Project Petridish: Efficient forward neural architecture search
| Debadeepta Dey
Having experience in deep learning doesn’t hurt when it comes to the often mysterious, time- and cost-consuming process of hunting down an appropriate neural architecture. But truth be told, no one really knows what works the best on a new…
In the news | Microsoft On the Issues
Employing data science, new research uncovers clues behind unexplainable infant death
Imagine losing your child in their first year of life and having no idea what caused it. This is the heartbreaking reality for thousands of families each year who lose a child to Sudden Unexpected Infant Death (SUID). Despite decades-long…
Game of Drones at NeurIPS 2019: Simulation-based drone-racing competition built on AirSim
| Ratnesh Madaan and Ashish Kapoor
Drone racing has transformed from a niche activity sparked by enthusiastic hobbyists to an internationally televised sport. In parallel, computer vision and machine learning are making rapid progress, along with advances in agile trajectory planning, control, and state estimation for…
Microsoft Research Open Data Project: Evolving our standards for data access and reproducible research
| Vani Mandava
Last summer we announced Microsoft Research Open Data—an Azure-based repository-as-a-service for sharing datasets—to encourage the reproducibility of research and make research data assets readily available in the cloud. Among other things, the project started a conversation between the community and…
Metalearned Neural Memory: Teaching neural networks how to remember
| Tsendsuren Munkhdalai, Alessandro Sordoni, Tong Wang, and Adam Trischler
Memory is an important part of human intelligence and the human experience. It grounds us in the current moment, helping us understand where we are and, consequently, what we should do next. Consider the simple example of reading a book.…
Going meta: learning algorithms and the self-supervised machine with Dr. Philip Bachman
Deep learning methodologies like supervised learning have been very successful in training machines to make predictions about the world. But because they’re so dependent upon large amounts of human-annotated data, they’ve been difficult to scale. Dr. Phil Bachman, a researcher…