Time-LLM reprograms frozen LLMs for time series forecasting via text prototypes and Prompt-as-Prefix, outperforming specialized models in standard, few-shot, and zero-shot settings.
Kingma and Jimmy Ba , title =
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Combining a soft physics residual with multi-IC multiple shooting yields the best data-driven Neural ODE on Lotka–Volterra across out-of-sample error, long-horizon stability, and Hamiltonian drift.
Constraining fine-tuning updates with LoRA mitigates performance degradation when switching from Adam to Muon on pretrained models.
citing papers explorer
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Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
Time-LLM reprograms frozen LLMs for time series forecasting via text prototypes and Prompt-as-Prefix, outperforming specialized models in standard, few-shot, and zero-shot settings.
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MPINeuralODE: Multiple-Initial-Condition Physics-Informed Neural ODEs for Globally Consistent Dynamical System Learning
Combining a soft physics residual with multi-IC multiple shooting yields the best data-driven Neural ODE on Lotka–Volterra across out-of-sample error, long-horizon stability, and Hamiltonian drift.
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Can Muon Fine-tune Adam-Pretrained Models?
Constraining fine-tuning updates with LoRA mitigates performance degradation when switching from Adam to Muon on pretrained models.