Semi-supervised Variational Autoencoder for Regression: Application on Soft Sensors

• URL: http://arxiv.org/abs/2211.05979v1
• Date: Fri, 11 Nov 2022 02:54:59 GMT
• Title: Semi-supervised Variational Autoencoder for Regression: Application on Soft Sensors
• Authors: Yilin Zhuang, Zhuobin Zhou, Burak Alakent, Mehmet Mercangoz
• Abstract summary: We motivate the use of semi-supervised learning considering the fact that process quality variables are not collected at the same frequency as other process variables. These unlabelled records are not possible to use for training quality variable predictions based on supervised learning methods. We extend this approach of supervised VAEs for regression (SVAER) to make it learn from unlabelled data leading to semi-supervised VAEs for regression (SSVAER)
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present the development of a semi-supervised regression method using variational autoencoders (VAE), which is customized for use in soft sensing applications. We motivate the use of semi-supervised learning considering the fact that process quality variables are not collected at the same frequency as other process variables leading to many unlabelled records in operational datasets. These unlabelled records are not possible to use for training quality variable predictions based on supervised learning methods. Use of VAEs for unsupervised learning is well established and recently they were used for regression applications based on variational inference procedures. We extend this approach of supervised VAEs for regression (SVAER) to make it learn from unlabelled data leading to semi-supervised VAEs for regression (SSVAER), then we make further modifications to their architecture using additional regularization components to make SSVAER well suited for learning from both labelled and unlabelled process data. The probabilistic regressor resulting from the variational approach makes it possible to estimate the variance of the predictions simultaneously, which provides an uncertainty quantification along with the generated predictions. We provide an extensive comparative study of SSVAER with other publicly available semi-supervised and supervised learning methods on two benchmark problems using fixed-size datasets, where we vary the percentage of labelled data available for training. In these experiments, SSVAER achieves the lowest test errors in 11 of the 20 studied cases, compared to other methods where the second best gets 4 lowest test errors out of the 20.

Related papers

• Hessian-Free Online Certified Unlearning [8.875278412741695]
  We develop an online unlearning algorithm that achieves near-instantaneous data removal. We prove that our proposed method outperforms the state-of-the-art methods in terms of the unlearning and generalization guarantees.
  arXiv  Detail & Related papers  (2024-04-02T07:54:18Z)
• Enhancing Consistency and Mitigating Bias: A Data Replay Approach for Incremental Learning [100.7407460674153]
  Deep learning systems are prone to catastrophic forgetting when learning from a sequence of tasks. To mitigate the problem, a line of methods propose to replay the data of experienced tasks when learning new tasks. However, it is not expected in practice considering the memory constraint or data privacy issue. As a replacement, data-free data replay methods are proposed by inverting samples from the classification model.
  arXiv  Detail & Related papers  (2024-01-12T12:51:12Z)
• ASPEST: Bridging the Gap Between Active Learning and Selective Prediction [56.001808843574395]
  Selective prediction aims to learn a reliable model that abstains from making predictions when uncertain. Active learning aims to lower the overall labeling effort, and hence human dependence, by querying the most informative examples. In this work, we introduce a new learning paradigm, active selective prediction, which aims to query more informative samples from the shifted target domain.
  arXiv  Detail & Related papers  (2023-04-07T23:51:07Z)
• Adaptive Negative Evidential Deep Learning for Open-set Semi-supervised Learning [69.81438976273866]
  Open-set semi-supervised learning (Open-set SSL) considers a more practical scenario, where unlabeled data and test data contain new categories (outliers) not observed in labeled data (inliers) We introduce evidential deep learning (EDL) as an outlier detector to quantify different types of uncertainty, and design different uncertainty metrics for self-training and inference. We propose a novel adaptive negative optimization strategy, making EDL more tailored to the unlabeled dataset containing both inliers and outliers.
  arXiv  Detail & Related papers  (2023-03-21T09:07:15Z)
• Semi-Supervised Deep Regression with Uncertainty Consistency and Variational Model Ensembling via Bayesian Neural Networks [31.67508478764597]
  We propose a novel approach to semi-supervised regression, namely Uncertainty-Consistent Variational Model Ensembling (UCVME) Our consistency loss significantly improves uncertainty estimates and allows higher quality pseudo-labels to be assigned greater importance under heteroscedastic regression. Experiments show that our method outperforms state-of-the-art alternatives on different tasks and can be competitive with supervised methods that use full labels.
  arXiv  Detail & Related papers  (2023-02-15T10:40:51Z)
• Leveraging Unlabeled Data to Predict Out-of-Distribution Performance [63.740181251997306]
  Real-world machine learning deployments are characterized by mismatches between the source (training) and target (test) distributions. In this work, we investigate methods for predicting the target domain accuracy using only labeled source data and unlabeled target data. We propose Average Thresholded Confidence (ATC), a practical method that learns a threshold on the model's confidence, predicting accuracy as the fraction of unlabeled examples.
  arXiv  Detail & Related papers  (2022-01-11T23:01:12Z)
• Regularizing Variational Autoencoder with Diversity and Uncertainty Awareness [61.827054365139645]
  Variational Autoencoder (VAE) approximates the posterior of latent variables based on amortized variational inference. We propose an alternative model, DU-VAE, for learning a more Diverse and less Uncertain latent space.
  arXiv  Detail & Related papers  (2021-10-24T07:58:13Z)
• Variation-Incentive Loss Re-weighting for Regression Analysis on Biased Data [8.115323786541078]
  We aim to improve the accuracy of the regression analysis by addressing the data skewness/bias during model training. We propose a Variation-Incentive Loss re-weighting method (VILoss) to optimize the gradient descent-based model training for regression analysis.
  arXiv  Detail & Related papers  (2021-09-14T10:22:21Z)
• Enhanced Doubly Robust Learning for Debiasing Post-click Conversion Rate Estimation [29.27760413892272]
  Post-click conversion, as a strong signal indicating the user preference, is salutary for building recommender systems. Currently, most existing methods utilize counterfactual learning to debias recommender systems. We propose a novel double learning approach for the MRDR estimator, which can convert the error imputation into the general CVR estimation.
  arXiv  Detail & Related papers  (2021-05-28T06:59:49Z)
• Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift [81.74795324629712]
  We call prediction-time batch normalization, which significantly improves model accuracy and calibration under covariate shift. We show that prediction-time batch normalization provides complementary benefits to existing state-of-the-art approaches for improving robustness. The method has mixed results when used alongside pre-training, and does not seem to perform as well under more natural types of dataset shift.
  arXiv  Detail & Related papers  (2020-06-19T05:08:43Z)
• Stable Prediction with Model Misspecification and Agnostic Distribution Shift [41.26323389341987]
  In machine learning algorithms, two main assumptions are required to guarantee performance. One is that the test data are drawn from the same distribution as the training data, and the other is that the model is correctly specified. Under model misspecification, distribution shift between training and test data leads to inaccuracy of parameter estimation and instability of prediction across unknown test data. We propose a novel Decorrelated Weighting Regression (DWR) algorithm which jointly optimize a variable decorrelation regularizer and a weighted regression model.
  arXiv  Detail & Related papers  (2020-01-31T08:56:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.