• 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 41 matches

• 2023
• 
  Bernhard Bermeitinger, Tomas Hrycej, Siegfried Handschuh:
  Make Deep Networks Shallow Again. KDIR 2023: 339-346
• 
  Ondrej Mamula, Tomás Pitner, Martin Strelec, David Hrycej, Frantisek Kasl, Petr Janecek:
  Technology Approaches to Metering in Flexibility Aggregation. ICUMT 2023: 122-127
• 
  Bernhard Bermeitinger, Tomas Hrycej, Siegfried Handschuh:
  Make Deep Networks Shallow Again. CoRR abs/2309.08414 (2023)
• 2022
• 
  Tomas Hrycej, Bernhard Bermeitinger, Siegfried Handschuh:
  Training Neural Networks in Single vs. Double Precision. KDIR 2022: 307-314
• 
  Tomas Hrycej, Bernhard Bermeitinger, Siegfried Handschuh:
  Number of Attention Heads vs. Number of Transformer-encoders in Computer Vision. KDIR 2022: 315-321
• 
  Martin Strelec, Ondrej Mamula, Tomás Pitner, Petr Janecek, David Hrycej, Frantisek Kasl:
  Requirements on Communication Technologies for Flexibility Aggregation Management. ICUMT 2022: 153-158
• 
  Tomas Hrycej, Bernhard Bermeitinger, Siegfried Handschuh:
  Training Neural Networks in Single vs Double Precision. CoRR abs/2209.07219 (2022)
• 
  Tomas Hrycej, Bernhard Bermeitinger, Siegfried Handschuh:
  Number of Attention Heads vs Number of Transformer-Encoders in Computer Vision. CoRR abs/2209.07221 (2022)
• 2019
• 
  Bernhard Bermeitinger, Tomas Hrycej, Siegfried Handschuh:
  Representational Capacity of Deep Neural Networks: A Computing Study. KDIR 2019: 532-538
• 
  Bernhard Bermeitinger, Tomas Hrycej, Siegfried Handschuh:
  Singular Value Decomposition and Neural Networks. ICANN (2) 2019: 153-164
• 
  Bernhard Bermeitinger, Tomas Hrycej, Siegfried Handschuh:
  Singular Value Decomposition and Neural Networks. CoRR abs/1906.11755 (2019)
• 
  Bernhard Bermeitinger, Tomas Hrycej, Siegfried Handschuh:
  Representational Capacity of Deep Neural Networks - A Computing Study. CoRR abs/1907.08475 (2019)
• 2008
• 
  Tomas Hrycej, Christian Manuel Strobel:
  Extraction of Maximum Support Rules for the Root Cause Analysis. Computational Intelligence in Automotive Applications 2008: 89-99
• 2007
• 
  Christian Manuel Strobel, Tomas Hrycej:
  Root Cause Analysis for Quality Management. GfKl 2007: 405-412
• 
  Tomas Hrycej, Matthias Grabert:
  Warranty Cost Forecast Based on Car Failure Data. IJCNN 2007: 108-113
• 2006
• 
  Rafael Schmidt, Tomas Hrycej, Eric Stützle:
  Multivariate distribution models with generalized hyperbolic margins. Comput. Stat. Data Anal. 50(8): 2065-2096 (2006)
• 
  Christian Manuel Strobel, Tomas Hrycej:
  A Data Mining Approach to the Joint Evaluation of Field and Manufacturing Data in Automotive Industry. PKDD 2006: 625-632
• 2005
• 
  Eric A. Sützle, Tomas Hrycej:
  Numerical method for estimating multivariate conditional distributions. Comput. Stat. 20(1): 151-176 (2005)
• 2004
• 
  Tomas Hrycej, Jochen Hipp:
  Outlier Detection by Rareness Assumption. GI Jahrestagung (1) 2004: 244-248
• 
  Matthias Grabert, Markus Prechtel, Tomas Hrycej, Winfried Günther:
  An Early Warning System for Vehicle Related Quality Data. ICDM 2004: 88-95
• 2002
• 
  Eric A. Stützle, Tomas Hrycej:
  Estimating Multivariate Conditional Distributions - An Application to the Truck Sales Forecast. OR 2002: 492-497
• 2001
• 
  Tomas Hrycej:
  Estimates of average complexity of neurocontrol algorithms. Neural Networks 14(8): 1089-1098 (2001)
• 2000
• 
  Wilm Eggert, Tomas Hrycej:
  Logit Demand Function with Embedded Neural Network Based Utility Function. IJCNN (5) 2000: 285-290
• 
  Tomas Hrycej:
  An Estimate of the Number of Samples to Convergence for Critic Algorithms. IJCNN (3) 2000: 227-232
• 1997
• 
  Tomas Hrycej:
  Error measure for identifying chaotic attractors. ESANN 1997
• 
  Tomas Hrycej:
  Global and local optimization in adaptive neurocontrol. ICNN 1997: 848-853
• 1995
• 
  Tomas Hrycej:
  Stability and equilibrium points in neurocontrol. ICNN 1995: 617-621
• 
  Tomas Hrycej:
  Identifying chaotic attractors with neural networks. ICNN 1995: 2664-2668
• 1993
• 
  Tomas Hrycej:
  Methods for decomposition of learning in neural networks. University of Ulm, Germany, 1993, pp. 1-147
• 
  Tomas Hrycej:
  A Temporal Extension of Prolog. J. Log. Program. 15(1&2): 113-145 (1993)

skipping 11 more matches