Exemplar-Based Hole-Filling Technique for Multiple Dynamic Objects. - Matteo Pagliardini; Yasuhiro Akagi; Marcos Slomp; Ryo Furukawa 001; Ryusuke Sagawa; Hiroshi Kawasaki (PSIVT, 2013)

Unsupervised Learning of Sentence Embeddings Using Compositional n-Gram Features. - Matteo Pagliardini; Prakhar Gupta; Martin Jaggi (NAACL-HLT, 2018)

Better Word Embeddings by Disentangling Contextual n-Gram Information. - Prakhar Gupta; Matteo Pagliardini; Martin Jaggi (NAACL-HLT (1), 2019)

Taming GANs with Lookahead. - Tatjana Chavdarova; Matteo Pagliardini; Martin Jaggi; François Fleuret (CoRR, 2020)

Taming GANs with Lookahead-Minmax. - Tatjana Chavdarova; Matteo Pagliardini; Sebastian U. Stich; François Fleuret; Martin Jaggi (ICLR, 2021)

The Peril of Popular Deep Learning Uncertainty Estimation Methods. - Yehao Liu; Matteo Pagliardini; Tatjana Chavdarova; Sebastian U. Stich (CoRR, 2021)

Improving Generalization via Uncertainty Driven Perturbations. - Matteo Pagliardini; Gilberto Manunza; Martin Jaggi; Michael I. Jordan; Tatjana Chavdarova (CoRR, 2022)

Revisiting the ACVI Method for Constrained Variational Inequalities. - Tatjana Chavdarova; Matteo Pagliardini; Tong Yang; Michael I. Jordan (CoRR, 2022)

Agree to Disagree: Diversity through Disagreement for Better Transferability. - Matteo Pagliardini; Martin Jaggi; François Fleuret; Sai Praneeth Karimireddy (ICLR, 2023)

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

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

CoTFormer: More Tokens With Attention Make Up For Less Depth. - Amirkeivan Mohtashami; Matteo Pagliardini; Martin Jaggi (CoRR, 2023)

MEDITRON-70B: Scaling Medical Pretraining for Large Language Models. - Zeming Chen 001; Alejandro Hernández-Cano; Angelika Romanou; Antoine Bonnet; Kyle Matoba; Francesco Salvi; Matteo Pagliardini; Simin Fan; Andreas Köpf; Amirkeivan Mohtashami; Alexandre Sallinen; Alireza Sakhaeirad; et al. (CoRR, 2023)

A Primal-Dual Approach to Solving Variational Inequalities with General Constraints. - Tatjana Chavdarova; Tong Yang; Matteo Pagliardini; Michael I. Jordan (ICLR, 2024)

DOGE: Domain Reweighting with Generalization Estimation. - Simin Fan; Matteo Pagliardini; Martin Jaggi (ICML, 2024)

DenseFormer: Enhancing Information Flow in Transformers via Depth Weighted Averaging. - Matteo Pagliardini; Amirkeivan Mohtashami; François Fleuret; Martin Jaggi (NeurIPS, 2024)

CoTFormer: A Chain of Thought Driven Architecture with Budget-Adaptive Computation Cost at Inference. - Amirkeivan Mohtashami; Matteo Pagliardini; Martin Jaggi (ICLR, 2025)

The AdEMAMix Optimizer: Better, Faster, Older. - Matteo Pagliardini; Pierre Ablin; David Grangier (ICLR, 2025)

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)