APPM 3310 Matrix Methods and Applications Introduces linear algebra and matrices, with an emphasis on applications, including methods to solve systems of linear algebraic and linear ordinary ...

The objectives of this course are: to develop competence in the basic concepts of linear algebra, including systems of linear equations, vector spaces, subspaces, linear transformations, the ...

If you’re emulating physics directly we know only a few applications would use dense linear algebra, that’s just fundamental to physics. Now, you can make the argument that ML can emulate these ...

Random Matrix Theory (RMT): A mathematical framework that explores the statistical properties of matrices with random elements, particularly focusing on eigenvalue and eigenvector distributions.

The motivation for this study comes partly from the possible relevance of such distributional assumptions to problems in econometrics and portfolio optimization, as well as robustness questions for ...

D-Matrix, a company focused on building accelerators for complex matrix math supporting machine learning, announced a $44 million series A round.

For AI models, reducing the number of computational steps in matrix math can lead to shorter training times and more efficient task performance. This will allow more complex models to be trained ...

Marvin Marcus, Some Properties and Applications of Doubly Stochastic Matrices, The American Mathematical Monthly, Vol. 67, No. 3 (Mar., 1960), pp. 215-221 ...

A UB study characterizes two-dimensional wave patterns, such as the undular bores seen in a Chinese river, that move along two directions.

When you put a cluster of 2,048 TPU cores on a tightly coupled toroidal mesh network and have a combined 32 TB of HBM2 memory and over 100 petaflops of single precision floating point math (with mixed ...