Timezone: »
Demonstration
MLPACK: Scalable Machine Learning Software
Alexander Gray
We will unveil MLPACK, a major open source machine learning software project intended to serve machine learning in the same way that LAPACK serves linear algebra. MLPACK is a software collection which will soon cover most or all of the major methods of machine learning. It is built using FASTlib, a well-designed C++ library with special attention to large datasets and the data structures which support them, memory and CPU efficiency, and seamless integration on top of LAPACK. We believe MLPACK fills a major need in machine learning. The initial release contains state-of-the-art fast algorithms for a moderate number of common machine learning methods.
Author Information
Alexander Gray (Skytree Inc. and Georgia Tech)
More from the Same Authors
-
2013 Poster: Which Space Partitioning Tree to Use for Search? »
Parikshit Ram · Alexander Gray -
2012 Poster: Minimax Multi-Task Learning and a Generalized Loss-Compositional Paradigm for MTL »
Nishant A Mehta · Dongryeol Lee · Alexander Gray -
2009 Workshop: Large-Scale Machine Learning: Parallelism and Massive Datasets »
Alexander Gray · Arthur Gretton · Alexander Smola · Joseph E Gonzalez · Carlos Guestrin -
2009 Poster: Submanifold density estimation »
Arkadas Ozakin · Alexander Gray -
2009 Poster: Linear-time Algorithms for Pairwise Statistical Problems »
Parikshit Ram · Dongryeol Lee · William B March · Alexander Gray -
2009 Spotlight: Linear-time Algorithms for Pairwise Statistical Problems »
Parikshit Ram · Dongryeol Lee · William B March · Alexander Gray -
2009 Poster: Rank-Approximate Nearest Neighbor Search: Retaining Meaning and Speed in High Dimensions »
Parikshit Ram · Dongryeol Lee · Hua Ouyang · Alexander Gray -
2008 Poster: QUIC-SVD: Fast SVD Using Cosine Trees »
Michael Holmes · Alexander Gray · Charles Isbell -
2008 Poster: Fast High-dimensional Kernel Summations Using the Monte Carlo Multipole Method »
Dongryeol Lee · Alexander Gray -
2007 Poster: Multi-Stage Monte Carlo Approximation for Fast Generalized Data Summations »
Michael Holmes · Alexander Gray · Charles Isbell