TIP: Typifying the Interpretability of Procedures

9 June 2017

Amit Dhurandhar

Vijay Iyengar

Ronny Luss

Karthikeyan Shanmugam

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Papers citing "TIP: Typifying the Interpretability of Procedures"

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Title
An Overview of Machine Teaching Xiaojin Zhu Adish Singla Sandra Zilles Anna N. Rafferty 59 178 0 18 Jan 2018
Efficient Data Representation by Selecting Prototypes with Importance Weights Karthik S. Gurumoorthy Amit Dhurandhar Guillermo Cecchi Charu Aggarwal 64 22 0 05 Jul 2017
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 280 2,264 0 24 Jun 2017
Interpreting Blackbox Models via Model Extraction Osbert Bastani Carolyn Kim Hamsa Bastani FAtt 64 172 0 23 May 2017
Learning with Changing Features Amit Dhurandhar Steve Hanneke Liu Yang OOD 25 1 0 29 Apr 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 388 3,785 0 28 Feb 2017
Algorithms for Fitting the Constrained Lasso Brian R. Gaines Hua Zhou 196 119 0 28 Oct 2016
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization Ramprasaath R. Selvaraju Michael Cogswell Abhishek Das Ramakrishna Vedantam Devi Parikh Dhruv Batra FAtt 289 19,981 0 07 Oct 2016
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 258 8,550 0 16 Aug 2016
Interpretable Two-level Boolean Rule Learning for Classification Guolong Su Dennis L. Wei Kush R. Varshney Dmitry Malioutov 59 52 0 18 Jun 2016
Rationalizing Neural Predictions Tao Lei Regina Barzilay Tommi Jaakkola 110 812 0 13 Jun 2016
The Mythos of Model Interpretability Zachary Chase Lipton FaML 178 3,699 0 10 Jun 2016
The Latin American Giant Observatory: a successful collaboration in Latin America based on Cosmic Rays and computer science domains Hernán Asorey R. Mayo-García L. Núñez M. Pascual A. J. Rubio-Montero M. Suárez-Durán L. A. Torres-Niño 81 5 0 30 May 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 16,954 0 16 Feb 2016
Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks Anh Totti Nguyen J. Yosinski Jeff Clune 54 329 0 11 Feb 2016
Engineering Safety in Machine Learning Kush R. Varshney 65 116 0 16 Jan 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 2.2K 193,814 0 10 Dec 2015
Unifying distillation and privileged information David Lopez-Paz Léon Bottou Bernhard Schölkopf V. Vapnik FedML 165 462 0 11 Nov 2015
Distilling the Knowledge in a Neural Network Geoffrey E. Hinton Oriol Vinyals J. Dean FedML 342 19,634 0 09 Mar 2015
FitNets: Hints for Thin Deep Nets Adriana Romero Nicolas Ballas Samira Ebrahimi Kahou Antoine Chassang C. Gatta Yoshua Bengio FedML 298 3,883 0 19 Dec 2014
Falling Rule Lists Fulton Wang Cynthia Rudin 52 258 0 21 Nov 2014
Do Deep Nets Really Need to be Deep? Lei Jimmy Ba R. Caruana 162 2,117 0 21 Dec 2013