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Found 6 papers, 1 papers with code
Learning Quantization in LDPC Decoders
Finding optimal message quantization is a key requirement for low complexity belief propagation (BP) decoding.
Artificial Neural Network and Its Application Research Progress in Chemical Process
Most chemical processes, such as distillation, absorption, extraction, and catalytic reactions, are extremely complex processes that are affected by multiple factors.
Application of Neural Network in Optimization of Chemical Process
Artificial neural network (ANN) has been widely used due to its strong nonlinear mapping ability, fault tolerance and self-learning ability.
Vector spaces as Kripke frames
In this paper, we build on this observation and extend it to a `vector space semantics' for the \emph{general} Lambek calculus, based on \emph{algebras over a field} $\mathbb{K}$ (or $\mathbb{K}$-algebras), i. e. vector spaces endowed with a bilinear binary product.
Towards Optimal Power Control via Ensembling Deep Neural Networks
A deep neural network (DNN) based power control method is proposed, which aims at solving the non-convex optimization problem of maximizing the sum rate of a multi-user interference channel.
An Iterative BP-CNN Architecture for Channel Decoding
The standard BP decoder is used to estimate the coded bits, followed by a CNN to remove the estimation errors of the BP decoder and obtain a more accurate estimation of the channel noise.
