95 citations · 170 across the 10 of their papers we have counts for
10 papers
Bag of Policies for Distributional Deep Exploration
Asen Nachkov, Luchen Li, Giulia Luise +2
Efficient exploration in complex environments remains a major challenge for reinforcement learning (RL). Compared to previous Thompson sampling-inspired mechanisms that enable temp…
Breaking Bad News in the Era of Artificial Intelligence and Algorithmic Medicine: An Exploration of Disclosure and its Ethical Justification using the Hedonic Calculus
Benjamin Post, Cosmin Badea, Aldo Faisal +1
An appropriate ethical framework around the use of Artificial Intelligence (AI) in healthcare has become a key desirable with the increasingly widespread deployment of this technol…
Inter-subject Deep Transfer Learning for Motor Imagery EEG Decoding
Xiaoxi Wei, Pablo Ortega, A. Aldo Faisal
Convolutional neural networks (CNNs) have become a powerful technique to decode EEG and have become the benchmark for motor imagery EEG Brain-Computer-Interface (BCI) decoding. How…
HemCNN: Deep Learning enables decoding of fNIRS cortical signals in hand grip motor tasks
Pablo Ortega, Aldo Faisal
We solve the fNIRS left/right hand force decoding problem using a data-driven approach by using a convolutional neural network architecture, the HemCNN. We test HemCNN's decoding c…
Optimizing Medical Treatment for Sepsis in Intensive Care: from Reinforcement Learning to Pre-Trial Evaluation
Luchen Li, Ignacio Albert-Smet, Aldo A. Faisal
Our aim is to establish a framework where reinforcement learning (RL) of optimizing interventions retrospectively allows us a regulatory compliant pathway to prospective clinical t…
Optimizing Sequential Medical Treatments with Auto-Encoding Heuristic Search in POMDPs
Luchen Li, Matthieu Komorowski, Aldo A. Faisal
Health-related data is noisy and stochastic in implying the true physiological states of patients, limiting information contained in single-moment observations for sequential clini…
The Actor Search Tree Critic (ASTC) for Off-Policy POMDP Learning in Medical Decision Making
Luchen Li, Matthieu Komorowski, Aldo A. Faisal
Off-policy reinforcement learning enables near-optimal policy from suboptimal experience, thereby provisions opportunity for artificial intelligence applications in healthcare. Pre…
Towards tailoring non-invasive brain stimulation using real-time fMRI and Bayesian optimization
Romy Lorenz, Ricardo Pio Monti, Adam Hampshire +7
Non-invasive brain stimulation, such as transcranial alternating current stimulation (tACS) provides a powerful tool to directly modulate brain oscillations that mediate complex co…
Stopping criteria for boosting automatic experimental design using real-time fMRI with Bayesian optimization
Romy Lorenz, Ricardo P Monti, Ines R Violante +4
Bayesian optimization has been proposed as a practical and efficient tool through which to tune parameters in many difficult settings. Recently, such techniques have been combined…
The Automatic Neuroscientist: automated experimental design with real-time fMRI
Romy Lorenz, Ricardo Pio Monti, Ines R. Violante +4
A standard approach in functional neuroimaging explores how a particular cognitive task activates a set of brain regions (one task-to-many regions mapping). Importantly though, the…