62 citations · 180 across the 16 of their papers we have counts for
16 papers
EOL: Transductive Few-Shot Open-Set Recognition by Enhancing Outlier Logits
Mateusz Ochal, Massimiliano Patacchiola, Malik Boudiaf +1
In Few-Shot Learning (FSL), models are trained to recognise unseen objects from a query set, given a few labelled examples from a support set. In standard FSL, models are evaluated…
Transformer Neural Autoregressive Flows
Massimiliano Patacchiola, Aliaksandra Shysheya, Katja Hofmann +1
Density estimation, a central problem in machine learning, can be performed using Normalizing Flows (NFs). NFs comprise a sequence of invertible transformations, that turn a comple…
Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy Imitation
Massimiliano Patacchiola, Mingfei Sun, Katja Hofmann +1
In this paper we explore few-shot imitation learning for control problems, which involves learning to imitate a target policy by accessing a limited set of offline rollouts. This s…
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification
Massimiliano Patacchiola, John Bronskill, Aliaksandra Shysheya +3
Recent years have seen a growth in user-centric applications that require effective knowledge transfer across tasks in the low-data regime. An example is personalization, where a p…
FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification
Aliaksandra Shysheya, John Bronskill, Massimiliano Patacchiola +2
Modern deep learning systems are increasingly deployed in situations such as personalization and federated learning where it is necessary to support i) learning on small amounts of…
Non-Gaussian Gaussian Processes for Few-Shot Regression
Marcin Sendera, Jacek Tabor, Aleksandra Nowak +5
Gaussian Processes (GPs) have been widely used in machine learning to model distributions over functions, with applications including multi-modal regression, time-series prediction…
Memory Efficient Meta-Learning with Large Images
John Bronskill, Daniela Massiceti, Massimiliano Patacchiola +3
Meta learning approaches to few-shot classification are computationally efficient at test time, requiring just a few optimization steps or single forward pass to learn a new task,…
How Sensitive are Meta-Learners to Dataset Imbalance?
Mateusz Ochal, Massimiliano Patacchiola, Amos Storkey +2
Meta-Learning (ML) has proven to be a useful tool for training Few-Shot Learning (FSL) algorithms by exposure to batches of tasks sampled from a meta-dataset. However, the standard…
Few-Shot Learning with Class Imbalance
Mateusz Ochal, Massimiliano Patacchiola, Amos Storkey +2
Few-Shot Learning (FSL) algorithms are commonly trained through Meta-Learning (ML), which exposes models to batches of tasks sampled from a meta-dataset to mimic tasks seen during…
Self-Supervised Relational Reasoning for Representation Learning
Massimiliano Patacchiola, Amos Storkey
In self-supervised learning, a system is tasked with achieving a surrogate objective by defining alternative targets on a set of unlabeled data. The aim is to build useful represen…
Defining Benchmarks for Continual Few-Shot Learning
Antreas Antoniou, Massimiliano Patacchiola, Mateusz Ochal +1
Both few-shot and continual learning have seen substantial progress in the last years due to the introduction of proper benchmarks. That being said, the field has still to frame a…
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…
Emotion Recognition in the Wild using Deep Neural Networks and Bayesian Classifiers
Luca Surace, Massimiliano Patacchiola, Elena Battini Sönmez +2
Group emotion recognition in the wild is a challenging problem, due to the unstructured environments in which everyday life pictures are taken. Some of the obstacles for an effecti…
Autonomous Quadrotor Landing using Deep Reinforcement Learning
Riccardo Polvara, Massimiliano Patacchiola, Sanjay Sharma +4
Landing an unmanned aerial vehicle (UAV) on a ground marker is an open problem despite the effort of the research community. Previous attempts mostly focused on the analysis of han…