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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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the shortcomings of these techniques, deep learning is more and more involved in static vulnerability localization and improving fuzzing efficiency. This project aims to deliver a smart software vulnerability
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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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to reproducible research, critical analysis, and publication Experience with deep learning, audio analysis, or affective computing is advantageous but not mandatory.
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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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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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., Pan, S., Aggarwal, C., & Salehi, M. (2022). Deep learning for time series anomaly detection: A survey. ACM Computing Surveys.
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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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analysis, or multi-omics integration, with strong competence in deep learning frameworks (e.g., PyTorch/TensorFlow) and data engineering for reproducible research. Familiarity with cloud/HPC workflows
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/learning-with-privacy-at-scale [3] Fang et al., "Local Model Poisoning Attacks to Byzantine-Robust Federated Learning". In USENIX Security Symposium, 2020. [4] Zhu et al., "Deep Leakage from Gradients