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Convolutional Neural Networks

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If you have a question about this talk, please contact Rowan McAllister .

Abstract: In this talk we outline convolutional neural networks (convnets) and discuss their contemporary applications and research. We begin by outlining prior assumptions and learning techniques for understanding data with a spatial structure. Secondly, we go through the key insights that have allowed convnets to surpass state-of-the-art performance in visual classification, regression, OCR , scene understanding and visual reinforcement learning.

Reading

Jarrett et al., What Is the Best Multi-Stage Architecture for Object Recognition?
Krizhevsky, Sutskever, and Hinton, ImageNet Classification with Deep Convolutional Neural Networks.
Szegedy, et al. Going deeper with convolutions.

This talk is part of the Machine Learning Reading Group @ CUED series.

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Abstract

Reading

Jarrett et al., What Is the Best Multi-Stage Architecture for Object Recognition?
Krizhevsky, Sutskever, and Hinton, ImageNet Classification with Deep Convolutional Neural Networks.
Szegedy, et al. Going deeper with convolutions.

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Convolutional Neural Networks

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Convolutional Neural Networks

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