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Multi-objective latent space optimization of generative molecular design models

PATTERNS 2024年第10期5卷 101042页

作者： Abeer, A. N. M. Nafiz Urban, Nathan M. Weil, M. Ryan Alexander, Francis J. Yoon, Byung-Jun Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA Brookhaven Natl Lab Computat Sci Initiat Upton NY 11973 USA Frederick Natl Lab Strateg & Data Sci Initiat Frederick MD 21702 USA Argonne Natl Lab Comp Environm & Life Sci Lemont IL 60439 USA

Molecular design based on generative models, such as variational autoencoders (VAEs), has become increasingly popular in recent years due to its efficiency for exploring high-dimensional molecular space to identify molecules with desired properties. While the efficacy of the initial model strongly depends on the training data, the sampling efficiency of the model for suggesting novel molecules with enhanced properties can be further enhanced via latent space optimization (LSO). In this paper, we propose a multi-objective LSO method that can significantly enhance the performance of generative molecular design (GMD). The proposed method adopts an iterative weighted retraining approach, where the respective weights of the molecules in the training data are determined by their Pareto efficiency. We demonstrate that our multi-objective GMD LSO method can significantly improve the performance of GMD for jointly optimizing multiple molecular properties.

关键词： generative molecular design GMD variational autoencoder VAE drug discovery latent space optimization LSO multi-objective optimization

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DISENTANGLEMENT LEARNING FOR variational autoencoderS APPLIED TO AUDIO-VISUAL SPEECH ENHANCEMENT

DISENTANGLEMENT LEARNING FOR VARIATIONAL AUTOENCODERS APPLIE...

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IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)

作者： Carbajal, Guillaume Richter, Julius Gerkmann, Timo Univ Hamburg Signal Proc SP Hamburg Germany

ISBN: (纸本)9781665448703

Recently, the standard variational autoencoder has been successfully used to learn a probabilistic prior over speech signals, which is then used to perform speech enhancement. variational autoencoders have then been conditioned on a label describing a high-level speech attribute (e.g. speech activity) that allows for a more explicit control of speech generation. However, the label is not guaranteed to be disentangled from the other latent variables, which results in limited performance improvements compared to the standard variational autoencoder. In this work, we propose to use an adversarial training scheme for variational autoencoders to disentangle the label from the other latent variables. At training, we use a discriminator that competes with the encoder of the variational autoencoder. Simultaneously, we also use an additional encoder that estimates the label for the decoder of the variational autoencoder, which proves to be crucial to learn disentanglement. We show the benefit of the proposed disentanglement learning when a voice activity label, estimated from visual data, is used for speech enhancement.

关键词： Speech enhancement conditional generative model variational autoencoder disentanglement learning adversarial training semi-supervised learning audio-visual.

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PriorVAE: encoding spatial priors with variational autoencoders for small-area estimation

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JOURNAL OF THE ROYAL SOCIETY INTERFACE 2022年第191期19卷 20220094-20220094页

作者： Semenova, Elizaveta Xu, Yidan Howes, Adam Rashid, Theo Bhatt, Samir Mishra, Swapnil Flaxman, Seth Univ Oxford Oxford Oxon England Univ Michigan Ann Arbor MI USA Imperial Coll London London England Univ Copenhagen Copenhagen Denmark

Gaussian processes (GPs), implemented through multivariate Gaussian distributions for a finite collection of data, are the most popular approach in small-area spatial statistical modelling. In this context, they are used to encode correlation structures over space and can generalize well in interpolation tasks. Despite their flexibility, off-the-shelf GPs present serious computational challenges which limit their scalability and practical usefulness in applied settings. Here, we propose a novel, deep generative modelling approach to tackle this challenge, termed PriorVAE: for a particular spatial setting, we approximate a class of GP priors through prior sampling and subsequent fitting of a variational autoencoder (VAE). Given a trained VAE, the resultant decoder allows spatial inference to become incredibly efficient due to the low dimensional, independently distributed latent Gaussian space representation of the VAE. Once trained, inference using the VAE decoder replaces the GP within a Bayesian sampling framework. This approach provides tractable and easy-to-implement means of approximately encoding spatial priors and facilitates efficient statistical inference. We demonstrate the utility of our VAE two-stage approach on Bayesian, small-area estimation tasks.

关键词： spatial modelling Gaussian process prior small-area estimation Bayesian inference variational autoencoder

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Uncertainty-aware structural damage warning system using deep variational composite neural networks

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EARTHQUAKE ENGINEERING & STRUCTURAL DYNAMICS 2023年第11期52卷 3345-3368页

作者： Eltouny, Kareem A. Liang, Xiao SUNY Buffalo Dept Civil Struct & Environm Engn Buffalo NY 14260 USA

Structural health monitoring (SHM) is, without a doubt, one of the most important assets for building resilient communities. The vast and rapidly advancing research in data science and machine learning has provided researchers in the civil engineering community with various tools that can facilitate the processing of significant amounts of gathered data. However, deep learning models are prone to mistakes, and with the catastrophic consequences that can happen due to damage misidentification, damage diagnosis models' predictions should not be taken for granted. In this study, we present an uncertainty-aware early-warning system that can provide near real-time SHM. The system utilizes a deep composite encoder-decoder network that combines elements from convolutional neural networks, recurrent neural networks, and variational inference (VI) to provide damage index distributions. The framework can detect anomalies in the structural system during seismic events and provide a measure of uncertainty that can be used to question the model's predictions. To assess the system's validity and practicality, we apply our proposal to three real structures, two of which suffered damage during the 1994 Northridge earthquake. We found that the early warning system delivers an accurate, yet cautious, continuous monitoring that is capable of sending warning signals when damage occurs in the course of seismic events. Source code is available at: https://***/keltouny/VSCAN.

关键词： anomaly detection deep learning recurrent neural network structural health monitoring uncertainty quantification variational autoencoder

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Joint Source-Channel Coding for a Multivariate Gaussian Over a Gaussian MAC Using variational Domain Adaptation

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2023年第6期9卷 1424-1437页

作者： Li, Yishen Chen, Xuechen Deng, Xiaoheng Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China

With the development of the distributed learning and edge computing, servers must often receive information from multiple terminal devices;thus, the importance of source-channel coding for distributed sources over multiple access channels (MACs) becomes more and more significant. This letter presents a deep joint source-channel coding (JSCC) design for a multivariate Gaussian source over a Gaussian MAC. The widely used autoencoder based deep-JSCC cannot perform stably under such conditions due to their easiness to fall into local optimum. Therefore we propose the variational domain adaptation (VDA)-JSCC scheme. Firstly, the loss function with an additional regularization term is introduced through variational analysis. The crucial prior distribution related to this item is obtained by domain adaptation, which is a transfer learning method. The proposed fine-tuning technique during the training process yields further performance improvement. Experiment results show that VDA-JSCC can always learn reasonable coding structures without artificial design and outperforms other state-of-the-art methods under different channel signal-to-noise ratios (CSNRs). We have also analyzed the reason why the performance of VDA-JSCC deteriorates in high CSNR range and then replace the encoder of VDA-JSCC with Mixture-of-Experts to improve its performance in high CSNR range. Finally, VDA-JSCC exhibits considerable robustness when the channel quality or correlation coefficient varies.

关键词： Encoding Symbols Feature extraction Decoding Standards Delays Adaptation models Deep joint source-channel coding domain adaptation variational autoencoder multivariate Gaussian multiple-access channel

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Joint Graph Attention and Asymmetric Convolutional Neural Network for Deep Image Compression

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2023年第1期33卷 421-433页

作者： Tang, Zhisen Wang, Hanli Yi, Xiaokai Zhang, Yun Kwong, Sam Kuo, C. -C. Jay Tongji Univ Dept Comp Sci & Technol Shanghai 201804 Peoples R China Tongji Univ Serv Comp Minist Educ Shanghai 200092 Peoples R China Tongji Univ Key Lab Embedded Syst Shanghai 200092 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Univ Southern Calif Los Angeles Signal & Image Proc Inst Ming Hsieh Dept Elect Engn Los Angeles CA 90089 USA

Recent deep image compression methods have achieved prominent progress by using nonlinear modeling and powerful representation capabilities of neural networks. However, most existing learning-based image compression approaches employ customized convolutional neural network (CNN) to utilize visual features by treating all pixels equally, neglecting the effect of local key features. Meanwhile, the convolutional filters in CNN usually express the local spatial relationship within the receptive field and seldom consider the long-range dependencies from distant locations. This results in the long-range dependencies of latent representations not being fully compressed. To address these issues, an end-to-end image compression method is proposed by integrating graph attention and asymmetric convolutional neural network (ACNN). Specifically, ACNN is used to strengthen the effect of local key features and reduce the cost of model training. Graph attention is introduced into image compression to address the bottleneck problem of CNN in modeling long-range dependencies. Meanwhile, regarding the limitation that existing attention mechanisms for image compression hardly share information, we propose a self-attention approach which allows information flow to achieve reasonable bit allocation. The proposed self-attention approach is in compliance with the perceptual characteristics of human visual system, as information can interact with each other via attention modules. Moreover, the proposed self-attention approach takes into account channel-level relationship and positional information to promote the compression effect of rich-texture regions. Experimental results demonstrate that the proposed method achieves state-of-the-art rate-distortion performances after being optimized by MS-SSIM compared to recent deep compression models on the benchmark datasets of Kodak and Tecnick.

关键词： Image coding Convolution Convolutional neural networks Training Kernel Visualization Rate-distortion Image compression graph attention network asymmetric convolutional neural network self-attention variational autoencoder

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Enhancing Load Forecasting with VAE-GAN-Based Data Cleaning for Electric Vehicle Charging Loads 10th

Enhancing Load Forecasting with VAE-GAN-Based Data Cleaning ...

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29th International Conference on Database Systems for Advanced Applications (DASFAA)

作者： Zhang, Wensi Lei, Shuya Jiang, Yuqing Yao, Tiechui Wang, Yishen Sun, Zhiqing State Grid Smart Grid Res Inst Co Ltd Beijing Peoples R China State Grid Hangzhou Power Supply Co Hangzhou Peoples R China

ISBN: (纸本)9789819609130;9789819609147

With the popularization of environmental protection ideas, people are increasingly valuing low-carbon lifestyles and the economy. Electric vehicles play a crucial role in this transformation to reduce carbon emissions. However, integrating electric vehicles into the power grid poses challenges, especially the possibility of destructive load peaks, which may endanger the stability and safety of the power grid. Accurately predicting the load of electric vehicles and managing grid scheduling are crucial for solving this problem. The current solutions are mainly divided into two categories: statistics-based methods and machine learning-based methods. Statistical methods require a large amount of long-term data modeling, making data collection a significant challenge. Similarly, machine learning-based methods have good long-term prediction performance on high-quality data, but they do not perform well in terms of short-term prediction accuracy. To overcome these obstacles, a comprehensive electric vehicle charging load prediction framework is proposed, which utilizes an innovative variational autoencoder to generate adversarial networks (VAE-GAN) for data processing, Principal Component Analysis (PCA) for feature extraction, and an improved CNN-GRU model for prediction. The experimental results show that the accuracy of short-term power load prediction is significantly improved, which verifies the effectiveness of the framework in processing small sample load data and provides advanced tools for intelligent management of electric vehicle charging stations.

关键词： load forecasting electric vehicles data cleaning variational autoencoder generate adversarial networks gated recurrent network

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Pedestrian Trajectory Prediction Using Spatio-Temporal VAE 7th

Pedestrian Trajectory Prediction Using Spatio-Temporal VAE

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7th Chinese Conference on Pattern Recognition and Computer Vision

作者： Yu, Qing Xu, Zhenwei Zhou, Yaoyong Liu, Zhida Silamu, Wushouer Xinjiang Univ XJU Sch Comp Sci & Technol 777 Huarui St Urumqi 830017 Xinjiang Uygur Peoples R China Xinjiang Univ XJU Sch Software 499 Xibei Rd Urumqi 830091 Xinjiang Uygur Peoples R China

ISBN: (纸本)9789819785049;9789819785056

To address the high uncertainty of pedestrian behavior and the complexity of the environment at traffic intersections, this paper proposes an innovative Spatio-temporal variational autoencoder model (ST-VAE). This model aims to efficiently capture the spatio-temporal characteristics of pedestrian behavior to enhance the accuracy of individual trajectory prediction at traffic intersections. To effectively handle the complex relationships in pedestrian social interactions, a Social Graph Attention Network (SGAT) was designed. This network dynamically identifies the relative importance among traffic participants and optimizes interaction analysis. Furthermore, a Complex Gated Recurrent Unit (CGRU) was introduced to accurately capture the dynamic changes in time series data, thereby optimizing future trajectory prediction. In the final prediction results, a Final Position Clustering (FPC) method was designed as a post-processing technique to eliminate bias and increase prediction accuracy. Evaluation results on four public datasets showed that the ST-VAE model reduces the average absolute error (ADE) and the final displacement error (FDE) by 27.2% and 18.2% respectively compared to the best baseline model,SocialCVAE, thereby demonstrating its significant performance advantage. Further ablation studies revealed the specific contributions of each component to the model's performance, confirming the effectiveness and robustness of ST-VAE in handling diverse environments and complex interaction scenarios. The ST-VAE model not only provides a new perspective for pedestrian trajectory prediction technology but also offers solid technical support for the development of autonomous driving planning systems, showcasing the immense potential of deep learning in smart transportation system applications.

关键词： Pedestrian trajectory prediction Complex Gated Recurrent Unit variational autoencoder Social Graph Attention Network

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Generating various airfoils with required lift coefficients by combining NACA and Joukowski airfoils using conditional variational autoencoders

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年第0期108卷 104560-104560页

作者： Yonekura, Kazuo Wada, Kazunari Suzuki, Katsuyuki Univ Tokyo Bunkyo Ku 7-3-1 Hongo Tokyo 1138656 Japan

The aim of inverse design problem in mechanical design is to obtain shapes that satisfy the required design specifications. It is also desired to obtain varieties of shapes, but finding multiple shapes in a short computation time is difficult using the conventional methods. This paper proposes the use of the conditional variational autoencoders (CVAE) with normal distribution, denoted by N-CVAE, along with the von Mises-Fischer distribution, denoted by S-CVAE, to find multiple solutions for the inverse design problems. Both the CVAE models embed shapes into a latent space. The S-CVAE enables the separation of data in the latent space, whereas the N-CVAE embeds the data in a narrow space. These different features are used for various tasks in this study. In the first task, the dataset consists of only one type of data. Here, S-CVAE outperforms N-CVAE because it can easily separate the data. In the second task, the dataset consists of two different types of airfoils. It is desired to combine two types and to generate new types of data. N-CVAE is useful in this task since it embeds different shapes in the same latent area, due to which, the model outputs intermediate shapes of different types. The shape-generation capability of S-CVAE and N-CVAE are experimentally compared in this study.

关键词： Design exploration Inverse problem variational autoencoder Airfoil design

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Adaptive Gait Trajectory and Event Prediction of Lower Limb Exoskeletons for Various Terrains Using Reinforcement Learning

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2023年第2期109卷 1-20页

作者： Yu, Zhipeng Zhao, Jianghai Chen, Danhui Chen, Shuyan Wang, Xiaojie Chinese Acad Sci Inst Intelligent Machines Hefei Inst Phys Sci Hefei 230031 Peoples R China Univ Sci & Technol China Dept Sci Isl Hefei 230026 Peoples R China Univ Sci & Technol China Dept Automat Hefei 230026 Peoples R China Chinese Acad Sci Changzhou Inst Adv Mfg Technol Changzhou 213164 Peoples R China

The recent development of lower limb exoskeletons has focus on assisting walking on a variety of terrains, such as level ground, stairs, and ramps. However, smooth transitions between different locomotion modes are challenging due to unknown future information. In this paper, we propose a method that adaptively generates a complete gait cycle of trajectory and event prediction for lower limb exoskeletons during the transition stage. First, we design a generative model with the variational autoencoder (VAE) structure to parameterize the gait cycles of trajectory and events into low-dimensional latent representations. Then, the gait trajectory and event prediction are generated by VAE reconstruction, stride time and gait phase alignment. Lastly, reinforcement learning (RL) is adopted to minimize the process error of continuous gait trajectory and event prediction by finding the optimal latent representations of a generative model. The experiments collected gait data from passive and active exoskeletons. The results show that our method significantly outperforms the extend Kalman filter (EKF)-based method and moving horizon estimation (MHE)-based method for both gait trajectory and event prediction tasks during transitions between and within locomotion modes. The capability of our method gives the opportunity to enrich the control mode by providing future information and contributes a valuable tool for natural and smooth transitions among different terrains for the lower limb exoskeleton.

关键词： Lower limb exoskeleton Gait trajectory prediction Gait event prediction variational autoencoder Reinforcement learning

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