activity
20182021
most citedReproducing Activation Function for Deep Learning

10 citations · 12 across the 4 of their papers we have counts for

collaborators

9 papers

math.NA2021

Stationary Density Estimation of Itô Diffusions Using Deep Learning

Yiqi Gu, John Harlim, Senwei Liang +1

In this paper, we consider the density estimation problem associated with the stationary measure of ergodic Itô diffusions from a discrete-time series that approximate the solution…

cs.LG20212 cited

AlterSGD: Finding Flat Minima for Continual Learning by Alternative Training

Zhongzhan Huang, Mingfu Liang, Senwei Liang +1

Deep neural networks suffer from catastrophic forgetting when learning multiple knowledge sequentially, and a growing number of approaches have been proposed to mitigate this probl…

cs.CV2021

Blending Pruning Criteria for Convolutional Neural Networks

Wei He, Zhongzhan Huang, Mingfu Liang +2

The advancement of convolutional neural networks (CNNs) on various vision applications has attracted lots of attention. Yet the majority of CNNs are unable to satisfy the strict re…

cs.LG202110 cited

Reproducing Activation Function for Deep Learning

Senwei Liang, Liyao Lyu, Chunmei Wang +1

We propose reproducing activation functions (RAFs) to improve deep learning accuracy for various applications ranging from computer vision to scientific computing. The idea is to e…

cs.CV2020

Efficient Attention Network: Accelerate Attention by Searching Where to Plug

Zhongzhan Huang, Senwei Liang, Mingfu Liang +2

Recently, many plug-and-play self-attention modules are proposed to enhance the model generalization by exploiting the internal information of deep convolutional neural networks (C…

math.NA2019

Machine Learning for Prediction with Missing Dynamics

John Harlim, Shixiao W. Jiang, Senwei Liang +1

This article presents a general framework for recovering missing dynamical systems using available data and machine learning techniques. The proposed framework reformulates the pre…