← Core Methods
Machine Learning › Core Methods ›

Sequence Modeling

123 directly classified papers

Papers per year

Papers

Spectral Learning of Mixture of Hidden Markov Models NIPS 2014
Small-Variance Asymptotics for Hidden Markov Models NIPS 2013
Relative Hidden Markov Models for Evaluating Motion Skill CVPR 2013
Representing and Discovering Adversarial Team Behaviors Using Player Roles CVPR 2013
Spectral learning of linear dynamics from generalised-linear observations with application to neural population data NIPS 2012
Forward-Backward Activation Algorithm for Hierarchical Hidden Markov Models NIPS 2012
Expectation Propagation in Gaussian Process Dynamical Systems NIPS 2012
Tensor Decomposition for Fast Parsing with Latent-Variable PCFGs NIPS 2012
Sequence learning with hidden units in spiking neural networks NIPS 2011
Comparative Analysis of Viterbi Training and Maximum Likelihood Estimation for HMMs NIPS 2011
A Model for Temporal Dependencies in Event Streams NIPS 2011
Dynamical segmentation of single trials from population neural data NIPS 2011
Movement extraction by detecting dynamics switches and repetitions NIPS 2010
Time-Varying Dynamic Bayesian Networks NIPS 2009
Maximum likelihood trajectories for continuous-time Markov chains NIPS 2009
Sharing Features among Dynamical Systems with Beta Processes NIPS 2009
Canonical Time Warping for Alignment of Human Behavior NIPS 2009
Kernel-ARMA for Hand Tracking and Brain-Machine interfacing During 3D Motor Control NIPS 2008
The Infinite Factorial Hidden Markov Model NIPS 2008
Unconstrained On-line Handwriting Recognition with Recurrent Neural Networks NIPS 2007
The Infinite Markov Model NIPS 2007
Dynamic Conditional Random Fields: Factorized Probabilistic Models for Labeling and Segmenting Sequence Data JMLR 2007
Modelling motion primitives and their timing in biologically executed movements NIPS 2007