Results 1 - 10 of 1,707

Gradient-based learning applied to document recognition

by Yann Lecun, Léon Bottou, Yoshua Bengio, Patrick Haffner - Proceedings of the IEEE , 1998
"... Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradientbased learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify hi ..."
Abstract - Cited by 1533 (84 self) - Add to MetaCart
Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradientbased learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify

Probabilistic Inference Using Markov Chain Monte Carlo Methods

by Radford M. Neal , 1993
"... Probabilistic inference is an attractive approach to uncertain reasoning and empirical learning in artificial intelligence. Computational difficulties arise, however, because probabilistic models with the necessary realism and flexibility lead to complex distributions over high-dimensional spaces. R ..."
Abstract - Cited by 736 (24 self) - Add to MetaCart
from data, and Bayesian learning for neural networks.

Face Recognition: A Convolutional Neural Network Approach

by Steve Lawrence, C. Lee Giles, Ah Chung Tsoi, Andrew D. Back - IEEE Transactions on Neural Networks , 1997
"... Faces represent complex, multidimensional, meaningful visual stimuli and developing a computational model for face recognition is difficult [43]. We present a hybrid neural network solution which compares favorably with other methods. The system combines local image sampling, a self-organizing map n ..."
Abstract - Cited by 234 (0 self) - Add to MetaCart
Faces represent complex, multidimensional, meaningful visual stimuli and developing a computational model for face recognition is difficult [43]. We present a hybrid neural network solution which compares favorably with other methods. The system combines local image sampling, a self-organizing map

Knowledge-Based Artificial Neural Networks

by Geoffrey G. Towell, Jude W. Shavlik , 1994
"... Hybrid learning methods use theoretical knowledge of a domain and a set of classified examples to develop a method for accurately classifying examples not seen during training. The challenge of hybrid learning systems is to use the information provided by one source of information to offset informat ..."
Abstract - Cited by 185 (13 self) - Add to MetaCart
information missing from the other source. By so doing, a hybrid learning system should learn more effectively than systems that use only one of the information sources. KBANN(Knowledge-Based Artificial Neural Networks) is a hybrid learning system built on top of connectionist learning techniques. It maps

using Convolutional Neural Networks

by Dimitri Palaz, Ronan Collobert, Mathew Magimai. -doss, Dimitri Palaz, Ronan Collobert, Mathew Magimai. -doss , 2013
"... In hybrid hidden Markov model/artificial neural networks (HMM/ANN) automatic speech recognition (ASR) system, the phoneme class conditional probabilities are estimated by first extracting acoustic features from the speech signal based on prior knowledge such as, speech perception or/and speech produ ..."
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In hybrid hidden Markov model/artificial neural networks (HMM/ANN) automatic speech recognition (ASR) system, the phoneme class conditional probabilities are estimated by first extracting acoustic features from the speech signal based on prior knowledge such as, speech perception or/and speech

Tiled convolutional neural networks

by Quoc V. Le, Jiquan Ngiam, Zhenghao Chen, Daniel Chia, Pang Wei Koh, Andrew Y. Ng - In NIPS, in press , 2010
"... Convolutional neural networks (CNNs) have been successfully applied to many tasks such as digit and object recognition. Using convolutional (tied) weights significantly reduces the number of parameters that have to be learned, and also allows translational invariance to be hard-coded into the archit ..."
Abstract - Cited by 54 (7 self) - Add to MetaCart
Convolutional neural networks (CNNs) have been successfully applied to many tasks such as digit and object recognition. Using convolutional (tied) weights significantly reduces the number of parameters that have to be learned, and also allows translational invariance to be hard

Large-scale Video Classification with Convolutional Neural Networks

by Andrej Karpathy, George Toderici Sanketh Shetty, Thomas Leung, Rahul Sukthankar, Li Fei-fei
"... Convolutional Neural Networks (CNNs) have been es-tablished as a powerful class of models for image recog-nition problems. Encouraged by these results, we pro-vide an extensive empirical evaluation of CNNs on large-scale video classification using a new dataset of 1 million YouTube videos belonging ..."
Abstract - Cited by 68 (5 self) - Add to MetaCart
Convolutional Neural Networks (CNNs) have been es-tablished as a powerful class of models for image recog-nition problems. Encouraged by these results, we pro-vide an extensive empirical evaluation of CNNs on large-scale video classification using a new dataset of 1 million YouTube videos belonging

Hybrid Evolution of Convolutional Networks

by Brian Cheung , Carl Sable - in 10th International Conference on Machine Learning and Applications, ICMLA. IEEE , 2011
"... Abstract-With the increasing trend of neural network models towards larger structures with more layers, we expect a corresponding exponential increase in the number of possible architectures. In this paper, we apply a hybrid evolutionary search procedure to define the initialization and architectur ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
Abstract-With the increasing trend of neural network models towards larger structures with more layers, we expect a corresponding exponential increase in the number of possible architectures. In this paper, we apply a hybrid evolutionary search procedure to define the initialization

Diffusion-Convolutional Neural Networks

by James Atwood , Don Towsley
"... Abstract We present diffusion-convolutional neural networks (DCNNs), a new model for graph-structured data. Through the introduction of a diffusion-convolution operation, we show how diffusion-based representations can be learned from graphstructured data and used as an effective basis for node cla ..."
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Abstract We present diffusion-convolutional neural networks (DCNNs), a new model for graph-structured data. Through the introduction of a diffusion-convolution operation, we show how diffusion-based representations can be learned from graphstructured data and used as an effective basis for node

Hybrid Algorithm for the Optimization of Training Convolutional Neural Network

by Hayder M. Albeahdili, Tony Han, Naz E. Islam
"... Abstract—The training optimization processes and efficient fast classification are vital elements in the development of a convolution neural network (CNN). Although stochastic gradient descend (SGD) is a Prevalence algorithm used by many researchers for the optimization of training CNNs, it has vast ..."
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Abstract—The training optimization processes and efficient fast classification are vital elements in the development of a convolution neural network (CNN). Although stochastic gradient descend (SGD) is a Prevalence algorithm used by many researchers for the optimization of training CNNs, it has
Results 1 - 10 of 1,707