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Deep Spectral Q-learning with Application to Mobile Health
Dynamic treatment regimes assign personalized treatments to patients sequentially over time based on their baseline information and time-varying covariates.
Sparse Winograd Convolutional neural networks on small-scale systolic arrays
The reconfigurability, energy-efficiency, and massive parallelism on FPGAs make them one of the best choices for implementing efficient deep learning accelerators.
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