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Michael E. Houle

3 accepted papers

2024

LDReg: Local Dimensionality Regularized Self-Supervised Learning

ICLR 2024poster

Representations learned via self-supervised learning (SSL) can be susceptible to dimensional collapse, where the learned representation subspace is of extremely low dimensionality and thus fails to represent the full data distribution and modalities. Dimensional collapse ––– also known as the "under…

2018

Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality

ICLR 2018oral

Deep Neural Networks (DNNs) have recently been shown to be vulnerable against adversarial examples, which are carefully crafted instances that can mislead DNNs to make errors during prediction. To better understand such attacks, a characterization is needed of the properties of regions (the so-calle…

2018

Dimensionality-Driven Learning with Noisy Labels

ICML 2018oral

Datasets with significant proportions of noisy (incorrect) class labels present challenges for training accurate Deep Neural Networks (DNNs). We propose a new perspective for understanding DNN generalization for such datasets, by investigating the dimensionality of the deep representation subspace o…