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.
Study improves crash rate forecasting in Washington, D.C. using stochastic volatility model.
This study analyzes how weather impacts bike sharing usage in Washington D.C.
Purpose: To determine if deep learning networks could be trained to forecast a future 24-2 Humphrey Visual Field (HVF). Participants: All patients who obtained a HVF 24-2 at the University of Washington. Methods: All datapoints from consecutive 24-2 HVFs from 1998 to 2018 were extracted from a University of Washington …
In this paper we deal with the offline handwriting text recognition (HTR) problem with reduced training datasets. Recent HTR solutions based on artificial neural networks exhibit remarkable solutions in referenced databases. These deep learning neural networks are composed of both convolutional (CNN) and long short-ter…
Time-lagged autoencoders (TAEs) have been proposed as a deep learning regression-based approach to the discovery of slow modes in dynamical systems. However, a rigorous analysis of nonlinear TAEs remains lacking. In this work, we discuss the capabilities and limitations of TAEs through both theoretical and numerical an…
A new neural approach for generating origin-destination matrices in ABMs.
Machine learning and deep learning infer surface/groundwater exchange from temperature data.
A new approach to newsvendor problems reduces loss by up to 40%.
Forecasting stock market decline and recovery post-COVID-19.
This paper uses machine learning to estimate how different types of crashes affect highway traffic.
Study analyzes data breach reporting patterns and frequency across U.S. states, finding increasing trends after 2020.