Abstract: The training process of a deep neural network commonly consists of three phases: forward propagation, backward propagation, and weight update.
Our approach applies to neural networks that use rectified linear unit. Considering that the backward propagation results in a zero activation gradient when the ...
[PDF] Acceleration of DNN Backward Propagation by Selective ...
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Jun 2, 2019 · Our approach exploits the backward propagation characteristics of ReLU layer, which makes backpropagated gradient zero when the corresponding ...
Oct 2, 2019 · This paper introduces Selective-Backprop, a technique that accelerates the training of deep neural networks (DNNs) by prioritizing examples ...
We propose LoCal+SGD , a new algorithmic approach to accelerate DNN training by selectively combining localized or Hebbian learning within a Stochastic Gradient ...
The MBGD algorithm can be divided into four iterative steps: a randomized minibatch selection, the Forward Propagation (FP), the cost computation, and the ...
○ Accelerates the convergence of Stochastic Gradient Descent (SGD) ... Backward pass: Compute gradients. f x,w = 1. 1+ ... Select training samples ...
Backpropagation. 1. Identify intermediate functions (forward prop). 2. Compute local gradients. 3. Combine with upstream error signal to get full gradient. Page ...
Jun 18, 2021 · This algorithm computes the gradient of the neural network parameters with respect to a loss function that measures the network's performance in ...
Mar 11, 2019 · The proposed method selects the top-k values of the gradient of the output vector, and backpropagates the loss through the corresponding subset.
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