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Neural Networks Architecture Evaluation in a Quantum Computer [PDF]

, 2017
In this work, we propose a quantum algorithm to evaluate neural networks architectures named Quantum Neural Network Architecture Evaluation (QNNAE). The proposed algorithm is based on a quantum associative memory and the learning algorithm for artificial neural networks.
da Silva, Adenilton José, de Oliveira, Rodolfo Luan F.   +1 more
arxiv   +3 more sources

Explanations for Neural Networks by Neural Networks [PDF]

Applied Sciences, 2022
Understanding the function learned by a neural network is crucial in many domains, e.g., to detect a model’s adaption to concept drift in online learning. Existing global surrogate model approaches generate explanations by maximizing the fidelity between the neural network and a surrogate model on a sample-basis, which can be very time-consuming ...
Sascha Marton, Stefan Lüdtke, Christian Bartelt   +2 more
openaire   +2 more sources

Neural Networks With Motivation [PDF]

Frontiers in Systems Neuroscience, 2021
Animals rely on internal motivational states to make decisions. The role of motivational salience in decision making is in early stages of mathematical understanding. Here, we propose a reinforcement learning framework that relies on neural networks to learn optimal ongoing behavior for dynamically changing motivation values. First, we show that neural
Marcus Stephenson-Jones, Marcus Stephenson-Jones, Ngoc B. Tran, Alexei A. Koulakov, Sergey Shuvaev, Bo Li   +5 more
openaire   +5 more sources

A Survey of Quantization Methods for Efficient Neural Network Inference [PDF]

Low-Power Computer Vision, 2021
As soon as abstract mathematical computations were adapted to computation on digital computers, the problem of efficient representation, manipulation, and communication of the numerical values in those computations arose.
A. Gholami, Sehoon Kim, Zhen Dong, Z. Yao, Michael W. Mahoney, K. Keutzer   +5 more
semanticscholar   +1 more source

Operational neural networks [PDF]

Neural Computing and Applications, 2020
AbstractFeed-forward, fully connected artificial neural networks or the so-called multi-layer perceptrons are well-known universal approximators. However, their learning performance varies significantly depending on the function or the solution space that they attempt to approximate. This is mainly because of their homogenous configuration based solely
Serkan Kiranyaz, Turker Ince, Alexandros Iosifidis, Moncef Gabbouj   +3 more
openaire   +6 more sources

Unet-Astar: A Deep Learning-Based Fast Routing Algorithm for Unified PCB Routing

IEEE Access, 2023
In recent years, there has been extensive research on the routing problem of printed circuit boards (PCBs). Due to the increasing number of pins, high pin density, and unique physical constraints, manual PCB routing has become a time-consuming task to ...
Shiyuan Yin, Min Jin, Gang Chen, Guoliang Gong, Wenyu Mao, Huaxiang Lu   +5 more
doaj   +1 more source

Reinforcement-learning-based parameter adaptation method for particle swarm optimization

Complex & Intelligent Systems, 2023
Particle swarm optimization (PSO) is a well-known optimization algorithm that shows good performances in solving different optimization problems. However, the PSO usually suffers from slow convergence.
Shiyuan Yin, Min Jin, Huaxiang Lu, Guoliang Gong, Wenyu Mao, Gang Chen, Wenchang Li   +6 more
doaj   +1 more source

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network [PDF]

Computer Vision and Pattern Recognition, 2016
Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR) input image is upscaled
Wenzhe Shi, Jose Caballero, Ferenc Huszár, J. Totz, Andrew P. Aitken, Rob Bishop, D. Rueckert, Zehan Wang   +7 more
semanticscholar   +1 more source

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices [PDF]

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017
We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e.g., 10-150 MFLOPs).
Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, Jian Sun   +3 more
semanticscholar   +1 more source

Prediction model of coal seam gas content based on ACSOA optimized BP neural network

Meikuang Anquan, 2022
For the problem of coal seam gas content prediction, the influencing factors of coal seam gas content were analyzed by taking No.2 coal seam of Chensilou Coal Mine as the research object.
Prediction model of coal seam gas content based on ACSOA optimized BP neural network
doaj   +1 more source