Paper2026
Maximum Likelihood Reinforcement Learning (MaxRL)
Tajwar et al.
A recent idea for training models on pass-fail tasks when sampling matters
Optimization
8 entries / Filter in the index →
Concept2017
Proximal Policy Optimization (PPO)
Schulman et al.
A stable, sample-efficient policy gradient algorithm for reinforcement learning
Concept2015
Batch Normalization
Ioffe & Szegedy
Normalizing layer inputs to accelerate deep network training
Concept2012
Dropout: Regularization for Neural Networks
Hinton et al.
Randomly dropping units during training to prevent overfitting
Concept1992
Policy Gradient Methods
Williams
Directly optimizing policies through gradient ascent on expected returns
Concept1986
Backpropagation
Rumelhart, Hinton & Williams
The algorithm that enables neural networks to learn by computing gradients efficiently
Misc1962
Stable Marriage Problem
Gale & Shapley
Finding a stable matching with the Gale-Shapley deferred acceptance algorithm