b'Zhihua Zhang'

Landscape of Sparse Linear Network: A Brief Investigation

Network pruning, or sparse network has a long history and practical sign...
09/16/2020 ∙ by Dachao Lin, et al. ∙ 1 ∙ share

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Optimal Quantization for Batch Normalization in Neural Network Deployments and Beyond

Quantized Neural Networks (QNNs) use low bit-width fixed-point numbers f...
08/30/2020 ∙ by Dachao Lin, et al. ∙ 0 ∙ share

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Intervention Generative Adversarial Networks

In this paper we propose a novel approach for stabilizing the training p...
08/09/2020 ∙ by Jiadong Liang, et al. ∙ 24 ∙ share

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An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter Optimization

The evaluation of hyperparameters, neural architectures, or data augment...
07/11/2020 ∙ by Yimin Huang, et al. ∙ 0 ∙ share

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Communication Efficient Decentralized Training with Multiple Local Updates

Communication efficiency plays a significant role in decentralized optim...
10/21/2019 ∙ by Xiang Li, et al. ∙ 0 ∙ share

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Distillation ≈ Early Stopping? Harvesting Dark Knowledge Utilizing Anisotropic Information Retrieval For Overparameterized Neural Network

Distillation is a method to transfer knowledge from one model to another...
10/02/2019 ∙ by Bin Dong, et al. ∙ 0 ∙ share

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A Stochastic Proximal Point Algorithm for Saddle-Point Problems

We consider saddle point problems which objective functions are the aver...
09/13/2019 ∙ by Luo Luo, et al. ∙ 0 ∙ share

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A General Analysis Framework of Lower Complexity Bounds for Finite-Sum Optimization

This paper studies the lower bound complexity for the optimization probl...
08/22/2019 ∙ by Guangzeng Xie, et al. ∙ 0 ∙ share

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Towards Better Generalization: BP-SVRG in Training Deep Neural Networks

Stochastic variance-reduced gradient (SVRG) is a classical optimization ...
08/18/2019 ∙ by Hao Jin, et al. ∙ 0 ∙ share

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On the Convergence of FedAvg on Non-IID Data

Federated learning enables a large amount of edge computing devices to l...
07/04/2019 ∙ by Xiang Li, et al. ∙ 0 ∙ share

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A Gram-Gauss-Newton Method Learning Overparameterized Deep Neural Networks for Regression Problems

First-order methods such as stochastic gradient descent (SGD) are curren...
05/28/2019 ∙ by Tianle Cai, et al. ∙ 0 ∙ share

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A Unified Framework for Regularized Reinforcement Learning

We propose and study a general framework for regularized Markov decision...
03/02/2019 ∙ by Xiang Li, et al. ∙ 0 ∙ share

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Lipschitz Generative Adversarial Nets

In this paper we study the convergence of generative adversarial network...
02/15/2019 ∙ by Zhiming Zhou, et al. ∙ 0 ∙ share

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Do Subsampled Newton Methods Work for High-Dimensional Data?

Subsampled Newton methods approximate Hessian matrices through subsampli...
02/13/2019 ∙ by Xiang Li, et al. ∙ 0 ∙ share

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Hierarchical Attention: What Really Counts in Various NLP Tasks

Attention mechanisms in sequence to sequence models have shown great abi...
08/10/2018 ∙ by Zehao Dou, et al. ∙ 0 ∙ share

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Interpolatron: Interpolation or Extrapolation Schemes to Accelerate Optimization for Deep Neural Networks

In this paper we explore acceleration techniques for large scale nonconv...
05/17/2018 ∙ by Guangzeng Xie, et al. ∙ 0 ∙ share

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A Unifying Framework for Convergence Analysis of Approximate Newton Methods

Many machine learning models are reformulated as optimization problems. ...
02/27/2017 ∙ by Haishan Ye, et al. ∙ 0 ∙ share

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An Efficient Character-Level Neural Machine Translation

Neural machine translation aims at building a single large neural networ...
08/16/2016 ∙ by Shenjian Zhao, et al. ∙ 0 ∙ share

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A Proximal Stochastic Quasi-Newton Algorithm

In this paper, we discuss the problem of minimizing the sum of two conve...
01/31/2016 ∙ by Luo Luo, et al. ∙ 0 ∙ share

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Wishart Mechanism for Differentially Private Principal Components Analysis

We propose a new input perturbation mechanism for publishing a covarianc...
11/18/2015 ∙ by Wuxuan Jiang, et al. ∙ 0 ∙ share

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Nonconvex Penalization in Sparse Estimation: An Approach Based on the Bernstein Function

In this paper we study nonconvex penalization using Bernstein functions ...
10/29/2015 ∙ by Zhihua Zhang, et al. ∙ 0 ∙ share

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A Parallel algorithm for X-Armed bandits

The target of X-armed bandit problem is to find the global maximum of an...
10/26/2015 ∙ by Cheng Chen, et al. ∙ 0 ∙ share

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A Scalable and Extensible Framework for Superposition-Structured Models

In many learning tasks, structural models usually lead to better interpr...
09/08/2015 ∙ by Shenjian Zhao, et al. ∙ 0 ∙ share

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Adjusting Leverage Scores by Row Weighting: A Practical Approach to Coherent Matrix Completion

Low-rank matrix completion is an important problem with extensive real-w...
12/26/2014 ∙ by Shusen Wang, et al. ∙ 0 ∙ share

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Group Orbit Optimization: A Unified Approach to Data Normalization

In this paper we propose and study an optimization problem over a matrix...
10/03/2014 ∙ by Shuchang Zhou, et al. ∙ 0 ∙ share

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The Bernstein Function: A Unifying Framework of Nonconvex Penalization in Sparse Estimation

In this paper we study nonconvex penalization using Bernstein functions....
12/17/2013 ∙ by Zhihua Zhang, et al. ∙ 0 ∙ share

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The Matrix Ridge Approximation: Algorithms and Applications

We are concerned with an approximation problem for a symmetric positive ...
12/17/2013 ∙ by Zhihua Zhang, et al. ∙ 0 ∙ share

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Compound Poisson Processes, Latent Shrinkage Priors and Bayesian Nonconvex Penalization

In this paper we discuss Bayesian nonconvex penalization for sparse lear...
08/28/2013 ∙ by Zhihua Zhang, et al. ∙ 0 ∙ share

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Kinetic Energy Plus Penalty Functions for Sparse Estimation

In this paper we propose and study a family of sparsity-inducing penalty...
07/22/2013 ∙ by Zhihua Zhang, et al. ∙ 0 ∙ share

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A Scalable CUR Matrix Decomposition Algorithm: Lower Time Complexity and Tighter Bound

The CUR matrix decomposition is an important extension of Nyström approx...
10/04/2012 ∙ by Shusen Wang, et al. ∙ 0 ∙ share

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Bayesian Multicategory Support Vector Machines

We show that the multi-class support vector machine (MSVM) proposed by L...
06/27/2012 ∙ by Zhihua Zhang, et al. ∙ 0 ∙ share

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EP-GIG Priors and Applications in Bayesian Sparse Learning

In this paper we propose a novel framework for the construction of spars...
04/19/2012 ∙ by Zhihua Zhang, et al. ∙ 0 ∙ share

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Coherence Functions with Applications in Large-Margin Classification Methods

Support vector machines (SVMs) naturally embody sparseness due to their ...
04/10/2012 ∙ by Zhihua Zhang, et al. ∙ 0 ∙ share

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Zhihua Zhang

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