{"list":"other","total":64,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"2203.00667","title":"Generative Adversarial Networks","created":"2022-03-01","cited_by_count":5627},{"id":"1901.00596","title":"A Comprehensive Survey on Graph Neural Networks","created":"2019-01-03","cited_by_count":2919},{"id":"1607.00653","title":"node2vec: Scalable Feature Learning for Networks","created":"2016-07-03","cited_by_count":1918},{"id":"1805.07836","title":"Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels","created":"2018-05-20","cited_by_count":997},{"id":"2107.03502","title":"CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation","created":"2021-07-07","cited_by_count":582},{"id":"1801.04264","title":"Real-world Anomaly Detection in Surveillance Videos","created":"2018-01-12","cited_by_count":577},{"id":"1802.04431","title":"Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding","created":"2018-02-13","cited_by_count":410},{"id":"2101.12072","title":"Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting","created":"2021-01-28","cited_by_count":337},{"id":"2106.06947","title":"Graph Neural Network-Based Anomaly Detection in Multivariate Time Series","created":"2021-06-13","cited_by_count":301},{"id":"2103.00111","title":"Graph Self-Supervised Learning: A Survey","created":"2021-02-27","cited_by_count":289},{"id":"1703.01925","title":"Grammar Variational Autoencoder","created":"2017-03-06","cited_by_count":285},{"id":"2007.04687","title":"Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervision","created":"2020-07-09","cited_by_count":238},{"id":"2106.07178","title":"A Comprehensive Survey on Graph Anomaly Detection with Deep Learning","created":"2021-06-14","cited_by_count":230},{"id":"1802.03903","title":"Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications","created":"2018-02-12","cited_by_count":224},{"id":"2201.07284","title":"TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data","created":"2022-01-18","cited_by_count":214},{"id":"2002.04236","title":"A review on outlier/anomaly detection in time series data","created":"2020-02-11","cited_by_count":189},{"id":"2102.10757","title":"Self-Supervised Learning of Graph Neural Networks: A Unified Review","created":"2021-02-22","cited_by_count":184},{"id":"1811.08055","title":"A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data","created":"2018-11-20","cited_by_count":182},{"id":"1903.07256","title":"Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection","created":"2019-03-18","cited_by_count":171},{"id":"2009.02040","title":"Multivariate Time-series Anomaly Detection via Graph Attention Network","created":"2020-09-04","cited_by_count":167},{"id":"1906.03821","title":"Time-Series Anomaly Detection Service at Microsoft","created":"2019-06-10","cited_by_count":165},{"id":"2009.13807","title":"Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress","created":"2020-09-29","cited_by_count":151},{"id":"2011.07491","title":"Anomaly Detection in Video via Self-Supervised and Multi-Task Learning","created":"2020-11-15","cited_by_count":148},{"id":"2109.12843","title":"A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions","created":"2021-09-27","cited_by_count":144},{"id":"2105.00696","title":"Graph Learning: A Survey","created":"2021-05-03","cited_by_count":141},{"id":"2104.01633","title":"MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection","created":"2021-04-04","cited_by_count":138},{"id":"2105.07342","title":"Self-supervised Learning on Graphs: Contrastive, Generative,or Predictive","created":"2021-05-16","cited_by_count":136},{"id":"2211.05244","title":"Deep Learning for Time Series Anomaly Detection: A Survey","created":"2022-11-09","cited_by_count":131},{"id":"2111.09099","title":"Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection","created":"2021-11-17","cited_by_count":119},{"id":"1903.03295","title":"Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos","created":"2019-03-08","cited_by_count":104},{"id":"2104.03466","title":"Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoT","created":"2021-04-08","cited_by_count":104},{"id":"2203.14506","title":"Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection","created":"2022-03-28","cited_by_count":104},{"id":"2109.05257","title":"Towards a Rigorous Evaluation of Time-series Anomaly Detection","created":"2021-09-11","cited_by_count":97},{"id":"1801.08085","title":"The Temple University Hospital Seizure Detection Corpus","created":"2018-01-03","cited_by_count":83},{"id":"2104.04883","title":"Graph Representation Learning in Biomedicine","created":"2021-04-11","cited_by_count":80},{"id":"2207.01463","title":"Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detection","created":"2022-07-04","cited_by_count":72},{"id":"2103.03036","title":"A Survey on Graph Structure Learning: Progress and Opportunities","created":"2021-03-04","cited_by_count":71},{"id":"2109.11428","title":"An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series","created":"2021-09-23","cited_by_count":68},{"id":"2204.01855","title":"A Survey on Graph Representation Learning Methods","created":"2022-04-04","cited_by_count":66},{"id":"2106.09876","title":"Anomaly Detection in Dynamic Graphs via Transformer","created":"2021-06-18","cited_by_count":59},{"id":"2012.08752","title":"Graph Neural Networks: Taxonomy, Advances and Trends","created":"2020-12-16","cited_by_count":48},{"id":"2107.07702","title":"Neural Contextual Anomaly Detection for Time Series","created":"2021-07-16","cited_by_count":43},{"id":"2202.07857","title":"Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series","created":"2022-02-16","cited_by_count":42},{"id":"2107.12626","title":"Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals","created":"2021-07-27","cited_by_count":41},{"id":"2009.02027","title":"Rethinking Graph Regularization for Graph Neural Networks","created":"2020-09-04","cited_by_count":36},{"id":"1812.06673","title":"Robust Graph Learning from Noisy Data","created":"2018-12-17","cited_by_count":34},{"id":"1911.04464","title":"MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams","created":"2019-11-11","cited_by_count":29},{"id":"2211.11176","title":"Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models","created":"2022-11-21","cited_by_count":29},{"id":"2208.07448","title":"Self-Supervised Learning for Anomalous Channel Detection in EEG Graphs: Application to Seizure Analysis","created":"2022-08-15","cited_by_count":28},{"id":"2202.04822","title":"Survey on Graph Neural Network Acceleration: An Algorithmic Perspective","created":"2022-02-10","cited_by_count":26},{"id":"2205.05173","title":"Self-Supervised Anomaly Detection in Computer Vision and Beyond: A Survey and Outlook","created":"2022-05-10","cited_by_count":24},{"id":"2209.14930","title":"Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges","created":"2022-09-29","cited_by_count":23},{"id":"2106.07451","title":"Noise-robust Graph Learning by Estimating and Leveraging Pairwise Interactions","created":"2021-06-14","cited_by_count":21},{"id":"2206.04149","title":"A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systems","created":"2022-06-08","cited_by_count":20},{"id":"2204.01637","title":"Do Deep Neural Networks Contribute to Multivariate Time Series Anomaly Detection?","created":"2022-04-04","cited_by_count":19},{"id":"2010.10016","title":"Action Sequence Augmentation for Early Graph-based Anomaly Detection","created":"2020-10-20","cited_by_count":15},{"id":"2212.05532","title":"Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges","created":"2022-12-11","cited_by_count":15},{"id":"1807.11637","title":"Deep Graph Laplacian Regularization for Robust Denoising of Real Images","created":"2018-07-31","cited_by_count":13},{"id":"2112.03196","title":"Online false discovery rate control for anomaly detection in time series","created":"2021-12-06","cited_by_count":13},{"id":"2010.15949","title":"Graph Regularized Autoencoder and its Application in Unsupervised Anomaly Detection","created":"2020-10-29","cited_by_count":11},{"id":"2203.09308","title":"Few-Shot Learning on Graphs","created":"2022-03-17","cited_by_count":11},{"id":"2202.06503","title":"Adaptive Graph Convolutional Networks for Weakly Supervised Anomaly Detection in Videos","created":"2022-02-14","cited_by_count":7},{"id":"2103.05976","title":"Robust graph-filter identification with graph denoising regularization","created":"2021-03-10","cited_by_count":4}],"next_cursor":null}