Emanuele Zappala

According to our database, Emanuele Zappala authored at least 17 papers between 2022 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Learning integral operators via neural integral equations.

Emanuele Zappala, Antonio Henrique de Oliveira Fonseca, Josue Ortega Caro, Andrew Henry Moberly, Michael James Higley, Jessica A. Cardin, David van Dijk

Nat. Mac. Intell., 2024

CaLMFlow: Volterra Flow Matching using Causal Language Models.

Sizhuang He, Daniel LeVine, Ivan Vrkic, Marco Francesco Bressana, David Zhang, Syed Asad Rizvi, Yangtian Zhang, Emanuele Zappala, David van Dijk

CoRR, 2024

Intelligence at the Edge of Chaos.

Shiyang Zhang, Aakash Patel, Syed Asad Rizvi, Nianchen Liu, Sizhuang He, Amin Karbasi, Emanuele Zappala, David van Dijk

CoRR, 2024

Leray-Schauder Mappings for Operator Learning.

Emanuele Zappala

CoRR, 2024

Universal Approximation of Operators with Transformers and Neural Integral Operators.

Emanuele Zappala, Maryam Bagherian

CoRR, 2024

Projection Methods for Operator Learning and Universal Approximation.

Emanuele Zappala

CoRR, 2024

BrainLM: A foundation model for brain activity recordings.

Josue Ortega Caro, Antonio Henrique de Oliveira Fonseca, Syed Asad Rizvi, Matteo Rosati, Christopher L. Averill, James Cross, Prateek Mittal, Emanuele Zappala, Rahul Madhav Dhodapkar, Chadi Abdallah, David van Dijk

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Spectral methods for Neural Integral Equations.

Emanuele Zappala

CoRR, 2023

Operator Learning Meets Numerical Analysis: Improving Neural Networks through Iterative Methods.

Emanuele Zappala, Daniel LeVine, Sizhuang He, Syed Asad Rizvi, Sacha Levy, David van Dijk

CoRR, 2023

Continuous Spatiotemporal Transformers.

Antonio Henrique de Oliveira Fonseca, Emanuele Zappala, Josue Ortega Caro, David van Dijk

CoRR, 2023

Continuous Spatiotemporal Transformer.

Antonio Henrique de Oliveira Fonseca, Emanuele Zappala, Josue Ortega Caro, David van Dijk

Proceedings of the International Conference on Machine Learning, 2023

Neural Integro-Differential Equations.

Emanuele Zappala, Antonio Henrique de Oliveira Fonseca, Andrew Henry Moberly, Michael James Higley, Chadi Abdallah, Jessica A. Cardin, David van Dijk

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Quantum Neural Networks and Topological Quantum Field Theories.

Antonino Marciano, Deen Chen, Filippo Fabrocini, Chris Fields, Enrico Greco, Niels Gresnigt, Krid Jinklub, Matteo Lulli, Kostas Terzidis, Emanuele Zappala

Neural Networks, 2022

Derivation problem for quandle algebras.

Mohamed Elhamdadi, Abdenacer Makhlouf, Sergei Silvestrov, Emanuele Zappala

Int. J. Algebra Comput., 2022

Deep Neural Networks as the Semi-classical Limit of Topological Quantum Neural Networks: The problem of generalisation.

Antonino Marciano, Deen Chen, Filippo Fabrocini, Chris Fields, Matteo Lulli, Emanuele Zappala

CoRR, 2022

AMPNet: Attention as Message Passing for Graph Neural Networks.

Syed Asad Rizvi, Nhi Nguyen, Haoran Lyu, Ben Christensen, Josue Ortega Caro, Emanuele Zappala, Maria Brbic, Rahul Madhav Dhodapkar, David van Dijk

CoRR, 2022

Neural Integral Equations.

Emanuele Zappala, Antonio Henrique de Oliveira Fonseca, Josue Ortega Caro, David van Dijk

CoRR, 2022