Knowledge-Based Neural Networks
Knowledge Based Descriptive Neural Networks
... while neural networks have proved to be far more effective at forecasting than more conventional linear techniques like regression analysis, their decision processes are not easily understandable in terms ...
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Incorporating Functional Knowledge in Neural Networks
... combination. Based on validation set performance, 50 models were retained and their validation set performances were ...chosen based on this average validation ...
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Enhancing Key Value Memory Neural Networks for Knowledge Based Question Answering
... 4.4 Query Updating In KV-MemNNs, it is important to properly up- date the query representation after each hop, since the updated query will be used to address more fo- cused information in the next hop, which is espe- ...
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Integration of knowledge-based system, artificial neural networks and multimedia for gear design
... the networks used within the design system is in such a form that the output is known for a given ...of networks used by the system are multi-layer feedforward networks employing the back propagation ...
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Clinical text classification with rule-based features and knowledge-guided convolutional neural networks
... applied CNN using pre-trained embeddings on clinical text for named entity recognization. They showed that their models outperformed the conditional random fields (CRF) baseline. Geraci et al. [29] applied deep learning ...
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Adaptive Knowledge Driven Regularization for Deep Neural Networks
... internal knowledge to reg- ularize training, we introduce a variable significance weight to the prior distribution of the model .... Based on this observation, we place the con- straints of θ i,j ≥ 0 and P ...
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Comparing Methods to Extract the Knowledge from Neural Networks
... Fan and Li (2002) present a method for rule extraction in which the hidden neurons are used to partition the input space into subspaces. The number of partitions created is equal to the number of hidden neurons in the ...
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Compressing Deep Neural Networks via Knowledge Distillation
... We discuss approaches to this problem through the rest of this chapter. 3.2 Saliency Based Network Pruning The earliest approaches to model compression were developed keeping the multilayer perceptron architecture ...
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MP3 Steganalysis Based on Neural Networks
... gather knowledge in this particular research field, we have concentrated on some techniques and methods which are described below. S.K.Bandyopadhyay, Debnath Bhattacharyya proposed introduced the most common ...
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A PROPOSED THEORY OF NEURAL NETWORKS IN KNOWLEDGE MANAGEMENT FOR AN EXPERT SYSTEM
... a neural networks for computer-aided knowledge management as , characterized in that all the elements are stored dynamically, and are administered dynamically via a list concatenated via pointers, in ...
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Knowledge Extraction from Survey Data Using Neural Networks
... and knowledge extraction from survey data is a very important step in the decision-making ...process. Based on this knowledge, decisions are taken to improve the area for which the survey was ...data ...
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Knowledge Extraction from Survey Data using Neural Networks
... data based on the patterns of the ...the knowledge extraction process from these classifiers becomes complex, and often the outcome of knowledge extraction process may not be ...unsupervised ...
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Blocked neural networks for knowledge extraction in the software development process
... are based on dynamic network fl ow models (min i mum cost dynamic fl ow, max i mum dynamic fl ow, universal maximum fl ow, quickest path and quickest fl ow) are ...
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E-learning System Based on Neural Networks
... The change of emotion state is Fig.5 ①surprise ②puzzle, bewilderment ③depression, despair ④ self-confidence With the learning process advancing, learner's emotion is changed. For instance, the learner has an idea to ...
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Quantum-based subgraph convolutional neural networks
... Figure 2: Example of regular grid data and irregular grid data. over local regions and mostly uncorrelated at a global scale. This works well for 165 data on a regular low-dimensional grid, for instance, images and sound ...
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Probabilistic Neural Networks for Rule Based Systems
... rule based system using Probabilistic Neural Networks ...rule based systems using probabilistic neural ...rule based system machine learning approach and to be able to produce ...
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The Research of Data Mining Based on Neural Networks
... Keywords: Data Mining (Dm), Neural Network (Nn), Self-Organizing Map 1. Introduction Along with the unceasing development of information technology and application, data rapid expansion, the surge of data behind ...
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Session-Based Recommendation with Graph Neural Networks
... Nowadays, neural network has been employed for generating representation for graph- structured data, ...nodes based on random ...classical neural net- work CNN and RNN are also deployed on ...
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Music composition based on Artificial Neural Networks
... 1.1. Motivation The motivation underlying this Bachelor Thesis relies in the idea of making the music creation process easier and more accessible to people. Learning to compose music might take many years from a formal ...
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Toward a Deep Neural Approach for Knowledge-Based IR
... structure neural net- ...enhanced knowledge-based representation of the docu- ment and the query or as a translation representation bridg- ing the semantic gap between the document and the query ...
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