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Found 5 papers, 0 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.
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).
The Boundary Forest Algorithm for Online Supervised and Unsupervised Learning
We describe a new instance-based learning algorithm called the Boundary Forest (BF) algorithm, that can be used for supervised and unsupervised learning.
A message-passing algorithm for multi-agent trajectory planning
We describe a novel approach for computing collision-free \emph{global} trajectories for $p$ agents with specified initial and final configurations, based on an improved version of the alternating direction method of multipliers (ADMM).
