62 citations · 180 across the 16 of their papers we have counts for
3 papers · 1 filter
Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels
Massimiliano Patacchiola, Jack Turner, Elliot J. Crowley +2
Recently, different machine learning methods have been introduced to tackle the challenging few-shot learning scenario that is, learning from a small labeled dataset related to a s…
Sample-efficient Deep Reinforcement Learning with Imaginary Rollouts for Human-Robot Interaction
Mohammad Thabet, Massimiliano Patacchiola, Angelo Cangelosi
Deep reinforcement learning has proven to be a great success in allowing agents to learn complex tasks. However, its application to actual robots can be prohibitively expensive. Fu…
Y-Autoencoders: disentangling latent representations via sequential-encoding
Massimiliano Patacchiola, Patrick Fox-Roberts, Edward Rosten
In the last few years there have been important advancements in generative models with the two dominant approaches being Generative Adversarial Networks (GANs) and Variational Auto…