Upcoming Events

Standard machine learning produces models that are highly accurate on average but that degrade dramatically when the test distribtion deviates from the training distribution. While one can train robust models, this often comes at the expense of standard accuracy (on the training distribution).

This webinar will go over tips on how to keep track of your data files more efficiently, better organize your data files, and how to manage your data, code and other research materials, to save yourself headaches down the road.

There is a profound optimism on the impact of deep learning (DL) and AI in the sciences with Geoffrey Hinton concluding that 'They should stop educating radiologists now'. However, DL has an Achilles heel: it is universally unstable so that small changes in the initial data can lead to large errors in the final result. This has been documented...

A two-day virtual workshop covering theory and hands-on tutorials on the software package for molecular simulation with machine learning (ML) tools developed at the Computational Chemical Science Center “Chemistry in Solution and at Interfaces” (http://chemlabs.princeton.edu/ccsc/(...