The big challenge in deep learning is that you need a lot of data to train the neural network. Fortunately, one of my ...

UM researchers have developed a deep learning model to predict compound protein interactions. GraphBAN is an inductive graph-based approach. The model is all about discovering new drug candidates ...

The findings are published in the Journal of Computational Physics ... deep learning on GPUs, we have reduced computation time by a factor of 1,000 compared to traditional CPU-based codes ...

NTT Research and NTT R&D co-authored papers explore LLMs’ uncertain and open-ended nature, the “emergence” phenomenon, ...

DeepNeuro, which has been trained on a large dataset of neuroimaging studies, is a Python-based deep learning (DL) framework designed to streamline the training and evaluation of DL models on new ...

Researchers from the Yunnan Observatories of the Chinese Academy of Sciences and Southwest Forestry University have developed an advanced neural network-based ... a deep learning approach using ...

This machine uses statistical physics principles to classify images and generate new examples based on learnt patterns. Hinton's techniques have been instrumental in advancing deep learning ...