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Synthesizing data by transferring information in data-intensive regions to enhance process monitoring performance in data-scarce region

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CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2021年第Sup1期99卷 S521-S539页

作者： Lyu, Yuting Chen, Junghui Song, Zhihuan Zhang, Qinghua Zhejiang Univ State Key Lab Ind Control Technol Hangzhou 310027 Zhejiang Peoples R China Chung Yuan Christian Univ Dept Chem Engn Chungli Taiwan Guangdong Univ Petrochem Technol Guangdong Prov Key Lab Petrochem Equipment Fault Maoming Peoples R China

Sufficient data are necessary for valid process monitoring results. However, modern industrial processes sometimes switch to new modes to meet the changes in market demand. The available data in such a new mode are initially quite scarce and it brings huge obstacles to data-based model construction. In this paper, a novel data synthesis method based on variational autoencoders is proposed to generate synthetic data for the data-scarce region. The proposed method utilizes not only the original data in the data-scarce region but also the data in other data-intensive regions, which share some common information with the scarce data. To avoid model biases caused by the data imbalance between these regions, a model correction mechanism is also developed. Once the ultimate synthetic data of the data-scarce region are acquired, they are combined with the original data to establish a local monitoring model. Finally, the effectiveness of the proposed method is demonstrated through a real ammonia synthesis process.

关键词： data synthesis deep learning fault detection process monitoring variational autoencoder

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variational model for low-resource natural language generation in spoken dialogue systems

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COMPUTER SPEECH AND LANGUAGE 2021年 65卷 101120-101120页

作者： Van-Khanh Tran Le-Minh Nguyen Japan Adv Inst Sci & Technol JAIST 1-1 Asahidai Nomi Ishikawa 9231292 Japan ICTU Thai Nguyen Univ Univ Informat & Commun Technol Thai Nguyen Vietnam

Natural Language Generation (NLG) plays a critical role in Spoken Dialogue Systems (SDSs), aims at converting a meaning representation into natural language utterances. Recent deep learning-based generators have shown improving results irrespective of providing sufficient annotated data. Nevertheless, how to build a generator that can effectively utilize as much of knowledge from a low-resource setting data is a crucial issue for NLG in SDSs. This paper presents a variational-based NLG framework to tackle the NLG problem of having limited annotated data in two scenarios, domain adaptation and low-resource in-domain training data. Based on this framework, we propose a novel adversarial domain adaptation NLG taclking the former issue, while the latter issue is also handled by a second proposed dual variational model. We extensively conducted the experiments on four different domains in a variety of training scenarios, in which the experimental results show that the proposed methods not only outperform previous methods when having sufficient training dataset but also show its ability to work acceptably well when there is a small amount of in-domain data or adapt quickly to a new domain with only a low-resource target domain data. (C) 2020 Published by Elsevier Ltd.

关键词： Neural language generation Domain adaptation Low-resource data variational autoencoder Deconvolutional neural network CNN RNN LSTM

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GenRadar: Self-Supervised Probabilistic Camera Synthesis Based on Radar Frequencies

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IEEE ACCESS 2021年 9卷 148994-149042页

作者： Ditzel, Carsten Dietmayer, Klaus Inst Measurement Control & Microtechnol D-89081 Ulm Germany

Autonomous systems require a continuous and dependable environment perception for navigation and decision-making, which is best achieved by combining different sensor types. Radar continues to function robustly in compromised circumstances in which cameras become impaired, guaranteeing a steady inflow of information. Yet, camera images provide a more intuitive and readily applicable impression of the world. This work combines the complementary strengths of both sensor types in a unique self-learning fusion approach for a probabilistic scene reconstruction in adverse surrounding conditions. After reducing the memory requirements of both high-dimensional measurements through a decoupled stochastic self-supervised compression technique, the proposed algorithm exploits similarities and establishes correspondences between both domains at different feature levels during training. Then, at inference time, relying exclusively on radio frequencies, the model successively predicts camera constituents in an autoregressive and self-contained process. These discrete tokens are finally transformed back into an instructive view of the respective surrounding, allowing to visually perceive potential dangers for important tasks downstream.

关键词： Radar Cameras Radar imaging Robot sensing systems Probabilistic logic Task analysis Signal processing algorithms Radar signal processing computer vision sensor fusion deep learning machine learning variational autoencoder self-supervised learning annotation-free learning scene understanding

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Data-Driven Discovery of 2D Materials for Solar Water Splitting

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FRONTIERS IN MATERIALS 2021年 8卷

作者： Agarwal, Abhishek Goverapet Srinivasan, Sriram Rai, Beena Tata Consulatncy Serv Ltd TCS Res Pune Maharashtra India

Hydrogen economy, wherein hydrogen is used as the fuel in the transport and energy sectors, holds significant promise in mitigating the deleterious effects of global warming. Photocatalytic water splitting using sunlight is perhaps the cleanest way of producing the hydrogen fuel. Among various other factors, widespread adoption of this technology has mainly been stymied by the lack of a catalyst material with high efficiency. 2D materials have shown significant promise as efficient photocatalysts for water splitting. The availability of open databases containing the "computed" properties of 2D materials and advancements in deep learning now enable us to do "inverse" design of these 2D photocatalysts for water splitting. We use one such database (Jain et al., ACS Energ. Lett. 2019, 4, 6, 1410-1411) to build a generative model for the discovery of novel 2D photocatalysts. The structures of the materials were converted into a 3D image-based representation that was used to train a cell, a basis autoencoder and a segmentation network to ascertain the lattice parameters as well as position of atoms from the images. Subsequently, the cell and basis encodings were used to train a conditional variational autoencoder (CVAE) to learn a continuous representation of the materials in a latent space. The latent space of the CVAE was then sampled to generate several new 2D materials that were likely to be efficient photocatalysts for water splitting. The bandgap of the generated materials was predicted using a graph neural network model while the band edge positions were obtained via empirical correlations. Although our generative modeling framework was used to discover novel 2D photocatalysts for water splitting reaction, it is generic in nature and can be used directly to discover novel materials for other applications as well.

关键词： generative modeling variational autoencoder inverse design photocatalysts water splitting reaction

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Recurrent variational autoencoders for Learning Nonlinear Generative Models in the Presence of Outliers

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2018年第6期12卷 1615-1627页

作者： Wang, Yu Dai, Bin Hua, Gang Aston, John Wipf, David Univ Cambridge Dept Pure Math & Stat Cambridge CB2 1TN England Tsinghua Univ Beijing 100084 Peoples R China Microsoft Res Redmond WA 98052 USA

This paper explores two useful modifications of the recent variational autoencoder (VAE), a popular deep generative modeling framework that dresses traditional autoencoders with probabilistic attire. The first involves a specially-tailored form of conditioning that allows us to simplify the VAE decoder structure while simultaneously introducing robustness to outliers. In a related vein, a second, complementary alteration is proposed to further build invariance to contaminated or dirty samples via a data augmentation process that amounts to recycling. In brief, to the extent that the VAE is legitimately a representative generative model, then each output from the decoder should closely resemble an authentic sample, which can then be resubmitted as a novel input ad infinitum. Moreover, this can be accomplished via special recurrent connections without the need for additional parameters to be trained. We evaluate these proposals on multiple practical outlier-removal and generative modeling tasks involving nonlinear low-dimensional manifolds, demonstrating considerable improvements over existing algorithms.

关键词： Deep generative models variational autoencoder robust PCA outlier removal variational Bayesian model deep learning

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Stochastic Curiosity Exploration for Dialogue Systems 21

Stochastic Curiosity Exploration for Dialogue Systems

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Interspeech Conference

作者： Chien, Jen-Tzung Hsu, Po-Chien Natl Chiao Tung Univ Dept Elect & Comp Engn Hsinchu Taiwan

ISBN: (纸本)9781713820697

Traditionally, task-oriented dialogue system is built by an autonomous agent which can be trained by reinforcement learning where the reward from environment is maximized. The agent is learned by updating the policy when the goal state is observed. However, in real world, the extrinsic reward is usually sparse or missing. The training efficiency is bounded. The system performance is degraded. It is challenging to tackle the issue of sample efficiency in sparse reward scenario for spoken dialogues. Accordingly, a dialogue agent needs additional information to update its policy even in the period when reward is absent in the environment. This paper presents a new dialogue agent which is learned by incorporating the intrinsic reward based on the information-theoretic approach via stochastic curiosity exploration. This agent encourages the exploration for future diversity based on a latent dynamic architecture which consists of encoder network, curiosity network, information network and policy network. The latent states and actions are drawn to predict stochastic transition for future. The curiosity learning are implemented with intrinsic reward in a metric of mutual information and prediction error in the predicted states and actions. Experiments on dialogue management using PyDial demonstrate the benefit by using the stochastic curiosity exploration.

关键词： dialogue management deep reinforcement learning information-theoretic learning variational autoencoder

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On the Low-density Latent Regions of VAE-based Language Models

On the Low-density Latent Regions of VAE-based Language Mode...

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NeurIPS Workshop on Pre-Registration in Machine Learning

作者： Li, Ruizhe Peng, Xutan Lin, Chenghua Rong, Wenge Chen, Zhigang Univ Sheffield Dept Comp Sci Sheffield S Yorkshire England Beihang Univ Sch Comp Sci & Engn Beijing Peoples R China Zhejiang Wanli Univ Coll Informat & Intelligent Engn Ningbo Peoples R China

By representing semantics in latent spaces, variational autoencoders (VAEs) have been proven powerful in modelling and generating signals such as image and text, even without supervision. However, previous studies suggest that in a learned latent space, some low-density regions (aka. holes) exist, which could harm the overall system performance. While existing studies focus on empirically mitigating these latent holes, how they distribute and how they affect different components of a VAE, are still unexplored. In addition, the hole issue in VAEs for language processing is rarely addressed. In our work, by introducing a simple hole-detection algorithm based on the neighbour consistency between VAE's input, latent, and output semantic spaces, we propose to deeply dive into these topics for the first time. Comprehensive experiments including automatic evaluation and human evaluation imply that large-scale low-density latent holes may not exist in the latent space. In addition, various sentence encoding strategies are explored and the native word embedding is the most suitable strategy for VAEs in language modelling task.

关键词： variational autoencoder Low-density Regions Latent Holes

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Learning Representations of Endoscopic Videos to Detect Tool Presence Without Supervision 10th

Learning Representations of Endoscopic Videos to Detect Tool...

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10th International Workshop on Multimodal Learning for Clinical Decision Support.(ML-CDS) / 9th International Workshop on Clinical Image-based Procedures - From Planning to Intervention (CLIP)

作者： Li, David Z. Ishii, Masaru Taylor, Russell H. Hager, Gregory D. Sinha, Ayushi Johns Hopkins Univ Dept Comp Sci Baltimore MD 21205 USA Johns Hopkins Med Inst Baltimore MD 21205 USA Johns Hopkins Univ Lab Computat Sensing & Robot Baltimore MD USA

ISBN: (纸本)9783030609450;9783030609467

In this work, we explore whether it is possible to learn representations of endoscopic video frames to perform tasks such as identifying surgical tool presence without supervision. We use a maximum mean discrepancy (MMD) variational autoencoder (VAE) to learn lowdimensional latent representations of endoscopic videos and manipulate these representations to distinguish frames containing tools from those without tools. We use three different methods to manipulate these latent representations in order to predict tool presence in each frame. Our fully unsupervised methods can identify whether endoscopic video frames contain tools with average precision of 71.56, 73.93, and 76.18, respectively, comparable to supervised methods. Our code is available at https:// ***/zdavidli/tool-presence/.

关键词： Endoscopic video Tool presence Representation learning variational autoencoder Maximum mean discrepancy

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Simulating real profiles for shilling attacks: A generative approach

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KNOWLEDGE-BASED SYSTEMS 2021年 230卷 107390-107390页

作者： Barbieri, Julio Alvim, Leandro G. M. Braida, Filipe Zimbrao, Geraldo Univ Fed Rio de Janeiro PESC COPPE CT Cidade UnivPOB 68511 Rio De Janeiro Brazil Univ Fed Rural Rio de Janeiro DCC IM BR-26020740 Rio De Janeiro Brazil

Collaborative Filtering (CF) approaches are vulnerable to Shilling Attacks, in which malicious users or companies inject a large number of fake profiles in a system in order to manipulate its recommendations. One problem of current Shilling Attack models is that they commonly use straightforward statistical templates, producing profiles with different rating patterns than actual system data, which facilitates its detection, requiring a larger amount of profiles to achieve its goals. To address this problem and create profiles closer to reality, we propose using a generative model, variational autoencoder (VAE) to map original data distribution. With VAE, it is possible to generate new profiles based on real data, without explicit copying their actual ratings. Its generated profiles are converted to malicious profiles by adding target item rating value. We test our attack model on MovieLens 100k data set and compare to literature attack models. Our results indicate that our model outperforms all other models in model-based CF system, especially using low attack sizes (from 3% to 5%). Also, analysis comparing profiles generated from it and other approaches shows that our model ratings pattern are very similar to real profiles, which may indicate that attacks mounted using our approach may be less likely to be detected by detection approaches. Thus, we show that our attack model represents an advance on Shilling Attack models, since its superior results in model-based CF and possible indistinction from real profiles may be useful as a baseline to test detection techniques and other tasks among Shilling Attack area. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Recommender systems Collaborative Filtering Shilling Attack Generative Model variational autoencoder

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Developing a Generative Model Utilizing Self-attention Networks: Application to Materials/Drug Discovery

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MOLECULAR INFORMATICS 2021年第10期40卷 e2100102-e2100102页

作者： Kondo, Masakazu JNC Petrochem Corp Ichihara Res Labs 5-1 Goi Kaigan Ichihara Chiba 2908551 Japan

A new generative model, in which the variational autoencoder network is combined with the Transformer architecture, is developed. The proposed model, the variational Autoencoding Transformer (VAT), is applied to the task of generating molecules, showing that, with proper training, the VAT model can not only produce similar molecules to input ones with high accuracy but also generate new molecules from a predefined prior almost perfectly. A desirable aspect of our VAT is that no heuristic setting is necessary for optimal performance, which suggests that the model can readily be available to a variety of datasets. As practical directions toward materials/drug discovery, two strategies: a fine-tuning method for directed molecular generation and a method of mixing molecules in the latent space, are demonstrated.

关键词： Generative model variational autoencoder Transformer

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