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5b85a0d7-85fb-476f-82e6-f8e00d2c0307 | buying-information-for-stochastic | 2306.03607 | null | https://arxiv.org/abs/2306.03607v1 | https://arxiv.org/pdf/2306.03607v1.pdf | Buying Information for Stochastic Optimization | Stochastic optimization is one of the central problems in Machine Learning and Theoretical Computer Science. In the standard model, the algorithm is given a fixed distribution known in advance. In practice though, one may acquire at a cost extra information to make better decisions. In this paper, we study how to buy i... | ['Christos Tzamos', 'Mingchen Ma'] | 2023-06-06 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [ 2.43494436e-01 1.39597267e-01 -6.18303359e-01 -6.04689181e-01
-1.56933260e+00 -7.76082397e-01 -4.11740512e-01 2.91963518e-01
-9.53274369e-01 8.11110795e-01 -2.58858681e-01 -5.87425530e-01
-4.26127791e-01 -9.29069757e-01 -1.35643494e+00 -1.07466900e+00
-8.78713578e-02 8.99258077e-01 -1.98012739e-01 6.47029951... | [4.6182332038879395, 3.402184247970581] |
f73d9093-798f-4d03-99d5-6a74931984e7 | qbitopt-fast-and-accurate-bitwidth | 2307.04535 | null | https://arxiv.org/abs/2307.04535v1 | https://arxiv.org/pdf/2307.04535v1.pdf | QBitOpt: Fast and Accurate Bitwidth Reallocation during Training | Quantizing neural networks is one of the most effective methods for achieving efficient inference on mobile and embedded devices. In particular, mixed precision quantized (MPQ) networks, whose layers can be quantized to different bitwidths, achieve better task performance for the same resource constraint compared to ne... | ['Tijmen Blankevoort', 'Mart van Baalen', 'Markus Nagel', 'Marios Fournarakis', 'Jorn Peters'] | 2023-07-10 | null | null | null | null | ['quantization'] | ['methodology'] | [ 1.74390540e-01 -1.20591663e-01 -7.82852411e-01 -4.47776169e-01
-8.13141823e-01 -4.25601482e-01 -4.49516177e-02 -1.48019969e-01
-8.81942093e-01 1.00948799e+00 -4.06955600e-01 -4.74663019e-01
-4.31339175e-01 -7.70646870e-01 -1.18148148e+00 -4.51112241e-01
-1.03620835e-01 4.41031337e-01 2.09178194e-01 1.46306649... | [8.653800964355469, 3.12553334236145] |
d4237f66-674e-45a9-8ccb-38a85c8b5d20 | oriented-object-detection-in-aerial-images-1 | 2109.10187 | null | https://arxiv.org/abs/2109.10187v5 | https://arxiv.org/pdf/2109.10187v5.pdf | Oriented Object Detection in Aerial Images Based on Area Ratio of Parallelogram | Oriented object detection is a challenging task in aerial images since the objects in aerial images are displayed in arbitrary directions and are frequently densely packed. The mainstream detectors describe rotating objects using a five-parament or eight-parament representations, which suffer from representation ambigu... | ['Xinyi Yu', 'Linlin Ou', 'Jiangping Lu', 'Mi Lin'] | 2021-09-21 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [ 2.40652397e-01 -4.51159060e-01 9.94211584e-02 -2.31916487e-01
-1.67533636e-01 -4.64444101e-01 1.69619918e-01 -1.09383032e-01
-4.63303864e-01 2.99913615e-01 -2.03445554e-01 -2.88219333e-01
-5.31577647e-01 -1.03323781e+00 -3.57547373e-01 -1.19900525e+00
-3.17593217e-01 -2.53538694e-03 3.55713755e-01 -2.59692311... | [8.724430084228516, -0.7974846363067627] |
b8915a94-619f-479d-865e-ca3002b2dd8a | dynamic-learning-with-frequent-new-product | 1904.12445 | null | http://arxiv.org/abs/1904.12445v1 | http://arxiv.org/pdf/1904.12445v1.pdf | Dynamic Learning with Frequent New Product Launches: A Sequential Multinomial Logit Bandit Problem | Motivated by the phenomenon that companies introduce new products to keep
abreast with customers' rapidly changing tastes, we consider a novel online
learning setting where a profit-maximizing seller needs to learn customers'
preferences through offering recommendations, which may contain existing
products and new prod... | ['Junyu Cao', 'Wei Sun'] | 2019-04-29 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [-7.12128207e-02 2.08029017e-01 -9.06503975e-01 -5.95606863e-01
-7.54351616e-01 -1.04116166e+00 -2.27624193e-01 2.79182822e-01
-3.67115468e-01 4.11448807e-01 -2.03062505e-01 -5.05001605e-01
-6.43777013e-01 -8.85391474e-01 -1.13417876e+00 -5.03167689e-01
-3.14895272e-01 6.00915968e-01 -2.58400917e-01 1.51385829... | [4.622377395629883, 3.3624491691589355] |
229061f8-ba83-48de-aeef-11f408c6bd0c | l3das22-challenge-learning-3d-audio-sources | 2202.10372 | null | https://arxiv.org/abs/2202.10372v1 | https://arxiv.org/pdf/2202.10372v1.pdf | L3DAS22 Challenge: Learning 3D Audio Sources in a Real Office Environment | The L3DAS22 Challenge is aimed at encouraging the development of machine learning strategies for 3D speech enhancement and 3D sound localization and detection in office-like environments. This challenge improves and extends the tasks of the L3DAS21 edition. We generated a new dataset, which maintains the same general c... | ['Danilo Comminiello', 'Aurelio Uncini', 'Bruno Masiero', 'Chen Zhang', 'Xiguang Zheng', 'Xinlei Ren', 'Marco Pennese', 'Christian Marinoni', 'Eric Guizzo'] | 2022-02-21 | null | null | null | null | ['sound-event-localization-and-detection'] | ['audio'] | [-3.33346128e-01 1.53912768e-01 4.07887906e-01 -1.18644543e-01
-1.31578422e+00 -7.09447563e-01 6.26359642e-01 -1.47966862e-01
-2.32565179e-01 1.94775358e-01 7.62301862e-01 -3.54912996e-01
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-2.31168211e-01 1.52772427e-01 4.67640638e-01 -1.71740159... | [14.825925827026367, 5.560122013092041] |
2d2aab58-70c8-4ea6-9655-e89b099d1b10 | l-cad-language-based-colorization-with-any | 2305.15217 | null | https://arxiv.org/abs/2305.15217v2 | https://arxiv.org/pdf/2305.15217v2.pdf | L-CAD: Language-based Colorization with Any-level Descriptions | Language-based colorization produces plausible and visually pleasing colors under the guidance of user-friendly natural language descriptions. Previous methods implicitly assume that users provide comprehensive color descriptions for most of the objects in the image, which leads to suboptimal performance. In this paper... | ['Boxin Shi', 'Si Li', 'Yu Li', 'Peixuan Zhang', 'Shuchen Weng', 'Zheng Chang'] | 2023-05-24 | null | null | null | null | ['colorization'] | ['computer-vision'] | [-1.60462320e-01 -2.88566709e-01 -8.84247571e-02 -4.00678009e-01
-7.38975823e-01 -6.99894190e-01 5.05026340e-01 -2.14233547e-01
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3.34373832e-01 -2.48594042e-02 3.02872155e-02 -3.12802792... | [11.391402244567871, -1.050082802772522] |
f841bc56-340b-41f0-8481-9afdab93bac2 | multilexnorm-a-shared-task-on-multilingual | null | null | https://aclanthology.org/2021.wnut-1.55 | https://aclanthology.org/2021.wnut-1.55.pdf | MultiLexNorm: A Shared Task on Multilingual Lexical Normalization | Lexical normalization is the task of transforming an utterance into its standardized form. This task is beneficial for downstream analysis, as it provides a way to harmonize (often spontaneous) linguistic variation. Such variation is typical for social media on which information is shared in a multitude of ways, includ... | ['Wladimir Sidorenko', 'Tommaso Caselli', 'Timothy Baldwin', 'Talha Çolakoğlu', 'Rahmad Mahendra', 'Özlem Çetinoğlu', 'Nikola Ljubešić', 'Iñaki San Vicente Roncal', 'Benjamin Muller', 'Barbara Plank', 'Arkaitz Zubiaga', 'Alan Ramponi', 'Rob van der Goot'] | null | null | null | null | emnlp-wnut-2021-11 | ['lexical-normalization'] | ['natural-language-processing'] | [-2.43000388e-02 -1.40233338e-01 -3.77116054e-01 -6.45423412e-01
-1.11058569e+00 -7.94398665e-01 5.20482183e-01 5.40427089e-01
-8.84823561e-01 7.44533658e-01 5.40000618e-01 -2.18362078e-01
3.80876809e-01 -3.88512999e-01 -6.30085349e-01 -3.39213967e-01
3.29878688e-01 2.81930327e-01 1.70653358e-01 -6.40660465... | [10.32082462310791, 9.917134284973145] |
37d1df7f-e4cb-4eba-b929-390c667f76ea | data-to-text-generation-with-macro-planning | 2102.02723 | null | https://arxiv.org/abs/2102.02723v1 | https://arxiv.org/pdf/2102.02723v1.pdf | Data-to-text Generation with Macro Planning | Recent approaches to data-to-text generation have adopted the very successful encoder-decoder architecture or variants thereof. These models generate text which is fluent (but often imprecise) and perform quite poorly at selecting appropriate content and ordering it coherently. To overcome some of these issues, we prop... | ['Mirella Lapata', 'Ratish Puduppully'] | 2021-02-04 | null | null | null | null | ['data-to-text-generation'] | ['natural-language-processing'] | [ 4.11067337e-01 9.52056289e-01 -9.87474471e-02 -4.62359309e-01
-1.00641072e+00 -5.39311647e-01 1.56989241e+00 4.18944925e-01
-3.22007418e-01 1.21754706e+00 1.15602255e+00 -1.28753290e-01
4.57206875e-01 -1.02074671e+00 -7.58148015e-01 -9.92236473e-03
2.63130795e-02 1.27326083e+00 2.42384389e-01 -5.49267709... | [11.61848258972168, 8.928973197937012] |
36ca9cc5-fe0a-4545-978c-5dd3ff7ed1d8 | a-few-shot-attention-recurrent-residual-u-net | 2303.01582 | null | https://arxiv.org/abs/2303.01582v1 | https://arxiv.org/pdf/2303.01582v1.pdf | A Few-Shot Attention Recurrent Residual U-Net for Crack Segmentation | Recent studies indicate that deep learning plays a crucial role in the automated visual inspection of road infrastructures. However, current learning schemes are static, implying no dynamic adaptation to users' feedback. To address this drawback, we present a few-shot learning paradigm for the automated segmentation of... | ['Athanasios Voulodimos', 'Nikolaos Doulamis', 'Anastasios Doulamis', 'Nikolaos Bakalos', 'Eftychios Protopapadakis', 'Iason Katsamenis'] | 2023-03-02 | null | null | null | null | ['crack-segmentation'] | ['computer-vision'] | [ 6.25673011e-02 4.26966399e-02 -1.31359801e-01 -3.16631228e-01
-6.23252571e-01 -1.89071402e-01 2.86256611e-01 -2.82368332e-01
-2.20584765e-01 4.04887319e-01 3.67473066e-02 -1.72309682e-01
-1.48957565e-01 -1.09698164e+00 -6.65195823e-01 -6.63061082e-01
2.60518938e-01 2.88033243e-02 7.67726898e-01 -2.36728415... | [9.47266960144043, 0.701022207736969] |
78179022-11b5-477c-85ea-ea62ecdd048f | demo2vec-reasoning-object-affordances-from | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Fang_Demo2Vec_Reasoning_Object_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Fang_Demo2Vec_Reasoning_Object_CVPR_2018_paper.pdf | Demo2Vec: Reasoning Object Affordances From Online Videos | Watching expert demonstrations is an important way for humans and robots to reason about affordances of unseen objects. In this paper, we consider the problem of reasoning object affordances through the feature embedding of demonstration videos. We design the Demo2Vec model which learns to extract embedded vectors of d... | ['Daniel Yang', 'Te-Lin Wu', 'Silvio Savarese', 'Kuan Fang', 'Joseph J. Lim'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['video-to-image-affordance-grounding'] | ['computer-vision'] | [-1.91108480e-01 2.67814666e-01 -4.29731488e-01 -6.57716691e-01
-1.06069788e-01 -3.84916961e-01 5.68292022e-01 -4.30107743e-01
-3.02668810e-01 3.75347614e-01 7.08929539e-01 -1.56601295e-01
1.14057042e-01 -2.12562397e-01 -1.06094360e+00 -4.12049651e-01
-1.17032222e-01 7.81551003e-02 -1.50173500e-01 8.67264997... | [4.9223222732543945, 0.24122387170791626] |
a09b84bf-9326-4ee5-86d4-15f2483ac454 | self-improving-safety-performance-of | 2210.16575 | null | https://arxiv.org/abs/2210.16575v3 | https://arxiv.org/pdf/2210.16575v3.pdf | Self-Improving Safety Performance of Reinforcement Learning Based Driving with Black-Box Verification Algorithms | In this work, we propose a self-improving artificial intelligence system to enhance the safety performance of reinforcement learning (RL)-based autonomous driving (AD) agents using black-box verification methods. RL algorithms have become popular in AD applications in recent years. However, the performance of existing ... | ['Nazim Kemal Ure', 'Halil Durmus', 'Resul Dagdanov'] | 2022-10-29 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [-9.61066186e-02 2.93174908e-02 -3.63869339e-01 -3.83777767e-01
-6.39804184e-01 -4.49714124e-01 4.54986811e-01 -8.10588896e-02
-5.20399451e-01 9.48476553e-01 -3.92623752e-01 -7.97687054e-01
-2.54708916e-01 -1.01141787e+00 -8.87417436e-01 -7.69027770e-01
-3.01348001e-01 4.24176276e-01 4.93255794e-01 -7.45352149... | [5.114373683929443, 1.3147997856140137] |
c4f3b9c5-45fb-4ee3-9691-6200ef35e90c | training-free-lexical-backdoor-attacks-on | 2302.04116 | null | https://arxiv.org/abs/2302.04116v1 | https://arxiv.org/pdf/2302.04116v1.pdf | Training-free Lexical Backdoor Attacks on Language Models | Large-scale language models have achieved tremendous success across various natural language processing (NLP) applications. Nevertheless, language models are vulnerable to backdoor attacks, which inject stealthy triggers into models for steering them to undesirable behaviors. Most existing backdoor attacks, such as dat... | ['Chunyang Chen', 'Xingliang Yuan', 'Han Hu', 'Qiongkai Xu', 'Terry Yue Zhuo', 'Yujin Huang'] | 2023-02-08 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [-2.67629445e-01 -1.84279129e-01 -5.62646210e-01 3.05072833e-02
-4.38906699e-01 -1.28412867e+00 6.85802221e-01 -1.90228317e-02
-2.55446434e-01 2.14462608e-01 7.64635131e-02 -1.10687304e+00
4.47373867e-01 -8.07229280e-01 -8.21049213e-01 -2.90184408e-01
-6.37025833e-02 -1.15593344e-01 3.38778526e-01 -3.45598966... | [6.053810119628906, 7.839073181152344] |
8e8107f5-f205-495b-a6e4-5ca5f2fb794c | adapting-crisp-dm-for-idea-mining-a-data | 2105.00574 | null | https://arxiv.org/abs/2105.00574v1 | https://arxiv.org/pdf/2105.00574v1.pdf | Adapting CRISP-DM for Idea Mining: A Data Mining Process for Generating Ideas Using a Textual Dataset | Data mining project managers can benefit from using standard data mining process models. The benefits of using standard process models for data mining, such as the de facto and the most popular, Cross-Industry-Standard-Process model for Data Mining (CRISP-DM) are reduced cost and time. Also, standard models facilitate ... | ['W. Y. Ayele'] | 2021-05-02 | null | null | null | null | ['dynamic-topic-modeling'] | ['natural-language-processing'] | [ 1.07962139e-01 2.37979725e-01 -5.26890993e-01 8.70831534e-02
-8.82751122e-02 -3.19289863e-01 6.88443124e-01 3.71286422e-01
-9.16589499e-02 4.67226535e-01 1.40005425e-01 -7.66289711e-01
-8.52559566e-01 -1.05359733e+00 -3.47858578e-01 -1.93382651e-01
2.39935100e-01 3.22536767e-01 -2.98992872e-01 1.29470468... | [8.967445373535156, 6.63814115524292] |
dc7fe704-093a-4953-83fe-a982efcf04a3 | drift-reduction-for-monocular-visual-odometry | 2207.00909 | null | https://arxiv.org/abs/2207.00909v1 | https://arxiv.org/pdf/2207.00909v1.pdf | Drift Reduction for Monocular Visual Odometry of Intelligent Vehicles using Feedforward Neural Networks | In this paper, an approach for reducing the drift in monocular visual odometry algorithms is proposed based on a feedforward neural network. A visual odometry algorithm computes the incremental motion of the vehicle between the successive camera frames, then integrates these increments to determine the pose of the vehi... | ['Sherif Hammad', 'Mohamed I. Awad', 'Mostafa Osman', 'Hassan Wagih'] | 2022-07-02 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-2.25892141e-01 -3.31275538e-02 -9.19986963e-02 -2.24065274e-01
3.51915538e-01 -3.28045756e-01 5.98632395e-01 -1.16848744e-01
-4.88995910e-01 6.62246287e-01 -8.41641128e-02 5.60974702e-02
3.69506190e-03 -4.53317016e-01 -9.20435727e-01 -4.19137269e-01
1.05850846e-01 5.01825690e-01 3.40024203e-01 -1.08204179... | [7.660608291625977, -2.089542865753174] |
b930cce7-99da-44b7-b4cd-777dc03473bc | improved-speech-enhancement-with-the-wave-u-1 | null | null | https://openreview.net/forum?id=B1zKGg3soX | https://openreview.net/pdf?id=B1zKGg3soX | Improved Speech Enhancement with the Wave-U-Net | We study the use of the Wave-U-Net architecture for speech enhancement, a model introduced by Stoller et al for the separation of music vocals and accompaniment. This end-to-end learning method for audio source separation operates directly in the time domain, permitting the integrated modelling of phase information an... | ['Anonymous'] | 2018-10-22 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 1.93941951e-01 -4.53771353e-02 2.88858205e-01 -1.03421688e-01
-9.78943169e-01 -5.43054223e-01 4.69231874e-01 4.40668687e-02
-6.12948895e-01 4.04434711e-01 4.32659686e-01 -2.25907043e-01
-4.95899528e-01 -2.08412871e-01 -1.96313009e-01 -8.45457435e-01
-2.47198939e-01 -4.62626517e-02 8.56150240e-02 -3.18670541... | [15.206711769104004, 5.776162147521973] |
ae3f4e82-5550-4781-af1f-77617d518562 | stylelight-hdr-panorama-generation-for | 2207.14811 | null | https://arxiv.org/abs/2207.14811v1 | https://arxiv.org/pdf/2207.14811v1.pdf | StyleLight: HDR Panorama Generation for Lighting Estimation and Editing | We present a new lighting estimation and editing framework to generate high-dynamic-range (HDR) indoor panorama lighting from a single limited field-of-view (LFOV) image captured by low-dynamic-range (LDR) cameras. Existing lighting estimation methods either directly regress lighting representation parameters or decomp... | ['Ziwei Liu', 'Chen Change Loy', 'Yinuo Yang', 'Guangcong Wang'] | 2022-07-29 | null | null | null | null | ['lighting-estimation'] | ['computer-vision'] | [ 5.51641822e-01 -4.41964954e-01 -7.79219270e-02 -4.40485746e-01
-6.38615906e-01 -6.83734953e-01 6.46928072e-01 -1.27151632e+00
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8.90469909e-01 1.17439823e-03 -5.57700932e-01 -1.42886728... | [9.903913497924805, -2.83371639251709] |
1c3c2254-3f58-438c-b20b-d4e50d38183c | enhancing-automated-essay-scoring-performance | null | null | https://aclanthology.org/2020.findings-emnlp.141 | https://aclanthology.org/2020.findings-emnlp.141.pdf | Enhancing Automated Essay Scoring Performance via Fine-tuning Pre-trained Language Models with Combination of Regression and Ranking | Automated Essay Scoring (AES) is a critical text regression task that automatically assigns scores to essays based on their writing quality. Recently, the performance of sentence prediction tasks has been largely improved by using Pre-trained Language Models via fusing representations from different layers, constructin... | ['Xiaodong He', 'Youzheng Wu', 'Zhiyuan Wen', 'Jiannong Cao', 'Ruosong Yang'] | 2020-11-01 | null | null | null | findings-of-the-association-for-computational | ['automated-essay-scoring'] | ['natural-language-processing'] | [-1.15555540e-01 -1.76111192e-01 -2.87327051e-01 -7.54381359e-01
-9.77108359e-01 -3.03801000e-01 3.81306559e-02 4.43438768e-01
-7.60272682e-01 1.00990784e+00 3.61341715e-01 -1.08698659e-01
-1.97427988e-01 -9.08822775e-01 -4.35543001e-01 -2.30876386e-01
6.28448725e-01 5.39192915e-01 1.89153463e-01 -4.38747108... | [11.293935775756836, 9.345186233520508] |
ee6d54bb-c9c6-4467-968b-f117b860d963 | a-pervasive-framework-for-human-detection-and | 2303.11170 | null | https://arxiv.org/abs/2303.11170v1 | https://arxiv.org/pdf/2303.11170v1.pdf | A Pervasive Framework for Human Detection and Tracking | The advent of the Edge Computing (EC) leads to a huge ecosystem where numerous nodes can interact with data collection devices located close to end users. Human detection and tracking can be realized at edge nodes that perform the surveillance of an area under consideration through the assistance of a set of sensors (e... | ['Kostas Kolomvatsos', 'Bratsos Dimitrios', 'Fesatidis Georgios'] | 2023-02-17 | null | null | null | null | ['human-detection'] | ['computer-vision'] | [-1.32324761e-02 2.00016230e-01 1.44423321e-01 1.70898616e-01
3.80819142e-01 -6.29616261e-01 4.23972726e-01 1.98809910e-04
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-1.24113098e-01 -8.34940553e-01 -2.25801960e-01 -3.26804906e-01
-4.87428665e-01 2.58732915e-01 6.94247961e-01 9.77546945... | [8.107170104980469, -0.7693142294883728] |
2af0f492-386c-441e-8121-29d6900dc5d1 | semantic-parsing-for-conversational-question | 2301.12217 | null | https://arxiv.org/abs/2301.12217v1 | https://arxiv.org/pdf/2301.12217v1.pdf | Semantic Parsing for Conversational Question Answering over Knowledge Graphs | In this paper, we are interested in developing semantic parsers which understand natural language questions embedded in a conversation with a user and ground them to formal queries over definitions in a general purpose knowledge graph (KG) with very large vocabularies (covering thousands of concept names and relations,... | ['Mirella Lapata', 'Emilio Monti', 'Parag Jain', 'Laura Perez-Beltrachini'] | 2023-01-28 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [-2.26710320e-01 7.60595858e-01 9.32437852e-02 -6.58771574e-01
-5.99358618e-01 -9.65386987e-01 6.07535124e-01 5.00783801e-01
-1.84795752e-01 1.02595901e+00 6.53729498e-01 -2.62857884e-01
-2.61819869e-01 -1.19375908e+00 -5.60274661e-01 3.29696476e-01
1.15510531e-01 1.26554060e+00 8.44291627e-01 -7.79833198... | [10.46121597290039, 7.916665077209473] |
73bf7f41-574f-45fd-9f6d-dd6012488d04 | does-manipulating-tokenization-aid-cross | 2304.10158 | null | https://arxiv.org/abs/2304.10158v1 | https://arxiv.org/pdf/2304.10158v1.pdf | Does Manipulating Tokenization Aid Cross-Lingual Transfer? A Study on POS Tagging for Non-Standardized Languages | One of the challenges with finetuning pretrained language models (PLMs) is that their tokenizer is optimized for the language(s) it was pretrained on, but brittle when it comes to previously unseen variations in the data. This can for instance be observed when finetuning PLMs on one language and evaluating them on data... | ['Barbara Plank', 'Hinrich Schütze', 'Verena Blaschke'] | 2023-04-20 | null | null | null | null | ['part-of-speech-tagging', 'cross-lingual-transfer'] | ['natural-language-processing', 'natural-language-processing'] | [-7.49683529e-02 -1.33100152e-01 -3.53101403e-01 -5.83038747e-01
-5.03664434e-01 -9.96628582e-01 4.34002727e-01 4.35379744e-01
-8.15204382e-01 5.15344381e-01 3.77356350e-01 -2.19842926e-01
1.24569334e-01 -6.82052374e-01 -6.40635848e-01 -3.95211577e-01
2.56413251e-01 6.24579906e-01 2.03041404e-01 -3.67568314... | [10.808675765991211, 9.9402494430542] |
ca3cf8b0-17a4-4bef-b800-395849cbd84a | proceedings-ninetheenth-conference-on | 2307.04005 | null | https://arxiv.org/abs/2307.04005v1 | https://arxiv.org/pdf/2307.04005v1.pdf | Proceedings Ninetheenth conference on Theoretical Aspects of Rationality and Knowledge | The TARK conference (Theoretical Aspects of Rationality and Knowledge) is a conference that aims to bring together researchers from a wide variety of fields, including computer science, artificial intelligence, game theory, decision theory, philosophy, logic, linguistics, and cognitive science. Its goal is to further o... | ['Rineke Verbrugge'] | 2023-07-08 | null | null | null | null | ['epistemic-reasoning', 'philosophy'] | ['miscellaneous', 'miscellaneous'] | [-3.24645370e-01 6.95700943e-01 -2.63843596e-01 8.71559978e-02
-4.41464037e-01 -7.50929415e-01 6.08387828e-01 3.46316993e-01
-4.87487406e-01 9.36281562e-01 2.11031497e-01 -5.33067763e-01
-7.07460999e-01 -9.32994843e-01 -1.37990788e-01 -2.53345430e-01
5.29201999e-02 5.97471118e-01 2.52701432e-01 -3.88815701... | [8.744461059570312, 6.714859962463379] |
1fdb3f45-31df-4d75-bcf6-eceec85caa9b | a-survey-on-hyperdimensional-computing-aka | 2111.06077 | null | https://arxiv.org/abs/2111.06077v1 | https://arxiv.org/pdf/2111.06077v1.pdf | A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part I: Models and Data Transformations | This two-part comprehensive survey is devoted to a computing framework most commonly known under the names Hyperdimensional Computing and Vector Symbolic Architectures (HDC/VSA). Both names refer to a family of computational models that use high-dimensional distributed representations and rely on the algebraic properti... | ['Abbas Rahimi', 'Evgeny Osipov', 'Dmitri A. Rachkovskij', 'Denis Kleyko'] | 2021-11-11 | null | null | null | null | ['electrical-engineering'] | ['miscellaneous'] | [ 2.22675413e-01 -6.85965121e-02 -1.40252084e-01 -2.12837130e-01
-2.52302121e-02 -6.70917034e-01 1.01183546e+00 8.92747715e-02
-1.22411206e-01 4.74737614e-01 3.04118037e-01 -2.79206216e-01
-7.92492986e-01 -1.11042535e+00 -1.26286224e-01 -7.54759848e-01
-4.55514818e-01 6.06120825e-01 -4.26464938e-02 -6.93494201... | [8.162091255187988, 3.912851333618164] |
d26222a6-9868-44bc-8a62-e828e5446e40 | detrs-with-collaborative-hybrid-assignments | 2211.12860 | null | https://arxiv.org/abs/2211.12860v4 | https://arxiv.org/pdf/2211.12860v4.pdf | DETRs with Collaborative Hybrid Assignments Training | In this paper, we provide the observation that too few queries assigned as positive samples in DETR with one-to-one set matching leads to sparse supervisions on the encoder's output which considerably hurt the discriminative feature learning of the encoder and vice visa for attention learning in the decoder. To allevia... | ['Yu Liu', 'Guanglu Song', 'Zhuofan Zong'] | 2022-11-22 | null | null | null | null | ['set-matching'] | ['computer-vision'] | [ 5.46488054e-02 2.37519126e-02 -3.00283879e-01 -4.46141511e-01
-1.33515227e+00 -6.81075752e-01 2.21148223e-01 -2.09812328e-01
-8.46263885e-01 5.48058152e-01 -1.10344782e-01 -8.76050293e-02
3.03550184e-01 -4.51108485e-01 -7.48817265e-01 -6.64031565e-01
4.14030939e-01 4.74361002e-01 5.16758442e-01 -2.11163908... | [9.464147567749023, 1.3052433729171753] |
85ccad78-cc13-4e17-bb03-8f020a9b42d1 | the-representation-jensen-shannon-divergence | 2305.16446 | null | https://arxiv.org/abs/2305.16446v1 | https://arxiv.org/pdf/2305.16446v1.pdf | The Representation Jensen-Shannon Divergence | Statistical divergences quantify the difference between probability distributions finding multiple uses in machine-learning. However, a fundamental challenge is to estimate divergence from empirical samples since the underlying distributions of the data are usually unknown. In this work, we propose the representation J... | ['Luis G. Sanchez-Giraldo', 'Jhoan K. Hoyos-Osorio'] | 2023-05-25 | null | null | null | null | ['hypothesis-testing', 'hypothesis-testing'] | ['methodology', 'miscellaneous'] | [ 5.18783815e-02 2.09076419e-01 2.06471160e-01 -2.97172546e-01
-1.02117789e+00 -7.22298563e-01 5.51029980e-01 -2.23082498e-01
-5.49766004e-01 9.37876821e-01 -9.82730612e-02 -2.02563077e-01
-2.96888947e-01 -5.84809005e-01 -9.15447950e-01 -9.46865499e-01
-2.59544253e-01 2.62484282e-01 5.18361144e-02 -1.57210350... | [7.327606678009033, 3.97904109954834] |
77661e1d-e9b0-4614-8c5e-48933d85e133 | towards-building-self-aware-object-detectors-1 | 2307.00934 | null | https://arxiv.org/abs/2307.00934v1 | https://arxiv.org/pdf/2307.00934v1.pdf | Towards Building Self-Aware Object Detectors via Reliable Uncertainty Quantification and Calibration | The current approach for testing the robustness of object detectors suffers from serious deficiencies such as improper methods of performing out-of-distribution detection and using calibration metrics which do not consider both localisation and classification quality. In this work, we address these issues, and introduc... | ['Puneet K. Dokania', 'Tom Joy', 'Kemal Oksuz'] | 2023-07-03 | towards-building-self-aware-object-detectors | http://openaccess.thecvf.com//content/CVPR2023/html/Oksuz_Towards_Building_Self-Aware_Object_Detectors_via_Reliable_Uncertainty_Quantification_and_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Oksuz_Towards_Building_Self-Aware_Object_Detectors_via_Reliable_Uncertainty_Quantification_and_CVPR_2023_paper.pdf | cvpr-2023-1 | ['out-of-distribution-detection'] | ['computer-vision'] | [-7.66428513e-03 -2.01003641e-01 -2.43189968e-02 -4.81326014e-01
-9.52218175e-01 -8.00408006e-01 6.20391607e-01 1.02269113e-01
-4.79440004e-01 4.49799091e-01 -3.12551975e-01 -2.69524813e-01
-1.49694130e-01 -4.18513000e-01 -7.47784734e-01 -4.51700598e-01
-2.07964685e-02 2.54565120e-01 1.06813073e+00 8.82419571... | [8.050240516662598, -1.2416661977767944] |
84f4580d-3a76-47d9-98c3-fba76db73b74 | dasps-a-database-for-anxious-states-based-on | 1901.02942 | null | https://arxiv.org/abs/1901.02942v2 | https://arxiv.org/pdf/1901.02942v2.pdf | DASPS: A Database for Anxious States based on a Psychological Stimulation | Anxiety affects human capabilities and behavior as much as it affects productivity and quality of life. It can be considered as the main cause of depression and suicide. Anxious states are easily detectable by humans due to their acquired cognition, humans interpret the interlocutor's tone of speech, gesture, facial ex... | ['Adel M. ALIMI', 'Rahma Fourati', 'Asma Baghdadi', 'Yassine Aribi', 'Najla Halouani', 'Patrick Siarry'] | 2019-01-09 | null | null | null | null | ['anxiety-detection'] | ['medical'] | [-6.34672567e-02 -1.32911995e-01 6.97364092e-01 -4.96287137e-01
-1.38673514e-01 -3.58281374e-01 4.99865450e-02 9.04312581e-02
-5.11257768e-01 7.01589704e-01 -3.32984030e-02 3.08392823e-01
-1.81745142e-01 -3.11578810e-01 -1.31845713e-01 -6.64877415e-01
-3.07299614e-01 -2.60959510e-02 -4.19280529e-01 -2.69399375... | [13.423893928527832, 3.154148578643799] |
ac9d4c98-a68c-4a9e-88b9-1107061ff195 | learning-long-term-style-preserving-blind | 2103.07278 | null | https://arxiv.org/abs/2103.07278v1 | https://arxiv.org/pdf/2103.07278v1.pdf | Learning Long-Term Style-Preserving Blind Video Temporal Consistency | When trying to independently apply image-trained algorithms to successive frames in videos, noxious flickering tends to appear. State-of-the-art post-processing techniques that aim at fostering temporal consistency, generate other temporal artifacts and visually alter the style of videos. We propose a postprocessing mo... | ['Matthieu Perrot', 'Robin Kips', 'Julien Despois', 'Hugo Thimonier'] | 2021-03-12 | null | null | null | null | ['video-temporal-consistency'] | ['computer-vision'] | [ 6.05793357e-01 -1.42091542e-01 1.83311135e-01 -2.36949362e-02
-4.61660206e-01 -7.11302161e-01 8.75383496e-01 -3.92834336e-01
-2.72999316e-01 6.84553623e-01 1.98653191e-01 3.41768488e-02
1.35690033e-01 -5.81961393e-01 -1.29819524e+00 -8.40971828e-01
-9.49929431e-02 -3.14247847e-01 3.45607549e-01 -2.74960876... | [11.1572265625, -0.790287435054779] |
2ba7ed58-a046-4262-bd63-98cc4a663eae | zero-resource-multilingual-model-transfer | 1810.03552 | null | https://arxiv.org/abs/1810.03552v3 | https://arxiv.org/pdf/1810.03552v3.pdf | Multi-Source Cross-Lingual Model Transfer: Learning What to Share | Modern NLP applications have enjoyed a great boost utilizing neural networks models. Such deep neural models, however, are not applicable to most human languages due to the lack of annotated training data for various NLP tasks. Cross-lingual transfer learning (CLTL) is a viable method for building NLP models for a low-... | ['Xilun Chen', 'Ahmed Hassan Awadallah', 'Wei Wang', 'Hany Hassan', 'Claire Cardie'] | 2018-10-08 | zero-resource-multilingual-model-transfer-1 | https://aclanthology.org/P19-1299 | https://aclanthology.org/P19-1299.pdf | acl-2019-7 | ['cross-lingual-ner'] | ['natural-language-processing'] | [-6.29581511e-02 -2.54325688e-01 -6.35588527e-01 -2.42994517e-01
-1.17791641e+00 -9.50921178e-01 7.07847774e-01 -6.66777790e-02
-7.30234802e-01 8.25992584e-01 1.86404347e-01 -5.32778621e-01
5.12249649e-01 -5.78069329e-01 -8.18080008e-01 -2.61563152e-01
1.54399812e-01 5.42920828e-01 -1.68415546e-01 -3.44360918... | [10.957636833190918, 9.851520538330078] |
203b5680-5c11-42ed-9938-35d193d2bd1e | one-model-to-rule-them-all-ranking-slovene | 2306.11518 | null | https://arxiv.org/abs/2306.11518v1 | https://arxiv.org/pdf/2306.11518v1.pdf | One model to rule them all: ranking Slovene summarizers | Text summarization is an essential task in natural language processing, and researchers have developed various approaches over the years, ranging from rule-based systems to neural networks. However, there is no single model or approach that performs well on every type of text. We propose a system that recommends the mo... | ['Marko Robnik-Šikonja', 'Aleš Žagar'] | 2023-06-20 | null | null | null | null | ['text-summarization'] | ['natural-language-processing'] | [ 1.78999290e-01 2.08870679e-01 -2.06860915e-01 -2.70418048e-01
-4.90963578e-01 -3.08274508e-01 5.90663016e-01 8.42358470e-01
-6.63039029e-01 7.24882066e-01 8.83491576e-01 -1.96307391e-01
-1.01737097e-01 -7.41205096e-01 -1.37274221e-01 -1.71191499e-01
4.79171365e-01 6.28570318e-01 3.82990316e-02 -5.09336591... | [12.508522033691406, 9.519389152526855] |
6bddaed7-eb07-4439-8097-2d01f82df47f | origin-destination-network-generation-via | 2306.03390 | null | https://arxiv.org/abs/2306.03390v1 | https://arxiv.org/pdf/2306.03390v1.pdf | Origin-Destination Network Generation via Gravity-Guided GAN | Origin-destination (OD) flow, which contains valuable population mobility information including direction and volume, is critical in many urban applications, such as urban planning, transportation management, etc. However, OD data is not always easy to access due to high costs or privacy concerns. Therefore, we must co... | ['Yong Li', 'Huandong Wang', 'Can Rong'] | 2023-06-06 | null | null | null | null | ['graph-attention'] | ['graphs'] | [-2.53451049e-01 -1.51979504e-02 -1.90130591e-01 -2.97527254e-01
-2.42518276e-01 -9.33669209e-02 8.14309955e-01 -6.81564063e-02
4.25348058e-02 1.04312646e+00 4.29430693e-01 -5.51034153e-01
-4.35403176e-02 -1.70382059e+00 -6.76438630e-01 -7.91287482e-01
1.08528242e-01 3.31812859e-01 2.24224761e-01 -5.32048047... | [6.464659214019775, 2.006385087966919] |
0fbf930f-2142-41d3-8b95-1c62d908e673 | asl-citizen-a-community-sourced-dataset-for | 2304.05934 | null | https://arxiv.org/abs/2304.05934v2 | https://arxiv.org/pdf/2304.05934v2.pdf | ASL Citizen: A Community-Sourced Dataset for Advancing Isolated Sign Language Recognition | Sign languages are used as a primary language by approximately 70 million D/deaf people world-wide. However, most communication technologies operate in spoken and written languages, creating inequities in access. To help tackle this problem, we release ASL Citizen, the first crowdsourced Isolated Sign Language Recognit... | ['Danielle Bragg', 'Naomi Caselli', 'Alex X. Lu', 'Hal Daumé III', 'Richard E. Ladner', 'Kriston Pumphrey', 'Chinmay Singh', 'Vanessa Milan', 'Fyodor O. Minakov', 'Lauren Berger', 'Aashaka Desai'] | 2023-04-12 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [-1.12242140e-01 -3.42618793e-01 -5.96357822e-01 -8.78895819e-02
-1.15968442e+00 -8.17418754e-01 5.25910556e-01 -2.35586345e-01
-6.77557707e-01 4.67528254e-01 7.45722830e-01 -1.55207440e-01
1.44520104e-01 -3.83980632e-01 -4.75211263e-01 -3.72900456e-01
2.96916395e-01 3.22943300e-01 3.36429656e-01 -1.04358986... | [9.156303405761719, -6.475452899932861] |
66d4ba05-6d51-41f0-bb3c-b74057824af4 | incnsa-detecting-communities-incrementally | null | null | https://www.worldscientific.com/doi/abs/10.1142/S0129183120500941 | https://www.worldscientific.com/doi/abs/10.1142/S0129183120500941 | IncNSA: Detecting communities incrementally from time-evolving networks based on node similarity | Many real-world systems can be abstracted as networks. As those systems always change dynamically in nature, the corresponding networks also evolve over time in general, and detecting communities from such time-evolving networks has become a critical task. In this paper, we propose an incremental detection method, whic... | ['Xiaoyun Chen', 'Wenbo Zhang', 'Mingwei Leng', 'Haijuan Yang', 'Jianjun Cheng', 'Xing Su'] | 2020-07-16 | null | null | null | international-journal-of-modern-physics-c-1 | ['dynamic-community-detection'] | ['graphs'] | [-5.41597307e-02 -2.35861540e-01 9.37550142e-02 1.54354185e-01
3.63092303e-01 -7.37780035e-01 1.95262879e-01 1.68174773e-01
-8.89723822e-02 7.36443222e-01 -1.66146606e-01 -8.50887075e-02
-1.54241219e-01 -1.18860984e+00 -6.01841696e-02 -8.98960590e-01
-5.86025953e-01 6.76579833e-01 1.09639597e+00 -1.07235707... | [7.062121868133545, 5.299328327178955] |
97040835-ad28-46d0-ab7e-e87533319fd5 | large-scale-transfer-learning-for-natural | null | null | https://aclanthology.org/P19-1608 | https://aclanthology.org/P19-1608.pdf | Large-Scale Transfer Learning for Natural Language Generation | Large-scale pretrained language models define state of the art in natural language processing, achieving outstanding performance on a variety of tasks. We study how these architectures can be applied and adapted for natural language generation, comparing a number of architectural and training schemes. We focus in parti... | ['er', 'Sergey Golovanov', 'Alex Tselousov', 'Rauf Kurbanov', 'Thomas Wolf', 'Sergey Nikolenko', 'Kyryl Truskovskyi'] | 2019-07-01 | null | null | null | acl-2019-7 | ['open-domain-dialog'] | ['natural-language-processing'] | [ 2.38772377e-01 8.70758414e-01 -4.36814176e-03 -5.03934205e-01
-7.31580138e-01 -5.59958100e-01 1.19940972e+00 -8.18326101e-02
-6.52326107e-01 1.24283242e+00 6.81559384e-01 -2.70132691e-01
3.14752758e-01 -9.82852340e-01 -2.57082433e-01 8.87918193e-03
9.34813544e-02 1.26596236e+00 1.38365719e-02 -1.06224012... | [11.651646614074707, 9.005457878112793] |
4d41fcc0-f22c-4517-8f1d-7bfd33525ee7 | efficient-flow-guided-multi-frame-de-fencing | 2301.10759 | null | https://arxiv.org/abs/2301.10759v1 | https://arxiv.org/pdf/2301.10759v1.pdf | Efficient Flow-Guided Multi-frame De-fencing | Taking photographs ''in-the-wild'' is often hindered by fence obstructions that stand between the camera user and the scene of interest, and which are hard or impossible to avoid. De-fencing is the algorithmic process of automatically removing such obstructions from images, revealing the invisible parts of the scene. W... | ['Alex Levinshtein', 'Allan Jepson', 'Fengjia Zhang', 'Stavros Tsogkas'] | 2023-01-25 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 5.54311097e-01 -2.96856780e-02 6.93985671e-02 1.74438685e-01
-5.73400676e-01 -7.00057924e-01 3.81628633e-01 -1.87426776e-01
-3.69079053e-01 7.32988834e-01 3.68403047e-01 -2.31709555e-01
4.77264859e-02 -6.11826479e-01 -8.72976363e-01 -4.73453909e-01
3.78954224e-02 -1.49196777e-02 3.35644275e-01 -1.63502157... | [10.667726516723633, -1.5245298147201538] |
14ccd3cf-81bd-46bb-83d7-881dfb0c7d91 | corruptencoder-data-poisoning-based-backdoor | 2211.08229 | null | https://arxiv.org/abs/2211.08229v3 | https://arxiv.org/pdf/2211.08229v3.pdf | CorruptEncoder: Data Poisoning based Backdoor Attacks to Contrastive Learning | Contrastive learning (CL) pre-trains general-purpose encoders using an unlabeled pre-training dataset, which consists of images or image-text pairs. CL is vulnerable to data poisoning based backdoor attacks (DPBAs), in which an attacker injects poisoned inputs into the pre-training dataset so the encoder is backdoored.... | ['Neil Zhenqiang Gong', 'Jinyuan Jia', 'Hongbin Liu', 'Jinghuai Zhang'] | 2022-11-15 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 6.92072362e-02 -3.36977979e-03 -3.02587837e-01 2.82702208e-01
-1.02319062e+00 -1.10792410e+00 6.45212948e-01 -5.82829490e-02
-4.50044960e-01 5.07638812e-01 -6.89693317e-02 -6.97097540e-01
4.75071967e-01 -9.17738795e-01 -1.06035995e+00 -7.74761617e-01
9.69923064e-02 -1.24217486e-02 2.62513012e-01 -1.38467744... | [5.7658185958862305, 7.755311489105225] |
6c77f9a8-d99f-4670-9021-16dd889e4c6a | protein-secondary-structure-prediction-using-2 | 1512.00843 | null | http://arxiv.org/abs/1512.00843v3 | http://arxiv.org/pdf/1512.00843v3.pdf | Protein secondary structure prediction using deep convolutional neural fields | Protein secondary structure (SS) prediction is important for studying protein
structure and function. When only the sequence (profile) information is used as
input feature, currently the best predictors can obtain ~80% Q3 accuracy, which
has not been improved in the past decade. Here we present DeepCNF (Deep
Convolutio... | ['Sheng Wang', 'Jianzhu Ma', 'Jinbo Xu', 'Jian Peng'] | 2015-12-02 | null | null | null | null | ['protein-secondary-structure-prediction'] | ['medical'] | [ 1.66504949e-01 -1.89875618e-01 -3.27385128e-01 -7.49071717e-01
-3.59418035e-01 -3.32759470e-01 2.66965419e-01 5.06756723e-01
-4.13363159e-01 1.26470768e+00 -5.69600053e-02 -6.33141339e-01
2.81297863e-01 -4.47486311e-01 -9.82813418e-01 -9.33267653e-01
-5.92752807e-02 3.10762644e-01 4.83635962e-01 -3.19504261... | [4.698616981506348, 5.6261067390441895] |
270d0087-52fe-46d5-9501-1ee907880b3d | optimally-coordinated-energy-management | 2307.00277 | null | https://arxiv.org/abs/2307.00277v1 | https://arxiv.org/pdf/2307.00277v1.pdf | Optimally Coordinated Energy Management Framework for Profit Maximization Considering Dispatchable and Non-Dispatchable Energy Resources | Contemporary distribution network can be seen with diverse dispatchable and non-dispatchable energy resources. The coordinated scheduling of these dispatchable resources with non-dispatchable resources can provide several techno-economic and social benefits. Since, battery energy storage systems (BESSs) and microturbin... | ['Jin Yang', 'Nand K. Meena', 'Anil Swarnkar', 'K. R. Niazi', 'Nikhil Gupta', 'Rayees Ahmad Thokar'] | 2023-07-01 | null | null | null | null | ['management', 'energy-management'] | ['miscellaneous', 'time-series'] | [-4.04036969e-01 -2.70060301e-01 4.57753614e-02 -1.52707040e-01
-1.01935253e-01 -7.36699641e-01 3.27725828e-01 2.40690261e-01
-3.17737281e-01 1.64965391e+00 -5.62929571e-01 -2.49864861e-01
-9.05905247e-01 -8.62873375e-01 -7.41941482e-02 -1.05272591e+00
-3.27878028e-01 1.03211510e+00 -3.03731203e-01 -3.04584712... | [5.680893898010254, 2.5635979175567627] |
c71fc03c-ae77-4f19-9ffc-8fcece7f6326 | translation-between-molecules-and-natural | 2204.11817 | null | https://arxiv.org/abs/2204.11817v3 | https://arxiv.org/pdf/2204.11817v3.pdf | Translation between Molecules and Natural Language | We present $\textbf{MolT5}$ $-$ a self-supervised learning framework for pretraining models on a vast amount of unlabeled natural language text and molecule strings. $\textbf{MolT5}$ allows for new, useful, and challenging analogs of traditional vision-language tasks, such as molecule captioning and text-based de novo ... | ['Heng Ji', 'Kyunghyun Cho', 'Garrett Honke', 'Kevin Ros', 'Tuan Lai', 'Carl Edwards'] | 2022-04-25 | null | null | null | null | ['molecule-captioning', 'text-based-de-novo-molecule-generation'] | ['medical', 'medical'] | [ 6.33697808e-01 3.19565147e-01 -2.68783659e-01 -4.11080807e-01
-1.06446004e+00 -9.18231905e-01 7.85881817e-01 5.21510661e-01
-4.27655131e-01 1.38702071e+00 5.88294491e-02 -5.73202908e-01
1.03756703e-01 -1.13604116e+00 -1.35664380e+00 -5.75187325e-01
8.35578293e-02 7.06426203e-01 -3.15341860e-01 -3.68896365... | [4.875363826751709, 5.923287868499756] |
1a2bdece-46c3-457f-b6c4-76ade02dda36 | hybrid-data-augmentation-and-deep-attention | 2109.09026 | null | https://arxiv.org/abs/2109.09026v1 | https://arxiv.org/pdf/2109.09026v1.pdf | Hybrid Data Augmentation and Deep Attention-based Dilated Convolutional-Recurrent Neural Networks for Speech Emotion Recognition | Speech emotion recognition (SER) has been one of the significant tasks in Human-Computer Interaction (HCI) applications. However, it is hard to choose the optimal features and deal with imbalance labeled data. In this article, we investigate hybrid data augmentation (HDA) methods to generate and balance data based on t... | ['Sy Dzung Nguyen', 'Duc Ngoc Minh Dang', 'Nhat Truong Pham'] | 2021-09-18 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [-5.22672758e-02 -1.85721386e-02 4.90613967e-01 -4.18473870e-01
-7.21247792e-01 7.57543966e-02 2.93819249e-01 -4.40848768e-01
-3.65445703e-01 7.64504075e-01 6.89089373e-02 -1.79940030e-01
2.55170971e-01 -6.85466588e-01 -5.11878550e-01 -7.75471270e-01
3.39669883e-01 1.02910586e-01 -2.79037565e-01 -4.40360099... | [13.906940460205078, 5.988748073577881] |
b1975f6a-b729-43c8-affb-1334ba0d76d1 | towards-better-instruction-following-language | 2304.07854 | null | https://arxiv.org/abs/2304.07854v1 | https://arxiv.org/pdf/2304.07854v1.pdf | Towards Better Instruction Following Language Models for Chinese: Investigating the Impact of Training Data and Evaluation | Recently, significant public efforts have been directed towards developing low-cost models with capabilities akin to ChatGPT, thereby fostering the growth of open-source conversational models. However, there remains a scarcity of comprehensive and in-depth evaluations of these models' performance. In this study, we exa... | ['Xiangang Li', 'Baochang Ma', 'Qiang Niu', 'Yiping Peng', 'Yong Deng', 'Yan Gong', 'Yunjie Ji'] | 2023-04-16 | null | null | null | null | ['instruction-following'] | ['natural-language-processing'] | [-3.00748408e-01 7.68842874e-03 -2.06665620e-01 -5.13343573e-01
-1.16539371e+00 -7.53754616e-01 4.15573031e-01 1.55021653e-01
-4.88892913e-01 8.29887569e-01 8.15242887e-01 -8.84415627e-01
2.30566889e-01 -4.69625831e-01 -6.08330011e-01 -7.68059418e-02
1.19328722e-01 4.87494707e-01 1.16160363e-01 -5.36683798... | [11.739022254943848, 8.277430534362793] |
5ae26d36-3b5b-44bb-bbc1-10e1a2634a60 | reservoir-of-diverse-adaptive-learners-and | 1709.02457 | null | http://arxiv.org/abs/1709.02457v1 | http://arxiv.org/pdf/1709.02457v1.pdf | Reservoir of Diverse Adaptive Learners and Stacking Fast Hoeffding Drift Detection Methods for Evolving Data Streams | The last decade has seen a surge of interest in adaptive learning algorithms
for data stream classification, with applications ranging from predicting ozone
level peaks, learning stock market indicators, to detecting computer security
violations. In addition, a number of methods have been developed to detect
concept dr... | ['Herna Viktor', 'Eric Paquet', 'Ali Pesaranghader'] | 2017-09-07 | null | null | null | null | ['computer-security'] | ['miscellaneous'] | [-1.30891457e-01 -8.29114854e-01 -7.34406114e-02 -2.92940736e-01
-2.80597210e-01 -6.51242495e-01 5.31956255e-01 6.69800520e-01
-2.75683701e-01 6.56503379e-01 -3.12602550e-01 -2.76632756e-01
-3.36853772e-01 -8.93394351e-01 -4.15561467e-01 -7.58905828e-01
-5.28979063e-01 3.33070189e-01 7.16221631e-01 -3.90697062... | [7.4659295082092285, 2.9898197650909424] |
90e36c2b-e26d-4031-9fe9-a497e5762f46 | challenges-and-strategies-in-cross-cultural | 2203.10020 | null | https://arxiv.org/abs/2203.10020v1 | https://arxiv.org/pdf/2203.10020v1.pdf | Challenges and Strategies in Cross-Cultural NLP | Various efforts in the Natural Language Processing (NLP) community have been made to accommodate linguistic diversity and serve speakers of many different languages. However, it is important to acknowledge that speakers and the content they produce and require, vary not just by language, but also by culture. Although l... | ['Anders Søgaard', 'Phillip Rust', 'Katerina Margatina', 'Constanza Fierro', 'Ruixiang Cui', 'Ilias Chalkidis', 'Laura Cabello Piqueras', 'Emanuele Bugliarello', 'Stephanie Brandl', 'Mostafa Abdou', 'Miryam de Lhoneux', 'Heather Lent', 'Stella Frank', 'Daniel Hershcovich'] | 2022-03-18 | null | https://aclanthology.org/2022.acl-long.482 | https://aclanthology.org/2022.acl-long.482.pdf | acl-2022-5 | ['multilingual-nlp'] | ['natural-language-processing'] | [-4.09188837e-01 -2.63539385e-02 -5.49683332e-01 -4.30668890e-01
-7.48388886e-01 -1.05822349e+00 6.01706684e-01 4.33132172e-01
-4.66588259e-01 6.57477200e-01 8.99019539e-01 -3.76084387e-01
1.81543112e-01 -4.52350914e-01 -1.00176193e-01 1.32969365e-01
3.87559175e-01 2.94978023e-01 -2.60964006e-01 -4.40088868... | [10.345733642578125, 9.87490463256836] |
01462367-211d-4ce2-b362-edcaef430062 | mastering-the-dungeon-grounded-language | 1711.07950 | null | http://arxiv.org/abs/1711.07950v3 | http://arxiv.org/pdf/1711.07950v3.pdf | Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent | Contrary to most natural language processing research, which makes use of
static datasets, humans learn language interactively, grounded in an
environment. In this work we propose an interactive learning procedure called
Mechanical Turker Descent (MTD) and use it to train agents to execute natural
language commands gro... | ['Douwe Kiela', 'Jack Urbanek', 'Saizheng Zhang', 'Jason Weston', 'Arthur Szlam', 'Alexander H. Miller', 'Zhilin Yang', 'Will Feng'] | 2017-11-21 | mastering-the-dungeon-grounded-language-1 | https://openreview.net/forum?id=SJ-C6JbRW | https://openreview.net/pdf?id=SJ-C6JbRW | iclr-2018-1 | ['grounded-language-learning'] | ['natural-language-processing'] | [-7.43891418e-01 3.16684395e-01 3.26205999e-01 -2.55042702e-01
3.09933424e-02 -8.35822761e-01 6.41043901e-01 -1.52148068e-01
-1.19691348e+00 7.23370314e-01 -2.04356596e-01 -1.80454314e-01
1.19036935e-01 -9.17121530e-01 -5.13299763e-01 -4.11607444e-01
-1.43497899e-01 1.01089728e+00 1.17626987e-01 -7.66993165... | [3.8692665100097656, 1.4357362985610962] |
b7fe8ffe-e838-443e-b288-13ee7f83a714 | lightweight-multi-person-total-motion-capture | 2108.10378 | null | https://arxiv.org/abs/2108.10378v1 | https://arxiv.org/pdf/2108.10378v1.pdf | Lightweight Multi-person Total Motion Capture Using Sparse Multi-view Cameras | Multi-person total motion capture is extremely challenging when it comes to handle severe occlusions, different reconstruction granularities from body to face and hands, drastically changing observation scales and fast body movements. To overcome these challenges above, we contribute a lightweight total motion capture ... | ['Yebin Liu', 'Tao Yu', 'Mengcheng Li', 'Liang An', 'Zhe Li', 'Yuxiang Zhang'] | 2021-08-23 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zhang_Lightweight_Multi-Person_Total_Motion_Capture_Using_Sparse_Multi-View_Cameras_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zhang_Lightweight_Multi-Person_Total_Motion_Capture_Using_Sparse_Multi-View_Cameras_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-multi-person-pose-estimation'] | ['computer-vision'] | [-7.83088282e-02 -5.14033079e-01 -1.53450593e-01 -1.38197497e-01
-7.78830051e-01 -4.74857211e-01 2.15592191e-01 -5.92783630e-01
-1.61148578e-01 6.12304926e-01 2.44224623e-01 6.59048259e-01
6.92467317e-02 -2.97801703e-01 -4.93127465e-01 -4.10534620e-01
3.12717557e-01 6.29780471e-01 3.28430742e-01 1.08809084... | [7.056490421295166, -1.030943751335144] |
58d9f0ac-f005-473f-b7bb-033014099554 | contextual-similarity-is-more-valuable-than | 2207.09217 | null | https://arxiv.org/abs/2207.09217v3 | https://arxiv.org/pdf/2207.09217v3.pdf | Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking | Chinese Spell Checking (CSC) task aims to detect and correct Chinese spelling errors. Recently, related researches focus on introducing character similarity from confusion set to enhance the CSC models, ignoring the context of characters that contain richer information. To make better use of contextual information, we ... | ['Hai-Tao Zheng', 'Yunbo Cao', 'Yangning Li', 'Shirong Ma', 'Qingyu Zhou', 'Yinghui Li', 'Ding Zhang'] | 2022-07-17 | null | null | null | null | ['chinese-spell-checking'] | ['natural-language-processing'] | [ 3.76197606e-01 -6.39405370e-01 -1.68513119e-01 -2.35765815e-01
-6.46505535e-01 -4.97371197e-01 4.56331551e-01 2.87332267e-01
-7.64010787e-01 7.56687164e-01 2.79967844e-01 -6.52623534e-01
2.47239783e-01 -4.28399563e-01 -4.28317815e-01 -5.26797652e-01
4.10481304e-01 -4.90403846e-02 6.31510377e-01 -3.22567105... | [10.93377685546875, 10.832954406738281] |
86938b92-8bb5-4fae-96d3-c617b0af1f01 | prompt-based-editing-for-text-style-transfer | 2301.11997 | null | https://arxiv.org/abs/2301.11997v1 | https://arxiv.org/pdf/2301.11997v1.pdf | Prompt-Based Editing for Text Style Transfer | Prompting approaches have been recently explored in text style transfer, where a textual prompt is used to query a pretrained language model to generate style-transferred texts word by word in an autoregressive manner. However, such a generation process is less controllable and early prediction errors may affect future... | ['Mauajama Firdaus', 'Lili Mou', 'Yu Tong Han', 'Guoqing Luo'] | 2023-01-27 | null | null | null | null | ['text-style-transfoer'] | ['natural-language-processing'] | [ 7.99115419e-01 1.81442887e-01 3.58773628e-03 -7.28466034e-01
-9.13477659e-01 -7.00550616e-01 9.49391425e-01 -2.02558234e-01
-5.19352913e-01 9.61182356e-01 3.21045846e-01 -2.95527846e-01
6.28379405e-01 -8.39059174e-01 -6.80698097e-01 -3.21540296e-01
6.67837918e-01 7.73083329e-01 5.95562533e-02 -4.16212916... | [11.657143592834473, 9.420083045959473] |
2e4b9a3f-c146-43e7-94d8-2effb639a00e | quantifying-the-effect-of-x-ray-scattering | 2305.12822 | null | https://arxiv.org/abs/2305.12822v1 | https://arxiv.org/pdf/2305.12822v1.pdf | Quantifying the effect of X-ray scattering for data generation in real-time defect detection | X-ray imaging is widely used for non-destructive detection of defects in industrial products on a conveyor belt. Real-time detection requires highly accurate, robust, and fast algorithms to analyze X-ray images. Deep convolutional neural networks (DCNNs) satisfy these requirements if a large amount of labeled data is a... | ['K. Joost Batenburg', 'Tristan van Leeuwen', 'Robert van Liere', 'Vladyslav Andriiashen'] | 2023-05-22 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 3.71730417e-01 -2.56234646e-01 4.73052055e-01 -4.41886663e-01
-6.65655315e-01 -3.14763784e-02 3.34820092e-01 3.23113322e-01
-2.99113065e-01 3.71798277e-01 -6.12301648e-01 -4.82720166e-01
-2.84571618e-01 -1.24092650e+00 -9.16034102e-01 -8.52027237e-01
2.66333167e-02 4.41425323e-01 4.29752886e-01 -2.53639221... | [7.337869644165039, 1.8456741571426392] |
4a1a2c81-92c3-4170-a7c8-50a04d478704 | a-modern-perspective-on-query-likelihood-with | 2106.13618 | null | https://arxiv.org/abs/2106.13618v1 | https://arxiv.org/pdf/2106.13618v1.pdf | A Modern Perspective on Query Likelihood with Deep Generative Retrieval Models | Existing neural ranking models follow the text matching paradigm, where document-to-query relevance is estimated through predicting the matching score. Drawing from the rich literature of classical generative retrieval models, we introduce and formalize the paradigm of deep generative retrieval models defined via the c... | ['Markus Schedl', 'Carsten Eickhoff', 'Klaus Antonius Grasserbauer', 'Daniel Cohen', 'Navid Rekabsaz', 'Oleg Lesota'] | 2021-06-25 | null | null | null | null | ['passage-re-ranking'] | ['natural-language-processing'] | [-4.86287177e-02 -1.43795386e-01 2.13925950e-02 -3.70252818e-01
-1.53291917e+00 -6.67596340e-01 1.41220129e+00 9.76378471e-02
-2.31826916e-01 6.17246807e-01 8.06568801e-01 -2.22049817e-01
-6.55629337e-01 -1.08348119e+00 -6.58285677e-01 -4.00065929e-01
-2.55728066e-01 1.26721406e+00 1.01453416e-01 -5.91657460... | [11.49854564666748, 7.641337871551514] |
f1183137-f885-4b80-a7ae-11502c686586 | ehanet-an-effective-hierarchical-aggregation | null | null | https://www.mdpi.com/2076-3417/10/9/3135 | https://www.researchgate.net/publication/341129398_EHANet_An_Effective_Hierarchical_Aggregation_Network_for_Face_Parsing | EHANet: An Effective Hierarchical Aggregation Network for Face Parsing | In recent years, benefiting from deep convolutional neural networks (DCNNs), face parsing has developed rapidly. However, it still has the following problems: (1) Existing state-of-the-art frameworks usually do not satisfy real-time while pursuing performance; (2) similar appearances cause incorrect pixel label assignm... | ['Xinglong Feng', 'Dingyu Xue', 'Ling Luo'] | 2020-04-07 | null | null | null | applied-sciences-2020-4 | ['face-parsing'] | ['computer-vision'] | [ 2.65567482e-01 -5.85196577e-02 -1.83450311e-01 -6.50838256e-01
-5.93126416e-01 1.53990015e-01 3.91173773e-02 -6.04211651e-02
-3.84763122e-01 4.31319326e-01 3.76470797e-02 -2.56944448e-01
1.09824799e-01 -9.19466138e-01 -6.05441928e-01 -6.71997905e-01
1.91805437e-01 1.57480054e-02 4.58108157e-01 -6.46277368... | [13.267311096191406, 0.6927266716957092] |
9e040888-31ba-4aba-bc9f-b7cc57992c86 | a-practical-test-for-a-planted-community-in | 2101.05928 | null | https://arxiv.org/abs/2101.05928v1 | https://arxiv.org/pdf/2101.05928v1.pdf | A practical test for a planted community in heterogeneous networks | One of the fundamental task in graph data mining is to find a planted community(dense subgraph), which has wide application in biology, finance, spam detection and so on. For a real network data, the existence of a dense subgraph is generally unknown. Statistical tests have been devised to testing the existence of dens... | ['Qian Wen', 'Mingao Yuan'] | 2021-01-15 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [ 1.10773720e-01 1.72874838e-01 -1.80083007e-01 -6.76410496e-02
2.00360745e-01 -5.75311124e-01 1.37642786e-01 3.75492036e-01
6.64464086e-02 1.06164169e+00 -5.71945727e-01 -6.92852914e-01
-5.03993154e-01 -1.25532186e+00 -5.72793543e-01 -9.71710920e-01
-4.36391503e-01 6.08600438e-01 5.07147431e-01 1.82190657... | [6.954839706420898, 5.262546539306641] |
6b85842e-bb0e-4298-aaec-201fe4558fb9 | discovering-pdes-from-multiple-experiments | 2109.11939 | null | https://arxiv.org/abs/2109.11939v2 | https://arxiv.org/pdf/2109.11939v2.pdf | Discovering PDEs from Multiple Experiments | Automated model discovery of partial differential equations (PDEs) usually considers a single experiment or dataset to infer the underlying governing equations. In practice, experiments have inherent natural variability in parameters, initial and boundary conditions that cannot be simply averaged out. We introduce a ra... | ['Remy Kusters', 'Gert-Jan Both', 'Georges Tod'] | 2021-09-24 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [ 3.97294424e-02 -1.17478356e-01 -6.68877140e-02 -1.31632671e-01
-6.61788464e-01 -5.96193790e-01 1.86851427e-01 -8.13170448e-02
1.52222021e-02 1.16665983e+00 -7.46560376e-03 -1.53362244e-01
-4.28007871e-01 -3.42652172e-01 -8.68307829e-01 -1.06990683e+00
-5.13958395e-01 6.91161096e-01 -2.88533241e-01 3.39271158... | [6.559925556182861, 3.5456602573394775] |
5c2fc1b0-f5b4-4d42-bd44-c4375ecf1a5e | boosting-active-learning-via-improving-test | 2112.05683 | null | https://arxiv.org/abs/2112.05683v2 | https://arxiv.org/pdf/2112.05683v2.pdf | Boosting Active Learning via Improving Test Performance | Central to active learning (AL) is what data should be selected for annotation. Existing works attempt to select highly uncertain or informative data for annotation. Nevertheless, it remains unclear how selected data impacts the test performance of the task model used in AL. In this work, we explore such an impact by t... | ['Min Xu', 'Cheng-Zhong Xu', 'Siyu Huang', 'Xiangrui Zeng', 'Guosheng Hu', 'Pengkun Yang', 'Xingjian Li', 'Tianyang Wang'] | 2021-12-10 | null | null | null | null | ['electron-tomography'] | ['medical'] | [ 3.40077609e-01 1.70323446e-01 -2.11440697e-01 -5.35138011e-01
-1.26994121e+00 -5.01509428e-01 1.55921265e-01 4.27002400e-01
-7.12564588e-01 1.14664543e+00 -2.44921237e-01 -1.81818202e-01
-2.69548949e-02 -5.46563387e-01 -6.71827972e-01 -1.08330059e+00
2.97701508e-01 5.06514609e-01 2.42428824e-01 4.09037620... | [14.67751407623291, -2.367584705352783] |
8627d7e3-8f46-493c-9e33-220be1d225d0 | chain-of-thought-prompting-elicits-knowledge | 2307.01640 | null | https://arxiv.org/abs/2307.01640v1 | https://arxiv.org/pdf/2307.01640v1.pdf | Chain of Thought Prompting Elicits Knowledge Augmentation | The knowledge-augmented deep learning paradigm refers to a paradigm in which domain knowledge is identified and integrated into deep models. Conventional methods typically employ task-specific approaches to gather external knowledge from various sources. In contrast, large language models are extensively pre-trained an... | ['Xinmei Huang', 'Jing Zhang', 'Dingjun Wu'] | 2023-07-04 | null | null | null | null | ['retrieval'] | ['methodology'] | [-4.49242383e-01 2.86624759e-01 -4.85329568e-01 -3.16498160e-01
-5.93076587e-01 -5.41882396e-01 8.61937523e-01 2.67639309e-01
-6.66338265e-01 9.50557530e-01 3.59708041e-01 -4.04679745e-01
9.92243662e-02 -1.11685276e+00 -9.08437371e-01 -3.40480953e-01
5.88485837e-01 6.48912311e-01 1.67941943e-01 -3.88349593... | [10.418241500854492, 8.215957641601562] |
9755a6b4-8492-4d99-b82c-a697777bf1cf | full-resolution-encoder-decoder-networks-with | 2106.00566 | null | https://arxiv.org/abs/2106.00566v1 | https://arxiv.org/pdf/2106.00566v1.pdf | Full-Resolution Encoder-Decoder Networks with Multi-Scale Feature Fusion for Human Pose Estimation | To achieve more accurate 2D human pose estimation, we extend the successful encoder-decoder network, simple baseline network (SBN), in three ways. To reduce the quantization errors caused by the large output stride size, two more decoder modules are appended to the end of the simple baseline network to get full output ... | ['Hong Wu', 'Mingjian Chen', 'Jie Ou'] | 2021-06-01 | null | null | null | null | ['2d-human-pose-estimation'] | ['computer-vision'] | [-2.10273549e-01 -1.82931330e-02 1.98717602e-02 -3.18953067e-01
-4.87842649e-01 3.43859121e-02 1.31817162e-01 -4.33726370e-01
-6.63250029e-01 6.62278473e-01 5.61616898e-01 2.70924568e-01
3.85481119e-01 -7.56150484e-01 -7.95505404e-01 -4.11670029e-01
4.52919900e-02 2.16358632e-01 6.59283161e-01 -4.86702472... | [7.181342124938965, -0.6591963171958923] |
457510fb-071f-4e26-934a-cede304a13c8 | one-button-machine-for-automating-feature | 1706.00327 | null | http://arxiv.org/abs/1706.00327v1 | http://arxiv.org/pdf/1706.00327v1.pdf | One button machine for automating feature engineering in relational databases | Feature engineering is one of the most important and time consuming tasks in
predictive analytics projects. It involves understanding domain knowledge and
data exploration to discover relevant hand-crafted features from raw data. In
this paper, we introduce a system called One Button Machine, or OneBM for
short, which ... | ['Johann-Michael Thiebaut', 'Hoang Thanh Lam', 'Bei Chen', 'Oznur Alkan', 'Mathieu Sinn', 'Tiep Mai'] | 2017-06-01 | null | null | null | null | ['automated-feature-engineering'] | ['methodology'] | [-5.11031866e-01 8.91882107e-02 -1.64010987e-01 -5.22012711e-01
-6.58353686e-01 -6.11354589e-01 3.25778633e-01 7.01937020e-01
-2.17395842e-01 5.34073710e-01 -2.34065861e-01 -4.94107634e-01
-4.09500003e-01 -1.01278448e+00 -8.47980559e-01 -2.62330379e-03
-1.04193501e-01 8.07501078e-01 2.22168714e-01 -3.74575078... | [8.894740104675293, 7.16386604309082] |
7ef0900c-003b-42b4-9a6f-ac8379b3ab5e | towards-an-end-to-end-framework-for-flow | 2204.02663 | null | https://arxiv.org/abs/2204.02663v2 | https://arxiv.org/pdf/2204.02663v2.pdf | Towards An End-to-End Framework for Flow-Guided Video Inpainting | Optical flow, which captures motion information across frames, is exploited in recent video inpainting methods through propagating pixels along its trajectories. However, the hand-crafted flow-based processes in these methods are applied separately to form the whole inpainting pipeline. Thus, these methods are less eff... | ['Ming-Ming Cheng', 'Chun-Le Guo', 'Jianhua Qin', 'Cheng-Ze Lu', 'Zhen Li'] | 2022-04-06 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Li_Towards_an_End-to-End_Framework_for_Flow-Guided_Video_Inpainting_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Li_Towards_an_End-to-End_Framework_for_Flow-Guided_Video_Inpainting_CVPR_2022_paper.pdf | cvpr-2022-1 | ['seeing-beyond-the-visible', 'video-inpainting'] | ['computer-vision', 'computer-vision'] | [-6.41450956e-02 -2.73034036e-01 -1.98541716e-01 -1.28311012e-02
-6.31199837e-01 -3.44731688e-01 4.02040988e-01 -3.33304882e-01
-2.68327266e-01 8.38039756e-01 3.55743915e-01 -6.14214465e-02
2.25761473e-01 -6.12193644e-01 -7.18141556e-01 -4.83161241e-01
4.76567559e-02 -1.18135437e-01 2.07017258e-01 -1.46791358... | [10.752904891967773, -1.4106210470199585] |
0d1f6e4c-ba0c-478f-bf9d-76b6eb649741 | can-string-kernels-pass-the-test-of-time-in | 1707.08349 | null | http://arxiv.org/abs/1707.08349v2 | http://arxiv.org/pdf/1707.08349v2.pdf | Can string kernels pass the test of time in Native Language Identification? | We describe a machine learning approach for the 2017 shared task on Native
Language Identification (NLI). The proposed approach combines several kernels
using multiple kernel learning. While most of our kernels are based on
character p-grams (also known as n-grams) extracted from essays or speech
transcripts, we also u... | ['Marius Popescu', 'Radu Tudor Ionescu'] | 2017-07-26 | can-string-kernels-pass-the-test-of-time-in-1 | https://aclanthology.org/W17-5024 | https://aclanthology.org/W17-5024.pdf | ws-2017-9 | ['native-language-identification'] | ['natural-language-processing'] | [-1.07907094e-01 -1.44334823e-01 -1.23277307e-01 -1.65437058e-01
-1.25606644e+00 -6.46318853e-01 5.34419000e-01 3.57471645e-01
-9.28037465e-01 4.18061376e-01 1.83270611e-02 -4.61358786e-01
-1.74907416e-01 -2.97802269e-01 -5.10781348e-01 -5.91467977e-01
-1.93929430e-02 4.32145357e-01 2.52025485e-01 4.00372706... | [10.344042778015137, 10.516022682189941] |
be5de5a0-d132-4946-8902-1f99720dd7c3 | evoquer-enhancing-temporal-grounding-with | 2109.04600 | null | https://arxiv.org/abs/2109.04600v1 | https://arxiv.org/pdf/2109.04600v1.pdf | EVOQUER: Enhancing Temporal Grounding with Video-Pivoted BackQuery Generation | Temporal grounding aims to predict a time interval of a video clip corresponding to a natural language query input. In this work, we present EVOQUER, a temporal grounding framework incorporating an existing text-to-video grounding model and a video-assisted query generation network. Given a query and an untrimmed video... | ['Rui Zhang', 'Huayan Wang', 'Xin Chen', 'Jason Wang', 'Lulu Liu', 'Yanjun Gao'] | 2021-09-10 | null | null | null | null | ['video-grounding'] | ['computer-vision'] | [ 2.80740291e-01 2.22158074e-01 -6.30421519e-01 -4.94470119e-01
-1.25968027e+00 -4.00248468e-01 5.83555281e-01 1.06766872e-01
-3.52681756e-01 5.67709327e-01 2.57275611e-01 -1.14953689e-01
1.79415092e-01 -6.76668584e-01 -1.33265591e+00 -6.58243448e-02
-5.51612139e-01 2.05078915e-01 4.56429750e-01 4.16216515... | [10.040645599365234, 0.6808129549026489] |
d17af62c-8b0c-476b-93d9-377108a6d752 | joined-prior-guided-multi-task-learning-for | 2203.00461 | null | https://arxiv.org/abs/2203.00461v1 | https://arxiv.org/pdf/2203.00461v1.pdf | JOINED : Prior Guided Multi-task Learning for Joint Optic Disc/Cup Segmentation and Fovea Detection | Fundus photography has been routinely used to document the presence and severity of various retinal degenerative diseases such as age-related macula degeneration, glaucoma, and diabetic retinopathy, for which the fovea, optic disc (OD), and optic cup (OC) are important anatomical landmarks. Identification of those anat... | ['Xiaoying Tang', 'Zhiyuan Cai', 'Li Lin', 'Huaqing He'] | 2022-03-01 | null | null | null | null | ['fovea-detection'] | ['medical'] | [ 7.92416260e-02 -1.93030074e-01 -4.43967655e-02 -1.48407906e-01
-7.83773124e-01 -3.74631822e-01 3.12440306e-01 2.44637460e-01
-6.37070477e-01 4.88795221e-01 3.32989693e-02 -4.09791440e-01
-3.18262950e-02 -4.53497350e-01 -5.39578259e-01 -8.57816935e-01
7.69720152e-02 1.79447353e-01 5.29689312e-01 4.74117786... | [15.783124923706055, -3.961388111114502] |
092dc9e1-198f-462b-80e2-c13dc906780e | a0c-alpha-zero-in-continuous-action-space | 1805.09613 | null | http://arxiv.org/abs/1805.09613v1 | http://arxiv.org/pdf/1805.09613v1.pdf | A0C: Alpha Zero in Continuous Action Space | A core novelty of Alpha Zero is the interleaving of tree search and deep
learning, which has proven very successful in board games like Chess, Shogi and
Go. These games have a discrete action space. However, many real-world
reinforcement learning domains have continuous action spaces, for example in
robotic control, na... | ['Joost Broekens', 'Thomas M. Moerland', 'Aske Plaat', 'Catholijn M. Jonker'] | 2018-05-24 | null | null | null | null | ['board-games'] | ['playing-games'] | [-9.11617950e-02 1.22013658e-01 -4.86441880e-01 7.44039640e-02
-1.28749445e-01 -4.08056408e-01 5.69815695e-01 -3.47855657e-01
-6.39848650e-01 1.40009272e+00 -4.75217909e-01 -6.31354153e-01
-6.21329963e-01 -9.29346204e-01 -4.35960829e-01 -7.13235438e-01
-4.99367386e-01 4.96390790e-01 7.71854043e-01 -9.65484619... | [3.791487216949463, 1.5569322109222412] |
14d3e119-5367-4046-b25b-66fb11b0444c | unsupervised-domain-adaptation-through-inter | 2004.10016 | null | https://arxiv.org/abs/2004.10016v1 | https://arxiv.org/pdf/2004.10016v1.pdf | Unsupervised Domain Adaptation through Inter-modal Rotation for RGB-D Object Recognition | Unsupervised Domain Adaptation (DA) exploits the supervision of a label-rich source dataset to make predictions on an unlabeled target dataset by aligning the two data distributions. In robotics, DA is used to take advantage of automatically generated synthetic data, that come with "free" annotation, to make effective ... | ['Barbara Caputo', 'Luca Robbiano', 'Mohammad Reza Loghmani', 'Mirco Planamente', 'Markus Vincze', 'Kiru Park'] | 2020-04-21 | null | null | null | null | ['object-categorization'] | ['computer-vision'] | [ 5.22294521e-01 2.42048204e-01 -1.66221976e-01 -8.69234204e-01
-6.98094249e-01 -7.44288146e-01 5.88450909e-01 -2.11599112e-01
-3.40730935e-01 3.08971524e-01 -1.29031569e-01 -2.01201797e-01
6.53302968e-02 -6.44025505e-01 -8.31937253e-01 -7.97308087e-01
4.39340800e-01 6.28964245e-01 2.90277451e-01 -8.75290334... | [8.193645477294922, -2.5165040493011475] |
8d3b28f3-0fae-4afd-abc5-78ef854bc973 | exascale-deep-learning-for-scientific-inverse | 1909.11150 | null | https://arxiv.org/abs/1909.11150v1 | https://arxiv.org/pdf/1909.11150v1.pdf | Exascale Deep Learning for Scientific Inverse Problems | We introduce novel communication strategies in synchronous distributed Deep Learning consisting of decentralized gradient reduction orchestration and computational graph-aware grouping of gradient tensors. These new techniques produce an optimal overlap between computation and communication and result in near-linear sc... | ['Junqi Yin', 'Albina Borisevich', 'Nouamane Laanait', 'Joshua Romero', 'Vitalii Starchenko', 'Sean Treichler', 'M. Todd Young', 'Michael Matheson', 'Alex Sergeev'] | 2019-09-24 | null | null | null | null | ['materials-imaging'] | ['computer-vision'] | [-4.51605052e-01 -1.06325641e-01 4.01864588e-01 -3.40065539e-01
-5.72681069e-01 -2.90227234e-01 3.47195387e-01 2.46283054e-01
-6.93055630e-01 5.69510520e-01 -2.16225367e-02 -6.22648299e-01
-2.18074918e-01 -1.02103102e+00 -9.11033988e-01 -8.89139473e-01
-5.89939952e-01 6.38853252e-01 2.59880811e-01 2.32690856... | [8.42798900604248, 3.2694342136383057] |
bae8efb9-fa17-4980-bbe1-88a537e19687 | unsupervised-discovery-of-object-radiance | 2107.07905 | null | https://arxiv.org/abs/2107.07905v2 | https://arxiv.org/pdf/2107.07905v2.pdf | Unsupervised Discovery of Object Radiance Fields | We study the problem of inferring an object-centric scene representation from a single image, aiming to derive a representation that explains the image formation process, captures the scene's 3D nature, and is learned without supervision. Most existing methods on scene decomposition lack one or more of these characteri... | ['Jiajun Wu', 'Leonidas J. Guibas', 'Hong-Xing Yu'] | 2021-07-16 | unsupervised-discovery-of-object-radiance-1 | https://openreview.net/forum?id=rwE8SshAlxw | https://openreview.net/pdf?id=rwE8SshAlxw | iclr-2022-4 | ['scene-segmentation'] | ['computer-vision'] | [ 7.63118267e-01 -3.30606732e-03 8.88444558e-02 -5.08740187e-01
-2.55344450e-01 -6.91310048e-01 6.76983893e-01 -3.05542946e-01
2.63440967e-01 3.27769011e-01 2.47626305e-01 -2.93642044e-01
-7.09044859e-02 -9.88394558e-01 -1.01640332e+00 -6.41000867e-01
3.72071385e-01 5.85018039e-01 2.08746456e-02 -2.22494662... | [9.146845817565918, -3.0601842403411865] |
070a7747-4d33-4188-8027-31b990bd3c19 | label-definitions-improve-semantic-role | null | null | https://aclanthology.org/2022.naacl-main.411 | https://aclanthology.org/2022.naacl-main.411.pdf | Label Definitions Improve Semantic Role Labeling | Argument classification is at the core of Semantic Role Labeling. Given a sentence and the predicate, a semantic role label is assigned to each argument of the predicate. While semantic roles come with meaningful definitions, existing work has treated them as symbolic. Learning symbolic labels usually requires ample tr... | ['Yunyao Li', 'Ishan Jindal', 'Li Zhang'] | null | null | null | null | naacl-2022-7 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 5.90554118e-01 4.94510293e-01 -7.64572799e-01 -7.23887146e-01
-5.42499602e-01 -8.97482514e-01 7.18165815e-01 7.39154994e-01
-3.79318178e-01 9.69780028e-01 5.14907062e-01 -2.65779912e-01
-2.08585218e-01 -7.21627474e-01 -5.72216153e-01 -3.16742808e-01
3.51911485e-01 5.70630014e-01 1.49145693e-01 -2.28930578... | [10.193902969360352, 9.272649765014648] |
32453d41-5322-4190-a5f3-b53a309ab985 | one-class-autoencoder-approach-for-optimal | 2104.04546 | null | https://arxiv.org/abs/2104.04546v3 | https://arxiv.org/pdf/2104.04546v3.pdf | One-class Autoencoder Approach for Optimal Electrode Set-up Identification in Wearable EEG Event Monitoring | A limiting factor towards the wide routine use of wearables devices for continuous healthcare monitoring is their cumbersome and obtrusive nature. This is particularly true for electroencephalography (EEG) recordings, which require the placement of multiple electrodes in contact with the scalp. In this work, we propose... | ['Maria A. Zuluaga', 'François Bremond', 'Esma Ismailova', 'Paolo Volpe', 'Guy Abi Hanna', 'Laura M. Ferrari'] | 2021-04-09 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [ 1.74536824e-01 -2.93065677e-03 4.53513175e-01 -2.39075407e-01
-4.88378406e-01 -4.15858060e-01 1.41793294e-02 5.65894127e-01
-7.45222926e-01 7.81193554e-01 -1.37096122e-01 -1.01948999e-01
-6.46137118e-01 -5.43662071e-01 -4.63908553e-01 -8.71716022e-01
-4.06275660e-01 1.82591066e-01 -2.86259949e-01 1.51715979... | [13.26679515838623, 3.3282532691955566] |
32f1cb72-026a-48d4-9de0-0121a25bbdd2 | on-gaze-deployment-to-audio-visual-cues-of | null | null | https://ieeexplore.ieee.org/document/9184838 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9184838 | On gaze deployment to audio-visual cues of social interactions | Attention supports our urge to forage on social cues. Under certain circumstances, we spend the majority of time scrutinising people, markedly their eyes and faces, and spotting persons that are talking. To account for such behaviour, this paper develops a computational model for the deployment of gaze within a multimo... | ['Raffaella Lanzarotti', 'Alessandro D’Amelio', 'Vittorio Cuculo', 'Giuseppe Boccignone', 'Giuliano Grossi'] | 2020-09-02 | null | null | null | ieee-access-2020-9 | ['scanpath-prediction'] | ['computer-vision'] | [ 2.06863135e-01 3.51272583e-01 2.66325265e-01 -4.13492531e-01
2.70614773e-01 -4.28817362e-01 2.92408437e-01 -3.48663300e-01
-5.52721620e-01 5.15014410e-01 1.49574608e-01 2.45708209e-02
-1.77002802e-01 -8.74995347e-03 -1.38399959e-01 -7.49112248e-01
-6.97486177e-02 -2.52798527e-01 2.25078203e-02 -1.05753437... | [14.000365257263184, 0.14033600687980652] |
0ef49c56-c5b4-4ee8-860f-323ff67ece15 | different-contexts-lead-to-different-word | null | null | https://aclanthology.org/C16-1073 | https://aclanthology.org/C16-1073.pdf | Different Contexts Lead to Different Word Embeddings | Recent work for learning word representations has applied successfully to many NLP applications, such as sentiment analysis and question answering. However, most of these models assume a single vector per word type without considering polysemy and homonymy. In this paper, we present an extension to the CBOW model which... | ['Nan Zheng', 'Jiajun Zhang', 'Wenpeng Hu'] | 2016-12-01 | different-contexts-lead-to-different-word-1 | https://aclanthology.org/C16-1073 | https://aclanthology.org/C16-1073.pdf | coling-2016-12 | ['learning-word-embeddings'] | ['methodology'] | [-4.44970690e-02 -2.06907481e-01 -3.75601143e-01 -3.40193570e-01
-1.90624177e-01 -4.73927617e-01 7.96066701e-01 6.83450460e-01
-1.03231633e+00 3.23865503e-01 6.45180404e-01 -3.81640255e-01
-3.60121392e-02 -9.15894568e-01 -1.71269670e-01 -6.18107021e-01
2.38524497e-01 3.74386817e-01 3.13243508e-01 -5.19789636... | [10.483474731445312, 8.677213668823242] |
86e3c363-d54b-428b-934f-9664a76d09bd | unh-at-semeval-2019-task-12-toponym | null | null | https://aclanthology.org/S19-2230 | https://aclanthology.org/S19-2230.pdf | UNH at SemEval-2019 Task 12: Toponym Resolution in Scientific Papers | The SemEval-2019 Task 12 is toponym resolution in scientific papers. We focus on Subtask 1: Toponym Detection which is the identification of spans of text for place names mentioned in a document. We propose two methods: 1) sliding window convolutional neural network using ELMo embeddings (cnn-elmo), and 2) sliding wind... | ['Laura Dietz', 'Matthew Magnusson'] | 2019-06-01 | null | null | null | semeval-2019-6 | ['toponym-resolution'] | ['natural-language-processing'] | [-1.83376670e-01 5.55709377e-02 -5.02005629e-02 5.73177636e-02
-9.56621945e-01 -5.15252352e-01 6.94688737e-01 4.63676542e-01
-8.60674083e-01 9.79977131e-01 2.25966156e-01 -3.16473693e-01
-1.55170828e-01 -5.68825901e-01 -8.41727197e-01 -2.21756488e-01
1.12210348e-01 4.06542182e-01 -7.24608004e-02 1.46014675... | [9.305785179138184, 9.209300994873047] |
ed8419bb-538c-40dd-8b23-d15402f7054f | bi-directional-weakly-supervised-knowledge | 2210.03664 | null | https://arxiv.org/abs/2210.03664v2 | https://arxiv.org/pdf/2210.03664v2.pdf | Bi-directional Weakly Supervised Knowledge Distillation for Whole Slide Image Classification | Computer-aided pathology diagnosis based on the classification of Whole Slide Image (WSI) plays an important role in clinical practice, and it is often formulated as a weakly-supervised Multiple Instance Learning (MIL) problem. Existing methods solve this problem from either a bag classification or an instance classifi... | ['Zhijian Song', 'Manning Wang', 'Xiaoyuan Luo', 'Linhao Qu'] | 2022-10-07 | null | null | null | null | ['multiple-instance-learning'] | ['methodology'] | [ 4.20062393e-01 3.50164890e-01 -4.71891552e-01 -5.09760797e-01
-8.69570851e-01 1.54655064e-02 9.56243053e-02 3.15514922e-01
-3.55242491e-01 9.10486162e-01 -1.80157483e-01 -2.33845457e-01
-2.64881432e-01 -9.33813393e-01 -6.33378744e-01 -1.24809265e+00
4.19152975e-01 5.69656849e-01 2.17368409e-01 -3.01842624... | [15.131327629089355, -2.739694833755493] |
184881c5-5d97-48b0-9778-b84182068c70 | accelerating-guided-diffusion-sampling-with | 2301.11558 | null | https://arxiv.org/abs/2301.11558v1 | https://arxiv.org/pdf/2301.11558v1.pdf | Accelerating Guided Diffusion Sampling with Splitting Numerical Methods | Guided diffusion is a technique for conditioning the output of a diffusion model at sampling time without retraining the network for each specific task. One drawback of diffusion models, however, is their slow sampling process. Recent techniques can accelerate unguided sampling by applying high-order numerical methods ... | ['Supasorn Suwajanakorn', 'Suttisak Wizadwongsa'] | 2023-01-27 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 4.65350807e-01 2.05672652e-01 1.40892014e-01 -7.37377927e-02
-8.61597538e-01 -2.89495975e-01 7.45394886e-01 -3.73858958e-01
-5.96750796e-01 1.00743854e+00 7.48037696e-02 -2.48482913e-01
2.84223169e-01 -6.95600212e-01 -7.98145771e-01 -7.34509826e-01
8.05627555e-02 5.50915360e-01 1.02735952e-01 -2.65866876... | [11.293133735656738, -0.4791332483291626] |
cce6a309-50d6-489a-ac5e-b32ec67f55cb | robust-iris-segmentation-based-on-fully | 1809.00769 | null | http://arxiv.org/abs/1809.00769v1 | http://arxiv.org/pdf/1809.00769v1.pdf | Robust Iris Segmentation Based on Fully Convolutional Networks and Generative Adversarial Networks | The iris can be considered as one of the most important biometric traits due
to its high degree of uniqueness. Iris-based biometrics applications depend
mainly on the iris segmentation whose suitability is not robust for different
environments such as near-infrared (NIR) and visible (VIS) ones. In this paper,
two appro... | ['Alceu S. Britto Jr.', 'Lucas F. Oliveira', 'Rayson Laroca', 'Diego R. Lucio', 'David Menotti', 'Evair Severo', 'Cides S. Bezerra'] | 2018-09-04 | null | null | null | null | ['iris-segmentation'] | ['medical'] | [ 2.29146495e-01 1.60747319e-01 2.32561585e-02 -9.76002812e-02
-3.05309653e-01 -4.84991193e-01 4.43843216e-01 -3.48553598e-01
-3.42802346e-01 7.77590215e-01 -7.90342242e-02 -3.89781773e-01
-3.22675318e-01 -7.27241218e-01 -6.40651822e-01 -1.04625380e+00
2.17628539e-01 3.49605322e-01 -2.52690166e-01 -1.54068738... | [3.744415760040283, -3.6317691802978516] |
af58f3c6-3535-4c97-a178-e7f01d5c3cd8 | language-model-prior-for-low-resource-neural | 2004.14928 | null | https://arxiv.org/abs/2004.14928v3 | https://arxiv.org/pdf/2004.14928v3.pdf | Language Model Prior for Low-Resource Neural Machine Translation | The scarcity of large parallel corpora is an important obstacle for neural machine translation. A common solution is to exploit the knowledge of language models (LM) trained on abundant monolingual data. In this work, we propose a novel approach to incorporate a LM as prior in a neural translation model (TM). Specifica... | ['Alexandra Birch', 'Christos Baziotis', 'Barry Haddow'] | 2020-04-30 | null | https://aclanthology.org/2020.emnlp-main.615 | https://aclanthology.org/2020.emnlp-main.615.pdf | emnlp-2020-11 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 2.01307610e-01 3.32164377e-01 -3.07511985e-01 -3.48908335e-01
-8.37547660e-01 -7.18363583e-01 8.11783612e-01 -2.00664476e-01
-6.66872978e-01 1.02211475e+00 2.05746338e-01 -8.43868732e-01
5.11212707e-01 -5.25860786e-01 -1.29768205e+00 -6.88726604e-01
7.08088517e-01 7.13065743e-01 -2.65035033e-01 -1.16265185... | [11.578855514526367, 10.235971450805664] |
960dc7b0-5eb4-4b8a-bde7-1b88f0629d0c | bayesian-optimization-enhanced-deep | 2212.13396 | null | https://arxiv.org/abs/2212.13396v1 | https://arxiv.org/pdf/2212.13396v1.pdf | Bayesian Optimization Enhanced Deep Reinforcement Learning for Trajectory Planning and Network Formation in Multi-UAV Networks | In this paper, we employ multiple UAVs coordinated by a base station (BS) to help the ground users (GUs) to offload their sensing data. Different UAVs can adapt their trajectories and network formation to expedite data transmissions via multi-hop relaying. The trajectory planning aims to collect all GUs' data, while th... | ['Dusit Niyato', 'Dinh Thai Hoang', 'Wenjie Zhang', 'Bo Gu', 'Meng Wang', 'Shimin Gong'] | 2022-12-27 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [-4.26646471e-01 2.32237294e-01 -4.90564466e-01 3.59753489e-01
-8.55539143e-02 -6.50211036e-01 -8.37415755e-02 2.06510052e-02
-3.96134615e-01 9.34775174e-01 -2.81077713e-01 -6.18332863e-01
-6.25347555e-01 -1.20115614e+00 -3.70553136e-01 -1.23188114e+00
-5.64010739e-01 1.43660247e-01 6.13816828e-02 -1.56769872... | [5.829674243927002, 1.5782639980316162] |
4c8eac26-4b34-41de-95bd-c41bc895c62d | object-category-aware-reinforcement-learning | 2210.07802 | null | https://arxiv.org/abs/2210.07802v1 | https://arxiv.org/pdf/2210.07802v1.pdf | Object-Category Aware Reinforcement Learning | Object-oriented reinforcement learning (OORL) is a promising way to improve the sample efficiency and generalization ability over standard RL. Recent works that try to solve OORL tasks without additional feature engineering mainly focus on learning the object representations and then solving tasks via reasoning based o... | ['Yunji Chen', 'Qi Guo', 'Xishan Zhang', 'Zidong Du', 'Xing Hu', 'Jiaming Guo', 'Shaohui Peng', 'Rui Zhang', 'Qi Yi'] | 2022-10-13 | null | null | null | null | ['object-discovery'] | ['computer-vision'] | [-2.88879257e-02 2.65317768e-01 -1.68826893e-01 -4.21128750e-01
-1.42720565e-01 -3.85118335e-01 3.95763040e-01 3.84340018e-01
-2.08016932e-01 4.04930264e-01 5.14151230e-02 2.86932409e-01
-6.19756639e-01 -1.16899335e+00 -8.12849522e-01 -6.40961885e-01
-2.23012585e-02 4.58181977e-01 4.30870712e-01 -2.52718836... | [10.013434410095215, 2.2797179222106934] |
66760079-a26c-4c88-852b-bc912094467b | is-synthetic-voice-detection-research-going | null | null | https://openaccess.thecvf.com/content/CVPR2022W/WMF/html/Borzi_Is_Synthetic_Voice_Detection_Research_Going_Into_the_Right_Direction_CVPRW_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022W/WMF/papers/Borzi_Is_Synthetic_Voice_Detection_Research_Going_Into_the_Right_Direction_CVPRW_2022_paper.pdf | Is Synthetic Voice Detection Research Going Into the Right Direction? | Machine Learning, and in general Artificial Intelligence approaches, brought a great advance in each and every field of Computer Science increasing accuracy levels of predictors in any known problem. Indeed, this evolution enabled the construction of effective frameworks and solutions able to be used in investigative a... | ['Dario Allegra', 'Filippo Stanco', 'Oliver Giudice', 'Stefano Borzì'] | 2022-06-19 | null | null | null | ieee-cvf-conference-on-computer-vision-and-5 | ['fake-voice-detection'] | ['audio'] | [ 2.72324253e-02 8.76686424e-02 -1.39406640e-02 -2.07601599e-02
-4.56764877e-01 -3.67978245e-01 4.87697095e-01 3.62992465e-01
-3.98170680e-01 5.93222618e-01 -1.64118305e-01 -7.39931107e-01
-1.28996342e-01 -5.87217987e-01 -3.06910783e-01 -7.90507317e-01
1.44958496e-03 3.60121965e-01 4.92193609e-01 -3.27360243... | [12.57474422454834, 1.2325940132141113] |
fa025c7e-2511-4ec6-8f2b-2e682b7755ef | the-shaped-transformer-attention-models-in | 2306.17759 | null | https://arxiv.org/abs/2306.17759v1 | https://arxiv.org/pdf/2306.17759v1.pdf | The Shaped Transformer: Attention Models in the Infinite Depth-and-Width Limit | In deep learning theory, the covariance matrix of the representations serves as a proxy to examine the network's trainability. Motivated by the success of Transformers, we study the covariance matrix of a modified Softmax-based attention model with skip connections in the proportional limit of infinite-depth-and-width.... | ['Daniel M. Roy', 'Chris Maddison', 'Thomas Hofmann', 'Bobby He', 'Mufan Bill Li', 'Chuning Li', 'Lorenzo Noci'] | 2023-06-30 | null | null | null | null | ['deep-attention', 'learning-theory', 'deep-attention'] | ['computer-vision', 'miscellaneous', 'natural-language-processing'] | [-1.80233177e-02 4.27358210e-01 -8.00510347e-02 -1.49663329e-01
-3.11602741e-01 -5.53354919e-01 7.61492491e-01 -2.39272282e-01
-5.10741472e-01 6.05873942e-01 1.58817351e-01 -4.28781748e-01
-3.83996338e-01 -5.33621490e-01 -8.57497454e-01 -1.23894620e+00
-1.35750830e-01 3.65014225e-01 3.30704629e-01 -2.25654751... | [7.926290035247803, 3.5731921195983887] |
ee4c9045-0e2d-495d-a39d-852f1e037349 | data-driven-response-regime-exploration-and | 2304.05822 | null | https://arxiv.org/abs/2304.05822v1 | https://arxiv.org/pdf/2304.05822v1.pdf | Data-Driven Response Regime Exploration and Identification for Dynamical Systems | Data-Driven Response Regime Exploration and Identification (DR$^2$EI) is a novel and fully data-driven method for identifying and classifying response regimes of a dynamical system without requiring human intervention. This approach is a valuable tool for exploring and discovering response regimes in complex dynamical ... | ['Maor Farid'] | 2023-04-07 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 3.00331503e-01 -4.47725832e-01 -2.33881339e-01 2.53780752e-01
-3.46745729e-01 -9.08233106e-01 7.35771179e-01 2.95140475e-01
-1.61553442e-01 7.77590632e-01 -8.89633745e-02 -5.56029320e-01
-8.77837837e-01 -5.08476853e-01 -1.65811647e-02 -1.17697644e+00
-7.06102848e-01 4.43752468e-01 -2.45478228e-02 -4.01804745... | [6.624209880828857, 3.552851676940918] |
3a492eaa-954e-4246-9bbe-616495a51dbf | sgeitl-scene-graph-enhanced-image-text | 2112.08587 | null | https://arxiv.org/abs/2112.08587v1 | https://arxiv.org/pdf/2112.08587v1.pdf | SGEITL: Scene Graph Enhanced Image-Text Learning for Visual Commonsense Reasoning | Answering complex questions about images is an ambitious goal for machine intelligence, which requires a joint understanding of images, text, and commonsense knowledge, as well as a strong reasoning ability. Recently, multimodal Transformers have made great progress in the task of Visual Commonsense Reasoning (VCR), by... | ['Shih-Fu Chang', 'Kai-Wei Chang', 'Yiqing Liang', 'Suji Park', 'Alireza Zareian', 'Liunian Harold Li', 'Haoxuan You', 'Zhecan Wang'] | 2021-12-16 | null | null | null | null | ['visual-commonsense-reasoning'] | ['reasoning'] | [ 4.12763864e-01 1.15771718e-01 1.04388865e-02 -4.48109150e-01
-4.40557986e-01 -5.15999556e-01 8.74677241e-01 2.34538943e-01
-1.85697332e-01 3.50088507e-01 5.49638927e-01 -4.05429631e-01
1.53482229e-01 -7.31692076e-01 -1.09033823e+00 -3.11976790e-01
5.27847469e-01 2.86419570e-01 4.33291137e-01 -4.63589579... | [10.672469139099121, 1.695701003074646] |
a8529c81-9bd1-42da-8a2a-751d49b07799 | object-detection-in-aerial-images-a-large | 2102.12219 | null | https://arxiv.org/abs/2102.12219v2 | https://arxiv.org/pdf/2102.12219v2.pdf | Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges | In the past decade, object detection has achieved significant progress in natural images but not in aerial images, due to the massive variations in the scale and orientation of objects caused by the bird's-eye view of aerial images. More importantly, the lack of large-scale benchmarks has become a major obstacle to the... | ['Liangpei Zhang', 'Marcello Pelillo', 'Mihai Datcu', 'Jiebo Luo', 'Serge Belongie', 'Micheal Ying Yang', 'Wen Yang', 'Xiang Bai', 'Gui-Song Xia', 'Nan Xue', 'Jian Ding'] | 2021-02-24 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [ 5.67833148e-03 -6.53560281e-01 2.40928486e-01 -2.64674783e-01
-2.44493470e-01 -9.88007724e-01 2.26864547e-01 -1.57144964e-01
-3.91577423e-01 2.33939707e-01 -2.02625349e-01 -8.50913897e-02
-1.38596758e-01 -6.49684787e-01 -5.34096181e-01 -5.41149378e-01
-5.93701661e-01 5.06207980e-02 8.48086476e-01 -2.93198079... | [8.712316513061523, -0.8011248111724854] |
12743006-8c1a-4251-9a9d-889431c06b1d | network-representation-learning-a-macro-and | 2111.10772 | null | https://arxiv.org/abs/2111.10772v1 | https://arxiv.org/pdf/2111.10772v1.pdf | Network representation learning: A macro and micro view | Graph is a universe data structure that is widely used to organize data in real-world. Various real-word networks like the transportation network, social and academic network can be represented by graphs. Recent years have witnessed the quick development on representing vertices in the network into a low-dimensional ve... | ['Jie Tang', 'Xueyi Liu'] | 2021-11-21 | null | null | null | null | ['network-embedding'] | ['methodology'] | [ 1.11621387e-01 4.61848706e-01 -8.53126526e-01 -1.05912164e-01
3.22809279e-01 -4.34125721e-01 5.92583537e-01 4.80193585e-01
8.64359811e-02 3.09998095e-01 4.64216709e-01 -4.91581857e-01
-5.49082816e-01 -1.37908530e+00 -7.31003582e-02 -4.46646720e-01
-4.16076154e-01 4.58311260e-01 2.67432164e-02 -2.53137469... | [7.123066425323486, 6.197125434875488] |
a7e22c9c-e71b-499c-b076-246b1d383fae | ontology-based-sms-controller-for-smart | 1509.01379 | null | http://arxiv.org/abs/1509.01379v1 | http://arxiv.org/pdf/1509.01379v1.pdf | Ontology Based SMS Controller for Smart Phones | Text analysis includes lexical analysis of the text and has been widely
studied and used in diverse applications. In the last decade, researchers have
proposed many efficient solutions to analyze / classify large text dataset,
however, analysis / classification of short text is still a challenge because
1) the data is ... | ['Mohammed A. Balubaid', 'Umar Manzoor'] | 2015-09-04 | null | null | null | null | ['lexical-analysis'] | ['natural-language-processing'] | [ 4.01906252e-01 -1.12925850e-01 -5.19737042e-02 -3.05359900e-01
9.24275741e-02 -5.55477977e-01 7.40146697e-01 8.94368529e-01
-5.21236837e-01 5.03375590e-01 3.90547007e-01 -3.69499296e-01
-4.62704718e-01 -9.61671710e-01 6.32614493e-02 -4.44765151e-01
5.65287530e-01 7.64526844e-01 4.51889873e-01 -5.46590328... | [10.870562553405762, 7.056601524353027] |
5427b273-d322-4e06-bbd7-676af182d701 | sql-palm-improved-large-language | 2306.00739 | null | https://arxiv.org/abs/2306.00739v3 | https://arxiv.org/pdf/2306.00739v3.pdf | SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL | One impressive emergent capability of large language models (LLMs) is generation of code, including Structured Query Language (SQL) for databases. For the task of converting natural language text to SQL queries, Text-to-SQL, adaptation of LLMs is of paramount importance, both in in-context learning and fine-tuning sett... | ['Sercan O. Arik', 'Tomas Pfister', 'Pengcheng Yin', 'Rajarishi Sinha', 'Hanjun Dai', 'Hootan Nakhost', 'Ruoxi Sun'] | 2023-05-26 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [-5.78044169e-02 -6.41784072e-02 -3.96469861e-01 -5.41164517e-01
-1.11701345e+00 -6.76396191e-01 5.83200216e-01 2.71735787e-01
-9.61811095e-02 1.97794154e-01 1.28728766e-02 -7.31768131e-01
-3.77329528e-01 -9.24738407e-01 -1.08483076e+00 7.58778378e-02
-2.55634010e-01 9.12046909e-01 4.91753876e-01 -5.11607885... | [9.771326065063477, 7.841807842254639] |
44d61e14-11fa-47e0-82c3-f1193281676c | paradigm-shift-in-sustainability-disclosure | 2306.15518 | null | https://arxiv.org/abs/2306.15518v1 | https://arxiv.org/pdf/2306.15518v1.pdf | Paradigm Shift in Sustainability Disclosure Analysis: Empowering Stakeholders with CHATREPORT, a Language Model-Based Tool | This paper introduces a novel approach to enhance Large Language Models (LLMs) with expert knowledge to automate the analysis of corporate sustainability reports by benchmarking them against the Task Force for Climate-Related Financial Disclosures (TCFD) recommendations. Corporate sustainability reports are crucial in ... | ['Markus Leippold', 'Tingyu Yu', 'Tobias Wekhof', 'Nicolas Webersinke', 'Qian Wang', 'Saeid Ashraf Vaghefi', 'Dominik Stammbach', 'Tobias Schimanski', 'Glen Gostlow', 'Mathias Kraus', 'Chiara Colesanti-Senni', 'Julia Bingler', 'Jingwei Ni'] | 2023-06-27 | null | null | null | null | ['benchmarking', 'benchmarking'] | ['miscellaneous', 'robots'] | [-1.36754319e-01 4.70436901e-01 -8.06132033e-02 -1.49734393e-01
-1.07891858e+00 -1.13326597e+00 6.69689596e-01 5.84169090e-01
-3.79202724e-01 6.98163688e-01 8.25218678e-01 -9.96092439e-01
1.32086903e-01 -9.43798304e-01 -3.50767106e-01 -3.01412605e-02
3.24723154e-01 1.18596062e-01 -3.07557043e-02 -7.98826665... | [9.601312637329102, 7.896562099456787] |
f67e29fd-aa33-41c9-8703-fddf6a87af91 | cosmic-commonsense-knowledge-for-emotion | 2010.02795 | null | https://arxiv.org/abs/2010.02795v1 | https://arxiv.org/pdf/2010.02795v1.pdf | COSMIC: COmmonSense knowledge for eMotion Identification in Conversations | In this paper, we address the task of utterance level emotion recognition in conversations using commonsense knowledge. We propose COSMIC, a new framework that incorporates different elements of commonsense such as mental states, events, and causal relations, and build upon them to learn interactions between interlocut... | ['Soujanya Poria', 'Rada Mihalcea', 'Alexander Gelbukh', 'Navonil Majumder', 'Deepanway Ghosal'] | 2020-10-06 | null | https://aclanthology.org/2020.findings-emnlp.224 | https://aclanthology.org/2020.findings-emnlp.224.pdf | findings-of-the-association-for-computational | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 2.69109279e-01 -2.12003458e-02 -1.74950287e-02 -7.59808064e-01
-5.39048612e-01 -6.18191004e-01 7.84386218e-01 2.88658351e-01
5.11807539e-02 6.84007406e-01 9.31718171e-01 5.30904792e-02
3.52856636e-01 -4.95523572e-01 -2.70924598e-01 -3.79518181e-01
8.16891901e-03 1.80173531e-01 -4.40999478e-01 -7.92524338... | [12.881413459777832, 6.31066370010376] |
2bd88cfa-ede2-4555-9736-80a44ce826de | portfolio-transformer-for-attention-based | 2206.03246 | null | https://arxiv.org/abs/2206.03246v1 | https://arxiv.org/pdf/2206.03246v1.pdf | Portfolio Transformer for Attention-Based Asset Allocation | Traditional approaches to financial asset allocation start with returns forecasting followed by an optimization stage that decides the optimal asset weights. Any errors made during the forecasting step reduce the accuracy of the asset weightings, and hence the profitability of the overall portfolio. The Portfolio Trans... | ['Denise Gorse', 'Damian Kisiel'] | 2022-06-07 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-7.50706196e-02 -7.56710172e-02 -1.49024770e-01 -3.67960066e-01
-7.33339548e-01 -6.48879766e-01 5.98105788e-01 9.47479997e-03
-4.24654275e-01 5.46915710e-01 4.65813398e-01 -5.33328950e-01
-4.23083097e-01 -1.01583230e+00 -5.16618848e-01 -4.29895163e-01
-2.00434610e-01 7.68291831e-01 -8.57947767e-02 -6.91970363... | [4.533115863800049, 4.112089157104492] |
ad068bb8-7a5e-44f6-a1e0-bb16a168bb28 | deep-learning-for-covid-19-diagnosis-based | 2104.07279 | null | https://arxiv.org/abs/2104.07279v4 | https://arxiv.org/pdf/2104.07279v4.pdf | COVID-19 detection using deep convolutional neural networks and binary-differential-algorithm-based feature selection on X-ray images | The new Coronavirus is spreading rapidly, and it has taken the lives of many people so far. The virus has destructive effects on the human lung, and early detection is very important. Deep Convolution neural networks are such powerful tools in classifying images. Therefore, in this paper, a hybrid approach based on a d... | ['Jafar Tanha', 'Mohammad-Reza Feizi-Derakhshi', 'Mohammad Saber Iraji'] | 2021-04-15 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [ 1.34829536e-01 -6.53177917e-01 -2.00534478e-01 -4.49882984e-01
2.54414836e-03 -9.90471765e-02 4.19263244e-01 3.46576609e-02
-7.85696805e-01 7.04169273e-01 -4.01021659e-01 -1.16464563e-01
-1.26020521e-01 -9.30802345e-01 -8.43673199e-02 -1.19824195e+00
-2.48846680e-01 5.06563783e-01 -4.84579988e-02 1.63548514... | [15.574479103088379, -1.7018190622329712] |
d46ad764-f8c0-4ef5-889e-4fca8ed31876 | the-inception-team-at-nsurl-2019-task-8 | 2004.11964 | null | https://arxiv.org/abs/2004.11964v1 | https://arxiv.org/pdf/2004.11964v1.pdf | The Inception Team at NSURL-2019 Task 8: Semantic Question Similarity in Arabic | This paper describes our method for the task of Semantic Question Similarity in Arabic in the workshop on NLP Solutions for Under-Resourced Languages (NSURL). The aim is to build a model that is able to detect similar semantic questions in the Arabic language for the provided dataset. Different methods of determining q... | ['Aisha Al-Sadi', 'Hana Al-Theiabat'] | 2020-04-24 | null | https://aclanthology.org/2019.nsurl-1.17 | https://aclanthology.org/2019.nsurl-1.17.pdf | nsurl-2019-9 | ['question-similarity'] | ['natural-language-processing'] | [-3.48199964e-01 3.59742045e-01 6.07648253e-01 -4.56687361e-01
-1.04008615e+00 -8.48096669e-01 7.79566288e-01 5.84462583e-01
-6.61499143e-01 5.94256639e-01 4.23876107e-01 -6.52199388e-02
-3.64613146e-01 -5.29971540e-01 -2.44614586e-01 -4.53102514e-02
3.55712444e-01 9.16274548e-01 5.39546669e-01 -1.04038227... | [11.385007858276367, 8.170435905456543] |
7d5f5206-159e-4d25-a04a-bb387c730175 | mars-a-motif-based-autoregressive-model-for | 2209.13178 | null | https://arxiv.org/abs/2209.13178v1 | https://arxiv.org/pdf/2209.13178v1.pdf | MARS: A Motif-based Autoregressive Model for Retrosynthesis Prediction | Retrosynthesis is a major task for drug discovery. It is formulated as a graph-generating problem by many existing approaches. Specifically, these methods firstly identify the reaction center, and break target molecule accordingly to generate synthons. Reactants are generated by either adding atoms sequentially to synt... | ['Peilin Zhao', 'Le Ou-Yang', 'Junzhou Huang', 'Chan Lu', 'Yang Yu', 'Chaochao Yan', 'Jiahan Liu'] | 2022-09-27 | null | null | null | null | ['retrosynthesis'] | ['medical'] | [ 6.54047191e-01 2.23731697e-01 -4.10039425e-01 1.77021697e-01
-3.28840226e-01 -8.31849992e-01 3.80197942e-01 6.47039652e-01
-1.00588903e-01 9.48961675e-01 1.40353918e-01 -4.00569379e-01
2.69610345e-01 -1.00936854e+00 -6.36919796e-01 -8.67611885e-01
2.57877767e-01 4.29871738e-01 6.09057605e-01 -2.78105974... | [4.499797821044922, 6.10117244720459] |
04df4efc-a9f4-4298-b8d7-50181ed728f6 | sentiment-analysis-for-modern-standard-arabic | 1505.03105 | null | http://arxiv.org/abs/1505.03105v1 | http://arxiv.org/pdf/1505.03105v1.pdf | Sentiment Analysis For Modern Standard Arabic And Colloquial | The rise of social media such as blogs and social networks has fueled
interest in sentiment analysis. With the proliferation of reviews, ratings,
recommendations and other forms of online expression, online opinion has turned
into a kind of virtual currency for businesses looking to market their
products, identify new ... | ['Hossam S. Ibrahim', 'Sherif M. Abdou', 'Mervat Gheith'] | 2015-05-12 | null | null | null | null | ['arabic-sentiment-analysis'] | ['natural-language-processing'] | [-1.32326081e-01 -1.25737920e-01 -7.52566010e-02 -5.78458726e-01
-2.72210300e-01 -1.00346768e+00 6.66547120e-01 9.00199115e-01
-4.80509311e-01 8.53447020e-01 3.75645936e-01 -9.91005376e-02
2.46194229e-02 -8.58964562e-01 -5.97347617e-02 -5.83163321e-01
-1.02562597e-02 4.44916964e-01 2.13003293e-01 -1.32392669... | [11.047333717346191, 6.912979602813721] |
7a06c8fb-dc7a-4cfe-b0e4-4a9dedb4d2bb | finding-covid-19-from-chest-x-rays-using-deep | 2004.02060 | null | https://arxiv.org/abs/2004.02060v4 | https://arxiv.org/pdf/2004.02060v4.pdf | Finding Covid-19 from Chest X-rays using Deep Learning on a Small Dataset | Testing for COVID-19 has been unable to keep up with the demand. Further, the false negative rate is projected to be as high as 30% and test results can take some time to obtain. X-ray machines are widely available and provide images for diagnosis quickly. This paper explores how useful chest X-ray images can be in dia... | ['Dmitry B. Goldgof', 'Gregory M. Goldgof', 'Lawrence O. Hall', 'Rahul Paul'] | 2020-04-05 | null | null | null | null | ['small-data'] | ['computer-vision'] | [-2.38227993e-02 -2.35281453e-01 4.90497015e-02 -3.84436786e-01
-7.60969639e-01 -6.30295932e-01 7.03145266e-02 1.64794385e-01
-5.35284281e-01 9.07134771e-01 -3.20113488e-02 -8.50274444e-01
-4.24964070e-01 -7.68569887e-01 -6.72934473e-01 -5.32768488e-01
-2.05848098e-01 1.11213839e+00 1.91742495e-01 3.51302534... | [15.50593376159668, -1.8056995868682861] |
ee48666e-3a84-4e9a-b5af-e9a64318b5f2 | real-time-system-of-hand-detection-and | 1502.07243 | null | http://arxiv.org/abs/1502.07243v1 | http://arxiv.org/pdf/1502.07243v1.pdf | Real-Time System of Hand Detection And Gesture Recognition In Cyber Presence Interactive System For E-Learning | The development of technologies of multimedia, linked to that of Internet and
democratization of high outflow, has made henceforth E-learning possible for
learners being in virtual classes and geographically distributed. The quality
and quantity of asynchronous and synchronous communications are the key
elements for E-... | ['Bousaaid Mourad', 'Ayaou Tarik', 'Estraillier Pascal', 'Afdel Karim'] | 2014-12-08 | null | null | null | null | ['hand-detection'] | ['computer-vision'] | [-2.78936833e-01 1.78293109e-01 8.87563266e-03 -1.99805856e-01
-1.10893488e-01 -6.21954441e-01 4.56388682e-01 1.86514005e-01
-7.72828460e-01 7.46256411e-01 -1.39761090e-01 -3.46812755e-01
-5.79072475e-01 -8.17243576e-01 -1.82171687e-01 -4.80317116e-01
3.50864202e-01 2.92494476e-01 3.60710174e-01 -3.69530320... | [13.29612922668457, 2.8421847820281982] |
b9adf4ac-e801-4719-9009-9c69853e49d6 | lfps-net-a-lightweight-fast-pulse-simulation | 2206.12558 | null | https://arxiv.org/abs/2206.12558v3 | https://arxiv.org/pdf/2206.12558v3.pdf | FastBVP-Net: a lightweight pulse extraction network for measuring heart rhythm via facial videos | Remote photoplethysmography (rPPG) is an attractive camera-based health monitoring method that can measure the heart rhythm from facial videos. Many well-established deep-learning models have been reported to measure heart rate (HR) and heart rate variability (HRV). However, most of these models usually require a 30-se... | ['Yun Zhang', 'Xiujuan Zheng', 'YuHeng Chen', 'Jialiang Zhuang'] | 2022-06-25 | null | null | null | null | ['heart-rate-variability', 'heart-rate-estimation'] | ['medical', 'medical'] | [-7.92143494e-02 -4.48549807e-01 -7.13963201e-03 -4.29389387e-01
-2.39032686e-01 7.35689178e-02 -1.59125254e-02 -5.18400908e-01
-2.66303986e-01 6.98726177e-01 -5.75114693e-03 2.25640491e-01
8.11040699e-02 -5.92865467e-01 -6.68543875e-02 -1.03395355e+00
7.47152716e-02 -3.05983752e-01 -1.88153967e-01 1.07536338... | [13.877423286437988, 2.697267532348633] |
abebb3c1-1003-48f2-aa5c-c845139a67c4 | opengait-revisiting-gait-recognition-towards | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Fan_OpenGait_Revisiting_Gait_Recognition_Towards_Better_Practicality_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Fan_OpenGait_Revisiting_Gait_Recognition_Towards_Better_Practicality_CVPR_2023_paper.pdf | OpenGait: Revisiting Gait Recognition Towards Better Practicality | Gait recognition is one of the most critical long-distance identification technologies and increasingly gains popularity in both research and industry communities. Despite the significant progress made in indoor datasets, much evidence shows that gait recognition techniques perform poorly in the wild. More importan... | ['Shiqi Yu', 'Yongzhen Huang', 'Saihui Hou', 'Chuanfu Shen', 'Junhao Liang', 'Chao Fan'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['gait-recognition'] | ['computer-vision'] | [-1.17321700e-01 -6.81662202e-01 -3.48819315e-01 -1.89679921e-01
-5.63528478e-01 -4.95761842e-01 3.03418070e-01 -2.81893194e-01
-2.58871228e-01 8.37594330e-01 2.77229875e-01 -1.69184521e-01
-2.53589869e-01 -6.62542224e-01 -3.66014898e-01 -8.74200881e-01
-3.43641788e-01 4.58850153e-02 2.89828569e-01 -2.03810051... | [14.27199935913086, 1.4283279180526733] |
bf71f16b-caed-4fd3-a49b-0993e0ccd970 | negative-deceptive-opinion-spam | null | null | https://aclanthology.org/N13-1053 | https://aclanthology.org/N13-1053.pdf | Negative Deceptive Opinion Spam | null | ['Myle Ott', 'Jeffrey T. Hancock', 'Claire Cardie'] | 2013-06-01 | null | null | null | naacl-2013-6 | ['deception-detection'] | ['miscellaneous'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.231036186218262, 3.591475248336792] |
ae744f25-c47f-41d3-a2d7-86c73c7f8b2a | residual-networks-as-flows-of-velocity-fields | 2106.11911 | null | https://arxiv.org/abs/2106.11911v1 | https://arxiv.org/pdf/2106.11911v1.pdf | Residual Networks as Flows of Velocity Fields for Diffeomorphic Time Series Alignment | Non-linear (large) time warping is a challenging source of nuisance in time-series analysis. In this paper, we propose a novel diffeomorphic temporal transformer network for both pairwise and joint time-series alignment. Our ResNet-TW (Deep Residual Network for Time Warping) tackles the alignment problem by compositing... | ['Yi Fang', 'Fan Zhu', 'Xichan Lin', 'Boulbaba Ben Amor', 'Hao Huang'] | 2021-06-22 | null | null | null | null | ['time-series-alignment'] | ['time-series'] | [-1.27248049e-01 -1.30876765e-01 1.57384008e-01 -5.72992302e-02
-3.83813918e-01 -7.64984429e-01 1.07367229e+00 -3.15036744e-01
-3.66546392e-01 6.30074024e-01 4.42861557e-01 -1.41727939e-01
-3.42757642e-01 -7.29838133e-01 -6.39506638e-01 -7.09622085e-01
-4.68133777e-01 3.43017250e-01 -2.00544193e-01 -6.43976390... | [7.182506561279297, 3.3752281665802] |
cec82c35-d411-4f65-9850-11e1c8b7c419 | contextformer-a-transformer-with-spatio | 2203.02452 | null | https://arxiv.org/abs/2203.02452v2 | https://arxiv.org/pdf/2203.02452v2.pdf | Contextformer: A Transformer with Spatio-Channel Attention for Context Modeling in Learned Image Compression | Entropy modeling is a key component for high-performance image compression algorithms. Recent developments in autoregressive context modeling helped learning-based methods to surpass their classical counterparts. However, the performance of those models can be further improved due to the underexploited spatio-channel d... | ['Eckehard Steinbach', 'Elena Alshina', 'Georgii Gaikov', 'Atanas Boev', 'Han Gao', 'A. Burakhan Koyuncu'] | 2022-03-04 | null | null | null | null | ['ms-ssim'] | ['computer-vision'] | [ 4.21282798e-01 -5.25138021e-01 -1.40253499e-01 -2.37783819e-01
-6.53559566e-01 1.07800208e-01 5.34995615e-01 -1.90594718e-01
-3.80027682e-01 5.74196458e-01 5.79326868e-01 -3.97346258e-01
-1.45375580e-01 -4.37308639e-01 -6.86020732e-01 -7.80906379e-01
-3.03031623e-01 -1.75290853e-01 1.63789123e-01 9.55705717... | [11.345636367797852, -1.6091408729553223] |
c26f65e2-18ad-4296-9eb3-5e2192085f20 | aim-2020-scene-relighting-and-illumination | 2009.12798 | null | https://arxiv.org/abs/2009.12798v1 | https://arxiv.org/pdf/2009.12798v1.pdf | AIM 2020: Scene Relighting and Illumination Estimation Challenge | We review the AIM 2020 challenge on virtual image relighting and illumination estimation. This paper presents the novel VIDIT dataset used in the challenge and the different proposed solutions and final evaluation results over the 3 challenge tracks. The first track considered one-to-one relighting; the objective was t... | ['Jiji C. V', 'Hrishikesh P. S', 'A. N. Rajagopalan', 'Maitreya Suin', 'Akashdeep Jassal', 'Nisarg A. Shah', 'Chu-Tak Li', 'Zhi-Song Liu', 'Li-Wen Wang', 'Sabine Süsstrunk', 'Tongtong Zhao', 'Sourya Dipta Das', 'Sabari Nathan', 'Ruofan Zhou', 'R. Suganya', 'Radu Timofte', 'M. Parisa Beham', 'Melvin Kuriakose', 'Chenghu... | 2020-09-27 | null | null | null | null | ['image-relighting'] | ['computer-vision'] | [ 1.92201450e-01 -3.07808101e-01 4.23214883e-01 -5.12620270e-01
-7.23073184e-01 -9.72883940e-01 4.64067340e-01 -9.70206633e-02
-5.53161502e-01 6.43612266e-01 -8.74167234e-02 -3.37513797e-02
4.56025064e-01 -2.87971050e-01 -9.21328545e-01 -9.00315404e-01
2.72661090e-01 1.88160911e-01 1.19735740e-01 -9.84302256... | [10.586980819702148, -2.2846853733062744] |
de656131-5c40-471d-87f5-57ac7a04b7a7 | qub-at-semeval-2017-task-6-cascaded | null | null | https://aclanthology.org/S17-2063 | https://aclanthology.org/S17-2063.pdf | QUB at SemEval-2017 Task 6: Cascaded Imbalanced Classification for Humor Analysis in Twitter | This paper presents our submission to SemEval-2017 Task 6: {\#}HashtagWars: Learning a Sense of Humor. There are two subtasks: A. Pairwise Comparison, and B. Semi-Ranking. Our assumption is that the distribution of humorous and non-humorous texts in real life language is naturally imbalanced. Using Na{\"\i}ve Bayes Mul... | ['Gregory Toner', 'Xiwu Han'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['humor-detection'] | ['natural-language-processing'] | [-3.00216734e-01 1.11985974e-01 -4.96243089e-02 -2.93864995e-01
-1.12820828e+00 -4.98283625e-01 5.54316461e-01 4.01465505e-01
-5.63454509e-01 9.22311783e-01 6.87453866e-01 -2.73108512e-01
4.00665477e-02 -4.59169239e-01 -5.18383086e-01 -4.09670144e-01
1.51801959e-01 9.56607342e-01 8.13892670e-03 -5.85010171... | [8.837709426879883, 11.085494995117188] |
058af64a-3ce5-411c-8579-257c1d7140ae | audio-visual-speech-recognition-is-worth-32 | 2109.09536 | null | https://arxiv.org/abs/2109.09536v1 | https://arxiv.org/pdf/2109.09536v1.pdf | Audio-Visual Speech Recognition is Worth 32$\times$32$\times$8 Voxels | Audio-visual automatic speech recognition (AV-ASR) introduces the video modality into the speech recognition process, often by relying on information conveyed by the motion of the speaker's mouth. The use of the video signal requires extracting visual features, which are then combined with the acoustic features to buil... | ['Olivier Siohan', 'Otavio Braga', 'Dmitriy Serdyuk'] | 2021-09-20 | null | null | null | null | ['audio-visual-speech-recognition'] | ['speech'] | [ 5.28331175e-02 -1.75757006e-01 -1.34828433e-01 -2.23915577e-01
-9.39929366e-01 -4.59012121e-01 7.48545468e-01 -4.44748253e-01
-5.13889790e-01 9.23964679e-02 4.61775631e-01 -3.94580245e-01
6.72250092e-01 -2.95906901e-01 -7.34971404e-01 -5.12307703e-01
3.40698451e-01 4.49967608e-02 1.61786780e-01 -1.76066626... | [14.362220764160156, 5.081820011138916] |
a8e4066b-b77f-47ef-91aa-9c81f17b0aec | a-unified-view-for-unsupervised | 2101.02083 | null | https://arxiv.org/abs/2101.02083v2 | https://arxiv.org/pdf/2101.02083v2.pdf | Representation learning for maximization of MI, nonlinear ICA and nonlinear subspaces with robust density ratio estimation | Contrastive learning is a recent promising approach in unsupervised representation learning where a feature representation of data is learned by solving a pseudo classification problem from unlabelled data. However, it is not straightforward to understand what representation contrastive learning yields. In addition, co... | ['Takashi Takenouchi', 'Hiroaki Sasaki'] | 2021-01-06 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [ 1.29718602e-01 -2.19551608e-01 -2.68863857e-01 -1.36098653e-01
-6.29403412e-01 -4.33417797e-01 4.10385340e-01 -1.81788668e-01
-1.57450140e-01 8.61787975e-01 1.62948365e-03 -1.44386627e-02
-8.14544261e-01 -4.95600671e-01 -7.53883362e-01 -1.19414258e+00
-2.74056438e-02 2.80849427e-01 -4.79198396e-01 1.05629951... | [7.820302963256836, 4.2173895835876465] |
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