Research
Microsoft is a silver sponsor of LISA17, the annual vendor-neutral meeting place for the wider system administration community. The program will address the overlap and differences between traditional and modern IT operations and engineering, curated…
Modern data often consists of feature vectors with a large number of features. High-dimensional geometry and Linear Algebra (Singular Value Decomposition) are two of the crucial areas which form the mathematical foundations of Data Science.…
Modern data often consists of feature vectors with a large number of features. High-dimensional geometry and Linear Algebra (Singular Value Decomposition) are two of the crucial areas which form the mathematical foundations of Data Science.…
We are excited to be a gold sponsor of Human Computation and Crowdsourcing (HCOMP) 2017, the premier venue for disseminating the latest research findings on crowdsourcing and human computation. While artificial intelligence (AI) and human-computer…
Modern data often consists of feature vectors with a large number of features. High-dimensional geometry and Linear Algebra (Singular Value Decomposition) are two of the crucial areas which form the mathematical foundations of Data Science.…
We are excited to be participating in SPLASH 2017, the ACM SIGPLAN conference that embraces all aspects of software construction and delivery to make it the premier conference at the intersection of programming, languages, and…
Linear algebra is a work-horse of numerical computing. Tensor algebra is a generalization of linear algebra with applications in scientific computing, machine learning, and data analytics. Tensors are often sparse and compound operations must frequently…
Noisy data, non-convex objectives, model misspecification, and numerical instability can all cause undesired behaviors in machine learning systems. As a result, detecting actual implementation errors can be extremely difficult. We demonstrate a methodology in which…
