This paper presents a new single-layer neural network which is based on orthogonal functions . This neural network is developed to avoid the problems of traditional feedforward neural networks such as the determination of initial weights and the numbers of layers and processing elements . The desired output accuracy determines the required number of processing elements . This article is designed to give the reader the key insights into the world of artificial neural networks ( ANNs ) , seen here as generalized mathematical models that emulate human cognition . The inner workings of ANNs are highlighted such as the topology itself , activation function , connection weights , combination function , distribution of outputs , sigmoid function , network architecture , its multilayer nature , high-order statistics , feedforward networks , learning process , training patterns , the perceptron model , backpropagation ( BP ) algorithm , among many other technical issues . The entry also includes many multiple academic research applications in marketing . In this conceptual paper we show , how we can combine both network architectures to benefit from their advantages . For this purpose , we consider learning vector quantizers in terms of feedforward network architectures and explain how it can be combined effectively with multilayer or single-layer feedforward network architectures . This approach includes deep and flat architectures as well as the popular extreme learning machines . Attention maps are used for adaptive feature refinement . STBTA can be plugged into any feedforward network architectures and is end-to-end trainable along with CNN . Extensive experiments on UCF101 , HMDB51 , Kinetics-400 datasets demonstrate that the proposed method can improve the performance for action recognition . Inspired by predictive coding in neuroscience , we designed a bi-directional and recurrent neural net , namely deep predictive coding networks ( PCN ) . It uses convolutional layers in both feedforward and feedback networks , and recurrent connections within each layer . Feedback connections from a higher layer carry the prediction of its lower-layer representation ; feedforward connections carry the prediction errors to its higher-layer . The focus of the research was to develop and test various data compression methods ( for eddy current data ) and the performance of different neural network paradigms for defect classification and defect parameter estimation . Feedforward , fully-connected neural networks , that use the back-propagation algorithm for network training , were implemented for defect classification and defect parameter estimation using a modular network architecture . A large eddy current tube inspection database was acquired from the Metals and Ceramics Division of Oak Ridge National Laboratory ( ORNL ) . We train a classifier on top of our multilingual encoder using the usual combination of the two sentence embeddings : , where and are the premise and hypothesis . For that purpose , we use a feed - forward neural network with two hidden layers of size 512 and 384 , trained with Adam . All hyperparameters were optimized on the English XNLI development corpus , and then , the same classifier was applied to all languages of the XNLI test set . j. In GNMT , the attention function is implemented as a feed forward neural network with a hidden layer of size n. Given a sequence of tokens , it is standard to form a context - independent token representation for each token position using pre - trained word embeddings and optionally character - based representations . Then , the model forms a context - sensitive representation , typically using either bidirectional RNNs , CNNs , or feed forward networks . To add ELMo to the supervised model , we first freeze the weights of the biLM and then concatenate the ELMo vector with and pass the ELMo enhanced representation into the task RNN . Avoid representational bottlenecks , especially early in the network . Feed - forward networks can be represented by an acyclic graph from the input layer ( s ) to the classifier or regressor . This defines a clear direction for the information flow . The highway data are derived from Ontario Department of Highways road maps from 1925 to 1971 . Observation of the set of maps shows a striking pattern in the growth of the improved highway network . From these analyses a lag regression model of changing spatial structure has been formulated and estimated . The US has been very successful in the construction and management of expressway . Its experiences are of great referential value to us , including the expressway numbering system , exclusive multilanes , tollcharging without stopping system , solutions to the tidal transportation problem , and its management ways of monofunctional police , highway control network system , fines substituting for education mode , traveling log recording , and free services . This paper recounts briefly to establish highway control network method , aiming to projection length deformation being bigger in Xinzhou highway survey , puts forward the method of horizontal coordinate system of option zone of central meridian being 11430 . And then using mass center formula to calculate four parameters of translation axes and rotation coordinate , mathematic model is established . Based on the index system of highway evaluation and the knowledge of GIS , GIS is applied in the highway network evaluation of Yangtze Delta . The frame of the highway network information system , layers about the highway network information system , and the evaluation result of the highway network in Yangtze Delta are presented . Expectation of GIS in the field of the highway evaluation is advanced . We present an improved three-step pipeline for the stereo matching problem and introduce multiple novelties at each stage . We propose a new highway network architecture for computing the matching cost at each possible disparity , based on multilevel weighted residual shortcuts , trained with a hybrid loss that supports multilevel comparison of image patches . A novel post-processing step is then introduced , which employs a second deep convolutional neural network for pooling global information from multiple disparities . The analytical results are validated by extensive simulations . Our derived highway network connectivity model could be applied in the study of a number of metrics related to connectivity in VANETs . A new approach to the highway network design model is presented that allows comparisonsof networks on the basis of multiple incommensurable objectives with different degrees of importance . The goal-programming approach is not only capable of solving the multiobjective network design problem in a relatively efficient manner , but it can also be used to generate the multidimensional trade-off curve that provides additional important information to that provided in the two-dimensional curve derived by using the linear programming model . The optimal path analysis is one of the important function in freeway emergency rescue system(FERS).To achieve the shortest time taken to reach the accident point , introduce the actual travel time as the road-section weight into the highway network topology model . Analyze deficiencies of the basic ant colony algorithm in solving the optimal path of FERS , and present an improved algorithm on the initialization and renewal mechanism of the pheromone and the heuristic direction information . In order to realize this goal , this paper improved the set of indicators for district 's spatial structure by supplementing the industry spatial harmony indicator and modifying the weight of adjustment matrix . The result shows : Xi'an 's spatial structure is turning harmonious gradually , though spatial structure should still be optimized;The distribution of towns in district should be modulated to build a centralized and correspondingly decentralizalized spatial structure;The radiation of central city should be sequentially strengthened , the trend to buid up same industries should be controlled , highway traffical network should be constructed and improve urbanlization and town density should improved as well . Very deep convolutional neural networks introduced new problems like vanishing gradient and degradation . The recent successful contributions towards solving these problems are Residual and Highway Networks . These networks introduce skip connections that allow the information ( from the input or those learned in earlier layers ) to flow more into the deeper layers . We used a modified version of the L regularization proposed in LoshchilovH17 , with . We additionally used a dropout SrivastavaHKSS14 rate of 0.3 for all highway networks and residual and scaled dot - product attention operations in the multi - head attention mechanism . We also used one - sided label smoothing SzegedyVISW16 for the passage relevance and answer possibility labels . ( Fig . 6 , middle ) . It has fewer parameters than other deep and thin networks such as FitNet [ reference ] and Highway [ reference ] ( Table 6 ) , yet is among the state - of - the - art results ( 6.43 % , Table 6 ) . Analysis of Layer Responses . Finally we apply a max - over - time pooling operation over the feature maps . To enhance the performance , we also add the highway architecture based on the pooled feature maps . Finally , a fully connected layer with sigmoid activation is used to output the probability that the input sequence is real . “ interleaved ” biLSTM with a 300 dimensional hidden size , in which the directions of the LSTM layers alternate per layer . This deep LSTM uses Highway connections Srivastava2015TrainingVD between layers and variational recurrent dropout Gal2016ATG . On the other hand , if we use the addition of the representation after highway network and the representation after self - attention as skip connection as in experiment 7 , the performance increase to 77.3 and 76.3 . The comparison indicates self - attention layer makes the training harder to converge while a skip connection could ease the gradient flow for both highway layer and self - attention layer . By comparing the base model and the model the in experiment 6 , we show that the fuse gate not only well serves as a skip connection , but also makes good decision upon which information the fuse for both representation . Densely-connected networks are prominent among natural systems , exhibiting structural characteristics often optimized for biological function . To reveal such features in highly-connected networks , we introduce a new network characterization determined by a decomposition of network-connectivity into low-rank and sparse components . Along this direction , here we demonstrate that given only one neuron in each layer , the shortcuts can be sparsely placed to let the slim network become an universal approximator . Potentially , our theoretically-guaranteed sparse network model can achieve a learning performance comparable to densely-connected networks on well-known benchmarks . Secondly , a self-attention mechanism is introduced to further learn the internal structure information of all pixels in the neighborhood window to generate a multidimensional representation . Finally , in order to avoid the loss of information transmitted between layers , Dense nets are used to implement information flow integration , and a multi-layered independent recurrent neural network is added between each densely connected module to solve the gradient disappearance . Experimental results show that this method is superior to traditional deep learning methods in image description and recognition . The size-invariant skill helps the deep layers create feature maps with high resolution . The dense connectivity technique is utilized to integrate those hierarchical features and reuse characteristic maps to enhance the network 's learning ability . Finally , an optimized threshold is used on the output image to obtain a binary image . In this paper , we develop an ACE-net that aims to enhance the feature representation and utilization by augmenting the contracting and expansive paths . In particular , we augment the paths by the recently proposed advanced techniques including ASPP , dense connection and deep supervision mechanisms , and novel connections such as directly connecting the raw image to the expansive side . With these augmentations , ACE-net can utilize features from multiple sources , scales and reception fields to segment while still maintains a relative simple architecture . In this paper , we present a novel hybrid deep learning network for human activity recognition that also employs multimodal sensor data ; however , our proposed model is a ConvLSTM pipeline that makes full use of the information in each layer extracted along the temporal domain . Thus , we propose a dense connection module ( DCM ) to ensure maximum information flow between the network layers . Furthermore , we employ a multilayer feature aggregation module ( MFAM ) to extract features along the spatial domain , and we aggregate the features obtained from every convolutional layer according to the importance of features in different spatial locations . The efficient 3D convolutional block is composed of 1 × 3 × 3 convolutional layers for obtaining spatial features of video and 3 × 1 × 1 convolutional layers for obtaining temporal features of video . Efficient 3D convolutional blocks are combined in multiple locations of dense residual network , which not only takes advantage of the advantages of easy optimization of residual blocks and feature reuse of dense connected network , but also can shorten the training time and improve the efficiency and performance of spatial-temporal feature extraction of the network . In the classical data set UCF101 , HMDB51 and the dynamic multi-view complicated 3D database of human activity ( DMV action3D ) , it is verified that the 3D-EDRNs combined with 3D convolutional block can significantly reduce the complexity of the model , effectively improve the classification performance of the network , and have the advantages of less computational resource demand , small number of parameters and short training time . Combining Dense network with U-Net network , Iris is segmented by taking advantage of dense U-Net network , which is narrower and has fewer parameters , and taking advantage of U-Net in semantic segmentation . Dense connected path is derived from dense connected network ( Dense U-Net ) , in which improved information and parameters are helpful to reduce the training difficulty of deep network . The final segmentation accuracy was 98 . 36 % . They require high compute density to support matrix-matrix and matrix-vector operations , but they rarely require 64bit or even 32bits of precision , thus architects are creating new instructions and new design points to accelerate training . Most current DNNs rely on dense fully connected networks and convolutional networks and thus are reasonably matched to current HPC accelerators . However future DNNs may rely less on dense communication patterns . In order to directly use in-phase and quadrature ( IQ ) data obtained by the receiver and enhance the efficiency of network extraction features to improve the recognition rate of modulation mode , this paper proposes a new network structure called Fully Dense Neural Network ( FDNN ) . This network uses residual blocks to extract features , dense connect to reduce model size , and adds attentions mechanism to recalibrate . Experiments on RML2016.10a show that this network has a higher recognition rate and lower model complexity . Abstract Image super-resolution ( SR ) techniques with deep convolutional network ( CNN ) have achieved significant improvements compared to previous shallow-learning-based methods . Especially for dense connection based networks , these methods have yielded unprecedented achievements but bring the higher complexity and more parameters . To this end , this paper considers both reconstruction performance and efficiency , and advocates a novel hierarchical dense connection network ( HDN ) for image SR . In this paper , we present a Convolutional AutoEncoder ( CAE ) for single image dehazing . Our CAE makes use of Densely Connection Networks as its encoder and decoder . It is trained with the corresponding hazy and clean images at the input and output , enabling it to remove the haze without having to rely on an atmospheric scattering model . In this work , we aim to study the advantages and limitations of both topologies and further enrich the path design by proposing a dual path architecture . In particular , we first provide a new understanding of the densely connected networks from the lens of a higher order recurrent neural network ( HORNN ) , and explore the relations between densely connected networks and residual networks . More specifically , we bridge the densely connected networks with the HORNNs , showing that the densely connected networks are HORNNs when the weights are shared across steps . Instead , they proposed the DRRN that applies both global and local learning while remaining parameter efficient via recursive learning . More recently , Tong et al . proposed making use of Densely Connected Networks ( DenseNet ) instead of ResNet as the building block for image SR . They demonstrated that the DenseNet structure is better at combining features at different levels , which boosts SR performance . We also recast HypER in terms of tensor operations showing that , despite the convolution operation , it is closely related to the established family of tensor factorization models . Our results suggest that convolution provides a good tradeoff between expressiveness and parameter number compared to a dense network . HypER is fast , robust to overfitting , has relatively few parameters , and achieves state - of - the - art results across almost all metrics on multiple link prediction datasets . Two different ways of excitation control of synchronous generator are considered in this article . First way is with adaptive neuron and the other way is with PI voltage controller . Control structure with adaptive neuron is implemented in digital control system . In this paper the fusion of artificial neural networks , granular computing and learning automata theory is proposed and we present as a final result ANLAGIS , an adaptive neuron-like network based on learning automata and granular inference systems . ANLAGIS can be applied to both pattern recognition and learning control problems . Applications to pattern recognition and machine learning . In this paper the fusion of artificial neural networks , granular computing and learning automata theory is proposed and we present as a final result ANLAGIS , an adaptive neuron-like network based on learning automata and granular inference systems . ANLAGIS can be applied to both pattern recognition and learning control problems . This paper gives a detailed description of the power quality analysis framework in networked environment and presents a fast and resolution-enhanced method for harmonic and interharmonic measurement . The proposed method first extracts harmonic and interharmonic components efficiently using the single-channel version of Robust Independent Component Analysis ( RobustICA ) , then estimates the high-resolution frequency from three discrete Fourier transform ( DFT ) samples with little additional computation , and finally computes the amplitudes and phases with the adaptive linear neuron network . The experiments show that the proposed method is time-efficient and leads to a better accuracy of the simulated and experimental signals in the presence of noise and fundamental frequency deviation , thus providing a deeper insight into the (inter)harmonic sources or even the whole system . However , the link between optimal plasticity and STDP in cortex remains loose , as does the relationship between STDP and adaptation processes . We investigate how STDP , as described by recent minimal models derived from experimental data , influences the quality of information transmission in an adapting neuron . We show that a phenomenological model based on triplets of spikes yields almost the same information rate as an optimal model specially designed to this end . Abstract We study an integrate and fire model of an adapting neuron . Using two dynamic thresholds , we account for the complex long-term behavior of single neurons under periodic pulsed inputs . Adaptive neuron circuit presented in this paper successfully exhibits spike-triggered adaptation with ratio between maximum and minimum firing rate ranging from 7.97 to 18.4 . Compared to conventional researches , adaptive neuron circuit proposed in this paper allows more versatile operation and easier fabrication due to utilization of out FG-MOSFET . On the other side , the representation of fuzzy knowledge in the learning neural networks has to be accurate to provide significant improvements for modeling of the studied system dynamic behavior . The results presented in this paper proves clearly that the proposed detection technique allows the perfect detection of the studied gas turbine malfunctions , furthermore it shows that the use of the proposed technique based on the Adaptive Neuro-Fuzzy Interference System ( ANFIS ) approach which uses the adaptive learning mechanism of neuron networks and fuzzy inference techniques , can be a promising technique to be applied in several industrial application for faults detection . It will be more flexibility to meet changing security environment of such a complex system requirements . The design and achievement of the adaptive learning neuron was stated in detail . Abstract An adaptive neuronal network ( synaptic matrix ) was simulated . After learning a number of randomly generated scenes , the network was tested for associative sequential recall in a stimulus-bound mode and in an image-bound mode . Three adaptive single-neuron models based on neural analogies of behavior modification episodes are proposed , which attempt to bridge the gap between psychology and neurophysiology . The proposed models capture the predictive nature of Pavlovian conditioning , which is essential to the theory of adaptive/learning systems . Its ' syntax , semantics and fuzzy tableau algorithm are presented in detail . Dur work enhances the expressiveness and reasoning ability of ALCN . IFALCN is the generalization of fuzzy ALCN based on single value and more expressive than the latter and can conform to human cognition better . These have been first introduced by Baader and Sattler as ALCN(M ) languages , with the adoption of role constructors M subset-of {o,-,And , Or}. In particular , they showed in 1999 that , although ALCN(o ) is decidable , the addition of other operators may easily lead to undecidability : in fact , ALCN(o , And ) and ALCN(o,-,Or ) were proved undecidable . In this work , we further investigate the computational properties of the ALCN family , aiming at narrowing the decidability gap left open by Baader and Sattler 's results . In particular , we will show that ALCN(o ) extended with inverse roles both in number and in value restrictions becomes undecidable , whereas it can be safely extended with qualified number restrictions without losing decidability . The original motivation for this work was to develop a description logic suitable for qualitative spatial reasoning problems . We quickly deene and discuss the DLs ALC RA , ALC RA , ALC RASG and ALCN RASG . All but ALC RASG are shown to be undecidable , and ALC RASG is of limited utility , even though it is still as expressive as ALC R + . In our research , the context will be adapted as meta-data to annotate ontological information elements ( concepts , roles , objects , attributes , etc . ) to describe their contextual information . Our specific objective is to express ontological information elements as a data set with the multiple perspectives using a modal description logics language ( Modal ALCN ) . Problems and objectives In the dissertation , we are investigating the multi-representations problem and its impact on semantic heterogeneity for domain-specific ontology . This work contributes to the field in three ways . First , the DL ALCN was used in a matchmaking process that yields a justified ranking of results . Second , the matchmaking process provides useful information to the requester/provider in order to modify or refine the original request/capability description for a better result . The tableau algorithms are developed in the style of so-called constraint propagation method . Extended fuzzy ALCN is more expressive than the existing fuzzy description logics and present more wide fuzzy information . In particular , we are mainly interested in Logics ( called ALC N ) allowing for the same kind of complex roles both in number and in value restrictions , which represent very expressive description languages and can be shown very useful for applications . We investigate the computational properties of various ALCN extensions and slightly improve the (un)decidability results following from the study published by Baader and Sattler in 1999 . In particular , we will show by reduction of a domino problem that ALC N(+ ; t ) and ALC N(+ ; u ) are undecidable . Motivated by a chemical engineering application , we introduce an extension of the concept description language ALCN by symbolic number restrictions . This first extension turns out to have an undecidable concept satisfiability problem .
This paper presents a new single-layer neural network which is based on orthogonal functions . This neural network is developed to avoid the problems of traditional feedforward neural networks such as the determination of initial weights and the numbers of layers and processing elements . The desired output accuracy determines the required number of processing elements .
This article is designed to give the reader the key insights into the world of artificial neural networks ( ANNs ) , seen here as generalized mathematical models that emulate human cognition . The inner workings of ANNs are highlighted such as the topology itself , activation function , connection weights , combination function , distribution of outputs , sigmoid function , network architecture , its multilayer nature , high-order statistics , feedforward networks , learning process , training patterns , the perceptron model , backpropagation ( BP ) algorithm , among many other technical issues . The entry also includes many multiple academic research applications in marketing .
In this conceptual paper we show , how we can combine both network architectures to benefit from their advantages . For this purpose , we consider learning vector quantizers in terms of feedforward network architectures and explain how it can be combined effectively with multilayer or single-layer feedforward network architectures . This approach includes deep and flat architectures as well as the popular extreme learning machines .
Attention maps are used for adaptive feature refinement . STBTA can be plugged into any feedforward network architectures and is end-to-end trainable along with CNN . Extensive experiments on UCF101 , HMDB51 , Kinetics-400 datasets demonstrate that the proposed method can improve the performance for action recognition .
Inspired by predictive coding in neuroscience , we designed a bi-directional and recurrent neural net , namely deep predictive coding networks ( PCN ) . It uses convolutional layers in both feedforward and feedback networks , and recurrent connections within each layer . Feedback connections from a higher layer carry the prediction of its lower-layer representation ; feedforward connections carry the prediction errors to its higher-layer .
The focus of the research was to develop and test various data compression methods ( for eddy current data ) and the performance of different neural network paradigms for defect classification and defect parameter estimation . Feedforward , fully-connected neural networks , that use the back-propagation algorithm for network training , were implemented for defect classification and defect parameter estimation using a modular network architecture . A large eddy current tube inspection database was acquired from the Metals and Ceramics Division of Oak Ridge National Laboratory ( ORNL ) .
We train a classifier on top of our multilingual encoder using the usual combination of the two sentence embeddings : , where and are the premise and hypothesis . For that purpose , we use a feed - forward neural network with two hidden layers of size 512 and 384 , trained with Adam . All hyperparameters were optimized on the English XNLI development corpus , and then , the same classifier was applied to all languages of the XNLI test set .
j. In GNMT , the attention function is implemented as a feed forward neural network with a hidden layer of size n.
Given a sequence of tokens , it is standard to form a context - independent token representation for each token position using pre - trained word embeddings and optionally character - based representations . Then , the model forms a context - sensitive representation , typically using either bidirectional RNNs , CNNs , or feed forward networks . To add ELMo to the supervised model , we first freeze the weights of the biLM and then concatenate the ELMo vector with and pass the ELMo enhanced representation into the task RNN .
Avoid representational bottlenecks , especially early in the network . Feed - forward networks can be represented by an acyclic graph from the input layer ( s ) to the classifier or regressor . This defines a clear direction for the information flow .
The highway data are derived from Ontario Department of Highways road maps from 1925 to 1971 . Observation of the set of maps shows a striking pattern in the growth of the improved highway network . From these analyses a lag regression model of changing spatial structure has been formulated and estimated .
The US has been very successful in the construction and management of expressway . Its experiences are of great referential value to us , including the expressway numbering system , exclusive multilanes , tollcharging without stopping system , solutions to the tidal transportation problem , and its management ways of monofunctional police , highway control network system , fines substituting for education mode , traveling log recording , and free services .
This paper recounts briefly to establish highway control network method , aiming to projection length deformation being bigger in Xinzhou highway survey , puts forward the method of horizontal coordinate system of option zone of central meridian being 11430 . And then using mass center formula to calculate four parameters of translation axes and rotation coordinate , mathematic model is established .
Based on the index system of highway evaluation and the knowledge of GIS , GIS is applied in the highway network evaluation of Yangtze Delta . The frame of the highway network information system , layers about the highway network information system , and the evaluation result of the highway network in Yangtze Delta are presented . Expectation of GIS in the field of the highway evaluation is advanced .
We present an improved three-step pipeline for the stereo matching problem and introduce multiple novelties at each stage . We propose a new highway network architecture for computing the matching cost at each possible disparity , based on multilevel weighted residual shortcuts , trained with a hybrid loss that supports multilevel comparison of image patches . A novel post-processing step is then introduced , which employs a second deep convolutional neural network for pooling global information from multiple disparities .
The analytical results are validated by extensive simulations . Our derived highway network connectivity model could be applied in the study of a number of metrics related to connectivity in VANETs .
A new approach to the highway network design model is presented that allows comparisonsof networks on the basis of multiple incommensurable objectives with different degrees of importance . The goal-programming approach is not only capable of solving the multiobjective network design problem in a relatively efficient manner , but it can also be used to generate the multidimensional trade-off curve that provides additional important information to that provided in the two-dimensional curve derived by using the linear programming model .
The optimal path analysis is one of the important function in freeway emergency rescue system(FERS).To achieve the shortest time taken to reach the accident point , introduce the actual travel time as the road-section weight into the highway network topology model . Analyze deficiencies of the basic ant colony algorithm in solving the optimal path of FERS , and present an improved algorithm on the initialization and renewal mechanism of the pheromone and the heuristic direction information .
In order to realize this goal , this paper improved the set of indicators for district 's spatial structure by supplementing the industry spatial harmony indicator and modifying the weight of adjustment matrix . The result shows : Xi'an 's spatial structure is turning harmonious gradually , though spatial structure should still be optimized;The distribution of towns in district should be modulated to build a centralized and correspondingly decentralizalized spatial structure;The radiation of central city should be sequentially strengthened , the trend to buid up same industries should be controlled , highway traffical network should be constructed and improve urbanlization and town density should improved as well .
Very deep convolutional neural networks introduced new problems like vanishing gradient and degradation . The recent successful contributions towards solving these problems are Residual and Highway Networks . These networks introduce skip connections that allow the information ( from the input or those learned in earlier layers ) to flow more into the deeper layers .
We used a modified version of the L regularization proposed in LoshchilovH17 , with . We additionally used a dropout SrivastavaHKSS14 rate of 0.3 for all highway networks and residual and scaled dot - product attention operations in the multi - head attention mechanism . We also used one - sided label smoothing SzegedyVISW16 for the passage relevance and answer possibility labels .
( Fig . 6 , middle ) . It has fewer parameters than other deep and thin networks such as FitNet [ reference ] and Highway [ reference ] ( Table 6 ) , yet is among the state - of - the - art results ( 6.43 % , Table 6 ) . Analysis of Layer Responses .
Finally we apply a max - over - time pooling operation over the feature maps . To enhance the performance , we also add the highway architecture based on the pooled feature maps . Finally , a fully connected layer with sigmoid activation is used to output the probability that the input sequence is real .
“ interleaved ” biLSTM with a 300 dimensional hidden size , in which the directions of the LSTM layers alternate per layer . This deep LSTM uses Highway connections Srivastava2015TrainingVD between layers and variational recurrent dropout Gal2016ATG .
On the other hand , if we use the addition of the representation after highway network and the representation after self - attention as skip connection as in experiment 7 , the performance increase to 77.3 and 76.3 . The comparison indicates self - attention layer makes the training harder to converge while a skip connection could ease the gradient flow for both highway layer and self - attention layer . By comparing the base model and the model the in experiment 6 , we show that the fuse gate not only well serves as a skip connection , but also makes good decision upon which information the fuse for both representation .
Densely-connected networks are prominent among natural systems , exhibiting structural characteristics often optimized for biological function . To reveal such features in highly-connected networks , we introduce a new network characterization determined by a decomposition of network-connectivity into low-rank and sparse components .
Along this direction , here we demonstrate that given only one neuron in each layer , the shortcuts can be sparsely placed to let the slim network become an universal approximator . Potentially , our theoretically-guaranteed sparse network model can achieve a learning performance comparable to densely-connected networks on well-known benchmarks .
Secondly , a self-attention mechanism is introduced to further learn the internal structure information of all pixels in the neighborhood window to generate a multidimensional representation . Finally , in order to avoid the loss of information transmitted between layers , Dense nets are used to implement information flow integration , and a multi-layered independent recurrent neural network is added between each densely connected module to solve the gradient disappearance . Experimental results show that this method is superior to traditional deep learning methods in image description and recognition .
The size-invariant skill helps the deep layers create feature maps with high resolution . The dense connectivity technique is utilized to integrate those hierarchical features and reuse characteristic maps to enhance the network 's learning ability . Finally , an optimized threshold is used on the output image to obtain a binary image .
In this paper , we develop an ACE-net that aims to enhance the feature representation and utilization by augmenting the contracting and expansive paths . In particular , we augment the paths by the recently proposed advanced techniques including ASPP , dense connection and deep supervision mechanisms , and novel connections such as directly connecting the raw image to the expansive side . With these augmentations , ACE-net can utilize features from multiple sources , scales and reception fields to segment while still maintains a relative simple architecture .
In this paper , we present a novel hybrid deep learning network for human activity recognition that also employs multimodal sensor data ; however , our proposed model is a ConvLSTM pipeline that makes full use of the information in each layer extracted along the temporal domain . Thus , we propose a dense connection module ( DCM ) to ensure maximum information flow between the network layers . Furthermore , we employ a multilayer feature aggregation module ( MFAM ) to extract features along the spatial domain , and we aggregate the features obtained from every convolutional layer according to the importance of features in different spatial locations .
The efficient 3D convolutional block is composed of 1 × 3 × 3 convolutional layers for obtaining spatial features of video and 3 × 1 × 1 convolutional layers for obtaining temporal features of video . Efficient 3D convolutional blocks are combined in multiple locations of dense residual network , which not only takes advantage of the advantages of easy optimization of residual blocks and feature reuse of dense connected network , but also can shorten the training time and improve the efficiency and performance of spatial-temporal feature extraction of the network . In the classical data set UCF101 , HMDB51 and the dynamic multi-view complicated 3D database of human activity ( DMV action3D ) , it is verified that the 3D-EDRNs combined with 3D convolutional block can significantly reduce the complexity of the model , effectively improve the classification performance of the network , and have the advantages of less computational resource demand , small number of parameters and short training time .
Combining Dense network with U-Net network , Iris is segmented by taking advantage of dense U-Net network , which is narrower and has fewer parameters , and taking advantage of U-Net in semantic segmentation . Dense connected path is derived from dense connected network ( Dense U-Net ) , in which improved information and parameters are helpful to reduce the training difficulty of deep network . The final segmentation accuracy was 98 . 36 % .
They require high compute density to support matrix-matrix and matrix-vector operations , but they rarely require 64bit or even 32bits of precision , thus architects are creating new instructions and new design points to accelerate training . Most current DNNs rely on dense fully connected networks and convolutional networks and thus are reasonably matched to current HPC accelerators . However future DNNs may rely less on dense communication patterns .
In order to directly use in-phase and quadrature ( IQ ) data obtained by the receiver and enhance the efficiency of network extraction features to improve the recognition rate of modulation mode , this paper proposes a new network structure called Fully Dense Neural Network ( FDNN ) . This network uses residual blocks to extract features , dense connect to reduce model size , and adds attentions mechanism to recalibrate . Experiments on RML2016.10a show that this network has a higher recognition rate and lower model complexity .
Abstract Image super-resolution ( SR ) techniques with deep convolutional network ( CNN ) have achieved significant improvements compared to previous shallow-learning-based methods . Especially for dense connection based networks , these methods have yielded unprecedented achievements but bring the higher complexity and more parameters . To this end , this paper considers both reconstruction performance and efficiency , and advocates a novel hierarchical dense connection network ( HDN ) for image SR .
In this paper , we present a Convolutional AutoEncoder ( CAE ) for single image dehazing . Our CAE makes use of Densely Connection Networks as its encoder and decoder . It is trained with the corresponding hazy and clean images at the input and output , enabling it to remove the haze without having to rely on an atmospheric scattering model .
In this work , we aim to study the advantages and limitations of both topologies and further enrich the path design by proposing a dual path architecture . In particular , we first provide a new understanding of the densely connected networks from the lens of a higher order recurrent neural network ( HORNN ) , and explore the relations between densely connected networks and residual networks . More specifically , we bridge the densely connected networks with the HORNNs , showing that the densely connected networks are HORNNs when the weights are shared across steps .
Instead , they proposed the DRRN that applies both global and local learning while remaining parameter efficient via recursive learning . More recently , Tong et al . proposed making use of Densely Connected Networks ( DenseNet ) instead of ResNet as the building block for image SR . They demonstrated that the DenseNet structure is better at combining features at different levels , which boosts SR performance .
We also recast HypER in terms of tensor operations showing that , despite the convolution operation , it is closely related to the established family of tensor factorization models . Our results suggest that convolution provides a good tradeoff between expressiveness and parameter number compared to a dense network . HypER is fast , robust to overfitting , has relatively few parameters , and achieves state - of - the - art results across almost all metrics on multiple link prediction datasets .
Two different ways of excitation control of synchronous generator are considered in this article . First way is with adaptive neuron and the other way is with PI voltage controller . Control structure with adaptive neuron is implemented in digital control system .
In this paper the fusion of artificial neural networks , granular computing and learning automata theory is proposed and we present as a final result ANLAGIS , an adaptive neuron-like network based on learning automata and granular inference systems . ANLAGIS can be applied to both pattern recognition and learning control problems .
Applications to pattern recognition and machine learning . In this paper the fusion of artificial neural networks , granular computing and learning automata theory is proposed and we present as a final result ANLAGIS , an adaptive neuron-like network based on learning automata and granular inference systems . ANLAGIS can be applied to both pattern recognition and learning control problems .
This paper gives a detailed description of the power quality analysis framework in networked environment and presents a fast and resolution-enhanced method for harmonic and interharmonic measurement . The proposed method first extracts harmonic and interharmonic components efficiently using the single-channel version of Robust Independent Component Analysis ( RobustICA ) , then estimates the high-resolution frequency from three discrete Fourier transform ( DFT ) samples with little additional computation , and finally computes the amplitudes and phases with the adaptive linear neuron network . The experiments show that the proposed method is time-efficient and leads to a better accuracy of the simulated and experimental signals in the presence of noise and fundamental frequency deviation , thus providing a deeper insight into the (inter)harmonic sources or even the whole system .
However , the link between optimal plasticity and STDP in cortex remains loose , as does the relationship between STDP and adaptation processes . We investigate how STDP , as described by recent minimal models derived from experimental data , influences the quality of information transmission in an adapting neuron . We show that a phenomenological model based on triplets of spikes yields almost the same information rate as an optimal model specially designed to this end .
Abstract We study an integrate and fire model of an adapting neuron . Using two dynamic thresholds , we account for the complex long-term behavior of single neurons under periodic pulsed inputs .
Adaptive neuron circuit presented in this paper successfully exhibits spike-triggered adaptation with ratio between maximum and minimum firing rate ranging from 7.97 to 18.4 . Compared to conventional researches , adaptive neuron circuit proposed in this paper allows more versatile operation and easier fabrication due to utilization of out FG-MOSFET .
On the other side , the representation of fuzzy knowledge in the learning neural networks has to be accurate to provide significant improvements for modeling of the studied system dynamic behavior . The results presented in this paper proves clearly that the proposed detection technique allows the perfect detection of the studied gas turbine malfunctions , furthermore it shows that the use of the proposed technique based on the Adaptive Neuro-Fuzzy Interference System ( ANFIS ) approach which uses the adaptive learning mechanism of neuron networks and fuzzy inference techniques , can be a promising technique to be applied in several industrial application for faults detection .
It will be more flexibility to meet changing security environment of such a complex system requirements . The design and achievement of the adaptive learning neuron was stated in detail .
Abstract An adaptive neuronal network ( synaptic matrix ) was simulated . After learning a number of randomly generated scenes , the network was tested for associative sequential recall in a stimulus-bound mode and in an image-bound mode .
Three adaptive single-neuron models based on neural analogies of behavior modification episodes are proposed , which attempt to bridge the gap between psychology and neurophysiology . The proposed models capture the predictive nature of Pavlovian conditioning , which is essential to the theory of adaptive/learning systems .
Its ' syntax , semantics and fuzzy tableau algorithm are presented in detail . Dur work enhances the expressiveness and reasoning ability of ALCN . IFALCN is the generalization of fuzzy ALCN based on single value and more expressive than the latter and can conform to human cognition better .
These have been first introduced by Baader and Sattler as ALCN(M ) languages , with the adoption of role constructors M subset-of {o,-,And , Or}. In particular , they showed in 1999 that , although ALCN(o ) is decidable , the addition of other operators may easily lead to undecidability : in fact , ALCN(o , And ) and ALCN(o,-,Or ) were proved undecidable . In this work , we further investigate the computational properties of the ALCN family , aiming at narrowing the decidability gap left open by Baader and Sattler 's results . In particular , we will show that ALCN(o ) extended with inverse roles both in number and in value restrictions becomes undecidable , whereas it can be safely extended with qualified number restrictions without losing decidability .
The original motivation for this work was to develop a description logic suitable for qualitative spatial reasoning problems . We quickly deene and discuss the DLs ALC RA , ALC RA , ALC RASG and ALCN RASG . All but ALC RASG are shown to be undecidable , and ALC RASG is of limited utility , even though it is still as expressive as ALC R + .
In our research , the context will be adapted as meta-data to annotate ontological information elements ( concepts , roles , objects , attributes , etc . ) to describe their contextual information . Our specific objective is to express ontological information elements as a data set with the multiple perspectives using a modal description logics language ( Modal ALCN ) . Problems and objectives In the dissertation , we are investigating the multi-representations problem and its impact on semantic heterogeneity for domain-specific ontology .
This work contributes to the field in three ways . First , the DL ALCN was used in a matchmaking process that yields a justified ranking of results . Second , the matchmaking process provides useful information to the requester/provider in order to modify or refine the original request/capability description for a better result .
The tableau algorithms are developed in the style of so-called constraint propagation method . Extended fuzzy ALCN is more expressive than the existing fuzzy description logics and present more wide fuzzy information .
In particular , we are mainly interested in Logics ( called ALC N ) allowing for the same kind of complex roles both in number and in value restrictions , which represent very expressive description languages and can be shown very useful for applications . We investigate the computational properties of various ALCN extensions and slightly improve the (un)decidability results following from the study published by Baader and Sattler in 1999 . In particular , we will show by reduction of a domino problem that ALC N(+ ; t ) and ALC N(+ ; u ) are undecidable .
Motivated by a chemical engineering application , we introduce an extension of the concept description language ALCN by symbolic number restrictions . This first extension turns out to have an undecidable concept satisfiability problem .
An orthogonal neural network for function approximation Artificial Neural Networks in Marketing Fusion of deep learning architectures, multilayer feedforward networks and learning vector quantizers for deep classification learning Spatial-Temporal Bottom-Up Top-Down Attention Model for Action Recognition Deep Predictive Coding Network for Object Recognition An integrated signal processing and neural networks system for steam generator tubing diagnostics using eddy current inspection Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer Deep contextualized word representations Rethinking the Inception Architecture for Computer Vision Lag Regression Models of the Spatial Spread of Highway Improvements The Illumination of the Construction and Management of the US Expressway to Us Actualization and Discussion on Xinzhou Highway Horizontal Control Network Survey Problem Design and Implementation of Evaluation System of Highway Network Based on GIS Improved Stereo Matching with Constant Highway Networks and Reflective Confidence Learning Performance analysis on network connectivity for vehicular ad hoc networks GOAL-PROGRAMMING APPROACH TO MULTIOBJECTIVE HIGHWAY NETWORK DESIGN MODEL (ABRIDGEMENT) Ant Colony Algorithm Optimization and Its Application in Freeway Emergency Rescue Comprehensive evaluation of Xi'an district spatial structure Deep Residual Networks with Exponential Linear Unit Multi-style Generative Reading Comprehension Deep Residual Learning for Image Recognition SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient Deep contextualized word representations Natural Language Inference over Interaction Space A Novel Characterization of Amalgamated Networks in Natural Systems Universal Approximation by a Slim Network with Sparse Shortcut Connections Multi-modal Remote Sensing Image Description Based on Word Embedding and Self-Attention Mechanism A size-invariant convolutional network with dense connectivity applied to retinal vessel segmentation measured by a unique index. ACE-Net: Biomedical Image Segmentation with Augmented Contracting and Expansive Paths A Hybrid Network Based on Dense Connection and Weighted Feature Aggregation for Human Activity Recognition Efficient 3D dense residual network and its application in human action recognition Study on Iris Segmentation Algorithm Based on Dense U-Net Deep Learning in Cancer and Infectious Disease: Novel Driver Problems for Future HPC Architecture Fully Dense Neural Network for the Automatic Modulation Recognition Hierarchical dense recursive network for image super-resolution Convolutional Autoencoder For Single Image Dehazing Dual Path Networks Image Super-Resolution via Dual-State Recurrent Networks Hypernetwork Knowledge Graph Embeddings Comparison of different methods of excitation control for a synchronous generator Neuro granular networks with self-learning stochastic connections: fusion of neuro granular networks and learning automata theory Fusion of learning automata theory and granular inference systems: ANLAGIS. Applications to pattern recognition and machine learning Resolution-Enhanced Harmonic and Interharmonic Measurement for Power Quality Analysis in Cyber-Physical Energy System STDP in Adaptive Neurons Gives Close-To-Optimal Information Transmission Complex dynamics of neuronal thresholds Input-modulating Adaptive Neuron Circuit Employing Asymmetric Floating-gate MOSFET with Two Independent Control Gates Faults detection in gas turbine using hybrid adaptive network based fuzzy inference systems A Method of Mining the Meta-association Rules for Dynamic Association Rule Based on the Model of AR-Markov Associative sequential recall in a synaptic matrix. Modeling the behavioral substrates of associate learning and memory: adaptive neural models Interval-valued fuzzy description logic IFALCN—Preliminary results On Decidability of Expressive Description Logics with Composition of Roles in Number Restrictions Obstacles on the Way to Qualitative Spatial Reasoning with Description Logics: Some Undecidability Results Towards contextual ontologies requirements and formalization A New Approach To Semantic Matchmaking Using Description Logics Extended Fuzzy ALCN and Its Tableau Algorithm On Expressive Number Restrictions in Description Logics Description Logics with Symbolic Number Restrictions
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Wiley Encyclopedia of Management WSOM ICIG (1) Transactions of the Association for Computational Linguistics ICLR NAACL-HLT 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) CVPR VisionNet'16 ACL 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) AAAI NAACL-HLT ICLR HPDC ICIP NIPS 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ICANN ICONIP (1) Description Logics FSKD (1) Description Logics ECAI
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