Upcoming Seminars

A Few Thoughts on Deep Network Approximation

Wed, Aug 12, 2020, 12:00 pm

Deep network approximation is a powerful tool of function approximation via composition. We will present a few new thoughts on deep network approximation from the point of view of scientific computing in practice: given an arbitrary width and depth of neural networks, what is the optimal approximation rate of various function...


Previous Seminars

Data Wrangling: How to Keep Your Data Workflows Orderly and Efficient

Thu, Jul 30, 2020, 12:00 pm

This webinar will provide several practical considerations to help you better manage your research data between the points of collection and analysis. We will review the principles of open research and cover best practices for documentation and metadata generation amidst collation, aggregation, and cleaning tasks.

Tradeoffs between Robustness and Accuracy

Wed, Jul 29, 2020, 12:00 pm

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).


Thematic Day on the Mean Field Training of Deep Neural Networks

Sat, Jul 25, 2020, 12:00 pm to 3:00 pm

12pm: Roberto I. Oliveira – TBA 

1pm: Konstantinos Spiliopoulos  - Mean field limits of neural networks: typical behavior and fluctuations

2pm: Huy Tuan Pham - A general framework for the mean field limit of multilayer neural networks

Managing Research Data

Thu, Jul 23, 2020, 12:00 pm

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.

On the foundations of computational mathematics, Smale’s 18th problem and the potential limits of AI

Wed, Jul 15, 2020, 12:00 pm

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...


Trainability and accuracy of artificial neural networks

Wed, Jul 8, 2020, 12:00 pm

The methods and models of machine learning (ML) are rapidly becoming de facto tools for the analysis and interpretation of large data sets. Complex classification tasks such as speech and image recognition, automatic translation, decision making, etc. that were out of reach a decade ago are now routinely performed by computers with a high...


Towards a mathematical understanding of supervised learning: What we know and what we don't know

Wed, Jul 1, 2020, 12:00 pm

Two of the biggest puzzles in machine learning are: Why is it so successful and why is it quite fragile? This talk will present a framework for unraveling these puzzles from the perspective of approximating functions in high dimensions.


Reimagining Digitized Newspapers with Machine Learning

Fri, May 15, 2020, 11:30 am

The 16 million digitized historic newspaper pages within Chronicling America, a joint initiative by the Library of Congress and the NEH, represent an incredibly rich resource for a wide range of users. Historians, journalists, genealogists, students, and members of the American public explore...


Running and Analyzing Large-scale Psychology Experiments

Fri, Mar 6, 2020, 12:00 pm

Psychology has traditionally been a laboratory discipline, focused on small-scale experiments conducted in person. However, recent technological innovations have made it possible to collect far more data from far more people than ever before.


Preference Modeling with Context-Dependent Salient Features

Mon, Feb 24, 2020, 4:00 pm

This talk considers the preference modeling problem and addresses the fact that pairwise comparison data often reflects irrational choice, e.g. intransitivity. Our key observation is that two items compared in isolation from other items may be compared based on only a salient subset of features.