Scientifically-Interpretable Reasoning Network (ScIReN): Uncovering the Black-Box of Nature

16 June 2025

Papers citing "Scientifically-Interpretable Reasoning Network (ScIReN): Uncovering the Black-Box of Nature"

9 / 9 papers shown

Title
Hybrid Phenology Modeling for Predicting Temperature Effects on Tree Dormancy Ron van Bree Diego Marcos Ioannis Athanasiadis 72 1 0 28 Jan 2025
KAN: Kolmogorov-Arnold Networks Ziming Liu Yixuan Wang Sachin Vaidya Fabian Ruehle James Halverson Marin Soljacic Thomas Y. Hou Max Tegmark 245 563 0 30 Apr 2024
Neural Additive Models: Interpretable Machine Learning with Neural Nets Rishabh Agarwal Levi Melnick Nicholas Frosst Xuezhou Zhang Ben Lengerich R. Caruana Geoffrey E. Hinton 92 420 0 29 Apr 2020
Achieving Conservation of Energy in Neural Network Emulators for Climate Modeling Tom Beucler S. Rasp Michael S. Pritchard Pierre Gentine 35 84 0 15 Jun 2019
Physics Guided RNNs for Modeling Dynamical Systems: A Case Study in Simulating Lake Temperature Profiles X. Jia J. Willard Anuj Karpatne J. Read Jacob Aaron Zwart M. Steinbach Vipin Kumar PINN AI4CE 32 213 0 31 Oct 2018
A Simple and Effective Model-Based Variable Importance Measure Brandon M. Greenwell Bradley C. Boehmke Andrew J. McCarthy FAtt TDI 45 230 0 12 May 2018
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 1.1K 22,002 0 22 May 2017
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 191 6,015 0 04 Mar 2017
Model-Agnostic Interpretability of Machine Learning Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 86 839 0 16 Jun 2016