NIPS Proceedingsβ

Padhraic Smyth

20 Papers

  • Continuous-Time Regression Models for Longitudinal Networks (2011)
  • Learning concept graphs from text with stick-breaking priors (2010)
  • Particle-based Variational Inference for Continuous Systems (2009)
  • Asynchronous Distributed Learning of Topic Models (2008)
  • Distributed Inference for Latent Dirichlet Allocation (2007)
  • Hierarchical Dirichlet Processes with Random Effects (2006)
  • Learning Time-Intensity Profiles of Human Activity using Non-Parametric Bayesian Models (2006)
  • Modeling General and Specific Aspects of Documents with a Probabilistic Topic Model (2006)
  • Joint Probabilistic Curve Clustering and Alignment (2004)
  • Gene Expression Clustering with Functional Mixture Models (2003)
  • Learning to Classify Galaxy Shapes Using the EM Algorithm (2002)
  • Bayesian Predictive Profiles With Applications to Retail Transaction Data (2001)
  • Model Complexity, Goodness of Fit and Diminishing Returns (2000)
  • Stacked Density Estimation (1997)
  • Clustering Sequences with Hidden Markov Models (1996)
  • Inferring Ground Truth from Subjective Labelling of Venus Images (1994)
  • Probabilistic Anomaly Detection in Dynamic Systems (1993)
  • Fault Diagnosis of Antenna Pointing Systems using Hybrid Neural Network and Signal Processing Models (1991)
  • On Stochastic Complexity and Admissible Models for Neural Network Classifiers (1990)
  • An Information Theoretic Approach to Rule-Based Connectionist Expert Systems (1988)