Learning Generative Models for Structured Data, research colloquium, CITEC, Bielefeld University, 01 August 2018
Deep Learning: Research Directions and Upcoming Challenges, Keynote Speech at CHPC 2017, Pretoria, 5 December 2017
Deep Learning: Trends and Challenges, Keynote at Dell EMC Accelerating Understanding Summit 2017, Pisa, 26 September 2017
Combining IoT and Intelligent Robotics: Challenges and Opportunities, Invited Panel at IoT Forum, Geneva, 7 June 2017
Learning Bayesian Network skeletons with high-dimensional and large-sample size data, Invited Lecture at Kings College, London, 21 February 2012
Bayesian network structure learning for high-dimensions and large samples, Invited Lecture at Computing for Graphical models, Royal Statistical Society, London, 16 December 2011
Unsupervised and Semi-Supervised Image Clustering by Multi-resolution Probabilistic Learning, Istituto di Scienza e Tecnologie dell’Informazione, CNR, Pisa, 16 February 2010
Understanding Visual Content: A Multi-resolution Neuro-Probabilistic Approach, Invited Seminar, IMT Lucca Job Market, 3rd June 2009
A Perceptual Learning Model to Discover the Hierarchical Latent Structure of Image Collections, E. R. Caianiello Invited Lecture at WIRN’09, Vietri sul Mare (SA), 29 May 2009
A Multilayered Latent Aspect Model for Multimodal Image Collections, Invited Seminar, HCI Colloquium, University of Heidelberg, 19 March 2009
Probabilistic Generative Models for Machine Vision, Invited Seminar, Università di Padova, 05 March 2009
A Perceptual Learning Model to Discover the Hierarchical Latent Structure of Image Collections, Invited Seminar, Liverpool John Moores University, November 2008
Talks at International Conferences and Workshops
TEACHING – Trustworthy autonomous cyber-physical applications through human-centred intelligence, COINS’21, August 2021
Deep learning for graphs, Tutorial, IJCNN’21, July 2021
Tensor Decompositions in Deep Learning, ESANN’20, October 2020
Deep learning for graphs: Processing symbolic relationships with neural networks, Tutorial, ECAI’20, July 2020
Deep learning for graphs, Tutorial, WCCI’20, July 2020
A non-negative factorization approach to node pooling in graph convolutional neural networks, AIIA’19, November 2019
Deep learning for graphs, Tutorial, IJCNN’19, July 2019
Deep learning for graphs, Tutorial, INNS-BDDL’19, April 2019
Deep learning for graphs, Tutorial, ECML-PKDD’18, September 2018
Concentric ESN: Assessing the Effect of Modularity in Cycle Reservoirs, WCCI’18, July 2018
Bioinformatics and medicine in the era of deep learning, ESANN’18, April 2018
Hidden Tree Markov Networks: Deep and Wide Learning for Structured Data, SSCI-DL’17, November 2017
On the Need of Machine Learning as a Service for the Internet of Things, IML’17, October 2017
DropIn: Making Neural Networks Robust to Missing Inputs by Dropout, IJCNN’17, May 2017
ELM Preference Learning for Physiological Data, ESANN’17, April 2017
Learning Neural-Generative Models for Structured Data, MLDM’16, November 2016
LOL: An Investigation into Cybernetic Humor, or: Can Machines Laugh?, FUN’16, June 2016 (co-starring with Vincenzo Gervasi)
A Reservoir Activation Kernel for Trees, ESANN’16, April 2016
ESNigma: efficient feature selection for Echo State Networks, ESANN’15, April 2015
Modeling Bi-Directional Tree Contexts by Generative Transductions, ICONIP’14, November 2014
An Iterative Feature Filter for Sensor Timeseries in Pervasive Computing Applications, EANN’14, September 2014
Learning Context-Aware Mobile Robot Navigation in Home Environments, IISA’14, July 2014
A General Purpose Distributed Learning Model for Robotic Ecologies, SYROCO’12, September 2012
Input-Output Hidden Markov Models for Trees, ESANN’12, 25th April 2012
Predicting user movements in heterogeneous indoor environments by reservoir computing, STAMI’11, July 2011
Bottom-up Generative Modeling of Tree-Structured Data, ICONIP’10, November 2010
Compositional Generative Mapping of Structured Data, IJCNN’10 – WCCI’10, July 2010
Are Model-based Clustering and Neural Clustering Consistent? A Case Study from Bioinformatics, KES’08, September 2008
Convergence Behavior of Competitive-Repetition Suppression Clustering, ICONIP’07, November 2007
A Robust Bio-Inspired Clustering Algorithm for the Automatic Determination of Unknown Cluster Number: IJCNN’07, August 2007
Simultaneous Clustering and Feature Ranking by Competitive Repetition Suppression Learning with Application to Gene Data: CIMED’07, July 2007
Fuzzy Agreement for Network Service Contracts: CIEF’07, July 2007
A fuzzy approach for negotiating quality of services: TCG’06, November 2006
Competitive Repetition Suppression Learning: ICANN’06, September 2006
How can Al help universities and researchers in Europe?, Panel member, Science|Business conference, Bruxelles, 10 September 2019
Creatività Artificiale: reti neurali, arte e probabilità, Amico Museo 2019, Museo degli Strumenti per il Calcolo, Pisa, 29 Maggio 2019
Bioinformatica intelligente – Il deep learning per grafi e le sue applicazioni biomediche e farmaceutiche, BIGDATATECH 2018 “Data for Human”, Milano, October 2018
Citizen Brain – La Comunicazione Politica al Tempo del Deep Learning, Internet Festival, Pisa, October 2018
Machine Learning tra IoT e Industria 4.0, TOI industrial seminars, 21/06/2018
Artificial Intelligence Research at DI.UNIPI, JRC meets UNIPI day, 17/05/2018
Intelligenza Artificiale: Illusioni, Rinascite e Prospettive, Open talk at Fondazione Palazzo Blu, Pisa, 21/03/2018
Machine Learning per Banking e Finanza, Seminar at Monte Paschi di Siena, Florence, 21 September 2017
I Neuroni alla Conquista di Google – Le reti neurali artificiali dal Percettrone al Deep Learning: Internet Festival, Pisa, October 2015
Repetita Iuvant? Constructive and destructive effects of redundancy and repetition in art, biology and computer science: CSE Seminars, IMT Lucca, May 2006