122 citations · 157 across the 6 of their papers we have counts for
4 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…
A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms
Yoshua Bengio, Tristan Deleu, Nasim Rahaman +5
We propose to meta-learn causal structures based on how fast a learner adapts to new distributions arising from sparse distributional changes, e.g. due to interventions, actions of…
Sparse Attentive Backtracking: Temporal CreditAssignment Through Reminding
Nan Rosemary Ke, Anirudh Goyal, Olexa Bilaniuk +4
Learning long-term dependencies in extended temporal sequences requires credit assignment to events far back in the past. The most common method for training recurrent neural netwo…
Feedforward Initialization for Fast Inference of Deep Generative Networks is biologically plausible
Yoshua Bengio, Benjamin Scellier, Olexa Bilaniuk +2
We consider deep multi-layered generative models such as Boltzmann machines or Hopfield nets in which computation (which implements inference) is both recurrent and stochastic, but…