Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vector quantization and signal compression
Vector quantization and signal compression
Improv: a system for scripting interactive actors in virtual worlds
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
Plausible motion simulation for computer graphics animation
Proceedings of the Eurographics workshop on Computer animation and simulation '96
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
EM algorithms for PCA and SPCA
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Learning in graphical models
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
SIGGRAPH '85 Proceedings of the 12th annual conference on Computer graphics and interactive techniques
Learning nonlinear dynamical systems using an EM algorithm
Proceedings of the 1998 conference on Advances in neural information processing systems II
Kernel PCA and de-noising in feature spaces
Proceedings of the 1998 conference on Advances in neural information processing systems II
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Sampling plausible solutions to multi-body constraint problems
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Composable controllers for physics-based character animation
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Motion texture: a two-level statistical model for character motion synthesis
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
The Role of Occam‘s Razor in Knowledge Discovery
Data Mining and Knowledge Discovery
The Paradoxical Success of Fuzzy Logic
IEEE Expert: Intelligent Systems and Their Applications
Face Identification by Fitting a 3D Morphable Model Using Linear Shape and Texture Error Functions
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Understanding belief propagation and its generalizations
Exploring artificial intelligence in the new millennium
Segmentation Using Eigenvectors: A Unifying View
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
A data-driven reflectance model
ACM SIGGRAPH 2003 Papers
An Introduction to the Kalman Filter
An Introduction to the Kalman Filter
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Variational Learning for Switching State-Space Models
Neural Computation
Journal of Cognitive Neuroscience
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Machine Learning to Boost the Next Generation of Visualization Technology
IEEE Computer Graphics and Applications
Global illumination with radiance regression functions
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
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I argue that computer graphics can benefit from a deeper use ofmachine learning techniques. I give an overview of what learninghas to offer the graphics community, with an emphasis on Bayesiantechniques. I also attempt to address some misconceptions aboutlearning, and to give a very brief tutorial on Bayesian reasoning.