200 citations · 371 across the 7 of their papers we have counts for
11 papers
Learning Robust Dynamics through Variational Sparse Gating
Arnav Kumar Jain, Shivakanth Sujit, Shruti Joshi +3
Learning world models from their sensory inputs enables agents to plan for actions by imagining their future outcomes. World models have previously been shown to improve sample-eff…
Accounting for Variance in Machine Learning Benchmarks
Xavier Bouthillier, Pierre Delaunay, Mirko Bronzi +14
Strong empirical evidence that one machine-learning algorithm A outperforms another one B ideally calls for multiple trials optimizing the learning pipeline over sources of variati…
HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery
Michel Deudon, Alfredo Kalaitzis, Israel Goytom +7
Generative deep learning has sparked a new wave of Super-Resolution (SR) algorithms that enhance single images with impressive aesthetic results, albeit with imaginary details. Mul…
An Empirical Study of Batch Normalization and Group Normalization in Conditional Computation
Vincent Michalski, Vikram Voleti, Samira Ebrahimi Kahou +4
Batch normalization has been widely used to improve optimization in deep neural networks. While the uncertainty in batch statistics can act as a regularizer, using these dataset st…
Towards Deep Conversational Recommendations
Raymond Li, Samira Kahou, Hannes Schulz +3
There has been growing interest in using neural networks and deep learning techniques to create dialogue systems. Conversational recommendation is an interesting setting for the sc…
ChatPainter: Improving Text to Image Generation using Dialogue
Shikhar Sharma, Dendi Suhubdy, Vincent Michalski +2
Synthesizing realistic images from text descriptions on a dataset like Microsoft Common Objects in Context (MS COCO), where each image can contain several objects, is a challenging…