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stat.ML2020
Adversarial domain adaptation to reduce sample bias of a high energy physics classifier
Jose M. Clavijo, Paul Glaysher, Judith M. Katzy +1
We apply adversarial domain adaptation in unsupervised setting to reduce sample bias in a supervised high energy physics events classifier training. We make use of a neural network…
cs.LG2020
Obstacle Tower Without Human Demonstrations: How Far a Deep Feed-Forward Network Goes with Reinforcement Learning
Marco Pleines, Jenia Jitsev, Mike Preuss +1
The Obstacle Tower Challenge is the task to master a procedurally generated chain of levels that subsequently get harder to complete. Whereas the most top performing entries of las…