12 citations · 12 across the 3 of their papers we have counts for
6 papers · 1 filter
CNT (Conditioning on Noisy Targets): A new Algorithm for Leveraging Top-Down Feedback
Alexia Jolicoeur-Martineau, Alex Lamb, Vikas Verma +1
We propose a novel regularizer for supervised learning called Conditioning on Noisy Targets (CNT). This approach consists in conditioning the model on a noisy version of the target…
Towards Domain-Agnostic Contrastive Learning
Vikas Verma, Minh-Thang Luong, Kenji Kawaguchi +2
Despite recent success, most contrastive self-supervised learning methods are domain-specific, relying heavily on data augmentation techniques that require knowledge about a partic…
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…
Modularity Matters: Learning Invariant Relational Reasoning Tasks
Jason Jo, Vikas Verma, Yoshua Bengio
We focus on two supervised visual reasoning tasks whose labels encode a semantic relational rule between two or more objects in an image: the MNIST Parity task and the colorized Pe…