An efficient early stopping method is proposed in this paper to reduce the average number of iterations for low density parity check ( LDPC ) decoders . The method is very efficient especially when the channel signal-to-noise ratio ( SNR ) is low . We then introduce EDNA * , a simple A * extension which provably solves the problem and give worst-case bounds on the number of edges explored by said algorithm . We compare the performance of this algorithm to a nonexploratory strategy using A * and discuss its relation to existing algorithms such as D * Lite , PHA * with early stopping , EWP or exploration algorithms . However , since turbo codes have been introduced into HARQ schemes , their complexity has increased owing to the iterative Bahl-Cocke-Jelinek-Raviv ( BCJR ) operations that are required following each retransmission . This paper explores the complexity of turbo HARQ schemes and proposes a new early stopping ( ES ) approach for iterative decoding based on mutual information ( MI ) , which dynamically determines the appropriate number of BCJR operations to be performed following each incremental redundancy ( IR ) transmission . We demonstrate that the proposed ES-based multiple-component turbo code (MCTC)-aided and systematic twin-component turbo code (TCTC)-assisted HARQ schemes exhibit 60%-85 % reduced complexity for signal-to-noise ratios ( SNRs ) below -2 dB without degrading the packet-loss ratio ( PLR ) and the throughput . In this paper a multi layers perceptron ( MLP ) neural network ( NN ) is designed for load forecasting in normal weather condition and ordinary days . The architecture of the proposed network is a three-layer feedforward neural network whose parameters are tuned by Levenberg-Marquardt backpropagation ( LMBP ) augmented by an early stopping ( ES ) method tried out for increasing the speed of convergence . For abrupt weather changes and special holidays , we have added a fuzzy inference systems ( FIS ) to modify the forecasted load appropriately . To find a good point to halt the optimizer , a common practice is to split the dataset into a training and a smaller validation set to obtain an ongoing estimate of the generalization performance . We propose a novel early stopping criterion based on fast-to-compute local statistics of the computed gradients and entirely removes the need for a held-out validation set . Our experiments show that this is a viable approach in the setting of least-squares and logistic regression , as well as neural networks . In this paper , to solve the problem of the Chinese character shortage , we implement a CNN model which has shared hidden layers and two distinct softmax layers for the Chinese and the alphanumeric character respectively . To avoid over-fitting problem in the training process , the early stopping rule is employed . By training and testing on our two small-scale car plate character databases , the model gets a 9.289 % and 9.632 % relative reduction in test errors for the Chinese and the alphanumeric characters respectively over conventional CNN models . In this paper , we present a novel technique for detecting surgical instruments by constructing a robust and reliable instrument-part detector . While such detectors are typically slow to use , we introduce a novel early stopping scheme for multiclass ensemble classifiers which acts as a cascade and significantly reduces the computational requirements at test time , ultimately allowing it to run at framerate . We evaluate the effectiveness of our approach on instrument detection in retinal microsurgery and laparoscopic image sequences and demonstrate significant improvements in both accuracy and speed . Both derived learning rates are the best ones among the existing generalization results of boosting-type methods for classification . Moreover , an efficient early stopping scheme is provided for the proposed method . A series of toy simulations and real data experiments are conducted to verify the developed theories and demonstrate the effectiveness of the proposed method . However , due to the model capacity required to capture such representations , they are often oversensitive to overfitting and therefore require proper regularization to generalize well . In this paper , we present a combination of regularization techniques which work together to get better performance , we built plain CNNs , and then we used data augmentation , dropout and customized early stopping function , we tested and evaluated these techniques by applying models on five famous datasets , MNIST , CIFAR10 , CIFAR100 , SVHN , STL10 , and we achieved three state-of-the-art-of ( MNIST , SVHN , STL10 ) and very high-Accuracy on the other two datasets . The high generalization capability of the Multi-Objective Algorithm and an automatic weight selection is aggregated by the LASSO approach , which generates networks with reduced number of weights when compared with Multi-Objective Algorithm solutions . Four data sets were chosen in order to compare and evaluate MOBJ , LASSO and Early-Stopping solutions . One generated from a function and tree available from a Machine Learning Repository . paragraph : Evaluation . We measure model performance with the official CoNLL evaluation script and report span - level named entity F - score on the test set using early stopping based on the performance on the validation set . We report average F - scores and standard deviation over 5 runs for our model . ’s performance on the development set during training and save parameters when this performance reaches a new peak . We use early stopping , evaluating on the test set using the parameters that perform best on the development set . We use random search to tune the hyperparameters of each model , setting the ranges for search for each hyperparameter heuristically ( and validating the reasonableness of the ranges on the development set ) , and then launching eight copies of each experiment each with newly sampled hyperparameters from those ranges . We learn a 50 - dimensional nonnegative vector for each concept in WordNet using the max - margin objective ( [ reference ] ) with margin , sampling 500 true and 500 false hypernym pairs in each batch . We train for 30 - 50 epochs using the Adam optimizer adam with learning rate and early stopping on the validation set . During evaluation , we find the optimal classification threshold on the validation set , then apply it to the test set . Both models are initialized using Glorot initialization [ reference ] and trained to minimize cross - entropy on the training nodes using the Adam SGD optimizer [ reference ] with an initial learning rate of 0.01 for Pubmed , and 0.005 for all other datasets . In both cases we use an early stopping strategy on both the cross - entropy loss and accuracy ( transductive ) or micro - F 1 ( inductive ) score on the validation nodes , with a patience of 100 epochs 1 . section : RESULTS However , DNNs usually contain a lot of parameters that make them prone to overfitting and slow in inference . In this paper , we apply variational dropout to sparsify YOLOv3 network for vehicle detection . We then prune redundant layers and compress the network to reduce the memory size and accelerate the inference speed of the model . This paper carries out an empirical analysis of various dropout techniques for language modelling , such as Bernoulli dropout , Gaussian dropout , Curriculum Dropout , Variational Dropout and Concrete Dropout . Moreover , we propose an extension of variational dropout to concrete dropout and curriculum dropout with varying schedules . We find these extensions to perform well when compared to standard dropout approaches , particularly variational curriculum dropout with a linear schedule . Variational dropout ( VD ) is a generalization of Gaussian dropout , which aims at inferring the posterior of network weights based on a log-uniform prior on them to learn these weights as well as dropout rate simultaneously . The log-uniform prior not only interprets the regularization capacity of Gaussian dropout in network training , but also underpins the inference of such posterior . We apply this technique to sparsify recurrent neural networks . To account for recurrent specifics we also rely on Binary Variational Dropout for RNN . We report 99.5 % sparsity level on sentiment analysis task without a quality drop and up to 87 % sparsity level on language modeling task with slight loss of accuracy . However , this method can not be generalized to regular neural network architectures . We present the activation norm penalty that is derived from the information bottleneck principle and is theoretically grounded in a variation dropout framework . Unlike in previous literature , it can be applied to any general neural network . In order to investigate the extent to which each of our proposed improvements helps with learning , we train 4 different models for each network size : ( 1 ) 2 - Layer LSTM with variational dropout ( VD - LSTM ) ( 2 ) 2 - Layer LSTM with variational dropout and augmented loss ( VD - LSTM + AL ) Regularization path algorithms have been proposed to deal with model selection problem in several machine learning approaches . These algorithms allow to compute the entire path of solutions for every value of regularization parameter using the fact that their solution paths have piecewise linear form . This paper presents two new algorithms . The first , called the path length minimization algorithm , is based on the same principles as the algorithm presented by Karp , and can also be applied to undirected graphs . It is purely combinatorial and has O(ʔUʔ3 ) time complexity . The population based harmony search algorithm has been dynamically adapted and used to search next global best pose for underwater robot while obstacle is identified near about robot ’s current pose . Each pose is evaluated based on their respective value for objective function which incorporates features of path length minimization as well as obstacle avoidance . Dynamic adaptation of control parameters and new perturbation schemes for solution vectors of harmony search has been proposed to strengthen both exploitation and randomization ability of present search process in a balanced manner . FFX identifies active bases among a huge set of candidate basis functions and their corresponding coefficients from recorded snapshot data . This approach uses a sparsity-promoting technique from compressive sensing and sparse optimization called pathwise regularized learning to perform feature selection and parameter estimation . Furthermore , it recovers several models of varying complexity ( number of basis terms ) . RNNs are replaced with self-attention blocks , which are powerful to model long-term dependencies inside sequences and able to be efficiently parallelized . Furthermore , a path-aware regularization is proposed to assist SA-T to learn alignments and improve the performance . Additionally , a chunk-flow mechanism is utilized to achieve online decoding . In this paper , we propose the Differentiated RF Power Transmission ( DRIFT ) scheme which is based on an Integer Linear Programming ( ILP ) model that maximizes the power received by the high priority sensor nodes . We compare the performance of DRIFT with a path length minimizing approach , namely Sustainable wireless Rechargeable Sensor network ( SuReSense ) . We show that DRIFT is able to provide more power to high priority nodes than SuReSense . In particular , we redesign the generator normalization , revisit progressive growing , and regularize the generator to encourage good conditioning in the mapping from latent codes to images . In addition to improving image quality , this path length regularizer yields the additional benefit that the generator becomes significantly easier to invert . This makes it possible to reliably attribute a generated image to a particular network . Compared to its actor-critic relatives , Dual-AC has the desired property that the actor and dual critic are updated cooperatively to optimize the same objective function , providing a more transparent way for learning the critic that is directly related to the objective function of the actor . We then provide a concrete algorithm that can effectively solve the minimax optimization problem , using techniques of multi-step bootstrapping , path regularization , and stochastic dual ascent algorithm . We demonstrate that the proposed algorithm achieves the state-of-the-art performances across several benchmarks . We revisit the choice of SGD for training deep neural networks by reconsidering the appropriate geometry in which to optimize the weights . We argue for a geometry invariant to rescaling of weights that does not affect the output of the network , and suggest Path-SGD , which is an approximate steepest descent method with respect to a path-wise regularizer related to max-norm regularization . Path-SGD is easy and efficient to implement and leads to empirical gains over SGD and Ada-Grad . Our registration technique is able to account for the axial movements experienced during recording as well as small transverse movements of the scan line from one scan to the next . The algorithm is based on a regularized shortest path formulation for a directed graph on a map formed by interimage ( B-scan ) correlations . The resulting image enhancement typically increases the contrast-to-noise ratio ( CNR ) with a factor of three or more and facilitates segmentation and quantitative characterization of pathologies . In this brief , we first present a new equivalent dual formulation for < inline-formula > < tex-math notation="LaTeX">$\nu $ < /tex-math></inline-formula>-SVC and , then , propose a robust < inline-formula > < tex-math notation="LaTeX">$\nu $ < /tex-math></inline-formula>-SvcPath , based on lower upper decomposition with partial pivoting . Theoretical analysis and experimental results verify that our proposed robust regularization path algorithm can avoid the exceptions completely , handle the singularities in the key matrix , and fit the entire solution path in a finite number of steps . Experimental results also show that our proposed algorithm fits the entire solution path with fewer steps and less running time than original one does . In this paper , we propose a joint conditional Gaussian graphical model ( JCGGM ) that aims for modeling biological processes based on multiple sources of data . This approach is able to integrate multiple sources of information by adopting conditional models combined with joint sparsity regularization . We apply our approach to a real dataset measuring gene expression in four tissues ( kidney , liver , heart , and fat ) from recombinant inbred rats . Based on shift- , rotation- , and scale-invariant requirements on the model , we show that the Lp-norm of an appropriate vector derivative is the most suitable choice of regularization for this purpose . In addition to Tikhonov-like quadratic regularization , this includes edge-preserving total-variation-like ( TV ) regularization . We give algorithms to recover the continuously defined model from noisy samples and also provide a data-driven scheme to determine the optimal amount of regularization . We validate our method with numerical examples where we demonstrate its superiority over an exact fit as well as the benefit of TV-like nonquadratic regularization over Tikhonov-like quadratic regularization . Furthermore , when regularization is applied to the CTC filter calculation , in order to limit the output levels at the loudspeakers , noncausal artifacts appear at the CTC filters and the resulting ear signals . We propose a global minimum-phase regularization to convert these anti-causal ringing artifacts into causal artifacts . Finally , an aspect that is especially critical for dynamic CTC systems is the filter switch between active loudspeakers distributed in a surround audio-visual display system with 360 ° of freedom of operator orientation . It is also proved that the effective measurement to improve the performance of ellipse detection is whitening the data noise and regularizing data observation . This theoretic analysis has strongly supported the Hartley 's regularization method . Then , an improved-GEVD algorithm has been developed . In this paper , we first use spatial fuzzy c-mean clustering to estimate region-of-interest in multimodal MRI images , and then extract some seed points from there for region growing based on a new notion “ affinity ” . In the end , we design a two-step strategy to refine the glioma border with region merging and improved distance regularization level set method . In BRATS 2015 database , we evaluate the accuracy and robustness of our method with performance scores , including dice , positive predictive value ( PPV ) , and sensitivity metrics , as well as Hausdorff and Euclidean distance ( HD&ED ) . A comparative analysis with real time series will be provided concerning cryptocurrency data , showing improvements in accuracy of about 5 % with respect to existing approaches , without requiring additional training data or further parameters . The presented approach constitutes an innovative , new step towards the statistical moments oriented regularization scheme for statistical forecasting . Keywords— Machine learning , deep learning , neural networks , forecasting , multitask learning , cryptocurrency . In this letter , we study this problem in the framework of statistical learning theory . Our main results show that implementing lq coefficient regularization schemes in the sample-dependent hypothesis space associated with a gaussian kernel can attain the same almost optimal learning rates for all . That is , the upper and lower bounds of learning rates for lq regularization learning are asymptotically identical for all . Thus , it is suitable to obtain sharp boundaries and blocky features with the Total Variation ( TV ) functional regularization . However , the TV function regularization leads to a non-differentiable objective function at zero in the inverse formulation and reduces the algorithm robustness . In this paper , we propose an improved primal-dual interior-point method ( PD-IPM ) based on the Lawson norm to get sharp spatial profiles of the anomalous bodies . When the conditions are not met exactly , the classifiers will pursue the minimum fitting error , running into the risk of overfitting . It turns out that $ \ell_2$-norm regularization can be applied to control overfitting . Its effect is explored under the spectral context . Experimental results . The node feature representations are input to a one - vs - rest logistic regression classifier with L2 regularization . The train and test data is split equally over 10 random instances . : 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 . paragraph : The objective function Our objective combines a cross - entropy objective for the SNLI classification task , cross - entropy objectives and for the parsing decision for each of the two sentences at each step , and an L2 regularization term on the trained parameters . The terms are weighted using the tuned hyperparameters and : Define as the set of filters used in f - lcon layer . The operation of f - lcon to can be formulated as follow : where “ ” denotes convolution , is a patch centered at position of channel in , is the corresponding regularization filter , and is a scalar output for and . To be specific for regularizing flow field from the cascaded flow inference , we replace to . The proposed method explicitly maximizes the ratio of the distance between generated images with respect to the corresponding latent codes , thus encouraging the generators to explore more minor modes during training . This mode seeking regularization term is readily applicable to various conditional generation tasks without imposing training overhead or modifying the original network structures . We validate the proposed algorithm on three conditional image synthesis tasks including categorical generation , image - to - image translation , and text - to - image synthesis with different baseline models . the parameters of the network . The time and space complexity of the gradient computation of one regularization layer are max ( O ( KCHW ) , O ( NCHW ) ) and O ( NCHW ) respectively . Note that we can cache the centered data This - norm constraint problem can be solved with a closed - form solution from the ridge regression [ reference ] theory . Based on the Tikhonov regularization / ridge - regression theory , the closed - form solution of the coefficients is given : We assume that the HR patches share the same coefficient α from their counterpart LR patches , i.e. , = D . The encoder ensures the aggregated posterior distribution can fool the discriminative adversarial network into thinking that the hidden code comes from the true prior distribution . Both , the adversarial network and the autoencoder are trained jointly with SGD in two phases – the reconstruction phase and the regularization phase – executed on each mini - batch . In the reconstruction phase , the autoencoder updates the encoder and the decoder to minimize the reconstruction error of the inputs .
An efficient early stopping method is proposed in this paper to reduce the average number of iterations for low density parity check ( LDPC ) decoders . The method is very efficient especially when the channel signal-to-noise ratio ( SNR ) is low .
We then introduce EDNA * , a simple A * extension which provably solves the problem and give worst-case bounds on the number of edges explored by said algorithm . We compare the performance of this algorithm to a nonexploratory strategy using A * and discuss its relation to existing algorithms such as D * Lite , PHA * with early stopping , EWP or exploration algorithms .
However , since turbo codes have been introduced into HARQ schemes , their complexity has increased owing to the iterative Bahl-Cocke-Jelinek-Raviv ( BCJR ) operations that are required following each retransmission . This paper explores the complexity of turbo HARQ schemes and proposes a new early stopping ( ES ) approach for iterative decoding based on mutual information ( MI ) , which dynamically determines the appropriate number of BCJR operations to be performed following each incremental redundancy ( IR ) transmission . We demonstrate that the proposed ES-based multiple-component turbo code (MCTC)-aided and systematic twin-component turbo code (TCTC)-assisted HARQ schemes exhibit 60%-85 % reduced complexity for signal-to-noise ratios ( SNRs ) below -2 dB without degrading the packet-loss ratio ( PLR ) and the throughput .
In this paper a multi layers perceptron ( MLP ) neural network ( NN ) is designed for load forecasting in normal weather condition and ordinary days . The architecture of the proposed network is a three-layer feedforward neural network whose parameters are tuned by Levenberg-Marquardt backpropagation ( LMBP ) augmented by an early stopping ( ES ) method tried out for increasing the speed of convergence . For abrupt weather changes and special holidays , we have added a fuzzy inference systems ( FIS ) to modify the forecasted load appropriately .
To find a good point to halt the optimizer , a common practice is to split the dataset into a training and a smaller validation set to obtain an ongoing estimate of the generalization performance . We propose a novel early stopping criterion based on fast-to-compute local statistics of the computed gradients and entirely removes the need for a held-out validation set . Our experiments show that this is a viable approach in the setting of least-squares and logistic regression , as well as neural networks .
In this paper , to solve the problem of the Chinese character shortage , we implement a CNN model which has shared hidden layers and two distinct softmax layers for the Chinese and the alphanumeric character respectively . To avoid over-fitting problem in the training process , the early stopping rule is employed . By training and testing on our two small-scale car plate character databases , the model gets a 9.289 % and 9.632 % relative reduction in test errors for the Chinese and the alphanumeric characters respectively over conventional CNN models .
In this paper , we present a novel technique for detecting surgical instruments by constructing a robust and reliable instrument-part detector . While such detectors are typically slow to use , we introduce a novel early stopping scheme for multiclass ensemble classifiers which acts as a cascade and significantly reduces the computational requirements at test time , ultimately allowing it to run at framerate . We evaluate the effectiveness of our approach on instrument detection in retinal microsurgery and laparoscopic image sequences and demonstrate significant improvements in both accuracy and speed .
Both derived learning rates are the best ones among the existing generalization results of boosting-type methods for classification . Moreover , an efficient early stopping scheme is provided for the proposed method . A series of toy simulations and real data experiments are conducted to verify the developed theories and demonstrate the effectiveness of the proposed method .
However , due to the model capacity required to capture such representations , they are often oversensitive to overfitting and therefore require proper regularization to generalize well . In this paper , we present a combination of regularization techniques which work together to get better performance , we built plain CNNs , and then we used data augmentation , dropout and customized early stopping function , we tested and evaluated these techniques by applying models on five famous datasets , MNIST , CIFAR10 , CIFAR100 , SVHN , STL10 , and we achieved three state-of-the-art-of ( MNIST , SVHN , STL10 ) and very high-Accuracy on the other two datasets .
The high generalization capability of the Multi-Objective Algorithm and an automatic weight selection is aggregated by the LASSO approach , which generates networks with reduced number of weights when compared with Multi-Objective Algorithm solutions . Four data sets were chosen in order to compare and evaluate MOBJ , LASSO and Early-Stopping solutions . One generated from a function and tree available from a Machine Learning Repository .
paragraph : Evaluation . We measure model performance with the official CoNLL evaluation script and report span - level named entity F - score on the test set using early stopping based on the performance on the validation set . We report average F - scores and standard deviation over 5 runs for our model .
’s performance on the development set during training and save parameters when this performance reaches a new peak . We use early stopping , evaluating on the test set using the parameters that perform best on the development set . We use random search to tune the hyperparameters of each model , setting the ranges for search for each hyperparameter heuristically ( and validating the reasonableness of the ranges on the development set ) , and then launching eight copies of each experiment each with newly sampled hyperparameters from those ranges .
We learn a 50 - dimensional nonnegative vector for each concept in WordNet using the max - margin objective ( [ reference ] ) with margin , sampling 500 true and 500 false hypernym pairs in each batch . We train for 30 - 50 epochs using the Adam optimizer adam with learning rate and early stopping on the validation set . During evaluation , we find the optimal classification threshold on the validation set , then apply it to the test set .
Both models are initialized using Glorot initialization [ reference ] and trained to minimize cross - entropy on the training nodes using the Adam SGD optimizer [ reference ] with an initial learning rate of 0.01 for Pubmed , and 0.005 for all other datasets . In both cases we use an early stopping strategy on both the cross - entropy loss and accuracy ( transductive ) or micro - F 1 ( inductive ) score on the validation nodes , with a patience of 100 epochs 1 . section : RESULTS
However , DNNs usually contain a lot of parameters that make them prone to overfitting and slow in inference . In this paper , we apply variational dropout to sparsify YOLOv3 network for vehicle detection . We then prune redundant layers and compress the network to reduce the memory size and accelerate the inference speed of the model .
This paper carries out an empirical analysis of various dropout techniques for language modelling , such as Bernoulli dropout , Gaussian dropout , Curriculum Dropout , Variational Dropout and Concrete Dropout . Moreover , we propose an extension of variational dropout to concrete dropout and curriculum dropout with varying schedules . We find these extensions to perform well when compared to standard dropout approaches , particularly variational curriculum dropout with a linear schedule .
Variational dropout ( VD ) is a generalization of Gaussian dropout , which aims at inferring the posterior of network weights based on a log-uniform prior on them to learn these weights as well as dropout rate simultaneously . The log-uniform prior not only interprets the regularization capacity of Gaussian dropout in network training , but also underpins the inference of such posterior .
We apply this technique to sparsify recurrent neural networks . To account for recurrent specifics we also rely on Binary Variational Dropout for RNN . We report 99.5 % sparsity level on sentiment analysis task without a quality drop and up to 87 % sparsity level on language modeling task with slight loss of accuracy .
However , this method can not be generalized to regular neural network architectures . We present the activation norm penalty that is derived from the information bottleneck principle and is theoretically grounded in a variation dropout framework . Unlike in previous literature , it can be applied to any general neural network .
In order to investigate the extent to which each of our proposed improvements helps with learning , we train 4 different models for each network size : ( 1 ) 2 - Layer LSTM with variational dropout ( VD - LSTM ) ( 2 ) 2 - Layer LSTM with variational dropout and augmented loss ( VD - LSTM + AL )
Regularization path algorithms have been proposed to deal with model selection problem in several machine learning approaches . These algorithms allow to compute the entire path of solutions for every value of regularization parameter using the fact that their solution paths have piecewise linear form .
This paper presents two new algorithms . The first , called the path length minimization algorithm , is based on the same principles as the algorithm presented by Karp , and can also be applied to undirected graphs . It is purely combinatorial and has O(ʔUʔ3 ) time complexity .
The population based harmony search algorithm has been dynamically adapted and used to search next global best pose for underwater robot while obstacle is identified near about robot ’s current pose . Each pose is evaluated based on their respective value for objective function which incorporates features of path length minimization as well as obstacle avoidance . Dynamic adaptation of control parameters and new perturbation schemes for solution vectors of harmony search has been proposed to strengthen both exploitation and randomization ability of present search process in a balanced manner .
FFX identifies active bases among a huge set of candidate basis functions and their corresponding coefficients from recorded snapshot data . This approach uses a sparsity-promoting technique from compressive sensing and sparse optimization called pathwise regularized learning to perform feature selection and parameter estimation . Furthermore , it recovers several models of varying complexity ( number of basis terms ) .
RNNs are replaced with self-attention blocks , which are powerful to model long-term dependencies inside sequences and able to be efficiently parallelized . Furthermore , a path-aware regularization is proposed to assist SA-T to learn alignments and improve the performance . Additionally , a chunk-flow mechanism is utilized to achieve online decoding .
In this paper , we propose the Differentiated RF Power Transmission ( DRIFT ) scheme which is based on an Integer Linear Programming ( ILP ) model that maximizes the power received by the high priority sensor nodes . We compare the performance of DRIFT with a path length minimizing approach , namely Sustainable wireless Rechargeable Sensor network ( SuReSense ) . We show that DRIFT is able to provide more power to high priority nodes than SuReSense .
In particular , we redesign the generator normalization , revisit progressive growing , and regularize the generator to encourage good conditioning in the mapping from latent codes to images . In addition to improving image quality , this path length regularizer yields the additional benefit that the generator becomes significantly easier to invert . This makes it possible to reliably attribute a generated image to a particular network .
Compared to its actor-critic relatives , Dual-AC has the desired property that the actor and dual critic are updated cooperatively to optimize the same objective function , providing a more transparent way for learning the critic that is directly related to the objective function of the actor . We then provide a concrete algorithm that can effectively solve the minimax optimization problem , using techniques of multi-step bootstrapping , path regularization , and stochastic dual ascent algorithm . We demonstrate that the proposed algorithm achieves the state-of-the-art performances across several benchmarks .
We revisit the choice of SGD for training deep neural networks by reconsidering the appropriate geometry in which to optimize the weights . We argue for a geometry invariant to rescaling of weights that does not affect the output of the network , and suggest Path-SGD , which is an approximate steepest descent method with respect to a path-wise regularizer related to max-norm regularization . Path-SGD is easy and efficient to implement and leads to empirical gains over SGD and Ada-Grad .
Our registration technique is able to account for the axial movements experienced during recording as well as small transverse movements of the scan line from one scan to the next . The algorithm is based on a regularized shortest path formulation for a directed graph on a map formed by interimage ( B-scan ) correlations . The resulting image enhancement typically increases the contrast-to-noise ratio ( CNR ) with a factor of three or more and facilitates segmentation and quantitative characterization of pathologies .
In this brief , we first present a new equivalent dual formulation for < inline-formula > < tex-math notation="LaTeX">$\nu $ < /tex-math></inline-formula>-SVC and , then , propose a robust < inline-formula > < tex-math notation="LaTeX">$\nu $ < /tex-math></inline-formula>-SvcPath , based on lower upper decomposition with partial pivoting . Theoretical analysis and experimental results verify that our proposed robust regularization path algorithm can avoid the exceptions completely , handle the singularities in the key matrix , and fit the entire solution path in a finite number of steps . Experimental results also show that our proposed algorithm fits the entire solution path with fewer steps and less running time than original one does .
In this paper , we propose a joint conditional Gaussian graphical model ( JCGGM ) that aims for modeling biological processes based on multiple sources of data . This approach is able to integrate multiple sources of information by adopting conditional models combined with joint sparsity regularization . We apply our approach to a real dataset measuring gene expression in four tissues ( kidney , liver , heart , and fat ) from recombinant inbred rats .
Based on shift- , rotation- , and scale-invariant requirements on the model , we show that the Lp-norm of an appropriate vector derivative is the most suitable choice of regularization for this purpose . In addition to Tikhonov-like quadratic regularization , this includes edge-preserving total-variation-like ( TV ) regularization . We give algorithms to recover the continuously defined model from noisy samples and also provide a data-driven scheme to determine the optimal amount of regularization . We validate our method with numerical examples where we demonstrate its superiority over an exact fit as well as the benefit of TV-like nonquadratic regularization over Tikhonov-like quadratic regularization .
Furthermore , when regularization is applied to the CTC filter calculation , in order to limit the output levels at the loudspeakers , noncausal artifacts appear at the CTC filters and the resulting ear signals . We propose a global minimum-phase regularization to convert these anti-causal ringing artifacts into causal artifacts . Finally , an aspect that is especially critical for dynamic CTC systems is the filter switch between active loudspeakers distributed in a surround audio-visual display system with 360 ° of freedom of operator orientation .
It is also proved that the effective measurement to improve the performance of ellipse detection is whitening the data noise and regularizing data observation . This theoretic analysis has strongly supported the Hartley 's regularization method . Then , an improved-GEVD algorithm has been developed .
In this paper , we first use spatial fuzzy c-mean clustering to estimate region-of-interest in multimodal MRI images , and then extract some seed points from there for region growing based on a new notion “ affinity ” . In the end , we design a two-step strategy to refine the glioma border with region merging and improved distance regularization level set method . In BRATS 2015 database , we evaluate the accuracy and robustness of our method with performance scores , including dice , positive predictive value ( PPV ) , and sensitivity metrics , as well as Hausdorff and Euclidean distance ( HD&ED ) .
A comparative analysis with real time series will be provided concerning cryptocurrency data , showing improvements in accuracy of about 5 % with respect to existing approaches , without requiring additional training data or further parameters . The presented approach constitutes an innovative , new step towards the statistical moments oriented regularization scheme for statistical forecasting . Keywords— Machine learning , deep learning , neural networks , forecasting , multitask learning , cryptocurrency .
In this letter , we study this problem in the framework of statistical learning theory . Our main results show that implementing lq coefficient regularization schemes in the sample-dependent hypothesis space associated with a gaussian kernel can attain the same almost optimal learning rates for all . That is , the upper and lower bounds of learning rates for lq regularization learning are asymptotically identical for all .
Thus , it is suitable to obtain sharp boundaries and blocky features with the Total Variation ( TV ) functional regularization . However , the TV function regularization leads to a non-differentiable objective function at zero in the inverse formulation and reduces the algorithm robustness . In this paper , we propose an improved primal-dual interior-point method ( PD-IPM ) based on the Lawson norm to get sharp spatial profiles of the anomalous bodies .
When the conditions are not met exactly , the classifiers will pursue the minimum fitting error , running into the risk of overfitting . It turns out that $ \ell_2$-norm regularization can be applied to control overfitting . Its effect is explored under the spectral context .
Experimental results . The node feature representations are input to a one - vs - rest logistic regression classifier with L2 regularization . The train and test data is split equally over 10 random instances .
: 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 .
paragraph : The objective function Our objective combines a cross - entropy objective for the SNLI classification task , cross - entropy objectives and for the parsing decision for each of the two sentences at each step , and an L2 regularization term on the trained parameters . The terms are weighted using the tuned hyperparameters and :
Define as the set of filters used in f - lcon layer . The operation of f - lcon to can be formulated as follow : where “ ” denotes convolution , is a patch centered at position of channel in , is the corresponding regularization filter , and is a scalar output for and . To be specific for regularizing flow field from the cascaded flow inference , we replace to .
The proposed method explicitly maximizes the ratio of the distance between generated images with respect to the corresponding latent codes , thus encouraging the generators to explore more minor modes during training . This mode seeking regularization term is readily applicable to various conditional generation tasks without imposing training overhead or modifying the original network structures . We validate the proposed algorithm on three conditional image synthesis tasks including categorical generation , image - to - image translation , and text - to - image synthesis with different baseline models .
the parameters of the network . The time and space complexity of the gradient computation of one regularization layer are max ( O ( KCHW ) , O ( NCHW ) ) and O ( NCHW ) respectively . Note that we can cache the centered data
This - norm constraint problem can be solved with a closed - form solution from the ridge regression [ reference ] theory . Based on the Tikhonov regularization / ridge - regression theory , the closed - form solution of the coefficients is given : We assume that the HR patches share the same coefficient α from their counterpart LR patches , i.e. , = D .
The encoder ensures the aggregated posterior distribution can fool the discriminative adversarial network into thinking that the hidden code comes from the true prior distribution . Both , the adversarial network and the autoencoder are trained jointly with SGD in two phases – the reconstruction phase and the regularization phase – executed on each mini - batch . In the reconstruction phase , the autoencoder updates the encoder and the decoder to minimize the reconstruction error of the inputs .
Efficient early stopping method for LDPC decoding based on check-node messages Exploratory Digraph Navigation Using A Low-Complexity Multiple-Component Turbo-Decoding-Aided Hybrid ARQ Short term load forecasting for Iran National Power System and its regions using multi layer perceptron and fuzzy inference systems Early Stopping without a Validation Set Car plate character recognition using a convolutional neural network with shared hidden layers Fast Part-Based Classification for Instrument Detection in Minimally Invasive Surgery Fully-Corrective Gradient Boosting with Squared Hinge: Fast Learning Rates and Early Stopping Stochastic Optimization of Plain Convolutional Neural Networks with Simple Methods New Multi-Objective Algorithms for Neural Network Training Applied to Genomic Classification Data Evaluating the Utility of Hand-crafted Features in Sequence Labelling A Fast Unified Model for Parsing and Sentence Understanding Order-Embeddings of Images and Language Graph Attention Networks YOLOv3-VD: A sparse network for vehicle detection using variational dropout Analysing Dropout and Compounding Errors in Neural Language Models Variational Bayesian Dropout Bayesian Sparsification of Recurrent Neural Networks. From Information Bottleneck To Activation Norm Penalty Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling Nonlinear regularization path for the modified Huber loss Support Vector Machines On Paths with the Shortest Average Arc Length in Weighted Graphs Navigation of underwater robot based on dynamically adaptive harmony search algorithm Equation Discovery Using Fast Function Extraction: a Deterministic Symbolic Regression Approach Self-Attention Transducers for End-to-End Speech Recognition DRIFT: Differentiated RF Power Transmission for Wireless Sensor Network deployment in the smart grid Analyzing and Improving the Image Quality of StyleGAN Boosting the Actor with Dual Critic Path-SGD: Path-Normalized Optimization in Deep Neural Networks Enhancing the signal-to-noise ratio in ophthalmic optical coherence tomography by image registration--method and clinical examples. A Robust Regularization Path Algorithm for $\nu $ -Support Vector Classification Joint conditional Gaussian graphical models with multiple sources of genomic data Regularized Interpolation for Noisy Images A Framework for the Calculation of Dynamic Crosstalk Cancellation Filters Ellipse detection based on improved-GEVD technique Glioma Segmentation With a Unified Algorithm in Multimodal MRI Images Multitask Machine Learning for Financial Forecasting Learning Rates of lq Coefficient Regularization Learning with Gaussian Kernel Improved Primal–Dual Interior-Point Method Using the Lawson-Norm for Inverse Problems Spectral-graph Based Classifications: Linear Regression for Classification and Normalized Radial Basis Function Network node2vec: Scalable Feature Learning for Networks Product-Based Neural Networks for User Response Prediction A Fast Unified Model for Parsing and Sentence Understanding LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis Learning Deep Parsimonious Representations Joint maximum purity forest with application to image super-resolution Adversarial Autoencoders
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IJCAI MICCAI (2) Foundations of Computational Intelligence (1) EMNLP ACL ICLR ICLR SoICT ICLR IJCNN INTERSPEECH GLOBECOM Workshops NIPS KDD 2016 IEEE 16th International Conference on Data Mining (ICDM) ACL 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) NIPS J. Electronic Imaging ArXiv
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