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Found 3 papers, 1 papers with code
BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial Optimization
In this paper, we present a novel formulation of Combinatorial Optimization Problems (COPs) as Markov Decision Processes (MDPs) that effectively leverages common symmetries of COPs to improve out-of-distribution robustness.
Combinatorial Optimization
Out-of-Distribution Generalization
Learning with Label Noise for Image Retrieval by Selecting Interactions
Learning with noisy labels is an active research area for image classification.
DEEP ADVERSARIAL FORWARD MODEL
More precisely, our forward model is trained to produce realistic observations of the future while a discriminator model is trained to distinguish between real images and the model’s prediction of the future.
