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IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP)

作者： Seki, Shogo Taga, Haruka Toda, Tomoki NTT Corp NTT Commun Sci Labs Atsugi Kanagawa Japan Nagoya Univ Grad Sch Informat Nagoya Aichi Japan Nagoya Univ Informat Technol Ctr Nagoya Aichi Japan

ISBN: (纸本)9781728163383

This paper proposes a method for achieving physically motivated and interpretable control of fundamental frequency (F0) contour generation in singing aid systems for laryngectomees. Recently proposed variational autoencoder (VAE)-based method, VAE-SPACE, has successfully generated singing F0 contours from musical scores. However, VAE-SPACE can generate physically deviated F0 contours. Moreover, to represent fluctuations in F0 contours, VAE-SPACE requires manual adjustment of noise components used as the input with musical scores. To address these issues, the proposed method 1) introduces a generalized command-response (GCR) model to represent an F0 contour as an approximation of a physical F0 production mechanism, and 2) employs a conditional VAE (CVAE) to treat musical scores and the noise components separately. The experimental results reveal that the proposed method achieves comparable performance as VAE-SPACE without the manual adjustment of noise components and makes it possible to control F0 contours more intuitively by using the trained GCR model.

关键词： Singing aid system F0 contour generation Generalized command-response model conditional variational autoencoder

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Autonomous Vehicle Path Prediction Using conditional variational autoencoder Networks 1

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25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Jagadish, D. N. Chauhan, Arun Mahto, Lakshman Indian Inst Informat Technol Dharwad Dharwad 580009 Karnataka India

ISBN: (数字)9783030757625

ISBN: (纸本)9783030757625;9783030757618

Path prediction of autonomous vehicles is an essential requirement under any given traffic scenario. Trajectory of several agent vehicles in the vicinity of ego vehicle, at least for a short future, is needed to be predicted in order to decide upon the maneuver of the ego vehicle. We explore variational autoencoder networks to obtain multimodal trajectories of agent vehicles. In our work, we condition the network on past trajectories of agents and traffic scenes as well. The latent space representation of traffic scenes is achieved by using another variational autoencoder network. The performance of the proposed networks is compared against a residual baseline model.

关键词： Autonomous vehicle path prediction conditional variational autoencoder Deep learning

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Recommending Changes on QoE Factors with conditional variational autoencoder 4

Recommending Changes on QoE Factors with Conditional Variati...

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4th ACM SIGCOMM FlexNets Workshop on Flexible Networks, Artificial Intelligence Supported Network Flexibility and Agility (FlexNets)

作者： Ickin, Selim Ericsson AB Stockholm Sweden

ISBN: (纸本)9781450386340

Increasing complexity in management of immense number of network elements and their dynamically changing environment necessitates machine learning based recommendation models to guide human experts in setting appropriate network configurations to sustain end-user Quality of Experience (QoE). In this paper, we present and demonstrate a generative conditional variational autoencoder (CVAE)-based technique to reconstruct realistic underlying QoE factors together with improvement suggestions in a video streaming use case. Based on our experiment setting consisting of a set of what-if scenarios, our approach pinpointed the potential required changes on the QoE factors to improve the estimated video Mean Opinion Scores (MOS).

关键词： Machine Learning conditional variational autoencoder Quality of Experience

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methCancer-gen: a DNA methylome dataset generator for user-specified cancer type based on conditional variational autoencoder

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BMC BIOINFORMATICS 2020年第1期21卷 1-10页

作者： Choi, Joungmin Chae, Heejoon Sookmyung Womens Univ Div Comp Sci Seoul South Korea

BackgroundRecently, DNA methylation has drawn great attention due to its strong correlation with abnormal gene activities and informative representation of the cancer status. As a number of studies focus on DNA methylation signatures in cancer, demand for utilizing publicly available methylome dataset has been increased. To satisfy this, large-scale projects were launched to discover biological insights into cancer, providing a collection of the dataset. However, public cancer data, especially for certain cancer types, is still limited to be used in research. Several simulation tools for producing epigenetic dataset have been introduced in order to alleviate the issue, still, to date, generation for user-specified cancer type dataset has not been *** this paper, we present methCancer-gen, a tool for generating DNA methylome dataset considering type for cancer. Employing conditional variational autoencoder, a neural network-based generative model, it estimates the conditional distribution with latent variables and data, and generates samples for specified cancer *** evaluate the simulation performance of methCancer-gen for the user-specified cancer type, our proposed model was compared to a benchmark method and it could successfully reproduce cancer type-wise data with high accuracy helping to alleviate the lack of condition-specific data issue. methCancer-gen is publicly available at https://***/cbi-bioinfo/methCancer-gen.

关键词： DNA methylation Cancer Generator conditional variational autoencoder Simulator

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Emotional Response Generation using conditional variational autoencoder

Emotional Response Generation using Conditional Variational ...

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IEEE International Conference on Big Data and Smart Computing (BigComp)

作者： Lee, Young-Jun Choi, Ho-Jin Korea Adv Inst Sci & Technol Sch Comp Daejeon South Korea

ISBN: (纸本)9781728160344

Neural response generation is to generate human-like response given human utterance by using a deep learning. In the previous studies, expressing emotion in response generation improve user performance, user engagement, and user satisfaction. Also, the conversational agents can communicate with users at the human level. however, the previous emotional response generation model cannot interpret why the model generates such response with emotions. We propose an interpretable emotional response generation model which generates emotional responses by using a latent space. The extraction part is to extract the emotion of input utterance as a vector form by using the Bidirectional GRU based classification model. The generation part is to generate an emotional response to the input utterance by exploiting emotion vector and latent space. All of these parts are jointly optimized at the training process. We will evaluate our model on the emotion-labeled dialogue dataset: DailyDialog.

关键词： Emotional response generation emotion classification model conditional variational autoencoder interpretability

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A conditional variational autoencoder Algorithm for Reconstructing Defect Data of Magnetic Flux Leakage 32

A Conditional Variational Autoencoder Algorithm for Reconstr...

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32nd Chinese Control And Decision Conference (CCDC)

作者： Lu, Senxiang Wu, Jing Zhang, Jianan Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Peoples R China Liaoning Prov Inst Measurement Shenyang 110004 Peoples R China

ISBN: (纸本)9781728158556

Magnetic flux leakage (MFL) testing is widely used in pipeline nondestructive testing. The acquisition quality of MFL signal is an important prerequisite for the accuracy of pipeline detection and evaluation. However, data loss often occurs in the process of detection, due to the interference of external environmental factors. Therefore, it is important to reconstruct the missing data. This paper analyzes four types of MFL data loss, and presents a conditional variational autoencoder algorithm to reconstruct defect data of MFL for these types of loss. The algorithm effectiveness is tested by comparing with traditional variational autoencoder method. The results demonstrate that the proposed method can improve the accuracy of reconstructing defect data loss.

关键词： Magnetic Flux Leakage Data Loss conditional variational autoencoder Defect Data Reconstructing

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Individualized Estimation of Baseline Retinal Nerve Fiber Layer Thickness Using conditional variational autoencoder

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Ophthalmology Science 2025年

作者： Ou Tan Keke Liu Aiyin Chen Dongseok Choi Jonathan C.H. Chan Bonnie N.K. Choy Kendrick C. Shih Jasper K.W. Wong Alex L.K. Ng Janice J.C. Cheung Michael Y. Ni Jimmy S.M. Lai Gabriel M. Leung Ian Y.H. Wong David Huang Casey Eye Institute Oregon Health & Science University Portland Oregon USA Department of Ophthalmology Duke University School of Medicine Durham North Carolina USA Department of Ophthalmology LKS Faculty of Medicine The University of Hong Kong The University of Hong Kong Hong Kong School of Public Health LKS Faculty of Medicine The University of Hong Kong Hong Kong Special Administrative Region China The State Key Laboratory of Brain and Cognitive Sciences. The University of Hong Kong Hong Kong Special Administrative Region China Healthy High Density Cities Lab HKUrbanLab The University of Hong Kong Hong Kong Special Administrative Region China Department of Ophthalmology Hong Kong Sanatorium & Hospital Hong Kong

Purpose Use generative deep learning (DL) models to estimate baseline reference nerve fiber layer thickness (NFLT) profile, taking into account individual ocular characteristics. Design Cross-sectional study. Participants 686 individuals from the Hong Kong Family cohort and 75 individuals from the Casey Eye Institute (CEI) cohort. Methods Healthy eyes were selected from the Hong Kong FAMILY and CEI cohorts. Circumpapillary NFLT profiles and vascular patterns were measured by a spectral-domain optical coherence tomography (OCT). Generative DL models were trained using the FAMILY data to reconstruct the individualized baseline NFLT, a customized normal reference based on each eye’s own vascular pattern, axial length (AL), spherical equivalent (SE) refractive error, disc size, and demography information. Two deep learning models were developed. The MAG model used actual AL and SE, while the REG model estimated AL and SE using vascular patterns as input. For comparison, a multiple linear regression (MLR) was trained to estimate baseline NFLT using AL and demographic information. Five-fold Cross-validation was used to assess performance. Main Outcome Measures The prediction error: root-mean-square of the difference between the actual NFLT profile and the predicted individualized baseline. Results A total of 1152 healthy eyes from 686 participants in the Hong Kong Family cohort were divided into four subgroups: high myopia (SE<-6D), low myopia (SE=-6D∼-1D), emmetropia (SE=-1D∼1D), and hyperopia (SE>1D). Compared with the population means, both DL models significantly reduced the prediction error for overall and quadrant NFLT and decreased the false-positive rate of identifying abnormal NFLT thinning in both myopia groups (from 13.0%-27.0% to 6.7%∼9.4%). Both DL models significantly reduced prediction error for the NFLT profiles compared to both the population mean and the MLR-adjusted NFLT. The reductions in prediction errors for NFLT profile and overall NFLT value were i

关键词： glaucoma optical coherence tomography nerve fiber layer thickness conditional variational autoencoder AL Axial Length CVAE conditional variational autoencoder rCNN Convolutional neural network for regression ILM Inner limiting membrane MLR Multiple linear regression OCT Optical coherence tomography RPE Retina pigment epithelium NFLT Retinal nerve fiber layer thickness SSI Signal strength index SE Spherical equivalent refractive error

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Trajectory Prediction with a conditional variational autoencoder

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JOURNAL OF ROBOTICS AND MECHATRONICS 2019年第3期31卷 493-499页

作者： Barbie, Thibault Nishio, Takaki Nishida, Takeshi Kyushu Inst Technol Dept Mech & Control Engn 1-1 Sensui Kitakyushu Fukuoka 8048550 Japan

Conventional motion planners do not rely on previous experience when presented with a new problem. Trajectory prediction algorithms solve this problem using a pre-existing dataset at runtime. We propose instead using a conditional variational autoencoder (CVAE) to learn the distribution of the motion dataset and hence to generate trajectories for use as priors within the traditional motion planning approaches. We demonstrate, through simulations and by using an industrial robot arm with six degrees of freedom, that our trajectory prediction algorithm generates more collision-free trajectories compared to the linear initialization, and reduces the computation time of optimization-based planners.

关键词： trajectory prediction machine learning conditional variational autoencoder

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Crustal model in eastern Qinghai-Tibet plateau and western Yangtze craton based on conditional variational autoencoder

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PHYSICS OF THE EARTH AND PLANETARY INTERIORS 2020年 309卷

作者： Cheng, Xianqiong Jiang, Kezhi Chengdu Univ Technol Coll Geophys Chengdu 610059 Peoples R China Chengdu Univ Technol Coll Engn & Technol Leshan 614000 Peoples R China

Eastern margin of the Qinghai-Tibet plateau and western margin of the Yangtze craton has a complex crust mantle structure and is an important region for studying the crust-mantle deformation mechanism. Because the Rayleigh surface wave group velocity has a strong constraint on the structure of the crust and upper mantle, a lot of researches have used the Rayleigh surface wave group velocity to invert the crust-mantle structure in this region, but the layered multi-parameter joint inversion and the uncertainty of the inversion result evaluation is still a research hotspot. Motived by conditional variational autoencoder (CVAE) which is composed of a decoding and encoding process that uses a deep neural network to predict the variational distribution of parameters, we propose a crustal model by CVAE to attain the forward and inversion relationship between the group velocity of Rayleigh wave and the crustal model. In which the CVAE decoding corresponds to the forward process and the encoding corresponds to the inverse process. Moreover, the inversion results of this method are constrained from three aspects as follows: The network inputs and outputs have the same group velocities;The decoded outputs are normal distribution of hidden variables representing the crustal model;The mean values of the decoded output during CVAE training are the sampled crustal models. The proposed method can invert multi-parameters and evaluate uncertainty of the inversion efficiently. Based on the latest group velocity model, we invert for mean and variance of thickness in the upper, middle, and lower sedimentary cover, and of thickness, P-wave velocity, S-wave velocity and density in the upper, middle, and lower crystalline crust for eastern Qinghai-Tibet plateau and western Yangtze craton. Compared with the existing crustal models obtained by different methods, the results of this study are in good agreement. We conclude that CVAE-based group velocity inversion of the crustal model is a fe

关键词： Crustal model conditional variational autoencoder Eastern Qinghai-Tibet plateau and western Yangtze craton Data-driven

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Optimization of force-field potential parameters using conditional variational autoencoder

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SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS-METHODS 2023年第1期3卷

作者： Matsunoshita, Koki Yamaguchi, Yudai Hamaie, Masato Horibe, Motoki Tanibata, Naoto Takeda, Hayami Nakayama, Masanobu Karasuyama, Masayuki Kobayashi, Ryo Nagoya Inst Technol Dept Adv Ceram Nagoya Aichi 4668555 Japan Nagoya Inst Technol Dept Comp Sci Nagoya Japan Nagoya Inst Technol Dept Appl Phys Nagoya Aichi 4668555 Japan

Owing to their high-speed, force-field (FF) calculations for inorganic solid-state materials using the parametric potential have been widely employed as an effective tool for high-throughput calculations such as exhaustive property evaluation of material databases and/or calculations for models containing tens of thousands of atoms or more, including amorphous and grain boundary models. However, the accuracy of such calculations strongly depends on the choice of the FF parameters. Today, compounds containing three or more elements are targeted in the research and development of functional materials, in which case the number of parameters often exceeds 10 dimensions. This significantly increases the search space, making it difficult to determine the parameters rationally. To resolve this issue, we previously reported an FF parameter determination approach based on meta-heuristics (APL Materials, 8, 081111 (2020)). In this study, we further investigated a methodology to efficiently determine the FF parameters using a conditional variational autoencoder (CVAE), a type of deep learning method, which can reduce the dimension of the input parameters and distribute them in a probabilistic manner in the latent space. As a demonstration, we applied the method to the optimization of 11 FF parameters for an argyrodite-type Li7PS6 material, which has attracted considerable attention as a solid electrolyte for all-solid-state batteries. The results confirmed that the proposed approach can generate valid FFs that are highly consistent with the results of first-principles calculations, even when using a limited set of meta-heuristics-generated FF parameters.

关键词： Force-field parameters cuckoo search conditional variational autoencoder

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