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20172025
most citedImproving Adversarial Robustness via Promoting Ensemble Diversity

190 citations · 498 across the 64 of their papers we have counts for

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Showing 2022Show all

5 papers · 1 filter

cs.LG2022★ 4 cited

Universal Antisymmetry in Fermionic Neural Networks

Tianyu Pang, Shuicheng Yan, Min Lin

Fermionic neural network (FermiNet) is a recently proposed wavefunction Ansatz, which is used in variational Monte Carlo (VMC) methods to solve the many-electron Schrödinger equati…

cs.LG2022

A Roadmap for Big Model

Sha Yuan, Hanyu Zhao, Shuai Zhao +97

With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm. Researchers have achieved various outcomes in the c…

cs.LG2022★ 1 cited

Query-Efficient Black-box Adversarial Attacks Guided by a Transfer-based Prior

Yinpeng Dong, Shuyu Cheng, Tianyu Pang +2

Adversarial attacks have been extensively studied in recent years since they can identify the vulnerability of deep learning models before deployed. In this paper, we consider the…

cs.CV2022★ 4 cited

Controllable Evaluation and Generation of Physical Adversarial Patch on Face Recognition

Xiao Yang, Yinpeng Dong, Tianyu Pang +3

Recent studies have revealed the vulnerability of face recognition models against physical adversarial patches, which raises security concerns about the deployed face recognition s…

cs.LG2022★ 26 cited

Robustness and Accuracy Could Be Reconcilable by (Proper) Definition

Tianyu Pang, Min Lin, Xiao Yang +2

The trade-off between robustness and accuracy has been widely studied in the adversarial literature. Although still controversial, the prevailing view is that this trade-off is inh…