With deep learning, you start with sample data, deploy the model, and then expose it to the real world. But models that work well on training data often perform poorly on real data.

Using this synthetic data, Uber sped up its neural architecture search (NAS) deep-learning optimization process by 9x. In a paper published on arXiv, the team described the system and a series of ...

Semi-Supervised Learning: Deep learning models receive both unlabeled and labeled data in their training set, requiring them to simultaneously give expected outputs and infer outputs based on ...

The visual data sets of images and videos amassed by the most powerful tech companies have been a competitive advantage, a moat that keeps the advances of machine learning out of reach from many.

Fueled by enterprises seeking greater insight from their analytics, deep learning is now seeing widespread adoption. While this artificial intelligence (AI) discipline was first conceived in the late ...

Data has always been a critical requirement for computer systems. Without enough data, testing is not robust. What’s important in artificial intelligence applications using deep learning (DL) is ...

Much of Panda’s work focuses on the optimized MPI stack, called MVAPICH, which was developed by his teams and now powers the #1 supercomputer in the world, the Sunway TaihuLight machine in China. He ...

Deep learning defined. Deep learning is a form of machine learning that models patterns in data as complex, multi-layered networks. Because deep learning is the most general way to model a problem ...