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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
a9e12292-36b5-459d-93e0-7e9a1551ae8b | towards-cross-language-prosody-transfer-for | 2307.04123 | null | https://arxiv.org/abs/2307.04123v1 | https://arxiv.org/pdf/2307.04123v1.pdf | Towards cross-language prosody transfer for dialog | Speech-to-speech translation systems today do not adequately support use for dialog purposes. In particular, nuances of speaker intent and stance can be lost due to improper prosody transfer. We present an exploration of what needs to be done to overcome this. First, we developed a data collection protocol in which bil... | ['Nigel G. Ward', 'Jonathan E. Avila'] | 2023-07-09 | null | null | null | null | ['speech-to-speech-translation'] | ['speech'] | [ 2.04925790e-01 2.49728486e-01 -2.57600754e-01 -7.37151444e-01
-1.27610826e+00 -7.83428669e-01 6.22880220e-01 2.77272224e-01
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3.39498788e-01 5.32891214e-01 6.98714182e-02 -4.57113504... | [14.139726638793945, 7.3188557624816895] |
8676fed3-4ec6-45a3-a1eb-c6c5acfb2919 | how-modular-should-neural-module-networks-be | 2106.08170 | null | https://arxiv.org/abs/2106.08170v2 | https://arxiv.org/pdf/2106.08170v2.pdf | How Modular Should Neural Module Networks Be for Systematic Generalization? | Neural Module Networks (NMNs) aim at Visual Question Answering (VQA) via composition of modules that tackle a sub-task. NMNs are a promising strategy to achieve systematic generalization, i.e., overcoming biasing factors in the training distribution. However, the aspects of NMNs that facilitate systematic generalizatio... | ['Xavier Boix', 'Tomotake Sasaki', "Vanessa D'Amario"] | 2021-06-15 | null | http://proceedings.neurips.cc/paper/2021/hash/c467978aaae44a0e8054e174bc0da4bb-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/c467978aaae44a0e8054e174bc0da4bb-Paper.pdf | neurips-2021-12 | ['systematic-generalization'] | ['reasoning'] | [-1.90770272e-02 1.95278093e-01 4.54490911e-03 -2.83296824e-01
-6.56278133e-02 -6.46122813e-01 5.76569259e-01 -1.40690401e-01
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2.69514233e-01 2.05910563e-01 1.44907460e-01 -5.72015345... | [10.526273727416992, 2.0699119567871094] |
52aa2ccd-93d7-409b-96e3-59885293a102 | fuzzy-logic-for-vagueness-management-in | null | null | https://aclanthology.org/2020.intellang-1.8 | https://aclanthology.org/2020.intellang-1.8.pdf | Fuzzy Logic for Vagueness Management in Referring Expression Generation | null | ['Daniel Sánchez', 'Gustavo Rivas-Gervilla', 'Nicolás Marín'] | null | null | null | null | intellang-2020-9-1 | ['referring-expression-generation'] | ['computer-vision'] | [-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... | [-7.243940353393555, 3.7471489906311035] |
86565ac7-d78b-49d1-b47c-9696cb98325e | robust-brain-magnetic-resonance-image | 2001.03857 | null | https://arxiv.org/abs/2001.03857v1 | https://arxiv.org/pdf/2001.03857v1.pdf | Robust Brain Magnetic Resonance Image Segmentation for Hydrocephalus Patients: Hard and Soft Attention | Brain magnetic resonance (MR) segmentation for hydrocephalus patients is considered as a challenging work. Encoding the variation of the brain anatomical structures from different individuals cannot be easily achieved. The task becomes even more difficult especially when the image data from hydrocephalus patients are c... | ['Lichi Zhang', 'Kai Xuan', 'Xuhua Ren', 'Qian Wang', 'Jiayu Huo', 'Dongming Wei'] | 2020-01-12 | null | null | null | null | ['hard-attention'] | ['methodology'] | [-1.05060562e-01 2.21868232e-01 5.03695607e-01 -4.80189025e-01
-4.47024077e-01 -3.46751474e-02 1.91744119e-02 -3.81366313e-02
-7.05970943e-01 6.43009067e-01 3.62532198e-01 9.72045511e-02
-7.19667375e-02 -4.46772903e-01 -2.46075526e-01 -7.36673594e-01
-3.92307192e-01 8.81188452e-01 4.08716023e-01 -2.21529692... | [14.273903846740723, -2.3706436157226562] |
9508b1fe-2d4e-4242-9fdd-d98f747aff9c | x-former-in-memory-acceleration-of | 2303.07470 | null | https://arxiv.org/abs/2303.07470v1 | https://arxiv.org/pdf/2303.07470v1.pdf | X-Former: In-Memory Acceleration of Transformers | Transformers have achieved great success in a wide variety of natural language processing (NLP) tasks due to the attention mechanism, which assigns an importance score for every word relative to other words in a sequence. However, these models are very large, often reaching hundreds of billions of parameters, and there... | ['Anand Raghunathan', 'Kaushik Roy', 'Jacob R. Stevens', 'Shrihari Sridharan'] | 2023-03-13 | null | null | null | null | ['blocking'] | ['natural-language-processing'] | [-5.10990545e-02 -5.85467696e-01 -1.93433344e-01 -2.10205063e-01
-2.09039092e-01 -3.57480705e-01 6.60281837e-01 3.97794187e-01
-7.65130818e-01 4.78032619e-01 7.19236583e-02 -7.21725583e-01
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2.86179394e-01 2.82888860e-01 4.54767764e-01 -1.16827227... | [8.414571762084961, 2.8957369327545166] |
7a4541c7-9f4f-4c37-a697-04bb5e30d6bb | static-and-dynamic-speaker-modeling-based-on | null | null | https://aclanthology.org/2022.naacl-srw.31 | https://aclanthology.org/2022.naacl-srw.31.pdf | Static and Dynamic Speaker Modeling based on Graph Neural Network for Emotion Recognition in Conversation | Each person has a unique personality which affects how they feel and convey emotions. Hence, speaker modeling is important for the task of emotion recognition in conversation (ERC). In this paper, we propose a novel graph-based ERC model which considers both conversational context and speaker personality. We model the ... | ['Sadao Kurohashi', 'Yin Jou Huang', 'Prakhar Saxena'] | null | null | null | null | naacl-acl-2022-7 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [-1.18726805e-01 3.71756911e-01 -6.62963167e-02 -8.44702661e-01
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-4.63067293e-01 3.31581712e-01 -2.55382597e-01 -4.08645988... | [12.978225708007812, 6.175746917724609] |
e20e4294-84f3-4a0f-9dbe-1712e743a626 | aiomics-exploring-more-of-the-proteome-using | 2305.09513 | null | https://arxiv.org/abs/2305.09513v1 | https://arxiv.org/pdf/2305.09513v1.pdf | AIomics: exploring more of the proteome using mass spectral libraries extended by AI | The unbounded permutations of biological molecules, including proteins and their constituent peptides, presents a dilemma in identifying the components of complex biosamples. Sequence search algorithms used to identify peptide spectra can be expanded to cover larger classes of molecules, including more modifications, i... | ['Stephen E. Stein', 'Tytus D. Mak', 'Douglas J. Slotta', 'Joel Lapin', 'Lewis Y. Geer'] | 2023-05-16 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [ 1.02226579e+00 -1.89173386e-01 -2.98313290e-01 -3.97063673e-01
-8.05522323e-01 -1.09874463e+00 -1.39883980e-02 5.86380780e-01
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1.61719322e-01 -3.73256326e-01 -5.84372938e-01 -6.21362507e-01
-2.45026071e-02 7.88087249e-01 5.47870755e-01 8.26423913... | [4.7564167976379395, 5.580986499786377] |
0c72a78f-8449-4e07-9cdc-5d6a94daefb0 | convolutional-neural-network-based-on-sparse | 2305.17898 | null | https://arxiv.org/abs/2305.17898v1 | https://arxiv.org/pdf/2305.17898v1.pdf | Convolutional neural network based on sparse graph attention mechanism for MRI super-resolution | Magnetic resonance imaging (MRI) is a valuable clinical tool for displaying anatomical structures and aiding in accurate diagnosis. Medical image super-resolution (SR) reconstruction using deep learning techniques can enhance lesion analysis and assist doctors in improving diagnostic efficiency and accuracy. However, e... | ['Jixin Maa', 'Hongjian Yu', 'Zhijiang Du', 'Xin Hua'] | 2023-05-29 | null | null | null | null | ['image-super-resolution', 'graph-attention'] | ['computer-vision', 'graphs'] | [ 7.17177689e-01 -1.74487561e-01 -1.11539505e-01 -3.49138170e-01
-6.70453548e-01 1.36659533e-01 2.17757180e-01 7.55166411e-02
-2.90903032e-01 5.28705120e-01 3.39185923e-01 -1.24205567e-01
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-5.55728972e-01 -7.30082244e-02 4.56294149e-01 -1.84345022... | [13.830326080322266, -2.373993396759033] |
54af5097-4fb6-4138-823a-e431550f1855 | the-online-behaviour-of-the-algerian-abusers | 2203.10369 | null | https://arxiv.org/abs/2203.10369v1 | https://arxiv.org/pdf/2203.10369v1.pdf | The Online Behaviour of the Algerian Abusers in Social Media Networks | Connecting to social media networks becomes a daily task for the majority of people around the world, and the amount of shared information is growing exponentially. Thus, controlling the way in which people communicate is necessary, in order to protect them from disorientation, conflicts, aggressions, etc. In this pape... | ['Kheireddine Abainia'] | 2022-03-19 | null | null | null | null | ['abuse-detection'] | ['natural-language-processing'] | [-5.20263910e-01 -1.14180960e-01 8.24423209e-02 -1.90005690e-01
2.31871471e-01 -4.94836539e-01 3.72536868e-01 4.61548030e-01
-6.60779715e-01 7.90765703e-01 2.72462755e-01 -1.99450403e-01
-1.70269147e-01 -1.00070846e+00 7.04475790e-02 -3.98307204e-01
-5.15143350e-02 3.50737661e-01 2.18400896e-01 -6.62241161... | [8.741109848022461, 10.491665840148926] |
048e0ac9-eeb4-407a-88df-e069c3a8a753 | mixformer-end-to-end-tracking-with-iterative-1 | 2203.11082 | null | https://arxiv.org/abs/2203.11082v2 | https://arxiv.org/pdf/2203.11082v2.pdf | MixFormer: End-to-End Tracking with Iterative Mixed Attention | Tracking often uses a multi-stage pipeline of feature extraction, target information integration, and bounding box estimation. To simplify this pipeline and unify the process of feature extraction and target information integration, we present a compact tracking framework, termed as MixFormer, built upon transformers. ... | ['Cheng Jiang', 'Gangshan Wu', 'LiMin Wang', 'Yutao Cui'] | 2022-03-21 | mixformer-end-to-end-tracking-with-iterative | http://openaccess.thecvf.com//content/CVPR2022/html/Cui_MixFormer_End-to-End_Tracking_With_Iterative_Mixed_Attention_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Cui_MixFormer_End-to-End_Tracking_With_Iterative_Mixed_Attention_CVPR_2022_paper.pdf | cvpr-2022-1 | ['visual-object-tracking'] | ['computer-vision'] | [-3.43601733e-01 -3.95321965e-01 -4.18718904e-01 -9.92989913e-02
-1.03731406e+00 -8.58311951e-01 5.07108092e-01 -2.14949518e-01
-4.29282397e-01 2.56269634e-01 1.43928632e-01 2.23798882e-02
8.76131579e-02 -5.50436020e-01 -6.36001587e-01 -4.82255340e-01
-1.89308420e-01 1.71630979e-01 6.47421420e-01 3.76388207... | [6.230373382568359, -2.139601469039917] |
f25ae47c-e03c-4fc6-ae16-b5b2106eefba | dorothie-spoken-dialogue-for-handling | 2210.12511 | null | https://arxiv.org/abs/2210.12511v1 | https://arxiv.org/pdf/2210.12511v1.pdf | DOROTHIE: Spoken Dialogue for Handling Unexpected Situations in Interactive Autonomous Driving Agents | In the real world, autonomous driving agents navigate in highly dynamic environments full of unexpected situations where pre-trained models are unreliable. In these situations, what is immediately available to vehicles is often only human operators. Empowering autonomous driving agents with the ability to navigate in a... | ['Joyce Chai', 'Matthew Marge', 'Felix Gervits', 'Eui-In Kim', 'Huang Yidong', 'Cristian-Paul Bara', 'Ben VanDerPloeg', 'Ziqiao Ma'] | 2022-10-22 | null | null | null | null | ['dialogue-act-classification', 'vision-and-language-navigation'] | ['natural-language-processing', 'robots'] | [-4.22444865e-02 3.76928926e-01 3.24399501e-01 -6.72766447e-01
-5.41563511e-01 -4.77117449e-01 1.13039422e+00 -3.51846099e-01
-4.93389875e-01 6.81637704e-01 1.92434967e-01 -7.87407219e-01
2.94850260e-01 -8.29935312e-01 -5.42861760e-01 -3.20684761e-01
-2.63547838e-01 7.94591725e-01 4.47857052e-01 -1.00735939... | [5.094977855682373, 1.122180461883545] |
a9ed62e7-f1b2-4897-8071-7f679f51826b | semeval-2016-task-12-clinical-tempeval | null | null | https://aclanthology.org/s16-1165 | https://aclanthology.org/s16-1165.pdf | SemEval-2016 Task 12: Clinical TempEval | null | ['Wei-Te Chen', 'Marc Verhagen', 'Leon Derczynski', 'Guergana Savova', 'Steven Bethard', 'James Pustejovsky'] | 2016-06-01 | semeval-2016-task-12-clinical-tempeval-1 | https://aclanthology.org/S16-1165 | https://aclanthology.org/S16-1165.pdf | semeval-2016-6 | ['temporal-information-extraction'] | ['natural-language-processing'] | [-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01
-8.60921741e-02 -7.76765764e-01 4.48510379e-01 -7.23253429e-01
-5.48377395e-01 1.31954515e+00 3.66348401e-02 -9.49533224e-01
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-7.42435038e-01 6.86515033e-01 1.44298598e-01 -6.52004302... | [-1.5392014980316162, 15.869209289550781] |
9c6237f7-abc1-42af-92e9-dd586c774d23 | direct-simultaneous-speech-to-text | 2106.06636 | null | https://arxiv.org/abs/2106.06636v1 | https://arxiv.org/pdf/2106.06636v1.pdf | Direct Simultaneous Speech-to-Text Translation Assisted by Synchronized Streaming ASR | Simultaneous speech-to-text translation is widely useful in many scenarios. The conventional cascaded approach uses a pipeline of streaming ASR followed by simultaneous MT, but suffers from error propagation and extra latency. To alleviate these issues, recent efforts attempt to directly translate the source speech int... | ['Liang Huang', 'Renjie Zheng', 'Mingbo Ma', 'Junkun Chen'] | 2021-06-11 | null | https://aclanthology.org/2021.findings-acl.406 | https://aclanthology.org/2021.findings-acl.406.pdf | findings-acl-2021-8 | ['speech-to-text-translation', 'simultaneous-speech-to-text-translation'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.87594700e-01 -7.56156966e-02 -1.07685238e-01 -3.97215039e-01
-1.58213675e+00 -6.78637624e-01 6.67541206e-01 -2.90863365e-01
-4.64899838e-01 7.33672440e-01 3.48988265e-01 -7.16038883e-01
6.66064620e-01 -1.59519777e-01 -9.38641846e-01 -5.37899733e-01
4.05876756e-01 6.68348193e-01 3.14403981e-01 -3.16794515... | [14.495304107666016, 7.150593280792236] |
ddd82dac-66e5-4146-8ccd-563c9947edf2 | surface-realisation-from-knowledge-bases | null | null | https://aclanthology.org/P14-1040 | https://aclanthology.org/P14-1040.pdf | Surface Realisation from Knowledge-Bases | null | ['Claire Gardent', 'Bikash Gyawali'] | 2014-06-01 | null | null | null | acl-2014-6 | ['concept-to-text-generation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
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-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.2352705001831055, 3.7084290981292725] |
0bb35921-e845-4b6c-abdb-ae639f632197 | fully-neuromorphic-vision-and-control-for | 2303.08778 | null | https://arxiv.org/abs/2303.08778v1 | https://arxiv.org/pdf/2303.08778v1.pdf | Fully neuromorphic vision and control for autonomous drone flight | Biological sensing and processing is asynchronous and sparse, leading to low-latency and energy-efficient perception and action. In robotics, neuromorphic hardware for event-based vision and spiking neural networks promises to exhibit similar characteristics. However, robotic implementations have been limited to basic ... | ['Guido de Croon', 'Yingfu Xu', 'Stein Stroobants', 'Julien Dupeyroux', 'Jesse Hagenaars', 'Federico Paredes-Vallés'] | 2023-03-15 | null | null | null | null | ['event-based-vision'] | ['computer-vision'] | [ 4.52161163e-01 -1.92373425e-01 5.56458950e-01 -1.64830223e-01
6.95336759e-02 -4.79123026e-01 4.05512542e-01 -5.66224642e-02
-9.40005004e-01 6.16219282e-01 -4.88288820e-01 2.47896224e-01
1.69337884e-01 -8.27142894e-01 -1.09869182e+00 -6.52415514e-01
-3.61521035e-01 2.15085581e-01 6.04833364e-01 -1.84962541... | [8.187663078308105, 2.295522928237915] |
d6db6019-97b4-4b54-b5b2-0d3c5584336a | a-multilayer-perceptron-based-ensemble | null | null | https://aclanthology.org/D17-1057 | https://aclanthology.org/D17-1057.pdf | A Multilayer Perceptron based Ensemble Technique for Fine-grained Financial Sentiment Analysis | In this paper, we propose a novel method for combining deep learning and classical feature based models using a Multi-Layer Perceptron (MLP) network for financial sentiment analysis. We develop various deep learning models based on Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) and Gated Recurrent Un... | ['Md. Shad Akhtar', 'Asif Ekbal', 'Deepanway Ghosal', 'Pushpak Bhattacharyya', 'Abhishek Kumar'] | 2017-09-01 | null | null | null | emnlp-2017-9 | ['stock-prediction'] | ['time-series'] | [-4.67869163e-01 -3.33749831e-01 -1.29193559e-01 -7.42709696e-01
-2.42416307e-01 -2.13184997e-01 8.12647641e-01 4.91359740e-01
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3.03329945e-01 -1.04668272e+00 -5.99076569e-01 -3.61267090e-01
-6.96066543e-02 1.13406047e-01 -1.86629221e-03 -5.43179989... | [11.047467231750488, 7.130095958709717] |
ea1a6fb8-a560-4600-955f-d9a899976512 | integrating-multiple-sources-of-ordinal | 2211.00420 | null | https://arxiv.org/abs/2211.00420v2 | https://arxiv.org/pdf/2211.00420v2.pdf | Integrating multiple sources of ordinal information in portfolio optimization | Active portfolio management tries to incorporate any source of meaningful information into the asset selection process. In this contribution we consider qualitative views specified as total orders of the expected asset returns and discuss two different approaches for incorporating this input in a mean-variance portfoli... | ['Ulrich Pferschy', 'Roland Mestel', 'Stephan Hafner', 'Eranda Çela'] | 2022-11-01 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-1.00150481e-01 4.19648200e-01 -4.72861156e-02 -4.61649179e-01
-8.26612890e-01 -8.79936159e-01 8.16467643e-01 4.93995011e-01
-5.92844844e-01 9.44786906e-01 4.29792464e-01 -2.25940481e-01
-1.03746426e+00 -9.90132093e-01 -5.38575053e-01 -7.71558106e-01
1.39264613e-01 9.13771331e-01 1.55980512e-01 -2.43141264... | [4.997137546539307, 3.9480481147766113] |
c2f8fc14-5494-4619-a856-4a3561716c16 | folding-and-stabilization-of-native-sequence | 1606.05373 | null | http://arxiv.org/abs/1606.05373v1 | http://arxiv.org/pdf/1606.05373v1.pdf | Folding and Stabilization of Native-Sequence-Reversed Proteins | Though the problem of sequence-reversed protein folding is largely
unexplored, one might speculate that reversed native protein sequences should
be significantly more foldable than purely random heteropolymer sequences. In
this article, we investigate how the reverse-sequences of native proteins might
fold by examining... | [] | 2016-06-16 | null | null | null | null | ['protein-design'] | ['medical'] | [ 6.78139389e-01 3.95025671e-01 2.72813868e-02 -5.23725927e-01
-2.51737326e-01 -1.09782791e+00 1.46011919e-01 3.16489965e-01
-3.02458078e-01 1.29969096e+00 1.80724978e-01 -1.23005521e+00
1.70746505e-01 -4.40636277e-01 -1.04929364e+00 -1.07699883e+00
-3.50791812e-01 2.99467087e-01 4.47079897e-01 -4.69850570... | [4.7212324142456055, 5.275047779083252] |
d6e5567a-7f3d-44b3-b408-fa24e1d6af32 | multi-label-cloud-segmentation-using-a-deep | 1903.06562 | null | http://arxiv.org/abs/1903.06562v1 | http://arxiv.org/pdf/1903.06562v1.pdf | Multi-label Cloud Segmentation Using a Deep Network | Different empirical models have been developed for cloud detection. There is
a growing interest in using the ground-based sky/cloud images for this purpose.
Several methods exist that perform binary segmentation of clouds. In this
paper, we propose to use a deep learning architecture (U-Net) to perform
multi-label sky/... | ['Yee Hui Lee', 'Soumyabrata Dev', 'Stefan Winkler', 'Shilpa Manandhar'] | 2019-03-15 | null | null | null | null | ['cloud-detection'] | ['computer-vision'] | [-6.22198097e-02 -7.17554867e-01 -1.63014278e-01 -7.31797576e-01
-8.56467247e-01 -6.64688766e-01 4.65605259e-01 7.73282629e-03
-4.22178447e-01 7.85686195e-01 -6.12725198e-01 -3.58599365e-01
2.92474329e-01 -1.02279854e+00 -5.64680696e-01 -9.11993682e-01
1.72598511e-01 6.87737942e-01 4.45854753e-01 5.18657744... | [9.711621284484863, -1.6792755126953125] |
81d405f2-a32b-4db8-99c1-683b818bb921 | learning-to-reason-over-visual-objects | 2303.02260 | null | https://arxiv.org/abs/2303.02260v1 | https://arxiv.org/pdf/2303.02260v1.pdf | Learning to reason over visual objects | A core component of human intelligence is the ability to identify abstract patterns inherent in complex, high-dimensional perceptual data, as exemplified by visual reasoning tasks such as Raven's Progressive Matrices (RPM). Motivated by the goal of designing AI systems with this capacity, recent work has focused on eva... | ['Jonathan D. Cohen', 'Taylor Webb', 'Shanka Subhra Mondal'] | 2023-03-03 | null | null | null | null | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [ 2.22077832e-01 1.72973067e-01 1.70125499e-01 -2.98711479e-01
1.20429568e-01 -4.79704559e-01 8.98501396e-01 4.07541841e-01
-5.42241871e-01 1.93286419e-01 4.74799931e-01 -6.92454696e-01
-4.57176179e-01 -7.41943657e-01 -7.45793581e-01 -2.80805171e-01
-1.39125749e-01 5.98827481e-01 8.34987909e-02 -4.72340673... | [10.572299003601074, 2.2604010105133057] |
a1479f80-090e-4ca2-ad30-e2d152d00d7f | evolutionary-generalized-zero-shot-learning | 2211.13174 | null | https://arxiv.org/abs/2211.13174v1 | https://arxiv.org/pdf/2211.13174v1.pdf | Evolutionary Generalized Zero-Shot Learning | An open problem on the path to artificial intelligence is generalization from the known to the unknown, which is instantiated as Generalized Zero-Shot Learning (GZSL) task. In this work, we propose a novel Evolutionary Generalized Zero-Shot Learning setting, which (i) avoids the domain shift problem in inductive GZSL, ... | ['Ling Shao', 'Yang Long', 'Yuming Shen', 'Haofeng Zhang', 'Dubing Chen'] | 2022-11-23 | null | null | null | null | ['generalized-zero-shot-learning', 'generalized-zero-shot-learning'] | ['computer-vision', 'methodology'] | [ 3.90755594e-01 4.99052584e-01 -7.79354945e-02 -4.22834083e-02
-5.33816993e-01 -2.11935326e-01 4.45092738e-01 -5.48435897e-02
-2.02182099e-01 8.96370769e-01 -1.67305991e-01 -1.58787332e-02
-3.76957625e-01 -9.39254045e-01 -9.31742430e-01 -6.85830593e-01
4.95554768e-02 8.11454594e-01 5.87425411e-01 -5.84859669... | [9.821194648742676, 3.3697619438171387] |
fcce4247-5a3b-4c6a-a4cf-a7147959465f | simultaneously-learning-neighborship-and | 1709.02896 | null | http://arxiv.org/abs/1709.02896v1 | http://arxiv.org/pdf/1709.02896v1.pdf | Simultaneously Learning Neighborship and Projection Matrix for Supervised Dimensionality Reduction | Explicitly or implicitly, most of dimensionality reduction methods need to
determine which samples are neighbors and the similarity between the neighbors
in the original highdimensional space. The projection matrix is then learned on
the assumption that the neighborhood information (e.g., the similarity) is
known and f... | ['Feiping Nie', 'Yanwei Pang', 'Bo Zhou'] | 2017-09-09 | null | null | null | null | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [-2.17403039e-01 -2.33238250e-01 -2.90193230e-01 -5.82338870e-01
-2.81306833e-01 -3.27296913e-01 2.94378728e-01 -3.02323282e-01
-4.46273714e-01 5.66526413e-01 1.87381938e-01 1.45300955e-01
-6.72433436e-01 -7.00188816e-01 -1.81346700e-01 -9.45748270e-01
1.76781237e-01 2.35229716e-01 6.51011392e-02 1.71134584... | [7.8477654457092285, 4.276310443878174] |
8e87d540-3a59-4dc4-9fe6-575547c9e3cd | sensitivity-and-robustness-of-large-language | 2305.08714 | null | https://arxiv.org/abs/2305.08714v2 | https://arxiv.org/pdf/2305.08714v2.pdf | Sensitivity and Robustness of Large Language Models to Prompt Template in Japanese Text Classification Tasks | Prompt engineering relevance research has seen a notable surge in recent years, primarily driven by advancements in pre-trained language models and large language models. However, a critical issue has been identified within this domain: the inadequate of sensitivity and robustness of these models towards Prompt Templat... | ['Tatsunori Mori', 'Chengguang Gan'] | 2023-05-15 | null | null | null | null | ['prompt-engineering'] | ['natural-language-processing'] | [ 1.26407981e-01 1.73660189e-01 -1.92999300e-02 -2.38662437e-01
-1.22021925e+00 -5.71028888e-01 6.61178172e-01 3.74450922e-01
-5.96619904e-01 6.36144757e-01 4.66212302e-01 -7.02114880e-01
-7.56238401e-02 -2.48492584e-01 -5.04745483e-01 -3.37054729e-01
3.31594676e-01 2.30443105e-01 1.66610464e-01 -4.78855193... | [10.978117942810059, 9.611851692199707] |
9c3cd049-063d-4b17-ac47-56da0fcef674 | non-homogeneous-haze-removal-via-artificial | 2104.01888 | null | https://arxiv.org/abs/2104.01888v2 | https://arxiv.org/pdf/2104.01888v2.pdf | Non-Homogeneous Haze Removal via Artificial Scene Prior and Bidimensional Graph Reasoning | Due to the lack of natural scene and haze prior information, it is greatly challenging to completely remove the haze from a single image without distorting its visual content. Fortunately, the real-world haze usually presents non-homogeneous distribution, which provides us with many valuable clues in partial well-prese... | ['Linfeng Xu', 'Fanman Meng', 'Hongliang Li', 'King Ngi Ngan', 'Hui Li', 'Qingbo Wu', 'Haoran Wei'] | 2021-04-05 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 1.12437010e-01 -1.10277615e-01 5.41057944e-01 -7.41694793e-02
-3.16010043e-02 -7.65504465e-02 2.53106862e-01 -9.82211754e-02
-9.92260780e-03 4.20330673e-01 3.09780896e-01 -3.49271223e-02
-1.96994424e-01 -1.04946625e+00 -6.48180544e-01 -1.14441109e+00
3.09057713e-01 -1.25878707e-01 6.64501548e-01 -5.10113478... | [10.915614128112793, -3.202021837234497] |
7a1ba392-ea69-42df-b3c7-0e56a93b1946 | temporal-action-segmentation-an-analysis-of | 2210.10352 | null | https://arxiv.org/abs/2210.10352v3 | https://arxiv.org/pdf/2210.10352v3.pdf | Temporal Action Segmentation: An Analysis of Modern Techniques | Temporal action segmentation (TAS) from videos aims at densely identifying video frames in minutes-long videos with multiple action classes. As a long-range video understanding task, researchers have developed an extended collection of methods and examined their performance using various benchmarks. Despite the rapid g... | ['Angela Yao', 'Fadime Sener', 'Guodong Ding'] | 2022-10-19 | null | null | null | null | ['action-segmentation'] | ['computer-vision'] | [ 4.33892071e-01 -2.34770000e-01 -9.00801361e-01 -2.52404243e-01
-5.99506021e-01 -5.48529029e-01 4.10528928e-01 -3.68744582e-01
-2.31580958e-01 5.30583322e-01 2.66011417e-01 1.22445665e-01
1.01189271e-01 -8.37203413e-02 -5.48068166e-01 -7.10796237e-01
-1.83468863e-01 -1.17678389e-01 6.07457578e-01 1.23272039... | [8.347220420837402, 0.5617870688438416] |
39b3147d-647b-41ec-89aa-f5b10e1d3e88 | nu-gan-high-resolution-neural-upsampling-with | 2010.11362 | null | https://arxiv.org/abs/2010.11362v1 | https://arxiv.org/pdf/2010.11362v1.pdf | NU-GAN: High resolution neural upsampling with GAN | In this paper, we propose NU-GAN, a new method for resampling audio from lower to higher sampling rates (upsampling). Audio upsampling is an important problem since productionizing generative speech technology requires operating at high sampling rates. Such applications use audio at a resolution of 44.1 kHz or 48 kHz, ... | ['Aaron Courville', 'Yoshua Bengio', 'Vicki Anand', 'Kundan Kumar', 'Rithesh Kumar'] | 2020-10-22 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 4.47460115e-01 6.50476873e-01 6.48812056e-02 -1.02381207e-01
-1.95099521e+00 -5.84781468e-01 6.11141622e-01 -4.49150115e-01
7.45816082e-02 8.88238251e-01 6.34689271e-01 -4.11680102e-01
4.02914733e-01 -6.29655421e-01 -7.40027845e-01 -5.01415372e-01
2.77096123e-01 4.34701651e-01 -1.22820891e-01 2.16769637... | [15.28525447845459, 6.181166172027588] |
79373d0c-e32a-4da9-aa1a-0688286dd570 | when-do-transformers-shine-in-rl-decoupling | 2307.03864 | null | https://arxiv.org/abs/2307.03864v1 | https://arxiv.org/pdf/2307.03864v1.pdf | When Do Transformers Shine in RL? Decoupling Memory from Credit Assignment | Reinforcement learning (RL) algorithms face two distinct challenges: learning effective representations of past and present observations, and determining how actions influence future returns. Both challenges involve modeling long-term dependencies. The transformer architecture has been very successful to solve problems... | ['Pierre-Luc Bacon', 'Benjamin Eysenbach', 'Michel Ma', 'Tianwei Ni'] | 2023-07-07 | null | null | null | null | ['reinforcement-learning-1'] | ['methodology'] | [-1.08475223e-01 -5.82348481e-02 -6.56690300e-01 -3.50489497e-01
-6.44536734e-01 -6.50871694e-01 6.98142588e-01 1.85466632e-01
-7.48331010e-01 1.12261999e+00 2.06154287e-01 -6.52974010e-01
-3.61436576e-01 -9.53352213e-01 -8.09246063e-01 -5.25623322e-01
-6.26661360e-01 5.97006202e-01 4.39559817e-02 -2.97336787... | [4.064801216125488, 1.974068522453308] |
e3f90abc-9de6-40d4-af69-256617ddf31e | robot-design-with-neural-networks-milp | 2010.09842 | null | https://arxiv.org/abs/2010.09842v4 | https://arxiv.org/pdf/2010.09842v4.pdf | Robot Design With Neural Networks, MILP Solvers and Active Learning | Central to the design of many robot systems and their controllers is solving a constrained blackbox optimization problem. This paper presents CNMA, a new method of solving this problem that is conservative in the number of potentially expensive blackbox function evaluations; allows specifying complex, even recursive co... | ['Karthik Narayan', 'Niraj K. Jha', 'Brendan Englot', 'Kishore Pochiraju', 'Jeremy Cohen', 'Todd Huster', 'Dana Chee', 'Emily Mak', 'Sanjai Narain'] | 2020-10-19 | null | null | null | null | ['acrobot'] | ['playing-games'] | [-7.75556117e-02 4.24759835e-01 -5.95240414e-01 -1.73633978e-01
-7.48916507e-01 -5.29161453e-01 3.05353433e-01 -3.31704795e-01
-3.86680722e-01 1.38515937e+00 -3.93344462e-01 -7.80214012e-01
-7.49474466e-01 -5.56409359e-01 -9.17632461e-01 -6.08512223e-01
-5.04907966e-01 9.99621511e-01 -2.91779011e-01 -4.35074031... | [4.519813060760498, 2.0783677101135254] |
3c1e3de2-98db-4750-94fc-5e5f37ddf2ec | traditional-saliency-reloaded-a-good-old | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Frintrop_Traditional_Saliency_Reloaded_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Frintrop_Traditional_Saliency_Reloaded_2015_CVPR_paper.pdf | Traditional Saliency Reloaded: A Good Old Model in New Shape | In this paper, we show that the seminal, biologically-inspired saliency model by Itti et al. is still competitive with current state-of-the-art methods for salient object segmentation if some important adaptions are made. We show which changes are necessary to achieve high performance, with special emphasis o... | ['German Martin Garcia', 'Thomas Werner', 'Simone Frintrop'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['object-proposal-generation'] | ['computer-vision'] | [ 4.36985850e-01 -5.22280149e-02 -1.78340271e-01 -1.78306535e-01
-4.80681926e-01 -3.73712689e-01 4.96530920e-01 1.59575328e-01
-3.05150658e-01 5.10884345e-01 3.63470726e-02 2.95539107e-02
1.36467040e-01 -5.88000596e-01 -6.96345627e-01 -5.99623978e-01
-2.15151384e-02 5.64344153e-02 1.27972031e+00 -5.39807200... | [9.793843269348145, -0.29204341769218445] |
e1732392-2bb2-4b95-8e02-484f2b1f00c6 | panoptic-animal-pose-estimators-are-zero-shot | 2203.07436 | null | https://arxiv.org/abs/2203.07436v2 | https://arxiv.org/pdf/2203.07436v2.pdf | SuperAnimal models pretrained for plug-and-play analysis of animal behavior | Quantification of behavior is critical in applications ranging from neuroscience, veterinary medicine and animal conservation efforts. A common key step for behavioral analysis is first extracting relevant keypoints on animals, known as pose estimation. However, reliable inference of poses currently requires domain kno... | ['Alexander Mathis', 'Tian Qiu', 'Steffen Schneider', 'Maxime Vidal', 'Jessy Lauer', 'Anastasiia Filippova', 'Mackenzie Weygandt Mathis', 'Shaokai Ye'] | 2022-03-14 | null | null | null | null | ['animal-pose-estimation'] | ['computer-vision'] | [ 7.70959929e-02 -2.76132017e-01 -1.89179018e-01 -4.95067239e-01
-4.14071232e-01 -7.43733823e-01 1.61659699e-02 -1.28334723e-02
-7.35922098e-01 6.52219296e-01 -4.36847180e-01 -2.37150826e-02
1.31756306e-01 -3.92408788e-01 -1.16332424e+00 -2.87468433e-01
-5.58046937e-01 2.97802687e-01 2.92630464e-01 7.95568302... | [7.641195774078369, -0.9213471412658691] |
c44a98b3-6729-4be4-aac8-a233cc8f5434 | stereopose-category-level-6d-transparent | 2211.01644 | null | https://arxiv.org/abs/2211.01644v1 | https://arxiv.org/pdf/2211.01644v1.pdf | StereoPose: Category-Level 6D Transparent Object Pose Estimation from Stereo Images via Back-View NOCS | Most existing methods for category-level pose estimation rely on object point clouds. However, when considering transparent objects, depth cameras are usually not able to capture meaningful data, resulting in point clouds with severe artifacts. Without a high-quality point cloud, existing methods are not applicable to ... | ['Qi Dou', 'Pieter Abbeel', 'Yun-hui Liu', 'Congying Sui', 'Stephen James', 'Kai Chen'] | 2022-11-03 | null | null | null | null | ['transparent-objects'] | ['computer-vision'] | [ 5.27311042e-02 1.91943836e-03 1.93809494e-01 -3.16477090e-01
-5.81310749e-01 -5.95034838e-01 3.83961052e-01 -1.65030435e-01
-3.81927416e-02 1.78622857e-01 8.48641098e-02 1.98650420e-01
-9.87339690e-02 -6.16833389e-01 -9.96712923e-01 -6.21238053e-01
4.17777270e-01 9.07958567e-01 6.49703622e-01 1.88436419... | [7.581234455108643, -2.6415231227874756] |
7e254a3d-d326-4f23-8a1a-34733997c5ba | incomplete-multi-view-clustering-via-cross | 2112.00739 | null | https://arxiv.org/abs/2112.00739v1 | https://arxiv.org/pdf/2112.00739v1.pdf | Incomplete Multi-view Clustering via Cross-view Relation Transfer | In this paper, we consider the problem of multi-view clustering on incomplete views. Compared with complete multi-view clustering, the view-missing problem increases the difficulty of learning common representations from different views. To address the challenge, we propose a novel incomplete multi-view clustering fram... | ['Yao Zhao', 'Zhiqiang Fu', 'Dongxia Chang', 'Yiming Wang'] | 2021-12-01 | null | null | null | null | ['incomplete-multi-view-clustering'] | ['computer-vision'] | [-7.82166421e-02 -2.70915143e-02 -1.83304399e-01 -4.71046567e-01
-7.83120990e-01 -4.07851994e-01 2.36645520e-01 -2.67230183e-01
2.98065037e-01 2.87172109e-01 5.23796320e-01 4.93148953e-01
-4.04270202e-01 -5.89488447e-01 -6.20162010e-01 -7.05010891e-01
4.75331277e-01 3.03450316e-01 -4.31552641e-02 1.80236921... | [8.365357398986816, 4.610386848449707] |
203a1743-38ed-4c86-8d25-95a20d5045e8 | alphapose-whole-body-regional-multi-person | 2211.03375 | null | https://arxiv.org/abs/2211.03375v1 | https://arxiv.org/pdf/2211.03375v1.pdf | AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time | Accurate whole-body multi-person pose estimation and tracking is an important yet challenging topic in computer vision. To capture the subtle actions of humans for complex behavior analysis, whole-body pose estimation including the face, body, hand and foot is essential over conventional body-only pose estimation. In t... | ['Cewu Lu', 'Yong-Lu Li', 'Yuliang Xiu', 'Haoyi Zhu', 'Chao Xu', 'Hongyang Tang', 'Jiefeng Li', 'Hao-Shu Fang'] | 2022-11-07 | null | null | null | null | ['multi-person-pose-estimation', 'multi-person-pose-estimation-and-tracking'] | ['computer-vision', 'computer-vision'] | [-2.51369834e-01 -2.03827709e-01 -7.46472329e-02 8.30937456e-03
-8.77649963e-01 -4.35550362e-01 3.79964679e-01 -2.20255375e-01
-5.34388900e-01 6.96319222e-01 1.09439455e-01 5.57427943e-01
1.60176545e-01 -1.92909151e-01 -7.58651674e-01 -4.21162009e-01
-3.23963985e-02 1.11655545e+00 3.89979482e-01 -2.78739840... | [7.034945487976074, -0.9152333736419678] |
543edd35-ece2-4b71-9fb4-c80816583c75 | medical-image-analysis-using-deep-relational | 2303.16099 | null | https://arxiv.org/abs/2303.16099v1 | https://arxiv.org/pdf/2303.16099v1.pdf | Medical Image Analysis using Deep Relational Learning | In the past ten years, with the help of deep learning, especially the rapid development of deep neural networks, medical image analysis has made remarkable progress. However, how to effectively use the relational information between various tissues or organs in medical images is still a very challenging problem, and it... | ['Zhihua Liu'] | 2023-03-28 | null | null | null | null | ['tumor-segmentation', 'homography-estimation', 'brain-tumor-segmentation', 'relational-reasoning'] | ['computer-vision', 'computer-vision', 'medical', 'natural-language-processing'] | [ 3.30675006e-01 1.43935546e-01 -2.39811204e-02 -3.79106075e-01
-7.40938127e-01 -1.73116580e-03 3.81680548e-01 -6.55418029e-04
-2.54557550e-01 3.76572102e-01 1.55353203e-01 -3.39766920e-01
-3.41702580e-01 -5.35178304e-01 -8.67005229e-01 -8.77890527e-01
5.73945493e-02 5.22425711e-01 1.73758462e-01 -2.33814090... | [14.585492134094238, -2.5006752014160156] |
8ac6cc22-d533-4b8d-ac6b-fdb34506682d | cure-tsr-challenging-unreal-and-real | 1712.02463 | null | http://arxiv.org/abs/1712.02463v2 | http://arxiv.org/pdf/1712.02463v2.pdf | CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition | In this paper, we investigate the robustness of traffic sign recognition
algorithms under challenging conditions. Existing datasets are limited in terms
of their size and challenging condition coverage, which motivated us to
generate the Challenging Unreal and Real Environments for Traffic Sign
Recognition (CURE-TSR) d... | ['Gukyeong Kwon', 'Ghassan AlRegib', 'Dogancan Temel', 'Mohit Prabhushankar'] | 2017-12-07 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 1.96861792e-02 -7.91894317e-01 -8.18242356e-02 -3.69668305e-01
-5.69086850e-01 -5.52982152e-01 5.24695575e-01 -8.22692394e-01
-3.80218089e-01 7.09036946e-01 8.24420899e-02 -6.45056367e-01
-5.19515276e-02 -4.42205846e-01 -6.94963276e-01 -5.84573150e-01
-1.90926298e-01 1.42826647e-01 6.52104437e-01 -2.60346621... | [8.020566940307617, -0.7913380265235901] |
3ae08a3f-c8e1-4861-b925-716a117f187c | researchers-eye-view-of-sarcasm-detection-in | 2304.08582 | null | https://arxiv.org/abs/2304.08582v1 | https://arxiv.org/pdf/2304.08582v1.pdf | Researchers eye-view of sarcasm detection in social media textual content | The enormous use of sarcastic text in all forms of communication in social media will have a physiological effect on target users. Each user has a different approach to misusing and recognising sarcasm. Sarcasm detection is difficult even for users, and this will depend on many things such as perspective, context, spec... | ['Vaibhav Khatavkar', 'Swapnil Mane'] | 2023-04-17 | null | null | null | null | ['sarcasm-detection'] | ['natural-language-processing'] | [-1.80364758e-01 2.85962541e-02 -2.37252474e-01 -5.05248368e-01
6.33277651e-03 -3.95014912e-01 3.94891351e-01 3.59741420e-01
-1.32198215e-01 3.77586424e-01 5.53047597e-01 3.75569426e-02
4.47845072e-01 -2.72944778e-01 4.36979055e-01 -3.12658548e-01
6.84062541e-01 2.68831402e-01 1.81014910e-01 -6.61986411... | [9.052032470703125, 10.643203735351562] |
2aa6f089-1f96-4028-9e27-8733931d2e97 | optimal-thermal-management-and-charging-of | 2210.03393 | null | https://arxiv.org/abs/2210.03393v1 | https://arxiv.org/pdf/2210.03393v1.pdf | Optimal Thermal Management and Charging of Battery Electric Vehicles over Long Trips | This paper studies optimal thermal management and charging of a battery electric vehicle driving over long distance trips. The focus is on the potential benefits of including a heat pump in the thermal management system for waste heat recovery, and charging point planning, in a way to achieve optimality in time, energy... | ['Jonas Fredriksson', 'Viktor Larsson', 'Mitra Pourabdollah', 'Jimmy Forsman', 'Nikolce Murgovski', 'Jiaming Zhao', 'Victor Hanson', 'Ahad Hamednia'] | 2022-10-07 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [-1.16812252e-01 6.43695965e-02 -2.37086222e-01 -1.60603523e-01
-2.53526121e-01 -6.40890479e-01 3.47360730e-01 2.40089238e-01
-4.44646060e-01 7.87763655e-01 -5.21978021e-01 -3.43334943e-01
-6.56059086e-01 -1.00139201e+00 -4.75680411e-01 -1.13952887e+00
-1.08671390e-01 6.05473220e-01 -2.98216164e-01 -1.51538566... | [5.603930473327637, 2.2235324382781982] |
7e902d44-1fc9-4e5d-87dd-243f06a3c610 | overview-of-the-third-workshop-on-scholarly | null | null | https://aclanthology.org/2022.sdp-1.1 | https://aclanthology.org/2022.sdp-1.1.pdf | Overview of the Third Workshop on Scholarly Document Processing | With the ever-increasing pace of research and high volume of scholarly communication, scholars face a daunting task. Not only must they keep up with the growing literature in their own and related fields, scholars increasingly also need to rebut pseudo-science and disinformation. These needs have motivated an increasin... | ['Lucy Lu Wang', 'Anita de Waard', 'Michal Shmueli-Scheuer', 'Philipp Mayr', 'Kyle Lo', 'Petr Knoth', 'Drahomira Herrmannova', 'Tirthankar Ghosal', 'Dayne Freitag', 'Guy Feigenblat', 'Arman Cohan'] | null | null | null | null | sdp-coling-2022-10 | ['scientific-article-summarization', 'document-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.91777587e-01 1.52786538e-01 -4.30863589e-01 1.83940321e-01
-1.16776013e+00 -9.29859698e-01 7.67686307e-01 6.98628426e-01
-7.53362626e-02 8.93714666e-01 6.36338472e-01 -5.68124771e-01
-4.05365378e-01 -5.28660893e-01 -2.95293301e-01 -2.81208009e-02
2.69246936e-01 6.80460989e-01 -1.00407310e-01 5.54646812... | [12.314988136291504, 9.520042419433594] |
66a4a7ed-2211-4b2a-9ffd-3683d7f2d740 | mixed-signals-sign-language-production-via-a | 2107.11317 | null | https://arxiv.org/abs/2107.11317v2 | https://arxiv.org/pdf/2107.11317v2.pdf | Mixed SIGNals: Sign Language Production via a Mixture of Motion Primitives | It is common practice to represent spoken languages at their phonetic level. However, for sign languages, this implies breaking motion into its constituent motion primitives. Avatar based Sign Language Production (SLP) has traditionally done just this, building up animation from sequences of hand motions, shapes and fa... | ['Richard Bowden', 'Necati Cihan Camgoz', 'Ben Saunders'] | 2021-07-23 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Saunders_Mixed_SIGNals_Sign_Language_Production_via_a_Mixture_of_Motion_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Saunders_Mixed_SIGNals_Sign_Language_Production_via_a_Mixture_of_Motion_ICCV_2021_paper.pdf | iccv-2021-1 | ['sign-language-production'] | ['natural-language-processing'] | [ 2.84379870e-01 2.60554217e-02 -1.95481628e-01 -3.28936309e-01
-9.94529307e-01 -6.40668452e-01 1.12082851e+00 -9.48571146e-01
-4.28130835e-01 4.54705805e-01 6.44111097e-01 -1.88356221e-01
4.98539984e-01 -2.64656425e-01 -8.77685964e-01 -8.22081923e-01
3.00833061e-02 9.15199399e-01 9.33264494e-02 -3.67167860... | [9.212303161621094, -6.537199020385742] |
cd1eaf63-c6aa-4797-9b12-be3d294c4902 | frozen-in-time-a-joint-video-and-image | 2104.00650 | null | https://arxiv.org/abs/2104.00650v2 | https://arxiv.org/pdf/2104.00650v2.pdf | Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval | Our objective in this work is video-text retrieval - in particular a joint embedding that enables efficient text-to-video retrieval. The challenges in this area include the design of the visual architecture and the nature of the training data, in that the available large scale video-text training datasets, such as HowT... | ['Andrew Zisserman', 'Gül Varol', 'Arsha Nagrani', 'Max Bain'] | 2021-04-01 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Bain_Frozen_in_Time_A_Joint_Video_and_Image_Encoder_for_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Bain_Frozen_in_Time_A_Joint_Video_and_Image_Encoder_for_ICCV_2021_paper.pdf | iccv-2021-1 | ['video-text-retrieval'] | ['computer-vision'] | [ 3.41364563e-01 -3.92416745e-01 -4.57382083e-01 -2.23211870e-01
-1.27842903e+00 -6.74863338e-01 9.95662093e-01 -2.81839281e-01
-6.16438329e-01 3.57707351e-01 4.18208480e-01 -2.80468583e-01
1.53164625e-01 -1.10575467e-01 -9.52135682e-01 -4.37339664e-01
-1.81300603e-02 4.63623673e-01 2.49033004e-01 -2.00572520... | [10.440245628356934, 1.0050828456878662] |
0efe45f3-c161-45e5-a162-18655c56909c | instant-nvr-instant-neural-volumetric | 2304.03184 | null | https://arxiv.org/abs/2304.03184v1 | https://arxiv.org/pdf/2304.03184v1.pdf | Instant-NVR: Instant Neural Volumetric Rendering for Human-object Interactions from Monocular RGBD Stream | Convenient 4D modeling of human-object interactions is essential for numerous applications. However, monocular tracking and rendering of complex interaction scenarios remain challenging. In this paper, we propose Instant-NVR, a neural approach for instant volumetric human-object tracking and rendering using a single RG... | ['Lan Xu', 'Haimin Luo', 'Zhehao Shen', 'Zhuo Su', 'Kaixin Yao', 'Yuheng Jiang'] | 2023-04-06 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Jiang_Instant-NVR_Instant_Neural_Volumetric_Rendering_for_Human-Object_Interactions_From_Monocular_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Jiang_Instant-NVR_Instant_Neural_Volumetric_Rendering_for_Human-Object_Interactions_From_Monocular_CVPR_2023_paper.pdf | cvpr-2023-1 | ['human-object-interaction-detection'] | ['computer-vision'] | [ 0.32014057 -0.36197728 0.32523346 -0.1569523 -0.52584136 -0.5498301
0.56683004 -0.29811767 -0.25130248 0.33778164 -0.13099287 0.03905035
0.14762117 -0.5717807 -0.80370486 -0.5600835 0.3457649 0.5086929
0.47583997 -0.19105178 -0.19375736 0.87399656 -1.8157705 0.06319552
0.5459038 1.1277076 0.30... | [7.253069877624512, -1.358171820640564] |
fc474c79-506c-444c-9aa6-2eef4d17e49e | semantic-helm-an-interpretable-memory-for | 2306.09312 | null | https://arxiv.org/abs/2306.09312v1 | https://arxiv.org/pdf/2306.09312v1.pdf | Semantic HELM: An Interpretable Memory for Reinforcement Learning | Reinforcement learning agents deployed in the real world often have to cope with partially observable environments. Therefore, most agents employ memory mechanisms to approximate the state of the environment. Recently, there have been impressive success stories in mastering partially observable environments, mostly in ... | ['Sepp Hochreiter', 'Markus Hofmarcher', 'Thomas Adler', 'Fabian Paischer'] | 2023-06-15 | null | null | null | null | ['starcraft-ii', 'dota-2', 'starcraft'] | ['playing-games', 'playing-games', 'playing-games'] | [ 1.78289130e-01 1.83889568e-01 -5.63694760e-02 -2.02926412e-01
-1.39789833e-02 -4.80736941e-01 8.42462599e-01 3.17965209e-01
-8.33658397e-01 7.17715323e-01 1.08240560e-01 -4.12762612e-01
1.69909596e-01 -1.09763610e+00 -7.21912622e-01 -4.31954563e-01
-1.59030221e-02 6.21695101e-01 2.24773183e-01 -4.60268199... | [4.188859462738037, 1.1589237451553345] |
0e35e183-051c-4008-8dae-253b76c19f82 | ledeepchef-deep-reinforcement-learning-agent | 1909.01646 | null | https://arxiv.org/abs/1909.01646v1 | https://arxiv.org/pdf/1909.01646v1.pdf | LeDeepChef: Deep Reinforcement Learning Agent for Families of Text-Based Games | While Reinforcement Learning (RL) approaches lead to significant achievements in a variety of areas in recent history, natural language tasks remained mostly unaffected, due to the compositional and combinatorial nature that makes them notoriously hard to optimize. With the emerging field of Text-Based Games (TBGs), re... | ['Leonard Adolphs', 'Thomas Hofmann'] | 2019-09-04 | null | null | null | null | ['text-based-games'] | ['playing-games'] | [-4.27096225e-02 3.70547771e-02 2.45494008e-01 -3.33860330e-02
-5.81308126e-01 -6.07261956e-01 5.74141681e-01 -1.05781533e-01
-7.03498125e-01 8.92003000e-01 2.23967597e-01 -3.42687845e-01
-2.51403868e-01 -9.58439648e-01 -5.22979438e-01 -6.87780917e-01
-1.63945809e-01 7.97689736e-01 2.84957886e-01 -1.17341340... | [3.8062973022460938, 1.4438252449035645] |
a485468a-c1b3-4a84-8826-3d21236054a3 | reasoning-with-transformer-based-models-deep | null | null | https://openreview.net/forum?id=Ozp1WrgtF5_ | https://openreview.net/pdf?id=Ozp1WrgtF5_ | Reasoning with Transformer-based Models: Deep Learning, but Shallow Reasoning | Recent years have seen impressive performance of transformer-based models on different natural language processing tasks. However, it is not clear to what degree the transformers can reason on natural language. To shed light on this question, this survey paper discusses the performance of transformers on different reas... | ['Fabian M. Suchanek', 'Chloé Clavel', 'Chadi Helwe'] | 2021-06-22 | null | null | null | akbc-2021-10 | ['mathematical-reasoning'] | ['natural-language-processing'] | [ 8.18592906e-02 4.84955460e-01 -2.13206522e-02 -4.47303951e-01
2.67817583e-02 -7.01957345e-01 1.00261450e+00 3.79556596e-01
-9.77641493e-02 7.41023540e-01 3.91241223e-01 -1.09989476e+00
-4.28227961e-01 -1.18148363e+00 -1.75178930e-01 9.41011682e-02
-1.93411391e-03 6.43009663e-01 4.39202309e-01 -7.13108718... | [9.260059356689453, 7.185478210449219] |
0bc75116-0893-4416-84e4-47a7ce43eb5b | a-quantum-neural-network-with-efficient | 2211.05793 | null | https://arxiv.org/abs/2211.05793v2 | https://arxiv.org/pdf/2211.05793v2.pdf | A fermion neural network with efficient optimization and quantum applicability | Classical artificial neural networks have witnessed widespread successes in machine-learning applications. Here, we propose fermion neural networks (FNNs) whose physical properties, such as local density of states or conditional conductance, serve as outputs, once the inputs are incorporated as an initial layer. Compar... | ['Yi Zhang', 'Jia-Bao Wang', 'Pei-Lin Zheng'] | 2022-11-10 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [ 4.84929085e-01 -1.55257778e-02 -3.70581597e-01 -2.40682423e-01
-2.38530692e-02 -4.14389133e-01 7.87429690e-01 1.35552153e-01
-3.33599865e-01 9.98929322e-01 -2.75344878e-01 -5.33534586e-01
-5.07641613e-01 -1.32568598e+00 -8.16363335e-01 -1.15729022e+00
-4.63222444e-01 3.80328923e-01 1.97742492e-01 -5.10050893... | [5.572151184082031, 5.0245232582092285] |
722a90ee-b8fa-4e57-aeab-46a689d0d5e8 | a-novel-approach-for-predicting | 2307.01157 | null | https://arxiv.org/abs/2307.01157v1 | https://arxiv.org/pdf/2307.01157v1.pdf | A novel approach for predicting epidemiological forecasting parameters based on real-time signals and Data Assimilation | This paper proposes a novel approach to predict epidemiological parameters by integrating new real-time signals from various sources of information, such as novel social media-based population density maps and Air Quality data. We implement an ensemble of Convolutional Neural Networks (CNN) models using various data so... | ['Ovidiu Şerban', 'Rossella Arcucci', 'César Quilodrán Casas', 'Romain Molinas'] | 2023-07-03 | null | null | null | null | ['decision-making'] | ['reasoning'] | [-1.87769845e-01 -1.19374886e-01 3.97190034e-01 -1.35007307e-01
-9.79963019e-02 -2.24718168e-01 1.00161898e+00 7.53655851e-01
-6.40698552e-01 1.04616296e+00 3.37415725e-01 -5.48500240e-01
-3.52859110e-01 -1.32016122e+00 -5.83584905e-01 -6.61266029e-01
-5.08337617e-01 4.81344312e-01 1.61980227e-01 -6.44612789... | [6.071933746337891, 4.240409851074219] |
7768c5b9-aa64-4dcb-8a2b-dae1c9b1593d | tallyqa-answering-complex-counting-questions | 1810.12440 | null | http://arxiv.org/abs/1810.12440v2 | http://arxiv.org/pdf/1810.12440v2.pdf | TallyQA: Answering Complex Counting Questions | Most counting questions in visual question answering (VQA) datasets are
simple and require no more than object detection. Here, we study algorithms for
complex counting questions that involve relationships between objects,
attribute identification, reasoning, and more. To do this, we created TallyQA,
the world's larges... | ['Manoj Acharya', 'Kushal Kafle', 'Christopher Kanan'] | 2018-10-29 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [-2.54944891e-01 -1.75187752e-01 2.06853189e-02 -4.10669029e-01
-7.64097333e-01 -9.35241342e-01 6.15934968e-01 6.20178461e-01
-7.75458455e-01 6.10710263e-01 2.48665988e-01 -5.06074429e-01
-2.41660699e-02 -1.14309597e+00 -6.43201888e-01 6.59189969e-02
-2.35419720e-02 1.20577300e+00 7.74830401e-01 -1.25236973... | [10.63117790222168, 1.697785496711731] |
95c0bea3-5524-43fd-9ab9-e3d720ea43e9 | optimal-operating-mr-contrast-for-brain | 2304.02056 | null | https://arxiv.org/abs/2304.02056v1 | https://arxiv.org/pdf/2304.02056v1.pdf | Optimal operating MR contrast for brain ventricle parcellation | Development of MR harmonization has enabled different contrast MRIs to be synthesized while preserving the underlying anatomy. In this paper, we use image harmonization to explore the impact of different T1-w MR contrasts on a state-of-the-art ventricle parcellation algorithm VParNet. We identify an optimal operating c... | ['Jerry L. Prince', 'Aaron Carass', 'Mark G. Luciano', 'Yuli Wang', 'Lianrui Zuo', 'Savannah P. Hays'] | 2023-04-04 | null | null | null | null | ['image-harmonization', 'anatomy'] | ['computer-vision', 'miscellaneous'] | [ 2.22037420e-01 3.16337883e-01 -2.38259099e-02 -2.63016403e-01
-6.62354887e-01 -4.43925530e-01 5.37786782e-01 1.03681922e-01
-5.20271540e-01 5.07479846e-01 4.68411058e-01 -1.85414717e-01
-2.23213539e-01 -2.95018077e-01 -3.88671637e-01 -7.01330960e-01
-4.04524088e-01 5.57362735e-01 5.54384768e-01 -1.74228311... | [13.958355903625488, -2.298020601272583] |
61a9a184-1906-46ac-b53f-a0490cc93339 | on-the-detection-to-track-association-for | 2107.00500 | null | https://arxiv.org/abs/2107.00500v1 | https://arxiv.org/pdf/2107.00500v1.pdf | On the detection-to-track association for online multi-object tracking | Driven by recent advances in object detection with deep neural networks, the tracking-by-detection paradigm has gained increasing prevalence in the research community of multi-object tracking (MOT). It has long been known that appearance information plays an essential role in the detection-to-track association, which l... | ['Carsten Maple', 'Victor Sanchez', 'Chang-Tsun Li', 'Xufeng Lin'] | 2021-07-01 | null | null | null | null | ['online-multi-object-tracking'] | ['computer-vision'] | [-2.64073104e-01 -6.31306171e-01 -1.25891492e-01 -1.23651534e-01
-3.71988475e-01 -5.33691049e-01 6.59925699e-01 4.16351467e-01
-5.62812269e-01 4.24328089e-01 -4.10369694e-01 -1.51551500e-01
-2.97030330e-01 -6.14289582e-01 -7.73459733e-01 -8.91666532e-01
-7.64195323e-02 5.60777009e-01 6.42895579e-01 8.85200277... | [6.432304382324219, -2.0422842502593994] |
969190fa-d369-4817-8a56-59ffd7c3b10e | vax-culture-a-dataset-for-studying-vaccine | 2304.06858 | null | https://arxiv.org/abs/2304.06858v3 | https://arxiv.org/pdf/2304.06858v3.pdf | Vax-Culture: A Dataset for Studying Vaccine Discourse on Twitter | Vaccine hesitancy continues to be a main challenge for public health officials during the COVID-19 pandemic. As this hesitancy undermines vaccine campaigns, many researchers have sought to identify its root causes, finding that the increasing volume of anti-vaccine misinformation on social media platforms is a key elem... | ['Majid Komeili', 'Sarah Everts', 'Michael Christensen', 'Mohammad Reza Zarei'] | 2023-04-13 | null | null | null | null | ['misinformation', 'culture'] | ['miscellaneous', 'speech'] | [ 3.12084049e-01 2.21869245e-01 -5.64252853e-01 -1.05566375e-01
-9.02142167e-01 -6.79756820e-01 8.97944033e-01 9.30809319e-01
-4.74971861e-01 7.79198527e-01 8.16153765e-01 -7.33667254e-01
4.23325419e-01 -7.93037772e-01 -9.48925614e-01 -5.29452920e-01
1.10424366e-02 7.69512653e-01 -2.32978031e-01 -7.38430500... | [8.472146034240723, 9.692351341247559] |
fb5557ec-c066-43d1-9f38-01bc33425e94 | spatial-state-action-features-for-general | 2201.06401 | null | https://arxiv.org/abs/2201.06401v2 | https://arxiv.org/pdf/2201.06401v2.pdf | Spatial State-Action Features for General Games | In many board games and other abstract games, patterns have been used as features that can guide automated game-playing agents. Such patterns or features often represent particular configurations of pieces, empty positions, etc., which may be relevant for a game's strategies. Their use has been particularly prevalent i... | ['Cameron Browne', 'Matthew Stephenson', 'Éric Piette', 'Dennis J. N. J. Soemers'] | 2022-01-17 | null | null | null | null | ['game-of-go', 'board-games'] | ['playing-games', 'playing-games'] | [ 6.66468367e-02 1.10630549e-01 -1.10066339e-01 5.06125838e-02
-3.96017879e-01 -8.48461926e-01 6.76055372e-01 2.18170375e-01
-4.88603920e-01 8.69962335e-01 2.94042043e-02 -5.66321135e-01
-7.48124421e-01 -1.27293921e+00 -3.15319687e-01 -5.36511719e-01
-4.77007687e-01 8.05817902e-01 6.95389032e-01 -9.44583535... | [3.450186252593994, 1.4577118158340454] |
547f99f9-66d4-4f6c-b790-c6fe86869734 | monocular-camera-localization-in-prior-lidar | 2004.00740 | null | https://arxiv.org/abs/2004.00740v2 | https://arxiv.org/pdf/2004.00740v2.pdf | Monocular Camera Localization in Prior LiDAR Maps with 2D-3D Line Correspondences | Light-weight camera localization in existing maps is essential for vision-based navigation. Currently, visual and visual-inertial odometry (VO\&VIO) techniques are well-developed for state estimation but with inevitable accumulated drifts and pose jumps upon loop closure. To overcome these problems, we propose an effic... | ['Ji Zhang', 'Huai Yu', 'Weikun Zhen', 'Sebastian Scherer', 'Wen Yang'] | 2020-04-01 | null | null | null | null | ['camera-localization'] | ['computer-vision'] | [-2.71669239e-01 -5.02475083e-01 -2.45765045e-01 -4.59081560e-01
-5.49606621e-01 -7.95218706e-01 5.42122185e-01 -1.51735649e-01
-6.24844551e-01 6.92222834e-01 -4.71008658e-01 -9.26029310e-02
2.08225548e-02 -4.49749887e-01 -1.01969182e+00 -3.94936725e-02
7.62805492e-02 9.94587004e-01 4.78830189e-01 1.14240482... | [7.395535945892334, -2.226337194442749] |
5a8a6de7-3cfb-4ea7-8966-f90bafe959d3 | the-effect-of-emission-lines-on-the | 2101.11368 | null | https://arxiv.org/abs/2101.11368v1 | https://arxiv.org/pdf/2101.11368v1.pdf | The effect of emission lines on the performance of photometric redshift estimation algorithms | We investigate the effect of strong emission line galaxies on the performance of empirical photometric redshift estimation methods. In order to artificially control the contribution of photometric error and emission lines to total flux, we develop a PCA-based stochastic mock catalogue generation technique that allows f... | ['István Csabai', 'László Dobos', 'Géza Csörnyei'] | 2021-01-27 | null | null | null | null | ['photometric-redshift-estimation'] | ['miscellaneous'] | [ 4.73636150e-01 -2.61671424e-01 4.21338350e-01 -1.65485397e-01
-6.83674872e-01 -6.47039890e-01 9.80453491e-01 -3.61451983e-01
-4.72637028e-01 8.28969777e-01 -3.33429426e-01 -1.61745548e-01
-3.74738842e-01 -8.39851797e-01 -3.02192897e-01 -1.31844187e+00
7.88398743e-01 9.31966782e-01 5.62621117e-01 1.12433933... | [7.359214782714844, 3.2737767696380615] |
b3deeb03-df7a-4114-b33d-23dbbdc193ff | complete-3d-human-reconstruction-from-a | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_Complete_3D_Human_Reconstruction_From_a_Single_Incomplete_Image_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_Complete_3D_Human_Reconstruction_From_a_Single_Incomplete_Image_CVPR_2023_paper.pdf | Complete 3D Human Reconstruction From a Single Incomplete Image | This paper presents a method to reconstruct a complete human geometry and texture from an image of a person with only partial body observed, e.g., a torso. The core challenge arises from the occlusion: there exists no pixel to reconstruct where many existing single-view human reconstruction methods are not designed... | ['Ulrich Neumann', 'Krishna Kumar Singh', 'Tuanfeng Y. Wang', 'Jae Shin Yoon', 'Junying Wang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['3d-human-reconstruction'] | ['computer-vision'] | [ 1.95400968e-01 3.47221911e-01 2.90978312e-01 -2.01144174e-01
-3.60949039e-01 -1.65878177e-01 2.61110604e-01 -4.52686042e-01
2.03030750e-01 6.96935892e-01 2.13863596e-01 4.74289685e-01
2.89351761e-01 -1.01078701e+00 -9.39947307e-01 -4.65602219e-01
4.29418087e-01 7.13469923e-01 -3.68505865e-02 -2.94921637... | [7.270761489868164, -1.3508002758026123] |
8a151229-b44a-40cd-a3fb-06d36b593abf | deep-implicit-moving-least-squares-functions | 2103.12266 | null | https://arxiv.org/abs/2103.12266v2 | https://arxiv.org/pdf/2103.12266v2.pdf | Deep Implicit Moving Least-Squares Functions for 3D Reconstruction | Point set is a flexible and lightweight representation widely used for 3D deep learning. However, their discrete nature prevents them from representing continuous and fine geometry, posing a major issue for learning-based shape generation. In this work, we turn the discrete point sets into smooth surfaces by introducin... | ['Yang Liu', 'Xin Tong', 'Peng-Shuai Wang', 'Hao Pan', 'Hao-Xiang Guo', 'Shi-Lin Liu'] | 2021-03-23 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Liu_Deep_Implicit_Moving_Least-Squares_Functions_for_3D_Reconstruction_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Liu_Deep_Implicit_Moving_Least-Squares_Functions_for_3D_Reconstruction_CVPR_2021_paper.pdf | cvpr-2021-1 | ['3d-object-reconstruction'] | ['computer-vision'] | [-7.33149573e-02 2.89576083e-01 -1.49518475e-02 -3.45792651e-01
-8.30774844e-01 -4.56472605e-01 6.07669353e-01 -1.50671052e-02
5.18451817e-02 5.34109771e-01 -4.45298329e-02 -1.83955222e-01
2.68410845e-03 -1.22549975e+00 -1.18827355e+00 -4.03520077e-01
-1.62002027e-01 8.30407500e-01 1.74994096e-01 -1.80654839... | [8.637205123901367, -3.607675075531006] |
a08d77e9-84ca-4b2a-a4d8-c1f74d38f2f7 | using-3d-convolutional-neural-networks-to | 1907.11454 | null | https://arxiv.org/abs/1907.11454v1 | https://arxiv.org/pdf/1907.11454v1.pdf | Using 3D Convolutional Neural Networks to Learn Spatiotemporal Features for Automatic Surgical Gesture Recognition in Video | Automatically recognizing surgical gestures is a crucial step towards a thorough understanding of surgical skill. Possible areas of application include automatic skill assessment, intra-operative monitoring of critical surgical steps, and semi-automation of surgical tasks. Solutions that rely only on the laparoscopic v... | ['Stefanie Speidel', 'Jürgen Weitz', 'Felix von Bechtolsheim', 'Sebastian Bodenstedt', 'Isabel Funke', 'Florian Oehme'] | 2019-07-26 | null | null | null | null | ['surgical-gesture-recognition'] | ['medical'] | [ 2.66985387e-01 -1.48227021e-01 -2.49510676e-01 -2.44859606e-01
-7.25593269e-01 -5.85431457e-01 3.17112654e-01 1.49365544e-01
-8.97631049e-01 1.87467635e-01 1.30255625e-01 -2.93563604e-01
-1.53311342e-01 -2.01282620e-01 -6.49528027e-01 -6.38974011e-01
-1.86816588e-01 2.73030791e-02 1.79496586e-01 -9.43958163... | [14.038775444030762, -3.32985258102417] |
bbee0fc0-edde-456c-b758-039351dc05f4 | maas-multi-modal-assignation-for-active | 2101.03682 | null | https://arxiv.org/abs/2101.03682v2 | https://arxiv.org/pdf/2101.03682v2.pdf | MAAS: Multi-modal Assignation for Active Speaker Detection | Active speaker detection requires a solid integration of multi-modal cues. While individual modalities can approximate a solution, accurate predictions can only be achieved by explicitly fusing the audio and visual features and modeling their temporal progression. Despite its inherent muti-modal nature, current methods... | ['Bernard Ghanem', 'Ali Thabet', 'Fabian Caba Heilbron', 'Juan León-Alcázar'] | 2021-01-11 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Alcazar_MAAS_Multi-Modal_Assignation_for_Active_Speaker_Detection_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Alcazar_MAAS_Multi-Modal_Assignation_for_Active_Speaker_Detection_ICCV_2021_paper.pdf | iccv-2021-1 | ['audio-visual-active-speaker-detection'] | ['computer-vision'] | [ 2.29746148e-01 8.05538669e-02 -3.56382281e-02 -3.78032148e-01
-1.33776462e+00 -5.98754585e-01 7.82709479e-01 3.68439913e-01
-1.96363956e-01 3.06116760e-01 3.49809706e-01 2.19732106e-01
-1.28193960e-01 -3.08656186e-01 -4.41682041e-01 -6.81562304e-01
-3.98983121e-01 5.59629202e-01 6.22947931e-01 6.17508031... | [14.47327995300293, 5.103629112243652] |
d5617d84-153a-4b64-b252-078e6ed1a37d | detection-of-adversarial-attacks-and | 1910.12084 | null | https://arxiv.org/abs/1910.12084v1 | https://arxiv.org/pdf/1910.12084v1.pdf | Detection of Adversarial Attacks and Characterization of Adversarial Subspace | Adversarial attacks have always been a serious threat for any data-driven model. In this paper, we explore subspaces of adversarial examples in unitary vector domain, and we propose a novel detector for defending our models trained for environmental sound classification. We measure chordal distance between legitimate a... | ['Alessandro Lameiras Koerich', 'Patrick Cardinal', 'Mohammad Esmaeilpour'] | 2019-10-26 | null | null | null | null | ['environmental-sound-classification', 'sound-classification'] | ['audio', 'audio'] | [ 3.35938632e-01 -1.89927235e-01 7.04067945e-01 2.20453367e-01
-9.53061998e-01 -1.40490139e+00 4.93280530e-01 -2.96173453e-01
-8.50133374e-02 2.88254052e-01 3.14016342e-01 -4.86336738e-01
3.03388461e-02 -5.74146569e-01 -6.73963010e-01 -7.47209728e-01
-6.84225619e-01 -1.87002078e-01 3.95912588e-01 -5.06481409... | [13.924002647399902, 5.820832252502441] |
a8f42f7e-0517-4403-b1c2-0f0c06b5d00a | natural-policy-gradients-in-reinforcement | 2209.01820 | null | https://arxiv.org/abs/2209.01820v1 | https://arxiv.org/pdf/2209.01820v1.pdf | Natural Policy Gradients In Reinforcement Learning Explained | Traditional policy gradient methods are fundamentally flawed. Natural gradients converge quicker and better, forming the foundation of contemporary Reinforcement Learning such as Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO). This lecture note aims to clarify the intuition behind natura... | ['W. J. A. van Heeswijk'] | 2022-09-05 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [-6.38775945e-01 -1.10878222e-01 -7.90427566e-01 -1.50941953e-01
-1.87517986e-01 -6.60896361e-01 6.07279778e-01 1.10203691e-01
-8.54437113e-01 1.46318221e+00 2.90460348e-01 -9.56277370e-01
-2.23769560e-01 -2.97784328e-01 -5.10377884e-01 -5.90209305e-01
-5.36870480e-01 1.32378504e-01 -5.70376664e-02 -7.87378490... | [4.095970630645752, 2.328688859939575] |
425b3a76-2834-4946-ba49-c1f237fc92ae | multimodal-driven-talking-face-generation | 2305.02594 | null | https://arxiv.org/abs/2305.02594v2 | https://arxiv.org/pdf/2305.02594v2.pdf | Multimodal-driven Talking Face Generation via a Unified Diffusion-based Generator | Multimodal-driven talking face generation refers to animating a portrait with the given pose, expression, and gaze transferred from the driving image and video, or estimated from the text and audio. However, existing methods ignore the potential of text modal, and their generators mainly follow the source-oriented feat... | ['Yong liu', 'Ying Tai', 'Jiangning Zhang', 'Tianxin Huang', 'Junwei Zhu', 'Shaoting Zhu', 'Chao Xu'] | 2023-05-04 | null | null | null | null | ['face-swapping', 'talking-face-generation', 'face-generation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.80914569e-01 1.49990901e-01 3.17826718e-01 -6.17274046e-01
-8.44177246e-01 -4.55369025e-01 6.22959733e-01 -9.47785139e-01
3.77681732e-01 5.01079023e-01 2.77015567e-01 3.18640679e-01
2.60718050e-03 -5.48655927e-01 -8.18942308e-01 -1.21457803e+00
4.93438452e-01 2.93703437e-01 -2.94276834e-01 -3.90821189... | [12.846172332763672, -0.327658474445343] |
ee36b828-2161-46d6-8a33-e618bbf41ae6 | autonomous-driving-with-deep-reinforcement | 2306.11217 | null | https://arxiv.org/abs/2306.11217v1 | https://arxiv.org/pdf/2306.11217v1.pdf | Autonomous Driving with Deep Reinforcement Learning in CARLA Simulation | Nowadays, autonomous vehicles are gaining traction due to their numerous potential applications in resolving a variety of other real-world challenges. However, developing autonomous vehicles need huge amount of training and testing before deploying it to real world. While the field of reinforcement learning (RL) has ev... | ['Jumman Hossain'] | 2023-06-20 | null | null | null | null | ['autonomous-vehicles', 'q-learning'] | ['computer-vision', 'methodology'] | [-3.65551859e-01 5.91462106e-02 -4.28705633e-01 -3.94342750e-01
-1.33793488e-01 -2.88476020e-01 6.30359769e-01 -4.25407320e-01
-6.83914781e-01 1.03377509e+00 -4.54780638e-01 -7.75258422e-01
-7.01431558e-02 -9.71519887e-01 -6.40412509e-01 -7.73168802e-01
-2.64080971e-01 3.72113824e-01 2.87793279e-01 -6.93520069... | [5.187730312347412, 1.2737840414047241] |
89fc73a7-501c-4cce-a3c9-959ec6e78648 | fusecap-leveraging-large-language-models-to | 2305.17718 | null | https://arxiv.org/abs/2305.17718v1 | https://arxiv.org/pdf/2305.17718v1.pdf | FuseCap: Leveraging Large Language Models to Fuse Visual Data into Enriched Image Captions | Image captioning is a central task in computer vision which has experienced substantial progress following the advent of vision-language pre-training techniques. In this paper, we highlight a frequently overlooked limitation of captioning models that often fail to capture semantically significant elements. This drawbac... | ['Ron Kimmel', 'Roy Ganz', 'Shaked Brody', 'David Bensaid', 'Noam Rotstein'] | 2023-05-28 | null | null | null | null | ['optical-character-recognition', 'image-captioning'] | ['computer-vision', 'computer-vision'] | [ 6.71775162e-01 3.29429120e-01 -1.02681935e-01 -2.41875410e-01
-1.23025656e+00 -7.21118271e-01 7.94409633e-01 2.17716545e-01
-3.50604743e-01 6.56797469e-01 3.71795833e-01 -9.35132131e-02
2.57246047e-01 -2.50810355e-01 -1.20008075e+00 -2.65739888e-01
6.34573519e-01 5.62372148e-01 1.01924255e-01 -2.16624960... | [10.924676895141602, 1.05076003074646] |
c2782780-2858-4d4e-bca4-2901042fa40f | trajectory-aware-body-interaction-transformer | 2303.05095 | null | https://arxiv.org/abs/2303.05095v2 | https://arxiv.org/pdf/2303.05095v2.pdf | Trajectory-Aware Body Interaction Transformer for Multi-Person Pose Forecasting | Multi-person pose forecasting remains a challenging problem, especially in modeling fine-grained human body interaction in complex crowd scenarios. Existing methods typically represent the whole pose sequence as a temporal series, yet overlook interactive influences among people based on skeletal body parts. In this pa... | ['Zizhao Wu', 'Siyuan Mao', 'Xiaogang Peng'] | 2023-03-09 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Peng_Trajectory-Aware_Body_Interaction_Transformer_for_Multi-Person_Pose_Forecasting_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Peng_Trajectory-Aware_Body_Interaction_Transformer_for_Multi-Person_Pose_Forecasting_CVPR_2023_paper.pdf | cvpr-2023-1 | ['multi-person-pose-forecasting'] | ['computer-vision'] | [-1.12294503e-01 -1.03294201e-01 1.71508357e-01 -3.56866032e-01
-3.76126587e-01 -3.28717023e-01 4.84628320e-01 -2.28679195e-01
-2.36639649e-01 5.17463803e-01 9.30540502e-01 4.81480956e-01
-1.20195925e-01 -6.19186997e-01 -7.53902435e-01 -3.89809102e-01
-3.07933331e-01 8.08411777e-01 5.47199011e-01 -6.14236057... | [7.216656684875488, -0.44388431310653687] |
047680ea-92f3-4a26-82c7-87785dd3d36a | localized-traffic-sign-detection-with-multi | 1804.10428 | null | http://arxiv.org/abs/1804.10428v2 | http://arxiv.org/pdf/1804.10428v2.pdf | Localized Traffic Sign Detection with Multi-scale Deconvolution Networks | Autonomous driving is becoming a future practical lifestyle greatly driven by
deep learning. Specifically, an effective traffic sign detection by deep
learning plays a critical role for it. However, different countries have
different sets of traffic signs, making localized traffic sign recognition
model training a tedi... | ['Yanfei Ji', 'Fuwu Tang', 'Zhong Ning', 'Songwen Pei', 'Jing Fan'] | 2018-04-27 | null | null | null | null | ['traffic-sign-recognition', 'traffic-sign-detection'] | ['computer-vision', 'computer-vision'] | [-1.40571445e-01 -8.16303194e-01 -7.64180496e-02 -4.11911190e-01
-4.67444777e-01 -2.55133599e-01 5.24667978e-01 -1.36225927e+00
-4.78654742e-01 6.72432721e-01 -1.03871800e-01 -8.40881169e-01
-6.66283891e-02 -4.50733453e-01 -5.97552061e-01 -8.98184717e-01
3.13743681e-01 2.14073882e-01 5.29243886e-01 -2.90453553... | [8.005793571472168, -0.7915133833885193] |
b0911aef-df32-495d-b9b0-98e095df8d73 | self-supervised-visual-feature-learning-with | 1902.06162 | null | http://arxiv.org/abs/1902.06162v1 | http://arxiv.org/pdf/1902.06162v1.pdf | Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey | Large-scale labeled data are generally required to train deep neural networks
in order to obtain better performance in visual feature learning from images or
videos for computer vision applications. To avoid extensive cost of collecting
and annotating large-scale datasets, as a subset of unsupervised learning
methods, ... | ['YingLi Tian', 'Longlong Jing'] | 2019-02-16 | null | null | null | null | ['self-supervised-image-classification'] | ['computer-vision'] | [-9.03824344e-02 -2.30992094e-01 -5.21550059e-01 -9.27456439e-01
-5.20160496e-01 -4.24290985e-01 3.45418304e-01 1.05279662e-01
-4.72222894e-01 5.40886343e-01 -3.97602692e-02 4.23754215e-01
2.07575545e-01 -5.37757754e-01 -7.56618381e-01 -7.04568863e-01
-3.00948262e-01 1.45064324e-01 3.62370014e-02 1.93456292... | [9.508500099182129, 2.256939172744751] |
ebbeb2ad-170d-44e2-a896-11534060fce3 | scientific-computing-with-diffractive-optical | 2302.10905 | null | https://arxiv.org/abs/2302.10905v1 | https://arxiv.org/pdf/2302.10905v1.pdf | Scientific Computing with Diffractive Optical Neural Networks | Diffractive optical neural networks (DONNs) have been emerging as a high-throughput and energy-efficient hardware platform to perform all-optical machine learning (ML) in machine vision systems. However, the current demonstrated applications of DONNs are largely straightforward image classification tasks, which undermi... | ['Weilu Gao', 'Jianzhu Ma', 'Yingheng Tang', 'Ruiyang Chen'] | 2023-02-12 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [ 6.34648800e-01 -8.31088647e-02 -3.74936573e-02 2.34377142e-02
-2.59002466e-02 -6.44344389e-01 3.52856278e-01 -2.81439871e-01
-3.27262491e-01 8.39723945e-01 -3.59146118e-01 -4.16917026e-01
-1.34389102e-01 -1.04286480e+00 -8.06497395e-01 -1.32823467e+00
3.27777743e-01 2.36626759e-01 1.07328735e-01 -3.12827796... | [8.191514015197754, 2.4691967964172363] |
362fd8fa-19cd-4d53-884a-6bafec39b92c | deep-neural-networks-based-modrec-some | 1811.06103 | null | http://arxiv.org/abs/1811.06103v1 | http://arxiv.org/pdf/1811.06103v1.pdf | Deep Neural Networks based Modrec: Some Results with Inter-Symbol Interference and Adversarial Examples | Recent successes and advances in Deep Neural Networks (DNN) in machine vision
and Natural Language Processing (NLP) have motivated their use in traditional
signal processing and communications systems. In this paper, we present results
of such applications to the problem of automatic modulation recognition.
Variations ... | ['S. Asim Ahmed', 'Michael Newhouse', 'Subhashish Chakravarty'] | 2018-11-14 | null | null | null | null | ['automatic-modulation-recognition'] | ['time-series'] | [ 3.95047665e-01 -6.76498413e-02 -1.28425270e-01 -3.54918182e-01
-3.56206059e-01 -2.78287411e-01 5.01847327e-01 -5.96350431e-01
-4.14602667e-01 1.07396162e+00 1.24757715e-01 -1.03133476e+00
-7.13073388e-02 -7.06188321e-01 -6.81061745e-01 -6.25520647e-01
-9.31357741e-01 -3.14156383e-01 -4.77020383e-01 -4.21763331... | [6.409473419189453, 1.501791000366211] |
ecf80ad0-71ba-4a8f-af40-ba68db95e56c | meloform-generating-melody-with-musical-form | 2208.14345 | null | https://arxiv.org/abs/2208.14345v1 | https://arxiv.org/pdf/2208.14345v1.pdf | MeloForm: Generating Melody with Musical Form based on Expert Systems and Neural Networks | Human usually composes music by organizing elements according to the musical form to express music ideas. However, for neural network-based music generation, it is difficult to do so due to the lack of labelled data on musical form. In this paper, we develop MeloForm, a system that generates melody with musical form us... | ['Tie-Yan Liu', 'Sheng Zhao', 'Tao Qin', 'Botao Yu', 'Xu Tan', 'Peiling Lu'] | 2022-08-30 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [-9.89542808e-03 -3.15255016e-01 5.94988354e-02 1.38467118e-01
-2.28414148e-01 -8.75640929e-01 2.95537800e-01 -3.07516515e-01
-1.11835867e-01 6.72678769e-01 3.54370624e-01 9.93370786e-02
-4.58237678e-01 -9.85282779e-01 -3.33143085e-01 -3.95006567e-01
3.67657572e-01 4.83844310e-01 2.75709834e-02 -7.71292329... | [16.033710479736328, 5.521481037139893] |
064c528b-e0f2-4fbb-902c-2945cc2a5073 | demystifying-ten-big-ideas-and-rules-every | 2111.13756 | null | https://arxiv.org/abs/2111.13756v1 | https://arxiv.org/pdf/2111.13756v1.pdf | Demystifying Ten Big Ideas and Rules Every Fire Scientist & Engineer Should Know About Blackbox, Whitebox & Causal Artificial Intelligence | Artificial intelligence (AI) is paving the way towards the fourth industrial revolution with the fire domain (Fire 4.0). As a matter of fact, the next few years will be elemental to how this technology will shape our academia, practice, and entrepreneurship. Despite the growing interest between fire research groups, AI... | ['M. Z. Naser'] | 2021-11-23 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [ 4.30135936e-01 1.39408261e-01 -8.05237442e-02 1.85691535e-01
2.09120542e-01 -5.42936504e-01 5.66194534e-01 -5.17897248e-01
7.05725886e-03 8.14360976e-01 -4.14571501e-02 -5.94770491e-01
-9.24210250e-01 -1.07543099e+00 -4.12574321e-01 -8.36057305e-01
2.34835833e-01 4.96579438e-01 -1.79589659e-01 -7.16178596... | [5.6537933349609375, 3.9028522968292236] |
c6e0f396-7dec-47e4-bb38-0e6e49233d39 | uatta-ens-uncertainty-aware-test-time | 2211.03148 | null | https://arxiv.org/abs/2211.03148v2 | https://arxiv.org/pdf/2211.03148v2.pdf | UATTA-ENS: Uncertainty Aware Test Time Augmented Ensemble for PIRC Diabetic Retinopathy Detection | Deep Ensemble Convolutional Neural Networks has become a methodology of choice for analyzing medical images with a diagnostic performance comparable to a physician, including the diagnosis of Diabetic Retinopathy. However, commonly used techniques are deterministic and are therefore unable to provide any estimate of pr... | ['Akshat Bhandari', 'Saurabh Kumar Mishra', 'Ananya Gupta', 'Adil Khan', 'Pratinav Seth'] | 2022-11-06 | null | null | null | null | ['diabetic-retinopathy-detection'] | ['medical'] | [ 1.66775912e-01 8.39565620e-02 1.00378007e-01 -9.44549143e-01
-1.04455483e+00 -2.56559134e-01 1.66262224e-01 2.08252117e-01
-2.26560175e-01 1.10000324e+00 -2.47875471e-02 -7.34400451e-01
-5.68583786e-01 -6.83107138e-01 -6.17572188e-01 -8.43031526e-01
1.57478735e-01 5.82506478e-01 -1.76283404e-01 3.79927933... | [14.297290802001953, -2.0893707275390625] |
014b363b-559b-4c3f-8967-1904186672af | schema-aware-reference-as-prompt-improves | 2210.10709 | null | https://arxiv.org/abs/2210.10709v4 | https://arxiv.org/pdf/2210.10709v4.pdf | Schema-aware Reference as Prompt Improves Data-Efficient Knowledge Graph Construction | With the development of pre-trained language models, many prompt-based approaches to data-efficient knowledge graph construction have been proposed and achieved impressive performance. However, existing prompt-based learning methods for knowledge graph construction are still susceptible to several potential limitations... | ['Huajun Chen', 'Xi Chen', 'Xiang Chen', 'Shumin Deng', 'Ningyu Zhang', 'Shengyu Mao', 'Yunzhi Yao'] | 2022-10-19 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [-4.20804881e-02 4.85562414e-01 -9.15267885e-01 -2.18501270e-01
-9.84341264e-01 -7.48526037e-01 8.00979197e-01 6.90097451e-01
-2.20472381e-01 6.20102048e-01 3.43963236e-01 -2.59373069e-01
-5.65310001e-01 -1.10360003e+00 -7.47889459e-01 -5.32150827e-03
-3.63356508e-02 7.44005203e-01 5.51296711e-01 -1.71789378... | [9.351644515991211, 8.078672409057617] |
20819599-375f-4b2c-82ed-c2845cd3e656 | self-supervised-semantic-segmentation | 2203.13868 | null | https://arxiv.org/abs/2203.13868v2 | https://arxiv.org/pdf/2203.13868v2.pdf | Self-supervised Semantic Segmentation Grounded in Visual Concepts | Unsupervised semantic segmentation requires assigning a label to every pixel without any human annotations. Despite recent advances in self-supervised representation learning for individual images, unsupervised semantic segmentation with pixel-level representations is still a challenging task and remains underexplored.... | ['Liu Ren', 'Liang Gou', 'Arvind Kumar Shekar', 'William Surmeier', 'Wenbin He'] | 2022-03-25 | null | null | null | null | ['unsupervised-semantic-segmentation'] | ['computer-vision'] | [ 5.79621851e-01 2.29224101e-01 -4.76230770e-01 -7.30157375e-01
-3.45943242e-01 -6.47556305e-01 5.62812865e-01 5.19921124e-01
-3.45094293e-01 3.96826237e-01 3.22945118e-01 4.37754672e-03
2.93348163e-01 -9.36942220e-01 -8.48218441e-01 -6.27494097e-01
1.17310002e-01 2.19606966e-01 4.17695791e-01 1.98220536... | [9.66912841796875, 0.9327367544174194] |
67afd259-b877-4d3f-8d7d-c37c6210728b | 4d-association-graph-for-realtime-multi | 2002.12625 | null | https://arxiv.org/abs/2002.12625v1 | https://arxiv.org/pdf/2002.12625v1.pdf | 4D Association Graph for Realtime Multi-person Motion Capture Using Multiple Video Cameras | This paper contributes a novel realtime multi-person motion capture algorithm using multiview video inputs. Due to the heavy occlusions in each view, joint optimization on the multiview images and multiple temporal frames is indispensable, which brings up the essential challenge of realtime efficiency. To this end, for... | ['Tao Yu', 'Liang An', 'Yuxiang Zhang', 'Xiu Li', 'Kun Li', 'Yebin Liu'] | 2020-02-28 | 4d-association-graph-for-realtime-multi-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_4D_Association_Graph_for_Realtime_Multi-Person_Motion_Capture_Using_Multiple_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_4D_Association_Graph_for_Realtime_Multi-Person_Motion_Capture_Using_Multiple_CVPR_2020_paper.pdf | cvpr-2020-6 | ['3d-multi-person-pose-estimation'] | ['computer-vision'] | [-1.50420383e-01 -4.87236768e-01 -1.46788448e-01 -1.16483048e-02
-7.43074298e-01 -5.82560778e-01 5.28752618e-02 -2.99236298e-01
-4.49754238e-01 2.28028074e-01 7.11080944e-03 1.88337773e-01
1.56706885e-01 -3.38757247e-01 -6.14242613e-01 -4.44566131e-01
1.81380183e-01 2.03972697e-01 5.05864084e-01 1.74784251... | [7.098141193389893, -1.0687003135681152] |
59084802-4952-4227-a7b2-b14796bfbb58 | folding-home-achievements-from-over-twenty | 2303.08993 | null | https://arxiv.org/abs/2303.08993v1 | https://arxiv.org/pdf/2303.08993v1.pdf | Folding@home: achievements from over twenty years of citizen science herald the exascale era | Simulations of biomolecules have enormous potential to inform our understanding of biology but require extremely demanding calculations. For over twenty years, the Folding@home distributed computing project has pioneered a massively parallel approach to biomolecular simulation, harnessing the resources of citizen scien... | ['Gregory R. Bowman', 'Vijay S. Pande', 'Vincent A. Voelz'] | 2023-03-15 | null | null | null | null | ['protein-folding'] | ['natural-language-processing'] | [-4.30892557e-02 -5.85566938e-01 -8.20334405e-02 -3.06937009e-01
-5.33716261e-01 -7.58431554e-01 3.39651674e-01 5.39463222e-01
-3.94224167e-01 8.44234943e-01 2.61155993e-01 -7.70362139e-01
4.10056144e-01 -6.49461031e-01 -4.48785335e-01 -7.91736305e-01
-3.70763063e-01 5.60249627e-01 8.03887248e-02 -3.16781014... | [4.77852201461792, 5.37705659866333] |
00cf2719-3edb-4a20-b392-11cff6a45bc3 | exploiting-completeness-and-uncertainty-of | 2212.04090 | null | https://arxiv.org/abs/2212.04090v1 | https://arxiv.org/pdf/2212.04090v1.pdf | Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detection | Weakly supervised video anomaly detection aims to identify abnormal events in videos using only video-level labels. Recently, two-stage self-training methods have achieved significant improvements by self-generating pseudo labels and self-refining anomaly scores with these labels. As the pseudo labels play a crucial ro... | ['Ming-Hsuan Yang', 'Qingming Huang', 'Laiyun Qing', 'Shuhui Wang', 'Yuankai Qi', 'Guorong Li', 'Chen Zhang'] | 2022-12-08 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Exploiting_Completeness_and_Uncertainty_of_Pseudo_Labels_for_Weakly_Supervised_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Exploiting_Completeness_and_Uncertainty_of_Pseudo_Labels_for_Weakly_Supervised_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-anomaly-detection'] | ['computer-vision'] | [ 2.50379533e-01 1.97240323e-01 -2.67477959e-01 -6.16985857e-01
-8.19539189e-01 -2.89526582e-01 5.47377527e-01 3.42759311e-01
-4.74180877e-01 7.32678711e-01 2.07566351e-01 1.42210573e-01
1.73225235e-02 -3.58576655e-01 -7.38266230e-01 -7.81897366e-01
-3.90735835e-01 5.02992809e-01 3.93566549e-01 2.60072827... | [7.82633113861084, 1.6396620273590088] |
d45860ed-7ea0-455a-a454-1eb0acbfe4b0 | economic-and-energetic-assessment-of-a-hybrid | 2301.02535 | null | https://arxiv.org/abs/2301.02535v1 | https://arxiv.org/pdf/2301.02535v1.pdf | Economic and Energetic Assessment of a Hybrid Vanadium Redox Flow and Lithium-ion batteries considering different Energy Management Strategies | Hybrid energy storage systems (HESS) combine different energy storage technologies aiming at overall system performance and lifetime improvement compared to a single technology system. In this work, control combinations for a vanadium redox flow battery (VRFB, 5/60 kW/kWh) and a lithium-ion battery (LIB, 3.3/9.8 kW/kWh... | ['Manuel Collares-Pereira', 'Pedro Horta', 'Luís Fialho', 'Ana Foles'] | 2023-01-06 | null | null | null | null | ['energy-management'] | ['time-series'] | [-5.93273938e-01 -2.18277231e-01 -8.26552138e-02 3.19713622e-01
-4.63357046e-02 -7.61268079e-01 1.09042835e+00 4.45895910e-01
-5.26110791e-02 1.10708177e+00 -1.27020925e-01 -2.29416415e-01
-6.24556422e-01 -9.46856499e-01 -3.07097375e-01 -1.24313068e+00
1.45626277e-01 3.16835016e-01 -1.64551541e-01 -3.61074150... | [5.6756744384765625, 2.5027191638946533] |
0c5fb90f-7bd7-4503-9509-6b36a5fb9bbc | benchmarking-state-of-the-art-gradient | 2305.17094 | null | https://arxiv.org/abs/2305.17094v1 | https://arxiv.org/pdf/2305.17094v1.pdf | Benchmarking state-of-the-art gradient boosting algorithms for classification | This work explores the use of gradient boosting in the context of classification. Four popular implementations, including original GBM algorithm and selected state-of-the-art gradient boosting frameworks (i.e. XGBoost, LightGBM and CatBoost), have been thoroughly compared on several publicly available real-world datase... | ['Adam Zagdański', 'Piotr Florek'] | 2023-05-26 | null | null | null | null | ['hyperparameter-optimization', 'bayesian-optimization'] | ['methodology', 'methodology'] | [-3.07172567e-01 -3.45477819e-01 -4.27180648e-01 -8.17380786e-01
-5.09882450e-01 -2.41306886e-01 8.96832108e-01 4.20829773e-01
-6.15393043e-01 1.21468747e+00 1.32773712e-01 -6.04164362e-01
-7.53212988e-01 -7.06575155e-01 -1.72597423e-01 -8.66517305e-01
-8.15296173e-02 6.44910514e-01 -4.10566991e-03 -2.95563310... | [8.31170654296875, 4.2854413986206055] |
1bdefdd8-1717-44a3-9f8b-fc37025afe30 | user-constrained-thumbnail-generation-using | 1810.13054 | null | http://arxiv.org/abs/1810.13054v3 | http://arxiv.org/pdf/1810.13054v3.pdf | User Constrained Thumbnail Generation using Adaptive Convolutions | Thumbnails are widely used all over the world as a preview for digital
images. In this work we propose a deep neural framework to generate thumbnails
of any size and aspect ratio, even for unseen values during training, with high
accuracy and precision. We use Global Context Aggregation (GCA) and a modified
Region Prop... | ['Ayan Kumar Bhunia', 'Perla Sai Raj Kishore', 'Shuvozit Ghose', 'Partha Pratim Roy'] | 2018-10-31 | null | null | null | null | ['user-constrained-thumbnail-generation'] | ['computer-vision'] | [ 2.96734631e-01 -3.27248663e-01 4.17466551e-01 -2.03989550e-01
-3.17526877e-01 -4.53187346e-01 6.21083319e-01 8.67416412e-02
-4.84343320e-01 6.49887919e-01 5.75789176e-02 -3.04222047e-01
3.33474427e-02 -1.13558757e+00 -6.97581649e-01 -5.87638378e-01
1.35007977e-01 8.61929283e-02 6.23289645e-01 -1.43943310... | [11.246367454528809, -1.0088026523590088] |
60ffdea5-a199-4957-a2bb-72746aadfd66 | bipartite-mixed-membership-distribution-free | 2211.00912 | null | https://arxiv.org/abs/2211.00912v2 | https://arxiv.org/pdf/2211.00912v2.pdf | Bipartite Mixed Membership Distribution-Free Model. A novel model for community detection in overlapping bipartite weighted networks | Modeling and estimating mixed memberships for overlapping unipartite un-weighted networks has been well studied in recent years. However, to our knowledge, there is no model for a more general case, the overlapping bipartite weighted networks. To close this gap, we introduce a novel model, the Bipartite Mixed Membershi... | ['Jingli Wang', 'Huan Qing'] | 2022-11-02 | null | null | null | null | ['community-detection'] | ['graphs'] | [ 1.45370424e-01 2.16492981e-01 -2.91523069e-01 -2.54203469e-01
-2.01069504e-01 -4.91342187e-01 1.25179708e-01 -2.56640255e-01
1.27665460e-01 9.98101771e-01 -1.78927064e-01 -1.91854045e-01
-8.02412093e-01 -9.68240619e-01 -6.28546298e-01 -8.00035894e-01
-4.93647873e-01 8.17248046e-01 4.38840538e-01 -3.32446136... | [6.969106197357178, 5.230407238006592] |
8eee0f7d-24c6-453d-8cae-1f878de7805a | cuisinenet-food-attributes-classification | 1805.12081 | null | http://arxiv.org/abs/1805.12081v2 | http://arxiv.org/pdf/1805.12081v2.pdf | CuisineNet: Food Attributes Classification using Multi-scale Convolution Network | Diversity of food and its attributes represents the culinary habits of
peoples from different countries. Thus, this paper addresses the problem of
identifying food culture of people around the world and its flavor by
classifying two main food attributes, cuisine and flavor. A deep learning model
based on multi-scale co... | ['Md. Mostafa Kamal Sarker', 'Petia Radeva', 'Mohammed Jabreel', 'Domenec Puig', 'Antonio Moreno', 'Syeda Furruka Banu', 'Hatem A. Rashwan'] | 2018-05-30 | null | null | null | null | ['multi-modal-classification'] | ['miscellaneous'] | [-5.05860984e-01 -5.67379594e-01 -1.99734017e-01 -6.69479847e-01
-4.74133432e-01 -5.64980865e-01 1.06101044e-01 6.65849388e-01
-5.04352391e-01 5.28487921e-01 2.61025488e-01 2.69149154e-01
2.44761407e-01 -1.14959848e+00 -8.21464419e-01 -7.01983213e-01
-1.37307703e-01 5.59091903e-02 -4.38811600e-01 -2.96617094... | [11.55945110321045, 4.38635778427124] |
c8c1ba88-b016-46f2-9ec3-d8005e7b416f | small-changes-make-big-differences-improving | 2111.10154 | null | https://arxiv.org/abs/2111.10154v2 | https://arxiv.org/pdf/2111.10154v2.pdf | Small Changes Make Big Differences: Improving Multi-turn Response Selection in Dialogue Systems via Fine-Grained Contrastive Learning | Retrieve-based dialogue response selection aims to find a proper response from a candidate set given a multi-turn context. Pre-trained language models (PLMs) based methods have yielded significant improvements on this task. The sequence representation plays a key role in the learning of matching degree between the dial... | ['Daxin Jiang', 'Yan Zhang', 'Lei Sha', 'Huang Hu', 'Can Xu', 'Yuntao Li'] | 2021-11-19 | null | null | null | null | ['conversational-response-selection'] | ['natural-language-processing'] | [ 6.45356476e-01 -1.85688585e-01 -4.58941191e-01 -7.94994235e-01
-1.09273875e+00 -5.46924055e-01 8.38358939e-01 3.50907147e-01
-3.02890956e-01 6.56625092e-01 5.68330526e-01 -2.68714219e-01
5.95052019e-02 -6.45106137e-01 -1.32495672e-01 -5.73359907e-01
4.18018758e-01 7.46895373e-01 4.02080327e-01 -8.90741408... | [12.532529830932617, 7.868793487548828] |
972c3efe-82d9-4a63-b7b7-069a808738e7 | debiasing-neural-retrieval-via-in-batch | 2205.09240 | null | https://arxiv.org/abs/2205.09240v1 | https://arxiv.org/pdf/2205.09240v1.pdf | Debiasing Neural Retrieval via In-batch Balancing Regularization | People frequently interact with information retrieval (IR) systems, however, IR models exhibit biases and discrimination towards various demographics. The in-processing fair ranking methods provide a trade-offs between accuracy and fairness through adding a fairness-related regularization term in the loss function. How... | ['Andrew Arnold', 'Xiaofei Ma', 'Parminder Bhatia', 'Shen Wang', 'Zijian Wang', 'Xiaokai Wei', 'Yuantong Li'] | 2022-05-18 | null | https://aclanthology.org/2022.gebnlp-1.5 | https://aclanthology.org/2022.gebnlp-1.5.pdf | naacl-gebnlp-2022-7 | ['passage-retrieval'] | ['natural-language-processing'] | [-7.45200813e-02 1.26019612e-01 -2.50205636e-01 -1.15228927e+00
-1.00807416e+00 -3.69673043e-01 5.12789130e-01 1.48950279e-01
-8.21460724e-01 6.56532884e-01 2.90740252e-01 -2.31422484e-01
-4.25675660e-01 -3.56513172e-01 -3.95987064e-01 -3.81191373e-01
1.84570462e-01 3.70998740e-01 -2.43392959e-01 -3.21247399... | [9.023911476135254, 5.312263011932373] |
81ec148e-efff-408d-9936-334beadaf655 | probing-multilingual-cognate-prediction | null | null | https://aclanthology.org/2022.findings-acl.299 | https://aclanthology.org/2022.findings-acl.299.pdf | Probing Multilingual Cognate Prediction Models | Character-based neural machine translation models have become the reference models for cognate prediction, a historical linguistics task. So far, all linguistic interpretations about latent information captured by such models have been based on external analysis (accuracy, raw results, errors). In this paper, we invest... | ['Benoît Sagot', 'Clémentine Fourrier'] | null | null | null | null | findings-acl-2022-5 | ['cognate-prediction'] | ['natural-language-processing'] | [ 1.54019818e-01 5.00446558e-01 -7.53495753e-01 -5.90055287e-01
-3.44999343e-01 -7.75238335e-01 1.14781201e+00 1.42309681e-01
-4.14615303e-01 9.58032012e-01 7.09016025e-01 -1.06900907e+00
4.38016593e-01 -8.76400709e-01 -8.35209787e-01 -1.49299562e-01
3.26474607e-01 6.18870080e-01 -4.92901774e-03 -2.65518218... | [10.85599136352539, 9.4702787399292] |
5b0a91ab-a87b-4a54-b015-49835bc3db9a | detection-and-recognition-of-malaysian | 1504.06921 | null | http://arxiv.org/abs/1504.06921v1 | http://arxiv.org/pdf/1504.06921v1.pdf | Detection and Recognition of Malaysian Special License Plate Based On SIFT Features | Automated car license plate recognition systems are developed and applied for
purpose of facilitating the surveillance, law enforcement, access control and
intelligent transportation monitoring with least human intervention. In this
paper, an algorithm based on SIFT feature points clustering and matching is
proposed to... | ['Yong Haur Tay', 'Hock Woon Hon', 'Kim Meng Liang', 'Hamam Mokayed', 'Hooi Sin Ng'] | 2015-04-27 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [-3.81093137e-02 -8.49095345e-01 1.22400135e-01 -7.31710047e-02
-1.29962757e-01 -9.83074427e-01 6.98025644e-01 1.02950104e-01
-4.13201421e-01 4.98039424e-01 -1.54339090e-01 -3.79384518e-01
-1.27906531e-01 -5.92815697e-01 -3.39928359e-01 -5.56186497e-01
4.77360427e-01 4.30844188e-01 7.27683544e-01 -2.13637710... | [9.799080848693848, -4.999486446380615] |
3ef13580-b35c-44aa-b50f-eaa5e18ed77e | ntuaails-at-semeval-2020-task-11-propaganda | null | null | https://aclanthology.org/2020.semeval-1.195 | https://aclanthology.org/2020.semeval-1.195.pdf | NTUAAILS at SemEval-2020 Task 11: Propaganda Detection and Classification with biLSTMs and ELMo | This paper describes the NTUAAILS submission for SemEval 2020 Task 11 Detection of Propaganda Techniques in News Articles. This task comprises of two different sub-tasks, namely A: Span Identification (SI), B: Technique Classification (TC). The goal for the SI sub-task is to identify specific fragments, in a given plai... | ['Georgios Siolas', 'Anastasios Arsenos'] | 2020-12-01 | null | null | null | semeval-2020 | ['propaganda-detection'] | ['natural-language-processing'] | [ 2.52950229e-02 -2.23061949e-01 -2.03757942e-01 3.72516364e-02
-9.30647731e-01 -5.70326328e-01 1.15078890e+00 3.32428843e-01
-7.93669581e-01 3.94296825e-01 5.34407437e-01 -6.59924626e-01
2.27665886e-01 -6.68684363e-01 -7.17574000e-01 -6.35566771e-01
-8.77869502e-02 1.25405446e-01 4.84084263e-02 -1.80706620... | [8.514561653137207, 10.679983139038086] |
f2b9821b-5ffa-4a17-93db-80d008f6e96e | tunbert-pretrained-contextualized-text | 2111.13138 | null | https://arxiv.org/abs/2111.13138v1 | https://arxiv.org/pdf/2111.13138v1.pdf | TunBERT: Pretrained Contextualized Text Representation for Tunisian Dialect | Pretrained contextualized text representation models learn an effective representation of a natural language to make it machine understandable. After the breakthrough of the attention mechanism, a new generation of pretrained models have been proposed achieving good performances since the introduction of the Transforme... | ['Amine Kerkeni', 'Faten Ghriss', 'Malek Naski', 'Abir Korched', 'Moez BenHajhmida', 'Nourchene Ferchichi', 'Hatem Haddad', 'Ahmed Cheikhrouhou', 'Abir Messaoudi'] | 2021-11-25 | null | null | null | null | ['dialect-identification'] | ['natural-language-processing'] | [-3.28382961e-02 2.33977303e-01 -3.46076861e-02 -5.52472413e-01
-1.03743970e+00 -5.78489602e-01 7.10609257e-01 2.95830965e-01
-6.11362994e-01 5.68519056e-01 6.28534615e-01 -6.22995079e-01
2.15996012e-01 -9.09679174e-01 -8.86414945e-01 -2.23183706e-01
3.59886527e-01 7.43009627e-01 3.34048569e-02 -8.19004893... | [10.941216468811035, 9.705794334411621] |
cfd420f3-cec4-447f-86fc-9b7b19a67415 | braitenberg-vehicles-as-developmental | 2003.07689 | null | https://arxiv.org/abs/2003.07689v3 | https://arxiv.org/pdf/2003.07689v3.pdf | Braitenberg Vehicles as Developmental Neurosimulation | Connecting brain and behavior is a longstanding issue in the areas of behavioral science, artificial intelligence, and neurobiology. As is standard among models of artificial and biological neural networks, an analogue of the fully mature brain is presented as a blank slate. However, this does not consider the realitie... | ['Bradly Alicea', 'Jesse Parent', 'Ankit Gupta', 'Ziyi Gong', 'Stefan Dvoretskii'] | 2020-02-28 | null | null | null | null | ['developmental-learning'] | ['robots'] | [-3.09789497e-02 4.24286127e-01 4.40434545e-01 1.74300849e-01
6.16966128e-01 -6.91576958e-01 8.69300723e-01 3.09077092e-03
-3.36135268e-01 5.21016836e-01 -1.00049876e-01 -1.65879384e-01
-6.48875594e-01 -7.61251211e-01 -6.86529160e-01 -8.01447213e-01
-2.72356123e-01 1.00039639e-01 1.32139966e-01 -7.17961490... | [5.567610263824463, 4.10671854019165] |
346c9a15-534f-4ea7-ad47-04c7145d66d7 | on-the-robustness-of-language-encoders | 2005.05683 | null | https://arxiv.org/abs/2005.05683v1 | https://arxiv.org/pdf/2005.05683v1.pdf | On the Robustness of Language Encoders against Grammatical Errors | We conduct a thorough study to diagnose the behaviors of pre-trained language encoders (ELMo, BERT, and RoBERTa) when confronted with natural grammatical errors. Specifically, we collect real grammatical errors from non-native speakers and conduct adversarial attacks to simulate these errors on clean text data. We use ... | ['Kai-Wei Chang', 'Tao Meng', 'Quanyu Long', 'Fan Yin'] | 2020-05-12 | on-the-robustness-of-language-encoders-1 | https://aclanthology.org/2020.acl-main.310 | https://aclanthology.org/2020.acl-main.310.pdf | acl-2020-6 | ['cloze-test', 'linguistic-acceptability'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.51380026e-02 3.58697891e-01 3.10221046e-01 -4.44840580e-01
-7.89719343e-01 -9.33993280e-01 1.65226713e-01 2.52784491e-01
-4.99749295e-02 3.83895844e-01 1.31636426e-01 -1.11591876e+00
4.88095045e-01 -5.81257105e-01 -1.28631914e+00 1.19903006e-01
-2.60053456e-01 -3.58009082e-03 3.48184444e-03 -3.29667836... | [7.880073070526123, 7.740659713745117] |
835f6018-94e8-437e-9ade-00526a85968c | videoofa-two-stage-pre-training-for-video-to | 2305.03204 | null | https://arxiv.org/abs/2305.03204v1 | https://arxiv.org/pdf/2305.03204v1.pdf | VideoOFA: Two-Stage Pre-Training for Video-to-Text Generation | We propose a new two-stage pre-training framework for video-to-text generation tasks such as video captioning and video question answering: A generative encoder-decoder model is first jointly pre-trained on massive image-text data to learn fundamental vision-language concepts, and then adapted to video data in an inter... | ['Wen-tau Yih', 'Yashar Mehdad', 'Barlas Oğuz', 'Wenhan Xiong', 'Lili Yu', 'Xilun Chen'] | 2023-05-04 | null | null | null | null | ['video-captioning', 'video-question-answering'] | ['computer-vision', 'computer-vision'] | [ 4.10766333e-01 2.51947075e-01 -2.06672907e-01 -4.21948373e-01
-1.08074164e+00 -5.03386438e-01 9.34823751e-01 -3.20035040e-01
-3.38687897e-01 5.29225647e-01 5.21154523e-01 -3.97462487e-01
5.26300967e-01 -3.44895899e-01 -1.45319855e+00 -1.01115771e-01
1.37987286e-01 6.06979489e-01 2.74444103e-01 -9.07703936... | [10.4539794921875, 0.9149399399757385] |
48722433-9fb8-447d-9548-d7f77e81b90f | probing-the-need-for-visual-context-in | 1903.08678 | null | https://arxiv.org/abs/1903.08678v2 | https://arxiv.org/pdf/1903.08678v2.pdf | Probing the Need for Visual Context in Multimodal Machine Translation | Current work on multimodal machine translation (MMT) has suggested that the visual modality is either unnecessary or only marginally beneficial. We posit that this is a consequence of the very simple, short and repetitive sentences used in the only available dataset for the task (Multi30K), rendering the source text su... | ['Loïc Barrault', 'Ozan Caglayan', 'Lucia Specia', 'Pranava Madhyastha'] | 2019-03-20 | probing-the-need-for-visual-context-in-1 | https://aclanthology.org/N19-1422 | https://aclanthology.org/N19-1422.pdf | naacl-2019-6 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 4.02517229e-01 1.41079098e-01 -9.28496718e-02 -9.58541632e-02
-6.20334864e-01 -8.28366458e-01 1.11430764e+00 1.64216742e-01
-4.51538891e-01 6.52126670e-01 4.37447786e-01 -8.96801949e-01
1.99620739e-01 -4.54061061e-01 -7.16641426e-01 -3.21036100e-01
5.27665675e-01 3.29004914e-01 6.03459515e-02 -3.46086293... | [11.43165111541748, 1.4420216083526611] |
cfd9e0ce-e908-43b7-9130-614b17d027f5 | reinforced-cross-modal-matching-and-self | 1811.10092 | null | http://arxiv.org/abs/1811.10092v2 | http://arxiv.org/pdf/1811.10092v2.pdf | Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation | Vision-language navigation (VLN) is the task of navigating an embodied agent
to carry out natural language instructions inside real 3D environments. In this
paper, we study how to address three critical challenges for this task: the
cross-modal grounding, the ill-posed feedback, and the generalization problems.
First, ... | ['Yuan-Fang Wang', 'Jianfeng Gao', 'William Yang Wang', 'Qiuyuan Huang', 'Asli Celikyilmaz', 'Lei Zhang', 'Xin Wang', 'Dinghan Shen'] | 2018-11-25 | reinforced-cross-modal-matching-and-self-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Wang_Reinforced_Cross-Modal_Matching_and_Self-Supervised_Imitation_Learning_for_Vision-Language_Navigation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_Reinforced_Cross-Modal_Matching_and_Self-Supervised_Imitation_Learning_for_Vision-Language_Navigation_CVPR_2019_paper.pdf | cvpr-2019-6 | ['vision-language-navigation'] | ['computer-vision'] | [ 4.33640182e-02 5.99616393e-02 -5.07544130e-02 -1.36032194e-01
-7.08020091e-01 -4.88070071e-01 8.54744375e-01 -2.51784921e-01
-6.58747375e-01 6.87027335e-01 8.81027505e-02 -4.96941477e-01
1.66756902e-02 -5.31010509e-01 -1.10786784e+00 -5.95838547e-01
-2.54579246e-01 3.00739855e-01 2.70289451e-01 -5.82270026... | [4.464812755584717, 0.6496798992156982] |
d8892b10-58c1-4829-9e6c-033b9e2c0a82 | how-to-find-a-unicorn-a-novel-model-free | 2004.11468 | null | https://arxiv.org/abs/2004.11468v3 | https://arxiv.org/pdf/2004.11468v3.pdf | How to find a unicorn: a novel model-free, unsupervised anomaly detection method for time series | Recognition of anomalous events is a challenging but critical task in many scientific and industrial fields, especially when the properties of anomalies are unknown. In this paper, we introduce a new anomaly concept called "unicorn" or unique event and present a new, model-free, unsupervised detection algorithm to dete... | ['Zoltán Somogyvári', 'Tamás Bábel', 'Zsigmond Benkő'] | 2020-04-23 | null | null | null | null | ['respiratory-failure'] | ['medical'] | [-1.55110300e-01 -2.90750086e-01 2.65110075e-01 -1.70765430e-01
-3.33676040e-01 -4.89507556e-01 5.26190758e-01 3.99475366e-01
-2.43184149e-01 8.12873065e-01 -6.69577643e-02 -2.96696842e-01
-6.35866582e-01 -4.08676773e-01 -4.57499921e-01 -7.99024642e-01
-5.28102458e-01 5.75343013e-01 4.60247666e-01 -7.22059309... | [7.25331974029541, 2.8260738849639893] |
b5c7ccae-a95e-4c20-abae-d78022044d04 | sgcn-exploiting-compressed-sparse-features-in | 2301.10388 | null | https://arxiv.org/abs/2301.10388v1 | https://arxiv.org/pdf/2301.10388v1.pdf | SGCN: Exploiting Compressed-Sparse Features in Deep Graph Convolutional Network Accelerators | Graph convolutional networks (GCNs) are becoming increasingly popular as they overcome the limited applicability of prior neural networks. A GCN takes as input an arbitrarily structured graph and executes a series of layers which exploit the graph's structure to calculate their output features. One recent trend in GCNs... | ['Jinho Lee', 'Youngsok Kim', 'Namhyung Kim', 'Jounghoo Lee', 'Jaeyong Song', 'Mingi Yoo'] | 2023-01-25 | null | null | null | null | ['feature-compression'] | ['computer-vision'] | [-1.54327706e-01 -1.21586822e-01 -4.74237621e-01 -1.26571342e-01
2.80574799e-01 -1.84258997e-01 3.46485049e-01 2.83973694e-01
-3.51669908e-01 2.25118265e-01 2.02369198e-01 -6.02835119e-01
6.23496063e-02 -1.27443707e+00 -6.55670941e-01 -5.73707998e-01
-4.67774868e-01 -1.97342321e-01 3.50584269e-01 -4.45270568... | [7.0393266677856445, 5.631537914276123] |
f6eff013-9f2e-4646-a69e-6edc5e8bdd66 | kartalol-transfer-learning-using-deep-neural | 2112.05236 | null | https://arxiv.org/abs/2112.05236v1 | https://arxiv.org/pdf/2112.05236v1.pdf | KartalOl: Transfer learning using deep neural network for iris segmentation and localization: New dataset for iris segmentation | Iris segmentation and localization in unconstrained environments is challenging due to long distances, illumination variations, limited user cooperation, and moving subjects. To address this problem, we present a U-Net with a pre-trained MobileNetV2 deep neural network method. We employ the pre-trained weights given wi... | ['Morteza Noshad', 'Yasin Amini', 'Rana Pourmohamad', 'Seyed Naeim Moafinejad', 'Farhang Jaryani', 'Samaneh Salehi Nasab', 'Jalil Nourmohammadi Khiarak'] | 2021-12-09 | null | null | null | null | ['iris-segmentation'] | ['medical'] | [-9.11200568e-02 -4.33440864e-01 -3.19018871e-01 -3.70644540e-01
-5.38312435e-01 -4.97186512e-01 3.60516489e-01 -2.48644337e-01
-6.75354064e-01 4.89341766e-01 -8.21530446e-03 -4.10364270e-01
-2.67288506e-01 -3.95166755e-01 -5.30870378e-01 -6.82811499e-01
-3.61763611e-02 2.35812828e-01 -6.46393821e-02 6.86580315... | [3.7498250007629395, -3.6278038024902344] |
48a9fb80-2a2d-4e6c-8ebf-97e3831efd8a | botnet-detection-using-recurrent-variational | 2004.00234 | null | https://arxiv.org/abs/2004.00234v1 | https://arxiv.org/pdf/2004.00234v1.pdf | Botnet Detection Using Recurrent Variational Autoencoder | Botnets are increasingly used by malicious actors, creating increasing threat to a large number of internet users. To address this growing danger, we propose to study methods to detect botnets, especially those that are hard to capture with the commonly used methods, such as the signature based ones and the existing an... | ['Jinoh Kim', 'Kesheng Wu', 'Jeeyung Kim', 'Alex Sim'] | 2020-04-01 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [-1.88620195e-01 -3.32087755e-01 2.14476436e-01 2.86626101e-01
2.27821201e-01 -6.50000691e-01 6.25956357e-01 1.47964254e-01
-5.44978976e-01 6.35556400e-01 -3.99453014e-01 -4.64140177e-01
-9.26275179e-02 -1.02349579e+00 -1.23660624e-01 -5.86623490e-01
-5.43858945e-01 5.76028705e-01 8.03053796e-01 -3.00978810... | [5.191184997558594, 7.263651371002197] |
d72f6caf-1326-4e21-aced-f6badaa0623a | iterative-linear-quadratic-optimization-for | 2207.06362 | null | https://arxiv.org/abs/2207.06362v1 | https://arxiv.org/pdf/2207.06362v1.pdf | Iterative Linear Quadratic Optimization for Nonlinear Control: Differentiable Programming Algorithmic Templates | We present the implementation of nonlinear control algorithms based on linear and quadratic approximations of the objective from a functional viewpoint. We present a gradient descent, a Gauss-Newton method, a Newton method, differential dynamic programming approaches with linear quadratic or quadratic approximations, v... | ['Zaid Harchaoui', 'Maryam Fazel', 'Siddhartha Srinivasa', 'Vincent Roulet'] | 2022-07-13 | null | null | null | null | ['carracing-v0'] | ['playing-games'] | [-4.53702271e-01 1.00937881e-01 -4.01792109e-01 -3.56408656e-01
-3.12313795e-01 -5.68393707e-01 3.38623673e-01 -4.79687154e-01
-4.20070946e-01 1.10193181e+00 -2.96735197e-01 -6.55236661e-01
-1.70736104e-01 -5.41764438e-01 -8.07627738e-01 -7.14557707e-01
-4.69185472e-01 4.83356863e-01 4.18717533e-01 -9.34078634... | [6.547861099243164, 4.061559677124023] |
f4044a9e-2279-4db6-8e6d-00fc9b2ef218 | evading-deepfake-detectors-via-adversarial | 2304.11670 | null | https://arxiv.org/abs/2304.11670v1 | https://arxiv.org/pdf/2304.11670v1.pdf | Evading DeepFake Detectors via Adversarial Statistical Consistency | In recent years, as various realistic face forgery techniques known as DeepFake improves by leaps and bounds,more and more DeepFake detection techniques have been proposed. These methods typically rely on detecting statistical differences between natural (i.e., real) and DeepFakegenerated images in both spatial and fre... | ['Jianjun Zhao', 'Lei Ma', 'Xiaofei Xie', 'Yihao Huang', 'Qing Guo', 'Yang Hou'] | 2023-04-23 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Hou_Evading_DeepFake_Detectors_via_Adversarial_Statistical_Consistency_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Hou_Evading_DeepFake_Detectors_via_Adversarial_Statistical_Consistency_CVPR_2023_paper.pdf | cvpr-2023-1 | ['face-swapping'] | ['computer-vision'] | [-1.58448964e-02 -3.02478939e-01 6.71160892e-02 -3.08715582e-01
-4.83350933e-01 -6.39148176e-01 5.01492202e-01 -4.38047826e-01
-1.08840548e-01 4.64505881e-01 -8.39959309e-02 2.20026691e-02
4.41015139e-02 -6.48745120e-01 -7.95919895e-01 -1.01132965e+00
9.72668976e-02 -3.14811200e-01 3.19965571e-01 -2.76543915... | [12.59003734588623, 1.0396888256072998] |
8ff45a89-6382-49a3-9206-6e5abc1addd0 | an-overview-of-indian-spoken-language | 2212.03812 | null | https://arxiv.org/abs/2212.03812v1 | https://arxiv.org/pdf/2212.03812v1.pdf | An Overview of Indian Spoken Language Recognition from Machine Learning Perspective | Automatic spoken language identification (LID) is a very important research field in the era of multilingual voice-command-based human-computer interaction (HCI). A front-end LID module helps to improve the performance of many speech-based applications in the multilingual scenario. India is a populous country with dive... | ['Goutam Saha', 'Md Sahidullah', 'Spandan Dey'] | 2022-11-30 | null | null | null | null | ['spoken-language-identification'] | ['speech'] | [-2.24311858e-01 -2.14723945e-01 -1.19841047e-01 -2.58907616e-01
-9.51945364e-01 -3.88347208e-01 4.04415041e-01 -3.95729780e-01
-5.69999933e-01 5.85705638e-01 5.20470083e-01 -4.92954820e-01
1.90816224e-01 -7.31663629e-02 3.26342434e-02 -6.60077929e-01
3.24151754e-01 5.28365433e-01 -1.61408648e-01 -3.68035823... | [14.229219436645508, 6.599114418029785] |
24e622eb-d15d-42d8-a489-8d48caaadbef | attentive-statistics-pooling-for-deep-speaker | 1803.10963 | null | http://arxiv.org/abs/1803.10963v2 | http://arxiv.org/pdf/1803.10963v2.pdf | Attentive Statistics Pooling for Deep Speaker Embedding | This paper proposes attentive statistics pooling for deep speaker embedding
in text-independent speaker verification. In conventional speaker embedding,
frame-level features are averaged over all the frames of a single utterance to
form an utterance-level feature. Our method utilizes an attention mechanism to
give diff... | [] | 2019-02-25 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [ 5.35268039e-02 -2.51179010e-01 1.56905204e-02 -9.06664729e-01
-1.15530920e+00 -2.71836966e-01 5.76955259e-01 1.11685349e-02
-5.04240215e-01 4.97971505e-01 7.48112917e-01 -2.97896005e-02
3.26205701e-01 -5.12077473e-02 -2.30411232e-01 -8.92275691e-01
-1.07103191e-01 -3.37024420e-01 -1.59271479e-01 -2.41646376... | [14.371933937072754, 6.086182594299316] |
c1fbb23f-10b8-460f-a02b-472a2ce4b9d8 | a-next-basket-recommendation-reality-check | 2109.14233 | null | https://arxiv.org/abs/2109.14233v2 | https://arxiv.org/pdf/2109.14233v2.pdf | A Next Basket Recommendation Reality Check | The goal of a next basket recommendation (NBR) system is to recommend items for the next basket for a user, based on the sequence of their prior baskets. Recently, a number of methods with complex modules have been proposed that claim state-of-the-art performance. They rarely look into the predicted basket and just pro... | ['Maarten de Rijke', 'Mozhdeh Ariannezhad', 'Sami Jullien', 'Ming Li'] | 2021-09-29 | null | null | null | null | ['next-basket-recommendation'] | ['miscellaneous'] | [-4.48653251e-02 -2.16525882e-01 -8.00708771e-01 -2.68128335e-01
-2.09376708e-01 -4.25813615e-01 3.96191955e-01 3.97515893e-01
-1.25776157e-01 5.80613673e-01 6.66467190e-01 -4.71952558e-01
-4.32587773e-01 -8.44747782e-01 -6.54358923e-01 -1.55211285e-01
-2.41989359e-01 4.56805140e-01 1.10271379e-01 -5.90314150... | [10.061765670776367, 5.691064834594727] |
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