most citedUnderstanding Adversarial Robustness Through Loss Landscape Geometries

11 citations · 22 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG20195 cited

Deep Connectomics Networks: Neural Network Architectures Inspired by Neuronal Networks

Nicholas Roberts, Dian Ang Yap, Vinay Uday Prabhu

The interplay between inter-neuronal network topology and cognition has been studied deeply by connectomics researchers and network scientists, which is crucial towards understandi…

cs.LG20191 cited

Grassmannian Packings in Neural Networks: Learning with Maximal Subspace Packings for Diversity and Anti-Sparsity

Dian Ang Yap, Nicholas Roberts, Vinay Uday Prabhu

Kernel sparsity ("dying ReLUs") and lack of diversity are commonly observed in CNN kernels, which decreases model capacity. Drawing inspiration from information theory and wireless…

cs.CV2019

Covering up bias in CelebA-like datasets with Markov blankets: A post-hoc cure for attribute prior avoidance

Vinay Uday Prabhu, Dian Ang Yap, Alexander Wang +1

Attribute prior avoidance entails subconscious or willful non-modeling of (meta)attributes that datasets are oft born with, such as the 40 semantic facial attributes associated wit…

cs.LG201911 cited

Understanding Adversarial Robustness Through Loss Landscape Geometries

Vinay Uday Prabhu, Dian Ang Yap, Joyce Xu +1

The pursuit of explaining and improving generalization in deep learning has elicited efforts both in regularization techniques as well as visualization techniques of the loss surfa…

cs.CV20195 cited

Fonts-2-Handwriting: A Seed-Augment-Train framework for universal digit classification

Vinay Uday Prabhu, Sanghyun Han, Dian Ang Yap +3

In this paper, we propose a Seed-Augment-Train/Transfer (SAT) framework that contains a synthetic seed image dataset generation procedure for languages with different numeral syste…