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Prognostics and Health Management Conference (PHM-2022 London)

作者： Proteau, Antoine Zemouri, Ryad Tahan, Antoine Thomas, Marc Bounouara, Wafa Agnard, Stephane Ecole Technol Super Dept Mech Engn Montreal PQ Canada Conservatoire Natl Arts & Metiers CNAM CEDRIC Lab Paris France APN Inc Quebec City PQ Canada

ISBN: (纸本)9781665479547

The purpose of this paper is to present and to describe a novel dataset acquired entirely in an industrial environment during multiple regular scheduled production runs. In the monitoring, prognostic and fault detection literature, researchers are often faced with work based on the same popular datasets;for instance, the milling dataset, the Pronostia Bearing Dataset, the IMS Bearing Dataset or the Turbofan Engine Degradation Simulation Dataset. On the one hand, these datasets are the results of either simulations or acquired in a laboratory under controlled environment. On the other hand, a real industrial context might not be adequately represented within these datasets due to less controlled parameters or increased complexity. Consequently, it becomes critical to have access to a way to test and validate research work on both experimental and industrial data. In that mindset, to accelerate the technological transfer to the industry and to ensure that it can quickly profit from the benefits that the monitoring, diagnostic and prognostic research area can provide them, a new dataset acquired at an industrial partner: a machining company located in Quebec City (Qc, Canada) is presented.

关键词： Dataset Machining GD&T Prognostic variational autoencoder

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Generative adversarial networks using variational autoencoder discrimination for super-resolution 6

Generative adversarial networks using variational autoencode...

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6th International Conference on Natural Language Processing (ICNLP)

作者： Xu, Jian Ren, Xiangbo Niu, Lijiao Guo, Jiayao Xian Univ Posts & Telecommun Sch Commun & Informat Engn Xian Peoples R China

ISBN: (纸本)9798350349122;9798350349115

Training generative adversarial networks (GANs) relies on the game between the generator and the discriminator, so the improvement of the discriminator can promote the improvement of the generator. A variational autoencoder (VAE) is useful for classification because it can learn the probability distribution of an image and provide latent variables as output features. Therefore, we propose a new network structure using a variational autoencoder in the GAN discriminator for super-resolution (SR). This network uses the latent variables generated by the VAE to extract the probability distribution of a reconstructed image and an original high-resolution (HR) image so the latent variables are used as features for discrimination. In addition, we propose to train the whole GAN and the VAE network alternately to optimize the network parameters. We verify the proposed method on five GAN-based image super-resolution methods. Experimental results show that the proposed algorithm leads to state-of-the-art results when plugged into existing GAN based SR methods.

关键词： Generative Adversarial Networks variational autoencoder Image Super-Resolution Algorithm

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The Dreaming variational autoencoder for Reinforcement Learning Environments 38th

The Dreaming Variational Autoencoder for Reinforcement Learn...

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38th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence (AI)

作者： Andersen, Per-Arne Goodwin, Morten Granmo, Ole-Christoffer Univ Agder Dept ICT Grimstad Norway

ISBN: (纸本)9783030041915;9783030041908

Reinforcement learning has shown great potential in generalizing over raw sensory data using only a single neural network for value optimization. There are several challenges in the current state-of-the-art reinforcement learning algorithms that prevent them from converging towards the global optima. It is likely that the solution to these problems lies in short- and long-term planning, exploration and memory management for reinforcement learning algorithms. Games are often used to benchmark reinforcement learning algorithms as they provide a flexible, reproducible, and easy to control environment. Regardless, few games feature a state-space where results in exploration, memory, and planning are easily perceived. This paper presents The Dreaming variational autoencoder (DVAE), a neural network based generative modeling architecture for exploration in environments with sparse feedback. We further present Deep Maze, a novel and flexible maze engine that challenges DVAE in partial and fully-observable state-spaces, long-horizon tasks, and deterministic and stochastic problems. We show initial findings and encourage further work in reinforcement learning driven by generative exploration.

关键词： Deep reinforcement learning Environment modeling Neural networks variational autoencoder Markov decision processes Exploration Artificial experience-replay

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One-Class Collaborative Filtering with the Queryable variational autoencoder 19

One-Class Collaborative Filtering with the Queryable Variati...

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42nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

作者： Wu, Ga Bouadjenek, Mohamed Reda Sanner, Scott Univ Toronto Toronto ON Canada Vector Inst Artificial Intelligence Toronto ON Canada

ISBN: (纸本)9781450361729

variational autoencoder (VAE) based methods for Collaborative Filtering (CF) demonstrate remarkable performance for one-class (implicit negative) recommendation tasks by extending autoencoders with relaxed but tractable latent distributions. Explicitly modeling a latent distribution over user preferences allows VAEs to learn user and item representations that not only reproduce observed interactions, but also generalize them by leveraging learning from similar users and items. Unfortunately, VAE-CF can exhibit suboptimal learning properties;e.g., VAE-CFs will increase their prediction confidence as they receive more preferences per user, even when those preferences may vary widely and create ambiguity in the user representation. To address this issue, we propose a novel Queryable variational autoencoder (Q-VAE) variant of the VAE that explicitly models arbitrary conditional relationships between observations. The proposed model appropriately increases uncertainty (rather than reduces it) in cases where a large number of user preferences may lead to an ambiguous user representation. Our experiments on two benchmark datasets show that the Q-VAE generally performs comparably or outperforms VAE-based recommenders as well as other state-of-the-art approaches and is generally competitive across the user preference density spectrum, where other methods peak for certain preference density levels.

关键词： One-Class Collaborative Filtering variational autoencoder Conditional Inference

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variational autoencoder based on knowledge sharing and correlation weighting for process-quality concurrent fault detection

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 133卷

作者： Wang, Ziyuan Wang, Chengzhu Li, Yonggang Cent South Univ Sch Automat Changsha 410083 Peoples R China China Tobacco Hunan Ind Co Ltd Changsha Cigarette Factory Changsha 410034 Peoples R China

The severity of faults and the corresponding solutions in the complex and large-scale modern manufacturing industry are determined based on their impact on product quality. Detecting faults that affect product quality is crucial for minimizing downtime and reducing maintenance costs. However, obtaining real-time indicators of product quality is often challenging. Therefore, in practical production settings, it becomes essential to develop methods for detecting process -quality faults without relying on real-time quality data. Existing processquality concurrent fault detection methods establish process -variable -quality -variable relationships to design monitoring statistics. variational autoencoder (VAE) is extensively utilized to disentangle quality -related information from quality -unrelated information by ensuring the independence of the extracted features. However, the current VAE-based model structures lack theoretical support from probability models, and the feature extraction solely from process variables may not fully capture quality indicators. Therefore, we propose a novel VAE based on knowledge sharing and correlation weighting (KSCW-VAE) with theoretical interpretability to realize process -quality concurrent fault detection. First, to extract information from quality variables, a novel VAE model with two input branches is constructed. The EncoderNet branch includes both process variables and quality variables, while the PriorNet branch comprises only process variables. Then, Kullback- Leibler (KL) divergence constraint is introduced to facilitate effective extraction of combined information from process variables and quality variables by leveraging knowledge sharing between EncoderNet and PriorNet. To differentiate quality -related and quality -unrelated information, separate regression networks for process variables and quality variables are constructed by segregating latent variables. In addition, a network weight regularization based on canonical c

关键词： Fault detection Process-quality concurrent variational autoencoder Knowledge sharing Zinc roaster

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variational autoencoder-Based Multiobjective Topology Optimization of Electrical Machines Using Vector Graphics

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IEEE ACCESS 2024年 12卷 184813-184826页

作者： Heroth, Michael Schmid, Helmut C. Gregorova, Magda Herrler, Rainer Hofmann, Wilfried ZF Friedrichshafen AG Adv Dev E Motor Simulat D-88046 Schweinfurt Germany TH Wurzburg Schweinfurt CAIRO D-97421 Wurzburg Germany Tech Univ Dresden Chair Elect Machines & Drives D-01069 Dresden Germany

The increasing automation in the design process of electrical machines for vehicles generates huge amounts of data, leading to a growing interest in using machine learning for faster predictions and optimization. This paper presents an innovative approach to simultaneously model and optimize different geometries of electrical machines. Unlike previous methods that rely on specific design variables or pixel-based representations, this approach is based on a flexible vector graphic representation that can easily be applied to any electrical machine configuration. This also sidesteps the problem of scalar-based design representation and, unlike pixel-based representation, can be directly imported into finite element simulations for validation. To speed up the optimization process, we transform the corresponding problem into a lower-dimensional latent space learned by a variational autoencoder. This is trained on a total of 6839 different 2D geometries of electrical machines, which were exported from the finite element simulation into the standardized scalable vector graphics format. The optimization results show that a sharp boundary is formed in the combined Pareto front of different rotor topologies. The validation results demonstrate that this novel method delivers results comparable to those of current techniques.

关键词： Geometry Optimization Topology Graphics Vectors Training data Rotors Training Databases Synchronous machines Deep neural network multiobjective optimization permanent magnet synchronous machine scalable vector graphics variational autoencoder

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Evaluating variational autoencoder as a Private Data Release Mechanism for Tabular Data 24

Evaluating Variational Autoencoder as a Private Data Release...

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IEEE 24th Pacific Rim International Symposium on Dependable Computing (PRDC)

作者： Li, Szu-Chuang Tai, Bo-Chen Huang, Yennun Acad Sinica Res Ctr Informat Technol Innovat Taipei Taiwan

ISBN: (纸本)9781728149615

Multi-market businesses can collect data from different business entities and aggregate data from various sources to create value. However, due to the restriction of privacy regulation, it could be illegal to exchange data between business entities of the same parent company, unless the users have opted-in to allow it. Regulations such as the EU's GDPR allows data exchange if data is anonymized appropriately. In this study, we use variational autoencoder as a mechanism to generate synthetic data. The privacy and utility of the generated data sets are measured. And its performance is compared with the performance of the plain autoencoder. The primary findings of this study are 1) variational autoencoder can be an option for data exchange with good accuracy even when the number of latent dimensions is low 2) plain autoencoder still provides better accuracy when the number of hidden nodes is high 3) variational autoencoder, as a generative model, can be given to a data user to generate his version of data that closely mimic the original data set.

关键词： variational autoencoder private data release k-anonymity k-Level

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Adversarial and Contrastive variational autoencoder for Sequential Recommendation 21

Adversarial and Contrastive Variational Autoencoder for Sequ...

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30th World Wide Web Conference (WWW)

作者： Xie, Zhe Liu, Chengxuan Zhang, Yichi Lu, Hongtao Wang, Dong Ding, Yue Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Cyber Sci & Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Univ Michigan Shanghai Jiao Tong Univ Joint Inst Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Software Shanghai Peoples R China

ISBN: (纸本)9781450383127

Sequential recommendation as an emerging topic has attracted increasing attention due to its important practical significance. Models based on deep learning and attention mechanism have achieved good performance in sequential recommendation. Recently, the generative models based on variational autoencoder (VAE) have shown the unique advantage in collaborative filtering. In particular, the sequential VAE model as a recurrent version of VAE can effectively capture temporal dependencies among items in user sequence and perform sequential recommendation. However, VAE-based models suffer from a common limitation that the representational ability of the obtained approximate posterior distribution is limited, resulting in lower quality of generated samples. This is especially true for generating sequences. To solve the above problem, in this work, we propose a novel method called Adversarial and Contrastive variational autoencoder (ACVAE) for sequential recommendation. Specifically, we first introduce the adversarial training for sequence generation under the Adversarial variational Bayes (AVB) framework, which enables our model to generate high-quality latent variables. Then, we employ the contrastive loss. The latent variables will be able to learn more personalized and salient characteristics by minimizing the contrastive loss. Besides, when encoding the sequence, we apply a recurrent and convolutional structure to capture global and local relationships in the sequence. Finally, we conduct extensive experiments on four real-world datasets. The experimental results show that our proposed ACVAE model outperforms other state-of-the-art methods.

关键词： Sequential Recommendation variational autoencoder Adversarial Learning Contrastive Learning

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Bilateral variational autoencoder for Collaborative Filtering 21

Bilateral Variational Autoencoder for Collaborative Filterin...

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14th ACM International Conference on Web Search and Data Mining (WSDM)

作者： Quoc-Tuan Truong Salah, Aghiles Lauw, Hady W. Singapore Management Univ Sch Informat Syst Singapore Singapore

ISBN: (纸本)9781450382977

Preference data is a form of dyadic data, with measurements associated with pairs of elements arising from two discrete sets of objects. These are users and items, as well as their interactions, e.g., ratings. We are interested in learning representations for both sets of objects, i.e., users and items, to predict unknown pairwise interactions. Motivated by the recent successes of deep latent variable models, we propose Bilateral variational autoencoder (BiVAE), which arises from a combination of a generative model of dyadic data with two inference models, user- and item-based, parameterized by neural networks. Interestingly, our model can take the form of a Bayesian variational autoencoder either on the user or item side. As opposed to the vanilla VAE model, BiVAE is "bilateral", in that users and items are treated similarly, making it more apt for two-way or dyadic data. While theoretically sound, we formally show that, similarly to VAE, our model might suffer from an over-regularized latent space. This issue, known as posterior collapse in the VAE literature, may appear due to assuming an over-simplified prior (isotropic Gaussian) over the latent space. Hence, we further propose a mitigation of this issue by introducing constrained adaptive prior (CAP) for learning user-and item-dependent prior distributions. Empirical results on several real-world datasets show that the proposed model outperforms conventional VAE and other comparative collaborative filtering models in terms of item recommendation. Moreover, the proposed CAP further boosts the performance of BiVAE. An implementation of BiVAE is available on Cornac recommender library.

关键词： Collaborative Filtering variational autoencoder Dyadic Data

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INVESTIGATION AND COMPARISON OF OPTIMIZATION METHODS FOR variational autoencoder-BASED UNDERDETERMINED MULTICHANNEL SOURCE SEPARATION 47

INVESTIGATION AND COMPARISON OF OPTIMIZATION METHODS FOR VAR...

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47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Seki, Shogo Kameoka, Hirokazu Li, Li NTT Corp NTT Commun Sci Labs Tokyo Japan Nagoya Univ Informat Technol Ctr Nagoya Aichi Japan

ISBN: (纸本)9781665405409

In this paper, we investigate two algorithms for variational autoencoder (VAE)-based underdetermined multichannel source separation. We previously extended the multichannel VAE (MVAE) method for determined multichannel source separation and proposed the generalized MVAE (GMVAE) method for underdetermined multichannel source separation. The GMVAE method employs a conditional VAE (CVAE) as the source model representing the power spectrograms of the underlying sources present in a mixture. While we developed a convergence-guaranteed parameter estimation algorithm using a majorization-minimization/minorization-maximization (MM) algorithm, an expectation-maximization (EM) algorithm also allows us to design another algorithm with the same property. However, a comparison of the MM-based and EM-based algorithms has not yet been revealed. To elucidate this, we investigate the MM-based and EM-based algorithms for the GMVAE method, using an improved CVAE variant called auxiliary classifier VAE (ACVAE). The experimental results suggest that the EM-based algorithm takes less computational cost, achieving comparable separation performance with the MM-based algorithm.

关键词： Underdetermined multichannel source separation variational autoencoder convergence-guaranteed algorithm

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