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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 4 5 Hits 1 – 20 of 93 1 XTREME-S: Evaluating Cross-lingual Speech Representations ... Conneau, Alexis; Bapna, Ankur; Zhang, Yu. - : arXiv, 2022 BASE Show details 2 Self-supervised Learning with Random-projection Quantizer for Speech Recognition ... Chiu, Chung-Cheng; Qin, James; Zhang, Yu. - : arXiv, 2022 BASE Show details 3 Unsupervised Data Selection via Discrete Speech Representation for ASR ... Lu, Zhiyun; Wang, Yongqiang; Zhang, Yu. - : arXiv, 2022 BASE Show details 4 mSLAM: Massively multilingual joint pre-training for speech and text ... Bapna, Ankur; Cherry, Colin; Zhang, Yu. - : arXiv, 2022 BASE Show details 5 Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations ... Meng, Yu; Zhang, Yunyi; Huang, Jiaxin. - : arXiv, 2022 BASE Show details 6 MAESTRO: Matched Speech Text Representations through Modality Matching ... Chen, Zhehuai; Zhang, Yu; Rosenberg, Andrew. - : arXiv, 2022 BASE Show details 7 LPInsider: a webserver for lncRNA–protein interaction extraction from the literature Li, Ying; Wei, Lizheng; Wang, Cankun... In: BMC Bioinformatics (2022) BASE Show details 8 Observation of new excited ${B} ^0_{s} $ states Aaij, Roel; Abellán Beteta, Carlos; Ackernley, Thomas... In: Eur.Phys.J.C ; https://hal.archives-ouvertes.fr/hal-03010999 ; Eur.Phys.J.C, 2021, 81 (7), pp.601. ⟨10.1140/epjc/s10052-021-09305-3⟩ (2021) BASE Show details 9 Parental use of relational language with 3-year-olds in math and spatial activities: A cross-cultural perspective Zhang, Yu. - : eScholarship, University of California, 2021 BASE Show details 10 Joint Unsupervised and Supervised Training for Multilingual ASR ... Bai, Junwen; Li, Bo; Zhang, Yu. - : arXiv, 2021 BASE Show details 11 Scaling End-to-End Models for Large-Scale Multilingual ASR ... Li, Bo; Pang, Ruoming; Sainath, Tara N.. - : arXiv, 2021 BASE Show details 12 Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Han, Jiawei; Huang, Jiaxin. - : Underline Science Inc., 2021 BASE Show details 13 Improving Confidence Estimation on Out-of-Domain Data for End-to-End Speech Recognition ... Li, Qiujia; Zhang, Yu; Qiu, David. - : arXiv, 2021 BASE Show details 14 Injecting Text in Self-Supervised Speech Pretraining ... Chen, Zhehuai; Zhang, Yu; Rosenberg, Andrew. - : arXiv, 2021 BASE Show details 15 Book Review: Data Collection Research Methods in Applied Linguistics Zhang, Yu In: Front Psychol (2021) BASE Show details 16 Observation of new excited ${B} ^0_{s} $ states Aaij, Roel; Abellán Beteta, Carlos; Ackernley, Thomas. - 2020 BASE Show details 17 Speech Sentiment Annotations Chen, Eric Y.; Lu, Zhiyun; Xu, Hao. - : Linguistic Data Consortium, 2020. : https://www.ldc.upenn.edu, 2020 BASE Show details 18 Large-scale multilingual audio visual dubbing ... Yang, Yi; Shillingford, Brendan; Assael, Yannis. - : arXiv, 2020 BASE Show details 19 Offline Handwritten Chinese Text Recognition with Convolutional Neural Networks ... Liu, Brian; Xu, Xianchao; Zhang, Yu. - : arXiv, 2020 Abstract: Deep learning based methods have been dominating the text recognition tasks in different and multilingual scenarios. The offline handwritten Chinese text recognition (HCTR) is one of the most challenging tasks because it involves thousands of characters, variant writing styles and complex data collection process. Recently, the recurrent-free architectures for text recognition appears to be competitive as its highly parallelism and comparable results. In this paper, we build the models using only the convolutional neural networks and use CTC as the loss function. To reduce the overfitting, we apply dropout after each max-pooling layer and with extreme high rate on the last one before the linear layer. The CASIA-HWDB database is selected to tune and evaluate the proposed models. With the existing text samples as templates, we randomly choose isolated character samples to synthesis more text samples for training. We finally achieve 6.81% character error rate (CER) on the ICDAR 2013 competition set, which is the ... : 6 pages, 5 figures, and 3 tables ... Keyword: Computer Vision and Pattern Recognition cs.CV; FOS Computer and information sciences URL: https://arxiv.org/abs/2006.15619 https://dx.doi.org/10.48550/arxiv.2006.15619 BASE Hide details 20 Is POS Tagging Necessary or Even Helpful for Neural Dependency Parsing? ... Zhou, Houquan; Zhang, Yu; Li, Zhenghua. - : arXiv, 2020 BASE Show details Page: 1 2 3 4 5

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