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Found 5 papers, 1 papers with code
Accelerating science with human-aware artificial intelligence
Artificial intelligence (AI) models trained on published scientific findings have been used to invent valuable materials and targeted therapies, but they typically ignore the human scientists who continually alter the landscape of discovery.
Complementary artificial intelligence designed to augment human discovery
When we evaluate the promise of our predictions with first-principles equations, we demonstrate that increased complementarity of our predictions does not decrease and in some cases increases the probability that the predictions possess the targeted properties.
Accelerating science with human versus alien artificial intelligences
These AI approaches typically ignore the distribution of human prediction engines -- scientists and inventor -- who continuously alter the landscape of discovery and invention.
Towards Robust and Reproducible Active Learning Using Neural Networks
Finally, we conclude with a set of recommendations on how to assess the results using a new AL algorithm to ensure results are reproducible and robust under changes in experimental conditions.
Ranked #6 on
Active Learning
on CIFAR10 (10,000)
Asymptotic Analysis of Objectives based on Fisher Information in Active Learning
In particular, we show that FIR can be asymptotically viewed as an upper bound of the expected variance of the log-likelihood ratio.
