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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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2 results for “wTDS”

Tackling water table depth modeling via machine learning: From proxy observations to verifiabilitycs.LG

ML models predict water table depth more accurately than PB models, especially in data-scarce regions.

problem Uncertainty in large-scale simulations of water table depth due to biased observational data and model flexibility.

method Constructed three ML models using XGBoost and over 20 million real and proxy observations across the US and Canada.

result ML models correlate 0.6-0.75 with real and proxy observations, significantly better than PB models (0.21-0.40).

water table depth machine learning proxy observations hydrogeology model verification

2024-04-30abs ↗pdf ↗

From text saliency to linguistic objects: learning linguistic interpretable markers with a multi-channels convolutional architecturestat.ML

A new method extracts linguistic objects from text using CNNs.

problem Lack of interpretability in deep learning models for text.

method Weighted extension of Text Deconvolution Saliency (wTDS) measure.

result Extracts interpretable linguistic objects from text.

deep learning text interpretation CNN linguistic objects wTDS

2020-04-07abs ↗pdf ↗