A paper on generative tree kernels has just been accepted for publication on the prestigious IEEE Transactions on Neural Networks and Learning Systems. Nice Christmas gift for my and my co-authors, Alessio Micheli and Alessandro Sperduti!
Generative Kernels for Tree-Structured Data. In: Neural Networks and Learning Systems, IEEE Transactions on, 2018, ISSN: 2162-2388 .
New Years news: in 2018 I will start serving as Associate Editor for the IEEE Transactions on Neural Networks and Learning Systems.
Thanks to the CHPC Organizers for inviting me over to deliver a keynote on upcoming research challenges of deep learning in HPC.
Today’s presentation day for my paper on deep and wide architecture for tree structured data processing.
Check out the article on Arxiv here.
Thanks to my student Federico Errica, it is now available the official Python release for the Bottom-up Hidden Tree Markov model.
The Python code for the model can be downloaded on Federico’s Github.
A paper on randomized neural networks for preference learning with physiological timeseries data has just been accepted for pubblication on the Neurocomputing journal. Congratulations to my Biobeats collaborators!
Randomized neural networks for preference learning with physiological data. In: Neurocomputing, vol. 298, pp. 9-20, 2018.
New upocoming special session: I am co-organizing a Deep Learning for Structured and Multimedia Information (DEEPSM) session at WCCI2018.
The session is meant to attract researchers working on the next generation of deep learning models for machine vision and multimedia information which are capable of extracting and processing information in a structured representation and/or with a multimodal nature.
Work in progress for securing sponsorship for best paper awards.
Deadline for paper submission: 15 January 2018.
Prospective contributors/participants can contact me (or another co-organizer) for details.
Organized by: Davide Bacciu (Università di Pisa, Italy), Silvio Jamil F. Guimarães (PUC Minas, Brazil) and Zenilton K. G. Patrocínio Jr (PUC Minas, Brazil).
Welcome to four new Ph.D. students joining the Machine Learning group under my supervision: Antonio Carta, Daniele Castellana, Francesco Crecchi and Marco Podda.
Looking forward to be working with you guys!
Congratulations to four of my students who just finished their M.Sc. and B.Sc discussing ML theses!!
Antonio Carta studied how to use deep learning to help CERN physicists filtering out fake trajectory hints in high energy physics (in co-supervision with Felice Pantaleo).
Francesco Crecchi proposed the DropIn technique to make recurrent neural network resilient to missing input data at inference time.
Andrea Cossu extended Echo State Networks and the ReCoPy Python framework with unsupervised anomaly detection mechanisms for sequential data.
Finally, Maurizio Idini presented his work supervised by Paolo Cignoni and Marco Di Benedetto (Visual Computing Lab @ISTI-CNR) regarding range maps denoising by deep learning techniques.
Ad maiora!
Tuesday 26/09 I will be giving a keynote at Dell-EMC Accelerating Understanding Summit on upcoming trends and challenges of Deep Learning.