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for monitoring and controlling the brain with medical devices and imaging brain activity in new and important ways. Required knowledge Statistical signal processing, Statistical Inference, Machine learning, Deep
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The existing deep learning based time series classification (TSC) algorithms have some success in multivariate time series, their accuracy is not high when we apply them on brain EEG time series (65
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the simple equation that more training data = better performance. Learning—in particular, the advanced deep learning methods, like BERT for NLP and ResNet for image processing—often require thousands
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unreliable techniques and is thus often not done so that infected colonies are discovered far too late. In this project, we aim to build Ai tools based on Deep Learning to automatically classify the health
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context for monologue and multi-party bilingual dialogue translation [1,2, 3], capitalizing on the flexibility and expressive power of deep learning and neural networks. In this project, we will push the
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for the relevant attributes or properties. General composition mechanisms will be learned such that applications can combine appropriate components to generate desired data. For example, the script of an emotion
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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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/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
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. This would provide thousands of diverse example images with corresponding body part locations. These data would be used to train a deep learning model 5, 7 . The model’s high-quality body part predictions may
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intelligence techniques (e.g., Deep Learning, Statistics, ML, Optimization) in order to (1) understand the nature of critical software defects like vulnerabilities; (2) predict; (3) highlight vulnerable code; (4