paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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dfc28557-eb9e-4cdb-99bf-549d87ab7ef8 | d-2conv3d-dynamic-dilated-convolutions-for | 2111.07774 | null | https://arxiv.org/abs/2111.07774v1 | https://arxiv.org/pdf/2111.07774v1.pdf | D^2Conv3D: Dynamic Dilated Convolutions for Object Segmentation in Videos | Despite receiving significant attention from the research community, the task of segmenting and tracking objects in monocular videos still has much room for improvement. Existing works have simultaneously justified the efficacy of dilated and deformable convolutions for various image-level segmentation tasks. This give... | ['Bastian Leibe', 'Sabarinath Mahadevan', 'Ali Athar', 'Christian Schmidt'] | 2021-11-15 | null | null | null | null | ['unsupervised-video-object-segmentation'] | ['computer-vision'] | [ 1.05711870e-01 -1.23140611e-01 -1.58157974e-01 -4.34994489e-01
-2.50868678e-01 -9.14967060e-01 4.98293966e-01 -4.23091888e-01
-5.79001606e-01 3.95402551e-01 -2.22579818e-02 -6.61474526e-01
2.70882010e-01 -5.38888872e-01 -9.24982965e-01 -4.46611792e-01
-1.91242427e-01 -5.79323806e-03 8.22544575e-01 -1.60933826... | [9.33808708190918, 0.08328895270824432] |
86913aea-bafd-4f6c-aaed-b3ac6ab4f2d1 | harvesting-detecting-and-characterizing-liver | 2006.15691 | null | https://arxiv.org/abs/2006.15691v2 | https://arxiv.org/pdf/2006.15691v2.pdf | Harvesting, Detecting, and Characterizing Liver Lesions from Large-scale Multi-phase CT Data via Deep Dynamic Texture Learning | Non-invasive radiological-based lesion characterization and identification, e.g., to differentiate cancer subtypes, has long been a major aim to enhance oncological diagnosis and treatment procedures. Here we study a specific population of human subjects, with the hope of reducing the need for invasive surgical biopsie... | ['Chien-Hung Liao', 'Chi-Tung Cheng', 'Ke Yan', 'Yuankai Huo', 'Le Lu', 'Jinzheng Cai', 'Jing Xiao', 'Bennett A. Landman', 'Ashwin Raju', 'Adam P. Harrison'] | 2020-06-28 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 4.62347968e-03 -1.63425714e-01 -2.96192974e-01 -2.69365553e-02
-1.16157353e+00 -6.42133713e-01 6.91382587e-01 5.17624915e-01
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-4.26037699e-01 1.03687632e+00 3.39661300e-01 3.10447931... | [14.52491283416748, -2.6548373699188232] |
f8467f1c-c3fe-48bd-883e-13420432adc8 | controlling-for-unknown-confounders-in | 2006.13135 | null | https://arxiv.org/abs/2006.13135v4 | https://arxiv.org/pdf/2006.13135v4.pdf | Estimation of Causal Effects in the Presence of Unobserved Confounding in the Alzheimer's Continuum | Studying the relationship between neuroanatomy and cognitive decline due to Alzheimer's has been a major research focus in the last decade. However, to infer cause-effect relationships rather than simple associations from observational data, we need to (i) express the causal relationships leading to cognitive decline i... | ['Sebastian Pölsterl', 'Christian Wachinger'] | 2020-06-23 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [ 2.40971714e-01 2.87214637e-01 -1.72107667e-01 -4.05237228e-01
-3.55481505e-01 -4.56291914e-01 4.77572531e-01 2.25154281e-01
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-7.52532065e-01 4.37022030e-01 -4.71459404e-02 2.35778525... | [7.835718154907227, 5.342631816864014] |
debad490-d015-4b43-b845-51d3f2f0d79a | sacha-soft-actor-critic-with-heuristic-based | 2307.02691 | null | https://arxiv.org/abs/2307.02691v1 | https://arxiv.org/pdf/2307.02691v1.pdf | SACHA: Soft Actor-Critic with Heuristic-Based Attention for Partially Observable Multi-Agent Path Finding | Multi-Agent Path Finding (MAPF) is a crucial component for many large-scale robotic systems, where agents must plan their collision-free paths to their given goal positions. Recently, multi-agent reinforcement learning has been introduced to solve the partially observable variant of MAPF by learning a decentralized sin... | ['Hang Ma', 'Qiushi Lin'] | 2023-07-05 | null | null | null | null | ['multi-agent-reinforcement-learning', 'multi-agent-path-finding'] | ['methodology', 'playing-games'] | [-0.5041956 0.3980175 -0.24580055 0.06391977 -0.8258258 -0.41844457
0.47724497 0.28878573 -0.7333846 1.1056882 0.01850952 -0.03428832
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0.44393188 -0.4718582 0.14384456 0.42179772 -1.1735337 -0.3706989
0.9643475 0.51718867 0.49... | [3.8047070503234863, 1.9526593685150146] |
7fa1ea59-27ef-4e2c-8e2d-d6c10358aece | peking-opera-synthesis-via-duration-informed | 2008.03029 | null | https://arxiv.org/abs/2008.03029v1 | https://arxiv.org/pdf/2008.03029v1.pdf | Peking Opera Synthesis via Duration Informed Attention Network | Peking Opera has been the most dominant form of Chinese performing art since around 200 years ago. A Peking Opera singer usually exhibits a very strong personal style via introducing improvisation and expressiveness on stage which leads the actual rhythm and pitch contour to deviate significantly from the original musi... | ['Heng Lu', 'Shengchen Li', 'Yusong Wu', 'Chao Weng', 'Liqiang Zhang', 'Chengzhu Yu', 'Dong Yu'] | 2020-08-07 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [ 3.04957330e-01 -6.43714843e-03 -2.70433314e-02 2.94953529e-02
-7.86375165e-01 -6.30446315e-01 4.93622944e-02 -6.62592590e-01
-1.67225450e-01 6.26921356e-01 2.39511728e-01 6.72950223e-02
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3.28748405e-01 1.35537565e-01 -2.63955206e-01 -3.01672578... | [15.630306243896484, 6.018380165100098] |
b08abd7a-18ad-473e-8196-ac45f53f56ba | simultaneous-speech-extraction-for-multiple | 2206.08525 | null | https://arxiv.org/abs/2206.08525v1 | https://arxiv.org/pdf/2206.08525v1.pdf | Simultaneous Speech Extraction for Multiple Target Speakers under the Meeting Scenarios(V1) | Recently, the target speech separation or extraction techniques under the meeting scenario have become a hot research trend. We propose a speaker diarization aware multiple target speech separation system (SD-MTSS) to simultaneously extract the voice of each speaker from the mixed speech, rather than requiring a succes... | ['Ming Li', 'Yuanyuan Bao', 'Weiqing Wang', 'Bang Zeng'] | 2022-06-17 | null | null | null | null | ['activity-detection', 'speech-separation', 'speech-extraction'] | ['computer-vision', 'speech', 'speech'] | [ 3.04950267e-01 -1.40844092e-01 -9.20188278e-02 -1.26713887e-01
-1.52853906e+00 -5.20790279e-01 3.73503029e-01 -2.51260847e-01
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1.45922169e-01 2.82858789e-01 3.48545909e-01 3.01209069... | [14.70956039428711, 6.040946960449219] |
51790190-1d3f-4b88-b4ad-d339a0a5cc09 | alisnet-accurate-and-lightweight-human | 2304.07533 | null | https://arxiv.org/abs/2304.07533v1 | https://arxiv.org/pdf/2304.07533v1.pdf | ALiSNet: Accurate and Lightweight Human Segmentation Network for Fashion E-Commerce | Accurately estimating human body shape from photos can enable innovative applications in fashion, from mass customization, to size and fit recommendations and virtual try-on. Body silhouettes calculated from user pictures are effective representations of the body shape for downstream tasks. Smartphones provide a conven... | ['Reza Shirvany', 'Anna Volokitin', 'Malte Alf', 'Alessandro Canopoli', 'Timon Künzle', 'Koen Vernooij', 'Amrollah Seifoddini'] | 2023-04-15 | null | null | null | null | ['virtual-try-on'] | ['computer-vision'] | [ 1.19901203e-01 2.87370950e-01 -3.46045136e-01 -3.90768051e-01
-2.89219409e-01 -3.64516288e-01 -1.64847717e-01 -9.40500572e-02
-2.60017097e-01 3.11824858e-01 -1.59928367e-01 -1.96248572e-03
3.38839501e-01 -9.69955802e-01 -7.32328653e-01 -2.62510609e-02
-4.75403443e-02 6.60729587e-01 2.96688229e-01 -7.80382007... | [7.038646221160889, -1.0682505369186401] |
62a491e1-a18b-4a87-a33b-12b7f5a8764a | detecting-and-correcting-learner-korean | null | null | https://aclanthology.org/I13-1199 | https://aclanthology.org/I13-1199.pdf | Detecting and Correcting Learner Korean Particle Omission Errors | null | ['Sun-Hee Lee', 'Markus Dickinson', 'Ross Israel'] | 2013-10-01 | detecting-and-correcting-learner-korean-1 | https://aclanthology.org/I13-1199 | https://aclanthology.org/I13-1199.pdf | ijcnlp-2013-10 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.416090488433838, 3.7533605098724365] |
ad28b87d-13a2-4c93-bf4e-c3c11b3d64ab | benchmarking-graph-neural-networks-on-dynamic | null | null | https://openreview.net/forum?id=I2KAe7x67JU | https://openreview.net/pdf?id=I2KAe7x67JU | Benchmarking Graph Neural Networks on Dynamic Link Prediction | Graph neural networks (GNNs) are rapidly becoming the dominant way to learn on graph-structured data. Link prediction is a near-universal benchmark for new GNN models. Many advanced models such as Dynamic graph neural networks(DGNNs) specifically target dynamic link prediction. However, these models, particularly... | ['Katarzyna Musial-Gabrys', 'Bogdan Gabrys', 'Matthew Hellmich', 'Joakim Skarding'] | 2021-09-29 | null | null | null | null | ['dynamic-link-prediction'] | ['graphs'] | [-3.38937372e-01 1.90644354e-01 -9.77376401e-01 8.69789533e-03
3.25063080e-01 -4.07936633e-01 6.00203395e-01 4.99284387e-01
5.67076690e-02 9.84720051e-01 -1.09197713e-01 -7.99925864e-01
-7.22200751e-01 -1.31144428e+00 -5.55291295e-01 -2.07275286e-01
-8.51583958e-01 7.31669724e-01 8.09382975e-01 -4.95483398... | [7.062472820281982, 6.158669948577881] |
68568195-19b5-4d30-91d0-1aec65530231 | synopses-of-movie-narratives-a-video-language | null | null | https://openreview.net/forum?id=ZLRckoIm-EH | https://openreview.net/pdf?id=ZLRckoIm-EH | Synopses of Movie Narratives: a Video-Language Dataset for Story Understanding | Despite recent advances of AI, story understanding remains an open and under-investigated problem. We collect, preprocess, and publicly release a video-language story dataset, Synopses of Movie Narratives(SyMoN), containing 5,193 video summaries of popular movies and TV series. SyMoN captures naturalistic storytelling... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['video-text-retrieval'] | ['computer-vision'] | [ 3.52708668e-01 -2.94566005e-01 -7.31062353e-01 -1.20895319e-01
-9.38936710e-01 -9.45572257e-01 1.07184887e+00 3.94375950e-01
-8.89244527e-02 5.16576946e-01 1.26483035e+00 3.47094297e-01
-4.23253281e-03 -2.82510310e-01 -5.41374207e-01 -2.16892928e-01
-4.88442462e-03 1.14033282e-01 1.89381316e-01 -6.74970746... | [10.526724815368652, 0.7926208972930908] |
14888e7e-13e3-4253-87aa-301f4ad6b246 | 190503711 | 1905.03711 | null | https://arxiv.org/abs/1905.03711v2 | https://arxiv.org/pdf/1905.03711v2.pdf | Processing Megapixel Images with Deep Attention-Sampling Models | Existing deep architectures cannot operate on very large signals such as megapixel images due to computational and memory constraints. To tackle this limitation, we propose a fully differentiable end-to-end trainable model that samples and processes only a fraction of the full resolution input image. The locations to p... | ['François Fleuret', 'Angelos Katharopoulos'] | 2019-05-03 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [ 4.33982402e-01 3.82072926e-01 3.42108220e-01 -4.10491645e-01
-1.31661355e+00 -4.40519124e-01 6.93732798e-01 -2.38560572e-01
-8.08414578e-01 7.91658759e-01 -1.66144054e-02 8.86359215e-02
2.13124171e-01 -6.80933893e-01 -1.31747830e+00 -7.30882943e-01
3.75698060e-01 7.50835001e-01 1.30305961e-01 4.63766605... | [11.20679759979248, -0.5238173604011536] |
43094d9e-262f-4737-a552-5db0e86673dd | what-and-how-well-you-performed-a-multitask | 1904.04346 | null | https://arxiv.org/abs/1904.04346v2 | https://arxiv.org/pdf/1904.04346v2.pdf | What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment | Can performance on the task of action quality assessment (AQA) be improved by exploiting a description of the action and its quality? Current AQA and skills assessment approaches propose to learn features that serve only one task - estimating the final score. In this paper, we propose to learn spatio-temporal features ... | ['Brendan Tran Morris', 'Paritosh Parmar'] | 2019-04-08 | what-and-how-well-you-performed-a-multitask-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Parmar_What_and_How_Well_You_Performed_A_Multitask_Learning_Approach_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Parmar_What_and_How_Well_You_Performed_A_Multitask_Learning_Approach_CVPR_2019_paper.pdf | cvpr-2019-6 | ['action-quality-assessment', 'skills-assessment', 'fine-grained-action-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.19854636e-01 -2.00126976e-01 -3.07077199e-01 -4.36290205e-01
-1.43075264e+00 -3.65380138e-01 7.21743107e-01 4.64584306e-02
-5.25319695e-01 6.64289176e-01 7.20329821e-01 1.86724111e-01
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-1.21225290e-01 3.60421389e-01 4.69043016e-01 -2.22268283... | [8.073412895202637, 0.6816800236701965] |
71bfac69-1699-4b20-b690-7eb6d3142f34 | open-set-relation-extraction-via-unknown | 2306.04950 | null | https://arxiv.org/abs/2306.04950v1 | https://arxiv.org/pdf/2306.04950v1.pdf | Open Set Relation Extraction via Unknown-Aware Training | The existing supervised relation extraction methods have achieved impressive performance in a closed-set setting, where the relations during both training and testing remain the same. In a more realistic open-set setting, unknown relations may appear in the test set. Due to the lack of supervision signals from unknown ... | ['Xuanjing Huang', 'Xiang Gao', 'Yunwen Chen', 'Zhongyu Wei', 'Tao Gui', 'Qi Zhang', 'WenYu Zhan', 'Xin Zhao', 'Jun Zhao'] | 2023-06-08 | null | null | null | null | ['relation-extraction'] | ['natural-language-processing'] | [ 7.13849127e-01 9.52419221e-01 -5.15976369e-01 -4.84680861e-01
-7.26039648e-01 -8.31720829e-01 4.03150618e-01 3.47877562e-01
-7.20662475e-02 1.08658206e+00 -5.15048385e-01 -5.82681537e-01
-1.57820851e-01 -1.14634860e+00 -9.10698473e-01 -5.89627147e-01
-1.73152819e-01 9.09374952e-01 1.59967333e-01 -2.14023247... | [9.153406143188477, 8.44236946105957] |
63f44822-f79f-4e45-9d55-e09693856dfb | forward-looking-sonar-patch-matching-modern | 2108.01066 | null | https://arxiv.org/abs/2108.01066v1 | https://arxiv.org/pdf/2108.01066v1.pdf | Forward-Looking Sonar Patch Matching: Modern CNNs, Ensembling, and Uncertainty | Application of underwater robots are on the rise, most of them are dependent on sonar for underwater vision, but the lack of strong perception capabilities limits them in this task. An important issue in sonar perception is matching image patches, which can enable other techniques like localization, change detection, a... | ['Matias Valdenegro-Toro', 'Paul Plöger', 'Arka Mallick'] | 2021-08-02 | null | null | null | null | ['patch-matching'] | ['computer-vision'] | [-7.51535594e-02 -2.94594821e-02 7.00611115e-01 -1.99891299e-01
-4.58745122e-01 -5.06828547e-01 3.85059148e-01 1.30648613e-01
-1.11356926e+00 5.14892995e-01 -9.89938900e-02 4.76782322e-02
-3.22310776e-01 -9.41106021e-01 -9.47774649e-01 -7.89320290e-01
-7.95125842e-01 2.24746734e-01 4.88421619e-01 -5.94171762... | [8.449810028076172, -1.3856148719787598] |
38cefbbc-fb30-43f2-9b4b-b4529cd6535a | see-few-seed-expand-and-entail-for-few-shot | 2210.05632 | null | https://arxiv.org/abs/2210.05632v1 | https://arxiv.org/pdf/2210.05632v1.pdf | SEE-Few: Seed, Expand and Entail for Few-shot Named Entity Recognition | Few-shot named entity recognition (NER) aims at identifying named entities based on only few labeled instances. Current few-shot NER methods focus on leveraging existing datasets in the rich-resource domains which might fail in a training-from-scratch setting where no source-domain data is used. To tackle training-from... | ['Deyu Zhou', 'Linhai Zhang', 'Zeng Yang'] | 2022-10-11 | null | https://aclanthology.org/2022.coling-1.224 | https://aclanthology.org/2022.coling-1.224.pdf | coling-2022-10 | ['few-shot-ner', 'low-resource-named-entity-recognition'] | ['natural-language-processing', 'natural-language-processing'] | [-2.04545204e-02 8.63753036e-02 -2.13512331e-01 -3.78022820e-01
-1.24118996e+00 -5.11047423e-01 3.44599843e-01 1.81601539e-01
-7.69777834e-01 9.25981641e-01 2.76771635e-01 -2.03964971e-02
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2.36351714e-01 2.25556716e-01 2.61491209e-01 -2.10080042... | [9.650466918945312, 9.361767768859863] |
9278a623-2a21-4162-95e3-89e11a0fba0d | dimensionality-reduction-using-similarity | 1706.05692 | null | http://arxiv.org/abs/1706.05692v3 | http://arxiv.org/pdf/1706.05692v3.pdf | Dimensionality Reduction using Similarity-induced Embeddings | The vast majority of Dimensionality Reduction (DR) techniques rely on
second-order statistics to define their optimization objective. Even though
this provides adequate results in most cases, it comes with several
shortcomings. The methods require carefully designed regularizers and they are
usually prone to outliers. ... | ['Anastasios Tefas', 'Nikolaos Passalis'] | 2017-06-18 | null | null | null | null | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [ 1.03303371e-02 -3.80851746e-01 -1.76474705e-01 -4.61626709e-01
-6.92524552e-01 -4.94093210e-01 7.48769045e-01 2.75883526e-01
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-6.31415248e-01 -6.36074722e-01 -9.64780152e-02 -7.97428310e-01
-4.16785441e-02 3.18033248e-01 2.90753320e-02 -1.59030885... | [7.905154228210449, 4.175537109375] |
688237dc-4452-4800-b198-445199ddcf29 | unsupervised-4d-lidar-moving-object | 2212.14750 | null | https://arxiv.org/abs/2212.14750v2 | https://arxiv.org/pdf/2212.14750v2.pdf | Unsupervised 4D LiDAR Moving Object Segmentation in Stationary Settings with Multivariate Occupancy Time Series | In this work, we address the problem of unsupervised moving object segmentation (MOS) in 4D LiDAR data recorded from a stationary sensor, where no ground truth annotations are involved. Deep learning-based state-of-the-art methods for LiDAR MOS strongly depend on annotated ground truth data, which is expensive to obtai... | ['Alejandro Sanchez Guinea', 'Max Mühlhäuser', 'Thomas Kreutz'] | 2022-12-30 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [ 5.16018271e-01 -3.00561517e-01 -2.51234353e-01 -6.06067300e-01
-8.21336746e-01 -3.00907165e-01 3.17490518e-01 2.84596920e-01
-6.17558658e-01 5.64745545e-01 -3.04293394e-01 -1.08610436e-01
-9.49312001e-02 -9.75416243e-01 -9.93232071e-01 -7.35880017e-01
-2.73278147e-01 9.79163766e-01 5.24238288e-01 3.89330208... | [8.077118873596191, -2.7461256980895996] |
381607d7-ffc1-49cf-aee5-f74589635068 | an-empirical-study-on-robustness-to-spurious | 2007.06778 | null | https://arxiv.org/abs/2007.06778v3 | https://arxiv.org/pdf/2007.06778v3.pdf | An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language Models | Recent work has shown that pre-trained language models such as BERT improve robustness to spurious correlations in the dataset. Intrigued by these results, we find that the key to their success is generalization from a small amount of counterexamples where the spurious correlations do not hold. When such minority examp... | ['Lifu Tu', 'Garima Lalwani', 'Spandana Gella', 'He He'] | 2020-07-14 | null | null | null | null | ['paraphrase-identification'] | ['natural-language-processing'] | [ 7.59015083e-02 -1.99008301e-01 -6.50758445e-01 -3.15572053e-01
-1.19059384e+00 -5.76693594e-01 6.74478412e-01 5.73669933e-02
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-2.79184997e-01 -3.69459629e-01 -9.41395283e-01 -4.83864784e-01
6.55689538e-02 4.34606045e-01 6.19121753e-02 -3.04497480... | [10.769022941589355, 8.453182220458984] |
f4c4fd6c-2a96-44bd-9b11-87c01402129a | predicting-phoneme-level-prosody-latents | 2211.01327 | null | https://arxiv.org/abs/2211.01327v1 | https://arxiv.org/pdf/2211.01327v1.pdf | Predicting phoneme-level prosody latents using AR and flow-based Prior Networks for expressive speech synthesis | A large part of the expressive speech synthesis literature focuses on learning prosodic representations of the speech signal which are then modeled by a prior distribution during inference. In this paper, we compare different prior architectures at the task of predicting phoneme level prosodic representations extracted... | ['Pirros Tsiakoulis', 'Aimilios Chalamandaris', 'Spyros Raptis', 'Inchul Hwang', 'June Sig Sung', 'Nikolaos Ellinas', 'Karolos Nikitaras', 'Konstantinos Klapsas'] | 2022-11-02 | null | null | null | null | ['expressive-speech-synthesis'] | ['speech'] | [ 6.85756505e-02 7.63162017e-01 -1.50524303e-01 -3.50488693e-01
-5.11312127e-01 -4.79451269e-01 6.22712553e-01 -2.12575346e-01
6.02891967e-02 8.50365937e-01 1.01546049e+00 1.57495454e-01
1.52946021e-02 -7.22761512e-01 -4.94184375e-01 -5.25506914e-01
3.55066568e-01 4.41594958e-01 6.82929680e-02 -3.96546543... | [15.043662071228027, 6.588314533233643] |
82aea945-f188-4b54-9a6c-4812129d113d | near-optimal-decentralized-momentum-method | 2304.10902 | null | https://arxiv.org/abs/2304.10902v1 | https://arxiv.org/pdf/2304.10902v1.pdf | Near-Optimal Decentralized Momentum Method for Nonconvex-PL Minimax Problems | Minimax optimization plays an important role in many machine learning tasks such as generative adversarial networks (GANs) and adversarial training. Although recently a wide variety of optimization methods have been proposed to solve the minimax problems, most of them ignore the distributed setting where the data is di... | ['Songcan Chen', 'Feihu Huang'] | 2023-04-21 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [-2.67692655e-01 2.67639663e-02 -3.35521698e-01 -1.65041506e-01
-1.21694267e+00 -5.43687046e-01 -8.15875903e-02 -3.10607143e-02
-4.15125251e-01 1.20402122e+00 -1.49811804e-01 -4.78806019e-01
-4.38228011e-01 -7.06993401e-01 -9.36762273e-01 -1.05884182e+00
1.30137756e-01 4.67731684e-01 -4.32627261e-01 -2.69325882... | [6.388952732086182, 4.786524295806885] |
6791504f-97f3-480a-9328-b8dded08197f | variable-decision-frequency-option-critic | 2212.04407 | null | https://arxiv.org/abs/2212.04407v3 | https://arxiv.org/pdf/2212.04407v3.pdf | Variable Decision-Frequency Option Critic | In classic reinforcement learning algorithms, agents make decisions at discrete and fixed time intervals. The duration between decisions becomes a crucial hyperparameter, as setting it too short may increase the difficulty of the problem by requiring the agent to make numerous decisions to achieve its goal, while setti... | ['Samuele Tosatto', 'Martin Jagersand', 'A. Rupam Mahmood', 'Jun Luo', 'Jun Jin', 'Amirmohammad Karimi'] | 2022-12-06 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [ 4.99982350e-02 9.69661400e-03 -3.25866103e-01 2.29453683e-01
-3.91534835e-01 -7.52934158e-01 6.92348123e-01 1.36890173e-01
-8.33990455e-01 1.26559985e+00 -2.86563575e-01 -4.33698446e-01
-4.29392844e-01 -6.37740433e-01 -4.47082400e-01 -8.65015507e-01
-5.01184046e-01 5.44137359e-01 2.31857583e-01 -2.95754820... | [4.275467872619629, 1.8837440013885498] |
acb03d76-5c86-4e00-b67d-95f9f896ea32 | facial-expression-recognition-with-swin | 2203.13472 | null | https://arxiv.org/abs/2203.13472v1 | https://arxiv.org/pdf/2203.13472v1.pdf | Facial Expression Recognition with Swin Transformer | The task of recognizing human facial expressions plays a vital role in various human-related systems, including health care and medical fields. With the recent success of deep learning and the accessibility of a large amount of annotated data, facial expression recognition research has been mature enough to be utilized... | ['Chee Sun Won', 'NamHo Kim', 'Jun-Hwa Kim'] | 2022-03-25 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 1.04554191e-01 -4.59106952e-01 -1.28566504e-01 -7.14013100e-01
-7.75637627e-01 -1.08732678e-01 3.84445041e-01 -2.19975755e-01
-2.46572778e-01 4.34779704e-01 2.15648741e-01 2.95453101e-01
9.31538343e-02 -3.88671279e-01 -2.18425006e-01 -9.88413155e-01
-1.15303434e-01 -1.05562918e-01 -2.23572701e-01 -4.28475082... | [13.606240272521973, 1.8743996620178223] |
bd0e99b0-18ab-4252-8243-3c0c5d4ee93f | dualmix-unleashing-the-potential-of-data | 2303.07864 | null | https://arxiv.org/abs/2303.07864v1 | https://arxiv.org/pdf/2303.07864v1.pdf | DualMix: Unleashing the Potential of Data Augmentation for Online Class-Incremental Learning | Online Class-Incremental (OCI) learning has sparked new approaches to expand the previously trained model knowledge from sequentially arriving data streams with new classes. Unfortunately, OCI learning can suffer from catastrophic forgetting (CF) as the decision boundaries for old classes can become inaccurate when per... | ['Song Guo', 'Junxiao Wang', 'Jiaqi Zhu', 'Haozhao Wang', 'Wenchao Xu', 'Yunfeng Fan'] | 2023-03-14 | null | null | null | null | ['class-incremental-learning'] | ['computer-vision'] | [ 3.29544514e-01 -1.66691095e-01 -4.53287870e-01 -4.76025343e-01
-4.30959076e-01 -4.43541795e-01 3.54005873e-01 4.61498648e-01
-2.13164791e-01 9.70139861e-01 3.77574861e-02 -5.46286814e-02
-1.44426271e-01 -6.24759853e-01 -6.93795979e-01 -9.41787839e-01
1.15681313e-01 4.90236789e-01 3.47371280e-01 4.46849875... | [9.677974700927734, 3.4867212772369385] |
2bda5617-1ca3-496a-a627-82095b0d38e4 | clip-reident-contrastive-training-for-player | 2303.11855 | null | https://arxiv.org/abs/2303.11855v1 | https://arxiv.org/pdf/2303.11855v1.pdf | CLIP-ReIdent: Contrastive Training for Player Re-Identification | Sports analytics benefits from recent advances in machine learning providing a competitive advantage for teams or individuals. One important task in this context is the performance measurement of individual players to provide reports and log files for subsequent analysis. During sport events like basketball, this invol... | ['Norbert Oswald', 'Fabian Deuser', 'Konrad Habel'] | 2023-03-21 | null | null | null | null | ['optical-character-recognition', 'sports-analytics'] | ['computer-vision', 'computer-vision'] | [ 4.17116016e-01 -3.20669860e-01 2.66864654e-02 -2.73105383e-01
-1.32536030e+00 -8.66836309e-01 4.47765499e-01 4.19057161e-01
-8.72898281e-01 1.62881762e-01 1.13458773e-02 3.43091488e-01
-7.96391070e-02 -5.82688928e-01 -8.49240720e-01 -2.77411312e-01
6.98016137e-02 7.02192307e-01 5.84724844e-01 -5.07857382... | [7.813141822814941, 0.20200839638710022] |
e35060c9-ad2e-46f1-a070-60424e95c621 | pixel-level-intra-domain-adaptation-for | null | null | https://dl.acm.org/doi/10.1145/3474085.3475174 | https://dl.acm.org/doi/pdf/10.1145/3474085.3475174 | Pixel-level Intra-domain Adaptation for Semantic Segmentation | Recent advances in unsupervised domain adaptation have achieved remarkable performance on semantic segmentation tasks. Despite such progress, existing works mainly focus on bridging the inter-domain gaps between the source and target domain, while only few of them noticed the intra-domain gaps within the target data. I... | ['Shuguang Cui', 'Xiaoguang Han', 'Yushuang Wu', 'Yipeng Qin', 'Xianggang Yu', 'Zizheng Yan'] | 2021-10-17 | null | null | null | acm-international-conference-on-multimedia-2 | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 6.43320084e-01 2.94368804e-01 -2.81150818e-01 -4.32031482e-01
-9.16255116e-01 -6.52982295e-01 3.14656228e-01 -9.16870236e-02
-3.30457687e-01 5.45147300e-01 -1.06580444e-01 4.53540571e-02
1.57543242e-01 -1.00109184e+00 -7.80276537e-01 -7.02015042e-01
6.22313023e-01 6.18280649e-01 8.71424496e-01 -1.09334655... | [9.68567180633545, 1.2757809162139893] |
4e151b23-3f90-4018-a2b5-5d4ca4755a71 | a-comparative-study-of-hyper-parameter | 2201.06433 | null | https://arxiv.org/abs/2201.06433v1 | https://arxiv.org/pdf/2201.06433v1.pdf | A Comparative study of Hyper-Parameter Optimization Tools | Most of the machine learning models have associated hyper-parameters along with their parameters. While the algorithm gives the solution for parameters, its utility for model performance is highly dependent on the choice of hyperparameters. For a robust performance of a model, it is necessary to find out the right hype... | ['Asif Salim', 'Adesh Bansode', 'Shashank Shekhar'] | 2022-01-17 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-3.50982286e-02 -3.10937613e-01 2.50805467e-01 -3.52331549e-01
-2.10063815e-01 -3.09219271e-01 4.60832030e-01 1.58855274e-01
-7.58701146e-01 7.05344021e-01 -2.83195198e-01 -1.30303591e-01
-7.42997050e-01 -6.41292632e-01 -2.97618836e-01 -9.76113915e-01
4.34541665e-02 9.11308169e-01 4.55175221e-01 -3.30966026... | [7.113138675689697, 3.7164254188537598] |
6b15d2ff-15ae-4cbd-8e8f-c6bc778bb7ae | abaw-facial-expression-recognition-in-the | 2303.09785 | null | https://arxiv.org/abs/2303.09785v1 | https://arxiv.org/pdf/2303.09785v1.pdf | ABAW : Facial Expression Recognition in the wild | The fifth Affective Behavior Analysis in-the-wild (ABAW) competition has multiple challenges such as Valence-Arousal Estimation Challenge, Expression Classification Challenge, Action Unit Detection Challenge, Emotional Reaction Intensity Estimation Challenge. In this paper we have dealt only expression classification c... | ['S Balasubramanian', 'Bobbili Veerendra Raj Kumar', 'Badveeti Naveen Siva Kumar', 'Darshan Gera'] | 2023-03-17 | null | null | null | null | ['action-unit-detection'] | ['computer-vision'] | [ 8.09219573e-03 1.46774366e-01 6.90902174e-02 -8.95986319e-01
-9.82567728e-01 -5.94957232e-01 3.04727077e-01 3.47814173e-01
-7.62966692e-01 1.14730752e+00 4.01246607e-01 7.91399360e-01
5.60026407e-01 -2.92624161e-03 3.33678350e-02 -4.55413908e-01
-2.34547228e-01 1.01010583e-01 -2.09941402e-01 -5.62619507... | [13.571680068969727, 2.260434865951538] |
5f6ed23d-497c-4ace-970e-0dd88114f8de | local-contrastive-loss-with-pseudo-label | 2112.09645 | null | https://arxiv.org/abs/2112.09645v1 | https://arxiv.org/pdf/2112.09645v1.pdf | Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation | Supervised deep learning-based methods yield accurate results for medical image segmentation. However, they require large labeled datasets for this, and obtaining them is a laborious task that requires clinical expertise. Semi/self-supervised learning-based approaches address this limitation by exploiting unlabeled dat... | ['Ender Konukoglu', 'Neerav Karani', 'Ertunc Erdil', 'Krishna Chaitanya'] | 2021-12-17 | null | null | null | null | ['semi-supervised-medical-image-segmentation'] | ['computer-vision'] | [ 5.29385626e-01 5.92485309e-01 -7.38866508e-01 -8.79428625e-01
-1.20069599e+00 -4.14295912e-01 2.22265735e-01 6.06240988e-01
-6.67304456e-01 8.22077036e-01 1.19913265e-01 7.98431411e-02
8.36098790e-02 -6.95289612e-01 -8.08108509e-01 -8.76650989e-01
6.08080067e-02 6.03947401e-01 3.06433588e-01 1.84752107... | [14.706171989440918, -2.1439599990844727] |
ea000ac7-cdb5-42a7-8ece-c2f37ba090e1 | wildgait-learning-of-gait-representations | 2105.05528 | null | https://arxiv.org/abs/2105.05528v5 | https://arxiv.org/pdf/2105.05528v5.pdf | WildGait: Learning Gait Representations from Raw Surveillance Streams | The use of gait for person identification has important advantages such as being non-invasive, unobtrusive, not requiring cooperation and being less likely to be obscured compared to other biometrics. Existing methods for gait recognition require cooperative gait scenarios, in which a single person is walking multiple ... | ['Emilian Radoi', 'Adrian Cosma'] | 2021-05-12 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [ 1.91608459e-01 -1.97194442e-01 -4.43555228e-02 -2.13705957e-01
-5.54323673e-01 -6.56197965e-01 2.40738869e-01 -1.94155738e-01
-6.95099533e-01 5.53983331e-01 2.52478272e-01 1.59365490e-01
2.77144492e-01 -5.99141002e-01 -5.34874618e-01 -6.07179105e-01
-3.33975226e-01 5.55405259e-01 2.07949758e-01 1.38122901... | [14.254825592041016, 1.4012690782546997] |
6ecef900-8cd7-4f4b-8675-120956504b25 | artificial-neural-networks-for-cloud-masking | null | null | https://www.tandfonline.com/doi/abs/10.1080/01431161.2020.1714776 | http://users.ntua.gr/vkristoll/assets/manuscript_cloud_masking_revised_technical_V2preprint.pdf | Artificial neural networks for cloud masking of Sentinel-2 ocean images with noise and sunglint | Cloudy regions in optical satellite images prevent the extraction of valuable information by image processing techniques. Several threshold, multi-temporal and machine learning approaches have been developed for the separation of clouds in land and ocean applications, but this task still remains a challenge. Concerning... | ['Vassilia Karathanassi', 'Viktoria Kristollari'] | 2020-01-26 | null | null | null | null | ['cloud-detection'] | ['computer-vision'] | [ 2.13298738e-01 -3.97260070e-01 5.11032939e-01 -1.27384171e-01
-4.12142903e-01 -5.78899205e-01 6.23832822e-01 1.54999793e-01
-7.36397088e-01 5.82627416e-01 -3.04232568e-01 -3.74110430e-01
-6.13838792e-01 -7.86098421e-01 -9.91168842e-02 -1.09329057e+00
-2.09681511e-01 1.88366547e-01 1.92468405e-01 -4.09482419... | [9.726019859313965, -1.7552011013031006] |
fcef9ff2-81a4-4c34-be92-fe1789084e18 | hierarchical-open-vocabulary-universal-image | 2307.00764 | null | https://arxiv.org/abs/2307.00764v1 | https://arxiv.org/pdf/2307.00764v1.pdf | Hierarchical Open-vocabulary Universal Image Segmentation | Open-vocabulary image segmentation aims to partition an image into semantic regions according to arbitrary text descriptions. However, complex visual scenes can be naturally decomposed into simpler parts and abstracted at multiple levels of granularity, introducing inherent segmentation ambiguity. Unlike existing metho... | ['Trevor Darrell', 'Kazuki Kozuka', 'Yusuke Kato', 'Konstantinos Kallidromitis', 'Shufan Li', 'Xudong Wang'] | 2023-07-03 | null | null | null | null | ['image-comprehension'] | ['computer-vision'] | [ 3.12070042e-01 2.00224414e-01 -1.87563702e-01 -4.10365134e-01
-8.48412335e-01 -8.62382770e-01 5.40765941e-01 2.75641263e-01
-3.81030053e-01 2.39945710e-01 1.53635312e-02 -2.31536388e-01
1.40438586e-01 -8.12113643e-01 -8.10044765e-01 -5.18693209e-01
6.44603968e-01 3.86335403e-01 4.67373312e-01 -1.08804114... | [9.663993835449219, 0.637100875377655] |
a8be424e-351f-421f-aec9-c800d4225ac7 | boundary-loss-for-highly-unbalanced | 1812.07032 | null | https://arxiv.org/abs/1812.07032v4 | https://arxiv.org/pdf/1812.07032v4.pdf | Boundary loss for highly unbalanced segmentation | Widely used loss functions for CNN segmentation, e.g., Dice or cross-entropy, are based on integrals over the segmentation regions. Unfortunately, for highly unbalanced segmentations, such regional summations have values that differ by several orders of magnitude across classes, which affects training performance and s... | ['Eric Granger', 'Hoel Kervadec', 'Christian Desrosiers', 'Jihene Bouchtiba', 'Jose Dolz', 'Ismail Ben Ayed'] | 2018-12-17 | null | null | null | null | ['unbalanced-segmentation', 'ischemic-stroke-lesion-segmentation', 'brain-lesion-segmentation-from-mri'] | ['computer-vision', 'medical', 'medical'] | [-1.21043742e-01 2.40571097e-01 -6.89068139e-02 -5.85593820e-01
-7.87459314e-01 -7.73834527e-01 5.38965128e-02 3.02650005e-01
-6.59784436e-01 5.68757057e-01 -3.75366151e-01 -2.35983089e-01
3.09982777e-01 -8.73107374e-01 -6.62235975e-01 -7.56060064e-01
-5.63462963e-03 1.87033247e-02 5.46589971e-01 -4.06698771... | [14.477165222167969, -2.191990375518799] |
eb3a4c9c-c5f5-4c6f-bcf8-3601cd26404c | vision-transformer-based-covid-19-detection | 2110.04458 | null | https://arxiv.org/abs/2110.04458v1 | https://arxiv.org/pdf/2110.04458v1.pdf | Vision Transformer based COVID-19 Detection using Chest X-rays | COVID-19 is a global pandemic, and detecting them is a momentous task for medical professionals today due to its rapid mutations. Current methods of examining chest X-rays and CT scan requires profound knowledge and are time consuming, which suggests that it shrinks the precious time of medical practitioners when peopl... | ['Karthik Sivarama Krishnan', 'Koushik Sivarama Krishnan'] | 2021-10-09 | null | null | null | null | ['covid-19-detection', 'covid-19-modelling'] | ['medical', 'time-series'] | [ 2.37496242e-01 -2.62800485e-01 -1.92694858e-01 4.06183153e-02
-6.90699935e-01 -4.20029253e-01 3.37648809e-01 2.44852573e-01
-4.49828684e-01 6.65670097e-01 2.61523500e-02 -6.16513014e-01
-2.14954212e-01 -7.51609862e-01 -3.12049419e-01 -6.52258933e-01
3.82527784e-02 7.23798215e-01 2.90572673e-01 -5.94313182... | [15.56101131439209, -1.7030218839645386] |
a3dc2806-2faa-4d28-a3a0-42590290f25d | attention-based-multi-input-deep-learning | 1906.05168 | null | https://arxiv.org/abs/1906.05168v3 | https://arxiv.org/pdf/1906.05168v3.pdf | Attention-based Multi-Input Deep Learning Architecture for Biological Activity Prediction: An Application in EGFR Inhibitors | Machine learning and deep learning have gained popularity and achieved immense success in Drug discovery in recent decades. Historically, machine learning and deep learning models were trained on either structural data or chemical properties by separated model. In this study, we proposed an architecture training simult... | ['Trung Hoang Le', 'Huy Ngoc Pham'] | 2019-06-12 | null | null | null | null | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 2.60546178e-01 5.09119704e-02 -3.83470714e-01 -4.56032306e-01
-5.42289257e-01 -4.08079982e-01 5.22764862e-01 3.41513425e-01
-2.38380060e-01 1.11524737e+00 1.21661551e-01 -6.52224362e-01
2.31206734e-02 -6.56574607e-01 -7.85178125e-01 -7.51939714e-01
-5.30206636e-02 1.77484557e-01 -2.26335645e-01 2.42573638... | [5.1200852394104, 5.811276912689209] |
45533852-9955-4e95-9e49-b546cfc32bee | skeleton-free-pose-transfer-for-stylized-3d | 2208.00790 | null | https://arxiv.org/abs/2208.00790v1 | https://arxiv.org/pdf/2208.00790v1.pdf | Skeleton-free Pose Transfer for Stylized 3D Characters | We present the first method that automatically transfers poses between stylized 3D characters without skeletal rigging. In contrast to previous attempts to learn pose transformations on fixed or topology-equivalent skeleton templates, our method focuses on a novel scenario to handle skeleton-free characters with divers... | ['Yang Zhou', 'Gerard Pons-Moll', 'Jun Saito', 'Jimei Yang', 'Zhouyingcheng Liao'] | 2022-07-28 | null | null | null | null | ['pose-transfer'] | ['computer-vision'] | [ 2.12154120e-01 2.55709916e-01 -8.88445750e-02 -2.02147007e-01
-5.06685913e-01 -9.27725196e-01 6.96203411e-01 -4.78073299e-01
6.22705445e-02 4.51045305e-01 1.62706494e-01 8.81923661e-02
1.68631598e-01 -7.29586720e-01 -8.79231095e-01 -2.96633959e-01
4.21283036e-01 1.05075026e+00 3.69094849e-01 -3.06414545... | [8.871909141540527, -3.358180522918701] |
ffbba140-ed59-469d-a290-a0b370765fa6 | sam-fails-to-segment-anything-sam-adapter | 2304.09148 | null | https://arxiv.org/abs/2304.09148v3 | https://arxiv.org/pdf/2304.09148v3.pdf | SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More | The emergence of large models, also known as foundation models, has brought significant advancements to AI research. One such model is Segment Anything (SAM), which is designed for image segmentation tasks. However, as with other foundation models, our experimental findings suggest that SAM may fail or perform poorly i... | ['Ying Zang', 'Papa Mao', 'Lingyun Sun', 'Zejian Li', 'Yan Wang', 'Runlong Cao', 'Chaotao Ding', 'Lanyun Zhu', 'Tianrun Chen'] | 2023-04-18 | null | null | null | null | ['shadow-detection', 'general-knowledge'] | ['computer-vision', 'miscellaneous'] | [ 8.56283188e-01 3.10896397e-01 -8.49424675e-02 -1.51170403e-01
-4.10383493e-01 -6.35635614e-01 1.02127418e-01 -2.96649367e-01
-2.94948131e-01 3.45781952e-01 -3.14022213e-01 -7.65827894e-01
1.88741848e-01 -7.06806362e-01 -8.19830835e-01 -7.05033481e-01
4.06402424e-02 2.80534327e-01 6.76368475e-01 -9.88716707... | [9.618502616882324, 0.17308221757411957] |
c76c36d3-e0c0-464e-8902-49dd108d5fd1 | medai-at-semeval-2021-task-5-start-to-end | null | null | https://aclanthology.org/2021.semeval-1.30 | https://aclanthology.org/2021.semeval-1.30.pdf | MedAI at SemEval-2021 Task 5: Start-to-end Tagging Framework for Toxic Spans Detection | This paper describes the system submitted to SemEval 2021 Task 5: Toxic Spans Detection. The task concerns evaluating systems that detect the spans that make a text toxic when detecting such spans are possible. To address the possibly multi-span detection problem, we develop a start-to-end tagging framework on top of R... | ['Junfei Liu', 'Hongjie Fan', 'Zhen Wang'] | 2021-08-01 | null | null | null | semeval-2021 | ['toxic-spans-detection'] | ['natural-language-processing'] | [-1.86676055e-01 1.72158718e-01 -8.69268253e-02 -4.11104262e-02
-1.25989628e+00 -8.67527902e-01 4.36091930e-01 4.63382065e-01
-5.37062705e-01 8.41487229e-01 1.40879601e-01 -3.23076755e-01
-1.94104668e-03 -5.74919999e-01 -6.33354783e-01 -1.13841161e-01
-3.04429710e-01 2.78127402e-01 4.56495166e-01 -1.04297869... | [8.965524673461914, 10.623862266540527] |
8b3b2bb1-e777-4be3-b7d2-94a1ff9bdbbf | a-distributional-view-on-multi-objective-1 | null | null | https://proceedings.icml.cc/static/paper_files/icml/2020/6749-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/6749-Paper.pdf | A distributional view on multi objective policy optimization | Many real-world problems require trading off multiple competing objectives. However, these objectives are often in different units with different scales, which can make it challenging for practitioners to express numerical preferences over objectives in their native units. In this paper we propose a novel algorithm for... | ['Sandy Huang', 'Martin Riedmiller', 'Abbas Abdolmaleki', 'Nicolas Heess', 'Francis Song', 'Raia Hadsell', 'Murilo Martins', 'Michael Neunert', 'Martina Zambelli', 'Leonard Hasenclever'] | null | null | https://proceedings.icml.cc/static/paper_files/icml/2020/6749-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/6749-Paper.pdf | icml-2020-1 | ['multi-objective-reinforcement-learning'] | ['methodology'] | [ 8.48504007e-02 -1.87547132e-01 -2.78369725e-01 -2.10879534e-01
-9.18127656e-01 -8.32592785e-01 2.99531549e-01 1.88042931e-02
-8.43809783e-01 1.10501313e+00 6.17776625e-02 -6.55350313e-02
-6.87044561e-01 -5.47977269e-01 -6.27498150e-01 -7.72916019e-01
-1.84650630e-01 9.21282351e-01 1.00126259e-01 -1.93286687... | [4.2324042320251465, 2.2639734745025635] |
38ef76ec-3fc1-4713-a316-96d35a3b4c1e | semsegdepth-a-combined-model-for-semantic | 2209.00381 | null | https://arxiv.org/abs/2209.00381v1 | https://arxiv.org/pdf/2209.00381v1.pdf | SemSegDepth: A Combined Model for Semantic Segmentation and Depth Completion | Holistic scene understanding is pivotal for the performance of autonomous machines. In this paper we propose a new end-to-end model for performing semantic segmentation and depth completion jointly. The vast majority of recent approaches have developed semantic segmentation and depth completion as independent tasks. Ou... | ['Esa Rahtu', 'Juan Pablo Lagos'] | 2022-09-01 | null | null | null | null | ['depth-completion'] | ['computer-vision'] | [ 3.00267369e-01 3.89594644e-01 -1.13236777e-01 -7.91976690e-01
-8.37356329e-01 -4.33873951e-01 5.64068496e-01 -8.59571397e-02
-5.67228436e-01 3.31646413e-01 8.14173669e-02 -7.70302936e-02
3.30726594e-01 -7.32485175e-01 -7.95467198e-01 -5.34137368e-01
3.47332895e-01 6.51237965e-01 4.56959933e-01 1.28221944... | [8.512372970581055, -2.4439613819122314] |
7ba05c0d-9434-4b16-bd00-653c3b410871 | 4d-unsupervised-object-discovery | 2210.04801 | null | https://arxiv.org/abs/2210.04801v1 | https://arxiv.org/pdf/2210.04801v1.pdf | 4D Unsupervised Object Discovery | Object discovery is a core task in computer vision. While fast progresses have been made in supervised object detection, its unsupervised counterpart remains largely unexplored. With the growth of data volume, the expensive cost of annotations is the major limitation hindering further study. Therefore, discovering obje... | ['Zhaoxiang Zhang', 'Yuntao Chen', 'Yuqi Wang'] | 2022-10-10 | null | null | null | null | ['3d-instance-segmentation-1', 'object-discovery'] | ['computer-vision', 'computer-vision'] | [ 5.81962802e-03 3.71899083e-03 -3.62846822e-01 -4.23408449e-01
-6.42948866e-01 -5.82401991e-01 3.66699606e-01 2.00770289e-01
-3.56870979e-01 1.54293686e-01 -5.76546907e-01 -1.08916596e-01
-1.60279021e-01 -3.91184628e-01 -7.50968039e-01 -7.12942481e-01
-1.61245897e-01 8.63396108e-01 7.83101976e-01 2.30054960... | [7.967505931854248, -3.038426637649536] |
64afe43a-97e3-44b8-8044-892137af8809 | lila-a-unified-benchmark-for-mathematical | 2210.17517 | null | https://arxiv.org/abs/2210.17517v2 | https://arxiv.org/pdf/2210.17517v2.pdf | Lila: A Unified Benchmark for Mathematical Reasoning | Mathematical reasoning skills are essential for general-purpose intelligent systems to perform tasks from grocery shopping to climate modeling. Towards evaluating and improving AI systems in this domain, we propose LILA, a unified mathematical reasoning benchmark consisting of 23 diverse tasks along four dimensions: (i... | ['Ashwin Kalyan', 'Peter Clark', 'Ashish Sabharwal', 'Oyvind Tafjord', 'Tanmay Rajpurohit', 'Chitta Baral', 'Sean Welleck', 'Leonard Tang', 'Pan Lu', 'Matthew Finlayson', 'Swaroop Mishra'] | 2022-10-31 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [-1.12464130e-01 -2.27468424e-02 2.33050391e-01 -2.89922804e-01
-8.25212061e-01 -9.25068736e-01 5.34589231e-01 2.69976318e-01
-4.36708927e-01 6.94644690e-01 1.05425984e-01 -8.04595947e-01
-4.28297669e-01 -1.00486887e+00 -9.72556889e-01 -3.15225542e-01
1.70288727e-01 5.71014643e-01 -1.72725379e-01 -5.01349986... | [9.668817520141602, 7.361785411834717] |
3388c5f1-dc50-4f17-9f98-515a981ae29a | learning-to-generate-piano-music-with-sustain | 2111.01216 | null | https://arxiv.org/abs/2111.01216v1 | https://arxiv.org/pdf/2111.01216v1.pdf | Learning To Generate Piano Music With Sustain Pedals | Recent years have witnessed a growing interest in research related to the detection of piano pedals from audio signals in the music information retrieval community. However, to our best knowledge, recent generative models for symbolic music have rarely taken piano pedals into account. In this work, we employ the transc... | ['Yi-Hsuan Yang', 'Joann Ching'] | 2021-11-01 | null | null | null | null | ['music-information-retrieval'] | ['music'] | [ 4.14524913e-01 1.06309004e-01 4.24735211e-02 7.91696385e-02
-1.01949775e+00 -7.19263911e-01 6.39835119e-01 -7.31023848e-02
-3.31838685e-03 3.73332679e-01 5.94698668e-01 5.83359338e-02
-3.36016119e-01 -5.75219631e-01 -5.89436293e-01 -6.28980935e-01
1.07791178e-01 4.91381496e-01 2.09861174e-01 -2.66933292... | [15.975661277770996, 5.455192565917969] |
c041246d-2be4-4ee6-bfc2-6922c565333e | one-shot-weakly-supervised-segmentation-in | 2111.10773 | null | https://arxiv.org/abs/2111.10773v1 | https://arxiv.org/pdf/2111.10773v1.pdf | One-shot Weakly-Supervised Segmentation in Medical Images | Deep neural networks usually require accurate and a large number of annotations to achieve outstanding performance in medical image segmentation. One-shot segmentation and weakly-supervised learning are promising research directions that lower labeling effort by learning a new class from only one annotated image and ut... | ['Shaoting Zhang', 'Xiaofan Zhang', 'Guotai Wang', 'Xinglong Liu', 'Na Wang', 'Ran Gu', 'Qi Su', 'Wenhui Lei'] | 2021-11-21 | null | null | null | null | ['one-shot-segmentation'] | ['computer-vision'] | [ 5.05658090e-01 4.37614202e-01 -3.86625081e-01 -7.68518329e-01
-9.75623965e-01 -2.62025714e-01 1.23195335e-01 3.86227906e-01
-4.71481591e-01 4.76222217e-01 -2.07073927e-01 -9.99470204e-02
1.30246878e-01 -5.29201806e-01 -5.76302469e-01 -8.66092682e-01
1.41119361e-01 6.65775597e-01 4.92164671e-01 2.47032225... | [14.609450340270996, -2.226905107498169] |
5f472be6-cdb9-43f2-87b1-0c1f478afcf9 | introducing-the-hearthstone-ai-competition | 1906.04238 | null | https://arxiv.org/abs/1906.04238v1 | https://arxiv.org/pdf/1906.04238v1.pdf | Introducing the Hearthstone-AI Competition | The Hearthstone AI framework and competition motivates the development of artificial intelligence agents that can play collectible card games. A special feature of those games is the high variety of cards, which can be chosen by the players to create their own decks. In contrast to simpler card games, the value of many... | ['Sanaz Mostaghim', 'Alexander Dockhorn'] | 2019-05-06 | null | null | null | null | ['card-games'] | ['playing-games'] | [-3.91660005e-01 -1.20816184e-02 -1.58198085e-02 1.76090240e-01
1.36763945e-01 -9.85260963e-01 5.75249612e-01 -3.69584858e-01
-3.97936821e-01 9.83176351e-01 -9.87154394e-02 -1.99478522e-01
-5.39374113e-01 -1.02757764e+00 -7.40203336e-02 -3.87183130e-01
-3.31141055e-02 9.88445759e-01 3.36664021e-01 -8.64743292... | [3.399207353591919, 1.4506036043167114] |
65aa92a0-1508-43e1-ba28-b5357b588d7a | fluid-mitigating-stragglers-in-federated | 2307.02623 | null | https://arxiv.org/abs/2307.02623v2 | https://arxiv.org/pdf/2307.02623v2.pdf | FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropout | Federated Learning (FL) allows machine learning models to train locally on individual mobile devices, synchronizing model updates via a shared server. This approach safeguards user privacy; however, it also generates a heterogeneous training environment due to the varying performance capabilities across devices. As a r... | ['Divya Mahajan', 'Prashant J. Nair', 'Irene Wang'] | 2023-07-05 | null | null | null | null | ['model-extraction', 'federated-learning', 'model-extraction'] | ['adversarial', 'methodology', 'methodology'] | [ 2.70352699e-02 -2.68343359e-01 -5.58665156e-01 -5.44451475e-01
-9.38123226e-01 -7.11429715e-01 2.24773616e-01 -3.87562424e-01
-5.19569695e-01 6.13711715e-01 -3.11075933e-02 -3.93146247e-01
3.34050157e-03 -5.58490574e-01 -1.08339918e+00 -7.33667016e-01
1.93286076e-01 8.16881582e-02 2.10692734e-01 2.89709061... | [5.988099098205566, 6.137306213378906] |
44145dbb-77fe-45ae-92ab-ba95b9ac73ee | graph-embedding-on-biomedical-networks | 1906.05017 | null | https://arxiv.org/abs/1906.05017v3 | https://arxiv.org/pdf/1906.05017v3.pdf | Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations | Graph embedding learning that aims to automatically learn low-dimensional node representations, has drawn increasing attention in recent years. To date, most recent graph embedding methods are evaluated on social and information networks and are not comprehensively studied on biomedical networks under systematic experi... | ['Wen Zhang', 'Yungui Huang', 'Srinivasan Parthasarathy', 'Ping Zhang', 'Soheil Moosavinasab', 'Xiang Yue', 'Simon M. Lin', 'Jingong Huang', 'Zhen Wang', 'Huan Sun'] | 2019-06-12 | null | null | null | null | ['protein-function-prediction'] | ['medical'] | [ 9.17133093e-02 5.66439867e-01 -5.04960120e-01 -8.26551765e-02
-5.22502325e-02 -1.27506062e-01 5.34727693e-01 7.91171074e-01
-2.15002880e-01 6.15513921e-01 1.62235424e-01 -5.99447548e-01
-4.17824715e-01 -8.23408186e-01 -1.82968840e-01 -9.60268915e-01
-5.30421436e-01 6.06991708e-01 1.70359984e-01 -5.92099428... | [7.320692539215088, 6.644371032714844] |
a16c3d08-7cf9-4281-9cc3-607a5a579cba | robust-authorship-verification-with-transfer | null | null | https://openreview.net/forum?id=BkgdPnjQ84 | https://openreview.net/pdf?id=BkgdPnjQ84 | Robust Authorship Verification with Transfer Learning | We address the problem of open-set authorship verification, a classification task that consists of attributing texts of unknown authorship to a given author when the unknown documents in the test set are excluded from the training set. We present an end-to-end model-building process that is universally applicable to a ... | ['Anonymous'] | 2019-02-27 | null | null | null | null | ['text-augmentation', 'authorship-verification'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.04822797e-01 2.41973296e-01 -1.61981374e-01 -4.29317683e-01
-8.75637472e-01 -9.71289158e-01 1.02010024e+00 3.71983089e-02
-4.54019576e-01 5.07852495e-01 1.38000185e-02 -4.32305455e-01
2.50432968e-01 -3.99042010e-01 -5.78368127e-01 -3.76477450e-01
4.83851731e-01 9.86739933e-01 -1.41655549e-01 -9.48449038... | [9.641075134277344, 10.567243576049805] |
febb2714-9f6f-434c-81cd-4da774a32117 | task-driven-graph-attention-for-hierarchical | 2306.13760 | null | https://arxiv.org/abs/2306.13760v1 | https://arxiv.org/pdf/2306.13760v1.pdf | Task-Driven Graph Attention for Hierarchical Relational Object Navigation | Embodied AI agents in large scenes often need to navigate to find objects. In this work, we study a naturally emerging variant of the object navigation task, hierarchical relational object navigation (HRON), where the goal is to find objects specified by logical predicates organized in a hierarchical structure - object... | ['Jiajun Wu', 'Li Fei-Fei', 'Ruohan Zhang', 'Roberto Martín-Martín', 'Alan Lou', 'Andrey Kurenkov', 'Minjune Hwang', 'Chengshu Li', 'Michael Lingelbach'] | 2023-06-23 | null | null | null | null | ['graph-attention', 'navigate'] | ['graphs', 'reasoning'] | [ 1.50002629e-01 3.57876539e-01 6.30590245e-02 -2.48929083e-01
-1.99314691e-02 -4.81720507e-01 5.57248652e-01 6.19946837e-01
-4.55419928e-01 4.75350946e-01 4.68301386e-01 -2.38445103e-01
-5.86443901e-01 -1.07999897e+00 -1.00465536e+00 -4.21950787e-01
-4.99459594e-01 8.93720090e-01 3.26238185e-01 -3.14107686... | [4.736565589904785, 0.4982045590877533] |
0c04971f-d8ef-46d6-b77b-c1341cbf3e72 | a-cross-corpus-study-on-speech-emotion | 2207.02104 | null | https://arxiv.org/abs/2207.02104v1 | https://arxiv.org/pdf/2207.02104v1.pdf | A cross-corpus study on speech emotion recognition | For speech emotion datasets, it has been difficult to acquire large quantities of reliable data and acted emotions may be over the top compared to less expressive emotions displayed in everyday life. Lately, larger datasets with natural emotions have been created. Instead of ignoring smaller, acted datasets, this study... | ['Thomas Hain', 'Raymond W. M. Ng', 'Md Asif Jalal', 'Rosanna Milner'] | 2022-07-05 | null | null | null | null | ['cross-corpus'] | ['computer-vision'] | [ 2.64825702e-01 3.72906029e-01 2.48784527e-01 -8.26255858e-01
-6.52470946e-01 -3.82627457e-01 6.75778031e-01 -1.15140490e-01
-6.79780126e-01 8.38197649e-01 3.87749970e-01 1.71984911e-01
2.44621411e-01 -3.41306418e-01 -6.35253310e-01 -4.26987231e-01
1.97411962e-02 4.21768606e-01 -2.67472863e-01 -4.91463691... | [13.482054710388184, 5.876943111419678] |
55c0e805-9153-4624-861c-368b283003e8 | deepsource-point-source-detection-using-deep | 1807.02701 | null | http://arxiv.org/abs/1807.02701v1 | http://arxiv.org/pdf/1807.02701v1.pdf | DeepSource: Point Source Detection using Deep Learning | Point source detection at low signal-to-noise is challenging for astronomical
surveys, particularly in radio interferometry images where the noise is
correlated. Machine learning is a promising solution, allowing the development
of algorithms tailored to specific telescope arrays and science cases. We
present DeepSourc... | ['A. Vafaei Sadr', 'Zafiirah Hosenie', 'Michelle Lochner', 'Etienne. E. Vos', 'N. Oozeer', 'Bruce A. Bassett'] | 2018-07-07 | null | null | null | null | ['radio-interferometry'] | ['miscellaneous'] | [ 3.07868011e-02 -1.96697667e-01 1.53269663e-01 1.64826423e-01
-1.02778602e+00 -6.50586128e-01 6.94646120e-01 -2.53522366e-01
-5.19478500e-01 4.54631150e-01 1.32954612e-01 -5.33730507e-01
-5.24981320e-01 -6.86714053e-01 -3.78024250e-01 -1.13009977e+00
-5.25037825e-01 2.35318169e-01 4.10798490e-01 -5.60382679... | [7.583398342132568, 3.1278133392333984] |
90164a67-f71b-4c3c-8f1e-fe5a463d34dd | evaluating-deep-neural-networks-for-image | 2106.15286 | null | https://arxiv.org/abs/2106.15286v1 | https://arxiv.org/pdf/2106.15286v1.pdf | Evaluating Deep Neural Networks for Image Document Enhancement | This work evaluates six state-of-the-art deep neural network (DNN) architectures applied to the problem of enhancing camera-captured document images. The results from each network were evaluated both qualitatively and quantitatively using Image Quality Assessment (IQA) metrics, and also compared with an existing approa... | ['Ricardo Ribani', 'Ricardo Piccoli', 'Lucas N. Kirsten'] | 2021-06-11 | null | null | null | null | ['document-enhancement'] | ['computer-vision'] | [ 4.01853055e-01 -2.22804695e-01 3.33093315e-01 -3.25342357e-01
-3.70971322e-01 -3.24387431e-01 9.94826257e-01 1.51630938e-01
-7.01747179e-01 4.33561057e-01 2.59609967e-01 -1.43759713e-01
-2.85994709e-01 -8.61806214e-01 -4.56636131e-01 -8.87086451e-01
8.81559029e-02 1.61239415e-01 3.96586545e-02 -3.12344968... | [11.3966064453125, -1.9728257656097412] |
6c51240c-5cea-4f54-abce-653218f9364c | egovsr-towards-high-quality-egocentric-video | 2305.14708 | null | https://arxiv.org/abs/2305.14708v1 | https://arxiv.org/pdf/2305.14708v1.pdf | EgoVSR: Towards High-Quality Egocentric Video Super-Resolution | Due to the limitations of capture devices and scenarios, egocentric videos frequently have low visual quality, mainly caused by high compression and severe motion blur. With the increasing application of egocentric videos, there is an urgent need to enhance the quality of these videos through super-resolution. However,... | ['Yapeng Tian', 'Wenming Yang', 'Jiamiao Zhang', 'Junhao Gu', 'Yichen Chi'] | 2023-05-24 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [-1.83927696e-02 -4.55988348e-01 -9.90091413e-02 -1.56366423e-01
-4.61317241e-01 -4.19908851e-01 3.50081861e-01 -9.92550731e-01
1.99572928e-02 6.43495619e-01 8.14876199e-01 1.13940984e-01
-3.91832702e-02 -3.20115387e-01 -8.45023990e-01 -4.77749944e-01
3.04759573e-02 -4.11152840e-01 1.15428790e-01 -1.41064823... | [11.292265892028809, -2.3315110206604004] |
06dde386-2f10-45ad-807c-ce87a3d4563a | asymmetric-proxy-loss-for-multi-view-acoustic | 2203.16080 | null | https://arxiv.org/abs/2203.16080v2 | https://arxiv.org/pdf/2203.16080v2.pdf | Asymmetric Proxy Loss for Multi-View Acoustic Word Embeddings | Acoustic word embeddings (AWEs) are discriminative representations of speech segments, and learned embedding space reflects the phonetic similarity between words. With multi-view learning, where text labels are considered as supplementary input, AWEs are jointly trained with acoustically grounded word embeddings (AGWEs... | ['Hoirin Kim', 'Myunghun Jung'] | 2022-03-30 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [-2.59367581e-02 -1.02701798e-01 -6.78192675e-02 -5.36583364e-01
-1.06553423e+00 -4.91865188e-01 3.46990258e-01 -7.81130567e-02
-6.53225780e-01 3.80844176e-01 4.92641121e-01 -7.16125816e-02
-1.16377376e-01 -6.78623915e-01 -4.07983214e-01 -9.89201844e-01
2.41734922e-01 3.08763981e-01 3.16497862e-01 -2.99542814... | [10.983842849731445, 8.516276359558105] |
d21452ea-31db-4da2-b7c5-3bc526c92513 | deep-occlusion-reasoning-for-multi-camera | 1704.05775 | null | http://arxiv.org/abs/1704.05775v2 | http://arxiv.org/pdf/1704.05775v2.pdf | Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection | People detection in single 2D images has improved greatly in recent years.
However, comparatively little of this progress has percolated into multi-camera
multi-people tracking algorithms, whose performance still degrades severely
when scenes become very crowded. In this work, we introduce a new architecture
that combi... | ['François Fleuret', 'Pierre Baqué', 'Pascal Fua'] | 2017-04-19 | deep-occlusion-reasoning-for-multi-camera-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Baque_Deep_Occlusion_Reasoning_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Baque_Deep_Occlusion_Reasoning_ICCV_2017_paper.pdf | iccv-2017-10 | ['multiview-detection'] | ['computer-vision'] | [-3.44861984e-01 -4.96092677e-01 1.08921394e-01 -4.56285238e-01
-4.45062816e-01 -5.09692073e-01 7.50100434e-01 5.89169152e-02
-9.94533956e-01 7.28654206e-01 2.38181859e-01 1.62944824e-01
2.68179089e-01 -5.53433418e-01 -4.33043182e-01 -2.52826184e-01
-1.32902548e-01 9.70711768e-01 5.94040155e-01 1.02177542... | [6.46295690536499, -1.9117021560668945] |
4a6806d3-8bf0-4f63-b9c6-dd259d257092 | probabilistic-lexicase-selection | 2305.11681 | null | https://arxiv.org/abs/2305.11681v1 | https://arxiv.org/pdf/2305.11681v1.pdf | Probabilistic Lexicase Selection | Lexicase selection is a widely used parent selection algorithm in genetic programming, known for its success in various task domains such as program synthesis, symbolic regression, and machine learning. Due to its non-parametric and recursive nature, calculating the probability of each individual being selected by lexi... | ['Lee Spector', 'Edward Pantridge', 'Li Ding'] | 2023-05-19 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [ 2.40578115e-01 -1.06015213e-01 -8.15197289e-01 -4.17597771e-01
-7.11501181e-01 -3.97210091e-01 2.06491172e-01 2.22722709e-01
8.06046054e-02 9.90427136e-01 -3.62640411e-01 -4.14129555e-01
-4.52482045e-01 -1.07531285e+00 -7.82082379e-01 -6.68175519e-01
-2.28622213e-01 8.40746522e-01 4.43719715e-01 -9.27763581... | [8.027497291564941, 7.235772132873535] |
a79205c8-66aa-42ea-86e5-1b439b09ac83 | applying-bert-and-chatgpt-for-sentiment | 2302.06474 | null | https://arxiv.org/abs/2302.06474v1 | https://arxiv.org/pdf/2302.06474v1.pdf | Applying BERT and ChatGPT for Sentiment Analysis of Lyme Disease in Scientific Literature | This chapter presents a practical guide for conducting Sentiment Analysis using Natural Language Processing (NLP) techniques in the domain of tick-borne disease text. The aim is to demonstrate the process of how the presence of bias in the discourse surrounding chronic manifestations of the disease can be evaluated. Th... | ['Teo Susnjak'] | 2023-02-07 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [ 5.09716749e-01 2.47657970e-01 -1.74243689e-01 -7.68401206e-01
-6.48590267e-01 -4.30652887e-01 5.62251568e-01 9.78558064e-01
-6.65661812e-01 7.82132804e-01 5.20397782e-01 -7.05534220e-01
-2.12215036e-02 -2.59014547e-01 -2.07549319e-01 -5.42293310e-01
-5.13215601e-01 7.35889673e-01 -3.42835069e-01 -5.92850387... | [8.47828197479248, 8.838959693908691] |
e2de1f35-826c-410c-aaf8-482d88469a1e | multimodal-machine-learning-based-knee | 1904.06236 | null | https://arxiv.org/abs/1904.06236v2 | https://arxiv.org/pdf/1904.06236v2.pdf | Multimodal Machine Learning-based Knee Osteoarthritis Progression Prediction from Plain Radiographs and Clinical Data | Knee osteoarthritis (OA) is the most common musculoskeletal disease without a cure, and current treatment options are limited to symptomatic relief. Prediction of OA progression is a very challenging and timely issue, and it could, if resolved, accelerate the disease modifying drug development and ultimately help to pr... | ['Jérôme Thevenot', 'Sita M. A. Bierma-Zeinstra', 'Stefan Klein', 'Esa Rahtu', 'Edwin H. G. Oei', 'Joyce van Meurs', 'Simo Saarakkala', 'Aleksei Tiulpin'] | 2019-04-12 | null | null | null | null | ['knee-osteoarthritis-prediction'] | ['medical'] | [-1.28005490e-01 -3.01956296e-01 -7.03659236e-01 -2.83830259e-02
-1.16289961e+00 -1.57111958e-01 1.35695010e-01 4.92025405e-01
-4.29198444e-01 1.07793784e+00 2.32264146e-01 -1.56097516e-01
-4.57397014e-01 -4.74872321e-01 -1.99725375e-01 -4.99429107e-01
-5.25758922e-01 7.62475908e-01 4.76587594e-01 1.84795350... | [14.564043998718262, -1.776610016822815] |
8267fd39-3fe9-4485-a356-1da582b9ec68 | pushing-the-performance-limit-of-scene-text | 2204.07714 | null | https://arxiv.org/abs/2204.07714v2 | https://arxiv.org/pdf/2204.07714v2.pdf | Pushing the Performance Limit of Scene Text Recognizer without Human Annotation | Scene text recognition (STR) attracts much attention over the years because of its wide application. Most methods train STR model in a fully supervised manner which requires large amounts of labeled data. Although synthetic data contributes a lot to STR, it suffers from the real-tosynthetic domain gap that restricts mo... | ['Peng Wang', 'Jae-Joon Han', 'Seungju Han', 'Seon-Min Rhee', 'Hui Li', 'Caiyuan Zheng'] | 2022-04-16 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zheng_Pushing_the_Performance_Limit_of_Scene_Text_Recognizer_Without_Human_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zheng_Pushing_the_Performance_Limit_of_Scene_Text_Recognizer_Without_Human_CVPR_2022_paper.pdf | cvpr-2022-1 | ['scene-text-recognition'] | ['computer-vision'] | [ 4.52427179e-01 -3.49501818e-01 -3.12265754e-01 -4.94267672e-01
-9.68731701e-01 -3.88170272e-01 6.99672639e-01 -4.31946695e-01
-2.70382285e-01 6.19318604e-01 -4.63489778e-02 -1.58781186e-02
2.73346841e-01 -3.16979468e-01 -7.59721160e-01 -6.98436856e-01
7.02269197e-01 4.54371035e-01 3.72312963e-01 -8.64950493... | [11.814221382141113, 2.098646402359009] |
2e8aa110-a01c-4667-b4e0-0853e2b7bb98 | radar-artifact-labeling-framework-ralf-method | 2012.01993 | null | https://arxiv.org/abs/2012.01993v1 | https://arxiv.org/pdf/2012.01993v1.pdf | Radar Artifact Labeling Framework (RALF): Method for Plausible Radar Detections in Datasets | Research on localization and perception for Autonomous Driving is mainly focused on camera and LiDAR datasets, rarely on radar data. Manually labeling sparse radar point clouds is challenging. For a dataset generation, we propose the cross sensor Radar Artifact Labeling Framework (RALF). Automatically generated labels ... | ['J. Marius Zoellner', 'Sascha Saralajew', 'Fabian E. Klein', 'Marcel P. Schilling', 'Simon T. Isele'] | 2020-12-03 | null | null | null | null | ['depth-image-estimation'] | ['computer-vision'] | [ 4.05821145e-01 3.97758419e-03 4.43206467e-02 -8.49798858e-01
-1.19135189e+00 -6.66332066e-01 7.23178983e-01 1.40350536e-02
-2.75398403e-01 6.33836746e-01 -2.04988778e-01 -1.83453321e-01
-3.59136909e-01 -9.52627182e-01 -7.44640291e-01 -3.60765576e-01
8.81637633e-02 5.80112100e-01 3.42644721e-01 4.57761213... | [7.823421478271484, -1.523889183998108] |
ac482ef2-349f-47b5-b27f-a41b3fd0f1a2 | diverse-probabilistic-trajectory-forecasting | 2302.03462 | null | https://arxiv.org/abs/2302.03462v1 | https://arxiv.org/pdf/2302.03462v1.pdf | Diverse Probabilistic Trajectory Forecasting with Admissibility Constraints | Predicting multiple trajectories for road users is important for automated driving systems: ego-vehicle motion planning indeed requires a clear view of the possible motions of the surrounding agents. However, the generative models used for multiple-trajectory forecasting suffer from a lack of diversity in their proposa... | ['Nicolas Thome', 'Patrick Pérez', 'Hedi Ben-Younes', 'Laura Calem'] | 2023-02-07 | null | null | null | null | ['trajectory-forecasting', 'motion-planning'] | ['computer-vision', 'robots'] | [-7.96391256e-03 3.28417957e-01 -1.12861276e-01 -4.51485068e-01
-6.18871689e-01 -7.91134000e-01 1.02120948e+00 -1.07720278e-01
-2.99400333e-02 8.55212867e-01 4.86358762e-01 -2.67744124e-01
-2.03829557e-01 -9.58048463e-01 -8.03610623e-01 -9.22785103e-01
1.61968648e-01 7.89565980e-01 6.10462487e-01 -5.71787655... | [5.945356845855713, 0.8974782228469849] |
0b3b0db8-5b0f-4073-b95b-2e46c8db5c6f | hybrid-rule-neural-coreference-resolution | 2212.10087 | null | https://arxiv.org/abs/2212.10087v1 | https://arxiv.org/pdf/2212.10087v1.pdf | Hybrid Rule-Neural Coreference Resolution System based on Actor-Critic Learning | A coreference resolution system is to cluster all mentions that refer to the same entity in a given context. All coreference resolution systems need to tackle two main tasks: one task is to detect all of the potential mentions, and the other is to learn the linking of an antecedent for each possible mention. In this pa... | ['Hongxia Jin', 'Yu Wang'] | 2022-12-20 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [ 5.88747822e-02 6.33428991e-01 -4.24996078e-01 -3.68053466e-01
-1.37096739e+00 -2.95344442e-01 4.54917312e-01 1.62307292e-01
-4.61297035e-01 8.32049429e-01 6.15027010e-01 -9.03154314e-02
-2.76119947e-01 -5.87700903e-01 -6.55016601e-01 -4.92080003e-01
-3.83298695e-02 1.14619803e+00 2.98812360e-01 -3.88066381... | [9.345355987548828, 9.571270942687988] |
c0295d28-d45b-4e4b-8a60-b4253a3fa938 | discriminant-analysis-in-contrasting | 2201.03029 | null | https://arxiv.org/abs/2201.03029v1 | https://arxiv.org/pdf/2201.03029v1.pdf | Discriminant Analysis in Contrasting Dimensions for Polycystic Ovary Syndrome Prognostication | A lot of prognostication methodologies have been formulated for early detection of Polycystic Ovary Syndrome also known as PCOS using Machine Learning. PCOS is a binary classification problem. Dimensionality Reduction methods impact the performance of Machine Learning to a greater extent and using a Supervised Dimensio... | ['Ronald Melwin Laban', 'Raunak Joshi', 'Himanshu Soni', 'Abhishek Gupta'] | 2022-01-09 | null | null | null | null | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [-9.52180475e-02 5.70448264e-02 -2.28604466e-01 -6.52791381e-01
4.09968793e-02 -4.61902678e-01 3.17019016e-01 6.67790115e-01
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-9.16243672e-01 -5.57703972e-01 2.36426204e-01 -5.60730517e-01
-5.39116681e-01 8.42917860e-01 -4.37896073e-01 -1.65280327... | [8.468485832214355, 4.870544910430908] |
ab6d7dbb-da6d-45b1-b0be-69f5ecab4dc1 | a-graph-kernel-based-on-context-vectors-for | null | null | https://www.sciencedirect.com/science/article/pii/S1532046416300053 | https://www.sciencedirect.com/science/article/pii/S1532046416300053 | A graph kernel based on context vectors for extracting drug–drug interactions | The clinical recognition of drug–drug interactions (DDIs) is a crucial issue for both patient safety and health care cost control. Thus there is an urgent need that DDIs be extracted automatically from biomedical literature by text-mining techniques. Although the top-ranking DDIs systems explore various features of tex... | ['Jian Wang', 'Zhihao Yang', 'Yijia Zhang', 'Bo Xu', 'Zhehuan Zhao', 'Hongfei Lin', 'Wei Zheng'] | 2016-03-21 | null | null | null | journal-of-biomedical-informatics-2016-3 | ['drug-drug-interaction-extraction'] | ['natural-language-processing'] | [ 1.82012916e-01 -1.07934847e-01 -4.80586141e-01 -2.12545887e-01
-3.98135632e-01 -4.82717007e-01 4.77925956e-01 1.03216028e+00
-3.23856562e-01 8.05366755e-01 5.57013229e-02 -6.35434270e-01
-6.40637696e-01 -7.24753261e-01 -3.50393444e-01 -7.67945528e-01
-4.02591437e-01 4.96872574e-01 3.24334688e-02 -1.31436978... | [8.361190795898438, 8.639373779296875] |
dd7d272e-3d0a-468a-ae4c-63cfa2448786 | strumming-to-the-beat-audio-conditioned | 2104.02687 | null | https://arxiv.org/abs/2104.02687v1 | https://arxiv.org/pdf/2104.02687v1.pdf | Strumming to the Beat: Audio-Conditioned Contrastive Video Textures | We introduce a non-parametric approach for infinite video texture synthesis using a representation learned via contrastive learning. We take inspiration from Video Textures, which showed that plausible new videos could be generated from a single one by stitching its frames together in a novel yet consistent order. This... | ['Trevor Darrell', 'Alexei A. Efros', 'Andrew Owens', 'Shiry Ginosar', 'Medhini Narasimhan'] | 2021-04-06 | null | null | null | null | ['texture-synthesis'] | ['computer-vision'] | [ 6.03845417e-01 -3.37020308e-02 -2.30582446e-01 -2.54077613e-01
-1.15478659e+00 -7.58570850e-01 1.01366282e+00 -3.41614068e-01
-3.41103151e-02 6.75494611e-01 6.00850940e-01 1.16370119e-01
1.24474086e-01 -4.85069633e-01 -1.26927805e+00 -7.02439904e-01
-3.32295209e-01 2.13812754e-01 3.40353549e-01 -1.67947784... | [10.87969970703125, -0.670634925365448] |
a76da5a6-c05d-4ebe-b0f6-b88c1ef0a9d4 | high-resolution-breast-cancer-screening-with | 1703.07047 | null | http://arxiv.org/abs/1703.07047v3 | http://arxiv.org/pdf/1703.07047v3.pdf | High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks | Advances in deep learning for natural images have prompted a surge of
interest in applying similar techniques to medical images. The majority of the
initial attempts focused on replacing the input of a deep convolutional neural
network with a medical image, which does not take into consideration the
fundamental differe... | ['Ujas Parikh', 'Nan Wu', 'Laura Heacock', 'Kyunghyun Cho', 'Krzysztof J. Geras', 'Stacey Wolfson', 'Linda Moy', 'Eric Kim', 'S. Gene Kim', 'Yiqiu Shen'] | 2017-03-21 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 5.14117002e-01 3.69507611e-01 8.41726065e-02 -5.48503399e-01
-8.68486404e-01 -2.57236481e-01 3.64450127e-01 3.52536321e-01
-7.07537234e-01 3.74496788e-01 2.41680279e-01 -5.68491161e-01
-1.59292549e-01 -9.86666739e-01 -7.87159443e-01 -5.20762503e-01
-8.63240585e-02 4.56321359e-01 4.88945127e-01 -2.09329352... | [15.018254280090332, -2.4602878093719482] |
58735911-0782-4b15-ab81-1736360e0f92 | end-to-end-dense-video-captioning-with-masked | 1804.00819 | null | http://arxiv.org/abs/1804.00819v1 | http://arxiv.org/pdf/1804.00819v1.pdf | End-to-End Dense Video Captioning with Masked Transformer | Dense video captioning aims to generate text descriptions for all events in
an untrimmed video. This involves both detecting and describing events.
Therefore, all previous methods on dense video captioning tackle this problem
by building two models, i.e. an event proposal and a captioning model, for
these two sub-probl... | ['Richard Socher', 'Yingbo Zhou', 'Luowei Zhou', 'Caiming Xiong', 'Jason J. Corso'] | 2018-04-03 | end-to-end-dense-video-captioning-with-masked-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Zhou_End-to-End_Dense_Video_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhou_End-to-End_Dense_Video_CVPR_2018_paper.pdf | cvpr-2018-6 | ['dense-video-captioning'] | ['computer-vision'] | [ 3.81437957e-01 3.48908931e-01 -1.82941213e-01 -3.49043697e-01
-9.61345851e-01 -3.03878725e-01 7.85239816e-01 -1.74574241e-01
-2.63268858e-01 7.02310681e-01 8.56150627e-01 1.13394998e-01
5.53153992e-01 -5.36789834e-01 -1.05111241e+00 -5.95133305e-01
1.96494851e-02 3.47481400e-01 1.61973551e-01 1.81814075... | [10.473143577575684, 0.6866409778594971] |
139320fe-3fd9-44c6-82f4-13a8dbbfa66a | online-multi-object-tracking-via-structural | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Yoon_Online_Multi-Object_Tracking_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Yoon_Online_Multi-Object_Tracking_CVPR_2016_paper.pdf | Online Multi-Object Tracking via Structural Constraint Event Aggregation | Multi-object tracking (MOT) becomes more challenging when objects of interest have similar appearances. In that case, the motion cues are particularly useful for discriminating multiple objects. However, for online 2D MOT in scenes acquired from moving cameras, observable motion cues are complicated by global camera mo... | ['Kuk-Jin Yoon', 'Ming-Hsuan Yang', 'Chang-Ryeol Lee', 'Ju Hong Yoon'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['online-multi-object-tracking'] | ['computer-vision'] | [ 1.84348449e-01 -6.66271031e-01 1.96842961e-02 -2.01780722e-02
-4.12185341e-01 -5.18053770e-01 3.02714914e-01 3.56249332e-01
-4.09916610e-01 7.59168327e-01 -3.81753623e-01 2.86261618e-01
-4.52028394e-01 -4.43716347e-01 -6.38497829e-01 -8.98507535e-01
-1.06759198e-01 3.15078825e-01 8.34154069e-01 3.62608582... | [6.4921698570251465, -2.0279595851898193] |
e03bc3e9-ebdd-4134-9a07-5607745d385b | review-dual-benefits-compositions-recommended | 1905.12405 | null | https://arxiv.org/abs/1905.12405v1 | https://arxiv.org/pdf/1905.12405v1.pdf | Review: dual benefits, compositions, recommended storage, and intake duration of mother's milk | Breastfeeding benefits both infants and mothers. Nutrients in mother's milk help protect infants from multiple diseases including infections, cancers, diabetes, gastrointestinal and respiratory diseases. We performed literature mining on 31,496 mother's-milk-related abstracts from PubMed and the results suggest the nee... | ['Suryani Lukman', 'Ammu Prasanna Kumar'] | 2019-05-28 | null | null | null | null | ['literature-mining'] | ['natural-language-processing'] | [ 2.16944277e-01 6.50160164e-02 -1.05837488e+00 -4.78363782e-01
1.09787874e-01 -5.57414830e-01 -2.41255462e-01 1.26493990e+00
-1.71693102e-01 4.76843894e-01 6.24540567e-01 -7.88295388e-01
7.44783506e-02 -8.27406168e-01 -1.19425869e+00 -7.44167089e-01
-2.29702946e-02 -1.27084866e-01 -5.69062047e-02 2.28523910... | [13.846563339233398, 3.0104565620422363] |
1bc0946b-c06d-4580-9afb-dba8c137d0c7 | is-chatgpt-equipped-with-emotional-dialogue | 2304.09582 | null | https://arxiv.org/abs/2304.09582v1 | https://arxiv.org/pdf/2304.09582v1.pdf | Is ChatGPT Equipped with Emotional Dialogue Capabilities? | This report presents a study on the emotional dialogue capability of ChatGPT, an advanced language model developed by OpenAI. The study evaluates the performance of ChatGPT on emotional dialogue understanding and generation through a series of experiments on several downstream tasks. Our findings indicate that while Ch... | ['Bing Qin', 'Yanpeng Tong', 'Shilong Wang', 'Xin Lu', 'Yanyan Zhao', 'Weixiang Zhao'] | 2023-04-19 | null | null | null | null | ['dialogue-understanding'] | ['natural-language-processing'] | [-0.4401266 1.021031 -0.04135167 -0.49719507 -0.5560196 -0.4087794
0.6535852 0.00867274 0.12772016 1.1058378 0.6501693 -0.25456086
0.41581476 -0.49323484 0.20083073 -0.22240563 -0.29381955 0.5722504
-0.5073857 -0.89335036 0.23562938 -0.05700759 -0.845816 0.64031154
0.85751545 0.3792336 -0.28... | [12.98897647857666, 7.800416946411133] |
e4527cbf-012f-4bea-8f53-ca22e1a23209 | toward-3d-object-reconstruction-from-stereo | 1910.08223 | null | https://arxiv.org/abs/1910.08223v2 | https://arxiv.org/pdf/1910.08223v2.pdf | Toward 3D Object Reconstruction from Stereo Images | Inferring the 3D shape of an object from an RGB image has shown impressive results, however, existing methods rely primarily on recognizing the most similar 3D model from the training set to solve the problem. These methods suffer from poor generalization and may lead to low-quality reconstructions for unseen objects. ... | ['Shangchen Zhou', 'Xiaoshuai Sun', 'Wenxiu Sun', 'Hongxun Yao', 'Haozhe Xie', 'Shengping Zhang'] | 2019-10-18 | null | null | null | null | ['3d-object-reconstruction'] | ['computer-vision'] | [ 9.24324710e-03 -1.60276145e-01 4.77283448e-01 -4.48691666e-01
-4.85445023e-01 -6.18531764e-01 5.01289070e-01 -4.52901959e-01
-1.16598204e-01 4.10556614e-01 3.38981226e-02 -4.70397174e-02
7.56145716e-02 -8.35297227e-01 -1.01544559e+00 -7.52970397e-01
5.43893099e-01 7.93070614e-01 3.19748938e-01 9.15873330... | [8.543987274169922, -2.857006788253784] |
b9ced89a-8695-48b9-a9dc-20b308b1c6d3 | bayesian-imbalanced-regression-debiasing | null | null | https://openreview.net/forum?id=IeYEepOLsFT | https://openreview.net/pdf?id=IeYEepOLsFT | Bayesian Imbalanced Regression Debiasing | Imbalanced regression, where the training data has an uneven distribution on its range, is widely encountered in the real world, e.g., age estimation (uni-dimensional regression) and pose estimation (multi-dimensional regression). Compared to imbalanced and long-tailed classification, imbalanced regression has its uniq... | ['Ziwei Liu', 'Cunjun Yu', 'Mingyuan Zhang', 'Jiawei Ren'] | 2021-09-29 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [ 9.36704725e-02 -2.60318667e-01 -5.44449210e-01 -4.93590534e-01
-1.00126565e+00 -2.75419414e-01 4.02966827e-01 1.47109643e-01
-2.56341964e-01 1.08218896e+00 -1.61716625e-01 -2.21885875e-01
-3.80177736e-01 -5.77783287e-01 -6.35677934e-01 -1.00084674e+00
1.38014227e-01 7.74666667e-01 -1.30663872e-01 6.61903322... | [8.425772666931152, 4.114441394805908] |
5eabcab0-9dd3-4a28-9f50-f264ddf94766 | an-analysis-of-abusive-language-data | null | null | https://aclanthology.org/2022.games-1.1 | https://aclanthology.org/2022.games-1.1.pdf | An Analysis of Abusive Language Data Collected through a Game with a Purpose | In this work we present an analysis of abusive language annotations collected through a 3D video game. With this approach, we are able to involve in the annotation teenagers, i.e. typical targets of cyberbullying, whose data are usually not available for research purposes. Using the game in the framework of educational... | ['Sara Tonelli', 'Federico Bonetti'] | null | null | null | null | games-lrec-2022-6 | ['abusive-language'] | ['natural-language-processing'] | [-2.26387918e-01 5.68140149e-01 -7.04996660e-02 -2.92642355e-01
-5.21717966e-01 -8.64026845e-01 5.02286434e-01 6.93848610e-01
-5.98648667e-01 5.40055275e-01 3.00987899e-01 -1.29185095e-01
6.64383993e-02 -7.66104341e-01 -2.46703073e-01 -4.40093160e-01
-3.61301042e-02 4.48999435e-01 5.81876040e-01 -3.84409577... | [8.714133262634277, 10.514253616333008] |
a368732b-a99f-47d0-8570-903d6a32bbb5 | spatio-temporal-dynamic-inference-network-for | 2108.11743 | null | https://arxiv.org/abs/2108.11743v1 | https://arxiv.org/pdf/2108.11743v1.pdf | Spatio-Temporal Dynamic Inference Network for Group Activity Recognition | Group activity recognition aims to understand the activity performed by a group of people. In order to solve it, modeling complex spatio-temporal interactions is the key. Previous methods are limited in reasoning on a predefined graph, which ignores the inherent person-specific interaction context. Moreover, they adopt... | ['Mang Wang', 'Dong Ni', 'Hangjie Yuan'] | 2021-08-26 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Yuan_Spatio-Temporal_Dynamic_Inference_Network_for_Group_Activity_Recognition_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Yuan_Spatio-Temporal_Dynamic_Inference_Network_for_Group_Activity_Recognition_ICCV_2021_paper.pdf | iccv-2021-1 | ['group-activity-recognition'] | ['computer-vision'] | [ 1.36796266e-01 -1.83974504e-02 -8.49953666e-02 -3.61782670e-01
1.53340930e-02 -9.39433724e-02 7.30629623e-01 2.27130756e-01
-3.63157243e-01 4.97526169e-01 3.72322828e-01 -2.44903490e-02
-6.07817292e-01 -1.21610105e+00 -2.98072904e-01 -5.47170460e-01
-1.33785605e-01 5.48789680e-01 3.67136389e-01 -2.91840043... | [8.092145919799805, 0.6646749973297119] |
d9e27c6e-e609-46fa-83bb-8a57122c0eb2 | 2d-human-pose-estimation-new-benchmark-and | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Andriluka_2D_Human_Pose_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Andriluka_2D_Human_Pose_2014_CVPR_paper.pdf | 2D Human Pose Estimation: New Benchmark and State of the Art Analysis | Human pose estimation has made significant progress during the last years. However current datasets are limited in their coverage of the overall pose estimation challenges. Still these serve as the common sources to evaluate, train and compare different models on. In this paper we introduce a novel benchmark "MPII Huma... | ['Peter Gehler', 'Mykhaylo Andriluka', 'Leonid Pishchulin', 'Bernt Schiele'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['2d-human-pose-estimation', 'art-analysis'] | ['computer-vision', 'computer-vision'] | [ 7.00947270e-02 -9.48066115e-02 -3.37315708e-01 -3.36395383e-01
-5.79902589e-01 -5.61145186e-01 7.03908801e-01 -2.23275155e-01
-3.42715979e-01 7.23095357e-01 7.36079454e-01 4.97310162e-01
8.94784331e-02 -9.02390182e-02 -3.46179456e-01 -4.22989190e-01
-4.03410137e-01 7.94621527e-01 2.31772885e-01 -3.77630174... | [6.968200206756592, -0.7838699817657471] |
88424a2b-df07-414a-9cd5-88a806a5dda9 | in-neural-machine-translation-what-does | null | null | https://aclanthology.org/2020.acl-main.688 | https://aclanthology.org/2020.acl-main.688.pdf | In Neural Machine Translation, What Does Transfer Learning Transfer? | Transfer learning improves quality for low-resource machine translation, but it is unclear what exactly it transfers. We perform several ablation studies that limit information transfer, then measure the quality impact across three language pairs to gain a black-box understanding of transfer learning. Word embeddings p... | ['Kenneth Heafield', 'Nikolay Bogoychev', 'Alham Fikri Aji', 'Rico Sennrich'] | 2020-07-01 | null | null | null | acl-2020-6 | ['learning-word-embeddings'] | ['methodology'] | [ 2.13254988e-01 1.00668557e-01 -3.94250959e-01 -1.93654448e-01
-1.31108832e+00 -9.10236239e-01 6.05898619e-01 -6.51274547e-02
-6.01294041e-01 1.11493015e+00 6.40355527e-01 -8.49682570e-01
3.45740527e-01 -8.45124960e-01 -1.02876329e+00 -3.34977001e-01
4.75616418e-02 6.82325423e-01 -6.24031499e-02 -5.72202682... | [11.582469940185547, 10.232970237731934] |
fbd81dac-65cf-459c-9b28-d551ce1cbc4d | the-autofeat-python-library-for-automatic | 1901.07329 | null | https://arxiv.org/abs/1901.07329v4 | https://arxiv.org/pdf/1901.07329v4.pdf | The autofeat Python Library for Automated Feature Engineering and Selection | This paper describes the autofeat Python library, which provides scikit-learn style linear regression and classification models with automated feature engineering and selection capabilities. Complex non-linear machine learning models, such as neural networks, are in practice often difficult to train and even harder to ... | ['Robert Pack', 'Franziska Horn', 'Michael Rieger'] | 2019-01-22 | null | null | null | null | ['automated-feature-engineering'] | ['methodology'] | [-1.99242294e-01 -2.25130841e-01 -2.63154179e-01 -7.32181370e-01
-5.47606111e-01 -6.21111214e-01 2.97781914e-01 4.07669306e-01
-2.71728393e-02 8.70372653e-01 -1.35277689e-01 -6.93014681e-01
-4.81581807e-01 -7.64864326e-01 -7.12447047e-01 -5.69383323e-01
-1.96652576e-01 4.40507561e-01 -1.35972798e-01 7.61587080... | [8.192610740661621, 4.864131450653076] |
4f14df83-df82-44ba-9203-50bd39a3de1b | cross-region-domain-adaptation-for-class | 2109.06422 | null | https://arxiv.org/abs/2109.06422v2 | https://arxiv.org/pdf/2109.06422v2.pdf | Cross-Region Domain Adaptation for Class-level Alignment | Semantic segmentation requires a lot of training data, which necessitates costly annotation. There have been many studies on unsupervised domain adaptation (UDA) from one domain to another, e.g., from computer graphics to real images. However, there is still a gap in accuracy between UDA and supervised training on nati... | ['Takayuki Okatani', 'Masanori Suganuma', 'Xing Liu', 'Zhijie Wang'] | 2021-09-14 | null | null | null | null | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 5.43661654e-01 1.72136694e-01 1.94626246e-02 -4.97885853e-01
-8.59182239e-01 -9.88196313e-01 6.64370179e-01 -3.03777575e-01
-3.16198707e-01 7.99737573e-01 -3.41651887e-01 -2.21094534e-01
2.94216186e-01 -7.12091148e-01 -8.74728322e-01 -8.90593112e-01
7.00362563e-01 5.62508225e-01 6.81271672e-01 -5.62047176... | [9.713386535644531, 1.4660032987594604] |
18f3dccd-3346-43a8-a9bc-11d4d97e22a0 | exploring-zero-and-few-shot-techniques-for | 2305.07157 | null | https://arxiv.org/abs/2305.07157v1 | https://arxiv.org/pdf/2305.07157v1.pdf | Exploring Zero and Few-shot Techniques for Intent Classification | Conversational NLU providers often need to scale to thousands of intent-classification models where new customers often face the cold-start problem. Scaling to so many customers puts a constraint on storage space as well. In this paper, we explore four different zero and few-shot intent classification approaches with t... | ['Mitul Tiwari', 'Prashil Tumbade', 'Quaizar Vohra', 'Soham Parikh'] | 2023-05-11 | null | null | null | null | ['intent-classification'] | ['natural-language-processing'] | [-2.09323496e-01 -2.61164367e-01 -5.05668163e-01 -7.85578549e-01
-1.09224963e+00 -4.87804383e-01 6.90490723e-01 1.97801031e-02
-4.92194116e-01 6.21276796e-01 5.17342389e-01 -5.66722691e-01
2.21799716e-01 -4.31895614e-01 -1.32817179e-01 -2.42747396e-01
3.76880914e-02 1.14310932e+00 -6.84724525e-02 -4.91772443... | [12.165809631347656, 7.7053446769714355] |
b2744136-fdc2-4f5a-8d9f-54c1e9474ce5 | joint-face-hallucination-and-deblurring-via | 1811.09019 | null | http://arxiv.org/abs/1811.09019v1 | http://arxiv.org/pdf/1811.09019v1.pdf | Joint Face Hallucination and Deblurring via Structure Generation and Detail Enhancement | We address the problem of restoring a high-resolution face image from a
blurry low-resolution input. This problem is difficult as super-resolution and
deblurring need to be tackled simultaneously. Moreover, existing algorithms
cannot handle face images well as low-resolution face images do not have much
texture which i... | ['Ming-Hsuan Yang', 'Lijun Gong', 'Linchao Bao', 'Jinshan Pan', 'Yibing Song', 'Shengfeng He', 'Qingxiong Yang', 'Jiawei Zhang'] | 2018-11-22 | null | null | null | null | ['face-hallucination'] | ['computer-vision'] | [ 4.70084995e-01 -2.86780387e-01 2.66893476e-01 -2.50716746e-01
-6.98554814e-01 -1.59324303e-01 3.23989719e-01 -8.90324295e-01
-1.10994399e-01 8.33090425e-01 4.49482828e-01 3.02889526e-01
-1.73875704e-01 -7.08787382e-01 -6.95750117e-01 -8.44239175e-01
4.47283119e-01 -1.23968750e-01 -2.47017026e-01 -2.94907123... | [12.814841270446777, -0.048775095492601395] |
c3ff6734-e9c7-4068-927f-52b8275a35dd | alignment-enhancement-network-for-fine | null | null | https://dl.acm.org/doi/abs/10.1145/3446208 | https://dl.acm.org/doi/abs/10.1145/3446208 | Alignment Enhancement Network for Fine-grained Visual Categorization | Fine-grained visual categorization (FGVC) aims to automatically recognize objects from different sub-ordinate categories. Despite attracting considerable attention from both academia and industry, it remains a challenging task due to subtle visual differences among different classes. Cross-layer feature aggregation an... | ['Yutao Hu'] | 2021-03-01 | null | null | null | null | ['fine-grained-visual-categorization'] | ['computer-vision'] | [ 3.20005924e-01 -5.51124871e-01 -5.06704524e-02 -5.33376515e-01
-4.21341538e-01 -4.85759526e-01 6.42115891e-01 1.53465956e-01
-4.26013142e-01 3.49750757e-01 2.40409672e-01 1.80615321e-01
-4.89556462e-01 -6.85233176e-01 -6.08308494e-01 -9.23167408e-01
2.02840224e-01 -1.29417226e-01 5.83991826e-01 -6.56967983... | [9.677705764770508, 1.9554895162582397] |
d92d91d5-0474-4d13-ad55-fd92c4a9f508 | on-the-optimization-landscape-of-burer | 2302.10963 | null | https://arxiv.org/abs/2302.10963v1 | https://arxiv.org/pdf/2302.10963v1.pdf | On the Optimization Landscape of Burer-Monteiro Factorization: When do Global Solutions Correspond to Ground Truth? | In low-rank matrix recovery, the goal is to recover a low-rank matrix, given a limited number of linear and possibly noisy measurements. Low-rank matrix recovery is typically solved via a nonconvex method called Burer-Monteiro factorization (BM). If the rank of the ground truth is known, BM is free of sub-optimal local... | ['Salar Fattahi', 'Jianhao Ma'] | 2023-02-21 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 2.32881546e-01 4.27545726e-01 -1.25314286e-02 3.94752294e-01
-1.26472366e+00 -9.30181742e-01 6.49843663e-02 -1.99488923e-01
-4.12887940e-03 7.89175868e-01 4.14163113e-01 -5.81360422e-02
-5.59781373e-01 -4.33186680e-01 -1.00251377e+00 -1.07140517e+00
-2.22354800e-01 5.06944835e-01 -3.35163683e-01 -1.97292417... | [6.944736003875732, 4.667161464691162] |
3bde5d6f-adac-48d3-94c5-5b4833ab94ab | generating-faithful-synthetic-data-with-large | 2305.15041 | null | https://arxiv.org/abs/2305.15041v1 | https://arxiv.org/pdf/2305.15041v1.pdf | Generating Faithful Synthetic Data with Large Language Models: A Case Study in Computational Social Science | Large Language Models (LLMs) have democratized synthetic data generation, which in turn has the potential to simplify and broaden a wide gamut of NLP tasks. Here, we tackle a pervasive problem in synthetic data generation: its generative distribution often differs from the distribution of real-world data researchers ca... | ['Robert West', 'Ashton Anderson', 'Martin Josifoski', 'Akhil Arora', 'Manoel Horta Ribeiro', 'Veniamin Veselovsky'] | 2023-05-24 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation', 'sarcasm-detection'] | ['medical', 'miscellaneous', 'natural-language-processing'] | [ 1.77178398e-01 5.47156155e-01 -2.39885315e-01 -1.94362313e-01
-8.64818692e-01 -7.45906293e-01 9.64939594e-01 2.77413070e-01
-5.04067004e-01 9.06341612e-01 8.40158343e-01 -2.87165642e-01
2.06298843e-01 -8.59221458e-01 -4.89296436e-01 -2.93364614e-01
5.87047219e-01 6.07597470e-01 -2.48317868e-01 -4.51530933... | [11.509605407714844, 8.979785919189453] |
0c093eaa-ecd2-4f0a-a848-9f0735ddb284 | pointview-gcn-3d-shape-classification-with | null | null | https://ieeexplore.ieee.org/abstract/document/9506426 | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9506426 | POINTVIEW-GCN: 3D SHAPE CLASSIFICATION WITH MULTI-VIEW POINT CLOUDS | We address 3D shape classification with partial point cloud inputs captured from multiple viewpoints around the object.
Different from existing methods that perform classification on the complete point cloud by first registering multi-view capturing, we propose PointView-GCN with multi-level Graph Convolutional Networ... | ['Alessio Del Bue', 'Yiming Wang', 'Seyed Saber Mohammadi'] | 2021-09-22 | null | null | null | ieee-international-conference-on-image-5 | ['3d-shape-retrieval', '3d-point-cloud-classification'] | ['computer-vision', 'computer-vision'] | [-3.89669240e-01 -2.50286669e-01 6.60961643e-02 -5.03648818e-01
-3.82500350e-01 -8.18585992e-01 8.36601734e-01 -1.86776444e-02
2.65390068e-01 -2.06645072e-01 -3.80748093e-01 -2.34133214e-01
-1.82027042e-01 -1.21227479e+00 -9.50204194e-01 -4.22740370e-01
1.55601814e-01 9.43383753e-01 3.78291845e-01 -4.77368146... | [8.092986106872559, -3.5409929752349854] |
297c7cf9-cf7c-4d8c-b262-b83f94bfd362 | autoregressive-gan-for-semantic-unconditional | 2211.00987 | null | https://arxiv.org/abs/2211.00987v2 | https://arxiv.org/pdf/2211.00987v2.pdf | Autoregressive GAN for Semantic Unconditional Head Motion Generation | In this work, we address the task of unconditional head motion generation to animate still human faces in a low-dimensional semantic space from a single reference pose. Different from traditional audio-conditioned talking head generation that seldom puts emphasis on realistic head motions, we devise a GAN-based archite... | ['Dominique Vaufreydaz', 'Stéphane Lathuilière', 'Xavier Alameda-Pineda', 'Louis Airale'] | 2022-11-02 | null | null | null | null | ['talking-head-generation'] | ['computer-vision'] | [ 4.69038822e-03 5.32340467e-01 2.68347025e-01 -4.33720291e-01
-1.11973882e+00 -8.74400213e-02 8.62187684e-01 -8.65317702e-01
-4.77776527e-02 8.78308833e-01 5.81206739e-01 3.18401963e-01
3.88981313e-01 -4.54521090e-01 -6.33343101e-01 -7.76663005e-01
7.10127503e-02 3.60929966e-01 -1.48552703e-02 -2.21840411... | [13.196324348449707, -0.4323963224887848] |
ca22e54f-e926-49b4-b90b-a4453a49ab79 | representation-learning-for-appliance | 2209.03759 | null | https://arxiv.org/abs/2209.03759v1 | https://arxiv.org/pdf/2209.03759v1.pdf | Representation Learning for Appliance Recognition: A Comparison to Classical Machine Learning | Non-intrusive load monitoring (NILM) aims at energy consumption and appliance state information retrieval from aggregated consumption measurements, with the help of signal processing and machine learning algorithms. Representation learning with deep neural networks is successfully applied to several related disciplines... | ['Hans-Arno Jacobsen', 'Daniel Jorde', 'Matthias Kahl'] | 2022-08-26 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 2.96036750e-01 -1.30227581e-02 -1.52016848e-01 -4.26272929e-01
-9.99907970e-01 -2.16956556e-01 5.09347618e-01 4.07694668e-01
-6.14886805e-02 4.60909218e-01 1.72216654e-01 -1.59743279e-01
-4.28050309e-01 -7.98947752e-01 -4.91633624e-01 -7.50977457e-01
-1.80680245e-01 4.69967157e-01 -3.93551350e-01 -1.42148450... | [16.06442642211914, 7.581260681152344] |
be09afd9-d604-4455-8996-59ca280c6346 | semi-supervised-single-view-3d-reconstruction | 2209.15383 | null | https://arxiv.org/abs/2209.15383v1 | https://arxiv.org/pdf/2209.15383v1.pdf | Semi-Supervised Single-View 3D Reconstruction via Prototype Shape Priors | The performance of existing single-view 3D reconstruction methods heavily relies on large-scale 3D annotations. However, such annotations are tedious and expensive to collect. Semi-supervised learning serves as an alternative way to mitigate the need for manual labels, but remains unexplored in 3D reconstruction. Inspi... | ['Yu-Gang Jiang', 'Zuxuan Wu', 'Hengduo Li', 'Zhen Xing'] | 2022-09-30 | null | null | null | null | ['single-view-3d-reconstruction', 'semi-supervised-image-classification', 'object-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.88695058e-01 4.29217130e-01 -2.53391117e-01 -3.52522671e-01
-9.69109476e-01 -7.50396132e-01 7.57150829e-01 -1.35908023e-01
-1.20708473e-01 3.20151508e-01 2.97385395e-01 -1.34261027e-01
2.07359746e-01 -6.56767190e-01 -8.37465048e-01 -4.94224608e-01
4.25779551e-01 7.14248240e-01 3.98206294e-01 -1.11768097... | [8.440689086914062, -3.0583293437957764] |
e9bcdec2-3a64-48f7-9ee3-55a0ed279ea6 | anticipating-traffic-accidents-with-adaptive | 1804.02675 | null | http://arxiv.org/abs/1804.02675v1 | http://arxiv.org/pdf/1804.02675v1.pdf | Anticipating Traffic Accidents with Adaptive Loss and Large-scale Incident DB | In this paper, we propose a novel approach for traffic accident anticipation
through (i) Adaptive Loss for Early Anticipation (AdaLEA) and (ii) a
large-scale self-annotated incident database for anticipation. The proposed
AdaLEA allows a model to gradually learn an earlier anticipation as training
progresses. The loss ... | ['Yoshimitsu Aoki', 'Tomoyuki Suzuki', 'Hirokatsu Kataoka', 'Yutaka Satoh'] | 2018-04-08 | anticipating-traffic-accidents-with-adaptive-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Suzuki_Anticipating_Traffic_Accidents_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Suzuki_Anticipating_Traffic_Accidents_CVPR_2018_paper.pdf | cvpr-2018-6 | ['accident-anticipation'] | ['computer-vision'] | [ 6.15313426e-02 6.58639148e-02 -1.11200735e-01 -4.84723777e-01
-1.42317092e+00 -2.28584856e-02 3.73226881e-01 3.12102765e-01
-9.23865736e-01 3.89736295e-01 1.96319476e-01 -3.65617365e-01
-6.84211403e-02 -4.09265548e-01 -7.72086203e-01 -4.87750322e-01
-7.24178731e-01 6.20882392e-01 8.96181226e-01 -2.00018659... | [7.333222389221191, 0.2565709054470062] |
5c9be18a-5b69-41f3-97cd-5184a3fe6a38 | knowledge-representation-via-joint-learning | 1609.07075 | null | http://arxiv.org/abs/1609.07075v1 | http://arxiv.org/pdf/1609.07075v1.pdf | Knowledge Representation via Joint Learning of Sequential Text and Knowledge Graphs | Textual information is considered as significant supplement to knowledge
representation learning (KRL). There are two main challenges for constructing
knowledge representations from plain texts: (1) How to take full advantages of
sequential contexts of entities in plain texts for KRL. (2) How to dynamically
select thos... | ['Ruobing Xie', 'Maosong Sun', 'Zhiyuan Liu', 'Jiawei Wu'] | 2016-09-22 | null | null | null | null | ['triple-classification'] | ['graphs'] | [ 4.79471087e-02 3.32907468e-01 -5.75654626e-01 -2.84807056e-01
-8.29155147e-01 -2.97538251e-01 3.63244534e-01 1.99578702e-01
-4.09014165e-01 8.35995972e-01 8.50949347e-01 -1.23343341e-01
-1.86229348e-01 -1.00543964e+00 -8.75934899e-01 -3.25300157e-01
5.00341840e-02 3.66828054e-01 2.70978600e-01 -2.55527407... | [9.303086280822754, 8.404169082641602] |
cea17c8d-d17c-4e23-8d68-75599ea53d9d | efficient-dense-point-cloud-object | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Kejie_Li_Efficient_Dense_Point_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Kejie_Li_Efficient_Dense_Point_ECCV_2018_paper.pdf | Efficient Dense Point Cloud Object Reconstruction using Deformation Vector Fields | Most existing CNN-based methods for single-view 3D object reconstruction represent a 3D object as either a 3D voxel occupancy grid or multiple depth-mask image pairs. However, these representations are inefficient since empty voxels or background pixels are wasteful. We propose a novel approach that addresses this limi... | ['Huangying Zhan', 'Trung Pham', 'Kejie Li', 'Ian Reid'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['3d-object-reconstruction'] | ['computer-vision'] | [ 3.05312634e-01 1.79433763e-01 7.65808299e-02 -5.50731897e-01
-7.58697808e-01 -3.49478394e-01 3.87541503e-01 -3.32588226e-01
-9.75359902e-02 3.57810318e-01 4.45498116e-02 2.45908871e-01
2.50797749e-01 -9.67815876e-01 -1.06049538e+00 -5.50391555e-01
4.65045363e-01 7.13375807e-01 4.80535060e-01 2.44817451... | [8.906929969787598, -3.1673736572265625] |
2132371e-dd4e-4121-8438-96e7755a4c80 | transcending-scaling-laws-with-0-1-extra | 2210.11399 | null | https://arxiv.org/abs/2210.11399v2 | https://arxiv.org/pdf/2210.11399v2.pdf | Transcending Scaling Laws with 0.1% Extra Compute | Scaling language models improves performance but comes with significant computational costs. This paper proposes UL2R, a method that substantially improves existing language models and their scaling curves with a relatively tiny amount of extra compute. The key idea is to continue training a state-of-the-art large lang... | ['Mostafa Dehghani', 'Quoc V. Le', 'Neil Houlsby', 'Slav Petrov', 'Donald Metzler', 'Denny Zhou', 'Aakanksha Chowdhery', 'Jinfeng Rao', 'Huaixiu Steven Zheng', 'Xavier Garcia', 'Siamak Shakeri', 'David R. So', 'Vinh Q. Tran', 'Hyung Won Chung', 'Jason Wei', 'Yi Tay'] | 2022-10-20 | null | null | null | null | ['multi-task-language-understanding', 'gsm8k', 'cross-lingual-question-answering', 'arithmetic-reasoning'] | ['methodology', 'natural-language-processing', 'natural-language-processing', 'reasoning'] | [-2.35596418e-01 6.89073280e-02 -1.38920248e-01 -7.38584325e-02
-1.70190620e+00 -8.18260789e-01 5.59195817e-01 -1.28790468e-01
-6.21111333e-01 7.48602390e-01 3.22201461e-01 -8.83169711e-01
-3.34145539e-02 -6.93346143e-01 -9.42341208e-01 -2.82767802e-01
2.36387298e-01 6.64896011e-01 -7.16850907e-02 -7.25613832... | [10.74299430847168, 8.218887329101562] |
623becb0-ad94-4cd1-afcc-dae02e89e2f9 | wav2seq-pre-training-speech-to-text-encoder | 2205.01086 | null | https://arxiv.org/abs/2205.01086v1 | https://arxiv.org/pdf/2205.01086v1.pdf | Wav2Seq: Pre-training Speech-to-Text Encoder-Decoder Models Using Pseudo Languages | We introduce Wav2Seq, the first self-supervised approach to pre-train both parts of encoder-decoder models for speech data. We induce a pseudo language as a compact discrete representation, and formulate a self-supervised pseudo speech recognition task -- transcribing audio inputs into pseudo subword sequences. This pr... | ['Yoav Artzi', 'Kilian Q. Weinberger', 'Ryan Mcdonald', 'Kyu Han', 'Shinji Watanabe', 'Kwangyoun Kim', 'Felix Wu'] | 2022-05-02 | null | null | null | null | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 6.52351201e-01 6.33231580e-01 -1.13346905e-01 -8.32846403e-01
-1.84186924e+00 -6.34358108e-01 6.49710298e-01 -2.96381086e-01
-6.01850390e-01 8.50319922e-01 7.83000052e-01 -6.44410789e-01
7.97905266e-01 -8.48613456e-02 -9.11494911e-01 -2.32574418e-01
-1.03827221e-02 9.08928633e-01 -4.77423817e-02 -2.02972740... | [14.491472244262695, 7.099369049072266] |
dce229a6-d8a5-413e-8a01-d9dcf278651f | the-area-under-the-roc-curve-as-a-measure-of | 2009.02400 | null | https://arxiv.org/abs/2009.02400v2 | https://arxiv.org/pdf/2009.02400v2.pdf | The Area Under the ROC Curve as a Measure of Clustering Quality | The Area Under the the Receiver Operating Characteristics (ROC) Curve, referred to as AUC, is a well-known performance measure in the supervised learning domain. Due to its compelling features, it has been employed in a number of studies to evaluate and compare the performance of different classifiers. In this work, we... | ['Ricardo José Gabrielli Barreto Campello', 'Pablo Andretta Jaskowiak', 'Ivan Gesteira Costa'] | 2020-09-04 | null | null | null | null | ['clustering-algorithms-evaluation'] | ['methodology'] | [ 5.86651228e-02 4.98554856e-02 -1.18458852e-01 -3.57040137e-01
-5.64346373e-01 -7.47462273e-01 5.18513620e-01 8.40489089e-01
-6.40851855e-01 4.15806204e-01 -1.83100566e-01 -5.47832787e-01
-5.99101484e-01 -5.98014534e-01 -1.76463917e-01 -9.74250257e-01
-2.60714442e-01 4.25589085e-01 9.28873345e-02 2.94823408... | [7.697799205780029, 4.5229010581970215] |
af52e238-9b61-4762-9a97-7a8c3325c006 | learning-task-aware-energy-disaggregation-a | 2204.06767 | null | https://arxiv.org/abs/2204.06767v2 | https://arxiv.org/pdf/2204.06767v2.pdf | Learning Task-Aware Energy Disaggregation: a Federated Approach | We consider the problem of learning the energy disaggregation signals for residential load data. Such task is referred as non-intrusive load monitoring (NILM), and in order to find individual devices' power consumption profiles based on aggregated meter measurements, a machine learning model is usually trained based on... | ['Yize Chen', 'Ruohong Liu'] | 2022-04-14 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 1.14550836e-01 -3.43076997e-02 -2.86794186e-01 -8.02215755e-01
-1.29372644e+00 -3.99124473e-01 3.35019469e-01 8.83958563e-02
1.14032272e-02 8.79884124e-01 3.69043887e-01 -2.44218810e-03
-1.67114407e-01 -8.27198505e-01 -4.65192646e-01 -1.04962683e+00
-1.45179376e-01 6.47623658e-01 -6.62626088e-01 5.26918292... | [16.030864715576172, 7.560460090637207] |
e27d3688-9343-46dd-a501-4c22285a8182 | a-survey-of-active-learning-algorithms-for | 2104.07784 | null | https://arxiv.org/abs/2104.07784v1 | https://arxiv.org/pdf/2104.07784v1.pdf | A survey of active learning algorithms for supervised remote sensing image classification | Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal datase... | ['Jordi Munoz-Mari', 'Mikhail Kanevski', 'Loris Copa', 'Michele Volpi', 'Devis Tuia'] | 2021-04-15 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 7.13896334e-01 2.39737406e-02 -5.51321387e-01 -6.55238450e-01
-9.66651618e-01 -6.23230278e-01 2.86018342e-01 2.14670539e-01
-4.99267578e-01 1.13247311e+00 -3.33070546e-01 -5.23328781e-01
-7.88287044e-01 -6.96018100e-01 -9.76878256e-02 -1.28586745e+00
-1.84374288e-01 7.45704532e-01 -1.17869295e-01 3.61998141... | [9.817048072814941, -1.5296695232391357] |
2ea300b8-5f88-42a1-98f3-6c27b8911449 | emogen-eliminating-subjective-bias-in | 2307.01229 | null | https://arxiv.org/abs/2307.01229v1 | https://arxiv.org/pdf/2307.01229v1.pdf | EmoGen: Eliminating Subjective Bias in Emotional Music Generation | Music is used to convey emotions, and thus generating emotional music is important in automatic music generation. Previous work on emotional music generation directly uses annotated emotion labels as control signals, which suffers from subjective bias: different people may annotate different emotions on the same music,... | ['Jiang Bian', 'Shikun Zhang', 'Wei Ye', 'Xu Tan', 'Botao Yu', 'Peiling Lu', 'Chenfei Kang'] | 2023-07-03 | null | null | null | null | ['music-generation', 'self-supervised-learning', 'clustering', 'music-generation'] | ['audio', 'computer-vision', 'methodology', 'music'] | [-1.20281659e-01 -7.56047070e-02 -2.01617554e-01 -1.17223382e-01
-6.31141484e-01 -7.04802036e-01 3.54213007e-02 -2.99456298e-01
1.01814605e-01 5.45043051e-01 4.67593223e-01 4.90707457e-01
4.45995145e-02 -8.08635116e-01 -3.17974031e-01 -6.90308809e-01
2.54202396e-01 2.54409850e-01 -7.16442525e-01 -2.80806035... | [15.967302322387695, 5.497159957885742] |
ff1ec6c5-00b4-4a8d-968a-923ab5ccdfc6 | relational-memory-based-knowledge-graph | 1907.06080 | null | https://arxiv.org/abs/1907.06080v2 | https://arxiv.org/pdf/1907.06080v2.pdf | A Relational Memory-based Embedding Model for Triple Classification and Search Personalization | Knowledge graph embedding methods often suffer from a limitation of memorizing valid triples to predict new ones for triple classification and search personalization problems. To this end, we introduce a novel embedding model, named R-MeN, that explores a relational memory network to encode potential dependencies in re... | ['Dai Quoc Nguyen', 'Dinh Phung', 'Tu Dinh Nguyen'] | 2019-07-13 | a-relational-memory-based-embedding-model-for | https://aclanthology.org/2020.acl-main.313 | https://aclanthology.org/2020.acl-main.313.pdf | acl-2020-6 | ['triple-classification'] | ['graphs'] | [-2.40072906e-02 4.42446291e-01 -9.39057410e-01 -3.47342402e-01
-6.37433052e-01 -1.24920301e-01 6.75329626e-01 4.30684566e-01
-5.04955649e-01 6.24092102e-01 5.88348925e-01 -3.47029805e-01
-1.05291203e-01 -1.18758988e+00 -1.06968260e+00 -2.61598617e-01
-6.33625239e-02 7.24342108e-01 7.47512579e-02 -4.92195338... | [8.825661659240723, 7.886193752288818] |
afac8bdd-a8de-4d6c-b945-1c635a1fba70 | generalizing-mlps-with-dropouts-batch | 2108.08186 | null | https://arxiv.org/abs/2108.08186v2 | https://arxiv.org/pdf/2108.08186v2.pdf | Generalizing MLPs With Dropouts, Batch Normalization, and Skip Connections | A multilayer perceptron (MLP) is typically made of multiple fully connected layers with nonlinear activation functions. There have been several approaches to make them better (e.g. faster convergence, better convergence limit, etc.). But the researches lack structured ways to test them. We test different MLP architectu... | ['Taewoon Kim'] | 2021-08-18 | generalizing-mlps-with-dropouts-batch-1 | https://openreview.net/forum?id=XbatFr32NRm | https://openreview.net/pdf?id=XbatFr32NRm | null | ['age-and-gender-classification', 'age-estimation', 'gender-prediction', 'age-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'miscellaneous'] | [-3.25158447e-01 4.50307839e-02 -3.41629446e-01 -8.61956179e-01
-1.19984955e-01 -1.80052943e-04 3.17458391e-01 -1.25674508e-03
-6.94832802e-01 1.12355864e+00 1.97363421e-01 -4.34974313e-01
7.08175078e-02 -8.83793771e-01 -1.00929856e+00 -7.12802112e-01
2.66366340e-02 3.99580002e-01 1.88235492e-01 4.25122172... | [8.888895034790039, 3.5130343437194824] |
d6e90b67-0144-4a94-8c0c-c77fd2158dca | a-computational-acquisition-model-for | 2205.05974 | null | https://arxiv.org/abs/2205.05974v1 | https://arxiv.org/pdf/2205.05974v1.pdf | A Computational Acquisition Model for Multimodal Word Categorization | Recent advances in self-supervised modeling of text and images open new opportunities for computational models of child language acquisition, which is believed to rely heavily on cross-modal signals. However, prior studies have been limited by their reliance on vision models trained on large image datasets annotated wi... | ['Lea Frermann', 'Omri Abend', 'Gabriel Stanovsky', 'Uri Berger'] | 2022-05-12 | null | https://aclanthology.org/2022.naacl-main.280 | https://aclanthology.org/2022.naacl-main.280.pdf | naacl-2022-7 | ['language-acquisition'] | ['natural-language-processing'] | [ 6.52463257e-01 2.68203348e-01 -1.28382310e-01 -6.91647470e-01
-2.77094722e-01 -8.49405468e-01 1.07800579e+00 5.74833632e-01
-5.77156126e-01 1.84086010e-01 3.37846875e-01 -2.82852590e-01
-2.17524678e-01 -5.98505795e-01 -9.18165684e-01 -3.05488884e-01
-9.54029325e-04 6.90040231e-01 1.37332767e-01 9.93778557... | [10.201016426086426, 8.61330795288086] |
9160dbc6-8fb7-41ee-a6aa-37b73d177f4b | evaluating-deep-music-generation-methods | 2201.00052 | null | https://arxiv.org/abs/2201.00052v1 | https://arxiv.org/pdf/2201.00052v1.pdf | Evaluating Deep Music Generation Methods Using Data Augmentation | Despite advances in deep algorithmic music generation, evaluation of generated samples often relies on human evaluation, which is subjective and costly. We focus on designing a homogeneous, objective framework for evaluating samples of algorithmically generated music. Any engineered measures to evaluate generated music... | ['Bjoern W. Schuller', 'Vincent Brisse', 'Najla D. Al Futaisi', 'Alice Baird', 'Georgios Rizos', 'Toby Godwin'] | 2021-12-31 | null | null | null | null | ['music-generation', 'genre-classification', 'music-generation'] | ['audio', 'computer-vision', 'music'] | [ 4.24713314e-01 2.08430558e-01 2.77608540e-02 -2.65724927e-01
-1.05408812e+00 -8.57060850e-01 5.48753619e-01 8.50836858e-02
-1.11043759e-01 6.26894951e-01 3.51695240e-01 4.24797624e-01
-1.98215425e-01 -7.50810504e-01 -3.27641398e-01 -5.95226228e-01
-2.47446653e-02 4.95940119e-01 -4.22447324e-01 -2.90774815... | [15.978036880493164, 5.464735984802246] |
afacbeb3-8741-4258-a9ce-415b3cc36f75 | privacy-preserving-image-queries-for-camera | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Speciale_Privacy_Preserving_Image_Queries_for_Camera_Localization_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Speciale_Privacy_Preserving_Image_Queries_for_Camera_Localization_ICCV_2019_paper.pdf | Privacy Preserving Image Queries for Camera Localization | Augmented/mixed reality and robotic applications are increasingly relying on cloud-based localization services, which require users to upload query images to perform camera pose estimation on a server. This raises significant privacy concerns when consumers use such services in their homes or in confidential industrial... | [' Marc Pollefeys', ' Sudipta N. Sinha', ' Johannes L. Schonberger', 'Pablo Speciale'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['camera-localization'] | ['computer-vision'] | [ 1.36820069e-02 1.61166146e-01 -7.44946348e-03 -2.44830742e-01
-9.20794129e-01 -1.15424764e+00 3.02083969e-01 -9.68389660e-02
-5.18127739e-01 5.40799141e-01 -3.66894990e-01 -2.77843568e-02
2.83945743e-02 -4.77059871e-01 -8.46685290e-01 -9.06929314e-01
-4.99764341e-04 3.94990772e-01 1.01803102e-01 2.40905825... | [7.554337501525879, -2.1682517528533936] |
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