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.

536papers

Papers - Page 2

February 19, 2025

Playing Hex and Counter Wargames using Reinforcement Learning and Recurrent Neural Networks
Guilherme Palma, Pedro A. Santos, João Dias
University of Lisbon●University of Algarve●INESC-ID●CISCA
Adversarial Game Strategy Logic Recurrent Neural Network Reinforcement Learning

February 13, 2025

SigGate: Enhancing Recurrent Neural Networks with Signature-Based Gating Mechanisms
Rémi Genet, Hugo Inzirillo
Recurrent Neural Network Architecture Gating Mechanism Recurrent Neural Network Gated Recurrent Unit Functional Mechanism Long Short Term Memory Path Signature

February 11, 2025

Exploring Neural Network Pruning with Screening Methods
Mingyuan Wang, Yangzi Guo, Sida Liu, Yanwen Xiao
Deep Neural Network Convolutional Neural Network Screening Process Recurrent Neural Network Neural Network

February 10, 2025

Implicit Language Models are RNNs: Balancing Parallelization and Expressivity
Mark Schöne, Babak Rahmani, Heiner Kremer, Fabian Falck, Hitesh Ballani, Jannes Gladrow
Behavior Expressivity Style Large Scale Language Model Neural Network Language Model Recurrent Neural Network Implicit Model

February 7, 2025

February 6, 2025

Decision Trees That Remember: Gradient-Based Learning of Recurrent Decision Trees with Memory
Sascha Marton, Moritz Schneider
Decision Tree Recurrent Neural Network Gradient Based Memory Trace Temporal Dependency Recurrent Neural Network Architecture Recurrent Event

February 3, 2025

January 30, 2025

January 25, 2025

Exploring the impact of Optimised Hyperparameters on Bi-LSTM-based Contextual Anomaly Detector
Aafan Ahmad Toor, Jia-Chun Lin, Ernst Gunnar Gran
Anomaly Detection Related Hyperparameters Global Impact Recurrent Neural Network

January 20, 2025

On the dimension of pullback attractors in recurrent neural networks
Muhammed Fadera
Recurrent Neural Network Fractal Dimension Invertible Linear Transformation Box Dimension State Space Continuous Attractor

January 14, 2025

Convergence Analysis of Real-time Recurrent Learning (RTRL) for a class of Recurrent Neural Networks
Samuel Chun-Hei Lam, Justin Sirignano, Konstantinos Spiliopoulos
Recurrent Neural Network Real Time Recurrent Learning \Sigma}{\Delta}$ Low Pas RNN Recurrent Network True Class Convergence Analysis

December 28, 2024

Predicting Customer Lifetime Value Using Recurrent Neural Net
Huigang Chen, Edwin Ng, Gavin Steininger, Slawek Smyl
Lifetime Value Recurrent Neural Network Customer Lifetime Value Long Short Term Memory

December 24, 2024

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis
Shicheng Yin, Kaixuan Yin, Weixing Chen, Enbo Huang, Yang Liu
Vision Transformer Linear RNN Convolutional Neural Network Recurrent Neural Network Supervised ImageNet

December 23, 2024

Brain-to-Text Benchmark '24: Lessons Learned
Francis R. Willett, Jingyuan Li, Trung Le, Chaofei Fan, Mingfei Chen, Eli Shlizerman, Yue Chen, Xin Zheng, Tatsuo S. Okubo, Tyler Benster+6
Brain to Text Recurrent Neural Network Critical Lesson

December 20, 2024

December 16, 2024

Optimal Gradient Checkpointing for Sparse and Recurrent Architectures using Off-Chip Memory
Wadjih Bencheikh, Jan Finkbeiner, Emre Neftci
Spiking Neural Network Model Checkpoint Recurrent Neural Network Architecture Recurrent Neural Network Many Sparse Chip Memory Neural Network Training

Recurrent Neural Network

Papers - Page 2

Playing Hex and Counter Wargames using Reinforcement Learning and Recurrent Neural Networks

SigGate: Enhancing Recurrent Neural Networks with Signature-Based Gating Mechanisms

Exploring Neural Network Pruning with Screening Methods

Implicit Language Models are RNNs: Balancing Parallelization and Expressivity

Comparison of Deep Recurrent Neural Networks and Bayesian Neural Networks for Detecting Electric Motor Damage Through Sound Signal Analysis

Memory Capacity of Nonlinear Recurrent Networks: Is it Informative?

Decision Trees That Remember: Gradient-Based Learning of Recurrent Decision Trees with Memory

Discovering Chunks in Neural Embeddings for Interpretability

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity

Predicting concentration levels of air pollutants by transfer learning and recurrent neural network

BARNN: A Bayesian Autoregressive and Recurrent Neural Network

Exploring the impact of Optimised Hyperparameters on Bi-LSTM-based Contextual Anomaly Detector

On the dimension of pullback attractors in recurrent neural networks

Convergence Analysis of Real-time Recurrent Learning (RTRL) for a class of Recurrent Neural Networks

Predicting Customer Lifetime Value Using Recurrent Neural Net

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis

Brain-to-Text Benchmark '24: Lessons Learned

Never Reset Again: A Mathematical Framework for Continual Inference in Recurrent Neural Networks

BabyHGRN: Exploring RNNs for Sample-Efficient Training of Language Models

Optimal Gradient Checkpointing for Sparse and Recurrent Architectures using Off-Chip Memory