The signal level of the neuron is represented by frequency of pulse signals . The proposed neuron provides adjustable nonlinear function , which resembles the sigmoid function . The proposed neuron and experimental multilayer neural network ( MNN ) are implemented on field programmable gate array ( FPGA ) and various experiments are conducted to test the performance of the proposed system . By this proposed method ANN works speedily , uses less resource and achieves high accuracy . Keywords : ANN , FPGA , sigmoid function , look up table , second order nonlinear function . To overcome such instabilities , in this paper , we have developed a unique halftoning technique for stable printing . Based on the charateristics of xerography , a combination of Gaussian filter with a sigmoid nonlinear function was used to calculate the probability of toner transfer in print . We can obtain a simulated i mage in print for xerography using this nonlinear printer model . The DICOM interface for TOPAS was extended to permit contour input , used to assign voxels to organs . The following dose response models were implemented : The Lyman-Kutcher-Burman model , the critical element model , the population based critical volume model , the parallel-serial model , a sigmoid-based model of Niemierko for normal tissue complication probability and tumor control probability ( TCP ) , and a Poisson-based model for TCP . The framework allows easy manipulation of the parameters of these models and the implementation of other models . The effectiveness of our proposed model can be clearly seen from mathematical analysis . We also compare results based on linear and sigmoid models . Highly similar optimal sources are obtained , but the linear model has a significant advantage over the sigmoid in terms of convergence rate and simulation time . Prediction results of the both stations reported that the OP-ELM and ANFIS-PSO are the best in modeling daily streamflows of upstream and downstream , respectively . For improving prediction accuracy of the OP-ELM method , various kernel types are tried and the linear , linear + sigmoid + Gaussian and linear + sigmoid provide the best results for both stations . The OP-ELM outperforms the other methods during estimation of downstream streamflow using hydro climatic data as input . In the channel routing results , all the activation functions are perfect , among which results from hyperbolic function and linear function are better than the Muskingum model , the result of linear function is the best . In the flood peak stage prediction , good results were obtained from sigmoid and linear function . This works addresses the said issue by proposing a new approach for histogram modification . The algorithm modifies the histogram by devices a sigmoid based function . The resulting images are further improved by applying an DCT based algorithm for local contrast enhancement . In this paper , corrective smoothed particle algorithm ( CSPM ) as a meshless particle method is used to study the mechanical behavior of a ceramic – metal functionally graded material ( FGM ) under high velocity impact conditions . A mixed strength model with sigmoid formulation has been used to describe both yielding and fracture phenomena in the FGM . The strength model includes the JC dynamic yield relation and Johnson – Holmquist – Beissel ( JHB ) fracture model with a continuum damage description approach . The methodology of design of a conventional model-reference-adaptive-control is extended to design a direct neural for a class of nonlinear system with structural uncertainty . A structured feedforward neural network , a Sigmoid-linear network , is used as the controller , which can be interpreted as a nonlinear extension of the conventional adaptive control . Without a specific pretraining stage , the weights of the neural network are adjusted online to minimise the error between the plant output and the desired output signal , according to a learning law derived in light of gradient-descent method . The operator produces two arrays of 1D Gaussian filter banks and whose filter locations and scales are computed from the generative LSTM hidden state ( same as defined in ) . The Gaussian filter banks are then applied to the generated - by - image patch , placing it onto the canvas : Finally , each entry from the final canvas matrix is transformed using a sigmoid function to produce a conditional Bernoulli distribution with mean vector over the image pixels given the latent variables and the input caption . In practice , when generating an image , instead of sampling from the conditional Bernoulli distribution , we simply use the conditional mean . Generally , with sentence input , the convolution unit for feature map of type - ( among of them ) on Layer - is and its matrix form is , where gives the output of feature map of type - for location in Layer - ; is the parameters for on Layer - , with matrix form ; is the activation function ( e.g. , Sigmoid or Relu ) denotes the segment of Layer - for the convolution at location , while concatenates the vectors for ( width of sliding window ) words from sentence input . paragraph : Max - Pooling Figure 5 compares these activation functions on FNN , IPNN and OPNN . From this figure , we find that tanh has better performance than sigmoid . This is supported by The other strategy is “ responsive ” to the characteristics of the region being pooled ; the learning process in this strategy results in a “ gating mask ” . This learned gating mask is then used to determine a “ responsive ” mix of max pooling and average pooling ; specifically , the value of the inner product between the gating mask and the current region being pooled is fed through a sigmoid , the output of which is used as the mixing proportion between max and average . To emphasize the role of the gating mask in determining the “ responsive ” mixing proportion , we refer to this strategy as gated max - average pooling . The word at the pointer - location is then copied into the summary . The switch is modeled as a sigmoid activation function over a linear layer based on the entire available context at each time - step as shown below . where is the probability of the switch turning on at the time - step of the decoder , is the hidden state , is the embedding vector of the emission from the previous time step , is the attention - weighted context vector , and and are the switch parameters . A fully connected layer is followed by the final pooling layer to generate the original quality score . After that , the origin scores of all images in a set are sent to sigmoid layer and group L1 - normalization layer to generate the final scores . For QAN split at Pool3 , we will add a block containing three 1 - stride convolution layer and a 2 - stride pooling layer at the beginning of quality generation unit . We remind the reader that maxout nets generalize ReLu units by employing non - linearities of the form where the subsets of neurons are typically disjoint . In we have shown that maxout DNNs and CNNs trained with annealed dropout outperform their sigmoid - based counterparts on both 300 hours and 2000 hours training regimes . What was missing there was a comparison between maxout and sigmoid for unfolded RNNs . The results show that the computational efficiency of the proposed method was better than the standard conjugate gradient algorithm . Keywords — Adaptive gain variation , back-propagation , activation function , conjugate gradient , search direction . Arc - II models for all tasks have eight layers ( three for convolution , three for pooling , and two for MLP ) , while Arc - I performs better with less layers ( two for convolution , two for pooling , and two for MLP ) and more hidden nodes . We use ReLu as the activation function for all of models ( convolution and MLP ) , which yields comparable or better results to sigmoid - like functions , but converges faster . section : Experiments ( 3 ) optimizer : Adam ; ( 4 ) activation function : tanh for IPNN , relu for other deep models . To be fair , our proposed DeepFM uses the same setting . In this paper , a new algebra method , i.e. , generalized elliptic equation rational expansion method is devised to uniformly construct a series of exact solutions for nonlinear partial differential equations . Compared with most existing Tanh methods , the proposed method not only recover some known solutions , but also find some new and general solutions . The efficiency of the method can be demonstrated on the (2 + 1)-dimensional Broer-Kaup-Kupershmidt system . Five personality traits named as EXT , NEU , AGR , CON , and OPN have been used for experimental analysis . The result showed that tanh and leaky ReLU performs over sigmoid in all datasets . The average F1-score of sigmoid , tanh and leaky ReLU showed 33.11 % , 47.25 % , and 49.07 % respectively . In the second approach , sigmoid function is used to transform continuous space into binary search space . As for the last binary approach of IWO algorithm , this approach is carried out by means of tanh function for converting to binary values . The accuracy and performance of the proposed approaches have been examined on well-known 12 benchmark instances of uncapacitated facility location problem . Methodologies and techniques by applying different learning rule , activation function and input layer structure are presented in this paper and a comparison for the short term runoff prediction results between them is also conducted . The prediction results of the general recurrent neural network with Momentum learning rule and Tanh activation function with Axon as input layer structure indicates a satisfactory performance in the three hours ahead of time prediction . The conclusions also indicate that general recurrent neural network with Momentum learning rule and Tanh activation function with Axon as input layer structure is more versatile than other combinations for general recurrent neural network and can be considered as an alternate and practical tool for predicting short term flood flow . We have also increased the fault rate significantly for the LeNet-5 network by randomly generated fault injection campaigns and observed that the training accuracy starts to degrade . When the fault rate increases , the network with Tanh activation function outperforms the one with Relu in terms of accuracy , e.g. , when the fault rate is 30 % the accuracy difference is 4.92 % . We introduce in this paper a new chaotic map with dynamical properties controlled by two free parameters . The map definition is based on the hyperbolic tangent function , so it is called the tanh map . We demonstrate that the Lyapunov exponent of the tanh map is robust , remaining practically unaltered with the variation of its parameters . As the main application , we consider a chaotic communication system based on symbolic dynamics with advantages over current approaches that use piecewise linear maps . Many neural networks use the tanh activation function , however when given a probability distribution as input , the problem of computing the output distribution in neural networks with tanh activation has not yet been addressed . One important example is the initialization of the echo state network in reservoir computing , where random initialization of the reservoir requires time to wash out the initial conditions , thereby wasting precious data and computational resources . It is also assumed that information about the road excitation is available in advance as a preview signal . The tanh-based model of the MR damper was identified according to the Bouc-Wen model response and used to obtain the inverse model that is necessary in the control scheme . The adaptive feedforward LMS ( Least Mean Squares ) algorithm with a filtered preview signal was modified in order to control the semi-active elements . [ 33 ] forget gate layer ) outputs a number between 0 and 1 telling how much information should be forgotten . The next step decides what new information will be stored in the state : a sigmoid layer ( called input gate layer ) decides which values will be updated and tanh layer creates a new vector of new candidate values that will be added in the state . Last step is to update the cell state and decide what to output . We propose and evaluate , ( a ) histogram-based mapping , ( b ) polynomial-fitting , ( c ) tanh-fitting , based methods to map confidences associated with false-recognitions and discuss their issues and benefits . In our tests , all of the above mapping methods fix the mean regression in CA of 21 % to a gain to 1 - 2 % , with tanh-mapping providing the best CA and FA tradeoff in our tests . We propose a compact face alignment method for fast training without decreasing its accuracy . Rectified linear unit is employed , which allows all networks approximately five times faster convergence than a tanh neuron . An eight learnable layer deep convolutional neural network ( DCNN ) based on local response normalization and a padding convolutional layer ( PCL ) is designed to provide reliable initial values during prediction . Similarly to , we found that removing it does not significantly affect the system performance , also due to a certain redundancy observed between update and reset gates . Second , we propose to replace hyperbolic tangent ( tanh ) with Rectified Linear Unit ( ReLU ) activations in the state update equation . ReLU units have been shown to be more effective than sigmoid non - linearities for feed - forward DNNs . Figure 5 compares these activation functions on FNN , IPNN and OPNN . From this figure , we find that tanh has better performance than sigmoid . This is supported by This produced a feature map that output the conditional loss on different classes . In the generator network , Tanh was used to produce images whose pixel values are scaled to . Thus , all image examples in datasets were preprocessed to have their pixel values in . We set the dimension of the bottleneck layer to 200 , and use a weighted square loss , equivalent to what the square loss with negative sampling used in the original paper . We apply tanh activation at both the bottleneck layer as well as the output layer . [ reference ] subsection : Influence of mini - batch size to LSUV initialization We have selected tanh activation as one , where LSUV initialization shows the worst performance and tested the influence of mini - batch size to training process . Note , that training mini - batch is the same for all initializations , the only difference is mini - batch used for variance estimation . The critic network has a layer with 400 units , followed by a tanh activation , followed by another layer of 300 units . The actor network has a layer with 300 units , followed by a tanh activation , followed by another layer of 200 units . The gradient used to update the actor network is clipped to , element - wise . The encoder - decoder architecture consists of : encoder : C64 - C128 - C256 - C512 - C512 - C512 - C512 - C512 decoder : CD512 - CD512 - CD512 - C512 - C256 - C128 - C64 After the last layer in the decoder , a convolution is applied to map to the number of output channels ( 3 in general , except in colorization , where it is 2 ) , followed by a Tanh function . As an exception to the above notation , BatchNorm is not applied to the first C64 layer in the encoder . ( [ reference ] ) and ( [ reference ] ) ) , we try linear function , sigmoid function and tanh function , and find the result of tanh function is optimal . This might be because the hyperbolic tangent often converges faster than the sigmoid function . In this work , we propose a new activation function to break the monotonicity property of SELU while still preserving the self-normalizing property . Differently from SELU , the new function introduces a bump-shaped function in the region of negative input by regularizing a linear function with a scaled exponential function , which is referred to as a scaled exponentially-regularized linear unit ( SERLU ) . The bump-shaped function has approximately zero response to large negative input while being able to push the output of SERLU towards zero mean statistically . To effectively combat over-fitting , we develop a so-called shift-dropout for SERLU , which includes standard dropout as a special case . Experimental results on MNIST , CIFAR10 and CIFAR100 show that SERLU-based neural networks provide consistently promising results in comparison to other 5 activation functions including ELU , SELU , Swish , Leakly ReLU and ReLU .
The signal level of the neuron is represented by frequency of pulse signals . The proposed neuron provides adjustable nonlinear function , which resembles the sigmoid function . The proposed neuron and experimental multilayer neural network ( MNN ) are implemented on field programmable gate array ( FPGA ) and various experiments are conducted to test the performance of the proposed system .
By this proposed method ANN works speedily , uses less resource and achieves high accuracy . Keywords : ANN , FPGA , sigmoid function , look up table , second order nonlinear function .
To overcome such instabilities , in this paper , we have developed a unique halftoning technique for stable printing . Based on the charateristics of xerography , a combination of Gaussian filter with a sigmoid nonlinear function was used to calculate the probability of toner transfer in print . We can obtain a simulated i mage in print for xerography using this nonlinear printer model .
The DICOM interface for TOPAS was extended to permit contour input , used to assign voxels to organs . The following dose response models were implemented : The Lyman-Kutcher-Burman model , the critical element model , the population based critical volume model , the parallel-serial model , a sigmoid-based model of Niemierko for normal tissue complication probability and tumor control probability ( TCP ) , and a Poisson-based model for TCP . The framework allows easy manipulation of the parameters of these models and the implementation of other models .
The effectiveness of our proposed model can be clearly seen from mathematical analysis . We also compare results based on linear and sigmoid models . Highly similar optimal sources are obtained , but the linear model has a significant advantage over the sigmoid in terms of convergence rate and simulation time .
Prediction results of the both stations reported that the OP-ELM and ANFIS-PSO are the best in modeling daily streamflows of upstream and downstream , respectively . For improving prediction accuracy of the OP-ELM method , various kernel types are tried and the linear , linear + sigmoid + Gaussian and linear + sigmoid provide the best results for both stations . The OP-ELM outperforms the other methods during estimation of downstream streamflow using hydro climatic data as input .
In the channel routing results , all the activation functions are perfect , among which results from hyperbolic function and linear function are better than the Muskingum model , the result of linear function is the best . In the flood peak stage prediction , good results were obtained from sigmoid and linear function .
This works addresses the said issue by proposing a new approach for histogram modification . The algorithm modifies the histogram by devices a sigmoid based function . The resulting images are further improved by applying an DCT based algorithm for local contrast enhancement .
In this paper , corrective smoothed particle algorithm ( CSPM ) as a meshless particle method is used to study the mechanical behavior of a ceramic – metal functionally graded material ( FGM ) under high velocity impact conditions . A mixed strength model with sigmoid formulation has been used to describe both yielding and fracture phenomena in the FGM . The strength model includes the JC dynamic yield relation and Johnson – Holmquist – Beissel ( JHB ) fracture model with a continuum damage description approach .
The methodology of design of a conventional model-reference-adaptive-control is extended to design a direct neural for a class of nonlinear system with structural uncertainty . A structured feedforward neural network , a Sigmoid-linear network , is used as the controller , which can be interpreted as a nonlinear extension of the conventional adaptive control . Without a specific pretraining stage , the weights of the neural network are adjusted online to minimise the error between the plant output and the desired output signal , according to a learning law derived in light of gradient-descent method .
The operator produces two arrays of 1D Gaussian filter banks and whose filter locations and scales are computed from the generative LSTM hidden state ( same as defined in ) . The Gaussian filter banks are then applied to the generated - by - image patch , placing it onto the canvas : Finally , each entry from the final canvas matrix is transformed using a sigmoid function to produce a conditional Bernoulli distribution with mean vector over the image pixels given the latent variables and the input caption . In practice , when generating an image , instead of sampling from the conditional Bernoulli distribution , we simply use the conditional mean .
Generally , with sentence input , the convolution unit for feature map of type - ( among of them ) on Layer - is and its matrix form is , where gives the output of feature map of type - for location in Layer - ; is the parameters for on Layer - , with matrix form ; is the activation function ( e.g. , Sigmoid or Relu ) denotes the segment of Layer - for the convolution at location , while concatenates the vectors for ( width of sliding window ) words from sentence input . paragraph : Max - Pooling
Figure 5 compares these activation functions on FNN , IPNN and OPNN . From this figure , we find that tanh has better performance than sigmoid . This is supported by
The other strategy is “ responsive ” to the characteristics of the region being pooled ; the learning process in this strategy results in a “ gating mask ” . This learned gating mask is then used to determine a “ responsive ” mix of max pooling and average pooling ; specifically , the value of the inner product between the gating mask and the current region being pooled is fed through a sigmoid , the output of which is used as the mixing proportion between max and average . To emphasize the role of the gating mask in determining the “ responsive ” mixing proportion , we refer to this strategy as gated max - average pooling .
The word at the pointer - location is then copied into the summary . The switch is modeled as a sigmoid activation function over a linear layer based on the entire available context at each time - step as shown below . where is the probability of the switch turning on at the time - step of the decoder , is the hidden state , is the embedding vector of the emission from the previous time step , is the attention - weighted context vector , and and are the switch parameters .
A fully connected layer is followed by the final pooling layer to generate the original quality score . After that , the origin scores of all images in a set are sent to sigmoid layer and group L1 - normalization layer to generate the final scores . For QAN split at Pool3 , we will add a block containing three 1 - stride convolution layer and a 2 - stride pooling layer at the beginning of quality generation unit .
We remind the reader that maxout nets generalize ReLu units by employing non - linearities of the form where the subsets of neurons are typically disjoint . In we have shown that maxout DNNs and CNNs trained with annealed dropout outperform their sigmoid - based counterparts on both 300 hours and 2000 hours training regimes . What was missing there was a comparison between maxout and sigmoid for unfolded RNNs .
The results show that the computational efficiency of the proposed method was better than the standard conjugate gradient algorithm . Keywords — Adaptive gain variation , back-propagation , activation function , conjugate gradient , search direction .
Arc - II models for all tasks have eight layers ( three for convolution , three for pooling , and two for MLP ) , while Arc - I performs better with less layers ( two for convolution , two for pooling , and two for MLP ) and more hidden nodes . We use ReLu as the activation function for all of models ( convolution and MLP ) , which yields comparable or better results to sigmoid - like functions , but converges faster . section : Experiments
( 3 ) optimizer : Adam ; ( 4 ) activation function : tanh for IPNN , relu for other deep models . To be fair , our proposed DeepFM uses the same setting .
In this paper , a new algebra method , i.e. , generalized elliptic equation rational expansion method is devised to uniformly construct a series of exact solutions for nonlinear partial differential equations . Compared with most existing Tanh methods , the proposed method not only recover some known solutions , but also find some new and general solutions . The efficiency of the method can be demonstrated on the (2 + 1)-dimensional Broer-Kaup-Kupershmidt system .
Five personality traits named as EXT , NEU , AGR , CON , and OPN have been used for experimental analysis . The result showed that tanh and leaky ReLU performs over sigmoid in all datasets . The average F1-score of sigmoid , tanh and leaky ReLU showed 33.11 % , 47.25 % , and 49.07 % respectively .
In the second approach , sigmoid function is used to transform continuous space into binary search space . As for the last binary approach of IWO algorithm , this approach is carried out by means of tanh function for converting to binary values . The accuracy and performance of the proposed approaches have been examined on well-known 12 benchmark instances of uncapacitated facility location problem .
Methodologies and techniques by applying different learning rule , activation function and input layer structure are presented in this paper and a comparison for the short term runoff prediction results between them is also conducted . The prediction results of the general recurrent neural network with Momentum learning rule and Tanh activation function with Axon as input layer structure indicates a satisfactory performance in the three hours ahead of time prediction . The conclusions also indicate that general recurrent neural network with Momentum learning rule and Tanh activation function with Axon as input layer structure is more versatile than other combinations for general recurrent neural network and can be considered as an alternate and practical tool for predicting short term flood flow .
We have also increased the fault rate significantly for the LeNet-5 network by randomly generated fault injection campaigns and observed that the training accuracy starts to degrade . When the fault rate increases , the network with Tanh activation function outperforms the one with Relu in terms of accuracy , e.g. , when the fault rate is 30 % the accuracy difference is 4.92 % .
We introduce in this paper a new chaotic map with dynamical properties controlled by two free parameters . The map definition is based on the hyperbolic tangent function , so it is called the tanh map . We demonstrate that the Lyapunov exponent of the tanh map is robust , remaining practically unaltered with the variation of its parameters . As the main application , we consider a chaotic communication system based on symbolic dynamics with advantages over current approaches that use piecewise linear maps .
Many neural networks use the tanh activation function , however when given a probability distribution as input , the problem of computing the output distribution in neural networks with tanh activation has not yet been addressed . One important example is the initialization of the echo state network in reservoir computing , where random initialization of the reservoir requires time to wash out the initial conditions , thereby wasting precious data and computational resources .
It is also assumed that information about the road excitation is available in advance as a preview signal . The tanh-based model of the MR damper was identified according to the Bouc-Wen model response and used to obtain the inverse model that is necessary in the control scheme . The adaptive feedforward LMS ( Least Mean Squares ) algorithm with a filtered preview signal was modified in order to control the semi-active elements .
[ 33 ] forget gate layer ) outputs a number between 0 and 1 telling how much information should be forgotten . The next step decides what new information will be stored in the state : a sigmoid layer ( called input gate layer ) decides which values will be updated and tanh layer creates a new vector of new candidate values that will be added in the state . Last step is to update the cell state and decide what to output .
We propose and evaluate , ( a ) histogram-based mapping , ( b ) polynomial-fitting , ( c ) tanh-fitting , based methods to map confidences associated with false-recognitions and discuss their issues and benefits . In our tests , all of the above mapping methods fix the mean regression in CA of 21 % to a gain to 1 - 2 % , with tanh-mapping providing the best CA and FA tradeoff in our tests .
We propose a compact face alignment method for fast training without decreasing its accuracy . Rectified linear unit is employed , which allows all networks approximately five times faster convergence than a tanh neuron . An eight learnable layer deep convolutional neural network ( DCNN ) based on local response normalization and a padding convolutional layer ( PCL ) is designed to provide reliable initial values during prediction .
Similarly to , we found that removing it does not significantly affect the system performance , also due to a certain redundancy observed between update and reset gates . Second , we propose to replace hyperbolic tangent ( tanh ) with Rectified Linear Unit ( ReLU ) activations in the state update equation . ReLU units have been shown to be more effective than sigmoid non - linearities for feed - forward DNNs .
Figure 5 compares these activation functions on FNN , IPNN and OPNN . From this figure , we find that tanh has better performance than sigmoid . This is supported by
This produced a feature map that output the conditional loss on different classes . In the generator network , Tanh was used to produce images whose pixel values are scaled to . Thus , all image examples in datasets were preprocessed to have their pixel values in .
We set the dimension of the bottleneck layer to 200 , and use a weighted square loss , equivalent to what the square loss with negative sampling used in the original paper . We apply tanh activation at both the bottleneck layer as well as the output layer . [ reference ]
subsection : Influence of mini - batch size to LSUV initialization We have selected tanh activation as one , where LSUV initialization shows the worst performance and tested the influence of mini - batch size to training process . Note , that training mini - batch is the same for all initializations , the only difference is mini - batch used for variance estimation .
The critic network has a layer with 400 units , followed by a tanh activation , followed by another layer of 300 units . The actor network has a layer with 300 units , followed by a tanh activation , followed by another layer of 200 units . The gradient used to update the actor network is clipped to , element - wise .
The encoder - decoder architecture consists of : encoder : C64 - C128 - C256 - C512 - C512 - C512 - C512 - C512 decoder : CD512 - CD512 - CD512 - C512 - C256 - C128 - C64 After the last layer in the decoder , a convolution is applied to map to the number of output channels ( 3 in general , except in colorization , where it is 2 ) , followed by a Tanh function . As an exception to the above notation , BatchNorm is not applied to the first C64 layer in the encoder .
( [ reference ] ) and ( [ reference ] ) ) , we try linear function , sigmoid function and tanh function , and find the result of tanh function is optimal . This might be because the hyperbolic tangent often converges faster than the sigmoid function .
In this work , we propose a new activation function to break the monotonicity property of SELU while still preserving the self-normalizing property . Differently from SELU , the new function introduces a bump-shaped function in the region of negative input by regularizing a linear function with a scaled exponential function , which is referred to as a scaled exponentially-regularized linear unit ( SERLU ) . The bump-shaped function has approximately zero response to large negative input while being able to push the output of SERLU towards zero mean statistically . To effectively combat over-fitting , we develop a so-called shift-dropout for SERLU , which includes standard dropout as a special case . Experimental results on MNIST , CIFAR10 and CIFAR100 show that SERLU-based neural networks provide consistently promising results in comparison to other 5 activation functions including ELU , SELU , Swish , Leakly ReLU and ReLU .
A new digital pulse-mode neuron with adjustable activation function Survey paper on Advanced Equipment Execution of ANN for FPGA A new halftoning technique to eliminate ambiguous pixels for stable printing A framework for implementation of organ effect models in TOPAS with benchmarks extended to proton therapy. Source optimization incorporating margin image average with conjugate gradient method Daily streamflow prediction using optimally pruned extreme learning machine Application of Neural Network in Channel Flow Forecasting Histogram modification and DCT based contrast enhancement Study of dynamic behavior of ceramic–metal FGM under high velocity impact conditions using CSPM method Direct neural control system: nonlinear extension of adaptive control Generating Images from Captions with Attention Convolutional Neural Network Architectures for Matching Natural Language Sentences Product-Based Neural Networks for User Response Prediction Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond Quality Aware Network for Set to Set Recognition The IBM 2016 English Conversational Telephone Speech Recognition System An Improved Conjugate Gradient Based Learning Algorithm for Back Propagation Neural Networks Convolutional Neural Network Architectures for Matching Natural Language Sentences DeepFM: A Factorization-Machine based Neural Network for CTR Prediction Generalized method and new exact wave solutions for (2+1)-dimensional Broer-Kaup-Kupershmidt system Personality Detection from Text using Convolutional Neural Network Binary Invasive Weed Optimization Algorithm Approaches for Binary Optimization Short Term Flood Forecasting Using General Recurrent Neural Network Modeling a Comparative Study On the Resilience of Deep Learning for Reduced-voltage FPGAs A smooth chaotic map with parameterized shape and symmetry Propagating Uncertainty through the tanh Function with Application to Reservoir Computing FxLMS algorithm with preview for vibration control of a half-car model with magnetorheological dampers Automatic text summarization Normalization of ASR confidence classifier scores via confidence mapping Multiple deep convolutional neural networks averaging for face alignment Light Gated Recurrent Units for Speech Recognition Product-Based Neural Networks for User Response Prediction Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities Variational Autoencoders for Collaborative Filtering All you need is a good init Distributed Prioritized Experience Replay Image-to-Image Translation with Conditional Adversarial Networks Deep Learning over Multi-field Categorical Data - - A Case Study on User Response Prediction Effectiveness of Scaled Exponentially-Regularized Linear Units (SERLUs)
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["Computer Science","Medicine"] ["Computer Science"] ["Engineering","Computer Science"] ["Medicine","Computer Science"] ["Computer Science","Engineering"] ["Geology"] ["Computer Science"] ["Computer Science"] ["Mathematics"] ["Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Engineering"] ["Computer Science","Mathematics"]
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Color Imaging: Processing, Hardcopy, and Applications ICLR NIPS 2016 IEEE 16th International Conference on Data Mining (ICDM) AISTATS CoNLL 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) INTERSPEECH NIPS IJCAI AIM INTERSPEECH IEEE Transactions on Emerging Topics in Computational Intelligence 2016 IEEE 16th International Conference on Data Mining (ICDM) International Journal of Computer Vision WWW ICLR ICLR 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ECIR
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