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.
DRN outperforms conventional neural networks in distribution regression tasks.
End-to-end DRNs outperform ConvNets in EEG decoding.
DRN improves actuarial distributional forecasting with interpretable neural networks.
Develops scalable autoencoder for document networks.
The study improves solar irradiance forecasts for Chile using machine learning.
Despite the superior performance of deep learning in many applications, challenges remain in the area of regression on function spaces. In particular, neural networks are unable to encode function inputs compactly as each node encodes just a real value. We propose a novel idea to address this shortcoming: to encode an …
Real-time traffic flow prediction can not only provide travelers with reliable traffic information so that it can save people's time, but also assist the traffic management agency to manage traffic system. It can greatly improve the efficiency of the transportation system. Traditional traffic flow prediction approaches…
Bayesian surrogate learning speeds up parameter estimation in complex systems.