Edinburgh. Senior Postdoctoral Researcher. University of Cambridge, Machine Learning Group.
EPSRC grant "Machine Learning for Tomorrow" (in collaboration with Microsoft Research).
Supervisor: Richard Turner

Edinburgh. Postdoctoral Researcher. University of Edinburgh, Machine Learning Group.
Few-shot learning with Deep Kernels in Gaussian Processes (in collaboration with Huawei).
Supervisor: Amos Storkey

Snap Inc. Research Intern in the Camera Platform team. Snapchat (London, UK).
Disentangling latent representations in deep autoencoders.
Supervisors: Patrick-Fox Roberts, Edward Rosten

Plymouth University PhD in Machine Learning and Robotics. University of Plymouth (UK).
Implementing social skills in humanoid robots using machine learning techniques.
Supervisors: Angelo Cangelosi, Torbjorn Dahl, Giorgio Metta

Eurolink Systems Robotics engineer (R&D Skunkworks). Eurolink Systems group (Rome, Italy).
Autonomous control of unmanned ground/aerial vehicles (UGVs/UAVs) in critical applications such as search-and-rescue, patrolling, and bomb disposal.

LARAL Rome Internship. Laboratory of Artificial Life and Robotics (LARAL). Rome (Italy).
Artificial neural networks and genetic algorithms in evolutionary robotics.
Supervisor: Domenico Parisi

La Sapienza University MSc Neuroscience. La Sapienza University (Rome, Italy).
Supervisors: Stefano Puglisi Allegra, Domenico Parisi, Gianluca Baldassarre

La Sapienza University BSc Experimental Cognitive Psychology. La Sapienza University (Rome, Italy).
Supervisors: Marta Olivetti Belardinelli, Valerio Santangelo

Recent Activity

First-author paper accepted at NeurIPS 2022

My first-author paper on a fast and efficient adaptation mechanism for few-shot learning has been accepted at NeurIPS 2022 [arXiv]

Joining the University of Cambridge

I am joining the Machine Learning Group at the University of Cambridge, working with Prof. Richard Turner and Microsoft Research.


Shysheya, A., Bronskill, J., Patacchiola, M., Nowozin, S., Turner, R.E. (2023). "FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification". International Conference on Learning Representations (ICLR). [arXiv] [GitHub]

Patacchiola, M., Bronskill, J., Shysheya, A., Hofmann, K., Nowozin, S., Turner, R.E. (2022). "Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification". Advances in Neural Information Processing Systems (NeurIPS). [arXiv] [GitHub]

Bronskill*, J., Massiceti*, D., Patacchiola*, M., Hofmann, K., Nowozin, S., & Turner, R.E. (2021). "Memory Efficient Meta-Learning with Large Images". Advances in Neural Information Processing Systems (NeurIPS). *Co-first authors. [arXiv]

Sendera, M., Tabor, J., Nowak, A., Bedychaj, A., Patacchiola, M., Trzcinski, T., Spurek, P., & Zieba, M. (2021). "Non-Gaussian Gaussian Processes for Few-Shot Regression". Advances in Neural Information Processing Systems (NeurIPS). [arXiv]

Patacchiola, M., & Storkey, A. (2020). "Self-Supervised Relational Reasoning for Representation Learning". Advances in Neural Information Processing Systems (NeurIPS). Spotlight (top 3%) [arXiv] [GitHub] [Poster]

Patacchiola, M., Turner, J., Crowley, E. J., O'Boyle, M., & Storkey, A. (2020). "Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels". Advances in Neural Information Processing Systems (NeurIPS). Spotlight (top 3%) [arXiv] [GitHub] [Poster]

Ochal, M., Patacchiola, M., Storkey, A., Vazquez J., & Wang S. (2021). "How Sensitive are Meta-Learners to Dataset Imbalance?". Learning to Learn Workshop - International Conference on Learning Representations (ICLR) [arXiv] [GitHub]

Antoniou, A., Patacchiola, M., Ochal, M., & Storkey, A. (2020). "Defining Benchmarks for Continual Few-Shot Learning". MetaLearn Workshop - Advances in Neural Information Processing Systems (NeurIPS) [arXiv] [GitHub] [YouTube] [Dataset]

Ochal, M., Patacchiola, M., Storkey, A., Vazquez, J., & Wang, S. (2021). "Few-Shot Learning with Class Imbalance". [arXiv] [GitHub]

Polvara*, R., Patacchiola*, M., Hanheide, M., & Neumann, G. (2020). "Sim-to-Real Quadrotor Landing via Sequential Deep Q-Networks and Domain Randomization". Robotics, 9(1), 8. *Co-first authors. [PDF] [GitHub]

Patacchiola, M., Fox-Roberts, P., Rosten, E. (2020). "Y-Autoencoders: Disentangling Latent Representations via Sequential Encoding". Pattern Recognition Letters, Elsevier, vol. 140, pp. 59-65. [arXiv] [DOI] [GitHub]

Thabet, M., Patacchiola, M., & Cangelosi, A. (2019). "Sample-efficient Deep Reinforcement Learning with Imaginary Rollouts for Human-Robot Interaction". IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) [arXiv]

Polvara* R., Patacchiola*, M., Sharma S., Wan J., Manning A., Sutton R., Cangelosi A. (2018). "Toward End-To-End Control for UAV Autonomous Landing Via Deep Reinforcement Learning". The 2018 International Conference on Unmanned Aircraft Systems (ICUAS). *Co-first authors. [PDF]

Surace, L., Patacchiola, M., Battini Sonmez, E., Spataro, W., & Cangelosi, A. (2017). "Emotion Recognition in the Wild using Deep Neural Networks and Bayesian Classifiers". In Proceeding of the Fifth Emotion Recognition in the Wild (EmotiW) Challenge, Glasgow, United Kingdom. [arXiv]

Patacchiola, M., & Cangelosi, A. (2017). "Head Pose Estimation in the Wild using Convolutional Neural Networks and Adaptive Gradient Methods". Pattern Recognition, Elsevier, vol. 71, pp. 132-143. [PDF] [DOI]

Patacchiola, M., & Cangelosi, A. (2016). "A Developmental Bayesian Model of Trust in Artificial Cognitive Systems". In Proceedings of the International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob). [PDF]

Zanatto, D., Patacchiola, M., Goslin, J., & Cangelosi, A. (2016). "Priming Anthropomorphism: Can the credibility of humanlike robots be transferred to non-humanlike robots?". In The Eleventh ACM/IEEE International Conference on Human Robot Interation (HRI). [PDF]