activity
20182022
most citedAn Adaptive and Momental Bound Method for Stochastic Learning

28 citations · 29 across the 3 of their papers we have counts for

collaborators

6 papers

cs.LG2022

GA-SAM: Gradient-Strength based Adaptive Sharpness-Aware Minimization for Improved Generalization

Zhiyuan Zhang, Ruixuan Luo, Qi Su +1

Recently, Sharpness-Aware Minimization (SAM) algorithm has shown state-of-the-art generalization abilities in vision tasks. It demonstrates that flat minima tend to imply better ge…

cs.LG20201 cited

Learning Robust Representation for Clustering through Locality Preserving Variational Discriminative Network

Ruixuan Luo, Wei Li, Zhiyuan Zhang +3

Clustering is one of the fundamental problems in unsupervised learning. Recent deep learning based methods focus on learning clustering oriented representations. Among those method…

cs.LG2020

Exploring the Vulnerability of Deep Neural Networks: A Study of Parameter Corruption

Xu Sun, Zhiyuan Zhang, Xuancheng Ren +2

We argue that the vulnerability of model parameters is of crucial value to the study of model robustness and generalization but little research has been devoted to understanding th…

cs.LG201928 cited

An Adaptive and Momental Bound Method for Stochastic Learning

Jianbang Ding, Xuancheng Ren, Ruixuan Luo +1

Training deep neural networks requires intricate initialization and careful selection of learning rates. The emergence of stochastic gradient optimization methods that use adaptive…

cs.CL2018

Exploration on Grounded Word Embedding: Matching Words and Images with Image-Enhanced Skip-Gram Model

Ruixuan Luo

Word embedding is designed to represent the semantic meaning of a word with low dimensional vectors. The state-of-the-art methods of learning word embeddings (word2vec and GloVe) o…

cs.CV2018

Acquisition of Localization Confidence for Accurate Object Detection

Borui Jiang, Ruixuan Luo, Jiayuan Mao +2

Modern CNN-based object detectors rely on bounding box regression and non-maximum suppression to localize objects. While the probabilities for class labels naturally reflect classi…