Unsupervised Cipher Cracking Using Discrete GANs. - Aidan N. Gomez; Sicong Huang 001; Ivan Zhang; Bryan M. Li; Muhammad Osama; Lukasz Kaiser (ICLR (Poster), 2018)

RL: Generic reinforcement learning codebase in TensorFlow. - Bryan M. Li; Alexander I. Cowen-Rivers; Piotr Kozakowski; David Tao; Siddhartha Kamalakara; Nitarshan Rajkumar; Hariharan Sezhiyan; Sicong Huang 001; Aidan N. Gomez (J. Open Source Softw., 2019)

CalciumGAN: A Generative Adversarial Network Model for Synthesising Realistic Calcium Imaging Data of Neuronal Populations. - Bryan M. Li; Theoklitos Amvrosiadis; Nathalie Rochefort; Arno Onken (CoRR, 2020)

Neuronal Learning Analysis using Cycle-Consistent Adversarial Networks. - Bryan M. Li; Theoklitos Amvrosiadis; Nathalie Rochefort; Arno Onken (CoRR, 2021)

Deep attention super-resolution of brain magnetic resonance images acquired under clinical protocols. - 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 J. Thrippleton; Michael S. Stringer; et al. (Frontiers Comput. Neurosci., 2022)

Wearable data from subjects playing Super Mario, sitting university exams, or performing physical exercise help detect acute mood episodes via self-supervised learning. - Filippo Corponi; Bryan M. Li; Gerard Anmella; Clàudia Valenzuela-Pascual; Ariadna Mas; Isabella Pacchiarotti; Marc Valentí; Iria Grande; Antonio Benabarre; Marina Garriga; Eduard Vieta; Allan H. Young; et al. (CoRR, 2023)

V1T: large-scale mouse V1 response prediction using a Vision Transformer. - Bryan M. Li; Isabel M. Cornacchia; Nathalie Rochefort; Arno Onken (Trans. Mach. Learn. Res., 2023)

AutoEmulate: A Python package for semi-automated emulation. - Martin A. Stoffel; Bryan M. Li; Kalle Westerling; Sophie Arana; Maximilian Balmus; Eric G. Daub; Steven A. Niederer (J. Open Source Softw., 2025)