default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDFederico Errica
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
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 - [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
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-10-21 20:31 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
