Francois Chollet (Google)
Francois Chollet is a software engineer at Google, where he leads the team that makes Keras, a major deep learning framework. He is the author of numerous publications in the field of deep learning, including a best-selling textbook. His current research focuses on abstraction generation, analogical reasoning, and how to achieve greater generality in artificial intelligence.
Melanie Mitchell (Santa Fe Institute)
Melanie Mitchell is the Davis Professor at the Santa Fe Institute. Her current research focuses on conceptual abstraction, analogy-making, and visual recognition in artificial intelligence systems. Melanie is the author or editor of six books and numerous scholarly papers in the fields of artificial intelligence, cognitive science, and complex systems. Her latest book is Artificial Intelligence: A Guide for Thinking Humans (Farrar, Straus, and Giroux).
Christian Szegedy (Google)
Christian Szegedy is a Machine Learning scientist at Google Research. He has a PhD in Mathematics from the University of Bonn, Germany. His most influential past works include the discovery of adversarial examples and various computer vision architectures for image recognition and object detection. He is the co-inventor of Batch-normalization. He is currently working on automated theorem proving and auto-formalization of mathematics via deep learning.
Related Events (a corresponding poster, oral, or spotlight)
2020 Tutorial: (Track1) Abstraction & Reasoning in AI systems: Modern Perspectives »
Mon Dec 7th 07:00 -- 09:30 PM Room None
More from the Same Authors
2016 Poster: DeepMath - Deep Sequence Models for Premise Selection »
Geoffrey Irving · Christian Szegedy · Alexander Alemi · Niklas Een · Francois Chollet · Josef Urban
2013 Poster: Deep Neural Networks for Object Detection »
Christian Szegedy · Alexander Toshev · Dumitru Erhan