{"list":"other","total":54,"rows":[{"id":"1910.10683","title":"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer","created":"2019-10-23","cited_by_count":14599},{"id":"2204.02311","title":"PaLM: Scaling Language Modeling with Pathways","created":"2022-04-05","cited_by_count":5326},{"id":"2104.09864","title":"RoFormer: Enhanced Transformer with Rotary Position Embedding","created":"2021-04-20","cited_by_count":4703},{"id":"1806.07366","title":"Neural Ordinary Differential Equations","created":"2018-06-19","cited_by_count":3779},{"id":"2111.00396","title":"Efficiently Modeling Long Sequences with Structured State Spaces","created":"2021-10-31","cited_by_count":2465},{"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":"2210.02186","title":"TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis","created":"2022-10-05","cited_by_count":1058},{"id":"2208.04933","title":"Simplified State Space Layers for Sequence Modeling","created":"2022-08-09","cited_by_count":788},{"id":"2403.07815","title":"Chronos: Learning the Language of Time Series","created":"2024-03-12","cited_by_count":759},{"id":"1905.10437","title":"N-BEATS: Neural basis expansion analysis for interpretable time series forecasting","created":"2019-05-24","cited_by_count":753},{"id":"2310.01728","title":"Time-LLM: Time Series Forecasting by Reprogramming Large Language Models","created":"2023-10-03","cited_by_count":700},{"id":"2310.10688","title":"A decoder-only foundation model for time-series forecasting","created":"2023-10-14","cited_by_count":692},{"id":"2402.02592","title":"Unified Training of Universal Time Series Forecasting Transformers","created":"2024-02-04","cited_by_count":614},{"id":"2310.07820","title":"Large Language Models Are Zero-Shot Time Series Forecasters","created":"2023-10-11","cited_by_count":500},{"id":"2402.03885","title":"MOMENT: A Family of Open Time-series Foundation Models","created":"2024-02-06","cited_by_count":482},{"id":"1703.07771","title":"Multitask learning and benchmarking with clinical time series data","created":"2017-03-22","cited_by_count":290},{"id":"2409.16040","title":"Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts","created":"2024-09-24","cited_by_count":287},{"id":"2202.02435","title":"On Neural Differential Equations","created":"2022-02-04","cited_by_count":268},{"id":"2403.14735","title":"Foundation Models for Time Series Analysis: A Tutorial and Survey","created":"2024-03-21","cited_by_count":239},{"id":"2402.02368","title":"Timer: Generative Pre-trained Transformers Are Large Time Series Models","created":"2024-02-04","cited_by_count":208},{"id":"1907.03907","title":"Latent ODEs for Irregularly-Sampled Time Series","created":"2019-07-08","cited_by_count":183},{"id":"2502.00816","title":"Sundial: A Family of Highly Capable Time Series Foundation Models","created":"2025-02-02","cited_by_count":180},{"id":"2310.03589","title":"TimeGPT-1","created":"2023-10-05","cited_by_count":158},{"id":"2310.09751","title":"UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting","created":"2023-10-15","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":"2310.08278","title":"Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting","created":"2023-10-12","cited_by_count":113},{"id":"2111.11344","title":"Modeling Irregular Time Series with Continuous Recurrent Units","created":"2021-11-22","cited_by_count":103},{"id":"2410.10469","title":"Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts","created":"2024-10-14","cited_by_count":95},{"id":"1606.04130","title":"Modeling Missing Data in Clinical Time Series with RNNs","created":"2016-06-13","cited_by_count":80},{"id":"2404.03264","title":"Foundation Model for Advancing Healthcare: Challenges, Opportunities, and Future Directions","created":"2024-04-04","cited_by_count":77},{"id":"2402.10635","title":"ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling","created":"2024-02-16","cited_by_count":63},{"id":"1911.12864","title":"Self-attention with Functional Time Representation Learning","created":"2019-11-28","cited_by_count":52},{"id":"2405.17766","title":"SleepFM: Multi-modal Representation Learning for Sleep Across Brain Activity, ECG and Respiratory Signals","created":"2024-05-28","cited_by_count":41},{"id":"2210.12156","title":"Improving Medical Predictions by Irregular Multimodal Electronic Health Records Modeling","created":"2022-10-18","cited_by_count":33},{"id":"2010.12493","title":"A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series Data","created":"2020-10-23","cited_by_count":31},{"id":"2311.11483","title":"A Multi-Center Study on the Adaptability of a Shared Foundation Model for Electronic Health Records","created":"2023-11-20","cited_by_count":31},{"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":"2503.02445","title":"BRIDGE: Bootstrapping Text to Control Time-Series Generation via Multi-Agent Iterative Optimization and Diffusion Modeling","created":"2025-03-04","cited_by_count":19},{"id":"2303.02320","title":"Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounders","created":"2023-03-04","cited_by_count":17},{"id":"2412.13823","title":"Prompt Categories Cluster for Weakly Supervised Semantic Segmentation","created":"2024-12-18","cited_by_count":16},{"id":"2102.07005","title":"Clustering Interval-Censored Time-Series for Disease Phenotyping","created":"2021-02-13","cited_by_count":11},{"id":"2307.02006","title":"PULSAR at MEDIQA-Sum 2023: Large Language Models Augmented by Synthetic Dialogue Convert Patient Dialogues to Medical Records","created":"2023-07-05","cited_by_count":11},{"id":"2402.13109","title":"CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Models","created":"2024-02-20","cited_by_count":11},{"id":"2302.12619","title":"T-Phenotype: Discovering Phenotypes of Predictive Temporal Patterns in Disease Progression","created":"2023-02-24","cited_by_count":9},{"id":"2405.03943","title":"Predictive Modeling with Temporal Graphical Representation on Electronic Health Records","created":"2024-05-07","cited_by_count":9},{"id":"2504.17613","title":"TarDiff: Target-Oriented Diffusion Guidance for Synthetic Electronic Health Record Time Series Generation","created":"2025-04-24","cited_by_count":8},{"id":"2306.02754","title":"PULSAR: Pre-training with Extracted Healthcare Terms for Summarising Patients' Problems and Data Augmentation with Black-box Large Language Models","created":"2023-06-05","cited_by_count":6},{"id":"2410.03794","title":"Repurposing Foundation Model for Generalizable Medical Time Series Classification","created":"2024-10-03","cited_by_count":6},{"id":"2302.02077","title":"Cross-Frequency Time Series Meta-Forecasting","created":"2023-02-04","cited_by_count":5},{"id":"2304.08593","title":"Forecasting with Sparse but Informative Variables: A Case Study in Predicting Blood Glucose","created":"2023-04-17","cited_by_count":5},{"id":"2210.04417","title":"Self-explaining Hierarchical Model for Intraoperative Time Series","created":"2022-10-10","cited_by_count":4},{"id":"2411.16346","title":"Towards Foundation Models for Critical Care Time Series","created":"2024-11-25","cited_by_count":4},{"id":"2207.06414","title":"Modeling Long-term Dependencies and Short-term Correlations in Patient Journey Data with Temporal Attention Networks for Health Prediction","created":"2022-07-13","cited_by_count":3},{"id":"2505.18189","title":"Generating Realistic Multi-Beat ECG Signals","created":"2025-05-19","cited_by_count":1}],"next_cursor":null}