Parallel Methods for Neuron Network

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Presentation transcript:

Parallel Methods for Neuron Network Haihao Lu and Yuanchu Dang

Machine Learning and Artificial Neuron Network

Problem of Interest: ANN W is the weight b is the bias ϕ is activation function x is feature y is label l is loss function

Drawback of SGD SGD is a first-order method, thus it converges slow. SGD suffers from vanishing gradient problem Most importantly, it is hard to parallelize SGD

Equivalent Problem Relaxed Problem

Alternating Minimization W-update z-update one or more steps of damped Newton cheap to compute Hessian parallel computing

RNN

RNN Equivalent Problem Relaxed Problem

With L_2 Regulation W-update z-update one or more steps of damped Newton cheap to compute Hessian parallel computing

Code neuralNetwork.jl https://github.com/Yuanchu/neuralNetwor k

Numerical Test: Auto-Encoder