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27th World Wide Web (WWW) Conference

作者： Liang, Dawen Krishnan, Rahul G. Hoffman, Matthew D. Jebara, Tony Netflix Los Gatos CA 95032 USA MIT 77 Massachusetts Ave Cambridge MA 02139 USA Google AI San Francisco CA USA

ISBN: (纸本)9781450356398

We extend variational autoencoders (VAEs) to collaborative filtering for implicit feedback. This non-linear probabilistic model enables us to go beyond the limited modeling capacity of linear factor models which still largely dominate collaborative filtering research. We introduce a generative model with multinomial likelihood and use Bayesian inference for parameter estimation. Despite widespread use in language modeling and economics, the multinomial likelihood receives less attention in the recommender systems literature. We introduce a different regularization parameter for the learning objective, which proves to be crucial for achieving competitive performance. Remarkably, there is an efficient way to tune the parameter using annealing. The resulting model and learning algorithm has information-theoretic connections to maximum entropy discrimination and the information bottleneck principle. Empirically, we show that the proposed approach significantly outperforms several state-of-the-art baselines, including two recently-proposed neural network approaches, on several real-world datasets. We also provide extended experiments comparing the multinomial likelihood with other commonly used likelihood functions in the latent factor collaborative filtering literature and show favorable results. Finally, we identify the pros and cons of employing a principled Bayesian inference approach and characterize settings where it provides the most significant improvements.

关键词： Recommender systems collaborative filtering implicit feedback variational autoencoder Bayesian models

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Latent space improved masked reconstruction model for human skeleton-based action recognition (vol 19, 1482281, 2025)

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FRONTIERS IN NEUROROBOTICS 2025年 19卷 1482281页

作者： Chen, E. Wang, X. Guo, X. Zhu, Y. Li, D. School of Electrical and Information Engineering Zhengzhou University Zhengzhou China. State Grid Henan Electric Power Company Information and Communication Branch Zhengzhou China.

Human skeleton-based action recognition is an important task in the field of computer vision. In recent years, masked autoencoder (MAE) has been used in various fields due to its powerful self supervised learning ability and has achieved good results in masked data reconstruction tasks. However, in visual classification tasks such as action recognition, the limited ability of the encoder to learn features in the autoencoder structure results in poor classification performance. We propose to enhance the encoder's feature extraction ability in classification tasks by leveraging the latent space of variational autoencoder (VAE) and further replace it with the latent space of vector quantized variational autoencoder (VQVAE). The constructed models are called SkeletonMVAE and SkeletonMVQVAE, respectively. In SkeletonMVAE, we constrain the latent variables to represent features in the form of distributions. And in SkeletonMVQVAE, we discretize the latent variables. These help the encoder learn deeper data structures and more discriminative and generalized feature representations. The experiment results on the NTU-60 and NTU-120 datasets demonstrate that our proposed method can effectively improve the classification accuracy of the encoder in classification tasks and its generalization ability in the case of few labeled data. SkeletonMVAE exhibits stronger classification ability, while SkeletonMVQVAE exhibits stronger generalization in situations with fewer labeled data.

关键词： human skeleton-based action recognition variational autoencoder vector quantized variational autoencoder masked reconstruction model self-supervised learning

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variational autoencoders for Baseball Player Evaluation 5

Variational Autoencoders for Baseball Player Evaluation

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5th International Conference on Fuzzy Systems and Data Mining (FSDM)

作者： Converse, Geoffrey Arnold, Brooke Curi, Mariana Oliveira, Suely Univ Iowa Iowa City IA 52242 USA Univ Sao Paulo Sao Paulo Brazil

ISBN: (纸本)9781643680194;9781643680187

In the sporting world, baseball has been quicker to embrace the use of data analytics than any other sport, as detailed baseball statistics have become readily available in large and diverse quantities to the general public. Professional baseball teams use this data to develop game plans and evaluate players. In this work, we explore the latter by using a variational autoencoder (VAE), a special class of artificial neural networks. Specifically, we wish to relate a player's season-long batting statistics with the latent skills that a professional athlete needs to succeed in the MLB. In the growing field of sports analytics, we find this work incredibly important as it provides a novel, flexible, and powerful method to predict specific athletic skills based on years of recorded statistics.

关键词： Neural Networks variational autoencoder Interpretability Sports Analytics

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variational autoencoders for Missing Data Imputation with Application to a Simulated Milling Circuit

Variational Autoencoders for Missing Data Imputation with Ap...

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5th IFAC Workshop on Mining, Mineral and Metal Processing (MMM)

作者： McCoy, John T. Kroon, Steve Auret, Lidia Stellenbosch Univ Dept Proc Engn Private Bag X1 ZA-7602 Matieland South Africa Stellenbosch Univ Div Comp Sci Private Bag X1 ZA-7602 Matieland South Africa

Missing data values and differing sampling rates, particularly for important parameters such as particle size and stream composition, are a common problem in minerals processing plants. Missing data imputation is used to avoid information loss (due to downsampling or discarding incomplete records). A recent deep -learning technique, variational autoencoders (VAEs), has been used for missing data imputation in image data, and was compared here to imputation by mean replacement and by principal component analysis (PCA) imputation. The techniques were compared using a synthetic, nonlinear dataset, and a simulated milling circuit dataset, which included process disturbances, measurement noise, and feedback control. Each dataset was corrupted with missing values in 20% of records (lightly corrupted) and in 90% of records (heavily corrupted). For both lightly and heavily corrupted datasets, the root mean squared error of prediction for VAE imputation was lower than the traditional methods. Possibilities for the extension of missing data imputation to inferential sensing are discussed. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Missing Data Imputation Machine Learning variational autoencoder

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Robustness Analysis of Deep Learning Models for Population Synthesis

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Transportation Research Procedia 2025年 82卷 3790-3806页

作者： Daniel Opoku Mensah Godwin Badu-Marfo Bilal Farooq Laboratory of Innovations in Transportation (LiTrans) Toronto Metropolitan University Toronto Canada

Deep generative models have become useful for synthetic data generation, particularly population synthesis. The models implicitly learn the probability distribution of a dataset and can draw samples from a distribution. Several models have been proposed, but their performance is only tested on a single cross-sectional sample. The implementation of population synthesis on single datasets is seen as a drawback that needs further studies to explore the robustness of the models on multiple datasets. While comparing with the real data can increase trust and interpretability of the models, techniques to evaluate deep generative models’ robustness for population synthesis remain underexplored. In this study, we present bootstrap confidence interval for the deep generative models, an approach that computes efficient confidence intervals for mean errors predictions to evaluate the robustness of the models to multiple datasets. Specifically, we adopt the tabular-based Composite Travel Generative Adversarial Network (CTGAN) and variational autoencoder (VAE), to estimate the distribution of the population, by generating agents that have tabular data using several samples over time from the same study area. The models are implemented on multiple travel diaries of Montréal Origin-Destination Survey of 2008, 2013, and 2018 and compare the predictive performance under varying sample sizes from multiple surveys. Results show that the predictive errors of CTGAN have narrower confidence intervals indicating its robustness to multiple datasets of the varying sample sizes when compared to VAE. Again, the evaluation of model robustness against varying sample size shows a minimal decrease in model performance with decrease in sample size. This study directly supports agent-based modelling by enabling finer synthetic generation of populations in a reliable environment.

关键词： Generative Adversarial Networks variational autoencoder population synthesis robustness

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Anomaly detection solutions: The dynamic loss approach in VAE for manufacturing and IoT environment

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RESULTS IN ENGINEERING 2025年 25卷

作者： Vijai, Praveen Sivakumar, P. Bagavathi Amrita Vishwa Vidyapeetham Dept Comp Sci & Engn Amrita Sch Comp Coimbatore India

Anomaly detection is critical for enhancing operational efficiency, safety, and maintenance in industrial applications, particularly in the era of Industry 4.0 and IoT. While traditional anomaly detection approaches face limitations such as scalability issues, high false alarm rates, and reliance on skilled expertise, this study proposes novel approach using a BiLSTM-variational autoencoder (BiLSTM-VAE) model with a dynamic loss function. The proposed model addresses key challenges, including data imbalance, interpretability issues, and computational complexity. By leveraging the bidirectional capability of BiLSTM in the encoder and decoder, the model captures comprehensive temporal dependencies, enabling more effective anomaly detection. The innovative dynamic loss function integrates a tempering index mechanism with tuneable parameters (alpha and gamma), which assigns higher weights to underrepresented classes and down-weights easily classified samples. This improves reconstruction and enhances detection accuracy, particularly for minority class anomalies. Experimental evaluations on the SKAB and TEP datasets demonstrate the superiority of the proposed framework. The model achieved an accuracy of 98% and an F1 score of 96% for binary classification on the SKAB dataset and a multiclass classification accuracy of 92% with an F1 score of 85% on the TEP dataset. These results significantly outperform state-ofthe-art models, including traditional VAE, LSTM, and transformer-based approaches. The proposed BiLSTM-VAE model not only demonstrates robust anomaly detection capabilities across diverse datasets but also effectively handles data imbalance and reduces false positives, making it a scalable and reliable solution for industrial anomaly detection in the context of Industry 4.0 and IoT environments.

关键词： Anomaly detection Deep learning variational autoencoder Bidirectional long short term memory model Dynamic loss function

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Advanced temporal deep learning framework for enhanced predictive modeling in industrial treatment systems

RESULTS IN ENGINEERING

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RESULTS IN ENGINEERING 2025年 25卷

作者： Ramya, S. Srinath, S. Tuppad, Pushpa JSS Sci & Technol Univ Dept Comp Sci & Engn Mysuru India JSS Sci & Technol Univ Dept Environm Engn Mysuru India

This research introduces an innovative hybrid modeling framework tailored for interval prediction, aimed at forecasting water quality parameters in industrial sewage treatment plants (STPs). It tackles key challenges in the field, including limited data availability, detecting anomalies, and selecting relevant features with precision. By addressing these critical gaps, the study advances predictive analytics for wastewater treatment systems. The main goal was to create a scalable and resilient model that consistently provides accurate forecasts for essential water quality indicators. To accomplish this, variational autoencoders (VAEs) were employed to generate synthetic datasets that mimic real-world patterns, improving data availability and enhancing the model's generalization capabilities. autoencoder paired with a Self-Organizing Map (SOM) was leveraged for anomaly detection and efficient feature selection. The study evaluated advanced architectures, including a Temporal Convolutional Network (TCN), TCN integrated with bidirectional Long Short-Term Memory (BiLSTM), and refined TCN_BiLSTM models featuring preand post-soft attention layers. The final model incorporated Multi- Head Attention mechanisms in both preand post-processing stages (TCN_BiLSTM_MultiHead_Attention), delivering a substantial performance improvement. The TCN_BiLSTM_MultiHead_Attention model proved to be the top performer, delivering state-of-the-art results with R2 scores of 0.9732, 0.9567, and 0.9638 for the training, validation, and test datasets, respectively. On the test set, it achieved an impressive MSE of 0.0008 and an MAE of 0.0198. These results underscore the model's exceptional accuracy in predicting key parameters, including BOD, COD, and AmmoniaNitrogen. The results highlight the significant potential of hybrid deep learning frameworks in capturing temporal patterns and complex dynamics within STP data. By integrating temporal pattern recognition, long-term dependency modeling, and

关键词： Treatment plant variational autoencoder Self-organizing map Temporal convolutional network Bidirectional long short-term memory Gated recurrent units Attention mechanism

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Low-carbon optimization scheduling of IES based on enhanced diffusion model for scenario deep generation

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International Journal of Electrical Power and Energy Systems 2025年 167卷

作者： Wang, Danhao Peng, Daogang Huang, Dongmei Zhao, Huirong Qu, Bogang College of Automation Engineering Shanghai University of Electric Power Shanghai China

With the widespread integration of renewable energy sources into IES (Integrated Energy Systems), the uncertainties in renewable energy outputs and the volatility of user loads present new challenges for energy scheduling optimization. To effectively address these uncertainties, this paper proposes a scheduling method for IES based on an enhanced DM (Diffusion Models). Firstly, a mathematical model of a regional IES encompassing energy supply devices, energy coupling devices, and energy storage devices is constructed. Secondly, an enhanced DM optimized with a VAE (variational autoencoder) is developed. By efficiently generating data in the latent space, this model enhances the representation capability of uncertainty factors, enabling precise modeling of fluctuations in wind and solar power outputs as well as heating, cooling, and electrical loads. Finally, leveraging the improved DM, a large number of high-quality error scenarios are generated, and scheduling optimization experiments are designed with the objectives of minimizing economic costs and carbon emissions. Experimental results demonstrate that the proposed method not only achieves more efficient energy distribution and lower carbon emissions but also maintains the continuous and stable operation of the system when faced with uncertainties such as renewable energy generation fluctuations and changes in load demand. © 2025 The Author(s)

关键词： variational autoencoder Diffusion Model Multi-Scenario Generation Integrated Energy Scheduling Uncertainty Modeling Low-Carbon Optimization

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Scalable variational quantum circuits for autoencoder-based drug discovery 22

Scalable variational quantum circuits for autoencoder-based ...

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Proceedings of the 2022 Conference & Exhibition on Design, Automation & Test in Europe

作者： Junde Li Swaroop Ghosh The Pennsylvania State University

ISBN: (纸本)9783981926361

The de novo design of drug molecules is recognized as a time-consuming and costly process, and computational approaches have been applied in each stage of the drug discovery pipeline. variational autoencoder is one of the computer-aided design methods which explores the chemical space based on an existing molecular dataset. Quantum machine learning has emerged as an atypical learning method that may speed up some classical learning tasks because of its strong expressive power. However, near-term quantum computers suffer from limited number of qubits which hinders the representation learning in high dimensional spaces. We present a scalable quantum generative autoencoder (SQ-VAE) for simultaneously reconstructing and sampling drug molecules, and a corresponding vanilla variant (SQ-AE) for better reconstruction. The architectural strategies in hybrid quantum classical networks such as, adjustable quantum layer depth, heterogeneous learning rates, and patched quantum circuits are proposed to learn high dimensional dataset such as, ligand-targeted drugs. Extensive experimental results are reported for different dimensions including 8x8 and 32x32 after choosing suitable architectural strategies. The performance of quantum generative autoencoder is compared with the corresponding classical counterpart throughout all experiments. The results show that quantum computing advantages can be achieved for normalized low-dimension molecules, and that high-dimension molecules generated from quantum generative autoencoders have better drug properties within the same learning period.

关键词： drug discovery quantum machine learning variational autoencoder

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Securing VANETs: Multi-Objective Intrusion Detection With variational autoencoders

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第1期70卷 3867-3874页

作者： Nissar, Nabil Naja, Najib Jamali, Abdellah Inst Natl Postes Telecommun RAISS Lab Rabat 10000 Morocco Inst Natl Postes Telecommun Rabat 10000 Morocco Hassan I Univ Settat 26002 Morocco

The rising prevalence of connected vehicles in Vehicular Ad-hoc Networks (VANETs) within Intelligent Transportation Systems (ITS) has introduced a heightened susceptibility to various cyber threats, particularly zero-day attacks. The magnitude of intercommunicating vehicles compounds the difficulty of identifying dynamic and spatially-distant anomalies that elude static rules. Existing solutions, often rule-bound and inflexible, face challenges in dealing with novel threats. Hence, the diverse and evolving nature of potential threats underscores the necessity for more adaptive and robust detection frameworks, especially when considering the end consumer's safety and privacy *** paper introduces an innovative anomaly detection framework designed for VANETs. It employs a variational autoencoder (VAE) and optimizes multiple objectives: divergence, KL-divergence, and reconstruction error, using the AGE-MOEA and R-NSGA-III algorithms. Deployable within Roadside Units, it captures and analyzes broadcast vehicular data sequences, classifying messages as anomalous or normal to address not only the technological intricacies but also the paramount concern of consumer safety within the vehicular ecosystem. Experiments involve exploring various hyperparameters, with performance assessed using key metrics. The proposed framework undergoes comprehensive benchmarking against prior research, considering accuracy, precision, recall, and ROC-AUC. This underscores the efficacy of fully unsupervised learning and multi-objective optimization in enhancing VANETs security against emerging threats.

关键词： Consumer electronics VANETs security intrusion detection systems anomaly detection NSL-KDD artificial intelligence deep learning unsupervised learning variational autoencoder AGE-MOEA R-NSGA-III

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