Neural Spline Search for Quantile Probabilistic Modeling
نویسندگان
چکیده
Accurate estimation of output quantiles is crucial in many use cases, where it desired to model the range possibility. Modeling target distribution at arbitrary quantile levels and input attribute are important offer a comprehensive picture data, requires function be expressive enough. The describing using critical for regression. Although various parametric forms distributions (that specifies) can adopted, an everlasting problem selecting most appropriate one that properly approximate data distributions. In this paper, we propose non-parametric data-driven approach, Neural Spline Search (NSS), represent observed without assumptions. NSS flexible modeling by transforming inputs with series monotonic spline regressions guided symbolic operators. We demonstrate outperforms previous methods on synthetic, real-world regression time-series forecasting tasks.
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ژورنال
عنوان ژورنال: Proceedings of the ... AAAI Conference on Artificial Intelligence
سال: 2023
ISSN: ['2159-5399', '2374-3468']
DOI: https://doi.org/10.1609/aaai.v37i8.26184