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1

It will recover the encrypted inputs. The algorithm starts with dummy data and dummy label, and then iteratively optimizes the dummy gradients close as to the original, also makes the dummy data close to the real training data: $$\mathbf{x}^{\prime *}, \mathbf{y}^{\prime *}=\underset{\mathbf{x}^{\prime}, \mathbf{y}^{\prime}}{\arg \min }\left\|\nabla W^{\...


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The 2015 article Cyclical Learning Rates for Training Neural Networks by Leslie N. Smith gives some good suggestions for finding an ideal range for the learning rate. The paper's primary focus is the benefit of using a learning rate schedule that varies learning rate cyclically between some lower and upper bound, instead of trying to choose a single fixed ...


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The visualisation can be found in The need for small learning rates on large problems. This paper by D. Randall Wilson and Tony R. Martinez from 2001 investigates the role of learning rates in gradient descent algorithms. In general, different algorithms assign different meaning to the same word 'learning rate'. For example, the learning rate in a gradient ...


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