|
|
||
|---|---|---|
|
inria-00176067v1
Communication dans un congrès
Nicolas Le Roux, Yoshua Bengio, Pascal Lamblin, Marc Joliveau, Balázs Kégl. Learning the 2-D Topology of Images Advances in Neural Information Processing Systems, 2007, Vancouver, Canada. 2007 |
||
|
inria-00176062v1
Article dans une revue
James Bergstra, Norman Casagrande, Dumitru Erhan, Douglas Eck, Balázs Kégl. Aggregate Features and AdaBoost for Music Classification Machine Learning Journal, Springer, 2006 |
||
|
inria-00176064v1
Communication dans un congrès
Guangyi Chen, Balázs Kégl. Invariant Radon-Wavelet Packet Signatures for Pattern Recognition IEEE Canadian Conference on Electrical and Computer Engineering, 2006, Ottawa, Canada. 2006 |
||
|
inria-00384970v1
Communication dans un congrès
Julien Perez, Cécile Germain, Balázs Kégl, Charles Loomis. Responsive Elastic Computing 2009 ACM/IEEE Conference on International Conference on Autonomic Computing, Jun 2009, Barcelone, Spain. ACM, pp.55-64, 2009, <10.1145/1555301.1555311> |
||
|
hal-00643001v1
Communication dans un congrès
Róbert Busa-Fekete, Balázs Kégl, Tamas Elteto, György Szarvas. Ranking by calibrated AdaBoost Yahoo! Learning to Rank Challenge, Jun 2010, Haifa, Israel. 2011 |
||
|
hal-00642998v1
Communication dans un congrès
James Bergstra, R. Bardenet, Yoshua Bengio, Balázs Kégl. Algorithms for Hyper-Parameter Optimization J. Shawe-Taylor, R.S. Zemel, P. Bartlett, F. Pereira, K.Q. Weinberger. 25th Annual Conference on Neural Information Processing Systems (NIPS 2011), Dec 2011, Granada, Spain. Neural Information Processing Systems Foundation, 24, 2011, Advances in Neural Information Processing Systems |
||
|
hal-00643000v1
Communication dans un congrès
Róbert Busa-Fekete, Balázs Kégl, Tamas Elteto, György Szarvas. A Robust Ranking Methodology based on Diverse Calibration of AdaBoost European Conference on Machine Learning (ECML 2011), Sep 2011, Athens, Greece. 2011 |
||
|
inria-00586504v1
Communication dans un congrès
Julien Perez, Balázs Kégl, Cécile Germain. Non-Markovian Reinforcement Learning for Reactive Grid scheduling Presses Universitaires des Antilles et de la Guyane. Conférence Francophone d'Apprentissage, May 2011, Chambéry, France. Publibook, 2011 |
||
|
inria-00174290v1
Communication dans un congrès
Julien Perez, Cécile Germain, Balázs Kégl. Predicting Bounds on Queuing Delay in the EGEE grid 2nd EGEE User Forum, May 2007, Manchester, United Kingdom. 2007 |
||
|
hal-01427570v1
Communication dans un congrès
Mehdi Cherti, Balázs Kégl, Akin Kazakçi. Out-of-class novelty generation: an experimental foundation * Neural Information Processing Systems, Dec 2016, Barcelona, Spain |
||
|
hal-01427556v1
Communication dans un congrès
Akin Kazakçi, Cherti Mehdi, Balázs Kégl. Digits that are not: Generating new types through deep neural nets International Conference on Computational Creativity, Jun 2016, Paris, France. <http://www.computationalcreativity.net/iccc2016/proceedings-2016/> |
||
|
inria-00287826v1
Communication dans un congrès
Cécile Germain, Julien Perez, Balázs Kégl, C. Loomis. Grid Differentiated Services: a Reinforcement Learning Approach 8th IEEE International Symposium on Cluster Computing and the Grid, May 2008, Lyon, France. 2008 |
||
|
inria-00287354v1
Communication dans un congrès
Julien Perez, Cécile Germain, Balázs Kégl, C. Loomis. Utility-based Reinforcement Learning for Reactive Grids The 5th IEEE International Conference on Autonomic Computing, May 2008, Chicago, United States. 2008 |
||
|
inria-00428923v1
Communication dans un congrès
Balázs Kégl, Thierry Bertin-Mahieux, Douglas Eck. Metropolis-Hastings sampling in a FilterBoost music classifier International Workshop on Machine Learning and Music (ICML08 Workshop), Jul 2008, Helsinki, Finland. 2008 |
||
|
inria-00428905v1
Communication dans un congrès
Balázs Kégl, Róbert Busa-Fekete. Boosting products of base classifiers Bottou L., Littman M. 26th International Conference on Machine Learning (ICML 2009), Jun 2009, Montreal, Canada. 26, pp.497-504, 2009 |
||
|
inria-00428924v1
Communication dans un congrès
András Bánhalmi, Róbert Busa-Fekete, Balázs Kégl. A One-Class Classification Approach for Protein Sequences and Structures MANDOIU I., NARASIMHAN G., ZHANG Y. 5th International Symposium on Bioinformatics Research and Applications (ISBRA'09), May 2009, Fort Lauderdale, Florida, United States. Springer, pp.310-322, 2009, Lecture Notes in Computer Science / Lecture Notes in Bioinformatics |
||
|
hal-01423097v1
Communication dans un congrès
Claire Adam-Bourdarios, G. Cowan, Cécile Germain, Isabelle Guyon, Balázs Kégl et al. How Machine Learning won the Higgs Boson Challenge European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Apr 2016, Bruges, Belgium. ESANN 2016 proceedings |
||
|
hal-01208587v1
Communication dans un congrès
Claire Adam-Bourdarios, Glen Cowan, Cécile Germain, Isabelle Guyon, Balázs Kégl et al. The Higgs boson machine learning challenge NIPS 2014 Workshop on High-energy Physics and Machine Learning, Dec 2014, Montreal, Canada. 42, pp.37, 2015, JMLR: Workshop and Conference Proceedings |
||
|
hal-01104487v1
Autre publication
Claire Adam-Bourdarios, Glen Cowan, Cécile Germain, Isabelle Guyon, Balázs Kégl et al. Learning to discover: the Higgs boson machine learning challenge The Higgs boson machine learning challenge has been set up to promote collaboration between high .. 2014, <10.7483/OPENDATA.ATLAS.MQ5J.GHXA> |
||
|
hal-01208543v1
Direction d'ouvrage, Proceedings
Glen Cowan, Cécile Germain, Isabelle Guyon, Balázs Kégl, David Rousseau. NIPS 2014 Workshop on High-energy Physics and Machine Learning NIPS 2014 Workshop on High-energy Physics and Machine Learning, Dec 2015, Montreal, Canada. 42, pp.134, 2015, JMLR Workshop and Conference Proceedings, <https://nips.cc/Conferences/2015/Schedule?type=Workshop> |
||
|
inria-00176059v1
Article dans une revue
Sebastien Gambs, Balázs Kégl, Esma Aïmeur. Privacy-Preserving Boosting Data Mining and Knowledge Discovery, Springer Verlag, 2007 |
||
|
hal-00491560v1
Article dans une revue
Julien Perez, Cécile Germain, Balázs Kégl, Charles Loomis. Multi-objective reinforcement learning for responsive grids Journal of Grid Computing, Springer Verlag, 2010, 8 (3), pp.473-492. <10.1007/s10723-010-9161-0> |
||
|
inria-00176061v1
Article dans une revue
Guangyi Chen, Balázs Kégl. Image Denoising with Complex Ridgelets Pattern Recognition, Elsevier, 2007 |
||
|
in2p3-00421717v1
Article dans une revue
G.Y. Chen, Balázs Kégl. Invariant pattern recognition using contourlets and AdaBoost Pattern Recognition, Elsevier, 2010, 43, pp.579-583. <10.1016/j.patcog.2009.08.020> |
||
|
|
||