Latent dirichlet allocation

DM Blei, AY Ng, MI Jordan - Journal of machine Learning research, 2003 - jmlr.org
Abstract We describe latent Dirichlet allocation (LDA), a generative probabilistic model for
collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian
model, in which each item of a collection is modeled as a finite mixture over an underlying

On spectral clustering: Analysis and an algorithm

AY Ng, MI Jordan, Y Weiss - Advances in neural information …, 2002 - books.google.com
Abstract Despite many empirical successes of spectral clustering methods—algorithms that
cluster points using eigenvectors of matrices derived from the data—there are several
unresolved issues. First, there are a wide variety of algorithms that use the eigenvectors in

[PDF][PDF] ROS: an open-source Robot Operating System

…, T Foote, J Leibs, R Wheeler, AY Ng - ICRA workshop on …, 2009 - willowgarage.com
Abstract—This paper gives an overview of ROS, an opensource robot operating system.
ROS is not an operating system in the traditional sense of process management and
scheduling; rather, it provides a structured communications layer above the host operating

[PDF][PDF] Distance metric learning with application to clustering with side-information

EP Xing, AY Ng, MI Jordan, S Russell - Advances in neural …, 2003 - papers.nips.cc
Abstract Many algorithms rely critically on being given a good metric over their inputs. For
instance, data can often be clustered in many “plausible” ways, and if a clustering algorithm
such as K-means initially fails to find one that is meaningful to a user, the only recourse may

[PDF][PDF] Efficient sparse coding algorithms

H Lee, A Battle, R Raina, AY Ng - Advances in neural …, 2006 - machinelearning.wustl.edu
Abstract Sparse coding provides a class of algorithms for finding succinct representations of
stimuli; given only unlabeled input data, it discovers basis functions that capture higher-level
features in the data. However, finding sparse codes remains a very difficult computational

Cheap and fast---but is it good?: evaluating non-expert annotations for natural language tasks

R Snow, B O'Connor, D Jurafsky, AY Ng - Proceedings of the conference …, 2008 - dl.acm.org
Abstract Human linguistic annotation is crucial for many natural language processing tasks
but can be expensive and time-consuming. We explore the use of Amazon's Mechanical
Turk system, a significantly cheaper and faster method for collecting annotations from a

Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations

H Lee, R Grosse, R Ranganath, AY Ng - Proceedings of the 26th annual …, 2009 - dl.acm.org
Abstract There has been much interest in unsupervised learning of hierarchical generative
models such as deep belief networks. Scaling such models to full-sized, high-dimensional
images remains a difficult problem. To address this problem, we present the convolutional

Map-reduce for machine learning on multicore

…, SK Kim, YA Lin, YY Yu, G Bradski, AY Ng… - Advances in neural …, 2007 - books.google.com
Abstract We are at the beginning of the multicore era. Computers will have increasingly
many cores (processors), but there is still no good programming framework for these
architectures, and thus no simple and unified way for machine learning to take advantage of

Apprenticeship learning via inverse reinforcement learning

P Abbeel, AY Ng - Proceedings of the twenty-first international …, 2004 - dl.acm.org
Abstract We consider learning in a Markov decision process where we are not explicitly
given a reward function, but where instead we can observe an expert demonstrating the task
that we want to learn to perform. This setting is useful in applications (such as the task of

Self-taught learning: transfer learning from unlabeled data

R Raina, A Battle, H Lee, B Packer, AY Ng - Proceedings of the 24th …, 2007 - dl.acm.org
Abstract We present a new machine learning framework called" self-taught learning" for
using unlabeled data in supervised classification tasks. We do not assume that the
unlabeled data follows the same class labels or generative distribution as the labeled data.

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