Nadav Cohen
News
Research
My research focuses on the theoretical and algorithmic foundations of deep learning.
In particular, I am interested in mathematically analyzing aspects of expressiveness, optimization and generalization, with the goal of deriving theoretically founded procedures and algorithms that will improve practical performance.
Blog Posts
Selected Talks
Simons Institute Workshop on Frontiers of Deep Learning 2019 (Berkeley, CA, USA): [videoslides]
ICERM Workshop on Theory and Practice in Machine Learning and Computer Vision 2019 (Providence, RI, USA): [videoslides]
ICML 2018 (Stockholm, Sweden): [videoslides]
ICLR 2018 (Vancouver, Canada): [videoslides]
Symposium on the Mathematical Theory of Deep Neural Networks 2018 (Princeton, NJ, USA): [videoslides]
Symposium on Physics and Machine Learning 2017 (New York City, NY, USA): [slides]
Mathematics of Deep Learning Workshop 2017 (Berlin, Germany): [slides]
AAAI Spring Symposium Series 2017 (Palo Alto, CA, USA): [slides]
CVPR 2017 (Honolulu, HI, USA): [slides]
GAMM 2017 (Weimar, Germany): [slides]
NIPS 2016 (Barcelona, Spain): [slides]
ICML 2016 (New York City, NY, USA): [videoslides]
COLT 2016 (New York City, NY, USA): [videoslides]
