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

October 4, 2022

October 3, 2022

An Embarrassingly Simple Approach for Intellectual Property Rights Protection on Recurrent Neural Networks
Zhi Qin Tan, Hao Shan Wong, Chee Seng Chan
Recurrent Neural Network Intellectual Property Protection

October 1, 2022

A Comparison of Transformer, Convolutional, and Recurrent Neural Networks on Phoneme Recognition
Kyuhong Shim, Wonyong Sung
Transformer Based Speech Recognition Recurrent Neural Network Deep Convolutional Neural Network Consistent Comparison \Sigma}{\Delta}$ Low Pas RNN Phoneme Recognition

September 28, 2022

September 24, 2022

A Deep Investigation of RNN and Self-attention for the Cyrillic-Traditional Mongolian Bidirectional Conversion
Muhan Na, Rui Liu, Feilong, Guanglai Gao
Recurrent Neural Network Self Attention Sequence to Sequence Comprehensive Investigation

September 23, 2022

Image Classification using Sequence of Pixels
Gajraj Kuldeep
Image Classification Recurrent Neural Network Long Short Term Memory LSTM Network Tetromino Pixel Sequence of Sequence

September 22, 2022

A Case Report On The "A.I. Locked-In Problem": social concerns with modern NLP
Yoshija Walter
Case Study Recurrent Neural Network Natural Language Processing Model Conversational Agent Societal Domain

September 21, 2022

September 16, 2022

Continual Learning with Dependency Preserving Hypernetworks
Dupati Srikar Chandra, Sakshi Varshney, P. K. Srijith, Sunil Gupta
Continual LEArning Recurrent Neural Network Continual Learning Performance Continual Learning Task

September 14, 2022

September 9, 2022

Autoencoder Based Iterative Modeling and Multivariate Time-Series Subsequence Clustering Algorithm
Jonas Köhne, Lars Henning, Clemens Gühmann
Recurrent Neural Network Multivariate Time Series Data Time Series Clustering Iterative Neural Network Time Series Subsequence Transient Imaging Data

September 8, 2022

Simpler is better: Multilevel Abstraction with Graph Convolutional Recurrent Neural Network Cells for Traffic Prediction
Naghmeh Shafiee Roudbari, Zachary Patterson, Ursula Eicker, Charalambos Poullis
Graph Neural Network Recurrent Neural Network Traffic Forecasting Spatio Temporal Forecasting Spatiotemporal Correlation Multilevel Abstraction

September 7, 2022

Cerberus: Exploring Federated Prediction of Security Events
Mohammad Naseri, Yufei Han, Enrico Mariconti, Yun Shen, Gianluca Stringhini, Emiliano De Cristofaro
Recurrent Neural Network

September 5, 2022

Bridging Music and Text with Crowdsourced Music Comments: A Sequence-to-Sequence Framework for Thematic Music Comments Generation
Peining Zhang, Junliang Guo, Linli Xu, Mu You, Junming Yin
Text Modality Recurrent Neural Network Text Generation Sequence to Sequence Bridging Text Text Description Crowd Annotation

September 3, 2022

Tree-Based Learning in RNNs for Power Consumption Forecasting
Roberto Baviera, Pietro Manzoni
Recurrent Neural Network Traditional RNNs Consumption Forecasting Tree Learning Tree LSTMs

Recurrent Neural Network

Papers

Public Transit Arrival Prediction: a Seq2Seq RNN Approach

Fast Saturating Gate for Learning Long Time Scales with Recurrent Neural Networks

The Surprising Computational Power of Nondeterministic Stack RNNs

An Embarrassingly Simple Approach for Intellectual Property Rights Protection on Recurrent Neural Networks

A Comparison of Transformer, Convolutional, and Recurrent Neural Networks on Phoneme Recognition

Breaking Time Invariance: Assorted-Time Normalization for RNNs

Less is More: Rethinking Few-Shot Learning and Recurrent Neural Nets

A Deep Investigation of RNN and Self-attention for the Cyrillic-Traditional Mongolian Bidirectional Conversion

Image Classification using Sequence of Pixels

A Case Report On The "A.I. Locked-In Problem": social concerns with modern NLP

Interneurons accelerate learning dynamics in recurrent neural networks for statistical adaptation

Dynamic Time-Alignment of Dimensional Annotations of Emotion using Recurrent Neural Networks

Continual Learning with Dependency Preserving Hypernetworks

Sequence Learning Using Equilibrium Propagation

TSFool: Crafting Highly-Imperceptible Adversarial Time Series through Multi-Objective Attack

Autoencoder Based Iterative Modeling and Multivariate Time-Series Subsequence Clustering Algorithm

Simpler is better: Multilevel Abstraction with Graph Convolutional Recurrent Neural Network Cells for Traffic Prediction

Cerberus: Exploring Federated Prediction of Security Events

Bridging Music and Text with Crowdsourced Music Comments: A Sequence-to-Sequence Framework for Thematic Music Comments Generation

Tree-Based Learning in RNNs for Power Consumption Forecasting