… This paper explores asynchronous stochastic optimization for sequence training of deep
neural networks. … We investigated asynchronous stochastic gradient descent for sequence …

… optimization criterion. Given the difficulty of guaranteeing high quality transcripts for large
training … Paper organization: The asynchronous optimization framework for sequence training …

… stochastic asynchronous optimization for … asynchronously updated by concurrent processes.
To this end, we use a probabilistic model which captures key features of real asynchronous …

… 6) For example on the QM9 dataset [30, 27] our implementation of gated graph sequence
neural network (GGSNN) [21] on a 16 core CPU runs 9x faster than a (manually optimized) …

… and asynchronous ND-CAO training, the algorithm is identified as an efficient scheme to
train neural networks in a novel gradient-independent, distributed, and asynchronous manner, …

… , extended training duration, and low equipment utilization in … optimization of large-scale
deep neural network (DNN) models, this paper proposes an asynchronous parallel optimization …

… In Section V, we include a numerical example illustrating how asynchronous decentralized
algorithms may be used for training deep neural networks. We conclude, in Section VI, with a …

… We introduce new training algorithms for a class of neural networks targeted at sequential …
on introducing new fast asynchronous parallel incremental optimization algorithms which can …

… Such variation is especially prominent in the training of deep neural network architectures, …
a new asynchronous EA called AES designed for complex problems such as optimizing the …

… train the models in a distributed manner using asynchronous … We compare two sequence
discriminative criteria – maximum … order to investigate all the above, we use ASGD optimization …