Higher-order Boltzmann machines
AIP Conference Proceedings 151 on Neural Networks for Computing
Learning invariance from transformation sequences
Neural Computation
Neural routing circuits for forming invariant representations of visual objects
Neural routing circuits for forming invariant representations of visual objects
Learning Lie groups for invariant visual perception
Proceedings of the 1998 conference on Advances in neural information processing systems II
Training products of experts by minimizing contrastive divergence
Neural Computation
Multilinear Analysis of Image Ensembles: TensorFaces
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Efficient Encoding of Natural Time Varying Images Produces Oriented Space-Time Receptive Fields
Efficient Encoding of Natural Time Varying Images Produces Oriented Space-Time Receptive Fields
Bilinear Sparse Coding for Invariant Vision
Neural Computation
Separating Style and Content with Bilinear Models
Neural Computation
Learning the Lie Groups of Visual Invariance
Neural Computation
Non-linear latent factor models for revealing structure in high-dimensional data
Non-linear latent factor models for revealing structure in high-dimensional data
Convolutional learning of spatio-temporal features
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Two Distributed-State Models For Generating High-Dimensional Time Series
The Journal of Machine Learning Research
Transformation equivariant Boltzmann machines
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
Learning image transformations without training examples
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
Bilinear deep learning for image classification
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Self-organization of topographic bilinear networks for invariant recognition
Neural Computation
Gated boltzmann machine in texture modeling
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Self-Avoiding Random Dynamics on Integer Complex Systems
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special Issue on Monte Carlo Methods in Statistics
Learning temporal coherent features through life-time sparsity
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
ACM Transactions on Reconfigurable Technology and Systems (TRETS)
Rotation-Invariant HOG Descriptors Using Fourier Analysis in Polar and Spherical Coordinates
International Journal of Computer Vision
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To allow the hidden units of a restricted Boltzmann machine to model the transformation between two successive images, Memisevic and Hinton (2007) introduced three-way multiplicative interactions that use the intensity of a pixel in the first image as a multiplicative gain on a learned, symmetric weight between a pixel in the second image and a hidden unit. This creates cubically many parameters, which form a three-dimensional interaction tensor. We describe a low-rank approximation to this interaction tensor that uses a sum of factors, each of which is a three-way outer product. This approximation allows efficient learning of transformations between larger image patches. Since each factor can be viewed as an image filter, the model as a whole learns optimal filter pairs for efficiently representing transformations. We demonstrate the learning of optimal filter pairs from various synthetic and real image sequences. We also show how learning about image transformations allows the model to perform a simple visual analogy task, and we show how a completely unsupervised network trained on transformations perceives multiple motions of transparent dot patterns in the same way as humans.