Braverman Readings in Machine Learning

Publications

volume 11100, 2018

Braverman Readings in Machine Learning. Key Ideas from Inception to Current State - International Conference Commemorating the 40th Anniversary of Emmanuil Braverman's Decease, Boston, MA, USA, April 28-30, 2017, Invited Talks Braverman Readings in Machine Learning 11100 (2018)

2017

Igor Mandel
Causality Modeling and Statistical Generative Mechanisms. Braverman Readings in Machine Learning (2017)
Valentina Sulimova, Vadim Mottl
Potential Functions for Signals and Symbolic Sequences. Braverman Readings in Machine Learning (2017)
Nicolas Borisov, Victor Tkachev, Anton Buzdin, Ilya Muchnik
Prediction of Drug Efficiency by Transferring Gene Expression Data from Cell Lines to Cancer Patients. Braverman Readings in Machine Learning (2017)
Evgeny Bauman, Konstantin Bauman
One-Class Semi-supervised Learning. Braverman Readings in Machine Learning (2017)
Lev I. Rozonoer
A Man of Unlimited Capabilities (in Memory of E. M. Braverman). Braverman Readings in Machine Learning (2017)
Peter J. Sadowski, Pierre Baldi
Deep Learning in the Natural Sciences: Applications to Physics. Braverman Readings in Machine Learning (2017)
Vladimir Vovk, Ilia Nouretdinov, Valery Manokhin, Alex J. Gammerman
Conformal Predictive Distributions with Kernels. Braverman Readings in Machine Learning (2017)
Boris G. Mirkin
Misha Braverman: My Mentor and My Model. Braverman Readings in Machine Learning (2017)
Mark Levin
Braverman and His Theory of Disequilibrium Economics. Braverman Readings in Machine Learning (2017)
Léon Bottou, Martín Arjovsky, David Lopez-Paz, Maxime Oquab
Geometrical Insights for Implicit Generative Modeling. Braverman Readings in Machine Learning (2017)
Mark A. Aizerman, Emmanuil M. Braverman, Lev I. Rozonoer
On the Choice of a Kernel Function in Symmetric Spaces. Braverman Readings in Machine Learning (2017)
Vadim Mottl, Oleg Seredin, Olga Krasotkina
Compactness Hypothesis, Potential Functions, and Rectifying Linear Space in Machine Learning. Braverman Readings in Machine Learning (2017)
Lev I. Rozonoer
On the Concept of Compositional Complexity. Braverman Readings in Machine Learning (2017)
Boris G. Mirkin
Braverman's Spectrum and Matrix Diagonalization Versus iK-Means: A Unified Framework for Clustering. Braverman Readings in Machine Learning (2017)
Vladimir Lumelsky
On One Approach to Robot Motion Planning. Braverman Readings in Machine Learning (2017)
Forest Agostinelli, Guillaume Hocquet, Sameer Singh, Pierre Baldi
From Reinforcement Learning to Deep Reinforcement Learning: An Overview. Braverman Readings in Machine Learning (2017)
Ilya Muchnik
List of Braverman's Papers Published in the "Avtomatika i telemekhanika" Journal, Moscow, Russia, and Translated to English as "Automation and Remote Control" Journal. Braverman Readings in Machine Learning (2017)