Publications associées à Theory of representation learning in cortical neural networks

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

Sabine Süsstrunk, Mathieu Salzmann, Yulun Jiang, Chen Liu, Zhuoyi Huang

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

...

2023

Theory of representation learning in cortical neural networks

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Comparing transferability in neural network approaches and linear models for machine-learning interaction potentials

Spatially adaptive machine learning models for predicting water quality in Hong Kong

Modeling Visual Impairments with Artificial Neural Networks: a Review

The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations

Beyond Spectral Gap: The Role of the Topology in Decentralized Learning

PePTM: An Efficient and Accurate Personalized P2P Learning Algorithm for Home Thermal Modeling

Machine Learning Methods for Robust Uncertainty Quantification and Controller Approximation

Self-Supervised Bayesian representation learning of acoustic emissions from laser powder bed Fusion process for in-situ monitoring

Source-Free Open-Set Domain Adaptation for Histopathological Images via Distilling Self-Supervised Vision Transformer

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

The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations

Comparing transferability in neural network approaches and linear models for machine-learning interaction potentials

Machine Learning Methods for Robust Uncertainty Quantification and Controller Approximation

Beyond Spectral Gap: The Role of the Topology in Decentralized Learning

Self-Supervised Bayesian representation learning of acoustic emissions from laser powder bed Fusion process for in-situ monitoring

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

Spatially adaptive machine learning models for predicting water quality in Hong Kong

Modeling Visual Impairments with Artificial Neural Networks: a Review

Source-Free Open-Set Domain Adaptation for Histopathological Images via Distilling Self-Supervised Vision Transformer

PePTM: An Efficient and Accurate Personalized P2P Learning Algorithm for Home Thermal Modeling