Search Results for author:
Found 7 papers, 5 papers with code
Large-scale graph representation learning with very deep GNNs and self-supervision
In doing so, we demonstrate evidence of scalable self-supervised graph representation learning, and utility of very deep GNNs -- both very important open issues.
Solving Mixed Integer Programs Using Neural Networks
Our approach constructs two corresponding neural network-based components, Neural Diving and Neural Branching, to use in a base MIP solver such as SCIP.
Neural Large Neighborhood Search
Results on several datasets show that it is possible to learn a neighbor selection policy that allows LNS to efficiently find good solutions.
Park: An Open Platform for Learning-Augmented Computer Systems
We present Park, a platform for researchers to experiment with Reinforcement Learning (RL) for computer systems.
Learning Generalizable Device Placement Algorithms for Distributed Machine Learning
We present Placeto, a reinforcement learning (RL) approach to efficiently find device placements for distributed neural network training.
Placeto: Learning Generalizable Device Placement Algorithms for Distributed Machine Learning
Unlike prior approaches that only find a device placement for a specific computation graph, Placeto can learn generalizable device placement policies that can be applied to any graph.
Understanding & Generalizing AlphaGo Zero
AlphaGo Zero (AGZ) introduced a new {\em tabula rasa} reinforcement learning algorithm that has achieved superhuman performance in the games of Go, Chess, and Shogi with no prior knowledge other than the rules of the game.
