Designing Interpretable Approximations to Deep Reinforcement Learning

28 October 2020

Papers citing "Designing Interpretable Approximations to Deep Reinforcement Learning"

3 / 3 papers shown

Title
ProtoX: Explaining a Reinforcement Learning Agent via Prototyping Ronilo Ragodos Tong Wang Qihang Lin Xun Zhou 24 7 0 06 Nov 2022
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges Cynthia Rudin Chaofan Chen Zhi Chen Haiyang Huang Lesia Semenova Chudi Zhong FaML AI4CE LRM 59 655 0 20 Mar 2021
NuCLS: A scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentation M. Amgad Lamees A. Atteya Hagar Hussein K. Mohammed Ehab Hafiz ... Critical Care David Manthey Atlanta D. Neurology Lurie Cancer Center 44 74 0 18 Feb 2021