• 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 17 matches

• 2020
• 
  Nuria Ferres-Forga, Justin Halberda:
  Approximate Number System Discrimination Training for 7-8 Year Olds Improves Approximate, but not Exact, Arithmetics, and Only in Children With Low Pre-Training Arithmetic Scores. J. Numer. Cogn. 6(3): 275-303 (2020)
• 
  Tolga Ergen, Mert Pilanci:
  Training Convolutional ReLU Neural Networks in Polynomial Time: Exact Convex Optimization Formulations. CoRR abs/2006.14798 (2020)
• 2019
• 
  Ping Li, Phan-Minh Nguyen:
  On Random Deep Weight-Tied Autoencoders: Exact Asymptotic Analysis, Phase Transitions, and Implications to Training. ICLR 2019
• 2018
• 
  Mathieu Guillame-Bert, Olivier Teytaud:
  Exact Distributed Training: Random Forest with Billions of Examples. CoRR abs/1804.06755 (2018)
• 2017
• 
  Tianze Shi, Liang Huang, Lillian Lee:
  Fast(er) Exact Decoding and Global Training for Transition-Based Dependency Parsing via a Minimal Feature Set. EMNLP 2017: 12-23
• 
  Tianze Shi, Liang Huang, Lillian Lee:
  Fast(er) Exact Decoding and Global Training for Transition-Based Dependency Parsing via a Minimal Feature Set. CoRR abs/1708.09403 (2017)
• 2016
• 
  Penghang Yin, Shuai Zhang, Jack Xin, Yingyong Qi:
  Training Ternary Neural Networks with Exact Proximal Operator. CoRR abs/1612.06052 (2016)
• 2015
• 
  Pascal Vincent, Alexandre de Brébisson, Xavier Bouthillier:
  Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets. NIPS 2015: 1108-1116
• 
  Pascal Vincent:
  Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets. ICLR (Workshop) 2015
• 2014
• 
  Francisco Javier Martínez López, José Antonio Torres Arriaza, Sergio Martínez Puertas, María Mercedes Peralta López:
  Multilevel RBF to resolve classification problems with large training sets: new pseudo-exact procedure. Soft Comput. 18(11): 2245-2252 (2014)
• 
  Masashi Kitamura, Akiko Takeda, Satoru Iwata:
  Exact SVM training by Wolfe's minimum norm point algorithm. MLSP 2014: 1-6
• 2008
• 
  Maaike Harbers, Karel van den Bosch, Frank Dignum, John-Jules Ch. Meyer:
  A Cognitive Model for the Generation and Explanation of Behaviour in Virtual Training Systems. ExaCt 2008: 96-107
• 
  Shigeo Abe:
  Batch Support Vector Training Based on Exact Incremental Training. ICANN (1) 2008: 295-304
• 
  Thomas Finley, Thorsten Joachims:
  Training structural SVMs when exact inference is intractable. ICML 2008: 304-311
• 2004
• 
  Ahmad Emami, Frederick Jelinek:
  Exact training of a neural syntactic language model. ICASSP (1) 2004: 245-248
• 1998
• 
  H. C. Rae, Peter Sollich, Anthony C. C. Coolen:
  On-Line Learning with Restricted Training Sets: Exact Solution as Benchmark for General Theories. NIPS 1998: 316-322
• 1993
• 
  Lars Kai Hansen:
  Stochastic linear learning: Exact test and training error averages. Neural Networks 6(3): 393-396 (1993)