Davide Buffelli

According to our database, Davide Buffelli authored at least 20 papers between 2020 and 2026.

Collaborative distances:

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

2026

Cross-Tokenizer LLM Distillation through a Byte-Level Interface.

Avyav Kumar Singh, Yen-Chen Wu, Alexandru Cioba, Alberto Bernacchia, Davide Buffelli

CoRR, April, 2026

2025

LGDC: Latent Graph Diffusion via Spectrum-Preserving Coarsening.

Nagham Osman, Keyue Jiang, Davide Buffelli, Xiaowen Dong, Laura Toni

CoRR, December, 2025

Towards a Foundation Model for Communication Systems.

Davide Buffelli, Sowmen Das, Yu-Wei Lin, Sattar Vakili, Chien-Yi Wang, Masoud Attarifar, Pritthijit Nath, Da-shan Shiu

CoRR, May, 2025

Group Think: Multiple Concurrent Reasoning Agents Collaborating at Token Level Granularity.

Chan-Jan Hsu, Davide Buffelli, Jamie McGowan, Feng-Ting Liao, Yi-Chang Chen, Sattar Vakili, Da-shan Shiu

CoRR, May, 2025

2024

Deep Equilibrium Algorithmic Reasoning.

Dobrik Georgiev, JJ Wilson, Davide Buffelli, Pietro Liò

CoRR, 2024

The Deep Equilibrium Algorithmic Reasoner.

Dobrik Georgiev, Pietro Liò, Davide Buffelli

CoRR, 2024

Deep Equilibrium Algorithmic Reasoning.

Dobrik Georgiev, Joseph Wilson, Davide Buffelli, Pietro Lió

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Exact, Tractable Gauss-Newton Optimization in Deep Reversible Architectures Reveal Poor Generalization.

Davide Buffelli, Jamie McGowan, Wangkun Xu, Alexandru Cioba, Da-shan Shiu, Guillaume Hennequin, Alberto Bernacchia

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

CliquePH: Higher-Order Information for Graph Neural Networks Through Persistent Homology on Clique Graphs.

Davide Buffelli, Farzin Soleymani, Bastian Rieck

Proceedings of the Learning on Graphs Conference, 26-29 November 2024, Virtual., 2024

2023

Improving the Effectiveness of Graph Neural Networks in Practical Scenarios.

Davide Buffelli

PhD thesis, 2023

Is Meta-Learning the Right Approach for the Cold-Start Problem in Recommender Systems?

Davide Buffelli, Ashish Gupta, Agnieszka Strzalka, Vassilis Plachouras

CoRR, 2023

Scalable Theory-Driven Regularization of Scene Graph Generation Models.

Davide Buffelli, Efthymia Tsamoura

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

2022

The Impact of Global Structural Information in Graph Neural Networks Applications.

Davide Buffelli, Fabio Vandin

Data, 2022

Scalable Regularization of Scene Graph Generation Models using Symbolic Theories.

Davide Buffelli, Efthymia Tsamoura

CoRR, 2022

SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks.

Davide Buffelli, Pietro Lió, Fabio Vandin

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Graph Representation Learning for Multi-Task Settings: a Meta-Learning Approach.

Davide Buffelli, Fabio Vandin

Proceedings of the International Joint Conference on Neural Networks, 2022

Extending Logic Explained Networks to Text Classification.

Rishabh Jain, Gabriele Ciravegna, Pietro Barbiero, Francesco Giannini, Davide Buffelli, Pietro Liò

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022

2020

A Meta-Learning Approach for Graph Representation Learning in Multi-Task Settings.

Davide Buffelli, Fabio Vandin

CoRR, 2020

Attention-Based Deep Learning Framework for Human Activity Recognition with User Adaptation.

Davide Buffelli, Fabio Vandin

CoRR, 2020

Are Graph Convolutional Networks Fully Exploiting Graph Structure?

Davide Buffelli, Fabio Vandin

CoRR, 2020