Upcoming Events

Generalized Lagrangian Networks

Generalized Lagrangian Networks
Fri, Jan 31, 2020, 12:00 pm

Even though neural networks enjoy widespread use, they still struggle to learn the basic laws of physics. How might we endow them with better inductive biases? Recent work (Greydanus et al. 2019) proposed Hamiltonian Neural Networks (HNNs) which can learn the Hamiltonian of a physical system from data using a neural network.

Location: 26 Prospect Ave, Classroom 103
Speaker(s):
Sam Greydanus
Google AI
Tags: ML for the Sciences
Subscribe to Events | More RSS feeds

Events Archive

Solving Inverse Problems with Data-driven Priors

Solving Inverse Problems with Data-driven Priors

I will present a Bayesian machine learning architecture that combines a physically motivated parameterization and an analytic error model for the likelihood with a deep generative model providing a powerful data-driven prior for complex signals.

Location: 26 Prospect Ave, Classroom 103
Speaker(s):
Peter Melchior
Princeton University
Tags: ML for the Sciences

Barks, Bubbles and Brownies!

Barks, Bubbles and Brownies!

We will have therapy dogs available to help calm you, bubble tea to hydrate you and brownies to assist with that chocolate fix.

RSVP not necessary but first come, first serve for the bubble tea!

Machine Learning for the Sciences

Machine Learning for the Sciences

Taking place every other Friday. Lunch will be provided.

Location: 26 Prospect Ave, Auditorium 103
Tags: Seminars

Grad Students: Interested in Data Science?

Grad Students: Interested in Data Science?

Center for Statistics and Machine Learning is holding an informal graduate information session about its certificate program.

Lunch will be served!

Location: 26 Prospect Ave

Recent Advances in Non-Convex Distributed Optimization and Learning

Recent Advances in Non-Convex Distributed Optimization and Learning
We consider a class of distributed non-convex optimization problems, in which a number of agents are connected by a communication network, and they collectively optimize a sum of (possibly non-convex and non-smooth) local objective functions. This type of problem has gained some recent popularities, especially in the application of distributed...
Location: B205 Engineering Quadrangle
Speaker(s):
Mingyi Hong
University of Minnesota
Tags: Seminars

Diving into TensorFlow 2.0

Diving into TensorFlow 2.0

Description: Please join us for this 90-minute workshop, taught at an intermediate level. We will briefly introduce TensorFlow 2.0, then dive in to writing a few flavors of neural networks. Attendees will need a laptop and an internet connection.

Location: Lewis Science Library 138
Speaker(s):
Josh Gordon
Google
Diving in to TensorFlow 2.0
Tags: Seminars

Can learning theory resist deep learning?

Can learning theory resist deep learning?

Abstract: 

Location: CS 105
Speaker(s):
Francis Bach
INRIA
Tags: Seminars

Machine Learning for the Sciences

Machine Learning for the Sciences

Taking place every other Friday. Lunch will be provided.

Location: 26 Prospect Ave, Auditorium 103
Tags: Seminars

Convergence Rates of Stochastic Algorithms in Nonsmooth Nonconvex Optimization

Convergence Rates of Stochastic Algorithms in Nonsmooth Nonconvex Optimization

Abstract:

Location: B205 Engineering Quadrangle
Speaker(s):
Dmitriy Drusvyatskiy
University of Washington
Convergence Rates of Stochastic Algorithms in Nonsmooth Nonconvex Optimization
Tags: Seminars

Exploration by Optimization in Partial Monitoring

Exploration by Optimization in Partial Monitoring

Abstract:   

Location: Sherrerd 101
Speaker(s):
Csaba Szepesvari
Professor of Computing Science, University of Alberta
Tags: Seminars

Pages