Deep learning is increasingly used in financial modeling, but its lack of transparency raises risks. Using the well-known ...

Integrating deep learning with traditional forecasting techniques can improve early warning systems by capitalizing on each ...

New deep-learning framework reconstructs hourly PM2.5 chemical composition using air-quality and meteorological data ...

Of 372 patients studied, 79.3% and 20.7% were in the completion group and the non-completion group, respectively. The final BERT model achieved average F1 scores of 0.91 and 0.98 for time to ...

Visualization of attention maps from the residual attention networks for the inspiratory convolutional neural network (I-CNN) and expiratory convolutional neural network (E-CNN) models. Attention maps ...

The video presentation below, “Deep Learning – Theory and Applications” is from the July 23rd SF Machine Learning Meetup at the Workday Inc. San Francisco office. The featured speaker is Ilya ...

Researchers have significantly enhanced an artificial intelligence tool used to rapidly detect bacterial contamination in food by eliminating misclassifications of food debris that looks like bacteria ...

In a significant breakthrough, researchers have developed an advanced explainable deep learning model to predict and analyze harmful algal blooms (HABs) in freshwater lakes and reservoirs across China ...

Deep Learning is a hot topic in statistical learning and many data scientists are seeking a place to start. Here is a presentation from the July 23rd SF Machine Learning Meetup at the Workday Inc. San ...

Researchers in China conceived a new PV forecasting approach that integrates causal convolution, recurrent structures, attention mechanisms, and the Kolmogorov–Arnold Network (KAN). Experimental ...