Chelsea Finn
(12 articles)
Meta-Learning
Learning to learn involves techniques that enable AI models to learn how to adapt quickly to new tasks with minimal data.
Generality: 858
Policy Gradient
Class of algorithms in RL that optimizes the parameters of a policy directly through gradient ascent on expected future rewards.
Generality: 675
IRL
Inverse Reinforcement Learning
Inverse Reinforcement Learning
Technique in which an algorithm learns the underlying reward function of an environment based on observed behavior from an agent, essentially inferring the goals an agent is trying to achieve.
Generality: 658
Data Efficient Learning
ML approach that requires fewer data to train a functional model.
Generality: 791
Conditional Generation
Process where models produce output based on specified conditions or constraints.
Generality: 830
Synthetic Data Generation
Creating artificial data programmatically, often used to train ML models where real data is scarce, sensitive, or biased.
Generality: 795
Imitation Learning
AI technique where models learn to perform tasks by mimicking human behavior or strategies demonstrated in training data.
Generality: 850
Sample Efficiency
Ability of a ML model to achieve high performance with a relatively small number of training samples.
Generality: 815
FSL
Few-Shot Learning
Few-Shot Learning
ML approach that enables models to learn and make accurate predictions from a very small dataset.
Generality: 575
Few Shot
ML technique designed to recognize patterns and make predictions based on a very limited amount of training data.
Generality: 675
Post-Training
Techniques and adjustments applied to neural networks after their initial training phase to enhance performance, efficiency, or adaptability to new data or tasks.
Generality: 650
Generative Model
A type of AI model that learns to generate new data instances that mimic the training data distribution.
Generality: 840