ERRICA, FEDERICO Statistiche
ERRICA, FEDERICO
DIPARTIMENTO DI INFORMATICA
Deep Graph Networks for Drug Repurposing with Multi-Protein Targets
2023-01-01 Bacciu, Davide; Errica, Federico; Gravina, Alessio; Madeddu, Lorenzo; Podda, Marco; Stilo, Giovanni
Graph Representation Learning
2023-01-01 Bacciu, Davide; Errica, Federico; Micheli, Alessio; Navarin, Nicolò; Pasa, Luca; Podda, Marco; Zambon, Daniele
Hidden Markov Models for Temporal Graph Representation Learning
2023-01-01 Errica, Federico; Gravina, Alessio; Bacciu, Davide; Micheli, Alessio
PyDGN: a Python Library for Flexible and Reproducible Research on Deep Learning for Graphs
2023-01-01 Errica, Federico; Bacciu, Davide; Micheli, Alessio
Catastrophic Forgetting in Deep Graph Networks: A Graph Classification Benchmark
2022-01-01 Carta, Antonio; Cossu, Andrea; Errica, Federico; Bacciu, Davide
The Infinite Contextual Graph Markov Model
2022-01-01 Castellana, D; Errica, F; Bacciu, D; Micheli, A
Towards learning trustworthily, automatically, and with guarantees on graphs: An overview
2022-01-01 Oneto, Luca; Navarin, Nicol??; Biggio, Battista; Errica, Federico; Micheli, Alessio; Scarselli, Franco; Bianchini, Monica; Demetrio, Luca; Bongini, Pietro; Tacchella, Armando; Sperduti, Alessandro
A Deep Graph Network–Enhanced Sampling Approach to Efficiently Explore the Space of Reduced Representations of Proteins
2021-01-01 Errica, F.; Giulini, M.; Bacciu, D.; Menichetti, R.; Micheli, A.; Potestio, R.
Complex Data: Learning Trustworthily, Automatically, and with Guarantees
2021-01-01 Oneto, Luca; Navarin, Nicolò; Biggio, Battista; Errica, Federico; Micheli, Alessio; Scarselli, Franco; Bianchini, Monica; Sperduti, Alessandro
Graph Mixture Density Networks
2021-01-01 Errica, F; Bacciu, D; Micheli, A
Modeling Edge Features with Deep Bayesian Graph Networks
2021-01-01 Atzeni, D.; Bacciu, D.; Errica, F.; Micheli, A.
A Fair Comparison of Graph Neural Networks for Graph Classification
2020-01-01 Errica, Federico; Podda, Marco; Bacciu, Davide; Micheli, Alessio
A gentle introduction to deep learning for graphs
2020-01-01 Bacciu, D.; Errica, F.; Micheli, A.; Podda, M.
Probabilistic learning on graphs via contextual architectures
2020-01-01 Bacciu, D.; Errica, F.; Micheli, A.
Theoretically Expressive and Edge-aware GraphLearning
2020-01-01 Errica, Federico; Bacciu, Davide; Micheli, Alessio
Contextual graph markov model: A deep and generative approach to graph processing
2018-01-01 Bacciu, Davide; Errica, Federico; Micheli, Alessio
| Titolo | Data di pubblicazione | Autore(i) | File |
|---|---|---|---|
| Deep Graph Networks for Drug Repurposing with Multi-Protein Targets | 1-gen-2023 | Bacciu, Davide; Errica, Federico; Gravina, Alessio; Madeddu, Lorenzo; Podda, Marco; Stilo, Giovanni | |
| Graph Representation Learning | 1-gen-2023 | Bacciu, Davide; Errica, Federico; Micheli, Alessio; Navarin, Nicolò; Pasa, Luca; Podda, Marco; Zambon, Daniele | |
| Hidden Markov Models for Temporal Graph Representation Learning | 1-gen-2023 | Errica, Federico; Gravina, Alessio; Bacciu, Davide; Micheli, Alessio | |
| PyDGN: a Python Library for Flexible and Reproducible Research on Deep Learning for Graphs | 1-gen-2023 | Errica, Federico; Bacciu, Davide; Micheli, Alessio | |
| Catastrophic Forgetting in Deep Graph Networks: A Graph Classification Benchmark | 1-gen-2022 | Carta, Antonio; Cossu, Andrea; Errica, Federico; Bacciu, Davide | |
| The Infinite Contextual Graph Markov Model | 1-gen-2022 | Castellana, D; Errica, F; Bacciu, D; Micheli, A | |
| Towards learning trustworthily, automatically, and with guarantees on graphs: An overview | 1-gen-2022 | Oneto, Luca; Navarin, Nicol??; Biggio, Battista; Errica, Federico; Micheli, Alessio; Scarselli, Franco; Bianchini, Monica; Demetrio, Luca; Bongini, Pietro; Tacchella, Armando; Sperduti, Alessandro | |
| A Deep Graph Network–Enhanced Sampling Approach to Efficiently Explore the Space of Reduced Representations of Proteins | 1-gen-2021 | Errica, F.; Giulini, M.; Bacciu, D.; Menichetti, R.; Micheli, A.; Potestio, R. | |
| Complex Data: Learning Trustworthily, Automatically, and with Guarantees | 1-gen-2021 | Oneto, Luca; Navarin, Nicolò; Biggio, Battista; Errica, Federico; Micheli, Alessio; Scarselli, Franco; Bianchini, Monica; Sperduti, Alessandro | |
| Graph Mixture Density Networks | 1-gen-2021 | Errica, F; Bacciu, D; Micheli, A | |
| Modeling Edge Features with Deep Bayesian Graph Networks | 1-gen-2021 | Atzeni, D.; Bacciu, D.; Errica, F.; Micheli, A. | |
| A Fair Comparison of Graph Neural Networks for Graph Classification | 1-gen-2020 | Errica, Federico; Podda, Marco; Bacciu, Davide; Micheli, Alessio | |
| A gentle introduction to deep learning for graphs | 1-gen-2020 | Bacciu, D.; Errica, F.; Micheli, A.; Podda, M. | |
| Probabilistic learning on graphs via contextual architectures | 1-gen-2020 | Bacciu, D.; Errica, F.; Micheli, A. | |
| Theoretically Expressive and Edge-aware GraphLearning | 1-gen-2020 | Errica, Federico; Bacciu, Davide; Micheli, Alessio | |
| Contextual graph markov model: A deep and generative approach to graph processing | 1-gen-2018 | Bacciu, Davide; Errica, Federico; Micheli, Alessio |