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Trung Trinh

2 accepted papers

2024

Improving robustness to corruptions with multiplicative weight perturbations

NeurIPS 2024spotlight

Deep neural networks (DNNs) excel on clean images but struggle with corrupted ones. Incorporating specific corruptions into the data augmentation pipeline can improve robustness to those corruptions but may harm performance on clean images and other types of distortion. In this paper, we introduce a…

2024

Input-gradient space particle inference for neural network ensembles

ICLR 2024spotlight

Deep Ensembles (DEs) demonstrate improved accuracy, calibration and robustness to perturbations over single neural networks partly due to their functional diversity. Particle-based variational inference (ParVI) methods enhance diversity by formalizing a repulsion term based on a network similarity k…