When the model was tested on 20,000 crystal structures of metal complexes containing Schiff bases, it successfully discovered the metal complexes reported as single-molecule magnets. "This is the ...

Key pros and cons of deep learning include its ability to handle large amounts of unstructured data and achieve high accuracy in challenging tasks, both of which are significant advantages.

Conventional robots, like those used in industry and hazardous environments, are easy to model and control, but are too rigid ...

Based on a unique deep learning architecture, a new computational model developed by researchers from the Center for Advanced Systems Understanding (CASUS) at HZDR and the Max Delbrück Center for ...

Neural architecture search promises to speed up the process of finding neural network architectures that will yield good models for a given dataset.

Single-molecule magnets (SMMs) are exciting materials. In a recent breakthrough, researchers have used deep learning to predict SMMs from 20,000 metal complexes. The predictions were made solely ...

A new computing architecture enables advanced machine-learning computations to be performed on a low-power, memory-constrained edge device. The technique may enable self-driving cars to make ...

Our work Our work includes, but is not limited to: model serving for deep learning, model architecture re-design, hyperparameter tuning during model serving, explainability, continuous model ...