David Silver
(25 articles)
RL
Reinforcement Learning
Reinforcement Learning
Type of ML where an agent learns to make decisions by performing actions in an environment to achieve a goal, guided by rewards.
Generality: 890
Motor Learning
Process by which robots or AI systems acquire, refine, and optimize motor skills through experience and practice.
Generality: 675
Function Approximation
Method used in AI to estimate complex functions using simpler, computationally efficient models.
Generality: 810
Overfitting
When a ML model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data.
Generality: 890
Universal Learning Algorithms
Theoretical frameworks aimed at creating systems capable of learning any task to human-level competency, leveraging principles that could allow for generalization across diverse domains.
Generality: 840
State Representation
The method by which an AI system formulates a concise and informative description of the environment's current situation or context.
Generality: 682
Temporal Difference Learning
A method in reinforcement learning that updates predictions based on the difference between successive predictions, rather than solely relying on final outcome errors.
Generality: 775
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
Catastrophic Forgetting
Phenomenon where a neural network forgets previously learned information upon learning new data.
Generality: 686
Policy Learning
Branch of reinforcement learning where the objective is to find an optimal policy that dictates the best action to take in various states to maximize cumulative reward.
Generality: 790
Policy Gradient Algorithm
Type of RL algorithm that optimizes the policy directly by computing gradients of expected rewards with respect to policy parameters.
Generality: 805
Policy Gradient
Class of algorithms in RL that optimizes the parameters of a policy directly through gradient ascent on expected future rewards.
Generality: 675
DRL
Deep Reinforcement Learning
Deep Reinforcement Learning
Combines neural networks with a reinforcement learning framework, enabling AI systems to learn optimal actions through trial and error to maximize a cumulative reward.
Generality: 855
Sequence Prediction
Involves forecasting the next item(s) in a sequence based on the observed pattern of prior sequences.
Generality: 825
Autoregressive Sequence Generator
A predictive model harnessed in AI tasks, particularly involving times series, which leverages its own prior outputs as inputs in subsequent predictions.
Generality: 650
Robustness
Ability of an algorithm or model to deliver consistent and accurate results under varying operating conditions and input perturbations.
Generality: 885
DQN
Deep Q-Networks
Deep Q-Networks
RL technique that combines Q-learning with deep neural networks to enable agents to learn how to make optimal decisions from high-dimensional sensory inputs.
Generality: 853
Move 37
Pivotal move made by AlphaGo in its second game against Go champion Lee Sedol, which showcased the superior strategic capabilities of AI in the game of Go.
Generality: 140
Sample Efficiency
Ability of a ML model to achieve high performance with a relatively small number of training samples.
Generality: 815
Expressive Hidden States
internal representations within a neural network that effectively capture and encode complex patterns and dependencies in the input data.
Generality: 695
Ablation
Method where components of a neural network are systematically removed or altered to study their impact on the model's performance.
Generality: 650
Precomputed Policy
A strategy computed in advance for decision-making processes in AI systems, particularly within reinforcement learning, to optimize future actions.
Generality: 550
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
1-N Systems
Architectures where one input or controller manages multiple outputs or agents, applicable in fields like neural networks and robotics.
Generality: 790
Instruction Following Model
AI system designed to execute tasks based on specific commands or instructions provided by users.
Generality: 640