Learning to Receive Help: Intervention-Aware Concept Embedding Models

29 September 2023

M. Zarlenga

Katherine M. Collins

Krishnamurthy Dvijotham

Papers citing "Learning to Receive Help: Intervention-Aware Concept Embedding Models"

20 / 20 papers shown

Title
If Concept Bottlenecks are the Question, are Foundation Models the Answer? Nicola Debole Pietro Barbiero Francesco Giannini Andrea Passerini Stefano Teso Emanuele Marconato 442 1 0 28 Apr 2025
Leakage and Interpretability in Concept-Based Models Enrico Parisini Tapabrata Chakraborti Chris Harbron Ben D. MacArthur Christopher R. S. Banerji 86 1 0 18 Apr 2025
Shortcuts and Identifiability in Concept-based Models from a Neuro-Symbolic Lens Samuele Bortolotti Emanuele Marconato Paolo Morettin Andrea Passerini Stefano Teso 147 5 0 16 Feb 2025
Label-Free Concept Bottleneck Models Tuomas P. Oikarinen Subhro Das Lam M. Nguyen Tsui-Wei Weng 83 175 0 12 Apr 2023
Interactive Concept Bottleneck Models Kushal Chauhan Rishabh Tiwari Jan Freyberg Pradeep Shenoy Krishnamurthy Dvijotham 51 55 0 14 Dec 2022
Encoding Concepts in Graph Neural Networks Lucie Charlotte Magister Pietro Barbiero Dmitry Kazhdan F. Siciliano Gabriele Ciravegna Fabrizio Silvestri M. Jamnik Pietro Lio 66 21 0 27 Jul 2022
GlanceNets: Interpretabile, Leak-proof Concept-based Models Emanuele Marconato Andrea Passerini Stefano Teso 159 68 0 31 May 2022
Promises and Pitfalls of Black-Box Concept Learning Models Anita Mahinpei Justin Clark Isaac Lage Finale Doshi-Velez Weiwei Pan 81 96 0 24 Jun 2021
Now You See Me (CME): Concept-based Model Extraction Dmitry Kazhdan B. Dimanov M. Jamnik Pietro Lio Adrian Weller 46 75 0 25 Oct 2020
Concept Bottleneck Models Pang Wei Koh Thao Nguyen Y. S. Tang Stephen Mussmann Emma Pierson Been Kim Percy Liang 94 821 0 09 Jul 2020
Generative causal explanations of black-box classifiers Matthew R. O’Shaughnessy Gregory H. Canal Marissa Connor Mark A. Davenport Christopher Rozell CML 58 73 0 24 Jun 2020
Concept Whitening for Interpretable Image Recognition Zhi Chen Yijie Bei Cynthia Rudin FAtt 70 320 0 05 Feb 2020
On Completeness-aware Concept-Based Explanations in Deep Neural Networks Chih-Kuan Yeh Been Kim Sercan O. Arik Chun-Liang Li Tomas Pfister Pradeep Ravikumar FAtt 228 305 0 17 Oct 2019
Explaining Classifiers with Causal Concept Effect (CaCE) Yash Goyal Amir Feder Uri Shalit Been Kim CML 73 177 0 16 Jul 2019
Towards Robust Interpretability with Self-Explaining Neural Networks David Alvarez-Melis Tommi Jaakkola MILM XAI 126 941 0 20 Jun 2018
Net2Vec: Quantifying and Explaining how Concepts are Encoded by Filters in Deep Neural Networks Ruth C. Fong Andrea Vedaldi FAtt 68 264 0 10 Jan 2018
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) Been Kim Martin Wattenberg Justin Gilmer Carrie J. Cai James Wexler F. Viégas Rory Sayres FAtt 211 1,842 0 30 Nov 2017
Categorical Reparameterization with Gumbel-Softmax Eric Jang S. Gu Ben Poole BDL 317 5,364 0 03 Nov 2016
TensorFlow: A system for large-scale machine learning Martín Abadi P. Barham Jianmin Chen Zhiwen Chen Andy Davis ... Vijay Vasudevan Pete Warden Martin Wicke Yuan Yu Xiaoqiang Zhang GNN AI4CE 433 18,350 0 27 May 2016
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Sergey Ioffe Christian Szegedy OOD 463 43,289 0 11 Feb 2015