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

May 28, 2023

On the Role of Noise in the Sample Complexity of Learning Recurrent Neural Networks: Exponential Gaps for Long Sequences
Alireza Fathollah Pour, Hassan Ashtiani
Neural Network Recurrent Neural Network Sample Complexity Industrial Disturbing Noise Long Sequence Filling Gap Linear Dependence Noise Tolerant Network

May 26, 2023

A Multi-Resolution Physics-Informed Recurrent Neural Network: Formulation and Application to Musculoskeletal Systems
Karan Taneja, Xiaolong He, Qizhi He, J. S. Chen
Application Proficiency Recurrent Neural Network Multi Resolution Surface Electromyography New Formulation sEMG Signal Muscle Dynamic Musculoskeletal System

May 25, 2023

Online learning of long-range dependencies
Nicolas Zucchet, Robert Meier, Simon Schug, Asier Mujika, João Sacramento
Recurrent Neural Network Online Learning Neuromorphic Computing Online Learning Algorithm Long Range Dependency Offline Pre Training Recurrent Module

May 22, 2023

RWKV: Reinventing RNNs for the Transformer Era
Bo Peng, Eric Alcaide, Quentin Anthony, Alon Albalak, Samuel Arcadinho, Stella Biderman, Huanqi Cao, Xin Cheng, Michael Chung, Matteo Grella, Kranthi Kiran GV, Xuzheng He, Haowen Hou, Jiaju Lin, Przemyslaw Kazienko, Jan Kocon, Jiaming Kong, Bartlomiej Koptyra, Hayden Lau, Krishna Sri Ipsit Mantri, Ferdinand Mom, Atsushi Saito, Guangyu Song, Xiangru Tang, Bolun Wang, Johan S. Wind, Stanislaw Wozniak, Ruichong Zhang, Zhenyuan Zhang, Qihang Zhao, Peng Zhou, Qinghua Zhou, Jian Zhu, Rui-Jie Zhu
Recurrent Neural Network \Sigma}{\Delta}$ Low Pas RNN Receptance Weighted Key Value Transformer Era

May 21, 2023

Temporal Fusion Transformers for Streamflow Prediction: Value of Combining Attention with Recurrence
Sinan Rasiya Koya, Tirthankar Roy
Recurrent Neural Network Long Short Term Memory Human Attention Net Present Value Temporal Difference Type Recurrence Temporal Fusion Transformer Streamflow Prediction

May 18, 2023

Support for Stock Trend Prediction Using Transformers and Sentiment Analysis
Harsimrat Kaeley, Ye Qiao, Nader Bagherzadeh
Transformer Megatron Decepticons Sentiment Analysis Recurrent Neural Network Customer Service Stock Price Prediction Technical Indicator Trend Prediction

May 17, 2023

Predicting Side Effect of Drug Molecules using Recurrent Neural Networks
Collin Beaudoin, Koustubh Phalak, Swaroop Ghosh
Recurrent Neural Network Side Effect Complex Model Drug Molecule

May 16, 2023

Empirical Analysis of the Inductive Bias of Recurrent Neural Networks by Discrete Fourier Transform of Output Sequences
Taiga Ishii, Ryo Ueda, Yusuke Miyao
Recurrent Neural Network Inductive Bias Structured Output Empirical Analysis Gated Recurrent Unit

May 11, 2023

IVP-VAE: Modeling EHR Time Series with Initial Value Problem Solvers
Jingge Xiao, Leonie Basso, Wolfgang Nejdl, Niloy Ganguly, Sandipan Sikdar
Time Series Recurrent Neural Network Neural ODE Recurrent Network VAE Model Continuous Time Model Value Problem

May 4, 2023

Employing Hybrid Deep Neural Networks on Dari Speech
Jawid Ahmad Baktash, Mursal Dawodi
Automatic Speech Recognition Recurrent Neural Network Text Classification Speech Recognition System Hybrid Deep Learning Mel Frequency Cepstral Coefficient

May 2, 2023

May 1, 2023

Low-Resourced Machine Translation for Senegalese Wolof Language
Derguene Mbaye, Moussa Diallo, Thierno Ibrahima Diop
Natural Language Processing Recurrent Neural Network Machine Translation Model Wolof Language

April 25, 2023

Nondeterministic Stacks in Neural Networks
Brian DuSell
Neural Network Recurrent Neural Network \Sigma}{\Delta}$ Low Pas RNN Nondeterministic Stack Compositional Nature Syntactic Generalization

April 24, 2023

April 22, 2023

Recurrent Neural Networks and Long Short-Term Memory Networks: Tutorial and Survey
Benyamin Ghojogh, Ali Ghodsi
Timely Survey Recurrent Neural Network Long Short Term Memory Tutorial Review

April 17, 2023

RNN-Guard: Certified Robustness Against Multi-frame Attacks for Recurrent Neural Networks
Yunruo Zhang, Tianyu Du, Shouling Ji, Peng Tang, Shanqing Guo
Adversarial Attack Native Robustness Recurrent Neural Network Mt RNN Frame Attack

April 16, 2023

Recurrent Neural Network

Papers

On the Role of Noise in the Sample Complexity of Learning Recurrent Neural Networks: Exponential Gaps for Long Sequences

A Multi-Resolution Physics-Informed Recurrent Neural Network: Formulation and Application to Musculoskeletal Systems

Online learning of long-range dependencies

RWKV: Reinventing RNNs for the Transformer Era

Temporal Fusion Transformers for Streamflow Prediction: Value of Combining Attention with Recurrence

Support for Stock Trend Prediction Using Transformers and Sentiment Analysis

Predicting Side Effect of Drug Molecules using Recurrent Neural Networks

Empirical Analysis of the Inductive Bias of Recurrent Neural Networks by Discrete Fourier Transform of Output Sequences

IVP-VAE: Modeling EHR Time Series with Initial Value Problem Solvers

Employing Hybrid Deep Neural Networks on Dari Speech

Sequence Modeling with Multiresolution Convolutional Memory

Memory of recurrent networks: Do we compute it right?

Low-Resourced Machine Translation for Senegalese Wolof Language

Nondeterministic Stacks in Neural Networks

Adaptive-saturated RNN: Remember more with less instability

Constraining Chaos: Enforcing dynamical invariants in the training of recurrent neural networks

Recurrent Neural Networks and Long Short-Term Memory Networks: Tutorial and Survey

RNN-Guard: Certified Robustness Against Multi-frame Attacks for Recurrent Neural Networks

A comparison between Recurrent Neural Networks and classical machine learning approaches In Laser induced breakdown spectroscopy

Obstacle-Transformer: A Trajectory Prediction Network Based on Surrounding Trajectories