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Convolutional neural network

Known as: Max norm constraint, Convolutional neural networks, ConvNet 
In machine learning, a convolutional neural network (CNN, or ConvNet) is a type of feed-forward artificial neural network in which the connectivity… 
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Related topics

Related topics

50 relations
Activation functionAlphaGoApache SparkArtificial neural network

Broader (1)

Computational neuroscience

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2020
2020
Structured Medical Pathology Data Hiding Information Association Mining Algorithm Based on Optimized Convolutional Neural Network
When using traditional algorithms to mine the association of hiding information in medical pathological data, there are some… 
2018
2018
Video Dynamics Detection Using Deep Neural Networks
In recent years, deep neural networks (DNNs) have achieved a remarkable progression in solving many complex problems. DNNs are… 
2017
2017
Self-Supervised Learning of Pose Embeddings from Spatiotemporal Relations in Videos
Human pose analysis is presently dominated by deep convolutional networks trained with extensive manual annotations of joint… 
Highly Cited
2015
Highly Cited
2015
Time-series modeling with undecimated fully convolutional neural networks
We present a new convolutional neural network-based time-series model. Typical convolutional neural network (CNN) architectures… 
Highly Cited
2014
Highly Cited
2014
Metric Learning Based Structural Appearance Model for Robust Visual Tracking
Appearance modeling is a key issue for the success of a visual tracker. Sparse representation based appearance modeling has… 
2013
2013
A new phase-based feature representation for robust speech recognition
The aim of this paper is to introduce a novel phase-based feature representation for robust speech recognition. This method… 
2011
2011
A Refined Performance Characterization of Longest-Queue-First Policy in Wireless Networks
One of the major challenges in wireless networking is how to optimize the link scheduling decisions under interference… 
2001
2001
Medical image segmentation using a contextual-constraint-based Hopfield neural cube
2000
2000
A Fast Hough Transform for the Parametrisation of Straight Lines using Fourier Methods
The Hough transform is a useful technique in the detection of straight lines and curves in an image. Due to the mathematical… 
Highly Cited
1995
Highly Cited
1995
Classifier and shift-invariant automatic target recognition neural networks
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