Most methods consider only pairs or triplets of samples within a mini-batch to compute the loss function , which is commonly based on the distance between embeddings . We propose Group Loss , a loss function based on a differentiable label-propagation method that enforces embedding similarity across all samples of a group while promoting , at the same time , low-density regions amongst data points belonging to different groups . Guided by the smoothness assumption that " similar objects should belong to the same group " , the proposed loss trains the neural network for a classification task , enforcing a consistent labelling amongst samples within a class . The learning rule is equally simple as the MSE rule ( i.e. , the Widrow-Hoff rule ) . It is shown by theoretical analysis as well as computational experiments that loss function based methods are clearly superior to the MSE-based methods in classification performance . With a small undercount , adjustment could easily degrade the accuracy of the data . The Bureau argued that errors in the census were more serious than errors in the proposed adjustment , using " loss function analysis " to balance the risks . This procedure turns out to depend on quite unreasonable assumptions . This paper reports a new framework for test-cost sensitive classification . It introduces a new loss function definition , in which misclassification cost and cost of feature extraction are combined qualitatively and the loss is conditioned with current and estimated decisions as well as their consistency . This loss function definition is motivated with the following issues . The advanced measurement approach requires financial institutions to develop internal models to evaluate regulatory capital . Traditionally , the loss distribution approach ( LDA ) is used , mixing frequencies and severities to build a loss distribution function ( LDF ) . This distribution represents annual losses ; consequently , the 99.9th percentile of the distribution providing the capital charge denotes the worst year in a thousand . In this correspondence , we present a simple argument that proves that under mild geometric assumptions on the class F and the set of target functions T , the empirical minimization algorithm can not yield a uniform error rate that is faster than 1/radic(k ) in the function learning setup . This result holds for various loss functionals and the target functions from T that cause the slow uniform error rate are clearly exhibited . To improve the colorization performance and simplify the model , two neural networks are respectively trained to obtain the value of U and V components since the model is in YUV color space . In the training stage , we alternately apply two loss metric functions in the deep model to suppress the training errors and verify our training scheme by quantitative analysis . To address the potential boundary artifacts , we propose three kinds of refinement schemes and make a comparison on their performances . In this sense it resembles the selective read and write operations developed for the Neural Turing Machine . The following section defines the DRAW architecture , along with the loss function used for training and the procedure for image generation . Section [ reference ] presents the selective attention model and shows how it is applied to reading and modifying images . In this paper , we have shown that we can achieve incremental learning in Person ReID tasks with simpler methods yet achieving state - of - the art results . We also propose a new loss that can be used to bring positive targets closer and vice versa in embedding space . We hope that our work would be build upon by ReID community to build more better and robust incremental learning systems that can be further adapted to other domains as well thus increasing real life usage of such systems . The loss term of stage 3 exhibits the following form : Here is the network outputs of this stage , representing a list of category predictions for all instances : . This loss term is dependent on and ( where is used for generating the RoI feature ) . section : End - to - End Training • We systematically explore issues in the architecture and loss function design for point generation network ; • [ reference ] . However , naively plugging G from Eq ( 1 ) into Loss ( 2 ) to predict S pred i wo n't work , as the loss minimization will nullify the randomness . It is also unclear how to make CGAN work in our scenario , as building a discriminator that directly consumes a point set is itself an open problem . q ( x t + 1 |x t , a t ) , H } consists of a loss function L , a distribution over initial observations q ( x 1 ) , a transition distribution q ( x As illustrated in Figure [ reference ] , the images were generated by halving used for generating images in Figure [ reference ] . A smaller means a relatively large weight was placed on the first loss minimization term of ( [ reference ] ) , which tends to collapse generated images to a single mode as it aggressively minimizes their losses to train the generator . This is also consistent with Theorem [ reference ] where the density of generated samples with a smaller could have a larger deviation from the underlying density . The utility model discloses a portable gas detector applicable to the petrochemical industry . The portable gas detector applicable to the petrochemical industry is mainly composed of an electronic circuit board and a waterproof shell , wherein the electronic circuit board comprises a data processing module , a sensor connected with the data processing module , an output module connected with the data processing module , a data export module connected with the data processing module , a key module connected with the data processing module , and a power module supplying power to all the modules in the electronic circuit board , wherein the data processing module comprises a processor and a low-power-consumption operational amplifier connected between the processor and the sensor , and a 16-bit MSP430 processor integrating an A/D converter , a time chip and an EEPROM memorizer is adopted for the processor . The concentration value of gas in the air is detected through the high-precision sensor , the concentration value is displayed through a display screen , an effect for warning about danger and harm is achieved , and meanwhile the gas detector has the functions of vibration notification , LED indication , buzzer alarming , monitoring information storage and the like . How close is all of this to what could be called deep understanding of the text ? The limit of these systems is primarily the need to explicitly design an output representation in advance . The system can only map free text to a formal representation to the extent that the representation can express the concepts in the text . The paper presents a method to classify electromyographic ( EMG ) signals according to the postures of rock-paper-scissors by using multi-layer perceptrons ( MLPs ) . The EMGs are first applied to He-Zajac-Levine bilinear activation model and then the output of model is utilized to be inputs of the MLPs . Cross validation method is used to evaluate the classification performance of MLPs and its outcome also shows that accuracy of the proposed method is over 97 % . The present invention relates to a client apparatus , a server apparatus , a multimedia redirection system and a method thereof . The client apparatus according to the present invention comprises a client interface part making communications with the server apparatus supporting a multimedia redirection function ; a connecting part connectable to a client local storage where client content is stored ; a data processing part for processing the client content ; a client controller transmitting information on the client content to the server apparatus and controlling the data processing part to process the client content in a format that can be outputted when a playback command for the client content is received from the server apparatus ; and an output part outputting the processed client content . To solve the problem that recognition rates of speech recognition systems decrease in the noisy environment presently , the Linear Predictive Mel cepstrum coefficient ( LPMCC ) is used as feature parameter and uses character possessing LPMCC and RBF neural network which have optimal approach capability and the fast training speed , adopts clustering algorithm and entire-supervised algorithm and realizes a noise-robust speech recognition system based on RBF neural net-work . The hidden layer training of clustering algorithm used K-means clustering algorithm and output layer learning used linear least mean square . The adjustment of the entire parameters of entire-supervised algorithm is based on grads decline method . TheRNNPSS consists ofarecurrent neural network identifier (RNNI)anda recurrent neuralnetworkcontroller (RNNC).The RNNI tracks thedynamics characteristics oftheplant , andtheRNNC todampthesystem 's lowfrequency oscillations . TheRNN consists ofaninput layer andanoutput layer . Eachneuronin theinput layer isarecurrent onewhichisconnected tooneself andother neurons , andthenconnected totheoutput layer . The invention discloses a user intention identification method and device and an automatic answering system and relates to the technical field of computers . User characteristic information which is input is processed via a neural network module so as to obtain a learning result , the learning result is subjected to classifying operation via an output layer module based on weight values and threshold values relative to the classifying operation , and therefore user intention can be identified . The user characteristic information which is received is subjected to convolution operation via the neural network module based on weight values and threshold values relative to the convolution operation , a convolution operation result is subjected to local normalization processing operation based on weight values and threshold values relative to the local normalization processing operation , a local normalization result is subjected to full connection superposition processing operation based on weight values and threshold values relative to the full connection superposition processing operation , and the learning result can be obtained . The CNN used in this paper consists of two convolution and pulling layers . We designed a database based neural network and a pulse feature based neural network according to the output layer design . To verify the performance of the algorithm , the data of 3110 CW ( Continuous Wave ) pulses and LFM ( Linear Frequency Modulated ) pulses received from the actual ocean were processed to construct training data and test data . In the hybrid circuit , the input-layer neurons have the proximal and basal/distal dendrites to combine sensory information with the temporal/location information from the brain ’s hippocampus . Using the same crossbar architecture , the output-layer neurons can perform a prediction by integrating the temporal information on the basal/distal dendrites . For training the proposed circuit , we used only simple Hebbian learning , not the complicated backpropagation algorithm . This method is suitable for general image recovery from oversampled diffuse elastic x-ray and electron-diffraction intensities . The relationship between this algorithm and the output-output algorithm is elucidated . We use the mini - batch size of 32 , and the initial learning rate is set to 0.0003 . We use the same model architecture as used in the character - level language model , except that the output layer is modified to predict real - valued outputs . We use the mixture density network as the output layer following [ reference ] , and use 400 units for each HM - LSTM layer and for the output embedding layer . The details of the QRN module is explained throughout this section ( 2.1 , 2.2 ) . Output module . Output module mapsŷ obtained from QRN to a natural language answerŷ . We were able to succesfully train maxout networks with rotational perturbations of the hidden layers . However , this did not yield nearly as strong of a regularizing effect as additive perturbation of the input layer . Our view of adversarial training is that it is only clearly useful when the model has the capacity to learn to resist adversarial examples . Sparse filtering does not attempt to explicitly model the input distribution but focuses on the properties of the output distribution instead . Sparsity is among the desirable properties of a good output representation . Sparse features consist of a large amount of outputs , which respond rarely and provide high responses when they do respond . — see for recent reviews of the different contexts and applications . The network architecture in the classification stage is that of a three layer neural network comprised from an input layer , a hidden layer of nonlinear units , and a linear output layer . The input weights are randomly chosen and untrained , and the output weights are trained in a single batch using least squares regression . σ ) : To stabilize the learning process of self - attention , we have found extending our mechanism to employ multi - head attention to be beneficial , similarly to [ reference ] . Specifically , K independent attention mechanisms execute the transformation of Equation 4 , and then their features are concatenated , resulting in the following output feature representation : where represents concatenation , α k ij are normalized attention coefficients computed by the k - th attention mechanism ( a k ) , and W k is the corresponding input linear transformation 's weight matrix . Our message function is a MLP . Our node function is , Our output function is a MLP with a dropout fraction of 0.5 in the penultimate layer . The last layer has 16 hidden linear units . learns node embeddings using sequence - based methods . map graphs to graph vectors then classify using an output neural network . There are several models that make use of similar propagation of node representations on a graph structure . We present a new method for the induction of classification trees with linear discriminants as the partitioning function at each internal node . This paper presents two main contributions : first , a novel objective function called soft entropy which is used to identify optimal coefficients for the linear discriminants , and second , a novel method for removing outliers called iterative re-faltering which boosts performance on many datasets . These two ideas are presented in the context of a single learning algorithm called DT-SEPIR , which is compared with the CART and OC1 algorithms . However , since the intention of cluster analysis is a global partition of a data set into clusters , it is not suitable to identify single clusters in a large data set where the majority of the data can not be assigned to meaningful clusters . This paper presents a new objective function-based approach to identify a single good cluster in a data set making use of techniques known from prototype-based , noise and fuzzy clustering . The proposed method can either be applied in order to identify single clusters or to carry out a standard cluster analysis by finding clusters step by step and determining the number of clusters automatically in this way . During the optimization procedure , the DE changes the parameters of the LSIPMSM dynamic model in such a way that the differences between the measured and calculated time behaviors of individual state variables are minimized . This paper focuses on the objective function definition , the constraint settings for individual parameters , the normalization of parameters , and , above all , the test and measurement procedures performed on the LSIPMSM , which all together make it possible to determine the LSIPMSM dynamic model parameters valid for a broad range of operation , thus ensuring proper evaluation of the LSIPMSM 's line-starting capability . Some of the LSIPMSM parameters that can be determined by finite-element analysis and experimental methods are compared to the values obtained by the DE , thus validating the DE-based approach . The adjacency graph with a data-adaptive neighborhood can extract dynamic manifold structure better than a specified neighborhood parameter . Secondly , a new objection function considered the sparse reconstruction and output weights is established to extract nonlinear dynamic manifold structure . Thirdly , the statistic SPE and T2 based on SRPE-ELM are built to monitor the whole process . Constrained independent component analysis ( C-ICA ) algorithms have been an effective way to introduce prior information into the ICA framework . The work in this area has focus on adding constraints to the objective function of algorithms that assume an orthogonal demixing matrix . Orthogonality is required in order to decouple-isolate-the constraints applied for each individual source . Our results show that the implementation of the combination strategy involving the prevention of treatment failure in drug-sensitive TB infectious individuals and the treatment of individuals with drug-resistant TB is the most cost-effective control strategy . Similar results were obtained with different objective functionals involving the minimization of the number of individuals with drug-sensitive TB-only and drug-resistant TB-only with the efforts involved in applying the control . As we know , genetic algorithm converges slowly . It is a natural contradiction when the situation appears with expensive objective function evaluating and satisfactory solutions being adequate . In this paper , a very fast convergent evolutionary algorithm ( VFEA ) is proposed with inner-outer hypercone crossover , problem dependent and search status involved mutation ( PdSiMu ) . In this study was considered the possibility of using a mathematical model for the basin used by a yacht harbor . As a result , it is shown that the optimal use of a basin by a yacht harbor can be modeled as an objective function problem , which according to previously known methods can turn into a mathematical programming problem . This means that there is no explicit objective function to allow for flexible behavior of the decision maker when forming the decisions . Such objective value function allows for quantitative analysis and removal of logical inconsistencies and errors ( Castagne et al. , 2009 ; Collopy & Hollingsworth , 2009 ) . Value driven design can be defined as a development paradigm , in which required human value considerations are engineered into best practices , activities and management ( Collopy , 2009 ) . Our CNN is trained end - to - end on MRI volumes depicting prostate , and learns to predict segmentation for the whole volume at once . We introduce a novel objective function , that we optimise during training , based on Dice coefficient . In this way we can deal with situations where there is a strong imbalance between the number of foreground and background voxels . It has a carefully designed objective function that preserves both the first - order and second - order proximities . We propose an edge - sampling algorithm for optimizing the objective . The algorithm tackles the limitation of the classical stochastic gradient decent and improves the effectiveness and efficiency of the inference . subsection : Model Optimization Optimizing objective ( [ reference ] ) is computationally expensive , which requires the summation over the entire set of vertices when calculating the conditional probability . To address this problem , we adopt the approach of negative sampling proposed in , which samples multiple negative edges according to some noisy distribution for each edge . This is an unsupervised deep learning approach . The features from the final / deepest layer are employed for subsequent analysis and classification . The seminal paper of stacked denoising autoencoders have shown that robust deep models can be learnt when noisy data is used for training stacked autoencoders instead of clean data . This relationship is exploited to regularise the representations of supervised tasks by backpropagating the error of the unsupervised task through the supervised tasks . We introduce a neural network where lower layers are supervised by junior downstream tasks and the final layer task is an auxiliary unsupervised task . The architecture shows improvements of up to two percentage points F1 for Chunking compared to a plausible baseline . We use a standard categorical cross-entropy loss over all output units ( including ROUs ) , and additionally penalize the entropy of softmax over just the ROUs , in order to encourage diverse representations ( middle ) . We also experiment with a loss which encourages the final-layer hidden representations to be close to pretrained word embeddings , in order to encourage meaningful semantics ( right ) . For architectural and training details see Section 4 : Experiments . To achieve the best of both worlds , we initiate the formal study of finality layers . Such a finality layer can be combined with a Nakamoto-style blockchain and periodically declare blocks as final , preventing rollbacks beyond final blocks . As conceptual contributions , we identify the following properties to be crucial for a finality layer : finalized blocks form a chain ( chain-forming ) , all parties agree on the finalized blocks ( agreement ) , the last finalized block does not fall too far behind the last block in the underlying blockchain ( updated ) , and all finalized blocks at some point have been on the chain adopted by at least k honest parties ( k-support ) . For this purpose , we propose an efficient sub - pixel convolution layer to learn the upscaling operation for image and video super - resolution . The advantages of these contributions are two fold : In our network , upscaling is handled by the last layer of the network . This means each LR image is directly fed to the network and feature extraction occurs through nonlinear convolutions in LR space . It is noticeable that despite each filter is independent in LR space , our independent filters is actually smooth in the HR space after . Compared to SRCNN ’s last layer filters , our final layer filters has complex patterns for different feature maps , it also has much richer and more meaningful representations . We also evaluated the effect of activation function based on the above model trained on 91 images and ImageNet images . Our CNN exploits both local features as well as more global contextual features simultaneously . Also , different from most traditional uses of CNNs , our networks use a final layer that is a convolutional implementation of a fully connected layer which allows a 40 fold speed up . We also describe a 2 - phase training procedure that allows us to tackle difficulties related to the imbalance of tumor labels .
Most methods consider only pairs or triplets of samples within a mini-batch to compute the loss function , which is commonly based on the distance between embeddings . We propose Group Loss , a loss function based on a differentiable label-propagation method that enforces embedding similarity across all samples of a group while promoting , at the same time , low-density regions amongst data points belonging to different groups . Guided by the smoothness assumption that " similar objects should belong to the same group " , the proposed loss trains the neural network for a classification task , enforcing a consistent labelling amongst samples within a class .
The learning rule is equally simple as the MSE rule ( i.e. , the Widrow-Hoff rule ) . It is shown by theoretical analysis as well as computational experiments that loss function based methods are clearly superior to the MSE-based methods in classification performance .
With a small undercount , adjustment could easily degrade the accuracy of the data . The Bureau argued that errors in the census were more serious than errors in the proposed adjustment , using " loss function analysis " to balance the risks . This procedure turns out to depend on quite unreasonable assumptions .
This paper reports a new framework for test-cost sensitive classification . It introduces a new loss function definition , in which misclassification cost and cost of feature extraction are combined qualitatively and the loss is conditioned with current and estimated decisions as well as their consistency . This loss function definition is motivated with the following issues .
The advanced measurement approach requires financial institutions to develop internal models to evaluate regulatory capital . Traditionally , the loss distribution approach ( LDA ) is used , mixing frequencies and severities to build a loss distribution function ( LDF ) . This distribution represents annual losses ; consequently , the 99.9th percentile of the distribution providing the capital charge denotes the worst year in a thousand .
In this correspondence , we present a simple argument that proves that under mild geometric assumptions on the class F and the set of target functions T , the empirical minimization algorithm can not yield a uniform error rate that is faster than 1/radic(k ) in the function learning setup . This result holds for various loss functionals and the target functions from T that cause the slow uniform error rate are clearly exhibited .
To improve the colorization performance and simplify the model , two neural networks are respectively trained to obtain the value of U and V components since the model is in YUV color space . In the training stage , we alternately apply two loss metric functions in the deep model to suppress the training errors and verify our training scheme by quantitative analysis . To address the potential boundary artifacts , we propose three kinds of refinement schemes and make a comparison on their performances .
In this sense it resembles the selective read and write operations developed for the Neural Turing Machine . The following section defines the DRAW architecture , along with the loss function used for training and the procedure for image generation . Section [ reference ] presents the selective attention model and shows how it is applied to reading and modifying images .
In this paper , we have shown that we can achieve incremental learning in Person ReID tasks with simpler methods yet achieving state - of - the art results . We also propose a new loss that can be used to bring positive targets closer and vice versa in embedding space . We hope that our work would be build upon by ReID community to build more better and robust incremental learning systems that can be further adapted to other domains as well thus increasing real life usage of such systems .
The loss term of stage 3 exhibits the following form : Here is the network outputs of this stage , representing a list of category predictions for all instances : . This loss term is dependent on and ( where is used for generating the RoI feature ) . section : End - to - End Training
• We systematically explore issues in the architecture and loss function design for point generation network ; •
[ reference ] . However , naively plugging G from Eq ( 1 ) into Loss ( 2 ) to predict S pred i wo n't work , as the loss minimization will nullify the randomness . It is also unclear how to make CGAN work in our scenario , as building a discriminator that directly consumes a point set is itself an open problem .
q ( x t + 1 |x t , a t ) , H } consists of a loss function L , a distribution over initial observations q ( x 1 ) , a transition distribution q ( x
As illustrated in Figure [ reference ] , the images were generated by halving used for generating images in Figure [ reference ] . A smaller means a relatively large weight was placed on the first loss minimization term of ( [ reference ] ) , which tends to collapse generated images to a single mode as it aggressively minimizes their losses to train the generator . This is also consistent with Theorem [ reference ] where the density of generated samples with a smaller could have a larger deviation from the underlying density .
The utility model discloses a portable gas detector applicable to the petrochemical industry . The portable gas detector applicable to the petrochemical industry is mainly composed of an electronic circuit board and a waterproof shell , wherein the electronic circuit board comprises a data processing module , a sensor connected with the data processing module , an output module connected with the data processing module , a data export module connected with the data processing module , a key module connected with the data processing module , and a power module supplying power to all the modules in the electronic circuit board , wherein the data processing module comprises a processor and a low-power-consumption operational amplifier connected between the processor and the sensor , and a 16-bit MSP430 processor integrating an A/D converter , a time chip and an EEPROM memorizer is adopted for the processor . The concentration value of gas in the air is detected through the high-precision sensor , the concentration value is displayed through a display screen , an effect for warning about danger and harm is achieved , and meanwhile the gas detector has the functions of vibration notification , LED indication , buzzer alarming , monitoring information storage and the like .
How close is all of this to what could be called deep understanding of the text ? The limit of these systems is primarily the need to explicitly design an output representation in advance . The system can only map free text to a formal representation to the extent that the representation can express the concepts in the text .
The paper presents a method to classify electromyographic ( EMG ) signals according to the postures of rock-paper-scissors by using multi-layer perceptrons ( MLPs ) . The EMGs are first applied to He-Zajac-Levine bilinear activation model and then the output of model is utilized to be inputs of the MLPs . Cross validation method is used to evaluate the classification performance of MLPs and its outcome also shows that accuracy of the proposed method is over 97 % .
The present invention relates to a client apparatus , a server apparatus , a multimedia redirection system and a method thereof . The client apparatus according to the present invention comprises a client interface part making communications with the server apparatus supporting a multimedia redirection function ; a connecting part connectable to a client local storage where client content is stored ; a data processing part for processing the client content ; a client controller transmitting information on the client content to the server apparatus and controlling the data processing part to process the client content in a format that can be outputted when a playback command for the client content is received from the server apparatus ; and an output part outputting the processed client content .
To solve the problem that recognition rates of speech recognition systems decrease in the noisy environment presently , the Linear Predictive Mel cepstrum coefficient ( LPMCC ) is used as feature parameter and uses character possessing LPMCC and RBF neural network which have optimal approach capability and the fast training speed , adopts clustering algorithm and entire-supervised algorithm and realizes a noise-robust speech recognition system based on RBF neural net-work . The hidden layer training of clustering algorithm used K-means clustering algorithm and output layer learning used linear least mean square . The adjustment of the entire parameters of entire-supervised algorithm is based on grads decline method .
TheRNNPSS consists ofarecurrent neural network identifier (RNNI)anda recurrent neuralnetworkcontroller (RNNC).The RNNI tracks thedynamics characteristics oftheplant , andtheRNNC todampthesystem 's lowfrequency oscillations . TheRNN consists ofaninput layer andanoutput layer . Eachneuronin theinput layer isarecurrent onewhichisconnected tooneself andother neurons , andthenconnected totheoutput layer .
The invention discloses a user intention identification method and device and an automatic answering system and relates to the technical field of computers . User characteristic information which is input is processed via a neural network module so as to obtain a learning result , the learning result is subjected to classifying operation via an output layer module based on weight values and threshold values relative to the classifying operation , and therefore user intention can be identified . The user characteristic information which is received is subjected to convolution operation via the neural network module based on weight values and threshold values relative to the convolution operation , a convolution operation result is subjected to local normalization processing operation based on weight values and threshold values relative to the local normalization processing operation , a local normalization result is subjected to full connection superposition processing operation based on weight values and threshold values relative to the full connection superposition processing operation , and the learning result can be obtained .
The CNN used in this paper consists of two convolution and pulling layers . We designed a database based neural network and a pulse feature based neural network according to the output layer design . To verify the performance of the algorithm , the data of 3110 CW ( Continuous Wave ) pulses and LFM ( Linear Frequency Modulated ) pulses received from the actual ocean were processed to construct training data and test data .
In the hybrid circuit , the input-layer neurons have the proximal and basal/distal dendrites to combine sensory information with the temporal/location information from the brain ’s hippocampus . Using the same crossbar architecture , the output-layer neurons can perform a prediction by integrating the temporal information on the basal/distal dendrites . For training the proposed circuit , we used only simple Hebbian learning , not the complicated backpropagation algorithm .
This method is suitable for general image recovery from oversampled diffuse elastic x-ray and electron-diffraction intensities . The relationship between this algorithm and the output-output algorithm is elucidated .
We use the mini - batch size of 32 , and the initial learning rate is set to 0.0003 . We use the same model architecture as used in the character - level language model , except that the output layer is modified to predict real - valued outputs . We use the mixture density network as the output layer following [ reference ] , and use 400 units for each HM - LSTM layer and for the output embedding layer .
The details of the QRN module is explained throughout this section ( 2.1 , 2.2 ) . Output module . Output module mapsŷ obtained from QRN to a natural language answerŷ .
We were able to succesfully train maxout networks with rotational perturbations of the hidden layers . However , this did not yield nearly as strong of a regularizing effect as additive perturbation of the input layer . Our view of adversarial training is that it is only clearly useful when the model has the capacity to learn to resist adversarial examples .
Sparse filtering does not attempt to explicitly model the input distribution but focuses on the properties of the output distribution instead . Sparsity is among the desirable properties of a good output representation . Sparse features consist of a large amount of outputs , which respond rarely and provide high responses when they do respond .
— see for recent reviews of the different contexts and applications . The network architecture in the classification stage is that of a three layer neural network comprised from an input layer , a hidden layer of nonlinear units , and a linear output layer . The input weights are randomly chosen and untrained , and the output weights are trained in a single batch using least squares regression .
σ ) : To stabilize the learning process of self - attention , we have found extending our mechanism to employ multi - head attention to be beneficial , similarly to [ reference ] . Specifically , K independent attention mechanisms execute the transformation of Equation 4 , and then their features are concatenated , resulting in the following output feature representation : where represents concatenation , α k ij are normalized attention coefficients computed by the k - th attention mechanism ( a k ) , and W k is the corresponding input linear transformation 's weight matrix .
Our message function is a MLP . Our node function is , Our output function is a MLP with a dropout fraction of 0.5 in the penultimate layer . The last layer has 16 hidden linear units .
learns node embeddings using sequence - based methods . map graphs to graph vectors then classify using an output neural network . There are several models that make use of similar propagation of node representations on a graph structure .
We present a new method for the induction of classification trees with linear discriminants as the partitioning function at each internal node . This paper presents two main contributions : first , a novel objective function called soft entropy which is used to identify optimal coefficients for the linear discriminants , and second , a novel method for removing outliers called iterative re-faltering which boosts performance on many datasets . These two ideas are presented in the context of a single learning algorithm called DT-SEPIR , which is compared with the CART and OC1 algorithms .
However , since the intention of cluster analysis is a global partition of a data set into clusters , it is not suitable to identify single clusters in a large data set where the majority of the data can not be assigned to meaningful clusters . This paper presents a new objective function-based approach to identify a single good cluster in a data set making use of techniques known from prototype-based , noise and fuzzy clustering . The proposed method can either be applied in order to identify single clusters or to carry out a standard cluster analysis by finding clusters step by step and determining the number of clusters automatically in this way .
During the optimization procedure , the DE changes the parameters of the LSIPMSM dynamic model in such a way that the differences between the measured and calculated time behaviors of individual state variables are minimized . This paper focuses on the objective function definition , the constraint settings for individual parameters , the normalization of parameters , and , above all , the test and measurement procedures performed on the LSIPMSM , which all together make it possible to determine the LSIPMSM dynamic model parameters valid for a broad range of operation , thus ensuring proper evaluation of the LSIPMSM 's line-starting capability . Some of the LSIPMSM parameters that can be determined by finite-element analysis and experimental methods are compared to the values obtained by the DE , thus validating the DE-based approach .
The adjacency graph with a data-adaptive neighborhood can extract dynamic manifold structure better than a specified neighborhood parameter . Secondly , a new objection function considered the sparse reconstruction and output weights is established to extract nonlinear dynamic manifold structure . Thirdly , the statistic SPE and T2 based on SRPE-ELM are built to monitor the whole process .
Constrained independent component analysis ( C-ICA ) algorithms have been an effective way to introduce prior information into the ICA framework . The work in this area has focus on adding constraints to the objective function of algorithms that assume an orthogonal demixing matrix . Orthogonality is required in order to decouple-isolate-the constraints applied for each individual source .
Our results show that the implementation of the combination strategy involving the prevention of treatment failure in drug-sensitive TB infectious individuals and the treatment of individuals with drug-resistant TB is the most cost-effective control strategy . Similar results were obtained with different objective functionals involving the minimization of the number of individuals with drug-sensitive TB-only and drug-resistant TB-only with the efforts involved in applying the control .
As we know , genetic algorithm converges slowly . It is a natural contradiction when the situation appears with expensive objective function evaluating and satisfactory solutions being adequate . In this paper , a very fast convergent evolutionary algorithm ( VFEA ) is proposed with inner-outer hypercone crossover , problem dependent and search status involved mutation ( PdSiMu ) .
In this study was considered the possibility of using a mathematical model for the basin used by a yacht harbor . As a result , it is shown that the optimal use of a basin by a yacht harbor can be modeled as an objective function problem , which according to previously known methods can turn into a mathematical programming problem .
This means that there is no explicit objective function to allow for flexible behavior of the decision maker when forming the decisions . Such objective value function allows for quantitative analysis and removal of logical inconsistencies and errors ( Castagne et al. , 2009 ; Collopy & Hollingsworth , 2009 ) . Value driven design can be defined as a development paradigm , in which required human value considerations are engineered into best practices , activities and management ( Collopy , 2009 ) .
Our CNN is trained end - to - end on MRI volumes depicting prostate , and learns to predict segmentation for the whole volume at once . We introduce a novel objective function , that we optimise during training , based on Dice coefficient . In this way we can deal with situations where there is a strong imbalance between the number of foreground and background voxels .
It has a carefully designed objective function that preserves both the first - order and second - order proximities . We propose an edge - sampling algorithm for optimizing the objective . The algorithm tackles the limitation of the classical stochastic gradient decent and improves the effectiveness and efficiency of the inference .
subsection : Model Optimization Optimizing objective ( [ reference ] ) is computationally expensive , which requires the summation over the entire set of vertices when calculating the conditional probability . To address this problem , we adopt the approach of negative sampling proposed in , which samples multiple negative edges according to some noisy distribution for each edge .
This is an unsupervised deep learning approach . The features from the final / deepest layer are employed for subsequent analysis and classification . The seminal paper of stacked denoising autoencoders have shown that robust deep models can be learnt when noisy data is used for training stacked autoencoders instead of clean data .
This relationship is exploited to regularise the representations of supervised tasks by backpropagating the error of the unsupervised task through the supervised tasks . We introduce a neural network where lower layers are supervised by junior downstream tasks and the final layer task is an auxiliary unsupervised task . The architecture shows improvements of up to two percentage points F1 for Chunking compared to a plausible baseline .
We use a standard categorical cross-entropy loss over all output units ( including ROUs ) , and additionally penalize the entropy of softmax over just the ROUs , in order to encourage diverse representations ( middle ) . We also experiment with a loss which encourages the final-layer hidden representations to be close to pretrained word embeddings , in order to encourage meaningful semantics ( right ) . For architectural and training details see Section 4 : Experiments .
To achieve the best of both worlds , we initiate the formal study of finality layers . Such a finality layer can be combined with a Nakamoto-style blockchain and periodically declare blocks as final , preventing rollbacks beyond final blocks . As conceptual contributions , we identify the following properties to be crucial for a finality layer : finalized blocks form a chain ( chain-forming ) , all parties agree on the finalized blocks ( agreement ) , the last finalized block does not fall too far behind the last block in the underlying blockchain ( updated ) , and all finalized blocks at some point have been on the chain adopted by at least k honest parties ( k-support ) .
For this purpose , we propose an efficient sub - pixel convolution layer to learn the upscaling operation for image and video super - resolution . The advantages of these contributions are two fold : In our network , upscaling is handled by the last layer of the network . This means each LR image is directly fed to the network and feature extraction occurs through nonlinear convolutions in LR space .
It is noticeable that despite each filter is independent in LR space , our independent filters is actually smooth in the HR space after . Compared to SRCNN ’s last layer filters , our final layer filters has complex patterns for different feature maps , it also has much richer and more meaningful representations . We also evaluated the effect of activation function based on the above model trained on 91 images and ImageNet images .
Our CNN exploits both local features as well as more global contextual features simultaneously . Also , different from most traditional uses of CNNs , our networks use a final layer that is a convolutional implementation of a fully connected layer which allows a 40 fold speed up . We also describe a 2 - phase training procedure that allows us to tackle difficulties related to the imbalance of tumor labels .
The Group Loss for Deep Metric Learning Loss Function Based Neural Classifiers Adjusting the U.S. Census of 1990 Qualitative test-cost sensitive classification Using a time series approach to correct serial correlation in Operational Risk capital calculation Lower Bounds for the Empirical Minimization Algorithm An edge-refined vectorized deep colorization model for grayscale-to-color images DRAW: A Recurrent Neural Network For Image Generation Incremental Learning in Person Re-Identification Instance-Aware Semantic Segmentation via Multi-task Network Cascades A Point Set Generation Network for 3D Object Reconstruction from a Single Image Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities Portable gas detector applicable to petrochemical industry Building a Machine Learning Based Text Understanding System Classification of rock-paper-scissors using electromyography and multi-layer perceptron Client apparatus, server apparatus, multimedia redirection system and the method thereof Noise-Robust Speech Recognition Based on LPMCC Feature and RBF Neural Network PowerSystemStabilizer UsingaNewRecurrent Neural NetworkforMulti-Machine User intention identification method and device and automatic answering system Active pulse classification algorithm using convolutional neural networks Memristor-CMOS Hybrid Circuit for Temporal-Pooling of Sensory and Hippocampal Responses of Cortical Neurons Iterative phase retrieval without support. Hierarchical Multiscale Recurrent Neural Networks Query-Reduction Networks for Question Answering Explaining and Harnessing Adversarial Examples No more meta-parameter tuning in unsupervised sparse feature learning Enhanced image classification with a fast-learning shallow convolutional neural network Graph Attention Networks Recurrent Relational Networks Gated Graph Sequence Neural Networks Robust Linear Discriminant Trees Identifying Single Good Clusters in Data Sets Differential-Evolution-Based Parameter Identification of a Line-Start IPM Synchronous Motor Sparse representation preserving embedding based on extreme learning machine for process monitoring General Non-Orthogonal Constrained ICA Optimal control of a two-strain tuberculosis-HIV/AIDS co-infection model A Very Fast Convergent Evolutionary Algorithm for Satisfactory Solutions A Goal Programming Model For Facility Location Planning Value Based Decision Control for Complex Systems V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation LINE: Large-scale Information Network Embedding Noisy deep dictionary learning: Application to Alzheimer's Disease classification Deep Semi-Supervised Learning with Linguistically Motivated Sequence Labeling Task Hierarchies Reserve Output Units for Deep Open-Set Learning Afgjort - A Semi-Synchronous Finality Layer for Blockchains Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network Brain tumor segmentation with Deep Neural Networks
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ICML ArXiv 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICML International Journal of Computer Vision URAI ICLR ICLR ICLR ArXiv 2015 International Joint Conference on Neural Networks (IJCNN) ICLR NeurIPS ICLR AISTATS IWICPAS ICSI (1) 2016 Fourth International Conference on 3D Vision (3DV) WWW '15 IJCNN 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Medical Image Anal.
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