Dissonance Between Human and Machine Understanding

18 January 2021

Papers citing "Dissonance Between Human and Machine Understanding"

20 / 20 papers shown

Title
Perception of Visual Content: Differences Between Humans and Foundation Models Nardiena A. Pratama Shaoyang Fan Gianluca Demartini VLM 122 0 0 28 Nov 2024
Generalisation in humans and deep neural networks Robert Geirhos Carlos R. Medina Temme Jonas Rauber Heiko H. Schutt Matthias Bethge Felix Wichmann OOD 94 605 0 27 Aug 2018
Human-like generalization in a machine through predicate learning L. Doumas Guillermo Puebla Andrea E. Martin NAI 40 9 0 05 Jun 2018
Ít's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisions Reuben Binns Max Van Kleek Michael Veale Ulrik Lyngs Jun Zhao N. Shadbolt FaML 48 527 0 31 Jan 2018
Building machines that adapt and compute like brains Brenden M. Lake J. Tenenbaum AI4CE FedML NAI AILaw 315 886 0 11 Nov 2017
Learning Credible Models Jiaxuan Wang Jeeheh Oh Haozhu Wang Jenna Wiens FaML 43 30 0 08 Nov 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 635 21,613 0 22 May 2017
Understanding Black-box Predictions via Influence Functions Pang Wei Koh Percy Liang TDI 142 2,854 0 14 Mar 2017
Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations A. Ross M. C. Hughes Finale Doshi-Velez FAtt 108 585 0 10 Mar 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 354 3,742 0 28 Feb 2017
Model-Agnostic Interpretability of Machine Learning Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 66 836 0 16 Jun 2016
The Mythos of Model Interpretability Zachary Chase Lipton FaML 132 3,672 0 10 Jun 2016
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning Christian Szegedy Sergey Ioffe Vincent Vanhoucke Alexander A. Alemi 306 14,196 0 23 Feb 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 650 16,828 0 16 Feb 2016
Embracing Error to Enable Rapid Crowdsourcing Ranjay Krishna Kenji Hata Stephanie Chen Joshua Kravitz David A. Shamma Li Fei-Fei Michael S. Bernstein LRM 62 91 0 14 Feb 2016
Rethinking the Inception Architecture for Computer Vision Christian Szegedy Vincent Vanhoucke Sergey Ioffe Jonathon Shlens Z. Wojna 3DV BDL 522 27,231 0 02 Dec 2015
Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model Benjamin Letham Cynthia Rudin Tyler H. McCormick D. Madigan FAtt 52 743 0 05 Nov 2015
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention Ke Xu Jimmy Ba Ryan Kiros Kyunghyun Cho Aaron Courville Ruslan Salakhutdinov R. Zemel Yoshua Bengio DiffM 286 10,034 0 10 Feb 2015
Going Deeper with Convolutions Christian Szegedy Wei Liu Yangqing Jia P. Sermanet Scott E. Reed Dragomir Anguelov D. Erhan Vincent Vanhoucke Andrew Rabinovich 342 43,511 0 17 Sep 2014
Very Deep Convolutional Networks for Large-Scale Image Recognition Karen Simonyan Andrew Zisserman FAtt MDE 1.0K 99,991 0 04 Sep 2014