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Found 3 papers, 1 papers with code
A structure-aware framework for learning device placements on computation graphs
To bridge the gap between encoder-placer and grouper-placer techniques, we propose a novel framework for the task of device placement, relying on smaller computation graphs extracted from the OpenVINO toolkit using reinforcement learning.
Pareto Policy Adaptation
We present a policy gradient method for Multi-Objective Reinforcement Learning under unknown, linear preferences.
Multi-Objective Reinforcement Learning
reinforcement-learning
Learning Hyperbolic Representations of Topological Features
Learning task-specific representations of persistence diagrams is an important problem in topological data analysis and machine learning.
