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- Element-Research dpnn: deep extensions to nn - GitHub
However, dpnn can be used without dp (for e g you can use it with optim), which is one of the main reasons why we made it Sagar Waghmare wrote a nice tutorial on how to use dpnn with nngraph to reproduce the Lateral Connections in Denoising Autoencoders Support Supervised Learning
- Deep-learning-based real-time individualization for reduce-order . . .
A double-path neural network (DPNN) was designed to input the waveforms of each haemodynamic indicator and their key features and then output the individual parameters of the LPM, which was labelled using a conventional optimization algorithm
- Bearing Degradation Prediction by WPD and DPNN: Introducing a Novel . . .
Bearing Degradation Prediction by WPD and DPNN: Introducing a Novel Deep Learning Method Abstract: In this article, a novel method of deep learning based on wavelet transform and deep perceptron neural networks (DPNNs) is proposed to predict the remaining useful life (RUL) of bearings
- DPNN-ac4C: A Dual-Path Neural Network with self-attention . . . - PubMed
Results: We present DPNN-ac4C, a dual-path neural network with a self-attention mechanism for the identification of ac4C sites in mRNA Our model integrates embedding modules, bidirectional GRU networks, convolutional neural networks, and self-attention to capture both local and global features of RNA sequences
- Multi-Wavelength Diffractive Photonic Neural Network for Multi-Task . . .
In this paper, a novel optical multi-task learning system is proposed by designing a multi-wavelength diffractive photonic neural network (DPNN) using a joint optimization method
- A Hybrid Deep Physics Neural Network Model for . . . - IEEE Xplore
This paper presents a new approach, in which a hybrid physics-based model and deep physics neural network (DPNN) model are used to simultaneously build a data conditioning autoencoder (DPNNAE) based on DPNN for enhancing the signal strength, compared to a conventional autoencoder (AE)
- Deep-learning-based real-time individualization for reduce-order . . .
The DPNN achieves accurate and real-time individualization for the LPM of the blood circulation system by inputting patient-specific haemodynamic waveforms that could be collected non-invasively Compared with the previous optimization algorithm, the DPNN significantly improves the calculation speed while ensuring the calculation accuracy
- Dual adaptive training of photonic neural networks
Dual adaptive training provides critical support for constructing large-scale PNNs to achieve advanced architectures and can be generalized to other types of artificial intelligence systems
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