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

July 27, 2023

July 16, 2023

Exploiting FPGA Capabilities for Accelerated Biomedical Computing
Kayode Inadagbo, Baran Arig, Nisanur Alici, Murat Isik
Recurrent Neural Network Long Short Term Memory Field Programmable Gate Array Neural Network Architecture

July 13, 2023

Learning fixed points of recurrent neural networks by reparameterizing the network model
Vicky Zhu, Robert Rosenbaum
LeArning Abstract Recurrent Neural Network Exceptional Point Computational Neuroscience Network Model

July 11, 2023

Hybrid hidden Markov LSTM for short-term traffic flow prediction
Agnimitra Sengupta, Adway Das, S. Ilgin Guler
Recurrent Neural Network Long Short Term Memory LSTM Network Efficient Hybrid Traffic Flow Prediction Traffic Dynamic Short Term Traffic

July 5, 2023

Facing Off World Model Backbones: RNNs, Transformers, and S4
Fei Deng, Junyeong Park, Sungjin Ahn
Transformer Megatron Decepticons Recurrent Neural Network World Model Long Term Memory Transformer Based World Model Deliberate Imagination

July 2, 2023

A Dual-Stream Recurrence-Attention Network With Global-Local Awareness for Emotion Recognition in Textual Dialog
Jiang Li, Xiaoping Wang, Zhigang Zeng
Recurrent Neural Network Emotion Recognition Global Attention Multi Head Attention Stream Attention

June 29, 2023

On the Relationship Between RNN Hidden State Vectors and Semantic Ground Truth
Edi Muškardin, Martin Tappler, Ingo Pill, Bernhard K. Aichernig, Thomas Pock
Recurrent Neural Network Ground Truth Human Relationship Hidden State

June 28, 2023

A Distributed Computation Model Based on Federated Learning Integrates Heterogeneous models and Consortium Blockchain for Solving Time-Varying Problems
Zhihao Hao, Guancheng Wang, Chunwei Tian, Bob Zhang
Recurrent Neural Network Time Varying Blockchain Network Heterogeneous Model

June 27, 2023

Coupling a Recurrent Neural Network to SPAD TCSPC Systems for Real-time Fluorescence Lifetime Imaging
Yang Lin, Paul Mos, Andrei Ardelean, Claudio Bruschini, Edoardo Charbon
Recurrent Neural Network Single Photon Avalanche Fluorescence Lifetime

June 24, 2023

Weighted Automata Extraction and Explanation of Recurrent Neural Networks for Natural Language Tasks
Zeming Wei, Xiyue Zhang, Yihao Zhang, Meng Sun
Recurrent Neural Network Line by Line Explanation \Sigma}{\Delta}$ Low Pas RNN Natural Language Task Weighted Finite Automaton Extraction

June 21, 2023

Probing the limit of hydrologic predictability with the Transformer network
Jiangtao Liu, Yuchen Bian, Chaopeng Shen
Recurrent Neural Network Long Short Term Memory Continuum Limit Transformer Network Vanilla Transformer

June 19, 2023

Grammatical gender in Swedish is predictable using recurrent neural networks
Edvin Listo Zec, Olof Mogren
Recurrent Neural Network Gender Inclusive Text Grammatical Gender

June 12, 2023

On the Dynamics of Learning Time-Aware Behavior with Recurrent Neural Networks
Peter DelMastro, Rushiv Arora, Edward Rietman, Hava T. Siegelmann
Recurrent Neural Network Recurrent Neural Network Architecture RNN Behavior Time Invariant

June 8, 2023

Deep Learning Method for Cell-Wise Object Tracking, Velocity Estimation and Projection of Sensor Data over Time
Marco Braun, Moritz Luszek, Mirko Meuter, Dominic Spata, Kevin Kollek, Anton Kummert
Deep Learning Recurrent Neural Network Sensor Data Temporal Correlation Projection Bias Velocity Estimation Convolutional Recurrent Neural Network Cell Tracking Environment Recognition

June 7, 2023

Generalized Teacher Forcing for Learning Chaotic Dynamics
Florian Hess, Zahra Monfared, Manuel Brenner, Daniel Durstewitz
Recurrent Neural Network Chaotic System Piecewise Linear Teacher Detector

June 1, 2023

A General Framework for Uncertainty Quantification via Neural SDE-RNN
Shweta Dahale, Sai Munikoti, Balasubramaniam Natarajan
Recurrent Neural Network Uncertainty Quantification General Framework Time Series Imputation Neural Stochastic Differential Equation Neural SDEs

May 30, 2023

May 29, 2023

Approximation Rate of the Transformer Architecture for Sequence Modeling
Haotian Jiang, Qianxiao Li
Recurrent Neural Network Transformer Architecture Sequence Modeling Layer Transformer Approximation Rate