IC Colloquium: Understanding machine learning via exactly solvable models
Event details
| Date | 17.02.2020 |
| Hour | 10:15 › 11:15 |
| Location | |
| Category | Conferences - Seminars |
By: Lenka Zdeborova, Institute of Theoretical Physics CEA Saclay
IC Faculty candidate
Abstract:
The affinity between statistical physics and machine learning has long history. I will describe the main lines of this long-lasting friendship in the context of current theoretical challenges and open questions about deep learning. Theoretical physics often proceeds in terms of solvable synthetic models, I will describe the related line of work on solvable models of simple feed-forward neural networks. I will highlight a path forward to capture the subtle interplay between the structure of the data, the architecture of the network, and the optimization algorithms commonly used for learning.
Bio:
Lenka Zdeborová is a researcher at CNRS working in the Institute of Theoretical Physics in CEA Saclay, France. She received a PhD in physics from University Paris-Sud and from Charles University in Prague in 2008. She spent two years in the Los Alamos National Laboratory as the Director's Postdoctoral Fellow. In 2014, she was awarded the CNRS bronze medal, in 2016 Philippe Meyer prize in theoretical physics and an ERC Starting Grant, in 2018 the Irène Joliot-Curie prize. She is editorial board member for Journal of Physics A, Physical review E, Physical Review X and SIMODS. Lenka's expertise is in applications of methods developed in statistical physics, such as advanced mean field methods, replica method and related message passing algorithms, to problems in machine learning, signal processing, statistical inference and optimization.
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IC Faculty candidate
Abstract:
The affinity between statistical physics and machine learning has long history. I will describe the main lines of this long-lasting friendship in the context of current theoretical challenges and open questions about deep learning. Theoretical physics often proceeds in terms of solvable synthetic models, I will describe the related line of work on solvable models of simple feed-forward neural networks. I will highlight a path forward to capture the subtle interplay between the structure of the data, the architecture of the network, and the optimization algorithms commonly used for learning.
Bio:
Lenka Zdeborová is a researcher at CNRS working in the Institute of Theoretical Physics in CEA Saclay, France. She received a PhD in physics from University Paris-Sud and from Charles University in Prague in 2008. She spent two years in the Los Alamos National Laboratory as the Director's Postdoctoral Fellow. In 2014, she was awarded the CNRS bronze medal, in 2016 Philippe Meyer prize in theoretical physics and an ERC Starting Grant, in 2018 the Irène Joliot-Curie prize. She is editorial board member for Journal of Physics A, Physical review E, Physical Review X and SIMODS. Lenka's expertise is in applications of methods developed in statistical physics, such as advanced mean field methods, replica method and related message passing algorithms, to problems in machine learning, signal processing, statistical inference and optimization.
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Practical information
- General public
- Free
- This event is internal
Contact
- Host: Jim Larus