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46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

作者： Wang, Siyu Chen, Xiaocong Sheng, Quan Z. Zhang, Yihong Yao, Lina Univ New South Wales Sydney NSW Australia Macquarie Univ Sydney NSW Australia Osaka Univ Osaka Japan CSIRO Data61 Eveleigh Australia

ISBN: (纸本)9781450394086

Recommendation models are typically trained on observational user interaction data, but the interactions between latent factors in users' decision-making processes lead to complex and entangled data. Disentangling these latent factors to uncover their underlying representation can improve the robustness, interpretability, and controllability of recommendation models. This paper introduces the Causal Disentangled variational Auto-Encoder (CaD-VAE), a novel approach for learning causal disentangled representations from interaction data in recommender systems. The CaD-VAE method considers the causal relationships between semantically related factors in real-world recommendation scenarios, rather than enforcing independence as in existing disentanglement methods. The approach utilizes structural causal models to generate causal representations that describe the causal relationship between latent factors. The results demonstrate that CaD-VAE outperforms existing methods, offering a promising solution for disentangling complex user behavior data in recommendation systems.

关键词： Recommender Systems Causal Disentangled Representation variational autoencoder

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Joint Inversion of Electrical Impedance, Microwave and Ultrasonic Data With Structural Feature Fusion for Human Thorax Imaging

Joint Inversion of Electrical Impedance, Microwave and Ultra...

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IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization (NEMO)

作者： Lin, Zhichao Guo, Rui Zhang, Ke Zhang, Haolin Li, Maokun Yang, Fan Xu, Shenheng Abubakar, Aria Beijing Natl Res Ctr Informat Sci & Technol BNRis Beijing Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Beijing Peoples R China Schlumberger Houston TX USA

ISBN: (纸本)9798350347401

In this study, we propose a novel joint inversion method for human thorax imaging that combines electrical impedance tomography (EIT), microwave tomography (MT), and ultrasound tomography (UT) through structural feature fusion. The structural and value features of the three modality images are decoupled and encoded into structure code and value code by a disentangled variational autoencoder (DVAE). The inversion is then performed at the feature level, with the structure code and value code treated as unknown parameters to be inverted under a deterministic framework by the Gauss-Newton method. To ensure the consistency of the structures in the three modalities, the structure codes of the three modalities are averaged every few iterations during the inversion process. Numerical results demonstrate an improved performance of our joint inversion approach over traditional separate inversion methods in human thorax imaging.

关键词： Electrical impedance tomography microwave joint inversion thorax imaging ultrasound variational autoencoder

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LyricJam Sonic: A Generative System for Real-Time Composition and Musical Improvisation 12th

LyricJam Sonic: A Generative System for Real-Time Compositio...

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12th International Conference on Artificial Intelligence in Music, Sound, Art and Design (EvoMUSART) Held as Part of EvoStar Conference

作者： Vechtomova, Olga Sahu, Gaurav Univ Waterloo Waterloo ON Canada

ISBN: (纸本)9783031299551;9783031299568

Electronic music artists and sound designers have unique workflow practices that necessitate specialized approaches for developing music information retrieval and creativity support tools. Furthermore, electronic music instruments, such as modular synthesizers, have near-infinite possibilities for sound creation and can be combined to create unique and complex audio paths. The process of discovering interesting sounds is often serendipitous and impossible to replicate. For this reason, many musicians in electronic genres record audio output at all times while they work in the studio. Subsequently, it is difficult for artists to rediscover audio segments that might be suitable for use in their compositions from thousands of hours of recordings. In this paper, we describe LyricJam Sonic, a creative tool for musicians to rediscover their previous recordings, re-contextualize them with other recordings, and create original live music compositions in real-time. A bi-modal AI-driven approach uses generated lyric lines to find compatible audio clips from the artist's past studio recordings, and uses them to generate new lyric lines, which in turn are used to find other clips, thus creating a continuous and evolving stream of music and lyrics. The intent is to keep the artists in a state of creative flow conducive to music creation rather than taking them into an analytical/critical state of deliberately searching for past audio segments. The system can run in either a fully autonomous mode without user input, or in a live performance mode, where the artist plays live music, while the system "listens" and creates a continuous stream of music and lyrics in response (LyricJam Sonic: https://*** Demo videos: https://***/view/supplementarymaterial-for-evo/home).

关键词： Generative Music Lyrics Generation Neural Network variational autoencoder Generative Adversarial Network

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Deep generative modelling of aircraft trajectories in terminal maneuvering areas

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MACHINE LEARNING WITH APPLICATIONS 2023年 11卷

作者： Krauth, Timothe Lafage, Adrien Morio, Jerome Olive, Xavier Waltert, Manuel Zurich Univ Appl Sci Ctr Aviat Winterthur Switzerland Univ Toulouse ONERA DTIS F-31055 Toulouse France Tech str 71 CH-8400 Winterthur Switzerland

Airspace design is subject to a multitude of constraints, which are mainly driven by the concern to keep the risk of mid-air collision below a target level of safety. For that purpose, Monte Carlo simulation methods can be applied to estimate aircraft conflict probability but require the accurate generation of artificial trajectories. Generative models allow to generate an infinite number of trajectories for air traffic procedures where only few observations are available. The generated trajectories must not only resemble observed trajectories in terms of statistical distributions but they should stay flyable and consider uncertainty due to weather, air traffic control, aircraft performances, or human factors. This paper focuses on the generation problem, and its main contribution lies in the adaptation of the variational autoencoder structure to the problem of 4 -dimensional aircraft trajectories modelling using Temporal Convolutional Networks and a prior distribution composed of a variational Mixture of Posteriors (VampPrior). The proposed model has been trained on trajectories in the Terminal Manoeuvre Area of Zurich airport, which have a particularly high degree of variability as air traffic controllers often take actions that deviate aircraft from the nominal approach procedure. The model has demonstrated great abilities to take into account such amount of uncertainty. Regarding metrics that evaluate the estimation of the statistical distribution of the observed trajectories, and the flyability of the generated ones, the proposed method outperforms traditional statistical methods by being able to generate more complex and realistic trajectories.

关键词： Air traffic management Deep Generative Models Multivariate time-series generation variational autoencoder Temporal Convolutional Networks

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Sequential Segment-based Level Generation and Blending using variational autoencoders 20

Sequential Segment-based Level Generation and Blending using...

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15th International Conference on the Foundations of Digital Games

作者： Sarkar, Anurag Cooper, Seth Northeastern Univ Boston MA 02115 USA

ISBN: (纸本)9781450388078

Existing methods of level generation using latent variable models such as VAEs and GANs do so in segments and produce the final level by stitching these separately generated segments together. In this paper, we build on these methods by training VAEs to learn a sequential model of segment generation such that generated segments logically follow from prior segments. By further combining the VAE with a classifier that determines whether to place the generated segment to the top, bottom, left or right of the previous segment, we obtain a pipeline that enables the generation of arbitrarily long levels that progress in any of these four directions and are composed of segments that logically follow one another. In addition to generating more coherent levels of non-fixed length, this method also enables implicit blending of levels from separate games that do not have similar orientation. We demonstrate our approach using levels from Super Mario Bros., Kid Icarus and Mega Man, showing that our method produces levels that are more coherent than previous latent variable-based approaches and are capable of blending levels across games.

关键词： procedural content generation variational autoencoder level generation level blending game blending PCGML

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Answer selection in community question answering exploiting knowledge graph and context information

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SEMANTIC WEB 2022年第3期13卷 339-356页

作者： Boroujeni, Golshan Afzali Faili, Heshaam Yaghoobzadeh, Yadollah Univ Tehran Coll Engn Sch Elect & Comp Engn Tehran Iran Inst Res Fundamental Sci IPM Sch Comp Sci Tehran Iran

With the increasing popularity of knowledge graph (KG), many applications such as sentiment analysis, trend prediction, and question answering use KG for better performance. Despite the obvious usefulness of commonsense and factual information in the KGs, to the best of our knowledge, KGs have been rarely integrated into the task of answer selection in community question answering (CQA). In this paper, we propose a novel answer selection method in CQA by using the knowledge embedded in KGs. We also learn a latent-variable model for learning the representations of the question and answer, jointly optimizing generative and discriminative objectives. It also uses the question category for producing context-aware representations for questions and answers. Moreover, the model uses variational autoencoders (VAE) in a multi-task learning process with a classifier to produce class-specific representations for answers. The experimental results on three widely used datasets demonstrate that our proposed method is effective and outperforms the existing baselines significantly.

关键词： Community question answering knowledge graph context convolutional-deconvolutional variational autoencoder

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PCovNet: A presymptomatic COVID-19 detection framework using deep learning model using wearables data

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COMPUTERS IN BIOLOGY AND MEDICINE 2022年 147卷 105682-105682页

作者： Abir, Farhan Fuad Alyafei, Khalid Chowdhury, Muhammad E. H. Khandakar, Amith Ahmed, Rashid Hossain, Muhammad Maqsud Mahmud, Sakib Rahman, Ashiqur Abbas, Tareq O. Zughaier, Susu M. Naji, Khalid Kamal Univ Dhaka Dept Elect & Elect Engn Dhaka 1000 Bangladesh Qatar Univ Coll Engn Dept Mech & Ind Engn Doha 2713 Qatar Qatar Univ Dept Elect Engn Doha 2713 Qatar Qatar Univ Biomed Res Ctr Doha 2713 Qatar North South Univ NSU Genome Res Inst NGRI Dhaka 1229 Bangladesh Tohoku Univ Inst Multidisciplinary Res Adv Mat Sendai Japan Surg Dept Urol Div Sidra Med Doha 26999 Qatar Qatar Univ Coll Med Dept Basic Med Sci QU Hlth Doha 2713 Qatar Qatar Univ Coll Engn Doha 2713 Qatar

While the advanced diagnostic tools and healthcare management protocols have been struggling to contain the COVID-19 pandemic, the spread of the contagious viral pathogen before the symptom onset acted as the Achilles' heel. Although reverse transcription-polymerase chain reaction (RT-PCR) has been widely used for COVID-19 diagnosis, they are hardly administered before any visible symptom, which provokes rapid transmission. This study proposes PCovNet, a Long Short-term Memory variational autoencoder (LSTM-VAE)-based anomaly detection framework, to detect COVID-19 infection in the presymptomatic stage from the Resting Heart Rate (RHR) derived from the wearable devices, i.e., smartwatch or fitness tracker. The framework was trained and evaluated in two configurations on a publicly available wearable device dataset consisting of 25 COVID-positive individuals in the span of four months including their COVID-19 infection phase. The first configuration of the framework detected RHR abnormality with average Precision, Recall, and F-beta scores of 0.946, 0.234, and 0.918, respectively. However, the second configuration detected aberrant RHR in 100% of the subjects (25 out of 25) during the infectious period. Moreover, 80% of the subjects (20 out of 25) were detected during the presymptomatic stage. These findings prove the feasibility of using wearable devices with such a deep learning framework as a secondary diagnosis tool to circumvent the presymptomatic COVID-19 detection problem.

关键词： Presymptomatic COVID-19 Smartwatch Resting heart rate Anomaly detection Long short -term memory variational autoencoder

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Efficient Local Image Descriptors Learned With autoencoders

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IEEE ACCESS 2022年 10卷 221-235页

作者： Zizakic, Nina Pizurica, Aleksandra Univ Ghent TELIN GAIM Dept Telecommun & Informat Proc B-9000 Ghent Belgium

Local image descriptors play a crucial role in many image processing tasks, such as object tracking, object recognition, panorama stitching, and image retrieval. In this paper, we focus on learning local image descriptors in an unsupervised way, using autoencoders and variational autoencoders. We perform a thorough comparative analysis of these two approaches along with an in-depth analysis of the most relevant hyperparameters to guide their optimal selection. In addition to this analysis, we give insights into the difficulties and the importance of selecting right evaluation techniques during the unsupervised learning of the local image descriptors. We explore the extent to which a simple perceptual metric during training can predict the performance on tasks such as patch matching, retrieval and verification. Finally, we propose an improvement to the encoder architecture that yields significant savings in memory complexity, especially in single-image tasks. As a proof of concept, we integrate our descriptor into an inpainting algorithm and illustrate its results when applied to the virtual restoration of master paintings. The source code required to reproduce the presented results has been made available as a repository on GitHub (https://***/nimpy/local-img-descr-ae).

关键词： Task analysis Computer architecture Benchmark testing Training Deep learning Object tracking Neural networks Local image descriptor autoencoder variational autoencoder inpainting unsupervised deep learning

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variational cold-start resistant recommendation

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INFORMATION SCIENCES 2022年 605卷 267-285页

作者： Walker, Joojo Zhang, Fengli Zhong, Ting Zhou, Fan Baagyere, Edward Yellakuor Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu Peoples R China CK Tedam Univ Technol & Appl Sci Dept Comp Sci Navrongo Ghana

Conventionally, cold-start limitations are managed by leveraging side information such as social-trust relationships. However, the relationships between users in social networks are complex, uncertain, and sparse. Therefore, it is necessary to extract beneficial social connections to make the recommendation models cold-start resistant. Towards this end, we propose a novel recommendation model called variational Cold-start Resistant Recommendation (CORE-VAE). More concretely, we employ a social-aware similarity function and a graph convolutional network (GCN) to generate robust social-aware user representations that account for the complexities, uncertainties, and sparse nature of the social-trust network. Subsequently, these powerful social-aware representations aid us in producing cold-start resistant rating vectors for all users. To explore the rich user rating information, we propose an expressive variational autoencoder (VAE) model. Unlike earlier VAE-based CF models, CORE-VAE utilizes a novel prior distribution and a well-designed skip-generative network to alleviate the posterior collapse issue considerably. Besides, CORE-VAE can also capture the latent space's uncertainty and ensure that observations and their accompanying latent variables have high mutual information. Overall, these novel techniques dramatically help produce better latent representations for generating more accurate recommendations. We show that CORE-VAE outperforms numerous competitive baseline models on real-world datasets through comprehensive empirical evaluation and analysis. (C) 2022 Elsevier Inc. All rights reserved.

关键词： variational autoencoder Cold-start Social-trust information Graph convolutional network

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Generative geomodeling based on flow responses in latent space

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2022年第0期211卷 110177-110177页

作者： Jo, Suryeom Ahn, Seongin Park, Changhyup Kim, Jaejun Korea Inst Geosci & Mineral Resources GeoICT Convergence Res Team Daejeon 34132 South Korea Kangwon Natl Univ Dept Energy & Resources Engn Chunchon 24341 Kangwon South Korea Seoul Natl Univ Dept Energy Syst Engn Seoul 08826 South Korea

This paper presents a new deep-learning-based generative method applicable to history matching without an inverse scheme. Multiple-point geostatistics is used to construct a prior population stochastically. A convolutional variational autoencoder (VAE) with probabilistic latent space is trained as the generative method, and kmeans clustering, nondominated sorting, and multilevel geomodel generations are performed based on flow responses. The applicability of the developed workflow was confirmed using a waterflooding problem with multiple wells in fluvial channel reservoirs. The VAE generates new geomodels based on the latent features and builds equiprobable models neighboring the representative models that reflect the observed production performance. The geomodels match the oil production profiles reliably as the steps progress and accurately forecast the water breakthrough time and liquid production trajectories. The density map of plausible geomodels explains reasonably the uncertainty of channel connectivity. The structural similarity index confirms that the generated geomodels become similar to the target reservoir and thus that the developed VAE-based framework creates geomodels that preserve geological realism. This proposed method involves relatively less time-consuming simulations without any inverse or optimization processes;nonetheless, it generates plausible geomodels in dimensionality-reduced latent space. The study methods and findings are thus applicable to scale-variant data integration and uncertainty assessment.

关键词： Generative model variational autoencoder Latent space History matching Uncertainty Flow response

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