Long Short Term Memory

Long Short-Term Memory (LSTM) networks are a type of recurrent neural network designed to process sequential data by learning long-term dependencies, enabling accurate predictions and classifications in various applications. Current research focuses on enhancing LSTM architectures, such as incorporating convolutional layers, attention mechanisms, and hybrid models combining LSTMs with other deep learning techniques like transformers or graph neural networks, to improve efficiency and accuracy. This work is significant because LSTMs are proving highly effective across diverse fields, from financial forecasting and environmental monitoring to medical image analysis and activity recognition, offering powerful tools for analyzing complex temporal data.

Papers

November 23, 2021

November 22, 2021

November 21, 2021

November 18, 2021

MS-nowcasting: Operational Precipitation Nowcasting with Convolutional LSTMs at Microsoft Weather
Sylwester Klocek, Haiyu Dong, Matthew Dixon, Panashe Kanengoni, Najeeb Kazmi, Pete Luferenko, Zhongjian Lv, Shikhar Sharma, Jonathan Weyn, Siqi Xiang
Deep Learning Model Long Short Term Memory Data Driven Nowcasting Precipitation Nowcasting Convolutional LSTM Radar Reflectivity

November 15, 2021

Short-Term Power Prediction for Renewable Energy Using Hybrid Graph Convolutional Network and Long Short-Term Memory Approach
Wenlong Liao, Birgitte Bak-Jensen, Jayakrishnan Radhakrishna Pillai, Zhe Yang, Kuangpu Liu
Long Short Term Memory Renewable Energy Wind Power Power Prediction

November 9, 2021

Analysis of Sectoral Profitability of the Indian Stock Market Using an LSTM Regression Model
Jaydip Sen, Saikat Mondal, Sidra Mehtab
General Analysis Long Short Term Memory Predictive Model Prediction Accuracy Indian Stock Market

November 8, 2021

November 7, 2021

Use of 1D-CNN for input data size reduction of LSTM in Hourly Rainfall-Runoff modeling
Kei Ishida, Ali Ercan, Takeyoshi Nagasato, Masato Kiyama, Motoki Amagasaki
Long Short Term Memory Greater Public Use CNN LSTM Input Compression

November 5, 2021

Sexism Identification in Tweets and Gabs using Deep Neural Networks
Amikul Kalra, Arkaitz Zubiaga
Deep Neural Network Long Short Term Memory Hate Speech Twitter Tweet Sexist Content Sexism Detection Sexism Identification

November 4, 2021

Model-assisted deep learning of rare extreme events from partial observations
Anna Asch, Ethan Brady, Hugo Gallardo, John Hood, Bryan Chu, Mohammad Farazmand
Deep Learning Deep Neural Network Long Short Term Memory Reservoir Computing Extreme Event Partial Observation

Long Short Term Memory

Papers

Using Language Model to Bootstrap Human Activity Recognition Ambient Sensors Based in Smart Homes

Appliance Level Short-term Load Forecasting via Recurrent Neural Network

pmSensing: A Participatory Sensing Network for Predictive Monitoring of Particulate Matter

Time Series Prediction about Air Quality using LSTM-Based Models: A Systematic Mapping

Subject-Independent Drowsiness Recognition from Single-Channel EEG with an Interpretable CNN-LSTM model

Design of an Novel Spectrum Sensing Scheme Based on Long Short-Term Memory and Experimental Validation

MS-nowcasting: Operational Precipitation Nowcasting with Convolutional LSTMs at Microsoft Weather

Short-Term Power Prediction for Renewable Energy Using Hybrid Graph Convolutional Network and Long Short-Term Memory Approach

Analysis of Sectoral Profitability of the Indian Stock Market Using an LSTM Regression Model

Stock Portfolio Optimization Using a Deep Learning LSTM Model

Learning via Long Short-Term Memory (LSTM) network for predicting strains in Railway Bridge members under train induced vibration

Use of 1D-CNN for input data size reduction of LSTM in Hourly Rainfall-Runoff modeling

Sexism Identification in Tweets and Gabs using Deep Neural Networks

Model-assisted deep learning of rare extreme events from partial observations