A multi-exposure video model predicts bracketed linear SDR sequences from single nonlinear SDR input, which a merging model combines into HDR video preserving shadow and highlight detail.
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7 Pith papers cite this work. Polarity classification is still indexing.
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LeapTS reformulates forecasting as adaptive multi-horizon scheduling via hierarchical control and NCDEs, delivering at least 7.4% better performance and 2.6-5.3x faster inference than Transformer baselines while adapting to non-stationary dynamics.
A new evaluation framework shows that blood glucose forecasting models with high overall accuracy often fail at timely hypoglycemia detection in high-risk periods and at predicting effects of changed insulin doses.
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.
NPMixer improves multivariate time series forecasting accuracy by combining a data-adaptive wavelet decomposition with hierarchical neighboring patch mixing via MLPs and channel mixing on high-frequency components.
PAMod models cyclical distribution shifts in non-stationary time series via phase-amplitude modulation in normalized space, proving equivalence to dynamic denormalization and achieving SOTA on twelve benchmarks.
GCSVR combines graph convolutions for spatial station dependencies with SVR for nonlinear temporal patterns, yielding more accurate and stable air pollution forecasts on Delhi and Mumbai datasets than standard benchmarks.
citing papers explorer
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Generating HDR Video from SDR Video
A multi-exposure video model predicts bracketed linear SDR sequences from single nonlinear SDR input, which a merging model combines into HDR video preserving shadow and highlight detail.
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LeapTS: Rethinking Time Series Forecasting as Adaptive Multi-Horizon Scheduling
LeapTS reformulates forecasting as adaptive multi-horizon scheduling via hierarchical control and NCDEs, delivering at least 7.4% better performance and 2.6-5.3x faster inference than Transformer baselines while adapting to non-stationary dynamics.
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From Prediction to Practice: A Task-Aware Evaluation Framework for Blood Glucose Forecasting
A new evaluation framework shows that blood glucose forecasting models with high overall accuracy often fail at timely hypoglycemia detection in high-risk periods and at predicting effects of changed insulin doses.
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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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NPMixer: Hierarchical Neighboring Patch Mixing for Time Series Forecasting
NPMixer improves multivariate time series forecasting accuracy by combining a data-adaptive wavelet decomposition with hierarchical neighboring patch mixing via MLPs and channel mixing on high-frequency components.
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PAMod: Modeling Cyclical Shifts via Phase-Amplitude Modulation for Non-stationary Time Series Forecasting
PAMod models cyclical distribution shifts in non-stationary time series via phase-amplitude modulation in normalized space, proving equivalence to dynamic denormalization and achieving SOTA on twelve benchmarks.
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Graph Convolutional Support Vector Regression for Robust Spatiotemporal Forecasting of Urban Air Pollution
GCSVR combines graph convolutions for spatial station dependencies with SVR for nonlinear temporal patterns, yielding more accurate and stable air pollution forecasts on Delhi and Mumbai datasets than standard benchmarks.