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17th ACM Conference on Recommender Systems (RecSys)

作者： Vassoy, Bjornar Langseth, Helge Kille, Benjamin Norwegian Univ Sci & Technol Trondheim Trondelag Norway

ISBN: (纸本)9798400702419

An emerging definition of fairness in machine learning requires that models are oblivious to demographic user information, e.g., a user's gender or age should not influence the model. Personalized recommender systems are particularly prone to violating this definition through their explicit user focus and user modelling. Explicit user modelling is also an aspect that makes many recommender systems incapable of providing hitherto unseen users with recommendations. We propose novel approaches for mitigating discrimination in variational autoencoder-based recommender systems by limiting the encoding of demographic information. The approaches are capable of, and evaluated on, providing users that are not represented in the training data with fair recommendations.

关键词： fairness variational autoencoder

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Digital pathology whole slide image compression with Vector Quantized variational autoencoders

Digital pathology whole slide image compression with Vector ...

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Conference on Medical Imaging - Digital and Computational Pathology

作者： Keighley, Jason de Kamps, Marc Wright, Alexander Treanor, Darren Univ Leeds Woodhouse Lane Leeds LS2 9JT W Yorkshire England Leeds Teaching Hosp NHS Trust Great George St Leeds LS1 3EX W Yorkshire England Linkoping Univ Dept Clin Pathol Linkoping Sweden Linkoping Univ Dept Clin & Expt Med Linkoping Sweden Linkoping Univ Ctr Med Image Sci & Visualizat CMIV Linkoping Sweden Univ Leeds LIDA Leeds W Yorkshire England Alan Turing Inst 96 Euston Rd London NW1 2DB England

ISBN: (纸本)9781510660472;9781510660489

Digital pathology Whole Slide Images (WSIs) are large images (similar to 30 GB/slide uncompressed) of high resolution (0.25 microns per pixel), presenting a significant data storage challenge for hospitals wishing to adopt digital pathology. Lossy compression has been adopted by scanner manufacturers to address this issue - we compare lossy Joint Photographic Experts Group (JPEG) compression for WSIs and investigate the Vector Quantised variational autoencoder 2 variant (VQVAE2) as a possible alternative to reduce file size while encoding useful features in the compressed representation. We trained three VQVAE2 models on a Camelyon 2016 subset to the Compression Ratio (CR) of 19.2:1 (CR1), 9.6:1 (CR2) and 4.8:1 (CR3) and tested on a Camelyon 2016 (DS1) subset;University of California (DS2) and Internal Validation Set (DS3). We then compared compression performance to ImageMagick JPEG and JPEG 2000 implementations. Both JPEG and JPEG 2000 compression outperformed the VQVAE2 implementation within the Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) metrics. The trained VQVAE2 models could visually reproduce WSI tissue structure, but used colours from the original training data within the reconstructions on other datasets.

关键词： variational autoencoder Quantisation Compression Pathology Whole Slide Images Digital Pathology

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Differentially Private Synthetic Data Generation via Lipschitz-Regularised variational autoencoders 9

Differentially Private Synthetic Data Generation via Lipschi...

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9th IEEE Smart World Congress, SWC 2023

作者： Gros, Benedikt Wunder, Gerhard Freie Universität Department of Computer Science Berlin Germany

ISBN: (纸本)9798350319804

Synthetic data has been hailed as the silver bullet for privacy preserving data analysis. If a record is not real, then how could it violate a person's privacy? In addition, deep-learning based generative models are employed successfully to approximate complex high-dimensional distributions from data and draw realistic samples from this learned distribution. It is often overlooked though that generative models are prone to memorising many details of individual training records and often generate synthetic data that too closely resembles the underlying sensitive training data, hence violating strong privacy regulations as, e.g., encountered in health care. Differential privacy is the well-known state-of-the-art framework for guaranteeing protection of sensitive individuals' data, allowing aggregate statistics and even machine learning models to be released publicly without compromising privacy. The training mechanisms however often add too much noise during the training process, and thus severely compromise the utility of these private models. Even worse, the tight privacy budgets do not allow for many training epochs so that model quality cannot be properly controlled in practice. In this paper we explore an alternative approach for privately generating data that makes direct use of the inherent stochasticity in generative models, e.g., variational autoencoders. The main idea is to appropriately constrain the continuity modulus of the deep models instead of adding another noise mechanism on top. For this approach, we derive mathematically rigorous privacy guarantees and illustrate its effectiveness with practical experiments. © 2023 IEEE.

关键词： differential privacy Lipschitz regularisation variational autoencoder

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Fast and Efficient Speech Enhancement with variational autoencoders 48

Fast and Efficient Speech Enhancement with Variational Autoe...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Sadeghi, Mostafa Serizel, Romain Université de Lorraine Cnrs Inria Loria NancyF-54000 France

ISBN: (纸本)9781728163277

Unsupervised speech enhancement based on variational autoencoders has shown promising performance compared with the commonly used supervised methods. This approach involves the use of a pre-trained deep speech prior along with a parametric noise model, where the noise parameters are learned from the noisy speech signal with an expectation-maximization (EM)-based method. The E-step involves an intractable latent posterior distribution. Existing algorithms to solve this step are either based on computationally heavy Monte Carlo Markov Chain sampling methods and variational inference, or inefficient optimization-based methods. In this paper, we propose a new approach based on Langevin dynamics that generates multiple sequences of samples and comes with a total variation-based regularization to incorporate temporal correlations of latent vectors. Our experiments demonstrate that the developed framework makes an effective compromise between computational efficiency and enhancement quality, and outperforms existing methods. © 2023 IEEE.

关键词： deep generative model Langevin dynamics Speech enhancement variational autoencoder

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Generation of Communication Interference Waveforms Based on variational autoencoders 23

Generation of Communication Interference Waveforms Based on ...

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23rd IEEE International Conference on Communication Technology, ICCT 2023

作者： Chen, Shunshun Zhou, Zhichao Zhang, Zhipeng Wu, Xiangyu Hou, Changbo College of Information and Communication Engineering Harbin Engineering University Harbin China

ISBN: (纸本)9798350325959

The generation of interference waveforms is one of the core technologies in communication countermeasures. Existing communication interference methods usually rely on signal information obtained from communication reconnaissance, but these methods are not ideal when facing unknown target signal characteristics or dynamically changing parameters. Therefore, this paper proposes a communication interference waveform generation method based on variational autoencoders (VAE). This method uses VAE to directly extract the latent features of the target signal and generate interference signals with similar characteristics. Through comparative experiments on interference effects, the results show that the interference effect of the interference waveform generated by VAE can approach the optimal interference effect, effectively meeting the requirements of communication interference. © 2023 IEEE.

关键词： Deep learning Interference waveform generation variational autoencoder

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Augmenting time series data: An interpretable approach with metric learning and variational autoencoders

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2024年 161卷

作者： Zhang, Chunfeng Qin, Hao Zhang, Yongjun Jiang, Chongying Zhang, Di Deng, Wenyang South China Univ Technol Sch Elect Power Guangzhou 510665 Peoples R China

In the field of time series classification, deep learning techniques have shown remarkable performance;however, their effectiveness is often compromised when confronted with challenges of insufficient data and class imbalance. To address this challenge, we propose an interpretable time series data augmentation algorithm integrating variational autoencoders (VAE) and metric learning. The core contribution of this algorithm is manifested in three aspects: First, it eliminates the heteroscedasticity and non-stationarity of the data, ensuring that the data satisfies the hypothesis of normal distribution in the potential space of the encoder, and effectively avoids the approximation error of the real data distribution;Secondly, the algorithm constructs a discriminant VAE potential space, suitable for data augmentation, with metric learning, ensuring that the hidden variable distribution accurately reflects the characteristics of the original data. Finally, this paper explores the multi-seasonal decomposition algorithm of time series to seamlessly integrate the structural features of the original time series in the generated data, thereby enhancing the interpretability of data generation. Through experimental verification on four multivariate time series data sets, including the electrical energy data set, the results demonstrate that the proposed algorithm outperforms existing methods in fidelity and prediction performance, exhibiting high stability and generalization ability, particularly in cases of limited data volume. The introduction of this algorithm not only contributes to enhancing the overall performance of time series classification models but also substantially reduces the cost of data collection and labeling, thereby demonstrating its significant value in practical applications.

关键词： Time series data Data augmentation variational autoencoder Metric learning Time series decomposition

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Dynamic behavior recognition in aerial deployment of multi-segmented foldable-wing drones using variational autoencoders

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CHINESE JOURNAL OF AERONAUTICS 2025年第6期38卷

作者： Dou, Yilin Zhou, Zhou Wang, Rui Northwestern Polytech Univ Sch Aeronaut Xian 710072 Peoples R China Natl Key Lab Aircraft Configurat Design Xian 710072 Peoples R China

The aerial deployment method enables Unmanned Aerial Vehicles (UAVs) to be directly positioned at the required altitude for their mission. This method typically employs folding technology to improve loading efficiency, with applications such as the gravity-only aerial deployment of high-aspect-ratio solar-powered UAVs, and aerial takeoff of fixed-wing drones in Mars research. However, the significant morphological changes during deployment are accompanied by strong nonlinear dynamic aerodynamic forces, which result in multiple degrees of freedom and an unstable character. This hinders the description and analysis of unknown dynamic behaviors, further leading to difficulties in the design of deployment strategies and flight control. To address this issue, this paper proposes an analysis method for dynamic behaviors during aerial deployment based on the variational autoencoder (VAE). Focusing on the gravity-only deployment problem of highaspect-ratio foldable-wing UAVs, the method encodes the multi-degree-of-freedom unstable motion signals into a low-dimensional feature space through a data-driven approach. By clustering in the feature space, this paper identifies and studies several dynamic behaviors during aerial deployment. The research presented in this paper offers a new method and perspective for feature extraction and analysis of complex and difficult-to-describe extreme flight dynamics, guiding the research on aerial deployment drones' design and control strategies. (c) 2025 Published by Elsevier Ltd on behalf of Chinese Society of Aeronautics and Astronautics. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Dynamic behavior recogni tion Aerial deployment technol ogy variational autoencoder Pattern recognition Multi-rigid-body dynamics

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Learning Optimal Priors for Task-Invariant Representations in variational autoencoders 22

Learning Optimal Priors for Task-Invariant Representations i...

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28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KKD)

作者： Takahashi, Hiroshi Iwata, Tomoharu Kumagai, Atsutoshi Kanai, Sekitoshi Yamada, Masanori Yamanaka, Yuuki Kashima, Hisashi NTT Nairobi Kenya Kyoto Univ Kyoto Japan

ISBN: (纸本)9781450393850

The variational autoencoder (VAE) is a powerful latent variable model for unsupervised representation learning. However, it does not work well in case of insufficient data points. To improve the performance in such situations, the conditional VAE (CVAE) is widely used, which aims to share task-invariant knowledge with multiple tasks through the task-invariant latent variable. In the CVAE, the posterior of the latent variable given the data point and task is regularized by the task-invariant prior, which is modeled by the standard Gaussian distribution. Although this regularization encourages independence between the latent variable and task, the latent variable remains dependent on the task. To reduce this task-dependency, the previous work introduced an additional regularizer. However, its learned representation does not work well on the target tasks. In this study, we theoretically investigate why the CVAE cannot sufficiently reduce the task-dependency and show that the simple standard Gaussian prior is one of the causes. Based on this, we propose a theoretical optimal prior for reducing the task-dependency. In addition, we theoretically show that unlike the previous work, our learned representation works well on the target tasks. Experiments on various datasets show that our approach obtains better task-invariant representations, which improves the performances of various downstream applications such as density estimation and classification.

关键词： variational autoencoder Multi-task learning

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Field-aware variational autoencoders for Billion-scale User Representation Learning 38

Field-aware Variational Autoencoders for Billion-scale User ...

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38th IEEE International Conference on Data Engineering (ICDE)

作者： Fan, Ge Zhang, Chaoyun Chen, Junyang Li, Baopu Xu, Zenglin Li, Yingjie Peng, Luyu Gong, Zhiguo Tencent Inc Shenzhen Peoples R China Shenzhen Univ Shenzhen Peoples R China Baidu USA Sunnyvale CA USA Harbin Inst Technol Shenzhen Shenzhen Peoples R China Univ Macau Macau Peoples R China

ISBN: (数字)9781665408837

ISBN: (纸本)9781665408837

User representation learning plays an essential role in Internet applications, such as recommender systems. Though developing a universal embedding for users is demanding, only few previous works are conducted in an unsupervised learning manner. The unsupervised method is however important as most of the user data is collected without specific labels. In this paper, we harness the unsupervised advantages of variational autoencoders (VAEs), to learn user representation from large-scale, high-dimensional, and multi-field data. We extend the traditional VAE by developing Field-aware VAE (FVAE) to model each feature field with an independent multinomial distribution. To reduce the complexity in training, we employ dynamic hash tables, a batched softmax function, and a feature sampling strategy to improve the efficiency of our method. We conduct experiments on multiple datasets, showing that the proposed FVAE significantly outperforms baselines on several tasks of data reconstruction and tag prediction. Moreover, we deploy the proposed method in real-world applications and conduct online A/B tests in a look-alike system. Results demonstrate that our method can effectively improve the quality of recommendation. To the best of our knowledge, it is the first time that the VAE-based user representation learning model is applied to real-world recommender systems.

关键词： User Representation Learning Recommender Systems Lookalike Systems variational autoencoder

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Towards Unsupervised Subject-Independent Speech-Based Relapse Detection in Patients with Psychosis using variational autoencoders 30

Towards Unsupervised Subject-Independent Speech-Based Relaps...

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30th European Signal Processing Conference (EUSIPCO)

作者： Garoufis, C. Zlatintsi, A. Filntisis, P. P. Efthymiou, N. Kalisperakis, E. Karantinos, T. Garyfalli, V Lazaridi, M. Smyrnis, N. Maragos, P. Natl Tech Univ Athens Sch ECE Athens 15773 Greece Univ Mental Hlth Res Inst Lab Cognit Neurosci Athens Greece Natl & Kapodistrian Univ Athens Med Sch Athens Greece

ISBN: (纸本)9789082797091

Generative models, such as variational autoencoders, are being increasingly utilized for various acoustic modeling tasks, such as anomaly detection from audio signals. Motivated by this, in this work we propose a Convolutional variational autoencoder (CVAE), in order to detect and predict the appearance of relapses in patients with psychotic disorders, such as schizophrenia and bipolar disorder. The proposed system utilizes speech segments of patients, isolated from interviews conducted with their clinicians containing spontaneous speech, and represented as log-mel spectrograms. The results from the analysis of each segment are then aggregated in a perinterview basis. We explore the performance of our system in both a personalized and a universal (patient-independent) setup. Evaluation of our method in data from 13 patients and 375 interviews, with a total duration of 30509 sec of isolated speech, indicate that the CVAE achieves similar results to a Convolutional autoencoder (CAE) baseline in a personalized setup. Furthermore, the proposed model significantly outperforms the CAE baseline when considering a universal relapse detection setup.

关键词： Psychotic Disorders Anomaly Detection variational autoencoder Spontaneous Speech Relapse Prediction

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