16 citations · 42 across the 14 of their papers we have counts for
14 papers
Inducing Neural Collapse in Deep Long-tailed Learning
Xuantong Liu, Jianfeng Zhang, Tianyang Hu +3
Although deep neural networks achieve tremendous success on various classification tasks, the generalization ability drops sheer when training datasets exhibit long-tailed distribu…
Federated Deep Learning with Bayesian Privacy
Hanlin Gu, Lixin Fan, Bowen Li +3
Federated learning (FL) aims to protect data privacy by cooperatively learning a model without sharing private data among users. For Federated Learning of Deep Neural Network with…
StrokeGAN: Reducing Mode Collapse in Chinese Font Generation via Stroke Encoding
Jinshan Zeng, Qi Chen, Yunxin Liu +2
The generation of stylish Chinese fonts is an important problem involved in many applications. Most of existing generation methods are based on the deep generative models, particul…
On Stochastic Variance Reduced Gradient Method for Semidefinite Optimization
Jinshan Zeng, Yixuan Zha, Ke Ma +1
The low-rank stochastic semidefinite optimization has attracted rising attention due to its wide range of applications. The nonconvex reformulation based on the low-rank factorizat…
Adversarial Robustness of Stabilized NeuralODEs Might be from Obfuscated Gradients
Yifei Huang, Yaodong Yu, Hongyang Zhang +2
In this paper we introduce a provably stable architecture for Neural Ordinary Differential Equations (ODEs) which achieves non-trivial adversarial robustness under white-box advers…
Leveraging both Lesion Features and Procedural Bias in Neuroimaging: An Dual-Task Split dynamics of inverse scale space
Xinwei Sun, Wenjing Han, Lingjing Hu +2
The prediction and selection of lesion features are two important tasks in voxel-based neuroimage analysis. Existing multivariate learning models take two tasks equivalently and op…
DessiLBI: Exploring Structural Sparsity of Deep Networks via Differential Inclusion Paths
Yanwei Fu, Chen Liu, Donghao Li +3
Over-parameterization is ubiquitous nowadays in training neural networks to benefit both optimization in seeking global optima and generalization in reducing prediction error. Howe…
How to trust unlabeled data? Instance Credibility Inference for Few-Shot Learning
Yikai Wang, Li Zhang, Yuan Yao +1
Deep learning based models have excelled in many computer vision tasks and appear to surpass humans' performance. However, these models require an avalanche of expensive human labe…
Learning the mapping : the cost of finding the needle in a haystack
Jiefu Zhang, Leonardo Zepeda-Núñez, Yuan Yao +1
The task of using machine learning to approximate the mapping with seems to be a trivial one. Given the knowledge of the separa…
Fast Stochastic Ordinal Embedding with Variance Reduction and Adaptive Step Size
Ke Ma, Jinshan Zeng, Qianqian Xu +3
Learning representation from relative similarity comparisons, often called ordinal embedding, gains rising attention in recent years. Most of the existing methods are based on semi…
iSplit LBI: Individualized Partial Ranking with Ties via Split LBI
Qianqian Xu, Xinwei Sun, Zhiyong Yang +3
Due to the inherent uncertainty of data, the problem of predicting partial ranking from pairwise comparison data with ties has attracted increasing interest in recent years. Howeve…
Characterizing Membership Privacy in Stochastic Gradient Langevin Dynamics
Bingzhe Wu, Chaochao Chen, Shiwan Zhao +6
Bayesian deep learning is recently regarded as an intrinsic way to characterize the weight uncertainty of deep neural networks~(DNNs). Stochastic Gradient Langevin Dynamics~(SGLD)…
On ADMM in Deep Learning: Convergence and Saturation-Avoidance
Jinshan Zeng, Shao-Bo Lin, Yuan Yao +1
In this paper, we develop an alternating direction method of multipliers (ADMM) for deep neural networks training with sigmoid-type activation functions (called \textit{sigmoid-ADM…
From Social to Individuals: a Parsimonious Path of Multi-level Models for Crowdsourced Preference Aggregation
Qianqian Xu, Jiechao Xiong, Xiaochun Cao +2
In crowdsourced preference aggregation, it is often assumed that all the annotators are subject to a common preference or social utility function which generates their comparison b…