Here , we introduce a set of null hypothesis tests that can be performed with Monte Carlo ( MC ) simulations of null models to rule out cases of overfitting with experimental data . We demonstrate the identification of these null models and null hypothesis testing on a generative model in two test cases , one with and one without overfitting . Importantly , we show that it is possible to distinguish an identified STDP rule from a null case where there are similar weight fluctuations which are activity-independent . In this work , a semi-parametric approach to one-shot learning is proposed , with the aim of bypassing the manual annotation step required for training perceptions systems used in autonomous driving . The proposed generative framework , coined Generative One-Shot Learning ( GOL ) , takes as input single one-shot objects , or generic patterns , and a small set of so-called regularization samples used to drive the generative process . New synthetic data is generated as Pareto optimal solutions from one-shot objects using a set of generalization functions built into a generalization generator . The term ‘ syntactocentrism ’ has been used to criticize the claim that syntax , as regarded in generative linguistics , plays the central role in modeling the mental architecture of the human language faculty . This research monograph explores the conjecture that many of the objections to the generative perspective , as they are formulated in alternative frameworks such as construction grammar , disappear once the consequences of recent minimalist theory are taken seriously . To show this , the book applies recent concepts of minimalist grammar to phenomena like the syntactic flexibility of idioms , the pragmatics of left-periphery-movement , or opacity effects involved in subextraction patterns . The former uses the carefully designed goodness measures for candidate segmentation , while the latter focuses on finding the optimal segmentation of the highest generative probability . However , while there exists a trivial way to extend the discriminative models into neural version by using neural language models , those of generative ones are non-trivial . In this paper , we propose the segmental language models ( SLMs ) for CWS . News . The results show that ( i ) query expansion improves both methods , ( ii ) that our discriminative models outperform the generative ones , and ( iii ) our best results surpass the state-of-the-art results by 10 % precision on average . Abstract Recent work has shown substantial performance improvements of discriminative probabilistic models over their generative counterparts . However , since discriminative models do not capture the input distribution of the data , their use in missing data scenarios is limited . Learning top - down generative models that can explain complex data distribution is a long - standing problem in machine learning research . The expressive power of deep neural networks makes them natural candidates for generative models , and several recent works have shown promising results . While state - of - the - art DNNs can rival human performance in certain discriminative tasks , current best deep generative models still fail when there are large variations in the data distribution . The restore - state command similarly resets the game to a previously saved state . This allows the use of ALE as a generative model to study topics such as planning and model - based reinforcement learning . subsection : Source Code The likelihood function of the PBN is straightforward to calculate , allowing it to be used as a generative classifier . An example is provided in which a generative classifier based on the PBN has comparable performance to a deep belief network in classifying handwritten characters . In addition , several examples are provided that demonstrate the duality relationship , for example , by training networks from either side of the duality . The essence of the approach is to use a resilient error backpropagation algorithm with dropout for “ tuning ” the polynomial neural network , obtained as a result of a multi-layered GMDH algorithm . The article compares the performance of the suggested algorithm on the time series with other popular forecasting methods : deep belief network , multi-layered GMDH algorithm , Box-Jenkins method and the ANFIS model . The minimum reached MAE of the proposed algorithm was approximately 25 % lower than the minimum MAE of the next best method – GMDH , thus indicating that the practical application of the algorithm can give good results compared with other well-known methods . It consists of two stages . The first one is unsupervised learning using layer by layer approach , which is intended for initialization of parameters ( pre-training of deep belief neural network ) . The second is supervised training in order to provide fine tuning of whole neural network . This paper presents our effort to the Audio/Visual Emotion Challenge ( AVEC'14 ) , whose goal is to predict the continuous values of the emotion dimensions arousal , valence and dominance at each moment in time . The proposed method utilizes deep belief network based models to recognize emotion states from audio and visual modalities . Firstly , we employ temporal pooling functions in the deep neutral network to encode dynamic information in the features , which achieves the first time scale temporal modeling . We test various RNN units and architectures including long short-term memory units , gated recurrent units , and recently proposed iRNN for in-depth design space exploration . According to our experimental results , the proposed approach significantly outperforms not only conventional machine learning-based methods but also a recent state-of-the-art deep belief network-based technique in terms of prediction accuracy . We propose a Deep Belief Network architecture for learning a joint representation of multimodal data . The model denes a probability distribution over the space of multimodal inputs and allows sampling from the conditional distributions over each data modality . It is also possible to create virtual labs on which students that are registered can carry out their experiments from remote with Interactive Intelligent Tutoring Systems . The current paper will develop a problem model using Markov Chain and greedy algorithms for Tutor or Instructor –Student interactive model for virtual labs and virtual class rooms which will be built on the Deep Belief Network architecture as a novel approach for state-of-art courses related to Energy and Wireless Communication for Higher Education proposed as part of Inter University Research Groups . Further , a Bio inspired Differential Evolution algorithm will be deployed for the virtual labs and virtual class rooms to meet the specific learning requirements for the Instructor-Student interactions specific to this E-learning platform . In order to further improve the robustness and recognition rate of deep belief network in Large Vocabulary Continuous Speech Recognition system , this paper presented a novel bottleneck deep belief network to extract new features , which was based on speaker adaptation and discriminative training . Firstly , a bottleneck deep belief network was adopted to get the feature . We propose a deep recurrent belief network with distributed time delays for learning multivariate Gaussians . Learning long time delays in deep belief networks is difficult due to the problem of van ... We propose a deep recurrent belief network with distributed time delays for learning multivariate Gaussians . Learning long time delays in deep belief networks is difficult due to the problem of vanishing or exploding gradients with increase in delay . The invention provides a fault diagnosis method for a rolling bearing based on a deep learning and SVM ( Support Vector Machine ) . The method comprises using a manure learning algorithm in a deep belief network theory to complete a characteristic extraction task needed by fault diagnosis ; automatically extracting the substantive characteristics of data input independent of manual selection from simple to complicate , from low to high , and automatically digging abundant information concealed in known data ; in addition , classifying and identifying a test sample by adopting an SVM classification method , seeking and finding a global minimum of a target function through an effective method previously designed , so as to solve the problem that a deep belief network may be trapped into a locally optimal solution . According to the fault diagnosis method for the rolling bearing based on the deep learning and SVM provided by the invention , the accuracy and effectiveness of the fault diagnosis method for a rolling bearing can be improved , and a new effective way can be provided to solve the accuracy and effectiveness of the fault diagnosis method , therefore the fault diagnosis method can be extensively applied complex systems in chemistry , metallurgy , electric power , aviation fields and the like . So as to improve the accuracy and stability of the network . The experimental results show that using the improved deep of belief network to establish the quantitative analysis model on the content of wheat , and the correlation coefficient of moisture , protein and ash content were 0.9978 , 0.9928 , 0.9920 , standard error of prediction were 0.0069 , 0.0628,0.0535 . Compared with the traditional deep belief network ( DBN ) and the traditional shallow learning BP neural network algorithm , the prediction results have been significantly improved . Instead of the usual approach to representing 3D shapes with voxel occupancy vectors , promising recent work has taken to learning simple latent representations of such objects . Neural architectures that have been developed with this goal in mind include those based on deep belief networks , deep autoencoders , and 3D convolutional networks . The positive progress made so far with neural networks has also led to the creation of several large - scale 3D CAD model benchmarks , notably ModelNet and ShapeNet . Deep generative models have recently drawn significant attention , and the ability of learning over large ( unlabeled ) data endows them with more potential and vitality . first proposed to use the contrastive divergence algorithm to efficiently training deep belief nets ( DBN ) . proposed denoising autoencoder ( DAE ) that learns the data distribution in a supervised learning fashion . We train a set of Restricted Boltzmann Machines ( RBMs ) on one- and two-dimensional Ising spin configurations at various values of temperature , generated using Monte Carlo simulations . We validate the training procedure by monitoring several estimators , including measurements of the log-likelihood , with the corresponding partition functions estimated using annealed importance sampling . With the aid of machine learning methods , we tested several approaches that not only rank candidate structures relative to each other but also predict how similar each candidate is to the native conformation . We trained a two-layer neural network , a multilayer neural network , and a network of Restricted Boltzmann Machines against extensive data sets of unbound complexes generated by RosettaDock and PyDock . We validated these methods with a set of refinement candidate structures . In order to solve this specific problem , we formulate approved drugs , side effect terms and drug-side effect associations as a recommender system , and transform the problem of predicting side effects into a recommender task . Two recommender methods , i.e. the integrated neighborhood-based method and the restricted Boltzmann machine-based method , are designed to make predictions . Further , in order to achieve better performances , we combine proposed methods and existing methods of the same type to develop ensemble models . We transform approved drugs , side effect terms and drug-side effect associations as a recommender system . We design two recommender methods , i.e. the integrated neighborhood-based method and the restricted Boltzmann machine-based method , to make predictions . Further , we combine proposed methods and existing methods of the same type to develop ensemble models . As one of the most important models of deep learning , Restricted Boltzmann Machine(RMB ) has been extensively studied and concerned in recent years . However , because RBM model has high redundancy , it greatly affects its convergence speed and training time . Moreover , the communication topology among the agents is assumed to be unknown and time-varying . A deep general type-2 fuzzy system ( DGT2FS ) using restricted Boltzmann machine ( RMB ) and contrastive divergence ( CD ) learning algorithm is proposed to estimate uncertainties . The simulation studies presented indicate that the proposed control method results in good performance under time-varying topology , unknown dynamics and external disturbances . In order to simplify the complexity of automobile driving conditions recognition , it is usually divided into three categories : driving , braking , and crashing , which are distinguished by a classifier . In this paper the 988-dimensional features of audio were extracted through openSMILE , and a classification of automobile driving conditions based on limited Boltzmann machine was proposed , of which the recognition rate could be reached to 93.7 % . Then the tagged data extracted from monitoring equipment are applied to train the prediction classifier which is the combination of deep belief network and softmax classifier . The deep belief network is composed of limited Boltzmann machine as well as BP network . BP network is used for parameters adjustment . Here we show that it is possible to construct an associative content-addressable memory ( ACAM ) with exponentially many stable states and robust error-correction . The network possesses expander graph connectivity on a restricted Boltzmann machine architecture . The expansion property allows simple neural network dynamics to perform at par with modern error-correcting codes . However , apart from a few special cases , they are notoriously difficult to compute even numerically due to the exponential complexity of generic quantum many-body systems . In this paper , we introduce a machine learning approach to OTOCs based on the restricted-Boltzmann-machine architecture , which features wide applicability and could work for arbitrary-dimensional systems with massive entanglement . We show , through a concrete example involving a two-dimensional transverse field Ising model , that our method is capable of computing early-time OTOCs with respect to random pure quantum states or infinite-temperature thermal ensembles . Deep learning has emerged as an effective approach in image-based robotic perception and actions . Towards cognitive robotic perception based on deep learning , this paper focuses the Constrained Restricted Boltzmann Machine ( RBM ) on visual images for sparse feature representation . Inspired by sparse coding , the sparse constraints are performed on the hidden layer of RBM to obtain sparse and effective feature representation from perceived visual images . In the proposed approach , a region-of-interest-based memorability preserving image compression algorithm which is accomplished via two sub-processes namely , memorability prediction and image compression is introduced . The memorability of images is predicted using convolutional neural network and restricted Boltzmann machine features . Based on these features , the memorability score of individual patches in an image is calculated and these scores are used to generate the memorability map . Building scalable generative models to capture rich distributions such as audio , images or video is one of the central challenges of machine learning . Until recently , deep generative models , such as Restricted Boltzmann Machines ( RBM ) , Deep Belief Networks ( DBNs ) and Deep Boltzmann Machines ( DBMs ) were trained primarily by MCMC - based algorithms geoff , russ . In these approaches the MCMC methods compute the gradient of log - likelihood which becomes more imprecise as training progresses . While standard back propagation gave about 118 errors , dropout decreased the errors to about 92 . Deep Boltzmann Machines - We also took a pretrained Deep Boltzmann Machine ( 784 - 500 - 1000 - 10 ) and finetuned it using dropout - backpropagation . The model uses a 1784 - 500 - 1000 - 10 architecture ( The extra 1000 input units come from the mean - field activations of the second layer of hidden units in the DBM , See for details ) . However , both works are still based on statistical PCA bases . Duong et al . address the problem of linearity in face modeling by using Deep Boltzmann Machines . However , they only work with 2D face and sparse landmarks ; and hence can not handle faces with large - pose variations or occlusion well . Recently several weakly supervised methods were developed to remove the need of explicitly labeling variations . disBM [ reference ] is a higher - order Boltzmann machine which learns a disentangled representation by " clamping " a part of the hidden units for a pair of data points that are known to match in all but one factors of variation . DC - IGN [ reference ] extends this " clamping " idea to VAE and successfully learns graphics codes that can represent pose and light in 3D rendered images . Unlike the cited prior works that strive to recover disentangled representations , InfoGAN requires no supervision of any kind . To the best of our knowledge , the only other unsupervised method that learns disentangled representations is hossRBM [ reference ] , a higher - order extension of the spike - and - slab restricted Boltzmann machine that can disentangle emotion from identity on the Toronto Face Dataset [ reference ] . However , hossRBM can only disentangle discrete latent factors , and its computation cost grows exponentially in the number of factors . A PER of % is reported in using RNNs . More recently , in a Mean - Covariance Restricted Boltzmann Machine ( RBM ) is used for recognizing phonemes in the TIMIT corpus using RBM for feature extraction . Along this line of research , in an approach called the Connectionist Temporal Classification ( CTC ) has been developed and can be used without an explicit input - output alignment . Specifically , FNN with a supervised - learning embedding layer using factorisation machines is proposed to efficiently reduce the dimension from sparse features to dense continuous features . The second model SNN is a deep neural network powered by a sampling - based restricted Boltzmann machine ( SNN - RBM ) or a sampling - based denoising auto - encoder ( SNN - DAE ) with a proposed negative sampling method . Based on the embedding layer , we build multiple layers neural nets with full connections to explore non - trivial data patterns . Our method , called CycleGAN-VC , uses a cycle-consistent adversarial network ( CycleGAN ) with gated convolutional neural networks ( CNNs ) and an identity-mapping loss . A CycleGAN learns forward and inverse mappings simultaneously using adversarial and cycle-consistency losses . This makes it possible to find an optimal pseudo pair from unpaired data . Furthermore , the adversarial loss contributes to reducing over-smoothing of the converted feature sequence . We configure a CycleGAN with gated CNNs and train it with an identity-mapping loss . This allows the mapping function to capture sequential and hierarchical structures while preserving linguistic information . Without explicit correspondence , it is difficult to generate a high quality cartoon face that captures the essential facial features of a person . In order to solve this problem , we propose landmark assisted CycleGAN , which utilizes face landmarks to define landmark consistency loss and to guide the training of local discriminator in CycleGAN . To enforce structural consistency in landmarks , we utilize the conditional generator and discriminator . PURPOSE To evaluate pix2pix and CycleGAN and to assess the effects of multiple combination strategies on accuracy for patch-based synthetic CT ( sCT ) generation for MR-only treatment planning in head and neck ( HN ) cancer patients . MATERIALS AND METHODS Twenty-three deformably registered pairs of CT and mDixon FFE MR datasets from HN cancer patients treated at our institution were retrospectively analyzed to evaluate patch-based sCT accuracy via the pix2pix and CycleGAN models . To test effects of overlapping sCT patches on estimations , we 1 ) trained the models for three orthogonal views to observe the effects of spatial context , 2 ) we increased effective set size by using per-epoch data augmentation , and 3 ) we evaluated the performance of three different approaches for combining overlapping Hounsfield Unit ( HU ) estimations for varied patch overlap parameters . This is an important development of learning-based methods for low-dose CT since it enables the model growth using previously acquired full-dose images and later acquired low-dose images from different patients . As a proof-of-concept study , we used the NIH-AAPM-Mayo Clinic Low Dose CT Grand Challenge data to test our CycleGAN denoising method . The results show that the proposed method not only achieves better peak signal-to-noise ratio ( PSNR ) for quarter-dose images than non-local mean and dictionary learning denoising methods , but also preserves more details reflected by images and structural similarity index ( SSIM ) . Yet , due to the existence of “ checkerboard artifacts ” , the original result can not be applied for real production . To reduce artifacts , the generator of CycleGAN is modified . As the final results suggest , the proposed method unlocks the bottleneck of vegetation greening when the infrared band is not available and artifacts are reduced . Lastly , we establish a new WHU VIS-NIR database including varies in face rotation and expressions to enrich the training data . Experimental results on the Oulu-CASIA and our WHU VIS-NIR databases show that the proposed FFE-based CycleGAN ( FFE-CycleGAN ) outperforms some state-of-the-art methods and achieves 96.5 % accuracy . In recent works , several models have been proposed that can do unsupervised learning which does not require an explicit pair of source domain image and target domain image , which is conventionally required for image translation . Two models called “ CycleGAN ” and “ DiscoGAN ” have appeared as state-of-the-art models in unsupervised learning-based image translation and succeeded in creating more realistic and highly accurate images . These models share the same network architecture , although there are differences in detailed parameter settings and learning algorithms . In this work , we formulate the Chinese handwritten character generation as a problem that learns a mapping from an existing printed font to a personalized handwritten style . We further propose DenseNet CycleGAN to generate Chinese handwritten characters . Our method is applied not only to commonly used Chinese characters but also to calligraphy work with aesthetic values . Recently , the cycle-consistent adversarial network ( CycleGAN ) , which does not require parallel training data , has been applied to voice conversion , showing the state-of-the-art performance . The CycleGAN based voice conversion , however , can be used only for a pair of speakers , i.e. , one-to-one voice conversion between two speakers . In this paper , we extend the CycleGAN by conditioning the network on speakers . Network Architecture . Adapted from CycleGAN , StarGAN has the generator network composed of two convolutional layers with the stride size of two for downsampling , six residual blocks , and two transposed convolutional layers with the stride size of two for upsampling . We use instance normalization for the generator but no normalization for the discriminator . subsection : Baseline Models As our baseline models , we adopt DIAT and CycleGAN , both of which performs image - to - image translation between two different domains . For comparison , we trained these models multiple times for every pair of two different domains . Ablation : No Semantic Consistency . We experiment without the addition of our semantic consistency loss and find that the standard unsupervised CycleGAN approach diverged when training SVHN to MNIST often suffering from random label flipping . Figure [ reference ] ( a ) demonstrates two examples where cycle constraints alone fail to have the desired behavior for our end task . Similarly , in cross - domain - nlp , zhangadversarial , an adversarial autoencoder is used to learn the cross - domain relations of the vector representations of words from two different languages . The architecture of the recent works of DiscoGAN discogan and CycleGAN cyclegan are also similar to an adversarial autoencoder in which the latent representation is enforced to have the distribution of the other domain . Here we describe how our proposed PixelGAN autoencoder can be potentially used in all these application areas to learn better cross - domain relations .
Here , we introduce a set of null hypothesis tests that can be performed with Monte Carlo ( MC ) simulations of null models to rule out cases of overfitting with experimental data . We demonstrate the identification of these null models and null hypothesis testing on a generative model in two test cases , one with and one without overfitting . Importantly , we show that it is possible to distinguish an identified STDP rule from a null case where there are similar weight fluctuations which are activity-independent .
In this work , a semi-parametric approach to one-shot learning is proposed , with the aim of bypassing the manual annotation step required for training perceptions systems used in autonomous driving . The proposed generative framework , coined Generative One-Shot Learning ( GOL ) , takes as input single one-shot objects , or generic patterns , and a small set of so-called regularization samples used to drive the generative process . New synthetic data is generated as Pareto optimal solutions from one-shot objects using a set of generalization functions built into a generalization generator .
The term ‘ syntactocentrism ’ has been used to criticize the claim that syntax , as regarded in generative linguistics , plays the central role in modeling the mental architecture of the human language faculty . This research monograph explores the conjecture that many of the objections to the generative perspective , as they are formulated in alternative frameworks such as construction grammar , disappear once the consequences of recent minimalist theory are taken seriously . To show this , the book applies recent concepts of minimalist grammar to phenomena like the syntactic flexibility of idioms , the pragmatics of left-periphery-movement , or opacity effects involved in subextraction patterns .
The former uses the carefully designed goodness measures for candidate segmentation , while the latter focuses on finding the optimal segmentation of the highest generative probability . However , while there exists a trivial way to extend the discriminative models into neural version by using neural language models , those of generative ones are non-trivial . In this paper , we propose the segmental language models ( SLMs ) for CWS .
News . The results show that ( i ) query expansion improves both methods , ( ii ) that our discriminative models outperform the generative ones , and ( iii ) our best results surpass the state-of-the-art results by 10 % precision on average .
Abstract Recent work has shown substantial performance improvements of discriminative probabilistic models over their generative counterparts . However , since discriminative models do not capture the input distribution of the data , their use in missing data scenarios is limited .
Learning top - down generative models that can explain complex data distribution is a long - standing problem in machine learning research . The expressive power of deep neural networks makes them natural candidates for generative models , and several recent works have shown promising results . While state - of - the - art DNNs can rival human performance in certain discriminative tasks , current best deep generative models still fail when there are large variations in the data distribution .
The restore - state command similarly resets the game to a previously saved state . This allows the use of ALE as a generative model to study topics such as planning and model - based reinforcement learning . subsection : Source Code
The likelihood function of the PBN is straightforward to calculate , allowing it to be used as a generative classifier . An example is provided in which a generative classifier based on the PBN has comparable performance to a deep belief network in classifying handwritten characters . In addition , several examples are provided that demonstrate the duality relationship , for example , by training networks from either side of the duality .
The essence of the approach is to use a resilient error backpropagation algorithm with dropout for “ tuning ” the polynomial neural network , obtained as a result of a multi-layered GMDH algorithm . The article compares the performance of the suggested algorithm on the time series with other popular forecasting methods : deep belief network , multi-layered GMDH algorithm , Box-Jenkins method and the ANFIS model . The minimum reached MAE of the proposed algorithm was approximately 25 % lower than the minimum MAE of the next best method – GMDH , thus indicating that the practical application of the algorithm can give good results compared with other well-known methods .
It consists of two stages . The first one is unsupervised learning using layer by layer approach , which is intended for initialization of parameters ( pre-training of deep belief neural network ) . The second is supervised training in order to provide fine tuning of whole neural network .
This paper presents our effort to the Audio/Visual Emotion Challenge ( AVEC'14 ) , whose goal is to predict the continuous values of the emotion dimensions arousal , valence and dominance at each moment in time . The proposed method utilizes deep belief network based models to recognize emotion states from audio and visual modalities . Firstly , we employ temporal pooling functions in the deep neutral network to encode dynamic information in the features , which achieves the first time scale temporal modeling .
We test various RNN units and architectures including long short-term memory units , gated recurrent units , and recently proposed iRNN for in-depth design space exploration . According to our experimental results , the proposed approach significantly outperforms not only conventional machine learning-based methods but also a recent state-of-the-art deep belief network-based technique in terms of prediction accuracy .
We propose a Deep Belief Network architecture for learning a joint representation of multimodal data . The model denes a probability distribution over the space of multimodal inputs and allows sampling from the conditional distributions over each data modality .
It is also possible to create virtual labs on which students that are registered can carry out their experiments from remote with Interactive Intelligent Tutoring Systems . The current paper will develop a problem model using Markov Chain and greedy algorithms for Tutor or Instructor –Student interactive model for virtual labs and virtual class rooms which will be built on the Deep Belief Network architecture as a novel approach for state-of-art courses related to Energy and Wireless Communication for Higher Education proposed as part of Inter University Research Groups . Further , a Bio inspired Differential Evolution algorithm will be deployed for the virtual labs and virtual class rooms to meet the specific learning requirements for the Instructor-Student interactions specific to this E-learning platform .
In order to further improve the robustness and recognition rate of deep belief network in Large Vocabulary Continuous Speech Recognition system , this paper presented a novel bottleneck deep belief network to extract new features , which was based on speaker adaptation and discriminative training . Firstly , a bottleneck deep belief network was adopted to get the feature .
We propose a deep recurrent belief network with distributed time delays for learning multivariate Gaussians . Learning long time delays in deep belief networks is difficult due to the problem of van ...
We propose a deep recurrent belief network with distributed time delays for learning multivariate Gaussians . Learning long time delays in deep belief networks is difficult due to the problem of vanishing or exploding gradients with increase in delay .
The invention provides a fault diagnosis method for a rolling bearing based on a deep learning and SVM ( Support Vector Machine ) . The method comprises using a manure learning algorithm in a deep belief network theory to complete a characteristic extraction task needed by fault diagnosis ; automatically extracting the substantive characteristics of data input independent of manual selection from simple to complicate , from low to high , and automatically digging abundant information concealed in known data ; in addition , classifying and identifying a test sample by adopting an SVM classification method , seeking and finding a global minimum of a target function through an effective method previously designed , so as to solve the problem that a deep belief network may be trapped into a locally optimal solution . According to the fault diagnosis method for the rolling bearing based on the deep learning and SVM provided by the invention , the accuracy and effectiveness of the fault diagnosis method for a rolling bearing can be improved , and a new effective way can be provided to solve the accuracy and effectiveness of the fault diagnosis method , therefore the fault diagnosis method can be extensively applied complex systems in chemistry , metallurgy , electric power , aviation fields and the like .
So as to improve the accuracy and stability of the network . The experimental results show that using the improved deep of belief network to establish the quantitative analysis model on the content of wheat , and the correlation coefficient of moisture , protein and ash content were 0.9978 , 0.9928 , 0.9920 , standard error of prediction were 0.0069 , 0.0628,0.0535 . Compared with the traditional deep belief network ( DBN ) and the traditional shallow learning BP neural network algorithm , the prediction results have been significantly improved .
Instead of the usual approach to representing 3D shapes with voxel occupancy vectors , promising recent work has taken to learning simple latent representations of such objects . Neural architectures that have been developed with this goal in mind include those based on deep belief networks , deep autoencoders , and 3D convolutional networks . The positive progress made so far with neural networks has also led to the creation of several large - scale 3D CAD model benchmarks , notably ModelNet and ShapeNet .
Deep generative models have recently drawn significant attention , and the ability of learning over large ( unlabeled ) data endows them with more potential and vitality . first proposed to use the contrastive divergence algorithm to efficiently training deep belief nets ( DBN ) . proposed denoising autoencoder ( DAE ) that learns the data distribution in a supervised learning fashion .
We train a set of Restricted Boltzmann Machines ( RBMs ) on one- and two-dimensional Ising spin configurations at various values of temperature , generated using Monte Carlo simulations . We validate the training procedure by monitoring several estimators , including measurements of the log-likelihood , with the corresponding partition functions estimated using annealed importance sampling .
With the aid of machine learning methods , we tested several approaches that not only rank candidate structures relative to each other but also predict how similar each candidate is to the native conformation . We trained a two-layer neural network , a multilayer neural network , and a network of Restricted Boltzmann Machines against extensive data sets of unbound complexes generated by RosettaDock and PyDock . We validated these methods with a set of refinement candidate structures .
In order to solve this specific problem , we formulate approved drugs , side effect terms and drug-side effect associations as a recommender system , and transform the problem of predicting side effects into a recommender task . Two recommender methods , i.e. the integrated neighborhood-based method and the restricted Boltzmann machine-based method , are designed to make predictions . Further , in order to achieve better performances , we combine proposed methods and existing methods of the same type to develop ensemble models .
We transform approved drugs , side effect terms and drug-side effect associations as a recommender system . We design two recommender methods , i.e. the integrated neighborhood-based method and the restricted Boltzmann machine-based method , to make predictions . Further , we combine proposed methods and existing methods of the same type to develop ensemble models .
As one of the most important models of deep learning , Restricted Boltzmann Machine(RMB ) has been extensively studied and concerned in recent years . However , because RBM model has high redundancy , it greatly affects its convergence speed and training time .
Moreover , the communication topology among the agents is assumed to be unknown and time-varying . A deep general type-2 fuzzy system ( DGT2FS ) using restricted Boltzmann machine ( RMB ) and contrastive divergence ( CD ) learning algorithm is proposed to estimate uncertainties . The simulation studies presented indicate that the proposed control method results in good performance under time-varying topology , unknown dynamics and external disturbances .
In order to simplify the complexity of automobile driving conditions recognition , it is usually divided into three categories : driving , braking , and crashing , which are distinguished by a classifier . In this paper the 988-dimensional features of audio were extracted through openSMILE , and a classification of automobile driving conditions based on limited Boltzmann machine was proposed , of which the recognition rate could be reached to 93.7 % .
Then the tagged data extracted from monitoring equipment are applied to train the prediction classifier which is the combination of deep belief network and softmax classifier . The deep belief network is composed of limited Boltzmann machine as well as BP network . BP network is used for parameters adjustment .
Here we show that it is possible to construct an associative content-addressable memory ( ACAM ) with exponentially many stable states and robust error-correction . The network possesses expander graph connectivity on a restricted Boltzmann machine architecture . The expansion property allows simple neural network dynamics to perform at par with modern error-correcting codes .
However , apart from a few special cases , they are notoriously difficult to compute even numerically due to the exponential complexity of generic quantum many-body systems . In this paper , we introduce a machine learning approach to OTOCs based on the restricted-Boltzmann-machine architecture , which features wide applicability and could work for arbitrary-dimensional systems with massive entanglement . We show , through a concrete example involving a two-dimensional transverse field Ising model , that our method is capable of computing early-time OTOCs with respect to random pure quantum states or infinite-temperature thermal ensembles .
Deep learning has emerged as an effective approach in image-based robotic perception and actions . Towards cognitive robotic perception based on deep learning , this paper focuses the Constrained Restricted Boltzmann Machine ( RBM ) on visual images for sparse feature representation . Inspired by sparse coding , the sparse constraints are performed on the hidden layer of RBM to obtain sparse and effective feature representation from perceived visual images .
In the proposed approach , a region-of-interest-based memorability preserving image compression algorithm which is accomplished via two sub-processes namely , memorability prediction and image compression is introduced . The memorability of images is predicted using convolutional neural network and restricted Boltzmann machine features . Based on these features , the memorability score of individual patches in an image is calculated and these scores are used to generate the memorability map .
Building scalable generative models to capture rich distributions such as audio , images or video is one of the central challenges of machine learning . Until recently , deep generative models , such as Restricted Boltzmann Machines ( RBM ) , Deep Belief Networks ( DBNs ) and Deep Boltzmann Machines ( DBMs ) were trained primarily by MCMC - based algorithms geoff , russ . In these approaches the MCMC methods compute the gradient of log - likelihood which becomes more imprecise as training progresses .
While standard back propagation gave about 118 errors , dropout decreased the errors to about 92 . Deep Boltzmann Machines - We also took a pretrained Deep Boltzmann Machine ( 784 - 500 - 1000 - 10 ) and finetuned it using dropout - backpropagation . The model uses a 1784 - 500 - 1000 - 10 architecture ( The extra 1000 input units come from the mean - field activations of the second layer of hidden units in the DBM , See for details ) .
However , both works are still based on statistical PCA bases . Duong et al . address the problem of linearity in face modeling by using Deep Boltzmann Machines . However , they only work with 2D face and sparse landmarks ; and hence can not handle faces with large - pose variations or occlusion well .
Recently several weakly supervised methods were developed to remove the need of explicitly labeling variations . disBM [ reference ] is a higher - order Boltzmann machine which learns a disentangled representation by " clamping " a part of the hidden units for a pair of data points that are known to match in all but one factors of variation . DC - IGN [ reference ] extends this " clamping " idea to VAE and successfully learns graphics codes that can represent pose and light in 3D rendered images .
Unlike the cited prior works that strive to recover disentangled representations , InfoGAN requires no supervision of any kind . To the best of our knowledge , the only other unsupervised method that learns disentangled representations is hossRBM [ reference ] , a higher - order extension of the spike - and - slab restricted Boltzmann machine that can disentangle emotion from identity on the Toronto Face Dataset [ reference ] . However , hossRBM can only disentangle discrete latent factors , and its computation cost grows exponentially in the number of factors .
A PER of % is reported in using RNNs . More recently , in a Mean - Covariance Restricted Boltzmann Machine ( RBM ) is used for recognizing phonemes in the TIMIT corpus using RBM for feature extraction . Along this line of research , in an approach called the Connectionist Temporal Classification ( CTC ) has been developed and can be used without an explicit input - output alignment .
Specifically , FNN with a supervised - learning embedding layer using factorisation machines is proposed to efficiently reduce the dimension from sparse features to dense continuous features . The second model SNN is a deep neural network powered by a sampling - based restricted Boltzmann machine ( SNN - RBM ) or a sampling - based denoising auto - encoder ( SNN - DAE ) with a proposed negative sampling method . Based on the embedding layer , we build multiple layers neural nets with full connections to explore non - trivial data patterns .
Our method , called CycleGAN-VC , uses a cycle-consistent adversarial network ( CycleGAN ) with gated convolutional neural networks ( CNNs ) and an identity-mapping loss . A CycleGAN learns forward and inverse mappings simultaneously using adversarial and cycle-consistency losses . This makes it possible to find an optimal pseudo pair from unpaired data . Furthermore , the adversarial loss contributes to reducing over-smoothing of the converted feature sequence . We configure a CycleGAN with gated CNNs and train it with an identity-mapping loss . This allows the mapping function to capture sequential and hierarchical structures while preserving linguistic information .
Without explicit correspondence , it is difficult to generate a high quality cartoon face that captures the essential facial features of a person . In order to solve this problem , we propose landmark assisted CycleGAN , which utilizes face landmarks to define landmark consistency loss and to guide the training of local discriminator in CycleGAN . To enforce structural consistency in landmarks , we utilize the conditional generator and discriminator .
PURPOSE To evaluate pix2pix and CycleGAN and to assess the effects of multiple combination strategies on accuracy for patch-based synthetic CT ( sCT ) generation for MR-only treatment planning in head and neck ( HN ) cancer patients . MATERIALS AND METHODS Twenty-three deformably registered pairs of CT and mDixon FFE MR datasets from HN cancer patients treated at our institution were retrospectively analyzed to evaluate patch-based sCT accuracy via the pix2pix and CycleGAN models . To test effects of overlapping sCT patches on estimations , we 1 ) trained the models for three orthogonal views to observe the effects of spatial context , 2 ) we increased effective set size by using per-epoch data augmentation , and 3 ) we evaluated the performance of three different approaches for combining overlapping Hounsfield Unit ( HU ) estimations for varied patch overlap parameters .
This is an important development of learning-based methods for low-dose CT since it enables the model growth using previously acquired full-dose images and later acquired low-dose images from different patients . As a proof-of-concept study , we used the NIH-AAPM-Mayo Clinic Low Dose CT Grand Challenge data to test our CycleGAN denoising method . The results show that the proposed method not only achieves better peak signal-to-noise ratio ( PSNR ) for quarter-dose images than non-local mean and dictionary learning denoising methods , but also preserves more details reflected by images and structural similarity index ( SSIM ) .
Yet , due to the existence of “ checkerboard artifacts ” , the original result can not be applied for real production . To reduce artifacts , the generator of CycleGAN is modified . As the final results suggest , the proposed method unlocks the bottleneck of vegetation greening when the infrared band is not available and artifacts are reduced .
Lastly , we establish a new WHU VIS-NIR database including varies in face rotation and expressions to enrich the training data . Experimental results on the Oulu-CASIA and our WHU VIS-NIR databases show that the proposed FFE-based CycleGAN ( FFE-CycleGAN ) outperforms some state-of-the-art methods and achieves 96.5 % accuracy .
In recent works , several models have been proposed that can do unsupervised learning which does not require an explicit pair of source domain image and target domain image , which is conventionally required for image translation . Two models called “ CycleGAN ” and “ DiscoGAN ” have appeared as state-of-the-art models in unsupervised learning-based image translation and succeeded in creating more realistic and highly accurate images . These models share the same network architecture , although there are differences in detailed parameter settings and learning algorithms .
In this work , we formulate the Chinese handwritten character generation as a problem that learns a mapping from an existing printed font to a personalized handwritten style . We further propose DenseNet CycleGAN to generate Chinese handwritten characters . Our method is applied not only to commonly used Chinese characters but also to calligraphy work with aesthetic values .
Recently , the cycle-consistent adversarial network ( CycleGAN ) , which does not require parallel training data , has been applied to voice conversion , showing the state-of-the-art performance . The CycleGAN based voice conversion , however , can be used only for a pair of speakers , i.e. , one-to-one voice conversion between two speakers . In this paper , we extend the CycleGAN by conditioning the network on speakers .
Network Architecture . Adapted from CycleGAN , StarGAN has the generator network composed of two convolutional layers with the stride size of two for downsampling , six residual blocks , and two transposed convolutional layers with the stride size of two for upsampling . We use instance normalization for the generator but no normalization for the discriminator .
subsection : Baseline Models As our baseline models , we adopt DIAT and CycleGAN , both of which performs image - to - image translation between two different domains . For comparison , we trained these models multiple times for every pair of two different domains .
Ablation : No Semantic Consistency . We experiment without the addition of our semantic consistency loss and find that the standard unsupervised CycleGAN approach diverged when training SVHN to MNIST often suffering from random label flipping . Figure [ reference ] ( a ) demonstrates two examples where cycle constraints alone fail to have the desired behavior for our end task .
Similarly , in cross - domain - nlp , zhangadversarial , an adversarial autoencoder is used to learn the cross - domain relations of the vector representations of words from two different languages . The architecture of the recent works of DiscoGAN discogan and CycleGAN cyclegan are also similar to an adversarial autoencoder in which the latent representation is enforced to have the distribution of the other domain . Here we describe how our proposed PixelGAN autoencoder can be potentially used in all these application areas to learn better cross - domain relations .
Monte Carlo validation of spike-timing-dependent plasticity identification from spiking activity Generative One-Shot Learning (GOL): A Semi-Parametric Approach to One-Shot Learning in Autonomous Vision Rethinking Syntactocentrism: Architectural issues and case studies at the syntax-pragmatics interface Unsupervised Neural Word Segmentation for Chinese via Segmental Language Modeling Improving People Search Using Query Expansions Hybrid generative-discriminative training of Gaussian mixture models Stacked Generative Adversarial Networks The Arcade Learning Environment: An Evaluation Platform for General Agents (Extended Abstract) On the Duality Between Belief Networks and Feed-Forward Neural Networks Forecasting aircraft miles flown time series using a deep learning-based hybrid approach A Learning Technique for Deep Belief Neural Networks Multi-scale Temporal Modeling for Dimensional Emotion Recognition in Video DNA-Level Splice Junction Prediction using Deep Recurrent Neural Networks Learning Representations for Multimodal Data with Deep Belief Nets Developing Virtual Class Room Models with Bio Inspired Algorithms for E-Learning: A Survey for Higher Technical Education for Saudi Arabia Vision 2030 A New Feature Extraction Method Based on Bottleneck Deep Belief Network Learning word dependencies in text by means of a deep recurrent belief network Learning word dependencies in text by means of a deep recurrent belief network Fault diagnosis method for rolling bearing based on deep learning and SVM (Support Vector Machine) Near infrared spectroscopy detection of the content of wheat based on improved deep belief network Learning a Hierarchical Latent-Variable Model of 3D Shapes SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient Machine learning determination of dynamical parameters: The Ising model case Machine Learning Approaches for Predicting Protein Complex Similarity Predicting potential side effects of drugs by recommender methods and ensemble learning A removing redundancy Restricted Boltzmann Machine A novel general type-2 fuzzy controller for fractional-order multi-agent systems under unknown time-varying topology Audio-Based Classification of Automobile Driving Conditions Fault Prediction for Satellite Communication Equipment Based on Deep Neural Network Associative content-addressable networks with exponentially many robust stable states Artificial Neural Network Based Computation for Out-of-Time-Ordered Correlators Analysis of Different Sparsity Methods in Constrained RBM for Sparse Representation in Cognitive Robotic Perception Memorability-based image compression Adversarial Autoencoders Improving neural networks by preventing co-adaptation of feature detectors Nonlinear 3D Face Morphable Model InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets Quaternion Convolutional Neural Networks for End-to-End Automatic Speech Recognition Deep Learning over Multi-field Categorical Data - - A Case Study on User Response Prediction Parallel-Data-Free Voice Conversion Using Cycle-Consistent Adversarial Networks Landmark Assisted CycleGAN for Cartoon Face Generation Patch-Based Generative Adversarial Neural Network Models for Head and Neck MR-Only Planning. Low-Dose CT Image Denoising Using Cycle-Consistent Adversarial Networks Vegetation Greening for Winter Oblique Photography Using Cycle-Consistence Adversarial Networks Facial Feature Embedded Cyclegan For Vis-Nir Translation Factors Affecting Accuracy in Image Translation based on Generative Adversarial Network Generating Handwritten Chinese Characters Using CycleGAN Many-To-Many Voice Conversion Using Conditional Cycle-Consistent Adversarial Networks StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation CyCADA: Cycle-Consistent Adversarial Domain Adaptation PixelGAN Autoencoders
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EMBC ICRA EMNLP ECCV (2) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) IJCAI AVEC@MM 2018 International Conference on 3D Vision (3DV) AAAI ICACI ArXiv ArXiv 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition NIPS INTERSPEECH ECIR ICAART (2) WACV ICASSP 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ICML NIPS
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