Multi-deme, twin adaptive strategy hp-HGS B Barabasz, S Migórski, R Schaefer, M Paszyński Inverse Problems in Science and Engineering 19 (1), 3-16, 2011 | 29 | 2011 |
Error analysis and improving the accuracy of Winograd convolution for deep neural networks B Barabasz, A Anderson, KM Soodhalter, D Gregg ACM Transactions on Mathematical Software (TOMS) 46 (4), 1-33, 2020 | 27 | 2020 |
A hybrid algorithm for solving inverse problems in elasticity B Barabasz, E Gajda-Zagórska, S Migórski, M Paszyński, R Schaefer, ... International Journal of Applied Mathematics and Computer Science 24 (4 …, 2014 | 27 | 2014 |
Efficient adaptive strategy for solving inverse problems M Paszyński, B Barabasz, R Schaefer Computational Science–ICCS 2007: 7th International Conference, Beijing …, 2007 | 24 | 2007 |
Winograd convolution for deep neural networks: Efficient point selection SA Alam, A Anderson, B Barabasz, D Gregg ACM Transactions on Embedded Computing Systems 21 (6), 1-28, 2022 | 21 | 2022 |
Winograd convolution for dnns: Beyond linear polynomials B Barabasz, D Gregg International Conference of the Italian Association for Artificial …, 2019 | 18 | 2019 |
Asymptotic Behavior of hp–HGS (hp–Adaptive Finite Element Method Coupled with the Hierarchic Genetic Strategy) by Solving Inverse Problems R Schaefer, B Barabasz International Conference on Computational Science, 682-691, 2008 | 14 | 2008 |
Quantaized winograd/toom-cook convolution for dnns: Beyond canonical polynomials base B Barabasz arXiv preprint arXiv:2004.11077, 2020 | 6 | 2020 |
Studying inverse problems in elasticity by hierarchic genetic search B Barabasz, E Gajda, S Migórski, M Paszynski, R Schaefer ECCOMAS thematic conference on Inverse Problems in Mechanics of Structures …, 2011 | 5 | 2011 |
Handling Ambiguous Inverse Problems by the Adaptive Genetic Strategy hp–HGS B Barabasz, R Schaefer, M Paszyński International Conference on Computational Science, 904-913, 2009 | 5 | 2009 |
Twin adaptive scheme for solving inverse problems R Schaefer, B Barabasz, M Paszyński Prace Naukowe Politechniki Warszawskiej. Elektronika, 241-249, 2007 | 5 | 2007 |
Speeding up multi-objective optimization of liquid fossil fuel reserve exploitation with parallel hybrid memory integration B Barabasz, S Barrett, L Siwik, M Łoś, K Podsiadło, M Woźniak Journal of Computational Science 31, 126-136, 2019 | 4 | 2019 |
Hardware and software performance in deep learning A Anderson, J Garland, Y Wen, B Barabasz, K Persand, A Vasudevan, ... by Geoff V. Merrett Bashir M. Al-Hashimi. Computing. Institution of …, 2019 | 3 | 2019 |
Coupled isogeometric finite element method and hierarchical genetic strategy with balanced accuracy for solving optimization inverse problem B Barabasz, M Łoś, M Woźniak, L Siwik, S Barrett Procedia Computer Science 108, 828-837, 2017 | 3 | 2017 |
Solving inverse problems by the multi-deme hierarchic genetic strategy R Schaefer, B Barabasz, M Paszynski 2009 IEEE Congress on Evolutionary Computation, 3157-3163, 2009 | 3 | 2009 |
Asymptotic guarantee of success of the hp–HGS strategy R Schaefer, B Barabasz, M Paszynski | 2 | 2008 |
Optimization of production chains using the nature-inspired techniques A Stanislawczyk, P Forys, L Sztangret, B Barabasz, J Kusiak Steel Research International 2, 617-624, 2008 | 2 | 2008 |
Winograd Convolution for Deep Neural Networks: Efficient Point Selection S Asad Alam, A Anderson, B Barabasz, D Gregg arXiv e-prints, arXiv: 2201.10369, 2022 | | 2022 |
hp-HGS twin adaptive strategy for inverse resistivity logging measurements B Barabasz, E Gajda, D Pardo, M Paszyński, R Schaefer, D Szeliga | | 2011 |
An algorithm for relating convergence ratios of inverse and direct problem solutions by means of the self-adaptive hp finite element method M Paszyński, D Szeliga, B Barabasz, P Macioł CMM-2007 : 17th international conference on Computer Methods in Mechanics, 2007 | | 2007 |