all AI news
Neural Additive Models for Location Scale and Shape: A Framework for Interpretable Neural Regression Beyond the Mean
March 4, 2024, 5:43 a.m. | Anton Thielmann, Ren\'e-Marcel Kruse, Thomas Kneib, Benjamin S\"afken
cs.LG updates on arXiv.org arxiv.org
Abstract: Deep neural networks (DNNs) have proven to be highly effective in a variety of tasks, making them the go-to method for problems requiring high-level predictive power. Despite this success, the inner workings of DNNs are often not transparent, making them difficult to interpret or understand. This lack of interpretability has led to increased research on inherently interpretable neural networks in recent years. Models such as Neural Additive Models (NAMs) achieve visual interpretability through the combination …
arxiv beyond cs.lg framework location mean regression scale stat.ml type
More from arxiv.org / cs.LG updates on arXiv.org
Digital Over-the-Air Federated Learning in Multi-Antenna Systems
2 days, 10 hours ago |
arxiv.org
Bagging Provides Assumption-free Stability
2 days, 10 hours ago |
arxiv.org
Jobs in AI, ML, Big Data
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
Research Scientist, Demography and Survey Science, University Grad
@ Meta | Menlo Park, CA | New York City
Computer Vision Engineer, XR
@ Meta | Burlingame, CA