no code implementations • 25 Jul 2023 • Ying Xiao, Hou-Biao Li, Yu-pu Zhang
In this paper, we address these problems by proposing a data-driven Bregman divergence parameter optimization clustering algorithm (DBGSA), which combines the Universal Gravitational Algorithm to bring similar points closer in the dataset.
no code implementations • 26 Jun 2023 • Yuanzheng Ma, Xinyue Wang, Benqi Zhao, Ying Xiao, Shijie Deng, Jian Song, Xun Guan
Deep learning-based super-resolution models have the potential to revolutionize biomedical imaging and diagnoses by effectively tackling various challenges associated with early detection, personalized medicine, and clinical automation.
no code implementations • 23 May 2023 • Ying Xiao, Shangwen Wang, Sicen Liu, Dingyuan Xue, Xian Zhan, Yepang Liu
The effectiveness and efficiency of these systems heavily depend on the quality of the training datasets.
no code implementations • 18 Mar 2023 • David A. Clunie, Adam Flanders, Adam Taylor, Brad Erickson, Brian Bialecki, David Brundage, David Gutman, Fred Prior, J Anthony Seibert, John Perry, Judy Wawira Gichoya, Justin Kirby, Katherine Andriole, Luke Geneslaw, Steve Moore, TJ Fitzgerald, Wyatt Tellis, Ying Xiao, Keyvan Farahani
Only technical issues of public sharing are addressed.
no code implementations • 12 May 2022 • Ahmed El-Kishky, Thomas Markovich, Kenny Leung, Frank Portman, Aria Haghighi, Ying Xiao
To this end, we introduce kNN-Embed, a general approach to improving diversity in dense ANN-based retrieval.
no code implementations • ICCV 2019 • Siyang Qin, Alessandro Bissacco, Michalis Raptis, Yasuhisa Fujii, Ying Xiao
We propose an end-to-end trainable network that can simultaneously detect and recognize text of arbitrary shape, making substantial progress on the open problem of reading scene text of irregular shape.
Instance Segmentation Optical Character Recognition (OCR) +3
no code implementations • 28 May 2019 • Behrooz Ghorbani, Ying Xiao, Shankar Krishnan
It is well-known that deeper neural networks are harder to train than shallower ones.
1 code implementation • 29 Jan 2019 • Behrooz Ghorbani, Shankar Krishnan, Ying Xiao
To understand the dynamics of optimization in deep neural networks, we develop a tool to study the evolution of the entire Hessian spectrum throughout the optimization process.
no code implementations • 5 Jan 2019 • Hongming Li, Pamela Boimel, James Janopaul-Naylor, Haoyu Zhong, Ying Xiao, Edgar Ben-Josef, Yong Fan
To improve existing survival analysis techniques whose performance is hinged on imaging features, we propose a deep learning method to build survival regression models by optimizing imaging features with deep convolutional neural networks (CNNs) in a proportional hazards model.
2 code implementations • ICML 2018 • RJ Skerry-Ryan, Eric Battenberg, Ying Xiao, Yuxuan Wang, Daisy Stanton, Joel Shor, Ron J. Weiss, Rob Clark, Rif A. Saurous
We present an extension to the Tacotron speech synthesis architecture that learns a latent embedding space of prosody, derived from a reference acoustic representation containing the desired prosody.
11 code implementations • ICML 2018 • Yuxuan Wang, Daisy Stanton, Yu Zhang, RJ Skerry-Ryan, Eric Battenberg, Joel Shor, Ying Xiao, Fei Ren, Ye Jia, Rif A. Saurous
In this work, we propose "global style tokens" (GSTs), a bank of embeddings that are jointly trained within Tacotron, a state-of-the-art end-to-end speech synthesis system.
no code implementations • ICLR 2018 • Shankar Krishnan, Ying Xiao, Rif A. Saurous
We demonstrate the effectiveness of our algorithm by successfully training large ImageNet models (Inception-V3, Resnet-50, Resnet-101 and Inception-Resnet-V2) with mini-batch sizes of up to 32000 with no loss in validation error relative to current baselines, and no increase in the total number of steps.
no code implementations • 1 Nov 2017 • Yuxuan Wang, RJ Skerry-Ryan, Ying Xiao, Daisy Stanton, Joel Shor, Eric Battenberg, Rob Clark, Rif A. Saurous
Prosodic modeling is a core problem in speech synthesis.
29 code implementations • 29 Mar 2017 • Yuxuan Wang, RJ Skerry-Ryan, Daisy Stanton, Yonghui Wu, Ron J. Weiss, Navdeep Jaitly, Zongheng Yang, Ying Xiao, Zhifeng Chen, Samy Bengio, Quoc Le, Yannis Agiomyrgiannakis, Rob Clark, Rif A. Saurous
A text-to-speech synthesis system typically consists of multiple stages, such as a text analysis frontend, an acoustic model and an audio synthesis module.
Ranked #5 on Speech Synthesis on North American English
no code implementations • 9 Dec 2014 • Santosh S. Vempala, Ying Xiao
We present a simple, general technique for reducing the sample complexity of matrix and tensor decomposition algorithms applied to distributions.
no code implementations • 17 Dec 2013 • Raffay Hamid, Ying Xiao, Alex Gittens, Dennis Decoste
Kernel approximation using randomized feature maps has recently gained a lot of interest.
1 code implementation • 25 Jun 2013 • Navin Goyal, Santosh Vempala, Ying Xiao
Fourier PCA is Principal Component Analysis of a matrix obtained from higher order derivatives of the logarithm of the Fourier transform of a distribution. We make this method algorithmic by developing a tensor decomposition method for a pair of tensors sharing the same vectors in rank-$1$ decompositions.