Stop the war!Остановите войну!
for scientists:
default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDDavide Bacciu
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2023
[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]- [i71]Lorenzo Simone, Davide Bacciu:
ECGAN: Self-supervised generative adversarial network for electrocardiography. CoRR abs/2301.09496 (2023) - [i70]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) - [i69]Danilo Numeroso, Davide Bacciu, Petar Velickovic:
Dual Algorithmic Reasoning. CoRR abs/2302.04496 (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) - [c92]Dario Balboni, Davide Bacciu:
An Empirical Verification of Wide Networks Theory. BMVC 2022: 517 - [c91]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 - [c90]Antonio Carta, Andrea Cossu, Vincenzo Lomonaco, Davide Bacciu:
Ex-Model: Continual Learning from a Stream of Trained Models. CVPR Workshops 2022: 3789-3798 - [c89]Davide Bacciu, Davide Serramazza:
Learning Image Captioning as a Structured Transduction Task. EANN 2022: 235-246 - [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 - [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 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 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 - [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 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]