Sciweavers

JMLR
2012
12 years 1 months ago
Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing
Open-text semantic parsers are designed to interpret any statement in natural language by inferring a corresponding meaning representation (MR – a formal representation of its s...
Antoine Bordes, Xavier Glorot, Jason Weston, Yoshu...
JMLR
2012
12 years 1 months ago
Factorized Asymptotic Bayesian Inference for Mixture Modeling
This paper proposes a novel Bayesian approximation inference method for mixture modeling. Our key idea is to factorize marginal log-likelihood using a variational distribution ove...
Ryohei Fujimaki, Satoshi Morinaga
JMLR
2012
12 years 1 months ago
Online Incremental Feature Learning with Denoising Autoencoders
While determining model complexity is an important problem in machine learning, many feature learning algorithms rely on cross-validation to choose an optimal number of features, ...
Guanyu Zhou, Kihyuk Sohn, Honglak Lee
JMLR
2012
12 years 1 months ago
Learning Low-order Models for Enforcing High-order Statistics
Models such as pairwise conditional random fields (CRFs) are extremely popular in computer vision and various other machine learning disciplines. However, they have limited expre...
Patrick Pletscher, Pushmeet Kohli
JMLR
2012
12 years 1 months ago
Statistical test for consistent estimation of causal effects in linear non-Gaussian models
This document contains supplementary material to the article ‘Statistical test for consistent estimation of causal effects in linear non-Gaussian models’, AISTATS 2012. A tabl...
Doris Entner, Patrik O. Hoyer, Peter Spirtes
JMLR
2012
12 years 1 months ago
Gaussian Processes for time-marked time-series data
In many settings, data is collected as multiple time series, where each recorded time series is an observation of some underlying dynamical process of interest. These observations...
John Cunningham, Zoubin Ghahramani, Carl Edward Ra...
JMLR
2012
12 years 1 months ago
On Sparse, Spectral and Other Parameterizations of Binary Probabilistic Models
This paper studies issues relating to the parameterization of probability distributions over binary data sets. Several such parameterizations of models for binary data are known, ...
David Buchman, Mark W. Schmidt, Shakir Mohamed, Da...
JMLR
2012
12 years 1 months ago
Metric and Kernel Learning Using a Linear Transformation
Metric and kernel learning arise in several machine learning applications. However, most existing metric learning algorithms are limited to learning metrics over low-dimensional d...
Prateek Jain, Brian Kulis, Jason V. Davis, Inderji...
JMLR
2012
12 years 1 months ago
Minimax-Optimal Rates For Sparse Additive Models Over Kernel Classes Via Convex Programming
Sparse additive models are families of d-variate functions with the additive decomposition f∗ = ∑j∈S f∗ j , where S is an unknown subset of cardinality s d. In this paper,...
Garvesh Raskutti, Martin J. Wainwright, Bin Yu