Video Resources on Machine Learning from Big Data Workshop NIPS2011

Big Learning Workshop: Algorithms, Systems, and Tools for Learning at Scale at NIPS 2011

Invited Talk: Machine Learning and Hadoop by Josh Wills

Abstract: We’ll review common use cases for machine learning and advanced analytics found in our customer base at Cloudera and ways in which Apache Hadoop supports these use cases. We’ll then discuss upcoming developments for Apache Hadoop that will enable new classes of applications to be supported by the system.

Tutorial: Vowpal Wabbit by John Langford

Abstract: We present a system and a set of techniques for learning linear predictors with convex losses on terascale datasets, with trillions of features,footnote{The number of features here refers to the number of non-zero entries in the data matrix.} billions of training examples and millions of parameters in an hour using a cluster of 1000 machines. One of the core techniques used is a new communication infrastructure–often referred to as AllReduce–implemented for compatibility with MapReduce clusters. The communication infrastructure appears broadly reusable for many other tasks.

Tutorial: Group Sparse Hidden Markov Models

Sparse Representation and Low-rank Approximation Workshop at NIPS 2011

Invited Talk: Group Sparse Hidden Markov Models by Jen-Tzung Chien, National Cheng Kung University, Taiwan

Invited Talk: A Common GPU n-Dimensional Array for Python and C by Arnaud Bergeron

Abstract: Currently there are multiple incompatible array/matrix/n-dimensional base object implementations for GPUs. This hinders the sharing of GPU code and causes duplicate development work.This paper proposes and presents a first version of a common GPU n-dimensional array(tensor) named GpuNdArray~citep{GpuNdArray} that works with both CUDA and OpenCL.It will be usable from python, C and possibly other languages.

Invited Talk: A Topic Model for Melodic Sequences by Athina Spiliopoulou

Athina is a PhD student in the Machine Learning group of the Institute for Adaptive and Neural Computation at the School of Informatics,University of Edinburgh. She works with Amos Storkey on Machine Learning methods for music, with a specific interest in unsupervised learning of musical structure from melodic sequences.


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