• case-insensitive prefix search: default
  e.g., sig matches "SIGIR" as well as "signal"
• exact word search: append dollar sign ($) to word
  e.g., graph$ matches "graph", but not "graphics"
• boolean and: separate words by space
  e.g., codd model
• boolean or: connect words by pipe symbol (|)
  e.g., graph|network

Update May 7, 2017: Please note that we had to disable the phrase search operator (.) and the boolean not operator (-) due to technical problems. For the time being, phrase search queries will yield regular prefix search result, and search terms preceded by a minus will be interpreted as regular (positive) search terms.

found 18 matches

• 2018
• 
  Lev I. Rozonoer, Boris G. Mirkin, Ilya Muchnik:
  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. Lecture Notes in Computer Science 11100, Springer 2018, ISBN 978-3-319-99491-8 [contents]
• 2017
• 
  Forest Agostinelli, Guillaume Hocquet, Sameer Singh, Pierre Baldi:
  From Reinforcement Learning to Deep Reinforcement Learning: An Overview. Braverman Readings in Machine Learning 2017: 298-328
• 
  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: 128-147
• 
  Evgeny Bauman, Konstantin Bauman:
  One-Class Semi-supervised Learning. Braverman Readings in Machine Learning 2017: 189-200
• 
  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: 201-212
• 
  Léon Bottou, Martín Arjovsky, David Lopez-Paz, Maxime Oquab:
  Geometrical Insights for Implicit Generative Modeling. Braverman Readings in Machine Learning 2017: 229-268
• 
  Mark Levin:
  Braverman and His Theory of Disequilibrium Economics. Braverman Readings in Machine Learning 2017: 333-340
• 
  Vladimir Lumelsky:
  On One Approach to Robot Motion Planning. Braverman Readings in Machine Learning 2017: 213-228
• 
  Igor Mandel:
  Causality Modeling and Statistical Generative Mechanisms. Braverman Readings in Machine Learning 2017: 148-186
• 
  Boris G. Mirkin:
  Braverman's Spectrum and Matrix Diagonalization Versus iK-Means: A Unified Framework for Clustering. Braverman Readings in Machine Learning 2017: 32-51
• 
  Boris G. Mirkin:
  Misha Braverman: My Mentor and My Model. Braverman Readings in Machine Learning 2017: 341-348
• 
  Vadim Mottl, Oleg Seredin, Olga Krasotkina:
  Compactness Hypothesis, Potential Functions, and Rectifying Linear Space in Machine Learning. Braverman Readings in Machine Learning 2017: 52-102
• 
  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: 349-351
• 
  Lev I. Rozonoer:
  On the Concept of Compositional Complexity. Braverman Readings in Machine Learning 2017: 122-127
• 
  Lev I. Rozonoer:
  A Man of Unlimited Capabilities (in Memory of E. M. Braverman). Braverman Readings in Machine Learning 2017: 331-332
• 
  Peter J. Sadowski, Pierre Baldi:
  Deep Learning in the Natural Sciences: Applications to Physics. Braverman Readings in Machine Learning 2017: 269-297
• 
  Valentina Sulimova, Vadim Mottl:
  Potential Functions for Signals and Symbolic Sequences. Braverman Readings in Machine Learning 2017: 3-31
• 
  Vladimir Vovk, Ilia Nouretdinov, Valery Manokhin, Alex Gammerman:
  Conformal Predictive Distributions with Kernels. Braverman Readings in Machine Learning 2017: 103-121