96 citations · 263 across the 8 of their papers we have counts for
5 papers · 1 filter
Introducing Milabench: Benchmarking Accelerators for AI
Pierre Delaunay, Xavier Bouthillier, Olivier Breuleux +12
AI workloads, particularly those driven by deep learning, are introducing novel usage patterns to high-performance computing (HPC) systems that are not comprehensively captured by…
LMEMs for post-hoc analysis of HPO Benchmarking
Anton Geburek, Neeratyoy Mallik, Danny Stoll +2
The importance of tuning hyperparameters in Machine Learning (ML) and Deep Learning (DL) is established through empirical research and applications, evident from the increase in ne…
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…
Fast Approximate Natural Gradient Descent in a Kronecker-factored Eigenbasis
Thomas George, César Laurent, Xavier Bouthillier +2
Optimization algorithms that leverage gradient covariance information, such as variants of natural gradient descent (Amari, 1998), offer the prospect of yielding more effective des…
EmoNets: Multimodal deep learning approaches for emotion recognition in video
Samira Ebrahimi Kahou, Xavier Bouthillier, Pascal Lamblin +15
The task of the emotion recognition in the wild (EmotiW) Challenge is to assign one of seven emotions to short video clips extracted from Hollywood style movies. The videos depict…