125 citations · 221 across the 28 of their papers we have counts for
5 papers · 1 filter
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…
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…
Continual Learning with Gated Incremental Memories for sequential data processing
Andrea Cossu, Antonio Carta, Davide Bacciu
The ability to learn in dynamic, nonstationary environments without forgetting previous knowledge, also known as Continual Learning (CL), is a key enabler for scalable and trustwor…
Learning Style-Aware Symbolic Music Representations by Adversarial Autoencoders
Andrea Valenti, Antonio Carta, Davide Bacciu
We address the challenging open problem of learning an effective latent space for symbolic music data in generative music modeling. We focus on leveraging adversarial regularizatio…
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…