1 code implementation • 20 Jun 2022 • Massimiliano Patacchiola, John Bronskill, Aliaksandra Shysheya, Katja Hofmann, Sebastian Nowozin, Richard E. Turner
In this paper we push this Pareto frontier in the few-shot image classification setting with a key contribution: a new adaptive block called Contextual Squeeze-and-Excitation (CaSE) that adjusts a pretrained neural network on a new task to significantly improve performance with a single forward pass of the user data (context).
Ranked #3 on Few-Shot Image Classification on Meta-Dataset
1 code implementation • 17 Jun 2022 • Aliaksandra Shysheya, John Bronskill, Massimiliano Patacchiola, Sebastian Nowozin, Richard E Turner
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 data, and ii) communication efficient distributed training protocols.
1 code implementation • NeurIPS 2021 • Marcin Sendera, Jacek Tabor, Aleksandra Nowak, Andrzej Bedychaj, Massimiliano Patacchiola, Tomasz Trzciński, Przemysław Spurek, Maciej Zięba
This makes the GP posterior locally non-Gaussian, therefore we name our method Non-Gaussian Gaussian Processes (NGGPs).
2 code implementations • NeurIPS 2021 • John Bronskill, Daniela Massiceti, Massimiliano Patacchiola, Katja Hofmann, Sebastian Nowozin, Richard E. Turner
This limitation arises because a task's entire support set, which can contain up to 1000 images, must be processed before an optimization step can be taken.
1 code implementation • ICLR Workshop Learning_to_Learn 2021 • Mateusz Ochal, Massimiliano Patacchiola, Amos Storkey, Jose Vazquez, Sen Wang
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.
1 code implementation • 7 Jan 2021 • Mateusz Ochal, Massimiliano Patacchiola, Amos Storkey, Jose Vazquez, Sen Wang
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 evaluation.
no code implementations • 1 Jan 2021 • Mateusz Ochal, Massimiliano Patacchiola, Jose Vazquez, Amos Storkey, Sen Wang
Few-shot learning aims to train models on a limited number of labeled samples from a support set in order to generalize to unseen samples from a query set.
1 code implementation • NeurIPS 2020 • 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.
Ranked #1 on Self-Supervised Learning on STL-10
2 code implementations • 15 Apr 2020 • Antreas Antoniou, Massimiliano Patacchiola, Mateusz Ochal, Amos Storkey
Both few-shot and continual learning have seen substantial progress in the last years due to the introduction of proper benchmarks.
2 code implementations • NeurIPS 2020 • Massimiliano Patacchiola, Jack Turner, Elliot J. Crowley, Michael O'Boyle, Amos Storkey
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 specific task.
no code implementations • 15 Aug 2019 • Mohammad Thabet, Massimiliano Patacchiola, Angelo Cangelosi
Deep reinforcement learning has proven to be a great success in allowing agents to learn complex tasks.
1 code implementation • 25 Jul 2019 • Massimiliano Patacchiola, Patrick Fox-Roberts, Edward Rosten
Additionally, the projection in the explicit manifold is monitored by a predictor, that is embedded in the encoder and trained end-to-end with no adversarial losses.
1 code implementation • 12 Sep 2017 • Luca Surace, Massimiliano Patacchiola, Elena Battini Sönmez, William Spataro, Angelo Cangelosi
Group emotion recognition in the wild is a challenging problem, due to the unstructured environments in which everyday life pictures are taken.
no code implementations • 11 Sep 2017 • Riccardo Polvara, Massimiliano Patacchiola, Sanjay Sharma, Jian Wan, Andrew Manning, Robert Sutton, Angelo Cangelosi
Landing an unmanned aerial vehicle (UAV) on a ground marker is an open problem despite the effort of the research community.