Advancing Deep Active Learning & Data Subset Selection: Unifying Principles With Information-Theory Intuitions
- - =#n/a
- - Academic And Stem
- - Accounting, Finance And Commerce
- - Archaeological Sciences And History
- - Biological And Life Sciences
- - Botanical Studies And Agriculture
- - Chemical Sciences And Chemistry
- - Computer Science & Engineering And It
- - Construction Management And Civil Engineering
- - Crime, Law And Forensics
- - Environmental Studies: Ecology, Conservation, And Management
- - Geological And Planetary Sciences
- - Healthcare, Medicine And Clinical Studies
- - Linguistics And Language Science
- - Mechanical, Manufacturing, And Design Engineering
- - Media And Journalism
- - Mental Health And Psychological Studies
- - Nutritional And Food Science
- - Pharmacy, Pharmacology And Pharmaceutical Sciences
- - Philosophy And Ethics
- - Physics: Science And Engineering
- - Political Studies
- - Probability, Statistics And Mathematics
- - Research In Education: Pedagogy And Learning Processes
- - Society, Culture And Social Studies
- - Systems Engineering: Communication, Electrical And Elecronics
Book Details :
Advancing Deep Active Learning & Data Subset Selection: Unifying Principles With Information-Theory Intuitions