3 citations · 8 across the 5 of their papers we have counts for
8 papers
Global Convergence Analysis of Deep Linear Networks with A One-neuron Layer
Kun Chen, Dachao Lin, Zhihua Zhang
In this paper, we follow Eftekhari's work to give a non-local convergence analysis of deep linear networks. Specifically, we consider optimizing deep linear networks which have a l…
Greedy and Random Broyden's Methods with Explicit Superlinear Convergence Rates in Nonlinear Equations
Haishan Ye, Dachao Lin, Zhihua Zhang
In this paper, we propose the greedy and random Broyden's method for solving nonlinear equations. Specifically, the greedy method greedily selects the direction to maximize a certa…
Explicit Superlinear Convergence Rates of The SR1 Algorithm
Haishan Ye, Dachao Lin, Zhihua Zhang +1
We study the convergence rate of the famous Symmetric Rank-1 (SR1) algorithm which has wide applications in different scenarios. Although it has been extensively investigated, SR1…
Directional Convergence Analysis under Spherically Symmetric Distribution
Dachao Lin, Zhihua Zhang
We consider the fundamental problem of learning linear predictors (i.e., separable datasets with zero margin) using neural networks with gradient flow or gradient descent. Under th…
Meta-Regularization: An Approach to Adaptive Choice of the Learning Rate in Gradient Descent
Guangzeng Xie, Hao Jin, Dachao Lin +1
We propose \textit{Meta-Regularization}, a novel approach for the adaptive choice of the learning rate in first-order gradient descent methods. Our approach modifies the objective…
Optimal Quantization for Batch Normalization in Neural Network Deployments and Beyond
Dachao Lin, Peiqin Sun, Guangzeng Xie +2
Quantized Neural Networks (QNNs) use low bit-width fixed-point numbers for representing weight parameters and activations, and are often used in real-world applications due to thei…