Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
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Self-Organization of Spatio-Temporal Hierarchy via Learning of Dynamic Visual Image Patterns on Action SequencesMulti-scale compositionality: identifying the compositional structures of social dynamics using deep learningProtein Secondary Structure Prediction Using Deep Convolutional Neural FieldsSixty-five years of the long march in protein secondary structure prediction: the final stretch?Central auditory neurons display flexible feature recombination functionsGranular computing with multiple granular layers for brain big data processing.Charles Bonnet syndrome: evidence for a generative model in the cortex?Sparsity-regularized HMAX for visual recognition.Unsupervised feature learning improves prediction of human brain activity in response to natural images.Active semi-supervised learning method with hybrid deep belief networks.QuantiFly: Robust Trainable Software for Automated Drosophila Egg CountingA Novel Cascade Classifier for Automatic Microcalcification Detection.Jointly Feature Learning and Selection for Robust Tracking via a Gating Mechanism.Enhanced HMAX model with feedforward feature learning for multiclass categorization.Unsupervised Learning in an Ensemble of Spiking Neural Networks Mediated by ITDP.Gaussian-binary restricted Boltzmann machines for modeling natural image statistics.The global landscape of cognition: hierarchical aggregation as an organizational principle of human cortical networks and functionsA building block for hardware belief networks.Color opponent receptive fields self-organize in a biophysical model of visual cortex via spike-timing dependent plasticity.Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review.AUCpreD: proteome-level protein disorder prediction by AUC-maximized deep convolutional neural fields.Deep Networks Can Resemble Human Feed-forward Vision in Invariant Object Recognition.Efficient training of convolutional deep belief networks in the frequency domain for application to high-resolution 2D and 3D images.Human-interpretable feature pattern classification system using learning classifier systems.Neural decoding with hierarchical generative models.A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs.Learning where to attend with deep architectures for image tracking.Deep Restricted Kernel Machines Using Conjugate Feature Duality.Enhanced gradient for training restricted Boltzmann machines.Simultaneous Whole-Brain Segmentation and White Matter Lesion Detection Using Contrast-Adaptive Probabilistic ModelsTransforming Auto-EncodersDeepLNC, a long non-coding RNA prediction tool using deep neural networkAccurate and High Throughput Cell Segmentation Method for Mouse Brain Nuclei Using Cascaded Convolutional Neural NetworkUsing Machine Learning for Labour Market IntelligenceAutomated Quality Assessment of Cardiac MR Images Using Convolutional Neural NetworksQuery Based Object Retrieval Using Neural CodesObject Detection for Crime Scene Evidence Analysis Using Deep LearningHuman Activity Recognition Using Recurrent Neural Networks
P2860
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P2860
Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
description
wetenschappelijk artikel
@nl
наукова стаття, опублікована у 2009
@uk
name
Convolutional deep belief netw ...... f hierarchical representations
@en
Convolutional deep belief netw ...... f hierarchical representations
@nl
type
label
Convolutional deep belief netw ...... f hierarchical representations
@en
Convolutional deep belief netw ...... f hierarchical representations
@nl
prefLabel
Convolutional deep belief netw ...... f hierarchical representations
@en
Convolutional deep belief netw ...... f hierarchical representations
@nl
P356
P1476
Convolutional deep belief netw ...... f hierarchical representations
@en
P2093
Honglak Lee
Rajesh Ranganath
P356
10.1145/1553374.1553453
P577
2009-01-01T00:00:00Z