1.4k citations · 3k across the 54 of their papers we have counts for
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Regularizing Deep Multi-Task Networks using Orthogonal Gradients
Mihai Suteu, Yike Guo
Deep neural networks are a promising approach towards multi-task learning because of their capability to leverage knowledge across domains and learn general purpose representations…
Biologically inspired architectures for sample-efficient deep reinforcement learning
Pierre H. Richemond, Arinbjörn Kolbeinsson, Yike Guo
Deep reinforcement learning requires a heavy price in terms of sample efficiency and overparameterization in the neural networks used for function approximation. In this work, we u…
Automatic Brain Tumour Segmentation and Biophysics-Guided Survival Prediction
Shuo Wang, Chengliang Dai, Yuanhan Mo +3
Gliomas are the most common malignant brain tumourswith intrinsic heterogeneity. Accurate segmentation of gliomas and theirsub-regions on multi-parametric magnetic resonance images…
Unsupervised Annotation of Phenotypic Abnormalities via Semantic Latent Representations on Electronic Health Records
Jingqing Zhang, Xiaoyu Zhang, Kai Sun +3
The extraction of phenotype information which is naturally contained in electronic health records (EHRs) has been found to be useful in various clinical informatics applications su…
Transfer Learning from Partial Annotations for Whole Brain Segmentation
Chengliang Dai, Yuanhan Mo, Elsa Angelini +2
Brain MR image segmentation is a key task in neuroimaging studies. It is commonly conducted using standard computational tools, such as FSL, SPM, multi-atlas segmentation etc, whic…
Blockchain-based Personal Data Management: From Fiction to Solution
Nguyen Truong, Kai Sun, Yike Guo
The emerging blockchain technology has enabled various decentralised applications in a trustless environment without relying on a trusted intermediary. It is expected as a promisin…
Integrated Multi-omics Analysis Using Variational Autoencoders: Application to Pan-cancer Classification
Xiaoyu Zhang, Jingqing Zhang, Kai Sun +3
Different aspects of a clinical sample can be revealed by multiple types of omics data. Integrated analysis of multi-omics data provides a comprehensive view of patients, which has…
Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction
Wenjia Bai, Chen Chen, Giacomo Tarroni +6
In the recent years, convolutional neural networks have transformed the field of medical image analysis due to their capacity to learn discriminative image features for a variety o…
Static Activation Function Normalization
Pierre H. Richemond, Yike Guo
Recent seminal work at the intersection of deep neural networks practice and random matrix theory has linked the convergence speed and robustness of these networks with the combina…
GDPR-Compliant Personal Data Management: A Blockchain-based Solution
Nguyen Binh Truong, Kai Sun, Gyu Myoung Lee +1
The General Data Protection Regulation (GDPR) gives control of personal data back to the owners by appointing higher requirements and obligations on service providers who manage an…
Integrating Semantic Knowledge to Tackle Zero-shot Text Classification
Jingqing Zhang, Piyawat Lertvittayakumjorn, Yike Guo
Insufficient or even unavailable training data of emerging classes is a big challenge of many classification tasks, including text classification. Recognising text documents of cla…
Trust Evaluation Mechanism for User Recruitment in Mobile Crowd-Sensing in the Internet of Things
Nguyen Binh Truong, Gyu Myoung Lee, Tai-Won Um +1
Mobile Crowd-Sensing (MCS) has appeared as a prospective solution for large-scale data collection, leveraging built-in sensors and social applications in mobile devices that enable…
Combining learning rate decay and weight decay with complexity gradient descent - Part I
Pierre H. Richemond, Yike Guo
The role of regularization, in the specific case of deep neural networks rather than more traditional machine learning models, is still not fully elucidated. We hypothesize t…