Sciweavers

ICML
2008
IEEE
14 years 8 months ago
Efficiently learning linear-linear exponential family predictive representations of state
Exponential Family PSR (EFPSR) models capture stochastic dynamical systems by representing state as the parameters of an exponential family distribution over a shortterm window of...
David Wingate, Satinder P. Singh
ICML
2008
IEEE
14 years 8 months ago
Memory bounded inference in topic models
What type of algorithms and statistical techniques support learning from very large datasets over long stretches of time? We address this question through a memory bounded version...
Ryan Gomes, Max Welling, Pietro Perona
ICML
2008
IEEE
14 years 8 months ago
Deep learning via semi-supervised embedding
We show how nonlinear embedding algorithms popular for use with shallow semisupervised learning techniques such as kernel methods can be applied to deep multilayer architectures, ...
Frédéric Ratle, Jason Weston, Ronan ...
ICML
2008
IEEE
14 years 8 months ago
On the quantitative analysis of deep belief networks
Deep Belief Networks (DBN's) are generative models that contain many layers of hidden variables. Efficient greedy algorithms for learning and approximate inference have allow...
Ruslan Salakhutdinov, Iain Murray
ICML
2008
IEEE
14 years 8 months ago
Fast Gaussian process methods for point process intensity estimation
Point processes are difficult to analyze because they provide only a sparse and noisy observation of the intensity function driving the process. Gaussian Processes offer an attrac...
John P. Cunningham, Krishna V. Shenoy, Maneesh Sah...
ICML
2008
IEEE
14 years 8 months ago
Nonextensive entropic kernels
André F. T. Martins, Eric P. Xing, Má...
ICML
2008
IEEE
14 years 8 months ago
Nearest hyperdisk methods for high-dimensional classification
In high-dimensional classification problems it is infeasible to include enough training samples to cover the class regions densely. Irregularities in the resulting sparse sample d...
Hakan Cevikalp, Bill Triggs, Robi Polikar
ICML
2008
IEEE
14 years 8 months ago
Apprenticeship learning using linear programming
In apprenticeship learning, the goal is to learn a policy in a Markov decision process that is at least as good as a policy demonstrated by an expert. The difficulty arises in tha...
Umar Syed, Michael H. Bowling, Robert E. Schapire
ICML
2008
IEEE
14 years 8 months ago
Semi-supervised learning of compact document representations with deep networks
Finding good representations of text documents is crucial in information retrieval and classification systems. Today the most popular document representation is based on a vector ...
Marc'Aurelio Ranzato, Martin Szummer
ICML
2008
IEEE
14 years 8 months ago
Democratic approximation of lexicographic preference models
Previous algorithms for learning lexicographic preference models (LPMs) produce a "best guess" LPM that is consistent with the observations. Our approach is more democra...
Fusun Yaman, Thomas J. Walsh, Michael L. Littman, ...