Choosing to ignore dynamic behaviors has allowed deep learning compilers to make significant strides in optimizing common deep learning workloads, but existing ...
This dissertation in particular focuses on an under served, yet important problem: the representation, optimization, differentiation, and execution of dynamic ...
Nov 28, 2023 · This paper offers an alternative to the classic training solutions: an in-depth study to find conditions under which the underlying Artificial Neural Networks ...
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Jul 12, 2023 · It is critical for dynamic optimizations to collect profiles of dynamism, which is hard for existing compilers because they have no knowledge ...
This paper presents a thorough examination of three intelligent methods: neural networks, intelligent systems, and optimization algorithms and strategies. It ...
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Feb 20, 2020 · We propose a new class of optimizer, DDP Neural Optimizer (DDPNOpt), for training feedforward and convolution networks.
This paper emphasizes enhancing the neural network via optimization algorithms by manipulating its tuned parameters or training parameters to obtain the best ...
Oct 22, 2024 · This article provides an in-depth exploration of the techniques related to dynamic construction and inference. Furthermore, it discusses the ...
Apr 17, 2024 · This paper presents SoD 2, a comprehensive framework for optimizing Dynamic DNNs. The basis of our approach is a classification of common operators that form ...
Jan 23, 2018 · In this paper, we propose a method to simultaneously optimize the network structure and weight parameters during neural network training. We ...
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