"Why Should I Trust You?": Explaining the Predictions of Any Classifier

16 February 2016

Papers citing ""Why Should I Trust You?": Explaining the Predictions of Any Classifier"

9 / 4,309 papers shown

Title
Safety Verification of Deep Neural Networks Xiaowei Huang Marta Kwiatkowska Sen Wang Min Wu AAML 183 933 0 21 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 89 19,634 0 07 Oct 2016
Correct classification for big/smart/fast data machine learning S. Stepanov 13 0 0 27 Sep 2016
A deep learning model for estimating story points Morakot Choetkiertikul Hoa Dam T. Tran Trang Pham A. Ghose Tim Menzies 24 170 0 02 Sep 2016
Towards Transparent AI Systems: Interpreting Visual Question Answering Models Yash Goyal Akrit Mohapatra Devi Parikh Dhruv Batra 25 74 0 31 Aug 2016
Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach Satoshi Hara K. Hayashi 17 91 0 29 Jun 2016
Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models Viktoriya Krakovna Finale Doshi-Velez AI4CE 39 69 0 16 Jun 2016
Rationalizing Neural Predictions Tao Lei Regina Barzilay Tommi Jaakkola 59 805 0 13 Jun 2016
Auditing Black-box Models for Indirect Influence Philip Adler Casey Falk Sorelle A. Friedler Gabriel Rybeck C. Scheidegger Brandon Smith Suresh Venkatasubramanian TDI MLAU 20 287 0 23 Feb 2016