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Found 5 papers, 2 papers with code
Bridging the Gap Between Target Networks and Functional Regularization
However, learning the value function via bootstrapping often leads to unstable training due to fast-changing target values.
Implicit Offline Reinforcement Learning via Supervised Learning
Despite the benefits of using implicit models to learn robotic skills via BC, offline RL via Supervised Learning algorithms have been limited to explicit models.
Bridging the Gap Between Target Networks and Functional Regularization
Our findings emphasize that Functional Regularization can be used as a drop-in replacement for Target Networks and result in performance improvement.
LOOC: Localize Overlapping Objects with Count Supervision
For localization, LOOC achieves a strong new baseline in the novel problem setup where only count supervision is available.
Objects of violence: synthetic data for practical ML in human rights investigations
We introduce a machine learning workflow to search for, identify, and meaningfully triage videos and images of munitions, weapons, and military equipment, even when limited training data exists for the object of interest.
