The Semi-Generative Approach
Semi-generative modelling: learning with cause and effect features
... Another interesting point of discussion is the behaviour of the semi-generative approach un- der model misspecification. From a quantitative point of view, our experiments show very clearly that ...
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Semi Supervised Structured Output Learning Based on a Hybrid Generative and Discriminative Approach
... a semi-supervised SOL framework based on a hybrid generative and discriminative ...hybrid approach was first proposed in a super- vised learning setting (Raina et ...a semi-supervised ...
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A Generative Model for Semi-Supervised Learning
... new semi-supervised generative model to overcome a drawback of existing ...new approach improves quality of latent ...our approach is capable of utilizing the nice latent representations ...
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Unbiased Generative Semi-Supervised Learning
... ML semi-supervised learning, as well as the desire to utilise unlabelled samples in non-generative models, a large number of alternative objective functions have been proposed to take advantage of ...
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Deep Generative Models for Semi-Supervised Machine Learning
... art semi-supervised machine learning based on probabilistic modeling, we show that we are able to perform condition monitoring in a photovoltaic system with high accuracy and only a small fraction of annotated ...
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Semi-Supervised Generation with Cluster-aware Generative Models
... top-down approach used in LVAEs does not enforce a clear separation between the role of each stochastic unit, as proven by the fact that all of them encode some class ...
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Learning disentangled representations with semi-supervised deep generative models
... and semi-supervised learning schemes in the domain of variational autoencoders ...hybrid generative models which incorporate both structured graphical models and unstructured random variables in the same ...
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Improving Semi-Supervised Learning with Auxiliary Deep Generative Models
... 4 Conclusion We have shown that making the discriminative distribution more flexible by introducing extra aux- iliary variables gives state-of-the-art performance on the 100 labeled examples MNIST benchmark. We are in ...
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Improving generative statistical parsing with semi-supervised word clustering
... 1 Introduction Lexical information is known crucial in natural language parsing. For probabilistic parsing, one main drawback of the plain PCFG approach is to lack sensitivity to the lexicon. The symbols acces- ...
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Semi-supervised learning with generative adversarial networks for pathological speech classification
... this approach is the shortage of suitable speech data for training ...a semi-supervised learning approach that employs a Generative Adversarial Network (GAN) to alleviate the problem of ...
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Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features
... with semi-supervised learn- ...a semi-generative model, P (Y, X E |X C , θ). Our approach is robust to domain shifts in the distribution of causal features and leverages unlabelled data by ...
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Multi-Label Latent Spaces with Semi-Supervised Deep Generative Models
... a generative model and an inference model [45, ...practical approach to merging the two is to use automatic differentiation variational inference (ADVI) [48] or its implementation in a library such as pymc3 ...
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Semi-supervised generative adversarial nets with multiple generators for SAR image recognition
... Sensors 2018, 18, x FOR PEER REVIEW 11 of 19 In the MCGAN’s training phase, we use all 2747 real SAR images to train our GANs, 600 of which are labeled. All the labeled images are randomly selected from the whole ...
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Semi-supervised Regression with Generative Adversarial Networks Using Minimal Labeled Data
... classification approach would result in each incorrect class being considered equally as ...classification approach may be acceptable, but these problems are more naturally framed as the original regression ...
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Generative adversarial network-based semi-supervised learning for pathological speech classification
... GAN-based semi-supervised approach for patho- logical speech classification ...the approach has the potential to mitigate the labelled data shortage problem faced by certain medical applications of ...
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Semi-supervised generative adversarial nets with multiple generators for SAR image recognition
... 4.2.1. Training of MCGAN with 600 Labeled Images In the MCGAN’s training phase, we use all 2747 real SAR images to train our GANs, 600 of which are labeled. All the labeled images are randomly selected from the whole ...
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Pre-trained Convolutional Networks and generative statiscial models: a study in semi-supervised learning
... these generative methods bring results in more distinctive features, allowing for more accu- rate classification using feature vectors of lower dimensionality and linear ...TSVM semi-supervised options, ...
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Semi Supervised QA with Generative Domain Adaptive Nets
... of semi-supervised question answering—-utilizing unlabeled text to boost the performance of ques- tion answering ...the Generative Domain-Adaptive Nets. In this framework, we train a generative model ...
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A Generative Approach for Building Database Federations
... Building federations is done in a completely generative manner: Generators automatically implement access layers to a set of heterogeneous data sources and provide integrated views and u[r] ...
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A NEW APPROACH TO WELFARE: GENERATIVE EXPERIENCES
... The debate on the definition and measurement of individual welfare that has developed over the past few years has achieved full recognition with the creation of the "Commission on the Measurement of Economic ...
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