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We leverage what are typically considered the worst qualities of deep learning algorithms - high computational cost, requirement for large data, no explainability, high dependence on hyper-parameter choice, overfitting, and vulnerability to adversarial perturbations - in order to create a method for the secure and efficient training of remotely deployed neural networks over unsecured channels.
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Prototype selection for nearest neighbor classification: Taxonomy and empirical study
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Energy and Policy Considerations for Deep Learning in NLP
Strubell, E.; Ganesh, A.; and McCallum, A. 2019 · 2019
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Flexible Dataset Distillation: Learn Labels Instead of Images
Bohdal, O.; Yang, Y.; and Hospedales, T. 2020 · 2020
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’Less Than One’-Shot Learning: Learning N Classes From M<N Samples
Sucholutsky, I.; and Schonlau, M. 2020 · 2020
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Dataset Condensation with Gradient Matching
Zhao, B.; Mopuri, K. R.; and Bilen, H. 2020 · 2020
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