685541e-01 -6.12367988e-01 2.16489345e-01 -1.39462322e-01
-4.82597262e-01 6.87798560e-01 -1.44308418e-01 -2.32795388e-01
4.03394818e-01 -1.04596579e+00 -3.88990670e-01 -8.08525741e-01
-3.24616488e-03 -5.16448915e-01 3.09765458e-01 -3.05833429... | [10.83838176727295, -2.4159348011016846] |
ebb684ed-ec00-4610-ade1-74d9ad0a5f7f | volta-vision-language-transformer-with-weakly | 2210.04135 | null | https://arxiv.org/abs/2210.04135v2 | https://arxiv.org/pdf/2210.04135v2.pdf | VoLTA: Vision-Language Transformer with Weakly-Supervised Local-Feature Alignment | Vision-language pre-training (VLP) has recently proven highly effective for various uni- and multi-modal downstream applications. However, most existing end-to-end VLP methods use high-resolution image-text box data to perform well on fine-grained region-level tasks, such as object detection, segmentation, and referrin... | ['Rama Chellappa', 'Yann Lecun', 'Hardik Shah', 'Jiachen Zhu', 'Sayan Nag', 'Li Jing', 'Shraman Pramanick'] | 2022-10-09 | null | null | null | null | ['referring-expression'] | ['computer-vision'] | [ 3.70896190e-01 1.70861498e-01 -3.26068640e-01 -5.97796321e-01
-1.41153491e+00 -6.81114674e-01 5.37537575e-01 6.84212372e-02
-3.72001857e-01 3.26422393e-01 2.51462907e-01 -2.41175249e-01
3.77150774e-01 -6.61332786e-01 -1.07377267e+00 -5.90971291e-01
6.61069155e-01 5.43810248e-01 4.75509107e-01 -2.80890226... | [10.381969451904297, 1.3462852239608765] |
6ba1bad4-8db5-46f2-a2cf-c05bf7a3301c | temporal-collaborative-ranking-via | 1908.05435 | null | https://arxiv.org/abs/1908.05435v1 | https://arxiv.org/pdf/1908.05435v1.pdf | Temporal Collaborative Ranking Via Personalized Transformer | The collaborative ranking problem has been an important open research question as most recommendation problems can be naturally formulated as ranking problems. While much of collaborative ranking methodology assumes static ranking data, the importance of temporal information to improving ranking performance is increasi... | ['Cho-Jui Hsieh', 'James Sharpnack', 'Shuqing Li', 'Liwei Wu'] | 2019-08-15 | null | null | null | null | ['collaborative-ranking'] | ['graphs'] | [-1.66160807e-01 -5.17197609e-01 -3.41872483e-01 -5.03878295e-01
-6.06151164e-01 -7.34108567e-01 8.41123939e-01 2.58874863e-01
-6.18282318e-01 3.43826771e-01 8.42803955e-01 -3.11085075e-01
-5.34575522e-01 -7.46847034e-01 -5.16134262e-01 -2.28778780e-01
-2.98872828e-01 6.06095433e-01 3.92735079e-02 -5.22574425... | [10.153741836547852, 5.705169677734375] |
7bb8e690-b892-48f7-8713-14151f78c788 | simulated-annealing-for-optimization-of | 2110.01384 | null | https://arxiv.org/abs/2110.01384v1 | https://arxiv.org/pdf/2110.01384v1.pdf | Simulated annealing for optimization of graphs and sequences | Optimization of discrete structures aims at generating a new structure with the better property given an existing one, which is a fundamental problem in machine learning. Different from the continuous optimization, the realistic applications of discrete optimization (e.g., text generation) are very challenging due to t... | ['Sen Song', 'Lili Mou', 'Jie zhou', 'Huasong Zhong', 'Hao Zhou', 'Fandong Meng', 'Pengyong Li', 'Xianggen Liu'] | 2021-10-01 | null | null | null | null | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 6.88014030e-01 1.12123741e-02 -1.33566141e-01 -1.11896969e-01
-6.92861497e-01 -6.15296602e-01 5.67951918e-01 4.81753290e-01
-1.82568401e-01 1.01956820e+00 1.33694798e-01 -3.30653697e-01
-6.05127104e-02 -1.21057081e+00 -1.00589800e+00 -7.09911704e-01
3.15361261e-01 7.42529392e-01 -5.75811006e-02 -5.67511201... | [4.965558052062988, 5.741209030151367] |
f0fad800-19b0-4cab-9226-eb2f7ba07ada | factorizable-net-an-efficient-subgraph-based | 1806.11538 | null | http://arxiv.org/abs/1806.11538v2 | http://arxiv.org/pdf/1806.11538v2.pdf | Factorizable Net: An Efficient Subgraph-based Framework for Scene Graph Generation | Generating scene graph to describe all the relations inside an image gains
increasing interests these years. However, most of the previous methods use
complicated structures with slow inference speed or rely on the external data,
which limits the usage of the model in real-life scenarios. To improve the
efficiency of s... | ['Chao Zhang', 'Yikang Li', 'Bolei Zhou', 'Wanli Ouyang', 'Xiaogang Wang', 'Jianping Shi'] | 2018-06-29 | factorizable-net-an-efficient-subgraph-based-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Yikang_LI_Factorizable_Net_An_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Yikang_LI_Factorizable_Net_An_ECCV_2018_paper.pdf | eccv-2018-9 | ['visual-relationship-detection'] | ['computer-vision'] | [ 2.39602968e-01 -8.62076879e-02 -2.00607195e-01 -5.45680583e-01
2.24332903e-02 -4.18352187e-01 4.21881229e-01 4.47835118e-01
-7.07219169e-02 3.01381558e-01 -4.11771275e-02 -2.33549222e-01
-2.62775958e-01 -1.23994339e+00 -5.29491842e-01 -6.37623489e-01
3.72758061e-02 3.37198287e-01 7.70446360e-01 -7.14077801... | [10.212055206298828, 1.638039469718933] |
efb5eb9d-b9c8-4364-bfb9-ae096dcdfefe | question-rewriting-assessing-its-importance | 2201.09146 | null | https://arxiv.org/abs/2201.09146v2 | https://arxiv.org/pdf/2201.09146v2.pdf | Question rewriting? Assessing its importance for conversational question answering | In conversational question answering, systems must correctly interpret the interconnected interactions and generate knowledgeable answers, which may require the retrieval of relevant information from a background repository. Recent approaches to this problem leverage neural language models, although different alternati... | ['Luísa Coheur', 'Bruno Martins', 'Rui Ribeiro', 'Gonçalo Raposo'] | 2022-01-22 | null | null | null | null | ['question-rewriting'] | ['natural-language-processing'] | [ 4.40472275e-01 4.62622583e-01 5.02738178e-01 -3.93203974e-01
-8.22805226e-01 -6.42380238e-01 1.16247761e+00 3.16678584e-01
-3.89843196e-01 6.21248662e-01 5.28868377e-01 -6.63091958e-01
-3.82487416e-01 -7.96664894e-01 -2.42457926e-01 -2.15444416e-01
2.37461969e-01 7.83919632e-01 4.70994800e-01 -6.53391004... | [12.119818687438965, 7.918738842010498] |
fb477c0b-dfb0-4178-888d-be255b0c92bd | towards-smooth-video-composition | 2212.07413 | null | https://arxiv.org/abs/2212.07413v1 | https://arxiv.org/pdf/2212.07413v1.pdf | Towards Smooth Video Composition | Video generation requires synthesizing consistent and persistent frames with dynamic content over time. This work investigates modeling the temporal relations for composing video with arbitrary length, from a few frames to even infinite, using generative adversarial networks (GANs). First, towards composing adjacent fr... | ['Bolei Zhou', 'Yinghao Xu', 'Yujun Shen', 'Ceyuan Yang', 'Qihang Zhang'] | 2022-12-14 | null | null | null | null | ['video-generation', 'single-image-generation', 'video-understanding'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.80055338e-01 2.05881119e-01 -7.53275156e-02 -7.35508231e-03
-9.15049434e-01 -8.21512282e-01 8.17731321e-01 -6.55303180e-01
5.41288033e-02 8.14793587e-01 3.82439882e-01 -1.80847794e-01
2.88935721e-01 -6.02086246e-01 -1.08874488e+00 -6.99714959e-01
-1.93035483e-01 -1.39509499e-01 1.54711023e-01 -9.72000360... | [10.912787437438965, -0.5932552218437195] |
ec885dc7-3508-474b-8534-333a7fd7ffc3 | a-robust-regression-approach-for | 1412.5126 | null | http://arxiv.org/abs/1412.5126v2 | http://arxiv.org/pdf/1412.5126v2.pdf | A Robust Regression Approach for Background/Foreground Segmentation | Background/foreground segmentation has a lot of applications in image and
video processing. In this paper, a segmentation algorithm is proposed which is
mainly designed for text and line extraction in screen content. The proposed
method makes use of the fact that the background in each block is usually
smoothly varying... | ['Yao Wang', 'Haoping Yu', 'Shervin Minaee'] | 2014-12-16 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 6.80457950e-01 -4.29012388e-01 -1.39918417e-01 -1.15955330e-01
-2.16877133e-01 -4.52690899e-01 3.09588432e-01 -1.07781626e-01
-7.91798234e-02 6.47132576e-01 -3.42793703e-01 -3.72524709e-01
8.16886351e-02 -5.78266859e-01 -4.57866818e-01 -9.79324758e-01
3.72227609e-01 4.85456079e-01 7.80728638e-01 5.12530543... | [8.988982200622559, -0.8355847001075745] |
e26212ff-33af-40f0-bae9-95d8c7988cc2 | transformative-machine-learning | 1811.03392 | null | http://arxiv.org/abs/1811.03392v1 | http://arxiv.org/pdf/1811.03392v1.pdf | Transformative Machine Learning | The key to success in machine learning (ML) is the use of effective data
representations. Traditionally, data representations were hand-crafted.
Recently it has been demonstrated that, given sufficient data, deep neural
networks can learn effective implicit representations from simple input
representations. However, fo... | ['Oghenejokpeme I. Orhobor', 'Ross D. King', 'Joaquin Vanschoren', 'Ivan Olier'] | 2018-11-08 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 5.69259048e-01 1.69935539e-01 -5.98710299e-01 -3.62838328e-01
-9.26151276e-01 -3.31228584e-01 6.02363527e-01 4.21449095e-01
-1.99189290e-01 1.03657603e+00 1.32942051e-01 -3.00940067e-01
-2.58652747e-01 -7.60393262e-01 -9.77067351e-01 -7.12909937e-01
1.40679553e-01 5.85642219e-01 -3.37795794e-01 -2.05756560... | [5.351639270782471, 5.64683723449707] |
4e166438-ba5f-4634-9811-ec6af5f5c268 | convgqr-generative-query-reformulation-for | 2305.15645 | null | https://arxiv.org/abs/2305.15645v2 | https://arxiv.org/pdf/2305.15645v2.pdf | ConvGQR: Generative Query Reformulation for Conversational Search | In conversational search, the user's real search intent for the current turn is dependent on the previous conversation history. It is challenging to determine a good search query from the whole conversation context. To avoid the expensive re-training of the query encoder, most existing methods try to learn a rewriting ... | ['Jian-Yun Nie', 'Kaiyu Huang', 'Yihong Wu', 'Yutao Zhu', 'Kelong Mao', 'Fengran Mo'] | 2023-05-25 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 1.13871962e-01 1.29053444e-01 -4.65966076e-01 -5.83870053e-01
-1.15445638e+00 -6.22834802e-01 8.09815705e-01 -2.10914403e-01
-2.94285744e-01 5.41425824e-01 6.25965893e-01 -4.10284787e-01
8.75056684e-02 -8.74373078e-01 -5.01433372e-01 -1.22195520e-01
5.29410481e-01 6.84182644e-01 2.15435416e-01 -7.18055546... | [12.023097038269043, 7.711638450622559] |
9e2b369d-bf37-4b5f-92be-8ae4cb1593fa | towards-spectral-estimation-from-a-single-rgb | 1812.00805 | null | http://arxiv.org/abs/1812.00805v1 | http://arxiv.org/pdf/1812.00805v1.pdf | Towards Spectral Estimation from a Single RGB Image in the Wild | In contrast to the current literature, we address the problem of estimating
the spectrum from a single common trichromatic RGB image obtained under
unconstrained settings (e.g. unknown camera parameters, unknown scene radiance,
unknown scene contents). For this we use a reference spectrum as provided by a
hyperspectral... | ['Radu Timofte', 'Yigit Baran Can', 'Berk Kaya'] | 2018-12-03 | null | null | null | null | ['spectral-reconstruction', 'spectral-estimation-from-a-single-rgb-image'] | ['computer-vision', 'computer-vision'] | [ 9.03774559e-01 -4.09740984e-01 3.18791211e-01 -1.23583257e-01
-9.59485590e-01 -9.37785566e-01 2.06780612e-01 -2.65687227e-01
-7.08051860e-01 6.83837831e-01 -2.63026655e-01 -1.17826499e-01
-2.34357730e-01 -7.04027057e-01 -8.97660315e-01 -8.25314760e-01
4.74777877e-01 1.28333732e-01 -4.94771935e-02 -2.24516049... | [10.230462074279785, -2.4066481590270996] |
5b5dc873-edec-447a-9310-0a3cd470cf72 | what-have-been-learned-what-should-be-learned | 2109.00175 | null | https://arxiv.org/abs/2109.00175v1 | https://arxiv.org/pdf/2109.00175v1.pdf | What Have Been Learned & What Should Be Learned? An Empirical Study of How to Selectively Augment Text for Classification | Text augmentation techniques are widely used in text classification problems to improve the performance of classifiers, especially in low-resource scenarios. Whilst lots of creative text augmentation methods have been designed, they augment the text in a non-selective manner, which means the less important or noisy wor... | ['Hailiang Huang', 'Sonqiao Han', 'Biyang Guo'] | 2021-09-01 | null | null | null | null | ['text-augmentation'] | ['natural-language-processing'] | [ 5.83247721e-01 -2.84593552e-02 -4.26795602e-01 -2.36239851e-01
-2.93126792e-01 -2.09890768e-01 6.17579699e-01 3.53755593e-01
-6.70735240e-01 8.90954137e-01 5.89873374e-01 -3.18521798e-01
2.61320740e-01 -6.51634753e-01 -5.23935445e-03 -9.30448174e-01
4.22647387e-01 3.36598784e-01 1.10107176e-01 -4.98479724... | [10.562234878540039, 7.662924289703369] |
4fcb216e-f502-4fc4-90a8-7eac3358448f | sequence-length-is-a-domain-length-based | 2109.07276 | null | https://arxiv.org/abs/2109.07276v1 | https://arxiv.org/pdf/2109.07276v1.pdf | Sequence Length is a Domain: Length-based Overfitting in Transformer Models | Transformer-based sequence-to-sequence architectures, while achieving state-of-the-art results on a large number of NLP tasks, can still suffer from overfitting during training. In practice, this is usually countered either by applying regularization methods (e.g. dropout, L2-regularization) or by providing huge amount... | ['Ondřej Bojar', 'Dušan Variš'] | 2021-09-15 | null | https://aclanthology.org/2021.emnlp-main.650 | https://aclanthology.org/2021.emnlp-main.650.pdf | emnlp-2021-11 | ['l2-regularization'] | ['methodology'] | [ 7.18930840e-01 2.23245740e-01 -4.13708352e-02 -2.52805054e-01
-9.96103108e-01 -7.81086802e-01 5.87573349e-01 3.96983288e-02
-5.04825234e-01 1.04108727e+00 2.61714160e-01 -7.07351863e-01
2.79862881e-01 -5.46377659e-01 -1.08352268e+00 -6.15503013e-01
3.68626952e-01 7.42738187e-01 -2.23196000e-01 -2.53585219... | [11.635783195495605, 10.01497745513916] |
a2c2b314-fbd7-4a23-8df4-db5abf6b2570 | go-with-the-flows-mixtures-of-normalizing | 2106.03135 | null | https://arxiv.org/abs/2106.03135v3 | https://arxiv.org/pdf/2106.03135v3.pdf | Go with the Flows: Mixtures of Normalizing Flows for Point Cloud Generation and Reconstruction | Recently normalizing flows (NFs) have demonstrated state-of-the-art performance on modeling 3D point clouds while allowing sampling with arbitrary resolution at inference time. However, these flow-based models still require long training times and large models for representing complicated geometries. This work enhances... | ['Federico Tombari', 'Luc van Gool', 'Riccardo Spezialetti', 'Mengya Liu', 'Janis Postels'] | 2021-06-06 | null | null | null | null | ['point-cloud-generation'] | ['computer-vision'] | [ 7.81513471e-03 -1.49391154e-02 1.71516240e-02 -2.66829818e-01
-6.01554990e-01 -9.31342781e-01 8.30508888e-01 -1.67762354e-01
1.05418742e-01 5.55361211e-01 5.98497242e-02 -1.83746859e-01
2.12937333e-02 -1.16839397e+00 -1.04082739e+00 -3.37354600e-01
-8.56932849e-02 1.21003914e+00 2.07614750e-01 1.15432218... | [8.880975723266602, -3.5914013385772705] |
575872d7-8ec9-4425-bd6e-7f590ac65c2d | revisiting-skeleton-based-action-recognition | 2104.13586 | null | https://arxiv.org/abs/2104.13586v2 | https://arxiv.org/pdf/2104.13586v2.pdf | Revisiting Skeleton-based Action Recognition | Human skeleton, as a compact representation of human action, has received increasing attention in recent years. Many skeleton-based action recognition methods adopt graph convolutional networks (GCN) to extract features on top of human skeletons. Despite the positive results shown in previous works, GCN-based methods a... | ['Bo Dai', 'Dahua Lin', 'Kai Chen', 'Yue Zhao', 'Haodong Duan'] | 2021-04-28 | revisiting-skeleton-based-action-recognition-1 | http://openaccess.thecvf.com//content/CVPR2022/html/Duan_Revisiting_Skeleton-Based_Action_Recognition_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Duan_Revisiting_Skeleton-Based_Action_Recognition_CVPR_2022_paper.pdf | cvpr-2022-1 | ['group-activity-recognition'] | ['computer-vision'] | [ 2.96224475e-01 -2.74065048e-01 -2.66805112e-01 -1.18729889e-01
-4.60681945e-01 -3.45016532e-02 5.20954728e-01 -1.13241069e-01
-4.61319387e-01 3.12352687e-01 5.80973268e-01 1.99287832e-01
-9.38781817e-03 -7.11044967e-01 -3.25047761e-01 -6.06948555e-01
6.62284791e-02 1.62014887e-01 6.50343955e-01 -1.83529884... | [7.84657096862793, 0.3700287640094757] |
40124800-4d18-4720-a5f5-cf62db2039ef | variable-rate-hierarchical-cpc-leads-to | 2206.02211 | null | https://arxiv.org/abs/2206.02211v3 | https://arxiv.org/pdf/2206.02211v3.pdf | Variable-rate hierarchical CPC leads to acoustic unit discovery in speech | The success of deep learning comes from its ability to capture the hierarchical structure of data by learning high-level representations defined in terms of low-level ones. In this paper we explore self-supervised learning of hierarchical representations of speech by applying multiple levels of Contrastive Predictive C... | ['Jan Chorowski', 'Paweł Rychlikowski', 'Ricard Marxer', 'Adrian Łańcucki', 'Santiago Cuervo'] | 2022-06-05 | null | null | null | null | ['acoustic-unit-discovery'] | ['speech'] | [ 6.23956740e-01 5.91852188e-01 -2.43544430e-01 -4.80337113e-01
-6.59292758e-01 -5.50696790e-01 9.57028151e-01 4.49022800e-01
-1.17344812e-01 3.23956937e-01 6.08027816e-01 -1.97130010e-01
-9.08871442e-02 -5.59836090e-01 -6.82886600e-01 -7.95524180e-01
-1.75555483e-01 4.59142238e-01 2.59002745e-01 -5.83240949... | [14.810212135314941, 6.511568069458008] |
40f82d16-460b-4a0f-8096-21966df9cef1 | hierarchical-deep-reinforcement-learning-for-1 | 2212.14670 | null | https://arxiv.org/abs/2212.14670v1 | https://arxiv.org/pdf/2212.14670v1.pdf | Hierarchical Deep Reinforcement Learning for VWAP Strategy Optimization | Designing an intelligent volume-weighted average price (VWAP) strategy is a critical concern for brokers, since traditional rule-based strategies are relatively static that cannot achieve a lower transaction cost in a dynamic market. Many studies have tried to minimize the cost via reinforcement learning, but there are... | ['Qing Li', 'Chenxin Zou', 'Pangjing Wu', 'XiaoDong Li'] | 2022-12-11 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [-9.17647600e-01 -1.77069142e-01 -5.58269143e-01 -2.19132319e-01
-7.97932506e-01 -5.97136438e-01 3.26903373e-01 9.93746594e-02
-4.19739544e-01 8.31137955e-01 3.63997221e-02 -6.30249023e-01
-3.05722505e-01 -1.24477696e+00 -5.91834724e-01 -5.61275303e-01
-4.88440365e-01 1.10234940e+00 1.46313742e-01 -4.73318279... | [4.448153972625732, 3.996840715408325] |
5b8a5c6a-653b-47a4-83be-7d281fab0c73 | avaya-conversational-intelligence-a-real-time | 1909.02851 | null | https://arxiv.org/abs/1909.02851v1 | https://arxiv.org/pdf/1909.02851v1.pdf | Avaya Conversational Intelligence: A Real-Time System for Spoken Language Understanding in Human-Human Call Center Conversations | Avaya Conversational Intelligence(ACI) is an end-to-end, cloud-based solution for real-time Spoken Language Understanding for call centers. It combines large vocabulary, real-time speech recognition, transcript refinement, and entity and intent recognition in order to convert live audio into a rich, actionable stream o... | ['Marzena Żyła-Hoppe', 'Cezary Kwiatkowski', 'Marcin Baran', 'Bartosz Borowik', 'Adam Wróbel', 'Łukasz Wójciak', 'Mikołaj Morzy', 'Łukasz Augustyniak', 'Piotr Żelasko', 'Piotr Szymański', 'Robert Głowski', 'Adam Artajew', 'Yishay Carmiel', 'Jeff Hodson', 'Jan Mizgajski', 'Daniel Smoczyk', 'Adrian Szymczak'] | 2019-09-02 | null | null | null | null | ['intent-recognition'] | ['natural-language-processing'] | [-1.09502189e-02 3.21807861e-02 3.80943157e-03 -7.24552691e-01
-1.08681107e+00 -7.55291700e-01 6.74460649e-01 5.07867217e-01
2.16857288e-02 3.06920439e-01 8.52204919e-01 -1.95178762e-01
-3.59796375e-01 -4.76098329e-01 -2.35842634e-02 -2.27463081e-01
-2.77276427e-01 1.15410686e+00 -1.68875605e-01 -2.98502386... | [12.5247163772583, 7.631338596343994] |
a79fa6d8-ae48-4e51-a916-90ae6ee1dd36 | dialogue-to-video-retrieval | 2303.16761 | null | https://arxiv.org/abs/2303.16761v1 | https://arxiv.org/pdf/2303.16761v1.pdf | Dialogue-to-Video Retrieval | Recent years have witnessed an increasing amount of dialogue/conversation on the web especially on social media. That inspires the development of dialogue-based retrieval, in which retrieving videos based on dialogue is of increasing interest for recommendation systems. Different from other video retrieval tasks, dialo... | ['Jennifer Foster', 'Cathal Gurrin', 'Liting Zhou', 'Van-Tu Ninh', 'Manh-Duy Nguyen', 'Chenyang Lyu'] | 2023-03-23 | null | null | null | null | ['video-retrieval'] | ['computer-vision'] | [-1.19165957e-01 -2.13886932e-01 -2.86415547e-01 4.42360668e-03
-1.02382255e+00 -6.36679590e-01 1.03543031e+00 2.49878302e-01
-5.58060884e-01 6.17838323e-01 4.87929881e-01 3.01505655e-01
-2.54077166e-01 -6.26399517e-01 -1.68912977e-01 -4.16383743e-01
-4.06152494e-02 2.69122511e-01 7.08980978e-01 -5.43038130... | [10.442185401916504, 0.7491852641105652] |
7c0159f4-497b-458b-821f-bd812e526d85 | dilated-convolution-with-dilated-gru-for | 1906.01203 | null | https://arxiv.org/abs/1906.01203v1 | https://arxiv.org/pdf/1906.01203v1.pdf | Dilated Convolution with Dilated GRU for Music Source Separation | Stacked dilated convolutions used in Wavenet have been shown effective for generating high-quality audios. By replacing pooling/striding with dilation in convolution layers, they can preserve high-resolution information and still reach distant locations. Producing high-resolution predictions is also crucial in music so... | ['Yi-Hsuan Yang', 'Jen-Yu Liu'] | 2019-06-04 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 2.27306306e-01 -3.29632431e-01 2.42003441e-01 -5.33689149e-02
-8.72077405e-01 -6.43754005e-01 9.73380916e-03 -1.03283294e-01
-2.33193174e-01 5.47547519e-01 4.13003564e-01 -7.90152326e-03
-2.49741212e-01 -6.83635414e-01 -5.52411199e-01 -8.33742797e-01
-1.83024973e-01 -4.52776670e-01 4.74924713e-01 -1.24818116... | [15.428714752197266, 5.569364547729492] |
ad6406cf-2bb1-4326-bd17-1bf825fa79fd | instance-optimal-cluster-recovery-in-the | 2306.12968 | null | https://arxiv.org/abs/2306.12968v1 | https://arxiv.org/pdf/2306.12968v1.pdf | Instance-Optimal Cluster Recovery in the Labeled Stochastic Block Model | We consider the problem of recovering hidden communities in the Labeled Stochastic Block Model (LSBM) with a finite number of clusters, where cluster sizes grow linearly with the total number $n$ of items. In the LSBM, a label is (independently) observed for each pair of items. Our objective is to devise an efficient a... | ['Se-Young Yun', 'Alexandre Proutiere', 'Kaito Ariu'] | 2023-06-18 | null | null | null | null | ['stochastic-block-model', 'clustering'] | ['graphs', 'methodology'] | [ 1.92689836e-01 -3.82084697e-02 -3.84278819e-02 -3.16846877e-01
-1.04472005e+00 -6.79682136e-01 1.30048092e-03 4.49132919e-01
-5.05625248e-01 4.73790854e-01 -5.57234526e-01 -4.60152715e-01
-4.28445697e-01 -6.72467947e-01 -9.06954467e-01 -1.10199571e+00
-4.70466971e-01 1.14026272e+00 3.36412042e-01 5.31028330... | [6.840945720672607, 5.0797810554504395] |
60860ca0-372c-4eaa-a575-211d9f8e405a | multi-scale-attention-flow-for-probabilistic | 2205.07493 | null | https://arxiv.org/abs/2205.07493v2 | https://arxiv.org/pdf/2205.07493v2.pdf | Multi-scale Attention Flow for Probabilistic Time Series Forecasting | The probability prediction of multivariate time series is a notoriously challenging but practical task. On the one hand, the challenge is how to effectively capture the cross-series correlations between interacting time series, to achieve accurate distribution modeling. On the other hand, we should consider how to capt... | ['Peilin Zhao', 'Fan Lin', 'Pengcheng Wu', 'Jiaxiang Wu', 'Ke Xu', 'Shibo Feng'] | 2022-05-16 | null | null | null | null | ['probabilistic-time-series-forecasting'] | ['time-series'] | [-8.48878846e-02 -5.71856260e-01 1.50277346e-01 -3.66468370e-01
-6.07831419e-01 -3.29823643e-01 4.92197365e-01 2.10996091e-01
-2.02340811e-01 5.35651684e-01 3.45564067e-01 -2.84854859e-01
-3.58436942e-01 -7.31695235e-01 -7.88454711e-01 -7.43625462e-01
-4.59928572e-01 3.10197562e-01 -6.67314157e-02 -1.30617442... | [6.984477519989014, 3.1182289123535156] |
621646cf-bdd1-4fde-87f2-76f5b3a8ac57 | suggestive-annotation-of-brain-mr-images-with | 2206.01014 | null | https://arxiv.org/abs/2206.01014v1 | https://arxiv.org/pdf/2206.01014v1.pdf | Suggestive Annotation of Brain MR Images with Gradient-guided Sampling | Machine learning has been widely adopted for medical image analysis in recent years given its promising performance in image segmentation and classification tasks. The success of machine learning, in particular supervised learning, depends on the availability of manually annotated datasets. For medical imaging applicat... | ['Wenjia Bai', 'Yike Guo', 'Elsa Angelini', 'Yuanhan Mo', 'Shuo Wang', 'Chengliang Dai'] | 2022-06-02 | null | null | null | null | ['brain-segmentation'] | ['medical'] | [ 6.42298102e-01 6.01864517e-01 1.67835262e-02 -7.37084985e-01
-1.11267734e+00 -1.37869850e-01 2.39953846e-01 4.80525047e-01
-1.07158458e+00 6.96311712e-01 -3.13068479e-01 -2.29249433e-01
-5.31885214e-02 -4.03669924e-01 -4.46930617e-01 -7.66712844e-01
1.95027310e-02 1.02934587e+00 5.85053027e-01 4.05496091... | [14.661480903625488, -2.3210179805755615] |
87339bd6-fc02-41ee-81c9-62169357d665 | systematic-comparison-of-neural-architectures | null | null | https://aclanthology.org/2020.emnlp-main.690 | https://aclanthology.org/2020.emnlp-main.690.pdf | Systematic Comparison of Neural Architectures and Training Approaches for Open Information Extraction | The goal of open information extraction (OIE) is to extract facts from natural language text, and to represent them as structured triples of the form {\textless}subject,predicate, object{\textgreater}. For example, given the sentence {``}Beethoven composed the Ode to Joy.{''}, we are expected to extract the triple {\te... | ['Thomas Lukasiewicz', 'Vid Kocijan', 'Frank Mtumbuka', 'Patrick Hohenecker'] | null | null | null | null | emnlp-2020-11 | ['open-information-extraction'] | ['natural-language-processing'] | [ 3.14805925e-01 5.59808314e-01 -1.26317546e-01 -2.70574868e-01
-8.66148829e-01 -8.99128914e-01 5.99419534e-01 5.39538383e-01
-7.08098829e-01 1.14348710e+00 1.36984149e-02 -4.35480863e-01
-2.96998501e-01 -8.22263181e-01 -1.08914006e+00 -3.09510052e-01
-1.08023122e-01 5.24666548e-01 -1.75177939e-02 -2.52217472... | [9.539167404174805, 8.787618637084961] |
715596c5-db79-454b-be0f-3df1935ff0ea | relate-auditory-speech-to-eeg-by-shallow-deep | 2303.10897 | null | https://arxiv.org/abs/2303.10897v1 | https://arxiv.org/pdf/2303.10897v1.pdf | Relate auditory speech to EEG by shallow-deep attention-based network | Electroencephalography (EEG) plays a vital role in detecting how brain responses to different stimulus. In this paper, we propose a novel Shallow-Deep Attention-based Network (SDANet) to classify the correct auditory stimulus evoking the EEG signal. It adopts the Attention-based Correlation Module (ACM) to discover the... | ['Dongmei Jiang', 'Yujun Wang', 'Ercheng Pei', 'Jiyao Liu', 'Lang He', 'Liyong Guo', 'Fan Cui'] | 2023-03-20 | null | null | null | null | ['deep-attention', 'eeg', 'deep-attention', 'eeg'] | ['computer-vision', 'methodology', 'natural-language-processing', 'time-series'] | [-2.36129574e-02 -5.10708332e-01 7.24877298e-01 -4.69826311e-01
-4.59104389e-01 1.26896054e-01 4.40211207e-01 5.14131561e-02
-4.44707960e-01 2.38776296e-01 5.42626262e-01 4.94645014e-02
-2.19719395e-01 -3.81713063e-01 -3.94303858e-01 -5.87214470e-01
-3.79352212e-01 -1.23630315e-01 -2.61874404e-02 -2.04937428... | [13.20036792755127, 3.4640486240386963] |
4f0b986b-78d3-48b8-b39c-eedb706db359 | to-answer-or-not-to-answer-improving-machine-1 | 2208.01299 | null | https://arxiv.org/abs/2208.01299v1 | https://arxiv.org/pdf/2208.01299v1.pdf | To Answer or Not to Answer? Improving Machine Reading Comprehension Model with Span-based Contrastive Learning | Machine Reading Comprehension with Unanswerable Questions is a difficult NLP task, challenged by the questions which can not be answered from passages. It is observed that subtle literal changes often make an answerable question unanswerable, however, most MRC models fail to recognize such changes. To address this prob... | ['Xiangang Li', 'Baochang Ma', 'Chenxiao Dou', 'Liangyu Chen', 'Yunjie Ji'] | 2022-08-02 | to-answer-or-not-to-answer-improving-machine | https://aclanthology.org/2022.findings-naacl.96 | https://aclanthology.org/2022.findings-naacl.96.pdf | findings-naacl-2022-7 | ['machine-reading-comprehension'] | ['natural-language-processing'] | [ 4.36357260e-01 2.79712826e-01 -5.39969504e-02 -4.65488672e-01
-1.32524645e+00 -1.06228006e+00 2.23195225e-01 4.01086837e-01
-3.32560718e-01 1.00385535e+00 5.40883422e-01 -5.86733222e-01
1.32919755e-02 -9.15852904e-01 -8.91246021e-01 5.23032360e-02
5.40462315e-01 2.94378817e-01 6.85888886e-01 -5.71016610... | [11.314651489257812, 8.076757431030273] |
9051f57a-8bb1-420d-bfb3-aca581595907 | focal-visual-text-attention-for-memex | null | null | https://ieeexplore.ieee.org/document/8603827 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8603827 | Focal Visual-Text Attention for Memex Question Answering | Recent insights on language and vision with neural networks have been successfully applied to simple single-image visual question answering. However, to tackle real-life question answering problems on multimedia collections such as personal photo albums, we have to look at whole collections with sequences of photos. Th... | ['Li-Jia Li', 'Yannis Kalantidis', 'Junwei Liang', 'and Alexander Hauptmann', 'Lu Jiang', 'Liangliang Cao'] | 2018-12-14 | null | null | null | ieee-transactions-on-pattern-analysis-and | ['memex-question-answering'] | ['natural-language-processing'] | [ 2.36788496e-01 3.23381759e-02 1.86514556e-01 -5.34390211e-01
-1.22351527e+00 -7.05088139e-01 6.01773739e-01 4.22799796e-01
-6.69143736e-01 5.10409176e-01 3.45644921e-01 -2.30052799e-01
2.09389910e-01 -4.81295437e-01 -1.07228851e+00 -2.55317003e-01
2.78671026e-01 7.33169377e-01 3.96183550e-01 -3.42684835... | [10.632328987121582, 1.3471585512161255] |
245235fc-92b9-4643-812a-c91ce8be6a02 | generating-large-labeled-data-sets-for | 1907.02882 | null | https://arxiv.org/abs/1907.02882v1 | https://arxiv.org/pdf/1907.02882v1.pdf | Generating large labeled data sets for laparoscopic image processing tasks using unpaired image-to-image translation | In the medical domain, the lack of large training data sets and benchmarks is often a limiting factor for training deep neural networks. In contrast to expensive manual labeling, computer simulations can generate large and fully labeled data sets with a minimum of manual effort. However, models that are trained on simu... | ['Stefanie Speidel', 'Jürgen Weitz', 'Lena Maier-Hein', 'Kurinchi Gurusamy', 'Tobias Roß', 'Sandy Engelhardt', 'Matthew J. Clarkson', 'Carina Riediger', 'Micha Pfeiffer', 'Sebastian Bodenstedt', 'Maria R. Robu', 'Isabel Funke', 'Thilo Welsch', 'Leon Strenger', 'Brian R. Davidson'] | 2019-07-05 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [ 1.78048924e-01 3.96517545e-01 1.11500651e-01 -5.80592930e-01
-7.97256231e-01 -8.60526443e-01 3.53925824e-01 9.00195241e-02
-3.81790161e-01 7.01269805e-01 -6.29146472e-02 -4.12085772e-01
3.61714333e-01 -6.18127227e-01 -1.02408731e+00 -4.45758462e-01
1.35616064e-01 7.88291693e-01 -6.86702430e-02 3.38901132... | [14.316856384277344, -2.2092673778533936] |
dde794de-c60f-464d-bfb7-13d0655c4e79 | a-unified-probabilistic-model-for-learning | 1805.09567 | null | http://arxiv.org/abs/1805.09567v1 | http://arxiv.org/pdf/1805.09567v1.pdf | A Unified Probabilistic Model for Learning Latent Factors and Their Connectivities from High-Dimensional Data | Connectivity estimation is challenging in the context of high-dimensional
data. A useful preprocessing step is to group variables into clusters, however,
it is not always clear how to do so from the perspective of connectivity
estimation. Another practical challenge is that we may have data from multiple
related classe... | ['Aapo Hyvärinen', 'Ricardo Pio Monti'] | 2018-05-24 | null | null | null | null | ['connectivity-estimation'] | ['graphs'] | [ 7.00302944e-02 7.48422742e-02 -1.37687236e-01 -3.34198684e-01
-3.82199697e-02 -6.58763111e-01 2.62458503e-01 6.39407486e-02
-1.70314521e-01 5.74283242e-01 2.80247867e-01 -2.25696415e-01
-6.25022292e-01 -7.64996409e-01 -2.81537145e-01 -9.04577374e-01
-4.63173091e-01 7.21354246e-01 -5.06826751e-02 3.63700598... | [7.154681205749512, 5.102121353149414] |
d9eaaf98-c037-4646-bbbd-103ff72a728d | real-time-document-image-classification-using | 1711.05862 | null | http://arxiv.org/abs/1711.05862v1 | http://arxiv.org/pdf/1711.05862v1.pdf | Real-Time Document Image Classification using Deep CNN and Extreme Learning Machines | This paper presents an approach for real-time training and testing for
document image classification. In production environments, it is crucial to
perform accurate and (time-)efficient training. Existing deep learning
approaches for classifying documents do not meet these requirements, as they
require much time for tra... | ['Andreas Kölsch', 'Muhammad Zeshan Afzal', 'Marcus Liwicki', 'Markus Ebbecke'] | 2017-11-03 | null | null | null | null | ['document-image-classification'] | ['computer-vision'] | [ 1.88215360e-01 -2.04276085e-01 -3.17444801e-02 -5.74726880e-01
-6.42679870e-01 -5.55547535e-01 7.22598195e-01 4.42065924e-01
-6.28961086e-01 3.04284602e-01 -5.46644330e-01 -6.78212285e-01
2.04717033e-02 -9.33348119e-01 -5.33594131e-01 -7.71692932e-01
2.12629050e-01 6.57667518e-01 -1.41071230e-02 1.63956627... | [11.440877914428711, 2.6288514137268066] |
9bdca7df-c1b5-4e3d-9841-b0fc6862d301 | bridging-anaphora-resolution-as-question | 2004.07898 | null | https://arxiv.org/abs/2004.07898v3 | https://arxiv.org/pdf/2004.07898v3.pdf | Bridging Anaphora Resolution as Question Answering | Most previous studies on bridging anaphora resolution (Poesio et al., 2004; Hou et al., 2013b; Hou, 2018a) use the pairwise model to tackle the problem and assume that the gold mention information is given. In this paper, we cast bridging anaphora resolution as question answering based on context. This allows us to fin... | ['Yufang Hou'] | 2020-04-16 | bridging-anaphora-resolution-as-question-1 | https://aclanthology.org/2020.acl-main.132 | https://aclanthology.org/2020.acl-main.132.pdf | acl-2020-6 | ['bridging-anaphora-resolution'] | ['natural-language-processing'] | [ 8.12960323e-03 7.39156067e-01 -5.46526968e-01 -3.66057485e-01
-1.50543821e+00 -7.14093745e-01 6.87119961e-01 1.63340032e-01
-3.83437812e-01 1.07178497e+00 5.88046551e-01 -1.93188295e-01
-3.81790936e-01 -9.19525981e-01 -8.47132921e-01 -1.24257416e-01
1.45280585e-01 1.31172943e+00 4.41956669e-01 -8.24490309... | [9.363304138183594, 9.49085521697998] |
fcbcc5c0-6c15-4278-95c2-27ac832412aa | fisheyesuperpoint-keypoint-detection-and | 2103.00191 | null | https://arxiv.org/abs/2103.00191v2 | https://arxiv.org/pdf/2103.00191v2.pdf | FisheyeSuperPoint: Keypoint Detection and Description Network for Fisheye Images | Keypoint detection and description is a commonly used building block in computer vision systems particularly for robotics and autonomous driving. However, the majority of techniques to date have focused on standard cameras with little consideration given to fisheye cameras which are commonly used in urban driving and a... | ['Senthil Yogamani', 'Rudi Villing', 'John McDonald', 'Ganesh Sistu', 'Ciarán Eising', 'Anna Konrad'] | 2021-02-27 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [-1.35875314e-01 -8.80584493e-02 -1.70519233e-01 -4.57651526e-01
-6.77937806e-01 -5.36588550e-01 1.05849946e+00 -2.27777194e-02
-8.21878135e-01 1.04754247e-01 -1.40725195e-01 -1.88707665e-01
1.35845646e-01 -4.97915477e-01 -8.75997722e-01 -3.09265763e-01
6.38812855e-02 3.28032762e-01 8.06888998e-01 -4.52956587... | [7.563199996948242, -2.0980358123779297] |
ad1786e6-519d-493b-94d8-6215d49c5e89 | refvsr-exploiting-reference-inputs-for | 2307.02897 | null | https://arxiv.org/abs/2307.02897v1 | https://arxiv.org/pdf/2307.02897v1.pdf | RefVSR++: Exploiting Reference Inputs for Reference-based Video Super-resolution | Smartphones equipped with a multi-camera system comprising multiple cameras with different field-of-view (FoVs) are becoming more prevalent. These camera configurations are compatible with reference-based SR and video SR, which can be executed simultaneously while recording video on the device. Thus, combining these tw... | ['Takayuki Okatani', 'Masanori Suganuma', 'Han Zou'] | 2023-07-06 | null | null | null | null | ['video-super-resolution', 'reference-based-video-super-resolution', 'super-resolution'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 5.90605676e-01 -6.07104123e-01 -1.67954385e-01 -8.26827139e-02
-8.52393508e-01 -4.54049051e-01 2.61993140e-01 -4.83728409e-01
-3.34215373e-01 3.88428748e-01 2.92135179e-01 -1.00350179e-01
-5.14595062e-02 -5.09835482e-01 -6.90862298e-01 -7.02858329e-01
2.85536736e-01 -5.20493805e-01 6.10148787e-01 -1.55191779... | [10.992293357849121, -2.0365986824035645] |
5fc618f6-f069-40a0-a848-5ea01ebced70 | differentiable-multi-granularity-human | 2103.04570 | null | https://arxiv.org/abs/2103.04570v1 | https://arxiv.org/pdf/2103.04570v1.pdf | Differentiable Multi-Granularity Human Representation Learning for Instance-Aware Human Semantic Parsing | To address the challenging task of instance-aware human part parsing, a new bottom-up regime is proposed to learn category-level human semantic segmentation as well as multi-person pose estimation in a joint and end-to-end manner. It is a compact, efficient and powerful framework that exploits structural information ov... | ['Luc van Gool', 'Yi Yang', 'Si Liu', 'Wenguan Wang', 'Tianfei Zhou'] | 2021-03-08 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Zhou_Differentiable_Multi-Granularity_Human_Representation_Learning_for_Instance-Aware_Human_Semantic_Parsing_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Zhou_Differentiable_Multi-Granularity_Human_Representation_Learning_for_Instance-Aware_Human_Semantic_Parsing_CVPR_2021_paper.pdf | cvpr-2021-1 | ['human-parsing'] | ['computer-vision'] | [ 3.65879059e-01 5.38097262e-01 -8.81048851e-03 -6.51976228e-01
-1.03271246e+00 -3.32300425e-01 3.53065878e-01 -7.78933018e-02
-5.24732649e-01 4.00829196e-01 3.69529963e-01 4.11194801e-01
-1.63558483e-01 -5.50059140e-01 -8.67834568e-01 -4.54091758e-01
6.86229169e-02 1.06218576e+00 2.82746583e-01 5.80276363... | [8.163106918334961, -0.238815039396286] |
53dab514-34d2-465a-9dcf-545292e59d1d | a-multi-task-learning-framework-for-sound | 2305.10729 | null | https://arxiv.org/abs/2305.10729v1 | https://arxiv.org/pdf/2305.10729v1.pdf | A Multi-Task Learning Framework for Sound Event Detection using High-level Acoustic Characteristics of Sounds | Sound event detection (SED) entails identifying the type of sound and estimating its temporal boundaries from acoustic signals. These events are uniquely characterized by their spatio-temporal features, which are determined by the way they are produced. In this study, we leverage some distinctive high-level acoustic ch... | ['Rohan Kumar Das', 'Tanmay Khandelwal'] | 2023-05-18 | null | null | null | null | ['sound-event-detection'] | ['audio'] | [-3.39633077e-02 -6.78618670e-01 3.90964746e-01 -2.17807963e-01
-1.59590614e+00 -7.40562320e-01 3.98451746e-01 2.32531682e-01
-6.67202771e-01 1.57776222e-01 3.25246900e-01 -8.73925444e-03
2.11572219e-02 -4.41561878e-01 -5.78890264e-01 -6.84900463e-01
-4.07194346e-01 -9.29965898e-02 4.65760291e-01 3.72711480... | [15.19951057434082, 5.182312965393066] |
e8fb4f30-b0da-4d54-b304-19eafaec0ce0 | universal-successor-representations-for | 1804.03758 | null | http://arxiv.org/abs/1804.03758v1 | http://arxiv.org/pdf/1804.03758v1.pdf | Universal Successor Representations for Transfer Reinforcement Learning | The objective of transfer reinforcement learning is to generalize from a set
of previous tasks to unseen new tasks. In this work, we focus on the transfer
scenario where the dynamics among tasks are the same, but their goals differ.
Although general value function (Sutton et al., 2011) has been shown to be
useful for k... | ['Yoshua Bengio', 'Junfeng Wen', 'Chen Ma'] | 2018-04-11 | null | null | null | null | ['transfer-reinforcement-learning'] | ['methodology'] | [ 3.58961225e-01 2.61442333e-01 -5.56637766e-03 -5.23185730e-02
-3.96834403e-01 -6.94599271e-01 8.00267398e-01 -5.46529368e-02
-5.13234735e-01 1.30197716e+00 -8.60631987e-02 -8.33319649e-02
-1.08254127e-01 -8.46398950e-01 -1.09550679e+00 -7.35480070e-01
-1.44540995e-01 5.90671480e-01 4.40597773e-01 -5.99458277... | [4.1726884841918945, 1.4765859842300415] |
548b5aa4-f317-40eb-bb3c-49645f9cf7d3 | st360iq-no-reference-omnidirectional-image | 2303.06907 | null | https://arxiv.org/abs/2303.06907v1 | https://arxiv.org/pdf/2303.06907v1.pdf | ST360IQ: No-Reference Omnidirectional Image Quality Assessment with Spherical Vision Transformers | Omnidirectional images, aka 360 images, can deliver immersive and interactive visual experiences. As their popularity has increased dramatically in recent years, evaluating the quality of 360 images has become a problem of interest since it provides insights for capturing, transmitting, and consuming this new media. Ho... | ['Aykut Erdem', 'Erkut Erdem', 'Cagri Ozcinar', 'Nevrez Imamoglu', 'Mohamed Hedi Elfkir', 'Nafiseh Jabbari Tofighi'] | 2023-03-13 | null | null | null | null | ['image-quality-assessment'] | ['computer-vision'] | [ 4.62811999e-02 -1.91004202e-01 4.53651249e-02 -2.94283032e-01
-6.36228740e-01 -4.16975290e-01 4.12729532e-01 2.65962481e-02
-1.75755858e-01 3.25143486e-01 4.56590265e-01 -1.48149207e-01
-2.09448606e-01 -7.91155875e-01 -6.86497152e-01 -4.30144042e-01
1.13844415e-02 -1.84403136e-01 3.25979799e-01 -3.27428907... | [11.753669738769531, -1.9467461109161377] |
4509b85b-879c-4960-ab3a-4deca69fa3f1 | tf-coder-program-synthesis-for-tensor | 2003.09040 | null | https://arxiv.org/abs/2003.09040v4 | https://arxiv.org/pdf/2003.09040v4.pdf | TF-Coder: Program Synthesis for Tensor Manipulations | The success and popularity of deep learning is on the rise, partially due to powerful deep learning frameworks such as TensorFlow and PyTorch that make it easier to develop deep learning models. However, these libraries also come with steep learning curves, since programming in these frameworks is quite different from ... | ['David Bieber', 'Rishabh Singh', 'Kensen Shi'] | 2020-03-19 | null | https://openreview.net/forum?id=nJ5Ij53umw2 | https://openreview.net/pdf?id=nJ5Ij53umw2 | neurips-workshop-cap-2020-12 | ['enumerative-search'] | ['computer-code'] | [-4.17617947e-01 -3.40600997e-01 -2.16669932e-01 -6.75069988e-01
-9.85965803e-02 -6.75541639e-01 1.93881169e-01 3.90296668e-01
-5.30954421e-01 8.67253318e-02 2.12263748e-01 -7.63544500e-01
-3.01110476e-01 -7.15560138e-01 -4.15842295e-01 -8.71491432e-02
-5.49088478e-01 4.62449789e-01 1.68133482e-01 -1.20719083... | [8.398140907287598, 3.4931483268737793] |
8ea9ed71-a60f-4bfb-8575-b2314a9a4ccf | blind-estimation-of-room-acoustic-parameters-1 | 2212.13009 | null | https://arxiv.org/abs/2212.13009v1 | https://arxiv.org/pdf/2212.13009v1.pdf | Blind estimation of room acoustic parameters from speech signals based on extended model of room impulse response | The speech transmission index (STI) and room acoustic parameters (RAPs), which are derived from a room impulse response (RIR), such as reverberation time and early decay time, are essential to assess speech transmission and to predict the listening difficulty in a sound field. Since it is difficult to measure RIR in da... | ['Masashi Unoki', 'Suradej Duangpummet', 'Lijun Wang'] | 2022-12-26 | null | null | null | null | ['room-impulse-response'] | ['audio'] | [ 1.25780210e-01 -9.49755669e-01 1.04916215e+00 -1.76421732e-01
-1.17174220e+00 -4.86931443e-01 1.33110955e-01 -1.74947500e-01
-2.63236165e-01 6.24873698e-01 6.26612186e-01 -4.68628347e-01
-3.58747661e-01 -3.02722275e-01 -5.20966686e-02 -9.72388029e-01
-3.63084018e-01 -3.57142538e-01 3.22541557e-02 -1.03305534... | [15.136185646057129, 5.765960693359375] |
a2c95bb8-1896-49a7-959b-eb01adae402e | combining-representation-learning-with-logic | 1712.09687 | null | http://arxiv.org/abs/1712.09687v1 | http://arxiv.org/pdf/1712.09687v1.pdf | Combining Representation Learning with Logic for Language Processing | The current state-of-the-art in many natural language processing and
automated knowledge base completion tasks is held by representation learning
methods which learn distributed vector representations of symbols via
gradient-based optimization. They require little or no hand-crafted features,
thus avoiding the need for... | ['Tim Rocktäschel'] | 2017-12-27 | null | null | null | null | ['formal-logic'] | ['reasoning'] | [ 1.94804475e-01 4.68366027e-01 -6.39709592e-01 -8.43548179e-01
-5.87218821e-01 -6.02077186e-01 7.00990140e-01 6.61300600e-01
-4.56094563e-01 1.10412943e+00 1.08560938e-02 -6.81028187e-01
-2.06399381e-01 -8.84549975e-01 -6.09276891e-01 -2.54740089e-01
5.25144227e-02 8.46779346e-01 -5.88277839e-02 -3.83521199... | [9.279653549194336, 7.482687950134277] |
2d92ccc5-533f-4f9a-a807-ec640560b72e | survey-on-various-gesture-recognition | 1012.00084 | null | http://arxiv.org/abs/1012.0084v1 | http://arxiv.org/pdf/1012.0084v1.pdf | Survey on Various Gesture Recognition Techniques for Interfacing Machines Based on Ambient Intelligence | Gesture recognition is mainly apprehensive on analyzing the functionality of
human wits. The main goal of gesture recognition is to create a system which
can recognize specific human gestures and use them to convey information or for
device control. Hand gestures provide a separate complementary modality to
speech for ... | ['Naveen Lakshmikhanth', 'Karthik R. Shastry', 'Manoj Ravindran', 'Harshith C', 'M. V. V. N. S. Srikanth'] | 2010-12-01 | null | null | null | null | ['contour-detection'] | ['computer-vision'] | [-8.41368064e-02 -1.53042197e-01 -2.24409327e-01 -2.79043198e-01
2.93850064e-01 -6.03203416e-01 5.67019224e-01 -4.36019629e-01
-4.63002056e-01 2.17450336e-01 2.04245389e-01 1.15147326e-02
3.08122896e-02 -4.49079275e-01 1.53837025e-01 -9.73328531e-01
5.20996034e-01 1.78708807e-01 1.86592460e-01 -2.49069810... | [6.506170272827148, -0.2459171861410141] |
7173c5b5-fcf8-4821-b27c-b5ec2e03c78e | api2com-on-the-improvement-of-automatically | 2103.10668 | null | https://arxiv.org/abs/2103.10668v1 | https://arxiv.org/pdf/2103.10668v1.pdf | API2Com: On the Improvement of Automatically Generated Code Comments Using API Documentations | Code comments can help in program comprehension and are considered as important artifacts to help developers in software maintenance. However, the comments are mostly missing or are outdated, specially in complex software projects. As a result, several automatic comment generation models are developed as a solution. Th... | ['Fatemeh H. Fard', 'Rishab Sharma', 'Ramin Shahbazi'] | 2021-03-19 | null | null | null | null | ['comment-generation'] | ['natural-language-processing'] | [ 8.51196125e-02 2.95058578e-01 -1.35703549e-01 -2.17656195e-01
-7.35418797e-01 -7.40834832e-01 5.25510073e-01 2.75056362e-01
-1.15543909e-01 5.23386598e-01 3.36704075e-01 -5.09510040e-01
2.61362255e-01 -7.58956909e-01 -7.59612024e-01 -1.47404626e-01
4.94120747e-01 -4.10733968e-02 2.05686450e-01 -2.80326515... | [7.709213733673096, 7.897206783294678] |
100b24d9-b211-45a6-9373-a27c95ca9894 | parsing-to-1-endpoint-crossing-pagenumber-2 | null | null | https://aclanthology.org/P17-1193 | https://aclanthology.org/P17-1193.pdf | Parsing to 1-Endpoint-Crossing, Pagenumber-2 Graphs | We study the Maximum Subgraph problem in deep dependency parsing. We consider two restrictions to deep dependency graphs: (a) 1-endpoint-crossing and (b) pagenumber-2. Our main contribution is an exact algorithm that obtains maximum subgraphs satisfying both restrictions simultaneously in time O(n5). Moreover, ignoring... | ['Weiwei Sun', 'Sheng Huang', 'Junjie Cao', 'Xiaojun Wan'] | 2017-07-01 | null | null | null | acl-2017-7 | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [-8.17373767e-02 7.52324402e-01 -3.26383412e-01 -5.95123053e-01
-1.05858588e+00 -9.15901303e-01 1.46693110e-01 4.44088280e-01
-5.36856711e-01 8.94988477e-01 -2.70488225e-02 -6.66774809e-01
7.96010252e-03 -8.68844748e-01 -6.08662784e-01 -5.50577462e-01
-4.10241812e-01 7.59262264e-01 6.78221345e-01 -1.98998645... | [10.296298027038574, 9.68644905090332] |
9643d681-9640-456f-8a12-874535b3561d | a-joint-convolutional-neural-networks-and | 1905.01574 | null | https://arxiv.org/abs/1905.01574v1 | https://arxiv.org/pdf/1905.01574v1.pdf | A Joint Convolutional Neural Networks and Context Transfer for Street Scenes Labeling | Street scene understanding is an essential task for autonomous driving. One important step towards this direction is scene labeling, which annotates each pixel in the images with a correct class label. Although many approaches have been developed, there are still some weak points. Firstly, many methods are based on the... | ['Junyu. Gao', 'Qi. Wang', 'Yuan Yuan'] | 2019-05-05 | null | null | null | null | ['scene-labeling'] | ['computer-vision'] | [ 5.47439992e-01 1.26920134e-01 -5.17637789e-01 -6.74035072e-01
-4.32514757e-01 -1.32910535e-01 5.38377404e-01 -6.32893369e-02
-3.36619526e-01 7.01034009e-01 -4.24236357e-02 -2.22501025e-01
1.13134302e-01 -1.12044585e+00 -8.19242060e-01 -6.92130923e-01
3.23187947e-01 1.39475748e-01 8.43701780e-01 -1.18050063... | [9.50399112701416, -0.4539680778980255] |
ebae8736-3ed7-4eca-ae8e-740147df3ed8 | object-level-targeted-selection-via-deep | 2207.01778 | null | https://arxiv.org/abs/2207.01778v1 | https://arxiv.org/pdf/2207.01778v1.pdf | Object-Level Targeted Selection via Deep Template Matching | Retrieving images with objects that are semantically similar to objects of interest (OOI) in a query image has many practical use cases. A few examples include fixing failures like false negatives/positives of a learned model or mitigating class imbalance in a dataset. The targeted selection task requires finding the r... | ['Christoph Angerer', 'Jose M. Alvarez', 'Elmar Haussmann', 'Michele Fenzi', 'Donna Roy', 'Suraj Kothawade'] | 2022-07-05 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [ 4.22805011e-01 -4.90719117e-02 -4.16240036e-01 -6.25082612e-01
-9.75227594e-01 -6.36830866e-01 2.66429514e-01 1.52516291e-01
-5.26277363e-01 2.73265392e-01 -1.84320942e-01 4.65030149e-02
-4.61660177e-01 -8.61130357e-01 -9.50464785e-01 -4.88090008e-01
3.43797840e-02 7.04601407e-01 5.78594565e-01 3.96578237... | [9.556206703186035, 1.6255439519882202] |
103ca730-6880-4354-bd6c-2b1283d04de9 | multi-scale-graphical-models-for-spatio | null | null | http://papers.nips.cc/paper/5473-multi-scale-graphical-models-for-spatio-temporal-processes | http://papers.nips.cc/paper/5473-multi-scale-graphical-models-for-spatio-temporal-processes.pdf | Multi-scale Graphical Models for Spatio-Temporal Processes | Learning the dependency structure between spatially distributed observations of a spatio-temporal process is an important problem in many fields such as geology, geophysics, atmospheric sciences, oceanography, etc. . However, estimation of such systems is complicated by the fact that they exhibit dynamics at multiple s... | ['Firdaus Janoos', 'Niranjan Subrahmanya', 'Huseyin Denli'] | 2014-12-01 | null | null | null | neurips-2014-12 | ['geophysics'] | ['miscellaneous'] | [-1.09273940e-01 -4.52204853e-01 4.11130279e-01 -1.34267509e-01
-1.47242367e-01 -6.13579690e-01 9.74896431e-01 3.11452806e-01
1.68897659e-02 9.54166234e-01 3.67085189e-01 -5.74917316e-01
-9.19463754e-01 -7.38507450e-01 -4.93957400e-01 -9.60430741e-01
-9.16189075e-01 7.04952717e-01 2.15824962e-01 -1.95035145... | [6.651901721954346, 3.499920129776001] |
37ce6478-8e01-4ec4-ab68-7fa812e068e0 | i2c2w-image-to-character-to-word-transformers | 2105.08383 | null | https://arxiv.org/abs/2105.08383v3 | https://arxiv.org/pdf/2105.08383v3.pdf | I2C2W: Image-to-Character-to-Word Transformers for Accurate Scene Text Recognition | Leveraging the advances of natural language processing, most recent scene text recognizers adopt an encoder-decoder architecture where text images are first converted to representative features and then a sequence of characters via `sequential decoding'. However, scene text images suffer from rich noises of different s... | ['Song Bai', 'Shijian Lu', 'Wenqing Zhang', 'Jiaxing Huang', 'Changhu Wang', 'Chuhui Xue'] | 2021-05-18 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 1.00844765e+00 -7.85029769e-01 2.87764698e-01 -4.14887607e-01
-9.04018700e-01 -5.89784801e-01 9.83737886e-01 1.55214518e-01
-4.42655414e-01 1.01829678e-01 1.72953695e-01 -5.09835072e-02
4.51577961e-01 -4.46695507e-01 -7.86942065e-01 -8.17983091e-01
6.90403581e-01 4.28392172e-01 6.38648689e-01 -7.85268769... | [11.92921257019043, 2.2532551288604736] |
29ce0c66-2032-41f4-bd10-67f7cbc77b9b | geo-nav-a-geometric-dataset-of-voltage-gated | 2306.12348 | null | https://arxiv.org/abs/2306.12348v1 | https://arxiv.org/pdf/2306.12348v1.pdf | GEO-Nav: a geometric dataset of voltage-gated sodium channels | Voltage-gated sodium (Nav) channels constitute a prime target for drug design and discovery, given their implication in various diseases such as epilepsy, migraine and ataxia to name a few. In this regard, performing morphological analysis is a crucial step in comprehensively understanding their biological function and... | ['Silvia Biasotti', 'Ulderico Fugacci', 'Andrea Raffo'] | 2023-06-21 | null | null | null | null | ['morphological-analysis'] | ['natural-language-processing'] | [ 3.25278848e-01 -4.24665719e-01 1.41145751e-01 -1.45116568e-01
-5.76171517e-01 -8.11878860e-01 3.74987930e-01 8.13442349e-01
-5.66689909e-01 1.05912173e+00 -2.10519597e-01 -7.19633937e-01
-1.60745814e-01 -5.51543593e-01 -3.95572037e-01 -9.46843684e-01
-4.75166082e-01 5.14576733e-01 1.82200298e-01 -2.40704045... | [13.394696235656738, -3.0392699241638184] |
532a91e3-151e-4f5d-882d-036efd55e901 | a-unifying-view-on-task-oriented-dialogue | null | null | https://aclanthology.org/2022.lrec-1.137 | https://aclanthology.org/2022.lrec-1.137.pdf | A Unifying View On Task-oriented Dialogue Annotation | Every model is only as strong as the data that it is trained on. In this paper, we present a new dataset, obtained by merging four publicly available annotated corpora for task-oriented dialogues in several domains (MultiWOZ 2.2, CamRest676, DSTC2 and Schema-Guided Dialogue Dataset). This way, we assess the feasibility... | ['Ondřej Dušek', 'Patrick Paroubek', 'Daniel Stancl', 'Leon-paul Schaub', 'Vojtěch Hudeček'] | null | null | null | null | lrec-2022-6 | ['dialogue-state-tracking'] | ['natural-language-processing'] | [-1.51575133e-01 8.59079301e-01 -2.81256363e-02 -2.72434831e-01
-8.31493556e-01 -8.88298333e-01 1.21228480e+00 4.09006178e-01
-5.99854112e-01 1.17881584e+00 7.55889177e-01 -2.51942396e-01
-3.20287831e-02 -4.60368931e-01 2.91191973e-02 -2.77601238e-02
1.88325748e-01 1.05443001e+00 4.23313379e-01 -1.04768574... | [12.77100944519043, 7.983251571655273] |
1975bf9a-f168-420a-b77e-e9fbe005bdf0 | generalised-image-outpainting-with-u | 2201.11403 | null | https://arxiv.org/abs/2201.11403v5 | https://arxiv.org/pdf/2201.11403v5.pdf | Generalised Image Outpainting with U-Transformer | In this paper, we develop a novel transformer-based generative adversarial neural network called U-Transformer for generalised image outpainting problem. Different from most present image outpainting methods conducting horizontal extrapolation, our generalised image outpainting could extrapolate visual context all-side... | ['Yujie Geng', 'John Y. Goulermas', 'Yuyao Yan', 'Kaizhu Huang', 'Rui Zhang', 'Xi Yang', 'Penglei Gao'] | 2022-01-27 | null | null | null | null | ['image-outpainting'] | ['computer-vision'] | [ 5.53382099e-01 5.08782923e-01 1.14479966e-01 -2.72204638e-01
-7.86459267e-01 -4.09897119e-01 4.85332757e-01 -5.37696362e-01
8.80732685e-02 9.16114807e-01 1.18345812e-01 -2.08094314e-01
3.67280364e-01 -9.15013671e-01 -1.46398664e+00 -6.25104427e-01
3.51578087e-01 2.41328657e-01 1.62059948e-01 -3.23306561... | [11.497297286987305, -0.9492271542549133] |
089f0c9d-9ecd-4043-b824-e6bfda0cf1a7 | multitask-learning-for-low-resource-spoken | 2211.13703 | null | https://arxiv.org/abs/2211.13703v1 | https://arxiv.org/pdf/2211.13703v1.pdf | Multitask Learning for Low Resource Spoken Language Understanding | We explore the benefits that multitask learning offer to speech processing as we train models on dual objectives with automatic speech recognition and intent classification or sentiment classification. Our models, although being of modest size, show improvements over models trained end-to-end on intent classification. ... | ['Hugo Van hamme', 'Marie-Francine Moens', 'Quentin Meeus'] | 2022-11-24 | null | null | null | null | ['spoken-language-understanding', 'intent-classification', 'spoken-language-understanding'] | ['natural-language-processing', 'natural-language-processing', 'speech'] | [ 1.69168770e-01 3.17561775e-01 -1.87364951e-01 -9.52607334e-01
-1.58452010e+00 -5.50203323e-01 7.41541803e-01 -7.02561662e-02
-1.00215173e+00 3.47392201e-01 6.86992407e-01 -4.68450665e-01
2.96002746e-01 -2.32256763e-02 -4.15205866e-01 -4.66666996e-01
9.30654705e-02 6.26832545e-01 -8.11145604e-02 -8.69382545... | [14.096991539001465, 6.956182479858398] |
35c83885-532a-4097-9a6a-47f58b8bf8db | multi-person-implicit-reconstruction-from-a | 2104.09283 | null | https://arxiv.org/abs/2104.09283v1 | https://arxiv.org/pdf/2104.09283v1.pdf | Multi-person Implicit Reconstruction from a Single Image | We present a new end-to-end learning framework to obtain detailed and spatially coherent reconstructions of multiple people from a single image. Existing multi-person methods suffer from two main drawbacks: they are often model-based and therefore cannot capture accurate 3D models of people with loose clothing and hair... | ['Adrian Hilton', 'Lourdes Agapito', 'Akin Caliskan', 'Armin Mustafa'] | 2021-04-19 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Mustafa_Multi-Person_Implicit_Reconstruction_From_a_Single_Image_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Mustafa_Multi-Person_Implicit_Reconstruction_From_a_Single_Image_CVPR_2021_paper.pdf | cvpr-2021-1 | ['3d-human-reconstruction'] | ['computer-vision'] | [-1.11868002e-01 -3.50628793e-01 4.99092937e-01 -3.51956367e-01
-8.21551800e-01 -4.02236462e-01 2.78968960e-01 -4.34391886e-01
-1.60907537e-01 6.54518604e-01 3.78858328e-01 5.88035762e-01
1.73363104e-01 -3.74110699e-01 -7.16036737e-01 -3.90502214e-01
9.52054933e-02 1.12679362e+00 1.65962443e-01 -1.29781708... | [7.1546630859375, -1.1513675451278687] |
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