A Novel Gradient Synchronization Algorithm in Training Distributed ...
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In this paper, a Gradient Truncated Stochastic Gradient Descent (GT-SGD) algorithm is proposed for distributed RNN training. This algorithm can aggressively ...
SGD is an iterative optimization algorithm that is often used to solve and optimize model parameters of machine learning algorithms. SGD is a deformed form ...
In this paper, a Gradient Truncated Stochastic Gradient Descent (GT-SGD) algorithm is proposed for distributed RNN training. This algorithm can aggressively ...
GT-SGD: A Novel Gradient Synchronization Algorithm in Training Distributed Recurrent Neural Network Language Models. Conference Paper. Oct 2017. Xiaoci Zhang ...
GT-SGD: A Novel Gradient Synchronization Algorithm in Training Distributed Recurrent Neural Network Language Models · Xiaoci ZhangNaijie GuR. YasrabHong Ye.
Oct 16, 2023 · Backpropagation allows us to compute the important gradient information that we need for gradient descent when we have a deep neural network.
Stochastic Gradient Descent (SGD) is a popular optimiza- tion algorithm to train neural networks (Bottou, 2012; Dean et al., 2012; Kingma & Ba, 2014). As for ...
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GT-SGD: A Novel Gradient Synchronization Algorithm in Training Distributed Recurrent Neural Network Language Models. 30 Sep 2017. Xiaoci Zhang, Naijie Gu ...
Jul 26, 2017 · Deep learning models (DLMs) are state-of-the-art techniques in speech recognition. However, training good DLMs can be.
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In this paper, we propose Deep Gradient Compression that can reduce the communication bandwidth by two orders of magnitude. We proposed four techniques that ...
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