{"list":"other","total":186,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"2006.11239","title":"Denoising Diffusion Probabilistic Models","created":"2020-06-19","cited_by_count":21076},{"id":"1609.02907","title":"Semi-Supervised Classification with Graph Convolutional Networks","created":"2016-09-09","cited_by_count":11361},{"id":"1710.10903","title":"Graph Attention Networks","created":"2017-10-30","cited_by_count":8923},{"id":"1706.02216","title":"Inductive Representation Learning on Large Graphs","created":"2017-06-07","cited_by_count":6320},{"id":"1806.07366","title":"Neural Ordinary Differential Equations","created":"2018-06-19","cited_by_count":3779},{"id":"1704.01212","title":"Neural Message Passing for Quantum Chemistry","created":"2017-04-04","cited_by_count":3487},{"id":"1901.00596","title":"A Comprehensive Survey on Graph Neural Networks","created":"2019-01-03","cited_by_count":2919},{"id":"2303.18223","title":"A Survey of Large Language Models","created":"2023-03-31","cited_by_count":2576},{"id":"1412.3555","title":"Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling","created":"2014-12-11","cited_by_count":2367},{"id":"1606.09375","title":"Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering","created":"2016-06-30","cited_by_count":2089},{"id":"1709.04875","title":"Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting","created":"2017-09-14","cited_by_count":1136},{"id":"1906.02691","title":"An Introduction to Variational Autoencoders","created":"2019-06-06","cited_by_count":1000},{"id":"2102.09844","title":"E(n) Equivariant Graph Neural Networks","created":"2021-02-19","cited_by_count":965},{"id":"1910.09457","title":"Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods","created":"2019-10-21","cited_by_count":913},{"id":"1703.07015","title":"Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks","created":"2017-03-21","cited_by_count":879},{"id":"1906.00121","title":"Graph WaveNet for Deep Spatial-Temporal Graph Modeling","created":"2019-05-31","cited_by_count":829},{"id":"2011.06225","title":"A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges","created":"2020-11-12","cited_by_count":819},{"id":"2306.08302","title":"Unifying Large Language Models and Knowledge Graphs: A Roadmap","created":"2023-06-14","cited_by_count":668},{"id":"1801.10247","title":"FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling","created":"2018-01-30","cited_by_count":584},{"id":"1809.04356","title":"Deep learning for time series classification: a review","created":"2018-09-12","cited_by_count":576},{"id":"2005.11650","title":"Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks","created":"2020-05-24","cited_by_count":566},{"id":"1606.01865","title":"Recurrent Neural Networks for Multivariate Time Series with Missing Values","created":"2016-06-06","cited_by_count":554},{"id":"1905.07953","title":"Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks","created":"2019-05-20","cited_by_count":541},{"id":"2007.02842","title":"Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting","created":"2020-07-06","cited_by_count":530},{"id":"1612.00222","title":"Interaction Networks for Learning about Objects, Relations and Physics","created":"2016-12-01","cited_by_count":509},{"id":"1611.05267","title":"Temporal Convolutional Networks for Action Segmentation and Detection","created":"2016-11-16","cited_by_count":508},{"id":"2005.08926","title":"Neural Controlled Differential Equations for Irregular Time Series","created":"2020-05-18","cited_by_count":506},{"id":"2202.07125","title":"Transformers in Time Series: A Survey","created":"2022-02-15","cited_by_count":497},{"id":"1809.09401","title":"Hypergraph Neural Networks","created":"2018-09-25","cited_by_count":488},{"id":"2004.13408","title":"Time Series Forecasting With Deep Learning: A Survey","created":"2020-04-28","cited_by_count":459},{"id":"2010.02803","title":"A Transformer-based Framework for Multivariate Time Series Representation Learning","created":"2020-10-06","cited_by_count":453},{"id":"1911.08415","title":"GMAN: A Graph Multi-Attention Network for Traffic Prediction","created":"2019-11-11","cited_by_count":434},{"id":"1810.07758","title":"The UCR Time Series Archive","created":"2018-10-17","cited_by_count":431},{"id":"2011.04573","title":"Parameterized Explainer for Graph Neural Network","created":"2020-11-09","cited_by_count":426},{"id":"1911.05485","title":"Diffusion Improves Graph Learning","created":"2019-10-28","cited_by_count":414},{"id":"1802.04431","title":"Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding","created":"2018-02-13","cited_by_count":410},{"id":"1910.12933","title":"Hyperbolic Graph Convolutional Neural Networks","created":"2019-10-28","cited_by_count":402},{"id":"2110.02642","title":"Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy","created":"2021-10-06","cited_by_count":394},{"id":"1806.02920","title":"GAIN: Missing Data Imputation using Generative Adversarial Nets","created":"2018-06-07","cited_by_count":352},{"id":"1910.13051","title":"ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels","created":"2019-10-29","cited_by_count":339},{"id":"1811.00075","title":"The UEA multivariate time series classification archive, 2018","created":"2018-10-31","cited_by_count":338},{"id":"1811.00855","title":"Session-based Recommendation with Graph Neural Networks","created":"2018-11-01","cited_by_count":312},{"id":"2106.06947","title":"Graph Neural Network-Based Anomaly Detection in Multivariate Time Series","created":"2021-06-13","cited_by_count":301},{"id":"2103.07719","title":"Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting","created":"2021-03-13","cited_by_count":299},{"id":"2103.00111","title":"Graph Self-Supervised Learning: A Survey","created":"2021-02-27","cited_by_count":289},{"id":"1511.02136","title":"Diffusion-Convolutional Neural Networks","created":"2015-11-06","cited_by_count":268},{"id":"2101.11174","title":"Graph Neural Network for Traffic Forecasting: A Survey","created":"2021-01-27","cited_by_count":261},{"id":"1704.02971","title":"A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction","created":"2017-04-07","cited_by_count":245},{"id":"1910.12892","title":"Hyperbolic Graph Neural Networks","created":"2019-10-28","cited_by_count":244},{"id":"2110.02905","title":"Geometric and Physical Quantities Improve E(3) Equivariant Message Passing","created":"2021-10-06","cited_by_count":228},{"id":"2005.08514","title":"Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction","created":"2020-05-18","cited_by_count":218},{"id":"2012.09641","title":"Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting","created":"2020-12-15","cited_by_count":213},{"id":"2106.12931","title":"Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting","created":"2021-06-24","cited_by_count":210},{"id":"1711.00614","title":"A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencoder","created":"2017-11-02","cited_by_count":209},{"id":"2002.04236","title":"A review on outlier/anomaly detection in time series data","created":"2020-02-11","cited_by_count":189},{"id":"1906.04928","title":"Deep Learning for Spatio-Temporal Data Mining: A Survey","created":"2019-06-11","cited_by_count":172},{"id":"2101.06861","title":"Discrete Graph Structure Learning for Forecasting Multiple Time Series","created":"2021-01-18","cited_by_count":171},{"id":"2202.07575","title":"Forecasting Global Weather with Graph Neural Networks","created":"2022-02-15","cited_by_count":168},{"id":"2009.02040","title":"Multivariate Time-series Anomaly Detection via Graph Attention Network","created":"2020-09-04","cited_by_count":167},{"id":"1802.08714","title":"Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction","created":"2018-02-23","cited_by_count":162},{"id":"2302.10035","title":"Large-scale Multi-Modal Pre-trained Models: A Comprehensive Survey","created":"2023-02-20","cited_by_count":153},{"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":"2007.13156","title":"Benchmarking Multivariate Time Series Classification Algorithms","created":"2020-07-26","cited_by_count":145},{"id":"2303.14483","title":"Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey","created":"2023-03-25","cited_by_count":140},{"id":"2112.03558","title":"Graph Neural Controlled Differential Equations for Traffic Forecasting","created":"2021-12-07","cited_by_count":133},{"id":"2211.05244","title":"Deep Learning for Time Series Anomaly Detection: A Survey","created":"2022-11-09","cited_by_count":131},{"id":"1809.04206","title":"Temporal Pattern Attention for Multivariate Time Series Forecasting","created":"2018-09-12","cited_by_count":125},{"id":"1703.04691","title":"Conditional Time Series Forecasting with Convolutional Neural Networks","created":"2017-03-14","cited_by_count":123},{"id":"1803.01254","title":"Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction","created":"2018-03-03","cited_by_count":122},{"id":"2104.07551","title":"HIVE-COTE 2.0: a new meta ensemble for time series classification","created":"2021-04-15","cited_by_count":118},{"id":"2206.09113","title":"Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting","created":"2022-06-18","cited_by_count":117},{"id":"2110.05357","title":"Graph-Guided Network for Irregularly Sampled Multivariate Time Series","created":"2021-10-11","cited_by_count":111},{"id":"1909.12790","title":"Hamiltonian Graph Networks with ODE Integrators","created":"2019-09-27","cited_by_count":108},{"id":"1912.12693","title":"A Gentle Introduction to Deep Learning for Graphs","created":"2019-12-29","cited_by_count":108},{"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":"2304.13029","title":"Bake off redux: a review and experimental evaluation of recent time series classification algorithms","created":"2023-04-25","cited_by_count":101},{"id":"1806.01738","title":"Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer's Disease","created":"2018-06-05","cited_by_count":99},{"id":"2306.10125","title":"Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects","created":"2023-06-16","cited_by_count":99},{"id":"2108.00298","title":"Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks","created":"2021-07-31","cited_by_count":95},{"id":"2006.07527","title":"Inductive Graph Neural Networks for Spatiotemporal Kriging","created":"2020-06-13","cited_by_count":91},{"id":"2102.00457","title":"MultiRocket: Multiple pooling operators and transformations for fast and effective time series classification","created":"2021-01-31","cited_by_count":90},{"id":"2205.07424","title":"Trustworthy Graph Neural Networks: Aspects, Methods and Trends","created":"2022-05-16","cited_by_count":89},{"id":"2211.14701","title":"Spatio-Temporal Meta-Graph Learning for Traffic Forecasting","created":"2022-11-27","cited_by_count":88},{"id":"2301.13629","title":"DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Models","created":"2023-01-31","cited_by_count":81},{"id":"2111.15367","title":"A Review on Graph Neural Network Methods in Financial Applications","created":"2021-11-27","cited_by_count":78},{"id":"2104.08336","title":"Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis","created":"2021-04-16","cited_by_count":76},{"id":"2207.00939","title":"An Empirical Survey on Long Document Summarization: Datasets, Models and Metrics","created":"2022-07-03","cited_by_count":74},{"id":"2202.08408","title":"Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs","created":"2022-02-17","cited_by_count":72},{"id":"2105.05682","title":"Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning","created":"2021-05-12","cited_by_count":70},{"id":"1911.12093","title":"Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting","created":"2019-11-27","cited_by_count":69},{"id":"2109.11428","title":"An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series","created":"2021-09-23","cited_by_count":68},{"id":"2302.02515","title":"Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey","created":"2023-02-06","cited_by_count":68},{"id":"2105.04100","title":"Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting","created":"2021-05-10","cited_by_count":67},{"id":"2107.03767","title":"Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic Space","created":"2021-07-08","cited_by_count":67},{"id":"2108.09896","title":"Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection","created":"2021-08-23","cited_by_count":67},{"id":"2302.09746","title":"PriSTI: A Conditional Diffusion Framework for Spatiotemporal Imputation","created":"2023-02-20","cited_by_count":66},{"id":"2007.03113","title":"Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks","created":"2020-07-06","cited_by_count":64},{"id":"2205.13479","title":"Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations","created":"2022-05-26","cited_by_count":64},{"id":"2101.10025","title":"A Review of Graph Neural Networks and Their Applications in Power Systems","created":"2021-01-25","cited_by_count":63}],"next_cursor":"100"}