Search Results for author: Ying Xiao

Found 18 papers, 5 papers with code

DBGSA: A Novel Data Adaptive Bregman Clustering Algorithm

no code implementations25 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.


Iterative-in-Iterative Super-Resolution Biomedical Imaging Using One Real Image

no code implementations26 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.


Towards Unconstrained End-to-End Text Spotting

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

The Effect of Network Depth on the Optimization Landscape

no code implementations28 May 2019 Behrooz Ghorbani, Ying Xiao, Shankar Krishnan

It is well-known that deeper neural networks are harder to train than shallower ones.

An Investigation into Neural Net Optimization via Hessian Eigenvalue Density

1 code implementation29 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.

Deep Convolutional Neural Networks for Imaging Data Based Survival Analysis of Rectal Cancer

no code implementations5 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.

Survival Analysis Survival Prediction

Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron

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.

Expressive Speech Synthesis

Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis

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.

Speech Synthesis Style Transfer +1

Neumann Optimizer: A Practical Optimization Algorithm for Deep Neural Networks

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.

Stochastic Optimization

Max vs Min: Tensor Decomposition and ICA with nearly Linear Sample Complexity

no code implementations9 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.

Tensor Decomposition

Compact Random Feature Maps

no code implementations17 Dec 2013 Raffay Hamid, Ying Xiao, Alex Gittens, Dennis Decoste

Kernel approximation using randomized feature maps has recently gained a lot of interest.

Fourier PCA and Robust Tensor Decomposition

1 code implementation25 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.

Tensor Decomposition

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