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Found 4 papers, 1 papers with code
Self-Play Learning Without a Reward Metric
The AlphaZero algorithm for the learning of strategy games via self-play, which has produced superhuman ability in the games of Go, chess, and shogi, uses a quantitative reward function for game outcomes, requiring the users of the algorithm to explicitly balance different components of the reward against each other, such as the game winner and margin of victory.
Noisier2Noise: Learning to Denoise from Unpaired Noisy Data
We present a method for training a neural network to perform image denoising without access to clean training examples or access to paired noisy training examples.
Monotone Learning with Rectified Wire Networks
This constraint is simply that the value of the output node associated with the correct class should be zero.
Proactive Message Passing on Memory Factor Networks
We introduce a new type of graphical model that we call a "memory factor network" (MFN).
