most citedGlobal Convergence and Geometric Characterization of Slow to Fast Weight Evolution in Neural Network Training for Classifying Linearly Non-Separable Data

1 citations · 1 across the 5 of their papers we have counts for

collaborators
Showing cs.LGShow all

3 papers · 1 filter

cs.LG2020

Recurrence of Optimum for Training Weight and Activation Quantized Networks

Ziang Long, Penghang Yin, Jack Xin

Deep neural networks (DNNs) are quantized for efficient inference on resource-constrained platforms. However, training deep learning models with low-precision weights and activatio…

cs.LG2020

Learning Quantized Neural Nets by Coarse Gradient Method for Non-linear Classification

Ziang Long, Penghang Yin, Jack Xin

Quantized or low-bit neural networks are attractive due to their inference efficiency. However, training deep neural networks with quantized activations involves minimizing a disco…

cs.LG2020★ 1 cited

Global Convergence and Geometric Characterization of Slow to Fast Weight Evolution in Neural Network Training for Classifying Linearly Non-Separable Data

Ziang Long, Penghang Yin, Jack Xin

In this paper, we study the dynamics of gradient descent in learning neural networks for classification problems. Unlike in existing works, we consider the linearly non-separable c…