Evaluating the Interpretability of Generative Models by Interactive Reconstruction

2 February 2021

Finale Doshi-Velez

Papers citing "Evaluating the Interpretability of Generative Models by Interactive Reconstruction"

31 / 31 papers shown

Title
On the Challenges and Opportunities in Generative AI Laura Manduchi Kushagra Pandey Robert Bamler Ryan Cotterell Sina Daubener ... F. Wenzel Frank Wood Stephan Mandt Vincent Fortuin Vincent Fortuin 224 21 0 28 Feb 2024
A Loss Function for Generative Neural Networks Based on Watson's Perceptual Model Steffen Czolbe Oswin Krause Ingemar Cox Christian Igel GAN 32 48 0 26 Jun 2020
Proxy Tasks and Subjective Measures Can Be Misleading in Evaluating Explainable AI Systems Zana Buçinca Phoebe Lin Krzysztof Z. Gajos Elena L. Glassman ELM 73 284 0 22 Jan 2020
The What-If Tool: Interactive Probing of Machine Learning Models James Wexler Mahima Pushkarna Tolga Bolukbasi Martin Wattenberg F. Viégas Jimbo Wilson VLM 79 492 0 09 Jul 2019
Assessing the Local Interpretability of Machine Learning Models Dylan Slack Sorelle A. Friedler C. Scheidegger Chitradeep Dutta Roy FAtt 43 71 0 09 Feb 2019
Human-Centered Tools for Coping with Imperfect Algorithms during Medical Decision-Making Carrie J. Cai Emily Reif Narayan Hegde J. Hipp Been Kim ... Martin Wattenberg F. Viégas G. Corrado Martin C. Stumpe Michael Terry 101 403 0 08 Feb 2019
An Evaluation of the Human-Interpretability of Explanation Isaac Lage Emily Chen Jeffrey He Menaka Narayanan Been Kim Sam Gershman Finale Doshi-Velez FAtt XAI 106 156 0 31 Jan 2019
TensorFlow.js: Machine Learning for the Web and Beyond D. Smilkov Nikhil Thorat Yannick Assogba Ann Yuan Nick Kreeger ... D. Sculley R. Monga G. Corrado F. Viégas Martin Wattenberg 80 174 0 16 Jan 2019
Towards a Definition of Disentangled Representations I. Higgins David Amos David Pfau S. Racanière Loic Matthey Danilo Jimenez Rezende Alexander Lerchner OCL DRL 103 480 0 05 Dec 2018
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations Francesco Locatello Stefan Bauer Mario Lucic Gunnar Rätsch Sylvain Gelly Bernhard Schölkopf Olivier Bachem OOD 118 1,467 0 29 Nov 2018
A Visual Interaction Framework for Dimensionality Reduction Based Data Exploration M. Cavallo Çağatay Demiralp 36 55 0 28 Nov 2018
What can AI do for me: Evaluating Machine Learning Interpretations in Cooperative Play Shi Feng Jordan L. Boyd-Graber HAI 47 129 0 23 Oct 2018
Recurrent World Models Facilitate Policy Evolution David R Ha Jürgen Schmidhuber SyDa TPM 117 944 0 04 Sep 2018
Towards Robust Interpretability with Self-Explaining Neural Networks David Alvarez-Melis Tommi Jaakkola MILM XAI 126 941 0 20 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 95 1,857 0 31 May 2018
Human-in-the-Loop Interpretability Prior Isaac Lage A. Ross Been Kim S. Gershman Finale Doshi-Velez 77 121 0 29 May 2018
Manipulating and Measuring Model Interpretability Forough Poursabzi-Sangdeh D. Goldstein Jake M. Hofman Jennifer Wortman Vaughan Hanna M. Wallach 88 698 0 21 Feb 2018
Disentangling by Factorising Hyunjik Kim A. Mnih CoGe OOD 62 1,350 0 16 Feb 2018
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric Richard Y. Zhang Phillip Isola Alexei A. Efros Eli Shechtman Oliver Wang EGVM 377 11,795 0 11 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 214 1,842 0 30 Nov 2017
Explanation in Artificial Intelligence: Insights from the Social Sciences Tim Miller XAI 242 4,265 0 22 Jun 2017
Network Dissection: Quantifying Interpretability of Deep Visual Representations David Bau Bolei Zhou A. Khosla A. Oliva Antonio Torralba MILM FAtt 146 1,515 1 19 Apr 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 399 3,798 0 28 Feb 2017
A Survey of Inductive Biases for Factorial Representation-Learning Karl Ridgeway DRL CML 66 76 0 15 Dec 2016
Embedding Projector: Interactive Visualization and Interpretation of Embeddings D. Smilkov Nikhil Thorat Charles Nicholson Emily Reif F. Viégas Martin Wattenberg 58 179 0 16 Nov 2016
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets Xi Chen Yan Duan Rein Houthooft John Schulman Ilya Sutskever Pieter Abbeel GAN 159 4,235 0 12 Jun 2016
The Mythos of Model Interpretability Zachary Chase Lipton FaML 180 3,701 0 10 Jun 2016
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 1.8K 150,115 0 22 Dec 2014
Auto-Encoding Variational Bayes Diederik P. Kingma Max Welling BDL 452 16,929 0 20 Dec 2013
Disentangling Factors of Variation via Generative Entangling Guillaume Desjardins Aaron Courville Yoshua Bengio CoGe CML DRL 90 104 0 19 Oct 2012
Representation Learning: A Review and New Perspectives Yoshua Bengio Aaron Courville Pascal Vincent OOD SSL 264 12,439 0 24 Jun 2012