Congratulations to Luigi di Sotto and Stefan Daniel Motoc for having completed their M.Sc. and B.Sc. in Computer Science with a final project on deep learning topics. Luigi discussed a novel pooling mechanism for graph convolutional networks. Stefan proposed a thorough analysis of the latent space of MusAE, our deep adversarial autoencoder for music generation.
I will be delivering a talk on computational creativity through neural networks this week at the Museo degli Strumenti del Calcolo in Pisa. More info on the seminar and on the logistics in the following (in Italian I am afraid).
Creatività Artificiale: reti neurali, arte e probabilità
Mercoledì 29 maggio, ore 17:00 Museo degli Strumenti per il Calcolo, Pisa, 29 Maggio 2019
L’intelligenza artificiale può cimentarsi con una prerogativa così fortemente umana come la creatività artistica? La conferenza racconterà come le reti neurali profonde possano essere utilizzate per generare lavori artistici che, in alcuni casi, sono indistinguibili da opere dell’ingegno umano. Spiegheremo poi come la generazione di un’opera d’arte artificiale sia frutto di un processo matematico basato sulla statistica.
A paper on Bayesian tensor factorization for efficient processing of structured data has just been accepted for IJCNN 2019. Check it out:
Daniele Castellana, Davide Bacciu: Bayesian Tensor Factorisation for Bottom-up Hidden Tree Markov Models. Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN 2019I) , IEEE, Forthcoming.
Congratulations to my student Francesco Crecchi for his first journal paper showing how Dropout can be used to enforce robustness to missing inputs at test time in several recurrent neural networks. Check out its applications to sensor data processing.
Davide Bacciu, Francesco Crecchi : Augmenting Recurrent Neural Networks Resilience by Dropout. In: Accepted for IEEE Transactions on Neural Networs and Learning Systems, Forthcoming.
A joint paper with the Hands and Haptics team at Centro Piaggio has just been accepted at the top-robotic confrence ICRA 2019 (and jointly to the IEEE Robotics and Automation Letters journal). A deep neural network learning, from humans, how to guide a robot arm in the manipulation of never-seen-before objects. Early access to the paper here: check out the upcoming videos of our system at work!
Della Santina Cosimo, Arapi Visar, Averta Giuseppe, Damiani Francesca, Fiore Gaia, Settimi Alessandro, Catalano Manuel Giuseppe, Bacciu Davide, Bicchi Antonio, Bianchi Matteo: Learning from humans how to grasp: a data-driven architecture for autonomous grasping with anthropomorphic soft hands. In: IEEE Robotics and Automation Letters, pp. 1-8, 2019, ISSN: 2377-3766, (Also accepted for presentation at ICRA 2019).