180 citations · 368 across the 30 of their papers we have counts for
5 papers · 2 filters
Short-Term Memory Optimization in Recurrent Neural Networks by Autoencoder-based Initialization
Antonio Carta, Alessandro Sperduti, Davide Bacciu
Training RNNs to learn long-term dependencies is difficult due to vanishing gradients. We explore an alternative solution based on explicit memorization using linear autoencoders f…
Conditional Constrained Graph Variational Autoencoders for Molecule Design
Davide Rigoni, Nicolò Navarin, Alessandro Sperduti
In recent years, deep generative models for graphs have been used to generate new molecules. These models have produced good results, leading to several proposals in the literature…
A Systematic Assessment of Deep Learning Models for Molecule Generation
Davide Rigoni, Nicolò Navarin, Alessandro Sperduti
In recent years the scientific community has devoted much effort in the development of deep learning models for the generation of new molecules with desirable properties (i.e. drug…
Incremental Training of a Recurrent Neural Network Exploiting a Multi-Scale Dynamic Memory
Antonio Carta, Alessandro Sperduti, Davide Bacciu
The effectiveness of recurrent neural networks can be largely influenced by their ability to store into their dynamical memory information extracted from input sequences at differe…
Encoding-based Memory Modules for Recurrent Neural Networks
Antonio Carta, Alessandro Sperduti, Davide Bacciu
Learning to solve sequential tasks with recurrent models requires the ability to memorize long sequences and to extract task-relevant features from them. In this paper, we study th…