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Digital twins based on bidirectional LSTM and GAN for modelling COVID-19
The outbreak of the coronavirus disease 2019 (COVID-19) has now spread t...
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OmiEmbed: reconstruct comprehensive phenotypic information from multi-omics data using multi-task deep learning
High-dimensional omics data contains intrinsic biomedical information th...
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A Blockchain-based Trust System for Decentralised Applications: When trustless needs trust
Blockchain technology has been envisaged to commence an era of decentral...
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BU-Trace: A Permissionless Mobile System for Privacy-Preserving Intelligent Contact Tracing
The coronavirus disease 2019 (COVID-19) pandemic has caused an unprecede...
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Adversarially trained LSTMs on reduced order models of urban air pollution simulations
This paper presents an approach to improve computational fluid dynamics ...
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Revealing the Transmission Dynamics of COVID-19: A Bayesian Framework for R_t Estimation
In epidemiological modelling, the instantaneous reproduction number, R_t...
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Privacy Preservation in Federated Learning: Insights from the GDPR Perspective
Along with the blooming of AI and Machine Learning-based applications an...
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Suggestive Annotation of Brain Tumour Images with Gradient-guided Sampling
Machine learning has been widely adopted for medical image analysis in r...
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An Epidemiological Modelling Approach for Covid19 via Data Assimilation
The global pandemic of the 2019-nCov requires the evaluation of policy i...
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Regularizing Deep Multi-Task Networks using Orthogonal Gradients
Deep neural networks are a promising approach towards multi-task learnin...
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Biologically inspired architectures for sample-efficient deep reinforcement learning
Deep reinforcement learning requires a heavy price in terms of sample ef...
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Automatic Brain Tumour Segmentation and Biophysics-Guided Survival Prediction
Gliomas are the most common malignant brain tumourswith intrinsic hetero...
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Unsupervised Annotation of Phenotypic Abnormalities via Semantic Latent Representations on Electronic Health Records
The extraction of phenotype information which is naturally contained in ...
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Transfer Learning from Partial Annotations for Whole Brain Segmentation
Brain MR image segmentation is a key task in neuroimaging studies. It is...
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Blockchain-based Personal Data Management: From Fiction to Solution
The emerging blockchain technology has enabled various decentralised app...
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Integrated Multi-omics Analysis Using Variational Autoencoders: Application to Pan-cancer Classification
Different aspects of a clinical sample can be revealed by multiple types...
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Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction
In the recent years, convolutional neural networks have transformed the ...
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A backdoor attack against LSTM-based text classification systems
With the widespread use of deep learning system in many applications, th...
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Static Activation Function Normalization
Recent seminal work at the intersection of deep neural networks practice...
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GDPR-Compliant Personal Data Management: A Blockchain-based Solution
The General Data Protection Regulation (GDPR) gives control of personal ...
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Integrating Semantic Knowledge to Tackle Zero-shot Text Classification
Insufficient or even unavailable training data of emerging classes is a ...
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Combining learning rate decay and weight decay with complexity gradient descent - Part I
The role of L^2 regularization, in the specific case of deep neural netw...
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Deep Sequence Learning with Auxiliary Information for Traffic Prediction
Predicting traffic conditions from online route queries is a challenging...
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Generative Creativity: Adversarial Learning for Bionic Design
Bionic design refers to an approach of generative creativity in which a ...
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The Future of Spreadsheets in the Big Data Era
The humble spreadsheet is the most widely used data storage, manipulatio...
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Dropping Activation Outputs with Localized First-layer Deep Network for Enhancing User Privacy and Data Security
Deep learning methods can play a crucial role in anomaly detection, pred...
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TensorLayer: A Versatile Library for Efficient Deep Learning Development
Deep learning has enabled major advances in the fields of computer visio...
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Semantic Image Synthesis via Adversarial Learning
In this paper, we propose a way of synthesizing realistic images directl...
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Deep De-Aliasing for Fast Compressive Sensing MRI
Fast Magnetic Resonance Imaging (MRI) is highly in demand for many clini...
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Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks
A major challenge in brain tumor treatment planning and quantitative eva...
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Deep Poincare Map For Robust Medical Image Segmentation
Precise segmentation is a prerequisite for an accurate quantification of...
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I2T2I: Learning Text to Image Synthesis with Textual Data Augmentation
Translating information between text and image is a fundamental problem ...
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DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG
The present study proposes a deep learning model, named DeepSleepNet, fo...
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Unsupervised Image-to-Image Translation with Generative Adversarial Networks
It's useful to automatically transform an image from its original form t...
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Mixed Neural Network Approach for Temporal Sleep Stage Classification
This paper proposes a practical approach to addressing limitations posed...
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Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks
We used convolutional neural networks (CNNs) for automatic sleep stage s...
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DropNeuron: Simplifying the Structure of Deep Neural Networks
Deep learning using multi-layer neural networks (NNs) architecture manif...
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