A transformation method from feedforward process neural networks to feedforward neural networks is proposed . Based on the prior knowledge of the feedforward neural networks , a learning algorithm is given . The simulation on the learning algorithm shows the results are satisfying . The networks were trained using the backpropagation algorithm . As a result , I found that feedforward neural networks are much better forecasters than recurrent neural networks and statistical models . KeywordsNeural networks , Time series analysis , Forecasting , Prediction , Statistical models , Sunspots , Autoregressive models . Nonlinear neural-network-based plant modeling is first discussed , based on the approximation capabilities of the multilayer perceptron . A structure is then proposed to utilize feedforward networks within a direct model reference adaptive control strategy . The difficulties involved in training this network , embedded within the closed-loop are discussed and a novel neural-network-based sensitivity modeling approach proposed to allow for the backpropagation of errors through the plant to the neural controller . Further , constructive algorithms have the advantage that polynomial-time learning is possible if network size is chosen by the learning algorithm so that the learning of the problem under consideration is simplified . This article considers the representational ability of feedforward networks ( FFNs ) in terms of the fan-in required by the hidden units of a network . We define network order to be the maximum fan-in of the hidden units of a network . In recent years neural computing has emerged as a practical technology , with successful applications in many fields . The majority of these applications are concerned with problems in pattern recognition , and make use of feedforward network architectures such as the multilayer perceptron and the radial basis function network . Also , it has become widely acknowledged that successful applications of neural computing require a principled , rather than ad hoc , approach . A state-space approach is used and a nonlinear-in-parameters neural network ( NLPNN ) is employed to identify the general unknown fault . The recurrent network configuration is obtained by a combination of feedforward network architectures and dynamical elements in the form of stable filters . The neural network weights are updated based on a modified backpropagation scheme . Next the different architectures are described , including single and multiple perceptron networks , Hopfield and Kohonen nets . A brief discussion of the learning rules employed in feedforward and feedback networks follows . The final section discusses hardware implementations of neural systems with emphasis on analog VLSI . 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 ORNL . Our approach to conditional computation is to introduce a new type of general purpose neural network component : a Sparsely - Gated Mixture - of - Experts Layer ( MoE ) . The MoE consists of a number of experts , each a simple feed - forward neural network , and a trainable gating network which selects a sparse combination of the experts to process each input ( see Figure 1 ) . All parts of the network are trained jointly by back - propagation . The experts are themselves neural networks , each with their own parameters . Although in principle we only require that the experts accept the same sized inputs and produce the same - sized outputs , in our initial investigations in this paper , we restrict ourselves to the case where the models are feed - forward networks with identical architectures , but with separate parameters . Let us denote by G ( x ) and E i ( x ) the output of the gating network and the output of the i - th expert network for a given input The score is based on the RNN hidden state and the - th node representation of the input graph . We parameterize the alignment model as a feed - forward neural network which is jointly trained with other components of the proposed system . Our model is jointly trained to maximize the conditional log - probability of the correct description given a source graph . section : Network Architectures for Segmentation Feed - Forward Networks . Until recently , the majority of feedforward networks , such as the VGG - variants [ reference ] , were composed of a linear sequence of layers . In this paper , a Higher Order Wavelet Neural Network ( HOWNN ) trained with an Extended Kalman Filter ( EKF ) is implemented to solve the wind forecasting problem . The Neural Network based scheme is composed of high order terms in the input layer , two hidden layers , one incorporating radial wavelets as activation functions and the other using classical logistic sigmoid , and an output layer with a linear activation function . A Kalman filter based algorithm is employed to update the synaptic weights of the wavelet network . We point out that , besides the well-known non-zero centered property , slope of the activation function near the origin is another possible reason making training deep networks with the logistic function difficult to train . We demonstrate that , with proper rescaling , the logistic sigmoid achieves comparable results with tanh . Then following the same argument , we improve tahn by penalizing in the negative part . In artificial neural networks ( ANNs ) , the activation function most used in practice are the logistic sigmoid function and the hyperbolic tangent function . The activation functions used in ANNs have been said to play an important role in the convergence of the learning algorithms . In this paper , we present two methods for transforming outlier scores into probabilities . The first approach assumes that the posterior probabilities follow a logistic sigmoid function and learns the parameters of the function from the distribution of outlier scores . The second approach models the score distributions as a mixture of exponential and Gaussian probability functions and calculates the posterior probabilites via the Bayes ' rule . A nature-inspired , integrated computational heuristic paradigm is developed for the piecewise solution of the nonlinear Bratu problem arising in fuel ignition model , electrically conducting solids and related fields , by exploiting the strength of Cascade Artificial Neural Networks ( CANN ) modeling , optimized with the memetic computing procedure based on global search efficacy of genetic algorithms ( GAs ) , aided with the efficient local search of teaching learning based optimization ( TLBO ) . The proposed technique incorporates the log-sigmoid activation function in the CANN model , trained by GAs hybridized with TLBO , i.e. , CANN-GA-TLBO . As a first application of CANN-GA-TLBO , 1D nonlinear Bratu 's system represented with a boundary value problem of the second-order ordinary different equation has been solved , which is a benchmark for testing new algorithms . In the case of GT compressor decay state coefficient , the best results are achieved if MLP is designed with four hidden layers of 100 , 50 , 50 and 20 ANs , respectively . This configuration is designed by using Logistic sigmoid activation function . The lowest mean relative error of 1.094 % is achieved if MLP is trained by using L-BFGS solver . Four different activation functions were tested . The results obtained from Logistic sigmoid activation function were comparatively better . The designed artificial neural network was optimised through genetic algorithm . The network was trained by the Levenberg-Marquardt method to minimise the sum of squares of the residuals . The output of each node was calculated by the logistic activation ( sigmoid ) function on the weighted sum of inputs to that node . It is possible in certain cases like this one to interpret the functions of the nodes in the hidden layer . Levenberg-Marquardt method was used for training the network by minimising the sum of squares of the residuals . The output of each node was calculated by the logistic activation ( sigmoid ) function on the weighted sum of inputs to that node , with different gain values . It is shown therein that this technique results in better predictions compared to conventional statistical correlation methods . This is why , an approach is introduced on which basis a technical system can automatically assess its user 's ability level and reconfigure itself or its interface accordingly . The proposed automated assessment is based on the task 's difficulty , the user 's reaction towards the task he/she is executing with the system at hand , and a logarithmic sigmoid function . Simulation experiments are presented and first results discussed , demonstrating that the proposed realization of the automated assessment allows reliably estimating the individual levels of the dependability-relevant user abilities . First , a Bayesian model is presented to estimate the posterior probability of lesion from a priori information , based on the test outcomes . Due to unavailable data alternative methods and models are presented : Regression Analysis , Monte Carlo simulation , and finally a new model known as Logistic Sigmoid Nonlinearity is proposed for probability estimation . This dissertation analyzes each of these models and alternative methodologies in detail . A three layer solution to the general feedforward neural network problem in the n-dimensional case with piecewise constant approximation requires the use of the corner function as the activation function . The corner function , a special case of n dimensional sigmoid function , is found to have desirable characteristics , and can be used to approximate functions with much weaker requirements ( only boundedness and piecewise continuity . ) Concave regions can be formed with a single layer of nodes with the corner function . We show that the Fuzzy Membership Function ( FMF ) is learnable with underlying chaotic neural networks for the open set probability . A sigmoid N-shaped function is used to generate chaotic signals . We postulate that such a chaotic set of innumerable realization forms a FMF exemplified by fuzzy feature maps of eyes , nose , etc . , for the invariant face classification . Each controller consists of different kinds of architectures of neural networks ( NN ) . Four types of NNs are as follows : Sigmoid NN , Radial NN , pulsed NN ( PNN ) , and PNN incorporating short term synaptic plasticity ( PNNSSP ) . In computer simulation , parameters of NNs are adjusted using genetic algorithm . [ reference ] ) . For the prediction of pair relatedness , we model the conditional probability distribution by sigmoid function : To maximise the log - likelihood we use the variational lower bound : Following the neural variational inference framework , we construct a deep neural network as the inference network : where and are also modelled by LSTMs , and the relatedness label is modelled by a simple linear transformation into the vector . Suppose that is the contextual embedding ( ) of [ CLS ] which can be viewed as the semantic representation of the input sentence pair . We introduce a task - specific parameter vector to compute the similarity score as : where is a sigmoid function that maps the score to a real value of the range . paragraph : Pairwise Text Classification Output : Let us describe how the activation of the - th hidden unit is computed . First , the reset gate is computed by where is the logistic sigmoid function , and denotes the - th element of a vector . and are the input and the previous hidden state , respectively . After applying a ReLU activation function , the result is combined by way of inner product with each and every object entity embedding , where varies over all entities in the dataset ( of size ) , to give a vector of scores . The logistic sigmoid is applied element - wise to the score vector to obtain the predicted probability of each prospective triple being true . For high level intuition , Figure [ reference ] shows a t - SNE plot of a subject entity embedding before and after it has been transformed by relation - specific filters , i.e. prior to combining it with the object embeddings . We define the scoring function for TuckER as : where are the rows of representing the subject and object entity embedding vectors , the rows of representing the relation embedding vector , is the core tensor of Tucker decomposition and is the tensor product along the - th mode . We apply logistic sigmoid to each score to obtain the predicted probability of a triple being true . Visualization of the TuckER model architecture can be seen in Figure [ reference ] . To facilitate this effect , we replace the unbounded exponential function of the softmax function in Eq . ( [ reference ] ) with the bounded logistic sigmoid such that This has the effect of smoothing the focus found by the attention mechanism . section : Related Work A 3D deconvolutional neural network with dimensions symmetrical to the encoder of Section [ reference ] is used to decode the learned latent features into a voxel . An element - wise logistic sigmoid is applied to the output layer in order to convert the learned features to occupancy probabilities for each voxel cell . subsection : Image Regressor : 2D - ConvNet The final layer is used for ( multi - label ) classification : K = 6 attention heads computing 121 features each , that are averaged and followed by a logistic sigmoid activation . The training sets for this task are sufficiently large and we found no need to apply L 2 regularization or dropout - we have , however , successfully employed skip connections [ reference ] across the intermediate attentional layer . In our mask network , ReLU is adopted for nonlinearity for the first 4 convolution layers . As for the fifth convolution layer , we adopt the sigmoid nonlinearity , and the binarization operation is then used to obtain the binary mask , where denotes an element of the feature map . However , the gradient of the binarizer is zero almost everywhere except that it is infinite when , making any layer before the binarizer never be updated during training . by mean - pooling ther j , concatenating them with the current ( topmost ) hidden state of the LSTM , and then feeding this concatenation via a 1 - layer ReLU MLP with hidden dimension 600 , and with a Sigmoid output layer . For the reconstruction - loss , we feed blocks ( of size at most 100 ) of the decoder 's LSTM hidden states through a [ reference ])- style convolutional model . To cope with this problem , we propose an Architecture-Driven Weight Prediction ( ADWP ) approach for neural architecture search ( NAS ) . In our approach , we first design an architecture-intensive search space and then train a HyperNetwork by inputting stochastic encoding architecture parameters . In the trained HyperNetwork , weights of convolution kernels can be well predicted for neural architectures in the search space . Consequently , the target architectures can be evaluated efficiently without any finetuning , thus enabling us to search fortheoptimalarchitectureinthespaceofgeneralnetworks ( macro-search ) . However , it can be prohibitively expensive as the search requires training thousands of different networks , while each can last for hours . In this work , we propose the Graph HyperNetwork ( GHN ) to amortize the search cost : given an architecture , it directly generates the weights by running inference on a graph neural network . GHNs model the topology of an architecture and therefore can predict network performance more accurately than regular hypernetworks and premature early stopping .
A transformation method from feedforward process neural networks to feedforward neural networks is proposed . Based on the prior knowledge of the feedforward neural networks , a learning algorithm is given . The simulation on the learning algorithm shows the results are satisfying .
The networks were trained using the backpropagation algorithm . As a result , I found that feedforward neural networks are much better forecasters than recurrent neural networks and statistical models . KeywordsNeural networks , Time series analysis , Forecasting , Prediction , Statistical models , Sunspots , Autoregressive models .
Nonlinear neural-network-based plant modeling is first discussed , based on the approximation capabilities of the multilayer perceptron . A structure is then proposed to utilize feedforward networks within a direct model reference adaptive control strategy . The difficulties involved in training this network , embedded within the closed-loop are discussed and a novel neural-network-based sensitivity modeling approach proposed to allow for the backpropagation of errors through the plant to the neural controller .
Further , constructive algorithms have the advantage that polynomial-time learning is possible if network size is chosen by the learning algorithm so that the learning of the problem under consideration is simplified . This article considers the representational ability of feedforward networks ( FFNs ) in terms of the fan-in required by the hidden units of a network . We define network order to be the maximum fan-in of the hidden units of a network .
In recent years neural computing has emerged as a practical technology , with successful applications in many fields . The majority of these applications are concerned with problems in pattern recognition , and make use of feedforward network architectures such as the multilayer perceptron and the radial basis function network . Also , it has become widely acknowledged that successful applications of neural computing require a principled , rather than ad hoc , approach .
A state-space approach is used and a nonlinear-in-parameters neural network ( NLPNN ) is employed to identify the general unknown fault . The recurrent network configuration is obtained by a combination of feedforward network architectures and dynamical elements in the form of stable filters . The neural network weights are updated based on a modified backpropagation scheme .
Next the different architectures are described , including single and multiple perceptron networks , Hopfield and Kohonen nets . A brief discussion of the learning rules employed in feedforward and feedback networks follows . The final section discusses hardware implementations of neural systems with emphasis on analog VLSI .
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 ORNL .
Our approach to conditional computation is to introduce a new type of general purpose neural network component : a Sparsely - Gated Mixture - of - Experts Layer ( MoE ) . The MoE consists of a number of experts , each a simple feed - forward neural network , and a trainable gating network which selects a sparse combination of the experts to process each input ( see Figure 1 ) . All parts of the network are trained jointly by back - propagation .
The experts are themselves neural networks , each with their own parameters . Although in principle we only require that the experts accept the same sized inputs and produce the same - sized outputs , in our initial investigations in this paper , we restrict ourselves to the case where the models are feed - forward networks with identical architectures , but with separate parameters . Let us denote by G ( x ) and E i ( x ) the output of the gating network and the output of the i - th expert network for a given input
The score is based on the RNN hidden state and the - th node representation of the input graph . We parameterize the alignment model as a feed - forward neural network which is jointly trained with other components of the proposed system . Our model is jointly trained to maximize the conditional log - probability of the correct description given a source graph .
section : Network Architectures for Segmentation Feed - Forward Networks . Until recently , the majority of feedforward networks , such as the VGG - variants [ reference ] , were composed of a linear sequence of layers .
In this paper , a Higher Order Wavelet Neural Network ( HOWNN ) trained with an Extended Kalman Filter ( EKF ) is implemented to solve the wind forecasting problem . The Neural Network based scheme is composed of high order terms in the input layer , two hidden layers , one incorporating radial wavelets as activation functions and the other using classical logistic sigmoid , and an output layer with a linear activation function . A Kalman filter based algorithm is employed to update the synaptic weights of the wavelet network .
We point out that , besides the well-known non-zero centered property , slope of the activation function near the origin is another possible reason making training deep networks with the logistic function difficult to train . We demonstrate that , with proper rescaling , the logistic sigmoid achieves comparable results with tanh . Then following the same argument , we improve tahn by penalizing in the negative part .
In artificial neural networks ( ANNs ) , the activation function most used in practice are the logistic sigmoid function and the hyperbolic tangent function . The activation functions used in ANNs have been said to play an important role in the convergence of the learning algorithms .
In this paper , we present two methods for transforming outlier scores into probabilities . The first approach assumes that the posterior probabilities follow a logistic sigmoid function and learns the parameters of the function from the distribution of outlier scores . The second approach models the score distributions as a mixture of exponential and Gaussian probability functions and calculates the posterior probabilites via the Bayes ' rule .
A nature-inspired , integrated computational heuristic paradigm is developed for the piecewise solution of the nonlinear Bratu problem arising in fuel ignition model , electrically conducting solids and related fields , by exploiting the strength of Cascade Artificial Neural Networks ( CANN ) modeling , optimized with the memetic computing procedure based on global search efficacy of genetic algorithms ( GAs ) , aided with the efficient local search of teaching learning based optimization ( TLBO ) . The proposed technique incorporates the log-sigmoid activation function in the CANN model , trained by GAs hybridized with TLBO , i.e. , CANN-GA-TLBO . As a first application of CANN-GA-TLBO , 1D nonlinear Bratu 's system represented with a boundary value problem of the second-order ordinary different equation has been solved , which is a benchmark for testing new algorithms .
In the case of GT compressor decay state coefficient , the best results are achieved if MLP is designed with four hidden layers of 100 , 50 , 50 and 20 ANs , respectively . This configuration is designed by using Logistic sigmoid activation function . The lowest mean relative error of 1.094 % is achieved if MLP is trained by using L-BFGS solver .
Four different activation functions were tested . The results obtained from Logistic sigmoid activation function were comparatively better . The designed artificial neural network was optimised through genetic algorithm .
The network was trained by the Levenberg-Marquardt method to minimise the sum of squares of the residuals . The output of each node was calculated by the logistic activation ( sigmoid ) function on the weighted sum of inputs to that node . It is possible in certain cases like this one to interpret the functions of the nodes in the hidden layer .
Levenberg-Marquardt method was used for training the network by minimising the sum of squares of the residuals . The output of each node was calculated by the logistic activation ( sigmoid ) function on the weighted sum of inputs to that node , with different gain values . It is shown therein that this technique results in better predictions compared to conventional statistical correlation methods .
This is why , an approach is introduced on which basis a technical system can automatically assess its user 's ability level and reconfigure itself or its interface accordingly . The proposed automated assessment is based on the task 's difficulty , the user 's reaction towards the task he/she is executing with the system at hand , and a logarithmic sigmoid function . Simulation experiments are presented and first results discussed , demonstrating that the proposed realization of the automated assessment allows reliably estimating the individual levels of the dependability-relevant user abilities .
First , a Bayesian model is presented to estimate the posterior probability of lesion from a priori information , based on the test outcomes . Due to unavailable data alternative methods and models are presented : Regression Analysis , Monte Carlo simulation , and finally a new model known as Logistic Sigmoid Nonlinearity is proposed for probability estimation . This dissertation analyzes each of these models and alternative methodologies in detail .
A three layer solution to the general feedforward neural network problem in the n-dimensional case with piecewise constant approximation requires the use of the corner function as the activation function . The corner function , a special case of n dimensional sigmoid function , is found to have desirable characteristics , and can be used to approximate functions with much weaker requirements ( only boundedness and piecewise continuity . ) Concave regions can be formed with a single layer of nodes with the corner function .
We show that the Fuzzy Membership Function ( FMF ) is learnable with underlying chaotic neural networks for the open set probability . A sigmoid N-shaped function is used to generate chaotic signals . We postulate that such a chaotic set of innumerable realization forms a FMF exemplified by fuzzy feature maps of eyes , nose , etc . , for the invariant face classification .
Each controller consists of different kinds of architectures of neural networks ( NN ) . Four types of NNs are as follows : Sigmoid NN , Radial NN , pulsed NN ( PNN ) , and PNN incorporating short term synaptic plasticity ( PNNSSP ) . In computer simulation , parameters of NNs are adjusted using genetic algorithm .
[ reference ] ) . For the prediction of pair relatedness , we model the conditional probability distribution by sigmoid function : To maximise the log - likelihood we use the variational lower bound : Following the neural variational inference framework , we construct a deep neural network as the inference network : where and are also modelled by LSTMs , and the relatedness label is modelled by a simple linear transformation into the vector .
Suppose that is the contextual embedding ( ) of [ CLS ] which can be viewed as the semantic representation of the input sentence pair . We introduce a task - specific parameter vector to compute the similarity score as : where is a sigmoid function that maps the score to a real value of the range . paragraph : Pairwise Text Classification Output :
Let us describe how the activation of the - th hidden unit is computed . First , the reset gate is computed by where is the logistic sigmoid function , and denotes the - th element of a vector . and are the input and the previous hidden state , respectively .
After applying a ReLU activation function , the result is combined by way of inner product with each and every object entity embedding , where varies over all entities in the dataset ( of size ) , to give a vector of scores . The logistic sigmoid is applied element - wise to the score vector to obtain the predicted probability of each prospective triple being true . For high level intuition , Figure [ reference ] shows a t - SNE plot of a subject entity embedding before and after it has been transformed by relation - specific filters , i.e. prior to combining it with the object embeddings .
We define the scoring function for TuckER as : where are the rows of representing the subject and object entity embedding vectors , the rows of representing the relation embedding vector , is the core tensor of Tucker decomposition and is the tensor product along the - th mode . We apply logistic sigmoid to each score to obtain the predicted probability of a triple being true . Visualization of the TuckER model architecture can be seen in Figure [ reference ] .
To facilitate this effect , we replace the unbounded exponential function of the softmax function in Eq . ( [ reference ] ) with the bounded logistic sigmoid such that This has the effect of smoothing the focus found by the attention mechanism . section : Related Work
A 3D deconvolutional neural network with dimensions symmetrical to the encoder of Section [ reference ] is used to decode the learned latent features into a voxel . An element - wise logistic sigmoid is applied to the output layer in order to convert the learned features to occupancy probabilities for each voxel cell . subsection : Image Regressor : 2D - ConvNet
The final layer is used for ( multi - label ) classification : K = 6 attention heads computing 121 features each , that are averaged and followed by a logistic sigmoid activation . The training sets for this task are sufficiently large and we found no need to apply L 2 regularization or dropout - we have , however , successfully employed skip connections [ reference ] across the intermediate attentional layer .
In our mask network , ReLU is adopted for nonlinearity for the first 4 convolution layers . As for the fifth convolution layer , we adopt the sigmoid nonlinearity , and the binarization operation is then used to obtain the binary mask , where denotes an element of the feature map . However , the gradient of the binarizer is zero almost everywhere except that it is infinite when , making any layer before the binarizer never be updated during training .
by mean - pooling ther j , concatenating them with the current ( topmost ) hidden state of the LSTM , and then feeding this concatenation via a 1 - layer ReLU MLP with hidden dimension 600 , and with a Sigmoid output layer . For the reconstruction - loss , we feed blocks ( of size at most 100 ) of the decoder 's LSTM hidden states through a [ reference ])- style convolutional model .
To cope with this problem , we propose an Architecture-Driven Weight Prediction ( ADWP ) approach for neural architecture search ( NAS ) . In our approach , we first design an architecture-intensive search space and then train a HyperNetwork by inputting stochastic encoding architecture parameters . In the trained HyperNetwork , weights of convolution kernels can be well predicted for neural architectures in the search space . Consequently , the target architectures can be evaluated efficiently without any finetuning , thus enabling us to search fortheoptimalarchitectureinthespaceofgeneralnetworks ( macro-search ) .
However , it can be prohibitively expensive as the search requires training thousands of different networks , while each can last for hours . In this work , we propose the Graph HyperNetwork ( GHN ) to amortize the search cost : given an architecture , it directly generates the weights by running inference on a graph neural network . GHNs model the topology of an architecture and therefore can predict network performance more accurately than regular hypernetworks and premature early stopping .
Aeroengine Condition Monitoring Based on Process Neural Networks and Aerothermo-parameters Forecasting Sunspot Numbers with Neural Networks Nonlinear control structures based on embedded neural system models Constructive higher-order network that is polynomial time Neural networks and machine learning A neural network-based fault detection scheme for satellite attitude control systems Artificial neural networks: principles and VLSI implementation Hybrid digital signal processing and neural networks for automated diagnostics using NDE methods Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes Higher Order Wavelet Neural Networks with Kalman learning for wind speed forecasting Revise Saturated Activation Functions Comparison of new activation functions in neural network for forecasting financial time series Converting Output Scores from Outlier Detection Algorithms into Probability Estimates Design of cascade artificial neural networks optimized with the memetic computing paradigm for solving the nonlinear Bratu system Multilayer Perceptron approach to Condition-Based Maintenance of Marine CODLAG Propulsion System Components Estimating the weight and the failure load of a spaghetti bridge: a deep learning approach Implementation of a chemical reactor selection expert system in an artificial neural network Artificial Neural Networks for Predicting Silicon Content in Raw Iron From Blast Furnaces Assessment of the User's Dependability-Relevant Abilities for Enhanced Human-Technology Interaction Computer-aided localization of neurological diseases Some analytical solutions to the general approximation problem for feedforward neural networks Chaotic neural network for learnable associative memory recall Applications of Pulsed Neural Network Incorporating Short Term Synaptic Plasticity to Autonomous Mobile Robots Neural Variational Inference for Text Processing Multi-Task Deep Neural Networks for Natural Language Understanding Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation Hypernetwork Knowledge Graph Embeddings TuckER: Tensor Factorization for Knowledge Graph Completion Attention-Based Models for Speech Recognition Learning a Hierarchical Latent-Variable Model of 3D Shapes Graph Attention Networks Deep Identity-aware Transfer of Facial Attributes Challenges in Data-to-Document Generation ADWPNAS: Architecture-Driven Weight Prediction for Neural Architecture Search Graph HyperNetworks for Neural Architecture Search
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["Computer Science"] ["Computer Science"] ["Computer Science","Medicine"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Engineering","Computer Science"] ["Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Physics"] ["Engineering"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Engineering","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"]
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