Dr Zhongtian Sun is a Lecturer in the School of Computing at the University of Kent, specialising in Artificial Intelligence (AI) and Machine Learning (ML). Dr Sun is Mila Quebec AI Institute AI ...
The practical adoption of deep learning in high-stakes forensic applications, such as dental age estimation, is often limited by the ‘black box’ nature of the models. This study introduces a framework ...
Melville Laboratory for Polymer Synthesis, Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Rd, Cambridge CB2 1EW, U.K. Melville Laboratory for Polymer Synthesis, Yusuf Hamied ...
This repo contains all my Deep Learning semester work, including implementations of FNNs, CNNs, autoencoders, CBOW, and transfer learning. I explored TensorFlow, Keras, PyTorch, and Theano while ...
There is a necessity to address the motivations for this project. TensorFlow is one of the deep learning frameworks available with the largest community. This repository is dedicated to suggesting a ...
Deep learning in medical imaging has the potential to minimize the risk of diagnostic errors, reduce radiologist workload, and accelerate diagnosis. Training such deep learning models requires large ...
Multifidelity Ensemble Kalman Filtering Using Surrogate Models Defined by Theory-Guided Autoencoders
Data assimilation is a Bayesian inference process that obtains an enhanced understanding of a physical system of interest by fusing information from an inexact physics-based model, and from noisy ...
Classification of electroencephalogram (EEG) is a key approach to measure the rhythmic oscillations of neural activity, which is one of the core technologies of brain-computer interface systems (BCIs) ...
Generating synthetic data is useful when you have imbalanced training data for a particular class, for example, generating synthetic females in a dataset of employees that has many males but few ...
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