Probabilistic machine learning and artificial intelligence
Nature, 2015. [All Versions]. Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The…
- from
- AGI & CoCoSci
- added
- 2026-10-10
- likes
- 0
similar
-
Probabilistic Machine Learning: An Introduction probml.github.io
-
Probabilistic models of cognition: Conceptual foundations cell.com
Trends in Cognitive Sciences, 2006. [All Versions]. Remarkable progress in the mathematics and computer science of probability has led to a revolution in the scope of probabilistic models. In particular, ‘sophisticated’ probabilistic methods apply to structured relational…
-
Building Machines That Learn and Think Like People cambridge.org
Behavioral and Brain Sciences, 2017. [All Versions]. [Preprint]. Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as…
-
Beneficial and harmful explanatory machine learning link.springer.com
Machine Learning, 2021. [All Versions]. Given the recent successes of Deep Learning in AI there has been increased interest in the role and need for explanations in machine learned theories. A distinct notion in this context is that of Michie’s definition of ultra-strong…
-
Human-level concept learning through probabilistic program induction science.org
Science, 2015. [All Versions]. [Preprint]. [Supplementary Material]. People learning new concepts can often generalize successfully from just a single example, yet machine learning algorithms typically require tens or hundreds of examples to perform with similar accuracy…
-
Scientific discovery in the age of artificial intelligence nature.com
Nature, 2023. [All Versions]. A review article that examines breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the…
AGI & CoCoSci › Papers > Bayesian Modeling > Bayesian Induction: “Nature, 2015. [All Versions]. Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The…”