Blaise Delattre

According to our database1, Blaise Delattre authored at least 14 papers between 2021 and 2025.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

On csauthors.net:

Bibliography

2025
On the Stability of Neural Networks in Deep Learning.
CoRR, October, 2025

Conditional Distribution Quantization in Machine Learning.
CoRR, February, 2025

On the Stability of Neural Networks in Deep Learning. (Stabilité des réseaux de neurones artificiels en apprentissage profond).
PhD thesis, 2025

Attention Chaînée et Causale pour un Suivi Efficace des Entités.
Proceedings of the Actes des 32ème Conférence sur le Traitement Automatique des Langues Naturelles, 2025

Accelerated training through iterative gradient propagation along the residual path.
Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Forward Only Learning for Orthogonal Neural Networks of Any Depth.
Proceedings of the ECAI 2025 - 28th European Conference on Artificial Intelligence, 25-30 October 2025, Bologna, Italy, 2025

Bridging the Theoretical Gap in Randomized Smoothing.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025

2024
Spectral Norm of Convolutional Layers with Circular and Zero Paddings.
CoRR, 2024

The Lipschitz-Variance-Margin Tradeoff for Enhanced Randomized Smoothing.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Chain and Causal Attention for Efficient Entity Tracking.
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

2023
Efficient Bound of Lipschitz Constant for Convolutional Layers by Gram Iteration.
Proceedings of the International Conference on Machine Learning, 2023

A Unified Algebraic Perspective on Lipschitz Neural Networks.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
A Dynamical System Perspective for Lipschitz Neural Networks.
Proceedings of the International Conference on Machine Learning, 2022

2021
Scalable Lipschitz Residual Networks with Convex Potential Flows.
CoRR, 2021


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