10 citations · 13 across the 5 of their papers we have counts for
5 papers
Selective Agent Guidance via Entropy: Learning Autonomous Policies from Imperfect VLM Teachers
Giovanni Bonetta, Matteo Merler, Davide Zago +2
Vision-Language Models (VLMs) provide useful priors for interactive decision-making, but using them directly as policies is expensive and brittle: they must be queried at every ste…
Job Shop Scheduling via Deep Reinforcement Learning: a Sequence to Sequence approach
Giovanni Bonetta, Davide Zago, Rossella Cancelliere +1
Job scheduling is a well-known Combinatorial Optimization problem with endless applications. Well planned schedules bring many benefits in the context of automated systems: among o…
Regularization-based Pruning of Irrelevant Weights in Deep Neural Architectures
Giovanni Bonetta, Matteo Ribero, Rossella Cancelliere
Deep neural networks exploiting millions of parameters are nowadays the norm in deep learning applications. This is a potential issue because of the great amount of computational r…
Retrieval-Augmented Transformer-XL for Close-Domain Dialog Generation
Giovanni Bonetta, Rossella Cancelliere, Ding Liu +1
Transformer-based models have demonstrated excellent capabilities of capturing patterns and structures in natural language generation and achieved state-of-the-art results in many…
Copy mechanism and tailored training for character-based data-to-text generation
Marco Roberti, Giovanni Bonetta, Rossella Cancelliere +1
In the last few years, many different methods have been focusing on using deep recurrent neural networks for natural language generation. The most widely used sequence-to-sequence…