266 citations · 1.7k across the 136 of their papers we have counts for
14 papers · 1 filter
REDAR: Reinforcement-Driven Adaptive Regularisation for Class Incremental Learning
Akila Ayanthi, Darshana Priyasad, Tharindu Fernando +3
Regularisation-based class-incremental learning (CIL) methods control the stability-plasticity trade-off through fixed coefficients tuned offline, independently of the continual le…
Bi Directional Feedback Fusion for Activity Aware Forecasting of Indoor CO2 and PM2.5
Harshala Gammulle, Lidia Morawska, Sridha Sridharan +1
Indoor air quality (IAQ) forecasting plays a critical role in safeguarding occupant health, ensuring thermal comfort, and supporting intelligent building control. However, predicti…
Few-Shot Radar Signal Recognition through Self-Supervised Learning and Radio Frequency Domain Adaptation
Zi Huang, Simon Denman, Akila Pemasiri +2
Radar signal recognition (RSR) plays a pivotal role in electronic warfare (EW), as accurately classifying radar signals is critical for informing decision-making. Recent advances i…
Part-based Quantitative Analysis for Heatmaps
Osman Tursun, Sinan Kalkan, Simon Denman +2
Heatmaps have been instrumental in helping understand deep network decisions, and are a common approach for Explainable AI (XAI). While significant progress has been made in enhanc…
Multi-stage Learning for Radar Pulse Activity Segmentation
Zi Huang, Akila Pemasiri, Simon Denman +2
Radio signal recognition is a crucial function in electronic warfare. Precise identification and localisation of radar pulse activities are required by electronic warfare systems t…
A Survey on Physics Informed Reinforcement Learning: Review and Open Problems
Chayan Banerjee, Kien Nguyen, Clinton Fookes +1
The inclusion of physical information in machine learning frameworks has revolutionized many application areas. This involves enhancing the learning process by incorporating physic…
Does Interference Exist When Training a Once-For-All Network?
Jordan Shipard, Arnold Wiliem, Clinton Fookes
The Once-For-All (OFA) method offers an excellent pathway to deploy a trained neural network model into multiple target platforms by utilising the supernet-subnet architecture. Onc…
A Survey on Graph-Based Deep Learning for Computational Histopathology
David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman +2
With the remarkable success of representation learning for prediction problems, we have witnessed a rapid expansion of the use of machine learning and deep learning for the analysi…
Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future
David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman +2
With the advances of data-driven machine learning research, a wide variety of prediction problems have been tackled. It has become critical to explore how machine learning and spec…
Deep Learning for Medical Anomaly Detection -- A Survey
Tharindu Fernando, Harshala Gammulle, Simon Denman +2
Machine learning-based medical anomaly detection is an important problem that has been extensively studied. Numerous approaches have been proposed across various medical applicatio…
Memory based fusion for multi-modal deep learning
Darshana Priyasad, Tharindu Fernando, Simon Denman +2
The use of multi-modal data for deep machine learning has shown promise when compared to uni-modal approaches with fusion of multi-modal features resulting in improved performance…
Memory Augmented Deep Generative models for Forecasting the Next Shot Location in Tennis
Tharindu Fernando, Simon Denman, Sridha Sridharan +1
This paper presents a novel framework for predicting shot location and type in tennis. Inspired by recent neuroscience discoveries we incorporate neural memory modules to model the…
Deep Classification of Epileptic Signals
David Ahmedt-Aristizabal, Clinton Fookes, Kien Nguyen +1
Electrophysiological observation plays a major role in epilepsy evaluation. However, human interpretation of brain signals is subjective and prone to misdiagnosis. Automating this…
Tree Memory Networks for Modelling Long-term Temporal Dependencies
Tharindu Fernando, Simon Denman, Aaron McFadyen +2
In the domain of sequence modelling, Recurrent Neural Networks (RNN) have been capable of achieving impressive results in a variety of application areas including visual question a…