We explore the added value of deep learning techniques for forecasting and nowcasting in official statistics as an alternative to classic time series models. Several neural network algorithms are ...

Methods A multimodal deep-learning model with transformers was developed for real-time recurrence prediction using baseline clinical, pathological, and molecular data with longitudinal laboratory and ...

Key Takeaways Neural nets are a type of machine learning model that mimic biological neurons—data comes in through an input layer and flows through nodes with various activation thresholds ...

During the COVID-19 crisis period, when GDP growth became unusually volatile, the advantages of deep learning became even ...

This study presents a valuable application of a video-text alignment deep neural network model to improve neural encoding of naturalistic stimuli in fMRI. The authors found that models based on ...

The model learns the important features from the data itself. Large Data Requirements: Deep learning models require vast amounts of labeled data to achieve high accuracy.

However, the background of OpenAI's birth is far more complex than a mere technological breakthrough. At that time, the fierce competition between Google and Meta for AI talent led to profound ...

A team of scientists at Georgia Southern University has combined both spatial and temporal attention mechanisms to develop a new approach for PV inverter fault detection. Training the new method on a ...