67 citations · 131 across the 29 of their papers we have counts for
4 papers · 1 filter
MixupE: Understanding and Improving Mixup from Directional Derivative Perspective
Yingtian Zou, Vikas Verma, Sarthak Mittal +6
Mixup is a popular data augmentation technique for training deep neural networks where additional samples are generated by linearly interpolating pairs of inputs and their labels.…
From Points to Functions: Infinite-dimensional Representations in Diffusion Models
Sarthak Mittal, Guillaume Lajoie, Stefan Bauer +1
Diffusion-based generative models learn to iteratively transfer unstructured noise to a complex target distribution as opposed to Generative Adversarial Networks (GANs) or the deco…
On Neural Architecture Inductive Biases for Relational Tasks
Giancarlo Kerg, Sarthak Mittal, David Rolnick +3
Current deep learning approaches have shown good in-distribution generalization performance, but struggle with out-of-distribution generalization. This is especially true in the ca…
Is a Modular Architecture Enough?
Sarthak Mittal, Yoshua Bengio, Guillaume Lajoie
Inspired from human cognition, machine learning systems are gradually revealing advantages of sparser and more modular architectures. Recent work demonstrates that not only do some…