Major advancements in kernel function approximation
Kernel based methods have become popular in a wide variety of machine learning tasks.
They rely on the computation of kernel functions, which implicitly transform the data in its …
They rely on the computation of kernel functions, which implicitly transform the data in its …
[HTML][HTML] A practical streaming approximate matrix multiplication algorithm
Abstract Approximate Matrix Multiplication (AMM) has emerged as a useful and
computationally inexpensive substitute for actual multiplication of large matrices …
computationally inexpensive substitute for actual multiplication of large matrices …
A fast and accurate explicit kernel map
Kernel functions are powerful techniques that have been used successfully in many
machine learning algorithms. Explicit kernel maps have emerged as an alternative to …
machine learning algorithms. Explicit kernel maps have emerged as an alternative to …
Dimensionality reduction of large datasets with explicit feature maps
Learning algorithms are often equipped with kernels that enable them to deal with non-
linearities in the data, which ensures increased performance in practice. However …
linearities in the data, which ensures increased performance in practice. However …
Building efficient algorithms by learning to compress
DW Blalock - 2020 - dspace.mit.edu
The amount of data in the world is doubling every two years. Such abundant data offers
immense opportunities, but also imposes immense computation, storage, and energy costs …
immense opportunities, but also imposes immense computation, storage, and energy costs …