An application of neural net chips: handwritten digit recognition

Jackel,; Gräf,; Hubbard,; Denker,; Henderson,; Guyon,

doi:10.1109/icnn.1988.23918

Cited by 26 publications

(4 citation statements)

References 4 publications

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“…It constitutes a connectionist approach to the navigational task of road following, which is the main success of the training using simulated images. ANN displays promising performance and flexibility in various domains that are characterized by high degrees of noise and variability, such as handwriting character recognition ( [56] & [57]) and speech recognition [58]. Specifically, ALVINN was designed to control the NAVLAB, the Carnegie Mellon autonomous navigation test vehicle.…”

Section: Environment St St+1mentioning

confidence: 99%

A Survey on Deep Learning for Steering Angle Prediction in Autonomous Vehicles

et al. 2020

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Steering angle prediction is critical in the control of Autonomous Vehicles (AVs) and has attracted the attention of researchers, manufacturers, and insurance companies in the automotive industry. Different Deep Learning (DL) architectures have been applied to predict the steering angle of AVs in various scenarios. A survey on steering angle prediction based on deep learning algorithms can help expert researchers identify those areas that require development. Also, novice researchers can use the survey as a starting point. In this paper, we present a broad study on the recent advances made in DL architectures that covers the steering angle prediction of AVs. A new comprehensive taxonomy of the application of DL in steering angle prediction of AVs is created. The survey presents a concise research summary synthesis, and analysis. It is found that most researchers depend on Convolutional Neural Network (CNN) over other DL architectures in predicting the steering angle of autonomous driving vehicles. Also identified are open research problems. The prominent challenge facing DL-based steering angle prediction of AVs is lack of sufficient real-world datasets, which means that researchers largely depend on data generated from simulated environments. Lastly, alternative viewpoints to solve the identified open research challenges are proposed, pointing towards promising future research directions.

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Section: Environment St St+1mentioning

confidence: 99%

A Survey on Deep Learning for Steering Angle Prediction in Autonomous Vehicles

et al. 2020

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“…The structure used is a multi-map procedure similar to those known to exist in the mid-level visual cortex [11]. As in previous work [12,13,14] the method must provide a parallel, multi-map, self-organizing, pattern classification procedure. This is achieved using a feed-forward architecture which allows multi-map features stored in weights acting as associative memories to be accessed in parallel and to trigger a symmetrically controlled parallel learning process.…”

Section: Systemsmentioning

confidence: 99%

Topological separation versus weight sharing in neural net optimization

Omidvar¹,

Wilson²

1992

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Recent advances in neural networks application development for real life problems have drawn attention to network optimization. Most of the known optimization methods rely heavily on a weight sharing concept for pattern separation and recognition. The shortcoming of the weight sharing method is attributed to a large number of extraneous weights which play a minimal role in pattern separation and recognition. Our experiments have shown that up to 97% of the connections in the network can be eliminated with little or no change in the network performance.Topological separation should be used when the size of the network is large enough to tackle real life problems such as fingerprint classification. Our research has focused on the network topology by changing the number of connections as secondary method of optimization. Our findings indicate that for large networks, topological separation yields smaller network size that is more suitable for VLSI implementation. Topological separation is based on the error surface and information content of the network. As such it is an economical way of size reduction which leads to overall optimization. The differential pruning of the connections is based on the weight contents rather than number of connections. The training error may vary with the topological dynamics but the correlation between the error surface and recognition rate decreases to a minimum. Topological separation reduces the size of the network by changing its architecture without degrading its performance.

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“…Both special purpose hardware [1] and software [2] approaches have been used on the character recognition problem with promising results. A set of features and a method of feature extraction are selected and the resulting classification problem is solved by a neural network.…”

Section: Network Architecturementioning

confidence: 99%

Methods for enhancing neural network handwritten character recognition

Garris

Wilkinson

Wilson

IJCNN-91-Seattle International Joint Conference on Neural Networks

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An efficient method for increasing the generalization capacity of neural character recognition is presented. The network uses a biologically inspired architecture for feature extraction and character classification. The numerical methods used are, however, optimized for use on massively parallel array processors. The method for training set construction, when applied to handwritten digit recognition, yielded a writer-independent recognition rate of 92%. The activation strength produced by network recognition is an effective statistical confidence measure of the accuracy of recognition. A method of using the activation strength for reclassification is described which when applied to handwritten digit recognition reduced substitutional errors to 2.2%. IntroductionThis paper uses a three part method for writer-independent digit recognition. First, character images are used to calculate least squares optimized Gabor components. For the digit recognition problem, 32 Gabor basis functions are used. Second, these coefficients are used as input feature vectors to a classification network trained using back-propagation learning. Finally, the activation strengths of the network are used as first-order Bayesian statistics for the separation of substitutional errors.The effectiveness of this method is strongly affected by the nature of the training set used. A new method of training set construction is presented which is based on measuring writer variance. This method is shown to increase the generalization ability of a neural character recognition model. Network ArchitectureThe usual method for designing character recognition systems has been top down. Both special purpose hardware [1] and software [2] approaches have been used on the character recognition problem with promising results. A set of features and a method of feature extraction are selected and the resulting classification problem is solved by a neural network. In this work, we have taken a different approach. The general form of input receptor fields which are used in tasks such as binocular vision by vertebrates has been modeled using parallel Gabor functions [3]. The output of these receptor fields is coupled to small networks for detection of position [4]. In this work, an approximation to this model was constructed as shown in Figure 1 and used for handwritten digit recognition.The model was not specifically designed for character recognition and could be taught any set of images which could be represented by the Gabor functions. Gabor functions are well suited to this I-695

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An application of neural net chips: handwritten digit recognition

Cited by 26 publications

References 4 publications

A Survey on Deep Learning for Steering Angle Prediction in Autonomous Vehicles

A Survey on Deep Learning for Steering Angle Prediction in Autonomous Vehicles

Topological separation versus weight sharing in neural net optimization

Methods for enhancing neural network handwritten character recognition

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