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Braverman Readings in Machine Learning. Key Ideas from Inception to Current State: International Conference Commemorating the 40th Anniversary of ... ... MA, USA, April 28-30, 2017, Invited Talks - Brossura
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Potential Functions for Signals and Symbolic Sequences.- Braverman's Spectrum and Matrix Diagonalization versus iK-Means: A Unified Framework for Clustering.- Compactness Hypothesis, Potential Functions, and Rectifying Linear Space in Machine Learning.- Conformal Predictive Distributions with Kernels.- On the Concept of Compositional Complexity.- On the Choice of a Kernel Function in Symmetric Spaces.- Causality Modeling and Statistical Generative Mechanisms.- One-Class Semi-Supervised Learning.- Prediction of Drug Efficiency by Transferring Gene Expression Data from Cell Lines to Cancer Patients.- On One Approach to Robot Motion Planning.- Geometrical Insights for Implicit Generative Modeling.- Deep Learning in the Natural Sciences: Applications to Physics.- From Reinforcement Learning to Deep Reinforcement Learning: An Overview.- A Man of Unlimited Capabilities.- Braverman and His Theory of Disequilibrium Economics.- Misha Braverman: My Mentor and My Model.- List of Braverman's papers published in the "Avtomatika itelemekhanika" journal, Moscow, Russia, and translated into English as "Automation and Remote Control" journal.
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