For unlabeled data , Pseudo-Labels , just picking up the class which has the maximum predicted probability , are used as if they were true labels . This is in effect equivalent to Entropy Regularization . It favors a low-density separation between classes , a commonly assumed prior for semi-supervised learning . Recommendation techniques primarily based on user or item similarity can suffer from the risk that users can not get expected information from the over-specified recommendation lists . In this paper , we propose an entropy regularizer to capture the notion of diversity . The entropy regularizer has good properties in that it satisfies monotonicity and submodularity , such that when we combine it with a modular rating set function , we get submodular objective function , which can be maximized approximately by efficient greedy algorithm , with provable constant factor guarantee of optimality . To resolve this issue , we propose the first quantization-based semi-supervised image retrieval scheme : Generalized Product Quantization ( GPQ ) network . We design a novel metric learning strategy that preserves semantic similarity between labeled data , and employ entropy regularization term to fully exploit inherent potentials of unlabeled data . Our solution increases the generalization capacity of the quantization network , which allows overcoming previous limitations in the retrieval community . Each input word is encoded into two latent representations with different coverage of the local context , that are exploited to predict the type of entity and of relation to which the word belongs . Our model combines an entropy-based regularizer and a set of First-Order Logic formulas that bridge the predictions on entity and relation types accordingly to the ontology structure . As a result , the system generates symbolic descriptions of the raw text that are interpretable and well-suited to attach human-level knowledge . Specifically , to improve the representation ability , we innovatively employ the deep convolutional neural networks for ambiguously-labeled image classification , in which the wellknown ResNet is adopted as our backbone . To enhance the discrimination ability , we design an entropy-based regularizer to maximize the margin between the potentially correct label and the unlikely ones of each image . In addition , we utilize the temporally assembled predictions of different epochs to guide the training process so that the latent groundtruth label can be confidently highlighted . Given an arbitrary loss function , the proposed approach , termed Supervised Fuzzy Partitioning ( SFP ) , incorporates labels information into its objective function through a surrogate term penalizing the empirical risk . Entropy-based regularization is also employed to fuzzify the partition and to weight features , enabling the method to capture more complex patterns , identify significant features , and yield better performance facing high-dimensional data . An iterative algorithm based on block coordinate descent scheme is formulated to efficiently find a local optimum . This work addresses the problem of learning video event grammars by employing semi-supervised learning . As opposed to starting from a grammar corpus , this work introduces a novel method for incrementally augmenting the grammar from the unlabeled data through bootstrapping from a small labeled data-set and an entropy minimization-based regularization . This is more realistic in generic environments . In order to learn invariant and discriminative representations for both domains , we propose a Cross-Domain Minimization with Deep Autoencoder method for unsupervised domain adaptation , which simultaneously learns label prediction on the source domain and input reconstruction on the target domain using shared feature representations aligned with correlation alignment in a unified framework . Furthermore , inspired by adversarial training and cluster assumption , a task-specific class label discriminator is incorporated to confuse the predicted target class labels with samples draw from categorical distribution , which can be regarded as entropy minimization regularization . Extensive empirical results demonstrate the superiority of our approach over the state-of-the-art unsupervised adaptation methods on both visual and non-visual cross-domain adaptation tasks . In [ reference ] , the effect of pseudo - labels in a neural network was investigated . They argued that the effect of training a classifier with pseudo - labels is equivalent to entropy regularization , thus leading to a low - density separation between classes . In addition , in our experiment , we observe that target samples are separated in hidden features . In this section , we verify our theoretical results empirically on several synthetic and real datasets . In particular , we evaluate whether the discriminator obtained from the entropy - regularized adversarial training can capture the density information ( in the form of energy ) , while making sure the generator distribution matches the data distribution . For convenience , we refer to the obtained models as EGAN - Ent . We notice that even without employing suggested techniques in [ reference ] , energy - based models perform quite similarly to the GAN model . Furthermore , the fact that our model scores higher than EGAN - Const highlights the importance of entropy regularization in obtaining good quality samples . section : CONCLUSION Run time is about 100 times faster than real time . As in , we use cross - entropy regularization . In all the lattice - free MMI ( LFMMI ) experiments mentioned below we use a trigram language model . Run time is about 100 times faster than real time . As in , we use cross - entropy regularization . In all the lattice - free MMI ( LFMMI ) experiments mentioned below we use a trigram language model . The conjecture that fine - grained nonterminal rules and labels can be discovered given weaker bracketing structures was based on several studies . In a similar work , sangati:09 proposed entropy minimization and greedy familiarity maximization techniques to obtain lexical heads from labeled phrase - structure trees in an unsupervised manner . In contrast , we used neural attention to obtain the “ head rules ” in the GA - RNNG ; the whole model is trained end - to - end to maximize the log probability of the correct action given the history . A representation discriminator is introduced at each feature hierarchy to encourage the representation manifold of the generator to align with that of the bottom - up discriminative network , leveraging the powerful discriminative representations to guide the generative model . In addition , we introduce a conditional loss that encourages the use of conditional information from the layer above , and a novel entropy loss that maximizes a variational lower bound on the conditional entropy of generator outputs . We first train each stack independently , and then train the whole model end - to - end . In our experiments , we applied VAT to supervised and semi - supervised learning tasks on multiple benchmark datasets . With a simple enhancement of the algorithm based on the entropy minimization principle , our VAT achieves state - of - the - art performance for semi - supervised learning tasks on SVHN and CIFAR - 10 . section : INTRODUCTION We use an infinite latent feature model to automatically infer this number ( depending on and limited by only the number of tasks ) . Furthermore , our approach is applicable when the underlying task parameter subspace is inherently sparse , drawing parallels with ‘ 1 regularization and LASSO-style models . We also propose an augmented model which can make use of ( labeled , and additionally unlabeled if available ) inputs to assist learning this subspace , leading to further improvements in the performance . It relies on two main elements : namely , the lower frequencies are treated by projecting the known part of the signal to be extended onto the space generated by ' ' Prolate Spheroidal Wave Functions ' ' ( PSWF , as originally proposed by Slepian ) , whereas the higher ones can be handled by the recent so-called ' ' Compressive Sampling ' ' ( CS , proposed by Candes ) algorithms which are independent of the largeness of the bandwidth . Slepian functions are recalled and their numerical computation is explained in full detail , whereas @?^1 regularization techniques are summarized together with a recent iterative algorithm which has been proved to work efficiently on so-called ' ' compressible signals ' ' , which appear to match rather well the class of smooth bandlimited functions . Numerical results are displayed for both numerical techniques and the accuracy of the process consisting of putting them all together is studied for some test-signals showing a quite fast Fourier decay . An intrinsic limitation of previous tensor reduction methods is the sensitivity to the presence of outliers , because they minimize the sum of squares errors ( L2 norm ) . In this paper , we propose a novel robust tensor factorization method using R1 norm for error accumulation function using robust covariance matrices , allowing the method to be efficiently implemented instead of resorting to quadratic programming software packages as in other L1 norm approaches . Experimental results on face representation and reconstruction show that our new robust tensor factorization method can effectively handle outliers compared to previous tensor based PCA methods . However , these methods suffer from low robustness and recommendation accuracy because they use the squared error function as the loss function that can be easily dominated by large residuals . In this paper we propose a robust collaborative filtering method based on non-negative matrix factorization and R1-norm . Firstly , we introduce R1-norm to construct a robust non-negative matrix factorization model for collaborative filtering and make an analysis of the stability of the model . This paper implements the L1/2 regularization and 2 reweighted L1 regularization on the reconstruction of ISAR image under down sampling rate . The results specify that the reweighted L1 regularization performs better than L1 regularization and performs equally with L1/2 regularization . 2 In this paper , we show that lscr1 regularization is an effective technique for feature selection in conditional random fields . We present results from a multi-robot tag domain with data from both real and simulated robots that compare the classification accuracy of models trained with lscr1 regularization , which simultaneously smoothes the model and selects features ; lscr2 regularization , which smoothes to avoid over-fitting , but performs no feature selection ; and models trained with no smoothing . Convex loss minimization with lscr1 regularization has been proposed as a promising method for feature selection in classification ( e.g. , lscr1-regularized logistic regression ) and regression ( e.g. , lscr1-regularized least squares ) . In this paper we describe an efficient interior-point method for solving large-scale lscr1-regularized convex loss minimization problems that uses a preconditioned conjugate gradient method to compute the search step . In order to solve the ill-posed problem in the process of reconstructing the spectral reflectance of the traditional matrix R method , a regularization matrix R method was proposed in this paper . Through analyzing the ill-posed equation of matrix R to reconstruct the spectral reflectance , the Tikhonov regularization method was researched to restrict the ill-posed problem to solve the Moore-Penrose pseudo inverse matrix . The L-curve method was used to obtain the optimal regularization parameter by training samples data in order to effectively restrict the ill-posed situation which was caused by the equation solving of spectral reconstruction . The experimental results verified that the proposed regularization matrix R method had higher spectral and chromatic accuracy of reconstructed spectrum than traditional matrix R method . At the same time , the proposed regularization matrix R method achieved good performance for color reproduction of real mural in practical application . Moreover , it can also help in data understanding and interpretation . OSCAR is a recent sparse modeling tool that achieves this by using a ‘ 1-regularizer and a pairwise ‘ 1-regularizer . However , its optimization is computationally expensive . Another possibility is to develop parallel algorithms which compute multiple updates simultaneously . Recent studies have considered distributing coordinate descent algorithms for general ` 1-regularized minimization ( Scherrer et al. , 2012a;b ; Bradley et al. , 2011 ; Yu et al. , 2012 ) . These papers propose synchronous algorithms using either locks or synchronizing steps to ensure the convergence in general cases . We develop an online homotopy method to reduce the computational complexity . Numerical simulations demonstrate that the proposed algorithm outperforms the ` 1 regularized RLS algorithm for a group sparse system identification problem and has lower implementation complexity than direct group lasso solvers . However , there are always Gaussian noises in natural images . In this paper , we propose a regularization LR-algorithm based on the Gaussian noises model . Our algorithm involves two regularization methods . As an implementation test case , the proposed approach was aggregated in a HW/SW co-design scheme in order to solve the nonlinear ill-posed inverse problem of nonparametric estimation of the power spatial spectrum pattern ( SSP ) from a remotely sensed scene . We show how such dual SSA core , drastically reduces the computational load of complex RS regularization techniques achieving the required real-time operational mode . The subject of our study is the latter . Regularization is a process of introducing additional information in order to manage this inevitable gap between the training error and the test error . In this study , we introduce a novel regularization method applicable to semisupervised learning that identifies the direction in which the classifier 's behavior is most sensitive . 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 . Recently , a regularization method named “ DisturbLabel ” is introduced by adding noise at the loss layer . However , in more recent work vanilla LSTMs ( ) with relatively large number of parameters have been shown to achieve state - of - the - art performance on several standard benchmark datasets both in word - level and character - level perplexity ( ) . A key component in these models is the use of several forms of regularization e.g. variational dropout on the token embeddings ( ) , dropout on the hidden - to - hidden weights in the LSTM ( ) , norm regularization on the outputs of the LSTM and classical dropout ( ) . By carefully tuning the hyperparameters associated with these regularizers combined with optimization algorithms like NT - ASGD ( a variant of the Averaged SGD ) , it is possible to achieve very good performance . Ideally all three terms ( center , size , heading ) should be jointly optimized for best 3D box estimation ( under IoU metric ) . To resolve this problem we propose a novel regularization loss , the corner loss : In essence , the corner loss is the sum of the distances between the eight corners of a predicted box and a ground truth box . Since corner positions are jointly determined by center , size and heading , the corner loss is able to regularize the multi - task training for those parameters . Finally , we comment on the regularizing effect of the Weighted Transformer . Given the improved results , a natural question is whether the results stem from improved regularization of the model . To investigate this , we report the testing loss of the Weighted Transformer and the baseline Transformer against the training loss in Figure [ reference ] . As the Penn Treebank is a relatively small dataset , preventing overfitting is of considerable importance and a major focus of recent research . It is not obvious in advance which of the many RNN regularization schemes would perform well when applied to the QRNN . Our tests showed encouraging results from zoneout applied to the QRNN ’s recurrent pooling layer , implemented as described in Section [ reference ] . The proposed phase-LASSO approach models not only the phase characteristics but also the within-phase and between-phase time-dependent information . In the meantime , it automatically selects the critical-to-quality phases via LASSO-type regularization . As a result , phase-LASSO has good prediction accuracy and model interpretation . Abstract A state-constrained optimal control problem with nonlocal radiation interface conditions arising from the modeling of crystal growth processes is considered . The problem is approximated by a Moreau-Yosida type regularization . Optimality conditions for the regularized problem are derived and the convergence of the regularized problems is shown . The majority of the degraded pixels are filled using multi-temporal images as referable information by building a regression model between the corresponding pixels . When the auxiliary multi-temporal data can not completely recover the missing pixels , a non-reference regularization algorithm is used to implement the pixel filling . To assess the efficacy of the proposed method , simulated and actual SLC-off ETM + images were tested . Various restoration methods are used . A new HSI restoration method based on low-rank matrix recovery ( LRMR ) is introduced , which can simultaneously remove the Gaussian noise , impulse noise , deadlines , and stripes but there is no spatial constraint applied on neighbouring pixels that causes large areas of missing pixels .To handle the problem of missing pixels non-reference regularization algorithm is used . However , the investigation of SRC for machinery vibration signals shows that the accuracy and sparsity concentration index are not high enough . In this paper , a new classification method for machinery vibration signals is proposed , in which the L1L2-norm regularization based sparse representation , i.e. group sparse representation , is recommended as a coding strategy . The method achieves its idea classification performance by three steps . We put forward a new variation model to solve this problem . In this model , a nonconvex weighted regularization is used in which the regularization parameters can change adaptively according to the noise level . We present the advantages of this model in detail . OBJECTIVE A new algorithm of adaptive super-resolution ( SR ) reconstruction based on the regularization parameter is proposed to reconstruct a high-resolution ( HR ) image from the low-resolution ( LR ) image sequence , which takes into full account the inaccurate estimates of motion error , point spread function ( PSF ) and the additive Gaussian noise in the LR image sequence . We established a novel nonlinear adaptive regularization function and analyzed experimentally its convexity to obtain the adaptive step size . This novel algorithm can effectively improve the spatial resolution of the image and the rate of convergence , which is verified by the experiment on optical images . We focus on the problem of low-rank Mahalanobis metric learning , where our objective is to learn an n × m projection matrix A , where m ≪ n. Low-rank metrics offer a “ plug-in ” enhancement to similarity-based classifiers that can reduce computation time and improve classification accuracy with fewer training samples , enabling operations in resource-constrained environments such as onboard spacecraft . Our results indicate that applying a simple shrinkage-based regularization procedure to multiclass Linear Discriminant Analysis ( LDA ) produces comparable or better classification accuracies than the low-rank extensions of several widely used Mahalanobis metric learning algorithms , at considerably lower computational cost . In this dissertation , we cover some recent advances in collaborative filtering and ranking . In chapter 1 , we give a brief introduction of the history and the current landscape of collaborative filtering and ranking ; chapter 2 we first talk about pointwise collaborative filtering problem with graph information , and how our proposed new method can encode very deep graph information which helps four existing graph collaborative filtering algorithms ; chapter 3 is on the pairwise approach for collaborative ranking and how we speed up the algorithm to near-linear time complexity ; chapter 4 is on the new listwise approach for collaborative ranking and how the listwise approach is a better choice of loss for both explicit and implicit feedback over pointwise and pairwise loss ; chapter 5 is about the new regularization technique Stochastic Shared Embeddings ( SSE ) we proposed for embedding layers and how it is both theoretically sound and empirically effectively for 6 different tasks across recommendation and natural language processing ; chapter 6 is how we introduce personalization for the state-of-the-art sequential recommendation model with the help of SSE , which plays an important role in preventing our personalized model from overfitting to the training data ; chapter 7 , we summarize what we have achieved so far and predict what the future directions can be ; chapter 8 is the appendix to all the chapters . The objective ( risk ) functional depends on functions from this RKHS and takes the form of a mathematical expectation ( integral ) of a nonnegative integrand ( loss function ) over a probability measure . The problem is generally ill-posed , a difficulty that in statistical learning is addressed through Tihonov regularization , with Monte Carlo approximation of integrals , which also makes it possible to solve the problem by finite dimensional ( convex ) quadratic optimization . The approximate solutions are referred to as kernel learning estimators and are expressed as a linear combination of kernels evaluated at the sample points . The third term is the mutual information between the latent code and the input . This is a regularization term that encourages and to be decoupled by removing the information of the data distribution from the hidden code . If the training set has examples , is bounded as follows ( see surgery ) . In the meantime , ensemble learning has become commonly used in several neural models , to improve robustness by reducing bias and variance . This paper is an expanded version of , with the following additional material : A comprehensive assessment of human performance on the NIST eval 2000 test set The description of a novel spatial regularization method which significantly boosts our LSTM acoustic model performance The use of LSTM rather than RNN - LMs , and the use of a letter - trigram input representation A two - level system combination , based on a subsystem of BLSTM - system variants that , by itself , surpasses the best previously reported results Expanded coverage of the Microsoft Cognitive Toolkit ( CNTK ) used to build our models An analysis of human versus machine errors , which indicates substantial equivalence , with the exception of the word classes of backchannel acknowledgments ( e.g. “ uh - huh ” ) and hesitations ( e.g. We improve on these architectures with the LACE model , which iteratively expands the effective window size , layer - by - layer , and adds an attention mask to differentially weight distant context . Our spatial regularization technique is similar in spirit to stimulated deep neural networks . Whereas stimulated networks use a supervision signal to encourage locality of activations in the model , our technique is automatic . Both works incorporate morphological and contextual features to better capture the heterogeneity of lesions . also incorporate brain structure segmentation results obtained from a multi - atlas label propagation approach ( ) to provide strong tissue - class priors to the Random Forests . additionally use a Markov Random Field ( MRF ) to incorporate spatial regularization . MRFs are commonly used to encourage spatial continuity of the segmentation ( ) . Even though TuckER has more parameters than some more simpler linear models ( DistMult , ComplEx and SimplE ) due to the presence of core tensor ( containing million parameters for FB15k and FB15k - 237 and million parameters for WN18 and WN18RR ) , it consistently obtains better results than any of those models . We believe this is achieved by exploiting knowledge sharing between relations through the core tensor and implicit regularization from dropout , which allows the model to learn which parameters to ignore rather than explicitly setting them to 0 . We find the value of the dropout parameter to have a significant influence on results , with lower dropout values required for datasets with a higher number of training triples per relation and thus less risk of overfitting ( WN18 and WN18RR ) and higher dropout values required for datasets with a large number of relations ( FB15k and FB15k - 237 ) . An explanation of why this scheme works is that blending these two trainings would help GAN get rid of some bad local minimums and alleviate mode collapse . Another justification is that the inserted supervised learning performs an implicit regularization on the generative model to prevent it from going too far away from the MLE solution . Temperature Control . however , when the MMD kernel is not based directly on image pixels , but on learned features of images . Wasserstein - inspired gradient regularization approaches can be used on the MMD critic when learning these features : [ reference ] uses weight clipping [ reference ] , and [ reference ][ reference ] use a gradient penalty [ reference ] . The recent Sobolev GAN [ reference ] uses a similar constraint on the expected gradient norm , but phrases it as estimating a Sobolev IPM rather than loosely approximating Wasserstein . ( 6 ) to the 2 - norm constraint , with least - squares minimization as the following equation , Based on the ridge regression [ reference ] theory , this 2 - norm constrained least square regression regularized problem has a closed - form solution , according to the Tikhonov regularization theory , as follows : With the assumption in [ reference ] , where HR patches and their counterpart LR patches share the same reconstructed coefficient α , i.e. = D ℎ , from Eqn . We used a batch - size of 50 and randomly shuffled the training data at every epoch , while sorting every 10 batches according to their lengths to speed up training . We did not use any dropout or regularization , but applied gradient clipping . We used early stopping based on the validation set and used the best model on the validation set to report all test performance numbers . Our best performance on a held - out development set was achieved using a four - layer densely - connected QRNN with 256 units per layer and word vectors initialized using 300 - dimensional cased GloVe embeddings Pennington2014 . Dropout of 0.3 was applied between layers , and we used regularization of . Optimization was performed on minibatches of 24 examples using RMSprop Tieleman2012 with learning rate of , , and . This method , while increasing data diversity against over - fitting , also incurs a considerable level of noise . In the effort to alleviate the impact of noise , the label smooth regularization ( LSR ) is adopted . The vanilla version of our method ( without LSR ) performs reasonably well on few - camera systems in which over - fitting often occurs . the total population . While our work is not the first attempt of this regularization idea , to our knowledge we are the first to apply it to the pain expression intensity estimation . Correspondingly , we propose three solutions to address the four issues mentioned above . This yields two loss values at each time step given by , and . Then the overall loss function of fraternal dropout is given by , where is the regularization coefficient , is the dimensions of and is the fraternal dropout regularization given by , We use Monte Carlo sampling to approximate where and are the same as the one used to calculate values . We mitigate the first problem by introducing a random variable on sample scaling parameters , whose distribution ’s parameters are jointly learnt with CNN and leads to what we call adaptive multi-scale sampling ( AMS ) . To address the second problem , we propose activation pattern regularization ( APR ) on only samples with the common causes such that CNN focuses on learning representations pertaining to the common factor . We demonstrate the effectiveness of both proposals in extensive experiments on real-world datasets . This paper presents a regularization scheme acting on the activation function output to improve the network interpretability and regularization . The proposed approach , referred to as activation regularization , encourages activation function outputs to satisfy a target pattern . By defining appropriate target patterns , different learning concepts can be imposed on the network . This method can aid network interpretability and also has the potential to reduce overfitting . The scheme is evaluated on several continuous speech recognition tasks : the Wall Street Journal continuous speech recognition task , eight conversational telephone speech tasks from the IARPA Babel program and a U.S. English broadcast news task . On all the tasks , the activation regularization achieved consistent performance gains over the standard DNN baselines . How much do we benefit from data augmentation ? What activation and regularization schemes are more beneficial ? How does the depth of the network affect the performance ? 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 .
For unlabeled data , Pseudo-Labels , just picking up the class which has the maximum predicted probability , are used as if they were true labels . This is in effect equivalent to Entropy Regularization . It favors a low-density separation between classes , a commonly assumed prior for semi-supervised learning .
Recommendation techniques primarily based on user or item similarity can suffer from the risk that users can not get expected information from the over-specified recommendation lists . In this paper , we propose an entropy regularizer to capture the notion of diversity . The entropy regularizer has good properties in that it satisfies monotonicity and submodularity , such that when we combine it with a modular rating set function , we get submodular objective function , which can be maximized approximately by efficient greedy algorithm , with provable constant factor guarantee of optimality .
To resolve this issue , we propose the first quantization-based semi-supervised image retrieval scheme : Generalized Product Quantization ( GPQ ) network . We design a novel metric learning strategy that preserves semantic similarity between labeled data , and employ entropy regularization term to fully exploit inherent potentials of unlabeled data . Our solution increases the generalization capacity of the quantization network , which allows overcoming previous limitations in the retrieval community .
Each input word is encoded into two latent representations with different coverage of the local context , that are exploited to predict the type of entity and of relation to which the word belongs . Our model combines an entropy-based regularizer and a set of First-Order Logic formulas that bridge the predictions on entity and relation types accordingly to the ontology structure . As a result , the system generates symbolic descriptions of the raw text that are interpretable and well-suited to attach human-level knowledge .
Specifically , to improve the representation ability , we innovatively employ the deep convolutional neural networks for ambiguously-labeled image classification , in which the wellknown ResNet is adopted as our backbone . To enhance the discrimination ability , we design an entropy-based regularizer to maximize the margin between the potentially correct label and the unlikely ones of each image . In addition , we utilize the temporally assembled predictions of different epochs to guide the training process so that the latent groundtruth label can be confidently highlighted .
Given an arbitrary loss function , the proposed approach , termed Supervised Fuzzy Partitioning ( SFP ) , incorporates labels information into its objective function through a surrogate term penalizing the empirical risk . Entropy-based regularization is also employed to fuzzify the partition and to weight features , enabling the method to capture more complex patterns , identify significant features , and yield better performance facing high-dimensional data . An iterative algorithm based on block coordinate descent scheme is formulated to efficiently find a local optimum .
This work addresses the problem of learning video event grammars by employing semi-supervised learning . As opposed to starting from a grammar corpus , this work introduces a novel method for incrementally augmenting the grammar from the unlabeled data through bootstrapping from a small labeled data-set and an entropy minimization-based regularization . This is more realistic in generic environments .
In order to learn invariant and discriminative representations for both domains , we propose a Cross-Domain Minimization with Deep Autoencoder method for unsupervised domain adaptation , which simultaneously learns label prediction on the source domain and input reconstruction on the target domain using shared feature representations aligned with correlation alignment in a unified framework . Furthermore , inspired by adversarial training and cluster assumption , a task-specific class label discriminator is incorporated to confuse the predicted target class labels with samples draw from categorical distribution , which can be regarded as entropy minimization regularization . Extensive empirical results demonstrate the superiority of our approach over the state-of-the-art unsupervised adaptation methods on both visual and non-visual cross-domain adaptation tasks .
In [ reference ] , the effect of pseudo - labels in a neural network was investigated . They argued that the effect of training a classifier with pseudo - labels is equivalent to entropy regularization , thus leading to a low - density separation between classes . In addition , in our experiment , we observe that target samples are separated in hidden features .
In this section , we verify our theoretical results empirically on several synthetic and real datasets . In particular , we evaluate whether the discriminator obtained from the entropy - regularized adversarial training can capture the density information ( in the form of energy ) , while making sure the generator distribution matches the data distribution . For convenience , we refer to the obtained models as EGAN - Ent .
We notice that even without employing suggested techniques in [ reference ] , energy - based models perform quite similarly to the GAN model . Furthermore , the fact that our model scores higher than EGAN - Const highlights the importance of entropy regularization in obtaining good quality samples . section : CONCLUSION
Run time is about 100 times faster than real time . As in , we use cross - entropy regularization . In all the lattice - free MMI ( LFMMI ) experiments mentioned below we use a trigram language model .
Run time is about 100 times faster than real time . As in , we use cross - entropy regularization . In all the lattice - free MMI ( LFMMI ) experiments mentioned below we use a trigram language model .
The conjecture that fine - grained nonterminal rules and labels can be discovered given weaker bracketing structures was based on several studies . In a similar work , sangati:09 proposed entropy minimization and greedy familiarity maximization techniques to obtain lexical heads from labeled phrase - structure trees in an unsupervised manner . In contrast , we used neural attention to obtain the “ head rules ” in the GA - RNNG ; the whole model is trained end - to - end to maximize the log probability of the correct action given the history .
A representation discriminator is introduced at each feature hierarchy to encourage the representation manifold of the generator to align with that of the bottom - up discriminative network , leveraging the powerful discriminative representations to guide the generative model . In addition , we introduce a conditional loss that encourages the use of conditional information from the layer above , and a novel entropy loss that maximizes a variational lower bound on the conditional entropy of generator outputs . We first train each stack independently , and then train the whole model end - to - end .
In our experiments , we applied VAT to supervised and semi - supervised learning tasks on multiple benchmark datasets . With a simple enhancement of the algorithm based on the entropy minimization principle , our VAT achieves state - of - the - art performance for semi - supervised learning tasks on SVHN and CIFAR - 10 . section : INTRODUCTION
We use an infinite latent feature model to automatically infer this number ( depending on and limited by only the number of tasks ) . Furthermore , our approach is applicable when the underlying task parameter subspace is inherently sparse , drawing parallels with ‘ 1 regularization and LASSO-style models . We also propose an augmented model which can make use of ( labeled , and additionally unlabeled if available ) inputs to assist learning this subspace , leading to further improvements in the performance .
It relies on two main elements : namely , the lower frequencies are treated by projecting the known part of the signal to be extended onto the space generated by ' ' Prolate Spheroidal Wave Functions ' ' ( PSWF , as originally proposed by Slepian ) , whereas the higher ones can be handled by the recent so-called ' ' Compressive Sampling ' ' ( CS , proposed by Candes ) algorithms which are independent of the largeness of the bandwidth . Slepian functions are recalled and their numerical computation is explained in full detail , whereas @?^1 regularization techniques are summarized together with a recent iterative algorithm which has been proved to work efficiently on so-called ' ' compressible signals ' ' , which appear to match rather well the class of smooth bandlimited functions . Numerical results are displayed for both numerical techniques and the accuracy of the process consisting of putting them all together is studied for some test-signals showing a quite fast Fourier decay .
An intrinsic limitation of previous tensor reduction methods is the sensitivity to the presence of outliers , because they minimize the sum of squares errors ( L2 norm ) . In this paper , we propose a novel robust tensor factorization method using R1 norm for error accumulation function using robust covariance matrices , allowing the method to be efficiently implemented instead of resorting to quadratic programming software packages as in other L1 norm approaches . Experimental results on face representation and reconstruction show that our new robust tensor factorization method can effectively handle outliers compared to previous tensor based PCA methods .
However , these methods suffer from low robustness and recommendation accuracy because they use the squared error function as the loss function that can be easily dominated by large residuals . In this paper we propose a robust collaborative filtering method based on non-negative matrix factorization and R1-norm . Firstly , we introduce R1-norm to construct a robust non-negative matrix factorization model for collaborative filtering and make an analysis of the stability of the model .
This paper implements the L1/2 regularization and 2 reweighted L1 regularization on the reconstruction of ISAR image under down sampling rate . The results specify that the reweighted L1 regularization performs better than L1 regularization and performs equally with L1/2 regularization . 2
In this paper , we show that lscr1 regularization is an effective technique for feature selection in conditional random fields . We present results from a multi-robot tag domain with data from both real and simulated robots that compare the classification accuracy of models trained with lscr1 regularization , which simultaneously smoothes the model and selects features ; lscr2 regularization , which smoothes to avoid over-fitting , but performs no feature selection ; and models trained with no smoothing .
Convex loss minimization with lscr1 regularization has been proposed as a promising method for feature selection in classification ( e.g. , lscr1-regularized logistic regression ) and regression ( e.g. , lscr1-regularized least squares ) . In this paper we describe an efficient interior-point method for solving large-scale lscr1-regularized convex loss minimization problems that uses a preconditioned conjugate gradient method to compute the search step .
In order to solve the ill-posed problem in the process of reconstructing the spectral reflectance of the traditional matrix R method , a regularization matrix R method was proposed in this paper . Through analyzing the ill-posed equation of matrix R to reconstruct the spectral reflectance , the Tikhonov regularization method was researched to restrict the ill-posed problem to solve the Moore-Penrose pseudo inverse matrix . The L-curve method was used to obtain the optimal regularization parameter by training samples data in order to effectively restrict the ill-posed situation which was caused by the equation solving of spectral reconstruction . The experimental results verified that the proposed regularization matrix R method had higher spectral and chromatic accuracy of reconstructed spectrum than traditional matrix R method . At the same time , the proposed regularization matrix R method achieved good performance for color reproduction of real mural in practical application .
Moreover , it can also help in data understanding and interpretation . OSCAR is a recent sparse modeling tool that achieves this by using a ‘ 1-regularizer and a pairwise ‘ 1-regularizer . However , its optimization is computationally expensive .
Another possibility is to develop parallel algorithms which compute multiple updates simultaneously . Recent studies have considered distributing coordinate descent algorithms for general ` 1-regularized minimization ( Scherrer et al. , 2012a;b ; Bradley et al. , 2011 ; Yu et al. , 2012 ) . These papers propose synchronous algorithms using either locks or synchronizing steps to ensure the convergence in general cases .
We develop an online homotopy method to reduce the computational complexity . Numerical simulations demonstrate that the proposed algorithm outperforms the ` 1 regularized RLS algorithm for a group sparse system identification problem and has lower implementation complexity than direct group lasso solvers .
However , there are always Gaussian noises in natural images . In this paper , we propose a regularization LR-algorithm based on the Gaussian noises model . Our algorithm involves two regularization methods .
As an implementation test case , the proposed approach was aggregated in a HW/SW co-design scheme in order to solve the nonlinear ill-posed inverse problem of nonparametric estimation of the power spatial spectrum pattern ( SSP ) from a remotely sensed scene . We show how such dual SSA core , drastically reduces the computational load of complex RS regularization techniques achieving the required real-time operational mode .
The subject of our study is the latter . Regularization is a process of introducing additional information in order to manage this inevitable gap between the training error and the test error . In this study , we introduce a novel regularization method applicable to semisupervised learning that identifies the direction in which the classifier 's behavior is most sensitive .
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 . Recently , a regularization method named “ DisturbLabel ” is introduced by adding noise at the loss layer .
However , in more recent work vanilla LSTMs ( ) with relatively large number of parameters have been shown to achieve state - of - the - art performance on several standard benchmark datasets both in word - level and character - level perplexity ( ) . A key component in these models is the use of several forms of regularization e.g. variational dropout on the token embeddings ( ) , dropout on the hidden - to - hidden weights in the LSTM ( ) , norm regularization on the outputs of the LSTM and classical dropout ( ) . By carefully tuning the hyperparameters associated with these regularizers combined with optimization algorithms like NT - ASGD ( a variant of the Averaged SGD ) , it is possible to achieve very good performance .
Ideally all three terms ( center , size , heading ) should be jointly optimized for best 3D box estimation ( under IoU metric ) . To resolve this problem we propose a novel regularization loss , the corner loss : In essence , the corner loss is the sum of the distances between the eight corners of a predicted box and a ground truth box . Since corner positions are jointly determined by center , size and heading , the corner loss is able to regularize the multi - task training for those parameters .
Finally , we comment on the regularizing effect of the Weighted Transformer . Given the improved results , a natural question is whether the results stem from improved regularization of the model . To investigate this , we report the testing loss of the Weighted Transformer and the baseline Transformer against the training loss in Figure [ reference ] .
As the Penn Treebank is a relatively small dataset , preventing overfitting is of considerable importance and a major focus of recent research . It is not obvious in advance which of the many RNN regularization schemes would perform well when applied to the QRNN . Our tests showed encouraging results from zoneout applied to the QRNN ’s recurrent pooling layer , implemented as described in Section [ reference ] .
The proposed phase-LASSO approach models not only the phase characteristics but also the within-phase and between-phase time-dependent information . In the meantime , it automatically selects the critical-to-quality phases via LASSO-type regularization . As a result , phase-LASSO has good prediction accuracy and model interpretation .
Abstract A state-constrained optimal control problem with nonlocal radiation interface conditions arising from the modeling of crystal growth processes is considered . The problem is approximated by a Moreau-Yosida type regularization . Optimality conditions for the regularized problem are derived and the convergence of the regularized problems is shown .
The majority of the degraded pixels are filled using multi-temporal images as referable information by building a regression model between the corresponding pixels . When the auxiliary multi-temporal data can not completely recover the missing pixels , a non-reference regularization algorithm is used to implement the pixel filling . To assess the efficacy of the proposed method , simulated and actual SLC-off ETM + images were tested .
Various restoration methods are used . A new HSI restoration method based on low-rank matrix recovery ( LRMR ) is introduced , which can simultaneously remove the Gaussian noise , impulse noise , deadlines , and stripes but there is no spatial constraint applied on neighbouring pixels that causes large areas of missing pixels .To handle the problem of missing pixels non-reference regularization algorithm is used .
However , the investigation of SRC for machinery vibration signals shows that the accuracy and sparsity concentration index are not high enough . In this paper , a new classification method for machinery vibration signals is proposed , in which the L1L2-norm regularization based sparse representation , i.e. group sparse representation , is recommended as a coding strategy . The method achieves its idea classification performance by three steps .
We put forward a new variation model to solve this problem . In this model , a nonconvex weighted regularization is used in which the regularization parameters can change adaptively according to the noise level . We present the advantages of this model in detail .
OBJECTIVE A new algorithm of adaptive super-resolution ( SR ) reconstruction based on the regularization parameter is proposed to reconstruct a high-resolution ( HR ) image from the low-resolution ( LR ) image sequence , which takes into full account the inaccurate estimates of motion error , point spread function ( PSF ) and the additive Gaussian noise in the LR image sequence . We established a novel nonlinear adaptive regularization function and analyzed experimentally its convexity to obtain the adaptive step size . This novel algorithm can effectively improve the spatial resolution of the image and the rate of convergence , which is verified by the experiment on optical images .
We focus on the problem of low-rank Mahalanobis metric learning , where our objective is to learn an n × m projection matrix A , where m ≪ n. Low-rank metrics offer a “ plug-in ” enhancement to similarity-based classifiers that can reduce computation time and improve classification accuracy with fewer training samples , enabling operations in resource-constrained environments such as onboard spacecraft . Our results indicate that applying a simple shrinkage-based regularization procedure to multiclass Linear Discriminant Analysis ( LDA ) produces comparable or better classification accuracies than the low-rank extensions of several widely used Mahalanobis metric learning algorithms , at considerably lower computational cost .
In this dissertation , we cover some recent advances in collaborative filtering and ranking . In chapter 1 , we give a brief introduction of the history and the current landscape of collaborative filtering and ranking ; chapter 2 we first talk about pointwise collaborative filtering problem with graph information , and how our proposed new method can encode very deep graph information which helps four existing graph collaborative filtering algorithms ; chapter 3 is on the pairwise approach for collaborative ranking and how we speed up the algorithm to near-linear time complexity ; chapter 4 is on the new listwise approach for collaborative ranking and how the listwise approach is a better choice of loss for both explicit and implicit feedback over pointwise and pairwise loss ; chapter 5 is about the new regularization technique Stochastic Shared Embeddings ( SSE ) we proposed for embedding layers and how it is both theoretically sound and empirically effectively for 6 different tasks across recommendation and natural language processing ; chapter 6 is how we introduce personalization for the state-of-the-art sequential recommendation model with the help of SSE , which plays an important role in preventing our personalized model from overfitting to the training data ; chapter 7 , we summarize what we have achieved so far and predict what the future directions can be ; chapter 8 is the appendix to all the chapters .
The objective ( risk ) functional depends on functions from this RKHS and takes the form of a mathematical expectation ( integral ) of a nonnegative integrand ( loss function ) over a probability measure . The problem is generally ill-posed , a difficulty that in statistical learning is addressed through Tihonov regularization , with Monte Carlo approximation of integrals , which also makes it possible to solve the problem by finite dimensional ( convex ) quadratic optimization . The approximate solutions are referred to as kernel learning estimators and are expressed as a linear combination of kernels evaluated at the sample points .
The third term is the mutual information between the latent code and the input . This is a regularization term that encourages and to be decoupled by removing the information of the data distribution from the hidden code . If the training set has examples , is bounded as follows ( see surgery ) .
In the meantime , ensemble learning has become commonly used in several neural models , to improve robustness by reducing bias and variance . This paper is an expanded version of , with the following additional material : A comprehensive assessment of human performance on the NIST eval 2000 test set The description of a novel spatial regularization method which significantly boosts our LSTM acoustic model performance The use of LSTM rather than RNN - LMs , and the use of a letter - trigram input representation A two - level system combination , based on a subsystem of BLSTM - system variants that , by itself , surpasses the best previously reported results Expanded coverage of the Microsoft Cognitive Toolkit ( CNTK ) used to build our models An analysis of human versus machine errors , which indicates substantial equivalence , with the exception of the word classes of backchannel acknowledgments ( e.g. “ uh - huh ” ) and hesitations ( e.g.
We improve on these architectures with the LACE model , which iteratively expands the effective window size , layer - by - layer , and adds an attention mask to differentially weight distant context . Our spatial regularization technique is similar in spirit to stimulated deep neural networks . Whereas stimulated networks use a supervision signal to encourage locality of activations in the model , our technique is automatic .
Both works incorporate morphological and contextual features to better capture the heterogeneity of lesions . also incorporate brain structure segmentation results obtained from a multi - atlas label propagation approach ( ) to provide strong tissue - class priors to the Random Forests . additionally use a Markov Random Field ( MRF ) to incorporate spatial regularization . MRFs are commonly used to encourage spatial continuity of the segmentation ( ) .
Even though TuckER has more parameters than some more simpler linear models ( DistMult , ComplEx and SimplE ) due to the presence of core tensor ( containing million parameters for FB15k and FB15k - 237 and million parameters for WN18 and WN18RR ) , it consistently obtains better results than any of those models . We believe this is achieved by exploiting knowledge sharing between relations through the core tensor and implicit regularization from dropout , which allows the model to learn which parameters to ignore rather than explicitly setting them to 0 . We find the value of the dropout parameter to have a significant influence on results , with lower dropout values required for datasets with a higher number of training triples per relation and thus less risk of overfitting ( WN18 and WN18RR ) and higher dropout values required for datasets with a large number of relations ( FB15k and FB15k - 237 ) .
An explanation of why this scheme works is that blending these two trainings would help GAN get rid of some bad local minimums and alleviate mode collapse . Another justification is that the inserted supervised learning performs an implicit regularization on the generative model to prevent it from going too far away from the MLE solution . Temperature Control .
however , when the MMD kernel is not based directly on image pixels , but on learned features of images . Wasserstein - inspired gradient regularization approaches can be used on the MMD critic when learning these features : [ reference ] uses weight clipping [ reference ] , and [ reference ][ reference ] use a gradient penalty [ reference ] . The recent Sobolev GAN [ reference ] uses a similar constraint on the expected gradient norm , but phrases it as estimating a Sobolev IPM rather than loosely approximating Wasserstein .
( 6 ) to the 2 - norm constraint , with least - squares minimization as the following equation , Based on the ridge regression [ reference ] theory , this 2 - norm constrained least square regression regularized problem has a closed - form solution , according to the Tikhonov regularization theory , as follows : With the assumption in [ reference ] , where HR patches and their counterpart LR patches share the same reconstructed coefficient α , i.e. = D ℎ , from Eqn .
We used a batch - size of 50 and randomly shuffled the training data at every epoch , while sorting every 10 batches according to their lengths to speed up training . We did not use any dropout or regularization , but applied gradient clipping . We used early stopping based on the validation set and used the best model on the validation set to report all test performance numbers .
Our best performance on a held - out development set was achieved using a four - layer densely - connected QRNN with 256 units per layer and word vectors initialized using 300 - dimensional cased GloVe embeddings Pennington2014 . Dropout of 0.3 was applied between layers , and we used regularization of . Optimization was performed on minibatches of 24 examples using RMSprop Tieleman2012 with learning rate of , , and .
This method , while increasing data diversity against over - fitting , also incurs a considerable level of noise . In the effort to alleviate the impact of noise , the label smooth regularization ( LSR ) is adopted . The vanilla version of our method ( without LSR ) performs reasonably well on few - camera systems in which over - fitting often occurs .
the total population . While our work is not the first attempt of this regularization idea , to our knowledge we are the first to apply it to the pain expression intensity estimation . Correspondingly , we propose three solutions to address the four issues mentioned above .
This yields two loss values at each time step given by , and . Then the overall loss function of fraternal dropout is given by , where is the regularization coefficient , is the dimensions of and is the fraternal dropout regularization given by , We use Monte Carlo sampling to approximate where and are the same as the one used to calculate values .
We mitigate the first problem by introducing a random variable on sample scaling parameters , whose distribution ’s parameters are jointly learnt with CNN and leads to what we call adaptive multi-scale sampling ( AMS ) . To address the second problem , we propose activation pattern regularization ( APR ) on only samples with the common causes such that CNN focuses on learning representations pertaining to the common factor . We demonstrate the effectiveness of both proposals in extensive experiments on real-world datasets .
This paper presents a regularization scheme acting on the activation function output to improve the network interpretability and regularization . The proposed approach , referred to as activation regularization , encourages activation function outputs to satisfy a target pattern . By defining appropriate target patterns , different learning concepts can be imposed on the network . This method can aid network interpretability and also has the potential to reduce overfitting . The scheme is evaluated on several continuous speech recognition tasks : the Wall Street Journal continuous speech recognition task , eight conversational telephone speech tasks from the IARPA Babel program and a U.S. English broadcast news task . On all the tasks , the activation regularization achieved consistent performance gains over the standard DNN baselines .
How much do we benefit from data augmentation ? What activation and regularization schemes are more beneficial ? How does the depth of the network affect the performance ?
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 .
Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks Promoting Diversity in Recommendation by Entropy Regularizer Generalized Product Quantization Network for Semi-Supervised Image Retrieval Combining Deep Learning and Symbolic Processing for Extracting Knowledge from Raw Text Deep Discriminative CNN with Temporal Ensembling for Ambiguously-Labeled Image Classification Supervised Fuzzy Partitioning Learning to extract interesting events from outdoor image sequences Unsupervised domain adaptation with target reconstruction and label confusion in the common subspace Asymmetric Tri-training for Unsupervised Domain Adaptation Calibrating Energy-based Generative Adversarial Networks The microsoft 2016 conversational speech recognition system Achieving Human Parity in Conversational Speech Recognition What Do Recurrent Neural Network Grammars Learn About Syntax? Stacked Generative Adversarial Networks Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning Infinite Predictor Subspace Models for Multitask Learning Effective band-limited extrapolation relying on Slepian series and l1 regularization Robust tensor factorization using R1 norm Robust collaborative filtering based on non-negative matrix factorization and R1-norm $L_{\frac{1}{2}}$ Regularization and Reweighted $L_{\frac{1}{2}}$ Regularization on ISAR Image Construction Feature selection in conditional random fields for activity recognition An Efficient Method for Large-Scale l1-Regularized Convex Loss Minimization Study of spectral reflectance reconstruction based on regularization matrix R method Sparse Modeling with Automatic Feature Grouping DICOD: Distributed Convolutional Coordinate Descent for Convolutional Sparse Coding Recursive ℓ1, ∞ Group Lasso A regularization LR-algorithm for restoring images on Gaussian noises model Dual Super-Systolic Core for Real-Time Reconstructive Algorithms of High-Resolution Radar/SAR Imaging Systems Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning Random Erasing Data Augmentation Frustum PointNets for 3D Object Detection from RGB-D Data Weighted Transformer Network for Machine Translation Quasi-Recurrent Neural Networks A LASSO-Based Batch Process Modeling and End-Product Quality Prediction Method Regularization of state-constrained elliptic optimal control problems with nonlocal radiation interface conditions Recovering missing pixels for Landsat ETM + SLC-off imagery using multi-temporal regression analysis and a regularization method HSI Image Restortion using Low Rank Matrix Recovery Classification of machinery vibration signals based on group sparse representation Noise removal in Shack-hartamnn wavefront sensor based on nonconvex weighted adaptively regularization [Adaptive super resolution algorithm for under-sampled images]. An Evaluation of Low-Rank Mahalanobis Metric Learning Techniques for Hyperspectral Image Classification Advances in Collaborative Filtering and Ranking On Convergence of Kernel Learning Estimators PixelGAN Autoencoders Achieving Human Parity in Conversational Speech Recognition Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation TuckER: Tensor Factorization for Knowledge Graph Completion Long Text Generation via Adversarial Training with Leaked Information On gradient regularizers for MMD GANs Image Super-resolution via Feature-augmented Random Forest Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond Quasi-Recurrent Neural Networks Camera Style Adaptation for Person Re-identification Transferring Face Verification Nets To Pain and Expression Regression Fraternal Dropout Binary Classification of Sequences Possessing Unilateral Common Factor with AMS and APR Improving Interpretability and Regularization in Deep Learning Study of Dense Network Approaches for Speech Emotion Recognition From Information Bottleneck To Activation Norm Penalty
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IJCAI ANNPR AAAI ICML ICLR 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ArXiv EACL 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) IEEE Transactions on Pattern Analysis and Machine Intelligence AISTATS CVPR IROS ICML IEEE Transactions on Pattern Analysis and Machine Intelligence AAAI 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ArXiv ICLR NIPS ArXiv Medical Image Anal. EMNLP/IJCNLP AAAI NeurIPS Signal Process. Image Commun. CoNLL ICLR 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ArXiv ICLR PAKDD (3) ICASSP
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