Bryan M. Li*, Filippo Corponi*, Gerard Anmella, Ariadna Mas, Miriam Sanabra, Isabella
Pacchiarotti, Marc Valentí, Anna Giménez-Palomo, Marina Garriga, Isabel Agasi, Anna
Bastidas,
Tabatha Fernández-Plaza, Néstor Arbelo, Myriam Cavero, Clemente García-Rizo, Miquel
Bioque, Norma
Verdolini, Santiago Madero, Andrea Murru, Iria Grande, Silvia Amoretti, Victoria Ruiz, Giovanna Fico,
Michele De Prisco, Vincenzo Oliva, Eduard Vieta, Diego Hidalgo-Mazzei. Can machine
learning with data from wearable devices distinguish disease severity levels and generalise across
patients? A pilot study in Mania and Depression. 2022. |
Bryan M. Li, Leonardo V. Castorina, Maria del C. Valdés-Hernández, Una Clancy,
Stewart J. Wiseman, Eleni Sakka, Amos J. Storkey, Daniela Jaime Garcia, Yajun Cheng, Fergus Doubal,
Michael T. Thrippleton, Michael Stringer, Joanna M. Wardlaw. Deep
Attention Super-Resolution of Brain Magnetic Resonance Images Acquired Under Clinical Protocols.
Frontiers in Computational Neuroscience. 2022. |
Bryan M. Li, Theoklitos Amvrosiadis, Nathalie Rochefort, Arno Onken. Neuronal Learning Analysis using
Cycle-Consistent Adversarial Networks. 2021. |
Bryan M. Li, Theoklitos Amvrosiadis, Nathalie Rochefort, Arno Onken. CalciumGAN: A Generative Adversarial Network
Model for Synthesising Realistic Calcium Imaging Data of neuronal populations. 2020. |
Bryan M. Li, Alexander Cowen-Rivers, Piotr Kozakowski, David Tao, Siddhartha Rao Kamalakara,
Nitarshan Rajkumar, Hariharan Sezhiyan, Sicong Huang, Aidan N. Gomez. RL: A generic reinforcement
learning codebase in TensorFlow. Journal of Open Source Software (JOSS). 2019. |
Aidan N. Gomez, Sicong Huang, Ivan Zhang, Bryan M. Li, Muhammad Osama, Lukasz Kaiser. CipherGAN: Unsupervised Cipher
Cracking Using Neural Networks. International Conference on Learning Representations (ICLR). 2018.
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