Search Results for author:
Found 2 papers, 2 papers with code
RLOR: A Flexible Framework of Deep Reinforcement Learning for Operation Research
In this work, we analyze the end-to-end autoregressive models for vehicle routing problems and show that these models can benefit from the recent advances in reinforcement learning with a careful re-implementation of the model architecture.
Robust Federated Learning with Attack-Adaptive Aggregation
To the best of our knowledge, our aggregation strategy is the first one that can be adapted to defend against various attacks in a data-driven fashion.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
