Publications

Most of my publications are available on Google Scholar and DBLP.

HDR and PhD

• 2016: Habilitation to conduct researches (HDR): University of Paris VI (UPMC)
  • Part 1: Computational Capabilities of Recurrent Neural Networks
  • Part 2: Limit Knowledge: A Topological Approach to Epistemic Game Theory
• 2007: PhD Thesis: Universities of Lausanne & Paris VII (Profs. Duparc and Pin)
  • A Game Theoretical Approach to the Algebraic Counterpart of the Wagner Hierarchy

Journals

• Jiří Šíma, Jérémie Cabessa, Petra Vidnerová: On energy complexity of fully-connected layers. Neural Networks 178: 106419 (2024)

• Christian W. Bach, Jérémie Cabessa. Lexicographic agreeing to disagree and perfect equilibrium. Journal of Mathematical Economics, 109:102908, 2023.

• Jérémie Cabessa and and Aubin Tchaptchet. Automata Complete Computation with Hodgkin-Huxley Neural Networks Composed of Synfire Rings. Neural networks 126: 312–334. 2020.

• Jérémie Cabessa. Turing Complete Neural Computation Based on Synaptic Plasticity. PLoS ONE 14(10):e0223451, 2019.

• Jérémie Cabessa and Olivier Finkel. Computational capabilities of analog and evolving neural networks over infinite input streams. Journal Comput. Sys. Sci. 101:86-99, 2019.

• Jérémie Cabessa and Alessandro E.P. Villa. Attractor dynamics of a Boolean model of a brain circuit controlled by multiple parameters. Chaos 28: 106318, 2018.

• Christian Bach and Jérémie Cabessa. Limit-agreeing to disagree. J. Logic Comput. 27(4): 1169-1187, 2017.

• Jérémie Cabessa and Alessandro E.P. Villa. Expressive power of first-order recurrent neural networks determined by their attractor dynamics. J. Comput. Sys. Sci. 82:1232-1250, 2016.

• Jérémie Cabessa and Jacques Duparc. Expressive power of nondeterministic recurrent neural networks in terms of their attractor dynamics. IJUC 12(1):25-50, 2016.

• Jérémie Cabessa and Hava T. Siegelmann. The super-Turing computational power of plastic recurrent neural networks. Int. J. Neural Syst. 24(8): 1450029 (22 p.), 2014.

• Jérémie Cabessa and Alessandro E.P. An Attractor-Based Complexity Measurement for Boolean Recurrent Neural Networks. PLoS ONE 9(4): e94204, 2014.

• Jérémie Cabessa and Alessandro E.P. Villa. The expressive power of analog recurrent neural networks on infinite input streams. Theor. Comput. Sci. 436: 23-34, 2012.

• Jérémie Cabessa and Hava T. Siegelmann. The computational power of interactive recurrent neural networks. Neural Comput. 24(4): 996-1019, 2012.

• Christian Bach and Jérémie Cabessa. Common knowledge and limit knowledge. Theory Dec., 73: 423-440, 2012.

• Jérémie Cabessa and Alessandro E.P. Villa. A hierarchical classification of first-order recurrent neural networks. Chinese J. Physiol. 53(6): 407-416, 2010.

• Jérémie Cabessa and Jacques Duparc. A game theoretical approach to the algebraic counterpart of the Wagner hierarchy: Part I. Theor. Inform. Appl. 43(3): 443-461, 2009.

• Jérémie Cabessa and Jacques Duparc. A game theoretical approach to the algebraic counterpart of the Wagner hierarchy: Part II. Theor. Inform. Appl. 43(3): 463-515, 2009.

Conferences

• Jérémie Cabessa, Hugo Hernault, Umer Mushtaq. Argument Mining with Fine-Tuned Large Language Models. In COLING 2025: Proceedings of the International Conference on Computational Linguistics, pp. 6624-6635, ACL, 2025.

• Jérémie Cabessa, Hugo Hernault, Umer Mushtaq. Argument Mining in BioMedicine: Zero-Shot, In-Context Learning and Fine-tuning with LLMs. In CLiC-it 2024: Proceedings of the Italian Conference on Computational Linguistics, CEUR-WS, 2024.

• Jiri Sima and Jérémie Cabessa. Energy Complexity of Fully-Connected Layers. In IWANN 2023: Proceedings of the International Work-Conference on Artificial Neural Networks (1), pp. 3-15, Springer, 2023.

• Umer Mushtaq and Jérémie Cabessa. Argument Mining with Modular BERT and Transfer Learning. In IJCNN 2023: Proceedings of the International Joint Conference on Artificial Neural Networks, pp. 1-8, IEEE, 2023.

• Jérémie Cabessa Hugo Hernault, Yves Lamonato, Mathieu Rochat, Yariv Z. Levy. The EsnTorch Library: Efficient Implementation of Transformer-Based Echo State Networks. In ICONIP 2022: Proceedings of the International Conference on Neural Information Processing (VII), CCIS vol. 1794, pp. 235-246, Springer, 2022.

• Umer Mushtaq and Jérémie Cabessa Argument Classification with BERT Plus Contextual, Structural and Syntactic Features as Text. In ICONIP 2022: Proceedings of the International Conference on Neural Information Processing (IV), CCIS vol. 1791, pp. 622-633, Springer, 2022.

• Jérémie Cabessa. Turing Computation with Neural Networks Composed of Synfire Rings. In IJCNN 2022: Proceedings of the International Joint Conference on Artificial Neural Networks, pp. 1-8, IEEE, 2022.

• Jérémie Cabessa Hugo Hernault, Heechang Kim, Yves Lamonato, Yariv Z. Levy. Efficient Text Classification with Echo State Networks. In IJCNN 2021: Proceedings of the International Joint Conference on Neural Networks, pp. 1-8, IEEE, 2021.

• Jérémie Cabessa and Jiri Sima. Robust Optimal-Size Implementation of Finite State Automata with Synfire Ring-Based Neural Networks. In ICANN 2019: Proceedings of the 27th International Conference on Artificial Neural Networks, vol. 1, pp. 806-818, Springer, 2019.

• Jérémie Cabessa and Alessandro E.P. Villa. A Memory-Based STDP Rule for Stable Attractor Dynamics in Boolean Recurrent Neural Networks. In IJCNN 2019: Proceedings of the International Joint Conference on Artificial Neural Networks, pp. 1-8, IEEE, 2019.

• Jérémie Cabessa and Alessandro E.P. Villa. An STDP Rule for the Improvement and Stabilization of the Attractor Dynamics of the Basal Ganglia-Thalamocortical Network. In ICANN 2018: Proceedings of the 26th International Conference on Artificial Neural Networks, vol. 3, pp. 693-702, Springer, 2018.

• Jérémie Cabessa and Aubin Tchaptchet. Automata Computation with Hodgkin-Huxley Based Neural Networks Composed of Synfire Rings. In IJCNN 2018: Proceedings of the 24rd International Joint Conference on Neural Networks, pp. 1-8, IEEE, 2018.

• Jérémie Cabessa and Olivier Finkel. Expressive Power of Evolving Neural Networwks Working on Infinite Input Streams. In FCT 2017: Proceedings of the 21st International Symposium on Fundamentals of Computation Theory, pp. 150-163, Springer, 2017.

• Jérémie Cabessa and Alessandro E.P. Villa Interactive Control of Computational Power in a Model of the Basal Ganglia-Thalamocortical Circuit by a Supervised Attractor-Based Learning Procedure. In ICANN 2017: Proceedings of the 26th International Conference on Artificial Neural Networks, vol. 1, pp. 334-342, Springer, 2017.

• Jérémie Cabessa and Paolo Masulli. Emulation of Finite State Automata with Networks of Synfire Rings. In IJCNN 2017: Proceedings of the 23rd International Joint Conference on Neural Networks, pp. 4641-4648, IEEE, 2017.

• Jérémie Cabessa, Ginette Horcholle-Bossavit and Brigitte Quenet. Interactive Control of Computational Power in a Model of the Basal Ganglia-Thalamocortical Circuit by a Supervised Attractor-Based Learning Procedure. In ICANN 2017: Proceedings of the 26th International Conference on Artificial Neural Networks, vol. 1, pp. 245-253, Springer, 2017.

• Jérémie Cabessa and Alessandro E.P. Villa. Attractor Dynamics Driven by Interactivity in Boolean Recurrent Neural Networks. In ICANN 2016: Proceedings of the 25th International Conference on Artificial Neural Networks, vol. 9886 of LNCS, pp. 115-122, Springer, 2016.

• Jérémie Cabessa and Alessandro E.P. Villa . Attractor-Based Complexity of a Boolean Model of the Basal Ganglia-Thalamocortical Netework. In IJCNN 2016: Proceedings of the 22nd International Joint Conference on Neural Networks, pp. 4664-4671, IEEE, 2016.

• Jérémie Cabessa and Jacques Duparc. Expressive power of nondeterministic evolving recurrent neural networks in Terms of their Attractor Dynamics. In UCNC 2015: Proceedings of the 14st International Conference on Unconventional Computation and Natural Computation vol. 9592 of LNCS, pp. 144-156, Springer, 2015.

• Jérémie Cabessa and Alessandro E.P. Villa. Computational Capabilities of Recurrent Neural Networks Based on their Attractor Dynamics. In IJCNN 2015: Proceedings of the 21st International Joint Conference on Neural Networks, IEEE, 2015.

• Jérémie Cabessa and Alessandro E.P. Villa. Interactive evolving recurrent neural networks are super-Turing universal. In ICANN 2014: Proceedings of the 24th International Conference on Artificial Neural Networks, vol. 8681 of LNCS, pp. 57-64, Springer, 2014.

• Jérémie Cabessa and Alessandro E.P. Villa. On interactively computable functions. In CiE 2014: 10th International Conference on Computability in Europe - Language, Life, Limits, accepted for presentation, 2014.

• Jérémie Cabessa and Alessandro E.P. Villa. The super-Turing computational power of interactive evolving recurrent neural networks. In ICANN 2013: Proceedings of the 23th International Conference on Artificial Neural Networks, vol. 8131 of LNCS, pp. 58-65, Springer, 2013.

• Jérémie Cabessa and Alessandro E.P. Villa. Recurrent neural networks: a natural model of computation beyond the Turing limits. In NCTA 2012: Proceedings of the 4th International Conference on Neural Computation, Theory and Applications, pp. 594-599, SciTePress, 2012.

• Jérémie Cabessa. Interactive evolving recurrent neural networks are super-Turing. In ICAART 2012: Proceedings of the 4th International Conference on Agents and Artificial Intelligence, vol. 1, pp. 328-333, SciTePress, 2012.

• Jérémie Cabessa, Yoshiyuki Asai, Javier Iglesias,  Pierre Dutoit, Alessandra Lintas and Alessandro E.P. Villa. Learning and memory phenomena in a complex sensory environment: a neuroheuristic approach. In NOLTA 2012: Proceedings of the 2012 International Symposium on Nonlinear Theory and its Applications, vol. 1, pp. 300-303, IEICE Proceedings series, 2012.

• Christian W. Bach and Jérémie Cabessa. Agreeing to disagree with limit knowledge. In LORI 2011: Proceedings of the 3rd International Workshop on Logic, Rationality and Interaction, vol. 6953 of LNCS, pp. 51-60, Springer, 2011.

• Jérémie Cabessa and Hava Siegelmann. Evolving recurrent neural networks are super-Turing. In IJCNN 2011: Proceedings of the 21st International Joint Conference on Neural Networks, pp. 3200-3206, IEEE, 2011.

• Jérémie Cabessa and Alessandro E.P. Villa. A hierarchical classification of first-order recurrent neural networks. In LATA 2010: Proceedings of the 4th International Conference on Language and Automata Theory and Applications, vol. 6031 of LNCS, pp. 142-153, Springer, 2010.

• Jérémie Cabessa, Jacques Duparc, Alessandro Facchini, and Filip Murlak. The Wadge hierarchy of max-regular languages. In FSTTCS 2009: Proceedings of the 29th IARCS Conference on Foundations of Software Technology and Theoretical Computer Science, vol. 4 of LIPIcs, pp. 121-132, Schloss Dagstuhl, 2009.

• Christian W. Bach and Jérémie Cabessa. Limit knowledge of rationality. In TARK 2009: Proceedings of the 12th Conference on Theoretical Aspects of Rationality and Knowledge, pp. 34-40, ACM, 2009.

• Jérémie Cabessa and Jacques Duparc. The algebraic counterpart of the wagner hierarchy. In CiE 2008: Proceedings of the 4th conference on Computability in Europe, vol. 5028 of LNCS, pp. 100-109, Springer, 2008.

• Jérémie Cabessa and Jacques Duparc. An infinite game over ω-semigroups. In FotFS V 2007: Proceedings of the 5th Conference on Foundations of the Formal Sciences V, vol. 11 of Studies in Logic, pp. 63-78, College Publications, 2007.

Book contributions

• Jérémie Cabessa and Alessandro E.P. Villa. Recurrent Neural Networks and Super-Turing Interactive Computation. In P. Koprinkova-Hristova et al., eds, Artificial Neural Networks - Springer Series in Bio-/Neuroinformatics, vol. 4, 2015, pp. 1-29, Springer, 2015.

• Jérémie Cabessa and Alessandro E. P. Villa. On Super-Turing Neural Computation. In H. Liljenström, editors, Advances in Cognitive Neurodynamics (IV), pp. 307-312, Springer, 2015.

• Alessandro E. P. Villa, Alessandra Lintas, and Jérémie Cabessa. Neural Dynamics Associated to Preferred Firing Sequences. In H. Liljenström, editors, Advances in Cognitive Neurodynamics (IV), pp. 597-604, Springer, 2015.

• Alessandro E.P. Villa, Yoshiyuki Asai, Javier Iglesias, Olga K. Chibirova, Jérémie Cabessa, Pierre Dutoit, and Vladyslav Shaposhnyk. Dynamical Systems and Accurate Temporal Information Transmission in Neural Networks In R. Wand and F. Gu, editors, Advances in Cognitive Neurodynamics (II), pp. 61-65, 2011.

Others

• Computer scientist leads the way to the next revolution in artificial intelligence. Science Daily, April 2, 2012. Link to the article

• Next Generation Artificial Intelligence. Wired Cosmos, April 2, 2012. Link to the article.