This paper proposed real-time de-mixture of speech mixture in enclosure environments . Making the real-time mixture of source signals as simultaneously as de-mixture in simulation based on adaptive NLMS and activation function in neural network are proposed . Separating the sources in the frequency domain is shown . The two major types are Long Short-Term Memory ( LSTM ) and Gated Recurrent Unit ( GRU ) networks . It is a challenging task to have real-time , efficient , and accurate hardware RNN implementations because of the high sensitivity to imprecision accumulation and the requirement of special activation function implementations . A key limitation of the prior works is the lack of a systematic design optimization framework of RNN model and hardware implementations , especially when the block size ( or compression ratio ) should be jointly optimized with RNN type , layer size , etc . The Verilog HDL has been used to model the network . Fixed point precision and activation function implementations have been investigated to monitor FPGA resource utilization for different network topologies . The network implemented has been adopted to different applications including handwritten recognition of the MNIST dataset . The present invention provides a method of preparing a microcontroller chip configured by different types of functional modules are provided in different chips , and connected between the activation of different chips is achieved by the silicon substrate , so that the user can choose required activation and function modules , as compared with the all functional modules disposed in a chip-on-chip system , the method of the present invention having a flexible , material and cost savings , advantages of a good data transfer capability . A novel single hand Sign Language Recognition ( SLR ) method utilizing Weightless Neural Network ( WNN ) known as a RAMnet or n-tuple network is evaluated as a memory efficient technique . In contrast with standard multilayer perceptron neural network ( MLPNN ) , the RAMnet does not require long iteration of presentation of training data in the training phase i.e. long training time , weights adjustments and activation function calculations . This alternative technique is based on WiSARD Neural Network . Adaptive kernels enable accurate recognition with significant lower memory requirements ; this is accomplished by reducing the number of kernels and the number of layers needed as compared to typical CNN configurations . Additionally , the use of adaptive kernels allow the decrease by 2X the number of epochs required for training , and the number of activation function computations . Our experimental results show a reduction of 66X of the parameters needed of a CNN compared to LeNet when evaluated with the MNIST dataset , maintaining > 99 % of accuracy . This work presents deep asymmetric networks with a set of node-wise variant activation functions . The nodes ' sensitivities are affected by activation function selections such that the nodes with smaller indices become increasingly more sensitive . As a result , features learned by the nodes are sorted by the node indices in the order of their importance . To overcome this problem , Differential Evolution ( DE ) has been used to determine optimal value for ANN parameters such as learning rate and momentum rate and also for weight optimization . In ANN , there are many elements need to be considered , and these include the number of input nodes , hidden nodes , output nodes , learning rate , momentum rate , bias parameter , minimum error and activation/transfer functions . These elements will affect the speed of neural network learning . Given a training dataset , the optimization objective is represented as where is the network parameters needed to be trained , is the estimated high frequency image with respect to the interpolation result of a LR image . In the literature , people suggest to use to recently proposed Parametric Rectified Linear Unit ( PReLU ) as the activation function instead of the commonly - used Rectified Linear Unit ( ReLU ) . But , in order to reduce parameters , ReLU is used after each convolution layer in our very deep network that has got a satisfactory result for comparison . The order is : 1 ) activation functions ; 2 ) dropout rate ; 3 ) number of neurons per layer ; 4 ) number of hidden layers ; 5 ) network shape . subsubsection : Activation Function According to , relu and tanh are more suitable for deep models than sigmoid . This matrix is processed by a bidirectional recurrent neural network , a hochreiter1997long in this work . The resulting output is further projected to by a fully - connected layer with activation ( Eq . [ reference ] ) . After the pretraining , we initialize a new decoder to be exclusively trained on the supervised problem . Depending on the task , this decoder may be a single linear layer with activation or an MLP . We also retain the original decoder and continue to train it by using language modeling as an auxiliary loss when finetuning on the new corpus . CNN - based models hold state - of - the - art performance in various computer vision tasks , including image classifcation , object detection , and image parsing . Recently , there has been considerable interest in enhancing CNN components , including pooling layers , activation units , and nonlinear layers . These enhancements either improve CNN training , or expand the network learning capacity . In this paper , we give some tools for the construction of homogeneous feedback which stabilizes homogeneous polynomial systems of odd degree in the plane . The algorithm is based on a local image decomposition technique using the Hermite polynomial basis . We show that any coefficient of the Hermite polynomial computed using the more blurred image is a function of the partial derivatives of the other image and the blur difference . Hence , the blur difference is computed by resolving a system of equations . This can be considered as a simple repetition block code , which generally has a limited coding gain . We propose a new UWB system employing super-orthogonal convolutional coding in conjunction with a set of orthogonal waveforms based on Hermite polynomial functions . Combination of the super-orthogonal convolutional code and orthogonal waveforms can effect high data rate communication with good error performance . In this paper novel modified Hermite polynomial functions for use in impulse radio ( ultra-wideband ) communications are proposed . With these functions pulse shapes which are orthogonal and have nearly constant pulse width regardless of the pulse order are generated . We give the multiplication method to multiply two elements of F"3"^"n in the Hermite polynomial representation with subquadratic computational complexity by using a divide-and-conquer idea . We show that in some cases there is a set of irreducible binomials in the Hermite polynomial representation to obtain modular reduction with a lower addition complexity than the standard polynomial representation . We also investigate the matrix vector product method for the multiplication of the field elements represented by Hermite polynomials . While our experiments with supervised learning do not provide a clear verdict , we find that this strategy offers considerable benefits in semi-supervised learning ( SSL ) / transductive learning settings . We carefully develop this idea and show how the use of Hermite polynomials based activations can yield improvements in pseudo-label accuracies and sizable financial savings ( due to concurrent runtime benefits ) . Further , we show via theoretical analysis , that the networks ( with Hermite activations ) offer robustness to noise and other attractive mathematical properties . While our experiments with supervised learning do not provide a clear verdict , we find that this strategy offers considerable benefits in semi-supervised learning ( SSL ) / transductive learning settings . We carefully develop this idea and show how the use of Hermite polynomials based activations can yield improvements in pseudo-label accuracies and sizable financial savings ( due to concurrent runtime benefits ) . Further , we show via theoretical analysis , that the networks ( with Hermite activations ) offer robustness to noise and other attractive mathematical properties . In general , the observable space related to the concept of single-experiment observability of the discrete-time nonlinear systems can not always be locally spanned by exact one-forms whose integrals would define the observable state coordinates . In this technical note , we show that for the special subclass of reversible polynomial systems , the observable space is integrable like in the continuous-time case . We also assess the eigenfunction of echo state networks and its criticality by the Hermite polynomials . A Hermite polynomial-based activation function design with fast convergence is proposed and the relation between long-term time dependence and edge-of-chaos criticality is given . A new particle swarm optimization-gravitational search algorithm is put forward to improve the parameters estimation that helps attain on the edge of chaos . In this paper , a numerical method based on polynomial approximation , using Hermite polynomial basis , to obtain the approximate solution of generalized pantograph equations with variable coefficients is presented . The technique we have used is an improved collocation method . The results motivate an addressing of a selected x – aircraft response . Keywords : Simulation modeling , Hermite polynomial forms , X – aircraft responses International Journal of Natural and Applied Sciences , 5(3 ) : 220 - 222 , 2009 Abstract This paper investigates the potential of using a novel Hermite polynomial neural network to model shoreline realignment along an urban beach fronted by a highly irregular beachrock reef . Modeling takes place on the basis of a number of input variables related to reef morphology and wave forcing , whereas the output variable is time series of shoreline position that have been recorded in high spatio-temporal resolution using a coastal video monitoring system . The single-hidden layer feedforward neural-network with scaled conjugate gradient was used as the classifier . We compared three activation functions : logistic sigmoid ( LOSI ) , rectified linear unit ( ReLU ) , and leaky rectified linear unit ( LReLU ) . Early stopping and K-fold cross validation ( CV ) was used to avoid overfitting and statistical analysis . The results show our method is promising . In addition , LReLU is a better activation function that may replace traditional logistic sigmoid function in other applications . However in the field of deep learning , they have been largely displaced by rectified ( ReLU ) or exponential ( ELU ) linear units to mitigate the effects of vanishing gradients associated with error back-propagation . The logistic sigmoid however does not represent the true input-output relation in real neurones under physiological conditions . Here , radical root unit ( RRU ) activation functions are introduced , exhibiting input-output non-linearities that are substantially more biologically plausible since their functional form is based on known current-frequency relationships . To address this problem , a novel gene selection strategy is proposed to identify the candidate gene biomarkers from the genomic data . Signal to Noise ratio with logistic sigmoid function , Hilbert – Schmidt Independence Criterion Lasso , and regularized genetic algorithm amalgamation finds the optimal features . The proposed system is tested with the microarray gene expression dataset of autism spectrum disorder ( ASD ) , accessed from gene expression omnibus repository . In this article , the artificial intelligence techniques have been used for the solution of Falkner – Skan ( FS ) equations based on neural networks optimized with three methods including active set technique , sequential quadratic programming and genetic algorithms ( GA ) hybridization . Log-sigmoid activation function is used in artificial neural network architecture . The proposed techniques are applied to a number of cases for Falkner – Skan problems , and results were compared with GA hybrid results in all cases and were found accurate . We reformulate the MLP model with the original perceptron in mind so that each node in the “ hidden layers ” can be considered as a latent ( that is , unobserved ) Bernoulli random variable . This maintains the property of binary output from the nodes , and with an imposed logistic regression of the hidden layer nodes on the inputs , the expected output of our model is identical to the MLP output with a logistic sigmoid activation function ( for the case of one hidden layer).We examine the usual MLP objective function — the sum of squares — and show its multi-modal form and the corresponding optimisation difficulty . We also construct the likelihood for the reformulated latent variable model and maximise it by standard finite mixture ML methods using an EM algorithm , which provides stable ML estimates from random starting positions without the need for regularisation or cross-validation . For a single example and its reconstructed output , we compute the MBCE loss as follows : Here , is the balanced cross - entropy loss with weighting factor , is the sigmoid function , is the Hadamard ( element - wise ) product , and is the boolean function : if , else . The same autoencoder architecture can be applied to a row vector in the augmented adjacency matrix . 2 M parameters . The output of the MoE layer is passed through a sigmoid function . We use the strictly - balanced gating function described in Appendix F. For each word - context pair in the training corpus , negative context words are sampled , and the objective function is defined as follows : where is the logistic sigmoid function , is the weight vector for the context word , and is a negative sample . It should be noted that the weight vectors for the context words are parameterized for the words without any character information . The remaining are GRU - like updates that incorporate information from the other nodes and from the previous timestep to update each node ’s hidden state . and are the update and reset gates , is the logistic sigmoid function , and is element - wise multiplication . We initially experimented with a vanilla recurrent neural network - style update , but in preliminary experiments we found this GRU - like propagation step to be more effective . In this brief , we investigate four different adaptation schemes for connectionist ( also known as hybrid ) ASR systems that learn microphone-specific hidden unit contributions , given some adaptation material . This solution is made possible adopting one of the following schemes : 1 ) the use of Hermite activation functions ; 2 ) the introduction of bias and slope parameters in the sigmoid activation functions ; 3 ) the injection of an amplitude parameter specific for each sigmoid unit ; or 4 ) the combination of 2 ) and 3 ) . Such a simple yet effective solution allows the adapted model to be stored in a small-sized storage space , a highly desirable property of adaptation algorithms for deep neural networks that are suitable for large-scale online deployment .
This paper proposed real-time de-mixture of speech mixture in enclosure environments . Making the real-time mixture of source signals as simultaneously as de-mixture in simulation based on adaptive NLMS and activation function in neural network are proposed . Separating the sources in the frequency domain is shown .
The two major types are Long Short-Term Memory ( LSTM ) and Gated Recurrent Unit ( GRU ) networks . It is a challenging task to have real-time , efficient , and accurate hardware RNN implementations because of the high sensitivity to imprecision accumulation and the requirement of special activation function implementations . A key limitation of the prior works is the lack of a systematic design optimization framework of RNN model and hardware implementations , especially when the block size ( or compression ratio ) should be jointly optimized with RNN type , layer size , etc .
The Verilog HDL has been used to model the network . Fixed point precision and activation function implementations have been investigated to monitor FPGA resource utilization for different network topologies . The network implemented has been adopted to different applications including handwritten recognition of the MNIST dataset .
The present invention provides a method of preparing a microcontroller chip configured by different types of functional modules are provided in different chips , and connected between the activation of different chips is achieved by the silicon substrate , so that the user can choose required activation and function modules , as compared with the all functional modules disposed in a chip-on-chip system , the method of the present invention having a flexible , material and cost savings , advantages of a good data transfer capability .
A novel single hand Sign Language Recognition ( SLR ) method utilizing Weightless Neural Network ( WNN ) known as a RAMnet or n-tuple network is evaluated as a memory efficient technique . In contrast with standard multilayer perceptron neural network ( MLPNN ) , the RAMnet does not require long iteration of presentation of training data in the training phase i.e. long training time , weights adjustments and activation function calculations . This alternative technique is based on WiSARD Neural Network .
Adaptive kernels enable accurate recognition with significant lower memory requirements ; this is accomplished by reducing the number of kernels and the number of layers needed as compared to typical CNN configurations . Additionally , the use of adaptive kernels allow the decrease by 2X the number of epochs required for training , and the number of activation function computations . Our experimental results show a reduction of 66X of the parameters needed of a CNN compared to LeNet when evaluated with the MNIST dataset , maintaining > 99 % of accuracy .
This work presents deep asymmetric networks with a set of node-wise variant activation functions . The nodes ' sensitivities are affected by activation function selections such that the nodes with smaller indices become increasingly more sensitive . As a result , features learned by the nodes are sorted by the node indices in the order of their importance .
To overcome this problem , Differential Evolution ( DE ) has been used to determine optimal value for ANN parameters such as learning rate and momentum rate and also for weight optimization . In ANN , there are many elements need to be considered , and these include the number of input nodes , hidden nodes , output nodes , learning rate , momentum rate , bias parameter , minimum error and activation/transfer functions . These elements will affect the speed of neural network learning .
Given a training dataset , the optimization objective is represented as where is the network parameters needed to be trained , is the estimated high frequency image with respect to the interpolation result of a LR image . In the literature , people suggest to use to recently proposed Parametric Rectified Linear Unit ( PReLU ) as the activation function instead of the commonly - used Rectified Linear Unit ( ReLU ) . But , in order to reduce parameters , ReLU is used after each convolution layer in our very deep network that has got a satisfactory result for comparison .
The order is : 1 ) activation functions ; 2 ) dropout rate ; 3 ) number of neurons per layer ; 4 ) number of hidden layers ; 5 ) network shape . subsubsection : Activation Function According to , relu and tanh are more suitable for deep models than sigmoid .
This matrix is processed by a bidirectional recurrent neural network , a hochreiter1997long in this work . The resulting output is further projected to by a fully - connected layer with activation ( Eq . [ reference ] ) .
After the pretraining , we initialize a new decoder to be exclusively trained on the supervised problem . Depending on the task , this decoder may be a single linear layer with activation or an MLP . We also retain the original decoder and continue to train it by using language modeling as an auxiliary loss when finetuning on the new corpus .
CNN - based models hold state - of - the - art performance in various computer vision tasks , including image classifcation , object detection , and image parsing . Recently , there has been considerable interest in enhancing CNN components , including pooling layers , activation units , and nonlinear layers . These enhancements either improve CNN training , or expand the network learning capacity .
In this paper , we give some tools for the construction of homogeneous feedback which stabilizes homogeneous polynomial systems of odd degree in the plane .
The algorithm is based on a local image decomposition technique using the Hermite polynomial basis . We show that any coefficient of the Hermite polynomial computed using the more blurred image is a function of the partial derivatives of the other image and the blur difference . Hence , the blur difference is computed by resolving a system of equations .
This can be considered as a simple repetition block code , which generally has a limited coding gain . We propose a new UWB system employing super-orthogonal convolutional coding in conjunction with a set of orthogonal waveforms based on Hermite polynomial functions . Combination of the super-orthogonal convolutional code and orthogonal waveforms can effect high data rate communication with good error performance .
In this paper novel modified Hermite polynomial functions for use in impulse radio ( ultra-wideband ) communications are proposed . With these functions pulse shapes which are orthogonal and have nearly constant pulse width regardless of the pulse order are generated .
We give the multiplication method to multiply two elements of F"3"^"n in the Hermite polynomial representation with subquadratic computational complexity by using a divide-and-conquer idea . We show that in some cases there is a set of irreducible binomials in the Hermite polynomial representation to obtain modular reduction with a lower addition complexity than the standard polynomial representation . We also investigate the matrix vector product method for the multiplication of the field elements represented by Hermite polynomials .
While our experiments with supervised learning do not provide a clear verdict , we find that this strategy offers considerable benefits in semi-supervised learning ( SSL ) / transductive learning settings . We carefully develop this idea and show how the use of Hermite polynomials based activations can yield improvements in pseudo-label accuracies and sizable financial savings ( due to concurrent runtime benefits ) . Further , we show via theoretical analysis , that the networks ( with Hermite activations ) offer robustness to noise and other attractive mathematical properties .
While our experiments with supervised learning do not provide a clear verdict , we find that this strategy offers considerable benefits in semi-supervised learning ( SSL ) / transductive learning settings . We carefully develop this idea and show how the use of Hermite polynomials based activations can yield improvements in pseudo-label accuracies and sizable financial savings ( due to concurrent runtime benefits ) . Further , we show via theoretical analysis , that the networks ( with Hermite activations ) offer robustness to noise and other attractive mathematical properties .
In general , the observable space related to the concept of single-experiment observability of the discrete-time nonlinear systems can not always be locally spanned by exact one-forms whose integrals would define the observable state coordinates . In this technical note , we show that for the special subclass of reversible polynomial systems , the observable space is integrable like in the continuous-time case .
We also assess the eigenfunction of echo state networks and its criticality by the Hermite polynomials . A Hermite polynomial-based activation function design with fast convergence is proposed and the relation between long-term time dependence and edge-of-chaos criticality is given . A new particle swarm optimization-gravitational search algorithm is put forward to improve the parameters estimation that helps attain on the edge of chaos .
In this paper , a numerical method based on polynomial approximation , using Hermite polynomial basis , to obtain the approximate solution of generalized pantograph equations with variable coefficients is presented . The technique we have used is an improved collocation method .
The results motivate an addressing of a selected x – aircraft response . Keywords : Simulation modeling , Hermite polynomial forms , X – aircraft responses International Journal of Natural and Applied Sciences , 5(3 ) : 220 - 222 , 2009
Abstract This paper investigates the potential of using a novel Hermite polynomial neural network to model shoreline realignment along an urban beach fronted by a highly irregular beachrock reef . Modeling takes place on the basis of a number of input variables related to reef morphology and wave forcing , whereas the output variable is time series of shoreline position that have been recorded in high spatio-temporal resolution using a coastal video monitoring system .
The single-hidden layer feedforward neural-network with scaled conjugate gradient was used as the classifier . We compared three activation functions : logistic sigmoid ( LOSI ) , rectified linear unit ( ReLU ) , and leaky rectified linear unit ( LReLU ) . Early stopping and K-fold cross validation ( CV ) was used to avoid overfitting and statistical analysis .
The results show our method is promising . In addition , LReLU is a better activation function that may replace traditional logistic sigmoid function in other applications .
However in the field of deep learning , they have been largely displaced by rectified ( ReLU ) or exponential ( ELU ) linear units to mitigate the effects of vanishing gradients associated with error back-propagation . The logistic sigmoid however does not represent the true input-output relation in real neurones under physiological conditions . Here , radical root unit ( RRU ) activation functions are introduced , exhibiting input-output non-linearities that are substantially more biologically plausible since their functional form is based on known current-frequency relationships .
To address this problem , a novel gene selection strategy is proposed to identify the candidate gene biomarkers from the genomic data . Signal to Noise ratio with logistic sigmoid function , Hilbert – Schmidt Independence Criterion Lasso , and regularized genetic algorithm amalgamation finds the optimal features . The proposed system is tested with the microarray gene expression dataset of autism spectrum disorder ( ASD ) , accessed from gene expression omnibus repository .
In this article , the artificial intelligence techniques have been used for the solution of Falkner – Skan ( FS ) equations based on neural networks optimized with three methods including active set technique , sequential quadratic programming and genetic algorithms ( GA ) hybridization . Log-sigmoid activation function is used in artificial neural network architecture . The proposed techniques are applied to a number of cases for Falkner – Skan problems , and results were compared with GA hybrid results in all cases and were found accurate .
We reformulate the MLP model with the original perceptron in mind so that each node in the “ hidden layers ” can be considered as a latent ( that is , unobserved ) Bernoulli random variable . This maintains the property of binary output from the nodes , and with an imposed logistic regression of the hidden layer nodes on the inputs , the expected output of our model is identical to the MLP output with a logistic sigmoid activation function ( for the case of one hidden layer).We examine the usual MLP objective function — the sum of squares — and show its multi-modal form and the corresponding optimisation difficulty . We also construct the likelihood for the reformulated latent variable model and maximise it by standard finite mixture ML methods using an EM algorithm , which provides stable ML estimates from random starting positions without the need for regularisation or cross-validation .
For a single example and its reconstructed output , we compute the MBCE loss as follows : Here , is the balanced cross - entropy loss with weighting factor , is the sigmoid function , is the Hadamard ( element - wise ) product , and is the boolean function : if , else . The same autoencoder architecture can be applied to a row vector in the augmented adjacency matrix .
2 M parameters . The output of the MoE layer is passed through a sigmoid function . We use the strictly - balanced gating function described in Appendix F.
For each word - context pair in the training corpus , negative context words are sampled , and the objective function is defined as follows : where is the logistic sigmoid function , is the weight vector for the context word , and is a negative sample . It should be noted that the weight vectors for the context words are parameterized for the words without any character information .
The remaining are GRU - like updates that incorporate information from the other nodes and from the previous timestep to update each node ’s hidden state . and are the update and reset gates , is the logistic sigmoid function , and is element - wise multiplication . We initially experimented with a vanilla recurrent neural network - style update , but in preliminary experiments we found this GRU - like propagation step to be more effective .
In this brief , we investigate four different adaptation schemes for connectionist ( also known as hybrid ) ASR systems that learn microphone-specific hidden unit contributions , given some adaptation material . This solution is made possible adopting one of the following schemes : 1 ) the use of Hermite activation functions ; 2 ) the introduction of bias and slope parameters in the sigmoid activation functions ; 3 ) the injection of an amplitude parameter specific for each sigmoid unit ; or 4 ) the combination of 2 ) and 3 ) . Such a simple yet effective solution allows the adapted model to be stored in a small-sized storage space , a highly desirable property of adaptation algorithms for deep neural networks that are suitable for large-scale online deployment .
Real time De-mixing system based on LMS adaptive algorithm for blind two source signals separation E-RNN: Design Optimization for Efficient Recurrent Neural Networks in FPGAs Flexible Modularized Artificial Neural Network Implementation on FPGA Preparation configurable chip microcontroller Memory efficient sign language recognition system based on WiSARD weightless neural network technique Adaptive Convolutional Kernels Deep Asymmetric Networks with a Set of Node-wise Variant Activation Functions Differential evolution for neural networks learning enhancement Single image super-resolution with dilated convolution based multi-scale information learning inception module DeepFM: A Factorization-Machine based Neural Network for CTR Prediction Dynamic Integration of Background Knowledge in Neural NLU Systems Practical Text Classification With Large Pre-Trained Language Models HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition Feedback stabilization of homogeneous polynomial systems of odd degree in the plane Depth from Defocus Estimation in Spatial Domain Super-orthogonal convolutional coding with orthogonal pulse waveform for ultra wideband communications A Novel UWB Pulse Shape Modulation System On the arithmetic operations over finite fields of characteristic three with low complexity Generating Accurate Pseudo-Labels in Semi-Supervised Learning and Avoiding Overconfident Predictions via Hermite Polynomial Activations Generating Accurate Pseudo-labels via Hermite Polynomials for SSL Confidently On Integrability of Observable Space for Discrete-Time Polynomial Control Systems Evolutionary echo state network for long-term time series prediction: on the edge of chaos A collocation method using Hermite polynomials for approximate solution of pantograph equations New Simulation Models for Addressing Like X–Aircraft Responses A Hermite neural network incorporating artificial bee colony optimization to model shoreline realignment at a reef-fronted beach Voxelwise detection of cerebral microbleed in CADASIL patients by leaky rectified linear unit and early stopping Deep learning improved by biological activation functions Predicting autism spectrum disorder from associative genetic markers of phenotypic groups using machine learning Stochastic numerical treatment for solving Falkner–Skan equations using feedforward neural networks Statistical modelling of artificial neural networks using the multi-layer perceptron Learning to Make Predictions on Graphs with Autoencoders Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks Gated Graph Sequence Neural Networks Adaptation to New Microphones Using Artificial Neural Networks With Trainable Activation Functions
3284b1de75a6ad1039f2436580b9d7909007444f 8f441ee1b62c915c689aca43ec48011fdd05daec 70395b7b3edee046f2400597b81288600ec6c2de 90b2c71443c00c7af5cc1116fd10118a96952d32 ec3555390e7301986d41ab9f1537a01fce9c7c0a 05b36844f98e48b99986901295e827dea9935d87 21f791d45a4f415f32e391bce9b9895f9599e573 5f53ff9708e73aee93c5c42c64389f8361107c38 758c180df56a11fc18250e414175d41445a74381 f3637ea3650bd5f051315a99b7c2e61a939ef5ca fe865a4c2b6a7276b200eb5668388c9412ad8088 76ab82d23b4dbc967d2fe91ce4fbe250a360b672 51c39a3dd3ac02ba4ccbbf7f2d834d7842e039cc ac06ecf1be380926f6aa7c51fb5e74185db655d0 05c924876aaa392f1d8ec3cefe829aa222eba378 0e54e593b0a3131b66887a76e2c0316e085e0767 14dcc231b24acf6081470f4d295754147acd2c26 51535a6df08638a34afdf72c7b1bf7f5b56c08d3 0f970281a05fbad1a9cb1b762f41e51e06460b04 35024520070f8a9e328e33bc1cc8fd1dfabd09d9 063e3c2345780b516dce1d21181fcf01b73e30b3 7952c461abeac8271aa5e562df3a5d8a4c222eb3 1e2f4beab8a0574aad7985cd158669850505dc9f 43cf6579bb8ec69a3f2dafef24b70634e89969fc 7e83ff3a1dd18783225b48aa601652affd5b0007 1463c2d95db135438486ead5f1a6db68408fe00c
["Computer Science"] ["Computer Science","Engineering"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Engineering","Mathematics"] ["Computer Science"] ["Computer Science","Mathematics"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Computer Science","Mathematics"] ["Mathematics","Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Medicine","Computer Science"]
2007 2019 2018 2014 2016 2019 2018 2008 2017 2017 2018 2018 2015 2007 2001 2004 2002 2014 2019 2019 2015 2020 2011 2009 2017 2018 2018 2020 2017 2003 2018 2017 2017 2016 2017
HPCA ICCV Workshops 2017 IEEE International Conference on Image Processing (ICIP) IJCAI ArXiv 2015 IEEE International Conference on Computer Vision (ICCV) 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) ICLR EMNLP ICLR
https://api.semanticscholar.org/3284b1de75a6ad1039f2436580b9d7909007444f https://api.semanticscholar.org/8f441ee1b62c915c689aca43ec48011fdd05daec https://api.semanticscholar.org/70395b7b3edee046f2400597b81288600ec6c2de https://api.semanticscholar.org/90b2c71443c00c7af5cc1116fd10118a96952d32 https://api.semanticscholar.org/ec3555390e7301986d41ab9f1537a01fce9c7c0a https://api.semanticscholar.org/05b36844f98e48b99986901295e827dea9935d87 https://api.semanticscholar.org/21f791d45a4f415f32e391bce9b9895f9599e573 https://api.semanticscholar.org/5f53ff9708e73aee93c5c42c64389f8361107c38 https://www.semanticscholar.org/paper/dd88e202b12a1650822c4086d091f882b582c7a4 https://www.semanticscholar.org/paper/1d122a074c936fcfd95faf44608e377a9d1799c8 https://www.semanticscholar.org/paper/2f92b10acf7c405e55c74c1043dabd9ded1b1800 https://www.semanticscholar.org/paper/14908a18ff831005b6b4fc953ce61e1b4e7b54ee https://www.semanticscholar.org/paper/006d5db0ef69e8070e8ecb2e11f01046f5179d3d https://api.semanticscholar.org/758c180df56a11fc18250e414175d41445a74381 https://api.semanticscholar.org/f3637ea3650bd5f051315a99b7c2e61a939ef5ca https://api.semanticscholar.org/fe865a4c2b6a7276b200eb5668388c9412ad8088 https://api.semanticscholar.org/76ab82d23b4dbc967d2fe91ce4fbe250a360b672 https://api.semanticscholar.org/51c39a3dd3ac02ba4ccbbf7f2d834d7842e039cc https://api.semanticscholar.org/ac06ecf1be380926f6aa7c51fb5e74185db655d0 https://api.semanticscholar.org/05c924876aaa392f1d8ec3cefe829aa222eba378 https://api.semanticscholar.org/0e54e593b0a3131b66887a76e2c0316e085e0767 https://api.semanticscholar.org/14dcc231b24acf6081470f4d295754147acd2c26 https://api.semanticscholar.org/51535a6df08638a34afdf72c7b1bf7f5b56c08d3 https://api.semanticscholar.org/0f970281a05fbad1a9cb1b762f41e51e06460b04 https://api.semanticscholar.org/35024520070f8a9e328e33bc1cc8fd1dfabd09d9 https://api.semanticscholar.org/063e3c2345780b516dce1d21181fcf01b73e30b3 https://api.semanticscholar.org/7952c461abeac8271aa5e562df3a5d8a4c222eb3 https://api.semanticscholar.org/1e2f4beab8a0574aad7985cd158669850505dc9f https://api.semanticscholar.org/43cf6579bb8ec69a3f2dafef24b70634e89969fc https://api.semanticscholar.org/7e83ff3a1dd18783225b48aa601652affd5b0007 https://www.semanticscholar.org/paper/2e4c06dd00c4c09ad5ac6be883cc66c19d88ea79 https://www.semanticscholar.org/paper/510e26733aaff585d65701b9f1be7ca9d5afc586 https://www.semanticscholar.org/paper/ade0c116120b54b57a91da51235108b75c28375a https://www.semanticscholar.org/paper/492f57ee9ceb61fb5a47ad7aebfec1121887a175 https://api.semanticscholar.org/1463c2d95db135438486ead5f1a6db68408fe00c