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variational Deep Clustering of Wafer Map Patterns

IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING 2020年第3期33卷 466-475页

作者： Hwang, Jonghyun Kim, Heeyoung Korea Adv Inst Sci & Technol Dept Ind & Syst Engn Daejeon 34141 South Korea

In semiconductor manufacturing, several measurement data called wafer maps are obtained in the metrology steps, and the variations in the process are detected by analyzing the wafer map data. Hidden processes or equipment affecting the process quality variations can be found by comparing the process tracking history and clustered groups of similar wafer maps;thus, clustering analysis is very important to reduce the process quality variations. Currently, clustering wafer maps are becoming more difficult as the wafer maps are formed into more complex patterns along with high-dimensional data. For more effective clustering of complex and high-dimensional wafer maps, we implement a Gaussian mixture model to a variational autoencoder framework to extract features that are more suitable to the clustering environment, and a Dirichlet process is further applied in the variational autoencoder mixture framework for automated one-step clustering. The proposed method is validated using a real dataset from a global semiconductor manufacturing company, and we demonstrate that it is more effective than other competitive methods in determining the number of clusters and clustering wafer map patterns.

关键词： Bayesian nonparametrics clustering deep neural network Dirichlet process Gaussian mixture model semiconductor manufacturing variational autoencoder

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Synthetic whole-slide image tile generation with gene expression profile-infused deep generative models

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CELL REPORTS METHODS 2023年第8期3卷 100534页

作者： Carrillo-Perez, Francisco Pizurica, Marija Ozawa, Michael G. Vogel, Hannes West, Robert B. Kong, Christina S. Herrera, Luis Javier Shen, Jeanne Gevaert, Olivier Stanford Univ Stanford Ctr Biomed Informat Res BMIR Dept Med 1265 Welch Rd Stanford CA 94305 USA Univ Granada Comp Engn Automatics & Robot Dept C Periodista Daniel Saucedo Aranda S-N Granada 18014 Spain Univ Ghent Internet Technol & Data Sci Lab IDLab Technol Pk Zwijnaarde 126 B-9052 Ghent Belgium Stanford Univ Sch Med Dept Pathol 300 Pasteur Dr Palo Alto CA 94304 USA Stanford Univ Sch Med Dept Biomed Data Sci Med Sch Off Bldg MSOB1265 Welch Rd Stanford CA 94305 USA

In this work, we propose an approach to generate whole-slide image (WSI) tiles by using deep generative models infused with matched gene expression profiles. First, we train a variational autoencoder (VAE) that learns a latent, lower-dimensional representation of multi-tissue gene expression profiles. Then, we use this representation to infuse generative adversarial networks (GANs) that generate lung and brain cortex tissue tiles, resulting in a new model that we call RNA-GAN. Tiles generated by RNA-GAN were preferred by expert pathologists compared with tiles generated using traditional GANs, and in addition, RNA-GAN needs fewer training epochs to generate high-quality tiles. Finally, RNA-GAN was able to generalize to gene expression profiles outside of the training set, showing imputation capabilities. A web-based quiz is available for users to play a game distinguishing real and synthetic tiles: https://***/, and the code for RNA-GAN is available here: https://***/gevaertlab/RNA-GAN.

关键词： deep learning generative adversarial network synthetic biomedical data variational autoencoder artificial intelligence generative model CP: Systems biology

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Single-cell multi-omics topic embedding reveals cell-type-specific and COVID-19 severity-related immune signatures

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CELL REPORTS METHODS 2023年第8期3卷 100563页

作者： Zhou, Manqi Zhang, Hao Bai, Zilong Mann-Krzisnik, Dylan Wang, Fei Li, Yue Cornell Univ Dept Computat Biol Ithaca NY 14853 USA Inst Artificial Intelligence Digital Hlth Weill Cornell Med New York NY 10021 USA Weill Cornell Med Div Hlth Informat Dept Populat Hlth Sci New York NY 10021 USA McGill Univ Quantitat Life Sci Montreal PQ H3A 0G4 Canada McGill Univ Sch Comp Sci Montreal PQ H3A 0G4 Canada Mila Quebec AI Inst Montreal PQ H2S 3H1 Canada

The advent of single-cell multi-omics sequencing technology makes it possible for researchers to leverage multiple modalities for individual cells and explore cell heterogeneity. However, the high-dimensional, discrete, and sparse nature of the data make the downstream analysis particularly challenging. Here, we propose an interpretable deep learning method called moETM to perform integrative analysis of high -dimensional single-cell multimodal data. moETM integrates multiple omics data via a product-of-experts in the encoder and employs multiple linear decoders to learn the multi-omics signatures. moETM demonstrates superior performance compared with six state-of-the-art methods on seven publicly available datasets. By applying moETM to the scRNA + scATAC data, we identified sequence motifs corresponding to the transcription factors regulating immune gene signatures. Applying moETM to CITE-seq data from the COVID-19 patients revealed not only known immune cell-type-specific signatures but also composite multi-omics biomarkers of critical conditions due to COVID-19, thus providing insights from both biological and clinical perspectives.

关键词： SM single-cell multiomics VAE variational autoencoder ETM embedding topipc model DL deep learning CP: Systems biology CP: Immunology

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Bayesian Multi-Temporal-Difference Learning

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APSIPA TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING 2022年第1期11卷

作者： Chien, Jen-Tzung Chiu, Yi-Chung Natl Yang Ming Chiao Tung Univ Inst Elect & Comp Engn Hsinchu Taiwan

This paper presents a new sequential learning via a planning strategy where the future samples are predicted by reflecting the past experiences. Such a strategy is appealing to implement an intelligent machine which foresees multiple time steps instead of predicting step by step. In particular, a flexible sequential learning is developed to directly predict future states without visiting all intermediate states. A Bayesian approach to multi-temporal-difference neural network is accordingly proposed to calculate the stochastic belief state for an abstract state machine so as to capture large-span context as well as make high-level prediction. Importantly, the sequence data are represented by multiple jumpy states with varying temporal differences. A Bayesian state machine is trained by maximizing the variational lower bound of log likelihood of sequence data. A generalized sequence model with various number of Markov states is derived with the simplified realization to the previous temporal-difference variational autoencoder. The predictive states are learned to roll forward with jumps. Experiments show that this approach is substantially trained to predict jumpy states in various types of sequence data.

关键词： Bayesian learning variational autoencoder sequential learning temporal-difference learning state machine

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Direct feature extraction from two-dimensional X-ray diffraction images of semiconductor thin films for fabrication analysis

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SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS-METHODS 2022年第1期2卷 23-37页

作者： Yamashita, Akihiro Nagata, Takahiro Yagyu, Shinjiro Asahi, Toru Chikyow, Toyohiro Waseda Univ Dept Adv Sci & Engn Tokyo Japan Natl Inst Mat Sci Res & Serv Div Mat Data & Integrated Syst Tsukuba Japan Natl Inst Mat Sci Res Ctr Funct Mat 1-1 Namiki Tsukuba 3050044 Japan

We built a workflow for the fabrication analysis of thin films by applying machine-learning (ML) techniques directly to the measurement data. This will lower the problem in cost of synthesizing and analyzing samples to improve the fabrication conditions. The workflow combines two ML techniques: non-negative matrix factorization (NMF) and variational autoencoder (VAE). The measurement data were two-dimensional X-ray diffraction of indium-gallium oxide system thin films. The thin films were fabricated by physical vapor techniques under multiple conditions. First, the workflow was applied to the data of the thin films fabricated through pulsed laser deposition as a proof of concept. We found that our workflow extracted features that represented crystallinity differences in addition to substrate differences. Second, VAE was analyzed to determine whether it could generate new data from its latent space. The latent space of the VAE, which learned the extracted features, represented the relationship between the fabrication conditions such as laser intensities and crystallinity. Third, the inference ability of the new data fabricated through sputtering was evaluated. The capability of the workflow we confirmed will support researchers in improving fabrication conditions by visually comparing various fabricated samples.

关键词： Two-dimensional x-ray diffraction thin films machine learning feature extraction dimensionality reduction non-negative matrix factorization variational autoencoder Materials data analysis and utilization

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Disentangled Human Body Embedding Based on Deep Hierarchical Neural Network

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2020年第8期26卷 2560-2575页

作者： Jiang, Boyi Zhang, Juyong Cai, Jianfei Zheng, Jianmin Univ Sci & Technol China Sch Math Sci Hefei 230052 Anhui Peoples R China Monash Univ Fac IT Clayton Vic 3800 Australia Nanyang Technol Univ Sch Engn & Comp Sci Singapore 639798 Singapore

Human bodies exhibit various shapes for different identities or poses, but the body shape has certain similarities in structure and thus can be embedded in a low-dimensional space. This article presents an autoencoder-like network architecture to learn disentangled shape and pose embedding specifically for the 3D human body. This is inspired by recent progress of deformation-based latent representation learning. To improve the reconstruction accuracy, we propose a hierarchical reconstruction pipeline for the disentangling process and construct a large dataset of human body models with consistent connectivity for the learning of the neural network. Our learned embedding can not only achieve superior reconstruction accuracy but also provide great flexibility in 3D human body generation via interpolation, bilinear interpolation, and latent space sampling. The results from extensive experiments demonstrate the powerfulness of our learned 3D human body embedding in various applications.

关键词： Shape Strain Three-dimensional displays Biological system modeling Computational modeling Solid modeling Face 3D body shape 3D human articulated body model variational autoencoder deformation representation hierarchical structure

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Towards safe reinforcement-learning in industrial grid-warehousing

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INFORMATION SCIENCES 2020年 537卷 467-484页

作者： Andersen, Per-Arne Goodwin, Morten Granmo, Ole-Christoffer Univ Agder Dept ICT Jon Lilletuns Vei 9 N-4879 Grimstad Norway

Reinforcement learning has shown to be profoundly successful at learning optimal policies for simulated environments using distributed training with extensive compute capacity. Model-free reinforcement learning uses the notion of trial and error, where the error is a vital part of learning the agent to behave optimally. In mission-critical, real-world environments, there is little tolerance for failure and can cause damaging effects on humans and equipment. In these environments, current state-of-the-art reinforcement learning approaches are not sufficient to learn optimal control policies safely. On the other hand, model-based reinforcement learning tries to encode environment transition dynamics into a predictive model. The transition dynamics describes the mapping from one state to another, conditioned on an action. If this model is accurate enough, the predictive model is sufficient to train agents for optimal behavior in real environments. This paper presents the Dreaming variational autoencoder (DVAE) for safely learning good policies with a significantly lower risk of catastrophes occurring during training. The algorithm combines variational autoencoders, risk-directed exploration, and curiosity to train deep-q networks inside "dream" states. We introduce a novel environment, ASRS-Lab, for research in the safe learning of autonomous vehicles in grid-based warehousing. The work shows that the proposed algorithm has better sample efficiency with similar performance to novel model-free deep reinforcement learning algorithms while maintaining safety during training. (C) 2020 The Author(s). Published by Elsevier Inc.

关键词： Model-based reinforcement learning Neural networks variational autoencoder Markov decision processes Exploration Safe reinforcement learning

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Scalable image generation and super resolution using generative adversarial networks

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GA...

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JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY 2020年第2期35卷 953-966页

作者： Turhan, Ceren Guzel Bilge, Hasan Sakir Gazi Univ Comp Engn Dept TR-06570 Ankara Turkey Gazi Univ Elect Elect Engn Dept TR-06570 Ankara Turkey

Generative adversarial training has been one of the most active research topics and many researchers have conducted their studies on Generative Adversarial Network (GAN) shortly after it is claimed to be one of the most promising research area of the last decade by pioneers of the deep learning community. On the other hand, the idea behind generators has also reemerged autoencoder models such as variational autoencoder (VAE). Therefore, autoencoder models have gained their popularity back. Some restrictions of GAN models such as lack of inference mechanism, GAN and VAE based hybrid models have proposed addressing image generation. With the effect of these notions and studies, we have also considered VAE and GAN hybrid models. For obtaining synthetic but at the same time high-resolution handwritten-looking images without any training, Compositional Pattern Producing Network (CPPN) is adapted from previous studies for combining with VAE and adversarial training. For improving generation capabilities, an objective from a previous VAE/GAN model is also adapted for our VAE/CPGAN hybrid model. For analyzing the proposed model performance, baseline models such as GAN, VAE and VAE/GAN are also evaluated for comparisons. In this paper. it is clearly seen the proposed model is capable of the generating realistic and scalable super resolution synthetic images on a common dataset.

关键词： Deep learning variational autoencoder generative adversarial network image generation scalable super resolution

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Enhancing Long Term Fairness in Recommendations with variational autoencoders 19

Enhancing Long Term Fairness in Recommendations with Variati...

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Proceedings of the 11th International Conference on Management of Digital EcoSystems

作者： Rodrigo Borges Kostas Stefanidis University of São Paulo & Tampere University São Paulo Brazil Tampere University Tampere Finland

ISBN: (纸本)9781450362382

Recommender systems have become indispensable for several Web sites, helping users deal with big amounts of data. They are capable of analyzing user/item interactions taking place on-line, and provide each user with a list of suggestions sorted by relevance. Items with the same or very close relevance, however, may occupy different positions in the ranking and may be exposed to completely different levels of attention. This promotes unfair treatment and can only be addressed by a long term *** autoencoders (VAEs) were recently proposed as the state-of-the-art for collaborative filtering recommendations, but as every other approach, they generate homogeneous prediction scores among the highest positions. In this paper, we propose incorporating randomness in the regular operation of VAEs in order to increase the fairness (mitigate the position bias) in multiple rounds of recommendation. We argue that adding a noise component when sampling values from VAE's latent representation provides long term fairness, despite of a tolerable decrease in ranking quality (NDCG). We calculate the trade-off between unfairness and NDCG when introducing 4 different noise *** solution has proved to be a very practical one and the results point for a clear positive effect of turning recommendation far more fair, despite some small NDCG loss in Movie Lens, Netflix and MSD datasets. In our best scenario, the unfairness was reduced by 76% despite a decrease of 5% in the quality of ranking.

关键词： Collaborative Filtering Recommendation Systems Position Bias variational autoencoder Fairness in Ranking

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Exploiting Missing Value Patterns for a Backdoor Attack on Machine Learning Models of Electronic Health Records: Development and Validation Study

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JMIR MEDICAL INFORMATICS 2022年第8期10卷 e38440页

作者： Joe, Byunggill Park, Yonghyeon Hamm, Jihun Shin, Insik Lee, Jiyeon Korea Adv Inst Sci & Technol Sch Comp Daejeon South Korea Elect & Telecommun Res Inst Daejeon South Korea Tulane Univ Dept Comp Sci New Orleans LA 70118 USA Soongsil Univ Sch AI Convergence Seoul South Korea

Background: A backdoor attack controls the output of a machine learning model in 2 stages. First, the attacker poisons the training data set, introducing a back door into the victim's trained model. Second, during test time, the attacker adds an imperceptible pattern called a trigger to the input values, which forces the victim's model to output the attacker's intended values instead of true predictions or decisions. While backdoor attacks pose a serious threat to the reliability of machine learning-based medical diagnostics, existing backdoor attacks that directly change the input values are detectable relatively easily. Objective: The goal of this study was to propose and study a robust backdoor attack on mortality-prediction machine learning models that use electronic health records. We showed that our backdoor attack grants attackers full control over classification outcomes for safety-critical tasks such as mortality prediction, highlighting the importance of undertaking safe artificial intelligence research in the medical field. Methods: We present a trigger generation method based on missing patterns in electronic health record data. Compared to existing approaches, which introduce noise into the medical record, the proposed backdoor attack makes it simple to construct backdoor triggers without prior knowledge. To effectively avoid detection by manual inspectors, we employ variational autoencoders to learn the missing patterns in normal electronic health record data and produce trigger data that appears similar to this data. Results: We experimented with the proposed backdoor attack on 4 machine learning models (linear regression, multilayer perceptron, long short-term memory, and gated recurrent units) that predict in-hospital mortality using a public electronic health record data set. The results showed that the proposed technique achieved a significant drop in the victim's discrimination performance (reducing the area under the precision-recall curve by at

关键词： medical machine learning neural network mortality prediction backdoor attack electronic health record data Medical Information Mart for Intensive Care-III missing value mask meta-information variational autoencoder

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