Variational auto-encoders ( VAE ) are scalable and powerful generative models . However , the choice of the variational posterior determines tractability and flexibility of the VAE . Every object acquires its shape from some kind of generative process , such as manufacture , biological growth , or self-organization , in response to external forces . Inferring such generative processes from an observed shape is computationally challenging because a given process can lead to radically different shapes , and similar shapes can result from different generative processes . The case of pronouns and clitics by Joyce Bruhn de Garavito .- 3 . L2 Acquisition of null subjects in Japanese : A new generative perspective and its pedagogical implications byMika Kizu .- 4 . Verb movement in generative SLA and the teaching of word order patterns by Tom Rankin .- 5 . These two approaches behave differently due to the unit they use ( word vs. character ) and the model form they adopt ( generative vs. discriminative ) . In general , character-based approaches are more robust than word-based ones , as the vocabulary of characters is a closed set ; and discriminative models are more robust than generative ones , since they can flexibly include all kinds of available information , such as future context . This article first proposes a character-based n-gram model to enhance the robustness of the generative approach . In this paper , the utilization of embeddings for answer retrieval is explored by using Locality-Sensitive Hashing Forest ( LSH Forest ) , an Approximate Nearest Neighbor ( ANN ) model , to find similar conversations in a corpus and rank possible candidates . Experimental results on the well-known Ubuntu Corpus ( in English ) and a customer service chat dataset ( in Dutch ) show that , in combination with a candidate selection method , retrieval-based approaches outperform generative ones and reveal promising future research directions towards the usability of such a system . We derive gradient based update equations for the parameters of the sequence classifiers in order to maximize the conditional likelihood function . Results of our experiments with data sets drawn from biological sequence classification ( specifically protein function and subcellular localization ) and text classification applications show that the discriminatively trained sequence classifiers outperform their generative counterparts , confirming the benefits of discriminative training when the primary objective is classification . Our experiments also show that the discriminatively trained MM(k - 1 ) sequence classifiers are competitive with the computationally much more expensive Support Vector Machines trained using k-gram representations of sequences . ( ) converges subsection : The Generative Model for Sequences We use recurrent neural networks ( RNNs ) as the generative model . Generative Adversarial Networks ( GANs ) [ reference ] represent an important milestone on the path towards more effective generative models . GANs cast generative model training as a minimax game between a generative network ( generator ) , which maps a random vector into the data space , and a discriminative network ( discriminator ) , whose objective is to distinguish generated samples from real samples . We present a novel approach with the combination of deep Boltzmann machine and multi-grained scanning forest ensemble , to effectively deal with industrial fault diagnosis based on big data . At first , we use deep Boltzmann machine to turn all features of data to be processed by forests into binary , and then utilize multi-grained scanning forest ensemble to process them in every layer of deep Boltzmann machine . By means of the collaborative method , we can address the aforementioned issues . Singular value decomposition is applied to suppress the artifacts and acquire the channel-individual characteristics . The deep boltzmann machine is employed to extract and model the characteristics and achieve the motor imagery classification . Results demonstrate that the proposed method has achieved a 14.2 % higher classification accuracy than the common spatial pattern on average . Therefore , two categories of intelligent learning approaches , i.e. , shallow learning and deep learning , are investigated and compared for manufacturing quality prediction in this paper . Specifically , the feed forward neural network ( FFNN ) with one hidden layer and the least squares support vector machine ( LSSVM ) with no hidden layers are selected as the representatives for shallow learning , and the deep restricted Boltzmann machine ( DRBM ) and the stack autoencoder ( SAE ) are chosen as the representatives for deep learning . The manufacturing data is collected from a competition about manufacturing quality control in the Tianchi Data Lab of China . It has been successfully applied in various application domains [ 4 ] . Given the development of deep learning model , based on a number of distribution and use of text analysis model -RSM ( Replicated Softmax Model , RSM ) [ 5 ] , this paper designs based on its depth Boltzmann machine ( Deep Boltzmann Machine , DBM ) [ 6 ] for text feature extraction , combined with K-mean clustering algorithm to text clustering . Background : DBMs and K-means Deep Boltzmann Machines . In this paper , we propose a patched-based deep Boltzmann shape priors for visual tracking . The shape priors are generated from deep Boltzmann machine network . The network consists of three layers of hidden and visible units . We show that deep narrow Boltzmann machines are universal approximators of probability distributions on the activities of their visible units , provided they have sufficiently many hidden layers , each containing the same number of units as the visible layer . We show that , within certain parameter domains , deep Boltzmann machines can be studied as feedforward networks . We provide upper and lower bounds on the sufficient depth and width of universal approximators . These results settle various intuitions regarding undirected networks and , in particular , they show that deep narrow Boltzmann machines are at least as compact universal approximators as narrow sigmoid belief networks and restricted Boltzmann machines , with respect to the currently available bounds for those models . The proposed pre-training method is evaluated in a phone recognition task . Experimental results on TIMIT dataset shows that the proposed method outperforms Deep Belief Network and basic Deep Boltzmann Machine . The mean field approximations and wake-sleep algorithm use simple models that are computationally efficient , but may be poor approximations to the true posterior densities when the latent variables have strong mutual dependencies . In this paper , we describe a variational EM learning method of DSBNs with a new inference model known as the conditional deep Boltzmann machine ( cDBM ) , which is an undirected graphical model capable of representing complex dependencies among latent variables . We show that this algorithm does not require the computation of the intractable partition function in the undirected cDBM model , and can be accelerated with contrastive learning . To solve these problems , this paper proposes an image segmentation algorithm combining MRF ( Markov Random Field ) with deep learning shape priori . The target shape priori information is modelled and generated from deep learning models : Restricted Boltzmann Machine ( RBM ) , Deep Belief Network ( DBN ) , and Deep the Boltzmann Machine ( DBM ) . Then the shape priori information is further defined and added to the energy function of the MRF image segmentation algorithm . There exists a large body of work on unsupervised representation learning . Early methods were based on stacked ( often denoising ) autoencoders or restricted Boltzmann machines [ reference ][ reference ][ reference ][ reference ] . A lot of promising recent work originates from the Skip - gram model [ reference ] , which inspired the skip - thought vectors [ reference ] and several techniques for unsupervised feature learning of images [ reference ] . Deep GMMs are currently only designed for continuous real - valued data , but our approach of reparametrizing the model into layers of successive transformations can also be applied to other types of mixture distributions . We would also like to compare this extension to other discrete density estimators such as Restricted Boltzmann Machines , Deep Belief Networks and the NADE model [ 15 ] . Inference Generation Substantial work on probabilistic generative models has focused on training models using maximum likelihood . One class of maximum likelihood models are those described by probabilistic undirected graphs , such as Restricted Boltzmann Machines smolensky1986information and Deep Boltzmann Machines salakhutdinov2009deep . These models are trained by taking advantage of the conditional independence property of their bipartite structure to allow efficient exact or approximate posterior inference on latent variables . subsection : Variational Auto - Encoders Numerous techniques exist for unsupervised learning in deep networks , e.g. sparse auto - encoders and sparse coding , denoising auto - encoders , deconvolutional networks , restricted Boltzmann machines , deep Boltzmann machines , generative adversarial networks and variational auto - encoders . In this paper we focus on the class of models in the variational auto - encoding framework . 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 ) . Sparse representation - based classification was extensively studied for face recognition with occlusions . Tang proposed Robust Boltzmann Machine to distinguish corrupted pixels and learn latent representations . These methods designed components explicitly handling occlusions , while we show that features learned by DeepID2 + have implicitly encoded invariance to occlusions . In this thesis , a novel iris presentation attack using deep learning based synthetically generated iris images is presented . Utilizing the generative capability of deep convolutional generative adversarial networks and iris quality metrics , a new framework , named as iDCGAN is proposed for creating realistic appearing synthetic iris images . In-depth analysis is performed using quality score distributions of real and synthetically generated iris images to understand the effectiveness of the proposed approach . We can handle the task with a small amount of finely labeled data . First , deep convolutional generative adversarial networks are utilized to generate training samples with weak labels , as data preparation . Then , through weakly supervised learning , our LR-CNN model can be trained effectively with a small amount of finely labeled data and a large amount of generated weakly labeled data . However , there are several shortcomings of these methods such as requiring expensive hardware , needing supervised training data , no ground truth data for comparison , and disregard for occlusion . In order to address these shortcomings , this work proposes a new deep convolutional generative adversarial network architecture , called Y-GAN , that uses data from three cameras to estimate a depth map for each frame in a multi-camera video stream . We present a novel pseudo-cyclic adversarial learning approach for unsupervised colorization of grayscale images . We investigate the use of a non-trainable , lightweight and well-defined Handcrafted Translation to enforce the generation of realistic images and replace one of the two deep convolutional generative adversarial neural networks classically used in cyclic models . Additionally , we propose to use Output Spatial Pyramids to jointly constrain the deep latent spaces of an encoder-decoder generator to preserve spatial structures and improve the quality of the generated images . It reduces significantly the performance of current SISR methods due to real-world image corruptions . To address these problems , we propose a deep Super-Resolution Residual Convolutional Generative Adversarial Network ( SRResCGAN1 ) to follow the real-world degradation settings by adversarial training the model with pixel-wise supervision in the HR domain from its generated LR counterpart . The proposed network exploits the residual learning by minimizing the energy-based objective function with powerful image regularization and convex optimization techniques . The received hand gesture signals are then processed with time-frequency analysis . Based on these big database of hand gesture , we propose a new classification architecture called deformable deep convolutional generative adversarial network . Experimental results shows the new architecture can upgrade the recognition rate by 10 % and the deformable kernel can reduce the testing time cost by 30 % . A promising solution to this problem is to leverage procedural content generation to automatically generate levels for educational games that incorporate the desired learning objectives . In this paper , we propose a multistep deep convolutional generative adversarial network for generating new levels within a game for middle school computer science education . The model operates in two phases : ( 1 ) train a generator with a small set of human-authored example levels and generate a much larger set of synthetic levels to augment the training data for a second generator , and ( 2 ) train a second generator using the augmented training data and use it to generate novel educational game levels with enhanced solvability . document : Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks In recent years , supervised learning with convolutional networks ( CNNs ) has seen huge adoption in computer vision applications . In this work we hope to help bridge the gap between the success of CNNs for supervised learning and unsupervised learning . We introduce a class of CNNs called deep convolutional generative adversarial networks ( DCGANs ) , that have certain architectural constraints , and demonstrate that they are a strong candidate for unsupervised learning . Training on various image datasets , we show convincing evidence that our deep convolutional adversarial pair learns a hierarchy of representations from object parts to scenes in both the generator and discriminator . Our main contributions can be summarized as follows . First , we devise novel deep convolutional network architectures that can naturally combine 1 ) the hierarchical sentence modeling through layer - by - layer composition and pooling , and 2 ) the capturing of the rich matching patterns at different levels of abstraction ; Second , we perform extensive empirical study on tasks with different scales and characteristics , and demonstrate the superior power of the proposed architectures over competitor methods . paragraph : Roadmap lenge in pixel - level labelling problems . Work on this topic includes : TextonBoost [ reference ] , TextonForest [ reference ] , and Random Forest - based classifiers [ reference ] . Recently , supervised deep learning approaches such as large - scale deep Convolutional Neural Networks ( CNNs ) have been immensely successful in many high - level computer vision tasks such as image recognition [ reference ] and object detection [ reference ] . document : StarGAN : Unified Generative Adversarial Networks for Multi - Domain Image - to - Image Translation Recent studies have shown remarkable success in image - to - image translation for two domains . Not only the proposed model is able to generate category sentences that enlarge the original dataset , but also it helps improve its generalization capability during supervised training . We evaluate the performance of CS-GAN for the task of sentiment analysis . Quantitative evaluation exhibits the accuracy improvement in polarity detection on a small dataset with high category information . Even with careful tuning , training may diverge or end up in a bad equilibrium with dropped modes . In this work , we introduce a new form of latent optimisation inspired by the CS-GAN and show that it improves adversarial dynamics by enhancing interactions between the discriminator and the generator . We develop supporting theoretical analysis from the perspectives of differentiable games and stochastic approximation .
Variational auto-encoders ( VAE ) are scalable and powerful generative models . However , the choice of the variational posterior determines tractability and flexibility of the VAE .
Every object acquires its shape from some kind of generative process , such as manufacture , biological growth , or self-organization , in response to external forces . Inferring such generative processes from an observed shape is computationally challenging because a given process can lead to radically different shapes , and similar shapes can result from different generative processes .
The case of pronouns and clitics by Joyce Bruhn de Garavito .- 3 . L2 Acquisition of null subjects in Japanese : A new generative perspective and its pedagogical implications byMika Kizu .- 4 . Verb movement in generative SLA and the teaching of word order patterns by Tom Rankin .- 5 .
These two approaches behave differently due to the unit they use ( word vs. character ) and the model form they adopt ( generative vs. discriminative ) . In general , character-based approaches are more robust than word-based ones , as the vocabulary of characters is a closed set ; and discriminative models are more robust than generative ones , since they can flexibly include all kinds of available information , such as future context . This article first proposes a character-based n-gram model to enhance the robustness of the generative approach .
In this paper , the utilization of embeddings for answer retrieval is explored by using Locality-Sensitive Hashing Forest ( LSH Forest ) , an Approximate Nearest Neighbor ( ANN ) model , to find similar conversations in a corpus and rank possible candidates . Experimental results on the well-known Ubuntu Corpus ( in English ) and a customer service chat dataset ( in Dutch ) show that , in combination with a candidate selection method , retrieval-based approaches outperform generative ones and reveal promising future research directions towards the usability of such a system .
We derive gradient based update equations for the parameters of the sequence classifiers in order to maximize the conditional likelihood function . Results of our experiments with data sets drawn from biological sequence classification ( specifically protein function and subcellular localization ) and text classification applications show that the discriminatively trained sequence classifiers outperform their generative counterparts , confirming the benefits of discriminative training when the primary objective is classification . Our experiments also show that the discriminatively trained MM(k - 1 ) sequence classifiers are competitive with the computationally much more expensive Support Vector Machines trained using k-gram representations of sequences .
( ) converges subsection : The Generative Model for Sequences We use recurrent neural networks ( RNNs ) as the generative model .
Generative Adversarial Networks ( GANs ) [ reference ] represent an important milestone on the path towards more effective generative models . GANs cast generative model training as a minimax game between a generative network ( generator ) , which maps a random vector into the data space , and a discriminative network ( discriminator ) , whose objective is to distinguish generated samples from real samples .
We present a novel approach with the combination of deep Boltzmann machine and multi-grained scanning forest ensemble , to effectively deal with industrial fault diagnosis based on big data . At first , we use deep Boltzmann machine to turn all features of data to be processed by forests into binary , and then utilize multi-grained scanning forest ensemble to process them in every layer of deep Boltzmann machine . By means of the collaborative method , we can address the aforementioned issues .
Singular value decomposition is applied to suppress the artifacts and acquire the channel-individual characteristics . The deep boltzmann machine is employed to extract and model the characteristics and achieve the motor imagery classification . Results demonstrate that the proposed method has achieved a 14.2 % higher classification accuracy than the common spatial pattern on average .
Therefore , two categories of intelligent learning approaches , i.e. , shallow learning and deep learning , are investigated and compared for manufacturing quality prediction in this paper . Specifically , the feed forward neural network ( FFNN ) with one hidden layer and the least squares support vector machine ( LSSVM ) with no hidden layers are selected as the representatives for shallow learning , and the deep restricted Boltzmann machine ( DRBM ) and the stack autoencoder ( SAE ) are chosen as the representatives for deep learning . The manufacturing data is collected from a competition about manufacturing quality control in the Tianchi Data Lab of China .
It has been successfully applied in various application domains [ 4 ] . Given the development of deep learning model , based on a number of distribution and use of text analysis model -RSM ( Replicated Softmax Model , RSM ) [ 5 ] , this paper designs based on its depth Boltzmann machine ( Deep Boltzmann Machine , DBM ) [ 6 ] for text feature extraction , combined with K-mean clustering algorithm to text clustering . Background : DBMs and K-means Deep Boltzmann Machines .
In this paper , we propose a patched-based deep Boltzmann shape priors for visual tracking . The shape priors are generated from deep Boltzmann machine network . The network consists of three layers of hidden and visible units .
We show that deep narrow Boltzmann machines are universal approximators of probability distributions on the activities of their visible units , provided they have sufficiently many hidden layers , each containing the same number of units as the visible layer . We show that , within certain parameter domains , deep Boltzmann machines can be studied as feedforward networks . We provide upper and lower bounds on the sufficient depth and width of universal approximators . These results settle various intuitions regarding undirected networks and , in particular , they show that deep narrow Boltzmann machines are at least as compact universal approximators as narrow sigmoid belief networks and restricted Boltzmann machines , with respect to the currently available bounds for those models .
The proposed pre-training method is evaluated in a phone recognition task . Experimental results on TIMIT dataset shows that the proposed method outperforms Deep Belief Network and basic Deep Boltzmann Machine .
The mean field approximations and wake-sleep algorithm use simple models that are computationally efficient , but may be poor approximations to the true posterior densities when the latent variables have strong mutual dependencies . In this paper , we describe a variational EM learning method of DSBNs with a new inference model known as the conditional deep Boltzmann machine ( cDBM ) , which is an undirected graphical model capable of representing complex dependencies among latent variables . We show that this algorithm does not require the computation of the intractable partition function in the undirected cDBM model , and can be accelerated with contrastive learning .
To solve these problems , this paper proposes an image segmentation algorithm combining MRF ( Markov Random Field ) with deep learning shape priori . The target shape priori information is modelled and generated from deep learning models : Restricted Boltzmann Machine ( RBM ) , Deep Belief Network ( DBN ) , and Deep the Boltzmann Machine ( DBM ) . Then the shape priori information is further defined and added to the energy function of the MRF image segmentation algorithm .
There exists a large body of work on unsupervised representation learning . Early methods were based on stacked ( often denoising ) autoencoders or restricted Boltzmann machines [ reference ][ reference ][ reference ][ reference ] . A lot of promising recent work originates from the Skip - gram model [ reference ] , which inspired the skip - thought vectors [ reference ] and several techniques for unsupervised feature learning of images [ reference ] .
Deep GMMs are currently only designed for continuous real - valued data , but our approach of reparametrizing the model into layers of successive transformations can also be applied to other types of mixture distributions . We would also like to compare this extension to other discrete density estimators such as Restricted Boltzmann Machines , Deep Belief Networks and the NADE model [ 15 ] .
Inference Generation Substantial work on probabilistic generative models has focused on training models using maximum likelihood . One class of maximum likelihood models are those described by probabilistic undirected graphs , such as Restricted Boltzmann Machines smolensky1986information and Deep Boltzmann Machines salakhutdinov2009deep . These models are trained by taking advantage of the conditional independence property of their bipartite structure to allow efficient exact or approximate posterior inference on latent variables .
subsection : Variational Auto - Encoders Numerous techniques exist for unsupervised learning in deep networks , e.g. sparse auto - encoders and sparse coding , denoising auto - encoders , deconvolutional networks , restricted Boltzmann machines , deep Boltzmann machines , generative adversarial networks and variational auto - encoders . In this paper we focus on the class of models in the variational auto - encoding framework .
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 ) .
Sparse representation - based classification was extensively studied for face recognition with occlusions . Tang proposed Robust Boltzmann Machine to distinguish corrupted pixels and learn latent representations . These methods designed components explicitly handling occlusions , while we show that features learned by DeepID2 + have implicitly encoded invariance to occlusions .
In this thesis , a novel iris presentation attack using deep learning based synthetically generated iris images is presented . Utilizing the generative capability of deep convolutional generative adversarial networks and iris quality metrics , a new framework , named as iDCGAN is proposed for creating realistic appearing synthetic iris images . In-depth analysis is performed using quality score distributions of real and synthetically generated iris images to understand the effectiveness of the proposed approach .
We can handle the task with a small amount of finely labeled data . First , deep convolutional generative adversarial networks are utilized to generate training samples with weak labels , as data preparation . Then , through weakly supervised learning , our LR-CNN model can be trained effectively with a small amount of finely labeled data and a large amount of generated weakly labeled data .
However , there are several shortcomings of these methods such as requiring expensive hardware , needing supervised training data , no ground truth data for comparison , and disregard for occlusion . In order to address these shortcomings , this work proposes a new deep convolutional generative adversarial network architecture , called Y-GAN , that uses data from three cameras to estimate a depth map for each frame in a multi-camera video stream .
We present a novel pseudo-cyclic adversarial learning approach for unsupervised colorization of grayscale images . We investigate the use of a non-trainable , lightweight and well-defined Handcrafted Translation to enforce the generation of realistic images and replace one of the two deep convolutional generative adversarial neural networks classically used in cyclic models . Additionally , we propose to use Output Spatial Pyramids to jointly constrain the deep latent spaces of an encoder-decoder generator to preserve spatial structures and improve the quality of the generated images .
It reduces significantly the performance of current SISR methods due to real-world image corruptions . To address these problems , we propose a deep Super-Resolution Residual Convolutional Generative Adversarial Network ( SRResCGAN1 ) to follow the real-world degradation settings by adversarial training the model with pixel-wise supervision in the HR domain from its generated LR counterpart . The proposed network exploits the residual learning by minimizing the energy-based objective function with powerful image regularization and convex optimization techniques .
The received hand gesture signals are then processed with time-frequency analysis . Based on these big database of hand gesture , we propose a new classification architecture called deformable deep convolutional generative adversarial network . Experimental results shows the new architecture can upgrade the recognition rate by 10 % and the deformable kernel can reduce the testing time cost by 30 % .
A promising solution to this problem is to leverage procedural content generation to automatically generate levels for educational games that incorporate the desired learning objectives . In this paper , we propose a multistep deep convolutional generative adversarial network for generating new levels within a game for middle school computer science education . The model operates in two phases : ( 1 ) train a generator with a small set of human-authored example levels and generate a much larger set of synthetic levels to augment the training data for a second generator , and ( 2 ) train a second generator using the augmented training data and use it to generate novel educational game levels with enhanced solvability .
document : Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks In recent years , supervised learning with convolutional networks ( CNNs ) has seen huge adoption in computer vision applications .
In this work we hope to help bridge the gap between the success of CNNs for supervised learning and unsupervised learning . We introduce a class of CNNs called deep convolutional generative adversarial networks ( DCGANs ) , that have certain architectural constraints , and demonstrate that they are a strong candidate for unsupervised learning . Training on various image datasets , we show convincing evidence that our deep convolutional adversarial pair learns a hierarchy of representations from object parts to scenes in both the generator and discriminator .
Our main contributions can be summarized as follows . First , we devise novel deep convolutional network architectures that can naturally combine 1 ) the hierarchical sentence modeling through layer - by - layer composition and pooling , and 2 ) the capturing of the rich matching patterns at different levels of abstraction ; Second , we perform extensive empirical study on tasks with different scales and characteristics , and demonstrate the superior power of the proposed architectures over competitor methods . paragraph : Roadmap
lenge in pixel - level labelling problems . Work on this topic includes : TextonBoost [ reference ] , TextonForest [ reference ] , and Random Forest - based classifiers [ reference ] . Recently , supervised deep learning approaches such as large - scale deep Convolutional Neural Networks ( CNNs ) have been immensely successful in many high - level computer vision tasks such as image recognition [ reference ] and object detection [ reference ] .
document : StarGAN : Unified Generative Adversarial Networks for Multi - Domain Image - to - Image Translation Recent studies have shown remarkable success in image - to - image translation for two domains .
Not only the proposed model is able to generate category sentences that enlarge the original dataset , but also it helps improve its generalization capability during supervised training . We evaluate the performance of CS-GAN for the task of sentiment analysis . Quantitative evaluation exhibits the accuracy improvement in polarity detection on a small dataset with high category information .
Even with careful tuning , training may diverge or end up in a bad equilibrium with dropped modes . In this work , we introduce a new form of latent optimisation inspired by the CS-GAN and show that it improves adversarial dynamics by enhancing interactions between the discriminator and the generator . We develop supporting theoretical analysis from the perspectives of differentiable games and stochastic approximation .
Improving Variational Auto-Encoders using Householder Flow Getting "fumpered": Classifying objects by what has been done to them. Universal grammar and the second language classroom Integrating Generative and Discriminative Character-Based Models for Chinese Word Segmentation A Retrieval-Based Dialogue System Utilizing Utterance and Context Embeddings Discriminatively trained Markov model for sequence classification SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient Calibrating Energy-based Generative Adversarial Networks A deep Boltzmann machine and multi-grained scanning forest ensemble collaborative method and its application to industrial fault diagnosis Multi-class motor imagery classification by singular value decomposition and deep boltzmann machine Manufacturing Quality Prediction Using Intelligent Learning Approaches: A Comparative Study Text Retrieval analysis based on Deep Learning Patched-based deep Boltzmann shape priors for visual tracking Deep Narrow Boltzmann Machines are Universal Approximators Gender aware Deep Boltzmann Machines for phone recognition Variational EM Learning of DSBNs with Conditional Deep Boltzmann Machines Image Segmentation Based on MRF Combining with Deep Learning Shape Priors InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets Factoring Variations in Natural Images with Deep Gaussian Mixture Models Density estimation using Real NVP Towards Conceptual Compression Improving neural networks by preventing co-adaptation of feature detectors Deeply learned face representations are sparse, selective, and robust On Generative Adversarial Network Based Synthetic Iris Presentation Attack And Its Detection Facial Component-Landmark Detection With Weakly-Supervised LR-CNN Y-GAN: A Generative Adversarial Network for Depthmap Estimation from Multi-camera Stereo Images Pseudo-Cyclic Network for Unsupervised Colorization with Handcrafted Translation and Output Spatial Pyramids Deep Generative Adversarial Residual Convolutional Networks for Real-World Super-Resolution Deformable deep convolutional generative adversarial network in microwave based hand gesture recognition system Generating Educational Game Levels with Multistep Deep Convolutional Generative Adversarial Networks Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Convolutional Neural Network Architectures for Matching Natural Language Sentences Conditional Random Fields as Recurrent Neural Networks StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation A Generative Model for category text generation LOGAN: Latent Optimisation for Generative Adversarial Networks
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["Computer Science","Mathematics"] ["Psychology","Medicine"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Engineering","Computer Science"] ["Computer Science"] ["Economics"] ["Mathematics"] ["Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science","Engineering"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Mathematics","Computer Science"]
2016 2019 2013 2012 2017 2005 2017 2017 2018 2017 2017 2015 2017 2015 2015 2014 2018 2016 2014 2017 2016 2012 2015 2019 2019 2019 2019 2020 2017 2019 2016 2014 2015 2018 2018 2019
Universal Grammar and the Second Language Classroom ICDM AAAI ICLR ICTC ICLR (Poster) IJCNN ICANN PRICAI NIPS NIPS ICLR NIPS ArXiv 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) SUMAC @ ACM Multimedia CoG ICLR NIPS 2015 IEEE International Conference on Computer Vision (ICCV) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
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