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ORCIDFederico Errica
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2020 – today
- 2024
[j6]Davide Bacciu
, Federico Errica, Alessio Gravina, Lorenzo Madeddu, Marco Podda, Giovanni Stilo:
Deep Graph Networks for Drug Repurposing With Multi-Protein Targets. IEEE Trans. Emerg. Top. Comput. 12(1): 177-189 (2024)- [c16]Federico Errica, Mathias Niepert:
Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks. ICLR 2024 - [c15]Julia Gastinger, Christian Meilicke, Federico Errica, Timo Sztyler, Anett Schülke, Heiner Stuckenschmidt:
History Repeats Itself: A Baseline for Temporal Knowledge Graph Forecasting. IJCAI 2024: 4016-4024 - [i17]Julia Gastinger, Christian Meilicke, Federico Errica, Timo Sztyler, Anett Schuelke, Heiner Stuckenschmidt:
History repeats Itself: A Baseline for Temporal Knowledge Graph Forecasting. CoRR abs/2404.16726 (2024) - [i16]Viktor Zaverkin, Francesco Alesiani, Takashi Maruyama, Federico Errica, Henrik Christiansen, Makoto Takamoto, Nicolas Weber, Mathias Niepert:
Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing. CoRR abs/2405.14253 (2024) - [i15]Federico Errica, Giuseppe Siracusano, Davide Sanvito, Roberto Bifulco:
What Did I Do Wrong? Quantifying LLMs' Sensitivity and Consistency to Prompt Engineering. CoRR abs/2406.12334 (2024) - 2023
- [j5]Federico Errica, Davide Bacciu, Alessio Micheli:
PyDGN: a Python Library for Flexible and Reproducible Research on Deep Learning for Graphs. J. Open Source Softw. 8(90): 5713 (2023) - [c14]Davide Bacciu, Federico Errica, Alessio Micheli, Nicolò Navarin, Luca Pasa, Marco Podda, Daniele Zambon:
Graph Representation Learning. ESANN 2023 - [c13]Federico Errica, Alessio Gravina, Davide Bacciu, Alessio Micheli:
Hidden Markov Models for Temporal Graph Representation Learning. ESANN 2023 - [c12]Federico Errica:
On Class Distributions Induced by Nearest Neighbor Graphs for Node Classification of Tabular Data. NeurIPS 2023 - [d1]Federico Errica, Davide Bacciu, Alessio Micheli:
PyDGN: a Python Library for Flexible and Reproducible Research on Deep Learning for Graphs. Zenodo, 2023
- [i14]Federico Errica, Mathias Niepert:
Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks. CoRR abs/2305.10544 (2023) - [i13]Daniele Atzeni, Federico Errica, Davide Bacciu, Alessio Micheli:
Modeling Edge Features with Deep Bayesian Graph Networks. CoRR abs/2308.09087 (2023) - [i12]Daniele Castellana, Federico Errica:
Investigating the Interplay between Features and Structures in Graph Learning. CoRR abs/2308.09570 (2023) - [i11]Henrik Christiansen, Federico Errica, Francesco Alesiani:
Self-Tuning Hamiltonian Monte Carlo for Accelerated Sampling. CoRR abs/2309.13593 (2023) - [i10]Federico Errica, Henrik Christiansen, Viktor Zaverkin, Takashi Maruyama, Mathias Niepert, Francesco Alesiani:
Adaptive Message Passing: A General Framework to Mitigate Oversmoothing, Oversquashing, and Underreaching. CoRR abs/2312.16560 (2023) - 2022
- [b1]Federico Errica:
Bayesian Deep Learning for Graphs. University of Pisa, Italy, 2022 - [j4]Antonio Carta, Andrea Cossu, Federico Errica, Davide Bacciu:
Catastrophic Forgetting in Deep Graph Networks: A Graph Classification Benchmark. Frontiers Artif. Intell. 5: 824655 (2022) - [j3]Luca Oneto, Nicolò Navarin, Battista Biggio, Federico Errica, Alessio Micheli, Franco Scarselli, Monica Bianchini, Luca Demetrio, Pietro Bongini, Armando Tacchella, Alessandro Sperduti:
Towards learning trustworthily, automatically, and with guarantees on graphs: An overview. Neurocomputing 493: 217-243 (2022) - [c11]Davide Bacciu, Federico Errica, Nicolò Navarin, Luca Pasa, Daniele Zambon:
Deep Learning for Graphs. ESANN 2022 - [c10]Daniele Castellana, Federico Errica, Davide Bacciu, Alessio Micheli:
The Infinite Contextual Graph Markov Model. ICML 2022: 2721-2737 - [i9]Federico Errica:
Bayesian Deep Learning for Graphs. CoRR abs/2202.12348 (2022) - 2021
- [c9]Federico Errica, Giacomo Iadarola, Fabio Martinelli, Francesco Mercaldo, Alessio Micheli:
Robust Malware Classification via Deep Graph Networks on Call Graph Topologies. ESANN 2021 - [c8]Luca Oneto, Nicolò Navarin, Battista Biggio, Federico Errica, Alessio Micheli, Franco Scarselli, Monica Bianchini, Alessandro Sperduti:
Complex Data: Learning Trustworthily, Automatically, and with Guarantees. ESANN 2021 - [c7]Federico Errica, Davide Bacciu, Alessio Micheli:
Graph Mixture Density Networks. ICML 2021: 3025-3035 - [c6]Daniele Atzeni, Davide Bacciu, Federico Errica, Alessio Micheli:
Modeling Edge Features with Deep Bayesian Graph Networks. IJCNN 2021: 1-8 - [c5]Federico Errica, Fabrizio Silvestri, Bora Edizel, Ludovic Denoyer, Fabio Petroni, Vassilis Plachouras, Sebastian Riedel:
Concept Matching for Low-Resource Classification. IJCNN 2021: 1-8 - [i8]Antonio Carta, Andrea Cossu, Federico Errica, Davide Bacciu:
Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph Classification. CoRR abs/2103.11750 (2021) - 2020
- [j2]Davide Bacciu, Federico Errica, Alessio Micheli:
Probabilistic Learning on Graphs via Contextual Architectures. J. Mach. Learn. Res. 21: 134:1-134:39 (2020) - [j1]Davide Bacciu, Federico Errica, Alessio Micheli, Marco Podda:
A gentle introduction to deep learning for graphs. Neural Networks 129: 203-221 (2020) - [c4]Federico Errica, Davide Bacciu, Alessio Micheli:
Theoretically Expressive and Edge-aware Graph Learning. ESANN 2020: 175-180 - [c3]Federico Errica, Marco Podda, Davide Bacciu, Alessio Micheli:
A Fair Comparison of Graph Neural Networks for Graph Classification. ICLR 2020 - [i7]Federico Errica, Davide Bacciu, Alessio Micheli:
Theoretically Expressive and Edge-aware Graph Learning. CoRR abs/2001.09005 (2020) - [i6]Federico Errica, Ludovic Denoyer, Bora Edizel, Fabio Petroni, Vassilis Plachouras, Fabrizio Silvestri, Sebastian Riedel:
Concept Matching for Low-Resource Classification. CoRR abs/2006.00937 (2020) - [i5]Federico Errica, Marco Giulini, Davide Bacciu, Roberto Menichetti, Alessio Micheli, Raffaello Potestio:
Accelerating the identification of informative reduced representations of proteins with deep learning for graphs. CoRR abs/2007.08658 (2020) - [i4]Federico Errica, Davide Bacciu, Alessio Micheli:
Graph Mixture Density Networks. CoRR abs/2012.03085 (2020)
2010 – 2019
- 2019
- [i3]Federico Errica, Marco Podda, Davide Bacciu, Alessio Micheli:
A Fair Comparison of Graph Neural Networks for Graph Classification. CoRR abs/1912.09893 (2019) - [i2]Davide Bacciu, Federico Errica, Alessio Micheli, Marco Podda:
A Gentle Introduction to Deep Learning for Graphs. CoRR abs/1912.12693 (2019) - 2018
- [c2]Davide Bacciu, Federico Errica, Alessio Micheli:
Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing. ICML 2018: 304-313 - [i1]Davide Bacciu, Federico Errica, Alessio Micheli:
Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing. CoRR abs/1805.10636 (2018) - 2017
- [c1]Giuseppe Attardi, Antonio Carta, Federico Errica, Andrea Madotto, Ludovica Pannitto:
FA3L at SemEval-2017 Task 3: A ThRee Embeddings Recurrent Neural Network for Question Answering. SemEval@ACL 2017: 299-304
Coauthor Index
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