2010
NIPS
NeurIPS 2010
Learning to combine foveal glimpses with a third-order Boltzmann machine
Abstract
We describe a model based on a Boltzmann machine with third-order connections that can learn how to accumulate information about a shape over several fixations. The model uses a retina that only has enough high resolution pixels to cover a small area of the image, so it must decide on a sequence of fixations and it must combine the glimpse" at each fixation with the location of the fixation before integrating the information with information from other glimpses of the same object. We evaluate this model on a synthetic dataset and two image classification datasets, showing that it can perform at least as well as a model trained on whole images."
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Interdisciplinary Bridge
— Artificial Intelligence and Computer Vision and Deep Learning and Machine Learning
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Trend Setter
— Multimodal Learning
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Keyword Pioneer
— foveal glimpses
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Deep Learning, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning
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Topic Pioneer
— Attention
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Hot Topic Early Bird
— image classification
Authors
Topics
Artificial Intelligence > Core AI > Multimodal Learning
Machine Learning > Learning Types > Self-Supervised Learning
Deep Learning > Architectures > Neural Networks
Computer Vision > Analysis > Object Detection
Computer Vision > Analysis > Scene Understanding
Computer Vision > Core AI > Multimodal Learning
Computer Vision > Core AI > Computer Vision
Deep Learning > Techniques > Attention