SUPA: A Lightweight Diagnostic Simulator for Machine Learning in Particle Physics. - Atul Kumar Sinha; Daniele Paliotta; Bálint Máté; Sebastian Pina-Otey; John A. Raine; Tobias Golling; François Fleuret (CoRR, 2022)

Fast Attention Over Long Sequences With Dynamic Sparse Flash Attention. - Matteo Pagliardini; Daniele Paliotta; Martin Jaggi; François Fleuret (NeurIPS, 2023)

SUPA: A Lightweight Diagnostic Simulator for Machine Learning in Particle Physics. - Atul Kumar Sinha; Daniele Paliotta; Bálint Máté; John A. Raine; Tobias Golling; François Fleuret (NeurIPS, 2023)

Graph Neural Networks Go Forward-Forward. - Daniele Paliotta; Mathieu Alain; Bálint Máté; François Fleuret (CoRR, 2023)

Faster Causal Attention Over Large Sequences Through Sparse Flash Attention. - Matteo Pagliardini; Daniele Paliotta; Martin Jaggi; François Fleuret (CoRR, 2023)

Understanding and Minimising Outlier Features in Transformer Training. - Bobby He; Lorenzo Noci; Daniele Paliotta; Imanol Schlag; Thomas Hofmann 001 (NeurIPS, 2024)

The Mamba in the Llama: Distilling and Accelerating Hybrid Models. - Junxiong Wang; Daniele Paliotta; Avner May; Alexander M. Rush; Tri Dao (NeurIPS, 2024)

Understanding and Minimising Outlier Features in Neural Network Training. - Bobby He; Lorenzo Noci; Daniele Paliotta; Imanol Schlag; Thomas Hofmann 001 (CoRR, 2024)

Leveraging the true depth of LLMs. - Ramón Calvo González; Daniele Paliotta; Matteo Pagliardini; Martin Jaggi; François Fleuret (CoRR, 2025)

Thinking Slow, Fast: Scaling Inference Compute with Distilled Reasoners. - Daniele Paliotta; Junxiong Wang; Matteo Pagliardini; Kevin Y. Li; Aviv Bick; J. Zico Kolter; Albert Gu; François Fleuret; Tri Dao (CoRR, 2025)

M1: Towards Scalable Test-Time Compute with Mamba Reasoning Models. - Junxiong Wang; Wen-Ding Li; Daniele Paliotta; Daniel Ritter 007; Alexander M. Rush; Tri Dao (CoRR, 2025)