30 citations · 44 across the 2 of their papers we have counts for
3 papers
Large-scale graph representation learning with very deep GNNs and self-supervision
Ravichandra Addanki, Peter W. Battaglia, David Budden +8
Effectively and efficiently deploying graph neural networks (GNNs) at scale remains one of the most challenging aspects of graph representation learning. Many powerful solutions ha…
Solving Mixed Integer Programs Using Neural Networks
Vinod Nair, Sergey Bartunov, Felix Gimeno +16
Mixed Integer Programming (MIP) solvers rely on an array of sophisticated heuristics developed with decades of research to solve large-scale MIP instances encountered in practice.…
Placeto: Learning Generalizable Device Placement Algorithms for Distributed Machine Learning
Ravichandra Addanki, Shaileshh Bojja Venkatakrishnan, Shreyan Gupta +2
We present Placeto, a reinforcement learning (RL) approach to efficiently find device placements for distributed neural network training. Unlike prior approaches that only find a d…