#optimization
8 articles
- Adam Optimizer Adaptive learning rates with momentum for deep learning
- Backpropagation The algorithm that enables neural networks to learn by computing gradients efficiently
- Batch Normalization Normalizing layer inputs to accelerate deep network training
- Dropout: Regularization for Neural Networks Randomly dropping units during training to prevent overfitting
- Maximum Likelihood Reinforcement Learning (MaxRL) A recent idea for training models on pass-fail tasks when sampling matters
- Policy Gradient Methods Directly optimizing policies through gradient ascent on expected returns
- Proximal Policy Optimization (PPO) A stable, sample-efficient policy gradient algorithm for reinforcement learning
- Stable Marriage Problem Finding a stable matching with the Gale-Shapley deferred acceptance algorithm