{"list":"other","total":61,"rows":[{"id":"2203.02155","title":"Training language models to follow instructions with human feedback","created":"2022-03-04","cited_by_count":15971},{"id":"1703.03400","title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","created":"2017-03-09","cited_by_count":4897},{"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":"1703.04730","title":"Understanding Black-box Predictions via Influence Functions","created":"2017-03-14","cited_by_count":1741},{"id":"2205.13504","title":"Are Transformers Effective for Time Series Forecasting?","created":"2022-05-26","cited_by_count":1424},{"id":"2305.17926","title":"Large Language Models are not Fair Evaluators","created":"2023-05-29","cited_by_count":1085},{"id":"2310.06625","title":"iTransformer: Inverted Transformers Are Effective for Time Series Forecasting","created":"2023-10-10","cited_by_count":931},{"id":"1703.07015","title":"Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks","created":"2017-03-21","cited_by_count":879},{"id":"2403.07815","title":"Chronos: Learning the Language of Time Series","created":"2024-03-12","cited_by_count":759},{"id":"2305.14387","title":"AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback","created":"2023-05-22","cited_by_count":748},{"id":"2310.01728","title":"Time-LLM: Time Series Forecasting by Reprogramming Large Language Models","created":"2023-10-03","cited_by_count":700},{"id":"1904.02868","title":"Data Shapley: Equitable Valuation of Data for Machine Learning","created":"2019-04-05","cited_by_count":629},{"id":"2402.02592","title":"Unified Training of Universal Time Series Forecasting Transformers","created":"2024-02-04","cited_by_count":614},{"id":"2002.08484","title":"Estimating Training Data Influence by Tracing Gradient Descent","created":"2020-02-19","cited_by_count":580},{"id":"1809.04356","title":"Deep learning for time series classification: a review","created":"2018-09-12","cited_by_count":576},{"id":"2306.11644","title":"Textbooks Are All You Need","created":"2023-06-20","cited_by_count":569},{"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":"2205.14415","title":"Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting","created":"2022-05-28","cited_by_count":391},{"id":"2405.14616","title":"TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting","created":"2024-05-23","cited_by_count":346},{"id":"2308.03296","title":"Studying Large Language Model Generalization with Influence Functions","created":"2023-08-07","cited_by_count":326},{"id":"2310.07641","title":"Evaluating Large Language Models at Evaluating Instruction Following","created":"2023-10-11","cited_by_count":302},{"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":"2303.14186","title":"TRAK: Attributing Model Behavior at Scale","created":"2023-03-24","cited_by_count":283},{"id":"1902.10275","title":"Towards Efficient Data Valuation Based on the Shapley Value","created":"2019-02-27","cited_by_count":281},{"id":"1907.05321","title":"Time2Vec: Learning a Vector Representation of Time","created":"2019-07-11","cited_by_count":213},{"id":"2402.02368","title":"Timer: Generative Pre-trained Transformers Are Large Time Series Models","created":"2024-02-04","cited_by_count":208},{"id":"2401.16380","title":"Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling","created":"2024-01-29","cited_by_count":161},{"id":"2402.09739","title":"QuRating: Selecting High-Quality Data for Training Language Models","created":"2024-02-15","cited_by_count":157},{"id":"1908.08619","title":"Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms","created":"2019-08-22","cited_by_count":148},{"id":"2112.03052","title":"Scaling Up Influence Functions","created":"2021-12-06","cited_by_count":132},{"id":"2402.09668","title":"How to Train Data-Efficient LLMs","created":"2024-02-15","cited_by_count":93},{"id":"2012.00073","title":"TimeSHAP: Explaining Recurrent Models through Sequence Perturbations","created":"2020-11-30","cited_by_count":74},{"id":"2108.13461","title":"Time Series Prediction using Deep Learning Methods in Healthcare","created":"2021-08-30","cited_by_count":74},{"id":"2002.02887","title":"Meta-learning framework with applications to zero-shot time-series forecasting","created":"2020-02-07","cited_by_count":70},{"id":"2402.02370","title":"AutoTimes: Autoregressive Time Series Forecasters via Large Language Models","created":"2024-02-04","cited_by_count":65},{"id":"2402.16132","title":"LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting","created":"2024-02-25","cited_by_count":59},{"id":"2410.11802","title":"TSFM-Bench: A Comprehensive and Unified Benchmark of Foundation Models for Time Series Forecasting","created":"2024-10-15","cited_by_count":58},{"id":"2502.10721","title":"A Comprehensive Survey of Deep Learning for Multivariate Time Series Forecasting: A Channel Strategy Perspective","created":"2025-02-15","cited_by_count":58},{"id":"2410.14752","title":"TimeSeriesExam: A time series understanding exam","created":"2024-10-18","cited_by_count":49},{"id":"2402.02713","title":"Position: What Can Large Language Models Tell Us about Time Series Analysis","created":"2024-02-05","cited_by_count":47},{"id":"2410.05440","title":"Can LLMs Understand Time Series Anomalies?","created":"2024-10-07","cited_by_count":41},{"id":"2503.01013","title":"TimeXL: Explainable Multi-modal Time Series Prediction with LLM-in-the-Loop","created":"2025-03-02","cited_by_count":39},{"id":"2304.07718","title":"Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value","created":"2023-04-16","cited_by_count":38},{"id":"2406.09130","title":"Time-Series Forecasting for Out-of-Distribution Generalization Using Invariant Learning","created":"2024-06-13","cited_by_count":27},{"id":"2211.06507","title":"WindowSHAP: An Efficient Framework for Explaining Time-series Classifiers based on Shapley Values","created":"2022-11-11","cited_by_count":24},{"id":"2502.15637","title":"Mantis: Lightweight Foundation Model for Time Series Classification","created":"2025-02-21","cited_by_count":24},{"id":"2501.05403","title":"TimeDP: Learning to Generate Multi-Domain Time Series with Domain Prompts","created":"2025-01-09","cited_by_count":21},{"id":"2402.02475","title":"TimeSiam: A Pre-Training Framework for Siamese Time-Series Modeling","created":"2024-02-04","cited_by_count":20},{"id":"2401.03038","title":"SPADE: Synthesizing Data Quality Assertions for Large Language Model Pipelines","created":"2024-01-05","cited_by_count":19},{"id":"2409.16341","title":"Quality Matters: Evaluating Synthetic Data for Tool-Using LLMs","created":"2024-09-24","cited_by_count":19},{"id":"2109.07497","title":"Sign-MAML: Efficient Model-Agnostic Meta-Learning by SignSGD","created":"2021-09-15","cited_by_count":14},{"id":"2405.03140","title":"TimeMIL: Advancing Multivariate Time Series Classification via a Time-aware Multiple Instance Learning","created":"2024-05-06","cited_by_count":12},{"id":"2411.01006","title":"Abstracted Shapes as Tokens -- A Generalizable and Interpretable Model for Time-series Classification","created":"2024-11-01","cited_by_count":12},{"id":"2308.14906","title":"BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decomposition","created":"2023-08-28","cited_by_count":10},{"id":"2404.11269","title":"DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Series","created":"2024-04-17","cited_by_count":7},{"id":"2505.24030","title":"From Images to Signals: Are Large Vision Models Useful for Time Series Analysis?","created":"2025-05-29","cited_by_count":7},{"id":"2407.15247","title":"TimeInf: Time Series Data Contribution via Influence Functions","created":"2024-07-21","cited_by_count":6},{"id":"2501.15070","title":"Unifying Prediction and Explanation in Time-Series Transformers via Shapley-based Pretraining","created":"2025-01-25","cited_by_count":5},{"id":"2505.21525","title":"Temporal Restoration and Spatial Rewiring for Source-Free Multivariate Time Series Domain Adaptation","created":"2025-05-23","cited_by_count":2},{"id":"2502.16637","title":"Time Series Domain Adaptation via Latent Invariant Causal Mechanism","created":"2025-02-23","cited_by_count":1}],"next_cursor":null}