Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Information processing in dynamical systems: foundations of harmony theory
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
On-line locomotion generation based on motion blending
Proceedings of the 2002 ACM SIGGRAPH/Eurographics symposium on Computer animation
Motion texture: a two-level statistical model for character motion synthesis
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Interactive motion generation from examples
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Interactive control of avatars animated with human motion data
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Motion capture assisted animation: texturing and synthesis
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Verbs and Adverbs: Multidimensional Motion Interpolation
IEEE Computer Graphics and Applications
Training products of experts by minimizing contrastive divergence
Neural Computation
Learning Dynamic Bayesian Networks
Adaptive Processing of Sequences and Data Structures, International Summer School on Neural Networks, "E.R. Caianiello"-Tutorial Lectures
Practical parameterization of rotations using the exponential map
Journal of Graphics Tools
Realistic synthesis of novel human movements from a database of motion capture examples
HUMO '00 Proceedings of the Workshop on Human Motion (HUMO'00)
Motion synthesis from annotations
ACM SIGGRAPH 2003 Papers
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Automated extraction and parameterization of motions in large data sets
ACM SIGGRAPH 2004 Papers
Modeling and Learning Contact Dynamics in Human Motion
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Geostatistical motion interpolation
ACM SIGGRAPH 2005 Papers
Learning physics-based motion style with nonlinear inverse optimization
ACM SIGGRAPH 2005 Papers
Style translation for human motion
ACM SIGGRAPH 2005 Papers
Estimation of Non-Normalized Statistical Models by Score Matching
The Journal of Machine Learning Research
Separating Style and Content with Bilinear Models
Neural Computation
The rate adapting poisson model for information retrieval and object recognition
ICML '06 Proceedings of the 23rd international conference on Machine learning
3D People Tracking with Gaussian Process Dynamical Models
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A fast learning algorithm for deep belief nets
Neural Computation
An empirical evaluation of deep architectures on problems with many factors of variation
Proceedings of the 24th international conference on Machine learning
Hierarchical Gaussian process latent variable models
Proceedings of the 24th international conference on Machine learning
Three new graphical models for statistical language modelling
Proceedings of the 24th international conference on Machine learning
Restricted Boltzmann machines for collaborative filtering
Proceedings of the 24th international conference on Machine learning
Multifactor Gaussian process models for style-content separation
Proceedings of the 24th international conference on Machine learning
Gaussian Process Dynamical Models for Human Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the quantitative analysis of deep belief networks
Proceedings of the 25th international conference on Machine learning
Training restricted Boltzmann machines using approximations to the likelihood gradient
Proceedings of the 25th international conference on Machine learning
Topologically-constrained latent variable models
Proceedings of the 25th international conference on Machine learning
Deep, narrow sigmoid belief networks are universal approximators
Neural Computation
Factored conditional restricted Boltzmann Machines for modeling motion style
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Using fast weights to improve persistent contrastive divergence
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Justifying and generalizing contrastive divergence
Neural Computation
Forecasting large scale conditional volatility and covariance using neural network on GPU
The Journal of Supercomputing
| Hi-index | 0.00 |
In this paper we develop a class of nonlinear generative models for high-dimensional time series. We first propose a model based on the restricted Boltzmann machine (RBM) that uses an undirected model with binary latent variables and real-valued "visible" variables. The latent and visible variables at each time step receive directed connections from the visible variables at the last few time-steps. This "conditional" RBM (CRBM) makes on-line inference efficient and allows us to use a simple approximate learning procedure. We demonstrate the power of our approach by synthesizing various sequences from a model trained on motion capture data and by performing on-line filling in of data lost during capture. We extend the CRBM in a way that preserves its most important computational properties and introduces multiplicative three-way interactions that allow the effective interaction weight between two variables to be modulated by the dynamic state of a third variable. We introduce a factoring of the implied three-way weight tensor to permit a more compact parameterization. The resulting model can capture diverse styles of motion with a single set of parameters, and the three-way interactions greatly improve its ability to blend motion styles or to transition smoothly among them. Videos and source code can be found at http://www.cs.nyu.edu/~gwtaylor/publications/jmlr2011.