Monday, Feb 11, 2019 |
|
Session Chair: Lenka Zdeborová (CEA Saclay)
|
| 8:50am |
Lars Bildsten (KITP Director) |
Welcome[Podcast][Aud][Cam] |
| 9:00am |
Andrea Montanari (Stanford) |
Mean Field Concepts in Machine Learning[Podcast][Aud][Cam] |
| 10:00am |
Florent Krzakala (ENS Paris) |
Statistical Physics and Machine Learning[Slides][Podcast][Aud][Cam] |
| 11:00am |
|
| 11:30am |
Greg Ver Steeg (UCS) |
Exact Information Bottlenecks for Arbitrary Distributions with Echo Noise[Slides][Podcast][Aud][Cam] |
| 12:30pm |
|
|
Session Chair: Kyle Cranmer (NYU)
|
| 2:00pm |
Chiara Cammarota (King's) |
Rough-glassy Landscapes from Inference to Machine Learning[Slides][Podcast][Aud][Cam] |
| 3:00pm |
Pratik Chaudhari (UCLA) |
A Picture of the Energy Landscape of Deep Neural Networks[Slides][Podcast][Aud][Cam] |
| 4:00pm |
|
| 4:30pm |
Michael Bronstein (Imperial) |
Deep Learning on Graphs: from Astrophysics to Fake News Detection[Podcast][Aud][Cam] |
| 5:30pm |
|
Tuesday, Feb 12, 2019 |
|
Session Chair: Giuseppe Carleo (Flatiron)
|
| 9:00am |
Michele Parrinello (ETH Zurich) |
Machine Learning and Enhanced Sampling[Podcast][Aud][Cam] |
| 10:00am |
Kieron Burke (UC Irvine) |
Machine Learning in Electronic Structure: Finding Better Density Functionals than Humans do[Slides][Podcast][Aud][Cam] |
| 11:00am |
|
| 11:30am |
Isaac Tamblyn (NRC Canada) |
Rubber Ducks and a Sea of Electrons - Designing Neural Networks which Incorporate Physics[Podcast][Aud][Cam] |
| 12:30pm |
|
| 2:00pm |
Mihai A. Petrovici (U of Bern) |
Computers Like Brains[Slides][Podcast][Aud][Cam] |
| 3:00pm |
Benjamin Wandelt (Flatiron) |
Bayesian Likelihood-free Inference in Cosmology and Information Maximizing Neural Networks[Slides][Podcast][Aud][Cam] |
| 4:00pm |
|
| 4:30pm |
Panel |
Open problems[Podcast][Aud][Cam] |
| 5:30pm |
|
| 6:00pm |
|
| 8:00pm |
|
Wednesday, Feb 13, 2019 |
|
|
| 9:00am |
Yashar Hezaveh (Stanford) |
Mapping Distant Galaxies with Machine Learning[Podcast][Aud][Cam] |
| 10:00am |
Fernanda Psihas (UT Austin) |
Successes and Perspectives of Deep Learning Applications to Neutrino Physics[Podcast][Aud][Cam] |
| 11:00am |
|
| 11:30am |
Jaan Altosaar (Princeton) |
Hierarchical Variational Approximations for Physics Models |
| 12:30pm |
|
| 2:00pm |
|
Thursday, Feb 14, 2019 |
|
Session Chair: Giuseppe Carleo (Flatiron)
|
| 9:00am |
Eun-Ah Kim (Cornell) |
Machine Learning Quantum Emergence[Slides][Podcast][Aud][Cam] |
| 10:00am |
Titus Neupert (U of Zurich) |
Excitations and Symmetries in Neural Network Variational Wave Functions[Slides][Podcast][Aud][Cam] |
| 11:00am |
|
| 11:30am |
Juan Carrasquilla (Vector) |
Learning Quantum States with Generative Models[Slides][Podcast][Aud][Cam] |
| 12:30pm |
|
|
Session Chair: Lenka Zdeborová (CEA Saclay)
|
| 2:00pm |
Masoud Mohseni (Google) |
Learning to Learn with Quantum Neural Networks |
| 3:00pm |
Andrey Lokhov (LANL) |
Uncovering the Behavior of Quantum Annealers with Statistical Learning[Slides][Podcast][Aud][Cam] |
| 4:00pm |
|
| 4:30pm |
Panel |
Open problems |
| 5:30pm |
|
| 6:00pm |
|
| 8:00pm |
|
Friday, Feb 15, 2019 |
|
Session Chair: Rose Yu (Northeastern)
|
| 9:00am |
James Halverson
(Northeastern) |
Machine Learning Geometry and String Theory[Slides][Podcast][Aud][Cam] |
| 10:00am |
Stephan Mandt (UC Irvine) |
Physics-inspired Machine Learning: Non-equilibrium, Perturbation Theory, and Goldstone Modes[Podcast][Aud][Cam] |
| 11:00am |
|
| 11:30am |
Phiala Shanahan (MIT) |
Machine Learning for Lattice Quantum Field Theory Calculations[Slides][Podcast][Aud][Cam] |
| 12:30pm |
|
|
Session Chair: Lenka Zdeborová (CEA Saclay)
|
| 2:00pm |
Yasaman Bahri (Google AI) |
Deep Neural Networks as Gaussian Processes[Slides][Podcast][Aud][Cam] |
| 3:00pm |
|
|
| 4:00pm |
|
