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Deepfakes, derived from "deep learning" and "fake," involve techniques that merge the face images of a target person with a video of a different source person. This process creates videos where
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of analytical data to guide the generation of highly accurate 3D/2D molecular graphs or SMILES representations. Research Aims and Objectives This project aims to develop a robust, data-efficient deep learning
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. This is indeed what AIC, BIC, MDL and MML would anticipate. And yet deep learning methods can often work despite this. This project investigates how deep learning can survive over-fitting and whether
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Over the past decades, we have witnessed the emergence and rapid development of deep learning. DL has been successfully deployed in many real-life applications, including face recognition, automatic
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This Ph.D. project aims to combine causal analysis with deep learning for mental health support. As deep learning is vulnerable to spurious correlations, novel causal discovery and inference methods
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‘dynamic graphs’. Although recently many studies on extending deep learning approaches for graph data have emerged, there is still a research gap on extending deep learning approaches for identifying
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While deep learning has shown remarkable performance in medical imaging benchmarks, translating these results to real-world clinical deployment remains challenging. Models trained on data from one
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to learn robotics or human-centered research methods will also be considered. Experience with programming languages (particularly Python), deep learning frameworks, and robotic simulation platforms (ROS
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Background and Motivation Modern deep learning models have achieved remarkable success in computer vision and natural language processing. However, they typically produce overconfident predictions
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The project involves building and curating a comprehensive food image dataset suitable for mobile AI applications. High-accuracy deep learning models will be trained on this dataset and then