AIMC Topic: Normal Distribution

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Analysis on the inherent noise tolerance of feedforward network and one noise-resilient structure.

Neural networks : the official journal of the International Neural Network Society Jun 17, 2023
In the past few decades, feedforward neural networks have gained much attraction in their hardware implementations. However, when we realize a neural network in analog circuits, the circuit-based model is sensitive to hardware nonidealities. The noni...
Neural Networks, Computer Noise Neurons Normal Distribution
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DyVGRNN: DYnamic mixture Variational Graph Recurrent Neural Networks.

Neural networks : the official journal of the International Neural Network Society Jun 5, 2023
Although graph representation learning has been studied extensively in static graph settings, dynamic graphs are less investigated in this context. This paper proposes a novel integrated variational framework called DYnamic mixture Variational Graph ...
Neural Networks, Computer Learning Cluster Analysis Normal Distribution
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Adversarial feature hybrid framework for steganography with shifted window local loss.

Neural networks : the official journal of the International Neural Network Society Jun 3, 2023
Image steganography is a long-standing image security problem that aims at hiding information in cover images. In recent years, the application of deep learning to steganography has the tendency to outperform traditional methods. However, the vigorou...
Neural Networks, Computer Normal Distribution
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Regularizing transformers with deep probabilistic layers.

Neural networks : the official journal of the International Neural Network Society Feb 9, 2023
Language models (LM) have grown non-stop in the last decade, from sequence-to-sequence architectures to attention-based Transformers. However, regularization is not deeply studied in those structures. In this work, we use a Gaussian Mixture Variation...
Language Natural Language Processing Normal Distribution
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Dual parallel net: A novel deep learning model for rectal tumor segmentation via CNN and transformer with Gaussian Mixture prior.

Journal of biomedical informatics Feb 2, 2023
Segmentation of rectal cancerous regions from Magnetic Resonance (MR) images can help doctor define the extent of the rectal cancer and judge the severity of rectal cancer, so rectal tumor segmentation is crucial to improve the accuracy of rectal can...
Humans Neural Networks, Computer Hospitals Deep Learning Image Processing, Computer-Assisted Rectal Neoplasms Normal Distribution
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Machine learning for better prediction of seepage flow through embankment dams: Gaussian process regression versus SVR and RVM.

Environmental science and pollution research international Jan 24, 2023
In the present study, three machine learning methods were applied for predicting seepage flow through embankment dams, namely (i) support vector regression (SVR), relevance vector machine (RVM), and Gaussian process regression (GPR). The three models...
Machine Learning Algorithms Normal Distribution
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Relating local connectivity and global dynamics in recurrent excitatory-inhibitory networks.

PLoS computational biology Jan 23, 2023
How the connectivity of cortical networks determines the neural dynamics and the resulting computations is one of the key questions in neuroscience. Previous works have pursued two complementary approaches to quantify the structure in connectivity. O...
Neural Networks, Computer Models, Neurological Neurons Homeostasis Nerve Net Normal Distribution
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An application of deep dual convolutional neural network for enhanced medical image denoising.

Medical & biological engineering & computing Jan 14, 2023
This work investigates the medical image denoising (MID) application of the dual denoising network (DudeNet) model for chest X-ray (CXR). The DudeNet model comprises four components: a feature extraction block with a sparse mechanism, an enhancement ...
Neural Networks, Computer Image Processing, Computer-Assisted Image Enhancement Signal-To-Noise Ratio Normal Distribution Data Compression
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Automatic Classification of Rotor Faults in Soft-Started Induction Motors, Based on Persistence Spectrum and Convolutional Neural Network Applied to Stray-Flux Signals.

Sensors (Basel, Switzerland) Dec 28, 2022
Due to their robustness, versatility and performance, induction motors (IMs) have been widely used in many industrial applications. Despite their characteristics, these machines are not immune to failures. In this sense, breakage of the rotor bars (B...
Neural Networks, Computer Reproducibility of Results Health Status Normal Distribution Records
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Disentangling Noise from Images: A Flow-Based Image Denoising Neural Network.

Sensors (Basel, Switzerland) Dec 14, 2022
The prevalent convolutional neural network (CNN)-based image denoising methods extract features of images to restore the clean ground truth, achieving high denoising accuracy. However, these methods may ignore the underlying distribution of clean ima...
Neural Networks, Computer Image Processing, Computer-Assisted Artifacts Signal-To-Noise Ratio Normal Distribution
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