The Algebraic Counterpart of the Wagner Hierarchy. - Jérémie Cabessa; Jacques Duparc (CiE, 2008)

The Wadge Hierarchy of Max-Regular Languages. - Jérémie Cabessa; Jacques Duparc; Alessandro Facchini; Filip Murlak (FSTTCS, 2009)

Limit knowledge of rationality. - Christian W. Bach; Jérémie Cabessa (TARK, 2009)

A game theoretical approach to the algebraic counterpart of the Wagner hierarchy: Part I. - Jérémie Cabessa; Jacques Duparc (RAIRO Theor. Informatics Appl., 2009)

A Game Theoretical Approach to The Algebraic Counterpart of The Wagner Hierarchy: Part II. - Jérémie Cabessa; Jacques Duparc (RAIRO Theor. Informatics Appl., 2009)

A Hierarchical Classification of First-Order Recurrent Neural Networks. - Jérémie Cabessa; Alessandro E. P. Villa (LATA, 2010)

Evolving recurrent neural networks are super-Turing. - Jérémie Cabessa; Hava T. Siegelmann (IJCNN, 2011)

Agreeing to Disagree with Limit Knowledge. - Christian W. Bach; Jérémie Cabessa (LORI, 2011)

Interactive Evolving Recurrent Neural Networks Are Super-turing. - Jérémie Cabessa (ICAART (1), 2012)

Recurrent Neural Networks - A Natural Model of Computation beyond the Turing Limits. - Jérémie Cabessa; Alessandro E. P. Villa (IJCCI, 2012)

The Computational Power of Interactive Recurrent Neural Networks. - Jérémie Cabessa; Hava T. Siegelmann (Neural Comput., 2012)

The expressive power of analog recurrent neural networks on infinite input streams. - Jérémie Cabessa; Alessandro E. P. Villa (Theor. Comput. Sci., 2012)

The Super-Turing Computational Power of Interactive Evolving Recurrent Neural Networks. - Jérémie Cabessa; Alessandro E. P. Villa (ICANN, 2013)

Interactive Evolving Recurrent Neural Networks Are Super-Turing Universal. - Jérémie Cabessa; Alessandro E. P. Villa (ICANN, 2014)

The Super-Turing Computational Power of plastic Recurrent Neural Networks. - Jérémie Cabessa; Hava T. Siegelmann (Int. J. Neural Syst., 2014)

Computational capabilities of recurrent neural networks based on their attractor dynamics. - Jérémie Cabessa; Alessandro E. P. Villa (IJCNN, 2015)

Expressive Power of Non-deterministic Evolving Recurrent Neural Networks in Terms of Their Attractor Dynamics. - Jérémie Cabessa; Jacques Duparc (UCNC, 2015)

Attractor Dynamics Driven by Interactivity in Boolean Recurrent Neural Networks. - Jérémie Cabessa; Alessandro E. P. Villa (ICANN (1), 2016)

Attractor-based complexity of a Boolean model of the basal ganglia-thalamocortical network. - Jérémie Cabessa; Alessandro E. P. Villa (IJCNN, 2016)

Expressive Power of Nondeterministic Recurrent Neural Networks in Terms of their Attractor Dynamics. - Jérémie Cabessa; Jacques Duparc (Int. J. Unconv. Comput., 2016)

Expressive power of first-order recurrent neural networks determined by their attractor dynamics. - Jérémie Cabessa; Alessandro E. P. Villa (J. Comput. Syst. Sci., 2016)

Expressive Power of Evolving Neural Networks Working on Infinite Input Streams. - Jérémie Cabessa; Olivier Finkel (FCT, 2017)

Neural Computation with Spiking Neural Networks Composed of Synfire Rings. - Jérémie Cabessa; Ginette Horcholle-Bossavit; Brigitte Quenet (ICANN (1), 2017)

Interactive Control of Computational Power in a Model of the Basal Ganglia-Thalamocortical Circuit by a Supervised Attractor-Based Learning Procedure. - Jérémie Cabessa; Alessandro E. P. Villa (ICANN (1), 2017)

Emulation of finite state automata with networks of synfire rings. - Jérémie Cabessa; Paolo Masulli (IJCNN, 2017)

Limit-agreeing to disagree. - Christian W. Bach; Jérémie Cabessa (J. Log. Comput., 2017)

An STDP Rule for the Improvement and Stabilization of the Attractor Dynamics of the Basal Ganglia-Thalamocortical Network. - Jérémie Cabessa; Alessandro E. P. Villa (ICANN (3), 2018)

Automata Computation with Hodgkin-Huxley Based Neural Networks Composed of Synfire Rings. - Jérémie Cabessa; Aubin Tchaptchet (IJCNN, 2018)

Robust Optimal-Size Implementation of Finite State Automata with Synfire Ring-Based Neural Networks. - Jérémie Cabessa; Jirí Síma (ICANN (1), 2019)

A Memory-Based STDP Rule for Stable Attractor Dynamics in Boolean Recurrent Neural Networks. - Jérémie Cabessa; Alessandro E. P. Villa (IJCNN, 2019)

Computational capabilities of analog and evolving neural networks over infinite input streams. - Jérémie Cabessa; Olivier Finkel (J. Comput. Syst. Sci., 2019)

Automata complete computation with Hodgkin-Huxley neural networks composed of synfire rings. - Jérémie Cabessa; Aubin Tchaptchet (Neural Networks, 2020)

Efficient Text Classification with Echo State Networks. - Jérémie Cabessa; Hugo Hernault; Heechang Kim; Yves Lamonato; Yariv Z. Levy (IJCNN, 2021)

The EsnTorch Library: Efficient Implementation of Transformer-Based Echo State Networks. - Jérémie Cabessa; Hugo Hernault; Yves Lamonato; Mathieu Rochat; Yariv Z. Levy (ICONIP (7), 2022)

Argument Classification with BERT Plus Contextual, Structural and Syntactic Features as Text. - Umer Mushtaq; Jérémie Cabessa (ICONIP (4), 2022)

Turing Computation with Neural Networks Composed of Synfire Rings. - Jérémie Cabessa (IJCNN, 2022)

Argument Mining with Modular BERT and Transfer Learning. - Umer Mushtaq; Jérémie Cabessa (IJCNN, 2023)

Energy Complexity of Fully-Connected Layers. - Jirí Síma; Jérémie Cabessa (IWANN (1), 2023)

Argument Mining in BioMedicine: Zero-Shot, In-Context Learning and Fine-tuning with LLMs. - Jérémie Cabessa; Hugo Hernault; Umer Mushtaq (CLiC-it, 2024)

In-Context Learning and Fine-Tuning GPT for Argument Mining. - Jérémie Cabessa; Hugo Hernault; Umer Mushtaq (CoRR, 2024)

On energy complexity of fully-connected layers. - Jirí Síma; Jérémie Cabessa; Petra Vidnerová (Neural Networks, 2024)

Argument Mining with Fine-Tuned Large Language Models. - Jérémie Cabessa; Hugo Hernault; Umer Mushtaq (COLING, 2025)

Refined Kolmogorov complexity of analog, evolving and stochastic recurrent neural networks. - Jérémie Cabessa; Yann Strozecki (Inf. Sci., 2025)