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8d3c1e40-6d92-48fb-ac6c-9f2e43c78750 | tweetnorm_es-an-annotated-corpus-for-spanish | null | null | https://aclanthology.org/L14-1379 | https://aclanthology.org/L14-1379.pdf | TweetNorm\_es: an annotated corpus for Spanish microtext normalization | In this paper we introduce TweetNorm{\_}es, an annotated corpus of tweets in Spanish language, which we make publicly available under the terms of the CC-BY license. This corpus is intended for development and testing of microtext normalization systems. It was created for Tweet-Norm, a tweet normalization workshop and ... | ['I{\\~n}aki San Vicente', "Lluis Padr{\\'o}", "V{\\'\\i}ctor Fresno", 'Pere Comas', 'I{\\~n}aki Alegria', 'Jordi Turmo', 'Pablo Gamallo', 'Nora Aranberri', 'Arkaitz Zubiaga'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['lexical-normalization'] | ['natural-language-processing'] | [ 5.47764637e-02 1.67785436e-01 -2.70622045e-01 -6.77818239e-01
-6.19476676e-01 -4.30097103e-01 1.17376924e+00 7.74743736e-01
-8.65461648e-01 7.75840700e-01 7.27263331e-01 -1.12088069e-01
-1.56243946e-02 -6.84713960e-01 -1.24420762e-01 -8.46763477e-02
4.44746166e-01 5.90610921e-01 2.29965776e-01 -7.38332212... | [10.0720796585083, 9.961713790893555] |
61ccb456-3da8-43ac-9530-9c0664235ec2 | data-oriented-scene-recognition | null | null | https://openreview.net/forum?id=Sb4hTI15hUZ | https://openreview.net/pdf?id=Sb4hTI15hUZ | Data-oriented Scene Recognition | Most deep learning backbones are evaluated on ImageNet. Using scenery images as an example, we conducted extensive experiments to demonstrate the widely accepted principles in network design may result in dramatic performance differences when the data is altered. Exploratory experiments are engaged to explain the under... | ['Jian Xia', 'Jianfang Shi', 'Chaoning Zhang', 'Xiaohui Yuan', 'Zhinan Qiao'] | 2021-09-29 | null | null | null | null | ['scene-recognition'] | ['computer-vision'] | [ 5.17374724e-02 2.01247662e-01 -5.06544439e-03 -5.70352018e-01
6.85029328e-01 -2.53866106e-01 3.60976815e-01 -2.69423574e-01
-8.71031523e-01 8.27676654e-01 -2.20026225e-02 -4.23986018e-01
-2.46923909e-01 -1.01022589e+00 -5.78688741e-01 -5.76156199e-01
-1.12340398e-01 -1.99613959e-01 5.66783965e-01 -2.71638662... | [9.078526496887207, 2.34324049949646] |
f7e5d47c-182d-4db9-a8f9-900ba6c73f04 | deep-spectrum-cartography-completing-radio | 2105.00177 | null | https://arxiv.org/abs/2105.00177v2 | https://arxiv.org/pdf/2105.00177v2.pdf | Deep Spectrum Cartography: Completing Radio Map Tensors Using Learned Neural Models | The spectrum cartography (SC) technique constructs multi-domain (e.g., frequency, space, and time) radio frequency (RF) maps from limited measurements, which can be viewed as an ill-posed tensor completion problem. Model-based cartography techniques often rely on handcrafted priors (e.g., sparsity, smoothness and low-r... | ['Mingyi Hong', 'Xiao Fu', 'Sagar Shrestha'] | 2021-05-01 | null | null | null | null | ['spectrum-cartography'] | ['computer-vision'] | [ 3.72312486e-01 -4.41603139e-02 2.41791457e-03 -1.40750632e-01
-6.11436486e-01 -3.77684832e-01 2.64265925e-01 -4.87664193e-01
-5.19257225e-02 8.50114584e-01 3.27016234e-01 -4.08396900e-01
-6.09638929e-01 -6.71932459e-01 -6.69294775e-01 -9.60904539e-01
-3.76245558e-01 1.03708170e-01 -7.65448749e-01 -3.36308092... | [6.377340316772461, 1.3300254344940186] |
09241005-76d4-48cb-ae28-8a8bc325ebbe | visual-question-answering-vqa-on-images-with | 2307.02489 | null | https://arxiv.org/abs/2307.02489v1 | https://arxiv.org/pdf/2307.02489v1.pdf | Visual Question Answering (VQA) on Images with Superimposed Text | Superimposed text annotations have been under-investigated, yet are ubiquitous, useful and important, especially in medical images. Medical images also highlight the challenges posed by low resolution, noise and superimposed textual meta-information. Therefor we probed the impact of superimposing text onto medical imag... | ['Daniel Berleant', 'Venkat Kodali'] | 2023-06-13 | null | null | null | null | ['visual-question-answering', 'visual-question-answering-1', 'question-answering'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 8.61602902e-01 4.46177781e-01 2.34773681e-01 -2.21441656e-01
-9.62499201e-01 -5.80716670e-01 5.40324450e-01 5.48853219e-01
-4.99185175e-01 3.49735290e-01 8.01016152e-01 -1.41654015e-01
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2.43924454e-01 1.21740341e-01 1.35770321e-01 -2.56896943... | [15.017894744873047, -1.4918005466461182] |
404d0c59-b2c5-440b-9d88-3cd27c070924 | model-adaptation-and-adaptive-training-for | null | null | https://aclanthology.org/W15-5112 | https://aclanthology.org/W15-5112.pdf | Model adaptation and adaptive training for the recognition of dysarthric speech | null | ['Stuart Cunningham', 'Siddharth Sehgal'] | 2015-09-01 | null | null | null | ws-2015-9 | ['acoustic-modelling'] | ['speech'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.341146469116211, 3.7670555114746094] |
cf6fb57b-6917-4f4d-9f4d-bf68a50678f2 | variational-interpretable-learning-from-multi | 2202.13503 | null | https://arxiv.org/abs/2202.13503v2 | https://arxiv.org/pdf/2202.13503v2.pdf | Variational Interpretable Learning from Multi-view Data | The main idea of canonical correlation analysis (CCA) is to map different views onto a common latent space with maximum correlation. We propose a deep interpretable variational canonical correlation analysis (DICCA) for multi-view learning. The developed model extends the existing latent variable model for linear CCA t... | ['Vernon M. Chinchilli', 'Lynn Lin', 'Lin Qiu'] | 2022-02-28 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [-3.24151367e-01 -4.86480482e-02 -1.58174828e-01 -4.73781079e-01
-5.87589025e-01 -7.05616295e-01 1.04352796e+00 -9.07740533e-01
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-7.33744875e-02 -5.41932762e-01 -5.14119148e-01 -7.83682764e-01
2.26261273e-01 5.64252257e-01 -4.76172984e-01 4.68566874... | [8.260746955871582, 4.584348201751709] |
ff4cfe00-634a-443f-b0d6-face046c3be0 | measuring-financial-time-series-similarity | 2107.03926 | null | https://arxiv.org/abs/2107.03926v1 | https://arxiv.org/pdf/2107.03926v1.pdf | Measuring Financial Time Series Similarity With a View to Identifying Profitable Stock Market Opportunities | Forecasting stock returns is a challenging problem due to the highly stochastic nature of the market and the vast array of factors and events that can influence trading volume and prices. Nevertheless it has proven to be an attractive target for machine learning research because of the potential for even modest levels ... | ['Ruihai Dong', 'Yang Xu', 'Barry Smyth', 'Rian Dolphin'] | 2021-07-07 | null | null | null | null | ['stock-prediction'] | ['time-series'] | [-4.11074162e-01 -6.01370335e-01 -2.60171164e-02 -3.44201744e-01
-5.35299063e-01 -6.29806578e-01 1.00357366e+00 2.89746493e-01
-3.78666192e-01 8.44400406e-01 5.76836616e-02 -4.89514172e-01
-5.02225459e-01 -8.75057042e-01 -1.77897915e-01 -4.07171935e-01
-4.76902932e-01 4.82969850e-01 4.24812585e-01 -6.27341330... | [4.617560863494873, 4.18320369720459] |
1d851d2b-d491-49a6-ae9a-1e8eceebeeac | a-novel-scheme-for-binarization-of-vehicle | 1003.6059 | null | http://arxiv.org/abs/1003.6059v2 | http://arxiv.org/pdf/1003.6059v2.pdf | A novel scheme for binarization of vehicle images using hierarchical histogram equalization technique | Automatic License Plate Recognition system is a challenging area of research
now-a-days and binarization is an integral and most important part of it. In
case of a real life scenario, most of existing methods fail to properly
binarize the image of a vehicle in a congested road, captured through a CCD
camera. In the cur... | ['Satadal Saha', 'Subhadip Basu', 'Mita Nasipuri', 'Dipak Kumar Basu'] | 2010-03-31 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [ 3.70238304e-01 -5.59812307e-01 6.64949417e-02 -3.39657128e-01
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-1.71733752e-01 -9.51655865e-01 -4.96219933e-01 -7.46087372e-01
4.22108352e-01 6.00772500e-01 7.52621770e-01 -2.09503129... | [9.804418563842773, -4.970078468322754] |
ceac966e-2b3d-48fb-a981-973c14c8e627 | vit-hgr-vision-transformer-based-hand-gesture | 2201.10060 | null | https://arxiv.org/abs/2201.10060v1 | https://arxiv.org/pdf/2201.10060v1.pdf | ViT-HGR: Vision Transformer-based Hand Gesture Recognition from High Density Surface EMG Signals | Recently, there has been a surge of significant interest on application of Deep Learning (DL) models to autonomously perform hand gesture recognition using surface Electromyogram (sEMG) signals. DL models are, however, mainly designed to be applied on sparse sEMG signals. Furthermore, due to their complex structure, ty... | ['Farnoosh Naderkhani', 'Arash Mohammadi', 'Elahe Rahimian', 'Soheil Zabihi', 'Mansooreh Montazerin'] | 2022-01-25 | null | null | null | null | ['gesture-recognition'] | ['computer-vision'] | [ 4.88543749e-01 -1.69511527e-01 -1.41926840e-01 -8.08058754e-02
-7.07966030e-01 -8.58253017e-02 3.29871833e-01 -6.11131668e-01
-6.45805955e-01 6.50676429e-01 2.50173621e-02 -2.51968235e-01
-2.65318662e-01 -3.17111582e-01 -6.15809917e-01 -9.09836650e-01
-9.33060721e-02 4.46243554e-01 -2.00119298e-02 -1.10917829... | [6.810060501098633, 0.08636351674795151] |
8afdc3bf-eec5-44bd-b3bf-2d22b52ecd55 | deep-learning-based-automatic-modulation | 2207.09647 | null | https://arxiv.org/abs/2207.09647v1 | https://arxiv.org/pdf/2207.09647v1.pdf | Deep Learning Based Automatic Modulation Recognition: Models, Datasets, and Challenges | Automatic modulation recognition (AMR) detects the modulation scheme of the received signals for further signal processing without needing prior information, and provides the essential function when such information is missing. Recent breakthroughs in deep learning (DL) have laid the foundation for developing high-perf... | ['FuChun Zheng', 'Yang Luo', 'Jialang Xu', 'Chunbo Luo', 'Fuxin Zhang'] | 2022-07-20 | null | null | null | null | ['automatic-modulation-recognition'] | ['time-series'] | [ 4.46801603e-01 -3.22319329e-01 -3.92716736e-01 -2.48465002e-01
-8.17973197e-01 3.06037404e-02 6.29346490e-01 -2.63368309e-01
-7.66191930e-02 7.34388113e-01 -1.10990845e-01 -6.90131426e-01
-3.26583654e-01 -5.31809449e-01 -2.66634136e-01 -9.08061147e-01
-5.98161101e-01 -2.94403046e-01 -4.73271012e-01 -3.30769688... | [6.445747375488281, 1.4797186851501465] |
1ec49621-f0cb-4011-84cc-659f93e70e1a | source-free-unsupervised-domain-adaptation-1 | 2207.08124 | null | https://arxiv.org/abs/2207.08124v2 | https://arxiv.org/pdf/2207.08124v2.pdf | Source-free Unsupervised Domain Adaptation for Blind Image Quality Assessment | Existing learning-based methods for blind image quality assessment (BIQA) are heavily dependent on large amounts of annotated training data, and usually suffer from a severe performance degradation when encountering the domain/distribution shift problem. Thanks to the development of unsupervised domain adaptation (UDA)... | ['Zhibo Chen', 'Shukun An', 'Xin Li', 'Jianzhao Liu'] | 2022-07-17 | null | null | null | null | ['blind-image-quality-assessment'] | ['computer-vision'] | [ 2.23240480e-01 -1.86663106e-01 2.39842404e-02 -5.18123567e-01
-9.85370278e-01 -4.85495359e-01 2.47579709e-01 -6.22452386e-02
-3.38197827e-01 8.18679035e-01 2.22933725e-01 1.54244411e-03
-4.53624457e-01 -5.06376207e-01 -4.53997254e-01 -9.98801470e-01
3.76505882e-01 2.13808745e-01 4.34314311e-02 -1.17119644... | [10.37947940826416, 3.12208890914917] |
fc78c118-df0c-4e23-956d-af31d56d82e4 | a-novel-multi-layer-modular-approach-for-real | 2206.06004 | null | https://arxiv.org/abs/2206.06004v3 | https://arxiv.org/pdf/2206.06004v3.pdf | A Novel Multi-Layer Modular Approach for Real-Time Gravitational-Wave Detection | Advanced LIGO and Advanced Virgo ground-based interferometers are poised to probe an unprecedentedly large volume of space, enhancing the discovery power of the observations to even new sources of gravitational wave emitters. In this scenario, the development of highly optimized gravitational wave detection algorithms ... | ['Marco Russo', "Daniele Dell'Aquila", 'Francesco Pio Barone'] | 2022-06-13 | null | null | null | null | ['gravitational-wave-detection'] | ['miscellaneous'] | [-3.24113630e-02 2.03852817e-01 4.32974547e-01 -2.96146777e-02
-3.51321995e-01 -5.39810836e-01 9.04651940e-01 -3.24809611e-01
-4.39972132e-01 5.44424117e-01 -2.78906554e-01 -5.33569217e-01
-5.45720398e-01 -1.04367888e+00 -3.94396603e-01 -9.52814341e-01
-4.94017601e-01 6.52513325e-01 4.09994155e-01 -5.48830092... | [7.50907564163208, 3.096998691558838] |
f545c646-171e-4816-b4b0-e58e1d7eaa68 | aspect-based-opinion-summarization-with | 1511.09128 | null | http://arxiv.org/abs/1511.09128v1 | http://arxiv.org/pdf/1511.09128v1.pdf | Aspect-based Opinion Summarization with Convolutional Neural Networks | This paper considers Aspect-based Opinion Summarization (AOS) of reviews on
particular products. To enable real applications, an AOS system needs to
address two core subtasks, aspect extraction and sentiment classification. Most
existing approaches to aspect extraction, which use linguistic analysis or
topic modeling, ... | ['Xiaodong Gu', 'Yiwei Gu', 'Shangdi Sun', 'Haibing Wu'] | 2015-11-30 | null | null | null | null | ['aspect-extraction'] | ['natural-language-processing'] | [ 1.56084806e-01 2.74023771e-01 -5.81894875e-01 -5.90390146e-01
-8.13914537e-01 -4.92766976e-01 6.38201296e-01 5.97852170e-01
-1.91661671e-01 5.37729204e-01 3.64545137e-01 -3.59958947e-01
3.41736436e-01 -9.85922575e-01 -5.34064412e-01 -4.78051275e-01
4.36915249e-01 1.30422384e-01 3.99774220e-03 -4.16984797... | [11.420624732971191, 6.696123123168945] |
ba690e4d-5a62-4aac-b692-0e84189de24a | two-level-explanations-in-music-emotion | 1905.11760 | null | https://arxiv.org/abs/1905.11760v1 | https://arxiv.org/pdf/1905.11760v1.pdf | Two-level Explanations in Music Emotion Recognition | Current ML models for music emotion recognition, while generally working quite well, do not give meaningful or intuitive explanations for their predictions. In this work, we propose a 2-step procedure to arrive at spectrogram-level explanations that connect certain aspects of the audio to interpretable mid-level percep... | ['Shreyan Chowdhury', 'Verena Haunschmid', 'Gerhard Widmer'] | 2019-05-28 | null | null | null | null | ['music-emotion-recognition'] | ['music'] | [ 3.50915253e-01 1.03250563e-01 -1.38113629e-02 -4.56282586e-01
-5.81803322e-01 -7.45979667e-01 3.44225794e-01 4.71385807e-01
3.68315756e-01 2.69347221e-01 6.42087162e-01 -2.07005456e-01
-4.54976648e-01 -4.59708095e-01 -3.67595851e-01 -2.32750937e-01
-2.51173228e-01 7.44207278e-02 -4.77090664e-02 -2.46003985... | [15.830620765686035, 5.322739124298096] |
ddbff9c8-367d-45da-8788-2b7db6d4940e | label-synchronous-neural-transducer-for-end | 2307.03088 | null | https://arxiv.org/abs/2307.03088v1 | https://arxiv.org/pdf/2307.03088v1.pdf | Label-Synchronous Neural Transducer for End-to-End ASR | Neural transducers provide a natural approach to streaming ASR. However, they augment output sequences with blank tokens which leads to challenges for domain adaptation using text data. This paper proposes a label-synchronous neural transducer (LS-Transducer), which extracts a label-level encoder representation before ... | ['Philip C. Woodland', 'Keqi Deng'] | 2023-07-06 | null | null | null | null | ['domain-adaptation'] | ['methodology'] | [ 7.72069395e-01 3.82727206e-01 -1.68021128e-01 -7.44618893e-01
-1.19276237e+00 -3.95695180e-01 2.86716402e-01 1.43848553e-01
-7.11875856e-01 5.58235168e-01 4.05305445e-01 -1.97559074e-01
4.20329541e-01 -4.07513291e-01 -6.87156022e-01 -2.87405014e-01
1.25251517e-01 2.38175839e-01 2.83858120e-01 -3.03272128... | [14.52154541015625, 6.870389461517334] |
bcc16abd-7fd3-4871-a765-4c9971f4886f | label-efficient-two-sample-test | 2111.08861 | null | https://arxiv.org/abs/2111.08861v5 | https://arxiv.org/pdf/2111.08861v5.pdf | A label-efficient two-sample test | Two-sample tests evaluate whether two samples are realizations of the same distribution (the null hypothesis) or two different distributions (the alternative hypothesis). We consider a new setting for this problem where sample features are easily measured whereas sample labels are unknown and costly to obtain. Accordin... | ['Visar Berisha', 'Karthikeyan Natesan Ramamurthy', 'Gautam Dasarathy', 'Weizhi Li'] | 2021-11-17 | null | null | null | null | ['hypothesis-testing', 'hypothesis-testing'] | ['methodology', 'miscellaneous'] | [ 1.47480518e-01 -1.21406475e-02 -6.50153935e-01 -7.74681628e-01
-1.57615364e+00 -7.58265018e-01 4.94533837e-01 2.72760987e-01
-4.19255525e-01 1.02648747e+00 -7.10377812e-01 -4.41528589e-01
-2.77472496e-01 -9.24627662e-01 -6.55068576e-01 -8.46320450e-01
1.76270038e-01 8.74233663e-01 2.25004345e-01 6.80818558... | [8.491340637207031, 4.16489315032959] |
0034f141-6dcd-4352-897b-7e810e290497 | dynamic-sliding-window-for-meeting | 2108.13629 | null | https://arxiv.org/abs/2108.13629v1 | https://arxiv.org/pdf/2108.13629v1.pdf | Dynamic Sliding Window for Meeting Summarization | Recently abstractive spoken language summarization raises emerging research interest, and neural sequence-to-sequence approaches have brought significant performance improvement. However, summarizing long meeting transcripts remains challenging. Due to the large length of source contents and targeted summaries, neural ... | ['Nancy F. Chen', 'Zhengyuan Liu'] | 2021-08-31 | null | null | null | null | ['meeting-summarization'] | ['natural-language-processing'] | [ 4.32322085e-01 2.27422372e-01 -3.46442848e-01 -3.76992285e-01
-1.25998235e+00 -3.91269147e-01 4.86039370e-01 4.06748593e-01
-2.10686788e-01 1.15229249e+00 1.08206224e+00 7.67035708e-02
2.40090802e-01 -3.27222288e-01 -4.16327327e-01 -4.36728895e-01
-4.50187027e-02 1.50756717e-01 7.35809747e-03 -1.44948229... | [12.588194847106934, 9.383347511291504] |
f1ff4f83-03df-433e-9fe7-f6b65d67bac4 | orientation-attentive-robot-grasp-synthesis | 2006.05123 | null | https://arxiv.org/abs/2006.05123v2 | https://arxiv.org/pdf/2006.05123v2.pdf | Orientation Attentive Robotic Grasp Synthesis with Augmented Grasp Map Representation | Inherent morphological characteristics in objects may offer a wide range of plausible grasping orientations that obfuscates the visual learning of robotic grasping. Existing grasp generation approaches are cursed to construct discontinuous grasp maps by aggregating annotations for drastically different orientations per... | ['Nikolaos Gkanatsios', 'Jan Peters', 'Georgia Chalvatzaki', 'Petros Maragos'] | 2020-06-09 | null | null | null | null | ['grasp-generation'] | ['computer-vision'] | [ 1.36892647e-01 4.22952950e-01 -1.52609736e-01 -2.93954074e-01
-7.07801878e-01 -9.25117433e-01 3.12654674e-01 7.43377432e-02
4.18884913e-03 4.31531847e-01 -9.78085119e-03 -1.45978689e-01
-4.39831376e-01 -8.91180754e-01 -1.17435873e+00 -9.88275945e-01
-6.10562146e-01 7.37246037e-01 1.96066007e-01 -2.51308233... | [5.745316505432129, -0.8511068224906921] |
a867bdf7-4cfa-415d-a0d4-5b300299b0e1 | reflection-removal-for-large-scale-3d-point | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Yun_Reflection_Removal_for_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Yun_Reflection_Removal_for_CVPR_2018_paper.pdf | Reflection Removal for Large-Scale 3D Point Clouds | Large-scale 3D point clouds (LS3DPCs) captured by terrestrial LiDAR scanners often exhibit reflection artifacts by glasses, which degrade the performance of related computer vision techniques. In this paper, we propose an efficient reflection removal algorithm for LS3DPCs. We first partition the unit sphere into local ... | ['Jae-Young Sim', 'Jae-Seong Yun'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['reflection-removal'] | ['computer-vision'] | [ 2.97675431e-01 -2.22294420e-01 6.52149677e-01 1.03837520e-01
-6.34131372e-01 -3.19505304e-01 3.05012584e-01 -2.85374582e-01
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1.14266768e-01 7.28563011e-01 7.53085375e-01 2.30843782... | [8.469706535339355, -2.631143569946289] |
bd05d12e-b260-4cdc-bfa3-bbf9ceded524 | cml-tts-a-multilingual-dataset-for-speech | 2306.10097 | null | https://arxiv.org/abs/2306.10097v1 | https://arxiv.org/pdf/2306.10097v1.pdf | CML-TTS A Multilingual Dataset for Speech Synthesis in Low-Resource Languages | In this paper, we present CML-TTS, a recursive acronym for CML-Multi-Lingual-TTS, a new Text-to-Speech (TTS) dataset developed at the Center of Excellence in Artificial Intelligence (CEIA) of the Federal University of Goias (UFG). CML-TTS is based on Multilingual LibriSpeech (MLS) and adapted for training TTS models, c... | ['Arlindo R. Galvão Filho', 'Anderson S. Soares', 'Arnaldo Cândido Júnior', 'Edresson Casanova', 'Frederico S. Oliveira'] | 2023-06-16 | null | null | null | null | ['speech-synthesis'] | ['speech'] | [-4.83364731e-01 -8.89344700e-03 -1.15685381e-01 -2.46115535e-01
-1.27180684e+00 -6.70493364e-01 7.62271881e-01 2.75566708e-02
-3.37711155e-01 5.69201946e-01 2.52723187e-01 -6.53021514e-01
2.24932849e-01 -2.02104896e-01 -7.07835495e-01 -3.33987117e-01
2.21626818e-01 8.34208190e-01 7.21343532e-02 -1.74207151... | [14.399086952209473, 7.018582820892334] |
9210fc3f-c487-48a4-a3de-36d20b00c0d2 | audio-embeddings-as-teachers-for-music | 2306.17424 | null | https://arxiv.org/abs/2306.17424v1 | https://arxiv.org/pdf/2306.17424v1.pdf | Audio Embeddings as Teachers for Music Classification | Music classification has been one of the most popular tasks in the field of music information retrieval. With the development of deep learning models, the last decade has seen impressive improvements in a wide range of classification tasks. However, the increasing model complexity makes both training and inference comp... | ['Alexander Lerch', 'Yiwei Ding'] | 2023-06-30 | null | null | null | null | ['classification-1', 'retrieval', 'transfer-learning', 'music-auto-tagging', 'music-classification', 'music-information-retrieval', 'information-retrieval'] | ['methodology', 'methodology', 'miscellaneous', 'music', 'music', 'music', 'natural-language-processing'] | [ 1.59215063e-01 -5.78194186e-02 1.33756533e-01 -2.50937045e-01
-4.83677804e-01 -7.53947318e-01 4.39939588e-01 1.24894030e-01
-6.77700162e-01 3.45294416e-01 1.82531819e-01 -2.15326294e-01
-3.29270780e-01 -8.63121927e-01 -5.81188619e-01 -6.09842420e-01
1.05243407e-01 5.16839683e-01 3.05198461e-01 -2.03238875... | [15.756868362426758, 5.2400407791137695] |
c902803f-c6ef-46be-8b09-bf97b95d9b5e | asm2tv-an-adaptive-semi-supervised-multi-task | 2105.08643 | null | https://arxiv.org/abs/2105.08643v2 | https://arxiv.org/pdf/2105.08643v2.pdf | ASM2TV: An Adaptive Semi-Supervised Multi-Task Multi-View Learning Framework for Human Activity Recognition | Many real-world scenarios, such as human activity recognition (HAR) in IoT, can be formalized as a multi-task multi-view learning problem. Each specific task consists of multiple shared feature views collected from multiple sources, either homogeneous or heterogeneous. Common among recent approaches is to employ a typi... | ['Xiuzhen Cheng', 'Xiao Zhang', 'Zekai Chen'] | 2021-05-18 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [ 1.45589799e-01 -2.84579366e-01 -5.45146644e-01 -4.81846422e-01
-9.77942705e-01 -4.63094443e-01 4.78989452e-01 -3.44166696e-01
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-1.67380720e-01 -1.76195502e-01 -7.13507891e-01 -1.04819548e+00
3.49775136e-01 3.87014747e-01 9.12265554e-02 2.32755557... | [8.504350662231445, 4.512551784515381] |
09821ee0-f2de-4826-9490-51498699978d | 3218ir-at-semeval-2020-task-11-conv1d-and | null | null | https://aclanthology.org/2020.semeval-1.225 | https://aclanthology.org/2020.semeval-1.225.pdf | 3218IR at SemEval-2020 Task 11: Conv1D and Word Embedding in Propaganda Span Identification at News Articles | In this paper, we present the result of our experiment with a variant of 1 Dimensional Convolutional Neural Network (Conv1D) hyper-parameters value. We describe the system entered by the team of Information Retrieval Lab. Universitas Indonesia (3218IR) in the SemEval 2020 Task 11 Sub Task 1 about propaganda span identi... | ['Muhammad Okky Ibrohim', 'Indra Budi', 'Dimas Sony Dewantara'] | 2020-12-01 | null | null | null | semeval-2020 | ['propaganda-span-identification'] | ['natural-language-processing'] | [-1.93169206e-01 6.79448098e-02 -2.06707060e-01 -4.55339216e-02
-5.01549006e-01 -6.86930716e-01 1.15622878e+00 1.48462251e-01
-8.11825156e-01 2.17617303e-01 7.39640534e-01 -8.47611547e-01
-2.19937220e-01 -8.04231644e-01 -5.12584209e-01 -4.36835915e-01
-1.19753674e-01 8.82974193e-02 -7.19055086e-02 -4.65723187... | [8.47994613647461, 10.673797607421875] |
a14dc2b1-8d57-4816-aeff-7eb4a9f5f876 | a-biologically-interpretable-two-stage-deep | 2004.08886 | null | https://arxiv.org/abs/2004.08886v2 | https://arxiv.org/pdf/2004.08886v2.pdf | A Biologically Interpretable Two-stage Deep Neural Network (BIT-DNN) For Vegetation Recognition From Hyperspectral Imagery | Spectral-spatial based deep learning models have recently proven to be effective in hyperspectral image (HSI) classification for various earth monitoring applications such as land cover classification and agricultural monitoring. However, due to the nature of "black-box" model representation, how to explain and interpr... | ['Darren Dancey', 'Sheng Chang', 'Wenjiang Huang', 'Yingying Dong', 'Yue Shi', 'Liangxiu Han', 'Lianghao Han'] | 2020-04-19 | null | null | null | null | ['scene-recognition'] | ['computer-vision'] | [ 6.38371468e-01 -5.21731004e-02 -1.43017590e-01 -3.00486952e-01
-1.63329230e-03 -5.86299658e-01 2.89941251e-01 4.60487843e-01
-4.04516980e-02 8.74611378e-01 -3.09989024e-02 -5.71681440e-01
-5.75829327e-01 -1.02603531e+00 -6.63781941e-01 -9.84505773e-01
-5.67440867e-01 3.20845336e-01 -2.90807575e-01 -3.06484967... | [9.728524208068848, -1.539516568183899] |
57ba2244-1d9f-4d24-bb93-b5769ab317bd | clid-controlled-length-image-descriptions | 2211.14835 | null | https://arxiv.org/abs/2211.14835v1 | https://arxiv.org/pdf/2211.14835v1.pdf | CLID: Controlled-Length Image Descriptions with Limited Data | Controllable image captioning models generate human-like image descriptions, enabling some kind of control over the generated captions. This paper focuses on controlling the caption length, i.e. a short and concise description or a long and detailed one. Since existing image captioning datasets contain mostly short cap... | ['Ayellet Tal', 'Elad Hirsch'] | 2022-11-27 | null | null | null | null | ['controllable-image-captioning'] | ['computer-vision'] | [ 5.79124272e-01 5.02727926e-01 -2.47572854e-01 -4.06286508e-01
-1.12137902e+00 -7.54293382e-01 7.41467774e-01 1.15706928e-01
-2.09785461e-01 1.12408817e+00 2.06515953e-01 -2.20498711e-01
2.48331919e-01 -6.01013601e-01 -1.11477089e+00 -5.68862617e-01
3.52264017e-01 8.55588794e-01 -1.14058889e-03 -1.16353668... | [11.030116081237793, 0.9028329849243164] |
99f494d6-0c91-4350-9534-c09354594b64 | on-a-scalable-entropic-breaching-of-the | 2002.03176 | null | https://arxiv.org/abs/2002.03176v1 | https://arxiv.org/pdf/2002.03176v1.pdf | On a scalable entropic breaching of the overfitting barrier in machine learning | Overfitting and treatment of "small data" are among the most challenging problems in the machine learning (ML), when a relatively small data statistics size $T$ is not enough to provide a robust ML fit for a relatively large data feature dimension $D$. Deploying a massively-parallel ML analysis of generic classificatio... | ['Illia Horenko'] | 2020-02-08 | null | null | null | null | ['small-data'] | ['computer-vision'] | [ 2.04424143e-01 2.73245871e-01 2.35807613e-01 -4.24451113e-01
-1.44847369e+00 -1.64883941e-01 7.32441694e-02 3.94008845e-01
-8.10703933e-01 9.15571570e-01 -4.88149345e-01 -5.32555580e-01
-7.10571527e-01 -6.47043109e-01 -9.57878351e-01 -1.19561279e+00
-1.48354009e-01 8.43354046e-01 -3.96044612e-01 4.13727164... | [7.885305881500244, 3.57230806350708] |
474e69e7-2d59-4b90-9039-643a18ef5110 | quasi-unsupervised-color-constancy | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Bianco_Quasi-Unsupervised_Color_Constancy_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Bianco_Quasi-Unsupervised_Color_Constancy_CVPR_2019_paper.pdf | Quasi-Unsupervised Color Constancy | We present here a method for computational color constancy in which a deep convolutional neural network is trained to detect achromatic pixels in color images after they have been converted to grayscale. The method does not require any information about the illuminant in the scene and relies on the weak assumption, ful... | [' Claudio Cusano', 'Simone Bianco'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['color-constancy'] | ['computer-vision'] | [ 3.87859493e-01 -4.82278764e-02 1.35174572e-01 -5.59073091e-01
6.53760433e-02 -6.44868433e-01 7.07392991e-01 8.89120176e-02
-8.13065112e-01 7.08110869e-01 -3.98711622e-01 -2.81758189e-01
1.49863213e-01 -9.18659151e-01 -6.53877854e-01 -7.32445598e-01
2.92201042e-02 2.63232887e-01 2.84024715e-01 -3.30346644... | [10.366981506347656, -2.435694694519043] |
35341216-8a6d-4248-b165-3a5ed47534b8 | icdbigbird-a-contextual-embedding-model-for | 2204.10408 | null | https://arxiv.org/abs/2204.10408v1 | https://arxiv.org/pdf/2204.10408v1.pdf | ICDBigBird: A Contextual Embedding Model for ICD Code Classification | The International Classification of Diseases (ICD) system is the international standard for classifying diseases and procedures during a healthcare encounter and is widely used for healthcare reporting and management purposes. Assigning correct codes for clinical procedures is important for clinical, operational, and f... | ['Helen Chen', 'Alexander Wong', 'Nicola Sahar', 'Michal Malyska', 'George Michalopoulos'] | 2022-04-21 | null | https://aclanthology.org/2022.bionlp-1.32 | https://aclanthology.org/2022.bionlp-1.32.pdf | bionlp-acl-2022-5 | ['code-classification'] | ['computer-code'] | [-9.56860259e-02 2.29344964e-01 -1.68028593e-01 -1.15572676e-01
-5.48867524e-01 -1.03835061e-01 4.70936358e-01 1.17111933e+00
-4.55095619e-01 1.81771800e-01 7.07272768e-01 -7.39409447e-01
-2.95136869e-01 -9.59929645e-01 -1.01248942e-01 -4.13829505e-01
-4.48361993e-01 7.40146160e-01 -7.18512759e-02 -5.29046878... | [7.985784530639648, 6.992125511169434] |
a4fc85cf-36dd-4740-801b-10646c3ff523 | re-basin-via-implicit-sinkhorn | 2212.12042 | null | https://arxiv.org/abs/2212.12042v1 | https://arxiv.org/pdf/2212.12042v1.pdf | Re-basin via implicit Sinkhorn differentiation | The recent emergence of new algorithms for permuting models into functionally equivalent regions of the solution space has shed some light on the complexity of error surfaces, and some promising properties like mode connectivity. However, finding the right permutation is challenging, and current optimization techniques... | ['Marco Pedersoli', 'Eric Granger', 'Masih Aminbeidokhti', 'Thomas Dubail', 'Heitor Rapela Medeiros', 'Fidel A. Guerrero Peña'] | 2022-12-22 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Pena_Re-Basin_via_Implicit_Sinkhorn_Differentiation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Pena_Re-Basin_via_Implicit_Sinkhorn_Differentiation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['linear-mode-connectivity', 'models-alignment', 're-basin'] | ['knowledge-base', 'knowledge-base', 'knowledge-base'] | [ 2.32938573e-01 -4.85083833e-02 -3.02518100e-01 4.08766121e-02
-4.75906998e-01 -5.16427338e-01 4.14882034e-01 1.81531817e-01
-3.52416277e-01 9.89617229e-01 -1.70987293e-01 -3.85465086e-01
-7.82600641e-01 -7.50685990e-01 -1.02384913e+00 -1.00467396e+00
-2.80741423e-01 2.23508984e-01 3.51087570e-01 -1.84296265... | [7.686289310455322, 4.128276348114014] |
8a93229c-6c41-405e-bdbb-0c85adeaebff | data-augmentation-free-unsupervised-learning | 2210.02798 | null | https://arxiv.org/abs/2210.02798v1 | https://arxiv.org/pdf/2210.02798v1.pdf | Data Augmentation-free Unsupervised Learning for 3D Point Cloud Understanding | Unsupervised learning on 3D point clouds has undergone a rapid evolution, especially thanks to data augmentation-based contrastive methods. However, data augmentation is not ideal as it requires a careful selection of the type of augmentations to perform, which in turn can affect the geometric and semantic information ... | ['Qiang Wu', 'Nicu Sebe', 'Elisa Ricci', 'Jian Zhang', 'Fabio Poiesi', 'Cristiano Saltori', 'Guofeng Mei'] | 2022-10-06 | null | null | null | null | ['3d-object-classification'] | ['computer-vision'] | [ 2.67828852e-01 4.31599498e-01 -2.45687261e-01 -6.19171321e-01
-6.05356395e-01 -5.01285434e-01 5.50626516e-01 3.59118819e-01
-1.78503349e-01 3.86394203e-01 -3.18996251e-01 -6.92796055e-03
-5.55321239e-02 -7.67370164e-01 -1.19089985e+00 -7.64519393e-01
-4.14618924e-02 9.21638250e-01 1.85102150e-01 1.32387996... | [8.019604682922363, -3.2335305213928223] |
97303cda-71a0-4c18-bc5b-b6294ada8a7e | low-light-image-enhancement-with-wavelet | 2306.00306 | null | https://arxiv.org/abs/2306.00306v1 | https://arxiv.org/pdf/2306.00306v1.pdf | Low-Light Image Enhancement with Wavelet-based Diffusion Models | Diffusion models have achieved promising results in image restoration tasks, yet suffer from time-consuming, excessive computational resource consumption, and unstable restoration. To address these issues, we propose a robust and efficient Diffusion-based Low-Light image enhancement approach, dubbed DiffLL. Specificall... | ['Shuaicheng Liu', 'Haoqiang Fan', 'Songchen Han', 'Ao Luo', 'Hai Jiang'] | 2023-06-01 | null | null | null | null | ['face-detection', 'image-enhancement', 'low-light-image-enhancement', 'image-restoration'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 2.30180442e-01 -6.06673360e-01 6.36145324e-02 -1.44987041e-02
-6.87971473e-01 3.89173813e-03 3.79155159e-01 -2.82385290e-01
-9.27735865e-02 4.49735999e-01 4.60584521e-01 -4.28785235e-02
-1.81792110e-01 -9.00635242e-01 -3.99208456e-01 -1.22998285e+00
2.00684428e-01 -4.88069832e-01 2.66733408e-01 -2.11332381... | [11.273582458496094, -2.403188467025757] |
ace74216-b5b7-47eb-bf95-1cd238386789 | imitation-learning-for-human-pose-prediction | 1909.03449 | null | https://arxiv.org/abs/1909.03449v1 | https://arxiv.org/pdf/1909.03449v1.pdf | Imitation Learning for Human Pose Prediction | Modeling and prediction of human motion dynamics has long been a challenging problem in computer vision, and most existing methods rely on the end-to-end supervised training of various architectures of recurrent neural networks. Inspired by the recent success of deep reinforcement learning methods, in this paper we pro... | ['De-An Huang', 'Hsu-kuang Chiu', 'Borui Wang', 'Juan Carlos Niebles', 'Ehsan Adeli'] | 2019-09-08 | imitation-learning-for-human-pose-prediction-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Wang_Imitation_Learning_for_Human_Pose_Prediction_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Imitation_Learning_for_Human_Pose_Prediction_ICCV_2019_paper.pdf | iccv-2019-10 | ['human-pose-forecasting'] | ['computer-vision'] | [ 1.01126529e-01 2.15060189e-01 -1.84043422e-01 -1.48820922e-01
-5.47501147e-01 -2.07763761e-01 6.00175440e-01 -4.90189701e-01
-6.20276272e-01 7.44833827e-01 3.46314937e-01 1.34367406e-01
1.99877843e-01 -4.32751209e-01 -9.69448507e-01 -3.47518772e-01
-2.31699005e-01 5.89829028e-01 3.20101261e-01 -4.85949963... | [7.310463905334473, -0.2218499779701233] |
818afc54-8961-4ff6-ae35-254d1aed5e6c | ifseg-image-free-semantic-segmentation-via | 2303.14396 | null | https://arxiv.org/abs/2303.14396v1 | https://arxiv.org/pdf/2303.14396v1.pdf | IFSeg: Image-free Semantic Segmentation via Vision-Language Model | Vision-language (VL) pre-training has recently gained much attention for its transferability and flexibility in novel concepts (e.g., cross-modality transfer) across various visual tasks. However, VL-driven segmentation has been under-explored, and the existing approaches still have the burden of acquiring additional t... | ['Jinwoo Shin', 'Paul Hongsuck Seo', 'Seong Hyeon Park', 'Sukmin Yun'] | 2023-03-25 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yun_IFSeg_Image-Free_Semantic_Segmentation_via_Vision-Language_Model_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yun_IFSeg_Image-Free_Semantic_Segmentation_via_Vision-Language_Model_CVPR_2023_paper.pdf | cvpr-2023-1 | ['novel-concepts'] | ['reasoning'] | [ 7.15768576e-01 2.17988804e-01 -1.59117669e-01 -5.54572821e-01
-8.47661257e-01 -6.39689028e-01 6.84557974e-01 -1.33121952e-01
-6.66613519e-01 4.75921303e-01 -1.38944998e-01 -1.70346990e-01
5.19238710e-01 -5.64972997e-01 -1.02070117e+00 -5.79737723e-01
5.93293607e-01 4.67476934e-01 6.66157603e-01 -6.76553696... | [9.751971244812012, 0.8718520998954773] |
d446deed-6fa3-4a88-8534-81c6b6e0b247 | pointmixer-mlp-mixer-for-point-cloud | 2111.11187 | null | https://arxiv.org/abs/2111.11187v5 | https://arxiv.org/pdf/2111.11187v5.pdf | PointMixer: MLP-Mixer for Point Cloud Understanding | MLP-Mixer has newly appeared as a new challenger against the realm of CNNs and transformer. Despite its simplicity compared to transformer, the concept of channel-mixing MLPs and token-mixing MLPs achieves noticeable performance in visual recognition tasks. Unlike images, point clouds are inherently sparse, unordered a... | ['In So Kweon', 'Jaesik Park', 'Francois Rameau', 'Chunghyun Park', 'Jaesung Choe'] | 2021-11-22 | null | null | null | null | ['3d-object-classification', 'point-cloud-reconstruction'] | ['computer-vision', 'computer-vision'] | [-4.30079475e-02 -1.53701827e-01 -2.06717715e-01 -3.39192152e-01
-9.73824501e-01 -5.41960537e-01 6.40510142e-01 4.21190411e-02
-1.85895145e-01 2.54111648e-01 -1.52661309e-01 -2.25940660e-01
2.16568336e-01 -9.50484276e-01 -1.18615377e+00 -7.10518360e-01
1.34869650e-01 6.39277041e-01 1.26699969e-01 -5.32457791... | [8.008304595947266, -3.4242000579833984] |
086de091-6977-47dd-b54a-b87929bcffb9 | 190412629 | 1904.12629 | null | https://arxiv.org/abs/1904.12629v1 | https://arxiv.org/pdf/1904.12629v1.pdf | Beauty Learning and Counterfactual Inference | This work showcases a new approach for causal discovery by leveraging user experiments and recent advances in photo-realistic image editing, demonstrating a potential of identifying causal factors and understanding complex systems counterfactually. We introduce the beauty learning problem as an example, which has been ... | ['Tao Li'] | 2019-04-24 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 6.07658565e-01 6.61011755e-01 -4.67339844e-01 -4.27952051e-01
-2.43017435e-01 -4.94297743e-01 1.24361348e+00 -3.71175230e-01
1.87952013e-03 1.16799653e+00 9.07907844e-01 -3.17454696e-01
-3.64869684e-01 -7.53380835e-01 -1.17746532e+00 -3.64852220e-01
-4.46854234e-01 1.11455414e-02 -6.10245109e-01 -1.35509253... | [8.090896606445312, 5.369482040405273] |
9c096563-046b-4203-84d8-a44ce686bd2a | hierarchical-feature-hashing-for-fast | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Zhao_Hierarchical_Feature_Hashing_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Zhao_Hierarchical_Feature_Hashing_2014_CVPR_paper.pdf | Hierarchical Feature Hashing for Fast Dimensionality Reduction | Curse of dimensionality is a practical and challenging problem in image categorization, especially in cases with a large number of classes. Multi-class classification encounters severe computational and storage problems when dealing with these large scale tasks. In this paper, we propose hierarchical feature hashing to... | ['Bin Zhao', 'Eric P. Xing'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['image-categorization'] | ['computer-vision'] | [ 4.10879180e-02 -7.19275236e-01 -3.14532399e-01 -3.85995209e-01
-8.20918798e-01 -6.94354713e-01 2.00626194e-01 4.33008730e-01
-4.70328659e-01 3.20150018e-01 3.41048017e-02 -8.32116604e-02
-3.59231234e-01 -8.02219689e-01 -1.31977737e-01 -8.19281638e-01
1.75484281e-03 2.66653091e-01 3.70308608e-01 3.26724380... | [11.331487655639648, 0.9457070231437683] |
90b05983-e8f0-4b0b-95a0-437064dd0517 | multi-intent-detection-in-user-provided | 2307.03966 | null | https://arxiv.org/abs/2307.03966v1 | https://arxiv.org/pdf/2307.03966v1.pdf | Multi-Intent Detection in User Provided Annotations for Programming by Examples Systems | In mapping enterprise applications, data mapping remains a fundamental part of integration development, but its time consuming. An increasing number of applications lack naming standards, and nested field structures further add complexity for the integration developers. Once the mapping is done, data transformation is ... | ['Hima Patel', 'Shanmukha Guttula', 'Nitin Gupta', 'Nischal Ashok Kumar'] | 2023-07-08 | null | null | null | null | ['program-synthesis', 'intent-detection'] | ['computer-code', 'natural-language-processing'] | [ 3.43083382e-01 -3.27940255e-01 -2.86427289e-01 -7.15323091e-01
-4.67361093e-01 -9.27659988e-01 1.28159270e-01 2.14561984e-01
2.09603250e-01 2.42664784e-01 -1.98542789e-01 -6.77167952e-01
-3.34616229e-02 -1.22668993e+00 -6.83074474e-01 8.14647377e-02
4.94717538e-01 4.40662235e-01 1.50471553e-01 -3.91599476... | [8.15756607055664, 7.613968849182129] |
bba6b95f-2582-4ab1-a753-534ea3ccc63f | shape-retrieval-of-non-rigid-3d-human-models | 2003.08763 | null | https://arxiv.org/abs/2003.08763v1 | https://arxiv.org/pdf/2003.08763v1.pdf | Shape retrieval of non-rigid 3d human models | 3D models of humans are commonly used within computer graphics and vision, and so the ability to distinguish between body shapes is an important shape retrieval problem. We extend our recent paper which provided a benchmark for testing non-rigid 3D shape retrieval algorithms on 3D human models. This benchmark provided ... | ['L. Sun', 'C. Li', 'A. Ben Hamza', 'Paul L. Rosin', 'X. Liu', 'J. Han', 'J. Ye', 'Ralph R. Martin', 'Roee Litman', 'S Bu', 'Z Cheng', 'Xianfang Sun', 'Valeria Garro', 'Luca Isaia', 'Bo Li', 'S Cheng', 'Masaki Aono', 'Henry Johan', 'Haisheng Li', 'David Pickup', 'Atsushi Tatsuma', 'Andrea Giachetti', 'Afzal Godil', 'Z ... | 2020-03-01 | null | null | null | null | ['3d-shape-retrieval'] | ['computer-vision'] | [-4.95584235e-02 -2.67699003e-01 1.24961421e-01 -5.53219974e-01
-8.82507324e-01 -7.92814195e-01 7.70898759e-01 1.26792639e-01
-4.34676975e-01 1.73523426e-01 3.76328140e-01 6.31615296e-02
-1.25900850e-01 -5.87081790e-01 -2.40065128e-01 -3.51816624e-01
-3.36307377e-01 1.26704288e+00 6.06253266e-01 -1.71153590... | [8.372315406799316, -3.4430418014526367] |
d073985a-f2c4-4ef8-96be-c7c72d1fae5c | detection-of-makeup-presentation-attacks | 2006.05074 | null | https://arxiv.org/abs/2006.05074v2 | https://arxiv.org/pdf/2006.05074v2.pdf | Detection of Makeup Presentation Attacks based on Deep Face Representations | Facial cosmetics have the ability to substantially alter the facial appearance, which can negatively affect the decisions of a face recognition. In addition, it was recently shown that the application of makeup can be abused to launch so-called makeup presentation attacks. In such attacks, the attacker might apply heav... | ['Christian Rathgeb', 'Pawel Drozdowski', 'Christoph Busch'] | 2020-06-09 | null | null | null | null | ['facial-makeup-transfer'] | ['computer-vision'] | [ 6.96779788e-01 1.51214242e-01 3.95047814e-01 -2.17641979e-01
-4.12210166e-01 -9.07549143e-01 8.20232987e-01 -2.64315933e-01
-6.53680414e-02 2.53727913e-01 -3.70353192e-01 -6.87798917e-01
2.94538766e-01 -7.55049407e-01 -7.67554104e-01 -9.14241493e-01
3.77204793e-04 -3.74850750e-01 -1.66115627e-01 -2.47298494... | [12.919063568115234, 1.1024786233901978] |
34bd50a6-0044-4b35-afbd-ddad8dd5fa41 | faithful-low-resource-data-to-text-generation | 2305.14793 | null | https://arxiv.org/abs/2305.14793v2 | https://arxiv.org/pdf/2305.14793v2.pdf | Faithful Low-Resource Data-to-Text Generation through Cycle Training | Methods to generate text from structured data have advanced significantly in recent years, primarily due to fine-tuning of pre-trained language models on large datasets. However, such models can fail to produce output faithful to the input data, particularly on out-of-domain data. Sufficient annotated data is often not... | ['Oleg Rokhlenko', 'Shervin Malmasi', 'Simone Filice', 'Nikhita Vedula', 'Marcus Collins', 'Zhuoer Wang'] | 2023-05-24 | null | null | null | null | ['data-to-text-generation'] | ['natural-language-processing'] | [ 2.94759721e-01 7.26699591e-01 -5.57693467e-02 -5.37620544e-01
-1.09276807e+00 -7.72883832e-01 9.05282080e-01 2.83918232e-01
-3.38408321e-01 1.05223203e+00 5.74774146e-01 -2.81778634e-01
1.63379043e-01 -9.09870625e-01 -7.98597455e-01 -3.63191329e-02
4.32065725e-01 9.99837697e-01 -8.47116020e-03 -4.75100040... | [11.461094856262207, 8.83066177368164] |
b36ced39-1554-4cd3-97c1-b1298bb62241 | terra-blockage-resilience-in-outdoor-mmwave | 2209.12296 | null | https://arxiv.org/abs/2209.12296v1 | https://arxiv.org/pdf/2209.12296v1.pdf | Terra: Blockage Resilience in Outdoor mmWave Networks | We address the problem of pedestrian blockage in outdoor mm-Wave networks, which can disrupt Line of Sight (LoS) communication and result in an outage. This necessitates either reacquisition as a new user that can take up to 1.28 sec in 5G New Radio, disrupting high-performance applications, or handover to a different ... | ['P. R. Kumar', 'Jaewon Kim', 'Santosh Ganji'] | 2022-09-25 | null | null | null | null | ['detect-ground-reflections'] | ['miscellaneous'] | [-1.16120681e-01 1.73498571e-01 3.01330477e-01 1.77371398e-01
-5.96549511e-01 -5.75510979e-01 -1.25706598e-01 1.92124605e-01
-2.30950698e-01 1.34902787e+00 -3.12310427e-01 -1.06149745e+00
4.90489490e-02 -1.23793876e+00 -3.93385559e-01 -1.16708684e+00
-6.29750371e-01 1.38217628e-01 6.25980139e-01 -1.46410286... | [6.256851673126221, 1.1428371667861938] |
4a7dabcd-ffc3-4fcb-b06b-7f866d2c42cb | domain-independent-extraction-of-scientific | 2001.03067 | null | https://arxiv.org/abs/2001.03067v1 | https://arxiv.org/pdf/2001.03067v1.pdf | Domain-independent Extraction of Scientific Concepts from Research Articles | We examine the novel task of domain-independent scientific concept extraction from abstracts of scholarly articles and present two contributions. First, we suggest a set of generic scientific concepts that have been identified in a systematic annotation process. This set of concepts is utilised to annotate a corpus of ... | ['Sören Auer', 'Anett Hoppe', "Jennifer D'Souza", 'Arthur Brack', 'Ralph Ewerth'] | 2020-01-09 | domain-independent-extraction-of-scientific-1 | null | null | accepted-for-publishing-in-42nd-european | ['scientific-concept-extraction'] | ['natural-language-processing'] | [ 1.87761337e-01 6.30282104e-01 -2.41728812e-01 -3.59202892e-01
-1.26445401e+00 -9.09053445e-01 8.67333531e-01 6.66452706e-01
-6.79839611e-01 9.73416507e-01 2.60107875e-01 -3.48138332e-01
-1.38359249e-01 -2.82751948e-01 -6.43797338e-01 -5.05956590e-01
6.95018172e-02 7.83266664e-01 1.27738535e-01 4.50807177... | [9.823522567749023, 8.330642700195312] |
fc1b7fe6-ddfe-45f4-b01f-3ec208394516 | co-training-with-high-confidence-pseudo | 2301.04465 | null | https://arxiv.org/abs/2301.04465v3 | https://arxiv.org/pdf/2301.04465v3.pdf | Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation | Consistency regularization and pseudo labeling-based semi-supervised methods perform co-training using the pseudo labels from multi-view inputs. However, such co-training models tend to converge early to a consensus, degenerating to the self-training ones, and produce low-confidence pseudo labels from the perturbed inp... | ['Osmar R. Zaiane', 'Jinzhu Yang', 'Xiaoli Liu', 'Hua Yang', 'Peng Cao', 'Zhiqiang Shen'] | 2023-01-11 | null | null | null | null | ['semi-supervised-medical-image-segmentation', 'semi-supervised-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 1.01957649e-01 7.11073875e-01 -4.16947782e-01 -6.94915771e-01
-1.25365853e+00 -3.39822918e-01 1.73915491e-01 -4.62437794e-02
-7.74156675e-02 7.34990776e-01 1.21679306e-01 -1.65633503e-02
-5.26466817e-02 -3.91271234e-01 -7.91054964e-01 -9.52267528e-01
3.44179511e-01 8.32120597e-01 2.40402907e-01 4.47599590... | [14.594900131225586, -1.951285719871521] |
9a9c8d05-f7f1-45eb-b3a1-c24a872ddc1a | universal-perceptual-grouping | 1808.02312 | null | http://arxiv.org/abs/1808.02312v1 | http://arxiv.org/pdf/1808.02312v1.pdf | Universal Perceptual Grouping | In this work we aim to develop a universal sketch grouper. That is, a grouper
that can be applied to sketches of any category in any domain to group
constituent strokes/segments into semantically meaningful object parts. The
first obstacle to this goal is the lack of large-scale datasets with grouping
annotation. To ov... | ['Yi-Zhe Song', 'Ke Li', 'Kaiyue Pang', 'Jifei Song', 'Timothy M. Hospedales', 'Tao Xiang', 'Honggang Zhang'] | 2018-08-07 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 1.16790392e-01 -2.01916948e-01 -2.35702768e-01 -4.09814596e-01
-7.38641739e-01 -7.84901083e-01 8.40518534e-01 -5.26807047e-02
1.00251278e-02 2.39909843e-01 4.58895527e-02 2.40152091e-01
-1.67322636e-01 -8.62994611e-01 -6.22101247e-01 -4.36733961e-01
1.68887451e-01 5.50393999e-01 4.13673431e-01 -6.93332106... | [11.684927940368652, 0.4978991150856018] |
3d3a3147-32f4-4def-b0af-602729a8c5c2 | convolutional-sequence-to-sequence-non | 1806.02078 | null | http://arxiv.org/abs/1806.02078v1 | http://arxiv.org/pdf/1806.02078v1.pdf | Convolutional Sequence to Sequence Non-intrusive Load Monitoring | A convolutional sequence to sequence non-intrusive load monitoring model is
proposed in this paper. Gated linear unit convolutional layers are used to
extract information from the sequences of aggregate electricity consumption.
Residual blocks are also introduced to refine the output of the neural network.
The partiall... | ['Jun Hu', 'Qin Wang', 'Kunlong Chen', 'Kunjin Chen', 'Ziyu He', 'Jinliang He'] | 2018-06-06 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 3.64700228e-01 -1.73632979e-01 7.60120451e-02 -6.36922300e-01
-3.89037877e-01 -7.15008914e-01 3.76518667e-01 -2.18678981e-01
-2.55478442e-01 8.50926399e-01 2.99194723e-01 -2.09979609e-01
1.72059625e-01 -9.13409770e-01 -4.83533829e-01 -8.77092063e-01
-1.39813364e-01 -2.79901981e-01 -2.35108882e-01 -1.99081525... | [16.062427520751953, 7.5762834548950195] |
4ae4c31b-3a5b-4697-adf1-6db14cfd229e | automated-classification-of-pre-defined | 2303.07107 | null | https://arxiv.org/abs/2303.07107v1 | https://arxiv.org/pdf/2303.07107v1.pdf | Automated classification of pre-defined movement patterns: A comparison between GNSS and UWB technology | Advanced real-time location systems (RTLS) allow for collecting spatio-temporal data from human movement behaviours. Tracking individuals in small areas such as schoolyards or nursing homes might impose difficulties for RTLS in terms of positioning accuracy. However, to date, few studies have investigated the performan... | ['Carolien Rieffe', 'Alexander Koutamanis', 'Mitra Baratchi', 'Richard van Dijk', 'Maedeh Nasri', 'Rodi Laanen'] | 2023-03-10 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [ 7.78513998e-02 -2.77405679e-01 2.27225989e-01 -1.26358300e-01
-5.35693109e-01 -5.05721271e-01 7.66701937e-01 3.05382997e-01
-8.39830160e-01 7.04582214e-01 1.20850809e-01 -7.74081528e-01
-8.71917427e-01 -8.89724195e-01 -2.65180767e-01 -7.98271418e-01
-4.00621414e-01 4.71504390e-01 4.99967992e-01 -3.79665583... | [6.543354511260986, 1.0241514444351196] |
980bf030-7685-4104-8130-6ff592106d6d | making-information-seeking-easier-an-improved | null | null | https://aclanthology.org/2020.findings-emnlp.354 | https://aclanthology.org/2020.findings-emnlp.354.pdf | Making Information Seeking Easier: An Improved Pipeline for Conversational Search | This paper presents a highly effective pipeline for passage retrieval in a conversational search setting. The pipeline comprises of two components: Conversational Term Selection (CTS) and Multi-View Reranking (MVR). CTS is responsible for performing the first-stage of passage retrieval. Given an input question, it uses... | ['Jamie Callan', 'Vaibhav Kumar'] | 2020-11-01 | null | null | null | findings-of-the-association-for-computational | ['passage-ranking', 'conversational-search'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.72665939e-01 -1.54141292e-01 -1.22622386e-01 -1.92732394e-01
-1.83160257e+00 -9.77490246e-01 9.37523246e-01 4.94871408e-01
-6.68799222e-01 4.49717432e-01 6.84991181e-01 -1.96602196e-01
-2.68236250e-01 -5.85174620e-01 -3.00774932e-01 -4.94844556e-01
1.63057879e-01 1.01619434e+00 7.27368593e-01 -5.95119059... | [12.010782241821289, 7.805692195892334] |
cbc45ce7-5eb7-4425-b818-91b5e9b9040a | 3d-high-fidelity-mask-face-presentation | 2108.06968 | null | https://arxiv.org/abs/2108.06968v1 | https://arxiv.org/pdf/2108.06968v1.pdf | 3D High-Fidelity Mask Face Presentation Attack Detection Challenge | The threat of 3D masks to face recognition systems is increasingly serious and has been widely concerned by researchers. To facilitate the study of the algorithms, a large-scale High-Fidelity Mask dataset, namely CASIA-SURF HiFiMask (briefly HiFiMask) has been collected. Specifically, it consists of a total amount of 5... | ['Guodong Guo', 'Zhen Lei', 'Hugo Jair Escalante', 'Sergio Escalera', 'Jun Wan', 'Zijian Kong', 'Xing Liu', 'Anyang Su', 'Zitong Yu', 'Chenxu Zhao', 'Ajian Liu'] | 2021-08-16 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 1.69324636e-01 -3.96355659e-01 1.37209758e-01 -3.69895965e-01
-5.97295761e-01 -6.19193971e-01 3.98744315e-01 -7.11549044e-01
-1.05589353e-01 2.94546962e-01 4.43404615e-02 7.51328990e-02
-2.66420543e-01 -2.45858982e-01 -5.58763206e-01 -6.28386021e-01
-5.67556441e-01 9.80992168e-02 2.03847229e-01 -1.93319619... | [13.02371883392334, 1.1287628412246704] |
6608e2b4-d7eb-40c5-ba5b-606d7ecfdd05 | rtformer-efficient-design-for-real-time | 2210.07124 | null | https://arxiv.org/abs/2210.07124v1 | https://arxiv.org/pdf/2210.07124v1.pdf | RTFormer: Efficient Design for Real-Time Semantic Segmentation with Transformer | Recently, transformer-based networks have shown impressive results in semantic segmentation. Yet for real-time semantic segmentation, pure CNN-based approaches still dominate in this field, due to the time-consuming computation mechanism of transformer. We propose RTFormer, an efficient dual-resolution transformer for ... | ['Jingdong Wang', 'Errui Ding', 'Junyu Han', 'Haocheng Feng', 'Qiman Wu', 'Chenhui Gou', 'Jian Wang'] | 2022-10-13 | null | null | null | null | ['real-time-semantic-segmentation'] | ['computer-vision'] | [-1.76087692e-01 2.28555445e-02 -2.94940472e-01 -2.89427459e-01
-1.02823198e+00 -4.40169245e-01 2.99938887e-01 -1.71259701e-01
-3.91588479e-01 3.03171933e-01 -1.17840683e-02 -4.79624212e-01
1.82183072e-01 -1.15285099e+00 -7.52081096e-01 -3.94449592e-01
1.99153885e-01 5.32138407e-01 7.52805710e-01 -1.42015263... | [9.451783180236816, -0.0023286007344722748] |
e9b6324a-0650-491c-9774-263576a09549 | sentiment-clustering-with-topic-and-temporal | null | null | https://aclanthology.org/Y16-3007 | https://aclanthology.org/Y16-3007.pdf | Sentiment Clustering with Topic and Temporal Information from Large Email Dataset | null | ['Ickjai Lee', 'Sisi Liu', 'Guochen Cai'] | 2016-10-01 | sentiment-clustering-with-topic-and-temporal-1 | https://aclanthology.org/Y16-3007 | https://aclanthology.org/Y16-3007.pdf | paclic-2016-10 | ['stock-market-prediction'] | ['time-series'] | [-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... | [-7.376011371612549, 3.6790597438812256] |
1e4ae847-7426-46a9-8249-ccc0cfe24d73 | why-so-toxic-measuring-and-triggering-toxic | 2209.03463 | null | https://arxiv.org/abs/2209.03463v2 | https://arxiv.org/pdf/2209.03463v2.pdf | Why So Toxic? Measuring and Triggering Toxic Behavior in Open-Domain Chatbots | Chatbots are used in many applications, e.g., automated agents, smart home assistants, interactive characters in online games, etc. Therefore, it is crucial to ensure they do not behave in undesired manners, providing offensive or toxic responses to users. This is not a trivial task as state-of-the-art chatbot models a... | ['Yang Zhang', 'Savvas Zannettou', 'Gianluca Stringhini', 'Emiliano De Cristofaro', 'Jeremy Blackburn', 'Michael Backes', 'Wai Man Si'] | 2022-09-07 | null | null | null | null | ['computer-security'] | ['miscellaneous'] | [-2.13317692e-01 1.30625054e-01 1.97423473e-01 -8.49273801e-02
-6.50267422e-01 -1.00714171e+00 5.15893817e-01 -1.95508644e-01
-3.55563253e-01 7.13399291e-01 -1.26233801e-01 -5.25151789e-01
1.68638065e-01 -9.71319616e-01 -2.60444611e-01 -6.08677924e-01
1.55299366e-01 4.98101681e-01 6.70541406e-01 -6.12439454... | [10.522488594055176, 7.571678638458252] |
d175f74e-1ea9-4dd7-8dde-f8fe38ab9e94 | rectified-max-value-entropy-search-for | 2202.13597 | null | https://arxiv.org/abs/2202.13597v1 | https://arxiv.org/pdf/2202.13597v1.pdf | Rectified Max-Value Entropy Search for Bayesian Optimization | Although the existing max-value entropy search (MES) is based on the widely celebrated notion of mutual information, its empirical performance can suffer due to two misconceptions whose implications on the exploration-exploitation trade-off are investigated in this paper. These issues are essential in the development o... | ['Patrick Jaillet', 'Bryan Kian Hsiang Low', 'Quoc Phong Nguyen'] | 2022-02-28 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [ 1.88712627e-01 9.69983488e-02 -2.69233525e-01 -4.03943509e-01
-7.22739518e-01 -4.25404191e-01 6.00833058e-01 4.49781343e-02
-7.00812697e-01 1.15351200e+00 1.66548476e-01 -2.95137137e-01
-7.45123923e-01 -7.55947053e-01 -3.77488434e-01 -7.69385040e-01
-2.49978095e-01 3.06098253e-01 -1.39282539e-01 -3.13279241... | [6.85791015625, 3.901007890701294] |
0cb491e2-c191-4890-87c1-f27ae9fc10cb | similarity-contrastive-estimation-for-self | 2111.14585 | null | https://arxiv.org/abs/2111.14585v2 | https://arxiv.org/pdf/2111.14585v2.pdf | Similarity Contrastive Estimation for Self-Supervised Soft Contrastive Learning | Contrastive representation learning has proven to be an effective self-supervised learning method. Most successful approaches are based on Noise Contrastive Estimation (NCE) and use different views of an instance as positives that should be contrasted with other instances, called negatives, that are considered as noise... | ['Stéphane Canu', 'Romain Hérault', 'Astrid Orcesi', 'Jaonary Rabarisoa', 'Julien Denize'] | 2021-11-29 | null | null | null | null | ['self-supervised-image-classification'] | ['computer-vision'] | [ 4.32246119e-01 8.55036974e-02 -1.37609079e-01 -6.05934262e-01
-8.53099883e-01 -4.51170385e-01 7.50533879e-01 3.45322579e-01
-5.64295173e-01 6.86664343e-01 -1.87163547e-01 1.76659793e-01
-1.79004833e-01 -9.04711664e-01 -1.09679103e+00 -7.79481769e-01
2.50000246e-02 6.83149636e-01 2.69312054e-01 -2.87502319... | [9.6097993850708, 2.9132466316223145] |
684bf755-b101-497f-be4f-37036f3b1e77 | dual-stream-reciprocal-disentanglement | 2106.13929 | null | https://arxiv.org/abs/2106.13929v2 | https://arxiv.org/pdf/2106.13929v2.pdf | Dual-Stream Reciprocal Disentanglement Learning for Domain Adaptation Person Re-Identification | Since human-labeled samples are free for the target set, unsupervised person re-identification (Re-ID) has attracted much attention in recent years, by additionally exploiting the source set. However, due to the differences on camera styles, illumination and backgrounds, there exists a large gap between source domain a... | ['David Zhang', 'Zhengtao Yu', 'Yong Xu', 'Guangming Lu', 'Jinxing Li', 'Kaixiong Xu', 'Huafeng Li'] | 2021-06-26 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 1.41678497e-01 -3.68461788e-01 -1.19739480e-01 -3.18481028e-01
-6.22146666e-01 -5.21321297e-01 6.58297658e-01 -1.85969636e-01
-5.10300994e-01 7.00321794e-01 2.95524031e-01 3.14956933e-01
-1.81582823e-01 -6.87948465e-01 -3.31711620e-01 -8.11857164e-01
3.93967479e-01 1.32512569e-01 -1.49842635e-01 -8.25230405... | [14.703422546386719, 1.0348873138427734] |
1dc3dca2-0592-4f00-9df4-6899a4144034 | decoupling-learning-and-remembering-a-bilevel | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Sun_Decoupling_Learning_and_Remembering_A_Bilevel_Memory_Framework_With_Knowledge_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Sun_Decoupling_Learning_and_Remembering_A_Bilevel_Memory_Framework_With_Knowledge_CVPR_2023_paper.pdf | Decoupling Learning and Remembering: A Bilevel Memory Framework With Knowledge Projection for Task-Incremental Learning | The dilemma between plasticity and stability arises as a common challenge for incremental learning. In contrast, the human memory system is able to remedy this dilemma owing to its multi-level memory structure, which motivates us to propose a Bilevel Memory system with Knowledge Projection (BMKP) for incremental le... | ['Yangli-ao Geng', 'Wen Wang', 'Jing Zhang', 'Qingyong Li', 'Wenju Sun'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['incremental-learning'] | ['methodology'] | [-2.74398774e-01 1.46010801e-01 -6.21507652e-02 -7.09373653e-02
1.48723200e-01 -2.37994850e-01 3.23962003e-01 -2.44120926e-01
-4.59233373e-01 7.25436807e-01 2.32477114e-01 9.33411568e-02
-4.65288460e-01 -9.64910328e-01 -9.34366941e-01 -9.47417557e-01
-1.18591987e-01 9.98209864e-02 5.93975842e-01 -7.98334926... | [9.897884368896484, 3.4017281532287598] |
9c88c8e6-8f7c-4a00-8141-a0b9bfa2f545 | tribert-human-centric-audio-visual | null | null | http://proceedings.neurips.cc/paper/2021/hash/51200d29d1fc15f5a71c1dab4bb54f7c-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/51200d29d1fc15f5a71c1dab4bb54f7c-Paper.pdf | TriBERT: Human-centric Audio-visual Representation Learning | The recent success of transformer models in language, such as BERT, has motivated the use of such architectures for multi-modal feature learning and tasks. However, most multi-modal variants (e.g., ViLBERT) have limited themselves to visual-linguistic data. Relatively few have explored its use in audio-visual modalitie... | ['Leonid Sigal', 'Mengyu Yang', 'Tanzila Rahman'] | 2021-12-01 | null | https://openreview.net/forum?id=YP1ham75vml | https://openreview.net/pdf?id=YP1ham75vml | neurips-2021-12 | ['pose-retrieval'] | ['computer-vision'] | [ 3.51070464e-02 -3.90082598e-01 1.48831487e-01 -1.48186460e-01
-1.51320434e+00 -9.05327559e-01 7.66869485e-01 6.88760579e-02
-3.61821890e-01 1.38124436e-01 5.32459438e-01 1.67329069e-02
-2.58039594e-01 -2.82195151e-01 -8.68830979e-01 -5.46292782e-01
-1.73386917e-01 3.13673615e-01 8.05724710e-02 -1.56703189... | [14.756865501403809, 4.946007251739502] |
9d7249c9-fdd2-4c19-bfdd-6edcae3aa6b5 | lasermix-for-semi-supervised-lidar-semantic | 2207.00026 | null | https://arxiv.org/abs/2207.00026v3 | https://arxiv.org/pdf/2207.00026v3.pdf | LaserMix for Semi-Supervised LiDAR Semantic Segmentation | Densely annotating LiDAR point clouds is costly, which restrains the scalability of fully-supervised learning methods. In this work, we study the underexplored semi-supervised learning (SSL) in LiDAR segmentation. Our core idea is to leverage the strong spatial cues of LiDAR point clouds to better exploit unlabeled dat... | ['Ziwei Liu', 'Liang Pan', 'Jiawei Ren', 'Lingdong Kong'] | 2022-06-30 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Kong_LaserMix_for_Semi-Supervised_LiDAR_Semantic_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Kong_LaserMix_for_Semi-Supervised_LiDAR_Semantic_Segmentation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['lidar-semantic-segmentation'] | ['computer-vision'] | [ 2.72249311e-01 8.21818262e-02 -6.78145885e-01 -7.33785629e-01
-1.19202924e+00 -7.29066551e-01 4.80530083e-01 -1.00921661e-01
-2.67033309e-01 7.09169090e-01 -2.13251814e-01 -4.21518832e-01
-7.51004070e-02 -6.63280785e-01 -9.67006207e-01 -5.95148504e-01
1.71352938e-01 7.07983017e-01 4.05598670e-01 3.37394238... | [8.022514343261719, -3.070784568786621] |
859147e7-8d43-4824-95dc-40c8ea1d7804 | dreamento-an-open-source-dream-engineering | 2207.03977 | null | https://arxiv.org/abs/2207.03977v3 | https://arxiv.org/pdf/2207.03977v3.pdf | Dreamento: an open-source dream engineering toolbox for sleep EEG wearables | We introduce Dreamento (Dream engineering toolbox), an open-source Python package for dream engineering using sleep electroencephalography (EEG) wearables. Dreamento main functions are (1) real-time recording, monitoring, analysis, and sensory stimulation, and (2) offline post-processing of the resulting data, both in ... | ['Martin Dresler', 'Frederik D. Weber', 'Paul Zerr', 'Amir Hossein Daraie', 'Mahdad Jafarzadeh Esfahani'] | 2022-07-08 | null | null | null | null | ['electromyography-emg'] | ['medical'] | [ 7.27886287e-03 -2.70311385e-01 3.48431289e-01 -2.62814224e-01
-5.62355399e-01 -5.29328763e-01 2.23675340e-01 2.71470815e-01
-6.57830596e-01 7.48194337e-01 6.55039132e-01 -4.12744552e-01
-1.86342895e-01 -3.22697014e-01 -3.08243692e-01 -6.02477074e-01
-4.41538483e-01 1.66050985e-01 -2.95338221e-02 -1.47889540... | [13.402176856994629, 3.4531261920928955] |
d61f7417-3a60-47f9-8b78-8e82803aed54 | an-attention-driven-hierarchical-multi-scale | 2110.12178 | null | https://arxiv.org/abs/2110.12178v1 | https://arxiv.org/pdf/2110.12178v1.pdf | An attention-driven hierarchical multi-scale representation for visual recognition | Convolutional Neural Networks (CNNs) have revolutionized the understanding of visual content. This is mainly due to their ability to break down an image into smaller pieces, extract multi-scale localized features and compose them to construct highly expressive representations for decision making. However, the convoluti... | ['Asish Bera', 'Ardhendu Behera', 'Zachary Wharton'] | 2021-10-23 | null | null | null | null | ['fine-grained-visual-recognition'] | ['computer-vision'] | [ 1.39197603e-01 -2.70108104e-01 -3.09359729e-01 -3.85453075e-01
-5.70235625e-02 -5.06211221e-01 7.49443054e-01 6.93228841e-01
-2.17731163e-01 4.24662173e-01 3.62246156e-01 -2.44943202e-01
-2.27880791e-01 -1.16022944e+00 -7.74215937e-01 -6.90551639e-01
-4.62601304e-01 -4.33205534e-03 5.39120197e-01 -3.31812918... | [9.68293285369873, 1.8899918794631958] |
76c2c800-11a6-401f-9198-e95c23393730 | supervised-adversarial-contrastive-learning | 2306.01505 | null | https://arxiv.org/abs/2306.01505v2 | https://arxiv.org/pdf/2306.01505v2.pdf | Supervised Adversarial Contrastive Learning for Emotion Recognition in Conversations | Extracting generalized and robust representations is a major challenge in emotion recognition in conversations (ERC). To address this, we propose a supervised adversarial contrastive learning (SACL) framework for learning class-spread structured representations in a supervised manner. SACL applies contrast-aware advers... | ['Songlin Hu', 'Wei Zhou', 'Lingwei Wei', 'Yinan Bao', 'Dou Hu'] | 2023-06-02 | null | null | null | null | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 5.68232358e-01 -1.84062928e-01 2.25606352e-01 -6.79598868e-01
-1.38149846e+00 -7.27247834e-01 4.67604637e-01 -3.65148485e-01
-2.82654822e-01 7.68416643e-01 4.71854419e-01 -5.24988994e-02
2.96988457e-01 -3.98063809e-01 -6.60495043e-01 -7.48138726e-01
-3.36213633e-02 -5.48733817e-03 -2.23856047e-01 -4.71207917... | [13.60180377960205, 5.83034086227417] |
c20694ed-1ed8-498c-8948-7c75954b2b59 | apollo-at-semeval-2018-task-9-detecting | null | null | https://aclanthology.org/S18-1146 | https://aclanthology.org/S18-1146.pdf | Apollo at SemEval-2018 Task 9: Detecting Hypernymy Relations Using Syntactic Dependencies | This paper presents the participation of Apollo{'}s team in the SemEval-2018 Task 9 {``}Hypernym Discovery{''}, Subtask 1: {``}General-Purpose Hypernym Discovery{''}, which tries to produce a ranked list of hypernyms for a specific term. We propose a novel approach for automatic extraction of hypernymy relations from a... | ['Daniela G{\\^\\i}fu', 'Diana ab{\\u{a}}{\\textcommabelow{t}}', 'Tr', 'Ionu{\\textcommabelow{t}} Hulub', 'Mihaela Onofrei'] | 2018-06-01 | null | null | null | semeval-2018-6 | ['hypernym-discovery'] | ['natural-language-processing'] | [ 1.35852635e-01 8.06524634e-01 -3.27885896e-01 -1.97762877e-01
-1.41602695e-01 -4.98455018e-01 7.87215233e-01 6.63985550e-01
-8.25384259e-01 8.65311980e-01 2.16671333e-01 -3.03498089e-01
-7.42389739e-01 -9.39730585e-01 -1.85242921e-01 -3.18451136e-01
-2.09714949e-01 1.10840011e+00 4.48374599e-01 -7.83368051... | [9.875913619995117, 8.7398681640625] |
3b70b485-905a-45ae-acb1-4475ad2140ee | cap-net-correspondence-aware-point-view | 2109.01291 | null | https://arxiv.org/abs/2109.01291v1 | https://arxiv.org/pdf/2109.01291v1.pdf | CAP-Net: Correspondence-Aware Point-view Fusion Network for 3D Shape Analysis | Learning 3D representations by fusing point cloud and multi-view data has been proven to be fairly effective. While prior works typically focus on exploiting global features of the two modalities, in this paper we argue that more discriminative features can be derived by modeling "where to fuse". To investigate this, w... | ['Xiang Bai', 'Song Bai', 'Silin Cheng', 'Xinwei He'] | 2021-09-03 | null | null | null | null | ['3d-object-classification'] | ['computer-vision'] | [-1.68001577e-01 -3.02991599e-01 -8.83848593e-02 -5.54443777e-01
-9.86636937e-01 -5.29679775e-01 8.30347955e-01 2.71884739e-01
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-2.35372782e-01 -8.47039700e-01 -5.40831566e-01 -6.99996114e-01
3.05497229e-01 1.14282236e-01 3.76959592e-01 -2.00697497... | [8.149913787841797, -3.811307668685913] |
d366c578-c0e6-4ec3-b3de-6f606391a6d7 | automated-timeline-length-selection-for | 2105.14201 | null | https://arxiv.org/abs/2105.14201v1 | https://arxiv.org/pdf/2105.14201v1.pdf | Automated Timeline Length Selection for Flexible Timeline Summarization | By producing summaries for long-running events, timeline summarization (TLS) underpins many information retrieval tasks. Successful TLS requires identifying an appropriate set of key dates (the timeline length) to cover. However, doing so is challenging as the right length can change from one topic to another. Existing... | ['Zheng Wang', 'JianXin Li', 'Hongdong Zhu', 'Hao Peng', 'Qianren Mao', 'Xi Li'] | 2021-05-29 | null | null | null | null | ['timeline-summarization'] | ['natural-language-processing'] | [ 3.23182195e-01 -2.76293427e-01 -3.21813494e-01 -3.61992508e-01
-1.28995848e+00 -7.98697948e-01 9.16860342e-01 7.30133176e-01
-3.89267862e-01 8.72970283e-01 6.32483780e-01 -2.62198508e-01
-4.82182950e-01 -6.97945893e-01 -3.42903048e-01 -4.84262168e-01
4.52434504e-03 5.94862223e-01 3.57032269e-01 -6.53236359... | [12.463996887207031, 9.46700668334961] |
0d0c43d3-6ed1-4521-b786-d1ce888f3ae6 | synthetic-data-augmentation-for-zero-shot | 2010.12643 | null | https://arxiv.org/abs/2010.12643v2 | https://arxiv.org/pdf/2010.12643v2.pdf | Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering | Coupled with the availability of large scale datasets, deep learning architectures have enabled rapid progress on the Question Answering task. However, most of those datasets are in English, and the performances of state-of-the-art multilingual models are significantly lower when evaluated on non-English data. Due to h... | ['Jacopo Staiano', 'Djamé Seddah', 'Benoît Sagot', 'Rachel Keraron', 'Thomas Scialom', 'Arij Riabi'] | 2020-10-23 | null | https://aclanthology.org/2021.emnlp-main.562 | https://aclanthology.org/2021.emnlp-main.562.pdf | emnlp-2021-11 | ['cross-lingual-question-answering'] | ['natural-language-processing'] | [-3.15209359e-01 3.25246006e-02 1.81670755e-01 -5.31096160e-01
-1.91171348e+00 -9.50549185e-01 5.86309910e-01 8.35943744e-02
-6.76076889e-01 1.12946963e+00 2.61806011e-01 -5.80097973e-01
2.30744347e-01 -7.93400228e-01 -9.42506850e-01 3.02112717e-02
4.48702395e-01 9.96909559e-01 1.36218444e-01 -7.09014177... | [11.348231315612793, 8.393497467041016] |
fe50b894-32f7-4bfa-9fce-fa311086208f | starcraft-ii-a-new-challenge-for | 1708.04782 | null | http://arxiv.org/abs/1708.04782v1 | http://arxiv.org/pdf/1708.04782v1.pdf | StarCraft II: A New Challenge for Reinforcement Learning | This paper introduces SC2LE (StarCraft II Learning Environment), a
reinforcement learning environment based on the StarCraft II game. This domain
poses a new grand challenge for reinforcement learning, representing a more
difficult class of problems than considered in most prior work. It is a
multi-agent problem with m... | ['Anthony Brunasso', 'Julian Schrittwieser', 'Heinrich Küttler', 'Michelle Yeo', 'Alexander Sasha Vezhnevets', 'Timo Ewalds', 'Stig Petersen', 'John Agapiou', 'Hado van Hasselt', 'Alireza Makhzani', 'Sergey Bartunov', 'Paul Keet', 'Karen Simonyan', 'David Silver', 'Anders Ekermo', 'Tom Schaul', 'Timothy Lillicrap', 'Ro... | 2017-08-16 | null | null | null | null | ['real-time-strategy-games'] | ['playing-games'] | [-2.84161121e-01 2.51103967e-01 4.59672138e-02 2.45435849e-01
-6.44377768e-01 -7.93909132e-01 5.63512802e-01 -3.64567228e-02
-8.38692009e-01 8.95354152e-01 -1.77929819e-01 -3.20052505e-01
-2.41935730e-01 -8.73268187e-01 -6.68417990e-01 -6.43692791e-01
-5.28601468e-01 9.93649602e-01 7.06587732e-01 -1.06432498... | [3.6451661586761475, 1.5151746273040771] |
bb680948-25fc-46e6-bb69-6fc38b24c7d9 | semantic-image-attack-for-visual-model | 2303.13010 | null | https://arxiv.org/abs/2303.13010v1 | https://arxiv.org/pdf/2303.13010v1.pdf | Semantic Image Attack for Visual Model Diagnosis | In practice, metric analysis on a specific train and test dataset does not guarantee reliable or fair ML models. This is partially due to the fact that obtaining a balanced, diverse, and perfectly labeled dataset is typically expensive, time-consuming, and error-prone. Rather than relying on a carefully designed test s... | ['Fernando de la Torre', 'Dong Huang', 'Chen Henry Wu', 'Zhaoning Wang', 'Jinqi Luo'] | 2023-03-23 | null | null | null | null | ['keypoint-detection'] | ['computer-vision'] | [ 3.54482204e-01 3.78031051e-03 1.74639001e-02 -1.87746167e-01
-9.60690022e-01 -1.10950744e+00 5.07000029e-01 2.61889666e-01
7.15850340e-03 5.19434452e-01 -1.40433028e-01 -4.82001156e-01
-1.53108791e-01 -8.93631518e-01 -8.43641579e-01 -5.97167790e-01
1.88288391e-02 3.56872171e-01 4.83106934e-02 -5.07452749... | [5.633944511413574, 7.869203090667725] |
24011ed5-75f1-43f2-b034-71f82e508cc2 | enhancing-continual-relation-extraction-via | 2305.04636 | null | https://arxiv.org/abs/2305.04636v1 | https://arxiv.org/pdf/2305.04636v1.pdf | Enhancing Continual Relation Extraction via Classifier Decomposition | Continual relation extraction (CRE) models aim at handling emerging new relations while avoiding catastrophically forgetting old ones in the streaming data. Though improvements have been shown by previous CRE studies, most of them only adopt a vanilla strategy when models first learn representations of new relations. I... | ['Zhifang Sui', 'Yunbo Cao', 'Binghuai Lin', 'Tianyu Liu', 'Peiyi Wang', 'Heming Xia'] | 2023-05-08 | null | null | null | null | ['relation-extraction', 'continual-relation-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.93175906e-01 5.76815546e-01 -6.44971550e-01 -3.61507863e-01
-1.57509595e-01 -1.63105890e-01 6.00346684e-01 5.10243118e-01
-2.10460201e-01 9.02724802e-01 2.82775387e-02 -2.84463406e-01
-3.38966474e-02 -1.03476417e+00 -7.26581335e-01 -5.25973082e-01
-2.27266788e-01 3.45473647e-01 3.36311340e-01 -4.13253933... | [9.1884126663208, 8.526650428771973] |
b15ef986-dc14-41ad-939e-53d50e229e4e | self-supervised-hybrid-inference-in-state | 2107.13349 | null | https://arxiv.org/abs/2107.13349v3 | https://arxiv.org/pdf/2107.13349v3.pdf | Self-Supervised Inference in State-Space Models | We perform approximate inference in state-space models with nonlinear state transitions. Without parameterizing a generative model, we apply Bayesian update formulas using a local linearity approximation parameterized by neural networks. This comes accompanied by a maximum likelihood objective that requires no supervis... | ['Patrick Forré', 'David Ruhe'] | 2021-07-28 | self-supervised-inference-in-state-space | https://openreview.net/forum?id=VPjw9KPWRSK | https://openreview.net/pdf?id=VPjw9KPWRSK | iclr-2022-4 | ['audio-denoising'] | ['audio'] | [-1.53493568e-01 2.81392545e-01 6.17928021e-02 -3.85652989e-01
-6.36990428e-01 -4.02061611e-01 1.02872658e+00 -2.45388210e-01
-5.00281513e-01 9.87942457e-01 2.13630050e-01 -3.22643220e-01
-5.93569614e-02 -6.27875984e-01 -6.64214909e-01 -9.84812796e-01
-1.10245891e-01 6.25316262e-01 5.68307266e-02 -8.58884305... | [6.900444507598877, 3.809490442276001] |
862b154d-0e7e-4026-bf84-d9cc128afd58 | a-comparison-of-strategies-for-source-free | null | null | https://openreview.net/forum?id=r0MNmnx4gHX | https://openreview.net/pdf?id=r0MNmnx4gHX | A Comparison of Strategies for Source-Free Domain Adaptation | Data sharing restrictions are common in NLP, especially in the clinical domain, but there is limited research on adapting models to new domains without access to the original training data, a setting known as source-free domain adaptation. We take algorithms that traditionally assume access to the source-domain trainin... | ['Anonymous'] | 2021-08-17 | null | null | null | acl-arr-august-2021-8 | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 5.60842812e-01 7.70167887e-01 -9.88948882e-01 -6.40237570e-01
-1.23215008e+00 -6.69982672e-01 5.92095494e-01 5.15504599e-01
-9.20258701e-01 1.42309070e+00 5.01308382e-01 -3.51508230e-01
-2.29868278e-01 -4.46470350e-01 -6.53123677e-01 -4.74216998e-01
4.32587042e-02 1.03620410e+00 8.55912864e-02 -1.17437176... | [10.600848197937012, 7.931034088134766] |
b4a578bd-1b61-4341-8186-5ab792c36c57 | identifying-the-module-structure-of-swarms | null | null | https://www.sciencedirect.com/science/article/abs/pii/S0952197621000610 | https://www.sciencedirect.com/science/article/abs/pii/S0952197621000610 | Identifying the module structure of swarms using a new framework of network-based time series clustering | Swarm is a collective motion phenomenon whose dynamic mechanism and cooperation structure could be identified based on observations. Unmanned Aerial Vehicles (UAV) is a special artificial swarm with unique rules and structures. Therefore, corresponding identification methods need to be developed. One critical identific... | ['Liang Yan', 'Guanlin Wu', 'Xiaojun Duan', 'Ziyang Mao', 'Kongjing Gu'] | 2021-03-01 | null | null | null | engineering-applications-of-artificial-3 | ['time-series-clustering'] | ['time-series'] | [-5.99337995e-01 -8.85408640e-01 1.83106199e-01 2.86032796e-01
3.31017226e-01 -9.15326893e-01 3.88250858e-01 2.56571770e-01
-3.59472958e-03 4.95798916e-01 -5.15488684e-01 -1.39195204e-01
-9.54966605e-01 -6.98742509e-01 -1.63985744e-01 -1.20713675e+00
-9.89453495e-01 1.15240306e-01 6.26616120e-01 -5.01821101... | [7.212841510772705, 3.429392099380493] |
390494ea-d020-44c8-be16-670f0c76f059 | masked-face-image-classification-with-sparse | 2011.04556 | null | https://arxiv.org/abs/2011.04556v1 | https://arxiv.org/pdf/2011.04556v1.pdf | Masked Face Image Classification with Sparse Representation based on Majority Voting Mechanism | Sparse approximation is the problem to find the sparsest linear combination for a signal from a redundant dictionary, which is widely applied in signal processing and compressed sensing. In this project, I manage to implement the Orthogonal Matching Pursuit (OMP) algorithm and Sparse Representation-based Classification... | ['Han Wang'] | 2020-11-09 | null | null | null | null | ['sparse-representation-based-classification'] | ['computer-vision'] | [ 4.92353946e-01 -2.45108858e-01 -1.24258883e-01 -3.54690015e-01
-4.85839009e-01 2.31630161e-01 2.96210438e-01 -5.04239142e-01
1.19608492e-01 5.91364563e-01 4.48663265e-01 -2.94083566e-03
-8.75605717e-02 -3.54519814e-01 -1.00455783e-01 -7.62741864e-01
-6.11596227e-01 7.90367126e-02 -4.39282835e-01 -2.54087269... | [12.47833251953125, 0.3700394332408905] |
16649795-1e58-4756-bbd3-a527727514f3 | multipath-time-delay-estimation-with | 2307.02113 | null | https://arxiv.org/abs/2307.02113v1 | https://arxiv.org/pdf/2307.02113v1.pdf | Multipath Time-delay Estimation with Impulsive Noise via Bayesian Compressive Sensing | Multipath time-delay estimation is commonly encountered in radar and sonar signal processing. In some real-life environments, impulse noise is ubiquitous and significantly degrades estimation performance. Here, we propose a Bayesian approach to tailor the Bayesian Compressive Sensing (BCS) to mitigate impulsive noises.... | ['Hangfang Zhao', 'Lei Cheng', 'Xingyu Ji'] | 2023-07-05 | null | null | null | null | ['compressive-sensing'] | ['computer-vision'] | [ 4.08333063e-01 -8.46142054e-01 2.16946766e-01 -2.39715174e-01
-1.04937673e+00 -2.44284347e-01 1.98797673e-01 -3.27384263e-01
-3.69326949e-01 7.38860607e-01 2.85619527e-01 -2.13746488e-01
-5.22103727e-01 -4.08159763e-01 -3.88378769e-01 -1.15814519e+00
-3.74925792e-01 -7.08228722e-02 4.18207943e-01 8.63606036... | [6.463106632232666, 1.3759515285491943] |
f75f7e11-9c6d-48a4-8a1a-06a86dc1d904 | bipartite-graph-network-with-adaptive-message | 2104.00308 | null | https://arxiv.org/abs/2104.00308v2 | https://arxiv.org/pdf/2104.00308v2.pdf | Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph Generation | Scene graph generation is an important visual understanding task with a broad range of vision applications. Despite recent tremendous progress, it remains challenging due to the intrinsic long-tailed class distribution and large intra-class variation. To address these issues, we introduce a novel confidence-aware bipar... | ['Xuming He', 'Bo Wan', 'Songyang Zhang', 'Rongjie Li'] | 2021-04-01 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Li_Bipartite_Graph_Network_With_Adaptive_Message_Passing_for_Unbiased_Scene_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Li_Bipartite_Graph_Network_With_Adaptive_Message_Passing_for_Unbiased_Scene_CVPR_2021_paper.pdf | cvpr-2021-1 | ['unbiased-scene-graph-generation'] | ['computer-vision'] | [ 5.44825196e-01 -1.67188257e-01 -2.04234436e-01 -4.62092876e-01
-4.51144606e-01 -2.83030421e-01 4.19998318e-01 2.08658278e-01
-2.65511870e-01 1.02617621e+00 1.94740951e-01 -2.79106528e-01
-1.11183776e-02 -8.50492775e-01 -8.98885906e-01 -7.31578946e-01
1.62798867e-01 3.16199511e-01 3.80348980e-01 1.81930423... | [10.202813148498535, 1.8824729919433594] |
8537b63a-1ea4-4dcf-bb41-6f7b702bd0a9 | measuring-axiomatic-soundness-of | 2303.01274 | null | https://arxiv.org/abs/2303.01274v1 | https://arxiv.org/pdf/2303.01274v1.pdf | Measuring axiomatic soundness of counterfactual image models | We present a general framework for evaluating image counterfactuals. The power and flexibility of deep generative models make them valuable tools for learning mechanisms in structural causal models. However, their flexibility makes counterfactual identifiability impossible in the general case. Motivated by these issues... | ['Ben Glocker', 'Daniel C. Castro', 'Nick Pawlowski', 'Fabio De Sousa Ribeiro', 'Miguel Monteiro'] | 2023-03-02 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 2.13375594e-02 4.23749059e-01 -6.32188082e-01 -2.08347172e-01
-1.41163260e-01 -7.36494839e-01 1.17501688e+00 -1.42300472e-01
-2.82453328e-01 1.09951389e+00 6.53543055e-01 -9.71661687e-01
-5.74350536e-01 -9.78471458e-01 -7.05219686e-01 -4.47879940e-01
-4.87567186e-01 4.76879388e-01 -1.82473645e-01 1.23229355... | [8.45376968383789, 5.543417453765869] |
a60e8ac7-6f3c-459a-bf14-0953336e767d | a-graph-based-neural-model-for-end-to-end | 2109.12319 | null | https://arxiv.org/abs/2109.12319v1 | https://arxiv.org/pdf/2109.12319v1.pdf | A Graph-Based Neural Model for End-to-End Frame Semantic Parsing | Frame semantic parsing is a semantic analysis task based on FrameNet which has received great attention recently. The task usually involves three subtasks sequentially: (1) target identification, (2) frame classification and (3) semantic role labeling. The three subtasks are closely related while previous studies model... | ['Meishan Zhang', 'Yueheng Sun', 'Zhichao Lin'] | 2021-09-25 | null | https://aclanthology.org/2021.emnlp-main.314 | https://aclanthology.org/2021.emnlp-main.314.pdf | emnlp-2021-11 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 5.97319603e-01 5.82194924e-01 -2.97995776e-01 -5.26736200e-01
-4.70979303e-01 -4.02594239e-01 6.18828833e-01 4.28934656e-02
-2.57056147e-01 6.81746304e-01 4.01663274e-01 -3.12596768e-01
3.69312674e-01 -8.71514797e-01 -7.88792074e-01 -4.50996071e-01
3.54758978e-01 1.71172336e-01 8.51269126e-01 -8.17711949... | [10.267305374145508, 9.216886520385742] |
e58fd489-230c-4166-9379-0b2e4121fc92 | learning-shadow-correspondence-for-video | 2208.00150 | null | https://arxiv.org/abs/2208.00150v1 | https://arxiv.org/pdf/2208.00150v1.pdf | Learning Shadow Correspondence for Video Shadow Detection | Video shadow detection aims to generate consistent shadow predictions among video frames. However, the current approaches suffer from inconsistent shadow predictions across frames, especially when the illumination and background textures change in a video. We make an observation that the inconsistent predictions are ca... | ['Xiaomeng Li', 'Xiaowei Hu', 'Jingweng Yang', 'Xinpeng Ding'] | 2022-07-30 | null | null | null | null | ['shadow-detection'] | ['computer-vision'] | [ 4.08029705e-01 -3.53932112e-01 -6.26832098e-02 -4.34862733e-01
-4.48481262e-01 -3.02393675e-01 3.54597420e-01 -4.91949171e-01
5.51560298e-02 6.70861661e-01 8.62201154e-02 -1.39722109e-01
2.39967689e-01 -3.64181846e-01 -8.28174233e-01 -1.02171147e+00
8.04276317e-02 -1.71357587e-01 1.32115793e+00 -1.18624736... | [10.838597297668457, -4.093729496002197] |
e59b381b-743d-4c6d-aebf-50bc9d2cb5a1 | goal-oriented-communications-for-the-iot-and | 2211.05378 | null | https://arxiv.org/abs/2211.05378v1 | https://arxiv.org/pdf/2211.05378v1.pdf | Goal-Oriented Communications for the IoT and Application to Data Compression | Internet of Things (IoT) devices will play an important role in emerging applications, since their sensing, actuation, processing, and wireless communication capabilities stimulate data collection, transmission and decision processes of smart applications. However, new challenges arise from the widespread popularity of... | ['Mehdi Bennis', 'Marios Kountouris', 'Walid Saad', 'Samson Lasaulce', 'Hang Zou', 'Chao Zhang'] | 2022-11-10 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 7.08515823e-01 -1.28549933e-01 -3.87863666e-01 -6.05965734e-01
-1.26749650e-02 -2.67682910e-01 5.83950222e-01 3.28389257e-01
-1.86068475e-01 7.83175826e-01 7.11762309e-01 1.25082657e-01
-7.06209123e-01 -1.27337360e+00 2.10640430e-01 -4.99657363e-01
-2.51553744e-01 4.45289552e-01 5.72098717e-02 -1.06199592... | [7.3905487060546875, 2.455141305923462] |
b00981e4-fa95-44f3-a650-d75b604a752e | quantifying-similarity-between-relations-with | 1907.08937 | null | https://arxiv.org/abs/1907.08937v1 | https://arxiv.org/pdf/1907.08937v1.pdf | Quantifying Similarity between Relations with Fact Distribution | We introduce a conceptually simple and effective method to quantify the similarity between relations in knowledge bases. Specifically, our approach is based on the divergence between the conditional probability distributions over entity pairs. In this paper, these distributions are parameterized by a very simple neural... | ['Weize Chen', 'Maosong Sun', 'Hao Zhu', 'Zhiyuan Liu', 'Xu Han'] | 2019-07-21 | quantifying-similarity-between-relations-with-1 | https://aclanthology.org/P19-1278 | https://aclanthology.org/P19-1278.pdf | acl-2019-7 | ['open-information-extraction'] | ['natural-language-processing'] | [-1.47307768e-01 6.74916267e-01 -4.14682180e-01 -6.00398242e-01
-6.99496627e-01 -6.50817275e-01 3.43928754e-01 3.29770416e-01
-2.00368434e-01 8.86962414e-01 4.56206538e-02 -4.17230964e-01
-2.29629412e-01 -9.48002815e-01 -7.66597688e-01 -2.22092569e-01
-1.05541870e-01 7.14786947e-01 2.15751514e-01 -4.32072550... | [9.391777992248535, 8.650940895080566] |
b162776c-1122-4da5-9a93-f5dac7839227 | multi-head-cross-attentional-ppg-and-motion | 2210.11415 | null | https://arxiv.org/abs/2210.11415v1 | https://arxiv.org/pdf/2210.11415v1.pdf | Multi-Head Cross-Attentional PPG and Motion Signal Fusion for Heart Rate Estimation | Nowadays, Hearth Rate (HR) monitoring is a key feature of almost all wrist-worn devices exploiting photoplethysmography (PPG) sensors. However, arm movements affect the performance of PPG-based HR tracking. This issue is usually addressed by fusing the PPG signal with data produced by inertial measurement units. Thus, ... | ['Charalampos Z. Patrikakis', 'Christos Chatzigeorgiou', 'Alessio Burrello', 'Lazaros Toumanidis', 'Panagiotis Kasnesis'] | 2022-10-14 | null | null | null | null | ['photoplethysmography-ppg', 'heart-rate-estimation'] | ['medical', 'medical'] | [-2.58312859e-02 1.50128782e-01 -1.51770309e-01 -3.51245701e-01
-5.59044898e-01 8.00150633e-02 1.22139558e-01 -3.37221831e-01
-2.77780145e-01 7.53510773e-01 5.86045921e-01 -4.38938290e-02
3.74700092e-02 -3.15959305e-01 -5.20240366e-01 -5.56925833e-01
-1.22347742e-01 -3.47790748e-01 -5.52373052e-01 2.11422630... | [13.918108940124512, 2.9943630695343018] |
6c797b5d-69a7-4315-add5-699fa07670d0 | a-bayesian-detect-to-track-system-for-robust | 2205.02371 | null | https://arxiv.org/abs/2205.02371v1 | https://arxiv.org/pdf/2205.02371v1.pdf | A Bayesian Detect to Track System for Robust Visual Object Tracking and Semi-Supervised Model Learning | Object tracking is one of the fundamental problems in visual recognition tasks and has achieved significant improvements in recent years. The achievements often come with the price of enormous hardware consumption and expensive labor effort for consecutive labeling. A missing ingredient for robust tracking is achieving... | ['Mingchen Gao', 'Chunwei Ma', 'Zhanghexuan Ji', 'Yan Shen'] | 2022-05-05 | null | null | null | null | ['visual-object-tracking'] | ['computer-vision'] | [ 1.00656733e-01 -3.29539299e-01 -3.19841295e-01 -1.91414356e-01
-5.89252889e-01 -3.86566103e-01 5.55772185e-01 -1.59992352e-01
-5.81969798e-01 7.06323743e-01 -4.74743873e-01 9.57667902e-02
-3.60664546e-01 -3.88088167e-01 -8.54226649e-01 -8.11433554e-01
8.19963291e-02 8.36118996e-01 7.24322021e-01 5.34416258... | [6.578737735748291, -1.9359240531921387] |
891a962a-6045-48d9-9445-c0843e33a613 | rigorous-agent-evaluation-an-adversarial | 1812.01647 | null | http://arxiv.org/abs/1812.01647v1 | http://arxiv.org/pdf/1812.01647v1.pdf | Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures | This paper addresses the problem of evaluating learning systems in safety
critical domains such as autonomous driving, where failures can have
catastrophic consequences. We focus on two problems: searching for scenarios
when learned agents fail and assessing their probability of failure. The
standard method for agent e... | ['Nicolas Heess', 'Csaba Szepesvari', 'Avraham Ruderman', 'Keith Anderson', 'Dvijotham', 'Tom Erez', 'Pushmeet Kohli', 'Krishmamurthy', 'Jonathan Uesato', 'Ananya Kumar'] | 2018-12-04 | rigorous-agent-evaluation-an-adversarial-1 | https://openreview.net/forum?id=B1xhQhRcK7 | https://openreview.net/pdf?id=B1xhQhRcK7 | iclr-2019-5 | ['humanoid-control'] | ['robots'] | [-7.84884840e-02 7.56537393e-02 2.02279195e-01 6.15023673e-02
-9.85450566e-01 -6.96060956e-01 7.83237696e-01 1.89513564e-01
-7.35568285e-01 1.30772495e+00 -3.02450269e-01 -4.57750320e-01
-1.85262293e-01 -8.09117794e-01 -9.82370079e-01 -7.28243172e-01
-6.02174580e-01 8.30268681e-01 4.41585422e-01 -5.31134963... | [4.611506938934326, 2.04848051071167] |
81ff01b6-7df5-408c-97ea-35a293bcd240 | detecting-human-and-non-human-vocal | 2302.07640 | null | https://arxiv.org/abs/2302.07640v1 | https://arxiv.org/pdf/2302.07640v1.pdf | Detecting human and non-human vocal productions in large scale audio recordings | We propose an automatic data processing pipeline to extract vocal productions from large-scale natural audio recordings. Through a series of computational steps (windowing, creation of a noise class, data augmentation, re-sampling, transfer learning, Bayesian optimisation), it automatically trains a neural network for ... | ['Arnaud Rey', 'Samuel Tronçon', 'Joël Fagot', 'Thierry Legou', 'Jean-Marc Freyermuth', 'Pierre Pudlo', 'Guillem Bonafos'] | 2023-02-14 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 6.80228472e-01 5.98114058e-02 7.94386506e-01 -5.20770550e-01
-9.13634002e-01 -5.81329644e-01 2.16056749e-01 3.10719520e-01
-8.87148440e-01 4.31909770e-01 1.49658591e-01 3.98535058e-02
5.05552534e-03 -5.32636642e-01 -5.69862902e-01 -3.86429995e-01
-6.17393672e-01 5.10292172e-01 4.92163569e-01 1.28656060... | [15.214920043945312, 5.252146244049072] |
b39f4163-4e70-4312-b7c7-52fcab8d10d1 | integrated-in-vehicle-monitoring-system-using | 2204.07946 | null | https://arxiv.org/abs/2204.07946v2 | https://arxiv.org/pdf/2204.07946v2.pdf | Integrated In-vehicle Monitoring System Using 3D Human Pose Estimation and Seat Belt Segmentation | Recently, along with interest in autonomous vehicles, the importance of monitoring systems for both drivers and passengers inside vehicles has been increasing. This paper proposes a novel in-vehicle monitoring system the combines 3D pose estimation, seat-belt segmentation, and seat-belt status classification networks. ... | ['Joseph Kihoon Kim', 'Suk-Ju Kang', 'Yeong-Hun Park', 'Sung-Sik Cho', 'Hyunsung Kim', 'Ginam Kim'] | 2022-04-17 | null | null | null | null | ['3d-pose-estimation'] | ['computer-vision'] | [-2.32235059e-01 9.00423527e-02 -5.18834174e-01 -9.73422468e-01
-6.32335901e-01 -4.42402631e-01 3.76531959e-01 -1.25043482e-01
-3.97774637e-01 2.31109306e-01 -3.37378979e-01 -4.43324685e-01
-5.79212382e-02 -6.39053464e-01 -8.16631854e-01 -5.40925205e-01
1.50378212e-01 5.66936493e-01 7.04530418e-01 -2.05260679... | [7.914276123046875, -1.1586940288543701] |
949b22b5-8b27-4bc3-ad39-9f69947ad2c9 | robust-one-shot-estimation-over-shared | 2302.14689 | null | https://arxiv.org/abs/2302.14689v1 | https://arxiv.org/pdf/2302.14689v1.pdf | Robust one-shot estimation over shared networks in the presence of denial-of-service attacks | Multi-agent systems often communicate over low-power shared wireless networks in unlicensed spectrum, prone to denial-of-service attacks. We consider the following scenario: multiple pairs of agents communicating strategically over shared communication networks in the presence of a jammer who may launch a denial-of-ser... | ['Marcos M. Vasconcelos', 'Xu Zhang'] | 2023-02-28 | null | null | null | null | ['blocking'] | ['natural-language-processing'] | [ 4.08111691e-01 6.87775016e-01 5.91803808e-03 1.74963757e-01
-9.72566068e-01 -1.07112598e+00 2.00259745e-01 -4.77473401e-02
-6.81345582e-01 8.87299955e-01 -2.99797595e-01 -4.61164266e-01
-6.36511207e-01 -8.68469357e-01 -2.52172351e-01 -1.39227569e+00
-6.76941395e-01 8.60865712e-01 -3.91926855e-01 -1.53440848... | [4.456560134887695, 2.913926362991333] |
7727d1d6-ab39-4e60-bb65-d4bc21d5ecee | sample-efficient-reinforcement-learning-in-3 | 2305.16483 | null | https://arxiv.org/abs/2305.16483v1 | https://arxiv.org/pdf/2305.16483v1.pdf | Sample Efficient Reinforcement Learning in Mixed Systems through Augmented Samples and Its Applications to Queueing Networks | This paper considers a class of reinforcement learning problems, which involve systems with two types of states: stochastic and pseudo-stochastic. In such systems, stochastic states follow a stochastic transition kernel while the transitions of pseudo-stochastic states are deterministic given the stochastic states/tran... | ['Lei Ying', 'Weina Wang', 'Xin Liu', 'Honghao Wei'] | 2023-05-25 | null | null | null | null | ['q-learning'] | ['methodology'] | [ 1.65651277e-01 1.68340523e-02 -3.36625278e-01 -8.87303650e-02
-8.83161426e-01 -4.32825059e-01 3.98344919e-02 1.40627280e-01
-7.06486344e-01 1.04091263e+00 -5.75977802e-01 -8.33849788e-01
-4.36197132e-01 -8.63965154e-01 -8.98371696e-01 -8.78657520e-01
-3.77280563e-01 4.69023019e-01 -1.86432973e-02 -2.02755705... | [4.329890727996826, 2.8107216358184814] |
4a10aba0-d9e3-4532-b186-40fadbffb320 | css-combining-self-training-and-self | 2210.05146 | null | https://arxiv.org/abs/2210.05146v1 | https://arxiv.org/pdf/2210.05146v1.pdf | CSS: Combining Self-training and Self-supervised Learning for Few-shot Dialogue State Tracking | Few-shot dialogue state tracking (DST) is a realistic problem that trains the DST model with limited labeled data. Existing few-shot methods mainly transfer knowledge learned from external labeled dialogue data (e.g., from question answering, dialogue summarization, machine reading comprehension tasks, etc.) into DST, ... | ['Shuguang Cui', 'Wenye Li', 'Huaishao Luo', 'Haipeng Sun', 'Junwei Bao', 'Haoning Zhang'] | 2022-10-11 | null | null | null | null | ['dialogue-state-tracking', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.30018282e-01 4.75109339e-01 -3.02125782e-01 -6.57259703e-01
-6.87841594e-01 -1.37060463e-01 5.47360122e-01 1.50691554e-01
-5.15008926e-01 9.04324293e-01 4.93099362e-01 1.04430191e-01
3.90127897e-01 -6.73846185e-01 -3.32655579e-01 -4.78597045e-01
4.13361102e-01 6.00455761e-01 6.24703586e-01 -6.53826475... | [12.734375953674316, 7.892882823944092] |
2b0c36c7-c6ad-4894-9f1d-8df684715ee3 | exploring-the-challenges-of-open-domain-multi | 2212.10526 | null | https://arxiv.org/abs/2212.10526v2 | https://arxiv.org/pdf/2212.10526v2.pdf | Towards multi-document summarization in the open-domain | Multi-document summarization (MDS) traditionally assumes a set of topic-related documents are provided. However, this document set is often an artifact of the dataset curation process; in practice, it is not necessarily available and would need to be retrieved given an information need, i.e. a question or topic stateme... | ['Arman Cohan', 'Lucy Lu Wang', 'Kyle Lo', 'Gary Bader', 'Bo wang', 'Luca Soldaini', 'John Giorgi'] | 2022-12-20 | null | null | null | null | ['document-summarization'] | ['natural-language-processing'] | [ 9.94446650e-02 -2.97495052e-02 -3.16234022e-01 -2.08257228e-01
-1.66163313e+00 -1.01865530e+00 7.75421500e-01 7.31163919e-01
-2.72519767e-01 9.57862854e-01 8.95686805e-01 -1.71845362e-01
-4.23831791e-01 -6.04769468e-01 -6.67298853e-01 -2.68002331e-01
-1.52862323e-02 8.50279331e-01 4.05424565e-01 -3.10102314... | [12.350746154785156, 9.372283935546875] |
64e68ecc-f7bf-410c-bc6e-a1649cf62219 | manifold-partition-discriminant-analysis | 2011.11521 | null | https://arxiv.org/abs/2011.11521v1 | https://arxiv.org/pdf/2011.11521v1.pdf | Manifold Partition Discriminant Analysis | We propose a novel algorithm for supervised dimensionality reduction named Manifold Partition Discriminant Analysis (MPDA). It aims to find a linear embedding space where the within-class similarity is achieved along the direction that is consistent with the local variation of the data manifold, while nearby data belon... | ['Shiliang Sun', 'Yang Zhou'] | 2020-11-23 | null | null | null | null | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [-5.84026039e-01 -2.43854284e-01 -3.19658101e-01 -1.57033101e-01
-3.97444993e-01 -6.69384062e-01 4.01776582e-01 1.20050192e-01
1.19919844e-01 3.87091152e-02 1.95529759e-01 1.05858207e-01
-6.76144063e-01 -6.51783228e-01 -7.25055560e-02 -9.51882362e-01
-4.70105678e-01 3.25066119e-01 -6.72055855e-02 1.79755911... | [7.881397247314453, 4.138488292694092] |
9da46d09-5077-453c-b2a7-490a2b73e211 | towards-accurate-data-free-quantization-for | 2305.18723 | null | https://arxiv.org/abs/2305.18723v3 | https://arxiv.org/pdf/2305.18723v3.pdf | Towards Accurate Data-free Quantization for Diffusion Models | In this paper, we propose an accurate data-free post-training quantization framework of diffusion models (ADP-DM) for efficient image generation. Conventional data-free quantization methods learn shared quantization functions for tensor discretization regardless of the generation timesteps, while the activation distrib... | ['Jiwen Lu', 'Jie zhou', 'Yansong Tang', 'Xiuwei Xu', 'Ziwei Wang', 'Changyuan Wang'] | 2023-05-30 | null | null | null | null | ['data-free-quantization', 'data-free-quantization'] | ['computer-vision', 'methodology'] | [-1.52481243e-01 -2.57531125e-02 -1.55669764e-01 -2.15153396e-01
-1.02228332e+00 -3.92084509e-01 5.17621338e-01 -8.75520855e-02
-3.68362904e-01 5.98436773e-01 2.58091480e-01 7.60737211e-02
-3.73644501e-01 -9.03237760e-01 -5.76870084e-01 -1.06624925e+00
7.65917972e-02 5.37048101e-01 2.17263788e-01 -8.71531963... | [11.245438575744629, -0.49560225009918213] |
99329e4e-83fe-48e5-befb-7f5e4c996fa6 | how-to-detect-unauthorized-data-usages-in | 2307.03108 | null | https://arxiv.org/abs/2307.03108v1 | https://arxiv.org/pdf/2307.03108v1.pdf | How to Detect Unauthorized Data Usages in Text-to-image Diffusion Models | Recent text-to-image diffusion models have shown surprising performance in generating high-quality images. However, concerns have arisen regarding the unauthorized usage of data during the training process. One example is when a model trainer collects a set of images created by a particular artist and attempts to train... | ['Shiqing Ma', 'Dimitris Metaxas', 'Lingjuan Lyu', 'Yuchen Liu', 'Chen Chen', 'Zhenting Wang'] | 2023-07-06 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 8.61068189e-01 -1.57065645e-01 1.38836913e-02 1.20729275e-01
-3.30818117e-01 -9.94860709e-01 8.29556525e-01 9.78232399e-02
-3.16470891e-01 5.29110968e-01 -3.24690878e-01 -5.66672087e-01
2.58644521e-01 -9.50089753e-01 -8.93310666e-01 -8.93879950e-01
-2.47943699e-02 -1.77256271e-01 2.04982027e-01 1.41904980... | [12.417925834655762, 1.115529179573059] |
21a4eebc-a6be-43cc-9d94-14611ac4f754 | embeddings-models-for-buddhist-sanskrit | null | null | https://aclanthology.org/2022.lrec-1.411 | https://aclanthology.org/2022.lrec-1.411.pdf | Embeddings models for Buddhist Sanskrit | The paper presents novel resources and experiments for Buddhist Sanskrit, broadly defined here including all the varieties of Sanskrit in which Buddhist texts have been transmitted. We release a novel corpus of Buddhist texts, a novel corpus of general Sanskrit and word similarity and word analogy datasets for intrinsi... | ['Senja Pollak', 'Andraž Pelicon', 'Matej Martinc', 'Ligeia Lugli'] | null | null | null | null | lrec-2022-6 | ['word-similarity'] | ['natural-language-processing'] | [-4.49188471e-01 -1.02636427e-01 -2.46333212e-01 -2.05210119e-01
-2.99920201e-01 -8.66167188e-01 1.18687832e+00 3.13294411e-01
-1.01358032e+00 2.87862331e-01 7.46807933e-01 -6.96583748e-01
-3.18214893e-01 -7.94538558e-01 1.47712678e-01 -7.44486034e-01
1.39103206e-02 1.10973203e+00 1.38287351e-01 -8.08523893... | [10.875288963317871, 9.911118507385254] |
e2cefcc4-352f-4d31-97e4-59b564079629 | adaptive-self-training-for-object-detection | 2212.05911 | null | https://arxiv.org/abs/2212.05911v1 | https://arxiv.org/pdf/2212.05911v1.pdf | Adaptive Self-Training for Object Detection | Deep learning has emerged as an effective solution for solving the task of object detection in images but at the cost of requiring large labeled datasets. To mitigate this cost, semi-supervised object detection methods, which consist in leveraging abundant unlabeled data, have been proposed and have already shown impre... | ['Marc Van Droogenbroeck', 'Gilles Louppe', 'Renaud Vandeghen'] | 2022-12-07 | null | null | null | null | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 1.70561418e-01 -4.47470369e-03 -1.36190578e-01 -5.04858017e-01
-9.64212060e-01 -7.06302047e-01 4.71767843e-01 2.36430451e-01
-6.65064633e-01 4.68568563e-01 -4.64477807e-01 -1.33637324e-01
8.47560987e-02 -6.90821230e-01 -7.80795813e-01 -9.15213168e-01
1.85266048e-01 9.09560800e-01 8.44341695e-01 1.74482599... | [9.21130657196045, 1.1997158527374268] |
dcffcf18-60e8-4c61-82ef-2f3959989c74 | numerical-data-imputation-for-multimodal-data | 2306.16906 | null | https://arxiv.org/abs/2306.16906v2 | https://arxiv.org/pdf/2306.16906v2.pdf | Numerical Data Imputation for Multimodal Data Sets: A Probabilistic Nearest-Neighbor Kernel Density Approach | Numerical data imputation algorithms replace missing values by estimates to leverage incomplete data sets. Current imputation methods seek to minimize the error between the unobserved ground truth and the imputed values. But this strategy can create artifacts leading to poor imputation in the presence of multimodal or ... | ['Florian Lalande', 'Kenji Doya'] | 2023-06-29 | null | null | null | null | ['imputation', 'density-estimation', 'imputation', 'imputation'] | ['computer-vision', 'methodology', 'miscellaneous', 'time-series'] | [-1.26006186e-01 2.70607304e-02 -2.63318360e-01 -7.94770360e-01
-1.13711333e+00 -4.08552617e-01 1.02873392e-01 1.21048398e-01
-2.29116306e-01 1.41722214e+00 5.04048645e-01 -2.00077489e-01
-4.76254582e-01 -1.04511178e+00 -7.92241633e-01 -4.82170969e-01
4.30250540e-02 5.79003751e-01 -5.41338742e-01 2.87736654... | [7.642595291137695, 4.849862098693848] |
323768fa-1df2-40a7-9148-704695592278 | a-study-of-bayesian-neural-network-surrogates | 2305.20028 | null | https://arxiv.org/abs/2305.20028v1 | https://arxiv.org/pdf/2305.20028v1.pdf | A Study of Bayesian Neural Network Surrogates for Bayesian Optimization | Bayesian optimization is a highly efficient approach to optimizing objective functions which are expensive to query. These objectives are typically represented by Gaussian process (GP) surrogate models which are easy to optimize and support exact inference. While standard GP surrogates have been well-established in Bay... | ['Andrew Gordon Wilson', 'Tim G. J. Rudner', 'Yucen Lily Li'] | 2023-05-31 | null | null | null | null | ['bayesian-optimization'] | ['methodology'] | [-1.29583627e-01 -2.23325342e-01 -2.35121399e-01 -2.72885352e-01
-1.29109430e+00 -4.03532654e-01 8.22268844e-01 5.74444160e-02
-4.20518458e-01 1.21854377e+00 2.55302787e-01 -3.68811101e-01
-6.56392574e-01 -7.92274058e-01 -8.19907665e-01 -8.25850129e-01
-1.29131958e-01 1.21416569e+00 -1.02222860e-01 1.70805514... | [6.888846397399902, 3.9453330039978027] |
f3d7aff2-5dd9-4f88-bfd0-e4bb7c10c2a2 | an-instantaneous-market-volatility-estimation | 1908.02847 | null | https://arxiv.org/abs/1908.02847v2 | https://arxiv.org/pdf/1908.02847v2.pdf | An instantaneous market volatility estimation | Working on different aspects of algorithmic trading we empirically discovered a new market invariant. It links together the volatility of the instrument with its traded volume, the average spread and the volume in the order book. The invariant has been tested on different markets and different asset classes. In all cas... | ['Bruce Bland', 'Oleh Danyliv'] | 2019-08-07 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-7.67127931e-01 -7.47072250e-02 1.18092977e-01 -1.53686389e-01
-9.02772769e-02 -1.15509343e+00 9.82767165e-01 1.24003656e-01
-4.04040754e-01 7.61163592e-01 -3.32274586e-02 -7.26691782e-01
-2.60461748e-01 -1.07894218e+00 -2.20104545e-01 -4.77029085e-01
-3.56826723e-01 8.70209694e-01 4.92945850e-01 -3.92851681... | [4.755692481994629, 4.072275638580322] |
9f35abac-aebc-4d7a-8e32-6d20525230a9 | domain-agnostic-batch-bayesian-optimization | 2306.05843 | null | https://arxiv.org/abs/2306.05843v1 | https://arxiv.org/pdf/2306.05843v1.pdf | Domain-Agnostic Batch Bayesian Optimization with Diverse Constraints via Bayesian Quadrature | Real-world optimisation problems often feature complex combinations of (1) diverse constraints, (2) discrete and mixed spaces, and are (3) highly parallelisable. (4) There are also cases where the objective function cannot be queried if unknown constraints are not satisfied, e.g. in drug discovery, safety on animal exp... | ['Michael A. Osborne', 'Harald Oberhauser', 'Martin Jørgensen', 'Xingchen Wan', 'Satoshi Hayakawa', 'Masaki Adachi'] | 2023-06-09 | null | null | null | null | ['drug-discovery', 'bayesian-optimisation', 'bayesian-optimization'] | ['medical', 'methodology', 'methodology'] | [ 5.43974519e-01 6.12110943e-02 -4.02308017e-01 7.01343119e-02
-9.08559322e-01 -9.48257744e-01 4.31396395e-01 2.97110319e-01
-6.33623004e-01 1.10239995e+00 -2.01349005e-01 -9.22999024e-01
-8.91198277e-01 -5.37760258e-01 -6.05914295e-01 -7.80586064e-01
-1.17700063e-01 1.02409327e+00 1.17897816e-01 -7.61003941... | [6.08876371383667, 3.8277504444122314] |
a2351122-ea4a-44ca-b433-b1bf7e82adbd | deep-convolutional-neural-networks-for-6 | 1605.09507 | null | http://arxiv.org/abs/1605.09507v3 | http://arxiv.org/pdf/1605.09507v3.pdf | Deep convolutional neural networks for predominant instrument recognition in polyphonic music | Identifying musical instruments in polyphonic music recordings is a
challenging but important problem in the field of music information retrieval.
It enables music search by instrument, helps recognize musical genres, or can
make music transcription easier and more accurate. In this paper, we present a
convolutional ne... | ['Yoonchang Han', 'Kyogu Lee', 'Jaehun Kim'] | 2016-05-31 | null | null | null | null | ['instrument-recognition', 'music-transcription'] | ['audio', 'music'] | [ 3.64295274e-01 -7.84189880e-01 -2.41480060e-02 1.16109557e-01
-8.45670938e-01 -9.09593999e-01 -1.77352846e-01 -1.59660414e-01
-3.30723464e-01 3.63088757e-01 1.05605006e-01 1.65278792e-01
-6.71394348e-01 -4.75527793e-01 -4.23121959e-01 -7.73841560e-01
-3.77278596e-01 -2.26129159e-01 -2.71035075e-01 3.25823277... | [15.795486450195312, 5.293245792388916] |
59101d91-7bcf-45ad-b41d-091ccf36dc84 | lungrn-nl-an-improved-adventitious-lung-sound | null | null | https://www.isca-speech.org/archive/interspeech_2020/ma20_interspeech.html | https://www.researchgate.net/publication/343524153_LungRNNL_An_Improved_Adventitious_Lung_Sound_Classification_Using_Non-Local_Block_ResNet_Neural_Network_with_Mixup_Data_Augmentation | LungRN+NL: An Improved Adventitious Lung Sound Classification Using Non-Local Block ResNet Neural Network with Mixup Data Augmentation | Performing an automated adventitious lung sound detection is a challenging task since the sound is susceptible to noises (heart-beat, motion artifacts, and audio sound) and there is subtle discrimination among different categories. An adventitious lung sound classification model, LungRN+NL, is proposed in this work, wh... | ['Yongfu Li', 'Xinzi Xu', 'Yi Ma'] | 2020-08-01 | null | null | null | interspeech-2020-8 | ['sound-classification'] | ['audio'] | [-2.26675114e-03 -1.39034078e-01 1.29997417e-01 2.23440394e-01
-8.43794346e-01 -2.98882443e-02 3.26166004e-01 -7.05549195e-02
-3.62410963e-01 4.73705471e-01 4.88135576e-01 -1.35082111e-01
3.31434578e-01 -5.37917435e-01 -2.66739935e-01 -4.99051839e-01
1.92719638e-01 3.02023172e-01 9.81115043e-01 7.40963146... | [14.5762300491333, 3.9329495429992676] |
2a83258d-649f-435e-b4ec-b468ba9c2106 | uncertainty-in-ontology-matching-a-decision | 1501.05724 | null | http://arxiv.org/abs/1501.05724v1 | http://arxiv.org/pdf/1501.05724v1.pdf | Uncertainty in Ontology Matching: A Decision Rule-Based Approach | Considering the high heterogeneity of the ontologies pub-lished on the web,
ontology matching is a crucial issue whose aim is to establish links between an
entity of a source ontology and one or several entities from a target ontology.
Perfectible similarity measures, consid-ered as sources of information, are
combined... | ['Grégory Smits', 'Arnaud Martin', 'Amira Essaid', 'Boutheina Ben Yaghlane'] | 2015-01-23 | null | null | null | null | ['ontology-matching'] | ['knowledge-base'] | [-3.53933901e-01 3.46920252e-01 -2.79728234e-01 -5.58229923e-01
-5.44776976e-01 -4.87328500e-01 6.36417985e-01 1.10168362e+00
-5.21702468e-01 9.46593165e-01 -1.78179815e-01 9.94623229e-02
-7.67083704e-01 -1.38840914e+00 -3.25362086e-01 -3.27807516e-01
-2.93361634e-01 7.46571541e-01 8.23379636e-01 -2.47452825... | [9.195296287536621, 8.104598045349121] |
f5a3fb19-1dcb-44df-8f27-58738e5a3c3d | a-simple-neural-network-module-for-relational | 1706.01427 | null | http://arxiv.org/abs/1706.01427v1 | http://arxiv.org/pdf/1706.01427v1.pdf | A simple neural network module for relational reasoning | Relational reasoning is a central component of generally intelligent
behavior, but has proven difficult for neural networks to learn. In this paper
we describe how to use Relation Networks (RNs) as a simple plug-and-play module
to solve problems that fundamentally hinge on relational reasoning. We tested
RN-augmented n... | ['Mateusz Malinowski', 'David G. T. Barrett', 'Adam Santoro', 'David Raposo', 'Timothy Lillicrap', 'Razvan Pascanu', 'Peter Battaglia'] | 2017-06-05 | a-simple-neural-network-module-for-relational-1 | http://papers.nips.cc/paper/7082-a-simple-neural-network-module-for-relational-reasoning | http://papers.nips.cc/paper/7082-a-simple-neural-network-module-for-relational-reasoning.pdf | neurips-2017-12 | ['multi-modal'] | ['miscellaneous'] | [-1.76920325e-01 7.19964504e-01 2.71151155e-01 -3.15771580e-01
-3.54150921e-01 -6.01648688e-01 8.68231714e-01 7.22417682e-02
-7.34536946e-02 4.67411846e-01 1.26006408e-02 -9.28549349e-01
-4.24825102e-01 -1.30034339e+00 -1.05784237e+00 -1.77301899e-01
-2.56169170e-01 9.27671313e-01 5.38730621e-01 -8.62903535... | [10.646239280700684, 2.246379852294922] |
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