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Active Learning for Discrete Latent Variable Models
Aditi Jha
, Zoe C. Ashwood
,
Jonathan W. Pillow
Princeton Neuroscience Institute
Princeton Language and Intelligence (PLI)
Research output
:
Contribution to journal
›
Letter
›
peer-review
6
Link opens in a new tab
Scopus citations
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Dive into the research topics of 'Active Learning for Discrete Latent Variable Models'. Together they form a unique fingerprint.
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Keyphrases
Active Learning
100%
Latent Variable Models
100%
Discrete Latent Variables
100%
Generalized Linear Model
40%
Machine Learning
20%
Input Selection
20%
Neuroscience
20%
Model Observations
20%
Data Required
20%
Infomax
20%
Large Gain
20%
Regression Model
20%
Hidden Markov Model
20%
Scientific Disciplines
20%
Discrete States
20%
Approximate Method
20%
Fisher Information
20%
Gaussian Process Regression
20%
Engineering Discipline
20%
Variational Inference
20%
Mutual Information Maximization
20%
Amortized Inference
20%
Information Input
20%
Mixture of Linear Regression
20%
Latent Variable Regression
20%
World Data
20%
Latent State
20%
Linear Gaussian
20%
Powerful Class
20%
Animal Decision-making
20%
Psychology
Regression Model
100%
Generalized Linear Model
100%
Hidden Markov Model
100%
Decision Making
50%
Neurobiology
50%
Gaussian Distribution
50%
Linear Regression
50%
Psychology
50%
Mathematics
Latent Variable Model
100%
Regression Model
50%
Hidden Markov Model
50%
Generalized Linear Model
50%
Mutual Information
25%
Approximates
25%
Gaussian Distribution
25%
Wide Variety
25%
Real-World Data
25%
Discrete State
25%
Scientific Discipline
25%
Fisher Information
25%
Linear Regression Analysis
25%
Computer Science
Active Learning
100%
Latent Variable Model
100%
Mutual Information
20%
Decision-Making
20%
Scientific Discipline
20%
Engineering Discipline
20%
Fisher Information
20%
Variable Regression
20%
Machine Learning
20%
Learning System
20%
Social Sciences
Latent Variable
100%
Hidden Markov Model
40%
Linear Models
40%
Psychology
20%
Scientific Discipline
20%
Decision Making
20%
Neurobiology
20%
Linear Regression Analysis
20%
Learning System
20%
Machine Learning
20%
Neurology
20%
Fisher Information
20%
Neuroscience
Latent Variable Model
100%
Generalized Linear Model
50%
Decision-Making
25%
Mutual Information
25%
Neuroscience
25%
Hidden Markov Model
25%