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The Journal of Machine Learning Research 2022年第1期23卷 9553-9603页

作者： Lars Henry Berge Olsen Ingrid Kristine Glad Martin Jullum Kjersti Aas Department of Mathematics University of Oslo Oslo Norway Norwegian Computing Center Oslo Norway

Shapley values are today extensively used as a model-agnostic explanation framework to explain complex predictive machine learning models. Shapley values have desirable theoretical properties and a sound mathematical foundation in the field of cooperative game theory. Precise Shapley value estimates for dependent data rely on accurate modeling of the dependencies between all feature combinations. In this paper, we use a variational autoencoder with arbitrary conditioning (VAEAC) to model all feature dependencies simultaneously. We demonstrate through comprehensive simulation studies that our VAEAC approach to Shapley value estimation outperforms the state-of-the-art methods for a wide range of settings for both continuous and mixed dependent features. For high-dimensional settings, our VAEAC approach with a non-uniform masking scheme significantly outperforms competing methods. Finally, we apply our VAEAC approach to estimate Shapley value explanations for the Abalone data set from the UCI Machine Learning Repository.

关键词： explainable artificial intelligence prediction explanation Shapley values variational autoencoder feature dependence

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CSCAD: Correlation Structure-Based Collective Anomaly Detection in Complex System

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第5期35卷 4634-4645页

作者： Qin, Huiling Zhan, Xianyuan Zheng, Yu Xidian Univ Beijing 100176 Peoples R China JD Technol JD iCity Beijing 100176 Peoples R China JD Intelligent Cities Res Beijing 100080 Peoples R China Tsinghua Univ Inst AI Ind Res AIR Beijing 100190 Peoples R China

Detecting anomalies in large complex systems is a critical and challenging task. The difficulties arise from several aspects. First, collecting ground truth labels or prior knowledge for anomalies is hard in real-world systems, which often lead to limited or no anomaly labels in the dataset. Second, anomalies in large systems usually occur in a collective manner due to the underlying dependency structure among devices or sensors. Lastly, real-time anomaly detection for high-dimensional data requires efficient algorithms that are capable of handling different types of data (i.e. continuous and discrete). We propose a correlation structure-based collective anomaly detection (CSCAD) model for high-dimensional anomaly detection problem in large systems, which is also generalizable to semi-supervised or supervised settings. Our framework utilize graph convolutional network combining a variational autoencoder to jointly exploit the feature space correlation and reconstruction deficiency of samples to perform anomaly detection. We propose an extended mutual information (EMI) metric to mine the internal correlation structure among different data features, which enhances the data reconstruction capability of CSCAD. The reconstruction loss and latent standard deviation vector of a sample obtained from reconstruction network can be perceived as two natural anomalous degree measures. An anomaly discriminating network can then be trained using low anomalous degree samples as positive samples, and high anomalous degree samples as negative samples. Experimental results on five public datasets demonstrate that our approach consistently outperforms all the competing baselines.

关键词： Anomaly detection Correlation Sensors Feature extraction Data models Loss measurement Complex systems complex system correlation mining unsupervised learning urban computing variational autoencoder

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VSCA: A Sentence Matching Model Incorporating Visual Perception

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COGNITIVE COMPUTATION 2023年第1期15卷 323-336页

作者： Zhang, Zhe Xiao, Guangli Qian, Yurong Ma, Mengnan Leng, Hongyong Zhang, Tao XinJiang Univ Sch Software Urumqi 830000 Peoples R China XinJiang Univ Xinjiang Uygur Autonomous Reg Key Lab Signal Detec Urumqi 830046 Peoples R China Beijing Inst Technol IT Acad Beijing 100081 Peoples R China

Stacking multiple layers of attention networks can significantly improve a model's performance. However, this also increases the model's time and space complexity, making it difficult for the model to capture detailed information on the underlying features. We propose a novel sentence matching model (VSCA) that uses a new attention mechanism based on variational autoencoders (VAE), which exploits the contextual information in sentences to construct a basic attention feature map and combines it with VAE to generate multiple sets of related attention feature maps for fusion. Furthermore, VSCA introduces a spatial attention mechanism that combines visual perception to capture multilevel semantic information. The experimental results show that our proposed model outperforms pretrained models such as BERT on the LCQMC dataset and performs well on the PAWS-X data. Our work consists of two parts. The first part compares the proposed sentence matching model with state-of-the-art pretrained models such as BERT. The second part conducts innovative research on applying VAE and spatial attention mechanisms in NLP. The experimental results on the related datasets show that the proposed method has satisfactory performance, and VSCA can capture rich attentional information and detailed information with less time and space complexity. This work provides insights into the application of VAE and spatial attention mechanisms in NLP.

关键词： Natural language processing Sentence matching variational autoencoder Spatial attention

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Functional Connectivity Networks with Latent Distributions for Mild Cognitive Impairment Identification

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JOURNAL OF DIGITAL IMAGING 2023年第5期36卷 2113-2124页

作者： Tang, Qiling Lu, Yuhong Cai, Bilian Wang, Yan South Cent Minzu Univ Sch Biomed Engn Wuhan 430074 Peoples R China

This work presents a novel approach to estimate brain functional connectivity networks via generative learning. Due to the complexity and variability of rs-fMRI signal, we consider it as a random variable, and utilize variational autoencoder networks to encode it as a confidence distribution in the latent space rather than as a fixed vector, so as to establish the relationship between them. First, the mean time series of each brain region of interest is mapped into a multivariate Gaussian distribution. The correlation between two brain regions is measured by the Jensen-Shannon divergence that describes the statistical similarity between two probability distributions, and then the adjacency matrix is created to indicate the functional connectivity strength of pairwise brain regions. Meanwhile, our findings show that the adjacency matrices obtained at VAE latent spaces of different dimensionalities have good complementarity for MCI identification in precision and recall, and the classification performance can be further boosted by an efficient cascade of classifiers. This proposal constructs brain functional networks from a statistical modeling standpoint, improving the statistical ability of population data and the generalization ability of observation data variability. We evaluate the proposed framework over the task of identifying subjects with MCI from normal controls, and the experimental results on the public dataset show that our method significantly outperforms both the baseline and current state-of-the-art methods.

关键词： Mild cognitive impairment Functional connectivity networks Generative learning variational autoencoder AdaBoost

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Integrating Adversarial Generative Network with variational autoencoders towards Cross-Modal Alignment for Zero-Shot Remote Sensing Image Scene Classification

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REMOTE SENSING 2022年第18期14卷 4533页

作者： Ma, Suqiang Liu, Chun Li, Zheng Yang, Wei Henan Univ Sch Comp & Informat Engn Kaifeng 475000 Peoples R China Henan Univ Henan Key Lab Big Data Anal & Proc Kaifeng 475004 Peoples R China Henan Univ Henan Engn Lab Spatial Informat Proc Kaifeng 475004 Peoples R China Henan Univ Henan Ind Technol Acad Spatio Temporal Big Data Zhengzhou 450046 Peoples R China

Remote sensing image scene classification takes image blocks as classification units and predicts their semantic descriptors. Because it is difficult to obtain enough labeled samples for all classes of remote sensing image scenes, zero-shot classification methods which can recognize image scenes that are not seen in the training stage are of great significance. By projecting the image visual features and the class semantic features into the latent space and ensuring their alignment, the variational autoencoder (VAE) generative model has been applied to address remote-sensing image scene classification under a zero-shot setting. However, the VAE model takes the element-wise square error as the reconstruction loss, which may not be suitable for measuring the reconstruction quality of the visual and semantic features. Therefore, this paper proposes to augment the VAE models with the generative adversarial network (GAN) to make use of the GAN's discriminator in order to learn a suitable reconstruction quality metric for VAE. To promote feature alignment in the latent space, we have also proposed cross-modal feature-matching loss to make sure that the visual features of one class are aligned with the semantic features of the class and not those of other classes. Based on a public dataset, our experiments have shown the effects of the proposed improvements. Moreover, taking the ResNet models of ResNet18, extracting 512-dimensional visual features, and ResNet50 and ResNet101, both extracting 2048-dimensional visual features for testing, the impact of the different visual feature extractors has also been investigated. The experimental results show that better performance is achieved by ResNet18. This indicates that more layers of the extractors and larger dimensions of the extracted features may not contribute to the image scene classification under a zero-shot setting.

关键词： zero-shot learning remote sensing image scene classification cross-modal feature alignment variational autoencoder generative adversarial network

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Two-step residual-error based approach for anomaly detection in engineering systems using variational autoencoders

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COMPUTERS & ELECTRICAL ENGINEERING 2022年 101卷

作者： Gonzalez-Muniz, Ana Diaz, Ignacio Cuadrado, Abel A. Garcia-Perez, Diego Perez, Daniel Univ Oviedo Elect Engn Dept Edif Dept Oeste 2Campus Viesques S-N Gijon 33204 Spain Univ Leon Escuela Ingn SUPPRESS Res Grp Campus Vegazana Leon 24007 Spain

Anomaly detection is a crucial task in the engineering systems field. However, there is usually little or no information about all possible abnormal modes in systems. Hence, a common approach is to build a model of healthy behaviour, based on normal operation data, so that anomaly detection would depend on how well new data fit this model. According to this idea, we propose a residual-error based approach consisting of: a variational autoencoder, used to model the probability density function of the system's healthy behaviour;and a two-step classification algorithm, which classifies the incoming samples based on their residuals, and reports not only their normal/anomalous nature but also that of their components. We have tested this proposal in three different engineering contexts and we have compared its performance with that of state-of-the-art approaches, demonstrating its capability to successfully detect and characterize anomalies.

关键词： Anomaly detection Novelty detection Deep autoencoder variational autoencoder Engineering systems

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ChartNavigator: An Interactive Pattern Identification and Annotation Framework for Charts

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第2期35卷 1258-1269页

作者： Zhang, Tianye Feng, Haozhe Chen, Wei Chen, Zexian Zheng, Wenting Luo, Xiaonan Huang, Wenqi Tung, Anthony Zhejiang Univ State Key Lab CAD & CG Hangzhou 310058 Peoples R China Zhejiang Univ China Southern Power Grid Joint Res Hangzhou 310058 Peoples R China Guilin Univ Elect Technol Guilin 541214 Peoples R China China Southern Power Grid Digital Grid Res Inst Guangzhou 510670 Peoples R China Natl Univ Singapore Dept Comp Sci Singapore 117417 Singapore

Patterns in charts refer to interesting visual features or forms. Identifying patterns not only helps analysts understand the 'shape' of the data but also supports better and faster decision-making. Existing solutions for identifying patterns in charts require a large number of labeled data instances, making it intractable without user supervision. In this paper, we propose ChartNavigator, an interactive pattern identification and annotation framework for unlabeled visualization charts. ChartNavigator leverages a novel chart-sensitive deep factor model to map patterns into a low-dimensional factor representation space, and facilitates rich analysis with the derived representations. We design and implement a visual interface to support efficient identification and annotation of potential patterns in charts. Evaluations with multiple datasets show that our approach outperforms the baseline models in identifying and annotating patterns.

关键词： Visualization Data models Annotations Data visualization Solid modeling Inference algorithms Estimation Pattern identification chart variational autoencoder user interaction visual analysis

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Combining High-Throughput Imaging in Visible and SWIR wavelengths for In-Situ Porosity Prediction in Laser Powder Bed Fusion 11

Combining High-Throughput Imaging in Visible and SWIR wavele...

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Conference on Laser 3D Manufacturing XI

作者： Ahar, Ayyoub Vandecasteele, Mathieu Booth, Brian G. De Grave, Kurt Verhees, Dries Philips, Wilfried Bey-Temsamani, Abdellatif Flanders Make B-3920 Lommel Belgium Univ Ghent Imec TELIN IPI B-3000 Leuven Belgium

ISBN: (纸本)9781510670136;9781510670129

Laser powder bed fusion is at the forefront of manufacturing metallic objects, particularly those with complex geometries or those produced in limited quantities. However, this 3D printing method is susceptible to several printing defects due to the complexities of using a high-power laser with ultra-fast actuation. Accurate online print defect detection is therefore in high demand, and this defect detection must maintain a low computational profile to enable low-latency process intervention. In this work, we propose a low-latency LPBF defect detection algorithm based on fusion of images from high-speed cameras in the visible and short-wave infrared (SWIR) spectrum ranges. First, we design an experiment to print an object while both imposing porosity defects on the print, and recording the laser's melt pool with the high-speed cameras. We then train variational autoencoders on images from both cameras to extract and fuse two sets of corresponding features. The melt pool recordings are then annotated with pore densities extracted from the printed object's CT scan. These annotations are then used to train and evaluate the ability of a fast neural network model to predict the occurrence of porosity from the fused features. We compare the prediction performance of our sensor fused model with models trained on image features from each camera separately. We observe that the SWIR imaging is sensitive to keyhole porosity while the visible-range optical camera is sensitive to lack-of-fusion porosity. By fusing features from both cameras, we are able to accurately predict both pore types, thus outperforming both single camera systems.

关键词： LPBF In-situ Monitoring Lack-of-Fusion Keyhole Porosity Additive Manufacturing SWIR variational autoencoder Sensor Fusion

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Multiple treatment effect estimation for business analytics using observational data

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COGENT ENGINEERING 2024年第1期11卷

作者： Tsuboi, Yuki Sakai, Yuta Shimizu, Ryotaro Goto, Masayuki Waseda Univ Grad Sch Creat Sci & Engn Tokyo Japan ZOZO Res Tokyo Japan Waseda Univ Sch Creat Sci & Engn Tokyo Japan

To correctly evaluate the effects of treatments, conducting randomized controlled trials (RCTs) is a reasonable approach. However, because it is generally difficult to implement RCTs for all treatments, methods to estimate the treatment effects using observational data have been actively studied and used in various decision-making processes. Observational data accumulated in business activities and elsewhere contains the results of various previously implemented treatments, and correctly estimating the effects of any given treatment without separating the impacts of other treatments is challenging. Against this background, this paper proposes a method to estimate the effects of multiple treatments of various types while considering various causal relationships. Specifically, the proposal is a variational inference method that estimates the effect of multiple treatments using four latent factors estimated from observations, making assumptions that are independent of the type and number of treatments. The proposed method makes it possible to appropriately estimate the effects of measures even in situations with complex causal relationships. In addition, in situations where measures with continuous parameters are being implemented, it is possible to estimate the effects of measures that have not been implemented in the past by treating the content of the measures as a continuous variable.

关键词： E-commerce marketing causal inference variational autoencoder machine learning business analytics treatment effect

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EASE-DR: Enhanced Sentence Embeddings for Dense Retrieval 47

EASE-DR: Enhanced Sentence Embeddings for Dense Retrieval

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47th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

作者： Zhou, Xixi Gao, Yang Jie, Xin Cai, Xiaoxu Bu, Jiajun Wang, Haishuai Zhejiang Univ Hangzhou Peoples R China

ISBN: (纸本)9798400704314

Recent neural information retrieval models using dense text representations generated by pre-trained models commonly face two issues. First, a pre-trained model (e.g., BERT) usually truncates a long document before giving its representation, which may cause the loss of some important semantic information. Second, although pre-training models like BERT have been widely used in generating sentence embeddings, a substantial body of literature has shown that the pre-training models often represent sentence embeddings in a homogeneous and narrow space, known as the problem of representation anisotropy, which hurts the quality of dense vector retrieval. In this paper, we split the query and the document in information retrieval into two sets of natural sentences and generate their sentence embeddings with BERT, the most popular pre-trained model. Before aggregating the sentence embeddings to get the entire embedding representations of the input query and document, to alleviate the usual representation degeneration problem of sentence embeddings from BERT, we sample the variational auto-encoder's latent space distribution to obtain isotropic sentence embeddings and utilize supervised contrastive learning to uniform the distribution of these sentence embeddings in the representation space. Our proposed model undergoes training optimization for both the query and the document in the abovementioned aspects. Our model performs well in evaluating three extensively researched neural information retrieval datasets.

关键词： Information Retrieval variational autoencoder Supervised Contrastive Learning

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