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NeurIPS Conference / Competition and Demonstration Track Conference

作者： Herruzo, Pedro Larriba-Pey, Josep L. Univ Politecn Cataluna Dept Comp Architecture North CampusC6 Bldg Barcelona 08034 Spain SEAT SA Barcelona Spain

The increasing complexity of mobility plus the growing population in cities, together with the importance of privacy when sharing data from vehicles or any device, makes traffic forecasting that uses data from infrastructure and citizens an open and challenging task. In this paper, we introduce a novel approach to deal with predictions of volume, speed and main traffic direction, in a new aggregated way of traffic data presented as videos. Our approach leverages the continuity in a sequence of frames, learning to embed them into a low dimensional space with an encoder and making predictions there using recurrent layers, ensuring good performance through an embedded loss, and then, recovering back spatial dimensions with a decoder using a second loss at a pixel level. Exogenous variables like weather, time and calendar are also added in the model. Furthermore, we introduce a novel sampling approach for sequences that ensures diversity when creating batches, running in parallel to the optimization process.

关键词： traffic forecasting video prediction recurrent autoencoder exogenous variables sequences' sampling

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Ratai: recurrent autoencoder with imputation units and temporal attention for multivariate time series imputation

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ARTIFICIAL INTELLIGENCE REVIEW 2024年第2期58卷 1-24页

作者： Lai, Xiaochen Yao, Yachen Mu, Jichong Lu, Wei Zhang, Liyong Dalian Univ Technol Sch Software Dalian 116600 Liaoning Peoples R China Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Liaoning Peoples R China Key Lab Ubiquitous Network & Serv Software Liaonin Dalian 116620 Liaoning Peoples R China Jilin Univ Sch Software Changchun 130022 Jilin Peoples R China

Multivariate time series is ubiquitous in real-world applications, yet it often suffers from missing values that impede downstream analytical tasks. In this paper, we introduce the Long Short-Term Memory Network based recurrent autoencoder with Imputation Units and Temporal Attention Imputation Model (RATAI), tailored for multivariate time series. RATAI is designed to address certain limitations of traditional RNN-based imputation methods, which often focus on predictive modeling to estimate missing values, sometimes neglecting the contextual impact of observed data at and beyond the target time step. Drawing inspiration from Kalman smoothing, which effectively integrates past and future information to refine state estimations, RATAI aims to extract feature representations from time series data and use them to reconstruct a complete time series, thus overcoming the shortcomings of existing approaches. It employs a dual-stage imputation process: the encoder utilizes temporal information and attribute correlations to predict and impute missing values, and extract feature representation of imputed time series. Subsequently, the decoder reconstructs the series from the feature representation, and the reconstructed values are used as the final imputation values. Additionally, RATAI incorporates a temporal attention mechanism, allowing the decoder to focus on highly relevant inputs during reconstruction. This model can be trained directly using data that contains missing values, avoiding the misleading effects on model training that can arise from setting initial values for missing values. Our experiments demonstrate that RATAI outperforms benchmark models in multivariate time series imputation.

关键词： Imputation Missing values Multivariate time series recurrent autoencoder LSTM

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RADAR: recurrent autoencoder Based Detector for Adversarial Examples on Temporal EHR

RADAR: Recurrent Autoencoder Based Detector for Adversarial ...

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European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Wang, Wenjie Tang, Pengfei Xiong, Li Jiang, Xiaoqian Emory Univ Atlanta GA 30322 USA UTHealth Houston TX USA

ISBN: (纸本)9783030676674;9783030676667

Leveraging the information-rich and large volume of Electronic Health Records (EHR), deep learning systems have shown great promise in assisting medical diagnosis and regulatory decisions. Although deep learning models have advantages over the traditional machine learning approaches in the medical domain, the discovery of adversarial examples has exposed great threats to the state-of-art deep learning medical systems. While most of the existing studies are focused on the impact of adversarial perturbation on medical images, few works have studied adversarial examples and potential defenses on temporal EHR data. In this work, we propose RADAR, a recurrent autoencoder based Detector for Adversarial examples on temporal EHR data, which is the first effort to defend adversarial examples on temporal EHR data. We evaluate RADAR on a mortality classifier using the MIMIC-III dataset. Experiments show that RADAR can filter out more than 90% of adversarial examples and improve the target model accuracy by more than 90% and F1 score by 60%. Besides, we also propose an enhanced attack by introducing the distribution divergence into the loss function such that the adversarial examples are more realistic and difficult to detect.

关键词： Adversarial example detection recurrent autoencoder Temporal Electronic Health Records (EHR)

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FEEDBACK recurrent autoencoder

FEEDBACK RECURRENT AUTOENCODER

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yang, Yang Sautiere, Guillaume Ryu, J. Jon Cohen, Taco S. Qualcomm Technol Inc Qualcomm AI Res San Diego CA 92121 USA Qualcomm Technol Netherlands BV Qualcomm AI Res Nijmegen Netherlands Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA

ISBN: (纸本)9781509066315

In this work, we propose a new recurrent autoencoder architecture, termed Feedback recurrent autoencoder (FRAE), for online compression of sequential data with temporal dependency. The recurrent structure of FRAE is designed to efficiently extract the redundancy along the time dimension and allows a compact discrete representation of the data to be learned. We demonstrate its effectiveness in speech spectrogram compression. Specifically, we show that the FRAE, paired with a powerful neural vocoder, can produce high-quality speech waveforms at a low, fixed bitrate. We further show that by adding a learned prior for the latent space and using an entropy coder, we can achieve an even lower variable bitrate.

关键词： recurrent autoencoder lossy compression speech coding prior model

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GRAE: Graph recurrent autoencoder for Multi-view Graph Clustering

GRAE: Graph Recurrent Autoencoder for Multi-view Graph Clust...

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2021年第四届算法、计算和人工智能国际会议

作者： Ercong Cai Jin Huang Bosong Huang Shi Xu Jia Zhu South China Normal University Zhejiang Normal University

Multi-view graph clustering aims to discover communities or groups in the graph with multiple views, which usually can supply more comprehensive information than that single-view graph clustering. With the increasing scale of complex data from the real world,multi-view graph clustering has drawn much attention. It has a solid theoretical foundation and high effectiveness in applications such as data mining and social network analysis. However, Most existing methods obtain the clustering result only through the shared feature representations, defectively overlooking the unique features of multiple views. To fill this gap, a Graph recurrent Auto Encoder(GRAE)is proposed for attributed multi-view graph clustering, which can attain node representation well by learning different view ***, we first design a global graph autoencoder and a partial graph autoencoder to extract the shared features and the unique features of all views, respectively, which can better represent the nodes in the graph. Then, from the perspective of representation fusion, we adopt an adaptive weight learning method to fuse the different features according to the importance of features. Moreover, we investigate a self-training clustering method to optimize a clustering objective for improving the clustering effect. Finally,we conducted a large number of experiments on three real-world datasets, demonstrating the superior performance of our proposed GRAE model on the multi-view graph clustering task.

关键词： multi-view graph clustering recurrent autoencoder adaptive weight learning

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GRAE: Graph recurrent autoencoder for Multi-view Graph Clustering 21

GRAE: Graph Recurrent Autoencoder for Multi-view Graph Clust...

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Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence

作者： Ercong Cai Jin Huang Bosong Huang Shi Xu Jia Zhu South China Normal University China Zhejiang Normal University China

ISBN: (纸本)9781450385053

Multi-view graph clustering aims to discover communities or groups in the graph with multiple views, which usually can supply more comprehensive information than that single-view graph clustering. With the increasing scale of complex data from the real world, multi-view graph clustering has drawn much attention. It has a solid theoretical foundation and high effectiveness in applications such as data mining and social network analysis. However, Most existing methods obtain the clustering result only through the shared feature representations, defectively overlooking the unique features of multiple views. To fill this gap, a Graph recurrent autoencoder (GRAE) is proposed for attributed multi-view graph clustering, which can attain node representation well by learning different view features. Specifically, we first design a global graph autoencoder and a partial graph autoencoder to extract the shared features and the unique features of all views, respectively, which can better represent the nodes in the graph. Then, from the perspective of representation fusion, we adopt an adaptive weight learning method to fuse the different features according to the importance of features. Moreover, we investigate a self-training clustering method to optimize a clustering objective for improving the clustering effect. Finally, we conducted a large number of experiments on three real-world datasets, demonstrating the superior performance of our proposed GRAE model on the multi-view graph clustering task.

关键词： multi-view adaptive weight learning recurrent autoencoder graph clustering

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Trend-pattern unlimited fuzzy information granule-based LSTM model for long-term time-series forecasting

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2025年 180卷

作者： Jiang, Yanan Yu, Fusheng Tang, Yuqing Ouyang, Chenxi Li, Fangyi Beijing Normal Univ Sch Math Sci Key Lab Math & Complex Syst Minist Educ Beijing 100875 Peoples R China

Trend fuzzy information granulation has shown promising results in long-term time-series forecasting and has attracted increasing attention. In the forecasting model based on trend fuzzy information granulation, the representation of trend granules plays a crucial role. The research focuses on developing trend granules and trend granular time series to effectively represent trend information and improve forecasting performance. However, the existing trend fuzzy information granulation methods make assumptions about the trend pattern of granules (i.e., assuming that granules have linear trends or definite nonlinear trends). Fuzzy information granules with presupposed trend patterns have limited expressive ability and struggle to capture complex nonlinear trends and temporal dependencies, thus limiting their forecasting performance. To address this issue, this paper proposes a novel kind of trend fuzzy information granules, named Trend-Pattern Unlimited Fuzzy Information Granules (TPUFIGs), which are constructed by the recurrent autoencoder with automatic feature learning and nonlinear modeling capabilities. Compared with the existing trend fuzzy information granules, TPUFIGs can better characterize potential trend patterns and temporal dependencies, and exhibit stronger robustness. With the TPUFIGs and Long Short-Term Memory (LSTM) neural network, we design the TPUFIG-LSTM forecasting model, which can effectively alleviate error accumulation and improve forecasting capability. Experimental results on six heterogeneous time series datasets demonstrate the superior performance of the proposed model. By combining deep learning and granular computing, this fuzzy information granulation method characterizes intricate dynamic features in time series more effectively, thus providing a novel solution for long-term time series forecasting with improved forecasting accuracy and generalization capability.

关键词： Long-term time-series forecasting Trend fuzzy information granule Trend-pattern unlimited fuzzy information granule recurrent autoencoder LSTM neural network

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Two-Stage Intelligent Multi-Type Artifact Removal for Single-Channel EEG Settings: A GRU autoencoder Based Approach

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2022年第10期69卷 3142-3154页

作者： Zhang, Wenzhe Yang, Wenxuan Jiang, Xiaofeng Qin, Xi Yang, Jian Du, Jiangfeng Univ Sci & Technol China Hefei Natl Lab Phys Sci Microscale Dept Modern Phys Hefei 230026 Anhui Peoples R China Univ Sci & Technol China CAS Key Lab Microscale Magnet Resonance Hefei 230026 Anhui Peoples R China Univ Sci & Technol China Synerget Innovat Ctr Quantum Informat & Quantum P Hefei 230026 Anhui Peoples R China Univ Sci & Technol China China Dept Automat Hefei Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei 230026 Anhui Peoples R China Univ Sci & Technol China Dept Automat Hefei 230026 Anhui Peoples R China

Objective: The wearable and portable Electroencephalogram (EEG) sensing systems are deeply interfered by unavoidable physiological artifacts due to the limited recording resources. In this work, an intelligent artifact removal system that handles single-channel EEG signals in the presence of mixed multi-type artifacts is investigated. Methods: The basic idea is to represent the mixed artifacts in contaminated varying EEG signals with the unchanged latent pattern features, and then employ the adaptive artifact removal scheme to separate the contamination and clean EEG signals in the encoded feature domain. To minimize the risks of corrupting clean signals and keeping artifacts by mistake, the artifact removal is formulated as an identification-removal two-stage minimization problem, and an attention based adaptive feature concentration mechanism is designed to improve the removal utility and reduce the calculation consumption. Results: In the real implementation on open real-world dataset, this study achieves the artifact identification accuracy of 98.52% and average correlation coefficient of 0.73 for the removal of strong mixed multi-type artifacts. Conclusion: This study can deal with single-channel EEG signals contaminated by mixed multi-type artifacts with high accuracy and low overhead, and is more effective and stable than traditional schemes with fixed criteria. Significance: This study can significantly improve the signal quality acquired by simplified EEG sensing systems, and may extend the application of wearable and portable EEG sensing systems to medical diagnosis, cognitive science research and other applications requiring clinical setups.

关键词： Electroencephalography Brain modeling Biomedical monitoring Signal to noise ratio Feature extraction Task analysis Sensors Two-stage identification and removal recurrent autoencoder mixed multi-type artifact single-channel wearable and portable EEG sensing systems

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Dynamic Hybrid Random Fields for the Probabilistic Graphical Modeling of Sequential Data: Definitions, Algorithms, and an Application to Bioinformatics

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NEURAL PROCESSING LETTERS 2018年第2期48卷 733-768页

作者： Bongini, Marco Freno, Antonino Laveglia, Vincenzo Trentin, Edmondo Univ Siena DIISM Via Roma 56 I-53100 Siena Italy Zalando SE Charlottenstr 4 D-10969 Berlin Germany Univ Firenze DINFO Via S Marta 3 I-50139 Florence Italy

The paper introduces a dynamic extension of the hybrid random field (HRF), called dynamic HRF (D-HRF). The D-HRF is aimed at the probabilistic graphical modeling of arbitrary-length sequences of sets of (time-dependent) discrete random variables under Markov assumptions. Suitable maximum likelihood algorithms for learning the parameters and the structure of the D-HRF are presented. The D-HRF inherits the computational efficiency and the modeling capabilities of HRFs, subsuming both dynamic Bayesian networks and Markov random fields. The behavior of the D-HRF is first evaluated empirically on synthetic data drawn from probabilistic distributions having known form. Then, D-HRFs (combined with a recurrent autoencoder) are successfully applied to the prediction of the disulfide-bonding state of cysteines from the primary structure of proteins in the Protein Data Bank.

关键词： Probabilistic graphical model Hybrid random field Dynamic Bayesian network recurrent autoencoder Disulfide bond

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A Hybrid recurrent Neural Network/Dynamic Probabilistic Graphical Model Predictor of the Disulfide Bonding State of Cysteines from the Primary Structure of Proteins 7th

A Hybrid Recurrent Neural Network/Dynamic Probabilistic Grap...

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7th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR)

作者： Bongini, Marco Laveglia, Vincenzo Trentin, Edmondo Univ Siena Dipartimento Ingn Informaz & Sci Matemat Siena Italy

ISBN: (纸本)9783319461823;9783319461816

Cysteines in a protein have a tendency to form mutual disulfide bonds. This affects the secondary and tertiary structure of the protein. Therefore, automatic prediction of the bonding state of cysteines from the primary structure of proteins has long been a relevant task in bioinformatics. The paper investigates the feasibility of a predictor based on a hybrid approach that combines the dynamic encoding capabilities of a recurrent autoencoder with the short-term/long-term dependencies modeling capabilities of a dynamic probabilistic graphical model (a dynamic extension of the hybrid random field). Results obtained using 1797 proteins from the May 2010 version of the Protein Data Bank show an average accuracy of 85% by relying only on the sub-sequences of the residue chains with no additional attributes (like global descriptors, or evolutionary information provided by multiple alignment).

关键词： Hybrid random field Probabilistic graphical model recurrent neural network recurrent autoencoder Disulfide bond Cysteine bonding state

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