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Lehel Csató
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2020 – today
- 2024
- [j10]Isah Charles Saidu, Lehel Csató:
Deep multiple affinity model for proposal-free single instance segmentation. Int. J. Comput. Vis. Robotics 14(5): 491-509 (2024) - 2023
- [j9]Anikó Kopacz, Lehel Csató, Camelia Chira:
Evaluating cooperative-competitive dynamics with deep Q-learning. Neurocomputing 550: 126507 (2023) - [c28]Tamás Kaszta, Hanna-Georgina Lieb, Lehel Csató:
Wavelet-Based Prototype Networks. SISY 2023: 277-282 - 2022
- [c27]Anikó Kopacz, Lehel Csató, Camelia Chira:
Applying Deep Q-learning for Multi-agent Cooperative-Competitive Environments. SOCO 2022: 626-634 - [c26]Csanád Sándor, Szabolcs Pável, Lehel Csató:
Neural Network Pruning based on Filter Importance Values Approximated with Monte Carlo Gradient Estimation. VISIGRAPP (5: VISAPP) 2022: 315-322 - [i4]Anikó Kopacz, Ágnes Mester, Sándor Kolumbán, Lehel Csató:
Standardized feature extraction from pairwise conflicts applied to the train rescheduling problem. CoRR abs/2204.03061 (2022) - 2021
- [j8]Isah Charles Saidu
, Lehel Csató
Active Learning with Bayesian UNet for Efficient Semantic Image Segmentation. J. Imaging 7(2): 37 (2021) - 2020
- [c25]Csanád Sándor, Szabolcs Pável
Pruning CNN's with Linear Filter Ensembles. ECAI 2020: 1435-1442 - [i3]Csanád Sándor, Szabolcs Pável, Lehel Csató:
Pruning CNN's with linear filter ensembles. CoRR abs/2001.08142 (2020)
2010 – 2019
- 2019
- [c24]Szabolcs Pável
Distortion Estimation Through Explicit Modeling of the Refractive Surface. ICANN (3) 2019: 17-28 - [c23]Szabolcs-Botond Lorincz
Single View Distortion Correction using Semantic Guidance. IJCNN 2019: 1-6 - [i2]Szabolcs Pável, Csanád Sándor, Lehel Csató:
Distortion Estimation Through Explicit Modeling of the Refractive Surface. CoRR abs/1909.10820 (2019) - [i1]Szabolcs-Botond Lorincz, Szabolcs Pável, Lehel Csató:
Single View Distortion Correction using Semantic Guidance. CoRR abs/1911.06505 (2019) - 2017
- [c22]Catalin F. Perticas, Bipin Indurkhya, Razvan V. Florian, Lehel Csató:
Finding Patterns in Visualizations of Programs. PPIG 2017: 10 - 2015
- [e1]Viktória Zsók, Zoltán Horváth
Central European Functional Programming School - 5th Summer School, CEFP 2013, Cluj-Napoca, Romania, July 8-20, 2013, Revised Selected Papers. Lecture Notes in Computer Science 8606, Springer 2015, ISBN 978-3-319-15939-3 [contents] - 2014
- [j7]Botond Attila Bócsi, Lehel Csató, Jan Peters:
Indirect robot model learning for tracking control. Adv. Robotics 28(9): 589-599 (2014) - [j6]Zalán Bodó
Linear spectral hashing. Neurocomputing 141: 117-123 (2014) - [c21]Zalán Bodó, Lehel Csató:
Augmented hashing for semi-supervised scenarios. ESANN 2014 - [c20]Botond Bocsi, Hunor Jakab, Lehel Csató:
Simulation-Extrapolation Gaussian Processes for Input Noise Modeling. SYNASC 2014: 189-195 - 2013
- [c19]Zalán Bodó, Lehel Csató:
Linear spectral hashing. ESANN 2013 - [c18]Botond Attila Bócsi, Lehel Csató:
Hessian Corrected Input Noise Models. ICANN 2013: 1-8 - [c17]Hunor Sandor Jakab, Lehel Csató:
Novel Feature Selection and Kernel-Based Value Approximation Method for Reinforcement Learning. ICANN 2013: 170-177 - [c16]Botond Bocsi, Lehel Csató, Jan Peters
Alignment-based transfer learning for robot models. IJCNN 2013: 1-7 - 2012
- [c15]Hunor Jakab, Lehel Csató:
Manifold-based non-parametric learning of action-value functions. ESANN 2012 - [c14]Botond Bocsi, Philipp Hennig, Lehel Csató, Jan Peters
Learning tracking control with forward models. ICRA 2012: 259-264 - [c13]Zalán Bodó
Improving kernel locality-sensitive hashing using pre-images and bounds. IJCNN 2012: 1-8 - [c12]Hunor Jakab, Lehel Csató:
Reinforcement learning with guided policy search using Gaussian processes. IJCNN 2012: 1-8 - 2011
- [j5]Remi Louis Barillec, Ben Ingram
Projected sequential Gaussian processes: A C++ tool for interpolation of large datasets with heterogeneous noise. Comput. Geosci. 37(3): 295-309 (2011) - [c11]Hunor Jakab, Lehel Csató:
Improving Gaussian Process Value Function Approximation in Policy Gradient Algorithms. ICANN (2) 2011: 221-228 - [c10]Botond Bocsi, Duy Nguyen-Tuong, Lehel Csató, Bernhard Schölkopf, Jan Peters:
Learning inverse kinematics with structured prediction. IROS 2011: 698-703 - [c9]Zalán Bodó, Zsolt Minier, Lehel Csató:
Active Learning with Clustering. Active Learning and Experimental Design @ AISTATS 2011: 127-139 - 2010
- [j4]Zalán Bodó
Hierarchical and Reweighting Cluster Kernels for Semi-Supervised Learning. Int. J. Comput. Commun. Control 5(4): 469-476 (2010)
2000 – 2009
- 2009
- [j3]Beáta Reiz, Lehel Csató:
Bayesian Network Classifier for Medical Data Analysis. Int. J. Comput. Commun. Control 4(1): 65-72 (2009) - [c8]Botond Bocsi, Lehel Csató:
Dirichlet process-based component detection in state-space models. ESANN 2009 - 2008
- [c7]Beáta Reiz, Lehel Csató, Dan Dumitrescu:
Prufer Number Encoding for Genetic Bayesian Network Structure Learning Algorithm. SYNASC 2008: 239-242 - 2007
- [c6]Zsolt Minier, Lehel Csató:
Kernel PCA based clustering for inducing features in text categorization. ESANN 2007: 349-354 - [c5]Zsolt Minier, Zalán Bodó
Wikipedia-Based Kernels for Text Categorization. SYNASC 2007: 157-164 - 2003
- [j2]Lehel Csató, Manfred Opper, Ole Winther
Tractable inference for probabilistic data models. Complex. 8(4): 64-68 (2003) - 2002
- [j1]Lehel Csató, Manfred Opper:
Sparse On-Line Gaussian Processes. Neural Comput. 14(3): 641-668 (2002) - 2001
- [c4]Lehel Csató, Dan Cornford
Online Approximations for Wind-Field Models. ICANN 2001: 300-307 - [c3]Lehel Csató, Manfred Opper, Ole Winther:
TAP Gibbs Free Energy, Belief Propagation and Sparsity. NIPS 2001: 657-663 - 2000
- [c2]Lehel Csató, Manfred Opper:
Sparse Representation for Gaussian Process Models. NIPS 2000: 444-450
1990 – 1999
- 1999
- [c1]Lehel Csató, Ernest Fokoué, Manfred Opper, Bernhard Schottky, Ole Winther:
Efficient Approaches to Gaussian Process Classification. NIPS 1999: 251-257
Coauthor Index
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last updated on 2024-09-18 01:09 CEST by the dblp team
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