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.
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!
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!
New paper accepted at forthcoming IEEE SSCI 2017 showing how you can go deep an wide on tree structured data processing using an hybrid neuro-probabilistic model.
New upocoming special session: I am co-organizing a Deep Learning in Bioinformatics and Medicine session at ESANN 2018.
The session is intended for researchers (from both the deep learning and the bioinformatics communities) who develop, investigate, or apply deep learning methods on biomedical and chemistry data.
Deadline for paper submission: 20 November 2017.
Prospective contributors/participants can contact me (or another co-organizer) for details.
Organized by: Miguel Atencia (Universidad de Málaga, Spain), Davide Bacciu (Università di Pisa, Italy), Paulo J. G. Lisboa (Liverpool John Moores University, United Kingdom), Jose D. Martin, (Universitat de València, Spain), Ruxandra Stoean (University of Craiova, Romania), Alfredo Vellido (Universitat Politècnica de Catalunya, Spain)
Congratulations to Daniele Castellana who has just completed his Computer Science master with a Cum Laude thesis on learning structured transductions between trees through a probabilistic approach.
Congratulations to two M.Sc. students who have just completed their Computer Science master with two theses on machine learning.
Marco Podda studied how to help clinicians in predicting and understanding the causes of mortality in low birth-weight infants.
Cosimo Ragusa developed ReCoPy, a state-of-the-art neural networks library for Reservoir Computing in Python.