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20182023
most citedFederated Deep Learning with Bayesian Privacy

10 citations · 21 across the 8 of their papers we have counts for

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Showing 2020Show all

5 papers · 1 filter

cs.LG2020

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…

cs.CV2020

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…

cs.CV2020

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…

cs.CV2020

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…

cs.LG2020

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…