Communications publiées lors de congrès ou colloques nationaux et internationaux (164)
  1. 6. Antonik, P., Hermans, M., Haelterman, M., & Massar, S. (2017). Chaotic Time Series Prediction Using a Photonic Reservoir Computer with Output Feedback. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence: AAAI-17: Vol. 6 (p. 4901) Palo Alto, California: AAAI Press.
  2. 7. Antonik, P., Haelterman, M., & Massar, S. (2017). Improving performance of analogue readout layers for photonic reservoir computers with online learning. In Proceedings of 31st AAAI Conference on Artificial Intelligence: Vol. 6 (p. 4899) Palo Alto, California.: AAAI Press.
  3. 8. Akrout, A., Antonik, P., Haelterman, M., & Massar, S. (2017). Towards autonomous photonic reservoir computer based on frequency parallelism of neurons. Proceedings of SPIE - The International Society for Optical Engineering: Proceedings Volume 10089, Real-time Measurements, Rogue Phenomena, and Single-Shot Applications II; 100890S (2017) (22 February 2017) doi:10.1117/12.2250865
  4. 9. Antonik, P., Hermans, M., Haelterman, M., & Massar, S. (2016). Pattern and Frequency Generation Using an Opto-Electronic Reservoir Computer with Output Feedback. Neural Information Processing: 23rd International Conference, ICONIP 2016, Kyoto, Japan, October 16-21, 2016, Proceedings, Part II: Lecture Notes in Computer Science. Vol. 9948 (pp. 318-325) ICONIP 2016(Kyoto, Japon). doi:10.1007/978-3-319-46672-9_36
  5. 10. Antonik, P., Hermans, M., Haelterman, M., & Massar, S. (2016). Towards Adjustable Signal Generation with Photonic Reservoir Computers. Artificial Neural Networks and Machine Learning - ICANN 2016: 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part I: Lecture Notes in Computer Science. Vol. 9886 (pp. 374-381) (Barcelone). doi:10.1007/978-3-319-44778-0_44
  6. 11. Vinckier, Q., Duport, F., Smerieri, A., Haelterman, M., & Massar, S. (2016). Autonomous bio-inspired photonic processor based on reservoir computing paradigm. In Proceedings of the IEEE 2016 Summer Topicals Meeting Series Institute of Electrical and Electronics Engineers.
  7. 12. Vinckier, Q., Bouwens, A., Haelterman, M., & Massar, S. (2016). Autonomous all-photonic processor based on reservoir computing paradigm. In Proceedings of CLEO:2016 (p. SF1F.1) Optical Society of America. doi:10.1364/CLEO_SI.2016.SF1F.1
  8. 13. Antonik, P., Hermans, M., Duport, F., Haelterman, M., & Massar, S. (2016). Towards pattern generation and chaotic series prediction with photonic reservoir computers. SPIE's 2016 Laser Technology and Industrial Laser Conference. Vol. 9732.
  9. 14. Antonik, P., Duport, F., Smerieri, A., Hermans, M., Haelterman, M., & Massar, S. (2015). Improving performance of opto-electronic reservoir computers with online learning. Annual Symposium of the IEEE Photonics Society Benelux Chapter
  10. 15. Antonik, P., Duport, F., Smerieri, A., Hermans, M., Haelterman, M., & Massar, S. (2015). Online Training of an Opto-Electronic Reservoir Computer. In S. Arik (Ed.), Lecture Notes in Computer Science: Vol. 9490 (pp. 233-240) Springer.
  11. 16. Vinckier, Q., Duport, F., Haelterman, M., & Massar, S. (2015). Information processing using an autonomous all-photonic reservoir computer based on coherently driven passive cavities. Frontiers in Optics 2015 - OSA Technical Digest (p. FTu3B.6) Frontiers in Optics 2015(18–22 October 2015: San Jose, California United States). doi:10.1364/FIO.2015.FTu3B.6
  12. 17. Vinckier, Q., Duport, F., Smerieri, A., Haelterman, M., & Massar, S. (2015). Information processing using a photonic reservoir computer based on a coherently driven passive cavity with an analog readout layer. CLEO Europe 2015 Proceedings 2015 European Conference on Lasers and Electro-Optics - European Quantum Electronics Conference(21-25 June 2015: Munich, Germany)
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