910 citations · 1.7k across the 16 of their papers we have counts for
7 papers · 1 filter
SketchTransfer: A Challenging New Task for Exploring Detail-Invariance and the Abstractions Learned by Deep Networks
Alex Lamb, Sherjil Ozair, Vikas Verma +1
Deep networks have achieved excellent results in perceptual tasks, yet their ability to generalize to variations not seen during training has come under increasing scrutiny. In thi…
GraphMix: Improved Training of GNNs for Semi-Supervised Learning
Vikas Verma, Meng Qu, Kenji Kawaguchi +4
We present GraphMix, a regularization method for Graph Neural Network based semi-supervised object classification, whereby we propose to train a fully-connected network jointly wit…
InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization
Fan-Yun Sun, Jordan Hoffmann, Vikas Verma +1
This paper studies learning the representations of whole graphs in both unsupervised and semi-supervised scenarios. Graph-level representations are critical in a variety of real-wo…
Towards Understanding Generalization in Gradient-Based Meta-Learning
Simon Guiroy, Vikas Verma, Christopher Pal
In this work we study generalization of neural networks in gradient-based meta-learning by analyzing various properties of the objective landscapes. We experimentally demonstrate t…
Interpolated Adversarial Training: Achieving Robust Neural Networks without Sacrificing Too Much Accuracy
Alex Lamb, Vikas Verma, Kenji Kawaguchi +4
Adversarial robustness has become a central goal in deep learning, both in the theory and the practice. However, successful methods to improve the adversarial robustness (such as a…
Interpolation Consistency Training for Semi-Supervised Learning
Vikas Verma, Kenji Kawaguchi, Alex Lamb +4
We introduce Interpolation Consistency Training (ICT), a simple and computation efficient algorithm for training Deep Neural Networks in the semi-supervised learning paradigm. ICT…