{"list":"other","total":53,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"2210.02747","title":"Flow Matching for Generative Modeling","created":"2022-10-06","cited_by_count":5702},{"id":"1806.07366","title":"Neural Ordinary Differential Equations","created":"2018-06-19","cited_by_count":3779},{"id":"2012.07436","title":"Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting","created":"2020-12-14","cited_by_count":2169},{"id":"2211.14730","title":"A Time Series is Worth 64 Words: Long-term Forecasting with Transformers","created":"2022-11-27","cited_by_count":2039},{"id":"2106.13008","title":"Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting","created":"2021-06-24","cited_by_count":1676},{"id":"1912.09363","title":"Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting","created":"2019-12-19","cited_by_count":761},{"id":"1606.01865","title":"Recurrent Neural Networks for Multivariate Time Series with Missing Values","created":"2016-06-06","cited_by_count":554},{"id":"2101.12072","title":"Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting","created":"2021-01-28","cited_by_count":337},{"id":"2304.08424","title":"Long-term Forecasting with TiDE: Time-series Dense Encoder","created":"2023-04-17","cited_by_count":332},{"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":"1701.05923","title":"Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks","created":"2017-01-20","cited_by_count":242},{"id":"1905.11190","title":"Model-Agnostic Counterfactual Explanations for Consequential Decisions","created":"2019-05-27","cited_by_count":204},{"id":"2002.00498","title":"DYNOTEARS: Structure Learning from Time-Series Data","created":"2020-02-02","cited_by_count":200},{"id":"2208.09399","title":"Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models","created":"2022-08-19","cited_by_count":198},{"id":"1511.06085","title":"Variable Rate Image Compression with Recurrent Neural Networks","created":"2015-11-19","cited_by_count":173},{"id":"2002.04083","title":"Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations","created":"2020-02-10","cited_by_count":155},{"id":"1910.03002","title":"High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes","created":"2019-10-07","cited_by_count":148},{"id":"2002.06103","title":"Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows","created":"2020-02-14","cited_by_count":144},{"id":"2306.09364","title":"TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting","created":"2023-06-14","cited_by_count":139},{"id":"2011.02268","title":"Causal Autoregressive Flows","created":"2020-11-04","cited_by_count":99},{"id":"2110.03224","title":"Darts: User-Friendly Modern Machine Learning for Time Series","created":"2021-10-07","cited_by_count":91},{"id":"2306.05043","title":"Non-autoregressive Conditional Diffusion Models for Time Series Prediction","created":"2023-06-08","cited_by_count":78},{"id":"1902.00450","title":"Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders","created":"2019-02-01","cited_by_count":75},{"id":"2007.16061","title":"Graph signal processing for machine learning: A review and new perspectives","created":"2020-07-31","cited_by_count":73},{"id":"2210.00035","title":"Neural Causal Models for Counterfactual Identification and Estimation","created":"2022-09-30","cited_by_count":57},{"id":"2306.05415","title":"Causal normalizing flows: from theory to practice","created":"2023-06-08","cited_by_count":51},{"id":"2302.02228","title":"Counterfactual Identifiability of Bijective Causal Models","created":"2023-02-04","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":"2305.05890","title":"CUTS+: High-dimensional Causal Discovery from Irregular Time-series","created":"2023-05-10","cited_by_count":41},{"id":"2205.13085","title":"Identifying Patient-Specific Root Causes with the Heteroscedastic Noise Model","created":"2022-05-25","cited_by_count":38},{"id":"2310.01753","title":"CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discovery","created":"2023-10-03","cited_by_count":36},{"id":"2010.09429","title":"Neural Additive Vector Autoregression Models for Causal Discovery in Time Series","created":"2020-10-19","cited_by_count":28},{"id":"2305.04933","title":"Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial","created":"2023-05-07","cited_by_count":27},{"id":"2405.06672","title":"Liouville Flow Importance Sampler","created":"2024-05-03","cited_by_count":25},{"id":"2302.00860","title":"Modeling Causal Mechanisms with Diffusion Models for Interventional and Counterfactual Queries","created":"2023-02-02","cited_by_count":24},{"id":"2008.13620","title":"Estimating Individual Treatment Effects with Time-Varying Confounders","created":"2020-08-27","cited_by_count":21},{"id":"2202.11316","title":"Multivariate Quantile Function Forecaster","created":"2022-02-23","cited_by_count":19},{"id":"2211.04258","title":"MetaLoc: Learning to Learn Wireless Localization","created":"2022-11-08","cited_by_count":18},{"id":"2303.02320","title":"Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounders","created":"2023-03-04","cited_by_count":17},{"id":"2305.15742","title":"Counterfactual Generative Models for Time-Varying Treatments","created":"2023-05-25","cited_by_count":17},{"id":"2409.20547","title":"Annealing Flow Generative Models Towards Sampling High-Dimensional and Multi-Modal Distributions","created":"2024-09-30","cited_by_count":11},{"id":"2412.09328","title":"Auto-Regressive Moving Diffusion Models for Time Series Forecasting","created":"2024-12-12","cited_by_count":10},{"id":"2401.01426","title":"Modular Learning of Deep Causal Generative Models for High-dimensional Causal Inference","created":"2024-01-02","cited_by_count":9},{"id":"2202.11286","title":"Deep Recurrent Modelling of Granger Causality with Latent Confounding","created":"2022-02-23","cited_by_count":8},{"id":"2503.15114","title":"DeCaFlow: A deconfounding causal generative model","created":"2025-03-19","cited_by_count":8},{"id":"1906.00226","title":"Patient-Specific Effects of Medication Using Latent Force Models with Gaussian Processes","created":"2019-06-01","cited_by_count":6},{"id":"2107.01126","title":"Interactive Causal Structure Discovery in Earth System Sciences","created":"2021-07-01","cited_by_count":6},{"id":"2410.10502","title":"A Practical Approach to Causal Inference over Time","created":"2024-10-14","cited_by_count":4},{"id":"2308.14824","title":"Bayesian-Boosted MetaLoc: Efficient Training and Guaranteed Generalization for Indoor Localization","created":"2023-08-28","cited_by_count":3},{"id":"2508.17221","title":"MC3G: Model Agnostic Causally Constrained Counterfactual Generation","created":"2025-08-24","cited_by_count":2},{"id":"2210.02850","title":"Evaluating the Impact of COVID-19 Vaccination in the United Kingdom: A Gaussian Process Approach","created":"2022-10-06","cited_by_count":1},{"id":"2404.01466","title":"TS-CausalNN: Learning Temporal Causal Relations from Non-linear Non-stationary Time Series Data","created":"2024-04-01","cited_by_count":1}],"next_cursor":null}