Publications by Chen Liu | EPFL Graph Search

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In the last decade, deep neural networks have achieved tremendous success in many fields of machine learning. However, they are shown vulnerable against adversarial attacks: well-designed, yet imperceptible, perturbations can make the state-of-the-art deep ...

This work studies the robustness certification problem of neural network models, which aims to find certified adversary-free regions as large as possible around data points. In contrast to the existing approaches that seek regions bounded uniformly along a ...

We analyze the influence of adversarial training on the loss landscape of machine learning models. To this end, we first provide analytical studies of the properties of adversarial loss functions under different adversarial budgets. We then demonstrate tha ...

We address the problem of stably and efficiently training a deep neural network robust to adversarial perturbations bounded by an

l_1

norm. We demonstrate that achieving robustness against

l_1

-bounded perturbations is more challenging than in the

l_2

...

Robustness to adversarial attacks was shown to require a larger model capacity, and thus a larger memory footprint. In this paper, we introduce an approach to obtain robust yet compact models by pruning randomly-initialized binary networks. Unlike adversar ...

We reconsider the training objective of Generative Adversarial Networks (GANs) from the mixed Nash Equilibria (NE) perspective. Inspired by the classical prox methods, we develop a novel algorithmic framework for GANs via an infinite-dimensional two-player ...

We report a dual-contrast method of simultaneously measuring and visualizing the volumetric structural information in live biological samples in three-dimensional (3D) space. By introducing a direct way of deriving the 3D scattering potential of the object ...

Chen Liu

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Towards Verifiable, Generalizable and Efficient Robust Deep Neural Networks

On Certifying Non-Uniform Bounds against Adversarial Attacks

On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Robust Binary Models by Pruning Randomly-initialized Networks

Finding Mixed Nash Equilibria of Generative Adversarial Networks

Simultaneous dual-contrast three-dimensional imaging in live cells via optical diffraction tomography and fluorescence