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Found 50 papers, 11 papers with code
REValueD: Regularised Ensemble Value-Decomposition for Factorisable Markov Decision Processes
We further dissect the factors influencing REValueD's performance, evaluating the significance of the regularisation loss and the scalability of REValueD with increasing sub-actions per dimension.
GOPlan: Goal-conditioned Offline Reinforcement Learning by Planning with Learned Models
In this work, we propose Goal-conditioned Offline Planning (GOPlan), a novel model-based framework that contains two key phases: (1) pretraining a prior policy capable of capturing multi-modal action distribution within the multi-goal dataset; (2) employing the reanalysis method with planning to generate imagined trajectories for funetuning policies.
Generating a Graph Colouring Heuristic with Deep Q-Learning and Graph Neural Networks
The graph colouring problem consists of assigning labels, or colours, to the vertices of a graph such that no two adjacent vertices share the same colour.
Balancing policy constraint and ensemble size in uncertainty-based offline reinforcement learning
Offline reinforcement learning agents seek optimal policies from fixed data sets.
Goal-conditioned Offline Reinforcement Learning through State Space Partitioning
Offline reinforcement learning (RL) aims to infer sequential decision policies using only offline datasets.
Model-based trajectory stitching for improved behavioural cloning and its applications
Furthermore, using the D4RL benchmarking suite, we demonstrate that state-of-the-art results are obtained by combining TS with two existing offline learning methodologies reliant on BC, model-based offline planning (MBOP) and policy constraint (TD3+BC).
Improving TD3-BC: Relaxed Policy Constraint for Offline Learning and Stable Online Fine-Tuning
The ability to discover optimal behaviour from fixed data sets has the potential to transfer the successes of reinforcement learning (RL) to domains where data collection is acutely problematic.
Model-based Trajectory Stitching for Improved Offline Reinforcement Learning
We propose a model-based data augmentation strategy, Trajectory Stitching (TS), to improve the quality of sub-optimal historical trajectories.
Assessing the Performance of Automated Prediction and Ranking of Patient Age from Chest X-rays Against Clinicians
Understanding the internal physiological changes accompanying the aging process is an important aspect of medical image interpretation, with the expected changes acting as a baseline when reporting abnormal findings.
LeNSE: Learning To Navigate Subgraph Embeddings for Large-Scale Combinatorial Optimisation
To solve CO problems, LeNSE is provided with a discriminative embedding trained using any existing heuristics using only on a small portion of the original graph.
A persistent homology-based topological loss for CNN-based multi-class segmentation of CMR
Multi-class segmentation of cardiac magnetic resonance (CMR) images seeks a separation of data into anatomical components with known structure and configuration.
Automated Knee X-ray Report Generation
Gathering manually annotated images for the purpose of training a predictive model is far more challenging in the medical domain than for natural images as it requires the expertise of qualified radiologists.
Solving Challenging Dexterous Manipulation Tasks With Trajectory Optimisation and Reinforcement Learning
Training agents to autonomously learn how to use anthropomorphic robotic hands has the potential to lead to systems capable of performing a multitude of complex manipulation tasks in unstructured and uncertain environments.
A persistent homology-based topological loss function for multi-class CNN segmentation of cardiac MRI
With respect to spatial overlap, CNN-based segmentation of short axis cardiovascular magnetic resonance (CMR) images has achieved a level of performance consistent with inter observer variation.
PlanGAN: Model-based Planning With Sparse Rewards and Multiple Goals
Learning with sparse rewards remains a significant challenge in reinforcement learning (RL), especially when the aim is to train a policy capable of achieving multiple different goals.
Adaptive Experience Selection for Policy Gradient
Existing sampling strategies for experience replay like uniform sampling or prioritised experience replay do not explicitly try to control the variance of the gradient estimates.
Learning Multi-Agent Coordination through Connectivity-driven Communication
In artificial multi-agent systems, the ability to learn collaborative policies is predicated upon the agents' communication skills: they must be able to encode the information received from the environment and learn how to share it with other agents as required by the task at hand.
Reinforcement Learning for Robotic Manipulation using Simulated Locomotion Demonstrations
In order to exploit this idea, we introduce a framework whereby an object locomotion policy is initially obtained using a realistic physics simulator.
Topology-preserving augmentation for CNN-based segmentation of congenital heart defects from 3D paediatric CMR
In a series of five-fold cross-validations, we demonstrate the performance gain produced by this pipeline and the relevance of topological considerations to the segmentation of congenital heart defects.
Skill Transfer in Deep Reinforcement Learning under Morphological Heterogeneity
We demonstrate its performance compared to a state-of-the-art approach and several ablation cases, visualize and interpret the hidden factors, and identify avenues for future improvements.
Spectral Multi-scale Community Detection in Temporal Networks with an Application
When dealing with real life data, communities at one or more scales can go undiscovered if appropriate parameter ranges are not selected.
Improving Coordination in Small-Scale Multi-Agent Deep Reinforcement Learning through Memory-driven Communication
In this work, we propose a framework for multi-agent training using deep deterministic policy gradients that enables concurrent, end-to-end learning of an explicit communication protocol through a memory device.
Multi-agent Deep Reinforcement Learning with Extremely Noisy Observations
An agent's policy depends on its own private observations as well as those explicitly shared by others through a communication medium.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
A Generative Adversarial Model for Right Ventricle Segmentation
The clinical management of several cardiovascular conditions, such as pulmonary hypertension, require the assessment of the right ventricular (RV) function.
Automated segmentation on the entire cardiac cycle using a deep learning work-flow
Our initial experiments suggest that significant improvement in performance can potentially be achieved by using a recurrent neural network component that explicitly learns cardiac motion patterns whilst performing LV segmentation.
V-FCNN: Volumetric Fully Convolution Neural Network For Automatic Atrial Segmentation
Atrial Fibrillation (AF) is a common electro-physiological cardiac disorder that causes changes in the anatomy of the atria.
Longitudinal detection of radiological abnormalities with time-modulated LSTM
Convolutional neural networks (CNNs) have been successfully employed in recent years for the detection of radiological abnormalities in medical images such as plain x-rays.
Temporal Convolution Networks for Real-Time Abdominal Fetal Aorta Analysis with Ultrasound
The automatic analysis of ultrasound sequences can substantially improve the efficiency of clinical diagnosis.
Deep metric learning for multi-labelled radiographs
Many radiological studies can reveal the presence of several co-existing abnormalities, each one represented by a distinct visual pattern.
Learning to detect chest radiographs containing lung nodules using visual attention networks
We propose two network architectures for the classification of images likely to contain pulmonary nodules using both weak labels and manually-delineated bounding boxes, when these are available.
Random Forest regression for manifold-valued responses
An increasing array of biomedical and computer vision applications requires the predictive modeling of complex data, for example images and shapes.
Learning what to look in chest X-rays with a recurrent visual attention model
X-rays are commonly performed imaging tests that use small amounts of radiation to produce pictures of the organs, tissues, and bones of the body.
Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker
Here we sought to further establish the credentials of "brain-predicted age" as a biomarker of individual differences in the brain ageing process, using a predictive modelling approach based on deep learning, and specifically convolutional neural networks (CNN), and applied to both pre-processed and raw T1-weighted MRI data.
Adaptive regularization for Lasso models in the context of non-stationary data streams
In this work consider the problem of learning $\ell_1$ regularized linear models in the context of streaming data.
Recurrent Convolutional Networks for Pulmonary Nodule Detection in CT Imaging
In this article we propose a deep artificial neural network architecture, ReCTnet, for the fully-automated detection of pulmonary nodules in CT scans.
Modelling Radiological Language with Bidirectional Long Short-Term Memory Networks
Motivated by the need to automate medical information extraction from free-text radiological reports, we present a bi-directional long short-term memory (BiLSTM) neural network architecture for modelling radiological language.
Medical Named Entity Recognition
named-entity-recognition
+5
Recurrent Fully Convolutional Neural Networks for Multi-slice MRI Cardiac Segmentation
In cardiac magnetic resonance imaging, fully-automatic segmentation of the heart enables precise structural and functional measurements to be taken, e. g. from short-axis MR images of the left-ventricle.
Studying the brain from adolescence to adulthood through sparse multi-view matrix factorisations
These latent factors can be used to produce low-dimensional visualisations of the data that emphasise age-specific effects once the shared effects have been accounted for.
Text-mining the NeuroSynth corpus using Deep Boltzmann Machines
Large-scale automated meta-analysis of neuroimaging data has recently established itself as an important tool in advancing our understanding of human brain function.
Learning population and subject-specific brain connectivity networks via Mixed Neighborhood Selection
In neuroimaging data analysis, Gaussian graphical models are often used to model statistical dependencies across spatially remote brain regions known as functional connectivity.
Stopping criteria for boosting automatic experimental design using real-time fMRI with Bayesian optimization
Bayesian optimization has been proposed as a practical and efficient tool through which to tune parameters in many difficult settings.
Streaming regularization parameter selection via stochastic gradient descent
We propose a framework to perform streaming covariance selection.
Community detection in multiplex networks using locally adaptive random walks
In this article we propose a community detection algorithm, LART (Locally Adaptive Random Transitions), for the detection of communities that are shared by either some or all the layers in the multiplex.
Sparse multi-view matrix factorisation: a multivariate approach to multiple tissue comparisons
The proposed methodology can be interpreted as an extension of principal component analysis in that it provides the means to decompose the total sample variance in each tissue into the sum of two components: one capturing the variance that is shared across tissues, and one isolating the tissue-specific variances.
Deep Neural Networks for Anatomical Brain Segmentation
To our knowledge, our technique is the first to tackle the anatomical segmentation of the whole brain using deep neural networks.
Predicting Alzheimer's disease: a neuroimaging study with 3D convolutional neural networks
Pattern recognition methods using neuroimaging data for the diagnosis of Alzheimer's disease have been the subject of extensive research in recent years.
Measuring the functional connectome "on-the-fly": towards a new control signal for fMRI-based brain-computer interfaces
Such studies have recently gained momentum and have been applied in a wide variety of settings; ranging from training of healthy subjects to self-regulate neuronal activity to being suggested as potential treatments for clinical populations.
Estimating Time-varying Brain Connectivity Networks from Functional MRI Time Series
We apply the SINGLE algorithm to functional MRI data from 24 healthy patients performing a choice-response task to demonstrate the dynamic changes in network structure that accompany a simple but attentionally demanding cognitive task.
Random Forests on Distance Matrices for Imaging Genetics Studies
We propose a non-parametric regression methodology, Random Forests on Distance Matrices (RFDM), for detecting genetic variants associated to quantitative phenotypes representing the human brain's structure or function, and obtained using neuroimaging techniques.
