[PDF] Top 20 Deep Neural Networks Constrained by Decision Rules
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Deep Neural Networks Constrained by Decision Rules
... of neural networks and the interpretability of decision rules, we propose a hybrid technique called rule-constrained networks, namely, neural networks that make ... See full document
10
Modelling Identity Rules with Neural Networks
... of deep neural networks over recent years, there has been an increasing awareness that there are some tasks that still elude neural network learning or need unrealistic amounts of ...identity ... See full document
26
Feedforward neural networks with constrained weights
... C.6 Decision surfaces after 6 consecutive training runs on problem CI: 2:3:1 network with double-precision weights a-f; 2:3:1 network with integer g-1.. 7 Decision surfaces after 6 conse[r] ... See full document
207
Deep Belief Networks Using Convolution Neural Networks Algorithm
... types of speech classes , feature extraction techniques, speech classifiers and performance evaluation are issues that require attention in designing of speech recognition system. The objective of this review paper is to ... See full document
8
Autonomous Decision Making for a Vehicle
... with Deep Learning and convolutional neural networks[11][17] to create an autonomous decision maker for vehicles which can do all the necessary calculations and computations and processing ... See full document
9
Speech dereverberation for enhancement and recognition using dynamic features constrained deep neural networks and feature adaptation
... other neural networks have been applied to many speech processing ...the deep recurrent neural network (DRNN), is used to estimate clean speech features (MFCC) from noisy ...recurrent ... See full document
18
Application of deep neural networks for security analysis of digital infrastructure componentsa
... a decision is not based on exponential attack of input data and search of expected features of vulnerabilities, at that, the application of neural networks with deep learning will provide ... See full document
10
Machine Learning Perspectives for Dental Imaging
... machine, decision tree, deep neural networks, convolution neural networks, super pixel segmentation, semantic segmentation, ... See full document
5
Unified Framework For Deep Learning Based Text Classification
... convolution networks also perform at par with other conventional ...artificial neural networks (ANN), k- nearest neighbor (KNN), naive Bayes classifier, decision trees, random forests, support ... See full document
5
AN OPTIMISED INTELLECTUAL AGENT BASED SECURE DECISION SYSTEM FOR HEALTH CARE
... functions. Decision Trees and Neural Networks use classification algorithms while Regression, Association Rules and Clustering use prediction algorithms [Charly ...(1998)]. Decision ... See full document
14
Harnessing Deep Neural Networks with Logic Rules
... transition rules (Row 2), the joint teacher model q achieves ...previous neural based methods (Rows 4-7), including the BLSTM-CRF model (Lample et ... See full document
11
Prediction Of Rainfall Using Machine Learning Techniques
... artificial neural networks, back propagation (BPNN), radial basis function (RBFNN) and generalized regression (GRNN) on the rainfall data of India mainly Nanded district, Maharashtra was considered and the ... See full document
5
Deep Convolution Neural Networks for Automatic Eyeglasses Removal
... Convolutional Neural Network (SRCNN) proposed by Dong [6] shows the great potential of an end-to-end DCN in image super- ...that deep learning is useful in the classical computer vision problem of ... See full document
8
A general purpose intelligent surveillance system for mobile devices using deep learning
... The system presented in this paper is only capable of using a pre-trained model for its classification. The truly game- changing capability is to allow a mobile device to learn as it collects samples of images. This is ... See full document
8
TDSNN: From Deep Neural Networks to Deep Spike Neural Networks with Temporal-Coding
... Continuous-valued deep convolutional networks (DNNs) can be converted into accurate rate-coding based spike neural net- works ...(LIF) neural model, we put forward a new coding princi- ple ... See full document
8
Leveraging big data for fuel oil consumption modelling
... shallow neural networks, deep neural networks, support vector machines, and random forest regressors are presented and implemented, comparing ... See full document
9
Events based Multimedia Indexing and Retrieval
... tools, deep neu- ral architectures demonstrated cutting-edge performance in the multimedia analysis, and proved to be effective in a variety of application ... See full document
171
Completeness Problem of the Deep Neural Networks
... Belief Networks (DBN) are generative neural network models with many layers of hidden explanatory factors, recently introduced by Hinton, Osindero, and Teh, along with a greedy layer-wise unsupervised ... See full document
13
Review of Deep Neural Network Based on Auto encoder
... hybrid neural network is to attach the deep belief networks to a sparse ...a deep belief network and also the pre-training initial value of the first restricted Boltzmann machine in the ... See full document
8
Computational methods for predicting functions at the mRNA isoform level
... The model was validated using single mRNA isoform gene pairs, that was referred to as the “gold standard dataset”, using cross-validation. The approach was shown to be accurate when the Area Under the Receiver Operating ... See full document
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