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2020 – today
- 2024
[j56]Giacomo Lanciano, Filippo Galli, Tommaso Cucinotta, Davide Bacciu, Andrea Passarella:
Extending OpenStack Monasca for Predictive Elasticity Control. Big Data Min. Anal. 7(2): 315-339 (2024)- [j55]Marco Lepri, Davide Bacciu
, Cosimo Della Santina:
Neural Autoencoder-Based Structure-Preserving Model Order Reduction and Control Design for High-Dimensional Physical Systems. IEEE Control. Syst. Lett. 8: 133-138 (2024) - [j54]Andrea Cossu, Francesco Spinnato, Riccardo Guidotti, Davide Bacciu:
Drifting explanations in continual learning. Neurocomputing 597: 127960 (2024) - [j53]Antonio Carta, Andrea Cossu, Vincenzo Lomonaco, Davide Bacciu, Joost van de Weijer:
Projected Latent Distillation for Data-Agnostic Consolidation in distributed continual learning. Neurocomputing 598: 127935 (2024) - [j52]Andrea Cossu, Antonio Carta, Lucia C. Passaro, Vincenzo Lomonaco, Tinne Tuytelaars, Davide Bacciu:
Continual pre-training mitigates forgetting in language and vision. Neural Networks 179: 106492 (2024) - [j51]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) - [j50]Kun Zhang, Ilya Shpitser, Sara Magliacane, Davide Bacciu, Fei Wu, Changshui Zhang, Peter Spirtes:
IEEE Transactions on Neural Networks and Learning Systems Special Issue on Causal Discovery and Causality-Inspired Machine Learning. IEEE Trans. Neural Networks Learn. Syst. 35(4): 4899-4901 (2024) - [j49]Alessio Gravina, Davide Bacciu:
Deep Learning for Dynamic Graphs: Models and Benchmarks. IEEE Trans. Neural Networks Learn. Syst. 35(9): 11788-11801 (2024) - [c131]Lorenzo Simone, Davide Bacciu, Vincenzo Gervasi:
Quasi-Orthogonal ECG-Frank XYZ Transformation with Energy-Based Models and Clinical Text. AIME (2) 2024: 249-253 - [c130]Andrea Ceni, Andrea Cossu, Maximilian W. Stölzle, Jingyue Liu, Cosimo Della Santina, Davide Bacciu, Claudio Gallicchio:
Random Oscillators Network for Time Series Processing. AISTATS 2024: 4807-4815 - [c129]Malio Li, Elia Piccoli, Vincenzo Lomonaco, Davide Bacciu:
I Know How: Combining Prior Policies to Solve New Tasks. CoG 2024: 1-4 - [c128]Lanpei Li, Elia Piccoli, Andrea Cossu, Davide Bacciu, Vincenzo Lomonaco:
Calibration of Continual Learning Models. CVPR Workshops 2024: 4160-4169 - [c127]Valerio De Caro, Christos Chronis, Massimo Coppola, Vincenzo Lomonaco, Claudio Gallicchio, Konstantinos Tserpes, Davide Bacciu:
TEACHING Platform for Human-Centric Autonomous Applications: Design and Overview. HPDC 2024: 381-384 - [c126]Riccardo Massidda, Francesco Landolfi, Martina Cinquini, Davide Bacciu:
Constraint-Free Structure Learning with Smooth Acyclic Orientations. ICLR 2024 - [c125]Alessio Gravina, Giulio Lovisotto, Claudio Gallicchio, Davide Bacciu, Claas Grohnfeldt:
Long Range Propagation on Continuous-Time Dynamic Graphs. ICML 2024 - [c124]Alessio Gravina, Daniele Zambon, Davide Bacciu, Cesare Alippi:
Temporal Graph ODEs for Irregularly-Sampled Time Series. IJCAI 2024: 4025-4034 - [c123]Asma Sattar, Georgios Deligiorgis, Marco Trincavelli, Davide Bacciu:
Multi-Relational Graph Neural Network for Out-of-Domain Link Prediction. IJCNN 2024: 1-8 - [c122]Andrea Valenti, Davide Bacciu, Antonio Vergari:
ChemAlgebra: Algebraic Reasoning on Chemical Reactions. IJCNN 2024: 1-8 - [c121]Michele Resta, Davide Bacciu:
Self-generated Replay Memories for Continual Neural Machine Translation. NAACL-HLT 2024: 175-191 - [c120]Matteo Ninniri, Marco Podda, Davide Bacciu:
Classifier-Free Graph Diffusion for Molecular Property Targeting. ECML/PKDD (4) 2024: 318-335 - [i94]Cosimo Della Santina, Carlos Hernández Corbato, Burak Sisman, Luis A. Leiva, Ioannis Arapakis, Michalis Vakalellis, Jean Vanderdonckt, Luis Fernando D'Haro, Guido Manzi, Cristina Becchio, Aïda Elamrani, Mohsen Alirezaei, Ginevra Castellano, Dimos V. Dimarogonas, Arabinda Ghosh, Sofie Haesaert, Sadegh Soudjani, Sybert Stroeve, Paul F. M. J. Verschure, Davide Bacciu, Ophelia Deroy, Bahador Bahrami, Claudio Gallicchio, Sabine Hauert, Ricardo Sanz, Pablo Lanillos, Giovanni Iacca, Stephan Sigg, Manel Gasulla, Luc Steels, Carles Sierra:
Awareness in robotics: An early perspective from the viewpoint of the EIC Pathfinder Challenge "Awareness Inside". CoRR abs/2402.09030 (2024) - [i93]Rudy Semola, Julio Hurtado, Vincenzo Lomonaco, Davide Bacciu:
Adaptive Hyperparameter Optimization for Continual Learning Scenarios. CoRR abs/2403.07015 (2024) - [i92]Asma Sattar, Georgios Deligiorgis, Marco Trincavelli, Davide Bacciu:
Multi-Relational Graph Neural Network for Out-of-Domain Link Prediction. CoRR abs/2403.11292 (2024) - [i91]Michele Resta, Davide Bacciu:
Self-generated Replay Memories for Continual Neural Machine Translation. CoRR abs/2403.13130 (2024) - [i90]Lanpei Li, Elia Piccoli, Andrea Cossu, Davide Bacciu, Vincenzo Lomonaco:
Calibration of Continual Learning Models. CoRR abs/2404.07817 (2024) - [i89]Alessandro Trenta, Davide Bacciu, Andrea Cossu, Pietro Ferrero:
MultiSTOP: Solving Functional Equations with Reinforcement Learning. CoRR abs/2404.14909 (2024) - [i88]Alessio Gravina, Daniele Zambon, Davide Bacciu, Cesare Alippi:
Temporal Graph ODEs for Irregularly-Sampled Time Series. CoRR abs/2404.19508 (2024) - [i87]Alessio Gravina, Moshe Eliasof, Claudio Gallicchio, Davide Bacciu, Carola-Bibiane Schönlieb:
Tackling Graph Oversquashing by Global and Local Non-Dissipativity. CoRR abs/2405.01009 (2024) - [i86]Simon Heilig, Alessio Gravina, Alessandro Trenta, Claudio Gallicchio, Davide Bacciu:
Injecting Hamiltonian Architectural Bias into Deep Graph Networks for Long-Range Propagation. CoRR abs/2405.17163 (2024) - [i85]Riccardo Massidda, Sara Magliacane, Davide Bacciu:
Learning Causal Abstractions of Linear Structural Causal Models. CoRR abs/2406.00394 (2024) - [i84]Alessio Gravina, Giulio Lovisotto, Claudio Gallicchio, Davide Bacciu, Claas Grohnfeldt:
Long Range Propagation on Continuous-Time Dynamic Graphs. CoRR abs/2406.02740 (2024) - [i83]Malio Li, Elia Piccoli, Vincenzo Lomonaco, Davide Bacciu:
I Know How: Combining Prior Policies to Solve New Tasks. CoRR abs/2406.09835 (2024) - 2023
- [j48]Giacomo Lanciano, Remo Andreoli, Tommaso Cucinotta, Davide Bacciu, Andrea Passarella:
A 2-Phase Strategy for Intelligent Cloud Operations. IEEE Access 11: 96841-96853 (2023) - [j47]Valerio De Caro, Claudio Gallicchio, Davide Bacciu:
Continual adaptation of federated reservoirs in pervasive environments. Neurocomputing 556: 126638 (2023) - [j46]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) - [j45]Asma Sattar, Davide Bacciu:
Graph Neural Network for Context-Aware Recommendation. Neural Process. Lett. 55(5): 5357-5376 (2023) - [j44]Davide Bacciu, Davide Morelli, Vlad Pandelea:
Modeling Mood Polarity and Declaration Occurrence by Neural Temporal Point Processes. IEEE Trans. Neural Networks Learn. Syst. 34(4): 1800-1807 (2023) - [j43]Davide Bacciu, Danilo Numeroso:
Explaining Deep Graph Networks via Input Perturbation. IEEE Trans. Neural Networks Learn. Syst. 34(12): 10334-10345 (2023) - [c119]Davide Bacciu, Alessio Conte, Francesco Landolfi:
Generalizing Downsampling from Regular Data to Graphs. AAAI 2023: 6718-6727 - [c118]Lorenzo Simone, Davide Bacciu:
ECGAN: Self-supervised Generative Adversarial Network for Electrocardiography. AIME 2023: 276-280 - [c117]Riccardo Massidda, Atticus Geiger, Thomas Icard, Davide Bacciu:
Causal Abstraction with Soft Interventions. CLeaR 2023: 68-87 - [c116]Hamed Hemati, Vincenzo Lomonaco, Davide Bacciu, Damian Borth:
Partial Hypernetworks for Continual Learning. CoLLAs 2023: 318-336 - [c115]Hamed Hemati, Andrea Cossu, Antonio Carta, Julio Hurtado, Lorenzo Pellegrini, Davide Bacciu, Vincenzo Lomonaco, Damian Borth:
Class-Incremental Learning with Repetition. CoLLAs 2023: 437-455 - [c114]Emanuele Cosenza, Andrea Valenti, Davide Bacciu:
Graph-based Polyphonic Multitrack Music Generation. CREAI@AI*IA 2023: 26-37 - [c113]Davide Bacciu, Federico Errica, Alessio Micheli, Nicolò Navarin, Luca Pasa, Marco Podda, Daniele Zambon:
Graph Representation Learning. ESANN 2023 - [c112]Valerio De Caro, Antonio Di Mauro, Davide Bacciu, Claudio Gallicchio:
Communication-Efficient Ridge Regression in Federated Echo State Networks. ESANN 2023 - [c111]Andrea Ceni, Davide Bacciu, Valerio De Caro, Claudio Gallicchio, Luca Oneto:
Improving Fairness via Intrinsic Plasticity in Echo State Networks. ESANN 2023 - [c110]Andrea Cossu, Francesco Spinnato, Riccardo Guidotti, Davide Bacciu:
A Protocol for Continual Explanation of SHAP. ESANN 2023 - [c109]Federico Errica, Alessio Gravina, Davide Bacciu, Alessio Micheli:
Hidden Markov Models for Temporal Graph Representation Learning. ESANN 2023 - [c108]Francesco Landolfi, Davide Bacciu, Danilo Numeroso:
A Tropical View of Graph Neural Networks. ESANN 2023 - [c107]Davide Bacciu, Konstantinos Tserpes, Massimo Coppola, Georg Macher, Claudio Gallicchio, Omar Veledar, Anna Maria Anaxagorou, Patrizio Dazzi:
TEACHING: A Computing Toolkit for Building Efficient Autonomous appliCations Leveraging Humanistic INtelliGence. FRAME@HPDC 2023: 37-39 - [c106]Valerio De Caro, Herbert Danzinger, Claudio Gallicchio, Clemens Könczöl, Vincenzo Lomonaco, Mina Marmpena, Sevasti Politi, Omar Veledar, Davide Bacciu:
Prediction of Driver's Stress Affection in Simulated Autonomous Driving Scenarios. ICASSP Workshops 2023: 1-5 - [c105]Vincenzo Lomonaco, Valerio De Caro, Claudio Gallicchio, Antonio Carta, Christos Sardianos, Iraklis Varlamis, Konstantinos Tserpes, Massimo Coppola, Mina Marmpena, Sevasti Politi, Erwin Schoitsch, Davide Bacciu:
AI-Toolkit: A Microservices Architecture for Low-Code Decentralized Machine Intelligence. ICASSP Workshops 2023: 1-5 - [c104]Julio Hurtado, Alain Raymond-Saez, Vladimir Araujo, Vincenzo Lomonaco, Alvaro Soto, Davide Bacciu:
Memory Population in Continual Learning via Outlier Elimination. ICCV (Workshops) 2023: 3473-3482 - [c103]Alessio Gravina, Davide Bacciu, Claudio Gallicchio:
Anti-Symmetric DGN: a stable architecture for Deep Graph Networks. ICLR 2023 - [c102]Danilo Numeroso, Davide Bacciu, Petar Velickovic:
Dual Algorithmic Reasoning. ICLR 2023 - [c101]Emanuele Cosenza, Andrea Valenti, Davide Bacciu:
Graph-based Polyphonic Multitrack Music Generation. IJCAI 2023: 5797-5805 - [c100]Yohannis Telila, Tommaso Cucinotta, Davide Bacciu:
Automatic Music Transcription using Convolutional Neural Networks and Constant-Q Transform. Ital-IA 2023: 526-531 - [c99]Dobrik Georgiev, Danilo Numeroso, Davide Bacciu, Pietro Lio:
Neural Algorithmic Reasoning for Combinatorial Optimisation. LoG 2023: 28 - [c98]Davide Bacciu, Antonio Carta, Claudio Gallicchio, Christoph Schmittner:
Safety and Robustness for Deep Neural Networks: An Automotive Use Case. SAFECOMP Workshops 2023: 95-107 - [e1]Marco Pegoraro, Davide Bacciu, Andrea Burattin, Antonio Carta, Patrizio Dazzi, Massimiliano de Leoni, Magdalini Eirinaki, Iraklis Varlamis:
Joint Proceedings of the 1st International Workshop on Computational Intelligence for Process Mining (CI4PM 2022) and the 1st International Workshop on Pervasive Artificial Intelligence (PAI 2022), co-located with the IEEE World Congress on Computational Intelligence (WCCI 2022), Padua, Italy, July 18-23, 2022., Padua, Italy, July 18-23, 2022. CEUR Workshop Proceedings 3350, CEUR-WS.org 2023 [contents] - [d13]Giacomo Lanciano, Remo Andreoli, Tommaso Cucinotta, Davide Bacciu, Andrea Passarella:
Archival bundle of the data used for "A 2-phase Strategy For Intelligent Cloud Operations". Version 2. Zenodo, 2023 [all versions]
- [d12]Giacomo Lanciano, Remo Andreoli, Tommaso Cucinotta, Davide Bacciu, Andrea Passarella:
Archival bundle of the code used for "A 2-phase Strategy For Intelligent Cloud Operations". Version v2.0.0. Zenodo, 2023 [all versions] - [d11]Giacomo Lanciano, Filippo Galli, Tommaso Cucinotta, Davide Bacciu, Andrea Passarella:
Archival bundle of the code used for "Extending OpenStack Monasca for Predictive Elasticity Control". Version v3.0.0. Zenodo, 2023 [all versions] - [i82]Lorenzo Simone, Davide Bacciu:
ECGAN: Self-supervised generative adversarial network for electrocardiography. CoRR abs/2301.09496 (2023) - [i81]Hamed Hemati, Andrea Cossu, Antonio Carta, Julio Hurtado, Lorenzo Pellegrini, Davide Bacciu, Vincenzo Lomonaco, Damian Borth:
Class-Incremental Learning with Repetition. CoRR abs/2301.11396 (2023) - [i80]Danilo Numeroso, Davide Bacciu, Petar Velickovic:
Dual Algorithmic Reasoning. CoRR abs/2302.04496 (2023) - [i79]Antonio Carta, Andrea Cossu, Vincenzo Lomonaco, Davide Bacciu, Joost van de Weijer:
Projected Latent Distillation for Data-Agnostic Consolidation in Distributed Continual Learning. CoRR abs/2303.15888 (2023) - [i78]Dario Balboni, Davide Bacciu:
ADLER - An efficient Hessian-based strategy for adaptive learning rate. CoRR abs/2305.16396 (2023) - [i77]Dobrik Georgiev, Danilo Numeroso, Davide Bacciu, Pietro Liò:
Neural Algorithmic Reasoning for Combinatorial Optimisation. CoRR abs/2306.06064 (2023) - [i76]Andrea Cossu, Francesco Spinnato, Riccardo Guidotti, Davide Bacciu:
A Protocol for Continual Explanation of SHAP. CoRR abs/2306.07218 (2023) - [i75]Hamed Hemati, Vincenzo Lomonaco, Davide Bacciu, Damian Borth:
Partial Hypernetworks for Continual Learning. CoRR abs/2306.10724 (2023) - [i74]Alessio Gravina, Davide Bacciu:
Deep learning for dynamic graphs: models and benchmarks. CoRR abs/2307.06104 (2023) - [i73]Emanuele Cosenza, Andrea Valenti, Davide Bacciu:
Graph-based Polyphonic Multitrack Music Generation. CoRR abs/2307.14928 (2023) - [i72]Daniele Atzeni, Federico Errica, Davide Bacciu, Alessio Micheli:
Modeling Edge Features with Deep Bayesian Graph Networks. CoRR abs/2308.09087 (2023) - [i71]Riccardo Massidda, Francesco Landolfi, Martina Cinquini, Davide Bacciu:
Constraint-Free Structure Learning with Smooth Acyclic Orientations. CoRR abs/2309.08406 (2023) - [i70]Marco Lepri, Davide Bacciu, Cosimo Della Santina:
Neural Autoencoder-Based Structure-Preserving Model Order Reduction and Control Design for High-Dimensional Physical Systems. CoRR abs/2312.06256 (2023) - [i69]Matteo Ninniri, Marco Podda, Davide Bacciu:
Classifier-free graph diffusion for molecular property targeting. CoRR abs/2312.17397 (2023) - 2022
- [j42]Giuseppe Averta, Federica Barontini, Irene Valdambrini, Paolo Cheli, Davide Bacciu, Matteo Bianchi:
Learning to Prevent Grasp Failure with Soft Hands: From Online Prediction to Dual-Arm Grasp Recovery. Adv. Intell. Syst. 4(3) (2022) - [j41]Antonio Carta, Andrea Cossu, Federico Errica, Davide Bacciu:
Catastrophic Forgetting in Deep Graph Networks: A Graph Classification Benchmark. Frontiers Artif. Intell. 5: 824655 (2022) - [j40]Andrea Cossu, Gabriele Graffieti, Lorenzo Pellegrini, Davide Maltoni, Davide Bacciu, Antonio Carta, Vincenzo Lomonaco:
Is Class-Incremental Enough for Continual Learning? Frontiers Artif. Intell. 5: 829842 (2022) - [j39]Francesco Crecchi, Marco Melis, Angelo Sotgiu, Davide Bacciu, Battista Biggio:
FADER: Fast adversarial example rejection. Neurocomputing 470: 257-268 (2022) - [j38]Daniele Castellana, Davide Bacciu:
A tensor framework for learning in structured domains. Neurocomputing 470: 405-426 (2022) - [j37]Haris Dukic, Shahab Mokarizadeh, Georgios Deligiorgis, Pierpaolo Sepe, Davide Bacciu, Marco Trincavelli:
Inductive-transductive learning for very sparse fashion graphs. Neurocomputing 504: 42-55 (2022) - [j36]Davide Bacciu, Gioele Bertoncini, Davide Morelli:
Topographic mapping for quality inspection and intelligent filtering of smart-bracelet data. Neural Comput. Appl. 34(1): 51-65 (2022) - [j35]Alessio Gravina, Jennifer L. Wilson, Davide Bacciu, Kevin Grimes, Corrado Priami:
Controlling astrocyte-mediated synaptic pruning signals for schizophrenia drug repurposing with deep graph networks. PLoS Comput. Biol. 18(5) (2022) - [j34]Lorenzo Collodi, Davide Bacciu, Matteo Bianchi, Giuseppe Averta:
Learning With Few Examples the Semantic Description of Novel Human-Inspired Grasp Strategies From RGB Data. IEEE Robotics Autom. Lett. 7(2): 2573-2580 (2022) - [j33]Daniele Atzeni, Davide Bacciu, Daniele Mazzei, Giuseppe Prencipe:
A Systematic Review of Wi-Fi and Machine Learning Integration with Topic Modeling Techniques. Sensors 22(13): 4925 (2022) - [c97]Dario Balboni, Davide Bacciu:
An Empirical Verification of Wide Networks Theory. BMVC 2022: 517 - [c96]Gabriele Lagani, Davide Bacciu, Claudio Gallicchio, Fabrizio Falchi, Claudio Gennaro, Giuseppe Amato:
Deep Features for CBIR with Scarce Data using Hebbian Learning. CBMI 2022: 136-141 - [c95]Antonio Carta, Andrea Cossu, Vincenzo Lomonaco, Davide Bacciu:
Ex-Model: Continual Learning from a Stream of Trained Models. CVPR Workshops 2022: 3789-3798 - [c94]Davide Bacciu, Davide Serramazza:
Learning Image Captioning as a Structured Transduction Task. EANN 2022: 235-246 - [c93]Davide Bacciu, Federico Errica, Nicolò Navarin, Luca Pasa, Daniele Zambon:
Deep Learning for Graphs. ESANN 2022 - [c92]Valerio De Caro, Claudio Gallicchio, Davide Bacciu:
Federated Adaptation of Reservoirs via Intrinsic Plasticity. ESANN 2022 - [c91]Federico Matteoni, Andrea Cossu, Claudio Gallicchio, Vincenzo Lomonaco, Davide Bacciu:
Continual Learning for Human State Monitoring. ESANN 2022 - [c90]Michele Resta, Davide Bacciu:
Continual Incremental Language Learning for Neural Machine Translation. ESANN 2022 - [c89]Andrea Valenti, Davide Bacciu:
Modular Representations for Weak Disentanglement. ESANN 2022 - [c88]Nicolò Lucchesi, Antonio Carta, Vincenzo Lomonaco, Davide Bacciu:
Avalanche RL: A Continual Reinforcement Learning Library. ICIAP (1) 2022: 524-535 - [c87]Gabriele Merlin, Vincenzo Lomonaco, Andrea Cossu, Antonio Carta, Davide Bacciu:
Practical Recommendations for Replay-Based Continual Learning Methods. ICIAP Workshops 2022: 548-559 - [c86]Daniele Castellana, Federico Errica, Davide Bacciu, Alessio Micheli:
The Infinite Contextual Graph Markov Model. ICML 2022: 2721-2737 - [c85]Mattia Sangermano, Antonio Carta, Andrea Cossu, Davide Bacciu:
Sample Condensation in Online Continual Learning. IJCNN 2022: 1-8 - [c84]Andrea Valenti, Davide Bacciu:
Leveraging Relational Information for Learning Weakly Disentangled Representations. IJCNN 2022: 1-8 - [c83]Valerio De Caro, Saira Bano, Achilles Machumilane, Alberto Gotta, Pietro Cassarà, Antonio Carta, Rudy Semola, Christos Sardianos, Christos Chronis, Iraklis Varlamis, Konstantinos Tserpes, Vincenzo Lomonaco, Claudio Gallicchio, Davide Bacciu:
AI-as-a-Service Toolkit for Human-Centered Intelligence in Autonomous Driving. PerCom Workshops 2022: 91-93 - [c82]Riccardo Massidda, Davide Bacciu:
Knowledge-Driven Interpretation of Convolutional Neural Networks. ECML/PKDD (1) 2022: 356-371 - [c81]Rudy Semola, Lorenzo Moro, Davide Bacciu, Enrico Prati:
Deep Reinforcement Learning Quantum Control on IBMQ Platforms and Qiskit Pulse. QCE 2022: 759-762 - [c80]Rudy Semola, Vincenzo Lomonaco, Davide Bacciu:
Continual-Learning-as-a-Service (CLaaS): On-Demand Efficient Adaptation of Predictive Models. CI4PM/PAI@WCCI 2022: 46-55 - [d10]Giacomo Lanciano, Tommaso Cucinotta, Davide Bacciu, Andrea Passarella:
Archival bundle of the data used for "A 2-phase Strategy For Intelligent Cloud Operations". Version 1. Zenodo, 2022 [all versions] - [d9]Giacomo Lanciano, Tommaso Cucinotta, Davide Bacciu, Andrea Passarella:
Archival bundle of the code used for "A 2-phase Strategy For Intelligent Cloud Operations". Version v1.0.0. Zenodo, 2022 [all versions] - [d8]Giacomo Lanciano, Filippo Galli, Tommaso Cucinotta, Davide Bacciu, Andrea Passarella:
Archival bundle of the data used for "Extending OpenStack Monasca for Predictive Elasticity Control". Version 1. Zenodo, 2022 [all versions] - [d7]Giacomo Lanciano, Filippo Galli, Tommaso Cucinotta, Davide Bacciu, Andrea Passarella:
Archival bundle of the code used for "Extending OpenStack Monasca for Predictive Elasticity Control". Version v1.0.0. Zenodo, 2022 [all versions] - [d6]Giacomo Lanciano, Filippo Galli, Tommaso Cucinotta, Davide Bacciu, Andrea Passarella:
Archival bundle of the data used for "Extending OpenStack Monasca for Predictive Elasticity Control". Version 2. Zenodo, 2022 [all versions] - [d5]Giacomo Lanciano, Filippo Galli, Tommaso Cucinotta, Davide Bacciu, Andrea Passarella:
Archival bundle of the code used for "Extending OpenStack Monasca for Predictive Elasticity Control". Version v2.0.0. Zenodo, 2022 [all versions] - [i68]Valerio De Caro, Saira Bano, Achilles Machumilane, Alberto Gotta, Pietro Cassarà, Antonio Carta, Rudy Semola, Christos Sardianos, Christos Chronis, Iraklis Varlamis, Konstantinos Tserpes, Vincenzo Lomonaco, Claudio Gallicchio, Davide Bacciu:
AI-as-a-Service Toolkit for Human-Centered Intelligence in Autonomous Driving. CoRR abs/2202.01645 (2022) - [i67]Gabriele Merlin, Vincenzo Lomonaco, Andrea Cossu, Antonio Carta, Davide Bacciu:
Practical Recommendations for Replay-based Continual Learning Methods. CoRR abs/2203.10317 (2022) - [i66]Danilo Numeroso, Davide Bacciu, Petar Velickovic:
Learning heuristics for A. CoRR abs/2204.08938 (2022) - [i65]Gabriele Lagani, Davide Bacciu, Claudio Gallicchio, Fabrizio Falchi, Claudio Gennaro, Giuseppe Amato:
Deep Features for CBIR with Scarce Data using Hebbian Learning. CoRR abs/2205.08935 (2022) - [i64]Andrea Cossu, Tinne Tuytelaars, Antonio Carta, Lucia C. Passaro, Vincenzo Lomonaco, Davide Bacciu:
Continual Pre-Training Mitigates Forgetting in Language and Vision. CoRR abs/2205.09357 (2022) - [i63]Andrea Valenti, Davide Bacciu:
Leveraging Relational Information for Learning Weakly Disentangled Representations. CoRR abs/2205.10056 (2022) - [i62]Rudy Semola, Vincenzo Lomonaco, Davide Bacciu:
Continual-Learning-as-a-Service (CLaaS): On-Demand Efficient Adaptation of Predictive Models. CoRR abs/2206.06957 (2022) - [i61]Valerio De Caro, Claudio Gallicchio, Davide Bacciu:
Federated Adaptation of Reservoirs via Intrinsic Plasticity. CoRR abs/2206.11087 (2022) - [i60]Mattia Sangermano, Antonio Carta, Andrea Cossu, Davide Bacciu:
Sample Condensation in Online Continual Learning. CoRR abs/2206.11849 (2022) - [i59]Federico Matteoni, Andrea Cossu, Claudio Gallicchio, Vincenzo Lomonaco, Davide Bacciu:
Continual Learning for Human State Monitoring. CoRR abs/2207.00010 (2022) - [i58]Francesco Corti, Rahim Entezari, Sara Hooker, Davide Bacciu, Olga Saukh:
Studying the impact of magnitude pruning on contrastive learning methods. CoRR abs/2207.00200 (2022) - [i57]Julio Hurtado, Alain Raymond-Saez, Vladimir Araujo, Vincenzo Lomonaco, Davide Bacciu:
It's all About Consistency: A Study on Memory Composition for Replay-Based Methods in Continual Learning. CoRR abs/2207.01145 (2022) - [i56]Davide Bacciu, Alessio Conte, Francesco Landolfi:
Graph Pooling with Maximum-Weight k-Independent Sets. CoRR abs/2208.03523 (2022) - [i55]Andrea Valenti, Davide Bacciu:
Modular Representations for Weak Disentanglement. CoRR abs/2209.05336 (2022) - [i54]Andrea Valenti, Davide Bacciu, Antonio Vergari:
ChemAlgebra: Algebraic Reasoning on Chemical Reactions. CoRR abs/2210.02095 (2022) - [i53]Alessio Gravina, Davide Bacciu, Claudio Gallicchio:
Anti-Symmetric DGN: a stable architecture for Deep Graph Networks. CoRR abs/2210.09789 (2022) - [i52]Riccardo Massidda, Atticus Geiger, Thomas Icard, Davide Bacciu:
Causal Abstraction with Soft Interventions. CoRR abs/2211.12270 (2022) - 2021
- [j32]Davide Bacciu, Alessio Conte, Roberto Grossi, Francesco Landolfi, Andrea Marino:
K-plex cover pooling for graph neural networks. Data Min. Knowl. Discov. 35(5): 2200-2220 (2021) - [j31]Michele Resta, Anna Monreale, Davide Bacciu:
Occlusion-Based Explanations in Deep Recurrent Models for Biomedical Signals. Entropy 23(8): 1064 (2021) - [j30]Gianluca Bontempi, Ricardo Chavarriaga, Hans ed Canck, Emanuela Girardi, Holger H. Hoos, Iarla Kilbane-Dawe, Tonio Ball, Ann Nowé, Jose Sousa, Davide Bacciu, Marco Aldinucci, Manlio ed Domenico, Alessandro Saffiotti, Marco Maratea:
The CLAIRE COVID-19 initiative: approach, experiences and recommendations. Ethics Inf. Technol. 23(S1): 127-133 (2021) - [j29]Davide Bacciu, Emanuela Girardi, Marco Maratea, Jose Sousa:
AI & COVID-19. Intelligenza Artificiale 15(2): 45-53 (2021) - [j28]Antonio Carta, Alessandro Sperduti, Davide Bacciu:
Encoding-based memory for recurrent neural networks. Neurocomputing 456: 407-420 (2021) - [j27]Andrea Cossu, Antonio Carta, Vincenzo Lomonaco, Davide Bacciu:
Continual learning for recurrent neural networks: An empirical evaluation. Neural Networks 143: 607-627 (2021) - [c79]Davide Bacciu, Siranush Akarmazyan, Eric Armengaud, Manlio Bacco, George Bravos, Calogero Calandra, Emanuele Carlini, Antonio Carta, Pietro Cassarà, Massimo Coppola, Charalampos Davalas, Patrizio Dazzi, Maria Carmela De Gennaro, Daniele Di Sarli, Jürgen Dobaj, Claudio Gallicchio, Sylvain Girbal, Alberto Gotta, Riccardo Groppo, Vincenzo Lomonaco, Georg Macher, Daniele Mazzei, Gabriele Mencagli, Dimitrios Michail, Alessio Micheli, Roberta Peroglio, Salvatore Petroni, Rosaria Potenza, Farank Pourdanesh, Christos Sardianos, Konstantinos Tserpes, Fulvio Tagliabo, Jakob Valtl, Iraklis Varlamis, Omar Veledar:
TEACHING - Trustworthy autonomous cyber-physical applications through human-centred intelligence. COINS 2021: 1-6 - [c78]Vincenzo Lomonaco, Lorenzo Pellegrini, Andrea Cossu, Antonio Carta, Gabriele Graffieti, Tyler L. Hayes, Matthias De Lange, Marc Masana, Jary Pomponi, Gido M. van de Ven, Martin Mundt, Qi She, Keiland W. Cooper, Jeremy Forest, Eden Belouadah, Simone Calderara, German Ignacio Parisi, Fabio Cuzzolin, Andreas S. Tolias, Simone Scardapane, Luca Antiga, Subutai Ahmad, Adrian Popescu, Christopher Kanan, Joost van de Weijer, Tinne Tuytelaars, Davide Bacciu, Davide Maltoni:
Avalanche: An End-to-End Library for Continual Learning. CVPR Workshops 2021: 3600-3610 - [c77]Davide Bacciu, Filippo Maria Bianchi, Benjamin Paassen, Cesare Alippi:
Deep learning for graphs. ESANN 2021 - [c76]Andrea Cossu, Davide Bacciu, Antonio Carta, Claudio Gallicchio, Vincenzo Lomonaco:
Continual Learning with Echo State Networks. ESANN 2021 - [c75]Marco Trincavelli, Haris Dukic, Georgios Deligiorgis, Pierpaolo Sepe, Davide Bacciu:
Inductive learning for product assortment graph completion. ESANN 2021 - [c74]Andrea Valenti, Stefano Berti, Davide Bacciu:
Calliope - A Polyphonic Music Transformer. ESANN 2021 - [c73]Federico Errica, Davide Bacciu, Alessio Micheli:
Graph Mixture Density Networks. ICML 2021: 3025-3035 - [c72]Daniele Atzeni, Davide Bacciu, Federico Errica, Alessio Micheli:
Modeling Edge Features with Deep Bayesian Graph Networks. IJCNN 2021: 1-8 - [c71]Davide Bacciu, Marco Podda:
Graphgen-redux: a Fast and Lightweight Recurrent Model for labeled Graph Generation. IJCNN 2021: 1-8 - [c70]Davide Bacciu, Daniele Di Sarli, Pouria Faraji, Claudio Gallicchio, Alessio Micheli:
Federated Reservoir Computing Neural Networks. IJCNN 2021: 1-7 - [c69]Davide Bacciu, Daniele Di Sarli, Claudio Gallicchio, Alessio Micheli, Niccolò Puccinelli:
Benchmarking Reservoir and Recurrent Neural Networks for Human State and Activity Recognition. IWANN (2) 2021: 168-179 - [c68]Asma Sattar, Davide Bacciu:
Context-Aware Graph Convolutional Autoencoder. IWANN (1) 2021: 279-290 - [c67]Georg Macher, Siranush Akarmazyan, Eric Armengaud, Davide Bacciu, Calogero Calandra, Herbert Danzinger, Patrizio Dazzi, Charalampos Davalas, Maria Carmela De Gennaro, Angela Dimitriou, Jürgen Dobaj, Maid Dzambic, Lorenzo Giraudi, Sylvain Girbal, Dimitrios Michail, Roberta Peroglio, Rosaria Potenza, Farank Pourdanesh, Matthias Seidl, Christos Sardianos, Konstantinos Tserpes, Jakob Valtl, Iraklis Varlamis, Omar Veledar:
Dependable Integration Concepts for Human-Centric AI-Based Systems. SAFECOMP Workshops 2021: 11-23 - [c66]Asma Sattar, Davide Bacciu:
Dynamic Context in Graph Neural Networks for Item Recommendation. SSCI 2021: 1-8 - [c65]Giacomo Lanciano, Filippo Galli, Tommaso Cucinotta, Davide Bacciu, Andrea Passarella:
Predictive auto-scaling with OpenStack Monasca. UCC 2021: 20:1-20:10 - [c64]Andrea Rosasco, Antonio Carta, Andrea Cossu, Vincenzo Lomonaco, Davide Bacciu:
Distilled Replay: Overcoming Forgetting Through Synthetic Samples. CSSL 2021: 104-117 - [d4]Giacomo Lanciano, Filippo Galli, Tommaso Cucinotta, Davide Bacciu, Andrea Passarella:
Archival bundle of the data used for "Predictive Auto-scaling with OpenStack Monasca" (UCC 2021). Zenodo, 2021 - [d3]Giacomo Lanciano, Filippo Galli, Tommaso Cucinotta, Davide Bacciu, Andrea Passarella:
Archival bundle of the code used for "Predictive Auto-scaling with OpenStack Monasca" (UCC 2021). Version v1.0.0. Zenodo, 2021 [all versions] - [d2]Giacomo Lanciano, Filippo Galli, Tommaso Cucinotta, Davide Bacciu, Andrea Passarella:
Archival bundle of the code used for "Predictive Auto-scaling with OpenStack Monasca" (UCC 2021). Version v1.0.1. Zenodo, 2021 [all versions] - [i51]Andrea Cossu, Antonio Carta, Vincenzo Lomonaco, Davide Bacciu:
Continual Learning for Recurrent Neural Networks: an Empirical Evaluation. CoRR abs/2103.07492 (2021) - [i50]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) - [i49]Andrea Rosasco, Antonio Carta, Andrea Cossu, Vincenzo Lomonaco, Davide Bacciu:
Distilled Replay: Overcoming Forgetting through Synthetic Samples. CoRR abs/2103.15851 (2021) - [i48]Vincenzo Lomonaco, Lorenzo Pellegrini, Andrea Cossu, Antonio Carta, Gabriele Graffieti, Tyler L. Hayes, Matthias De Lange, Marc Masana, Jary Pomponi, Gido M. van de Ven, Martin Mundt, Qi She, Keiland W. Cooper, Jeremy Forest, Eden Belouadah, Simone Calderara, German Ignacio Parisi, Fabio Cuzzolin, Andreas S. Tolias, Simone Scardapane, Luca Antiga, Subutai Amhad, Adrian Popescu, Christopher Kanan, Joost van de Weijer, Tinne Tuytelaars, Davide Bacciu, Davide Maltoni:
Avalanche: an End-to-End Library for Continual Learning. CoRR abs/2104.00405 (2021) - [i47]Danilo Numeroso, Davide Bacciu:
MEG: Generating Molecular Counterfactual Explanations for Deep Graph Networks. CoRR abs/2104.08060 (2021) - [i46]Elisa Ferrari, Davide Bacciu:
Addressing Fairness, Bias and Class Imbalance in Machine Learning: the FBI-loss. CoRR abs/2105.06345 (2021) - [i45]Elisa Ferrari, Luna Gargani, Greta Barbieri, Lorenzo Ghiadoni, Francesco Faita, Davide Bacciu:
A causal learning framework for the analysis and interpretation of COVID-19 clinical data. CoRR abs/2105.06998 (2021) - [i44]Andrea Cossu, Davide Bacciu, Antonio Carta, Claudio Gallicchio, Vincenzo Lomonaco:
Continual Learning with Echo State Networks. CoRR abs/2105.07674 (2021) - [i43]Andrea Valenti, Stefano Berti, Davide Bacciu:
Calliope - A Polyphonic Music Transformer. CoRR abs/2107.05546 (2021) - [i42]Davide Bacciu, Siranush Akarmazyan, Eric Armengaud, Manlio Bacco, George Bravos, Calogero Calandra, Emanuele Carlini, Antonio Carta, Pietro Cassarà, Massimo Coppola, Charalampos Davalas, Patrizio Dazzi, Maria Carmela De Gennaro, Daniele Di Sarli, Jürgen Dobaj, Claudio Gallicchio, Sylvain Girbal, Alberto Gotta, Riccardo Groppo, Vincenzo Lomonaco, Georg Macher, Daniele Mazzei, Gabriele Mencagli, Dimitrios Michail, Alessio Micheli, Roberta Peroglio, Salvatore Petroni, Rosaria Potenza, Farank Pourdanesh, Christos Sardianos, Konstantinos Tserpes, Fulvio Tagliabo, Jakob Valtl, Iraklis Varlamis, Omar Veledar:
TEACHING - Trustworthy autonomous cyber-physical applications through human-centred intelligence. CoRR abs/2107.06543 (2021) - [i41]Marco Podda, Davide Bacciu:
GraphGen-Redux: a Fast and Lightweight Recurrent Model for labeled Graph Generation. CoRR abs/2107.08396 (2021) - [i40]Haris Dukic, Georgios Deligiorgis, Pierpaolo Sepe, Davide Bacciu, Marco Trincavelli:
Inductive learning for product assortment graph completion. CoRR abs/2110.01677 (2021) - [i39]Giacomo Lanciano, Filippo Galli, Tommaso Cucinotta, Davide Bacciu, Andrea Passarella:
Predictive Auto-scaling with OpenStack Monasca. CoRR abs/2111.02133 (2021) - [i38]Andrea Cossu, Gabriele Graffieti, Lorenzo Pellegrini, Davide Maltoni, Davide Bacciu, Antonio Carta, Vincenzo Lomonaco:
Is Class-Incremental Enough for Continual Learning? CoRR abs/2112.02925 (2021) - [i37]Antonio Carta, Andrea Cossu, Vincenzo Lomonaco, Davide Bacciu:
Ex-Model: Continual Learning from a Stream of Trained Models. CoRR abs/2112.06511 (2021) - [i36]Davide Bacciu, Patrizio Dazzi, Alberto Gotta:
Supporting Privacy Preservation by Distributed and Federated Learning on the Edge. ERCIM News 2021(126): 0 (2021) - 2020
- [j26]Elisa Ferrari, Alessandra Retico, Davide Bacciu:
Measuring the effects of confounders in medical supervised classification problems: the Confounding Index (CI). Artif. Intell. Medicine 103: 101804 (2020) - [j25]Davide Bacciu, Alessio Micheli, Marco Podda:
Edge-based sequential graph generation with recurrent neural networks. Neurocomputing 416: 177-189 (2020) - [j24]Davide Bacciu, Federico Errica, Alessio Micheli:
Probabilistic Learning on Graphs via Contextual Architectures. J. Mach. Learn. Res. 21: 134:1-134:39 (2020) - [j23]Davide Bacciu, Federico Errica, Alessio Micheli, Marco Podda:
A gentle introduction to deep learning for graphs. Neural Networks 129: 203-221 (2020) - [j22]Davide Bacciu, Francesco Crecchi:
Augmenting Recurrent Neural Networks Resilience by Dropout. IEEE Trans. Neural Networks Learn. Syst. 31(1): 345-351 (2020) - [c63]Marco Podda, Davide Bacciu, Alessio Micheli:
A Deep Generative Model for Fragment-Based Molecule Generation. AISTATS 2020: 2240-2250 - [c62]Daniele Castellana, Davide Bacciu:
Learning from Non-Binary Constituency Trees via Tensor Decomposition. COLING 2020: 3899-3910 - [c61]Andrea Valenti, Antonio Carta, Davide Bacciu:
Learning Style-Aware Symbolic Music Representations by Adversarial Autoencoders. ECAI 2020: 1563-1570 - [c60]Marco Podda, Alessio Micheli, Davide Bacciu, Paolo Milazzo:
Biochemical Pathway Robustness Prediction with Graph Neural Networks. ESANN 2020: 121-126 - [c59]Federico Errica, Davide Bacciu, Alessio Micheli:
Theoretically Expressive and Edge-aware Graph Learning. ESANN 2020: 175-180 - [c58]Francesco Crecchi, Cyril de Bodt, Michel Verleysen, John A. Lee, Davide Bacciu:
Perplexity-free Parametric t-SNE. ESANN 2020: 387-392 - [c57]Davide Bacciu, Danilo P. Mandic:
Tensor Decompositions in Deep Learning. ESANN 2020: 441-450 - [c56]Daniele Castellana, Davide Bacciu:
Tensor Decompositions in Recursive Neural Networks for Tree-Structured Data. ESANN 2020: 451-456 - [c55]Federico Errica, Marco Podda, Davide Bacciu, Alessio Micheli:
A Fair Comparison of Graph Neural Networks for Graph Classification. ICLR 2020 - [c54]Daniele Castellana, Davide Bacciu:
Generalising Recursive Neural Models by Tensor Decomposition. IJCNN 2020: 1-8 - [c53]Andrea Cossu, Antonio Carta, Davide Bacciu:
Continual Learning with Gated Incremental Memories for sequential data processing. IJCNN 2020: 1-8 - [c52]Antonio Carta, Alessandro Sperduti, Davide Bacciu:
Incremental Training of a Recurrent Neural Network Exploiting a Multi-scale Dynamic Memory. ECML/PKDD (1) 2020: 677-693 - [c51]Andrea Valenti, Michele Barsotti, Raffaello Brondi, Davide Bacciu, Luca Ascari:
ROS-Neuro Integration of Deep Convolutional Autoencoders for EEG Signal Compression in Real-time BCIs. SMC 2020: 2019-2024 - [i35]Andrea Valenti, Antonio Carta, Davide Bacciu:
Learning a Latent Space of Style-Aware Symbolic Music Representations by Adversarial Autoencoders. CoRR abs/2001.05494 (2020) - [i34]Federico Errica, Davide Bacciu, Alessio Micheli:
Theoretically Expressive and Edge-aware Graph Learning. CoRR abs/2001.09005 (2020) - [i33]Antonio Carta, Alessandro Sperduti, Davide Bacciu:
Encoding-based Memory Modules for Recurrent Neural Networks. CoRR abs/2001.11771 (2020) - [i32]Davide Bacciu, Alessio Micheli, Marco Podda:
Edge-based sequential graph generation with recurrent neural networks. CoRR abs/2002.00102 (2020) - [i31]Davide Bacciu, Danilo P. Mandic:
Tensor Decompositions in Deep Learning. CoRR abs/2002.11835 (2020) - [i30]Marco Podda, Davide Bacciu, Alessio Micheli:
A Deep Generative Model for Fragment-Based Molecule Generation. CoRR abs/2002.12826 (2020) - [i29]Andrea Cossu, Antonio Carta, Davide Bacciu:
Continual Learning with Gated Incremental Memories for sequential data processing. CoRR abs/2004.04077 (2020) - [i28]Daniele Castellana, Davide Bacciu:
Generalising Recursive Neural Models by Tensor Decomposition. CoRR abs/2006.10021 (2020) - [i27]Daniele Castellana, Davide Bacciu:
Tensor Decompositions in Recursive NeuralNetworks for Tree-Structured Data. CoRR abs/2006.10619 (2020) - [i26]Antonio Carta, Alessandro Sperduti, Davide Bacciu:
Incremental Training of a Recurrent Neural Network Exploiting a Multi-Scale Dynamic Memory. CoRR abs/2006.16800 (2020) - [i25]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) - [i24]Andrea Valenti, Michele Barsotti, Raffaello Brondi, Davide Bacciu, Luca Ascari:
ROS-Neuro Integration of Deep Convolutional Autoencoders for EEG Signal Compression in Real-time BCIs. CoRR abs/2008.13485 (2020) - [i23]Francesco Crecchi, Cyril de Bodt, Michel Verleysen, John A. Lee, Davide Bacciu:
Perplexity-free Parametric t-SNE. CoRR abs/2010.01359 (2020) - [i22]Francesco Crecchi, Marco Melis, Angelo Sotgiu, Davide Bacciu, Battista Biggio:
FADER: Fast Adversarial Example Rejection. CoRR abs/2010.09119 (2020) - [i21]Matteo Ronchetti, Davide Bacciu:
Generative Tomography Reconstruction. CoRR abs/2010.14933 (2020) - [i20]Daniele Castellana, Davide Bacciu:
Learning from Non-Binary Constituency Trees via Tensor Decomposition. CoRR abs/2011.00860 (2020) - [i19]Antonio Carta, Alessandro Sperduti, Davide Bacciu:
Short-Term Memory Optimization in Recurrent Neural Networks by Autoencoder-based Initialization. CoRR abs/2011.02886 (2020) - [i18]Danilo Numeroso, Davide Bacciu:
Explaining Deep Graph Networks with Molecular Counterfactuals. CoRR abs/2011.05134 (2020) - [i17]Federico Errica, Davide Bacciu, Alessio Micheli:
Graph Mixture Density Networks. CoRR abs/2012.03085 (2020)
2010 – 2019
- 2019
- [j21]Davide Bacciu, Maurizio Di Rocco, Mauro Dragone, Claudio Gallicchio, Alessio Micheli, Alessandro Saffiotti:
An ambient intelligence approach for learning in smart robotic environments. Comput. Intell. 35(4): 1060-1087 (2019) - [j20]Davide Bacciu, Daniele Castellana:
Bayesian mixtures of Hidden Tree Markov Models for structured data clustering. Neurocomputing 342: 49-59 (2019) - [j19]Cosimo Della Santina, Visar Arapi, Giuseppe Averta, Francesca Damiani, Gaia Fiore, Alessandro Settimi, Manuel G. Catalano, Davide Bacciu, Antonio Bicchi, Matteo Bianchi:
Learning From Humans How to Grasp: A Data-Driven Architecture for Autonomous Grasping With Anthropomorphic Soft Hands. IEEE Robotics Autom. Lett. 4(2): 1533-1540 (2019) - [c50]Davide Bacciu, Luigi Di Sotto:
A Non-negative Factorization Approach to Node Pooling in Graph Convolutional Neural Networks. AI*IA 2019: 294-306 - [c49]Michele Cafagna, Lorenzo De Mattei, Davide Bacciu, Malvina Nissim:
Suitable Doesn't Mean Attractive. Human-Based Evaluation of Automatically Generated Headlines. CLiC-it 2019 - [c48]Davide Bacciu, Battista Biggio, Paulo Lisboa, José D. Martín, Luca Oneto, Alfredo Vellido:
Societal Issues in Machine Learning: When Learning from Data is Not Enough. ESANN 2019 - [c47]Davide Bacciu, Alessio Micheli, Marco Podda:
Graph generation by sequential edge prediction. ESANN 2019 - [c46]Francesco Crecchi, Davide Bacciu, Battista Biggio:
Detecting Black-box Adversarial Examples through Nonlinear Dimensionality Reduction. ESANN 2019 - [c45]Davide Bacciu, Antonio Carta, Alessandro Sperduti:
Linear Memory Networks. ICANN (1) 2019: 513-525 - [c44]Daniele Castellana, Davide Bacciu:
Bayesian Tensor Factorisation for Bottom-up Hidden Tree Markov Models. IJCNN 2019: 1-8 - [c43]Davide Bacciu, Alessio Micheli:
Deep Learning for Graphs. INNSBDDL (Tutorials) 2019: 99-127 - [c42]Davide Bacciu, Antonio Bruno:
Deep Tree Transductions - A Short Survey. INNSBDDL 2019: 236-245 - [c41]Antonio Carta, Davide Bacciu:
Sequential Sentence Embeddings for Semantic Similarity. SSCI 2019: 1354-1361 - [i16]Davide Bacciu, Antonio Bruno:
Deep Tree Transductions - A Short Survey. CoRR abs/1902.01737 (2019) - [i15]Francesco Crecchi, Davide Bacciu, Battista Biggio:
Detecting Adversarial Examples through Nonlinear Dimensionality Reduction. CoRR abs/1904.13094 (2019) - [i14]Elisa Ferrari, Alessandra Retico, Davide Bacciu:
Measuring the effects of confounders in medical supervised classification problems: the Confounding Index (CI). CoRR abs/1905.08871 (2019) - [i13]Daniele Castellana, Davide Bacciu:
Bayesian Tensor Factorisation for Bottom-up Hidden Tree Markov Models. CoRR abs/1905.13528 (2019) - [i12]Davide Bacciu, Luigi Di Sotto:
A Non-Negative Factorization approach to node pooling in Graph Convolutional Neural Networks. CoRR abs/1909.03287 (2019) - [i11]Federico Errica, Marco Podda, Davide Bacciu, Alessio Micheli:
A Fair Comparison of Graph Neural Networks for Graph Classification. CoRR abs/1912.09893 (2019) - [i10]Davide Bacciu, Federico Errica, Alessio Micheli, Marco Podda:
A Gentle Introduction to Deep Learning for Graphs. CoRR abs/1912.12693 (2019) - 2018
- [j18]Visar Arapi, Cosimo Della Santina, Davide Bacciu, Matteo Bianchi, Antonio Bicchi:
DeepDynamicHand: A Deep Neural Architecture for Labeling Hand Manipulation Strategies in Video Sources Exploiting Temporal Information. Frontiers Neurorobotics 12: 86 (2018) - [j17]Davide Bacciu, Michele Colombo, Davide Morelli, David Plans:
Randomized neural networks for preference learning with physiological data. Neurocomputing 298: 9-20 (2018) - [j16]Davide Bacciu, Alessio Micheli, Alessandro Sperduti:
Generative Kernels for Tree-Structured Data. IEEE Trans. Neural Networks Learn. Syst. 29(10): 4932-4946 (2018) - [c40]Davide Bacciu, Daniele Castellana:
Mixture of Hidden Markov Model as Tree Encoder. ESANN 2018 - [c39]Davide Bacciu, Paulo Lisboa, José D. Martín, Ruxandra Stoean, Alfredo Vellido:
Bioinformatics and medicine in the era of deep learning. ESANN 2018 - [c38]Davide Bacciu, Federico Errica, Alessio Micheli:
Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing. ICML 2018: 304-313 - [c37]Davide Bacciu, Andrea Bongiorno:
Concentric ESN: Assessing the Effect of Modularity in Cycle Reservoirs. IJCNN 2018: 1-8 - [c36]Davide Bacciu, Antonio Bruno:
Text Summarization as Tree Transduction by Top-Down TreeLSTM. SSCI 2018: 1411-1418 - [i9]Davide Bacciu, Paulo J. G. Lisboa, José D. Martín, Ruxandra Stoean, Alfredo Vellido:
Bioinformatics and Medicine in the Era of Deep Learning. CoRR abs/1802.09791 (2018) - [i8]Davide Bacciu, Andrea Bongiorno:
Concentric ESN: Assessing the Effect of Modularity in Cycle Reservoirs. CoRR abs/1805.09244 (2018) - [i7]Davide Bacciu, Federico Errica, Alessio Micheli:
Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing. CoRR abs/1805.10636 (2018) - [i6]Davide Bacciu, Daniele Castellana:
Learning Tree Distributions by Hidden Markov Models. CoRR abs/1805.12372 (2018) - [i5]Davide Bacciu, Antonio Bruno:
Text Summarization as Tree Transduction by Top-Down TreeLSTM. CoRR abs/1809.09096 (2018) - [i4]Davide Bacciu, Antonio Carta, Alessandro Sperduti:
Linear Memory Networks. CoRR abs/1811.03356 (2018) - 2017
- [j15]Davide Bacciu, Stefano Chessa, Claudio Gallicchio, Alessio Micheli, Luca Pedrelli, Erina Ferro, Luigi Fortunati, Davide La Rosa, Filippo Palumbo, Federico Vozzi, Oberdan Parodi:
A learning system for automatic Berg Balance Scale score estimation. Eng. Appl. Artif. Intell. 66: 60-74 (2017) - [j14]Davide Bacciu, Antonio Carta, Stefania Gnesi, Laura Semini:
An experience in using machine learning for short-term predictions in smart transportation systems. J. Log. Algebraic Methods Program. 87: 52-66 (2017) - [j13]Filippo Palumbo, Davide La Rosa, Erina Ferro, Davide Bacciu, Claudio Gallicchio, Alessio Micheli, Stefano Chessa, Federico Vozzi, Oberdan Parodi:
Reliability and human factors in Ambient Assisted Living environments - The DOREMI case study. J. Reliab. Intell. Environ. 3(3): 139-157 (2017) - [c35]Davide Bacciu, Michele Colombo, Davide Morelli, David Plans:
ELM Preference Learning for Physiological Data. ESANN 2017 - [c34]Davide Bacciu, Francesco Crecchi, Davide Morelli:
DropIn: Making reservoir computing neural networks robust to missing inputs by dropout. IJCNN 2017: 2080-2087 - [c33]Davide Bacciu, Stefano Chessa, Claudio Gallicchio, Alessio Micheli:
On the need of machine learning as a service for the internet of things. IML 2017: 22:1-22:8 - [c32]Davide Bacciu:
Hidden tree Markov networks: Deep and wide learning for structured data. SSCI 2017: 1-8 - [i3]Davide Bacciu, Francesco Crecchi, Davide Morelli:
DropIn: Making Reservoir Computing Neural Networks Robust to Missing Inputs by Dropout. CoRR abs/1705.02643 (2017) - [i2]Davide Bacciu:
Hidden Tree Markov Networks: Deep and Wide Learning for Structured Data. CoRR abs/1711.07784 (2017) - 2016
- [j12]Davide Bacciu:
Unsupervised feature selection for sensor time-series in pervasive computing applications. Neural Comput. Appl. 27(5): 1077-1091 (2016) - [c31]Davide Bacciu, Claudio Gallicchio, Alessio Micheli:
A reservoir activation kernel for trees. ESANN 2016 - [c30]Davide Bacciu, Vincenzo Gervasi, Giuseppe Prencipe:
LOL: An Investigation into Cybernetic Humor, or: Can Machines Laugh?. FUN 2016: 3:1-3:15 - [c29]Davide Bacciu, Stefano Chessa, Erina Ferro, Luigi Fortunati, Claudio Gallicchio, Davide La Rosa, Miguel Llorente, Alessio Micheli, Filippo Palumbo, Oberdan Parodi, Andrea Valenti, Federico Vozzi:
Detecting Socialization Events in Ageing People: The Experience of the DOREMI Project. Intelligent Environments 2016: 132-135 - [c28]Giuseppe Amato, Davide Bacciu, Stefano Chessa, Mauro Dragone, Claudio Gallicchio, Claudio Gennaro, Héctor Lozano Peiteado, Alessio Micheli, Gregory M. P. O'Hare, Arantxa Rentería, Claudio Vairo:
A Benchmark Dataset for Human Activity Recognition and Ambient Assisted Living. ISAmI 2016: 1-9 - [d1]Davide Bacciu, Paolo Barsocchi, Stefano Chessa, Claudio Gallicchio, Alessio Micheli:
Indoor User Movement Prediction from RSS data. UCI Machine Learning Repository, 2016 - [i1]Davide Bacciu, Antonio Carta, Stefania Gnesi, Laura Semini:
Adopting a Machine Learning Approach in the Design of Smart Transportation Systems. ERCIM News 2016(105) (2016) - 2015
- [j11]Mauro Dragone, Giuseppe Amato, Davide Bacciu, Stefano Chessa, Sonya A. Coleman, Maurizio Di Rocco, Claudio Gallicchio, Claudio Gennaro, Héctor Lozano Peiteado, Liam P. Maguire, T. Martin McGinnity, Alessio Micheli, Gregory M. P. O'Hare, Arantxa Rentería, Alessandro Saffiotti, Claudio Vairo, Philip J. Vance:
A cognitive robotic ecology approach to self-configuring and evolving AAL systems. Eng. Appl. Artif. Intell. 45: 269-280 (2015) - [j10]Giuseppe Amato, Davide Bacciu, Mathias Broxvall, Stefano Chessa, Sonya A. Coleman, Maurizio Di Rocco, Mauro Dragone, Claudio Gallicchio, Claudio Gennaro, Héctor Lozano Peiteado, T. Martin McGinnity, Alessio Micheli, Anjan Kumar Ray, Arantxa Rentería, Alessandro Saffiotti, David Swords, Claudio Vairo, Philip J. Vance:
Robotic Ubiquitous Cognitive Ecology for Smart Homes. J. Intell. Robotic Syst. 80(Supplement-1): 57-81 (2015) - [c27]Davide Bacciu, Stefano Chessa, Claudio Gallicchio, Alessio Micheli, Erina Ferro, Luigi Fortunati, Filippo Palumbo, Oberdan Parodi, Federico Vozzi, Sten Hanke, Johannes Kropf, Karl Kreiner:
Smart Environments and Context-Awareness for Lifestyle Management in a Healthy Active Ageing Framework. EPIA 2015: 54-66 - [c26]Davide Bacciu, Filippo Benedetti, Alessio Micheli:
ESNigma: efficient feature selection for echo state networks. ESANN 2015 - [c25]Davide Bacciu, Stefania Gnesi, Laura Semini:
Using a Machine Learning Approach to Implement and Evaluate Product Line Features. WWV 2015: 75-83 - [p1]Davide Bacciu, Paulo J. G. Lisboa, Alessandro Sperduti, Thomas Villmann:
Probabilistic Modeling in Machine Learning. Handbook of Computational Intelligence 2015: 545-575 - 2014
- [j9]Davide Bacciu, Paolo Barsocchi, Stefano Chessa, Claudio Gallicchio, Alessio Micheli:
An experimental characterization of reservoir computing in ambient assisted living applications. Neural Comput. Appl. 24(6): 1451-1464 (2014) - [c24]Davide Bacciu:
An Iterative Feature Filter for Sensor Timeseries in Pervasive Computing Applications. EANN 2014: 39-48 - [c23]Davide Bacciu, Alessio Micheli, Alessandro Sperduti:
Modeling Bi-directional Tree Contexts by Generative Transductions. ICONIP (1) 2014: 543-550 - [c22]Davide Bacciu, Claudio Gallicchio, Alessio Micheli, Maurizio Di Rocco, Alessandro Saffiotti:
Learning context-aware mobile robot navigation in home environments. IISA 2014: 57-62 - [c21]Davide Bacciu, Alessio Micheli, Alessandro Sperduti:
Integrating bi-directional contexts in a generative kernel for trees. IJCNN 2014: 4145-4151 - 2013
- [j8]Davide Bacciu, Terence A. Etchells, Paulo J. G. Lisboa, Joe Whittaker:
Efficient identification of independence networks using mutual information. Comput. Stat. 28(2): 621-646 (2013) - [j7]Nicola Di Mauro, Paolo Frasconi, Fabrizio Angiulli, Davide Bacciu, Marco de Gemmis, Floriana Esposito, Nicola Fanizzi, Stefano Ferilli, Marco Gori, Francesca A. Lisi, Pasquale Lops, Donato Malerba, Alessio Micheli, Marcello Pelillo, Francesco Ricci, Fabrizio Riguzzi, Lorenza Saitta, Giovanni Semeraro:
Italian Machine Learning and Data Mining research: The last years. Intelligenza Artificiale 7(2): 77-89 (2013) - [j6]Davide Bacciu, Alessio Micheli, Alessandro Sperduti:
An input-output hidden Markov model for tree transductions. Neurocomputing 112: 34-46 (2013) - [j5]Davide Bacciu, Alessio Micheli, Alessandro Sperduti:
Compositional Generative Mapping for Tree-Structured Data - Part II: Topographic Projection Model. IEEE Trans. Neural Networks Learn. Syst. 24(2): 231-247 (2013) - [c20]Davide Bacciu, Claudio Gallicchio, Alessandro Lenzi, Stefano Chessa, Alessio Micheli, Susanna Pelagatti, Claudio Vairo:
Distributed Neural Computation over WSN in Ambient Intelligence. ISAmI 2013: 147-154 - 2012
- [j4]Davide Bacciu, Alessio Micheli, Alessandro Sperduti:
Compositional Generative Mapping for Tree-Structured Data - Part I: Bottom-Up Probabilistic Modeling of Trees. IEEE Trans. Neural Networks Learn. Syst. 23(12): 1987-2002 (2012) - [c19]Davide Bacciu, Alessio Micheli, Alessandro Sperduti:
Input-Output Hidden Markov Models for trees. ESANN 2012 - [c18]Davide Bacciu, Alessio Micheli, Alessandro Sperduti:
A Generative Multiset Kernel for Structured Data. ICANN (1) 2012: 57-64 - [c17]Davide Bacciu, Mathias Broxvall, Sonya A. Coleman, Mauro Dragone, Claudio Gallicchio, Claudio Gennaro, Roberto Guzmán, Rafa López, Héctor Lozano Peiteado, A. K. Ray, Arantxa Rentería, Alessandro Saffiotti, Claudio Vairo:
Self-sustaining Learning for Robotic Ecologies. SENSORNETS 2012: 99-103 - [c16]Davide Bacciu, Stefano Chessa, Claudio Gallicchio, Alessandro Lenzi, Alessio Micheli, Susanna Pelagatti:
A General Purpose Distributed Learning Model for Robotic Ecologies. SyRoCo 2012: 435-440 - [c15]Davide Bacciu, Stefano Chessa, Claudio Gallicchio, Alessio Micheli, Paolo Barsocchi:
An Experimental Evaluation of Reservoir Computation for Ambient Assisted Living. WIRN 2012: 41-50 - 2011
- [j3]Ian H. Jarman, Terence A. Etchells, Davide Bacciu, Jonathan M. Garibaldi, Ian O. Ellis, Paulo J. G. Lisboa:
Clustering of protein expression data: a benchmark of statistical and neural approaches. Soft Comput. 15(8): 1459-1469 (2011) - [c14]Paulo J. G. Lisboa, Ian H. Jarman, Terence A. Etchells, Simon J. Chambers, Davide Bacciu, Joe Whittaker, Jonathan M. Garibaldi, Sandra Ortega-Martorell, Alfredo Vellido, Ian O. Ellis:
Discovering Hidden Pathways in Bioinformatics. CIBB 2011: 49-60 - [c13]Davide Bacciu, Alessio Micheli, Alessandro Sperduti:
Adaptive tree kernel by multinomial generative topographic mapping. IJCNN 2011: 1651-1658 - 2010
- [c12]Davide Bacciu, Alessio Micheli, Alessandro Sperduti:
Bottom-Up Generative Modeling of Tree-Structured Data. ICONIP (1) 2010: 660-668 - [c11]Davide Bacciu, Alessio Micheli, Alessandro Sperduti:
Compositional generative mapping of structured data. IJCNN 2010: 1-8 - [c10]Davide Bacciu, Maria Grazia Buscemi, Lusine Mkrtchyan:
Adaptive fuzzy-valued service selection. SAC 2010: 2467-2471
2000 – 2009
- 2009
- [j2]Davide Bacciu, Antonina Starita:
Expansive competitive learning for kernel vector quantization. Pattern Recognit. Lett. 30(6): 641-651 (2009) - [c9]Ana S. Fernandes, Davide Bacciu, Ian H. Jarman, Terence A. Etchells, José Manuel Fonseca, Paulo J. G. Lisboa:
Different Methodologies for Patient Stratification Using Survival Data. CIBB 2009: 276-290 - [c8]Davide Bacciu, Ian H. Jarman, Terence A. Etchells, Paulo J. G. Lisboa:
Patient stratification with competing risks by multivariate Fisher distance. IJCNN 2009: 213-220 - 2008
- [j1]Davide Bacciu, Antonina Starita:
Competitive Repetition Suppression (CoRe) Clustering: A Biologically Inspired Learning Model With Application to Robust Clustering. IEEE Trans. Neural Networks 19(11): 1922-1941 (2008) - [c7]Davide Bacciu, Elia Biganzoli, Paulo J. G. Lisboa, Antonina Starita:
Are Model-Based Clustering and Neural Clustering Consistent? A Case Study from Bioinformatics. KES (2) 2008: 181-188 - 2007
- [c6]Davide Bacciu, Antonina Starita:
Convergence Behavior of Competitive Repetition-Suppression Clustering. ICONIP (1) 2007: 497-506 - [c5]Davide Bacciu, Antonina Starita:
A Robust Bio-Inspired Clustering Algorithm for the Automatic Determination of Unknown Cluster Number. IJCNN 2007: 1314-1319 - [c4]Davide Bacciu, Alessio Botta, Dan C. Stefanescu:
Augmenting the Distributed Evaluation of Path Queries on Data-Graphs with Information Granules. MLG 2007 - 2006
- [c3]Davide Bacciu, Antonina Starita:
Competitive Repetition-suppression (CoRe) Learning. ICANN (1) 2006: 130-139 - [c2]Davide Bacciu, Alessio Botta, Hernán C. Melgratti:
A Fuzzy Approach for Negotiating Quality of Services. TGC 2006: 200-217 - 2004
- [c1]Davide Bacciu, Loredana Zollo, Eugenio Guglielmelli, Fabio Leoni, Antonina Starita:
A RLWPR network for learning the internal model of an anthropomorphic robot arm. IROS 2004: 260-265
Coauthor Index
[j46] [c113] [c109] [i72] [c86] [c79] [c73] [c72] [c70] [c69] [i42] [j25] [j24] [j23] [c63] [c60] [c59] [c55] [i34] [i32] [i30] [i25] [i17] [j21] [c47] [c43] [i11] [i10] [j16] [c38] [i7] [j15] [j13] [c33] [c31] [c29] [c28] [d1] [j11] [j10] [c27] [c26] [j9] [c23] [c22] [c21] [j7] [j6] [j5] [c20] [j4] [c19] [c18] [c16] [c15] [c13] [c12] [c11]
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