Automatic methods for designing artificial neural nets are desired to avoid the laborious and erratically human expert’s job. Evolutionary computation has been used as a search technique to find appropriate NN architectures. Direct and indirect encoding methods are used to codify the net architecture into the chromosome. A reformulation of an indirect encoding method, based on two bi-dimensiona...

We use a stereo disparity predictor, implemented as layered neural nets in the PILUT architecture, to encode the disparity flow field for the ground plane at various viewing positions over the work space. A deviation of disparity, computed using a correspondence algorithm, from its prediction may then indicate a potential obstacle. A casual bayes net model is used to estimate the probability th...

The paper addresses the problem of using contextual information by neural nets solving problems of contextual nature. The models of a context-dependent neuron and a multi-layer net are recalled and supplemented by the analysis of context-dependent and hybrid nets’ architecture. The context-dependent nets’ properties are discussed and compared with the properties of traditional nets considering ...

We show how eld-programable gate arrays can be used to eeciently implement neural nets. By implementing the training phase in software and the actual application in hardware, connicting demands can be met: training beneets from a fast edit-debug cycle, and once the design has stabilized, a hardware implementation results in higher performance. While neural nets have been implemented in hardware...

In previous work, the author and Dr. Jer-Nan Juang contributed a new neural net architecture, within the framework of “second generation” neural models. We showed how to implement backpropagation learning in a massively parallel architecture involving only local computations – thereby capturing one of the principal advantages of biological neural nets. Since then, a large body of neural-biologi...

This paper explores and investigates Deep Convolutional Neural Networks (DCNNs) architectures to increase efficiency and robustness of semantic segmentation tasks. The proposed solutions are based on Up-Convolutional Networks. We introduce three different architectures in this work. The first architecture, called Part-Net, is designed to tackle the specific problem of human body part segmentati...

ProjectH, a research group of a hundred researchers, produced a huge amount of data from computer mediated discussions. The data classified several thousand postings from over 30 newsgroups into 46 categories. One approach to extract typical examples from this database is presented in this paper. An autoassociative neural network is trained on all 3000 coded messages and then used to construct ...

In this article, we propose DeepSLAM, a novel unsupervised deep learning based visual simultaneous localization and mapping (SLAM) system. The DeepSLAM training is fully since it only requires stereo imagery instead of annotating ground-truth poses. Its testing takes monocular image sequence as the input. Therefore, SLAM paradigm. consists several essential components, including Mapping-Net, Tr...

ÐAesthetically appealing patterns are produced by the dynamical behavior of arti®cial neural networks with randomly chosen connection strengths. These feed-forward networks have a single hidden layer of neurons and a single output, which is fed back to the input to produce a scalar time series that is always bounded and often chaotic. Sample attractors are shown and simple computer code is prov...