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cs.LG2024
Guarantees of confidentiality via Hammersley-Chapman-Robbins bounds
Kamalika Chaudhuri, Chuan Guo, Laurens van der Maaten +2
Protecting privacy during inference with deep neural networks is possible by adding noise to the activations in the last layers prior to the final classifiers or other task-specifi…
cs.LG2016
Poor starting points in machine learning
Mark Tygert
Poor (even random) starting points for learning/training/optimization are common in machine learning. In many settings, the method of Robbins and Monro (online stochastic gradient…