Improving Prototypical Visual Explanations with Reward Reweighing, Reselection, and Retraining

8 July 2023

Papers citing "Improving Prototypical Visual Explanations with Reward Reweighing, Reselection, and Retraining"

8 / 8 papers shown

Title
ProtoPFormer: Concentrating on Prototypical Parts in Vision Transformers for Interpretable Image Recognition Mengqi Xue Qihan Huang Haofei Zhang Lechao Cheng Mingli Song Ming-hui Wu Mingli Song ViT 78 54 0 22 Aug 2022
A General Language Assistant as a Laboratory for Alignment Amanda Askell Yuntao Bai Anna Chen Dawn Drain Deep Ganguli ... Tom B. Brown Jack Clark Sam McCandlish C. Olah Jared Kaplan ALM 94 762 0 01 Dec 2021
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges Cynthia Rudin Chaofan Chen Zhi Chen Haiyang Huang Lesia Semenova Chudi Zhong FaML AI4CE LRM 118 662 0 20 Mar 2021
Reward-rational (implicit) choice: A unifying formalism for reward learning Hong Jun Jeon S. Milli Anca Dragan 45 177 0 12 Feb 2020
Towards Explainable Deep Neural Networks (xDNN) Plamen Angelov Eduardo Soares AAML 41 259 0 05 Dec 2019
Fine-Tuning Language Models from Human Preferences Daniel M. Ziegler Nisan Stiennon Jeff Wu Tom B. Brown Alec Radford Dario Amodei Paul Christiano G. Irving ALM 422 1,664 0 18 Sep 2019
Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs W. James Murdoch Peter J. Liu Bin Yu 51 209 0 16 Jan 2018
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 587 16,828 0 16 Feb 2016