IEEE

Probabilistic Approximation of Stochastic Time Series Using Bayesian Recurrent Neural Network

Abstract: In this brief, we investigate the approximation theory (AT) of Bayesian recurrent neural network (BRNN) for stochastic time series forecasting (TSF) from a probabilistic standpoint. Due to ...
GitHub

GitHub - Speedtrust/test

2017-07-06 Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks. A paper by Pranav Rajpurkar, Awni Y. Hannun et al. that uses a 34-layer convolutional neural network which maps a ...
IEEE

FranSys—A Fast Non-Autoregressive Recurrent Neural Network for Multi-Step Ahead Prediction

Abstract: Neural network-based nonlinear system identification is crucial for various multi-step ahead prediction tasks, including model predictive control and digital twins. These applications demand ...
C&EN

A Multi-Stage Graph Neural Network–Physics-Informed Neural Network (GNN–PINN) Framework for Thermodynamic Property Prediction

Department of Chemistry and Chemical Engineering, Education and Research Center for Smart Energy and Materials, Inha University, Incheon 22212, Republic of Korea ...
eLife

Online reinforcement learning of state representation in recurrent network supported by the power of random feedback and biological constraints

In this important study, the authors model reinforcement-learning experiments using a recurrent neural network. The work examines if the detailed credit assignment necessary for back-propagation ...
eLife

Neural dynamics of reversal learning in the prefrontal cortex and recurrent neural networks

The findings of this study are valuable, offering insights into the neural representation of reversal probability in decision-making tasks, with potential implications for understanding flexible ...
GitHub

Recurrent Network #7

def __init__(self, positional_embedding): super().__init__() mamba_config = { "d_model": self.config["d_model"], "d_state": self.config["d_state"], "d_conv": self ...