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

December 10, 2021

Neural Multi-Quantile Forecasting for Optimal Inventory Management
Federico Garza Ramírez
Recurrent Neural Network Autoregressive Model Time Scale Inventory Management

December 7, 2021

December 5, 2021

ES-dRNN: A Hybrid Exponential Smoothing and Dilated Recurrent Neural Network Model for Short-Term Load Forecasting
Slawek Smyl, Grzegorz Dudek, Paweł Pełka
Recurrent Neural Network Load Forecasting Complex Time Series Exponential Smoothing Hierarchical Deep Learning

November 29, 2021

Low-bit Quantization of Recurrent Neural Network Language Models Using Alternating Direction Methods of Multipliers
Junhao Xu, Xie Chen, Shoukang Hu, Jianwei Yu, Xunying Liu, Helen Meng
Language Model Recurrent Neural Network Low Bit Quantization Neural Network Quantization Class Wise Multiplier

November 27, 2021

Video Content Classification using Deep Learning
Pradyumn Patil, Vishwajeet Pawar, Yashraj Pawar, Shruti Pisal
Deep Learning Convolutional Neural Network Recurrent Neural Network Video Classification Key Frame

November 26, 2021

November 24, 2021

Animal Behavior Classification via Accelerometry Data and Recurrent Neural Networks
Liang Wang, Reza Arablouei, Flavio A. P. Alvarenga, Greg J. Bishop-Hurley
Recurrent Neural Network Long Short Term Memory Accelerometer Data Behavior Classification

November 23, 2021

Appliance Level Short-term Load Forecasting via Recurrent Neural Network
Yuqi Zhou, Arun Sukumaran Nair, David Ganger, Abhinandan Tripathi, Chaitanya Baone, Hao Zhu
Recurrent Neural Network Long Short Term Memory Load Forecasting Short Term Load Forecasting

November 22, 2021

November 20, 2021

Predicting High-Flow Nasal Cannula Failure in an ICU Using a Recurrent Neural Network with Transfer Learning and Input Data Perseveration: A Retrospective Analysis
George A. Pappy, Melissa D. Aczon, Randall C. Wetzel, David R. Ledbetter
Transfer Learning Recurrent Neural Network Intensive Care Unit Long Input

November 16, 2021

Comparative Analysis of Machine Learning Models for Predicting Travel Time
Armstrong Aboah, Elizabeth Arthur
Deep Learning Model Machine Learning Model Recurrent Neural Network Comparative Study Travel Time Integrated Moving Average Travel Time Estimation Speed Map

November 15, 2021

Category-orthogonal object features guide information processing in recurrent neural networks trained for object categorization
Sushrut Thorat, Giacomo Aldegheri, Tim C. Kietzmann
Recurrent Neural Network Information Flow Object Classification Class Agnostic Object Data Processing Recurrent Connection

November 13, 2021

Learning to Evolve on Dynamic Graphs
Xintao Xiang, Tiancheng Huang, Donglin Wang
LeArning Abstract Representation Learning Recurrent Neural Network Dynamic Graph Graph Data Temporal Embeddings

November 11, 2021

November 10, 2021

Gradients are Not All You Need
Luke Metz, C. Daniel Freeman, Samuel S. Schoenholz, Tal Kachman
Optimization Purpose Recurrent Neural Network Differentiable Programming

Recurrent Neural Network

Papers

Neural Multi-Quantile Forecasting for Optimal Inventory Management

CCasGNN: Collaborative Cascade Prediction Based on Graph Neural Networks

Graph Neural Controlled Differential Equations for Traffic Forecasting

ES-dRNN: A Hybrid Exponential Smoothing and Dilated Recurrent Neural Network Model for Short-Term Load Forecasting

Low-bit Quantization of Recurrent Neural Network Language Models Using Alternating Direction Methods of Multipliers

Video Content Classification using Deep Learning

On Recurrent Neural Networks for learning-based control: recent results and ideas for future developments

Towards Explainable End-to-End Prostate Cancer Relapse Prediction from H&E Images Combining Self-Attention Multiple Instance Learning with a Recurrent Neural Network

DSNet: Dynamic Skin Deformation Prediction by Recurrent Neural Network

Animal Behavior Classification via Accelerometry Data and Recurrent Neural Networks

Appliance Level Short-term Load Forecasting via Recurrent Neural Network

pmSensing: A Participatory Sensing Network for Predictive Monitoring of Particulate Matter

Modeling Irregular Time Series with Continuous Recurrent Units

Predicting High-Flow Nasal Cannula Failure in an ICU Using a Recurrent Neural Network with Transfer Learning and Input Data Perseveration: A Retrospective Analysis

Comparative Analysis of Machine Learning Models for Predicting Travel Time

Category-orthogonal object features guide information processing in recurrent neural networks trained for object categorization

Learning to Evolve on Dynamic Graphs

Observation Error Covariance Specification in Dynamical Systems for Data assimilation using Recurrent Neural Networks

Longitudinal patient stratification of electronic health records with flexible adjustment for clinical outcomes

Gradients are Not All You Need