Tomas Hrycej - researchr alias

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2023

Make Deep Networks Shallow AgainBernhard Bermeitinger, Tomas Hrycej, Siegfried Handschuh. ic3k 2023: 339-346 [doi]

2022

Training Neural Networks in Single vs. Double PrecisionTomas Hrycej, Bernhard Bermeitinger, Siegfried Handschuh. ic3k 2022: 307-314 [doi]

Number of Attention Heads vs. Number of Transformer-encoders in Computer VisionTomas Hrycej, Bernhard Bermeitinger, Siegfried Handschuh. ic3k 2022: 315-321 [doi]

2019

Singular Value Decomposition and Neural NetworksBernhard Bermeitinger, Tomas Hrycej, Siegfried Handschuh. icann 2019: 153-164 [doi]

Representational Capacity of Deep Neural Networks: A Computing StudyBernhard Bermeitinger, Tomas Hrycej, Siegfried Handschuh. ic3k 2019: 532-538 [doi]

2008

Extraction of Maximum Support Rules for the Root Cause AnalysisTomas Hrycej, Christian Manuel Strobel. In Danil V. Prokhorov, editor, Computational Intelligence in Automotive Applications. Volume 132 of Studies in Computational Intelligence, pages 89-99, Springer, 2008. [doi]

2007

Warranty Cost Forecast Based on Car Failure DataTomas Hrycej, Matthias Grabert. ijcnn 2007: 108-113 [doi]

Root Cause Analysis for Quality ManagementChristian Manuel Strobel, Tomas Hrycej. gfkl 2008: 405-412 [doi]

2006

Multivariate distribution models with generalized hyperbolic marginsRafael Schmidt, Tomas Hrycej, Eric Stützle. csda, 50(8):2065-2096, 2006. [doi]

A Data Mining Approach to the Joint Evaluation of Field and Manufacturing Data in Automotive IndustryChristian Manuel Strobel, Tomas Hrycej. pkdd 2006: 625-632 [doi]

2005

Numerical method for estimating multivariate conditional distributionsEric A. Sützle, Tomas Hrycej. cstat, 20(1):151-176, 2005. [doi]

2004

An Early Warning System for Vehicle Related Quality DataMatthias Grabert, Markus Prechtel, Tomas Hrycej, Winfried Günther. icdm2 2004: 88-95 [doi]

Outlier Detection by Rareness AssumptionTomas Hrycej, Jochen Hipp. gi 2004: 244-248

2002

Estimating Multivariate Conditional Distributions - An Application to the Truck Sales ForecastEric A. Stützle, Tomas Hrycej. or 2003: 492-497 [doi]

2001

Estimates of average complexity of neurocontrol algorithmsTomas Hrycej. NN, 14(8):1089-1098, 2001. [doi]

2000

Logit Demand Function with Embedded Neural Network Based Utility FunctionWilm Eggert, Tomas Hrycej. ijcnn 2000: 285-290 [doi]

An Estimate of the Number of Samples to Convergence for Critic AlgorithmsTomas Hrycej. ijcnn 2000: 227-232 [doi]

1997

Error measure for identifying chaotic attractorsTomas Hrycej. esann 1997:

Global and local optimization in adaptive neurocontrolTomas Hrycej. icnn 1997: 848-853 [doi]

1995

Identifying chaotic attractors with neural networksTomas Hrycej. icnn 1995: 2664-2668 [doi]

Stability and equilibrium points in neurocontrolTomas Hrycej. icnn 1995: 617-621 [doi]

1993

A Temporal Extension of PrologTomas Hrycej. jlp, 15(1&2):113-145, 1993.

Methods for decomposition of learning in neural networksTomas Hrycej. PhD thesis, University of Ulm, Germany, 1993. [doi]

1992

Modular learning in neural networks - a modularized approach to neural network classificationTomas Hrycej. Sixth-Generation computer technology series, Wiley, 1992.

Supporting supervised learning by self-organizationTomas Hrycej. ijon, 4(1):17-30, 1992.

1990

Self-organization by delta ruleTomas Hrycej. ijcnn 1990: 307-312 [doi]

A modular architecture for efficient learningTomas Hrycej. ijcnn 1990: 557-562 [doi]

Gibbs Sampling in Bayesian NetworksTomas Hrycej. ai, 46(3):351-363, 1990.

1989

Unsupervised Learning by Backward InhibitionTomas Hrycej. IJCAI 1989: 170-175

1988

Intelligent Backtracking with Structured ContextsTomas Hrycej. ecai 1988: 589-594

Temporal PrologTomas Hrycej. ecai 1988: 296-301

1987

An Efficient Algorithm for Reasoning about Time IntervalsTomas Hrycej. xps 1987: 327-340

1986

Transitivity in Relations between Time IntervalsTomas Hrycej. wi 1988: 272-292

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