Cascaded neural network based small array synthesis with robustness to noise

2020 ◽  
Vol 31 (1) ◽  
Author(s):  
Sagar Dutta ◽  
Banani Basu ◽  
Fazal Ahmed Talukdar
Keyword(s):  
Neural Network ◽  
Robustness To Noise ◽  
Small Array

scholarly journals Quantum learning with noise and decoherence: a robust quantum neural network

2019 ◽  
Author(s):  
Elizabeth Behrman ◽  
Nam Nguyen ◽  
James Steck
Keyword(s):  
Neural Network ◽  
Quantum Computing ◽  
Large Scale ◽  
Learning Approaches ◽  
Quantum Neural Network ◽  
Quantum Learning ◽  
Learning With Noise ◽  
Robustness To Noise ◽  
Robust To Noise ◽  
Arbitrary Quantum

<p>Noise and decoherence are two major obstacles to the implementation of large-scale quantum computing. Because of the no-cloning theorem, which says we cannot make an exact copy of an arbitrary quantum state, simple redundancy will not work in a quantum context, and unwanted interactions with the environment can destroy coherence and thus the quantum nature of the computation. Because of the parallel and distributed nature of classical neural networks, they have long been successfully used to deal with incomplete or damaged data. In this work, we show that our model of a quantum neural network (QNN) is similarly robust to noise, and that, in addition, it is robust to decoherence. Moreover, robustness to noise and decoherence is not only maintained but improved as the size of the system is increased. Noise and decoherence may even be of advantage in training, as it helps correct for overfitting. We demonstrate the robustness using entanglement as a means for pattern storage in a qubit array. Our results provide evidence that machine learning approaches can obviate otherwise recalcitrant problems in quantum computing. </p> <p> </p>

Infrared Image Mosaic Based on Pulse Coupled Neural Network

2012 ◽  
Vol 263-266 ◽  
pp. 2493-2497
Author(s):  
Xue Wu Zhang ◽  
Yan Wang ◽  
Wei Li ◽  
Yun Zhou
Keyword(s):  
Neural Network ◽  
Signal To Noise Ratio ◽  
Infrared Image ◽  
Weighted Average ◽  
Image Mosaic ◽  
Signal To Noise ◽  
Edge Information ◽  
Noise Interference ◽  
Pulse Coupled Neural Network ◽  
Robustness To Noise

According to the infrared image noise interference serious and low signal to noise ratio characteristics, an image mosaic algorithm based on pulse coupled neural network is presented in this paper. The algorithm uses the PCNN imitation of biological vision to extract edge information, adopts the Hausdorff distance as the registration similarity measure, afterwards, computes the optimal registration parameters, simultaneously, uses the weighted average fusion method to achieve image mosaic. The experimental results show that the algorithm can achieve precise image mosaic and have better robustness to noise.

scholarly journals A novel image edge smoothing method based on convolutional neural network

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142092167 ◽  
Author(s):  
Hui-hong Xu ◽  
Dong-yuan Ge
Keyword(s):  
Neural Network ◽  
Edge Detection ◽  
Convolutional Neural Network ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Image Smoothing ◽  
Step Effect ◽  
Frequency Components ◽  
Robustness To Noise ◽  
Image Edge

In the field of visual perception, the edges of images tend to be rich in effective visual stimuli, which contribute to the neural network’s understanding of various scenes. Image smoothing is an image processing method used to highlight the wide area, low-frequency components, main part of the image or to suppress image noise and high-frequency interference components, which could make the image’s brightness smooth and gradual, reduce the abrupt gradient, and improve the image quality. At present, there are still problems such as easy blurring of the edges of the image, poor overall smoothing effect, obvious step effect, and lack of robustness to noise on image smoothing. Based on the convolutional neural network, this article proposes a method for edge detection and deep learning for image smoothing. The results show that the research method proposed in this article solves the problem of edge detection and information capture better, significantly improves the edge effect, and protects the effectiveness of edge information. At the same time, it reduces the signal-to-noise ratio of the smoothed image and greatly improves the effect of image smoothing.

scholarly journals Robust Embedding with Multi-Level Structures for Link Prediction

2019 ◽  
Author(s):  
Zihan Wang ◽  
Zhaochun Ren ◽  
Chunyu He ◽  
Peng Zhang ◽  
Yue Hu
Keyword(s):  
Neural Network ◽  
Recent Work ◽  
Link Prediction ◽  
Robustness Analysis ◽  
Hierarchical Structures ◽  
Knowledge Graph ◽  
Structure Information ◽  
Robustness To Noise ◽  
Multi Level ◽  
Representational Power

Knowledge Graph (KG) embedding has become crucial for the task of link prediction. Recent work applies encoder-decoder models to tackle this problem, where an encoder is formulated as a graph neural network (GNN) and a decoder is represented by an embedding method. These approaches enforce embedding techniques with structure information. Unfortunately, existing GNN-based frameworks still confront 3 severe problems: low representational power, stacking in a flat way, and poor robustness to noise. In this work, we propose a novel multi-level graph neural network (M-GNN) to address the above challenges. We first identify an injective aggregate scheme and design a powerful GNN layer using multi-layer perceptrons (MLPs). Then, we define graph coarsening schemes for various kinds of relations, and stack GNN layers on a series of coarsened graphs, so as to model hierarchical structures. Furthermore, attention mechanisms are adopted so that our approach can make predictions accurately even on the noisy knowledge graph. Results on WN18 and FB15k datasets show that our approach is effective in the standard link prediction task, significantly and consistently outperforming competitive baselines. Furthermore, robustness analysis on FB15k-237 dataset demonstrates that our proposed M-GNN is highly robust to sparsity and noise. 

scholarly journals Quantum learning with noise and decoherence: a robust quantum neural network

2019 ◽  
Author(s):  
Elizabeth Behrman ◽  
Nam Nguyen ◽  
James Steck
Keyword(s):  
Neural Network ◽  
Quantum Computing ◽  
Large Scale ◽  
Learning Approaches ◽  
Quantum Neural Network ◽  
Quantum Learning ◽  
Learning With Noise ◽  
Robustness To Noise ◽  
Robust To Noise ◽  
Arbitrary Quantum

<p>Noise and decoherence are two major obstacles to the implementation of large-scale quantum computing. Because of the no-cloning theorem, which says we cannot make an exact copy of an arbitrary quantum state, simple redundancy will not work in a quantum context, and unwanted interactions with the environment can destroy coherence and thus the quantum nature of the computation. Because of the parallel and distributed nature of classical neural networks, they have long been successfully used to deal with incomplete or damaged data. In this work, we show that our model of a quantum neural network (QNN) is similarly robust to noise, and that, in addition, it is robust to decoherence. Moreover, robustness to noise and decoherence is not only maintained but improved as the size of the system is increased. Noise and decoherence may even be of advantage in training, as it helps correct for overfitting. We demonstrate the robustness using entanglement as a means for pattern storage in a qubit array. Our results provide evidence that machine learning approaches can obviate otherwise recalcitrant problems in quantum computing. </p> <p> </p>

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural
Sign in / Sign up

Export Citation Format

Share Document