暂无分享,去创建一个
[1] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[2] Jian Tang,et al. Enhancing Effectiveness of Outlier Detections for Low Density Patterns , 2002, PAKDD.
[3] Douglas Eck,et al. Music Transformer , 2018, 1809.04281.
[4] Natalia Gimelshein,et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.
[5] Charles C. Kemp,et al. A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-Based Variational Autoencoder , 2017, IEEE Robotics and Automation Letters.
[6] Radford M. Neal. Pattern Recognition and Machine Learning , 2007, Technometrics.
[7] Bin Zhou,et al. BeatGAN: Anomalous Rhythm Detection using Adversarially Generated Time Series , 2019, IJCAI.
[8] Stephen Lin,et al. Swin Transformer: Hierarchical Vision Transformer using Shifted Windows , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[9] Bo Zong,et al. Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection , 2018, ICLR.
[10] Chandan Srivastava,et al. Support Vector Data Description , 2011 .
[11] Yang Feng,et al. Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications , 2018, WWW.
[12] Zhuanghua Liu,et al. Practical Approach to Asynchronous Multivariate Time Series Anomaly Detection and Localization , 2021, KDD.
[13] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[14] Dan Pei,et al. Multivariate Time Series Anomaly Detection and Interpretation using Hierarchical Inter-Metric and Temporal Embedding , 2021, KDD.
[15] Yue Zhao,et al. Revisiting Time Series Outlier Detection: Definitions and Benchmarks , 2021, NeurIPS Datasets and Benchmarks.
[16] Georg Heigold,et al. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale , 2021, ICLR.
[17] Bernhard Schölkopf,et al. Estimating the Support of a High-Dimensional Distribution , 2001, Neural Computation.
[18] Mark Chen,et al. Language Models are Few-Shot Learners , 2020, NeurIPS.
[19] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[20] Lukasz Kaiser,et al. Reformer: The Efficient Transformer , 2020, ICLR.
[21] Wei Sun,et al. Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network , 2019, KDD.
[22] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[23] Hui Xiong,et al. Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting , 2020, AAAI.
[24] Alexander Binder,et al. Deep One-Class Classification , 2018, ICML.
[25] Lifeng Shen,et al. Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network , 2020, NeurIPS.
[26] Jianmin Wang,et al. Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting , 2021, NeurIPS.
[27] Georg Langs,et al. f‐AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks , 2019, Medical Image Anal..
[28] Xiuzhen Cheng,et al. Learning Graph Structures With Transformer for Multivariate Time-Series Anomaly Detection in IoT , 2021, IEEE Internet of Things Journal.
[29] Lei Shi,et al. MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks , 2019, ICANN.
[30] Wenhu Chen,et al. Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting , 2019, NeurIPS.
[31] Nils Ole Tippenhauer,et al. SWaT: a water treatment testbed for research and training on ICS security , 2016, 2016 International Workshop on Cyber-physical Systems for Smart Water Networks (CySWater).