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检索条件"主题词=Auto-Encoder"

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Rating Prediction in Review-based Recommendations via Adversarial auto-encoder 18

Rating Prediction in Review-based Recommendations via Advers...

引用

IEEE/WIC/ACM International Conference on Web Intelligence (WI)

作者： Yi, Jin Huang, Jiajin Qin, Jin Guizhou Univ Coll Comp Sci & Technol Guiyang Guizhou Peoples R China Beijing Univ Technol Int WIC Inst Beijing Peoples R China

ISBN: (纸本)9781538673256

Recommendation methods usually use users' historical ratings on items to predict ratings on their unrated items to make recommendations. However, the sparse rating data limit the recommendation quality. In order to solve the sparsity problem, other auxiliary information is combined to mine users' preferences for higher recommendation quality. This paper proposes a novel recommendation model, which harnesses an adversarial learning among auto-encoders to improve recommendation quality by minimizing the gap of rating and review relation of users and items. The empirical studies on real-world datasets prove that the proposed method improves recommendation performance.

关键词： Recommendations Adversarial Learning auto-encoder

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A Resilient to Faults auto-encoder Enabled Kalman based Multi-Sensorial Fusion 30

A Resilient to Faults Auto-Encoder Enabled Kalman based Mult...

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30th Mediterranean Conference on Control and automation (MED)

作者： Mukherjee, Moumita Banerjee, Avijit Nikolakopoulos, George Lulea Univ Technol Dept Comp Elect & Space Engn Robot & AI Lulea Sweden

ISBN: (纸本)9781665406734

This article presents a novel auto-encoder-enabled fault resilient multi-sensor fusion architecture while incorporating an extended Kalman filter framework. The auto-encoder facilitate reconstruction of the faulty measurements from multiple onboard sensors, while the centralized extended Kalman filter enables an accurate fusion architecture. Moreover, the process is capable of successfully eliminating the additive noise appearing from the raw sensor data. The proposed method provides a robust reconstruction mechanism in the presence of time-dependent anomalies and faulty sensor measurement. The efficacy of the proposed scheme is extensively evaluated in the context of pose estimation for a micro aerial vehicle equipped with multiple onboard sensors. In addition, the evaluation process incorporates various realistic failure scenarios with artificially introduced inaccurate measurements. The superiority of the proposed auto-encoder enabled centralized Kalman filter (AEKF) fusion is demonstrated through an extensive comparison with a recently developed Fault Resilient Optimal Information Filter (FROIF) method.

关键词： auto-encoder FROIF Kalman Filter Aerial Robot White Gaussian Noise

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Semi-supervised auto-encoder Based Event Detection in Constructing Knowledge Graph for Social Good 19

Semi-supervised Auto-encoder Based Event Detection in Constr...

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19th IEEE/WIC/ACM International Conference on Web Intelligence (WI)

作者： Zhao, Yue Jin, Xiaolong Wang, Yuanzhuo Cheng, Xueqi Univ CAS CAS Key Lab Network Data Sci & Technol Inst Comp Technol CAS Sch Comp & Control Engn Beijing Peoples R China

ISBN: (纸本)9781450369343

Knowledge graphs have recently been extensively applied in many different areas (e.g., disaster management and relief, disease diagnosis). For example, event-centric knowledge graphs have been developed to improve decision making in disaster management and relief. This paper focuses on the task of event detection, which is the precondition of event extraction for constructing event-centric knowledge graphs. Event detection identifies trigger words of events in the sentences of a document and further classifies the types of events. It is straightforward that context information is useful for event detection. Therefore, the feature-based methods adopt cross-sentence information. However, they suffer from the complication of human-designed features. On the other hand, the representation-based methods learn document-level embeddings, which, however, contain much noise caused by unsupervised learning. To overcome these problems, in this paper we propose a new model based on Semi-supervised auto-encoder, which learns Context information to Enhance Event Detection, thus called SAE-CEED. This model first applies large-scale unlabeled texts to pre-train an auto-encoder, so that the embeddings of segments learned by the encoder contain the semantic and order information of the original text. It then uses the decoder to extract the context embeddings and fine-tunes them to enhance a bidirectional neural network model to identify event triggers and their types in sentences. Through experiments on the benchmark ACE-2005 dataset, we demonstrate the effectiveness of the proposed SAE-CEED model. In addition, we systematically conduct a series of experiments to verify the impact of different lengths of text segments in the pre-training of the auto-encoder on event detection.

关键词： knowledge graph event detection neural network auto-encoder

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Dual Attention Mechanisms Based auto-encoder for Video Anomaly Detection 8th

Dual Attention Mechanisms Based Auto-Encoder for Video Anoma...

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8th International Conference on Artificial Intelligence and Security (ICAIS)

作者： Gu, Jiatao Zeng, Jing Ji, Genlin Nanjing Normal Univ Sch Comp & Elect Informat Nanjing Peoples R China

ISBN: (纸本)9783031067945;9783031067938

Video anomaly detection refers to the identification of abnormal behaviors that do not conform to normal patterns. Reconstruction of video frames based on auto-encoder is the current mainstream video anomaly detection method. If frames have higher reconstruction error than the threshold, these frames will be treated as the anomalous frames. However, auto-encoders lack attention to global information and channel dependence. The attention mechanism enables the neural network to accurately focus on input-related elements and becomes an important part of the neural network. In order to focus the feature of both channel and spatial dimensions, we propose dual attention mechanisms based auto-encoder (DAMAE) for video anomaly detection. After each down-sampling, the feature map is operated by two kinds of attention processing. The feature map is divided into specific groups. Every individual group can autonomously enhance its learnt expression and suppress possible noise. By fusing channel attention and spatial attention, DAMAE is able to capture the pixel-level pairwise relationship and channel dependence. Compared with traditional auto-encoder in the process of each up-sampling, the feature with channel attention and spatial attention can reconstruct the normal pattern of the video better. Experimental results show that our method is superior to other advanced methods, which proves the effectiveness of our method.

关键词： Video anomaly detection auto-encoder Attention mechanisms

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A Deep Learning Method Based on Hybrid auto-encoder Model 2

A Deep Learning Method Based on Hybrid Auto-Encoder Model

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2017 IEEE 2nd Information Technology, Networking, Electronic and automation Control Conference (ITNEC 2017)

作者： ZhenYu Yang Hui Jing School of Information Qilu University of Technology

ISBN: (纸本)9781509064151;9781509064144

In order to make auto-encoder improve the ability of feature learning in training, reduce dimensionality and extract advanced features of more abstract features from mass original data, it can improve the classification results ultimately. The paper proposes a deep learning method based on hybrid auto-encoder model, the method is that CAE(Contractive auto-encoder) and traditional neural network SRBM(Sparse Restricted Boltzmann Machine) are combined to form a hybrid auto-encoder network to train dataset. This model is constructed to achieve a variety of neural network between functionally complementary advantages and strengthening reducing dimensionality, extracting more "good" feature and enhancing the learning ability of the hybrid model. In order to speed up the convergence rate of hybrid model parameters, when training update parameters, we add a method of updating parameters which is Polyak Averaging. The experimental results show that the hybrid model can extract more accurate features and improve the accuracy of classification. Polyak Averaging accelerates the speed of convergence of the model. It is demonstrated a comparison of the classification accuracy with the single stacked two layer CAE training model experimentally, the classification accuracy of the model is proved to be better.

关键词： deep learning auto-encoder SRBM CAE hybrid auto-encoder Polyak Averaging

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Attention-Based auto-encoder Framework for Abnormal Driving Detection

Attention-Based Auto-Encoder Framework for Abnormal Driving ...

引用

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Liu, Jing Liu, Yang Wei, Donglai Ni, Wei Zeng, Xinhua Song, Liang Fudan Univ Acad Engn & Technol Shanghai Peoples R China Shanghai East Bund Res Inst NSAI Shanghai Peoples R China

ISBN: (纸本)9781665484855

With the popularity of smartphones, abnormal driving detection via smartphone sensors has been proposed in recent years. However, existing methods are insufficient in exploring feature extraction, so the practical value is limited due to the low accuracy. To address this problem, we propose an attentionbased auto-encoder framework for abnormal driving detection that combines the advantages of bi-directional long short-term memory and self-attention. Specifically, these two modules are embedded in the auto-encoder for modeling latent vector and exploring the internal correlations of spatial-temporal features, respectively, so as to improve the capability of reconstructing driving time series using small and representative features. We conduct experiments on the real-world datasets, and the results show that the proposed framework achieves significant performance with recall and F1-score of 96.2% and 95.0%, superior to the other baselines.

关键词： Abnormal driving detection bi-directional long short-term memory auto-encoder time series self-attention

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A Performance Comparison of auto-encoder and Its Variants for Classification 1

A Performance Comparison of Auto-Encoder and Its Variants fo...

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1st International Conference on Signals and Systems (ICSigSys)

作者： Lee, Jae-Neung Kwak, Keun-Chang Chosun Univ Dept Control & Instrumentat Engn 375 Seosuk Dong Gwangju 501759 South Korea

ISBN: (纸本)9781509067480

In this paper, we present auto-encoder (AE), stacked auto-encoder (SAE) and sparse auto-encoder (SPAE) to classify gaits of horse riding for real riding coaching. The parameters of each auto-encoder are adjusted to compare the performance. The data is collected from 16 inertial sensors attached to a motion capture suit to construct a motion database. We build the motion features as the method of gaits classification with the database. The experiment shows that the performance is 95% when applied AE. SPAE was the best in terms of time and AE was the best in performance. We can apply to coaching system by each horse gait for rider under real or horse simulator environments using the SPAE algorithm.

关键词： auto-encoder stacked auto-encoder sparse auto-encoder classification

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L2G auto-encoder: Understanding Point Clouds by Local-to-Global Reconstruction with Hierarchical Self-Attention 19

L2G Auto-encoder: Understanding Point Clouds by Local-to-Glo...

引用

27th ACM International Conference on Multimedia (MM)

作者： Liu, Xinhai Han, Zhizhong Wen, Xin Liu, Yu-Shen Zwicker, Matthias Tsinghua Univ Sch Software Beijing Peoples R China Beijing Natl Res Ctr Informat Sci & Technol BNRis Beijing Peoples R China Univ Maryland Dept Comp Sci College Pk MD 20742 USA

ISBN: (纸本)9781450368896

auto-encoder is an important architecture to understand point clouds in an encoding and decoding procedure of self reconstruction. Current auto-encoder mainly focuses on the learning of global structure by global shape reconstruction, while ignoring the learning of local structures. To resolve this issue, we propose Local-to-Global auto-encoder (L2G-AE) to simultaneously learn the local and global structure of point clouds by local to global reconstruction. Specifically, L2G-AE employs an encoder to encode the geometry information of multiple scales in a local region at the same time. In addition, we introduce a novel hierarchical self-attention mechanism to highlight the important points, scales and regions at different levels in the information aggregation of the encoder. Simultaneously, L2G-AE employs a recurrent neural network (RNN) as decoder to reconstruct a sequence of scales in a local region, based on which the global point cloud is incrementally reconstructed. Our outperforming results in shape classification, retrieval and upsampling show that L2G-AE can understand point clouds better than state-of-the-art methods.

关键词： Point Cloud auto-encoder Unsupervised Learning Hierarchical Attention Interpolation Layer Recurrent Neural Network

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Non-Compression auto-encoder for Detecting Road Surface Abnormality via Vehicle Driving Noise 3

Non-Compression Auto-Encoder for Detecting Road Surface Abno...

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3rd IEEE International Conference on Architecture, Construction, Environment and Hydraulics

作者： Park, YeongHyeon Jung, JongHee SK Planet Co Ltd IoT Solut Business Grp Seongnam South Korea

ISBN: (纸本)9781665405300

Road accidents can be triggered by wet roads because it decreases skid resistance. To prevent road accidents, detecting abnormal road surfaces is highly useful. In this paper, we propose the deep learning-based cost-effective real-time anomaly detection architecture, naming with non-compression auto-encoder (NCAE). The proposed architecture can reflect forward and backward causality of time-series information via convolutional operation. Moreover, the above architecture shows higher anomaly detection performance of published anomaly detection models via experiments. We conclude that NCAE is a cutting-edge model for road surface anomaly detection with 4.20% higher AUROC and 2.99 times faster decisions than before.

关键词： Anomaly Detection auto-encoder Non-Compression Road Safety Vehicle Noise

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Regularized Stacked auto-encoder Based Pre-training for Generalization of Multi-layer Perceptron 1

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6th International Conference on the Theory and Practice of Natural Computing (TPNC)

作者： Dey, Prasenjit Ghosh, Abhijit Pal, Tandra Natl Inst Technol Dept Comp Sci & Engn Durgapur 713209 W Bengal India

ISBN: (数字)9783319710693

ISBN: (纸本)9783319710693;9783319710686

Generalization capability of multi-layer perceptron (MLP) depends on the initialization of its weights. If the weights of an MLP are not initialized properly, it may fail to achieve good generalization. In this article, we propose a weight initialization technique for MLP to improve its generalization. This is achieved by a regularized stacked auto-encoder based pre-trainingmethod. During pre-training, the weights between each adjacent layers of an MLP, upto the penultimate layer, are trained layer wise by an auto-encoder. To train the auto-encoder, we use weighted sum of two terms: (i) mean squared error (MSE) and (ii) sum of squares of the first order derivatives of the outputs with respect to inputs. Here, the second term acts as a regularizer. It is used to penalize the training of autoencoder during pre-training to generate better initial values of the weights for each successive layers of MLP. To compare the proposed initialization technique with random weight initialization, we have considered ten standard classification data sets. Empirical results show that the proposed initialization technique improves the generalization of MLP.

关键词： auto-encoder Initialization Multi-layer perceptron Pre-training Regularization

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