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remaining Useful Life (RUL) Prediction of Mechanical Bearings using Continuous Wavelet Transform (CWT), Convolution Neural Network (CNN), and Long Short Term Memory (LSTM) unit
to prediction the remain useful life of bearing based on 2012 PHM data
Transformer Network for Remaining Useful Life Prediction of Lithium-Ion Batteries
This repository includes code for the paper: Lithology Identification Based on One-dimensional Convolutional Neural Network and Recurrent Neural Network with Attention Mechanism.
Remaining useful life prediction by Transformer-based Model
pytorch implementation of LSTM + Self Attention for character level name classification
PyTorch Dual-Attention LSTM-Autoencoder For Multivariate Time Series
BiLSTM 加普通Attention中文文本多分类Pytorch实现
🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.
Using LSTM to predict Remaining Useful Life of CMAPSS Dataset
Multivariate Time-Series Forecasting with LSTM and Attention Mechanism - SJSU (2021): Used Python and TensorFlow package to predict the remaining useful life of Turbofan engine applying LSTM and At…
Seq2Seq: Bidirectional LSTM with Attention in Pytorch
Pytorch Implementation of Attention-Based BiLSTM for Relation Extraction ("Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification" ACL 2016 http://www.aclweb.org/…
文本分类， 双向lstm + attention 算法
Sentiment Analysis: Deep Bi-LSTM+attention model
Pytorch implemention of Deep CNN Encoder + LSTM Decoder with Attention for Image to Latex
PTB Language Modelling task with LSTM + Attention layer
A PyTorch Tutorials of Sentiment Analysis Classification (RNN, LSTM, Bi-LSTM, LSTM+Attention, CNN)
"Attention in Convolutional LSTM for Gesture Recognition" in NIPS 2018
这是一个YoloV4-tiny-pytorch的源码，可以用于训练自己的模型。
PyTorch implementation of remaining useful life prediction with long-short term memories (LSTM), performing on NASA C-MAPSS data sets. Partially inspired by Zheng, S., Ristovski, K., Farahat, A., &…
Transformer implementation with PyTorch for remaining useful life prediction on turbofan engine with NASA CMAPSS data set. Inspired by Mo, Y., Wu, Q., Li, X., & Huang, B. (2021). Remaining useful l…