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Found 30 papers, 1 papers with code
The 'Sandwich' meta-framework for architecture agnostic deep privacy-preserving transfer learning for non-invasive brainwave decoding
It outperforms conventional deep learning methods, showcasing the potential for effective use of larger, heterogeneous data sets with enhanced privacy as a model-agnostic meta-framework.
Speaker-Independent Dysarthria Severity Classification using Self-Supervised Transformers and Multi-Task Learning
We develop a transformer framework, called Speaker-Agnostic Latent Regularisation (SALR), incorporating a multi-task learning objective and contrastive learning for speaker-independent multi-class dysarthria severity classification.
Learning to Optimise Wind Farms with Graph Transformers
This work proposes a novel data-driven model capable of providing accurate predictions for the power generation of all wind turbines in wind farms of arbitrary layout, yaw angle configurations and wind conditions.
Physics-informed reinforcement learning via probabilistic co-adjustment functions
Reinforcement learning of real-world tasks is very data inefficient, and extensive simulation-based modelling has become the dominant approach for training systems.
EEG Decoding for Datasets with Heterogenous Electrode Configurations using Transfer Learning Graph Neural Networks
This indicates that the GNN-based transfer learning framework can effectively aggregate knowledge from multiple datasets with different electrode layouts, leading to improved generalization in subject-independent MI EEG classification.
Towards AI-controlled FES-restoration of movements: Learning cycling stimulation pattern with reinforcement learning
By using just 100 seconds of cycling data, our method can deliver a fine-tuned pattern that gives better cycling performance.
Towards AI-controlled FES-restoration of arm movements: neuromechanics-based reinforcement learning for 3-D reaching
In combination, our customisable models and RL-based control method open the possibility of delivering customised FES controls for different subjects and settings with minimal engineering intervention.
Towards AI-controlled FES-restoration of arm movements: Controlling for progressive muscular fatigue with Gaussian state-space models
Yet, one remaining challenge of using RL to control FES is unobservable muscle fatigue that progressively changes as an unknown function of the stimulation, breaking the Markovian assumption of RL.
Federated deep transfer learning for EEG decoding using multiple BCI tasks
Here, we demonstrate a federated deep transfer learning technique, the Multi-dataset Federated Separate-Common-Separate Network (MF-SCSN) based on our previous work of SCSN, which integrates privacy-preserving properties into deep transfer learning to utilise data sets with different tasks.
Neuromuscular Reinforcement Learning to Actuate Human Limbs through FES
Functional Electrical Stimulation (FES) is a technique to evoke muscle contraction through low-energy electrical signals.
The role of haptic communication in dyadic collaborative object manipulation tasks
The task requires that the two sides coordinate with each other, in real-time, to balance the ball at the target.
The Response Shift Paradigm to Quantify Human Trust in AI Recommendations
Explainability, interpretability and how much they affect human trust in AI systems are ultimately problems of human cognition as much as machine learning, yet the effectiveness of AI recommendations and the trust afforded by end-users are typically not evaluated quantitatively.
2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets
Task 2 is centred on Brain-Computer Interfacing (BCI), addressing motor imagery decoding across both subjects and data sets.
MIDAS: Deep learning human action intention prediction from natural eye movement patterns
Our evaluation shows that intention prediction is not a naive result of the data, but rather relies on non-linear temporal processing of gaze cues.
Enabling risk-aware Reinforcement Learning for medical interventions through uncertainty decomposition
Reinforcement Learning (RL) is emerging as tool for tackling complex control and decision-making problems.
Bayesian Distributional Policy Gradients
Distributional Reinforcement Learning (RL) maintains the entire probability distribution of the reward-to-go, i. e. the return, providing more learning signals that account for the uncertainty associated with policy performance, which may be beneficial for trading off exploration and exploitation and policy learning in general.
Inter-subject Deep Transfer Learning for Motor Imagery EEG Decoding
We propose a multi-branch deep transfer network, the Separate-Common-Separate Network (SCSN) based on splitting the network's feature extractors for individual subjects.
I am Robot: Neuromuscular Reinforcement Learning to Actuate Human Limbs through Functional Electrical Stimulation
However, an open challenge remains on how to restore motor abilities to human limbs through FES, as the problem of controlling the stimulation is unclear.
Neuromechanics-based Deep Reinforcement Learning of Neurostimulation Control in FES cycling
Functional Electrical Stimulation (FES) can restore motion to a paralysed person's muscles.
Gaze-contingent decoding of human navigation intention on an autonomous wheelchair platform
We have pioneered the Where-You-Look-Is Where-You-Go approach to controlling mobility platforms by decoding how the user looks at the environment to understand where they want to navigate their mobility device.
Non-invasive Cognitive-level Human Interfacing for the Robotic Restoration of Reaching & Grasping
We combine wearable eye tracking, the visual context of the environment and the structural grammar of human actions to create a cognitive-level assistive robotic setup that enables the users in fulfilling activities of daily living, while conserving interpretability, and the agency of the user.
Real-World Human-Robot Collaborative Reinforcement Learning
We then use this setup to perform systematic experiments on human/agent behaviour and adaptation when co-learning a policy for the collaborative game.
Human-Robot Collaboration via Deep Reinforcement Learning of Real-World Interactions
We present a robotic setup for real-world testing and evaluation of human-robot and human-human collaborative learning.
Reinforcement Learning with Structured Hierarchical Grammar Representations of Actions
From a young age humans learn to use grammatical principles to hierarchically combine words into sentences.
Human Visual Attention Prediction Boosts Learning & Performance of Autonomous Driving Agents
Autonomous driving is a multi-task problem requiring a deep understanding of the visual environment.
Dot-to-Dot: Explainable Hierarchical Reinforcement Learning for Robotic Manipulation
This is currently a challenge in deep learning systems.
Understanding the Artificial Intelligence Clinician and optimal treatment strategies for sepsis in intensive care
In this document, we explore in more detail our published work (Komorowski, Celi, Badawi, Gordon, & Faisal, 2018) for the benefit of the AI in Healthcare research community.
Towards an Embodied Semantic Fovea: Semantic 3D scene reconstruction from ego-centric eye-tracker videos
Incorporating the physical environment is essential for a complete understanding of human behavior in unconstrained every-day tasks.
FastOrient: Lightweight Computer Vision for Wrist Control in Assistive Robotic Grasping
Here we are demonstrating the off-loading of low-level control of assistive robotics and active orthotics, through automatic end-effector orientation control for grasping.
Generalised Structural CNNs (SCNNs) for time series data with arbitrary graph topology
Deep Learning methods, specifically convolutional neural networks (CNNs), have seen a lot of success in the domain of image-based data, where the data offers a clearly structured topology in the regular lattice of pixels.
