Skip to main navigation
Skip to search
Skip to main content
Princeton University Home
Help & FAQ
Link opens in a new tab
Search content at Princeton University
Home
Profiles
Research units
Facilities
Projects
Research output
Press/Media
Recasting gradient-based meta-learning as hierarchical bayes
Erin Grant
, Chelsea Finn
, Sergey Levine
, Trevor Darrell
,
Thomas Griffiths
Research output
:
Contribution to conference
›
Paper
›
peer-review
412
Link opens in a new tab
Scopus citations
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Recasting gradient-based meta-learning as hierarchical bayes'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Meta-learning
100%
Hierarchical Bayes
100%
Model-agnostic Meta-learning
100%
Gradient-based Meta-learning
100%
Meta-learning Strategy
100%
Performance Improvement
25%
Intelligent Agents
25%
Complex Function
25%
Gradient Descent
25%
Learning Procedure
25%
Probabilistic Inference
25%
Curvature Estimation
25%
Efficient Inference
25%
Computational Strategies
25%
Hierarchical Bayesian Model
25%
Model Identification
25%
Prior Learning
25%
Posterior Inference
25%
Approximate Inference
25%
Descent Procedure
25%
Function Approximator
25%
Bayesian Hierarchical Model
25%
Learning Episodes
25%
Mathematics
Approximates
100%
Bayesian Model
100%
Complex Variable Function
100%
Hierarchical Bayes Model
100%
Probabilistic Inference
100%
Bayesian Hierarchical Modeling
100%
Computer Science
Meta-Learning
100%
Learning Algorithm
44%
Intelligent Agent
11%
Theoretical Framework
11%
Gradient Descent
11%
Curvature Estimation
11%
Probabilistic Inference
11%
Complex Function
11%
Hierarchical Bayesian Model
11%