Publications & Technical Reports


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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, Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS), 2024.
    [Paper coming soon] [PDF (arXiv)]
  • Deep Equilibrium Algorithmic Reasoning
    Dobrik Georgiev, JJ Wilson, Davide Buffelli, Pietro Liò, Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS), 2024.
    [Paper coming soon] [PDF (arXiv)]
  • CliquePH: Higher-Order Information for Graph Neural Networks through Persistent Homology on Clique Graphs
    Davide Buffelli*, Farzin Soleymani*, Bastian Rieck, Learning on Graphs (LoG), 2024.
    [Paper coming soon] [PDF (arXiv)]
  • The Deep Equilibrium Algorithmic Reasoner
    Dobrik Georgiev, Pietro Liò, Davide Buffelli, CVPR Workshop on Multimodal Algorithmic Reasoning, 2024. (Spotlight)
    [Paper] [PDF (arXiv)]

2023

  • Is Meta-Learning the Right Approach for the Cold-Start Problem in Recommender Systems?
    Davide Buffelli, Ashish Gupta, Agnieszka Strzalka, Vassilis Plachouras, Preprint, 2023.
    [PDF (arXiv)]
  • Improving the Effectiveness of Graph Neural Networks in Practical Scenarios
    Davide Buffelli, PhD Thesis, University of Padova, 2023.
    [Full Text]
  • Scalable Theory-Driven Regularization of Scene Graph Generation Models
    Davide Buffelli*, Efthymia Tsamoura*, Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI), 2023.
    [Paper] [PDF (arXiv)]

2022

  • SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks
    Davide Buffelli, Pietro Liò, Fabio Vandin, Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS), 2022.
    [Paper] [PDF (arXiv)] [Code]
  • Graph Representation Learning for Multi-Task Settings: a Meta-Learning Approach
    Davide Buffelli, Fabio Vandin, International Joint Conference on Neural Networks (IJCNN), 2022. (Oral)
    [Paper] [PDF (arXiv)] [Code]
  • The Impact of Global Structural Information in Graph Neural Networks Applications
    Davide Buffelli, Fabio Vandin, Data (special issue "Knowledge Extraction from Data Using Machine Learning"), 2022.
    [Paper] [PDF (arXiv)] [Code]
  • Extending Logic Explained Networks to Text Classification
    Rishabh Jain, Gabriele Ciravegna, Pietro Barbiero, Francesco Giannini, Davide Buffelli, Pietro Liò, The 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022.
    [Paper] [PDF (arXiv)]

2021

  • Attention-Based Deep Learning Framework for Human Activity Recognition with User Adaptation
    Davide Buffelli, Fabio Vandin, IEEE Sensors Journal, 2021.
    [Paper] [PDF (arXiv)] [Code]

2020