Machine learning can make these calculations using samples of simulated data, a process that takes closer to 24 hours and creates a database that astrophysicists can sample from in a matter of seconds ...

To flexibly and robustly handle diverse problems, AI systems can leverage dual-process theories of human cognition that ...

A new machine learning approach tries to better emulate the human brain, in hopes of creating more capable agentic AI.

The aim of the course will be to cover interesting, influential papers in learning theory (with a particular emphasis on deep learning theory) . There are two goals (1) explore different frameworks, ...

Catalog description: Presents the underlying theory behind machine learning in proofs-based format. Answers fundamental questions about what learning means and what can be learned via formal models of ...

Researchers have used machine learning to dramatically speed up the processing time when simulating galaxy evolution coupled with supernova explosion. This approach could help us understand the ...

Maarten Sap, a computer scientist at Carnegie Mellon University, fed more than 1,000 theory of mind tests into large language models and found that the most advanced transformers, like ChatGPT and ...

Machine learning techniques, including neural networks and reinforcement learning, are being deployed to approximate metrics on Calabi–Yau manifolds, classify geometric phases, and even generate ...

“It’s good that people do this machine learning business, because I’m sure we will need it at some point,” Van Riet said. But first “we need to think about the underlying principles, the patterns.

According to Indeed.com, job postings for Machine Learning Engineers have grown 344% from 2015-2018 and a Machine Learning Engineer position commands an average base salary of $146,085 per year.