Zobrazeno 1 - 10
of 126
pro vyhledávání: '"Faisal, Aldo A."'
Efficient exploration in complex environments remains a major challenge for reinforcement learning (RL). Compared to previous Thompson sampling-inspired mechanisms that enable temporally extended exploration, i.e., deep exploration, we focus on deep
Externí odkaz:
http://arxiv.org/abs/2308.01759
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 technology. Advances in AI hold the promise of improving the precision of out
Externí odkaz:
http://arxiv.org/abs/2207.01431
Autor:
Ortega, Pablo, Faisal, Aldo
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 capabilities to decode in a streaming way the hand, left or right, from
Externí odkaz:
http://arxiv.org/abs/2103.05338
We introduce here the idea of Meta-Learning for training EEG BCI decoders. Meta-Learning is a way of training machine learning systems so they learn to learn. We apply here meta-learning to a simple Deep Learning BCI architecture and compare it to tr
Externí odkaz:
http://arxiv.org/abs/2103.08664
Non-invasive cortical neural interfaces have only achieved modest performance in cortical decoding of limb movements and their forces, compared to invasive brain-computer interfaces (BCIs). While non-invasive methodologies are safer, cheaper and vast
Externí odkaz:
http://arxiv.org/abs/2103.05334
Our aim is to establish a framework where reinforcement learning (RL) of optimizing interventions retrospectively allows us a regulatory compliant pathway to prospective clinical testing of the learned policies in a clinical deployment. We focus on i
Externí odkaz:
http://arxiv.org/abs/2003.06474
Autor:
Lange, Robert Tjarko, Faisal, Aldo
Hierarchical Reinforcement Learning algorithms have successfully been applied to temporal credit assignment problems with sparse reward signals. However, state-of-the-art algorithms require manual specification of sub-task structures, a sample ineffi
Externí odkaz:
http://arxiv.org/abs/1907.12477
Health-related data is noisy and stochastic in implying the true physiological states of patients, limiting information contained in single-moment observations for sequential clinical decision making. We model patient-clinician interactions as partia
Externí odkaz:
http://arxiv.org/abs/1905.07465
Nonlinear optimal control problems are often solved with numerical methods that require knowledge of system's dynamics which may be difficult to infer, and that carry a large computational cost associated with iterative calculations. We present a nov
Externí odkaz:
http://arxiv.org/abs/1903.03064
Autor:
Peng, Xuefeng, Ding, Yi, Wihl, David, Gottesman, Omer, Komorowski, Matthieu, Lehman, Li-wei H., Ross, Andrew, Faisal, Aldo, Doshi-Velez, Finale
Sepsis is the leading cause of mortality in the ICU. It is challenging to manage because individual patients respond differently to treatment. Thus, tailoring treatment to the individual patient is essential for the best outcomes. In this paper, we t
Externí odkaz:
http://arxiv.org/abs/1901.04670