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ACL
2012
7 years 4 months ago
Modeling Sentences in the Latent Space
Sentence Similarity is the process of computing a similarity score between two sentences. Previous sentence similarity work finds that latent semantics approaches to the problem ...
Weiwei Guo, Mona T. Diab
KDD
2012
ACM
244views Data Mining» more  KDD 2012»
7 years 4 months ago
Open domain event extraction from twitter
Tweets are the most up-to-date and inclusive stream of information and commentary on current events, but they are also fragmented and noisy, motivating the need for systems that c...
Alan Ritter, Mausam, Oren Etzioni, Sam Clark
JMLR
2012
7 years 4 months ago
Max-Margin Min-Entropy Models
We propose a new family of latent variable models called max-margin min-entropy (m3e) models, which define a distribution over the output and the hidden variables conditioned on ...
Kevin Miller, M. Pawan Kumar, Benjamin Packer, Dan...
AAAI
2011
8 years 2 months ago
Incorporating Boosted Regression Trees into Ecological Latent Variable Models
Important ecological phenomena are often observed indirectly. Consequently, probabilistic latent variable models provide an important tool, because they can include explicit model...
Rebecca A. Hutchinson, Li-Ping Liu, Thomas G. Diet...
FTCGV
2011
122views more  FTCGV 2011»
8 years 5 months ago
Structured Learning and Prediction in Computer Vision
Powerful statistical models that can be learned efficiently from large amounts of data are currently revolutionizing computer vision. These models possess a rich internal structur...
Sebastian Nowozin, Christoph H. Lampert
CVPR
2011
IEEE
8 years 6 months ago
Nonlinear Shape Manifolds as Shape Priors in Level Set Segmentation and Tracking
We propose a novel nonlinear, probabilistic and variational method for adding shape information to level setbased segmentation and tracking. Unlike previous work, we represent sha...
Victor Prisacariu, Ian Reid
JMLR
2010
93views more  JMLR 2010»
8 years 9 months ago
Distinguishing between cause and effect
We propose a novel method for inferring whether X causes Y or vice versa from joint observations of X and Y . The basic idea is to model the observed data using probabilistic late...
Joris M. Mooij, Dominik Janzing
SAC
2008
ACM
9 years 1 months ago
Particle methods for maximum likelihood estimation in latent variable models
Standard methods for maximum likelihood parameter estimation in latent variable models rely on the Expectation-Maximization algorithm and its Monte Carlo variants. Our approach is ...
Adam M. Johansen, Arnaud Doucet, Manuel Davy
JIIS
2002
114views more  JIIS 2002»
9 years 2 months ago
A Dynamic Probabilistic Model to Visualise Topic Evolution in Text Streams
Abstract. We propose a novel probabilistic method, based on latent variable models, for unsupervised topographic visualisation of dynamically evolving, coherent textual information...
Ata Kabán, Mark Girolami
JMLR
2007
137views more  JMLR 2007»
9 years 2 months ago
Building Blocks for Variational Bayesian Learning of Latent Variable Models
We introduce standardised building blocks designed to be used with variational Bayesian learning. The blocks include Gaussian variables, summation, multiplication, nonlinearity, a...
Tapani Raiko, Harri Valpola, Markus Harva, Juha Ka...
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