To address this issue , this paper implements a self-normalizing neural network ( SNN ) in order to extract high-level abstract representations without losing information due to the data initialization . The selected activation function ( scaled exponential linear units or SELU ) normalizes the data considering their neighborhood 's information and a special dropout technique ( a-dropout ) , obtaining good classification performance while maintaining the data characteristics across the successive layers . Obtained results show that the proposal improves the performance with few training samples . While ReLU employs predefined values for ( typically equal to 0 ) , PReLU requires learning this coefficient . Thus , this activation function can adapt the rectifiers to their inputs , improving the network ’s accuracy at a negligible extra computational cost . Their basic components ( convolution , pooling , and activation functions ) operate on local input regions , and depend only on relative spatial coordinates . Writing for the data vector at location in a particular layer , and for the following layer , these functions compute outputs by where is called the kernel size , is the stride or subsampling factor , and determines the layer type : a matrix multiplication for convolution or average pooling , a spatial max for max pooling , or an elementwise nonlinearity for an activation function , and so on for other types of layers . This functional form is maintained under composition , with kernel size and stride obeying the transformation rule Deep neural networks have recently achieved state-of-the-art results in many machine learning problems , e.g. , speech recognition or object recognition . Hitherto , work on rectified linear units ( ReLU ) provides empirical and theoretical evidence on performance increase of neural networks comparing to typically used sigmoid activation function . In this paper , we investigate a new manner of improving neural networks by introducing a bunch of copies of the same neuron modeled by the generalized Kumaraswamy distribution . We use Parkinson 's disease data in the University of California , Irvine ( UCI ) . In order to optimize the SVM algorithm , different kernel functions and C parameters have been used and our results show that SVM with C parameter ( C-SVM ) with average of 99.18 % accuracy with Polynomial Kernel function in testing step , has better performance compared to the other Kernel functions such as RBF and Sigmoid as well as Bayesian Network algorithm . It is also shown that ten important factors in SVM algorithm are Jitter ( Abs ) , Subject # , RPDE , PPE , Age , NHR , Shimmer APQ 11 , NHR , Total-UPDRS , Shimmer ( dB ) and Shimmer . This study uses linear and nonlinear viscoelastic models to describe the dynamic distention of the aorta induced by time-varying arterial blood pressure . We employ an inverse mathematical modeling approach on a four-parameter ( linear ) Kelvin viscoelastic model and two five-parameter nonlinear viscoelastic models ( arctangent and sigmoid ) to infer vascular biomechanical properties under in vivo and ex vivo experimental conditions in ten and eleven male Merino sheep , respectively . We used the Akaike Information Criterion ( AIC ) as a goodness-of-fit measure . There are three key steps in designing a robust system . First , in contrast to sigmoid activation and min-max normalization in state-of-the-art algorithms , a linear activation function at the output layer and global mean-variance normalization of target features are adopted to learn the complicated nonlinear mapping function from reverberant to anechoic speech and to improve the restoration of the low-frequency and intermediate-frequency contents . Next , two key design parameters , namely , frame shift size in speech framing and acoustic context window size at the DNN input , are investigated to show that RT60-dependent parameters are needed in the DNN training stage in order to optimize the system performance in diverse reverberant environments . Experimental results using a Doherty PA will show that the proposed DNN-DPD can achieve an additional 3 to 4 dB of linearity compensation compared to a conventional Volterra-DPD or shallow NN . Furthermore , to achieve comparable performance to sigmoid activation , the number of multipliers can be reduced by a factor of 100 . This confirms that the DNN-DPD approach has a large potential to overcome the complicated memory effects of Doherty PAs with reduced DPD complexity . When using Monte Carlo calculations with TOPAS , despite the relatively good match to the published DVH 's , differences up to 9 % were found for the parallel-serial model ( for a maximum DVH difference of 2 % ) and up to 0.5 % for the Poisson model ( for a maximum DVH difference of 0.5 % ) . However , differences of 74.5 % ( in Rectangle1 ) , 34.8 % ( in PTV ) and 52.1 % ( in Triangle ) for the critical element , critical volume and the sigmoid-based models were found respectively . We propose a new benchmark for verification of organ effect models in proton therapy . This paper proposes a new class loss function as an alternative to the standard sigmoid class loss function for optimizing the parameters of decoding graphs using discriminative training based on minimum classification error ( MCE ) criterion . The standard sigmoid based approach tends to ignore a significant number of training samples that have a large difference between the scores of the reference and their corresponding competing hypotheses and this affects the parameters optimization . The proposed function overcomes this limitation through considering almost all the training samples and thus improved the parameter optimization when tested on large decoding graphs . This chapter discusses FCM application in relationship modeling context using some agile inference mechanisms . A sigmoid-based activation function is discussed with application in modeling hexapod locomotion gait . The activation algorithm is then added with a Hebbian weight training technique to enable automatic construction of FCMs . In order to apply artificial neutral networks in intelligence processing of sensing signal using combination of FPGA chip and sensors , Sigmoid activated function should be implemented on FPGA . Accordingly , an approach is proposed to implement the Sigmoid function activating method using pipeline mode , and two key points in this process are discussed in detail : first is to approximate exponential function by CORDIC algorithm after several iteration and second is implementation of an improved high-efficient divider , and simulation results are analyzed . To some extent , this scheme is able to meet the requirements of neural networks and can be used in intellectualization of sensors . Then , a curve fitting algorithm is used to curve the lines through the detection of cliff locations . Cliff location represents the edge of the pectoral muscle and it is detected using sigmoid operation . Our algorithm is compared with an algorithm that detects the pectoral muscle using one line only . Based on neural network techniques , our objective was to identify those factors for HIV/HCV co-infected patients ( to which clinicians have given careful consideration before treatment uptake ) that have not being included among the prioritisation criteria . This study was based on the Spanish HERACLES cohort ( NCT02511496 ) ( April-September 2015 , 2940 patients ) and involved application of different neural network models with different basis functions ( product-unit , sigmoid unit and radial basis function neural networks ) for automatic classification of patients for treatment . An evolutionary algorithm was used to determine the architecture and estimate the coefficients of the model . It creates convolutions of convolutions , thereby capturing more complex higher - order features from the dataset . In a recurrent neural network ( RNN ) , each hidden representation is calculated based on the current token embedding and the hidden vector at the previous time step : where is a nonlinear function , such as the sigmoid function . Instead of a fixed context window , information is passed through the sentence using a recursive function and the network is able to learn which patterns to disregard or pass forward . 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 architecture of the maxout RNNs comprises one recurrent layer with 2828 units projected to 1414 units via non - overlapping maxout operations . We then normalize the logits into probabilities . Note that we follow previous work and employ softmax and sigmoid for multi - class and multi - label classifications , respectively . subsection : Loss Function Xavier initialization has provided a theoretically sound estimation of the variance of weight . It assumes that the weights are initialized close to zero , hence the nonlinear activations like Sigmoid and Tanh can be regarded as linear functions . This assumption does not hold for rectifier activations . The bottom layer of the SNN is fully connected with the input vector while the bottom layer for FNN is partially connected and thus the FNN is more robust when some hidden units are dropped out . Furthermore , the sigmoid activation function tend to more effective than the linear activation function in terms of dropout . Therefore , the dropout rates at the best performance of FNN and SNN are quite different . Similarly , DC ( 256 , 128 , 4c2s ) denotes a corresponding transposed convolutional operation . Compared to the original DCGAN architecture , the discriminator under our formulation does not have the last sigmoid layer which squashes a scalar value into a probability in [ reference ][ reference ] . For celebA experiment with 64 × 64 color images , we use the following architecture Given the chosen architectures , we follow [ reference ] and use Adam as the optimization algorithm . However , unlike the DCGAN , the LS - GAN model ( unconditional version in Section [ reference ] ) did not use a sigmoid layer as the output for the loss function network . Instead , we removed it and directly output the activation before the removed sigmoid layer . On the other hand , for the loss function network in CLS - GAN , a global mean - pooling layer was added on top of convolutional layers . The classification subnet applies four convolution layers each with 256 filters , followed by a convolution layer with filters where means the number of classes and means the number of anchors per location . For face detection since we use sigmoid activation , and we use in most experiments . All convolution layers in this subnet share parameters across all pyramid levels just like the original RetinaNet . We also experimented with a log - likelihood loss by modeling the output distribution with a Mixture Density Network to avoid assuming a constant variance over time , but found that these were more difficult to train and they did not lead to better sounding samples . In parallel to spectrogram frame prediction , the concatenation of decoder LSTM output and the attention context is projected down to a scalar and passed through a sigmoid activation to predict the probability that the output sequence has completed . This “ stop token ” prediction is used during inference to allow the model to dynamically determine when to terminate generation instead of always generating for a fixed duration . The batch normalization ( BN ) layers are applied after each convolutional layer , which are followed by the parameterized rectified linear unit ( PReLU ) processing . The sigmoid units and the hyperbolic tangent units are applied to the output layers of the generative models for generating images with desired pixel range values . appendix : Visualization We use rectified linear units ( ReLU ) for neurons in the convolutional layers and the DeepID2 layer . ReLU has better fitting abilities than the sigmoid units for large training datasets . An illustration of the ConvNet structure used to extract DeepID2 is shown in Fig . Gradient - based optimization is the workhorse of modern AI . Using a network that has been designed to be sufficiently linear – whether it is a ReLU or maxout network , an LSTM , or a sigmoid network that has been carefully configured not to saturate too much– we are able to fit most problems we care about , at least on the training set . The existence of adversarial examples suggests that being able to explain the training data or even being able to correctly label the test data does not imply that our models truly understand the tasks we have asked them to perform . We extended one portable and available language that meets these requirements , Tcl/Tk [ 30 ] , with the framework for a traditional patch-based audio system . The resulting package , " swish , " lets the user create applications that implement , control , and interact with audio processes at a high level . Since Tk provides access to a widget set , the interaction can be either textual or graphical . In order to verify the validity and practicability of the proposed three new activation functions are tested and compared with complex swish activation function on complex valued four bit XOR problem , three inputs symmetry detection and the fading equalization problems . We show that complex E-swish ( β=1.4 ) has the best overall performance when compared to other networks using complex swish , complex modified swish and complex Flatten-T swish activation functions on the considered tasks . The use of multimedia as a means of presenting information in an application will be very helpful . Information about Alcatel Mobile technology created using Swish v2.0 application . In the Swish v2.0 provided a place to create text , animations , so the Alcatel Mobile pictures and text can be moved so that it can look more attractive . Penamaan File : 30101883 So the value of information that can have a sale value in making these ikln . So the writer the chance to create animated ads handphone Nokia 6110 , N70 , N77 using swish v2.0 software that can create vector and bitmap animation object so that the resulting file size . With the increasing development of technology in graphic design , advertising , etc . , then we need good software with adequate facilities to assist users in improving work efficiency . Scholarly writing explains how to create a simple ad that looks interesting and interactive by using swish software version 2.0 . By utilizing a variety of effects and animation facilities available at the swish version 2.0 is expected to help users to create or design a compelling ad , as well as the use of interest to try to be creative with the use of various facilities and services provided . An effective script not only works on a given level , but more importantly relates the activities at each level to each other to form an effective integrated pedagogical process . The authors propose their suggestive SWISH principle as a stimulus for collaborative learning . Collaboration , they argue , takes place most effectively in a relatively unconstrained small group process of peers working together to overcome some cognitive barrier to the shared accomplishment of a joint task . Consequently , the deep neural network has not been benefited from the negative representations . In this work , an activation function called Flatten-T Swish ( FTS ) that leverage the benefit of the negative values is proposed . To verify its performance , this study evaluates FTS with ReLU and several recent activation functions . To enhance the student 's visualization of calculating volumes of solid , a 3D graphics multimedia courseware has been developed using the ADDIE model . The tools that are used to develop the prototype are Blender for 3D modeling and SWiSH Max 4 for layout and interactivity of courseware . Questionnaire has been distributed to get feedback from 12 students pertaining to 3 categories which are visualization , navigation and content .
To address this issue , this paper implements a self-normalizing neural network ( SNN ) in order to extract high-level abstract representations without losing information due to the data initialization . The selected activation function ( scaled exponential linear units or SELU ) normalizes the data considering their neighborhood 's information and a special dropout technique ( a-dropout ) , obtaining good classification performance while maintaining the data characteristics across the successive layers . Obtained results show that the proposal improves the performance with few training samples .
While ReLU employs predefined values for ( typically equal to 0 ) , PReLU requires learning this coefficient . Thus , this activation function can adapt the rectifiers to their inputs , improving the network ’s accuracy at a negligible extra computational cost .
Their basic components ( convolution , pooling , and activation functions ) operate on local input regions , and depend only on relative spatial coordinates . Writing for the data vector at location in a particular layer , and for the following layer , these functions compute outputs by where is called the kernel size , is the stride or subsampling factor , and determines the layer type : a matrix multiplication for convolution or average pooling , a spatial max for max pooling , or an elementwise nonlinearity for an activation function , and so on for other types of layers . This functional form is maintained under composition , with kernel size and stride obeying the transformation rule
Deep neural networks have recently achieved state-of-the-art results in many machine learning problems , e.g. , speech recognition or object recognition . Hitherto , work on rectified linear units ( ReLU ) provides empirical and theoretical evidence on performance increase of neural networks comparing to typically used sigmoid activation function . In this paper , we investigate a new manner of improving neural networks by introducing a bunch of copies of the same neuron modeled by the generalized Kumaraswamy distribution .
We use Parkinson 's disease data in the University of California , Irvine ( UCI ) . In order to optimize the SVM algorithm , different kernel functions and C parameters have been used and our results show that SVM with C parameter ( C-SVM ) with average of 99.18 % accuracy with Polynomial Kernel function in testing step , has better performance compared to the other Kernel functions such as RBF and Sigmoid as well as Bayesian Network algorithm . It is also shown that ten important factors in SVM algorithm are Jitter ( Abs ) , Subject # , RPDE , PPE , Age , NHR , Shimmer APQ 11 , NHR , Total-UPDRS , Shimmer ( dB ) and Shimmer .
This study uses linear and nonlinear viscoelastic models to describe the dynamic distention of the aorta induced by time-varying arterial blood pressure . We employ an inverse mathematical modeling approach on a four-parameter ( linear ) Kelvin viscoelastic model and two five-parameter nonlinear viscoelastic models ( arctangent and sigmoid ) to infer vascular biomechanical properties under in vivo and ex vivo experimental conditions in ten and eleven male Merino sheep , respectively . We used the Akaike Information Criterion ( AIC ) as a goodness-of-fit measure .
There are three key steps in designing a robust system . First , in contrast to sigmoid activation and min-max normalization in state-of-the-art algorithms , a linear activation function at the output layer and global mean-variance normalization of target features are adopted to learn the complicated nonlinear mapping function from reverberant to anechoic speech and to improve the restoration of the low-frequency and intermediate-frequency contents . Next , two key design parameters , namely , frame shift size in speech framing and acoustic context window size at the DNN input , are investigated to show that RT60-dependent parameters are needed in the DNN training stage in order to optimize the system performance in diverse reverberant environments .
Experimental results using a Doherty PA will show that the proposed DNN-DPD can achieve an additional 3 to 4 dB of linearity compensation compared to a conventional Volterra-DPD or shallow NN . Furthermore , to achieve comparable performance to sigmoid activation , the number of multipliers can be reduced by a factor of 100 . This confirms that the DNN-DPD approach has a large potential to overcome the complicated memory effects of Doherty PAs with reduced DPD complexity .
When using Monte Carlo calculations with TOPAS , despite the relatively good match to the published DVH 's , differences up to 9 % were found for the parallel-serial model ( for a maximum DVH difference of 2 % ) and up to 0.5 % for the Poisson model ( for a maximum DVH difference of 0.5 % ) . However , differences of 74.5 % ( in Rectangle1 ) , 34.8 % ( in PTV ) and 52.1 % ( in Triangle ) for the critical element , critical volume and the sigmoid-based models were found respectively . We propose a new benchmark for verification of organ effect models in proton therapy .
This paper proposes a new class loss function as an alternative to the standard sigmoid class loss function for optimizing the parameters of decoding graphs using discriminative training based on minimum classification error ( MCE ) criterion . The standard sigmoid based approach tends to ignore a significant number of training samples that have a large difference between the scores of the reference and their corresponding competing hypotheses and this affects the parameters optimization . The proposed function overcomes this limitation through considering almost all the training samples and thus improved the parameter optimization when tested on large decoding graphs .
This chapter discusses FCM application in relationship modeling context using some agile inference mechanisms . A sigmoid-based activation function is discussed with application in modeling hexapod locomotion gait . The activation algorithm is then added with a Hebbian weight training technique to enable automatic construction of FCMs .
In order to apply artificial neutral networks in intelligence processing of sensing signal using combination of FPGA chip and sensors , Sigmoid activated function should be implemented on FPGA . Accordingly , an approach is proposed to implement the Sigmoid function activating method using pipeline mode , and two key points in this process are discussed in detail : first is to approximate exponential function by CORDIC algorithm after several iteration and second is implementation of an improved high-efficient divider , and simulation results are analyzed . To some extent , this scheme is able to meet the requirements of neural networks and can be used in intellectualization of sensors .
Then , a curve fitting algorithm is used to curve the lines through the detection of cliff locations . Cliff location represents the edge of the pectoral muscle and it is detected using sigmoid operation . Our algorithm is compared with an algorithm that detects the pectoral muscle using one line only .
Based on neural network techniques , our objective was to identify those factors for HIV/HCV co-infected patients ( to which clinicians have given careful consideration before treatment uptake ) that have not being included among the prioritisation criteria . This study was based on the Spanish HERACLES cohort ( NCT02511496 ) ( April-September 2015 , 2940 patients ) and involved application of different neural network models with different basis functions ( product-unit , sigmoid unit and radial basis function neural networks ) for automatic classification of patients for treatment . An evolutionary algorithm was used to determine the architecture and estimate the coefficients of the model .
It creates convolutions of convolutions , thereby capturing more complex higher - order features from the dataset . In a recurrent neural network ( RNN ) , each hidden representation is calculated based on the current token embedding and the hidden vector at the previous time step : where is a nonlinear function , such as the sigmoid function . Instead of a fixed context window , information is passed through the sentence using a recursive function and the network is able to learn which patterns to disregard or pass forward .
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 architecture of the maxout RNNs comprises one recurrent layer with 2828 units projected to 1414 units via non - overlapping maxout operations .
We then normalize the logits into probabilities . Note that we follow previous work and employ softmax and sigmoid for multi - class and multi - label classifications , respectively . subsection : Loss Function
Xavier initialization has provided a theoretically sound estimation of the variance of weight . It assumes that the weights are initialized close to zero , hence the nonlinear activations like Sigmoid and Tanh can be regarded as linear functions . This assumption does not hold for rectifier activations .
The bottom layer of the SNN is fully connected with the input vector while the bottom layer for FNN is partially connected and thus the FNN is more robust when some hidden units are dropped out . Furthermore , the sigmoid activation function tend to more effective than the linear activation function in terms of dropout . Therefore , the dropout rates at the best performance of FNN and SNN are quite different .
Similarly , DC ( 256 , 128 , 4c2s ) denotes a corresponding transposed convolutional operation . Compared to the original DCGAN architecture , the discriminator under our formulation does not have the last sigmoid layer which squashes a scalar value into a probability in [ reference ][ reference ] . For celebA experiment with 64 × 64 color images , we use the following architecture Given the chosen architectures , we follow [ reference ] and use Adam as the optimization algorithm .
However , unlike the DCGAN , the LS - GAN model ( unconditional version in Section [ reference ] ) did not use a sigmoid layer as the output for the loss function network . Instead , we removed it and directly output the activation before the removed sigmoid layer . On the other hand , for the loss function network in CLS - GAN , a global mean - pooling layer was added on top of convolutional layers .
The classification subnet applies four convolution layers each with 256 filters , followed by a convolution layer with filters where means the number of classes and means the number of anchors per location . For face detection since we use sigmoid activation , and we use in most experiments . All convolution layers in this subnet share parameters across all pyramid levels just like the original RetinaNet .
We also experimented with a log - likelihood loss by modeling the output distribution with a Mixture Density Network to avoid assuming a constant variance over time , but found that these were more difficult to train and they did not lead to better sounding samples . In parallel to spectrogram frame prediction , the concatenation of decoder LSTM output and the attention context is projected down to a scalar and passed through a sigmoid activation to predict the probability that the output sequence has completed . This “ stop token ” prediction is used during inference to allow the model to dynamically determine when to terminate generation instead of always generating for a fixed duration .
The batch normalization ( BN ) layers are applied after each convolutional layer , which are followed by the parameterized rectified linear unit ( PReLU ) processing . The sigmoid units and the hyperbolic tangent units are applied to the output layers of the generative models for generating images with desired pixel range values . appendix : Visualization
We use rectified linear units ( ReLU ) for neurons in the convolutional layers and the DeepID2 layer . ReLU has better fitting abilities than the sigmoid units for large training datasets . An illustration of the ConvNet structure used to extract DeepID2 is shown in Fig .
Gradient - based optimization is the workhorse of modern AI . Using a network that has been designed to be sufficiently linear – whether it is a ReLU or maxout network , an LSTM , or a sigmoid network that has been carefully configured not to saturate too much– we are able to fit most problems we care about , at least on the training set . The existence of adversarial examples suggests that being able to explain the training data or even being able to correctly label the test data does not imply that our models truly understand the tasks we have asked them to perform .
We extended one portable and available language that meets these requirements , Tcl/Tk [ 30 ] , with the framework for a traditional patch-based audio system . The resulting package , " swish , " lets the user create applications that implement , control , and interact with audio processes at a high level . Since Tk provides access to a widget set , the interaction can be either textual or graphical .
In order to verify the validity and practicability of the proposed three new activation functions are tested and compared with complex swish activation function on complex valued four bit XOR problem , three inputs symmetry detection and the fading equalization problems . We show that complex E-swish ( β=1.4 ) has the best overall performance when compared to other networks using complex swish , complex modified swish and complex Flatten-T swish activation functions on the considered tasks .
The use of multimedia as a means of presenting information in an application will be very helpful . Information about Alcatel Mobile technology created using Swish v2.0 application . In the Swish v2.0 provided a place to create text , animations , so the Alcatel Mobile pictures and text can be moved so that it can look more attractive . Penamaan File : 30101883
So the value of information that can have a sale value in making these ikln . So the writer the chance to create animated ads handphone Nokia 6110 , N70 , N77 using swish v2.0 software that can create vector and bitmap animation object so that the resulting file size .
With the increasing development of technology in graphic design , advertising , etc . , then we need good software with adequate facilities to assist users in improving work efficiency . Scholarly writing explains how to create a simple ad that looks interesting and interactive by using swish software version 2.0 . By utilizing a variety of effects and animation facilities available at the swish version 2.0 is expected to help users to create or design a compelling ad , as well as the use of interest to try to be creative with the use of various facilities and services provided .
An effective script not only works on a given level , but more importantly relates the activities at each level to each other to form an effective integrated pedagogical process . The authors propose their suggestive SWISH principle as a stimulus for collaborative learning . Collaboration , they argue , takes place most effectively in a relatively unconstrained small group process of peers working together to overcome some cognitive barrier to the shared accomplishment of a joint task .
Consequently , the deep neural network has not been benefited from the negative representations . In this work , an activation function called Flatten-T Swish ( FTS ) that leverage the benefit of the negative values is proposed . To verify its performance , this study evaluates FTS with ReLU and several recent activation functions .
To enhance the student 's visualization of calculating volumes of solid , a 3D graphics multimedia courseware has been developed using the ADDIE model . The tools that are used to develop the prototype are Blender for 3D modeling and SWiSH Max 4 for layout and interactivity of courseware . Questionnaire has been distributed to get feedback from 12 students pertaining to 3 categories which are visualization , navigation and content .
An Investigation on Self-Normalized Deep Neural Networks for Hyperspectral Image Classification Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation Fully Convolutional Networks for Semantic Segmentation Improving neural networks with bunches of neurons modeled by Kumaraswamy units: Preliminary study Impact of Patients’ Gender on Parkinson’s disease using Classification Algorithms Linear and nonlinear viscoelastic modeling of ovine aortic biomechanical properties under in vivo and ex vivo conditions A Reverberation-Time-Aware Approach to Speech Dereverberation Based on Deep Neural Networks Deep Neural Network Based Predistorter with ReLU Activation for Doherty Power Amplifiers A framework for implementation of organ effect models in TOPAS with benchmarks extended to proton therapy. Optimizing the parameters of decoding graphs using new log-based MCE FCM Relationship Modeling for Engineering Systems Implementation scheme for activated function of neutral networks in intelligent sensors Improved method of pectoral muscle detection based on two straight line fitting algorithm Using machine learning methods to determine a typology of patients with HIV-HCV infection to be treated with antivirals Compositional Sequence Labeling Models for Error Detection in Learner Writing The IBM 2016 English Conversational Telephone Speech Recognition System Explicit Interaction Model towards Text Classification Learning Feature Pyramids for Human Pose Estimation Deep Learning over Multi-field Categorical Data - - A Case Study on User Response Prediction Calibrating Energy-based Generative Adversarial Networks Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities Face Attention Network: An Effective Face Detector for the Occluded Faces Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions Coupled Generative Adversarial Networks Deep Learning Face Representation by Joint Identification-Verification Explaining and Harnessing Adversarial Examples Programming models for the development of interactive audio applications NEW COMPLEX VALUED ACTIVATIONFUNCTIONS: COMPLEX MODIFIEDSWISH, COMPLEX E-SWISH ANDCOMPLEX FLATTEN-TSWISH ALCATEL HANDPHONE USING ANIMATED SWiSH V2.0 MAKING ANIMATED ADVERTISING HANDPHONE NOKIA 6110, N70, N77 USING SWISH V2.0 ADVERTISING MAKING BY USING SWISH MEDIA. The many levels of CSCL Flatten-T Swish: a thresholded ReLU-Swish-like activation function for deep learning Enhancing 3D visualization for learning volumes of solid, an application in integral calculus: A multimedia courseware development
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["Computer Science"] ["Computer Science"] ["Mathematics"] ["Computer Science","Medicine"] ["Computer Science"] ["Engineering"] ["Medicine","Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Medicine"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"]
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IGARSS Comput. Medical Imaging Graph. IEEE Transactions on Pattern Analysis and Machine Intelligence APSIPA Fuzzy Cognitive Maps for Applied Sciences and Engineering ACL INTERSPEECH AAAI 2017 IEEE International Conference on Computer Vision (ICCV) ECIR ICLR International Journal of Computer Vision ArXiv 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) NIPS NIPS ICLR
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