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Bryan M. Li
Bryan M. Li
School of Informatics, University of Edinburgh
Verified email at ed.ac.uk - Homepage
Title
Cited by
Cited by
Year
Unsupervised cipher cracking using discrete gans
AN Gomez, S Huang, I Zhang, BM Li, M Osama, L Kaiser
International Conference on Learning Representations (ICLR), 2018
802018
Synthesising Realistic Calcium Traces of Neuronal Populations Using GAN
BM Li, T Amvrosiadis, N Rochefort, A Onken
arXiv preprint arXiv:2009.02707, 2020
9*2020
Deep attention super-resolution of brain magnetic resonance images acquired under clinical protocols
BM Li, LV Castorina, MC Valdés Hernández, U Clancy, SJ Wiseman, ...
Frontiers in Computational Neuroscience 16, 887633, 2022
8*2022
Exploring digital biomarkers of illness activity in mood episodes: hypotheses generating and model development study.
G Anmella, F Corponi, BM Li, A Mas, M Sanabra, I Pacchiarotti, M Valentí, ...
JMIR mHealth and uHealth, 2023
42023
Inferring mood disorder symptoms from multivariate time-series sensory data
BM Li, F Corponi, G Anmella, A Mas, M Sanabra, D Hidalgo-Mazzei, ...
NeurIPS 2022 Workshop on Learning from Time Series for Health, 2022
42022
Automated mood disorder symptoms monitoring from multivariate time-series sensory data: Getting the full picture beyond a single number
F Corponi, BM Li, G Anmella, A Mas, I Pacchiarotti, M Valentí, I Grande, ...
Nature Translational Psychiatry 14 (1), 161, 2024
32024
Can machine learning with data from wearable devices distinguish disease severity levels and generalise across patients? A pilot study in Mania and Depression
BM Li, F Corponi, G Anmella, A Mas, M Sanabra, I Pacchiarotti, M Valentí, ...
medRxiv, 2022.05. 19.22274670, 2022
22022
Neuronal learning analysis using cycle-consistent adversarial networks
BM Li, T Amvrosiadis, N Rochefort, A Onken
arXiv preprint arXiv:2111.13073, 2021
22021
Metrics for quality control of results from super-resolution machine-learning algorithms – Data extracted from publications in the period 2017- May 2021
LV Castorina, BM Li, A Storkey, MV Hernández
University of Edinburgh. Centre for Clinical Brain Sciences and School of …, 2021
22021
V1T: large-scale mouse V1 response prediction using a Vision Transformer
BM Li, IM Cornacchia, NL Rochefort, A Onken
Transactions on Machine Learning Research, 2023
12023
Does heart rate variability change over acute episodes of bipolar disorder? A Bayesian analysis
F Corponi, BM Li, G Anmella, C Valenzuela-Pascual, I Pacchiarotti, ...
OSF, 2024
2024
Electrodermal activity in bipolar disorder: Differences between mood episodes and clinical remission using a wearable device in a real-world clinical setting
G Anmella, A Mas, M Sanabra, C Valenzuela-Pascual, M Valentí, ...
Journal of Affective Disorders 345, 43-50, 2024
2024
Wearable data from subjects playing Super Mario, sitting university exams, or performing physical exercise help detect acute mood episodes via self-supervised learning
F Corponi, BM Li, G Anmella, C Valenzuela-Pascual, A Mas, I Pacchiarotti, ...
arXiv preprint arXiv:2311.04215, 2023
2023
The TIMEBASE Study: IdenTifying dIgital bioMarkers of illnEss activity in BipolAr diSordEr. Preliminary results
G Anmella, A Mas, I Pacchiarotti, T Fernández, A Bastidas, I Agasi, ...
European Psychiatry 65 (S1), S221-S221, 2022
2022
Timebase: identifying digital biomarkers of illness activity and treatment response in bipolar disorder: an exploratory study.
G Anmella, F Corponi, B Li, AM Musons, M Sanabra, P Isabella, V Marc, ...
Neuroscience Applied 1, 100176, 2022
2022
RL: Generic reinforcement learning codebase in TensorFlow
BM Li, A Cowen-Rivers, P Kozakowski, D Tao, SR Kamalakara, ...
Journal of Open Source Software (JOSS) 4 (42), 1524, 2019
2019
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Articles 1–16