Recent seminars

Europe/Lisbon — Online

Masoud Mohseni

Masoud Mohseni, Google Quantum Artificial Intelligence Laboratory.
TensorFlow Quantum: An open source framework for hybrid quantum-classical machine learning.

In this talk, I introduce TensorFlow Quantum (TFQ), an open source library that was launched by Google in March 2020, for the rapid prototyping of hybrid quantum-classical models for classical or quantum data.This framework offers high-level abstractions for the design, training, and testing of both discriminative and generative quantum models under TensorFlow and supports high-performance quantum circuit simulators. I provide an overview of the software architecture and building blocks through several examples and illustrate TFQ functionalities via constructing hybrid quantum-classical convolutional neural networks for quantum state classification.

Video

Additional file

Mohseni_slides.pdf

Europe/Lisbon — Online

Marylou Gabrié

Marylou Gabrié, Center for Data Science, NYU and Flatiron Institute, CCM.
Progress and hurdles in the statistical mechanics of deep learning.

Understanding the great performances of deep neural networks is a very active direction of research with contributions coming from a wide variety of fields. The statistical mechanics of learning is a theoretical framework dating back to the 80s studying learning problems from a physicist viewpoint and using tools from the physics of disordered systems. In this talk, I will first go over this traditional framework, which relies on the teacher-student scenario, bayesian analysis and mean-field approximations. Then I will discuss some recent advances in the corresponding analysis of modern deep neural network, and highlight remaining challenges.

Video

Additional file

Gabrie_slides.pdf

Europe/Lisbon — Online

João Miranda Lemos

João Miranda Lemos, Instituto Superior Técnico and INESC-ID.
Reinforcement Learning and Adaptive Control.

The aim of this seminar is to explain, to a wide audience, how to combine optimal control techniques with reinforcement learning, by using approximate dynamic programming, and artificial neural networks, to obtain adaptive optimal controllers. Although with roots since the end of the XX century, this problem has been the subject of an increasing attention. In addition to the promising tools that it offers to tackle difficult nonlinear problems with major engineering importance (ranging from robotics to biomedical engineering and beyond), it has the charm of creating a meeting point between the control and machine learning research communities.

Video

Additional file

Lemos_JM_slides.pdf

Europe/Lisbon — Online

Francisco C. Santos

Francisco C. Santos, Instituto Superior Técnico and INESC-ID.
Climate action and cooperation dynamics under uncertainty.

When attempting to avoid global warming, individuals often face a social dilemma in which, besides securing future benefits, it is also necessary to reduce the chances of future losses. In this talk, I will resort to game theory and populations of adaptive agents to offer a theoretical analysis of this type of dilemmas, in which the risk of failure plays a central role in individual decisions. I will discuss both deterministic dynamics in large populations, and stochastic social learning dynamics in finite populations. This class of models can be shown to capture some of the essential features discovered in recent key experiments while allowing one to extend in non-trivial ways the experimental conditions to regions of practical interest. Moreover, this approach leads us to identify useful parallels between ecological and socio-economic systems, particularly in what concerns the evolution and self-organization of their institutions. Particularly, our results suggest that global coordination for a common good should be attempted through a polycentric structure of multiple small-scale agreements, in which perception of risk is high and uncertainty in collective goals is minimized. Whenever the perception of risk is low, our results indicate that sanctioning institutions may significantly enhance the chances of coordinating to tame the planet's climate, as long as they are implemented in a bottom-up manner. I will discuss the impact on public goods dilemmas of heterogeneous political networks and wealth inequality, including distribution of wealth representative of existing inequalities among nations. Finally, I will briefly discuss the impact of scientific uncertainty — both in what concerns the collective targets and the time window available for action — on individuals' strategies and polarization of preferences.

Video

Europe/Lisbon — Online

Kyle Cranmer

Kyle Cranmer, New York University.
On the Interplay between Physics and Deep Learning.

The interplay between physics and deep learning is typically divided into two themes.

The first is “physics for deep learning,” where techniques from physics are brought to bear on understanding dynamics of learning. The second is “deep learning for physics,” which focuses on application of deep learning techniques to physics problems. I will present a more nuanced view of this interplay with examples of how the structure of physics problems have inspired advances in deep learning and how it yields insights on topics such as inductive bias, interpretability, and causality.

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Session slides