7 citations · 11 across the 9 of their papers we have counts for
9 papers
Variational Autoencoder Learns Better Feature Representations for EEG-based Obesity Classification
Yuan Yue, Jeremiah D. Deng, Dirk De Ridder +2
Obesity is a common issue in modern societies today that can lead to various diseases and significantly reduced quality of life. Currently, research has been conducted to investiga…
Sizing up the Batteries: Modelling of Energy-Harvesting Sensor Nodes in a Delay Tolerant Network
Jeremiah D. Deng
For energy-harvesting sensor nodes, rechargeable batteries play a critical role in sensing and transmissions. By coupling two simple Markovian queue models in a delay-tolerant netw…
Finding neural signatures for obesity through feature selection on source-localized EEG
Yuan Yue, Dirk De Ridder, Patrick Manning +2
Obesity is a serious issue in the modern society and is often associated to significantly reduced quality of life. Current research conducted to explore obesity-related neurologica…
Variational Transfer Learning using Cross-Domain Latent Modulation
Jinyong Hou, Jeremiah D. Deng, Stephen Cranefield +1
To successfully apply trained neural network models to new domains, powerful transfer learning solutions are essential. We propose to introduce a novel cross-domain latent modulati…
Semi-Supervised Semantic Segmentation of Vessel Images using Leaking Perturbations
Jinyong Hou, Xuejie Ding, Jeremiah D. Deng
Semantic segmentation based on deep learning methods can attain appealing accuracy provided large amounts of annotated samples. However, it remains a challenging task when only lim…
Cross-Domain Latent Modulation for Variational Transfer Learning
Jinyong Hou, Jeremiah D. Deng, Stephen Cranefield +1
We propose a cross-domain latent modulation mechanism within a variational autoencoders (VAE) framework to enable improved transfer learning. Our key idea is to procure deep repres…
Resting-state EEG sex classification using selected brain connectivity representation
Jean Li, Jeremiah D. Deng, Divya Adhia +1
Effective analysis of EEG signals for potential clinical applications remains a challenging task. So far, the analysis and conditioning of EEG have largely remained sex-neutral. Th…
Deep Adversarial Transition Learning using Cross-Grafted Generative Stacks
Jinyong Hou, Xuejie Ding, Stephen Cranefield +1
Current deep domain adaptation methods used in computer vision have mainly focused on learning discriminative and domain-invariant features across different domains. In this paper,…
Unsupervised Domain Adaptation using Deep Networks with Cross-Grafted Stacks
Jinyong Hou, Xuejie Ding, Jeremiah D. Deng +1
Current deep domain adaptation methods used in computer vision have mainly focused on learning discriminative and domain-invariant features across different domains. In this paper,…