Right for the Right Reasons: Training Differentiable Models by
Constraining their Explanations

Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations

10 March 2017

Finale Doshi-Velez

Papers citing "Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations"

15 / 115 papers shown

Title
Saliency Learning: Teaching the Model Where to Pay Attention Reza Ghaeini Xiaoli Z. Fern Hamed Shahbazi Prasad Tadepalli FAtt XAI 21 30 0 22 Feb 2019
Interpretable machine learning: definitions, methods, and applications W. James Murdoch Chandan Singh Karl Kumbier R. Abbasi-Asl Bin-Xia Yu XAI HAI 21 1,416 0 14 Jan 2019
Multimodal Explanations by Predicting Counterfactuality in Videos Atsushi Kanehira Kentaro Takemoto S. Inayoshi Tatsuya Harada 18 35 0 04 Dec 2018
Interpretable Neuron Structuring with Graph Spectral Regularization Alexander Tong David van Dijk Jay S. Stanley Matthew Amodio Kristina M. Yim R. Muhle J. Noonan Guy Wolf Smita Krishnaswamy 19 6 0 30 Sep 2018
Women also Snowboard: Overcoming Bias in Captioning Models (Extended Abstract) Lisa Anne Hendricks Kaylee Burns Kate Saenko Trevor Darrell Anna Rohrbach 25 478 0 02 Jul 2018
Learning Qualitatively Diverse and Interpretable Rules for Classification A. Ross Weiwei Pan Finale Doshi-Velez 16 13 0 22 Jun 2018
Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems Richard J. Tomsett Dave Braines Daniel Harborne Alun D. Preece Supriyo Chakraborty FaML 23 164 0 20 Jun 2018
Detecting and interpreting myocardial infarction using fully convolutional neural networks Nils Strodthoff C. Strodthoff 33 150 0 18 Jun 2018
Explaining Explanations: An Overview of Interpretability of Machine Learning Leilani H. Gilpin David Bau Ben Z. Yuan Ayesha Bajwa Michael A. Specter Lalana Kagal XAI 40 1,840 0 31 May 2018
Human-in-the-Loop Interpretability Prior Isaac Lage A. Ross Been Kim S. Gershman Finale Doshi-Velez 32 120 0 29 May 2018
Seq2Seq-Vis: A Visual Debugging Tool for Sequence-to-Sequence Models Hendrik Strobelt Sebastian Gehrmann M. Behrisch Adam Perer Hanspeter Pfister Alexander M. Rush VLM HAI 28 239 0 25 Apr 2018
How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation Menaka Narayanan Emily Chen Jeffrey He Been Kim S. Gershman Finale Doshi-Velez FAtt XAI 16 241 0 02 Feb 2018
Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients A. Ross Finale Doshi-Velez AAML 26 675 0 26 Nov 2017
Beyond Sparsity: Tree Regularization of Deep Models for Interpretability Mike Wu M. C. Hughes S. Parbhoo Maurizio Zazzi Volker Roth Finale Doshi-Velez AI4CE 20 281 0 16 Nov 2017
Human Understandable Explanation Extraction for Black-box Classification Models Based on Matrix Factorization Jaedeok Kim Ji-Hoon Seo FAtt 13 8 0 18 Sep 2017