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946a1734-e488-4568-8ddd-61fa1ea2d736 | free-headgan-neural-talking-head-synthesis | 2208.02210 | null | https://arxiv.org/abs/2208.02210v1 | https://arxiv.org/pdf/2208.02210v1.pdf | Free-HeadGAN: Neural Talking Head Synthesis with Explicit Gaze Control | We present Free-HeadGAN, a person-generic neural talking head synthesis system. We show that modeling faces with sparse 3D facial landmarks are sufficient for achieving state-of-the-art generative performance, without relying on strong statistical priors of the face, such as 3D Morphable Models. Apart from 3D pose and ... | ['Stefanos Zafeiriou', 'Viktoriia Sharmanska', 'Evangelos Ververas', 'Michail Christos Doukas'] | 2022-08-03 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [ 1.01658534e-02 8.49557698e-01 2.80398101e-01 -5.98998189e-01
-7.66048670e-01 -4.20420438e-01 9.26327527e-01 -1.01255119e+00
-6.53211996e-02 5.01175821e-01 4.24417585e-01 3.39548886e-01
5.91509581e-01 -3.05891633e-01 -8.57153714e-01 -6.17538750e-01
2.43238404e-01 6.39999568e-01 -2.43770316e-01 -2.73879856... | [12.827779769897461, -0.29549139738082886] |
61b10d7d-1a91-4ae2-995c-71e129055a17 | pmct-patched-multi-condition-training-for | 2207.04949 | null | https://arxiv.org/abs/2207.04949v1 | https://arxiv.org/pdf/2207.04949v1.pdf | pMCT: Patched Multi-Condition Training for Robust Speech Recognition | We propose a novel Patched Multi-Condition Training (pMCT) method for robust Automatic Speech Recognition (ASR). pMCT employs Multi-condition Audio Modification and Patching (MAMP) via mixing {\it patches} of the same utterance extracted from clean and distorted speech. Training using patch-modified signals improves ro... | ['Mete Ozay', 'Karthikeyan Saravanan', 'Agnieszka Dobrowolska', 'Pablo Peso Parada'] | 2022-07-11 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 5.15461266e-01 -1.61402375e-01 7.21973300e-01 -7.90301263e-02
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8.80686417e-02 -4.65617329e-03 -7.52466172e-02 -2.83142477... | [14.835609436035156, 6.068288326263428] |
b3409b4f-d556-4306-9160-ca44128a8af4 | edge-aware-regional-message-passing | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_Edge-Aware_Regional_Message_Passing_Controller_for_Image_Forgery_Localization_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_Edge-Aware_Regional_Message_Passing_Controller_for_Image_Forgery_Localization_CVPR_2023_paper.pdf | Edge-Aware Regional Message Passing Controller for Image Forgery Localization | Digital image authenticity has promoted research on image forgery localization. Although deep learning-based methods achieve remarkable progress, most of them usually suffer from severe feature coupling between the forged and authentic regions. In this work, we propose a two-step Edge-aware Regional Message Passing... | ['Zheng-Jun Zha', 'Xueyang Fu', 'Jiawei Liu', 'Menglu Wang', 'Jiaying Zhu', 'Dong Li'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['graph-construction'] | ['graphs'] | [ 6.87686875e-02 -1.25583231e-01 -5.62587641e-02 -1.12463437e-01
-7.42824852e-01 -2.94886619e-01 4.42288816e-01 5.34858443e-02
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1.44486755e-01 -1.32029459e-01 2.84072518e-01 -3.07727098... | [12.365924835205078, 0.9017300009727478] |
0dd2f335-100f-45df-aa5e-875cddf14dc8 | medical-matting-a-new-perspective-on-medical | 2106.09887 | null | https://arxiv.org/abs/2106.09887v3 | https://arxiv.org/pdf/2106.09887v3.pdf | Medical Matting: A New Perspective on Medical Segmentation with Uncertainty | It is difficult to accurately label ambiguous and complex shaped targets manually by binary masks. The weakness of binary mask under-expression is highlighted in medical image segmentation, where blurring is prevalent. In the case of multiple annotations, reaching a consensus for clinicians by binary masks is more chal... | ['ZongYuan Ge', 'Xiufen Ye', 'Xin Zhao', 'Xuan Yao', 'Zhiwen Yang', 'Yelin Huang', 'Wanji He', 'Xin Wang', 'Donghao Zhang', 'Lie Ju', 'Lin Wang'] | 2021-06-18 | null | null | null | null | ['image-matting'] | ['computer-vision'] | [ 5.05694926e-01 5.97502053e-01 -1.32394647e-02 -4.36646640e-01
-9.06510949e-01 -3.70146751e-01 3.53349835e-01 1.64433151e-01
-4.71645087e-01 1.12382913e+00 3.18479016e-02 -1.44755438e-01
-3.37578803e-01 -4.30875719e-01 -5.87889791e-01 -8.25908482e-01
2.66442835e-01 7.46467590e-01 3.58566374e-01 2.14311466... | [14.397488594055176, -2.1147522926330566] |
8ff4a0ba-51fc-4d16-a5d7-aec2d0d8a538 | real-time-instance-segmentation-with | 2106.12204 | null | https://arxiv.org/abs/2106.12204v2 | https://arxiv.org/pdf/2106.12204v2.pdf | Real-time Instance Segmentation with Discriminative Orientation Maps | Although instance segmentation has made considerable advancement over recent years, it's still a challenge to design high accuracy algorithms with real-time performance. In this paper, we propose a real-time instance segmentation framework termed OrienMask. Upon the one-stage object detector YOLOv3, a mask head is adde... | ['Tingming Bai', 'Yiman Chen', 'Chengyu Qiao', 'Shuya Chen', 'Zhiyu Xiang', 'Wentao Du'] | 2021-06-23 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Du_Real-Time_Instance_Segmentation_With_Discriminative_Orientation_Maps_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Du_Real-Time_Instance_Segmentation_With_Discriminative_Orientation_Maps_ICCV_2021_paper.pdf | iccv-2021-1 | ['real-time-instance-segmentation', 'foreground-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.24883550e-01 -4.21397611e-02 -2.96001881e-01 -3.85376871e-01
-8.43893647e-01 -4.97638404e-01 1.00775793e-01 4.99390550e-02
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1.82010323e-01 4.37666297e-01 1.06837404e+00 4.12269145... | [9.409461975097656, -0.009846139699220657] |
c91bc98d-035f-4978-a583-7b1615f47101 | virtual-reality-to-study-the-gap-between | 1912.09380 | null | https://arxiv.org/abs/1912.09380v2 | https://arxiv.org/pdf/1912.09380v2.pdf | A Transferable Adaptive Domain Adversarial Neural Network for Virtual Reality Augmented EMG-Based Gesture Recognition | Within the field of electromyography-based (EMG) gesture recognition, disparities exist between the offline accuracy reported in the literature and the real-time usability of a classifier. This gap mainly stems from two factors: 1) The absence of a controller, making the data collected dissimilar to actual control. 2) ... | ['François Laviolette', 'Erik Scheme', 'Gabriel Gagnon-Turcotte', 'Ulysse Côté-Allard', 'Kyrre Glette', 'Angkoon Phinyomark', 'Benoit Gosselin'] | 2019-12-16 | null | null | null | null | ['emg-gesture-recognition'] | ['medical'] | [ 2.59791613e-01 -3.38672757e-01 -2.74785250e-01 7.88937509e-02
-6.09959424e-01 -6.83636189e-01 3.97102922e-01 -2.32004583e-01
-7.25732505e-01 4.34601486e-01 2.72489786e-01 -1.00659490e-01
-2.91350365e-01 -2.18008593e-01 -5.27928770e-01 -5.18220127e-01
-4.34250027e-01 7.34787583e-02 3.18822712e-01 -2.19581380... | [6.8321428298950195, 0.1981944888830185] |
c484f571-61b6-40e2-95a1-127749e66f07 | enhancing-chinese-multi-label-text | null | null | https://aclanthology.org/2022.rocling-1.4 | https://aclanthology.org/2022.rocling-1.4.pdf | Enhancing Chinese Multi-Label Text Classification Performance with Response-based Knowledge Distillation | It’s difficult to optimize individual label performance of multi-label text classification, especially in those imbalanced data containing long-tailed labels. Therefore, this study proposes a response-based knowledge distillation mechanism comprising a teacher model that optimizes binary classifiers of the correspondin... | ['Kuo-Kai Shyu', 'Po-Lei Lee', 'Lung-Hao Lee', 'Po-Hsun Liao', 'Cheng-Fu Cao', 'Szu-Chi Huang'] | null | null | null | null | rocling-2022-11 | ['multi-label-text-classification', 'multi-label-text-classification'] | ['methodology', 'natural-language-processing'] | [ 6.02050386e-02 1.07062034e-01 -5.21770537e-01 -7.35147238e-01
-7.87784219e-01 -4.27038312e-01 -4.01567481e-02 6.88399434e-01
-4.65424240e-01 1.02276659e+00 -4.10023965e-02 -2.62980670e-01
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5.78496516e-01 7.12004423e-01 1.49353966e-01 -1.05195992... | [9.528101921081543, 4.4637885093688965] |
9598a41a-a816-4bbd-8c12-e4ae18aea8d0 | a-dataset-for-telling-the-stories-of-social | null | null | https://aclanthology.org/D18-1117 | https://aclanthology.org/D18-1117.pdf | A Dataset for Telling the Stories of Social Media Videos | Video content on social media platforms constitutes a major part of the communication between people, as it allows everyone to share their stories. However, if someone is unable to consume video, either due to a disability or network bandwidth, this severely limits their participation and communication. Automatically t... | ['ana', 'Sp Gella', 'Mike Lewis', 'Marcus Rohrbach'] | 2018-10-01 | null | null | null | emnlp-2018-10 | ['video-description'] | ['computer-vision'] | [ 5.15824072e-02 -8.52940083e-02 -6.22208059e-01 -5.05584419e-01
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3.14243175e-02 2.21103832e-01 2.82647640e-01 -1.04718357... | [10.478250503540039, 0.7812630534172058] |
f0b552bd-3b06-4ccd-b522-ed7cb4b53315 | the-pytorch-kaldi-speech-recognition-toolkit | 1811.07453 | null | http://arxiv.org/abs/1811.07453v2 | http://arxiv.org/pdf/1811.07453v2.pdf | The PyTorch-Kaldi Speech Recognition Toolkit | The availability of open-source software is playing a remarkable role in the
popularization of speech recognition and deep learning. Kaldi, for instance, is
nowadays an established framework used to develop state-of-the-art speech
recognizers. PyTorch is used to build neural networks with the Python language
and has re... | ['Titouan Parcollet', 'Mirco Ravanelli', 'Yoshua Bengio'] | 2018-11-19 | null | null | null | null | ['noisy-speech-recognition', 'distant-speech-recognition'] | ['speech', 'speech'] | [-3.80695939e-01 -2.97016412e-01 2.57056653e-01 -4.13797259e-01
-4.09678280e-01 -3.44021380e-01 5.07123053e-01 -2.02711254e-01
-5.93304694e-01 9.52263325e-02 -4.67225499e-02 -5.91836989e-01
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-7.96570107e-02 6.32870555e-01 3.96242589e-01 -4.05973077... | [14.349201202392578, 6.40721321105957] |
33c39291-f8f6-4381-8894-3bcf2aff32c0 | a-network-community-detection-method-with | 2305.13012 | null | https://arxiv.org/abs/2305.13012v1 | https://arxiv.org/pdf/2305.13012v1.pdf | A network community detection method with integration of data from multiple layers and node attributes | Multilayer networks are in the focus of the current complex network study. In such networks multiple types of links may exist as well as many attributes for nodes. To fully use multilayer -- and other types of complex networks in applications, the merging of various data with topological information renders a powerful ... | ['Tomi Räty', 'Lasse Leskelä', 'Hannu Reittu'] | 2023-05-22 | null | null | null | null | ['community-detection'] | ['graphs'] | [ 1.25953943e-01 2.01767743e-01 7.62658268e-02 3.02386340e-02
4.04670656e-01 -8.38216603e-01 5.12597024e-01 4.80693221e-01
-1.23824179e-01 6.67921364e-01 1.12000354e-01 -4.40755874e-01
-5.38863361e-01 -1.29686773e+00 -3.81547987e-01 -5.30801535e-01
-1.04012108e+00 7.58375585e-01 6.02953255e-01 -5.30699909... | [6.952853679656982, 5.283052444458008] |
4bfc942d-c9bb-4ff6-9768-c5a31cf144b9 | principled-analysis-of-energy-discourse | null | null | https://aclanthology.org/2021.alta-1.11 | https://aclanthology.org/2021.alta-1.11.pdf | Principled Analysis of Energy Discourse across Domains with Thesaurus-based Automatic Topic Labeling | With the increasing impact of Natural Language Processing tools like topic models in social science research, the experimental rigor and comparability of models and datasets has come under scrutiny. Especially when contributing to research on topics with worldwide impacts like energy policy, objective analyses and reli... | ['Lea Frermann', 'Alfonso Martinez Arranz', 'Thomas Scelsi'] | null | null | null | null | alta-2021-12 | ['topic-models'] | ['natural-language-processing'] | [ 1.68699086e-01 5.62698662e-01 -5.31751096e-01 -4.41750258e-01
-1.03660738e+00 -9.87215042e-01 1.30263424e+00 7.38508224e-01
-4.39708292e-01 6.24486744e-01 1.03778934e+00 -7.36910880e-01
-1.95851266e-01 -9.43861961e-01 -4.14141059e-01 -4.73460793e-01
2.55250335e-01 5.55552781e-01 4.65096273e-02 1.71322394... | [9.150323867797852, 9.666997909545898] |
79e03c34-8eea-4c5d-9031-f545f573ab79 | compositional-generalization-in-a-deep | 1904.09708 | null | https://arxiv.org/abs/1904.09708v3 | https://arxiv.org/pdf/1904.09708v3.pdf | Compositional generalization in a deep seq2seq model by separating syntax and semantics | Standard methods in deep learning for natural language processing fail to capture the compositional structure of human language that allows for systematic generalization outside of the training distribution. However, human learners readily generalize in this way, e.g. by applying known grammatical rules to novel words.... | ["Randall C. O'Reilly", 'Jason Jo', 'Yoshua Bengio', 'Jake Russin'] | 2019-04-22 | null | null | null | null | ['systematic-generalization'] | ['reasoning'] | [ 5.06071091e-01 5.22369683e-01 -1.44405171e-01 -9.07517672e-01
-3.78629327e-01 -7.91349769e-01 9.28272724e-01 2.51804620e-01
-5.80342352e-01 6.60199106e-01 4.84937847e-01 -6.58630967e-01
8.75383466e-02 -7.93518007e-01 -1.13684821e+00 -3.80001634e-01
1.34119928e-01 7.07473099e-01 7.80708045e-02 -4.63185221... | [10.703245162963867, 9.100137710571289] |
e76aa31c-7557-4674-84dd-cb90550f990a | gadbench-revisiting-and-benchmarking | 2306.12251 | null | https://arxiv.org/abs/2306.12251v1 | https://arxiv.org/pdf/2306.12251v1.pdf | GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection | With a long history of traditional Graph Anomaly Detection (GAD) algorithms and recently popular Graph Neural Networks (GNNs), it is still not clear (1) how they perform under a standard comprehensive setting, (2) whether GNNs outperform traditional algorithms such as tree ensembles, and (3) their efficiency on large-s... | ['Jia Li', 'Peilin Zhao', 'Ziqi Gao', 'Fengrui Hua', 'Jianheng Tang'] | 2023-06-21 | null | null | null | null | ['graph-anomaly-detection', 'anomaly-detection', 'benchmarking', 'benchmarking'] | ['graphs', 'methodology', 'miscellaneous', 'robots'] | [ 1.19019665e-01 2.21823901e-01 -1.92111686e-01 -4.87984857e-03
-3.87343258e-01 -4.72272784e-01 3.47130984e-01 6.55769289e-01
-1.20297574e-01 5.73249102e-01 3.10015511e-02 -8.97687972e-01
-1.22671865e-01 -1.13212240e+00 -5.91109395e-01 -3.60622436e-01
-9.10814881e-01 5.37309945e-01 4.99875486e-01 -2.74890125... | [6.721827983856201, 5.897677898406982] |
01d8ab35-f60a-4eb7-8e86-dbb4ca0b7496 | self-supervised-training-for-blind-multi | 2004.06957 | null | https://arxiv.org/abs/2004.06957v4 | https://arxiv.org/pdf/2004.06957v4.pdf | Self-Supervised training for blind multi-frame video denoising | We propose a self-supervised approach for training multi-frame video denoising networks. These networks predict frame t from a window of frames around t. Our self-supervised approach benefits from the video temporal consistency by penalizing a loss between the predicted frame t and a neighboring target frame, which are... | ['Jérémy Anger', 'Valéry Dewil', 'Gabriele Facciolo', 'Thibaud Ehret', 'Pablo Arias', 'Axel Davy'] | 2020-04-15 | null | null | null | null | ['video-denoising', 'video-temporal-consistency'] | ['computer-vision', 'computer-vision'] | [ 2.41081104e-01 -2.60641545e-01 1.02183565e-01 -3.27123195e-01
-7.08284974e-01 -3.75508219e-01 4.78237748e-01 -2.41385028e-01
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-6.77092448e-02 -6.82936534e-02 3.48395586e-01 3.16494703... | [11.36500358581543, -2.1828155517578125] |
3602c97c-63ac-46b8-b7ef-c07b7fc01003 | image-augmentation-for-multitask-few-shot | 2102.12295 | null | https://arxiv.org/abs/2102.12295v1 | https://arxiv.org/pdf/2102.12295v1.pdf | Image Augmentation for Multitask Few-Shot Learning: Agricultural Domain Use-Case | Large datasets' availability is catalyzing a rapid expansion of deep learning in general and computer vision in particular. At the same time, in many domains, a sufficient amount of training data is lacking, which may become an obstacle to the practical application of computer vision techniques. This paper challenges s... | ['Mariia Pukalchik', 'Dmitrii Shadrin', 'Sergey Nesteruk'] | 2021-02-24 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [ 5.76239526e-01 -1.12358641e-04 -4.41889539e-02 -2.76310384e-01
-3.07839870e-01 -4.38089609e-01 2.17221841e-01 4.48559940e-01
-4.75280464e-01 6.15626514e-01 -6.38584137e-01 -3.69830757e-01
2.60839075e-01 -9.95360970e-01 -7.60305643e-01 -6.25384748e-01
3.20702881e-01 6.45502865e-01 1.30804315e-01 -4.11359556... | [9.100342750549316, -1.4821122884750366] |
74c69c71-1b44-4a41-b639-9eb8590809c8 | online-multi-object-tracking-framework-with | 1907.13347 | null | https://arxiv.org/abs/1907.13347v1 | https://arxiv.org/pdf/1907.13347v1.pdf | Online Multi-Object Tracking Framework with the GMPHD Filter and Occlusion Group Management | In this paper, we propose an efficient online multi-object tracking framework based on the GMPHD filter and occlusion group management scheme where the GMPHD filter utilizes hierarchical data association to reduce the false negatives caused by miss detection. The hierarchical data association consists of two steps: det... | ['Kin-Choong Yow', 'Young-chul Yoon', 'Young-min Song', 'Kwangjin Yoon', 'Moongu Jeon'] | 2019-07-31 | null | null | null | null | ['online-multi-object-tracking', 'real-time-multi-object-tracking'] | ['computer-vision', 'computer-vision'] | [-3.52646798e-01 -4.22808766e-01 -1.50528073e-01 3.45920138e-02
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-1.40093207e-01 -7.39437819e-01 -8.30337286e-01 -7.34824181e-01
-1.77950580e-02 6.56143010e-01 1.01888764e+00 2.25667417... | [6.505015850067139, -1.9859169721603394] |
b2af89d6-7040-426d-b2f5-ec976d79908e | multi-label-ecg-classification-using | null | null | https://ieeexplore.ieee.org/abstract/document/9662750 | https://www.cinc.org/archives/2021/pdf/CinC2021-075.pdf | Multi-Label ECG Classification Using Convolutional Neural Networks in a Classifier Chain | Over the last decade, AI has shown its feasibility in classifying heart-related diagnoses from ECGs. Earlier studies have mainly focused on 12 and 2-lead ECGs, but we aim to classify 26 different diagnoses based on 12, 6, 4, 3, and 2-lead ECGs in this study. We trained a supervised model on a dataset containing 88 253 ... | ['Pål Haugar Brekke', 'Eraraya Morenzo Muten', 'Bjørn-Jostein Singstad'] | 2022-01-10 | null | null | null | computing-in-cardiology-2022-1 | ['ecg-classification'] | ['medical'] | [ 1.19745329e-01 2.53455900e-03 2.25090623e-01 -3.22374851e-01
-8.35491598e-01 -4.52309728e-01 -2.88707286e-01 3.59482467e-01
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-3.93611789e-01 -6.83878422e-01 -1.46477312e-01 -6.14809096e-01
-4.94301379e-01 5.19867539e-01 -1.56389307e-02 -2.84716487... | [14.333642959594727, 3.296204090118408] |
d9624b0c-e9e1-4dd2-af1e-379684808c71 | beyond-one-model-fits-all-a-survey-of-domain | 2305.18703 | null | https://arxiv.org/abs/2305.18703v3 | https://arxiv.org/pdf/2305.18703v3.pdf | Large Language Models, Natural Language Processing, Domain Specialization | Large language models (LLMs) have significantly advanced the field of natural language processing (NLP), providing a highly useful, task-agnostic foundation for a wide range of applications. However, directly applying LLMs to solve sophisticated problems in specific domains meets many hurdles, caused by the heterogenei... | ['Liang Zhao', 'Jian Pei', 'Quanquan Gu', 'Xuchao Zhang', 'Carl Yang', 'Chris White', 'Haifeng Chen', 'Zhengzhang Chen', 'Yanchi Liu', 'Haoyu Wang', 'Wei Cheng', 'Amit Panalkar', 'Tianjiao Zhao', 'Hejie Cui', 'Yun Li', 'Tanmoy Chowdhury', 'Junxiang Wang', 'Can Zheng', 'Chengyuan Deng', 'Jiaying Lu', 'Xujiang Zhao', 'Ch... | 2023-05-30 | null | null | null | null | ['chatbot', 'chatbot'] | ['methodology', 'natural-language-processing'] | [ 9.72543135e-02 1.11708730e-01 -8.24300706e-01 -3.88431340e-01
-4.52453524e-01 -9.57071304e-01 6.60340667e-01 7.40970448e-02
-2.78063655e-01 7.28079498e-01 2.55824089e-01 -3.77042949e-01
-3.85146916e-01 -5.38879275e-01 -1.75914809e-01 -5.52363582e-02
1.15894884e-01 6.22579694e-01 4.42745350e-02 -3.74299198... | [10.775446891784668, 8.78807544708252] |
1ce24a5c-f5b5-4612-894d-2f8b54eaf5dd | playing-codenames-with-language-graphs-and | 2105.05885 | null | https://arxiv.org/abs/2105.05885v1 | https://arxiv.org/pdf/2105.05885v1.pdf | Playing Codenames with Language Graphs and Word Embeddings | Although board games and video games have been studied for decades in artificial intelligence research, challenging word games remain relatively unexplored. Word games are not as constrained as games like chess or poker. Instead, word game strategy is defined by the players' understanding of the way words relate to eac... | ['Cynthia Rudin', 'Rachel Lea Draelos', 'Anna Sun', 'Divya Koyyalagunta'] | 2021-05-12 | null | null | null | null | ['board-games'] | ['playing-games'] | [-2.85519004e-01 -1.12586662e-01 -2.58502245e-01 1.51957020e-01
-7.33396590e-01 -9.51206684e-01 5.70354521e-01 4.27642077e-01
-7.73944318e-01 3.86390418e-01 5.58762193e-01 -6.45523965e-01
-3.04011643e-01 -1.11356878e+00 -2.42569372e-01 -1.92453459e-01
1.26518518e-01 4.78988647e-01 4.77746069e-01 -9.70343411... | [10.474672317504883, 8.764177322387695] |
8edd4d18-311e-41b8-85c7-2e7c58edfd4c | audio-retrieval-with-wavtext5k-and-clap | 2209.14275 | null | https://arxiv.org/abs/2209.14275v1 | https://arxiv.org/pdf/2209.14275v1.pdf | Audio Retrieval with WavText5K and CLAP Training | Audio-Text retrieval takes a natural language query to retrieve relevant audio files in a database. Conversely, Text-Audio retrieval takes an audio file as a query to retrieve relevant natural language descriptions. Most of the literature train retrieval systems with one audio captioning dataset, but evaluating the ben... | ['Huaming Wang', 'Benjamin Elizalde', 'Soham Deshmukh'] | 2022-09-28 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 2.70366132e-01 -1.57082126e-01 -1.05055846e-01 -2.07919404e-01
-2.05404043e+00 -7.26513088e-01 5.28124511e-01 3.97423387e-01
-3.99074197e-01 5.06796122e-01 5.24501383e-01 2.60120749e-01
-2.58326322e-01 -3.89261782e-01 -8.92461181e-01 -2.07146540e-01
-2.30864182e-01 6.33480310e-01 3.85177314e-01 -3.25434655... | [15.322793960571289, 4.950413703918457] |
e3ffcf32-8983-4c48-b022-2cbdc97d76bc | uot-uwf-partai-at-semeval-2021-task-5-self | 2104.13164 | null | https://arxiv.org/abs/2104.13164v1 | https://arxiv.org/pdf/2104.13164v1.pdf | UoT-UWF-PartAI at SemEval-2021 Task 5: Self Attention Based Bi-GRU with Multi-Embedding Representation for Toxicity Highlighter | Toxic Spans Detection(TSD) task is defined as highlighting spans that make a text toxic. Many works have been done to classify a given comment or document as toxic or non-toxic. However, none of those proposed models work at the token level. In this paper, we propose a self-attention-based bidirectional gated recurrent... | ['Jafar Razmara', 'Mostafa Rahgouy', 'Taher Rahgooy', 'Hamed Babaei Giglou'] | 2021-04-27 | null | https://aclanthology.org/2021.semeval-1.129 | https://aclanthology.org/2021.semeval-1.129.pdf | semeval-2021 | ['toxic-spans-detection'] | ['natural-language-processing'] | [ 2.04207506e-02 -1.15324974e-01 -2.38636747e-01 3.38273495e-02
-6.34701610e-01 -2.29803517e-01 6.44812226e-01 5.33145189e-01
-1.95918590e-01 4.93207902e-01 8.95013571e-01 -2.48044223e-01
2.12853968e-01 -7.68276215e-01 -2.21777171e-01 -5.73676229e-01
9.36140418e-02 -3.14068615e-01 2.40927905e-01 -1.91312909... | [8.966055870056152, 10.574906349182129] |
f99dde8b-6699-444c-803b-2d41124928f6 | explainable-hierarchical-imitation-learning | 2105.07348 | null | https://arxiv.org/abs/2105.07348v1 | https://arxiv.org/pdf/2105.07348v1.pdf | Explainable Hierarchical Imitation Learning for Robotic Drink Pouring | To accurately pour drinks into various containers is an essential skill for service robots. However, drink pouring is a dynamic process and difficult to model. Traditional deep imitation learning techniques for implementing autonomous robotic pouring have an inherent black-box effect and require a large amount of demon... | ['Zhengyou Zhang', 'Dongsheng Zhang', 'Lei Wei', 'Qiang Li', 'Yu Zheng', 'Dandan Zhang'] | 2021-05-16 | null | null | null | null | ['action-generation'] | ['computer-vision'] | [-2.78962016e-01 2.57480294e-01 -9.65511203e-02 -3.67163241e-01
1.12692453e-01 -3.94716620e-01 2.88078129e-01 -9.84471068e-02
1.15212716e-01 5.33161938e-01 -2.53934622e-01 -3.04597206e-02
-2.57475019e-01 -6.41676009e-01 -8.88817668e-01 -5.58393776e-01
-1.42456487e-01 4.85022724e-01 7.02682212e-02 -3.23306441... | [4.589056491851807, 0.9433034062385559] |
e88a56f0-3f4d-4c39-ae9b-bb2b19ebb16b | exploring-transfer-learning-for-low-resource | 1901.04276 | null | http://arxiv.org/abs/1901.04276v1 | http://arxiv.org/pdf/1901.04276v1.pdf | Exploring Transfer Learning for Low Resource Emotional TTS | During the last few years, spoken language technologies have known a big
improvement thanks to Deep Learning. However Deep Learning-based algorithms
require amounts of data that are often difficult and costly to gather.
Particularly, modeling the variability in speech of different speakers,
different styles or differen... | ['Kevin El Haddad', 'Noé Tits', 'Thierry Dutoit'] | 2019-01-14 | exploring-transfer-learning-for-low-resource-1 | null | null | advances-in-intelligent-systems-and-computing | ['emotional-speech-synthesis', 'expressive-speech-synthesis'] | ['speech', 'speech'] | [-2.91610152e-01 3.10913205e-01 7.58316666e-02 -8.97018790e-01
-6.43394947e-01 -5.27645171e-01 5.44362068e-01 -1.53257445e-01
-1.90270200e-01 7.19372571e-01 2.39781186e-01 -2.98646856e-02
4.25030798e-01 -3.28673691e-01 -5.45005679e-01 -4.14462119e-01
8.05796534e-02 6.13521278e-01 -2.90781319e-01 -5.12383699... | [14.275425910949707, 6.282937049865723] |
eaaf06bb-0dc5-4ba2-ae8b-fc32e613a009 | factorized-attention-self-attention-with | 1812.01243 | null | https://arxiv.org/abs/1812.01243v9 | https://arxiv.org/pdf/1812.01243v9.pdf | Efficient Attention: Attention with Linear Complexities | Dot-product attention has wide applications in computer vision and natural language processing. However, its memory and computational costs grow quadratically with the input size. Such growth prohibits its application on high-resolution inputs. To remedy this drawback, this paper proposes a novel efficient attention me... | ['Shuai Yi', 'Haiyu Zhao', 'Hongsheng Li', 'Zhuoran Shen', 'Mingyuan Zhang'] | 2018-12-04 | null | null | null | null | ['stereo-depth-estimation', 'extractive-document-summarization'] | ['computer-vision', 'natural-language-processing'] | [-1.26210049e-01 -6.43771365e-02 -2.50224978e-01 -2.11934999e-01
-5.11290014e-01 -1.37224153e-01 6.29409790e-01 -1.34459168e-01
-6.94787800e-01 2.89441943e-01 8.35975260e-02 -3.05390984e-01
2.34751597e-01 -7.20477343e-01 -6.63483024e-01 -3.66784513e-01
1.28620520e-01 1.58038765e-01 3.52273464e-01 1.21339764... | [9.554512023925781, 0.7786177396774292] |
b4262131-1a39-46aa-bbbd-090152a440c9 | instrument-independent-dastgah-recognition-of | 1812.07017 | null | http://arxiv.org/abs/1812.07017v3 | http://arxiv.org/pdf/1812.07017v3.pdf | Instrument-Independent Dastgah Recognition of Iranian Classical Music Using AzarNet | In this paper, AzarNet, a deep neural network (DNN), is proposed to
recognizing seven different Dastgahs of Iranian classical music in Maryam
Iranian classical music (MICM) dataset. Over the last years, there has been
remarkable interest in employing feature learning and DNNs which lead to
decreasing the required engin... | ['Ali Ahmadi', 'Shahla RezezadehAzar', 'Saber Malekzadeh', 'Maryam Samami'] | 2018-12-17 | null | null | null | null | ['recognizing-seven-different-dastgahs-of'] | ['music'] | [ 3.10986429e-01 -6.49288476e-01 2.81920195e-01 2.95777433e-03
-4.02509212e-01 -6.96783245e-01 4.06003535e-01 -1.68165594e-01
-3.60990793e-01 6.03998780e-01 1.36693031e-01 1.55155957e-01
-8.01677704e-01 -6.98700428e-01 -2.46293560e-01 -7.80609190e-01
-3.12893391e-01 1.41043961e-01 -1.59366384e-01 -2.42303252... | [15.793532371520996, 5.173585891723633] |
26ccda84-b8f5-4a36-83ad-a56961e9ab7c | reasoning-with-language-model-prompting-a | 2212.09597 | null | https://arxiv.org/abs/2212.09597v5 | https://arxiv.org/pdf/2212.09597v5.pdf | Reasoning with Language Model Prompting: A Survey | Reasoning, as an essential ability for complex problem-solving, can provide back-end support for various real-world applications, such as medical diagnosis, negotiation, etc. This paper provides a comprehensive survey of cutting-edge research on reasoning with language model prompting. We introduce research works with ... | ['Huajun Chen', 'Fei Huang', 'Chuanqi Tan', 'Shumin Deng', 'Yunzhi Yao', 'Xiang Chen', 'Ningyu Zhang', 'Yixin Ou', 'Shuofei Qiao'] | 2022-12-19 | null | null | null | null | ['mathematical-reasoning', 'arithmetic-reasoning', 'logical-reasoning', 'common-sense-reasoning'] | ['natural-language-processing', 'reasoning', 'reasoning', 'reasoning'] | [-2.91626394e-01 8.92326653e-01 -9.45424438e-01 -5.25286734e-01
-5.02612770e-01 -6.13470316e-01 5.67764342e-01 4.36289608e-01
-1.27778992e-01 7.88135171e-01 6.81592941e-01 -9.71195757e-01
-3.23529482e-01 -6.63612068e-01 -1.85208656e-02 -1.00510143e-01
2.44328484e-01 7.11889446e-01 -9.15894192e-03 -5.60606480... | [9.50910758972168, 7.4019389152526855] |
b5738911-95bf-4de7-8f38-fb19f766218d | adaptive-multi-view-ica-estimation-of-noise | 2102.10964 | null | https://arxiv.org/abs/2102.10964v1 | https://arxiv.org/pdf/2102.10964v1.pdf | Adaptive Multi-View ICA: Estimation of noise levels for optimal inference | We consider a multi-view learning problem known as group independent component analysis (group ICA), where the goal is to recover shared independent sources from many views. The statistical modeling of this problem requires to take noise into account. When the model includes additive noise on the observations, the like... | ['Bertrand Thirion', 'Alexandre Gramfort', 'Aapo Hyvärinen', 'Pierre Ablin', 'Hugo Richard'] | 2021-02-22 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [ 9.09548774e-02 6.14663959e-02 9.96588320e-02 -3.70161325e-01
-1.04970300e+00 -5.42125881e-01 5.51941693e-01 -5.48672915e-01
-2.42331237e-01 6.51834428e-01 5.99324882e-01 2.06405431e-01
-2.94767022e-01 -3.17118078e-01 -9.10639107e-01 -9.69265521e-01
-2.18607321e-01 2.60439456e-01 -5.56884646e-01 3.48270088... | [7.757248401641846, 4.403214931488037] |
8069a57f-e413-4c86-a954-23bb8fcd1d0c | learning-bone-suppression-from-dual-energy | 1811.02628 | null | http://arxiv.org/abs/1811.02628v1 | http://arxiv.org/pdf/1811.02628v1.pdf | Learning Bone Suppression from Dual Energy Chest X-rays using Adversarial Networks | Suppressing bones on chest X-rays such as ribs and clavicle is often expected
to improve pathologies classification. These bones can interfere with a broad
range of diagnostic tasks on pulmonary disease except for musculoskeletal
system. Current conventional method for acquisition of bone suppressed X-rays
is dual ener... | ['Dong Yul Oh', 'Il Dong Yun'] | 2018-11-05 | null | null | null | null | ['bone-suppression-from-dual-energy-chest-x'] | ['medical'] | [ 5.31338036e-01 6.35117590e-02 -2.17391048e-02 5.50167561e-02
-1.09927928e+00 -1.94826052e-01 3.27181458e-01 -2.19574451e-01
-2.50200659e-01 7.30178535e-01 2.13972166e-01 -9.58540887e-02
-2.38016903e-01 -9.79428411e-01 -6.49732769e-01 -1.00136900e+00
3.44286919e-01 4.00496006e-01 3.45602393e-01 -7.24753663... | [13.515238761901855, -2.51350736618042] |
bcfcbf4e-2d5e-4ef7-9833-1b0175994b41 | a-context-integrated-relational-spatio | 2009.12469 | null | https://arxiv.org/abs/2009.12469v1 | https://arxiv.org/pdf/2009.12469v1.pdf | A Context Integrated Relational Spatio-Temporal Model for Demand and Supply Forecasting | Traditional methods for demand forecasting only focus on modeling the temporal dependency. However, forecasting on spatio-temporal data requires modeling of complex nonlinear relational and spatial dependencies. In addition, dynamic contextual information can have a significant impact on the demand values, and therefor... | ['Hongjie Chen', 'Ryan A. Rossi', 'Hoda Eldardiry', 'Kanak Mahadik'] | 2020-09-25 | null | null | null | null | ['irregular-time-series'] | ['time-series'] | [-2.68766046e-01 -4.70933199e-01 -5.03842354e-01 -4.90177572e-01
3.97331044e-02 -6.78342879e-01 7.46801019e-01 3.72236729e-01
3.86783004e-01 5.70566773e-01 6.30880356e-01 -5.92317939e-01
-4.72471029e-01 -1.24972773e+00 -6.26732111e-01 -4.73819137e-01
-7.49231756e-01 3.52455795e-01 2.04259366e-01 -6.08658016... | [6.6675004959106445, 2.5791690349578857] |
0ff3b05f-4014-4bf9-a66c-70bcad9e381c | parameterizing-the-cost-function-of-dynamic | 2301.10350 | null | https://arxiv.org/abs/2301.10350v2 | https://arxiv.org/pdf/2301.10350v2.pdf | Parameterizing the cost function of Dynamic Time Warping with application to time series classification | Dynamic Time Warping (DTW) is a popular time series distance measure that aligns the points in two series with one another. These alignments support warping of the time dimension to allow for processes that unfold at differing rates. The distance is the minimum sum of costs of the resulting alignments over any allowabl... | ['Geoffrey I. Webb', 'Chang Wei Tan', 'Matthieu Herrmann'] | 2023-01-24 | null | null | null | null | ['dynamic-time-warping'] | ['time-series'] | [ 3.23886484e-01 -3.28576982e-01 -2.38362372e-01 -4.37518448e-01
-4.08350348e-01 -8.35039020e-01 7.05644727e-01 5.93819499e-01
-7.26216912e-01 5.18888593e-01 2.02340260e-01 -4.00292307e-01
-3.97209376e-01 -7.62157023e-01 -3.66354644e-01 -7.22812414e-01
-6.61228716e-01 2.82872945e-01 6.31650090e-01 -2.79729724... | [7.306468963623047, 3.4372661113739014] |
8bea1121-7dd7-4a9c-bd2a-250e1791482e | retrievalfuse-neural-3d-scene-reconstruction | 2104.00024 | null | https://arxiv.org/abs/2104.00024v2 | https://arxiv.org/pdf/2104.00024v2.pdf | RetrievalFuse: Neural 3D Scene Reconstruction with a Database | 3D reconstruction of large scenes is a challenging problem due to the high-complexity nature of the solution space, in particular for generative neural networks. In contrast to traditional generative learned models which encode the full generative process into a neural network and can struggle with maintaining local de... | ['Angela Dai', 'Matthias Nießner', 'Qi Shan', 'Fangchang Ma', 'Justus Thies', 'Yawar Siddiqui'] | 2021-03-31 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Siddiqui_RetrievalFuse_Neural_3D_Scene_Reconstruction_With_a_Database_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Siddiqui_RetrievalFuse_Neural_3D_Scene_Reconstruction_With_a_Database_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-scene-reconstruction', 'scene-generation'] | ['computer-vision', 'computer-vision'] | [ 3.94416094e-01 2.40770161e-01 4.83572811e-01 -4.35559809e-01
-1.17305017e+00 -5.43882191e-01 6.03982449e-01 -2.70621292e-02
4.80117723e-02 4.71521556e-01 3.82206857e-01 1.90380931e-01
1.75468046e-02 -1.12559831e+00 -1.17451203e+00 -5.27430952e-01
9.21063200e-02 1.05887175e+00 1.61130562e-01 -3.86688411... | [8.957077980041504, -3.327646017074585] |
6891c9bb-798a-43be-b982-6a33fd524921 | synthetic-pre-training-tasks-for-neural | 2212.09864 | null | https://arxiv.org/abs/2212.09864v2 | https://arxiv.org/pdf/2212.09864v2.pdf | Synthetic Pre-Training Tasks for Neural Machine Translation | Pre-training models with large crawled corpora can lead to issues such as toxicity and bias, as well as copyright and privacy concerns. A promising way of alleviating such concerns is to conduct pre-training with synthetic tasks and data, since no real-world information is ingested by the model. Our goal in this paper ... | ['Rogerio Feris', 'Julian McAuley', 'Rameswar Panda', 'Graeme Blackwood', 'Zexue He'] | 2022-12-19 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 5.24750113e-01 1.19568102e-01 -1.50356725e-01 -5.94662540e-02
-1.13791251e+00 -9.56151307e-01 8.08689356e-01 6.71563968e-02
-7.59421349e-01 1.05184686e+00 2.75237679e-01 -8.63554180e-01
4.08008754e-01 -7.00187624e-01 -1.05407739e+00 -3.32614481e-01
2.80529886e-01 4.98019964e-01 -2.63849169e-01 -2.94598192... | [11.571885108947754, 10.20050048828125] |
2816c351-be36-47e2-a095-db2d1129b9a9 | kg-cruse-recurrent-walks-over-knowledge-graph | null | null | https://aclanthology.org/2022.nlp4convai-1.9 | https://aclanthology.org/2022.nlp4convai-1.9.pdf | KG-CRuSE: Recurrent Walks over Knowledge Graph for Explainable Conversation Reasoning using Semantic Embeddings | Knowledge-grounded dialogue systems utilise external knowledge such as knowledge graphs to generate informative and appropriate responses. A crucial challenge of such systems is to select facts from a knowledge graph pertinent to the dialogue context for response generation. This fact selection can be formulated as pat... | ['John McCrae', 'Mihael Arcan', 'Rajdeep Sarkar'] | null | null | null | null | nlp4convai-acl-2022-5 | ['fact-selection'] | ['natural-language-processing'] | [ 3.14851999e-01 1.07915437e+00 -9.61387604e-02 -5.18213451e-01
-7.30426610e-01 -5.48848331e-01 8.39460850e-01 2.51649171e-01
-2.10415736e-01 1.13871253e+00 9.20155942e-01 -2.30407923e-01
-4.72013094e-02 -1.03243387e+00 -4.55329359e-01 -9.67178345e-02
1.55957162e-01 1.06466067e+00 2.75608987e-01 -8.64819705... | [12.460657119750977, 8.137899398803711] |
7971a063-78c5-4faa-93f2-88da483a5817 | learning-from-task-descriptions | 2011.08115 | null | https://arxiv.org/abs/2011.08115v1 | https://arxiv.org/pdf/2011.08115v1.pdf | Learning from Task Descriptions | Typically, machine learning systems solve new tasks by training on thousands of examples. In contrast, humans can solve new tasks by reading some instructions, with perhaps an example or two. To take a step toward closing this gap, we introduce a framework for developing NLP systems that solve new tasks after reading t... | ['Matthew E. Peters', 'Matt Gardner', 'Nicholas Lourie', 'Orion Weller'] | 2020-11-16 | null | https://aclanthology.org/2020.emnlp-main.105 | https://aclanthology.org/2020.emnlp-main.105.pdf | emnlp-2020-11 | ['systematic-generalization'] | ['reasoning'] | [ 3.64643455e-01 5.36972821e-01 -6.92601427e-02 -6.70177341e-01
-8.87567937e-01 -1.17028701e+00 5.98689735e-01 -6.65507913e-02
-4.85853910e-01 8.74612629e-01 1.19840220e-01 -5.36839426e-01
-1.35572493e-01 -5.21160245e-01 -6.61240876e-01 -9.87555459e-02
3.75861436e-01 9.58915234e-01 3.65894079e-01 -4.10993725... | [11.039196968078613, 8.16624641418457] |
36cf18b1-6b02-49f6-a984-092dd22098b2 | english-to-chinese-transliteration-with-1 | 2112.10321 | null | https://arxiv.org/abs/2112.10321v2 | https://arxiv.org/pdf/2112.10321v2.pdf | English-to-Chinese Transliteration with Phonetic Back-transliteration | Transliteration is a task of translating named entities from a language to another, based on phonetic similarity. The task has embraced deep learning approaches in recent years, yet, most ignore the phonetic features of the involved languages. In this work, we incorporate phonetic information into neural networks in tw... | ['Songpeng Yan', 'Zhuofei Ding', 'Shi Cheng'] | 2021-12-20 | null | null | null | null | ['transliteration'] | ['natural-language-processing'] | [ 4.08770293e-02 -6.06082603e-02 -3.38864595e-01 -6.51177526e-01
-8.81532490e-01 -6.36400223e-01 7.02738464e-01 -5.72831094e-01
-5.35243392e-01 9.60058928e-01 6.79180980e-01 -7.95001268e-01
5.00036359e-01 -7.98874021e-01 -8.71234298e-01 -2.96564817e-01
5.25453925e-01 6.56001985e-01 -4.83835936e-02 -2.25480065... | [11.510931968688965, 10.30536937713623] |
c606e087-ea79-4a4c-9c6d-1e12e9776c4b | mvss-net-multi-view-multi-scale-supervised | 2112.08935 | null | https://arxiv.org/abs/2112.08935v3 | https://arxiv.org/pdf/2112.08935v3.pdf | MVSS-Net: Multi-View Multi-Scale Supervised Networks for Image Manipulation Detection | As manipulating images by copy-move, splicing and/or inpainting may lead to misinterpretation of the visual content, detecting these sorts of manipulations is crucial for media forensics. Given the variety of possible attacks on the content, devising a generic method is nontrivial. Current deep learning based methods a... | ['Xirong Li', 'Juan Cao', 'Ruohan Hu', 'Xinru Chen', 'Chengbo Dong'] | 2021-12-16 | null | null | null | null | ['image-manipulation-detection'] | ['computer-vision'] | [ 6.49316609e-01 -4.73868966e-01 6.59227669e-02 -6.71333149e-02
-9.89812136e-01 -8.55923891e-01 5.39898396e-01 -5.19362204e-02
-2.72721440e-01 4.88977820e-01 -1.28642190e-02 -2.20132068e-01
-1.95133965e-02 -5.42043805e-01 -1.18101025e+00 -7.03979075e-01
-1.27980337e-01 -5.12857251e-02 2.65239686e-01 -1.48134986... | [12.327141761779785, 0.9770194888114929] |
328d51a3-4607-4618-a6fd-7c78870135ca | improved-algorithm-on-online-clustering-of | 1902.09162 | null | https://arxiv.org/abs/1902.09162v2 | https://arxiv.org/pdf/1902.09162v2.pdf | Improved Algorithm on Online Clustering of Bandits | We generalize the setting of online clustering of bandits by allowing non-uniform distribution over user frequencies. A more efficient algorithm is proposed with simple set structures to represent clusters. We prove a regret bound for the new algorithm which is free of the minimal frequency over users. The experiments ... | ['Kwong-Sak Leung', 'Wei Chen', 'Shuai Li'] | 2019-02-25 | null | null | null | null | ['online-clustering'] | ['computer-vision'] | [-2.89501995e-01 1.25414893e-01 -9.36775744e-01 -2.95648843e-01
-8.27529073e-01 -8.61827374e-01 4.29174155e-02 2.25560516e-02
-3.13774884e-01 1.03302801e+00 1.18585765e-01 -5.27587771e-01
-6.65623188e-01 -8.13403785e-01 -9.38421249e-01 -7.90823579e-01
-6.63011193e-01 8.32942069e-01 -1.02302739e-02 2.34544173... | [4.565731048583984, 3.3419275283813477] |
acabeb00-6e88-43db-aff5-3c8baf7fcfde | ernie-sat-speech-and-text-joint-pretraining-1 | 2211.03545 | null | https://arxiv.org/abs/2211.03545v2 | https://arxiv.org/pdf/2211.03545v2.pdf | ERNIE-SAT: Speech and Text Joint Pretraining for Cross-Lingual Multi-Speaker Text-to-Speech | Speech representation learning has improved both speech understanding and speech synthesis tasks for single language. However, its ability in cross-lingual scenarios has not been explored. In this paper, we extend the pretraining method for cross-lingual multi-speaker speech synthesis tasks, including cross-lingual mul... | ['Hua Wu', 'Yu Sun', 'Liang Huang', 'Zeyu Chen', 'Junkun Chen', 'Shuohuan Wang', 'Pengfei Zhu', 'Renjie Zheng', 'He Bai', 'Tian Yuan', 'Chao Pang', 'Xiaoran Fan'] | 2022-11-07 | ernie-sat-speech-and-text-joint-pretraining | https://arxiv.org/abs/2211.03545 | https://arxiv.org/pdf/2211.03545.pdf | null | ['text-to-speech-synthesis', 'voice-cloning'] | ['speech', 'speech'] | [ 3.02243203e-01 2.50674844e-01 -2.23600909e-01 -6.40853643e-01
-1.50735867e+00 -5.36226869e-01 4.88353133e-01 -4.92428422e-01
-1.71512023e-01 4.40216959e-01 5.38456500e-01 -7.61763394e-01
5.31461000e-01 -1.41446814e-01 -9.81721997e-01 -4.24248606e-01
4.20398802e-01 5.57650566e-01 -2.53848374e-01 -4.76796064... | [14.722244262695312, 6.840377330780029] |
cc2ca970-12db-445d-9f31-51fc10894290 | on-granularity-of-prosodic-representations-in | 2301.11446 | null | https://arxiv.org/abs/2301.11446v1 | https://arxiv.org/pdf/2301.11446v1.pdf | On granularity of prosodic representations in expressive text-to-speech | In expressive speech synthesis it is widely adopted to use latent prosody representations to deal with variability of the data during training. Same text may correspond to various acoustic realizations, which is known as a one-to-many mapping problem in text-to-speech. Utterance, word, or phoneme-level representations ... | ['Viacheslav Klimkov', 'Daniel Korzekwa', 'Rafal Sienkiewicz', 'Raahil Shah', 'Kamil Pokora', 'Mikolaj Babianski'] | 2023-01-26 | null | null | null | null | ['expressive-speech-synthesis'] | ['speech'] | [ 1.77925587e-01 5.34414411e-01 -1.45722955e-01 -4.97600615e-01
-7.52568245e-01 -4.61277068e-01 4.74397212e-01 -1.97950244e-01
-1.42678618e-02 6.95782959e-01 9.09370601e-01 -1.06979506e-02
1.97179750e-01 -6.06911004e-01 -4.76795256e-01 -5.53104043e-01
2.46225655e-01 2.58418024e-01 -3.27683636e-03 -2.56879002... | [14.916479110717773, 6.626264572143555] |
5fb66e03-5866-4a4a-a413-95c6074a128c | deep-sparse-coding-for-non-intrusive-load | 1912.12128 | null | https://arxiv.org/abs/1912.12128v1 | https://arxiv.org/pdf/1912.12128v1.pdf | Deep Sparse Coding for Non-Intrusive Load Monitoring | Energy disaggregation is the task of segregating the aggregate energy of the entire building (as logged by the smartmeter) into the energy consumed by individual appliances. This is a single channel (the only channel being the smart-meter) blind source (different electrical appliances) separation problem. The tradition... | ['Angshul Majumdar', 'Shikha Singh'] | 2019-12-11 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 1.40714899e-01 -1.21754780e-01 -3.33036363e-01 -8.51591676e-02
-1.19953346e+00 -8.75489116e-01 6.06703699e-01 2.99765915e-01
-8.69824216e-02 4.20084059e-01 5.35386443e-01 -2.65581965e-01
2.82696158e-01 -8.20872843e-01 -6.55365109e-01 -1.26563680e+00
1.38453931e-01 4.42245543e-01 -3.04664284e-01 8.97007510... | [16.09954071044922, 7.610836029052734] |
3e16c48d-b988-45c5-8616-81213acca2de | ice-core-dating-using-probabilistic | 2210.16568 | null | https://arxiv.org/abs/2210.16568v1 | https://arxiv.org/pdf/2210.16568v1.pdf | Ice Core Dating using Probabilistic Programming | Ice cores record crucial information about past climate. However, before ice core data can have scientific value, the chronology must be inferred by estimating the age as a function of depth. Under certain conditions, chemicals locked in the ice display quasi-periodic cycles that delineate annual layers. Manually count... | ['Markus Kaiser', 'Neil D. Lawrence', 'J. Scott Hosking', 'Richard E. Turner', 'Will Tebbutt', 'Ieva Kazlauskaite', 'Tom R. Andersson', 'Aditya Ravuri'] | 2022-10-29 | null | null | null | null | ['probabilistic-programming'] | ['methodology'] | [-4.06985320e-02 -8.04198757e-02 2.36836940e-01 -3.45601022e-01
-5.41509926e-01 -9.59812999e-01 5.75368762e-01 2.85571009e-01
-3.56072485e-01 1.03847456e+00 6.50412291e-02 -5.91573775e-01
1.50590986e-01 -1.01639116e+00 -5.28087854e-01 -6.07670724e-01
-5.55496812e-01 9.48894739e-01 4.10864353e-01 1.31060556... | [6.62251615524292, 3.6701743602752686] |
fc213ae7-f6d4-4d5f-a8ee-0f5f14e77605 | why-only-micro-f1-class-weighting-of-measures-1 | 2205.09460 | null | https://arxiv.org/abs/2205.09460v1 | https://arxiv.org/pdf/2205.09460v1.pdf | Why only Micro-F1? Class Weighting of Measures for Relation Classification | Relation classification models are conventionally evaluated using only a single measure, e.g., micro-F1, macro-F1 or AUC. In this work, we analyze weighting schemes, such as micro and macro, for imbalanced datasets. We introduce a framework for weighting schemes, where existing schemes are extremes, and two new interme... | ['Christoph Alt', 'Leonhard Hennig', 'Yuxuan Chen', 'David Harbecke'] | 2022-05-19 | why-only-micro-f1-class-weighting-of-measures | https://aclanthology.org/2022.nlppower-1.4 | https://aclanthology.org/2022.nlppower-1.4.pdf | nlppower-acl-2022-5 | ['relation-classification'] | ['natural-language-processing'] | [ 1.56225994e-01 4.11105812e-01 -8.23436975e-01 -7.45594680e-01
-5.34091055e-01 -4.64227438e-01 3.99997503e-01 1.01544201e+00
-3.28094661e-01 1.06389797e+00 2.23899856e-01 -5.04838228e-01
-7.07948387e-01 -8.39921236e-01 -5.01314960e-02 -3.22237343e-01
3.78472358e-03 4.25273597e-01 2.67936051e-01 -3.10493201... | [9.258954048156738, 8.674219131469727] |
4fa5bea0-8751-4596-b8a4-a72a7da98320 | curriculum-learning-for-relative | 2212.02733 | null | https://arxiv.org/abs/2212.02733v2 | https://arxiv.org/pdf/2212.02733v2.pdf | Curriculum Learning for Relative Overgeneralization | In multi-agent reinforcement learning (MARL), many popular methods, such as VDN and QMIX, are susceptible to a critical multi-agent pathology known as relative overgeneralization (RO), which arises when the optimal joint action's utility falls below that of a sub-optimal joint action in cooperative tasks. RO can cause ... | ['Bei Peng', 'Lin Shi'] | 2022-12-06 | null | null | null | null | ['starcraft-ii', 'starcraft'] | ['playing-games', 'playing-games'] | [-5.21214753e-02 -3.45735475e-02 -1.70140743e-01 3.11800539e-01
-9.65371788e-01 -4.16954637e-01 4.57738072e-01 1.37752846e-01
-6.93757713e-01 1.30996180e+00 -1.02048337e-01 -1.68354642e-02
-4.64722663e-01 -6.81908011e-01 -8.59430730e-01 -9.68903005e-01
-2.83233136e-01 7.19674706e-01 4.60376948e-01 -6.60057127... | [3.7998526096343994, 1.9158456325531006] |
93a96fe9-f580-4e60-8812-67bc94348c58 | pai-at-semeval-2023-task-2-a-universal-system | 2305.06099 | null | https://arxiv.org/abs/2305.06099v1 | https://arxiv.org/pdf/2305.06099v1.pdf | PAI at SemEval-2023 Task 2: A Universal System for Named Entity Recognition with External Entity Information | The MultiCoNER II task aims to detect complex, ambiguous, and fine-grained named entities in low-context situations and noisy scenarios like the presence of spelling mistakes and typos for multiple languages. The task poses significant challenges due to the scarcity of contextual information, the high granularity of th... | ['Xuan Li', 'Weiguo Gao', 'Hailong Huang', 'Tianbo Che', 'Kai Lu', 'Long Ma'] | 2023-05-10 | null | null | null | null | ['named-entity-recognition-ner'] | ['natural-language-processing'] | [-3.33115280e-01 -6.38777688e-02 3.37436087e-02 -3.26572388e-01
-1.13132989e+00 -9.54958081e-01 4.53080684e-01 3.05815816e-01
-7.36316741e-01 1.00134850e+00 4.90674555e-01 -3.13703120e-01
6.27475381e-02 -8.81350815e-01 -6.41825378e-01 -1.39177993e-01
2.99356639e-01 5.15915453e-01 3.50841790e-01 -3.87964427... | [9.629878044128418, 9.476466178894043] |
5bb2f581-ce6e-4c2a-9971-05be4e90efb9 | intelligent-frame-selection-as-a-privacy | 2101.07529 | null | https://arxiv.org/abs/2101.07529v2 | https://arxiv.org/pdf/2101.07529v2.pdf | Intelligent Frame Selection as a Privacy-Friendlier Alternative to Face Recognition | The widespread deployment of surveillance cameras for facial recognition gives rise to many privacy concerns. This study proposes a privacy-friendly alternative to large scale facial recognition. While there are multiple techniques to preserve privacy, our work is based on the minimization principle which implies minim... | ['Pieter Simoens', 'Sam Leroux', 'Mattijs Baert'] | 2021-01-19 | null | null | null | null | ['face-image-quality', 'face-image-quality-assessment'] | ['computer-vision', 'computer-vision'] | [ 2.72535205e-01 1.02363363e-01 -6.69824630e-02 -8.21424663e-01
-7.58289695e-01 -4.31752384e-01 3.17843080e-01 -3.37544888e-01
-6.53617918e-01 5.07596612e-01 5.35633527e-02 8.43563825e-02
-2.56888062e-01 -7.76687920e-01 -5.30747294e-01 -9.20934260e-01
2.39686400e-01 7.70656113e-03 -4.43340480e-01 3.19992304... | [13.10615348815918, 0.7857871055603027] |
0ff3b347-15fc-4f36-b606-f5544ff7e767 | ensembling-instance-and-semantic-segmentation | 2304.10326 | null | https://arxiv.org/abs/2304.10326v1 | https://arxiv.org/pdf/2304.10326v1.pdf | Ensembling Instance and Semantic Segmentation for Panoptic Segmentation | We demonstrate our solution for the 2019 COCO panoptic segmentation task. Our method first performs instance segmentation and semantic segmentation separately, then combines the two to generate panoptic segmentation results. To enhance the performance, we add several expert models of Mask R-CNN in instance segmentation... | ['Yogesh Langhe', 'Mehmet Yildirim'] | 2023-04-20 | null | null | null | null | ['panoptic-segmentation'] | ['computer-vision'] | [-4.72328905e-03 4.42598127e-02 -3.93566191e-01 -5.66280425e-01
-8.75669241e-01 -6.91869199e-01 3.40186536e-01 -5.08921184e-02
-2.34708995e-01 4.75966662e-01 -2.34205917e-01 -4.26301390e-01
3.10363099e-02 -9.52721655e-01 -7.08172321e-01 -7.87070096e-01
5.69604188e-02 7.84663856e-01 2.75068015e-01 1.86014399... | [9.515697479248047, 0.25471174716949463] |
4db95ac9-b8db-4045-9ac7-39497a8b49e6 | non-autoregressive-sign-language-production | 2208.06183 | null | https://arxiv.org/abs/2208.06183v1 | https://arxiv.org/pdf/2208.06183v1.pdf | Non-Autoregressive Sign Language Production via Knowledge Distillation | Sign Language Production (SLP) aims to translate expressions in spoken language into corresponding ones in sign language, such as skeleton-based sign poses or videos. Existing SLP models are either AutoRegressive (AR) or Non-Autoregressive (NAR). However, AR-SLP models suffer from regression to the mean and error propa... | ['Jong C. Park', 'Suk min Cho', 'Jung Ho Kim', 'Eui Jun Hwang'] | 2022-08-12 | null | null | null | null | ['sign-language-production'] | ['natural-language-processing'] | [ 2.78850734e-01 2.23612972e-02 -2.68330663e-01 -5.68048239e-01
-1.06248927e+00 -4.28533643e-01 6.20558321e-01 -8.78270924e-01
-2.92174518e-01 5.92225373e-01 7.74667919e-01 -9.30181146e-02
5.51987626e-02 -3.29834968e-01 -7.01320589e-01 -7.06547022e-01
1.80728227e-01 4.81037915e-01 3.05849671e-01 -1.23247884... | [9.194982528686523, -6.4980573654174805] |
e2f3ed5c-0924-4ec8-84cb-f52faf76f268 | improved-vocal-effort-transfer-vector | 2305.02147 | null | https://arxiv.org/abs/2305.02147v3 | https://arxiv.org/pdf/2305.02147v3.pdf | Improved Vocal Effort Transfer Vector Estimation for Vocal Effort-Robust Speaker Verification | Despite the maturity of modern speaker verification technology, its performance still significantly degrades when facing non-neutrally-phonated (e.g., shouted and whispered) speech. To address this issue, in this paper, we propose a new speaker embedding compensation method based on a minimum mean square error (MMSE) e... | ['Eduardo Lleida', 'Alfonso Ortega', 'Santi Prieto', 'Iván López-Espejo'] | 2023-05-03 | null | null | null | null | ['speaker-verification'] | ['speech'] | [ 1.64597839e-01 4.22335044e-02 5.66442870e-03 -3.24885577e-01
-9.37471867e-01 -3.36207986e-01 2.93837428e-01 -4.06353921e-01
-4.43834960e-01 3.98728579e-01 3.89939040e-01 -3.40924323e-01
1.51149612e-02 2.01464798e-02 -1.39605343e-01 -6.79485798e-01
2.03330696e-01 -1.31112054e-01 -2.03721046e-01 -9.34334546... | [14.488577842712402, 6.090006351470947] |
e38cc29d-d356-4839-a2f6-12845fbbea26 | event-transformer | 2204.05172 | null | https://arxiv.org/abs/2204.05172v1 | https://arxiv.org/pdf/2204.05172v1.pdf | Event Transformer | The event camera is a bio-vision inspired camera with high dynamic range, high response speed, and low power consumption, recently attracting extensive attention for its use in vast vision tasks. Unlike the conventional cameras that output intensity frame at a fixed time interval, event camera records the pixel brightn... | ['Zhan Ma', 'M. Salman Asif', 'Zhihao LI'] | 2022-04-11 | null | null | null | null | ['event-based-vision'] | ['computer-vision'] | [ 4.53431487e-01 -8.56036365e-01 1.17703013e-01 -2.77396381e-01
-3.17794740e-01 -3.40660304e-01 9.47956860e-01 1.31525472e-01
-4.97858196e-01 4.91950423e-01 2.84242809e-01 2.59759724e-01
-3.53005797e-01 -5.59304893e-01 -3.44250172e-01 -1.18170404e+00
-2.60615975e-01 -2.73742467e-01 3.58706862e-01 3.18385035... | [8.638672828674316, -1.258387565612793] |
33ebef80-364c-4b6c-bb3c-4031b2da8c94 | hit-qmul-at-semeval-2022-task-9-label | null | null | https://aclanthology.org/2022.semeval-1.177 | https://aclanthology.org/2022.semeval-1.177.pdf | HIT&QMUL at SemEval-2022 Task 9: Label-Enclosed Generative Question Answering (LEG-QA) | This paper presents the second place system for the R2VQ: competence-based multimodal question answering shared task. The purpose of this task is to involve semantic&cooking roles and text-images objects when querying how well a system understands the procedure of a recipe. This task is approached with text-to-text gen... | ['Bingquan Liu', 'Arkaitz Zubiaga', 'Mingqiang Feng', 'Weihe Zhai'] | null | null | null | null | semeval-naacl-2022-7 | ['generative-question-answering'] | ['natural-language-processing'] | [ 5.00466764e-01 5.61002731e-01 6.22654736e-01 -9.60383177e-01
-1.32825887e+00 -9.15571034e-01 8.85738969e-01 1.25175178e-01
-4.93363827e-01 3.84581417e-01 4.44131553e-01 -1.79907084e-01
3.28937247e-02 -5.45440435e-01 -6.20963693e-01 -5.80225646e-01
5.29373288e-01 9.34896648e-01 -8.50712694e-03 -9.08936322... | [11.28055191040039, 7.898998260498047] |
333c6c85-8079-4909-9d7a-a54d4aae6043 | video-denoising-and-enhancement-via-dynamic | 1710.02213 | null | http://arxiv.org/abs/1710.02213v1 | http://arxiv.org/pdf/1710.02213v1.pdf | Video Denoising and Enhancement via Dynamic Video Layering | Video denoising refers to the problem of removing "noise" from a video
sequence. Here the term "noise" is used in a broad sense to refer to any
corruption or outlier or interference that is not the quantity of interest. In
this work, we develop a novel approach to video denoising that is based on the
idea that many noi... | ['Namrata Vaswani', 'Han Guo'] | 2017-10-05 | null | null | null | null | ['video-denoising'] | ['computer-vision'] | [ 3.34370047e-01 -3.96050394e-01 4.63313907e-01 -6.28974065e-02
-8.26565504e-01 -2.12983564e-01 2.92736501e-01 -1.25676781e-01
-2.00351357e-01 5.95887542e-01 5.51603258e-01 1.57360017e-01
1.02650054e-01 -4.08802271e-01 -9.75680530e-01 -1.12021863e+00
-2.83646822e-01 -3.58192921e-01 1.16327778e-01 -3.04953873... | [11.404804229736328, -2.210583448410034] |
aad3544c-43df-4889-8149-790ed8d8aad6 | in-silico-identification-of-tipifarnib-like | 2305.16156 | null | https://arxiv.org/abs/2305.16156v1 | https://arxiv.org/pdf/2305.16156v1.pdf | In silico Identification of tipifarnib-like compounds by structure-based pharmacophore, virtual screening and molecular docking against K-Ras post-translation in colorectal cancer | Colorectal cancer is a public health problem.Approximately 30 to 50 \% of colorectal tumors are caused by mutations in the KRAS gene.These mutations induce uncontrolled proliferation.To date,There is no approved effective treatment for the mutated KRAS oncogene.Farnesyltransferase (FTI) inhibitors are considered a ther... | ['Houda Filali', 'Imane Rahmoune', 'Youness Kadil1', 'Mohammed Mouhcine'] | 2023-05-07 | null | null | null | null | ['molecular-docking'] | ['medical'] | [ 3.67027998e-01 -2.09830031e-01 -7.78778017e-01 2.50675917e-01
-3.98542494e-01 -7.43001223e-01 4.95390072e-02 5.60292244e-01
-5.21324337e-01 1.29392624e+00 2.80455977e-01 -7.35546768e-01
2.10945278e-01 -6.39211237e-01 -5.48590183e-01 -1.07714665e+00
1.64428234e-01 4.01420385e-01 1.94148690e-01 -2.10241646... | [4.663742542266846, 5.130850791931152] |
90a34453-1452-41a7-8531-fbf6f345782d | automated-control-and-optimisation-of-laser | 2303.00823 | null | https://arxiv.org/abs/2303.00823v1 | https://arxiv.org/pdf/2303.00823v1.pdf | Automated control and optimisation of laser driven ion acceleration | The interaction of relativistically intense lasers with opaque targets represents a highly non-linear, multi-dimensional parameter space. This limits the utility of sequential 1D scanning of experimental parameters for the optimisation of secondary radiation, although to-date this has been the accepted methodology due ... | ['C. A. J. Palmer', 'N. Xu', 'F. Treffert', 'A. G. R. Thomas', 'D. R. Symes', 'C. Spindloe', 'P. Parsons', 'C. Parisuaña', 'Z. Najmudin', 'P. McKenna', 'O. McCusker', 'D. Margarone', 'M. King', 'V. Istokskaia', 'C. Hyland', 'G. S. Hicks', 'R. J. Gray', 'J. S. Green', 'S. H. Glenzer', 'G. D. Glenn', 'L. Giuffrida', 'M. ... | 2023-03-01 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 4.64070708e-01 2.03097854e-02 -8.07332546e-02 -2.37323225e-01
-9.09918845e-01 -5.26807643e-02 7.95720100e-01 6.72335699e-02
-8.52425754e-01 6.17297053e-01 2.19134018e-01 -5.33017039e-01
-5.47143579e-01 -5.54567695e-01 -2.28392839e-01 -1.27357841e+00
7.43094534e-02 1.28582096e+00 2.96223909e-01 9.52972472... | [6.289713382720947, 3.683126449584961] |
ea3ebcbf-6d36-4027-a7a0-91ab79cad62a | action-agnostic-human-pose-forecasting | 1810.09676 | null | http://arxiv.org/abs/1810.09676v1 | http://arxiv.org/pdf/1810.09676v1.pdf | Action-Agnostic Human Pose Forecasting | Predicting and forecasting human dynamics is a very interesting but
challenging task with several prospective applications in robotics,
health-care, etc. Recently, several methods have been developed for human pose
forecasting; however, they often introduce a number of limitations in their
settings. For instance, previ... | ['De-An Huang', 'Borui Wang', 'Hsu-kuang Chiu', 'Juan Carlos Niebles', 'Ehsan Adeli'] | 2018-10-23 | null | null | null | null | ['human-pose-forecasting', 'human-dynamics'] | ['computer-vision', 'computer-vision'] | [ 6.21562488e-02 -1.01608373e-01 -1.91812932e-01 -4.46963012e-01
-3.91528100e-01 -1.46136060e-01 3.41548622e-01 -3.79025877e-01
-3.71052116e-01 6.55196786e-01 6.47111893e-01 2.23363653e-01
-6.95304526e-03 -4.61120814e-01 -7.84959376e-01 -5.45914888e-01
-3.88146073e-01 5.20343065e-01 4.25979644e-01 -3.47106785... | [7.2829718589782715, -0.2952585816383362] |
ecb53d1d-0e00-47ac-ba0b-abfbf9987756 | improving-code-switching-and-named-entity | 2306.08588 | null | https://arxiv.org/abs/2306.08588v1 | https://arxiv.org/pdf/2306.08588v1.pdf | Improving Code-Switching and Named Entity Recognition in ASR with Speech Editing based Data Augmentation | Recently, end-to-end (E2E) automatic speech recognition (ASR) models have made great strides and exhibit excellent performance in general speech recognition. However, there remain several challenging scenarios that E2E models are not competent in, such as code-switching and named entity recognition (NER). Data augmenta... | ['Xie Chen', 'Kai Yu', 'Chenpeng Du', 'Ziyang Ma', 'Zheshu Song', 'Zheng Liang'] | 2023-06-14 | null | null | null | null | ['named-entity-recognition-ner', 'automatic-speech-recognition'] | ['natural-language-processing', 'speech'] | [ 3.59299839e-01 1.27186313e-01 1.76597178e-01 -5.11152148e-01
-8.11437309e-01 -1.87404767e-01 5.66015422e-01 -2.82880843e-01
-3.97385240e-01 4.77467895e-01 5.44487059e-01 -5.74178994e-01
3.90108883e-01 -1.95136502e-01 -4.28528696e-01 -3.74407470e-01
3.00422519e-01 1.65424988e-01 -1.58714354e-02 -4.38764155... | [14.606691360473633, 6.682539463043213] |
42c29c4e-6695-42ab-ae9d-5497a680f822 | a-ccnn-adaptive-ccnn-for-density-estimation | 1804.06958 | null | http://arxiv.org/abs/1804.06958v2 | http://arxiv.org/pdf/1804.06958v2.pdf | A-CCNN: adaptive ccnn for density estimation and crowd counting | Crowd counting, for estimating the number of people in a crowd using
vision-based computer techniques, has attracted much interest in the research
community. Although many attempts have been reported, real-world problems, such
as huge variation in subjects' sizes in images and serious occlusion among
people, make it st... | ['Michelle Zeibots', 'Saeed Amirgholipour Kasmani', 'Xiangjian He', 'Wenjing Jia', 'Dadong Wang'] | 2018-04-19 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [-3.27848554e-01 -8.50588441e-01 3.07113200e-01 -3.65481019e-01
3.29436399e-02 -1.52328178e-01 5.13887525e-01 2.70019710e-01
-1.05700350e+00 8.74233007e-01 8.67378712e-02 2.34334879e-02
3.70826513e-01 -8.51718664e-01 -3.42107177e-01 -4.88173187e-01
1.09433001e-02 6.62049770e-01 7.76038766e-01 8.65144879... | [8.419402122497559, -0.3017589747905731] |
a19f5585-3943-4c77-a4b4-d680e05559b1 | learning-to-segment-moving-objects | 1712.01127 | null | http://arxiv.org/abs/1712.01127v1 | http://arxiv.org/pdf/1712.01127v1.pdf | Learning to Segment Moving Objects | We study the problem of segmenting moving objects in unconstrained videos.
Given a video, the task is to segment all the objects that exhibit independent
motion in at least one frame. We formulate this as a learning problem and
design our framework with three cues: (i) independent object motion between a
pair of frames... | ['Karteek Alahari', 'Pavel Tokmakov', 'Cordelia Schmid'] | 2017-12-01 | null | null | null | null | ['unsupervised-video-object-segmentation'] | ['computer-vision'] | [ 2.88224816e-01 -2.56092042e-01 -3.24484617e-01 -2.29772836e-01
-3.05878490e-01 -4.73901629e-01 3.80075246e-01 -3.09817970e-01
-5.48122942e-01 4.03401345e-01 -1.26289099e-01 5.44582270e-02
2.83439010e-01 -5.47604322e-01 -1.13373661e+00 -8.87813628e-01
-1.85942709e-01 6.74695075e-02 7.07775116e-01 3.82021874... | [9.068084716796875, -0.1794736385345459] |
f495199e-d9b3-43ed-82a9-a90bc9e62359 | estimating-the-value-of-evidence-based | 2306.13681 | null | https://arxiv.org/abs/2306.13681v1 | https://arxiv.org/pdf/2306.13681v1.pdf | Estimating the Value of Evidence-Based Decision Making | Business/policy decisions are often based on evidence from randomized experiments and observational studies. In this article we propose an empirical framework to estimate the value of evidence-based decision making (EBDM) and the return on the investment in statistical precision. | ['Serguei Stepaniants', 'James McQueen', 'Siwei Jia', 'Guido Imbens', 'Anish Agarwal', 'Alberto Abadie'] | 2023-06-21 | null | null | null | null | ['decision-making'] | ['reasoning'] | [-2.96260267e-01 2.94350475e-01 -1.21273005e+00 -3.33364367e-01
-4.16312665e-01 -1.39979139e-01 8.13549519e-01 4.77374643e-01
-7.91747212e-01 9.80649412e-01 3.12788248e-01 -1.40954196e+00
-4.87907708e-01 -6.40858293e-01 -7.16048896e-01 -1.77869052e-01
1.60641819e-01 1.39671028e-01 -1.11407273e-01 4.36675727... | [7.976164817810059, 5.229262351989746] |
cd5fa524-d137-4010-bb22-e039b6108d18 | layout-design-for-intelligent-warehouse-by | 1811.05685 | null | http://arxiv.org/abs/1811.05685v1 | http://arxiv.org/pdf/1811.05685v1.pdf | Layout Design for Intelligent Warehouse by Evolution with Fitness Approximation | With the rapid growth of the express industry, intelligent warehouses that
employ autonomous robots for carrying parcels have been widely used to handle
the vast express volume. For such warehouses, the warehouse layout design plays
a key role in improving the transportation efficiency. However, this work is
still done... | ['Wei-Nan Zhang', 'Zilong Guo', 'Yong Yu', 'Jun Wang', 'Wenxin Li', 'Han Cai', 'Haifeng Zhang', 'Chris Wang'] | 2018-11-14 | null | null | null | null | ['layout-design'] | ['computer-vision'] | [-2.68963188e-01 -1.53788209e-01 1.53925091e-01 -2.95264900e-01
4.68332181e-03 -3.72035950e-01 3.81793967e-03 2.42654026e-01
-4.19346988e-01 6.89441502e-01 -1.22196428e-01 -3.29567820e-01
-3.90734702e-01 -1.26537931e+00 -4.06187981e-01 -8.42109740e-01
1.86077014e-01 5.00303328e-01 1.26774982e-01 -6.36456788... | [5.702325820922852, 3.4919850826263428] |
1ff008a3-9762-4d7b-b88e-daebd671ff08 | mixskd-self-knowledge-distillation-from-mixup | 2208.05768 | null | https://arxiv.org/abs/2208.05768v1 | https://arxiv.org/pdf/2208.05768v1.pdf | MixSKD: Self-Knowledge Distillation from Mixup for Image Recognition | Unlike the conventional Knowledge Distillation (KD), Self-KD allows a network to learn knowledge from itself without any guidance from extra networks. This paper proposes to perform Self-KD from image Mixture (MixSKD), which integrates these two techniques into a unified framework. MixSKD mutually distills feature maps... | ['Qian Zhang', 'Yongjun Xu', 'Jiwen Wu', 'Xiang Zhi', 'Linhang Cai', 'Helong Zhou', 'Zhulin An', 'Chuanguang Yang'] | 2022-08-11 | null | null | null | null | ['self-knowledge-distillation'] | ['computer-vision'] | [ 6.33320063e-02 4.48715657e-01 -3.70834559e-01 -4.61582094e-01
-6.57205641e-01 -3.97396356e-01 7.34141886e-01 -1.82241082e-01
-3.73632848e-01 6.20413244e-01 -1.01828285e-01 -1.51243106e-01
8.47679526e-02 -7.33450234e-01 -9.07929361e-01 -9.47260559e-01
4.05660719e-01 3.78071487e-01 4.02301401e-01 1.05762206... | [9.415118217468262, 1.656624674797058] |
066c6410-f9d4-44bc-bb57-43e9c259a1f8 | machine-learning-strategies-to-improve | 2212.08744 | null | https://arxiv.org/abs/2212.08744v1 | https://arxiv.org/pdf/2212.08744v1.pdf | Machine Learning Strategies to Improve Generalization in EEG-based Emotion Assessment: \\a Systematic Review | A systematic review on machine-learning strategies for improving generalizability (cross-subjects and cross-sessions) electroencephalography (EEG) based in emotion classification was realized. In this context, the non-stationarity of EEG signals is a critical issue and can lead to the Dataset Shift problem. Several arc... | ['Roberto Prevete', 'Nicola Moccaldi', 'Giovanna Mastrati', 'Davide Marocco', "Giovanni D'Errico", 'Pasquale Arpaia', 'Andrea Apicella'] | 2022-12-16 | null | null | null | null | ['emotion-classification', 'emotion-classification'] | ['computer-vision', 'natural-language-processing'] | [ 5.46534993e-02 -1.98402286e-01 -5.69189608e-01 -3.99102926e-01
-5.68648636e-01 -3.05485606e-01 7.19575882e-02 4.38733637e-01
-7.75735140e-01 1.06141019e+00 -2.04332530e-01 -2.35064298e-01
-7.61398792e-01 -2.50746518e-01 -5.93659043e-01 -6.91443086e-01
-4.13513184e-01 -5.42651340e-02 -2.06649229e-01 2.04932913... | [13.253623962402344, 3.361152172088623] |
545c5383-2802-44a9-98ae-7eb4dbff74dc | dense-and-aligned-captions-dac-promote | 2305.19595 | null | https://arxiv.org/abs/2305.19595v2 | https://arxiv.org/pdf/2305.19595v2.pdf | Dense and Aligned Captions (DAC) Promote Compositional Reasoning in VL Models | Vision and Language (VL) models offer an effective method for aligning representation spaces of images and text, leading to numerous applications such as cross-modal retrieval, visual question answering, captioning, and more. However, the aligned image-text spaces learned by all the popular VL models are still sufferin... | ['Paola Cascante-Bonilla', 'Donghyun Kim', 'Leonid Karlinsky', 'Shimon Ullman', 'Rameswar Panda', 'Rogerio Feris', 'Raja Giryes', 'Roei Herzig', 'Amit Alfassy', 'Sivan Harary', 'Assaf Arbelle', 'Sivan Doveh'] | 2023-05-31 | null | null | null | null | ['cross-modal-retrieval'] | ['miscellaneous'] | [ 2.95347929e-01 2.67505944e-02 -2.15755880e-01 -3.31820816e-01
-8.03153515e-01 -4.81141567e-01 1.01582849e+00 3.18527073e-01
-4.79015470e-01 3.33726555e-01 4.78658289e-01 -1.78732052e-01
1.14948131e-01 -4.53030556e-01 -8.94967735e-01 -6.81062818e-01
3.56937855e-01 4.77322757e-01 2.99681902e-01 -1.18720382... | [10.775211334228516, 1.651843786239624] |
72b836fe-b63e-4951-bbf4-5d83b4f5cc96 | convolutional-neural-network-architecture-for | 1703.05593 | null | http://arxiv.org/abs/1703.05593v2 | http://arxiv.org/pdf/1703.05593v2.pdf | Convolutional neural network architecture for geometric matching | We address the problem of determining correspondences between two images in
agreement with a geometric model such as an affine or thin-plate spline
transformation, and estimating its parameters. The contributions of this work
are three-fold. First, we propose a convolutional neural network architecture
for geometric ma... | ['Relja Arandjelović', 'Ignacio Rocco', 'Josef Sivic'] | 2017-03-16 | convolutional-neural-network-architecture-for-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Rocco_Convolutional_Neural_Network_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Rocco_Convolutional_Neural_Network_CVPR_2017_paper.pdf | cvpr-2017-7 | ['geometric-matching'] | ['computer-vision'] | [ 2.09408119e-01 -1.83065999e-02 4.95155230e-02 -4.98344362e-01
-8.44499409e-01 -6.36606932e-01 5.70235491e-01 -1.70145005e-01
-4.02251959e-01 3.18033010e-01 -9.50062945e-02 -1.08225979e-01
1.62087251e-02 -5.32849908e-01 -8.88626397e-01 -5.66722266e-02
-2.34014496e-01 6.27784669e-01 3.13743353e-01 -1.04019143... | [8.533690452575684, -2.093501567840576] |
ccecc160-66c4-463b-9aed-80d7b3fba5fd | indoor-scene-recognition-in-3d | 2002.12819 | null | https://arxiv.org/abs/2002.12819v2 | https://arxiv.org/pdf/2002.12819v2.pdf | Indoor Scene Recognition in 3D | Recognising in what type of environment one is located is an important perception task. For instance, for a robot operating in indoors it is helpful to be aware whether it is in a kitchen, a hallway or a bedroom. Existing approaches attempt to classify the scene based on 2D images or 2.5D range images. Here, we study s... | ['Mikhail Usvyatsov', 'Shengyu Huang', 'Konrad Schindler'] | 2020-02-28 | null | null | null | null | ['scene-recognition'] | ['computer-vision'] | [ 5.03400922e-01 -4.28548068e-01 1.65576816e-01 -4.50405985e-01
-2.47062579e-01 -7.24224567e-01 5.18415034e-01 6.06896460e-01
-4.04047459e-01 2.10054427e-01 -1.37336493e-01 -2.38279641e-01
-3.42958897e-01 -7.12709665e-01 -6.13735497e-01 -8.14669847e-01
1.33782759e-01 7.57141948e-01 1.87711298e-01 -9.16100740... | [8.203076362609863, -2.3267805576324463] |
b4b41153-4f8c-4b22-b65a-0f92f4e7332f | beyond-planar-symmetry-modeling-human | 1704.03568 | null | http://arxiv.org/abs/1704.03568v2 | http://arxiv.org/pdf/1704.03568v2.pdf | Beyond Planar Symmetry: Modeling human perception of reflection and rotation symmetries in the wild | Humans take advantage of real world symmetries for various tasks, yet
capturing their superb symmetry perception mechanism with a computational model
remains elusive. Motivated by a new study demonstrating the extremely high
inter-person accuracy of human perceived symmetries in the wild, we have
constructed the first ... | ['Yanxi Liu', 'Christopher Funk'] | 2017-04-11 | beyond-planar-symmetry-modeling-human-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Funk_Beyond_Planar_Symmetry_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Funk_Beyond_Planar_Symmetry_ICCV_2017_paper.pdf | iccv-2017-10 | ['symmetry-detection'] | ['computer-vision'] | [ 1.84539735e-01 6.02318831e-02 1.61286205e-01 -5.90858042e-01
-4.09789205e-01 -8.32791269e-01 9.42098796e-01 -4.08480376e-01
6.17108569e-02 -7.60763586e-02 5.75882971e-01 1.85218096e-01
-3.60410601e-01 -4.13636714e-01 -6.23338997e-01 -1.61721393e-01
-7.14952499e-03 9.05530751e-01 2.91876607e-02 -2.09549397... | [8.624198913574219, -2.051072597503662] |
a7bafb69-e9f9-46a5-82e9-44b8d687029a | your-room-is-not-private-gradient-inversion | 2306.09273 | null | https://arxiv.org/abs/2306.09273v1 | https://arxiv.org/pdf/2306.09273v1.pdf | Your Room is not Private: Gradient Inversion Attack for Deep Q-Learning | The prominence of embodied Artificial Intelligence (AI), which empowers robots to navigate, perceive, and engage within virtual environments, has attracted significant attention, owing to the remarkable advancements in computer vision and large language models. Privacy emerges as a pivotal concern within the realm of e... | ['Ding Zhao', 'Wenhao Ding', 'Miao Li'] | 2023-06-15 | null | null | null | null | ['q-learning', 'navigate'] | ['methodology', 'reasoning'] | [ 3.31606656e-01 3.30744743e-01 1.50941312e-01 -4.03350204e-01
-7.54425049e-01 -6.40108466e-01 4.39578891e-01 1.63201198e-01
-8.77681315e-01 7.95648754e-01 -1.65123846e-02 -3.47099900e-01
-8.47465545e-02 -7.20115840e-01 -8.27269733e-01 -9.64597344e-01
-2.83377916e-01 -1.22619025e-01 -4.79577422e-01 -2.02040777... | [5.831775188446045, 6.789862632751465] |
d7abb4c6-cec2-4eb2-871a-a5ca1adcb1b9 | multimodal-emotion-recognition-using-transfer | 2202.08974 | null | https://arxiv.org/abs/2202.08974v1 | https://arxiv.org/pdf/2202.08974v1.pdf | Multimodal Emotion Recognition using Transfer Learning from Speaker Recognition and BERT-based models | Automatic emotion recognition plays a key role in computer-human interaction as it has the potential to enrich the next-generation artificial intelligence with emotional intelligence. It finds applications in customer and/or representative behavior analysis in call centers, gaming, personal assistants, and social robot... | ['Ram D. Sriram', 'Dinesh Manocha', 'Seyed Omid Sadjadi', 'Sarala Padi'] | 2022-02-16 | null | null | null | null | ['multimodal-emotion-recognition', 'emotional-intelligence', 'multimodal-emotion-recognition'] | ['computer-vision', 'natural-language-processing', 'speech'] | [ 5.29676601e-02 -2.87927061e-01 1.04846261e-01 -5.47118366e-01
-7.28288412e-01 -1.89697102e-01 3.01338017e-01 -2.59743869e-01
-5.35019577e-01 4.75715965e-01 3.01758319e-01 1.48329765e-01
-1.85197741e-01 -3.12408209e-01 -4.09907281e-01 -7.69890308e-01
8.85975957e-02 2.52798855e-01 -3.52006704e-01 -5.26165724... | [13.346765518188477, 5.295775890350342] |
bd4f9c35-b372-48b4-963b-cf71eb3b122c | unrealcv-connecting-computer-vision-to-unreal | 1609.01326 | null | http://arxiv.org/abs/1609.01326v1 | http://arxiv.org/pdf/1609.01326v1.pdf | UnrealCV: Connecting Computer Vision to Unreal Engine | Computer graphics can not only generate synthetic images and ground truth but
it also offers the possibility of constructing virtual worlds in which: (i) an
agent can perceive, navigate, and take actions guided by AI algorithms, (ii)
properties of the worlds can be modified (e.g., material and reflectance),
(iii) physi... | ['Weichao Qiu', 'Alan Yuille'] | 2016-09-05 | null | null | null | null | ['physical-simulations'] | ['miscellaneous'] | [-2.58539975e-01 2.33870372e-01 5.68210900e-01 -8.61627609e-02
-4.51846421e-02 -7.66294122e-01 7.17572033e-01 -3.24970096e-01
-2.21698165e-01 7.44516730e-01 -1.00909606e-01 -4.41799551e-01
3.70864242e-01 -1.39184129e+00 -7.20143914e-01 -3.44814241e-01
-1.34946123e-01 4.68816549e-01 8.12409997e-01 -4.16143626... | [4.589593410491943, 0.5154894590377808] |
2b20dc87-a38e-47b4-b030-917e9def0cf7 | revisiting-dense-retrieval-with-unanswerable | 2304.03031 | null | https://arxiv.org/abs/2304.03031v5 | https://arxiv.org/pdf/2304.03031v5.pdf | Revisiting Dense Retrieval with Unanswerable Counterfactuals | The retriever-reader framework is popular for open-domain question answering (ODQA), where a retriever samples for the reader a set of relevant candidate passages from a large corpus. A key assumption behind this method is that high relevance scores from the retriever likely indicate high answerability from the reader,... | ['Jinyeong Yeo', 'Kyungjae Lee', 'Dahyun Lee', 'Yongho Song'] | 2023-04-06 | null | null | null | null | ['passage-retrieval', 'open-domain-question-answering'] | ['natural-language-processing', 'natural-language-processing'] | [-1.05335668e-01 1.06355637e-01 -2.69477934e-01 -5.20434231e-02
-1.78606367e+00 -8.37658644e-01 9.22934532e-01 4.88433897e-01
-4.45725828e-01 7.45251417e-01 9.77971613e-01 -3.04310411e-01
-4.53338116e-01 -1.03630483e+00 -8.63838851e-01 -2.65799791e-01
1.72558039e-01 7.26870000e-01 3.17548096e-01 -6.43737137... | [11.427091598510742, 7.756728649139404] |
c2ddec18-69c8-47c2-84c6-87b0a3a423d4 | multi-label-learning-with-missing-values | 2008.07234 | null | https://arxiv.org/abs/2008.07234v1 | https://arxiv.org/pdf/2008.07234v1.pdf | Multi-label Learning with Missing Values using Combined Facial Action Unit Datasets | Facial action units allow an objective, standardized description of facial micro movements which can be used to describe emotions in human faces. Annotating data for action units is an expensive and time-consuming task, which leads to a scarce data situation. By combining multiple datasets from different studies, the a... | ['Jaspar Pahl', 'Dominik Seuss', 'Ines Rieger'] | 2020-08-17 | null | null | null | null | ['action-unit-detection'] | ['computer-vision'] | [ 4.87364858e-01 1.10318139e-01 -3.85181367e-01 -5.95645785e-01
-8.77258658e-01 -6.12740636e-01 3.91829103e-01 -2.83806268e-02
-3.24792534e-01 7.55504549e-01 1.35819465e-01 4.36499208e-01
4.72829700e-01 -2.87607372e-01 -2.89831191e-01 -8.29737425e-01
1.69016853e-01 4.65849578e-01 6.94484189e-02 -3.87056284... | [13.571832656860352, 1.7895984649658203] |
e12e1459-a52c-4632-ae38-095bd265ff5b | the-adapter-bot-all-in-one-controllable | 2008.12579 | null | https://arxiv.org/abs/2008.12579v2 | https://arxiv.org/pdf/2008.12579v2.pdf | The Adapter-Bot: All-In-One Controllable Conversational Model | Considerable progress has been made towards conversational models that generate coherent and fluent responses by training large language models on large dialogue datasets. These models have little or no control of the generated responses and miss two important features: continuous dialogue skills integration and seamle... | ['Pascale Fung', 'Yejin Bang', 'Andrea Madotto', 'Zhaojiang Lin'] | 2020-08-28 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [-1.49322018e-01 4.68544990e-01 1.54289648e-01 -3.50049198e-01
-4.37510759e-01 -1.12838817e+00 8.94201159e-01 7.07207397e-02
-3.47741097e-01 8.37688148e-01 6.46528840e-01 -3.76367986e-01
6.89817145e-02 -1.03494918e+00 -2.00178444e-01 -1.77905008e-01
2.73109972e-01 9.47277188e-01 4.94029820e-01 -8.35359931... | [12.795272827148438, 8.075533866882324] |
5d41a9e0-eda1-432b-a751-3ae8203ddf09 | incomplete-multimodal-learning-for-complex | 2305.16222 | null | https://arxiv.org/abs/2305.16222v1 | https://arxiv.org/pdf/2305.16222v1.pdf | Incomplete Multimodal Learning for Complex Brain Disorders Prediction | Recent advancements in the acquisition of various brain data sources have created new opportunities for integrating multimodal brain data to assist in early detection of complex brain disorders. However, current data integration approaches typically need a complete set of biomedical data modalities, which may not alway... | ['Paul M. Thompson', 'Li Shen', 'Heng Huang', 'Liang Zhan', 'Reza Shirkavand'] | 2023-05-25 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [ 3.27210486e-01 -2.40191668e-02 -1.03279296e-02 -5.93011320e-01
-8.49842250e-01 -3.80290419e-01 3.57741475e-01 -8.37063566e-02
-5.57755828e-01 9.93499637e-01 5.89974038e-02 -4.53515530e-01
-1.86427772e-01 -7.49229193e-01 -6.93640530e-01 -6.01367533e-01
-2.22665295e-02 6.52219534e-01 -1.54989481e-01 8.13076049... | [14.231600761413574, -1.6506675481796265] |
c751600b-5b45-4235-a8ae-8c1dfab8a83b | beyond-classification-financial-reasoning-in | 2305.01505 | null | https://arxiv.org/abs/2305.01505v2 | https://arxiv.org/pdf/2305.01505v2.pdf | Beyond Classification: Financial Reasoning in State-of-the-Art Language Models | Large Language Models (LLMs), consisting of 100 billion or more parameters, have demonstrated remarkable ability in complex multi-step reasoning tasks. However, the application of such generic advancements has been limited to a few fields, such as clinical or legal, with the field of financial reasoning remaining large... | ['Sol Jin', 'Keonju Na', 'Moonjeong Hahm', 'Hanearl Jung', 'Guijin Son'] | 2023-04-30 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [-3.56589779e-02 4.39765304e-01 -5.02041221e-01 -3.80945683e-01
-8.02264810e-01 -6.88626885e-01 4.38361347e-01 2.60250479e-01
-2.30013713e-01 7.51435995e-01 1.52149811e-01 -1.09906554e+00
-4.00080681e-01 -9.29893851e-01 -5.45428038e-01 -2.46461451e-01
2.39507839e-01 8.00826609e-01 -1.45522803e-01 -3.83628458... | [10.480300903320312, 7.925762176513672] |
11310cba-59bb-4fc9-bd89-57746b214e29 | performance-efficiency-trade-offs-in | 2109.06870 | null | https://arxiv.org/abs/2109.06870v1 | https://arxiv.org/pdf/2109.06870v1.pdf | Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition | This paper is a study of performance-efficiency trade-offs in pre-trained models for automatic speech recognition (ASR). We focus on wav2vec 2.0, and formalize several architecture designs that influence both the model performance and its efficiency. Putting together all our observations, we introduce SEW (Squeezed and... | ['Yoav Artzi', 'Kilian Q. Weinberger', 'Kyu Han', 'Jing Pan', 'Kwangyoun Kim', 'Felix Wu'] | 2021-09-14 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [-1.41913608e-01 2.50767358e-02 6.41196361e-03 -4.71091926e-01
-9.61048543e-01 -4.04000193e-01 5.10209441e-01 -3.73359695e-02
-7.09331453e-01 3.34592074e-01 7.16593981e-01 -1.07263041e+00
2.86882013e-01 -3.03540856e-01 -4.35726672e-01 -2.73714602e-01
6.48091035e-03 5.62036693e-01 9.03714597e-02 -4.12255496... | [14.35218334197998, 6.678980827331543] |
a81f68f4-1123-42ab-95fd-a12f2425b1ab | parallel-scale-wise-attention-network-for | 2104.12076 | null | https://arxiv.org/abs/2104.12076v1 | https://arxiv.org/pdf/2104.12076v1.pdf | Parallel Scale-wise Attention Network for Effective Scene Text Recognition | The paper proposes a new text recognition network for scene-text images. Many state-of-the-art methods employ the attention mechanism either in the text encoder or decoder for the text alignment. Although the encoder-based attention yields promising results, these schemes inherit noticeable limitations. They perform th... | ['Guanghui Wang', 'Taejoon Kim', 'Jin Zhang', 'Michael Chow', 'Usman Sajid'] | 2021-04-25 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 3.26968879e-01 -4.74797726e-01 8.49813148e-02 -3.74458879e-01
-8.38787317e-01 -9.34266225e-02 7.97541320e-01 -1.13565013e-01
-5.26886165e-01 2.60229349e-01 2.90082425e-01 -2.42263842e-02
2.44568422e-01 -4.24246430e-01 -6.75504565e-01 -7.37680078e-01
7.97836244e-01 2.11640075e-01 2.46676117e-01 -7.18211243... | [11.866340637207031, 2.144838809967041] |
f04d0d26-69b6-456f-8d07-e803789d634c | learnable-multi-level-frequency-decomposition | 2109.07950 | null | https://arxiv.org/abs/2109.07950v3 | https://arxiv.org/pdf/2109.07950v3.pdf | Learnable Multi-level Frequency Decomposition and Hierarchical Attention Mechanism for Generalized Face Presentation Attack Detection | With the increased deployment of face recognition systems in our daily lives, face presentation attack detection (PAD) is attracting much attention and playing a key role in securing face recognition systems. Despite the great performance achieved by the hand-crafted and deep-learning-based methods in intra-dataset eva... | ['Arjan Kuijper', 'Florian Kirchbuchner', 'Naser Damer', 'Meiling Fang'] | 2021-09-16 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 2.79151589e-01 -2.36890450e-01 -9.59587283e-03 -4.08951163e-01
-3.99143010e-01 -4.87925351e-01 4.98472244e-01 -1.67258278e-01
-4.69393075e-01 2.51417190e-01 -1.15058914e-01 -9.47450846e-02
-2.49529094e-01 -6.67984545e-01 -7.37655103e-01 -7.01179147e-01
-2.58826673e-01 -2.17085943e-01 9.55646783e-02 -2.17758834... | [13.097590446472168, 0.8885894417762756] |
1f560371-e91a-4053-acbd-3e3abbb76eaf | variance-dependent-regret-bounds-for-linear | 2302.10371 | null | https://arxiv.org/abs/2302.10371v1 | https://arxiv.org/pdf/2302.10371v1.pdf | Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency | Recently, several studies (Zhou et al., 2021a; Zhang et al., 2021b; Kim et al., 2021; Zhou and Gu, 2022) have provided variance-dependent regret bounds for linear contextual bandits, which interpolates the regret for the worst-case regime and the deterministic reward regime. However, these algorithms are either computa... | ['Quanquan Gu', 'Tong Zhang', 'Dongruo Zhou', 'Jiafan He', 'Heyang Zhao'] | 2023-02-21 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 4.21365872e-02 7.28436708e-02 -5.34080803e-01 -2.88940161e-01
-1.30604053e+00 -5.87632656e-01 1.47844478e-01 1.93061437e-02
-6.78941667e-01 1.17489398e+00 -2.20670074e-01 -8.25709224e-01
-8.28527331e-01 -7.94523239e-01 -8.05409849e-01 -9.92721617e-01
-2.47588217e-01 4.88337606e-01 -1.34967407e-02 1.26929432... | [4.470541477203369, 3.1813783645629883] |
7e653ce8-fd90-4f1e-a772-5a43500df10d | memonet-memorizing-representations-of-all | 2211.01334 | null | https://arxiv.org/abs/2211.01334v2 | https://arxiv.org/pdf/2211.01334v2.pdf | MemoNet:Memorizing Representations of All Cross Features Efficiently via Multi-Hash Codebook Network for CTR Prediction | New findings in natural language processing(NLP) demonstrate that the strong memorization capability contributes a lot to the success of large language models.This inspires us to explicitly bring an independent memory mechanism into CTR ranking model to learn and memorize all cross features'representations. In this pap... | ['Junlin Zhang', 'PengTao Zhang'] | 2022-10-25 | null | null | null | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [-4.45616633e-01 -2.25905269e-01 -1.91234201e-01 -1.80562809e-01
-3.65816444e-01 -3.69558781e-01 7.26141989e-01 2.27812544e-01
-7.08713710e-01 4.81481135e-01 4.08493906e-01 -2.07613975e-01
-9.13360640e-02 -1.14955056e+00 -5.40149927e-01 -6.11765563e-01
1.83929980e-01 3.36105347e-01 6.45287931e-01 -5.58894336... | [11.02379035949707, 8.364947319030762] |
0fe42637-038c-45b1-a0b0-1b79a2f2844d | glass-global-to-local-attention-for-scene | 2208.03364 | null | https://arxiv.org/abs/2208.03364v1 | https://arxiv.org/pdf/2208.03364v1.pdf | GLASS: Global to Local Attention for Scene-Text Spotting | In recent years, the dominant paradigm for text spotting is to combine the tasks of text detection and recognition into a single end-to-end framework. Under this paradigm, both tasks are accomplished by operating over a shared global feature map extracted from the input image. Among the main challenges that end-to-end ... | ['R. Manmatha', 'Amir Markovitz', 'Inbal Lavi', 'Oron Anschel', 'Shahar Tsiper', 'Roi Ronen'] | 2022-08-05 | null | null | null | null | ['text-spotting'] | ['computer-vision'] | [ 7.06125259e-01 -6.78946793e-01 -1.28839344e-01 -2.00827360e-01
-8.97681653e-01 -5.64987540e-01 7.85872340e-01 7.35350624e-02
-5.53423822e-01 7.71561041e-02 3.55135739e-01 9.49738026e-02
2.10637361e-01 -4.49276149e-01 -5.03187656e-01 -6.76787674e-01
5.84096074e-01 6.98995069e-02 2.51410484e-01 -1.38671637... | [11.918320655822754, 2.2436444759368896] |
704d1284-a71d-475e-bada-6dcd15b477c2 | mi-segnet-mutual-information-based-us | 2303.12649 | null | https://arxiv.org/abs/2303.12649v1 | https://arxiv.org/pdf/2303.12649v1.pdf | MI-SegNet: Mutual Information-Based US Segmentation for Unseen Domain Generalization | Generalization capabilities of learning-based medical image segmentation across domains are currently limited by the performance degradation caused by the domain shift, particularly for ultrasound (US) imaging. The quality of US images heavily relies on carefully tuned acoustic parameters, which vary across sonographer... | ['Nassir Navab', 'Angelos Karlas', 'Reza Ghotbi', 'Ricarda Clarenbach', 'Zhongliang Jiang', 'Yuan Bi'] | 2023-03-22 | null | null | null | null | ['anatomy'] | ['miscellaneous'] | [ 6.16588950e-01 3.56046021e-01 -1.64000809e-01 -5.15118539e-01
-1.16413689e+00 -5.52441120e-01 2.14350000e-01 4.57049683e-02
-4.96069998e-01 3.73569995e-01 2.04510957e-01 -2.53648728e-01
-2.86491871e-01 -5.17062545e-01 -7.84787774e-01 -9.05157506e-01
-2.71442056e-01 1.56660497e-01 2.54132569e-01 9.19837505... | [14.621288299560547, -2.066981792449951] |
a2e7fb91-a7ec-4242-990e-d450710885c4 | not-enough-data-deep-learning-to-the-rescue | 1911.03118 | null | https://arxiv.org/abs/1911.03118v2 | https://arxiv.org/pdf/1911.03118v2.pdf | Not Enough Data? Deep Learning to the Rescue! | Based on recent advances in natural language modeling and those in text generation capabilities, we propose a novel data augmentation method for text classification tasks. We use a powerful pre-trained neural network model to artificially synthesize new labeled data for supervised learning. We mainly focus on cases wit... | ['Naama Tepper', 'Esther Goldbraich', 'Ateret Anaby-Tavor', 'George Kour', 'Segev Shlomov', 'Naama Zwerdling', 'Boaz Carmeli', 'Amir Kantor'] | 2019-11-08 | null | null | null | null | ['lambada'] | ['natural-language-processing'] | [ 7.30604768e-01 5.69721699e-01 -3.45633626e-01 -4.11226422e-01
-6.77460313e-01 -4.18979943e-01 9.71821547e-01 5.22306442e-01
-5.82834303e-01 9.15367544e-01 3.22184145e-01 -4.40791488e-01
6.07152224e-01 -9.35885191e-01 -5.63101053e-01 -3.80484313e-01
3.63625288e-01 7.53781557e-01 -2.09018111e-01 -4.70666498... | [10.872079849243164, 8.32581615447998] |
db906030-4956-4d21-bbd1-57a04204d241 | nilc-at-cwi-2018-exploring-feature | null | null | https://aclanthology.org/W18-0540 | https://aclanthology.org/W18-0540.pdf | NILC at CWI 2018: Exploring Feature Engineering and Feature Learning | This paper describes the results of NILC team at CWI 2018. We developed solutions following three approaches: (i) a feature engineering method using lexical, n-gram and psycholinguistic features, (ii) a shallow neural network method using only word embeddings, and (iii) a Long Short-Term Memory (LSTM) language model, w... | ['ro Borges', 'Nathan Hartmann', 'Le dos Santos'] | 2018-06-01 | null | null | null | ws-2018-6 | ['complex-word-identification'] | ['natural-language-processing'] | [-3.16267721e-02 1.56421121e-02 1.29367903e-01 -3.88733625e-01
-6.94338799e-01 -2.64922321e-01 6.86306059e-01 3.86982530e-01
-1.17142010e+00 5.28015733e-01 5.00542521e-01 -7.35271692e-01
-4.61052656e-02 -8.33932638e-01 -3.31873626e-01 -1.52229533e-01
8.97388626e-03 5.76801479e-01 -1.16254009e-01 -3.61374170... | [10.507673263549805, 10.323017120361328] |
cce88547-9c3a-4707-b699-b97c9b7263cd | abstractive-timeline-summarization | null | null | https://aclanthology.org/D19-5403 | https://aclanthology.org/D19-5403.pdf | Abstractive Timeline Summarization | Timeline summarization (TLS) automatically identifies key dates of major events and provides short descriptions of what happened on these dates. Previous approaches to TLS have focused on extractive methods. In contrast, we suggest an abstractive timeline summarization system. Our system is entirely unsupervised, which... | ['Katja Markert', 'Julius Steen'] | 2019-11-01 | null | null | null | ws-2019-11 | ['timeline-summarization'] | ['natural-language-processing'] | [ 2.11458534e-01 2.85911947e-01 -4.49739426e-01 -1.07881568e-01
-1.37658095e+00 -9.36376810e-01 8.90235066e-01 8.59057307e-01
-4.81990933e-01 9.53410923e-01 8.23802948e-01 -2.73389757e-01
-3.35049070e-02 -6.11120462e-01 -5.15331864e-01 -2.02556625e-01
-3.86122940e-03 7.98820436e-01 3.74497086e-01 -1.70494005... | [12.565291404724121, 9.568561553955078] |
448c490c-0fd7-4974-9d9e-422877dad6ec | how-to-turn-your-knowledge-graph-embeddings | 2305.15944 | null | https://arxiv.org/abs/2305.15944v1 | https://arxiv.org/pdf/2305.15944v1.pdf | How to Turn Your Knowledge Graph Embeddings into Generative Models via Probabilistic Circuits | Some of the most successful knowledge graph embedding (KGE) models for link prediction -- CP, RESCAL, TuckER, ComplEx -- can be interpreted as energy-based models. Under this perspective they are not amenable for exact maximum-likelihood estimation (MLE), sampling and struggle to integrate logical constraints. This wor... | ['Antonio Vergari', 'Robert Peharz', 'Nicola Di Mauro', 'Lorenzo Loconte'] | 2023-05-25 | null | null | null | null | ['graph-embedding', 'link-prediction', 'knowledge-graph-embedding', 'knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'graphs', 'graphs', 'graphs', 'methodology'] | [ 1.76350009e-02 9.61994469e-01 -6.12991095e-01 -7.06790686e-02
-4.41597998e-01 -6.94806695e-01 6.74482405e-01 1.83513135e-01
1.28477842e-01 9.36624348e-01 2.59958476e-01 -6.18004441e-01
-3.63904864e-01 -1.23739290e+00 -1.22009075e+00 -4.40682620e-01
-4.33967382e-01 8.47194791e-01 2.78907806e-01 2.85072532... | [8.647440910339355, 7.475470542907715] |
09039f11-6d03-4ba6-aa61-281955c0c278 | transferring-rich-deep-features-for-facial | 1803.07253 | null | http://arxiv.org/abs/1803.07253v1 | http://arxiv.org/pdf/1803.07253v1.pdf | Transferring Rich Deep Features for Facial Beauty Prediction | Feature extraction plays a significant part in computer vision tasks. In this
paper, we propose a method which transfers rich deep features from a pretrained
model on face verification task and feeds the features into Bayesian ridge
regression algorithm for facial beauty prediction. We leverage the deep neural
networks... | ['Lu Xu', 'Jinhai Xiang', 'Xiaohui Yuan'] | 2018-03-20 | null | null | null | null | ['facial-beauty-prediction'] | ['computer-vision'] | [-9.71135795e-02 -2.04952713e-02 -2.22607553e-01 -1.07793117e+00
-3.23060155e-01 5.78219332e-02 6.63127542e-01 -6.19867265e-01
1.29066664e-03 4.74809378e-01 1.31594449e-01 6.56519830e-02
-3.17846358e-01 -5.65381885e-01 -5.56040287e-01 -6.25720978e-01
6.74127694e-03 -1.83623478e-01 -4.90111709e-01 -2.31914908... | [13.369916915893555, 0.8413964509963989] |
36b206b0-f1b6-4093-9365-9e840aa0e421 | fast-global-registration | null | null | http://vladlen.info/papers/fast-global-registration.pdf | http://vladlen.info/papers/fast-global-registration.pdf | Fast Global Registration | We present an algorithm for fast global registration of partially overlapping 3D surfaces. The algorithm operates on candidate matches that cover the
surfaces. A single objective is optimized to align the surfaces and disable false
matches. The objective is defined densely over the surfaces and the optimization achie... | ['Vladlen Koltun', 'Jaesik Park', 'Qian-Yi Zhou'] | 2016-10-08 | null | null | null | eccv-2016-10 | ['point-cloud-registration'] | ['computer-vision'] | [ 4.04216439e-01 3.86538535e-01 4.73014742e-01 -9.66773629e-02
-1.27312148e+00 -2.83444017e-01 6.23016298e-01 5.00967622e-01
-3.30843627e-01 3.65211546e-01 -3.40178460e-01 2.08099231e-01
2.36673607e-03 -8.27781677e-01 -8.39798450e-01 -4.21693951e-01
-1.11669958e-01 1.26769412e+00 9.02402043e-01 -2.79534012... | [7.735663414001465, -2.9240684509277344] |
fd46d1cf-75d7-40ea-9ed2-6ca004a75063 | integral-probability-metrics-pac-bayes-bounds | 2207.00614 | null | https://arxiv.org/abs/2207.00614v8 | https://arxiv.org/pdf/2207.00614v8.pdf | Integral Probability Metrics PAC-Bayes Bounds | We present a PAC-Bayes-style generalization bound which enables the replacement of the KL-divergence with a variety of Integral Probability Metrics (IPM). We provide instances of this bound with the IPM being the total variation metric and the Wasserstein distance. A notable feature of the obtained bounds is that they ... | ['Ron Meir', 'Shay Moran', 'Baruch Epstein', 'Ron Amit'] | 2022-07-01 | null | null | null | null | ['style-generalization'] | ['computer-vision'] | [ 1.56921238e-01 -5.19637503e-02 -3.10151517e-01 -1.84108868e-01
-7.33654261e-01 -8.17651868e-01 6.29724085e-01 4.07674521e-01
-6.36412382e-01 9.31383312e-01 -2.85661697e-01 -2.72312850e-01
-4.93579000e-01 -7.58571148e-01 -5.11836886e-01 -1.00004494e+00
-4.43852127e-01 6.20911062e-01 6.78941369e-01 -1.08480595... | [7.352199554443359, 4.131488800048828] |
734371ad-7713-44b0-9911-cad86a5d831e | extract-and-attend-improving-entity | 2306.02242 | null | https://arxiv.org/abs/2306.02242v1 | https://arxiv.org/pdf/2306.02242v1.pdf | Extract and Attend: Improving Entity Translation in Neural Machine Translation | While Neural Machine Translation(NMT) has achieved great progress in recent years, it still suffers from inaccurate translation of entities (e.g., person/organization name, location), due to the lack of entity training instances. When we humans encounter an unknown entity during translation, we usually first look up in... | ['Tie-Yan Liu', 'Tao Qin', 'Xu Tan', 'Junliang Guo', 'Yichong Leng', 'Rui Wang', 'Zixin Zeng'] | 2023-06-04 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 3.93116891e-01 1.07564680e-01 -1.96318537e-01 -3.22283566e-01
-1.11259544e+00 -5.40341198e-01 4.51630145e-01 2.35083282e-01
-5.30326247e-01 9.96535480e-01 3.31625283e-01 -2.69207776e-01
4.42212820e-01 -8.70967865e-01 -9.06450510e-01 -3.19654107e-01
5.75909913e-01 6.14851058e-01 -1.64295226e-01 -2.87178993... | [11.661026000976562, 10.266135215759277] |
9938e3f4-9429-41a7-a607-0eac71b6af62 | a-hierarchical-subspace-model-for-language | 2011.03115 | null | https://arxiv.org/abs/2011.03115v2 | https://arxiv.org/pdf/2011.03115v2.pdf | A Hierarchical Subspace Model for Language-Attuned Acoustic Unit Discovery | In this work, we propose a hierarchical subspace model for acoustic unit discovery. In this approach, we frame the task as one of learning embeddings on a low-dimensional phonetic subspace, and simultaneously specify the subspace itself as an embedding on a hyper-subspace. We train the hyper-subspace on a set of transc... | ['Murat Saraclar', 'Jan Cernocky', 'Lukas Burget', 'Lucas Ondel', 'Bolaji Yusuf'] | 2020-11-04 | null | null | null | null | ['acoustic-unit-discovery'] | ['speech'] | [ 4.16837931e-02 1.42206997e-02 -3.24006349e-01 -3.12685519e-01
-9.95651245e-01 -9.18488145e-01 3.70783687e-01 -3.46669257e-01
-3.96706998e-01 2.40847185e-01 6.25246823e-01 -3.48277211e-01
3.79201323e-01 -3.81288439e-01 -7.73846984e-01 -7.56657302e-01
1.63491279e-01 9.26437020e-01 -7.05980733e-02 4.40633953... | [14.478325843811035, 6.642317771911621] |
02b228f4-3fbe-4e07-9d15-9ed6f4825d9f | cyclical-self-supervision-for-semi-supervised | 2210.11291 | null | https://arxiv.org/abs/2210.11291v2 | https://arxiv.org/pdf/2210.11291v2.pdf | Cyclical Self-Supervision for Semi-Supervised Ejection Fraction Prediction from Echocardiogram Videos | Left-ventricular ejection fraction (LVEF) is an important indicator of heart failure. Existing methods for LVEF estimation from video require large amounts of annotated data to achieve high performance, e.g. using 10,030 labeled echocardiogram videos to achieve mean absolute error (MAE) of 4.10. Labeling these videos i... | ['Kwang-Ting Cheng', 'Xinpeng Ding', 'Xiaomeng Li', 'Weihang Dai'] | 2022-10-20 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [ 1.03522837e-01 1.57458082e-01 -4.58699375e-01 -6.00241184e-01
-7.54445195e-01 -5.47620177e-01 -1.76953040e-02 -1.64940134e-01
-1.04594551e-01 8.00920606e-01 1.11027114e-01 -2.69317031e-01
3.54172975e-01 -4.03814048e-01 -6.63875401e-01 -5.16634643e-01
-1.56444609e-01 5.07947803e-01 2.16358528e-01 3.71689558... | [14.340490341186523, -2.2567079067230225] |
95d736ee-c7f1-4343-8403-9bfdc2f230f3 | graph-neural-networks-for-image | 2203.03457 | null | https://arxiv.org/abs/2203.03457v2 | https://arxiv.org/pdf/2203.03457v2.pdf | Graph Neural Networks for Image Classification and Reinforcement Learning using Graph representations | In this paper, we will evaluate the performance of graph neural networks in two distinct domains: computer vision and reinforcement learning. In the computer vision section, we seek to learn whether a novel non-redundant representation for images as graphs can improve performance over trivial pixel to node mapping on a... | ['David Steiner', 'Naman Goyal'] | 2022-03-07 | null | null | null | null | ['rubik-s-cube'] | ['graphs'] | [ 5.94683886e-01 8.33211124e-01 -2.10338384e-01 -3.50286961e-01
-3.62452060e-01 -2.65473634e-01 3.42492908e-01 2.71058261e-01
-1.85772315e-01 5.53062201e-01 -3.92714776e-02 -5.94602048e-01
-1.09505035e-01 -8.49541426e-01 -1.11571670e+00 -5.15612721e-01
-3.81232500e-01 3.67932618e-01 -1.57547474e-01 -4.67978865... | [6.951810836791992, 6.215753078460693] |
7165f2c2-ed2a-41d1-bc2a-6bc49a6a10ad | a-text-editing-approach-to-joint-japanese | null | null | https://aclanthology.org/2021.wnut-1.9 | https://aclanthology.org/2021.wnut-1.9.pdf | A Text Editing Approach to Joint Japanese Word Segmentation, POS Tagging, and Lexical Normalization | Lexical normalization, in addition to word segmentation and part-of-speech tagging, is a fundamental task for Japanese user-generated text processing. In this paper, we propose a text editing model to solve the three task jointly and methods of pseudo-labeled data generation to overcome the problem of data deficiency. ... | ['Eiichiro Sumita', 'Taro Watanabe', 'Masao Utiyama', 'Shohei Higashiyama'] | null | null | null | null | wnut-acl-2021-11 | ['lexical-normalization'] | ['natural-language-processing'] | [ 3.13601255e-01 -6.22954732e-03 -2.81130284e-01 -6.81295574e-01
-8.77933979e-01 -4.03097510e-01 1.71441957e-01 5.30081950e-02
-1.04655087e+00 1.08971906e+00 5.62400579e-01 -3.63304257e-01
5.60264349e-01 -5.25800586e-01 -5.55377938e-02 -3.07769746e-01
9.10590231e-01 4.86740023e-01 2.57849187e-01 -4.55816239... | [10.219980239868164, 10.090128898620605] |
95d4ee66-d66c-4c73-8931-6d9624424d92 | meta-learning-pathologies-from-radiology | 2210.13979 | null | https://arxiv.org/abs/2210.13979v2 | https://arxiv.org/pdf/2210.13979v2.pdf | Meta-learning Pathologies from Radiology Reports using Variance Aware Prototypical Networks | Large pretrained Transformer-based language models like BERT and GPT have changed the landscape of Natural Language Processing (NLP). However, fine tuning such models still requires a large number of training examples for each target task, thus annotating multiple datasets and training these models on various downstrea... | ['Benjamin Odry', 'Anasuya Das', 'Nabila Abraham', 'Kawshik Kannan', 'Arijit Sehanobish'] | 2022-10-22 | null | null | null | null | ['few-shot-text-classification'] | ['natural-language-processing'] | [-2.21742198e-01 1.58373490e-02 -3.19021761e-01 -7.18502045e-01
-8.09476912e-01 -6.03563368e-01 6.23424947e-01 5.57874262e-01
-5.62286556e-01 4.19803679e-01 1.22981519e-01 -3.44290942e-01
-6.06056675e-02 -7.32677042e-01 -4.44726944e-01 -5.16489685e-01
-3.94633785e-02 8.31894755e-01 3.65720898e-01 -3.09215933... | [10.4635648727417, 7.350512981414795] |
a9ae8ae6-cfac-40a6-8a9b-08ab35dc89d0 | yake-keyword-extraction-from-single-documents | null | null | https://repositorio.inesctec.pt/server/api/core/bitstreams/ef121a01-a0a6-4be8-945d-3324a58fc944/content | https://repositorio.inesctec.pt/server/api/core/bitstreams/ef121a01-a0a6-4be8-945d-3324a58fc944/content | YAKE! Keyword extraction from single documents using multiple local features | In this paper, we present YAKE!, a novel feature-based system for
multi-lingual keyword extraction from single documents, which supports texts
of different sizes, domains or languages. Unlike most systems, YAKE! does not
rely on dictionaries or thesauri, neither it is trained against any corpora. Instead,
we follow... | ['A. Jatowt', 'C. Nunes', 'A. Jorge', 'Arian Pasquali', 'Vítor Mangaravite', 'Ricardo Campos'] | 2018-03-01 | null | null | null | ecir-2018-2018-3 | ['keyword-extraction'] | ['natural-language-processing'] | [-3.57338399e-01 -1.55971006e-01 -2.53776401e-01 -1.85879514e-01
-9.60480213e-01 -9.34443116e-01 8.12479079e-01 2.96049058e-01
-6.70875072e-01 7.35597670e-01 1.52750790e-01 -6.19990468e-01
9.21302065e-02 -6.07757270e-01 -4.34749216e-01 -4.00909901e-01
3.29126626e-01 5.89458168e-01 2.65813291e-01 -4.58314598... | [10.203770637512207, 9.687365531921387] |
c7d0b6bb-8bf9-484a-94ba-cb180073628e | navigation-as-the-attacker-wishes-towards | 2211.14769 | null | https://arxiv.org/abs/2211.14769v3 | https://arxiv.org/pdf/2211.14769v3.pdf | Navigation as Attackers Wish? Towards Building Byzantine-Robust Embodied Agents under Federated Learning | Federated embodied agent learning protects the data privacy of individual visual environments by keeping data locally at each client (the individual environment) during training. However, since the local data is inaccessible to the server under federated learning, attackers may easily poison the training data of the lo... | ['Xin Eric Wang', 'Cihang Xie', 'Kaiwen Zhou', 'Zonglin Di', 'Yunchao Zhang'] | 2022-11-27 | null | null | null | null | ['vision-and-language-navigation'] | ['robots'] | [-3.42562586e-01 4.25563082e-02 2.41383567e-01 -1.71355128e-01
-5.92888415e-01 -1.32804060e+00 8.01934719e-01 -4.40205634e-01
-7.86874354e-01 2.41306692e-01 -2.28587419e-01 -6.96686029e-01
-8.26672316e-02 -7.49735177e-01 -9.63003039e-01 -1.20039809e+00
-4.59979564e-01 9.15872827e-02 1.38912931e-01 -1.81407735... | [5.698072910308838, 7.090514183044434] |
6c913da0-1c81-4f53-ad58-2cb7ff9e4d73 | adding-3d-geometry-control-to-diffusion | 2306.08103 | null | https://arxiv.org/abs/2306.08103v1 | https://arxiv.org/pdf/2306.08103v1.pdf | Adding 3D Geometry Control to Diffusion Models | Diffusion models have emerged as a powerful method of generative modeling across a range of fields, capable of producing stunning photo-realistic images from natural language descriptions. However, these models lack explicit control over the 3D structure of the objects in the generated images. In this paper, we propose... | ['Alan Yuille', 'Adam Kortylewski', 'Yaoyao Liu', 'Angtian Wang', 'Jiahao Wang', 'Qihao Liu', 'Wufei Ma'] | 2023-06-13 | null | null | null | null | ['pose-estimation', '3d-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-2.17905432e-01 3.10888216e-02 2.31841043e-01 -1.88911334e-01
-3.71487468e-01 -9.58231390e-01 8.47696126e-01 -3.84189159e-01
-1.31370947e-01 2.34649479e-01 1.87559444e-02 -1.65090159e-01
3.52527142e-01 -8.95267785e-01 -9.13765609e-01 -5.72943449e-01
2.86596328e-01 8.33087027e-01 5.35429120e-01 -3.16134572... | [9.288121223449707, -3.042825222015381] |
19c4f579-6a07-4491-88ac-d91e679c0c31 | a-hybrid-approach-for-learning-program | 1907.02136 | null | https://arxiv.org/abs/1907.02136v2 | https://arxiv.org/pdf/1907.02136v2.pdf | Learning Blended, Precise Semantic Program Embeddings | Learning neural program embeddings is key to utilizing deep neural networks in program languages research --- precise and efficient program representations enable the application of deep models to a wide range of program analysis tasks. Existing approaches predominately learn to embed programs from their source code, a... | ['Zhendong Su', 'Ke Wang'] | 2019-07-03 | null | null | null | null | ['method-name-prediction'] | ['natural-language-processing'] | [-2.10063964e-01 -1.54893667e-01 -9.12811935e-01 -4.07221675e-01
-4.59840417e-01 -5.05748510e-01 2.30511829e-01 4.96821612e-01
-3.43638271e-01 5.74491099e-02 3.01414430e-01 -9.14978862e-01
4.37051773e-01 -1.08158898e+00 -1.08477354e+00 -9.00634155e-02
-2.48451605e-01 8.46660510e-02 2.29123216e-02 -2.64020652... | [7.469923496246338, 7.803621292114258] |
6eacd036-1b55-4854-86fb-3f2f0f08aade | cross-paradigm-pretraining-of-convolutional | 1806.09532 | null | http://arxiv.org/abs/1806.09532v2 | http://arxiv.org/pdf/1806.09532v2.pdf | Cross-paradigm pretraining of convolutional networks improves intracranial EEG decoding | When it comes to the classification of brain signals in real-life
applications, the training and the prediction data are often described by
different distributions. Furthermore, diverse data sets, e.g., recorded from
various subjects or tasks, can even exhibit distinct feature spaces. The fact
that data that have to be... | [] | 2018-07-20 | null | null | null | null | ['eeg-decoding', 'eeg-decoding'] | ['medical', 'time-series'] | [ 3.53490412e-01 -3.04919444e-02 3.60000283e-01 -4.68072146e-01
-5.75367808e-01 -2.22558200e-01 4.34251904e-01 2.45598584e-01
-7.95494914e-01 1.17660677e+00 -3.62099379e-01 -1.31477267e-01
-5.14378607e-01 -7.02842891e-01 -7.83894360e-01 -7.78777719e-01
-4.82127517e-01 7.68263713e-02 1.19861346e-02 -2.13687003... | [13.068241119384766, 3.432602643966675] |
b12f85d8-30b2-424b-873e-414df891533f | molecular-dynamics-simulations-reveal-the | 1808.08375 | null | http://arxiv.org/abs/1808.08375v1 | http://arxiv.org/pdf/1808.08375v1.pdf | Molecular dynamics simulations reveal the role of ceramicine B as novel PPAR{\gamma} partial agonist against type 2 diabetes | Peroxisome proliferator-activated receptors gamma (PPAR{\gamma}) are
ligand-activated controllers of various metabolic actions and insulin
sensitivity. PPAR{\gamma} is thus considered as an important target to treat
type 2 diabetes. Available PPAR{\gamma} drugs (full agonists) have robust
insulin-sensitizing properties... | [] | 2018-08-25 | null | null | null | null | ['molecular-docking'] | ['medical'] | [ 4.27158743e-01 -4.04195517e-01 -7.10927367e-01 -8.80741999e-02
-5.38894892e-01 -6.44781351e-01 5.92255220e-02 4.92685884e-01
-3.39606255e-01 1.19533300e+00 2.46008441e-01 -5.62589943e-01
8.91143084e-02 -5.85286021e-01 -5.25806069e-01 -1.14631355e+00
-1.23738974e-01 3.19063991e-01 -3.59754893e-03 -8.09501484... | [4.702845573425293, 5.105762958526611] |
89c4e303-d7f4-4181-b737-22a2bdb482a1 | knowledge-based-review-generation-by | 2105.03815 | null | https://arxiv.org/abs/2105.03815v1 | https://arxiv.org/pdf/2105.03815v1.pdf | Knowledge-based Review Generation by Coherence Enhanced Text Planning | As a natural language generation task, it is challenging to generate informative and coherent review text. In order to enhance the informativeness of the generated text, existing solutions typically learn to copy entities or triples from knowledge graphs (KGs). However, they lack overall consideration to select and arr... | ['Ji-Rong Wen', 'Nicholas Jing Yuan', 'Zhicheng Wei', 'Wayne Xin Zhao', 'Junyi Li'] | 2021-05-09 | null | null | null | null | ['review-generation'] | ['natural-language-processing'] | [ 2.58812726e-01 1.04189730e+00 -3.65609020e-01 -4.55446333e-01
-5.80556571e-01 -1.55026495e-01 8.15595567e-01 2.52718747e-01
1.50580645e-01 9.97002542e-01 9.24497128e-01 -7.12672621e-02
-6.42739758e-02 -1.12909877e+00 -8.57681334e-01 -2.81432569e-01
3.14376950e-01 3.55485797e-01 1.15473501e-01 -3.80586445... | [11.910406112670898, 8.908956527709961] |
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