An AI model that learns without human input—by posing interesting queries for itself—might point the way to superintelligence. Save this story Save this story Even the smartest artificial intelligence ...
Abstract: Using a privacy-preserving federated hybrid architecture that combines Long Short-Term Memory (LSTM) and Multilayer Perceptron networks (MLP), the research suggests a novel method. Our ...
PyTorch Lightning implementation of an LSTM-based encoder-decoder for battery aging prediction. The model is optimized for training/prediction speed and prediction accuracy and can be applied for ...
In the context of global energy shortages, traditional energy sources face issues of limited reserves and high prices. As a result, the importance of energy storage technology is increasingly ...
In a new comparative analysis of artificial intelligence applications in retail, researchers have revealed that advanced deep learning models can dramatically enhance the accuracy of demand ...
This project implements state-of-the-art deep learning models for financial time series forecasting with a focus on uncertainty quantification. The system provides not just point predictions, but ...
With the widespread application of lithium-ion batteries in electric vehicles and energy storage systems, health monitoring and remaining useful life prediction have become critical components of ...
Abstract: Long Short-Term Memory (LSTM) networks are particularly useful in recommender systems since user preferences change over time. Unlike traditional recommender models which assume static ...
ABSTRACT: The application of artificial intelligence in stock price forecasting is an important area of research at the intersection of finance and computer science, with machine learning techniques ...
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