May 16, 2024, 4:42 a.m. | Jose A. Lopez, Georg Stemmer, Hector A. Cordourier

cs.LG updates on

arXiv:2405.09305v1 Announce Type: new
Abstract: Gradient boosted decision trees have achieved remarkable success in several domains, particularly those that work with static tabular data. However, the application of gradient boosted models to signal processing is underexplored. In this work, we introduce gradient boosted filters for dynamic data, by employing Hammerstein systems in place of decision trees. We discuss the relationship of our approach to the Volterra series, providing the theoretical underpinning for its application. We demonstrate the effective generalizability of …

abstract application arxiv cs.lg data decision decision trees domains dynamic filters gradient however processing signal success systems tabular tabular data trees type work

Doctoral Researcher (m/f/div) in Automated Processing of Bioimages

@ Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI) | Jena

Seeking Developers and Engineers for AI T-Shirt Generator Project

@ Chevon Hicks | Remote

Software Engineer for AI Training Data (School Specific)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Python)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Tier 2)

@ G2i Inc | Remote

AI Engineer

@ Holcim Group | Navi Mumbai, MH, IN, 400708