We present a novel hashing-based technique to drastically reduce the amount of computation needed to train and test neural networks . Our approach combines two recent ideas , Adaptive Dropout and Randomized Hashing for Maximum Inner Product Search ( MIPS ) , to select the nodes with the highest activations efficiently . Our new algorithm for deep learning reduces the overall computational cost of the forward and backward propagation steps by operating on significantly fewer nodes . This paper presents a new approach to constructing factorized approximate inverses for a symmetric and positive definite matrix $ A$ . The proposed strategy is based on adaptive dropping that reflects the quality of preserving the relation $ UZ = I$ between the direct factor $ U$ and the inverse factor $ Z$ satisfying $ A = U^TU$ and $ A^{-1}=ZZ^T$. An important part of the approach is column pivoting , used to minimize the growth of the condition number of leading principal submatrices of $ U$ that occurs explicitly in the dropping criterion . Video playback is improved by employing an adaptive scalable video layer dropping algorithm which drops a progressively larger number of scalable video layers as network utilization increases as measured by the moving average of the video packet delay . The influence of the algorithm 's three parameters on its performance is investigated in detail , and the results of the optimized adaptive dropping algorithm are compared to baseline static dropping algorithm . Thesis Mentor : _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ Dr. Rami J. Haddad Honors Director : We describe a method called ' standout ' in which a binary belief network is overlaid on a neural network and is used to regularize of its hidden units by selectively setting activities to zero . This ' adaptive dropout network ' can be trained jointly with the neural network by approximately computing local expectations of binary dropout variables , computing derivatives using back-propagation , and using stochastic gradient descent . Interestingly , experiments show that the learnt dropout network parameters recapitulate the neural network parameters , suggesting that a good dropout network regularizes activities according to magnitude . The dropout voltage of the LDO is fixed based on its peak load . An adaptive LDO dropout technique , based on LDO load-current sensing , has been proposed to improve the moderate and light load system efficiency of the cascaded DC-DC and LDO combination . The feedback from LDO output to DC-DC output is stable and has minimal overhead ( < ; 2 % ) in terms of area and current consumption . Moreover , the issue of model over-fitting is prone to occur in the process of using deep learning for complex structures learning . Based on this argument , this paper has proposed an improved large-scale task processing approach , Tard ( large-scale Tasks processing based on Adaptive dropout deep computing and Reinforcement learning for big Data ) . We design an adaptive Dropout deep computing model based virtual network mapping method to achieve large-scale task allocation . However , most of current algorithms are not effective to cluster heterogeneous data which is prevalent in big data . In this paper , we propose a high-order CFS algorithm ( HOCFS ) to cluster heterogeneous data by combining the CFS clustering algorithm and the dropout deep learning model , whose functionality rests on three pillars : ( i ) an adaptive dropout deep learning model to learn features from each type of data , ( ii ) a feature tensor model to capture the correlations of heterogeneous data , and ( iii ) a tensor distance-based high-order CFS algorithm to cluster heterogeneous data . Furthermore , we verify our proposed algorithm on different datasets , by comparison with other two clustering schemes , that is , HOPCM and CFS . The soft dropout method can introduce continuous mask coefficients in the interval of [ 0 , 1 ] , rather than only zero and one . Meanwhile , in order to implement the adaptive dropout rate via adaptive distribution parameters , we respectively utilize the half-Gaussian distributed and the half-Laplace distributed variables to approximate the beta distributed masks and apply a variation of variational Bayes optimization called stochastic gradient variational Bayes ( SGVB ) algorithm to optimize the distribution parameters . In the experiments , compared with the standard soft dropout with fixed dropout rate , the adaptive soft dropout method generally improves the performance . In addition , the proposed soft dropout and its adaptive versions achieve performance improvement compared with the referred methods on both image classification and regression tasks . The proposed circuit has been designed in AMS 0.35 m High-Voltage CMOS process and simulated across wide supply voltage ( 6V to 18V ) , wide load range ( 2mA to 300mA ) and wide temperature range ( -40 ° C to 150 ° C ) to meet the automotive requirements . The adaptive dropout scheme works for any output voltage and load current of the LDOs . The overall system efficiency is improved by about 3 % to 10 % with minimal overhead ( < ; 2 % ) in terms of area compared to conventional fixed dropout architecture . The sampling periods are controlled based on the real-time available bandwidth and the overall control performance is improved significantly . The stability analysis is provided by considering the characteristic of adaptive packet dropout method , and then control gain is obtained . Simulation results verify the effectiveness and advantage of our approach . Latter , Wan et al . propose a generalization of dropout approach , DropConect , which instead randomly selects weights to zero during training . In addition , Adaptive dropout is proposed where the dropout probability for each hidden neuron is estimated through a binary belief network . Stochastic Pooling randomly selects activation from a multinomial distribution during training , which is parameter free and can be applied with other regularization techniques . The gaussian process prior is then imposed on another transformation to non-linearly and efficiently map the learned subspace to a shared manifold space . In order to apply the proposed approach to the classification , a discriminative regularization is also embedded to exploit the label information . Experimental results on three real-world databases substantiate the effectiveness and superiority of the proposed approach as compared with several state-of-the-art approaches . As a consequence , samples close to the boundary are quite likely to be misclassified . In this paper , we introduce an approach called semi-supervised classification by discriminative regularization ( SSCDR for short ) to address this problem . SSCDR first constructs a k nearest neighborhood graph to capture the local manifold structure of samples , and a discriminative graph to encode the discriminative information derived from constrained clustering on labeled and unlabeled samples . Most existing approaches explore the domaininvariant features shared by domains but ignore the discriminative information of source domain . To address this issue , we propose a discriminative domain adaptation method ( DDA ) to reduce domain shift by seeking a common latent subspace jointly using supervised sparse coding ( SSC ) and discriminative regularization term . Particularly , DDA adapts SSC to yield discriminative coefficients of target data and further unites with discriminative regularization term to induce a common latent subspace across domains . In this paper , we rigorously validate that the discriminative regularizer actually coincides with the definition on the inner product in Reproducing Kernel [ email protected]?n Space ( RKKS ) naturally . As a result , we further present a new discriminality-driven regularization framework for indefinite kernel machine based on the discriminative regularizer . According to the framework , we firstly reintroduce the original DRLSC from the viewpoint of the proper indefinite kernelization rather than the empirical kernel mapping . We propose discriminant GANs , where , for generated samples , we maximize between-class distance and minimize within-class distance , so that generated samples in different classes are more separable and different betweenclass distances are explicitly allowed . Empirical experiments show that ( 1 ) discriminant regularization improves the quality of generated samples , ( 2 ) discriminant regularized GANs extract efficient features for equipment health classification . To enforce the Lipschitz constraint , instead of weight clipping strategy , recent work adds a gradient penalty term to the critic loss . In this paper , we combine a more discriminative gradient penalty term with the importance weighting strategy and further propose more effective algorithms for Lipschitz constraint enforcement of the critic in WGAN . Our algorithms do not adding any computation burden . In the proposed DCCRC , the class discrimination information is fully utilized for promoting the true class of each testing sample to dominantly represent the testing sample during collaborative representation . The class discrimination information is well considered in the newly designed discriminative -norm regularization that can decrease the ability of representation from the interclasses of each testing sample . Simultaneously , a competitive -norm regularization is introduced to the DCCRC model with the class discrimination information with the aim of enhancing the competitive ability of representation from the true class of each testing sample . We show that synergistically combining automatically-learned and handcrafted features can significantly improve the classification performance . Our method utilizes features extracted from convolutional neural networks ( CNNs ) , texture features learned via a discriminative sparsity-regularized approach , and hand-crafted statistical features . To assess the efficacy of different feature sets , we use AdaBoost with decision trees to classify prostate cancer lesions using different sets of features . We propose a generative model training objective that uses a black-box discriminative model as a regularizer to learn representations that preserve this predictive variation . With these discriminatively regularized latent variable models , we visualize and measure variation in the data that influence a black-box predictive model , enabling an expert to better understand each prediction . With this technique , we study models that use electrocardiograms to predict outcomes of clinical interest . We show it is in fact possible to train a language GAN from scratch -- without maximum likelihood pre-training . We combine existing techniques such as large batch sizes , dense rewards and discriminator regularization to stabilize and improve language GANs . The resulting model , ScratchGAN , performs comparably to maximum likelihood training on EMNLP2017 News and WikiText-103 corpora according to quality and diversity metrics . Table [ reference ] shows that using more than one output neuron in the discriminator significantly improved the performance of repulsive loss over the one - neuron case on CIFAR - 10 dataset . The reason may be that using insufficient output neurons makes it harder for the discriminator to learn an injective and discriminative representation of the data ( see Fig . [ reference ] ) . We first outline a novel generalized framework for adversarial adaptation , which subsumes recent state - of - the - art approaches as special cases , and we use this generalized view to better relate the prior approaches . We propose a previously unexplored instance of our general framework which combines discriminative modeling , untied weight sharing , and a GAN loss , which we call Adversarial Discriminative Domain Adaptation ( ADDA ) . We show that ADDA is more effective yet considerably simpler than competing domain - adversarial methods , and demonstrate the promise of our approach by exceeding state - of - the - art unsupervised adaptation results on standard cross - domain digit classification tasks and a new more difficult cross - modality object classification task . Our framework unifies design choices such as weight - sharing , base models , and adversarial losses and subsumes previous work , while also facilitating the design of novel instantiations that improve upon existing ones . In particular , we observe that generative modeling of input image distributions is not necessary , as the ultimate task is to learn a discriminative representation . On the other hand , asymmetric mappings can better model the difference in low level features than symmetric ones . The second type of CNN - based re - ID methods focuses on feature learning , which categorizes the training samples into pre - defined classes and the FC descriptor is used for retrieval . In , the classification CNN model is fine - tuned using either the video frames or image bounding boxes to learn a discriminative embedding for pedestrian retrieval . Xiao propose learning generic feature representations from multiple re - ID datasets jointly . The network is trained using the Softmax loss function for multiclass classification using the Caffe deep learning platform . section : Learning a Discriminative Embedding In this section , we describe our algorithm for learning a low - dimensional embedding such that the resulting projections are more discriminative . document : Semantic Instance Segmentation with a Discriminative Loss Function Semantic instance segmentation remains a challenging task . Whereas these works employ a discriminative loss function to optimize distances between images in a dataset , our method operates at the pixel level , optimizing distances between individual pixels in an image . To our knowledge , we are the first to successfully use a discriminative loss based on distance metric learning principles for the task of instance segmentation with deep networks . section : Method He et al . proposed the Mask R - CNN , which is extended from Faster R - CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition . Brabandere et al . proposed to tackle instance segmentation with a discriminative loss function , operating at the pixel level , which encourages a convolutional network to produce a representation of the image that can be easily clustered into instances with a simple post - processing step . However , these methods either only consider coarse semantic information or are agnostic to different instances . RNNGs maintain the algorithmic convenience of transition - based parsing but incorporate top - down ( i.e. , root - to - terminal ) syntactic information ( § [ reference ] ) . The top - down transition set that RNNGs are based on lends itself to discriminative modeling as well , where sequences of transitions are modeled conditional on the full input sentence along with the incrementally constructed syntactic structures . Similar to previously published discriminative bottom - up transition - based parsers , greedy prediction with our model yields a linear - time deterministic parser ( provided an upper bound on the number of actions taken between processing subsequent terminal symbols is imposed ) ; however , our algorithm generates arbitrary tree structures directly , without the binarization required by shift - reduce parsers . Though a single CNN model ( i.e. , DnCNN - B ) is trained in for Gaussian denoising , it does not generalize well to real noisy images and works only if the noise level is in the preset range , e.g. , . Besides , all the existing discriminative learning based methods lack flexibility to deal with spatially variant noise . To overcome the drawbacks of existing CNN based denoising methods , we present a fast and flexible denoising convolutional neural network ( FFDNet ) . Therefore , the MAP solution of ( non - blind ) SISR can be formulated as where denotes the parameters of the MAP inference . By treating CNN as a discriminative learning solution to Eqn . ( [ reference ] ) For example , unsupervised feature learning is known to be beneficial for image restoration [ 5 ] and recent results show that it outperforms supervised feature learning also on descriptor matching [ 6 ] . In this work we combine the power of a discriminative objective with the major advantage of unsupervised feature learning : cheap data acquisition . We introduce a novel training procedure for convolutional neural networks that does not require any labeled data . The unfolded maxout RNN architecture is depicted in Figure [ reference ] . The network is trained one hidden layer at a time with discriminative pretraining followed by 12 epochs of SGD CE training on randomized minibatches of 250 samples . The model is refined with Hessian - free sequence discriminative training using the state - based MBR criterion for 10 iterations . This thesis combines these lines of research to develop new tools for learning in modular systems . As data-dependent regularization has been shown to be critical to success in deep modular systems , several significant contributions are provided in this area . A novel , differentiable formulation of sparse coding is presented and shown to be a powerful semi-supervised learning algorithm . Data dependent regularization is known to benefit a wide variety of problems in machine learning . Often , these regularizers can not be easily decomposed into a sum over a finite number of terms , e.g. , a sum over individual example-wise terms . It is a primary challenge in the fault diagnosis community of the gearbox to extract the weak fault features under heavy background noise and nonstationary conditions . For this purpose , a novel weak fault detection approach based on majorization – minimization ( MM ) and asymmetric convex penalty regularization ( ACPR ) is proposed in this paper . The proposed objective cost function ( OCF ) consisting of a signal-fidelity term , and two parameterized penalty terms ( i.e. , one is an asymmetric nonconvex penalty regularization term , and another is a symmetric nonconvex penalty regularization term).To begin with , the asymmetric and symmetric penalty functions are established on the basis of an L1-norm model , then , according to the splitting idea , the majorizer of the symmetric function and the majorizer of the asymmetric function are respectively calculated via the MM algorithm . We present an expository , general analysis of valid post-selection or post-regularization inference about a low-dimensional target parameter in the presence of a very high-dimensional nuisance parameter that is estimated using selection or regularization methods . Our analysis provides a set of high-level conditions under which inference for the low-dimensional parameter based on testing or point estimation methods will be regular despite selection or regularization biases occurring in the estimation of the high-dimensional nuisance parameter . For this purpose , supporting HR face is computed using the proposed ISLcR model . Further , reconstruction residual of both data fidelity is handled by the locality with sparsity regularization term . The use of both types of data fidelity and locality with sparsity regularization help in reduction of noise , generate a more discriminable outcome , and makes the process computationally viable . We present a novel regularization-based Multitask Learning ( MTL ) formulation for Structured Output ( SO ) prediction for the case of hierarchical task relations . Structured output prediction often leads to difficult inference problems and hence requires large amounts of training data to obtain accurate models . We also show that any cluster point of the sequence generated by the proposed algorithm converges to a stationary point . Numerical experiments demonstrate the efficiency of the proposed s-difference regularization in comparison with some other existing penalty functions . We study the nonlocal total variation ( NL-TV ) regularization technique for image zooming , which exploits the spatial interactions in images . To solve the nonlinear Euler-Lagrange equation associated with the NL-TV regularization framework , we propose a split Bregman NL-TV image-zooming method . Furthermore , we give the convergence analysis of the proposed algorithm . Abstract This paper presents an intelligent design methodology of microstrip filters in which a dynamic neural network model based on Bayesian Regularization Back-Propagation ( BRBP ) learning algorithm is used . In this approach , a Low-Pass Filter ( LPF ) composed of multiple open stubs , and stepped impedance resonators is initially designed for which an Artificial Neural Network ( ANN ) is trained to improve the performance of the filter . In this paper , we propose an improved deterministic regularization algorithm to handle the sparse angle data problem in optical diffraction tomography . Based on optical diffraction tomography and the deterministic regularization algorithm , the regularization iteration is performed in the space domain and the frequency domain simultaneously , which greatly reduces the computational cost . By applying piecewise-smoothness and positivity constraints as the penalty function , the missing frequency spectrum is effectively recovered and the internal refractive index distribution of the specimen is accurately reconstructed . The 1D convolution kernel size for character embedding is 5 . All weights are constraint by L2 regularization , and the L2 regularization at step is calculated as follows : where determines the maximum L2 regularization ratio , and determines at which step the maximum L2 regularization ratio would be applied on the L2 regularization . We choose as and as 100 , 000 . We train and validate on all 20 tasks jointly using the 9 , 000 training and 1 , 000 validation samples defined in the en_valid_10k split . We use the Adam optimizer with a batch size of 512 , a learning rate of 2e - 4 and L2 regularization with a rate of 1e - 5 . We train for 5 M gradient steps . We can see that HypER performs comparably or better than ConvE on 8 out of 11 relations in the dataset . To ensure that the difference between reported results for HypER and ConvE is not simply due to HypER having a reduced number of parameters ( implicit regularization ) , we trained ConvE reducing the number of feature maps to 16 instead of 32 to have a comparable number of parameters to HypER ( explicit regularization ) . This showed no improvement in ConvE results , indicating HypER ’s architecture does more than merely reducing the number of parameters . LR - Bi - LSTM : LR_LSTM imposes linguistic roles into neural networks by applying linguistic regularization on intermediate outputs with KL divergence . Self - attention : self - attention proposes a self - attention mechanism to learn structured sentence embedding . document : 3D MRI brain tumor segmentation using autoencoder regularization Automated segmentation of brain tumors from 3D magnetic resonance images ( MRIs ) is necessary for the diagnosis , monitoring , and treatment planning of the disease . To represent the distributions effectively , we use the kernel embedding of distributions that hold high-order moment information about a distribution as an element in a reproducing kernel Hilbert space , which enables us to calculate the distance between the distributions without density estimation . The structural information encoded in the latent vectors are preserved by using the orthogonality regularizer . We demonstrate the effectiveness of the proposed method with experiments using real-world multi-lingual document-word relational datasets and multiple user-item relational datasets . First , we construct an orthogonal initialization of weights . Second , to avoid destroying the orthogonality of the weights in the training process , orthogonality regularizations are proposed to maintain the orthogonality of weights . Simultaneously , a squeeze-and-excitation ( SE ) block is employed to perform feature recalibration across different channels . After that , we build an objective function of the projected vectors to regularize their behaviours towards comparable eigenvalue magnitudes of WWT . Experimental results on training VGG16 for CIFAR10 show that PWR combined with centered weight normalization ( CWN ) yields promising validation performance compared to orthonormal regularisation combined with CWN . Specifically , at each iteration , we first approximate (WW^T)^(-1/2 ) using its Taylor expansion before multiplying the matrix W. After that , the matrix product is then normalised by applying the spectral normalisation ( SN ) technique to obtain h(W ) . Conceptually speaking , AON is designed to turn orthonormal regularisation into orthonormal normalisation to avoid manual balancing the original and penalty functions . Experimental results show that AON yields promising validation performance compared to orthonormal regularisation . In this work , we focus on the weight matrix learnt by the neural networks and hypothesize that ill conditioned weight matrix is one of the contributing factors in neural network 's susceptibility towards adversarial examples . For ensuring that the learnt weight matrix 's condition number remains sufficiently low , we suggest using orthogonal regularizer . We show that this indeed helps in increasing the adversarial accuracy on MNIST and F-MNIST datasets . Following a popular method , minimizing the maximum overall error probability with respect to the selection matrix can be approximated by maximizing the minimum Chernoff distance between the distributions of the selected measurements under null hypothesis and alternative hypothesis to be detected . Then , we utilize Danskin ’s theorem to compute the gradient of the objective function of the converted maximization problem , and apply the orthogonal constraint-preserving gradient algorithm ( OCPGA ) to solve the relaxed maximization problem without 0/1 constraints . It is shown that the OCPGA can obtain a stationary point of the relaxed problem . We compare three different weight orthogonalization techniques with regards to their convergence properties , their ability to ensure the Lipschitz condition and the achieved quality of the learned distribution . In addition , we provide a comparison to Wasserstein-GANs trained with current state-of-the-art methods , where we demonstrate the potential of solely using orthogonality-based regularization . In this context , we propose an improved training procedure for Wasserstein-GANs which utilizes orthogonalization to further increase its generalization capability . To address this problem , this paper presents a novel robust dictionary learning framework via Bayesian inference . In particular , we employ the orthogonality-promoting regularization to mitigate correlations among dictionary atoms . Such a regularization , encouraging the dictionary atoms to be close to being orthogonal , can alleviate overfitting to training data and improve the discrimination of the model . Similar to general multi-label image classification task , facial attribute recognition suffers from several typical problems : discriminative feature learning , handling the correlations among attributes , and imbalanced training data . In this paper , we propose a unified facial attribute recognition solution via feature decoupling and graph convolutional networks ( GCN ) : 1 ) We utilize the orthonormal regularizer to constrain that each dimension of the general facial representation represents a certain visual pattern . Meanwhile , a learnable matrix is unveiled , which can convert general facial feature into attribute-specific representation through modeling the importance of different visual patterns . We sweep values and select , finding this small added penalty sufficient to improve the likelihood that our models will be amenable to truncation . Across runs in Table [ reference ] , we observe that without Orthogonal Regularization , only 16 % of models are amenable to truncation , compared to 60 % when trained with Orthogonal Regularization . subsection : Summary
We present a novel hashing-based technique to drastically reduce the amount of computation needed to train and test neural networks . Our approach combines two recent ideas , Adaptive Dropout and Randomized Hashing for Maximum Inner Product Search ( MIPS ) , to select the nodes with the highest activations efficiently . Our new algorithm for deep learning reduces the overall computational cost of the forward and backward propagation steps by operating on significantly fewer nodes .
This paper presents a new approach to constructing factorized approximate inverses for a symmetric and positive definite matrix $ A$ . The proposed strategy is based on adaptive dropping that reflects the quality of preserving the relation $ UZ = I$ between the direct factor $ U$ and the inverse factor $ Z$ satisfying $ A = U^TU$ and $ A^{-1}=ZZ^T$. An important part of the approach is column pivoting , used to minimize the growth of the condition number of leading principal submatrices of $ U$ that occurs explicitly in the dropping criterion .
Video playback is improved by employing an adaptive scalable video layer dropping algorithm which drops a progressively larger number of scalable video layers as network utilization increases as measured by the moving average of the video packet delay . The influence of the algorithm 's three parameters on its performance is investigated in detail , and the results of the optimized adaptive dropping algorithm are compared to baseline static dropping algorithm . Thesis Mentor : _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ Dr. Rami J. Haddad Honors Director :
We describe a method called ' standout ' in which a binary belief network is overlaid on a neural network and is used to regularize of its hidden units by selectively setting activities to zero . This ' adaptive dropout network ' can be trained jointly with the neural network by approximately computing local expectations of binary dropout variables , computing derivatives using back-propagation , and using stochastic gradient descent . Interestingly , experiments show that the learnt dropout network parameters recapitulate the neural network parameters , suggesting that a good dropout network regularizes activities according to magnitude .
The dropout voltage of the LDO is fixed based on its peak load . An adaptive LDO dropout technique , based on LDO load-current sensing , has been proposed to improve the moderate and light load system efficiency of the cascaded DC-DC and LDO combination . The feedback from LDO output to DC-DC output is stable and has minimal overhead ( < ; 2 % ) in terms of area and current consumption .
Moreover , the issue of model over-fitting is prone to occur in the process of using deep learning for complex structures learning . Based on this argument , this paper has proposed an improved large-scale task processing approach , Tard ( large-scale Tasks processing based on Adaptive dropout deep computing and Reinforcement learning for big Data ) . We design an adaptive Dropout deep computing model based virtual network mapping method to achieve large-scale task allocation .
However , most of current algorithms are not effective to cluster heterogeneous data which is prevalent in big data . In this paper , we propose a high-order CFS algorithm ( HOCFS ) to cluster heterogeneous data by combining the CFS clustering algorithm and the dropout deep learning model , whose functionality rests on three pillars : ( i ) an adaptive dropout deep learning model to learn features from each type of data , ( ii ) a feature tensor model to capture the correlations of heterogeneous data , and ( iii ) a tensor distance-based high-order CFS algorithm to cluster heterogeneous data . Furthermore , we verify our proposed algorithm on different datasets , by comparison with other two clustering schemes , that is , HOPCM and CFS .
The soft dropout method can introduce continuous mask coefficients in the interval of [ 0 , 1 ] , rather than only zero and one . Meanwhile , in order to implement the adaptive dropout rate via adaptive distribution parameters , we respectively utilize the half-Gaussian distributed and the half-Laplace distributed variables to approximate the beta distributed masks and apply a variation of variational Bayes optimization called stochastic gradient variational Bayes ( SGVB ) algorithm to optimize the distribution parameters . In the experiments , compared with the standard soft dropout with fixed dropout rate , the adaptive soft dropout method generally improves the performance . In addition , the proposed soft dropout and its adaptive versions achieve performance improvement compared with the referred methods on both image classification and regression tasks .
The proposed circuit has been designed in AMS 0.35 m High-Voltage CMOS process and simulated across wide supply voltage ( 6V to 18V ) , wide load range ( 2mA to 300mA ) and wide temperature range ( -40 ° C to 150 ° C ) to meet the automotive requirements . The adaptive dropout scheme works for any output voltage and load current of the LDOs . The overall system efficiency is improved by about 3 % to 10 % with minimal overhead ( < ; 2 % ) in terms of area compared to conventional fixed dropout architecture .
The sampling periods are controlled based on the real-time available bandwidth and the overall control performance is improved significantly . The stability analysis is provided by considering the characteristic of adaptive packet dropout method , and then control gain is obtained . Simulation results verify the effectiveness and advantage of our approach .
Latter , Wan et al . propose a generalization of dropout approach , DropConect , which instead randomly selects weights to zero during training . In addition , Adaptive dropout is proposed where the dropout probability for each hidden neuron is estimated through a binary belief network . Stochastic Pooling randomly selects activation from a multinomial distribution during training , which is parameter free and can be applied with other regularization techniques .
The gaussian process prior is then imposed on another transformation to non-linearly and efficiently map the learned subspace to a shared manifold space . In order to apply the proposed approach to the classification , a discriminative regularization is also embedded to exploit the label information . Experimental results on three real-world databases substantiate the effectiveness and superiority of the proposed approach as compared with several state-of-the-art approaches .
As a consequence , samples close to the boundary are quite likely to be misclassified . In this paper , we introduce an approach called semi-supervised classification by discriminative regularization ( SSCDR for short ) to address this problem . SSCDR first constructs a k nearest neighborhood graph to capture the local manifold structure of samples , and a discriminative graph to encode the discriminative information derived from constrained clustering on labeled and unlabeled samples .
Most existing approaches explore the domaininvariant features shared by domains but ignore the discriminative information of source domain . To address this issue , we propose a discriminative domain adaptation method ( DDA ) to reduce domain shift by seeking a common latent subspace jointly using supervised sparse coding ( SSC ) and discriminative regularization term . Particularly , DDA adapts SSC to yield discriminative coefficients of target data and further unites with discriminative regularization term to induce a common latent subspace across domains .
In this paper , we rigorously validate that the discriminative regularizer actually coincides with the definition on the inner product in Reproducing Kernel [ email protected]?n Space ( RKKS ) naturally . As a result , we further present a new discriminality-driven regularization framework for indefinite kernel machine based on the discriminative regularizer . According to the framework , we firstly reintroduce the original DRLSC from the viewpoint of the proper indefinite kernelization rather than the empirical kernel mapping .
We propose discriminant GANs , where , for generated samples , we maximize between-class distance and minimize within-class distance , so that generated samples in different classes are more separable and different betweenclass distances are explicitly allowed . Empirical experiments show that ( 1 ) discriminant regularization improves the quality of generated samples , ( 2 ) discriminant regularized GANs extract efficient features for equipment health classification .
To enforce the Lipschitz constraint , instead of weight clipping strategy , recent work adds a gradient penalty term to the critic loss . In this paper , we combine a more discriminative gradient penalty term with the importance weighting strategy and further propose more effective algorithms for Lipschitz constraint enforcement of the critic in WGAN . Our algorithms do not adding any computation burden .
In the proposed DCCRC , the class discrimination information is fully utilized for promoting the true class of each testing sample to dominantly represent the testing sample during collaborative representation . The class discrimination information is well considered in the newly designed discriminative -norm regularization that can decrease the ability of representation from the interclasses of each testing sample . Simultaneously , a competitive -norm regularization is introduced to the DCCRC model with the class discrimination information with the aim of enhancing the competitive ability of representation from the true class of each testing sample .
We show that synergistically combining automatically-learned and handcrafted features can significantly improve the classification performance . Our method utilizes features extracted from convolutional neural networks ( CNNs ) , texture features learned via a discriminative sparsity-regularized approach , and hand-crafted statistical features . To assess the efficacy of different feature sets , we use AdaBoost with decision trees to classify prostate cancer lesions using different sets of features .
We propose a generative model training objective that uses a black-box discriminative model as a regularizer to learn representations that preserve this predictive variation . With these discriminatively regularized latent variable models , we visualize and measure variation in the data that influence a black-box predictive model , enabling an expert to better understand each prediction . With this technique , we study models that use electrocardiograms to predict outcomes of clinical interest .
We show it is in fact possible to train a language GAN from scratch -- without maximum likelihood pre-training . We combine existing techniques such as large batch sizes , dense rewards and discriminator regularization to stabilize and improve language GANs . The resulting model , ScratchGAN , performs comparably to maximum likelihood training on EMNLP2017 News and WikiText-103 corpora according to quality and diversity metrics .
Table [ reference ] shows that using more than one output neuron in the discriminator significantly improved the performance of repulsive loss over the one - neuron case on CIFAR - 10 dataset . The reason may be that using insufficient output neurons makes it harder for the discriminator to learn an injective and discriminative representation of the data ( see Fig . [ reference ] ) .
We first outline a novel generalized framework for adversarial adaptation , which subsumes recent state - of - the - art approaches as special cases , and we use this generalized view to better relate the prior approaches . We propose a previously unexplored instance of our general framework which combines discriminative modeling , untied weight sharing , and a GAN loss , which we call Adversarial Discriminative Domain Adaptation ( ADDA ) . We show that ADDA is more effective yet considerably simpler than competing domain - adversarial methods , and demonstrate the promise of our approach by exceeding state - of - the - art unsupervised adaptation results on standard cross - domain digit classification tasks and a new more difficult cross - modality object classification task .
Our framework unifies design choices such as weight - sharing , base models , and adversarial losses and subsumes previous work , while also facilitating the design of novel instantiations that improve upon existing ones . In particular , we observe that generative modeling of input image distributions is not necessary , as the ultimate task is to learn a discriminative representation . On the other hand , asymmetric mappings can better model the difference in low level features than symmetric ones .
The second type of CNN - based re - ID methods focuses on feature learning , which categorizes the training samples into pre - defined classes and the FC descriptor is used for retrieval . In , the classification CNN model is fine - tuned using either the video frames or image bounding boxes to learn a discriminative embedding for pedestrian retrieval . Xiao propose learning generic feature representations from multiple re - ID datasets jointly .
The network is trained using the Softmax loss function for multiclass classification using the Caffe deep learning platform . section : Learning a Discriminative Embedding In this section , we describe our algorithm for learning a low - dimensional embedding such that the resulting projections are more discriminative .
document : Semantic Instance Segmentation with a Discriminative Loss Function Semantic instance segmentation remains a challenging task .
Whereas these works employ a discriminative loss function to optimize distances between images in a dataset , our method operates at the pixel level , optimizing distances between individual pixels in an image . To our knowledge , we are the first to successfully use a discriminative loss based on distance metric learning principles for the task of instance segmentation with deep networks . section : Method
He et al . proposed the Mask R - CNN , which is extended from Faster R - CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition . Brabandere et al . proposed to tackle instance segmentation with a discriminative loss function , operating at the pixel level , which encourages a convolutional network to produce a representation of the image that can be easily clustered into instances with a simple post - processing step . However , these methods either only consider coarse semantic information or are agnostic to different instances .
RNNGs maintain the algorithmic convenience of transition - based parsing but incorporate top - down ( i.e. , root - to - terminal ) syntactic information ( § [ reference ] ) . The top - down transition set that RNNGs are based on lends itself to discriminative modeling as well , where sequences of transitions are modeled conditional on the full input sentence along with the incrementally constructed syntactic structures . Similar to previously published discriminative bottom - up transition - based parsers , greedy prediction with our model yields a linear - time deterministic parser ( provided an upper bound on the number of actions taken between processing subsequent terminal symbols is imposed ) ; however , our algorithm generates arbitrary tree structures directly , without the binarization required by shift - reduce parsers .
Though a single CNN model ( i.e. , DnCNN - B ) is trained in for Gaussian denoising , it does not generalize well to real noisy images and works only if the noise level is in the preset range , e.g. , . Besides , all the existing discriminative learning based methods lack flexibility to deal with spatially variant noise . To overcome the drawbacks of existing CNN based denoising methods , we present a fast and flexible denoising convolutional neural network ( FFDNet ) .
Therefore , the MAP solution of ( non - blind ) SISR can be formulated as where denotes the parameters of the MAP inference . By treating CNN as a discriminative learning solution to Eqn . ( [ reference ] )
For example , unsupervised feature learning is known to be beneficial for image restoration [ 5 ] and recent results show that it outperforms supervised feature learning also on descriptor matching [ 6 ] . In this work we combine the power of a discriminative objective with the major advantage of unsupervised feature learning : cheap data acquisition . We introduce a novel training procedure for convolutional neural networks that does not require any labeled data .
The unfolded maxout RNN architecture is depicted in Figure [ reference ] . The network is trained one hidden layer at a time with discriminative pretraining followed by 12 epochs of SGD CE training on randomized minibatches of 250 samples . The model is refined with Hessian - free sequence discriminative training using the state - based MBR criterion for 10 iterations .
This thesis combines these lines of research to develop new tools for learning in modular systems . As data-dependent regularization has been shown to be critical to success in deep modular systems , several significant contributions are provided in this area . A novel , differentiable formulation of sparse coding is presented and shown to be a powerful semi-supervised learning algorithm .
Data dependent regularization is known to benefit a wide variety of problems in machine learning . Often , these regularizers can not be easily decomposed into a sum over a finite number of terms , e.g. , a sum over individual example-wise terms .
It is a primary challenge in the fault diagnosis community of the gearbox to extract the weak fault features under heavy background noise and nonstationary conditions . For this purpose , a novel weak fault detection approach based on majorization – minimization ( MM ) and asymmetric convex penalty regularization ( ACPR ) is proposed in this paper . The proposed objective cost function ( OCF ) consisting of a signal-fidelity term , and two parameterized penalty terms ( i.e. , one is an asymmetric nonconvex penalty regularization term , and another is a symmetric nonconvex penalty regularization term).To begin with , the asymmetric and symmetric penalty functions are established on the basis of an L1-norm model , then , according to the splitting idea , the majorizer of the symmetric function and the majorizer of the asymmetric function are respectively calculated via the MM algorithm .
We present an expository , general analysis of valid post-selection or post-regularization inference about a low-dimensional target parameter in the presence of a very high-dimensional nuisance parameter that is estimated using selection or regularization methods . Our analysis provides a set of high-level conditions under which inference for the low-dimensional parameter based on testing or point estimation methods will be regular despite selection or regularization biases occurring in the estimation of the high-dimensional nuisance parameter .
For this purpose , supporting HR face is computed using the proposed ISLcR model . Further , reconstruction residual of both data fidelity is handled by the locality with sparsity regularization term . The use of both types of data fidelity and locality with sparsity regularization help in reduction of noise , generate a more discriminable outcome , and makes the process computationally viable .
We present a novel regularization-based Multitask Learning ( MTL ) formulation for Structured Output ( SO ) prediction for the case of hierarchical task relations . Structured output prediction often leads to difficult inference problems and hence requires large amounts of training data to obtain accurate models .
We also show that any cluster point of the sequence generated by the proposed algorithm converges to a stationary point . Numerical experiments demonstrate the efficiency of the proposed s-difference regularization in comparison with some other existing penalty functions .
We study the nonlocal total variation ( NL-TV ) regularization technique for image zooming , which exploits the spatial interactions in images . To solve the nonlinear Euler-Lagrange equation associated with the NL-TV regularization framework , we propose a split Bregman NL-TV image-zooming method . Furthermore , we give the convergence analysis of the proposed algorithm .
Abstract This paper presents an intelligent design methodology of microstrip filters in which a dynamic neural network model based on Bayesian Regularization Back-Propagation ( BRBP ) learning algorithm is used . In this approach , a Low-Pass Filter ( LPF ) composed of multiple open stubs , and stepped impedance resonators is initially designed for which an Artificial Neural Network ( ANN ) is trained to improve the performance of the filter .
In this paper , we propose an improved deterministic regularization algorithm to handle the sparse angle data problem in optical diffraction tomography . Based on optical diffraction tomography and the deterministic regularization algorithm , the regularization iteration is performed in the space domain and the frequency domain simultaneously , which greatly reduces the computational cost . By applying piecewise-smoothness and positivity constraints as the penalty function , the missing frequency spectrum is effectively recovered and the internal refractive index distribution of the specimen is accurately reconstructed .
The 1D convolution kernel size for character embedding is 5 . All weights are constraint by L2 regularization , and the L2 regularization at step is calculated as follows : where determines the maximum L2 regularization ratio , and determines at which step the maximum L2 regularization ratio would be applied on the L2 regularization . We choose as and as 100 , 000 .
We train and validate on all 20 tasks jointly using the 9 , 000 training and 1 , 000 validation samples defined in the en_valid_10k split . We use the Adam optimizer with a batch size of 512 , a learning rate of 2e - 4 and L2 regularization with a rate of 1e - 5 . We train for 5 M gradient steps .
We can see that HypER performs comparably or better than ConvE on 8 out of 11 relations in the dataset . To ensure that the difference between reported results for HypER and ConvE is not simply due to HypER having a reduced number of parameters ( implicit regularization ) , we trained ConvE reducing the number of feature maps to 16 instead of 32 to have a comparable number of parameters to HypER ( explicit regularization ) . This showed no improvement in ConvE results , indicating HypER ’s architecture does more than merely reducing the number of parameters .
LR - Bi - LSTM : LR_LSTM imposes linguistic roles into neural networks by applying linguistic regularization on intermediate outputs with KL divergence . Self - attention : self - attention proposes a self - attention mechanism to learn structured sentence embedding .
document : 3D MRI brain tumor segmentation using autoencoder regularization Automated segmentation of brain tumors from 3D magnetic resonance images ( MRIs ) is necessary for the diagnosis , monitoring , and treatment planning of the disease .
To represent the distributions effectively , we use the kernel embedding of distributions that hold high-order moment information about a distribution as an element in a reproducing kernel Hilbert space , which enables us to calculate the distance between the distributions without density estimation . The structural information encoded in the latent vectors are preserved by using the orthogonality regularizer . We demonstrate the effectiveness of the proposed method with experiments using real-world multi-lingual document-word relational datasets and multiple user-item relational datasets .
First , we construct an orthogonal initialization of weights . Second , to avoid destroying the orthogonality of the weights in the training process , orthogonality regularizations are proposed to maintain the orthogonality of weights . Simultaneously , a squeeze-and-excitation ( SE ) block is employed to perform feature recalibration across different channels .
After that , we build an objective function of the projected vectors to regularize their behaviours towards comparable eigenvalue magnitudes of WWT . Experimental results on training VGG16 for CIFAR10 show that PWR combined with centered weight normalization ( CWN ) yields promising validation performance compared to orthonormal regularisation combined with CWN .
Specifically , at each iteration , we first approximate (WW^T)^(-1/2 ) using its Taylor expansion before multiplying the matrix W. After that , the matrix product is then normalised by applying the spectral normalisation ( SN ) technique to obtain h(W ) . Conceptually speaking , AON is designed to turn orthonormal regularisation into orthonormal normalisation to avoid manual balancing the original and penalty functions . Experimental results show that AON yields promising validation performance compared to orthonormal regularisation .
In this work , we focus on the weight matrix learnt by the neural networks and hypothesize that ill conditioned weight matrix is one of the contributing factors in neural network 's susceptibility towards adversarial examples . For ensuring that the learnt weight matrix 's condition number remains sufficiently low , we suggest using orthogonal regularizer . We show that this indeed helps in increasing the adversarial accuracy on MNIST and F-MNIST datasets .
Following a popular method , minimizing the maximum overall error probability with respect to the selection matrix can be approximated by maximizing the minimum Chernoff distance between the distributions of the selected measurements under null hypothesis and alternative hypothesis to be detected . Then , we utilize Danskin ’s theorem to compute the gradient of the objective function of the converted maximization problem , and apply the orthogonal constraint-preserving gradient algorithm ( OCPGA ) to solve the relaxed maximization problem without 0/1 constraints . It is shown that the OCPGA can obtain a stationary point of the relaxed problem .
We compare three different weight orthogonalization techniques with regards to their convergence properties , their ability to ensure the Lipschitz condition and the achieved quality of the learned distribution . In addition , we provide a comparison to Wasserstein-GANs trained with current state-of-the-art methods , where we demonstrate the potential of solely using orthogonality-based regularization . In this context , we propose an improved training procedure for Wasserstein-GANs which utilizes orthogonalization to further increase its generalization capability .
To address this problem , this paper presents a novel robust dictionary learning framework via Bayesian inference . In particular , we employ the orthogonality-promoting regularization to mitigate correlations among dictionary atoms . Such a regularization , encouraging the dictionary atoms to be close to being orthogonal , can alleviate overfitting to training data and improve the discrimination of the model .
Similar to general multi-label image classification task , facial attribute recognition suffers from several typical problems : discriminative feature learning , handling the correlations among attributes , and imbalanced training data . In this paper , we propose a unified facial attribute recognition solution via feature decoupling and graph convolutional networks ( GCN ) : 1 ) We utilize the orthonormal regularizer to constrain that each dimension of the general facial representation represents a certain visual pattern . Meanwhile , a learnable matrix is unveiled , which can convert general facial feature into attribute-specific representation through modeling the importance of different visual patterns .
We sweep values and select , finding this small added penalty sufficient to improve the likelihood that our models will be amenable to truncation . Across runs in Table [ reference ] , we observe that without Orthogonal Regularization , only 16 % of models are amenable to truncation , compared to 60 % when trained with Orthogonal Regularization . subsection : Summary
Scalable and Sustainable Deep Learning via Randomized Hashing Factorized Approximate Inverses with Adaptive Dropping Employing H.264 Coarse and Medium Grain Scalable Video to Optimize Video Playback over Passive Optical Networks Adaptive dropout for training deep neural networks Automotive hybrid voltage regulator design with adaptive LDO dropout using load-sense technique Explore adaptive dropout deep computing and reinforcement learning to large-scale tasks processing for big data A High-Order CFS Algorithm for Clustering Big Data Soft Dropout And Its Variational Bayes Approximation System Efficiency Improvement Technique for Automotive Power Management IC Using Maximum Load Current Selector Circuit Feedback control for Wireless Networked Control Systems based on adaptive packet dropout Random Erasing Data Augmentation Shared Linear Encoder-based Gaussian Process Latent Variable Model for Visual Classification Semi-supervised classification by discriminative regularization Unsupervised domain adaptation with joint supervised sparse coding and discriminative regularization term Discriminality-driven regularization framework for indefinite kernel machine Discriminant Generative Adversarial Networks with its Application to Equipment Health Classification Effective Lipschitz constraint enforcement for Wasserstein GAN training Discriminative, Competitive, and Collaborative Representation-Based Classification with l2-Norm Regularizations Synergistic Combination of Learned and Hand-Crafted Features for Prostate Lesion Classification in Multiparametric Magnetic Resonance Imaging Discriminative Regularization for Latent Variable Models with Applications to Electrocardiography Training language GANs from Scratch Improving MMD-GAN Training with Repulsive Loss Function Adversarial Discriminative Domain Adaptation SVDNet for Pedestrian Retrieval Triplet probabilistic embedding for face verification and clustering Semantic Instance Segmentation with a Discriminative Loss Function Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing Recurrent Neural Network Grammars FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image Denoising Learning a Single Convolutional Super-Resolution Network for Multiple Degradations Discriminative Unsupervised Feature Learning with Convolutional Neural Networks The IBM 2016 English Conversational Telephone Speech Recognition System Learning in modular systems Optimizing Nondecomposable Data Dependent Regularizers via Lagrangian Reparameterization offers Significant Performance and Efficiency Gains Weak Fault Detection for Gearboxes Using Majorization-Minimization and Asymmetric Convex Penalty Regularization Valid Post-Selection and Post-Regularization Inference: An Elementary, General Approach Robust face super-resolution via iterative sparsity and locality-constrained representation Hierarchical Multitask Structured Output Learning for Large-scale Sequence Segmentation Sparse Optimization Problem with s-difference Regularization Image-zooming technique based on Bregmanized nonlocal total variation regularization A novel neural-based approach for design of microstrip filters Improved regularization reconstruction from sparse angle data in optical diffraction tomography. Natural Language Inference over Interaction Space Recurrent Relational Networks Hypernetwork Knowledge Graph Embeddings A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification 3D MRI brain tumor segmentation using autoencoder regularization Unsupervised Object Matching for Relational Data Orthogonal convolutional neural networks for automatic sleep stage classification based on single-channel EEG Projected Weight Regularization to Improve Neural Network Generalization Approximated Orthonormal Normalisation in Training Neural Networks Neural Networks in Adversarial Setting and Ill-Conditioned Weight Space A Gradient-Based Method for Robust Sensor Selection in Hypothesis Testing Orthogonal Wasserstein GANs Orthogonality-Promoting Dictionary Learning via Bayesian Inference Facial Attribute Recognition With Feature Decoupling and Graph Convolutional Networks Large Scale GAN Training for High Fidelity Natural Image Synthesis
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KDD NIPS APCCAS ICCC MLSP VLSI Design AAAI ACM Multimedia ICIP ICASSP MICCAI (3) ICML ICLR 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE International Conference on Computer Vision (ICCV) 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS) ArXiv ArXiv HLT-NAACL IEEE Transactions on Image Processing 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition NIPS INTERSPEECH NIPS ICLR NeurIPS ICANN ACL BrainLes@MICCAI ICASSP AAAI ICLR
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