The AttendSeg deep learning model performs semantic segmentation at an accuracy that is almost on-par with RefineNet while cutting down the number of parameters to 1.19 million.

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 ...

Deep learning models owe their initial success to large servers with large amounts of memory and clust This article is part of our reviews of AI research papers, a series of posts that explore the ...

Neural Radiance Fields (NeRFs): This genAI model uses a deep learning technique to represent 3D scenes based on 2D image inputs. Generative AI models are highly scalable and accessible AI ...

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 ...

Deep neural networks have a huge advantage: They replace “feature engineering”—a difficult and arduous part of the classic machine learning cycle—with an end-to-end process that ...

Neural architecture search promises to speed up the process of finding neural network architectures that will yield good models for a given dataset. Topics Spotlight: AI-ready data centers ...

Deep learning (DL) algorithms have the promise to improve the quality of diagnostic image interpretation within oncology. 1,2 Models generated from DL algorithms have been validated across a variety ...