Training products of experts by minimizing contrastive divergence
Neural Computation
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
Shape Matching and Object Recognition Using Low Distortion Correspondences
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Multiclass Object Recognition with Sparse, Localized Features
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A fast learning algorithm for deep belief nets
Neural Computation
Self-taught learning: transfer learning from unlabeled data
Proceedings of the 24th international conference on Machine learning
Backpropagation applied to handwritten zip code recognition
Neural Computation
Deep learning via semi-supervised embedding
Proceedings of the 25th international conference on Machine learning
Large-scale deep unsupervised learning using graphics processors
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Large-scale deep unsupervised learning using graphics processors
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Learning Deep Architectures for AI
Foundations and Trends® in Machine Learning
Why Does Unsupervised Pre-training Help Deep Learning?
The Journal of Machine Learning Research
A dynamically configurable coprocessor for convolutional neural networks
Proceedings of the 37th annual international symposium on Computer architecture
Unsupervised Layer-Wise Model Selection in Deep Neural Networks
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
A deep-learning model-based and data-driven hybrid architecture for image annotation
Proceedings of the international workshop on Very-large-scale multimedia corpus, mining and retrieval
Convolutional learning of spatio-temporal features
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Research frontier: deep machine learning--a new frontier in artificial intelligence research
IEEE Computational Intelligence Magazine
Neural decoding with hierarchical generative models
Neural Computation
Exploiting local structure in Boltzmann machines
Neurocomputing
Discriminative deep belief networks for visual data classification
Pattern Recognition
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
Stacked convolutional auto-encoders for hierarchical feature extraction
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
Weakly supervised learning of foreground-background segmentation using masked RBMs
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
Comparing probabilistic models for melodic sequences
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Recursive Compositional Models for Vision: Description and Review of Recent Work
Journal of Mathematical Imaging and Vision
On the expressive power of deep architectures
ALT'11 Proceedings of the 22nd international conference on Algorithmic learning theory
Bilinear deep learning for image classification
MM '11 Proceedings of the 19th ACM international conference on Multimedia
A cascade fusion scheme for gait and cumulative foot pressure image recognition
Pattern Recognition
XCS-based versus UCS-based feature pattern classification system
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Learning where to attend with deep architectures for image tracking
Neural Computation
Learning a generative model of images by factoring appearance and shape
Neural Computation
The impact of images on user clicks in product search
Proceedings of the Twelfth International Workshop on Multimedia Data Mining
Unsupervised and supervised visual codes with restricted boltzmann machines
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Information theoretic learning for pixel-based visual agents
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Exploring bag of words architectures in the facial expression domain
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
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
Hierarchical k-means algorithm for modeling visual area v2 neurons
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Energy-based temporal neural networks for imputing missing values
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
An abstract deep network for image classification
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Enhanced gradient for training restricted boltzmann machines
Neural Computation
Learning hierarchical bag of words using naive bayes clustering
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Hierarchical object representations for visual recognition via weakly supervised learning
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Convolutional Deep Networks for Visual Data Classification
Neural Processing Letters
A local descriptor based on Laplacian pyramid coding for action recognition
Pattern Recognition Letters
Optimizing cepstral features for audio classification
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Learning discriminative representations from RGB-D video data
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Deep feature learning using target priors with applications in ECoG signal decoding for BCI
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Hierarchical spatiotemporal feature extraction using recurrent online clustering
Pattern Recognition Letters
Hierarchical kernel-based rotation and scale invariant similarity
Pattern Recognition
Training energy-based models for time-series imputation
The Journal of Machine Learning Research
The Shape Boltzmann Machine: A Strong Model of Object Shape
International Journal of Computer Vision
Hi-index | 0.00 |
There has been much interest in unsupervised learning of hierarchical generative models such as deep belief networks. Scaling such models to full-sized, high-dimensional images remains a difficult problem. To address this problem, we present the convolutional deep belief network, a hierarchical generative model which scales to realistic image sizes. This model is translation-invariant and supports efficient bottom-up and top-down probabilistic inference. Key to our approach is probabilistic max-pooling, a novel technique which shrinks the representations of higher layers in a probabilistically sound way. Our experiments show that the algorithm learns useful high-level visual features, such as object parts, from unlabeled images of objects and natural scenes. We demonstrate excellent performance on several visual recognition tasks and show that our model can perform hierarchical (bottom-up and top-down) inference over full-sized images.