Inducing Causal Structure for Interpretable Neural Networks

Inducing Causal Structure for Interpretable Neural Networks

1 December 2021

Thomas F. Icard

Noah D. Goodman

Christopher Potts

Papers citing "Inducing Causal Structure for Interpretable Neural Networks"

11 / 61 papers shown

Title
Breakpoint Transformers for Modeling and Tracking Intermediate Beliefs Kyle Richardson Ronen Tamari Oren Sultan Reut Tsarfaty Dafna Shahaf Ashish Sabharwal KELM 21 8 0 15 Nov 2022
Interpretable Deep Tracking Benjamin Thérien Krzysztof Czarnecki 28 0 0 03 Oct 2022
Causal Proxy Models for Concept-Based Model Explanations Zhengxuan Wu Karel DÓosterlinck Atticus Geiger Amir Zur Christopher Potts MILM 77 35 0 28 Sep 2022
Towards Faithful Model Explanation in NLP: A Survey Qing Lyu Marianna Apidianaki Chris Callison-Burch XAI 109 107 0 22 Sep 2022
Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks Tilman Raukur A. Ho Stephen Casper Dylan Hadfield-Menell AAML AI4CE 23 124 0 27 Jul 2022
Causal Discovery and Knowledge Injection for Contestable Neural Networks (with Appendices) Fabrizio Russo Francesca Toni CML 35 7 0 19 May 2022
A Safety Assurable Human-Inspired Perception Architecture Rick Salay Krzysztof Czarnecki AAML AI4CE 21 1 0 10 May 2022
Causal Distillation for Language Models Zhengxuan Wu Atticus Geiger J. Rozner Elisa Kreiss Hanson Lu Thomas F. Icard Christopher Potts Noah D. Goodman 89 25 0 05 Dec 2021
A Framework for Learning to Request Rich and Contextually Useful Information from Humans Khanh Nguyen Yonatan Bisk Hal Daumé 47 16 0 14 Oct 2021
ReaSCAN: Compositional Reasoning in Language Grounding Zhengxuan Wu Elisa Kreiss Desmond C. Ong Christopher Potts CoGe LRM 29 22 0 18 Sep 2021
Relational reasoning and generalization using non-symbolic neural networks Atticus Geiger Alexandra Carstensen Michael C. Frank Michael C. Frank Christopher Potts NAI 19 20 0 14 Jun 2020