We are interested in Euclidean distance-based solutions to spatial queries as Euclidean distance is significantly cheaper to compute than road network distance . A common view is that Euclidean distance is not suitable for solving any spatial query in road networks as road network distance can be significantly different to Euclidean distance . We challenge this view by evaluating the performance of an Euclidean distance-based approach in solving Group Nearest Neighbor queries , which can be used in transportation applications . Experiments were conducted using twenty UCR benchmark datasets . Also , the proposed modified DTW result has been compared with the state of the art competitive distance measures which is based on precision , recall and f-measure including the original DTW , Minkowski distance measure and Euclidean distance measure . The results showed that the proposed modified DTW reveal superior results in compared to the standard DTW , either using Minkowski or Euclidean . The first feature set is the energy density of power spectra of Discrete Fourier Transform ( DFT ) or Discrete Cosine Transform ; the second one is the set of statistical moments of Discrete Wavelet Transform ( DWT ) . Euclidean distance metric is used to accomplish feature set matching task . The combinations of features from two EEG channels showed high accuracy for the identification system , and competitive results for the verification system . There are two approaches used to recognize the speech signal . The first approach is linear predictive coding combined with Euclidean squared distance . LPC is used as the feature extraction method and Euclidean squared distance is used as the recognition method . The analysis of the relative importance of components of MFCC for both speech recognition and speaker recognition using DTW recognizer in various noise environments are given . For English digit and under the Euclidean distance definition , the experiment results show cepstral components from C 2 to C 16 contain the most useful speaker information , while C 0 and C 1 are usually harm to speaker recognition . Cepstral terms from C 1 to C 12 are found to contain the most useful speech information . Unlike point and surface registers , our method based on the ICP algorithm better captures the bulk nature of data such as bone thickness . The proposed ICP algorithm is divided into three main steps : the 3D function extraction , comparison of the Euclidean metric distance and gross alignment and ICP enhancement . The input to our system are biomedical data ( CT , MRI ) . This paper puts forward an algorithm which was based on Hasudorff distance and nonlocal means ( Hasudorff-Non Local Means , HDNLM ) . This algorithm makes use of Hasudorff distance more efficient than Euclidean distance distance in preserving signal details . It is aim to improve the weight model of traditional NLM of Euclidean Distance . The buggy will have remote code written in embedded C. Buggy will consist of device controller , heat sink , transformer , motor and a device driver which will be connected in serial connection RS-232 . The algorithm used will be Euclidean distance means and cosine similarity algorithm , this algorithm helps to identify and execute the gestures provided in the leap motion . There are total twelve element inputs which can be passed using fingers and the palm . The inference stage of the RF - based image super - resolution process is similar to the content - based image retrieval ( CBIR ) framework , as shown in Fig . 1 . The general approximated nearest neighbor ( ANN ) search framework [ reference ][ reference ] is an efficient strategy for large - scale image retrieval , which mainly consists of 4 parts : ( 1 ) extracting compact features ( e.g. , locality - sensitive Hashing ( LSH ) [ reference ] feature ) for a query image ; ( 2 ) coarse - level search using Hamming distance to measure the similarity between binary compact Hash features , then narrow the search scope into a smaller candidate group ; ( 3 ) fine - level search by using Euclidean distance to measure the similarity between their corresponding feature vectors ; and ( 4 ) finding the object in the smaller candidate group that is the nearest one to the query image . In the inference stage of conventional RF - based SISR , PCA projection is worked as a Hash - like function to compress the feature dimension for decreasing the search range , which can speed up the searching as the coarse - level search in a CBIR framework , but the impact of using PCA on feature dimensionality reduction has been overlooked in previous works [ reference ][ reference ][ reference ][ reference ][ reference ][ reference ] . Inspired by the finelevel search using augmented features in CBIR frameworks , the high dimensional features in the leaf nodes in an RF can further improve the prediction accuracy in the regression step , which has not been studied previously . subsection : Relationship to existing models Related prior few - shot work uses fixed pre - specified distance metrics such as Euclidean or cosine distance to perform classification . These studies can be seen as distance metric learning , but where all the learning occurs in the feature embedding , and a fixed metric is used given the learned embedding . The mini - batch size is 100 . The autoencoder is trained with a Euclidean cost function for reconstruction . On the MNIST dataset we use the sigmoid activation function in the last layer of the autoencoder and on the TFD dataset we use the linear activation function . Despite only training on PASCAL , we outperform these models which were trained on the much larger COCO dataset . In Table [ reference ] we report the total average recall at IoU for some recently proposed proposal detection methods , including unpublished work inst - DML which is similar in spirit to our model but learns a Euclidean distance based metric to group pixels . We can clearly see that our method achieves significantly better results than existing methods . Traditional methods : Many early skeleton detection algorithms are based on gradient intensity maps . In , the authors study the limiting average outward flux of the gradient of a Euclidean distance function to a 2D or 3D object boundary . The skeleton is associated with those locations where an energy principle is violated , where there is a net inward flux . ’s features followed by a deep matching function from the convolutional features or the Fully Connected ( FC ) features . In addition to the deep metric learning , some work directly learns image representation through pair - wise contrastive loss or triplet ranking loss , and use Euclidean metric for comparison . With the increasing sample size of ReID dataset , the IDE feature which is learned through multi - class person identification tasks , has shown great potentials on current large - scale person ReID datasets . In least squares support vector machine ( LS-SVM ) classiers the original SVM formulation of Vapnik is modi ed by considering equalit y constraints within a form of ridge regression instead of inequality constraints . As a result the solution follows from solving a set of linear equations instead of a quadratic programming problem . In particular , the use of high-dimensional multivariate input data in comparison to small parameter sets is assessed . As prediction methods , Simple Linear Regression , Multiple Linear Regression , Partial Least Square Regression , and Ridge Linear Regression utilizing the Partial Least Square Estimate algorithm are compared . Regression parameter optimization and model selection is performed and evaluated via cross validation and grid search , using the Root Mean Squared Error . This paper presents a comprehensive analysis method based on ridge trace analysis and fuzzy linear regression method . First , take into account the multicollinearity among input variables , the critical intersection is elicited based on ridge regression analysis , and then the fuzzy regression method is used to get flow forecasting model , and the non-detector intersection traffic flow is directly calculated by model . The experiment results demonstrate that the method proposed in the paper is applicable and effective . This work is a preliminary investigation into the energy demand of the Nepali Information and Communications Technology ( ICT ) sector . We formulate a logistic growth model for telecommunication users ' diffusion and then present a ridge regression based model to analyse the contribution of the ICT sector in the national energy consumption scenario with a specific focus on the telecommunication sector . Although our analysis leaves out some important dimensions due to lack of publicly available data , the stable ridge regression model can easily be expanded to accommodate new dimensions ( indicators ) . Triangular membership function is adopted . And to reduce the strong interaction among discrete input variables , coefficient contraction method is employed ; ridge regression function is used in the THEN parts of fuzzy rules . At last , pyrimidines medical data is used in simulations ; results show that our new algorithm gets an advantage in accuracy over the existing training algorithms for HHFNN and standard BP algorithm . After fulfilling one of these criteria , a density model ( ) in the leaf - node is estimated by all samples falling into this leaf - node for predicting the target value in the testing stage . A simple way to estimate the probability distribution ( ) is averaging all the samples in the leaf - node , while there are also variant methods , such as fitting a Gaussian distribution or kernel density estimation , ridge regression [ reference ][ reference ][ reference ] , and so on . ( ) is the local score for a set of samples ( is either or ) , which normally is calculated using entropy as in Eqn . This solution is equivalent to least squares regression applied to an overcomplete set of linear equations , with an - dimensional target . It is known to often be useful to regularise such problems , and instead solve the following ridge regression problem : where is a hyper - parameter and is the identity matrix . In practice , it is efficient to avoid explicit calculation of the inverse in Equation ( [ reference ] ) and instead use QR factorisation to solve the following set of linear equations for the unknown variables in : — see the discussion about this in Section [ reference ] . subsection : Stage 2 Design : Ridge Regression parameter With these choices , there remains only two hyper - parameters for the Classifier stage : the regression parameter , , and the number of hidden - units , . A + assumes a partition of the LR space around the dictionary atoms , called anchors . For each anchor j a ridge regressor is trained on the local neighborhood N l of fixed size of LR training patches ( features ) . Thus , for each LR input patch y we minimize Secondly , controller and observer design techniques for LPV systems are examined . In particular , the stability conditions in the form of parameter dependent linear matrix inequalities ( LMI ) that result from the applications of the standard Lyapunov stability theory and other advanced techniques such as L2 and H∞ to LPV systems are discussed in detail . Also discussed are the some of the techniques for solving these LMIs . Thank you very much for downloading l2 gain and passivity techniques in nonlinear control . Maybe you have knowledge that , people have search numerous times for their chosen books like this l2 gain and passivity techniques in nonlinear control , but end up in malicious downloads . This approach , named projected ELM ( PELM ) , achieves more than 2 % advance in average accuracy . The final contribution of this paper was implementing Tikhonov regularization in the form of the L2 -penalty with ELM ( TRELM ) , which regularizes and improves the matrix computations utilizing the L-curve criterion and SVD . The L-curve , unlike iterative methods , can estimate the optimum regularization parameter by illustrating a curve with few points that represents the tradeoff between minimizing the training error and the residual of output weight . an essential complex competence for the Internet Age Yang Yang , University of Exeter , UK , y.yang@exeter.ac.uk Rupert Wegerif , University of Exeter , UK , r.b.wegerif@exeter.ac.uk Toby Dragon , Saarland University , Germany , toby.dragon@celtech.de Manolis Mavrikis , London Knowledge Lab , UK , m.mavrikis@lkl.ac.uk Bruce M. McLaren , Saarland Univeristy , Germany , bmclaren@cs.cmu.edu Abstract : Learning to learn together ( L2L2 ) is a complex competence requiring that all the group members are able to coordinate , regulate and plan the learning task by balancing issues of individual ability , motivation and expectations through constant dialogue . In this paper we report on a project to define the complex competence of L2L2 and to support it with a set of web-based tools and associated pedagogy , the Metafora Project . The system we develop embodies our theory of L2L2 and the results of our design-based research suggest that this system can succeed in making key elements of L2L2 explicit in the talk and actions of groups of learners . Most of the regression-based Deep Learning ( DL ) algorithms which were recently proposed are based on Convolutional Neural Networks ( CNN ) trained by using l2 loss function . In order to avoid the vulnerability of it to outliers , some authors propose to combine regression methods with a classification function like combination of l2 loss function and softmax or a strategy based on bounding boxes what improves the performance of the DL neural network . However , the original algorithm was developed for binary classification problems . In this paper , we study multi-class boosting algorithms under the l2-loss framework , and devise two multi-class l2-Boost algorithms . These are based on coordinate descent and gradient descent to minimize the multi-class l2-loss function . The present study examines the validity of the feature (re)-assembly account by providing arguments from the second language acquisition of English by speakers of two varieties of Arabic . In particular , the findings from the comparison of the written samples of two groups of students are argued to support the claim that L2 impairment can be explained by a failure to (re)-assemble features . However , certain problems in L2 acquisition are left unaccounted for , suggesting that more is involved in SLA than just feature selection and feature assembly . The neural network is used to extract the feature map . The CNN network and the L2 loss calculation are used to extract the detected confidence map and the part affinity field . The maximum weight bipartite graph is used to calculate the optimal connection between the limb parts . The performance of several recent and existing regularization techniques are compared in the framework of 3D seismic tomography . These include traditional l2 penalties , but also a sparsity promoting l1 penalty and a total variation penalty . Which of these algorithms is judged optimal depends on the specific requirements of the scientific experiment . Discarding significant variables or keeping irrelevant variables has significant effects on the performance of the learning algorithm during knowledge discovery . In this paper , a feature selection method called Least Loss ( L2 ) is proposed that significantly reduces the dimensionality of data by disposing weakly correlated variables in a robust manner without diminishing the predictive performance of classifiers . The proposed method is based on quantifying the similarity between the observed and expected probabilities and generating scores for each independent variable , which makes it simple and intuitive . The identification signal is the same ( classifying the identities ) for all the comparisons . DeepID2 features learned with the L2 + verification signal are only slightly worse than those learned with L2 . In contrast , the L2 - verification signal helps little in feature learning and gives almost the same result as no verification signal is used . is a robust L 1 loss that is less sensitive to outliers than the L 2 loss used in R - CNN and SPPnet . When the regression targets are unbounded , training with L 2 loss can require careful tuning of learning rates in order to prevent exploding gradients . Eq . Given this unorthodox network output , one of our challenges is how to measure loss during training , as the same geometry may admit different point cloud representations at the same degree of approximation . Unlike the usual L 2 type losses , we use the solution of a transportation problem based on the Earth Mover 's distance ( EMD ) , effectively solving an assignment problem . We exploit an approximation to the EMD to provide speed as well as ensure differentiability for end - to - end training . Our goal is to predict the coordinates of the 3D vertices of each facial scan from the corresponding 2D image via CNN regression . As a number of works have pointed out ( see for example ) , direct regression of all 3D points concatenated as a vector using the standard L2 loss might cause difficulties in learning because a single correct value for each 3D vertex must be predicted . Additionally , such an approach requires interpolating all scans to a vector of a fixed dimension , a pre - processing step not required by our method . These feature extraction algorithms are compared to other well-known techniques : principal component analysis , the discrete cosine transform , and the direct use of pixel intensities . The extracted features are given to three different types of support vector machines for classification , namely a linear SVM , an SVM with the RBF kernel , and a linear SVM using L2-regularization . We have evaluated the six different feature descriptors and three SVM classifiers on three different handwritten character datasets : Bangla , Odia and MNIST . And then , considering ill-condition of transfer matrix and sparsity of impact-force , FIST algorithm is employed to solve sparse regularization model by minimizing the l1-norm . Numerical and experimental studies indicate that the proposed reconstruction strategy can be used to accurately reconstruct force and response under impact excitation , and compared with typical l2-norm regularization methods , FIST algorithm is more efficient and accurate in both single-time impact and consecutive impact cases . It takes advantage of the special features of tensor singular value decomposition ( t-SVD ) along with regularizations using l1/2-norm with half thresholding operation and tensor total variation ( TTV ) to develop a noise robust MOD system with improved detection accuracy . While t-SVD exploits the spatio-temporal correlation of the video background , l1/2 regularization provides noise robustness besides removing the sparser but discontinuous dynamic elements in the spatio-temporal direction . Moreover , TTV enhances the spatio-temporal continuity and fills up the gaps due to the lingering objects and thereby extracting the foreground precisely . Recent works have shown that on sufficiently over-parametrized neural nets , gradient descent with relatively large initialization optimizes a prediction function in the RKHS of the Neural Tangent Kernel ( NTK ) . This analysis leads to global convergence results but does not work when there is a standard l2 regularizer , which is useful to have in practice . We show that sample efficiency can indeed depend on the presence of the regularizer : we construct a simple distribution in d dimensions which the optimal regularized neural net learns with O(d ) samples but the NTK requires \Omega(d^2 ) samples to learn . In this paper , we extend the DN by incorporating the L1/2 sparsity constraint , which we name the L1/2-DN . The L1/2 regularizer not only induces sparsity but is also a better choice among Lq , ( 0<;q<;1 ) regularizers . Furthermore , the L1/2-DN algorithm is more efficient , provides a sparser representation , and results in more accurate recovery than the DN . Due to limited training data for SAR target recognition tasks , different regularization techniques have been used in the deep convolution neural networks ( CNN ) to improve generalization ability . In this paper , the influences of three regularization techniques , including data augmentation , L2 regularization term , dropout , are studied under standard operating conditions ( SOC ) when moving and stationary target recognition ( MSTAR ) dataset is used for SAR target recognition . Four representative CNN models based on classical models , such as AlexNet and ResNet , are selected and trained to recognize 10-classes targets . The learned GLM thus , preserves privacy of each of the individual training points while providing accurate predictions for new test points . Finally , we empirically validate our stability assertions for dropout in the context of convex ERMs and show that surprisingly , dropout significantly outperforms ( in terms of prediction accuracy ) the L2 regularization based methods for several benchmark datasets . Our model is trained for 30k SGD iterations using a mini - batch of 6 images , momentum of 0.9 , an initialize learning rate ( LR ) of and ” polynomial ” learning rate policy . All layers are trained with L2 - regularization ( weight decay of ) . We do not use any data - augmentation , multi - scale training / testing or post - processing using CRFs for this task . subsection : Regularization We use L2 norm regularization on the convolutional kernel parameters with a weight of . We also use the spatial dropout with a rate of after the initial encoder convolution . : This model has a product layer , which is a concatenation of inner product and outer product . Additionally , in order to prevent over - fitting , the popular L2 regularization term is added to the loss function L ( y , ŷ ) when training LR and FM . And we also employ dropout as a regularization method to prevent over - fitting when training neural networks . Removing Dropout from Modified BN - Inception speeds up training , without increasing overfitting . Reduce the L2 weight regularization . While in Inception an loss on the model parameters controls overfitting , in Modified BN - Inception the weight of this loss is reduced by a factor of 5 . [ reference ] in NVDM , since we adopt as a standard Gaussian prior , the KL divergence term can be analytically computed as . It is not difficult to find that it actually acts as L2 regulariser when we update the . Similarly , in NASM ( Eq . Processing noise data from data stream is one of the most important fields in data stream classification . In this paper , a new classification algorithm called FDBCA is proposed , which deals with noise data by Fast-DBSCAN that improved density based spatial clustering of applications with noise algorithm , and use mean square error to detect concept drifting . The experimental results show that FDBCA can improve classification accuracy on classifying noise data stream compared with existed data stream classification algorithm . TCC enables us to tune a color correction matrix between linear and polynomial models by a tuning parameter . We also present a way of selecting a suitable parameter value based on the mean squared error calculation model for PCC . Experimental results demonstrate that TCC effectively balances the trade-off and outperforms both LCC and PCC for noisy images . This paper concerns minimizing the expected distortion of a Gaussian source , transmitted over a two-hop block fading channel , under the mean square-error measure . It is assumed that there is not a direct link between the source and destination , and a simple Decode and Forward ( DF ) relay , handles the communication . The result of this research is number of audio latency can be reduced by accessing and applying native method to play sounds in both desktop and mobile platform . Also the model accuracy between gamelan synthetic signals envelope and recorded samples using the method that described in this paper are close to zero by measure it using Mean Square Error equation . From each cluster a representative gene was selected by text mining in the gene descriptions and evaluating gene ontology terms . The expression profiles of these genes were simulated by a differential equation system , whose structure and parameters were optimized minimizing both the number of non-vanishing parameters and the mean square error of model fit to the microarray data . In this work we explore possibilities for coding and decoding tailor-made for mean squared error evaluation of error in contexts such as image transmission . To do so , we introduce a loss function that expresses the overall performance of a coding and decoding scheme for discrete channels and that exchanges the usual goal of minimizing the error probability to that of minimizing the expected loss . The proposed method provides many local estimates that allow tracking of the variation of noise statistics in frequency and time . The MMSE filter coefficients are obtained from the mean-squared-error expression , which can be calculated using the noise statistics . Evaluation of the performance with computer simulations shows that the proposed method tracks the local statistics of the noise more efficiently than conventional methods . The detailed architecture is shown in Figure [ reference ] . We then add a regression loss to the network , namely a mean - squared error loss , in order to improve fine - grained predictions . We have three final losses , one for each angle , and each is a linear combination of both the respective classification and the regression losses . Different from the previous techniques , for a given rational Bezier curve r ( t ) , a polynomial curve q ( s ) with a parameter transformation s = ϕ ( t ) , such that q ( ϕ ( t ) ) is the closest point to the point r ( t ) , is considered to approximate it . To minimize the distance between these two curves in the L 2 norm produces a similar effect as that of the Hausdorff distance . We use a rational function s ( t ) of a Mobius parameter transformation to approximate the function ϕ ( t ) . To solve the problem effectively , the Nesterov 's method was used to accelerate the computation . To evaluate the performance of the proposed l 2,1-norm method , numerical phantom experiments and in vivo mouse experiments are conducted . The results show that the proposed method not only achieves accurate and desirable fluorescent source reconstruction , but also demonstrates enhanced robustness to noise . To further improve the accuracy of hyperspectral unmixing , we propose a novel linear hyperspectral unmixing method based on l1−l2 sparsity and total variation ( TV ) regularization . First , the enhanced sparsity based on the l1−l2 norm is explored to depict the intrinsic sparse characteristic of the fractional abundances in a sparse regression unmixing model because the l1−l2 norm promotes stronger sparsity than the l1 norm . Then , TV is minimized to enforce the spatial smoothness by considering the spatial correlation between neighbouring pixels . For the speed of convergence and lemniscate asymptotics , the notion of quasilemniscate arises . In the $ $ L^2$$L2-norm algorithms for solving the problem are devised . The base learning rate is set to be 0.01 which is halved every six epoches . Regularization , dropout and L2 norm are cross - validated and used . For the VQA dataset , we use the simple iBOWIMG model in as one baseline model , which beats most existing VQA models currently on arxiv.org . Consequently , this anti - group - sparsity representation is expected to realize the desired diversity . Different from the commonly utilized l 2 , 1 norm , our utilized group - sparsity term is concave , leading to the convexity of its negative . This on one side simplifies the designation of the solving strategy , and on the other hand well fits the previous axiomatic definition for the SPL regularizer . is the parameter to be learned in the verification loss function . Loss functions based on the L1 norm could have similar formulations . The cosine similarity was used in as where is the cosine similarity between DeepID2 vectors , are learnable scaling and shifting parameters , is the sigmoid function , and is the binary target of whether the two compared face images belong to the same identity . The numerical results show that the new channel estimation schemes considerably improve the estimation accuracy and the bit error rate performance over conventional channel estimators . Further , a mean squared error analysis of the proposed estimators is conducted in two asymptotic regimes ( high SNR and low SNR ) enabling a simple characterization and thus comparison of the proposed estimators . Now we should change our way to take some research in the models of degradation of images , the reasons of image degradation , and the relations among different degraded images and different image quality evaluation methods . In this paper , we build models of even noise and pulse noise based on their definition and get degraded images using these models , and we take research in six kinds of usual image quality evaluation methods such as square error method , sum of multi-power of grey scale method , entropy method , Fisher function method , Sobel method , and sum of grads method , and we make computer software for these methods to use easily to evaluate all kinds of images input . Then we evaluate the images ’ qualities with different evaluation methods and analyze the results of six kinds of methods , and finally we get many important results . In order to improve the positional accuracy of CNC machine tools , to ensure production quality , the character of positional precision error classification , identification method , the error compensation and error compensation effect evaluation method were studied . By using technical square-error analysis to separate systematic errors and random errors in position precision of the machine tool was proposed , and based on the F-test method to inference system error relative size in occupied proportion , whether needed to compensate and making evaluations on the effect of compensation for position precision errors . The examples are used to demonstrate the effectiveness of the method . In the testing stage , we take the ' SubjectAggr ' approach described before to build a highly - compact face representation for each gallery subject . Identification is perform simply by comparing the L 2 distances between aggregated face representations . The results in both Table 5 and Fig . The objective of a conditional GAN can be expressed as where tries to minimize this objective against an adversarial that tries to maximize it , i.e. . To test the importance of conditioning the discriminator , we also compare to an unconditional variant in which the discriminator does not observe : Previous approaches have found it beneficial to mix the GAN objective with a more traditional loss , such as L2 distance . The discriminator
We are interested in Euclidean distance-based solutions to spatial queries as Euclidean distance is significantly cheaper to compute than road network distance . A common view is that Euclidean distance is not suitable for solving any spatial query in road networks as road network distance can be significantly different to Euclidean distance . We challenge this view by evaluating the performance of an Euclidean distance-based approach in solving Group Nearest Neighbor queries , which can be used in transportation applications .
Experiments were conducted using twenty UCR benchmark datasets . Also , the proposed modified DTW result has been compared with the state of the art competitive distance measures which is based on precision , recall and f-measure including the original DTW , Minkowski distance measure and Euclidean distance measure . The results showed that the proposed modified DTW reveal superior results in compared to the standard DTW , either using Minkowski or Euclidean .
The first feature set is the energy density of power spectra of Discrete Fourier Transform ( DFT ) or Discrete Cosine Transform ; the second one is the set of statistical moments of Discrete Wavelet Transform ( DWT ) . Euclidean distance metric is used to accomplish feature set matching task . The combinations of features from two EEG channels showed high accuracy for the identification system , and competitive results for the verification system .
There are two approaches used to recognize the speech signal . The first approach is linear predictive coding combined with Euclidean squared distance . LPC is used as the feature extraction method and Euclidean squared distance is used as the recognition method .
The analysis of the relative importance of components of MFCC for both speech recognition and speaker recognition using DTW recognizer in various noise environments are given . For English digit and under the Euclidean distance definition , the experiment results show cepstral components from C 2 to C 16 contain the most useful speaker information , while C 0 and C 1 are usually harm to speaker recognition . Cepstral terms from C 1 to C 12 are found to contain the most useful speech information .
Unlike point and surface registers , our method based on the ICP algorithm better captures the bulk nature of data such as bone thickness . The proposed ICP algorithm is divided into three main steps : the 3D function extraction , comparison of the Euclidean metric distance and gross alignment and ICP enhancement . The input to our system are biomedical data ( CT , MRI ) .
This paper puts forward an algorithm which was based on Hasudorff distance and nonlocal means ( Hasudorff-Non Local Means , HDNLM ) . This algorithm makes use of Hasudorff distance more efficient than Euclidean distance distance in preserving signal details . It is aim to improve the weight model of traditional NLM of Euclidean Distance .
The buggy will have remote code written in embedded C. Buggy will consist of device controller , heat sink , transformer , motor and a device driver which will be connected in serial connection RS-232 . The algorithm used will be Euclidean distance means and cosine similarity algorithm , this algorithm helps to identify and execute the gestures provided in the leap motion . There are total twelve element inputs which can be passed using fingers and the palm .
The inference stage of the RF - based image super - resolution process is similar to the content - based image retrieval ( CBIR ) framework , as shown in Fig . 1 . The general approximated nearest neighbor ( ANN ) search framework [ reference ][ reference ] is an efficient strategy for large - scale image retrieval , which mainly consists of 4 parts : ( 1 ) extracting compact features ( e.g. , locality - sensitive Hashing ( LSH ) [ reference ] feature ) for a query image ; ( 2 ) coarse - level search using Hamming distance to measure the similarity between binary compact Hash features , then narrow the search scope into a smaller candidate group ; ( 3 ) fine - level search by using Euclidean distance to measure the similarity between their corresponding feature vectors ; and ( 4 ) finding the object in the smaller candidate group that is the nearest one to the query image . In the inference stage of conventional RF - based SISR , PCA projection is worked as a Hash - like function to compress the feature dimension for decreasing the search range , which can speed up the searching as the coarse - level search in a CBIR framework , but the impact of using PCA on feature dimensionality reduction has been overlooked in previous works [ reference ][ reference ][ reference ][ reference ][ reference ][ reference ] . Inspired by the finelevel search using augmented features in CBIR frameworks , the high dimensional features in the leaf nodes in an RF can further improve the prediction accuracy in the regression step , which has not been studied previously .
subsection : Relationship to existing models Related prior few - shot work uses fixed pre - specified distance metrics such as Euclidean or cosine distance to perform classification . These studies can be seen as distance metric learning , but where all the learning occurs in the feature embedding , and a fixed metric is used given the learned embedding .
The mini - batch size is 100 . The autoencoder is trained with a Euclidean cost function for reconstruction . On the MNIST dataset we use the sigmoid activation function in the last layer of the autoencoder and on the TFD dataset we use the linear activation function .
Despite only training on PASCAL , we outperform these models which were trained on the much larger COCO dataset . In Table [ reference ] we report the total average recall at IoU for some recently proposed proposal detection methods , including unpublished work inst - DML which is similar in spirit to our model but learns a Euclidean distance based metric to group pixels . We can clearly see that our method achieves significantly better results than existing methods .
Traditional methods : Many early skeleton detection algorithms are based on gradient intensity maps . In , the authors study the limiting average outward flux of the gradient of a Euclidean distance function to a 2D or 3D object boundary . The skeleton is associated with those locations where an energy principle is violated , where there is a net inward flux .
’s features followed by a deep matching function from the convolutional features or the Fully Connected ( FC ) features . In addition to the deep metric learning , some work directly learns image representation through pair - wise contrastive loss or triplet ranking loss , and use Euclidean metric for comparison . With the increasing sample size of ReID dataset , the IDE feature which is learned through multi - class person identification tasks , has shown great potentials on current large - scale person ReID datasets .
In least squares support vector machine ( LS-SVM ) classiers the original SVM formulation of Vapnik is modi ed by considering equalit y constraints within a form of ridge regression instead of inequality constraints . As a result the solution follows from solving a set of linear equations instead of a quadratic programming problem .
In particular , the use of high-dimensional multivariate input data in comparison to small parameter sets is assessed . As prediction methods , Simple Linear Regression , Multiple Linear Regression , Partial Least Square Regression , and Ridge Linear Regression utilizing the Partial Least Square Estimate algorithm are compared . Regression parameter optimization and model selection is performed and evaluated via cross validation and grid search , using the Root Mean Squared Error .
This paper presents a comprehensive analysis method based on ridge trace analysis and fuzzy linear regression method . First , take into account the multicollinearity among input variables , the critical intersection is elicited based on ridge regression analysis , and then the fuzzy regression method is used to get flow forecasting model , and the non-detector intersection traffic flow is directly calculated by model . The experiment results demonstrate that the method proposed in the paper is applicable and effective .
This work is a preliminary investigation into the energy demand of the Nepali Information and Communications Technology ( ICT ) sector . We formulate a logistic growth model for telecommunication users ' diffusion and then present a ridge regression based model to analyse the contribution of the ICT sector in the national energy consumption scenario with a specific focus on the telecommunication sector . Although our analysis leaves out some important dimensions due to lack of publicly available data , the stable ridge regression model can easily be expanded to accommodate new dimensions ( indicators ) .
Triangular membership function is adopted . And to reduce the strong interaction among discrete input variables , coefficient contraction method is employed ; ridge regression function is used in the THEN parts of fuzzy rules . At last , pyrimidines medical data is used in simulations ; results show that our new algorithm gets an advantage in accuracy over the existing training algorithms for HHFNN and standard BP algorithm .
After fulfilling one of these criteria , a density model ( ) in the leaf - node is estimated by all samples falling into this leaf - node for predicting the target value in the testing stage . A simple way to estimate the probability distribution ( ) is averaging all the samples in the leaf - node , while there are also variant methods , such as fitting a Gaussian distribution or kernel density estimation , ridge regression [ reference ][ reference ][ reference ] , and so on . ( ) is the local score for a set of samples ( is either or ) , which normally is calculated using entropy as in Eqn .
This solution is equivalent to least squares regression applied to an overcomplete set of linear equations , with an - dimensional target . It is known to often be useful to regularise such problems , and instead solve the following ridge regression problem : where is a hyper - parameter and is the identity matrix . In practice , it is efficient to avoid explicit calculation of the inverse in Equation ( [ reference ] ) and instead use QR factorisation to solve the following set of linear equations for the unknown variables in :
— see the discussion about this in Section [ reference ] . subsection : Stage 2 Design : Ridge Regression parameter With these choices , there remains only two hyper - parameters for the Classifier stage : the regression parameter , , and the number of hidden - units , .
A + assumes a partition of the LR space around the dictionary atoms , called anchors . For each anchor j a ridge regressor is trained on the local neighborhood N l of fixed size of LR training patches ( features ) . Thus , for each LR input patch y we minimize
Secondly , controller and observer design techniques for LPV systems are examined . In particular , the stability conditions in the form of parameter dependent linear matrix inequalities ( LMI ) that result from the applications of the standard Lyapunov stability theory and other advanced techniques such as L2 and H∞ to LPV systems are discussed in detail . Also discussed are the some of the techniques for solving these LMIs .
Thank you very much for downloading l2 gain and passivity techniques in nonlinear control . Maybe you have knowledge that , people have search numerous times for their chosen books like this l2 gain and passivity techniques in nonlinear control , but end up in malicious downloads .
This approach , named projected ELM ( PELM ) , achieves more than 2 % advance in average accuracy . The final contribution of this paper was implementing Tikhonov regularization in the form of the L2 -penalty with ELM ( TRELM ) , which regularizes and improves the matrix computations utilizing the L-curve criterion and SVD . The L-curve , unlike iterative methods , can estimate the optimum regularization parameter by illustrating a curve with few points that represents the tradeoff between minimizing the training error and the residual of output weight .
an essential complex competence for the Internet Age Yang Yang , University of Exeter , UK , y.yang@exeter.ac.uk Rupert Wegerif , University of Exeter , UK , r.b.wegerif@exeter.ac.uk Toby Dragon , Saarland University , Germany , toby.dragon@celtech.de Manolis Mavrikis , London Knowledge Lab , UK , m.mavrikis@lkl.ac.uk Bruce M. McLaren , Saarland Univeristy , Germany , bmclaren@cs.cmu.edu Abstract : Learning to learn together ( L2L2 ) is a complex competence requiring that all the group members are able to coordinate , regulate and plan the learning task by balancing issues of individual ability , motivation and expectations through constant dialogue . In this paper we report on a project to define the complex competence of L2L2 and to support it with a set of web-based tools and associated pedagogy , the Metafora Project . The system we develop embodies our theory of L2L2 and the results of our design-based research suggest that this system can succeed in making key elements of L2L2 explicit in the talk and actions of groups of learners .
Most of the regression-based Deep Learning ( DL ) algorithms which were recently proposed are based on Convolutional Neural Networks ( CNN ) trained by using l2 loss function . In order to avoid the vulnerability of it to outliers , some authors propose to combine regression methods with a classification function like combination of l2 loss function and softmax or a strategy based on bounding boxes what improves the performance of the DL neural network .
However , the original algorithm was developed for binary classification problems . In this paper , we study multi-class boosting algorithms under the l2-loss framework , and devise two multi-class l2-Boost algorithms . These are based on coordinate descent and gradient descent to minimize the multi-class l2-loss function .
The present study examines the validity of the feature (re)-assembly account by providing arguments from the second language acquisition of English by speakers of two varieties of Arabic . In particular , the findings from the comparison of the written samples of two groups of students are argued to support the claim that L2 impairment can be explained by a failure to (re)-assemble features . However , certain problems in L2 acquisition are left unaccounted for , suggesting that more is involved in SLA than just feature selection and feature assembly .
The neural network is used to extract the feature map . The CNN network and the L2 loss calculation are used to extract the detected confidence map and the part affinity field . The maximum weight bipartite graph is used to calculate the optimal connection between the limb parts .
The performance of several recent and existing regularization techniques are compared in the framework of 3D seismic tomography . These include traditional l2 penalties , but also a sparsity promoting l1 penalty and a total variation penalty . Which of these algorithms is judged optimal depends on the specific requirements of the scientific experiment .
Discarding significant variables or keeping irrelevant variables has significant effects on the performance of the learning algorithm during knowledge discovery . In this paper , a feature selection method called Least Loss ( L2 ) is proposed that significantly reduces the dimensionality of data by disposing weakly correlated variables in a robust manner without diminishing the predictive performance of classifiers . The proposed method is based on quantifying the similarity between the observed and expected probabilities and generating scores for each independent variable , which makes it simple and intuitive .
The identification signal is the same ( classifying the identities ) for all the comparisons . DeepID2 features learned with the L2 + verification signal are only slightly worse than those learned with L2 . In contrast , the L2 - verification signal helps little in feature learning and gives almost the same result as no verification signal is used .
is a robust L 1 loss that is less sensitive to outliers than the L 2 loss used in R - CNN and SPPnet . When the regression targets are unbounded , training with L 2 loss can require careful tuning of learning rates in order to prevent exploding gradients . Eq .
Given this unorthodox network output , one of our challenges is how to measure loss during training , as the same geometry may admit different point cloud representations at the same degree of approximation . Unlike the usual L 2 type losses , we use the solution of a transportation problem based on the Earth Mover 's distance ( EMD ) , effectively solving an assignment problem . We exploit an approximation to the EMD to provide speed as well as ensure differentiability for end - to - end training .
Our goal is to predict the coordinates of the 3D vertices of each facial scan from the corresponding 2D image via CNN regression . As a number of works have pointed out ( see for example ) , direct regression of all 3D points concatenated as a vector using the standard L2 loss might cause difficulties in learning because a single correct value for each 3D vertex must be predicted . Additionally , such an approach requires interpolating all scans to a vector of a fixed dimension , a pre - processing step not required by our method .
These feature extraction algorithms are compared to other well-known techniques : principal component analysis , the discrete cosine transform , and the direct use of pixel intensities . The extracted features are given to three different types of support vector machines for classification , namely a linear SVM , an SVM with the RBF kernel , and a linear SVM using L2-regularization . We have evaluated the six different feature descriptors and three SVM classifiers on three different handwritten character datasets : Bangla , Odia and MNIST .
And then , considering ill-condition of transfer matrix and sparsity of impact-force , FIST algorithm is employed to solve sparse regularization model by minimizing the l1-norm . Numerical and experimental studies indicate that the proposed reconstruction strategy can be used to accurately reconstruct force and response under impact excitation , and compared with typical l2-norm regularization methods , FIST algorithm is more efficient and accurate in both single-time impact and consecutive impact cases .
It takes advantage of the special features of tensor singular value decomposition ( t-SVD ) along with regularizations using l1/2-norm with half thresholding operation and tensor total variation ( TTV ) to develop a noise robust MOD system with improved detection accuracy . While t-SVD exploits the spatio-temporal correlation of the video background , l1/2 regularization provides noise robustness besides removing the sparser but discontinuous dynamic elements in the spatio-temporal direction . Moreover , TTV enhances the spatio-temporal continuity and fills up the gaps due to the lingering objects and thereby extracting the foreground precisely .
Recent works have shown that on sufficiently over-parametrized neural nets , gradient descent with relatively large initialization optimizes a prediction function in the RKHS of the Neural Tangent Kernel ( NTK ) . This analysis leads to global convergence results but does not work when there is a standard l2 regularizer , which is useful to have in practice . We show that sample efficiency can indeed depend on the presence of the regularizer : we construct a simple distribution in d dimensions which the optimal regularized neural net learns with O(d ) samples but the NTK requires \Omega(d^2 ) samples to learn .
In this paper , we extend the DN by incorporating the L1/2 sparsity constraint , which we name the L1/2-DN . The L1/2 regularizer not only induces sparsity but is also a better choice among Lq , ( 0<;q<;1 ) regularizers . Furthermore , the L1/2-DN algorithm is more efficient , provides a sparser representation , and results in more accurate recovery than the DN .
Due to limited training data for SAR target recognition tasks , different regularization techniques have been used in the deep convolution neural networks ( CNN ) to improve generalization ability . In this paper , the influences of three regularization techniques , including data augmentation , L2 regularization term , dropout , are studied under standard operating conditions ( SOC ) when moving and stationary target recognition ( MSTAR ) dataset is used for SAR target recognition . Four representative CNN models based on classical models , such as AlexNet and ResNet , are selected and trained to recognize 10-classes targets .
The learned GLM thus , preserves privacy of each of the individual training points while providing accurate predictions for new test points . Finally , we empirically validate our stability assertions for dropout in the context of convex ERMs and show that surprisingly , dropout significantly outperforms ( in terms of prediction accuracy ) the L2 regularization based methods for several benchmark datasets .
Our model is trained for 30k SGD iterations using a mini - batch of 6 images , momentum of 0.9 , an initialize learning rate ( LR ) of and ” polynomial ” learning rate policy . All layers are trained with L2 - regularization ( weight decay of ) . We do not use any data - augmentation , multi - scale training / testing or post - processing using CRFs for this task .
subsection : Regularization We use L2 norm regularization on the convolutional kernel parameters with a weight of . We also use the spatial dropout with a rate of after the initial encoder convolution .
: This model has a product layer , which is a concatenation of inner product and outer product . Additionally , in order to prevent over - fitting , the popular L2 regularization term is added to the loss function L ( y , ŷ ) when training LR and FM . And we also employ dropout as a regularization method to prevent over - fitting when training neural networks .
Removing Dropout from Modified BN - Inception speeds up training , without increasing overfitting . Reduce the L2 weight regularization . While in Inception an loss on the model parameters controls overfitting , in Modified BN - Inception the weight of this loss is reduced by a factor of 5 .
[ reference ] in NVDM , since we adopt as a standard Gaussian prior , the KL divergence term can be analytically computed as . It is not difficult to find that it actually acts as L2 regulariser when we update the . Similarly , in NASM ( Eq .
Processing noise data from data stream is one of the most important fields in data stream classification . In this paper , a new classification algorithm called FDBCA is proposed , which deals with noise data by Fast-DBSCAN that improved density based spatial clustering of applications with noise algorithm , and use mean square error to detect concept drifting . The experimental results show that FDBCA can improve classification accuracy on classifying noise data stream compared with existed data stream classification algorithm .
TCC enables us to tune a color correction matrix between linear and polynomial models by a tuning parameter . We also present a way of selecting a suitable parameter value based on the mean squared error calculation model for PCC . Experimental results demonstrate that TCC effectively balances the trade-off and outperforms both LCC and PCC for noisy images .
This paper concerns minimizing the expected distortion of a Gaussian source , transmitted over a two-hop block fading channel , under the mean square-error measure . It is assumed that there is not a direct link between the source and destination , and a simple Decode and Forward ( DF ) relay , handles the communication .
The result of this research is number of audio latency can be reduced by accessing and applying native method to play sounds in both desktop and mobile platform . Also the model accuracy between gamelan synthetic signals envelope and recorded samples using the method that described in this paper are close to zero by measure it using Mean Square Error equation .
From each cluster a representative gene was selected by text mining in the gene descriptions and evaluating gene ontology terms . The expression profiles of these genes were simulated by a differential equation system , whose structure and parameters were optimized minimizing both the number of non-vanishing parameters and the mean square error of model fit to the microarray data .
In this work we explore possibilities for coding and decoding tailor-made for mean squared error evaluation of error in contexts such as image transmission . To do so , we introduce a loss function that expresses the overall performance of a coding and decoding scheme for discrete channels and that exchanges the usual goal of minimizing the error probability to that of minimizing the expected loss .
The proposed method provides many local estimates that allow tracking of the variation of noise statistics in frequency and time . The MMSE filter coefficients are obtained from the mean-squared-error expression , which can be calculated using the noise statistics . Evaluation of the performance with computer simulations shows that the proposed method tracks the local statistics of the noise more efficiently than conventional methods .
The detailed architecture is shown in Figure [ reference ] . We then add a regression loss to the network , namely a mean - squared error loss , in order to improve fine - grained predictions . We have three final losses , one for each angle , and each is a linear combination of both the respective classification and the regression losses .
Different from the previous techniques , for a given rational Bezier curve r ( t ) , a polynomial curve q ( s ) with a parameter transformation s = ϕ ( t ) , such that q ( ϕ ( t ) ) is the closest point to the point r ( t ) , is considered to approximate it . To minimize the distance between these two curves in the L 2 norm produces a similar effect as that of the Hausdorff distance . We use a rational function s ( t ) of a Mobius parameter transformation to approximate the function ϕ ( t ) .
To solve the problem effectively , the Nesterov 's method was used to accelerate the computation . To evaluate the performance of the proposed l 2,1-norm method , numerical phantom experiments and in vivo mouse experiments are conducted . The results show that the proposed method not only achieves accurate and desirable fluorescent source reconstruction , but also demonstrates enhanced robustness to noise .
To further improve the accuracy of hyperspectral unmixing , we propose a novel linear hyperspectral unmixing method based on l1−l2 sparsity and total variation ( TV ) regularization . First , the enhanced sparsity based on the l1−l2 norm is explored to depict the intrinsic sparse characteristic of the fractional abundances in a sparse regression unmixing model because the l1−l2 norm promotes stronger sparsity than the l1 norm . Then , TV is minimized to enforce the spatial smoothness by considering the spatial correlation between neighbouring pixels .
For the speed of convergence and lemniscate asymptotics , the notion of quasilemniscate arises . In the $ $ L^2$$L2-norm algorithms for solving the problem are devised .
The base learning rate is set to be 0.01 which is halved every six epoches . Regularization , dropout and L2 norm are cross - validated and used . For the VQA dataset , we use the simple iBOWIMG model in as one baseline model , which beats most existing VQA models currently on arxiv.org .
Consequently , this anti - group - sparsity representation is expected to realize the desired diversity . Different from the commonly utilized l 2 , 1 norm , our utilized group - sparsity term is concave , leading to the convexity of its negative . This on one side simplifies the designation of the solving strategy , and on the other hand well fits the previous axiomatic definition for the SPL regularizer .
is the parameter to be learned in the verification loss function . Loss functions based on the L1 norm could have similar formulations . The cosine similarity was used in as where is the cosine similarity between DeepID2 vectors , are learnable scaling and shifting parameters , is the sigmoid function , and is the binary target of whether the two compared face images belong to the same identity .
The numerical results show that the new channel estimation schemes considerably improve the estimation accuracy and the bit error rate performance over conventional channel estimators . Further , a mean squared error analysis of the proposed estimators is conducted in two asymptotic regimes ( high SNR and low SNR ) enabling a simple characterization and thus comparison of the proposed estimators .
Now we should change our way to take some research in the models of degradation of images , the reasons of image degradation , and the relations among different degraded images and different image quality evaluation methods . In this paper , we build models of even noise and pulse noise based on their definition and get degraded images using these models , and we take research in six kinds of usual image quality evaluation methods such as square error method , sum of multi-power of grey scale method , entropy method , Fisher function method , Sobel method , and sum of grads method , and we make computer software for these methods to use easily to evaluate all kinds of images input . Then we evaluate the images ’ qualities with different evaluation methods and analyze the results of six kinds of methods , and finally we get many important results .
In order to improve the positional accuracy of CNC machine tools , to ensure production quality , the character of positional precision error classification , identification method , the error compensation and error compensation effect evaluation method were studied . By using technical square-error analysis to separate systematic errors and random errors in position precision of the machine tool was proposed , and based on the F-test method to inference system error relative size in occupied proportion , whether needed to compensate and making evaluations on the effect of compensation for position precision errors . The examples are used to demonstrate the effectiveness of the method .
In the testing stage , we take the ' SubjectAggr ' approach described before to build a highly - compact face representation for each gallery subject . Identification is perform simply by comparing the L 2 distances between aggregated face representations . The results in both Table 5 and Fig .
The objective of a conditional GAN can be expressed as where tries to minimize this objective against an adversarial that tries to maximize it , i.e. . To test the importance of conditioning the discriminator , we also compare to an unconditional variant in which the discriminator does not observe : Previous approaches have found it beneficial to mix the GAN objective with a more traditional loss , such as L2 distance . The discriminator
Is Euclidean Distance Really that Bad with Road Networks? Modified Dynamic Time Warping for Hierarchical Clustering User Identification and Verification from a Pair of Simultaneous EEG Channels Using Transform Based Features Implementation of speech recognition on MCS51 microcontroller for controlling wheelchair On the Importance of Components of the MFCC in Speech and Speaker Recognition 3D registration of the point cloud data using ICP algorithm in medical image analysis One Dimension NLM Denoising Method Based on Hasudorff Distance and Its Application in OTLC HCI Using Leap Motion Camera Image Super-resolution via Feature-augmented Random Forest Learning to Compare: Relation Network for Few-Shot Learning Adversarial Autoencoders Recurrent Pixel Embedding for Instance Grouping DeepFlux for Skeletons in the Wild Learning Deep Context-Aware Features over Body and Latent Parts for Person Re-identification Sparse least squares Support Vector Machine classifiers Regression methods for prediction of PECVD Silicon Nitride layer thickness Research on Prediction of Traffic Flow at Non-detector Intersections Based on Ridge Trace and Fuzzy Linear Regression Analysis A regression analysis into Nepali ICT's energy consumption and its implications An improved training algorithm of T-S model HHFNN based on ridge regression function Joint maximum purity forest with application to image super-resolution Enhanced image classification with a fast-learning shallow convolutional neural network Seven Ways to Improve Example-Based Single Image Super Resolution Identification and State Estimation for Linear Parameter Varying Systems with Application to Battery Management System Design L2 Gain And Passivity Techniques In Nonlinear Control Optimum, projected, and regularized extreme learning machine methods with singular value decomposition and L 2 -Tikhonov regularization Learning How to Learn Together (L2L2): Developing Tools to Support an Essential Complex Competence for the Internet Age Solving Regression Models with First Order Stochastic Based Optimizers Multi-class ℓ2-Boost with the scoring coding Variability in L2 Acquisition: Investigating the Role of Formal Features Pose Comparison Based on Part Affinity Fields* Comparison of novel regularization techniques in seismic tomography Least Loss: A simplified filter method for feature selection Deep Learning Face Representation by Joint Identification-Verification Fast R-CNN A Point Set Generation Network for 3D Object Reconstruction from a Single Image Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression Recognizing Handwritten Characters with Local Descriptors and Bags of Visual Words A novel strategy for response and force reconstruction under impact excitation A Three-Way Optimization Technique for Noise Robust Moving Object Detection Using Tensor Low-Rank Approximation, l1/2, and TTV Regularizations. Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel $L_{1/2}$-Regularized Deconvolution Network for the Representation and Restoration of Optical Remote Sensing Images Research of regularization techniques for SAR target recognition using deep CNN models To Drop or Not to Drop: Robustness, Consistency and Differential Privacy Properties of Dropout Revisiting Unreasonable Effectiveness of Data in Deep Learning Era 3D MRI brain tumor segmentation using autoencoder regularization Product-Based Neural Networks for User Response Prediction Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Neural Variational Inference for Text Processing Classification method of noise data stream Tunable color correction between linear and polynomial models for noisy images Minimizing the expected distortion of two-hop networks using multi-layer source coding Gamelan Simulator Multiplatform Application Development Discovery of Gene Regulatory Networks in Aspergillus fumigatus Coding and Decoding Schemes for MSE and Image Transmission MMSE Noise Plus Interference Power Estimation in Adaptive OFDM Systems Fine-Grained Head Pose Estimation Without Keypoints Constrained polynomial approximation of rational Bézier curves using reparameterization Novel l 2,1-norm optimization method for fluorescence molecular tomography reconstruction. Hyperspectral unmixing employing l1−l2 sparsity and total variation regularization Polynomials and lemniscates of indefiniteness Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering Bridging Saliency Detection to Weakly Supervised Object Detection Based on Self-Paced Curriculum Learning Deep Learning Face Representation by Joint Identification-Verification Hybrid sparse/diffuse channels: A new model and estimators for wideband channels The simulation of adaptive optical image even and pulse noise and research of image quality evaluation Error Compensation Evaluation Research of the CNC Machine Tool Neural Aggregation Network for Video Face Recognition Image-to-Image Translation with Conditional Adversarial Networks
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IWCTS@SIGSPATIAL Signal Process. Image Commun. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ArXiv 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ESANN CASE CIS (2) SKIMA ICNC J. Electronic Imaging 2015 International Joint Conference on Neural Networks (IJCNN) 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) CSCL (2) NER NIPS 2015 IEEE International Conference on Computer Vision (ICCV) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE International Conference on Computer Vision (ICCV) EANN NeurIPS 2017 IEEE International Conference on Computer Vision (ICCV) BrainLes@MICCAI 2016 IEEE 16th International Conference on Data Mining (ICDM) ICML ICML ICIP IST KDECB 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) ECCV IJCAI NIPS Allerton 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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