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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
bedae3bf-97bb-4ee3-83a4-4ae995c45841 | mtgflow-unsupervised-multivariate-time-series | 2208.02108 | null | https://arxiv.org/abs/2208.02108v3 | https://arxiv.org/pdf/2208.02108v3.pdf | Detecting Multivariate Time Series Anomalies with Zero Known Label | Multivariate time series anomaly detection has been extensively studied under the semi-supervised setting, where a training dataset with all normal instances is required. However, preparing such a dataset is very laborious since each single data instance should be fully guaranteed to be normal. It is, therefore, desire... | ['Wenchao Meng', 'Shibo He', 'Haoyu Liu', 'Jiming Chen', 'Qihang Zhou'] | 2022-08-03 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [-0.09098624 -0.06473374 0.0522949 -0.34641436 -0.44929737 -0.4874422
0.48225737 0.7311957 -0.01152911 0.38658112 -0.22248608 -0.3879252
-0.26564574 -0.9054872 -0.56953806 -0.89024776 -0.5621235 0.54276323
0.0421562 0.04174276 -0.03845495 0.389207 -1.3427432 -0.28999922
1.259354 1.1539416 -0.36... | [7.322405815124512, 2.7496509552001953] |
f1eda516-f966-4720-9209-d2ff6430ceee | metaphysica-ood-robustness-in-physics | 2303.03181 | null | https://arxiv.org/abs/2303.03181v1 | https://arxiv.org/pdf/2303.03181v1.pdf | MetaPhysiCa: OOD Robustness in Physics-informed Machine Learning | A fundamental challenge in physics-informed machine learning (PIML) is the design of robust PIML methods for out-of-distribution (OOD) forecasting tasks. These OOD tasks require learning-to-learn from observations of the same (ODE) dynamical system with different unknown ODE parameters, and demand accurate forecasts ev... | ['Bruno Ribeiro', 'Muhammad Ashraful Alam', 'S Chandra Mouli'] | 2023-03-06 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [-2.98522860e-01 2.53746778e-01 -3.27518016e-01 -2.58699924e-01
-8.94172132e-01 -4.27406251e-01 1.25901413e+00 1.87137559e-01
1.94454640e-01 8.24536204e-01 3.51171166e-01 -5.05739272e-01
-5.82969427e-01 -5.15802145e-01 -9.24915910e-01 -6.91881061e-01
-5.14903545e-01 9.82294679e-01 -1.21129796e-01 4.88275290... | [6.912353038787842, 3.785682201385498] |
1cd693f8-3fe3-4332-acaf-94568602fb1a | speech-reconstruction-with-reminiscent-sound | null | null | https://ieeexplore.ieee.org/document/9618777 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9618777 | Speech Reconstruction with Reminiscent Sound via Visual Voice Memory | The goal of this work is to reconstruct speech from silent video, in both speaker dependent and independent ways. Unlike previous works that have been mostly restricted to a speaker dependent setting, we propose Visual Voice memory to restore essential auditory information to generate proper speech from different speak... | ['Yong Man Ro', 'Se Jin Park', 'Minsu Kim', 'Joanna Hong'] | 2021-11-17 | null | null | null | ieee-acm-transactions-on-audio-speech-and-5 | ['speaker-specific-lip-to-speech-synthesis'] | ['computer-vision'] | [ 1.35163367e-01 2.14561243e-02 -1.50198400e-01 -9.09176096e-02
-8.93071592e-01 -4.27288324e-01 4.72302407e-01 -5.96015453e-02
-9.79254320e-02 5.94192147e-01 3.22108954e-01 -1.48344517e-01
8.16319138e-02 -7.39059627e-01 -9.72935200e-01 -6.87271535e-01
1.80080935e-01 3.58090643e-03 3.20307910e-01 6.74216524... | [14.352807998657227, 5.060187816619873] |
46b59dd4-3139-4392-bc20-4b5f9829f7e6 | safe-exploration-incurs-nearly-no-additional | 2206.14057 | null | https://arxiv.org/abs/2206.14057v3 | https://arxiv.org/pdf/2206.14057v3.pdf | Safe Exploration Incurs Nearly No Additional Sample Complexity for Reward-free RL | Reward-free reinforcement learning (RF-RL), a recently introduced RL paradigm, relies on random action-taking to explore the unknown environment without any reward feedback information. While the primary goal of the exploration phase in RF-RL is to reduce the uncertainty in the estimated model with minimum number of tr... | ['Yingbin Liang', 'Jing Yang', 'Ruiquan Huang'] | 2022-06-28 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 1.18524820e-01 4.63846415e-01 -5.13760626e-01 2.01793656e-01
-1.03793991e+00 -8.79425347e-01 4.64752942e-01 2.08144754e-01
-6.47549272e-01 1.12169421e+00 5.33665344e-02 -4.76362079e-01
-8.14632952e-01 -9.00567770e-01 -8.95726204e-01 -9.47409332e-01
-4.38159674e-01 5.30409694e-01 2.90501455e-04 -2.12079078... | [4.395223617553711, 2.504487991333008] |
3db8a78f-29e2-450e-8515-493abdeaf2be | nonlinear-trend-removal-should-be-carefully | 1605.05891 | null | http://arxiv.org/abs/1605.05891v1 | http://arxiv.org/pdf/1605.05891v1.pdf | Nonlinear trend removal should be carefully performed in heart rate variability analysis | $\bullet$ Background : In Heart rate variability analysis, the rate-rate time
series suffer often from aperiodic non-stationarity, presence of ectopic beats
etc. It would be hard to extract helpful information from the original signals.
10 $\bullet$ Problem : Trend removal methods are commonly practiced to reduce
the i... | [] | 2016-05-19 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [-1.09268732e-01 -5.39239883e-01 2.79648483e-01 -3.39782871e-02
1.36784017e-01 -6.65301383e-01 2.16489464e-01 -2.27493137e-01
-1.84296697e-01 1.16352522e+00 -2.83225439e-02 -3.48902404e-01
-5.69872618e-01 -4.85663325e-01 8.70499387e-02 -8.68010879e-01
-8.38263631e-01 8.78901258e-02 -9.28565040e-02 -2.94504672... | [14.07344913482666, 3.1172266006469727] |
797128e7-2bca-42c9-86e1-b925e6c0acbc | eel-efficiently-encoding-lattices-for | 2306.00947 | null | https://arxiv.org/abs/2306.00947v1 | https://arxiv.org/pdf/2306.00947v1.pdf | EEL: Efficiently Encoding Lattices for Reranking | Standard decoding approaches for conditional text generation tasks typically search for an output hypothesis with high model probability, but this may not yield the best hypothesis according to human judgments of quality. Reranking to optimize for "downstream" metrics can better optimize for quality, but many metrics o... | ['Greg Durrett', 'Xi Ye', 'Jiacheng Xu', 'Prasann Singhal'] | 2023-06-01 | null | null | null | null | ['conditional-text-generation'] | ['natural-language-processing'] | [ 5.67095280e-01 5.59906900e-01 -1.30415335e-01 -2.46905267e-01
-1.55619347e+00 -7.16207683e-01 8.97441208e-01 4.47506011e-01
-4.75848258e-01 1.09101605e+00 8.73473227e-01 -4.08941031e-01
3.68181020e-01 -7.96665192e-01 -7.05784082e-01 -2.98846066e-01
-3.72547619e-02 8.91660631e-01 3.81937295e-01 -3.83813173... | [11.720924377441406, 9.086136817932129] |
87e08da3-ac7d-44cc-8689-aad55b66de49 | sa-text-simple-but-accurate-detector-for-text | 1911.07046 | null | https://arxiv.org/abs/1911.07046v3 | https://arxiv.org/pdf/1911.07046v3.pdf | A method for detecting text of arbitrary shapes in natural scenes that improves text spotting | Understanding the meaning of text in images of natural scenes like highway signs or store front emblems is particularly challenging if the text is foreshortened in the image or the letters are artistically distorted. We introduce a pipeline-based text spotting framework that can both detect and recognize text in variou... | ['Margrit Betke', 'Yi Zheng', 'Qitong Wang'] | 2019-11-16 | null | null | null | null | ['text-spotting'] | ['computer-vision'] | [ 4.66784239e-01 -3.57322454e-01 2.48699903e-01 -1.96693882e-01
-7.55427599e-01 -7.37479389e-01 8.87549400e-01 -2.34458193e-01
-1.02722801e-01 3.11183487e-03 2.19401047e-02 -5.65103590e-01
3.74314725e-01 -6.62425816e-01 -7.24828362e-01 -3.91630441e-01
6.61820233e-01 7.57895052e-01 6.34951711e-01 -2.54672378... | [12.009273529052734, 2.1955580711364746] |
67cc16ca-7951-45c2-a8d8-1c6cabf5bb2c | enhancing-task-bot-engagement-with | 2212.10008 | null | https://arxiv.org/abs/2212.10008v1 | https://arxiv.org/pdf/2212.10008v1.pdf | Enhancing Task Bot Engagement with Synthesized Open-Domain Dialog | Many efforts have been made to construct dialog systems for different types of conversations, such as task-oriented dialog (TOD) and open-domain dialog (ODD). To better mimic human-level conversations that usually fuse various dialog modes, it is essential to build a system that can effectively handle both TOD and ODD ... | ['Zhu Zhang', 'Jianfeng Gao', 'Michel Galley', 'Baolin Peng', 'Miaoran Li'] | 2022-12-20 | null | null | null | null | ['open-domain-dialog'] | ['natural-language-processing'] | [-3.97944182e-01 4.19102788e-01 5.33659607e-02 -4.33840871e-01
-5.70005476e-01 -8.97768617e-01 9.31697190e-01 -2.17474490e-01
-1.12315372e-01 1.06296086e+00 5.54945529e-01 -4.27968323e-01
-1.46388337e-01 -6.88400269e-01 2.73538768e-01 -1.02453426e-01
6.76108599e-01 9.05803442e-01 6.11498237e-01 -7.54976869... | [12.757184982299805, 8.0005521774292] |
b0d4c104-f7ed-4ea5-9645-1f736ea66b41 | boosting-high-level-vision-with-joint | 2010.08919 | null | https://arxiv.org/abs/2010.08919v2 | https://arxiv.org/pdf/2010.08919v2.pdf | Boosting High-Level Vision with Joint Compression Artifacts Reduction and Super-Resolution | Due to the limits of bandwidth and storage space, digital images are usually down-scaled and compressed when transmitted over networks, resulting in loss of details and jarring artifacts that can lower the performance of high-level visual tasks. In this paper, we aim to generate an artifact-free high-resolution image f... | ['Jan P. Allebach', 'Qian Lin', 'Xiaoyu Xiang'] | 2020-10-18 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 7.39101887e-01 -5.04572451e-01 1.23736240e-01 -2.48322859e-01
-9.31186199e-01 -2.47369613e-02 4.50321823e-01 -4.35302049e-01
-3.93581241e-01 4.62753922e-01 1.48759067e-01 7.11031705e-02
1.00938819e-01 -9.18036938e-01 -8.78743052e-01 -5.57926357e-01
4.32297796e-01 -4.70345803e-02 2.29427859e-01 -3.94936986... | [11.271931648254395, -1.9614120721817017] |
55d53fa7-070c-4988-af56-97ccd968961c | proxyformer-proxy-alignment-assisted-point | 2302.14435 | null | https://arxiv.org/abs/2302.14435v1 | https://arxiv.org/pdf/2302.14435v1.pdf | ProxyFormer: Proxy Alignment Assisted Point Cloud Completion with Missing Part Sensitive Transformer | Problems such as equipment defects or limited viewpoints will lead the captured point clouds to be incomplete. Therefore, recovering the complete point clouds from the partial ones plays an vital role in many practical tasks, and one of the keys lies in the prediction of the missing part. In this paper, we propose a no... | ['Mingqiang Wei', 'Xiaoyang Tan', 'Pan Gao', 'Shanshan Li'] | 2023-02-28 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_ProxyFormer_Proxy_Alignment_Assisted_Point_Cloud_Completion_With_Missing_Part_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_ProxyFormer_Proxy_Alignment_Assisted_Point_Cloud_Completion_With_Missing_Part_CVPR_2023_paper.pdf | cvpr-2023-1 | ['point-cloud-completion'] | ['computer-vision'] | [-6.56252950e-02 -1.10186785e-01 -1.23680644e-02 -3.96018296e-01
-8.23466301e-01 -4.51765627e-01 2.34917611e-01 -4.61945646e-02
8.69487002e-02 4.39143807e-01 2.23485783e-01 1.50716260e-01
-7.21645132e-02 -9.37697887e-01 -1.07958376e+00 -6.20142281e-01
3.36089671e-01 9.19496357e-01 5.20577312e-01 -1.29299834... | [8.31652545928955, -3.551234722137451] |
54621054-1352-4394-a675-ac983e03f03f | score-refinement-for-confidence-based-3d | 2107.04327 | null | https://arxiv.org/abs/2107.04327v1 | https://arxiv.org/pdf/2107.04327v1.pdf | Score refinement for confidence-based 3D multi-object tracking | Multi-object tracking is a critical component in autonomous navigation, as it provides valuable information for decision-making. Many researchers tackled the 3D multi-object tracking task by filtering out the frame-by-frame 3D detections; however, their focus was mainly on finding useful features or proper matching met... | ['Andreas Zell', 'Jona Schröder', 'Nuri Benbarka'] | 2021-07-09 | null | null | null | null | ['3d-multi-object-tracking'] | ['computer-vision'] | [-4.13254410e-01 -5.16151428e-01 -6.66152015e-02 7.60124698e-02
-8.66823912e-01 -7.51180053e-01 5.51371038e-01 3.71125974e-02
-8.73049438e-01 6.29800797e-01 -2.02942744e-01 -6.09628446e-02
-1.03037573e-01 -5.63429654e-01 -5.97828090e-01 -8.25776279e-01
2.05749571e-02 5.33828616e-01 1.14438963e+00 -2.74246305... | [6.586888313293457, -2.093747854232788] |
1180af78-7231-4203-be33-af27ce9b6e91 | ontology-matching-through-absolute | 2204.0404 | null | https://arxiv.org/abs/2204.04040v1 | https://arxiv.org/pdf/2204.04040v1.pdf | Ontology Matching Through Absolute Orientation of Embedding Spaces | Ontology matching is a core task when creating interoperable and linked open datasets. In this paper, we explore a novel structure-based mapping approach which is based on knowledge graph embeddings: The ontologies to be matched are embedded, and an approach known as absolute orientation is used to align the two embedd... | ['Heiko Paulheim', 'Michael Hladik', 'Katharina Kreplin', 'Karolin Stefani', 'Guilherme Costa', 'Jan Portisch'] | 2022-04-08 | null | null | null | null | ['knowledge-graph-embeddings', 'ontology-matching', 'knowledge-graph-embeddings'] | ['graphs', 'knowledge-base', 'methodology'] | [-2.01789383e-02 6.00271106e-01 -6.04527406e-02 -2.90325940e-01
-1.25727654e-01 -5.74785352e-01 6.53035760e-01 6.33301675e-01
-4.38451231e-01 5.71679235e-01 4.93804544e-01 -2.24068433e-01
-7.72402823e-01 -1.03227007e+00 -4.32842702e-01 7.44460523e-02
-4.41811740e-01 8.30342650e-01 6.19761527e-01 -5.76478541... | [9.210307121276855, 8.093515396118164] |
5700c4bd-f637-40e1-8493-e68f5c0b3208 | show-attend-and-interact-perceivable-human | 1702.08626 | null | http://arxiv.org/abs/1702.08626v1 | http://arxiv.org/pdf/1702.08626v1.pdf | Show, Attend and Interact: Perceivable Human-Robot Social Interaction through Neural Attention Q-Network | For a safe, natural and effective human-robot social interaction, it is
essential to develop a system that allows a robot to demonstrate the
perceivable responsive behaviors to complex human behaviors. We introduce the
Multimodal Deep Attention Recurrent Q-Network using which the robot exhibits
human-like social intera... | ['Hiroshi Ishiguro', 'Ahmed Hussain Qureshi', 'Yutaka Nakamura', 'Yuichiro Yoshikawa'] | 2017-02-28 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [-3.88963044e-01 6.81385159e-01 5.38178027e-01 -3.28046620e-01
-6.04562089e-03 1.61546282e-02 3.80468607e-01 -2.85820752e-01
-6.28163159e-01 1.00260627e+00 2.66050629e-04 4.86230105e-01
1.22161798e-01 -3.88837785e-01 -4.68082160e-01 -4.80803192e-01
-5.70399880e-01 7.93147087e-01 1.32164173e-02 -8.02979350... | [4.826992511749268, 1.0419059991836548] |
c47c5c1b-9cfd-4a32-977b-3e66865b5b18 | cascade-transformers-for-end-to-end-person | 2203.09642 | null | https://arxiv.org/abs/2203.09642v1 | https://arxiv.org/pdf/2203.09642v1.pdf | Cascade Transformers for End-to-End Person Search | The goal of person search is to localize a target person from a gallery set of scene images, which is extremely challenging due to large scale variations, pose/viewpoint changes, and occlusions. In this paper, we propose the Cascade Occluded Attention Transformer (COAT) for end-to-end person search. Our three-stage cas... | ['Brian Clipp', 'Anthony Hoogs', 'Christopher Funk', 'Daniel Davila', 'Rodney LaLonde', 'Dawei Du', 'Rui Yu'] | 2022-03-17 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Yu_Cascade_Transformers_for_End-to-End_Person_Search_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Yu_Cascade_Transformers_for_End-to-End_Person_Search_CVPR_2022_paper.pdf | cvpr-2022-1 | ['person-search'] | ['computer-vision'] | [-1.59679711e-01 -5.58230102e-01 3.80994558e-01 -2.38970980e-01
-6.46053255e-01 -4.62813795e-01 5.73608220e-01 -1.47575766e-01
-7.24328279e-01 3.77763987e-01 2.93786824e-01 4.89054650e-01
2.71791369e-01 -5.51032007e-01 -3.17334354e-01 -4.72856075e-01
1.26974255e-01 6.66306973e-01 2.61913329e-01 4.56312858... | [14.80836009979248, 0.8138939738273621] |
a11c0e89-8880-4f3d-9714-e95b16d59160 | quantifying-model-uncertainty-for-semantic | 2211.01999 | null | https://arxiv.org/abs/2211.01999v1 | https://arxiv.org/pdf/2211.01999v1.pdf | Quantifying Model Uncertainty for Semantic Segmentation using Operators in the RKHS | Deep learning models for semantic segmentation are prone to poor performance in real-world applications due to the highly challenging nature of the task. Model uncertainty quantification (UQ) is one way to address this issue of lack of model trustworthiness by enabling the practitioner to know how much to trust a segme... | ['Jose C. Principe', 'Rishabh Singh'] | 2022-11-03 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 2.71642543e-02 2.75100648e-01 -3.30360159e-02 -3.89596283e-01
-1.23990846e+00 -4.42988575e-01 8.26600075e-01 1.58363655e-01
-5.16009927e-01 7.14929938e-01 -1.54993027e-01 -2.08126992e-01
-3.26890588e-01 -8.76607358e-01 -9.19194400e-01 -8.11361969e-01
1.67075336e-01 9.05257046e-01 4.05103654e-01 1.60890043... | [7.267406940460205, 3.4886274337768555] |
57905948-686e-4bb0-adab-33234eee0052 | jacobian-norm-for-unsupervised-source-free | 2204.03467 | null | https://arxiv.org/abs/2204.03467v1 | https://arxiv.org/pdf/2204.03467v1.pdf | Jacobian Norm for Unsupervised Source-Free Domain Adaptation | Unsupervised Source (data) Free domain adaptation (USFDA) aims to transfer knowledge from a well-trained source model to a related but unlabeled target domain. In such a scenario, all conventional adaptation methods that require source data fail. To combat this challenge, existing USFDAs turn to transfer knowledge by a... | ['Songcan Chen', 'Meng Cao', 'Weikai Li'] | 2022-04-07 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 3.17943126e-01 3.25554498e-02 -5.39673984e-01 -4.56368715e-01
-6.26474977e-01 -4.85134155e-01 5.04772246e-01 -1.37304083e-01
-9.28905308e-02 9.03174281e-01 1.21872425e-01 -8.91708136e-02
-1.62746787e-01 -6.70092165e-01 -6.16141737e-01 -9.15828824e-01
4.21287745e-01 1.90351013e-04 9.51682106e-02 -2.89061934... | [10.349784851074219, 3.208911895751953] |
ea239d62-9239-48bf-9cdf-cd78183888d0 | learning-bilingual-word-embeddings-with | null | null | https://aclanthology.org/P17-1042 | https://aclanthology.org/P17-1042.pdf | Learning bilingual word embeddings with (almost) no bilingual data | Most methods to learn bilingual word embeddings rely on large parallel corpora, which is difficult to obtain for most language pairs. This has motivated an active research line to relax this requirement, with methods that use document-aligned corpora or bilingual dictionaries of a few thousand words instead. In this wo... | ['Gorka Labaka', 'Mikel Artetxe', 'Eneko Agirre'] | 2017-07-01 | null | null | null | acl-2017-7 | ['multilingual-word-embeddings'] | ['methodology'] | [-3.93232286e-01 -1.70071289e-01 -5.30010641e-01 -2.38020808e-01
-3.96052361e-01 -8.98689926e-01 9.87160683e-01 5.91889679e-01
-1.07969594e+00 8.14583063e-01 5.45588434e-01 -7.00998485e-01
2.19892979e-01 -9.26886499e-01 -3.33517671e-01 -3.07990760e-01
3.75910550e-01 7.09004164e-01 1.75663531e-01 -8.00731301... | [10.968047142028809, 10.04788875579834] |
37e7cb69-b67c-4132-9b55-ab21162f1a72 | anomaly-detection-in-image-or-latent-space-of | 2307.02495 | null | https://arxiv.org/abs/2307.02495v1 | https://arxiv.org/pdf/2307.02495v1.pdf | Anomaly detection in image or latent space of patch-based auto-encoders for industrial image analysis | We study several methods for detecting anomalies in color images, constructed on patch-based auto-encoders. Wecompare the performance of three types of methods based, first, on the error between the original image and its reconstruction,second, on the support estimation of the normal image distribution in the latent sp... | ['Carole Lartizien', 'Robin Trombetta', 'Nicolas Pinon'] | 2023-07-04 | null | null | null | null | ['anomaly-detection'] | ['methodology'] | [ 3.10857415e-01 -1.40633807e-01 3.33173543e-01 -5.83927631e-02
-7.04427302e-01 -2.77023584e-01 6.06299222e-01 -2.42704246e-02
3.66411619e-02 3.63855213e-01 -3.85828167e-01 -1.25131667e-01
-1.20414168e-01 -7.61415958e-01 -8.40276241e-01 -9.68231618e-01
-6.69929981e-02 1.97500616e-01 3.51211280e-01 -1.55748557... | [11.7147216796875, -1.9319080114364624] |
636fadf4-671e-4cba-bd6e-c489d084fa22 | regression-or-classification-automated-essay | null | null | https://aclanthology.org/W19-4409 | https://aclanthology.org/W19-4409.pdf | Regression or classification? Automated Essay Scoring for Norwegian | In this paper we present first results for the task of Automated Essay Scoring for Norwegian learner language. We analyze a number of properties of this task experimentally and assess (i) the formulation of the task as either regression or classification, (ii) the use of various non-neural and neural machine learning a... | ['Lilja {\\O}vrelid', 'Stig Johan Berggren', 'Taraka Rama'] | 2019-08-01 | null | null | null | ws-2019-8 | ['automated-essay-scoring', 'native-language-identification'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.13557419e-02 -9.56469476e-02 -9.81609076e-02 -3.61693859e-01
-1.17423642e+00 -5.76422215e-01 4.80651408e-01 1.55408949e-01
-7.72668123e-01 8.83701682e-01 3.24660718e-01 -8.28929245e-01
-2.93118268e-01 -4.12324876e-01 -4.20139760e-01 -2.09974870e-01
5.11723280e-01 7.92073369e-01 2.08464190e-01 -4.44979638... | [11.312026023864746, 9.371820449829102] |
932dcdae-f9d1-4ee0-86ba-1f69f2d372f0 | prefix-tuning-optimizing-continuous-prompts | 2101.0019 | null | https://arxiv.org/abs/2101.00190v1 | https://arxiv.org/pdf/2101.00190v1.pdf | Prefix-Tuning: Optimizing Continuous Prompts for Generation | Fine-tuning is the de facto way to leverage large pretrained language models to perform downstream tasks. However, it modifies all the language model parameters and therefore necessitates storing a full copy for each task. In this paper, we propose prefix-tuning, a lightweight alternative to fine-tuning for natural lan... | ['Percy Liang', 'Xiang Lisa Li'] | 2021-01-01 | null | https://aclanthology.org/2021.acl-long.353 | https://aclanthology.org/2021.acl-long.353.pdf | acl-2021-5 | ['table-to-text-generation'] | ['natural-language-processing'] | [ 2.78724134e-01 4.96823519e-01 -4.98600036e-01 -2.32762992e-01
-1.20180106e+00 -8.29063118e-01 1.00211072e+00 1.03980750e-01
-4.74262506e-01 9.39654112e-01 7.24629760e-01 -5.47384381e-01
3.23295265e-01 -7.07453132e-01 -8.84123862e-01 -4.88000244e-01
1.02205269e-01 8.06462348e-01 4.47987393e-02 -3.47736716... | [11.549721717834473, 8.944610595703125] |
78e6637c-3ebf-4397-ae4a-0b694de6dfe4 | melon-nerf-with-unposed-images-using | 2303.08096 | null | https://arxiv.org/abs/2303.08096v1 | https://arxiv.org/pdf/2303.08096v1.pdf | MELON: NeRF with Unposed Images Using Equivalence Class Estimation | Neural radiance fields enable novel-view synthesis and scene reconstruction with photorealistic quality from a few images, but require known and accurate camera poses. Conventional pose estimation algorithms fail on smooth or self-similar scenes, while methods performing inverse rendering from unposed views require a r... | ['Dmitry Lagun', 'Gordon Wetzstein', 'Matan Sela', 'Mark Matthews', 'Axel Levy'] | 2023-03-14 | null | null | null | null | ['inverse-rendering'] | ['computer-vision'] | [ 5.94765306e-01 1.89245448e-01 3.07198763e-01 -3.22703868e-01
-7.09301174e-01 -1.02465177e+00 5.35436988e-01 -6.72182500e-01
-2.48214826e-01 4.92488950e-01 5.27315140e-02 7.61813149e-02
1.08777188e-01 -7.35565960e-01 -1.22708094e+00 -6.45215511e-01
4.79445666e-01 3.89937371e-01 -1.87730372e-01 -2.24813998... | [9.25918197631836, -2.9649791717529297] |
cc1f65c6-b9af-4c21-bfa5-c05d1bebaef7 | video-text-as-game-players-hierarchical | 2303.14369 | null | https://arxiv.org/abs/2303.14369v1 | https://arxiv.org/pdf/2303.14369v1.pdf | Video-Text as Game Players: Hierarchical Banzhaf Interaction for Cross-Modal Representation Learning | Contrastive learning-based video-language representation learning approaches, e.g., CLIP, have achieved outstanding performance, which pursue semantic interaction upon pre-defined video-text pairs. To clarify this coarse-grained global interaction and move a step further, we have to encounter challenging shell-breaking... | ['Jie Chen', 'Li Yuan', 'Xiangyang Ji', 'Chang Liu', 'Shangxuan Tian', 'Pengfei Xiong', 'Jinfa Huang', 'Peng Jin'] | 2023-03-25 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Jin_Video-Text_As_Game_Players_Hierarchical_Banzhaf_Interaction_for_Cross-Modal_Representation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Jin_Video-Text_As_Game_Players_Hierarchical_Banzhaf_Interaction_for_Cross-Modal_Representation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-question-answering', 'video-retrieval'] | ['computer-vision', 'computer-vision'] | [-2.47621492e-01 -2.26136908e-01 -8.66484195e-02 -9.39595327e-02
-9.68055248e-01 -4.83812183e-01 5.97398937e-01 -1.10248074e-01
-2.54538298e-01 3.27982843e-01 3.82912189e-01 -1.18528731e-01
-6.09930873e-01 -4.83760774e-01 -6.29653811e-01 -7.87474334e-01
-1.71604306e-01 3.53623122e-01 2.04950169e-01 -3.20526272... | [10.327431678771973, 1.003305435180664] |
945db439-7130-433c-a82c-b3596f17d6e7 | swin-deformable-attention-hybrid-u-net-for | 2302.1445 | null | https://arxiv.org/abs/2302.14450v1 | https://arxiv.org/pdf/2302.14450v1.pdf | Swin Deformable Attention Hybrid U-Net for Medical Image Segmentation | How to harmonize convolution and multi-head self-attention mechanisms has recently emerged as a significant area of research in the field of medical image segmentation. Various combination methods have been proposed. However, there is a common flaw in these works: failed to provide a direct explanation for their hybrid... | ['Guang Yang', 'Jiahao Huang', 'Lichao Wang'] | 2023-02-28 | null | null | null | null | ['lesion-segmentation'] | ['medical'] | [ 1.92326438e-02 4.07527953e-01 -4.30687070e-02 -5.55356443e-01
-7.44390845e-01 -2.40171850e-01 2.50168145e-01 -3.35138179e-02
-2.77316570e-01 5.00008583e-01 1.86798707e-01 -2.01098919e-01
-3.09155166e-01 -5.54944456e-01 -6.60906553e-01 -7.62685239e-01
3.93544108e-01 3.44270587e-01 5.71155727e-01 -4.02272046... | [14.613574981689453, -2.5345144271850586] |
399a919c-f1a9-40d8-9e5e-b5047c0372a7 | text-with-knowledge-graph-augmented | 2303.12423 | null | https://arxiv.org/abs/2303.12423v2 | https://arxiv.org/pdf/2303.12423v2.pdf | Text with Knowledge Graph Augmented Transformer for Video Captioning | Video captioning aims to describe the content of videos using natural language. Although significant progress has been made, there is still much room to improve the performance for real-world applications, mainly due to the long-tail words challenge. In this paper, we propose a text with knowledge graph augmented trans... | ['Longyin Wen', 'Tiejian Luo', 'Libo Zhang', 'YuFei Wang', 'Guang Chen', 'Xin Gu'] | 2023-03-22 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Gu_Text_With_Knowledge_Graph_Augmented_Transformer_for_Video_Captioning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Gu_Text_With_Knowledge_Graph_Augmented_Transformer_for_Video_Captioning_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-captioning'] | ['computer-vision'] | [ 3.09008900e-02 3.26080644e-03 -3.83051813e-01 -9.93884653e-02
-7.35781252e-01 -3.51886064e-01 5.46488583e-01 -1.19464375e-01
-9.33508426e-02 5.53124905e-01 5.79929709e-01 1.53334156e-01
3.14669579e-01 -2.56539583e-01 -1.00264609e+00 -6.51733398e-01
9.44632739e-02 1.19672917e-01 3.83112252e-01 -2.15024669... | [10.473132133483887, 0.7882286310195923] |
4d12865c-fc80-427c-bcd8-fead10aa9618 | limitr-leveraging-local-information-for | 2303.11755 | null | https://arxiv.org/abs/2303.11755v1 | https://arxiv.org/pdf/2303.11755v1.pdf | LIMITR: Leveraging Local Information for Medical Image-Text Representation | Medical imaging analysis plays a critical role in the diagnosis and treatment of various medical conditions. This paper focuses on chest X-ray images and their corresponding radiological reports. It presents a new model that learns a joint X-ray image & report representation. The model is based on a novel alignment sch... | ['Ayellet Tal', 'Elad Hirsch', 'Gefen Dawidowicz'] | 2023-03-21 | null | null | null | null | ['phrase-grounding'] | ['natural-language-processing'] | [ 1.92993656e-01 8.95230025e-02 -6.46340072e-01 -6.73996091e-01
-1.51249945e+00 -3.70787978e-01 5.06956041e-01 7.74035752e-01
-2.22521871e-01 3.18757951e-01 5.57322025e-01 -2.73984909e-01
-4.72255200e-01 -5.00109494e-01 -3.54938805e-01 -5.11751652e-01
-1.51706515e-02 6.96974874e-01 2.85966754e-01 2.02933952... | [14.93877124786377, -1.6190842390060425] |
4edc7d0c-fa65-414f-9d0a-73eaa46db9dd | exploring-better-text-image-translation-with | 2305.17415 | null | https://arxiv.org/abs/2305.17415v2 | https://arxiv.org/pdf/2305.17415v2.pdf | Exploring Better Text Image Translation with Multimodal Codebook | Text image translation (TIT) aims to translate the source texts embedded in the image to target translations, which has a wide range of applications and thus has important research value. However, current studies on TIT are confronted with two main bottlenecks: 1) this task lacks a publicly available TIT dataset, 2) do... | ['Jinsong Su', 'Degen Huang', 'Bin Wang', 'Jian Luan', 'Wen Zhang', 'Xiang Li', 'Jiawei Yu', 'Zhibin Lan'] | 2023-05-27 | null | null | null | null | ['optical-character-recognition'] | ['computer-vision'] | [ 6.88714206e-01 -4.68369186e-01 -1.96816817e-01 -1.89160496e-01
-1.02872217e+00 -6.39768660e-01 7.77635515e-01 -4.66140389e-01
-5.38025856e-01 5.25683880e-01 1.97217062e-01 -3.12750101e-01
3.97267789e-01 -1.73410326e-01 -7.12715447e-01 -6.55097902e-01
8.53583872e-01 5.60408950e-01 8.14383104e-02 3.13035101... | [11.530354499816895, 1.5865225791931152] |
a93c78fd-5997-4cc5-8845-47ca1513f2eb | are-pretrained-multilingual-models-equally | null | null | https://openreview.net/forum?id=GnlD4Dzr1t | https://openreview.net/pdf?id=GnlD4Dzr1t | Are Pretrained Multilingual Models Equally Fair Across Languages? | Pretrained multilingual language models can help bridge the digital language divide, enabling high-quality NLP models for lower-resourced languages. Studies of multilingual models have so far focused on performance, consistency, and cross-lingual generalization. However, with their wide-spread application in the wild a... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['cloze-test', 'pretrained-multilingual-language-models'] | ['natural-language-processing', 'natural-language-processing'] | [-6.80243015e-01 -1.20832950e-01 -8.01223397e-01 -2.79799223e-01
-1.17628419e+00 -9.91026640e-01 7.91795075e-01 2.88993239e-01
-7.88763821e-01 8.65950823e-01 5.13964057e-01 -7.72588789e-01
-6.87643811e-02 -3.63847852e-01 -5.33089101e-01 -1.77108228e-01
1.11239418e-01 5.71420968e-01 -3.12610477e-01 -1.92170277... | [10.44095230102539, 10.089821815490723] |
da16a271-cc81-4ab4-b197-ce916b076f18 | comparison-of-synthetic-dataset-generation | 2209.11493 | null | https://arxiv.org/abs/2209.11493v1 | https://arxiv.org/pdf/2209.11493v1.pdf | Comparison of synthetic dataset generation methods for medical intervention rooms using medical clothing detection as an example | The availability of real data from areas with high privacy requirements, such as the medical intervention space, is low and the acquisition legally complex. Therefore, this work presents a way to create a synthetic dataset for the medical context, using medical clothing as an example. The goal is to close the reality g... | ['Marcus Vetter', 'Steffen Diehl', 'Nils Rathmann', 'Anke Siebert', 'Marcus Pfister', 'Yannick Bukschat', 'Indira Emter', 'Ronja Vorpahl', 'Hannah Teufel', 'Patrick Schülein'] | 2022-09-23 | null | null | null | null | ['mixed-reality'] | ['computer-vision'] | [ 2.72999346e-01 7.92092264e-01 1.69443175e-01 -5.12291789e-01
-9.97242093e-01 -7.44845688e-01 4.23913598e-01 7.54082575e-02
-6.08202994e-01 8.55089724e-01 6.12906814e-02 -3.96554917e-01
2.33873744e-02 -7.27320254e-01 -1.05627906e+00 -3.10573667e-01
1.73876718e-01 6.21702492e-01 2.29099348e-01 -2.33337507... | [14.346242904663086, -1.9897027015686035] |
9b953d6c-c0bc-4113-81d9-7c6a0089fefb | camera-aware-proxies-for-unsupervised-person | 2012.10674 | null | https://arxiv.org/abs/2012.10674v2 | https://arxiv.org/pdf/2012.10674v2.pdf | Camera-aware Proxies for Unsupervised Person Re-Identification | This paper tackles the purely unsupervised person re-identification (Re-ID) problem that requires no annotations. Some previous methods adopt clustering techniques to generate pseudo labels and use the produced labels to train Re-ID models progressively. These methods are relatively simple but effective. However, most ... | ['Xian-Sheng Hua', 'Xiaojin Gong', 'Jianqiang Huang', 'Baisheng Lai', 'Menglin Wang'] | 2020-12-19 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [-1.14179747e-02 -3.19265902e-01 -2.19597653e-01 -5.86621284e-01
-8.97224188e-01 -5.99030852e-01 6.58849537e-01 -1.06030531e-01
-5.22709191e-01 6.47832930e-01 1.88082635e-01 2.19385535e-01
-7.65567347e-02 -4.34123874e-01 -4.94089186e-01 -6.98388755e-01
2.65258163e-01 7.12345660e-01 2.67724134e-02 4.69377398... | [14.814281463623047, 1.0823616981506348] |
23600fb0-9bf7-4383-a97d-bd517147a820 | accelerating-self-play-learning-in-go | 1902.10565 | null | https://arxiv.org/abs/1902.10565v5 | https://arxiv.org/pdf/1902.10565v5.pdf | Accelerating Self-Play Learning in Go | By introducing several improvements to the AlphaZero process and architecture, we greatly accelerate self-play learning in Go, achieving a 50x reduction in computation over comparable methods. Like AlphaZero and replications such as ELF OpenGo and Leela Zero, our bot KataGo only learns from neural-net-guided Monte Carl... | ['David J. Wu'] | 2019-02-27 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [-4.76631373e-01 1.94001049e-02 -5.19728899e-01 1.79414839e-01
-9.49858963e-01 -7.76610911e-01 3.80899251e-01 -4.27121930e-02
-6.21051371e-01 1.04572630e+00 -1.36698514e-01 -8.31050634e-01
-7.85615157e-06 -1.01269305e+00 -8.68757188e-01 -4.34499502e-01
-3.95444125e-01 7.58829892e-01 7.46577442e-01 -3.49213928... | [3.5497488975524902, 1.435394048690796] |
d77e6273-071a-4a37-906f-645cf724f978 | spot-evasion-attacks-adversarial-examples-for | 1911.00927 | null | https://arxiv.org/abs/1911.00927v2 | https://arxiv.org/pdf/1911.00927v2.pdf | Spot Evasion Attacks: Adversarial Examples for License Plate Recognition Systems with Convolutional Neural Networks | Recent studies have shown convolution neural networks (CNNs) for image recognition are vulnerable to evasion attacks with carefully manipulated adversarial examples. Previous work primarily focused on how to generate adversarial examples closed to source images, by introducing pixel-level perturbations into the whole o... | ['Jing-sheng Lei', 'Wu-jie Zhou', 'Jian-hai Chen', 'Jia-min Wang', 'Dan-feng Ma', 'Ya-guan Qian', 'Jun Pan', 'Bin Wang'] | 2019-10-27 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [ 4.55231190e-01 -4.92627583e-02 4.52633142e-01 6.13845885e-02
-3.14315230e-01 -1.09175777e+00 6.07502997e-01 -6.78884208e-01
-3.87461990e-01 6.87817752e-01 -4.29572701e-01 -5.18589079e-01
3.78324896e-01 -1.04129231e+00 -1.17240691e+00 -8.70689631e-01
1.56556249e-01 -9.65896249e-02 2.69333869e-01 -4.68060911... | [5.49254035949707, 7.952006816864014] |
66ef6e0d-13bc-4e64-87e3-1cbb72e4f68d | deep-stereo-video-inpainting | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wu_Deep_Stereo_Video_Inpainting_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wu_Deep_Stereo_Video_Inpainting_CVPR_2023_paper.pdf | Deep Stereo Video Inpainting | Stereo video inpainting aims to fill the missing regions on the left and right views of the stereo video with plausible content simultaneously. Compared with the single video inpainting that has achieved promising results using deep convolutional neural networks, inpainting the missing regions of stereo video has n... | ['Yan Yan', 'Hanyu Xuan', 'Changchang Sun', 'Zhiliang Wu'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['video-inpainting'] | ['computer-vision'] | [ 2.96367332e-02 -1.00025080e-01 -5.17829955e-02 -2.04738736e-01
-5.14792323e-01 -2.15889201e-01 2.22960919e-01 -5.02779067e-01
2.81051230e-02 7.68117249e-01 4.50750709e-01 1.04186058e-01
1.81481451e-01 -5.64616978e-01 -9.64653611e-01 -5.59100032e-01
4.71147329e-01 -1.97250377e-02 2.64722496e-01 -6.73617572... | [10.826117515563965, -1.4238476753234863] |
3313b5a5-bee8-4a28-90ca-7b6effb1b855 | towards-feature-selection-for-ranking-and | 2205.04346 | null | https://arxiv.org/abs/2205.04346v1 | https://arxiv.org/pdf/2205.04346v1.pdf | Towards Feature Selection for Ranking and Classification Exploiting Quantum Annealers | Feature selection is a common step in many ranking, classification, or prediction tasks and serves many purposes. By removing redundant or noisy features, the accuracy of ranking or classification can be improved and the computational cost of the subsequent learning steps can be reduced. However, feature selection can ... | ['Paolo Cremonesi', 'Guglielmo Faggioli', 'Nicola Ferro', 'Riccardo Nembrini', 'Fabio Moroni', 'Maurizio Ferrari Dacrema'] | 2022-05-09 | null | null | null | null | ['classification'] | ['methodology'] | [ 3.36665630e-01 -2.18809605e-01 -1.12633921e-01 -4.13506150e-01
-1.05199349e+00 -2.64937550e-01 6.07441008e-01 5.95263004e-01
-6.56576037e-01 9.67743337e-01 -5.04919171e-01 8.12800881e-03
-4.18163091e-01 -1.17621648e+00 -1.63217410e-01 -1.00883079e+00
-2.60804713e-01 9.71894324e-01 2.55356729e-01 -5.94798684... | [5.612372875213623, 4.905333042144775] |
dea562a1-d455-441f-ac61-49b272860312 | automatic-noisy-label-correction-for-fine | 2205.03011 | null | https://arxiv.org/abs/2205.03011v2 | https://arxiv.org/pdf/2205.03011v2.pdf | Automatic Noisy Label Correction for Fine-Grained Entity Typing | Fine-grained entity typing (FET) aims to assign proper semantic types to entity mentions according to their context, which is a fundamental task in various entity-leveraging applications. Current FET systems usually establish on large-scale weakly-supervised/distantly annotation data, which may contain abundant noise a... | ['Feida Zhu', 'Wei Wei', 'Weiran Pan'] | 2022-05-06 | null | null | null | null | ['entity-typing'] | ['natural-language-processing'] | [-1.01778610e-02 8.03792700e-02 -3.12522382e-01 -5.37809610e-01
-1.12188983e+00 -7.03460813e-01 4.21892434e-01 3.75308961e-01
-5.97621679e-01 1.01822913e+00 2.61013269e-01 -9.39573646e-02
1.74348488e-01 -7.18231678e-01 -6.16774082e-01 -6.64428771e-01
3.99632782e-01 5.33018112e-01 2.89885104e-01 2.26720080... | [9.535276412963867, 8.910539627075195] |
0e5ec53b-9ad9-43d6-abe0-ae69e57e0c26 | edog-adversarial-edge-detection-for-graph | 2212.13607 | null | https://arxiv.org/abs/2212.13607v1 | https://arxiv.org/pdf/2212.13607v1.pdf | EDoG: Adversarial Edge Detection For Graph Neural Networks | Graph Neural Networks (GNNs) have been widely applied to different tasks such as bioinformatics, drug design, and social networks. However, recent studies have shown that GNNs are vulnerable to adversarial attacks which aim to mislead the node or subgraph classification prediction by adding subtle perturbations. Detect... | ['Bo Li', 'Carl A. Gunter', 'Alok Lal', 'Hanzhang Wang', 'Yue Yu', 'Xiaojun Xu'] | 2022-12-27 | null | null | null | null | ['edge-detection'] | ['computer-vision'] | [ 3.89880419e-01 4.45597231e-01 -1.06876761e-01 2.55578756e-01
-3.64864647e-01 -9.84697819e-01 4.25351590e-01 4.52761531e-01
1.06944472e-01 6.57561004e-01 -4.16506261e-01 -7.95635700e-01
6.82175020e-03 -1.32896280e+00 -1.30578554e+00 -4.94470268e-01
-5.22090137e-01 4.73763794e-01 5.99816501e-01 -2.75369614... | [6.117924213409424, 7.350695610046387] |
06948395-95cd-4d6a-8c24-07e0887b0e8d | exploring-representation-level-augmentation | 2210.12285 | null | https://arxiv.org/abs/2210.12285v1 | https://arxiv.org/pdf/2210.12285v1.pdf | Exploring Representation-Level Augmentation for Code Search | Code search, which aims at retrieving the most relevant code fragment for a given natural language query, is a common activity in software development practice. Recently, contrastive learning is widely used in code search research, where many data augmentation approaches for source code (e.g., semantic-preserving progr... | ['Yanlin Wang', 'Hongyu Zhang', 'Yuan Huang', 'Yanxian Huang', 'Cyril Leung', 'Chunyan Miao', 'Haochen Li'] | 2022-10-21 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [ 1.36693031e-01 -4.09792125e-01 -7.89439619e-01 -1.75814882e-01
-9.66296017e-01 -3.36756527e-01 4.60470825e-01 5.66901684e-01
-2.79141456e-01 1.62132412e-01 4.92850132e-02 -6.40000165e-01
3.51982787e-02 -7.62332499e-01 -7.76815891e-01 -2.82621980e-01
5.73292673e-02 1.30628929e-01 2.78289735e-01 -2.91914701... | [7.449542999267578, 8.038731575012207] |
68997de3-8590-4679-acda-75e1f770aaf0 | sgem-test-time-adaptation-for-automatic | 2306.01981 | null | https://arxiv.org/abs/2306.01981v4 | https://arxiv.org/pdf/2306.01981v4.pdf | SGEM: Test-Time Adaptation for Automatic Speech Recognition via Sequential-Level Generalized Entropy Minimization | Automatic speech recognition (ASR) models are frequently exposed to data distribution shifts in many real-world scenarios, leading to erroneous predictions. To tackle this issue, an existing test-time adaptation (TTA) method has recently been proposed to adapt the pre-trained ASR model on unlabeled test instances witho... | ['Eunho Yang', 'Hajin Shim', 'Joonhyung Park', 'Changhun Kim'] | 2023-06-03 | null | null | null | null | ['automatic-speech-recognition'] | ['speech'] | [ 5.61364710e-01 -8.94003361e-02 -1.80103078e-01 -4.64760661e-01
-1.10414183e+00 -3.97108406e-01 5.24050236e-01 -2.49681219e-01
-3.90301883e-01 8.91672611e-01 1.24292485e-01 -5.10105968e-01
1.60601035e-01 -1.45102143e-01 -6.16361916e-01 -6.35799229e-01
3.96432519e-01 5.47309697e-01 3.04681689e-01 -1.11576907... | [14.447153091430664, 6.644789695739746] |
10107c44-8c5b-4200-99b1-e8260a0644a9 | quantum-inspired-representation-for-long-tail | null | null | https://openreview.net/forum?id=r6z-A8wyD-W | https://openreview.net/pdf?id=r6z-A8wyD-W | Quantum-inspired Representation for Long-tail Senses of Word Sense Disambiguation | Data imbalance, also known as the long-tailed distribution of data, is an important challenge for data-driven models. Due to the long tail phenomenon of word sense distribution in linguistics, it is difficult to learn accurate representations for Long-Tail Senses (LTSs) in Word Sense Disambiguation (WSD) tasks. Without... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 1.21306315e-01 -8.15825909e-02 -3.85255754e-01 -3.76407862e-01
-6.38467014e-01 -1.98695138e-01 3.86312306e-01 4.95309085e-01
-5.70912123e-01 7.44256556e-01 3.27636242e-01 -4.69286978e-01
-2.01191783e-01 -7.66566217e-01 -3.45235437e-01 -7.54267097e-01
5.81231900e-02 3.21551025e-01 -1.44176528e-01 -6.12197161... | [10.268644332885742, 8.900481224060059] |
5de06204-dfab-4ff7-b1b1-b4dae5859585 | regression-and-classification-for-direction | 1904.08452 | null | https://arxiv.org/abs/1904.08452v3 | https://arxiv.org/pdf/1904.08452v3.pdf | Regression and Classification for Direction-of-Arrival Estimation with Convolutional Recurrent Neural Networks | We present a novel learning-based approach to estimate the direction-of-arrival (DOA) of a sound source using a convolutional recurrent neural network (CRNN) trained via regression on synthetic data and Cartesian labels. We also describe an improved method to generate synthetic data to train the neural network using st... | ['Kevin Hogan', 'Dinesh Manocha', 'John D. Kanu', 'Zhenyu Tang'] | 2019-04-17 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [ 3.35493237e-01 1.17691346e-01 8.72305691e-01 -5.66003799e-01
-1.35367203e+00 -4.30723786e-01 5.87461770e-01 -3.51515919e-01
-1.45833910e-01 5.01431644e-01 3.75254542e-01 -4.79780883e-01
1.79907352e-01 -9.89142656e-01 -9.23974574e-01 -8.91138792e-01
-8.28510746e-02 2.70718426e-01 1.07021883e-01 -5.20236455... | [15.186945915222168, 5.771124839782715] |
674734a0-8a2a-40be-af7e-3ce4b1f6643a | algorithmic-probability-guided-supervised | 1910.02758 | null | https://arxiv.org/abs/1910.02758v2 | https://arxiv.org/pdf/1910.02758v2.pdf | Algorithmic Probability-guided Supervised Machine Learning on Non-differentiable Spaces | We show how complexity theory can be introduced in machine learning to help bring together apparently disparate areas of current research. We show that this new approach requires less training data and is more generalizable as it shows greater resilience to random attacks. We investigate the shape of the discrete algor... | ['Jesper Tegnér', 'Jürgen Riedel', 'Santiago Hernández-Orozco', 'Narsis A. Kiani', 'Adam Uccello', 'Hector Zenil'] | 2019-10-07 | null | null | null | null | ['small-data'] | ['computer-vision'] | [ 5.79138160e-01 1.78039789e-01 4.61455528e-03 5.53651899e-02
-5.43177903e-01 -7.18494952e-01 8.10738504e-01 2.73899376e-01
-4.94383782e-01 7.73061931e-01 -4.34029907e-01 -6.71761572e-01
-7.37474740e-01 -8.22122514e-01 -6.88147008e-01 -1.06886792e+00
-7.00658202e-01 2.88396508e-01 1.95292756e-01 -3.91695470... | [7.7939982414245605, 3.790597915649414] |
e0650a89-dd29-42a1-b04a-7a2125cecb4d | scod-active-object-detection-for-embodied | 2107.02069 | null | https://arxiv.org/abs/2107.02069v1 | https://arxiv.org/pdf/2107.02069v1.pdf | SCOD: Active Object Detection for Embodied Agents using Sensory Commutativity of Action Sequences | We introduce SCOD (Sensory Commutativity Object Detection), an active method for movable and immovable object detection. SCOD exploits the commutative properties of action sequences, in the scenario of an embodied agent equipped with first-person sensors and a continuous motor space with multiple degrees of freedom. SC... | ['David Filliat', 'Michael Garcia-Ortiz', 'Hugo Caselles-Dupré'] | 2021-07-05 | null | null | null | null | ['active-object-detection'] | ['computer-vision'] | [ 3.30157608e-01 1.79979682e-01 2.18980432e-01 2.01028273e-01
2.36948747e-02 -8.30392420e-01 8.57812762e-01 -1.81099981e-01
-8.24097931e-01 5.77497780e-01 -1.54216483e-01 2.09280685e-01
-4.33454782e-01 -2.71424174e-01 -8.15676570e-01 -6.22829199e-01
-7.46721923e-01 3.63899529e-01 5.85846543e-01 -3.47575396... | [4.576280117034912, 0.8487626910209656] |
680e29bb-31e6-49df-9996-5cf25e096dd3 | among-us-adversarially-robust-collaborative | 2303.09495 | null | https://arxiv.org/abs/2303.09495v2 | https://arxiv.org/pdf/2303.09495v2.pdf | Among Us: Adversarially Robust Collaborative Perception by Consensus | Multiple robots could perceive a scene (e.g., detect objects) collaboratively better than individuals, although easily suffer from adversarial attacks when using deep learning. This could be addressed by the adversarial defense, but its training requires the often-unknown attacking mechanism. Differently, we propose RO... | ['Chen Feng', 'Felix Juefei-Xu', 'Siheng Chen', 'Jiamu Bai', 'Qi Fang', 'Yiming Li'] | 2023-03-16 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 1.21789023e-01 4.60185528e-01 6.88901842e-01 -1.64836556e-01
-7.37247288e-01 -1.14322269e+00 5.84837794e-01 1.74107343e-01
-7.60447919e-01 6.18782759e-01 -3.85887742e-01 1.72545135e-01
-7.23963529e-02 -9.34522450e-01 -9.06920552e-01 -8.00063252e-01
-3.29908103e-01 6.23586297e-01 2.91370660e-01 -2.09358901... | [3.9270174503326416, 2.4597625732421875] |
432e5eb4-dd5e-47af-ae00-35d35877a661 | festa-flow-estimation-via-spatial-temporal | 2104.00798 | null | https://arxiv.org/abs/2104.00798v2 | https://arxiv.org/pdf/2104.00798v2.pdf | FESTA: Flow Estimation via Spatial-Temporal Attention for Scene Point Clouds | Scene flow depicts the dynamics of a 3D scene, which is critical for various applications such as autonomous driving, robot navigation, AR/VR, etc. Conventionally, scene flow is estimated from dense/regular RGB video frames. With the development of depth-sensing technologies, precise 3D measurements are available via p... | ['Dong Tian', 'YingLi Tian', 'Muhammad A. Lodhi', 'Jiahao Pang', 'HaiYan Wang'] | 2021-04-01 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Wang_FESTA_Flow_Estimation_via_Spatial-Temporal_Attention_for_Scene_Point_Clouds_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Wang_FESTA_Flow_Estimation_via_Spatial-Temporal_Attention_for_Scene_Point_Clouds_CVPR_2021_paper.pdf | cvpr-2021-1 | ['scene-flow-estimation'] | ['computer-vision'] | [ 7.22251311e-02 -4.46997941e-01 -1.97732113e-02 -7.46075958e-02
-9.72704291e-02 -2.50454664e-01 4.74104226e-01 -2.06667557e-02
-2.25451544e-01 5.79800129e-01 2.59996086e-01 -1.65941536e-01
-2.05359071e-01 -8.47827494e-01 -5.88607907e-01 -6.91768050e-01
9.40340385e-02 -1.21892132e-02 3.71137321e-01 -3.06089878... | [8.540590286254883, -2.057718515396118] |
344f7121-f06b-44da-9878-d5c53d126421 | multi-dimensional-and-multi-scale-modeling | 2303.03737 | null | https://arxiv.org/abs/2303.03737v1 | https://arxiv.org/pdf/2303.03737v1.pdf | Multi-Dimensional and Multi-Scale Modeling for Speech Separation Optimized by Discriminative Learning | Transformer has shown advanced performance in speech separation, benefiting from its ability to capture global features. However, capturing local features and channel information of audio sequences in speech separation is equally important. In this paper, we present a novel approach named Intra-SE-Conformer and Inter-T... | ['Wenjing Zhu', 'Xinyu Yang', 'Zhaoxi Mu'] | 2023-03-07 | null | null | null | null | ['speech-separation'] | ['speech'] | [-0.03181819 -0.5565229 -0.10706517 -0.35037386 -0.94813675 -0.3813049
0.18629506 -0.2387086 -0.07354158 0.299 0.38322937 -0.19241309
-0.4214418 0.07603276 -0.09501217 -0.9178478 -0.139646 0.08608121
0.18544869 -0.10879592 -0.13610446 0.49723244 -1.303584 0.3561807
1.1158435 1.0901062 0.40... | [14.884148597717285, 5.888002872467041] |
ec6c9442-9c06-4956-b2e2-6349509f0e7a | skeleton-cloud-colorization-for-unsupervised | 2108.01959 | null | https://arxiv.org/abs/2108.01959v3 | https://arxiv.org/pdf/2108.01959v3.pdf | Skeleton Cloud Colorization for Unsupervised 3D Action Representation Learning | Skeleton-based human action recognition has attracted increasing attention in recent years. However, most of the existing works focus on supervised learning which requiring a large number of annotated action sequences that are often expensive to collect. We investigate unsupervised representation learning for skeleton ... | ['Alex C. Kot', 'Meng Hwa Er', 'Shijian Lu', 'Jun Liu', 'Siyuan Yang'] | 2021-08-04 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Yang_Skeleton_Cloud_Colorization_for_Unsupervised_3D_Action_Representation_Learning_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Yang_Skeleton_Cloud_Colorization_for_Unsupervised_3D_Action_Representation_Learning_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-human-action-recognition'] | ['computer-vision'] | [ 6.28166258e-01 -2.60520846e-01 -6.35627031e-01 -3.39372814e-01
-5.88907182e-01 -3.52662683e-01 3.99499416e-01 -5.41675687e-01
-5.21363080e-01 4.01412904e-01 4.16385800e-01 2.02495605e-02
3.23193878e-01 -2.64448583e-01 -7.12531865e-01 -6.93054259e-01
-1.20688854e-02 4.83428299e-01 4.28688347e-01 2.69513786... | [7.860272407531738, 0.38868314027786255] |
0be47ba3-1917-4ce3-a695-8cbd7419ca98 | generalized-label-propagation-methods-for | 1901.09993 | null | https://arxiv.org/abs/1901.09993v3 | https://arxiv.org/pdf/1901.09993v3.pdf | Label Efficient Semi-Supervised Learning via Graph Filtering | Graph-based methods have been demonstrated as one of the most effective approaches for semi-supervised learning, as they can exploit the connectivity patterns between labeled and unlabeled data samples to improve learning performance. However, existing graph-based methods either are limited in their ability to jointly ... | ['Xiao-Ming Wu', 'Zhichao Guan', 'Xiaotong Zhang', 'Qimai Li', 'Han Liu'] | 2019-01-28 | label-efficient-semi-supervised-learning-via | http://openaccess.thecvf.com/content_CVPR_2019/html/Li_Label_Efficient_Semi-Supervised_Learning_via_Graph_Filtering_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Li_Label_Efficient_Semi-Supervised_Learning_via_Graph_Filtering_CVPR_2019_paper.pdf | cvpr-2019-6 | ['graph-similarity'] | ['graphs'] | [ 4.54460591e-01 4.45585757e-01 -4.71502244e-01 -4.51452315e-01
-1.50784358e-01 -6.82993114e-01 7.84139931e-01 7.30034053e-01
-2.28300378e-01 4.46854293e-01 -8.62615854e-02 -3.41058075e-01
-3.52432191e-01 -1.05888844e+00 -5.09360909e-01 -6.14648700e-01
-1.30673930e-01 4.40493315e-01 3.62558335e-01 -1.12938425... | [7.2489800453186035, 6.23140287399292] |
b3b2dab6-e2b6-4f6f-aba2-afc706f1ae58 | adaptive-selection-of-informative-path | 2108.06618 | null | https://arxiv.org/abs/2108.06618v1 | https://arxiv.org/pdf/2108.06618v1.pdf | Adaptive Selection of Informative Path Planning Strategies via Reinforcement Learning | In our previous work, we designed a systematic policy to prioritize sampling locations to lead significant accuracy improvement in spatial interpolation by using the prediction uncertainty of Gaussian Process Regression (GPR) as "attraction force" to deployed robots in path planning. Although the integration with Trave... | ['Grzegorz Cielniak', 'Taeyeong Choi'] | 2021-08-14 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 2.56716073e-01 5.46392918e-01 -5.90614751e-02 -2.35046551e-01
-9.60366607e-01 -4.55519617e-01 5.55707872e-01 2.07410142e-01
-3.48419040e-01 1.11472964e+00 -7.80919120e-02 -4.41433579e-01
-7.59933829e-01 -1.01755667e+00 -7.28965878e-01 -7.09745526e-01
-4.90725249e-01 9.37141359e-01 3.90241206e-01 -3.19064170... | [4.830384254455566, 1.745438575744629] |
e93a3294-1777-4cee-a53d-27e55ee42b78 | first-place-solution-to-the-cvpr-2023-aqtc | 2306.1338 | null | https://arxiv.org/abs/2306.13380v1 | https://arxiv.org/pdf/2306.13380v1.pdf | First Place Solution to the CVPR'2023 AQTC Challenge: A Function-Interaction Centric Approach with Spatiotemporal Visual-Language Alignment | Affordance-Centric Question-driven Task Completion (AQTC) has been proposed to acquire knowledge from videos to furnish users with comprehensive and systematic instructions. However, existing methods have hitherto neglected the necessity of aligning spatiotemporal visual and linguistic signals, as well as the crucial i... | ['Chen Chen', 'Wei Sun', 'Wei Miao', 'Jingwen Wang', 'Zechuan Li', 'Ming Li', 'Zhengeng Yang', 'Hongshan Yu', 'Tom Tongjia Chen'] | 2023-06-23 | null | null | null | null | ['human-object-interaction-detection'] | ['computer-vision'] | [ 1.13364249e-01 -1.92173988e-01 -1.68360211e-02 -4.16515946e-01
-9.63869929e-01 -5.63187540e-01 6.39554858e-01 6.57633021e-02
-5.20565510e-01 4.52831239e-01 4.74950999e-01 -1.28889561e-01
4.40540873e-02 6.33335188e-02 -6.30322516e-01 -2.59785146e-01
8.11376497e-02 1.11941926e-01 1.59594476e-01 -2.23788261... | [9.84041690826416, 0.8389043807983398] |
a1f59634-2c8b-4532-9451-e993a240c6be | improving-cnn-based-person-re-identification | 2307.00397 | null | https://arxiv.org/abs/2307.00397v2 | https://arxiv.org/pdf/2307.00397v2.pdf | Improving CNN-based Person Re-identification using score Normalization | Person re-identification (PRe-ID) is a crucial task in security, surveillance, and retail analysis, which involves identifying an individual across multiple cameras and views. However, it is a challenging task due to changes in illumination, background, and viewpoint. Efficient feature extraction and metric learning al... | ['Chahrazed Boudellal', 'Afaf Benzaibak', 'Shadi Atalla', 'Wathiq Mansoor', 'Yassine Himeur', 'Abdelmalik Ouamane', 'Ammar Chouchane'] | 2023-07-01 | null | null | null | null | ['person-re-identification', 'metric-learning', 'metric-learning'] | ['computer-vision', 'computer-vision', 'methodology'] | [-1.99817806e-01 -7.35138834e-01 2.98833221e-01 -3.88399482e-01
-6.23653769e-01 -4.93022323e-01 4.85923976e-01 1.12909622e-01
-6.64158881e-01 7.10490942e-01 -7.20242113e-02 3.49260956e-01
-3.01546484e-01 -6.15277946e-01 -9.75815579e-02 -7.15876579e-01
1.13660865e-01 1.53795496e-01 -1.35964036e-01 -9.52772945... | [14.659543991088867, 0.9857585430145264] |
caf0d8ab-e68a-429a-9312-2dcaabfca4d9 | v3ctron-data-retrieval-access-system-for | null | null | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4430463 | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4430463 | V3CTRON | Data Retrieval & Access System For Flexible Semantic Search & Retrieval Of Proprietary Document Collections Using Natural Language Queries. | V3CTRON is an open source vector database that allows users to upload text based documents & document collections, which are automatically embedded for super-accurate semantic search & retrieval using natural language queries. V3CTRON supports multiple vector database providers, including Milvus, LlamaIndex and Qdrant,... | ['Devin Schumacher'] | 2023-04-26 | null | null | null | social-science-research-network-ssrn-2023-4 | ['conversational-search'] | ['natural-language-processing'] | [-3.89071673e-01 -3.85644317e-01 -4.79589224e-01 -2.01393411e-01
-7.54245996e-01 -8.06330681e-01 5.71607292e-01 1.27629980e-01
-5.19009233e-01 5.71291268e-01 7.44546294e-01 -2.53870577e-01
-3.98126870e-01 -7.22794533e-01 1.55144662e-01 -1.24654226e-01
2.63802558e-01 7.20092952e-01 3.89567703e-01 -7.59911597... | [11.48068904876709, 7.904420375823975] |
f30cde1e-cf99-41a7-b04a-92ca3ea443fe | graph-neural-networks-for-3d-multi-object | 2008.09506 | null | https://arxiv.org/abs/2008.09506v1 | https://arxiv.org/pdf/2008.09506v1.pdf | Graph Neural Networks for 3D Multi-Object Tracking | 3D Multi-object tracking (MOT) is crucial to autonomous systems. Recent work often uses a tracking-by-detection pipeline, where the feature of each object is extracted independently to compute an affinity matrix. Then, the affinity matrix is passed to the Hungarian algorithm for data association. A key process of this ... | ['Kris Kitani', 'Xinshuo Weng', 'Yunze Man', 'Yongxin Wang'] | 2020-08-20 | null | null | null | null | ['3d-multi-object-tracking'] | ['computer-vision'] | [-4.39284518e-02 -5.01949310e-01 -1.09947257e-01 -1.42742053e-01
-4.30220097e-01 -4.64664310e-01 5.90819716e-01 -1.50820076e-01
-4.37499762e-01 3.80331814e-01 -1.99167818e-01 -8.10536742e-02
-1.24237493e-01 -7.14920759e-01 -6.30143642e-01 -7.50573099e-01
-4.03594822e-02 6.92209065e-01 7.84753561e-01 5.28595224... | [6.515285015106201, -2.2098731994628906] |
0f7fa78d-f966-413e-8a1b-d22fa8ce7377 | pebble-feedback-efficient-interactive | 2106.05091 | null | https://arxiv.org/abs/2106.05091v1 | https://arxiv.org/pdf/2106.05091v1.pdf | PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training | Conveying complex objectives to reinforcement learning (RL) agents can often be difficult, involving meticulous design of reward functions that are sufficiently informative yet easy enough to provide. Human-in-the-loop RL methods allow practitioners to instead interactively teach agents through tailored feedback; howev... | ['Pieter Abbeel', 'Laura Smith', 'Kimin Lee'] | 2021-06-09 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [ 2.17880487e-01 3.64109755e-01 -2.68903702e-01 -2.62728125e-01
-8.95826697e-01 -8.21043909e-01 6.67407572e-01 2.34413415e-01
-7.32925773e-01 1.12844551e+00 -2.65494317e-01 -3.36489052e-01
-1.26724884e-01 -7.00109661e-01 -9.04538453e-01 -5.97419083e-01
-4.49825764e-01 7.03759313e-01 4.28089261e-01 -5.10045230... | [4.12812614440918, 1.6280169486999512] |
78f20d98-92a5-4bc1-b7f2-523c4344ba37 | hub-dravidianlangtech-eacl2021-meme | null | null | https://aclanthology.org/2021.dravidianlangtech-1.28 | https://aclanthology.org/2021.dravidianlangtech-1.28.pdf | HUB@DravidianLangTech-EACL2021: Meme Classification for Tamil Text-Image Fusion | This article describes our system for task DravidianLangTech - EACL2021: Meme classification for Tamil. In recent years, we have witnessed the rapid development of the Internet and social media. Compared with traditional TV and radio media platforms, there are not so many restrictions on the use of online social media ... | ['Yang Bai', 'Bo Huang'] | null | null | null | null | eacl-dravidianlangtech-2021-4 | ['meme-classification'] | ['natural-language-processing'] | [-3.49633187e-01 -4.89955992e-01 1.03964970e-01 8.04583877e-02
-1.96329311e-01 -4.69141573e-01 7.79335260e-01 4.87096906e-01
-6.60198927e-01 3.93164843e-01 1.72746763e-01 2.32015513e-02
4.64785546e-01 -1.08843076e+00 -1.00455955e-01 -4.43536192e-01
3.36174130e-01 1.89790595e-02 3.66436630e-01 -7.83615232... | [8.516146659851074, 10.72548770904541] |
e6920023-7a6f-490f-a00d-29749fdf4fcb | large-language-models-as-counterfactual | 2305.14791 | null | https://arxiv.org/abs/2305.14791v1 | https://arxiv.org/pdf/2305.14791v1.pdf | Large Language Models as Counterfactual Generator: Strengths and Weaknesses | Large language models (LLMs) have demonstrated remarkable performance in a range of natural language understanding and generation tasks. Yet, their ability to generate counterfactuals, which can be used for areas like data augmentation, remains under-explored. This study aims to investigate the counterfactual generatio... | ['Tieyun Qian', 'Shen Zhou', 'Xin Miao', 'Mayi Xu', 'Yongqi Li'] | 2023-05-24 | null | null | null | null | ['sentiment-analysis', 'relation-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.56129909e-01 7.00211346e-01 -4.05072957e-01 -3.75062495e-01
-6.03341937e-01 -4.92867380e-01 1.18046439e+00 4.00921553e-02
-4.05010074e-01 1.29504979e+00 5.89653730e-01 -7.85105228e-01
1.28959715e-01 -7.74993598e-01 -8.28116179e-01 -5.58134839e-02
-9.22980011e-02 2.04516321e-01 -6.91351593e-01 -4.74886566... | [10.220510482788086, 8.157829284667969] |
69088377-090e-49ae-b3b1-2e0d44584b8a | space-air-ground-integrated-multi-domain | 2202.02459 | null | https://arxiv.org/abs/2202.02459v1 | https://arxiv.org/pdf/2202.02459v1.pdf | Space-Air-Ground Integrated Multi-domain Network Resource Orchestration based on Virtual Network Architecture: a DRL Method | Traditional ground wireless communication networks cannot provide high-quality services for artificial intelligence (AI) applications such as intelligent transportation systems (ITS) due to deployment, coverage and capacity issues. The space-air-ground integrated network (SAGIN) has become a research focus in the indus... | ['Lei Liu', 'Neeraj Kumar', 'Chao Wang', 'Peiying Zhang'] | 2022-02-03 | null | null | null | null | ['network-embedding'] | ['methodology'] | [-4.18871582e-01 5.46816625e-02 -7.02421963e-01 8.98139849e-02
2.56463587e-01 -4.17478591e-01 2.38824159e-01 -3.15977246e-01
-1.45909443e-01 1.10818708e+00 -2.14551777e-01 -4.46125537e-01
-7.14797854e-01 -1.49983454e+00 -3.23930353e-01 -8.18291664e-01
-3.92013699e-01 7.77246475e-01 3.23274434e-01 -2.87372947... | [5.875757694244385, 1.7134358882904053] |
5207146f-3b67-48f5-93ad-fffa3733e8f3 | this-email-could-save-your-life-introducing | 1906.03497 | null | https://arxiv.org/abs/1906.03497v1 | https://arxiv.org/pdf/1906.03497v1.pdf | This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation | Given the overwhelming number of emails, an effective subject line becomes essential to better inform the recipient of the email's content. In this paper, we propose and study the task of email subject line generation: automatically generating an email subject line from the email body. We create the first dataset for t... | ['Joel Tetreault', 'Rui Zhang'] | 2019-06-08 | this-email-could-save-your-life-introducing-1 | https://aclanthology.org/P19-1043 | https://aclanthology.org/P19-1043.pdf | acl-2019-7 | ['headline-generation'] | ['natural-language-processing'] | [ 5.72278023e-01 4.07058716e-01 -3.44445743e-02 -2.53170580e-01
-1.25601053e+00 -5.74427068e-01 1.20892131e+00 5.35364449e-01
-4.37631369e-01 1.19608176e+00 9.42649066e-01 -2.06863225e-01
1.43589705e-01 -5.81677794e-01 -5.93184173e-01 -1.83606502e-02
5.48791587e-01 8.40477824e-01 7.67211840e-02 -3.34594518... | [12.397648811340332, 9.393540382385254] |
3ba2f93b-087a-499b-969f-922935a96a1c | a-subjective-study-of-the-perceptual | 2212.01686 | null | https://arxiv.org/abs/2212.01686v1 | https://arxiv.org/pdf/2212.01686v1.pdf | A subjective study of the perceptual acceptability of audio-video desynchronization in sports videos | This paper presents the results of a study conducted on the perceptual acceptability of audio-video desynchronization for sports videos. The study was conducted with 45 videos generated by applying 8 audio-video offsets on 5 source contents. 20 subjects participated in the study. The results show that humans are more s... | ['Joshua Peter Ebenezer'] | 2022-12-03 | null | null | null | null | ['video-synchronization'] | ['computer-vision'] | [ 2.58021027e-01 -2.49438882e-01 -1.32222697e-01 -3.57882708e-01
-9.36983347e-01 -4.30218846e-01 2.31525321e-02 2.19533727e-01
-3.92047942e-01 5.24336100e-01 6.27578318e-01 1.69538528e-01
1.34296298e-01 -4.81969193e-02 -8.29271376e-01 -4.23643142e-01
-4.99884039e-01 -4.06434268e-01 6.86158955e-01 -1.89197600... | [15.052556991577148, 5.661042213439941] |
5497b00a-1a6e-44e3-902e-63ce3f76dfd5 | coordinet-uncertainty-aware-pose-regressor | 2103.10796 | null | https://arxiv.org/abs/2103.10796v2 | https://arxiv.org/pdf/2103.10796v2.pdf | CoordiNet: uncertainty-aware pose regressor for reliable vehicle localization | In this paper, we investigate visual-based camera re-localization with neural networks for robotics and autonomous vehicles applications. Our solution is a CNN-based algorithm which predicts camera pose (3D translation and 3D rotation) directly from a single image. It also provides an uncertainty estimate of the pose. ... | ['Arnaud de La Fortelle', 'Bogdan Stanciulescu', 'Dzmitry Tsishkou', 'Nathan Piasco', 'Arthur Moreau'] | 2021-03-19 | null | null | null | null | ['camera-localization'] | ['computer-vision'] | [-2.80806482e-01 2.88606673e-01 8.78822953e-02 -4.82804418e-01
-8.23911071e-01 -7.69906402e-01 7.21282184e-01 -1.18331723e-02
-1.08551073e+00 6.06173158e-01 -3.49453211e-01 -2.37609029e-01
2.99411416e-01 -5.65644622e-01 -1.53214216e+00 -4.53069955e-01
-1.30694807e-01 9.86204743e-01 4.80961502e-01 -1.68788627... | [7.618219375610352, -2.125690460205078] |
b620f5cc-fa42-44a8-8b4d-3faa836d2664 | utility-theory-of-synthetic-data-generation | 2305.10015 | null | https://arxiv.org/abs/2305.10015v1 | https://arxiv.org/pdf/2305.10015v1.pdf | Utility Theory of Synthetic Data Generation | Evaluating the utility of synthetic data is critical for measuring the effectiveness and efficiency of synthetic algorithms. Existing results focus on empirical evaluations of the utility of synthetic data, whereas the theoretical understanding of how utility is affected by synthetic data algorithms remains largely une... | ['Guang Cheng', 'Will Wei Sun', 'SHIRONG XU'] | 2023-05-17 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 2.98160970e-01 9.42414925e-02 -1.05926074e-01 -4.61329669e-01
-7.13000536e-01 -6.64840043e-01 6.46810830e-01 6.25410154e-02
-5.45784891e-01 8.64174724e-01 -4.81765941e-02 -1.99097902e-01
-4.33817595e-01 -7.62899876e-01 -8.25506568e-01 -8.04890037e-01
5.11592533e-03 2.55573392e-01 -2.20567614e-01 1.39865875... | [8.593117713928223, 4.244696140289307] |
fffbfedb-9074-4837-8324-2f966c3ac459 | flexible-and-structured-knowledge-grounded | 2209.08284 | null | https://arxiv.org/abs/2209.08284v3 | https://arxiv.org/pdf/2209.08284v3.pdf | Structured Knowledge Grounding for Question Answering | Can language models (LM) ground question-answering (QA) tasks in the knowledge base via inherent relational reasoning ability? While previous models that use only LMs have seen some success on many QA tasks, more recent methods include knowledge graphs (KG) to complement LMs with their more logic-driven implicit knowle... | ['Kairui Zhou', 'Siqi Ouyang', 'Yujie Lu'] | 2022-09-17 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [-1.27450541e-01 8.63772631e-01 -3.53941709e-01 -2.46425405e-01
-1.05402088e+00 -8.52279544e-01 6.55278444e-01 2.91309118e-01
-1.53152063e-01 7.10655034e-01 2.78089702e-01 -8.55279505e-01
-5.03240407e-01 -1.44452286e+00 -9.50299740e-01 1.14097677e-01
1.45450160e-01 8.14445376e-01 8.67142379e-01 -8.06791067... | [10.200711250305176, 7.835104942321777] |
c067f6bb-8b54-42a3-b728-e16674b8bef9 | spatialsense-an-adversarially-crowdsourced | 1908.0266 | null | https://arxiv.org/abs/1908.02660v2 | https://arxiv.org/pdf/1908.02660v2.pdf | SpatialSense: An Adversarially Crowdsourced Benchmark for Spatial Relation Recognition | Understanding the spatial relations between objects in images is a surprisingly challenging task. A chair may be "behind" a person even if it appears to the left of the person in the image (depending on which way the person is facing). Two students that appear close to each other in the image may not in fact be "next t... | ['Olga Russakovsky', 'Kaiyu Yang', 'Jia Deng'] | 2019-08-07 | spatialsense-an-adversarially-crowdsourced-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Yang_SpatialSense_An_Adversarially_Crowdsourced_Benchmark_for_Spatial_Relation_Recognition_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Yang_SpatialSense_An_Adversarially_Crowdsourced_Benchmark_for_Spatial_Relation_Recognition_ICCV_2019_paper.pdf | iccv-2019-10 | ['spatial-relation-recognition'] | ['computer-vision'] | [-1.19059309e-01 -1.13255540e-02 6.98656291e-02 -4.20384020e-01
-6.56130910e-01 -1.03120303e+00 9.30790603e-01 1.52249545e-01
-5.83550453e-01 4.10060614e-01 1.67699650e-01 -4.91291076e-01
-1.16945006e-01 -6.09470129e-01 -1.08515561e+00 -7.18232632e-01
1.93160757e-01 7.21484721e-01 4.60713059e-01 -3.66405666... | [10.463980674743652, 1.7307357788085938] |
c618b637-fc6e-4fea-8fd7-7117388506c5 | a-survey-on-evolutionary-computation-for | 2209.06399 | null | https://arxiv.org/abs/2209.06399v1 | https://arxiv.org/pdf/2209.06399v1.pdf | A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends | Computer vision (CV) is a big and important field in artificial intelligence covering a wide range of applications. Image analysis is a major task in CV aiming to extract, analyse and understand the visual content of images. However, image-related tasks are very challenging due to many factors, e.g., high variations ac... | ['Mengjie Zhang', 'Stefano Cagnoni', 'Pablo Mesejo', 'Bing Xue', 'Ying Bi'] | 2022-09-14 | null | null | null | null | ['edge-detection'] | ['computer-vision'] | [ 7.19164848e-01 -6.03091896e-01 7.35874996e-02 -1.42751291e-01
-9.34883878e-02 -5.08263409e-01 1.71610162e-01 1.54095158e-01
-3.35109890e-01 2.85479844e-01 -6.14792824e-01 2.71038488e-02
-2.63983518e-01 -4.97491062e-01 -2.62188286e-01 -9.91380453e-01
2.00845838e-01 -6.10223822e-02 1.67804897e-01 -7.15226457... | [9.770865440368652, -0.44271019101142883] |
5c63e00b-e5a9-4200-b4a7-1c8a8b0a80c5 | spatial-temporal-attention-network-for-open | 2211.1394 | null | https://arxiv.org/abs/2211.13940v1 | https://arxiv.org/pdf/2211.13940v1.pdf | Spatial-Temporal Attention Network for Open-Set Fine-Grained Image Recognition | Triggered by the success of transformers in various visual tasks, the spatial self-attention mechanism has recently attracted more and more attention in the computer vision community. However, we empirically found that a typical vision transformer with the spatial self-attention mechanism could not learn accurate atten... | ['Qiulei Dong', 'Hong Wang', 'Jiayin Sun'] | 2022-11-25 | null | null | null | null | ['fine-grained-image-recognition', 'open-set-learning'] | ['computer-vision', 'miscellaneous'] | [ 8.02144930e-02 -3.12974244e-01 1.75855577e-01 -4.31957275e-01
-5.11850297e-01 -2.42699504e-01 6.35870814e-01 -7.73419961e-02
-2.01284558e-01 5.07092476e-01 1.61289126e-01 7.44284317e-02
-6.13760889e-01 -8.44889998e-01 -8.49838436e-01 -8.73749077e-01
-1.28754765e-01 2.05921665e-01 6.65383399e-01 1.19298451... | [9.677848815917969, 2.0548439025878906] |
1a80665f-26bd-40dc-86c3-16ab88c9613c | a-novel-method-for-comparative-analysis-of | 1403.1523 | null | http://arxiv.org/abs/1403.1523v2 | http://arxiv.org/pdf/1403.1523v2.pdf | A Novel Method for Comparative Analysis of DNA Sequences by Ramanujan-Fourier Transform | Alignment-free sequence analysis approaches provide important alternatives
over multiple sequence alignment (MSA) in biological sequence analysis because
alignment-free approaches have low computation complexity and are not dependent
on high level of sequence identity, however, most of the existing
alignment-free metho... | ['Changchuan Yin', 'Xuemeng E. Yin', 'Jiasong Wang'] | 2014-03-06 | null | null | null | null | ['multiple-sequence-alignment'] | ['medical'] | [ 8.99765074e-01 -7.53436208e-01 -1.27958804e-01 -2.58437663e-01
-4.01279628e-01 -8.20956230e-01 1.28071070e-01 4.70274925e-01
-6.31032884e-01 8.59875321e-01 -1.75309107e-01 -3.26878786e-01
-3.43202889e-01 -7.94385135e-01 -3.35145414e-01 -1.33750987e+00
-1.58283412e-01 2.91500181e-01 3.33896995e-01 -2.05608636... | [4.874583721160889, 5.296675682067871] |
99d9432d-84c5-4841-9385-ad407805d017 | docut5-seq2seq-sql-generation-with-table | 2211.06193 | null | https://arxiv.org/abs/2211.06193v1 | https://arxiv.org/pdf/2211.06193v1.pdf | DocuT5: Seq2seq SQL Generation with Table Documentation | Current SQL generators based on pre-trained language models struggle to answer complex questions requiring domain context or understanding fine-grained table structure. Humans would deal with these unknowns by reasoning over the documentation of the tables. Based on this hypothesis, we propose DocuT5, which uses off-th... | ['Jeffrey Dalton', 'Iain Mackie', 'Elena Soare'] | 2022-11-11 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [-2.53400922e-01 3.36161733e-01 -2.88379937e-01 -5.36624134e-01
-1.37160897e+00 -1.01653397e+00 3.28981340e-01 3.63373429e-01
-4.43502590e-02 8.80035520e-01 4.08468992e-01 -6.60181046e-01
7.46558793e-03 -1.15161407e+00 -1.32464850e+00 4.48179007e-01
1.71463639e-01 9.76658821e-01 5.66483974e-01 -6.80049002... | [9.912863731384277, 7.84554386138916] |
46b20ca8-e3f2-4510-b583-2004db1dc816 | patchwork-learning-a-paradigm-towards | 2305.06217 | null | https://arxiv.org/abs/2305.06217v2 | https://arxiv.org/pdf/2305.06217v2.pdf | Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sources | Machine learning (ML) in healthcare presents numerous opportunities for enhancing patient care, population health, and healthcare providers' workflows. However, the real-world clinical and cost benefits remain limited due to challenges in data privacy, heterogeneous data sources, and the inability to fully leverage mul... | ['Yong Chen', 'Fei Wang', 'Jiayu Zhou', 'Mert R. Sabuncu', 'Weishen Pan', 'Suraj Rajendran'] | 2023-05-10 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [ 1.61751315e-01 1.12666413e-01 -7.38310635e-01 -4.59602058e-01
-1.15497017e+00 -5.53428233e-01 1.19005233e-01 9.02383029e-01
-2.82114357e-01 6.01579666e-01 5.81801832e-01 -4.07795787e-01
-4.58594292e-01 -4.99407858e-01 -3.10849965e-01 -6.25646770e-01
-3.81821468e-02 2.50446141e-01 -7.69364715e-01 2.49672219... | [6.192500591278076, 6.502936840057373] |
b751f020-2d4a-4d3d-ba5d-d6ad6766ffb8 | a-robust-iris-authentication-system-on-gpu | 1912.00756 | null | https://arxiv.org/abs/1912.00756v2 | https://arxiv.org/pdf/1912.00756v2.pdf | Learning scale-variant features for robust iris authentication with deep learning based ensemble framework | In recent years, mobile Internet has accelerated the proliferation of smart mobile development. The mobile payment, mobile security and privacy protection have become the focus of widespread attention. Iris recognition becomes a high-security authentication technology in these fields, it is widely used in distinct scie... | ['Nurul Amelina Nasharuddin', 'Rahmita Wirza O. K. Rahmat', 'Fatimah Khalid', 'Siming Zheng'] | 2019-12-02 | null | null | null | null | ['mobile-security'] | ['miscellaneous'] | [ 2.24393964e-01 -5.22518277e-01 -2.23364934e-01 -1.61132038e-01
1.60153195e-01 -1.08770445e-01 1.79271758e-01 -2.90441632e-01
-5.56604147e-01 2.16176212e-01 -1.41197518e-01 -6.25444353e-01
-3.84623557e-01 -7.35938728e-01 -2.97538221e-01 -9.61423337e-01
3.20224315e-01 -1.49172783e-01 -2.34675273e-01 -2.10811213... | [3.7703969478607178, -3.6165342330932617] |
ba0bc57d-5f5d-49ee-baa0-3b6dba7d74f7 | survival-analysis-meets-counterfactual | 2006.07756 | null | https://arxiv.org/abs/2006.07756v2 | https://arxiv.org/pdf/2006.07756v2.pdf | Enabling Counterfactual Survival Analysis with Balanced Representations | Balanced representation learning methods have been applied successfully to counterfactual inference from observational data. However, approaches that account for survival outcomes are relatively limited. Survival data are frequently encountered across diverse medical applications, i.e., drug development, risk profiling... | ['Michael J. Pencina', 'Paidamoyo Chapfuwa', 'Shuxi Zeng', 'Lawrence Carin', 'Serge Assaad', 'Ricardo Henao'] | 2020-06-14 | enabling-counterfactual-survival-analysis | https://openreview.net/forum?id=3ZeGLibhFo0 | https://openreview.net/pdf?id=3ZeGLibhFo0 | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 5.47351837e-01 -2.15065077e-01 -8.66846025e-01 -5.53189754e-01
-1.00460112e+00 -1.61161825e-01 2.76584208e-01 5.95781624e-01
-2.18506634e-01 1.39306772e+00 2.50153124e-01 -7.95764506e-01
-5.37312388e-01 -6.97140694e-01 -6.02498651e-01 -9.58192885e-01
-4.00282204e-01 3.11966985e-01 -7.65830576e-01 3.20901781... | [8.007707595825195, 5.415359020233154] |
e697e367-25c6-44e9-9781-466d312facb0 | urban-scene-semantic-segmentation-with-low | 2212.07911 | null | https://arxiv.org/abs/2212.07911v1 | https://arxiv.org/pdf/2212.07911v1.pdf | Urban Scene Semantic Segmentation with Low-Cost Coarse Annotation | For best performance, today's semantic segmentation methods use large and carefully labeled datasets, requiring expensive annotation budgets. In this work, we show that coarse annotation is a low-cost but highly effective alternative for training semantic segmentation models. Considering the urban scene segmentation sc... | ['Bernt Schiele', 'Zeynep Akata', 'Yang He', 'Yongqin Xian', 'Anurag Das'] | 2022-12-15 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 2.41284370e-01 4.95873898e-01 -4.70804483e-01 -6.02245510e-01
-1.14782214e+00 -6.36945307e-01 4.70650047e-01 2.66997628e-02
-5.88371933e-01 8.36854577e-01 -1.14137689e-02 -1.04598194e-01
4.75955039e-01 -7.77033269e-01 -8.77175570e-01 -3.38207394e-01
3.24505448e-01 1.05788124e+00 7.62407601e-01 -1.68109108... | [9.508354187011719, 0.6279402375221252] |
f9adf6a4-4f30-4efb-a8f7-1ae8eb99b2d9 | restricted-forensic-levenshtein-distance | 2203.06138 | null | https://arxiv.org/abs/2203.06138v3 | https://arxiv.org/pdf/2203.06138v3.pdf | A New String Edit Distance and Applications | String edit distances have been used for decades in applications ranging from spelling correction and web search suggestions to DNA analysis. Most string edit distances are variations of the Levenshtein distance and consider only single-character edits. In forensic applications polymorphic genetic markers such as short... | ['Hari Iyer', 'Tunde I Huszar', 'Jan Hannig', 'Taylor Petty'] | 2022-03-11 | null | null | null | null | ['dna-analysis', 'spelling-correction'] | ['medical', 'natural-language-processing'] | [ 8.60683322e-01 -3.48343283e-01 1.25466689e-01 -4.53258395e-01
-2.12277532e-01 -9.55735266e-01 4.60287601e-01 8.69076312e-01
-7.29718626e-01 8.17213178e-01 -2.79141694e-01 -3.81366879e-01
-2.03007817e-01 -8.32736313e-01 -4.15421307e-01 -9.01866317e-01
-3.26077431e-01 5.68544984e-01 5.99434972e-01 -1.98059946... | [4.909133434295654, 5.192310810089111] |
ae6d9ebd-c864-4099-8f5d-3f2db8a36c8c | anea-distant-supervision-for-low-resource | 2102.13129 | null | https://arxiv.org/abs/2102.13129v2 | https://arxiv.org/pdf/2102.13129v2.pdf | ANEA: Distant Supervision for Low-Resource Named Entity Recognition | Distant supervision allows obtaining labeled training corpora for low-resource settings where only limited hand-annotated data exists. However, to be used effectively, the distant supervision must be easy to gather. In this work, we present ANEA, a tool to automatically annotate named entities in texts based on entity ... | ['Dietrich Klakow', 'Lukas Lange', 'Michael A. Hedderich'] | 2021-02-25 | null | null | null | null | ['low-resource-named-entity-recognition'] | ['natural-language-processing'] | [-8.35129693e-02 5.06036341e-01 -2.66107589e-01 -7.21744776e-01
-1.21709561e+00 -1.03004670e+00 4.08705175e-01 5.56041121e-01
-7.42248297e-01 1.07009017e+00 1.74906403e-01 -3.43332946e-01
-2.41269413e-02 -2.92939395e-01 -6.59954906e-01 -2.27308005e-01
2.29404554e-01 8.53014648e-01 2.95008123e-01 -1.27245143... | [9.697881698608398, 9.245559692382812] |
78567fbf-8799-4d17-b524-fdb7ad5dc593 | mgtr-end-to-end-mutual-gaze-detection-with | 2209.1093 | null | https://arxiv.org/abs/2209.10930v2 | https://arxiv.org/pdf/2209.10930v2.pdf | MGTR: End-to-End Mutual Gaze Detection with Transformer | People's looking at each other or mutual gaze is ubiquitous in our daily interactions, and detecting mutual gaze is of great significance for understanding human social scenes. Current mutual gaze detection methods focus on two-stage methods, whose inference speed is limited by the two-stage pipeline and the performanc... | ['Jingtai Liu', 'Zhengxi Hu', 'Hang Guo'] | 2022-09-22 | null | null | null | null | ['mutual-gaze'] | ['computer-vision'] | [-9.64009762e-02 9.46813822e-02 -2.27429047e-02 -4.62606549e-01
-9.60506871e-02 -2.13165253e-01 3.65044355e-01 -1.83527201e-01
-3.19620341e-01 1.10070765e-01 8.37119371e-02 -1.20230347e-01
-6.02617511e-05 -3.75953317e-01 -4.03211117e-01 -4.59475100e-01
3.15232962e-01 -1.01125045e-02 4.42545921e-01 -1.38528794... | [14.093866348266602, 0.04494749382138252] |
7cb390fa-413d-4be6-a847-c3b4500a42ab | towards-end-to-end-generative-modeling-of | 2303.11251 | null | https://arxiv.org/abs/2303.11251v3 | https://arxiv.org/pdf/2303.11251v3.pdf | Towards End-to-End Generative Modeling of Long Videos with Memory-Efficient Bidirectional Transformers | Autoregressive transformers have shown remarkable success in video generation. However, the transformers are prohibited from directly learning the long-term dependency in videos due to the quadratic complexity of self-attention, and inherently suffering from slow inference time and error propagation due to the autoregr... | ['Seunghoon Hong', 'Chiheon Kim', 'Doyup Lee', 'Semin Kim', 'Jaehoon Yoo'] | 2023-03-20 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yoo_Towards_End-to-End_Generative_Modeling_of_Long_Videos_With_Memory-Efficient_Bidirectional_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yoo_Towards_End-to-End_Generative_Modeling_of_Long_Videos_With_Memory-Efficient_Bidirectional_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-generation'] | ['computer-vision'] | [ 1.68524772e-01 4.21770439e-02 7.83363059e-02 -2.20856071e-01
-1.10811317e+00 -4.39798594e-01 5.78196585e-01 -4.68679518e-01
3.14589753e-03 5.88577628e-01 4.71347481e-01 -1.81799129e-01
2.70722806e-02 -5.71999192e-01 -1.35597646e+00 -7.86122143e-01
-3.08295429e-01 2.21621007e-01 -5.19635826e-02 3.31292301... | [10.668998718261719, -0.668192446231842] |
9224b1d1-dfe9-43de-9670-697be4bbcbb2 | cut-and-approximate-3d-shape-reconstruction | 2210.12509 | null | https://arxiv.org/abs/2210.12509v1 | https://arxiv.org/pdf/2210.12509v1.pdf | Cut-and-Approximate: 3D Shape Reconstruction from Planar Cross-sections with Deep Reinforcement Learning | Current methods for 3D object reconstruction from a set of planar cross-sections still struggle to capture detailed topology or require a considerable number of cross-sections. In this paper, we present, to the best of our knowledge the first 3D shape reconstruction network to solve this task which additionally uses or... | ['Azimkhon Ostonov'] | 2022-10-22 | null | null | null | null | ['3d-object-reconstruction', '3d-shape-reconstruction', 'object-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 8.41015428e-02 5.62907100e-01 2.10004076e-01 -1.00357033e-01
-5.48521876e-01 -5.12443841e-01 6.39979482e-01 1.77512094e-01
-3.28759491e-01 8.86545062e-01 -3.41645390e-01 -1.89739168e-01
-3.04090418e-02 -8.74947786e-01 -1.14043415e+00 -3.21145117e-01
-2.89654285e-01 1.17413330e+00 2.44886130e-01 -1.89444840... | [4.758941650390625, 0.4571479558944702] |
aa4f2f9e-f628-4e83-9468-67935ebdfa5a | learning-emotion-aware-contextual-1 | null | null | https://openreview.net/forum?id=8yJ0RpqNppY | https://openreview.net/pdf?id=8yJ0RpqNppY | Learning Emotion-Aware Contextual Representations for Emotion Cause Analysis | Emotion Cause Analysis has been a key topic in natural language processing. Previous works focus on Emotion Cause Extraction (ECE), a clause-level classification task aimed at extracting causes of certain given emotion in text. The task has been expanded to Emotion Cause Pair Extraction (ECPE) that focus on extracting ... | ['Anonymous'] | 2021-09-17 | null | null | null | acl-arr-september-2021-9 | ['emotion-cause-pair-extraction', 'emotion-cause-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.36650485e-01 2.67890126e-01 -3.04235388e-02 -6.02031648e-01
-9.10937786e-01 -5.59005916e-01 6.68739796e-01 4.66645062e-01
-3.32598805e-01 6.71685159e-01 4.06670123e-01 -1.05336038e-02
-1.12536503e-03 -6.52097285e-01 -4.30003852e-01 -4.89628136e-01
-8.90539438e-02 -4.97241393e-02 -1.09566934e-01 -2.93669194... | [12.630476951599121, 6.216465950012207] |
66b9a793-2659-4b04-b5f8-6829fc98b16a | counterfactual-reasoning-for-fair-clinical | 1907.0626 | null | https://arxiv.org/abs/1907.06260v1 | https://arxiv.org/pdf/1907.06260v1.pdf | Counterfactual Reasoning for Fair Clinical Risk Prediction | The use of machine learning systems to support decision making in healthcare raises questions as to what extent these systems may introduce or exacerbate disparities in care for historically underrepresented and mistreated groups, due to biases implicitly embedded in observational data in electronic health records. To ... | ['Nigam H. Shah', 'Tony Duan', 'Stephen Pfohl', 'Daisy Yi Ding'] | 2019-07-14 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 5.87730348e-01 9.67630088e-01 -4.02243406e-01 -5.61115682e-01
-1.62088960e-01 -2.97216445e-01 6.16052806e-01 5.81427753e-01
-6.65897429e-01 8.85825336e-01 7.60785758e-01 -9.35753405e-01
-5.55145204e-01 -1.07872605e+00 -7.64903724e-01 -4.86799330e-01
-2.26701081e-01 6.35141969e-01 -6.84706211e-01 2.12361097... | [8.2918701171875, 5.519822120666504] |
1fce28c9-d175-4df1-a61d-df71506aec38 | word-substitution-in-short-answer-extraction | null | null | https://aclanthology.org/2016.gwc-1.11 | https://aclanthology.org/2016.gwc-1.11.pdf | Word Substitution in Short Answer Extraction: A WordNet-based Approach | We describe the implementation of a short answer extraction system. It consists of a simple sentence selection front-end and a two phase approach to answer extraction from a sentence. In the first phase sentence classification is performed with a classifier trained with the passive aggressive algorithm utilizing the UI... | ['Adam Pease', 'Gerald Kurlandski', 'Maochen Guan', 'James Gung', 'Qingqing Cai'] | null | null | null | null | gwc-2016-1 | ['sentence-classification'] | ['natural-language-processing'] | [ 4.39009845e-01 4.72111069e-02 -5.24467342e-02 -5.89198232e-01
-9.64458883e-01 -8.34370792e-01 4.28008169e-01 8.01952660e-01
-1.05489445e+00 9.78423417e-01 4.86371219e-01 -5.07284343e-01
-4.48337160e-02 -7.79101968e-01 2.60862559e-01 -1.85660452e-01
1.72587961e-01 8.91201854e-01 7.28186011e-01 -6.94471478... | [11.987848281860352, 9.427606582641602] |
dc35a310-19f6-41b5-aaa5-a578f8294a9b | attention-based-modeling-for-emotion | 1906.0702 | null | https://arxiv.org/abs/1906.07020v1 | https://arxiv.org/pdf/1906.07020v1.pdf | Attention-based Modeling for Emotion Detection and Classification in Textual Conversations | This paper addresses the problem of modeling textual conversations and detecting emotions. Our proposed model makes use of 1) deep transfer learning rather than the classical shallow methods of word embedding; 2) self-attention mechanisms to focus on the most important parts of the texts and 3) turn-based conversationa... | ['Jérôme Azé', 'Waleed Ragheb', 'Sandra Bringay', 'Maximilien Servajean'] | 2019-06-14 | null | null | null | null | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [-1.31878287e-01 2.86256760e-01 -1.01011053e-01 -6.38339520e-01
-5.60278714e-01 -1.50343224e-01 9.44692612e-01 4.53310281e-01
-7.21353531e-01 6.68028355e-01 7.41741598e-01 -3.08920264e-01
3.90653163e-01 -5.84523857e-01 -1.84659749e-01 -4.58422363e-01
-7.44946003e-02 5.70949674e-01 -2.33974785e-01 -6.86231136... | [12.979376792907715, 6.19352388381958] |
54ee8427-921a-4a8f-9c0d-2bf94998743f | aim-adapting-image-models-for-efficient-video | 2302.03024 | null | https://arxiv.org/abs/2302.03024v1 | https://arxiv.org/pdf/2302.03024v1.pdf | AIM: Adapting Image Models for Efficient Video Action Recognition | Recent vision transformer based video models mostly follow the ``image pre-training then finetuning" paradigm and have achieved great success on multiple video benchmarks. However, full finetuning such a video model could be computationally expensive and unnecessary, given the pre-trained image transformer models have ... | ['Mu Li', 'Chen Chen', 'Aston Zhang', 'Yusheng Xie', 'Yi Zhu', 'Taojiannan Yang'] | 2023-02-06 | null | null | null | null | ['action-classification', 'video-understanding'] | ['computer-vision', 'computer-vision'] | [ 3.27272087e-01 6.79525435e-02 -3.74495745e-01 -2.42623851e-01
-5.54511309e-01 -6.06925368e-01 6.62869573e-01 -6.65308475e-01
-3.01486820e-01 4.45071161e-01 3.44774187e-01 -2.36400589e-01
1.28641814e-01 -5.20964444e-01 -1.23680639e+00 -3.71951759e-01
2.08106995e-01 1.84062093e-01 5.36490262e-01 -1.25491202... | [9.11372184753418, 0.7227661609649658] |
79cd2143-6069-4100-8a5f-691e2940e99a | a-densely-connected-criss-cross-attention | 2203.13953 | null | https://arxiv.org/abs/2203.13953v1 | https://arxiv.org/pdf/2203.13953v1.pdf | A Densely Connected Criss-Cross Attention Network for Document-level Relation Extraction | Document-level relation extraction (RE) aims to identify relations between two entities in a given document. Compared with its sentence-level counterpart, document-level RE requires complex reasoning. Previous research normally completed reasoning through information propagation on the mention-level or entity-level doc... | ['Yidong Cheng', 'Liang Zhang'] | 2022-03-26 | null | null | null | null | ['document-level-relation-extraction'] | ['natural-language-processing'] | [-3.37757796e-01 4.97505516e-01 -4.33945835e-01 -3.10798645e-01
-6.94100559e-01 -4.30777699e-01 7.25584686e-01 5.16952813e-01
-1.10598959e-01 6.30961239e-01 6.51416838e-01 -6.15469217e-01
-5.15354276e-01 -1.35691857e+00 -9.35627520e-01 1.28784357e-02
-7.16237025e-03 6.65995777e-01 1.55255094e-01 -4.77617592... | [9.178617477416992, 8.530251502990723] |
3fcd544e-c51a-4d80-97fd-74e64693b2aa | trajectory-guided-control-prediction-for-end | 2206.08129 | null | https://arxiv.org/abs/2206.08129v2 | https://arxiv.org/pdf/2206.08129v2.pdf | Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline | Current end-to-end autonomous driving methods either run a controller based on a planned trajectory or perform control prediction directly, which have spanned two separately studied lines of research. Seeing their potential mutual benefits to each other, this paper takes the initiative to explore the combination of the... | ['Yu Qiao', 'Hongyang Li', 'Junchi Yan', 'Li Chen', 'Xiaosong Jia', 'Penghao Wu'] | 2022-06-16 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [-4.30968404e-02 4.64368105e-01 -4.60195661e-01 -4.41745281e-01
-6.61520243e-01 -6.84361398e-01 1.05904448e+00 1.92406196e-02
-2.91193843e-01 6.61194503e-01 6.04305193e-02 -5.88397563e-01
-1.86854154e-01 -8.96216929e-01 -7.28225589e-01 -6.85272753e-01
-4.01800647e-02 4.58416015e-01 5.16737044e-01 -5.32090366... | [5.692533016204834, 0.9589014053344727] |
abf420c1-9b3e-4468-aaf1-67ac4062063e | detecting-label-errors-in-token | 2210.0392 | null | https://arxiv.org/abs/2210.03920v1 | https://arxiv.org/pdf/2210.03920v1.pdf | Detecting Label Errors in Token Classification Data | Mislabeled examples are a common issue in real-world data, particularly for tasks like token classification where many labels must be chosen on a fine-grained basis. Here we consider the task of finding sentences that contain label errors in token classification datasets. We study 11 different straightforward methods t... | ['Jonas Mueller', 'Wei-Chen Wang'] | 2022-10-08 | null | null | null | null | ['classification'] | ['methodology'] | [ 1.49526700e-01 -1.49788022e-01 -4.06869739e-01 -7.70281732e-01
-1.03254378e+00 -6.55834973e-01 5.75826228e-01 8.45733583e-01
-8.96654606e-01 1.08927071e+00 9.62937996e-03 -1.20657295e-01
1.90682545e-01 -7.13290751e-01 -4.85484660e-01 -3.48819077e-01
1.76117733e-01 6.72627509e-01 7.47904703e-02 2.27862328... | [9.78543758392334, 9.60146427154541] |
b47e7e2b-bb76-4a31-a7e2-4ede716ba62c | reviewrobot-explainable-paper-review | 2010.06119 | null | https://arxiv.org/abs/2010.06119v3 | https://arxiv.org/pdf/2010.06119v3.pdf | ReviewRobot: Explainable Paper Review Generation based on Knowledge Synthesis | To assist human review process, we build a novel ReviewRobot to automatically assign a review score and write comments for multiple categories such as novelty and meaningful comparison. A good review needs to be knowledgeable, namely that the comments should be constructive and informative to help improve the paper; an... | ['Nazneen Fatema Rajani', 'Heng Ji', 'Kevin Knight', 'Lifu Huang', 'Qi Zeng', 'Qingyun Wang'] | 2020-10-13 | null | https://aclanthology.org/2020.inlg-1.44 | https://aclanthology.org/2020.inlg-1.44.pdf | inlg-acl-2020-12 | ['review-generation'] | ['natural-language-processing'] | [-6.87164292e-02 6.24305665e-01 -7.32410252e-01 -4.95180726e-01
-9.36578870e-01 -6.45734489e-01 4.74780440e-01 5.08191288e-01
-8.22811052e-02 9.23003435e-01 9.64534134e-02 -3.44992906e-01
-1.51250586e-01 -7.16896057e-01 -5.60988247e-01 4.13679667e-02
4.94902760e-01 2.27257088e-01 3.75559449e-01 1.03405029... | [12.294317245483398, 9.500164031982422] |
c141237d-be41-4fc4-8fad-730795ba32e1 | uncertainty-sensitive-learning-and-planning | null | null | https://openreview.net/forum?id=SkglVlSFPS | https://openreview.net/pdf?id=SkglVlSFPS | Uncertainty - sensitive learning and planning with ensembles | We propose a reinforcement learning framework for discrete environments in which an agent optimizes its behavior on two timescales. For the short one, it uses tree search methods to perform tactical decisions. The long strategic level is handled with an ensemble of value functions learned using $TD$-like backups. Combi... | ['Maciej Klimek', 'Piotr Kozakowski', 'Konrad Czechowski', 'Łukasz Kuciński', 'Piotr Miłoś'] | 2019-09-25 | null | null | null | null | ['montezumas-revenge'] | ['playing-games'] | [-2.31865510e-01 4.02754515e-01 1.87844597e-02 8.61710906e-02
-7.72848189e-01 -6.93625569e-01 6.27984703e-01 4.02707636e-01
-8.73859704e-01 1.29589593e+00 1.38117343e-01 -2.66280502e-01
-5.72407067e-01 -9.93882000e-01 -7.20034063e-01 -1.03543973e+00
-6.84059680e-01 5.05131900e-01 1.69846609e-01 -6.38923466... | [3.9930639266967773, 2.0512382984161377] |
610155cc-4988-4f1b-8c96-6c41a8c616a4 | bayesian-optimization-with-formal-safety | 2306.17815 | null | https://arxiv.org/abs/2306.17815v1 | https://arxiv.org/pdf/2306.17815v1.pdf | Bayesian Optimization with Formal Safety Guarantees via Online Conformal Prediction | Black-box zero-th order optimization is a central primitive for applications in fields as diverse as finance, physics, and engineering. In a common formulation of this problem, a designer sequentially attempts candidate solutions, receiving noisy feedback on the value of each attempt from the system. In this paper, we ... | ['Osvaldo Simeone', 'Sangwoo Park', 'Yunchuan Zhang'] | 2023-06-30 | null | null | null | null | ['conformal-prediction', 'bayesian-optimization', 'conformal-prediction'] | ['computer-vision', 'methodology', 'reasoning'] | [ 2.54210621e-01 3.76465172e-01 -4.38198969e-02 -1.33918494e-03
-4.72568661e-01 -3.76600206e-01 3.04345667e-01 4.97238159e-01
-3.90322745e-01 7.92433858e-01 -3.34984094e-01 -2.77477652e-01
-6.87249780e-01 -8.86071801e-01 -7.92132437e-01 -9.87787068e-01
6.85921311e-02 -6.24512555e-03 4.21639793e-02 -1.05696060... | [5.338906288146973, 3.343168258666992] |
a9c8a7f0-a6b8-413d-aa60-608721b2e731 | robust-channel-wise-illumination-estimation | 2111.05681 | null | https://arxiv.org/abs/2111.05681v1 | https://arxiv.org/pdf/2111.05681v1.pdf | Robust channel-wise illumination estimation | Recently, Convolutional Neural Networks (CNNs) have been widely used to solve the illuminant estimation problem and have often led to state-of-the-art results. Standard approaches operate directly on the input image. In this paper, we argue that this problem can be decomposed into three channel-wise independent and sym... | ['Moncef Gabbouj', 'Alexandros Iosifidis', 'Jarno Nikkanen', 'Jenni Raitoharju', 'Firas Laakom'] | 2021-11-10 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 2.96998739e-01 -1.94814846e-01 2.41982684e-01 -3.90238047e-01
-8.55495751e-01 -3.48826319e-01 3.70386899e-01 -1.23149976e-01
-4.86414909e-01 8.86823058e-01 -4.96640176e-01 -1.92403734e-01
-1.13442034e-01 -5.54116964e-01 -8.63228142e-01 -1.05587375e+00
3.14238727e-01 1.00953966e-01 -6.33841455e-02 2.56279856... | [10.310770034790039, -2.5118136405944824] |
e9eeec4c-67f4-45c2-90c7-28493bec7826 | back-to-the-future-knowledge-distillation-for | 1904.04868 | null | https://arxiv.org/abs/1904.04868v2 | https://arxiv.org/pdf/1904.04868v2.pdf | Knowledge Distillation for Human Action Anticipation | We consider the task of training a neural network to anticipate human actions in video. This task is challenging given the complexity of video data, the stochastic nature of the future, and the limited amount of annotated training data. In this paper, we propose a novel knowledge distillation framework that uses an act... | ['Vinh Tran', 'Minh Hoai', 'Yang Wang'] | 2019-04-09 | null | null | null | null | ['action-anticipation'] | ['computer-vision'] | [ 5.02159655e-01 3.14186662e-01 -2.68870592e-01 -5.07596970e-01
-1.67843997e-01 -3.01409483e-01 5.85953295e-01 -1.50745228e-01
-6.90713286e-01 7.12980390e-01 3.27352524e-01 -5.04554398e-02
-1.75925121e-01 -5.78931212e-01 -9.38530684e-01 -5.13746500e-01
-3.45209718e-01 3.67106318e-01 6.24008358e-01 -8.73402283... | [8.395657539367676, 0.5376204252243042] |
4dcc1a41-9e82-4a7e-89d0-c5adfb07b273 | learning-symmetry-consistent-deep-cnns-for | 1812.07741 | null | http://arxiv.org/abs/1812.07741v1 | http://arxiv.org/pdf/1812.07741v1.pdf | Learning Symmetry Consistent Deep CNNs for Face Completion | Deep convolutional networks (CNNs) have achieved great success in face
completion to generate plausible facial structures. These methods, however, are
limited in maintaining global consistency among face components and recovering
fine facial details. On the other hand, reflectional symmetry is a prominent
property of f... | ['WangMeng Zuo', 'Guosheng Hu', 'Meng Wang', 'Lei Zhang', 'Jieru Zhu', 'Xiaoming Li', 'Ming Liu'] | 2018-12-19 | null | null | null | null | ['facial-inpainting'] | ['computer-vision'] | [ 1.66456893e-01 3.80959570e-01 9.64195952e-02 -5.66599667e-01
-3.31197023e-01 -2.47489899e-01 5.50212502e-01 -1.03802443e+00
3.36816519e-01 5.04832208e-01 5.27121842e-01 1.83736056e-01
-3.74624468e-02 -8.03127348e-01 -9.53685999e-01 -7.32765615e-01
3.51784497e-01 4.90790419e-02 -4.88079607e-01 -1.86526865... | [12.776800155639648, -0.05653838440775871] |
4c4f0f12-0de7-48f9-96e1-aff5f2098406 | uncertainty-aware-label-distribution-learning | 2209.10448 | null | https://arxiv.org/abs/2209.10448v1 | https://arxiv.org/pdf/2209.10448v1.pdf | Uncertainty-aware Label Distribution Learning for Facial Expression Recognition | Despite significant progress over the past few years, ambiguity is still a key challenge in Facial Expression Recognition (FER). It can lead to noisy and inconsistent annotation, which hinders the performance of deep learning models in real-world scenarios. In this paper, we propose a new uncertainty-aware label distri... | ['Anh Nguyen', 'Bac Le', 'Erman Tjiputra', 'Quang Tran', 'Khanh Nguyen', 'Nhat Le'] | 2022-09-21 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [-2.77533904e-02 -1.30774662e-01 -2.95581967e-01 -1.24577272e+00
-9.06380594e-01 -4.65272665e-01 1.62898704e-01 -3.44246656e-01
-3.67085278e-01 8.06286931e-01 1.93658508e-02 1.03495635e-01
1.59000233e-01 -2.79262275e-01 -5.12139201e-01 -6.37156844e-01
2.24047273e-01 3.52315634e-01 -3.90763313e-01 3.85941081... | [13.67630672454834, 1.6823756694793701] |
f76747c9-c520-43af-81f1-188ed60f1e0d | few-shot-fine-grained-image-classification | 2207.08547 | null | https://arxiv.org/abs/2207.08547v2 | https://arxiv.org/pdf/2207.08547v2.pdf | Few-shot Fine-grained Image Classification via Multi-Frequency Neighborhood and Double-cross Modulation | Traditional fine-grained image classification typically relies on large-scale training samples with annotated ground-truth. However, some sub-categories have few available samples in real-world applications, and current few-shot models still have difficulty in distinguishing subtle differences among fine-grained catego... | ['Chengqing Li', 'Yange Zhou', 'Jiayi Wang', 'Zhan Gao', 'Hegui Zhu'] | 2022-07-18 | null | null | null | null | ['fine-grained-image-classification'] | ['computer-vision'] | [ 3.24859768e-02 -6.59000456e-01 -3.81908834e-01 -4.27165866e-01
-6.69795930e-01 -2.42183074e-01 5.57850599e-01 6.14399500e-02
-2.66714752e-01 6.94769382e-01 1.52612731e-01 4.31285173e-01
-2.44497493e-01 -8.90289009e-01 -4.57590342e-01 -7.24571884e-01
2.28164509e-01 -6.24731109e-02 8.38040113e-01 -2.58615136... | [9.686807632446289, 2.0359251499176025] |
688872ca-8253-43bc-81b8-694c806abbff | spts-v2-single-point-scene-text-spotting | 2301.01635 | null | https://arxiv.org/abs/2301.01635v2 | https://arxiv.org/pdf/2301.01635v2.pdf | SPTS v2: Single-Point Scene Text Spotting | End-to-end scene text spotting has made significant progress due to its intrinsic synergy between text detection and recognition. Previous methods commonly regard manual annotations such as horizontal rectangles, rotated rectangles, quadrangles, and polygons as a prerequisite, which are much more expensive than using s... | ['Lianwen Jin', 'Xiang Bai', 'Chunhua Shen', 'Dahua Lin', 'Can Huang', 'Jingqun Tang', 'Xinyu Wang', 'Mingxin Huang', 'Dezhi Peng', 'Jiaxin Zhang', 'Yuliang Liu'] | 2023-01-04 | null | null | null | null | ['text-spotting'] | ['computer-vision'] | [ 4.51312065e-01 -2.09586307e-01 -7.40643218e-02 -2.31986508e-01
-9.03996766e-01 -4.42544252e-01 5.79771161e-01 -2.08142456e-02
-3.72300625e-01 1.64645329e-01 -1.67792231e-01 -5.39450765e-01
2.53419697e-01 -6.51974857e-01 -8.41920257e-01 -6.48344815e-01
4.35888380e-01 6.88341856e-01 3.40342015e-01 -8.55804980... | [12.027835845947266, 2.271174907684326] |
0c161e8b-782c-4007-a712-fa02046e4f31 | parametrically-retargetable-decision-makers | 2206.13477 | null | https://arxiv.org/abs/2206.13477v2 | https://arxiv.org/pdf/2206.13477v2.pdf | Parametrically Retargetable Decision-Makers Tend To Seek Power | If capable AI agents are generally incentivized to seek power in service of the objectives we specify for them, then these systems will pose enormous risks, in addition to enormous benefits. In fully observable environments, most reward functions have an optimal policy which seeks power by keeping options open and stay... | ['Prasad Tadepalli', 'Alexander Matt Turner'] | 2022-06-27 | null | null | null | null | ['montezumas-revenge'] | ['playing-games'] | [ 7.88304433e-02 7.60183215e-01 -5.65945864e-01 -1.73198104e-01
-2.33545408e-01 -1.00006497e+00 8.06689382e-01 -3.46992075e-01
-8.93031836e-01 1.17091048e+00 2.51787066e-01 -5.78968167e-01
-4.54738975e-01 -7.03528941e-01 -5.34518898e-01 -6.37839019e-01
-5.85220814e-01 6.62821054e-01 -2.04212740e-01 -2.57540166... | [4.163037300109863, 2.4064903259277344] |
eb01338c-382a-4dfb-aa93-dbd3e16e4a05 | machine-love | 2302.09248 | null | https://arxiv.org/abs/2302.09248v2 | https://arxiv.org/pdf/2302.09248v2.pdf | Machine Love | While ML generates much economic value, many of us have problematic relationships with social media and other ML-powered applications. One reason is that ML often optimizes for what we want in the moment, which is easy to quantify but at odds with what is known scientifically about human flourishing. Thus, through its ... | ['Joel Lehman'] | 2023-02-18 | null | null | null | null | ['artificial-life', 'philosophy'] | ['miscellaneous', 'miscellaneous'] | [ 3.03396005e-02 6.46958530e-01 -5.34595490e-01 -2.39871949e-01
2.21400574e-01 -2.22565636e-01 7.65923500e-01 2.88943022e-01
-3.77989262e-01 4.09288675e-01 6.71146214e-01 -4.87196267e-01
-2.32248828e-01 -8.23079348e-01 -1.54820368e-01 -3.42882276e-01
1.67865589e-01 4.98943418e-01 -7.22998142e-01 -7.71009266... | [9.046924591064453, 6.318998336791992] |
b946326e-3ad2-40b5-8bb9-58d340536b3b | a-new-dataset-and-transformer-for | 2204.10039 | null | https://arxiv.org/abs/2204.10039v1 | https://arxiv.org/pdf/2204.10039v1.pdf | A New Dataset and Transformer for Stereoscopic Video Super-Resolution | Stereo video super-resolution (SVSR) aims to enhance the spatial resolution of the low-resolution video by reconstructing the high-resolution video. The key challenges in SVSR are preserving the stereo-consistency and temporal-consistency, without which viewers may experience 3D fatigue. There are several notable works... | ['Lai-Kuan Wong', 'Md Baharul Islam', 'Hassan Imani'] | 2022-04-21 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 2.77495444e-01 -4.69007879e-01 -1.22696437e-01 -1.96288243e-01
-8.08317840e-01 -1.36425197e-01 2.49510854e-01 -8.98424089e-01
2.95357071e-02 8.04545760e-01 7.46336639e-01 1.35543302e-01
3.59065272e-02 -6.64615035e-01 -8.01747978e-01 -6.58630311e-01
1.38297752e-01 -4.11446124e-01 7.23771274e-01 -3.80424798... | [10.92693042755127, -2.0580759048461914] |
20aa37ed-ff55-438d-9ddb-65086a1aa539 | simple-online-and-realtime-tracking-with-a | 1703.07402 | null | http://arxiv.org/abs/1703.07402v1 | http://arxiv.org/pdf/1703.07402v1.pdf | Simple Online and Realtime Tracking with a Deep Association Metric | Simple Online and Realtime Tracking (SORT) is a pragmatic approach to
multiple object tracking with a focus on simple, effective algorithms. In this
paper, we integrate appearance information to improve the performance of SORT.
Due to this extension we are able to track objects through longer periods of
occlusions, eff... | ['Alex Bewley', 'Nicolai Wojke', 'Dietrich Paulus'] | 2017-03-21 | null | null | null | null | ['large-scale-person-re-identification', 'video-instance-segmentation'] | ['computer-vision', 'computer-vision'] | [-1.38235509e-01 -4.27639991e-01 2.41257716e-02 -4.92835075e-01
-7.53494263e-01 -8.01812649e-01 4.82279956e-01 8.80458504e-02
-7.86285579e-01 4.40311253e-01 5.10156602e-02 -1.58577319e-02
9.67979133e-02 -4.65863347e-01 -8.00377071e-01 -9.07905176e-02
-1.24464452e-01 6.48955524e-01 4.98052418e-01 1.53115228... | [6.504465103149414, -1.8163576126098633] |
9c2ef47d-37ec-43e6-b6c9-5ca36d70119b | conversations-with-search-engines | 2004.14162 | null | https://arxiv.org/abs/2004.14162v2 | https://arxiv.org/pdf/2004.14162v2.pdf | Conversations with Search Engines: SERP-based Conversational Response Generation | In this paper, we address the problem of answering complex information needs by conversing conversations with search engines, in the sense that users can express their queries in natural language, and directly receivethe information they need from a short system response in a conversational manner. Recently, there have... | ['Evangelos Kanoulas', 'Zhaochun Ren', 'Christof Monz', 'Zhumin Chen', 'Pengjie Ren', 'Maarten de Rijke'] | 2020-04-29 | null | null | null | null | ['conversational-search', 'conversational-response-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.23192282e-03 8.37130770e-02 -4.42465022e-02 -2.75042832e-01
-1.28893661e+00 -8.85819614e-01 1.32238066e+00 7.99260139e-02
-5.55066168e-01 6.09960258e-01 7.14717925e-01 -4.73693699e-01
2.42814124e-01 -5.24295390e-01 -3.17612410e-01 -1.79764077e-01
4.66098875e-01 8.85911644e-01 4.88141388e-01 -7.31589913... | [12.368642807006836, 7.945367813110352] |
47f6f5a0-95b7-4ae8-bf24-7bee9a12c0b9 | do-not-train-it-a-linear-neural-architecture | 2305.14065 | null | https://arxiv.org/abs/2305.14065v3 | https://arxiv.org/pdf/2305.14065v3.pdf | Do Not Train It: A Linear Neural Architecture Search of Graph Neural Networks | Neural architecture search (NAS) for Graph neural networks (GNNs), called NAS-GNNs, has achieved significant performance over manually designed GNN architectures. However, these methods inherit issues from the conventional NAS methods, such as high computational cost and optimization difficulty. More importantly, previ... | ['Haiqin Yang', 'Bei Yu', 'Yue Zhao', 'Jiaqi Sun', 'Xuanzhou Liu', 'Lin Zhang', 'Peng Xu'] | 2023-05-23 | null | null | null | null | ['architecture-search'] | ['methodology'] | [ 1.08638786e-01 3.31779987e-01 -3.72534603e-01 -2.34243661e-01
-4.73550469e-01 -2.82156318e-01 1.66508108e-01 -3.87688935e-01
-3.77668262e-01 4.64047343e-01 1.06564343e-01 -5.86533189e-01
-2.25846320e-01 -6.87250495e-01 -8.70369256e-01 -5.89240432e-01
-1.10636607e-01 4.59962904e-01 -4.32842597e-02 -2.64473081... | [8.575203895568848, 3.3967554569244385] |
55fec37a-7fc9-416a-b1b5-09098a2c0498 | reinforcement-learning-approach-for-real-time | 1602.04936 | null | http://arxiv.org/abs/1602.04936v1 | http://arxiv.org/pdf/1602.04936v1.pdf | Reinforcement Learning approach for Real Time Strategy Games Battle city and S3 | In this paper we proposed reinforcement learning algorithms with the
generalized reward function. In our proposed method we use Q-learning and SARSA
algorithms with generalised reward function to train the reinforcement learning
agent. We evaluated the performance of our proposed algorithms on two real-time
strategy ga... | ['Harshit Sethy', 'Amit Patel'] | 2016-02-16 | null | null | null | null | ['real-time-strategy-games'] | ['playing-games'] | [-2.16738030e-01 2.89982617e-01 3.35204214e-01 -1.13653038e-02
-1.19201951e-01 -7.13745296e-01 5.20896673e-01 8.17470402e-02
-1.07645595e+00 1.39786398e+00 -3.73070985e-01 -5.73920071e-01
-3.95239800e-01 -1.08127725e+00 -4.33243483e-01 -4.24096704e-01
-2.56241322e-01 9.99047577e-01 9.70423996e-01 -1.20312369... | [3.5862066745758057, 1.5481213331222534] |
79691bcf-65e4-4d3b-97dd-f5f0fb4951a9 | identifying-and-extracting-rare-disease | 2306.12656 | null | https://arxiv.org/abs/2306.12656v1 | https://arxiv.org/pdf/2306.12656v1.pdf | Identifying and Extracting Rare Disease Phenotypes with Large Language Models | Rare diseases (RDs) are collectively common and affect 300 million people worldwide. Accurate phenotyping is critical for informing diagnosis and treatment, but RD phenotypes are often embedded in unstructured text and time-consuming to extract manually. While natural language processing (NLP) models can perform named ... | ['Hua Xu', 'Paul A. Harris', 'Yan Hu', 'Cathy Shyr'] | 2023-06-22 | null | null | null | null | ['prompt-engineering', 'named-entity-recognition-ner'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.20498240e-01 1.82923213e-01 -1.06938697e-01 -3.62073094e-01
-1.07139754e+00 -5.79517126e-01 2.79844373e-01 5.57772100e-01
-4.60637122e-01 8.68929267e-01 1.19947232e-01 -4.26213950e-01
-2.98088372e-01 -6.95157111e-01 -5.21541238e-01 -2.37465620e-01
8.62190407e-03 7.85289168e-01 -1.43343598e-01 2.10990787... | [8.5006685256958, 8.577235221862793] |
4ed565ff-4d68-402a-bbbf-f29567c0a8ea | locally-smoothed-gaussian-process-regression | 2210.09998 | null | https://arxiv.org/abs/2210.09998v1 | https://arxiv.org/pdf/2210.09998v1.pdf | Locally Smoothed Gaussian Process Regression | We develop a novel framework to accelerate Gaussian process regression (GPR). In particular, we consider localization kernels at each data point to down-weigh the contributions from other data points that are far away, and we derive the GPR model stemming from the application of such localization operation. Through a s... | ['Maurizio Filippone', 'Bogdan Kozyrskiy', 'Davit Gogolashvili'] | 2022-10-18 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [-1.50579259e-01 -1.07917935e-01 4.41546410e-01 -9.44512710e-02
-1.19090319e+00 -3.64631154e-02 8.19781423e-01 4.66659129e-01
-3.91510338e-01 2.92516083e-01 2.82996148e-01 -1.27505928e-01
-1.15788572e-01 -6.70906544e-01 -7.00727284e-01 -1.08225453e+00
-2.76087046e-01 6.97969615e-01 3.55121158e-02 2.60538220... | [6.980855464935303, 3.763324737548828] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.