#deep-learning

#deep-learning

LLVM has quietly emerged as the secret sauce that makes AI workloads not just tolerable but genuinely exciting to optimize, turning legacy model execution pipelines into blazing-fast, hardware-friendly deployment flows.

Whole slide images (WSIs) can be too large to be processed directly by Deep Learning models due to their sizes, often exceeding 6 GB of memory.

Cognitiv's innovative awareness campaign, highlighting the positive benefits of advertising with deep learning, presents a vision of what advertisers can achieve with reclaimed time.

In radiation therapy, precision can save lives. Oncologists must carefully map the size and location of a tumor before delivering high-dose radiation to destroy cancer cells while sparing healthy tissue.

Traditional keyword matching in information retrieval fails to understand user intent, which leads to irrelevant results and limits the diversity of responses, requiring query alterations to be effective.

Recent advancements suggest that employing small Graph Neural Networks to craft preconditioners could significantly enhance performance while preserving necessary sparsity, thereby optimizing computational efficacy.

Although our Transformer-based deep learning model provides state-of-the-art performance in both resolution enhancement and noise reduction for moderate noise levels, restoration becomes impossible when the noise level exceeds a threshold.

The new LieBN framework provides a systematic way to extend batch normalization to Riemannian manifolds, specifically catering to SPD (symmetric positive definite) matrices.