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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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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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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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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
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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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., Pan, S., Aggarwal, C., & Salehi, M. (2022). Deep learning for time series anomaly detection: A survey. ACM Computing Surveys.
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of the AAAI Conference on Artificial Intelligence (Vol. 26, No. 1, pp. 267-273). - Blau, T., Bonilla, E. V., Chades, I., & Dezfouli, A. (2022, June). Optimizing sequential experimental design with deep
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Discovery Project, this research aims to develop highly novel physics-informed deep learning methods for Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) and applications in image
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addition to audio. Candidates will be expected to devise novel multi-modal generation models by incorporating ideas and techniques from various techniques, such as causality, deep learning, deep reinforcement
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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