Recurrent Neural Network

Recurrent Neural Networks (RNNs) are a class of neural networks designed to process sequential data by maintaining an internal state that is updated over time. Current research focuses on improving RNN efficiency and stability, exploring variations like LSTMs and GRUs, and investigating their application in diverse fields such as time series forecasting, natural language processing, and dynamical systems modeling. This includes developing novel architectures like selective state space models for improved memory efficiency and exploring the use of RNNs in conjunction with other architectures, such as transformers and convolutional neural networks. The resulting advancements have significant implications for various applications requiring sequential data processing, offering improved accuracy, efficiency, and interpretability.

Papers

April 14, 2023

Continuous time recurrent neural networks: overview and application to forecasting blood glucose in the intensive care unit
Oisin Fitzgerald, Oscar Perez-Concha, Blanca Gallego-Luxan, Alejandro Metke-Jimenez, Lachlan Rudd, Louisa Jorm
Application Proficiency Time Series Recurrent Neural Network Long Short Term Memory Intensive Care Unit Continuous Time Ordinary Differential Equation Autoregressive Neural Network Blood Glucose

April 12, 2023

GDP nowcasting with artificial neural networks: How much does long-term memory matter?
Kristóf Németh, Dániel Hadházi
Neural Network Recurrent Neural Network Long Term Memory Data Driven Nowcasting

April 5, 2023

Inferring Population Dynamics in Macaque Cortex
Ganga Meghanath, Bryan Jimenez, Joseph G. Makin
Recurrent Neural Network Neural Activity \Sigma}{\Delta}$ Low Pas RNN Neural Recording

April 3, 2023

Neuroevolution of Recurrent Architectures on Control Tasks
Maximilien Le Clei, Pierre Bellec
Recurrent Neural Network Evolutionary Algorithm Reinforcement Learning Benchmark Recurrent Neural Network Architecture

April 2, 2023

Modelling customer churn for the retail industry in a deep learning based sequential framework
Juan Pablo Equihua, Henrik Nordmark, Maged Ali, Berthold Lausen
Deep Learning Recurrent Neural Network Sequential Model Deep Survival Model Retail Store Customer Behavior Customer Churn

April 1, 2023

Sequential Learning from Noisy Data: Data-Assimilation Meets Echo-State Network
Debdipta Goswami
Training Data Recurrent Neural Network Data Assimilation Noisy Data Sequential Learning Echo State Network Ensemble Kalman Filter

March 29, 2023

March 25, 2023

Complexity-calibrated Benchmarks for Machine Learning Reveal When Next-Generation Reservoir Computer Predictions Succeed and Mislead
Sarah E. Marzen, Paul M. Riechers, James P. Crutchfield
Recurrent Neural Network Memory Trace Reservoir Computer Recurrent Neural Network Architecture

March 23, 2023

Noise impact on recurrent neural network with linear activation function
V. M. Moskvitin, N. Semenova
Recurrent Neural Network Environmental Noise Multiplicative Noise Linear Activation Internal Noise

March 21, 2023

March 20, 2023

Investigating Topological Order using Recurrent Neural Networks
Mohamed Hibat-Allah, Roger G. Melko, Juan Carrasquilla
Neural Network Recurrent Neural Network Many Body Spin Chain Topological Ordering

March 19, 2023

Active hypothesis testing in unknown environments using recurrent neural networks and model free reinforcement learning
George Stamatelis, Nicholas Kalouptsidis
Deep Reinforcement Learning Full Model Recurrent Neural Network Supervised Learning Unknown Environment Continuous Variable Active Hypothesis Testing

March 16, 2023

Embedding Theory of Reservoir Computing and Reducing Reservoir Network Using Time Delays
Xing-Yue Duan, Xiong Ying, Si-Yang Leng, Jürgen Kurths, Wei Lin, Huan-Fei Ma
Recurrent Neural Network Theoretical Understanding Reservoir Computing Dimensional Embeddings Reservoir Dynamic Single Neuron Time Delay

March 15, 2023

Forecasting Intraday Power Output by a Set of PV Systems using Recurrent Neural Networks and Physical Covariates
Pierrick Bruneau, David Fiorelli, Christian Braun, Daniel Koster
Recurrent Neural Network Relevant Covariates Consumption Forecasting Closing Set Photovoltaic System Output Distribution Neural Autoregressive Model