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

February 15, 2022

Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoT
Jingjing Zheng, Kai Li, Naram Mhaisen, Wei Ni, Eduardo Tovar, Mohsen Guizani
Federated Learning Long Short Term Memory Deep Reinforcement Online Resource Allocation Delayed Deep Deterministic Policy Gradient

February 14, 2022

BiFSMN: Binary Neural Network for Keyword Spotting
Haotong Qin, Xudong Ma, Yifu Ding, Xiaoyang Li, Yang Zhang, Yao Tian, Zejun Ma, Jie Luo, Xianglong Liu
Neural Network Long Short Term Memory Keyword Spotting Binary Neural Network Full Precision

February 11, 2022

February 10, 2022

Learning the Pedestrian-Vehicle Interaction for Pedestrian Trajectory Prediction
Chi Zhang, Christian Berger
Long Short Term Memory Deep Convolutional Neural Network Pedestrian Trajectory Prediction Pedestrian Vehicle Interaction

February 9, 2022

February 6, 2022

Portfolio Optimization on NIFTY Thematic Sector Stocks Using an LSTM Model
Jaydip Sen, Saikat Mondal, Sidra Mehtab
Long Short Term Memory LSTM Model Portfolio Optimization Indian Stock Market Eigen Portfolio

February 4, 2022

Data Scaling Laws in NMT: The Effect of Noise and Architecture
Yamini Bansal, Behrooz Ghorbani, Ankush Garg, Biao Zhang, Maxim Krikun, Colin Cherry, Behnam Neyshabur, Orhan Firat
Long Short Term Memory Mixed Effect Neural Machine Translation Industrial Disturbing Noise Architecture Design Scaling Law Encoder Decoder Transformer Model Language Modeling Loss

January 31, 2022

January 27, 2022

January 26, 2022

SkiM: Skipping Memory LSTM for Low-Latency Real-Time Continuous Speech Separation
Chenda Li, Lei Yang, Weiqin Wang, Yanmin Qian
Long Short Term Memory Speech Separation Time Domain Speech Separation Block SkiM

January 25, 2022

Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based reduced order models
Federico Fatone, Stefania Fresca, Andrea Manzoni
Long Short Term Memory Long Term Prediction Proper Orthogonal Decomposition Data Driven Reduced Order Based Reduced Order Model

January 22, 2022

Supervised and Self-supervised Pretraining Based COVID-19 Detection Using Acoustic Breathing/Cough/Speech Signals
Xing-Yu Chen, Qiu-Shi Zhu, Jie Zhang, Li-Rong Dai
Long Short Term Memory Speech Signal Self Supervised Pre Training Patient Cough

January 20, 2022

Long Short-Term Memory Neural Network for Financial Time Series
Carmina Fjellström
Neural Network Long Short Term Memory Financial Time Series

January 17, 2022

A Brief Survey of Machine Learning Methods for Emotion Prediction using Physiological Data
Maryam Khalid, Emily Willis
Machine Learning Long Short Term Memory Emotion Prediction Physiological Data Ecological Momentary Assessment

Long Short Term Memory

Papers

Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoT

BiFSMN: Binary Neural Network for Keyword Spotting

Early Disease Stage Characterization in Parkinson's Disease from Resting-state fMRI Data Using a Long Short-term Memory Network

Hybridization of Capsule and LSTM Networks for unsupervised anomaly detection on multivariate data

Learning the Pedestrian-Vehicle Interaction for Pedestrian Trajectory Prediction

Social Media as an Instant Source of Feedback on Water Quality

Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid Framework for Rotating Machinery

Portfolio Optimization on NIFTY Thematic Sector Stocks Using an LSTM Model

Data Scaling Laws in NMT: The Effect of Noise and Architecture

Comparative Study of Machine Learning Models for Stock Price Prediction

Similarity Learning based Few Shot Learning for ECG Time Series Classification

Dual-CLVSA: a Novel Deep Learning Approach to Predict Financial Markets with Sentiment Measurements

LiteLSTM Architecture for Deep Recurrent Neural Networks

Deep Recurrent Learning for Heart Sounds Segmentation based on Instantaneous Frequency Features

Micro-level Reserving for General Insurance Claims using a Long Short-Term Memory Network

SkiM: Skipping Memory LSTM for Low-Latency Real-Time Continuous Speech Separation

Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based reduced order models

Supervised and Self-supervised Pretraining Based COVID-19 Detection Using Acoustic Breathing/Cough/Speech Signals

Long Short-Term Memory Neural Network for Financial Time Series

A Brief Survey of Machine Learning Methods for Emotion Prediction using Physiological Data