Distill Archive

Vol 1, Issue 9

• Attention and Augmented Recurrent Neural Networks
  http://doi.org/10.23915/distill.00001
  Sept. 8, 2016
  Olah, Chris and Carter, Shan

Vol 1, Issue 10

• Deconvolution and Checkerboard Artifacts
  http://doi.org/10.23915/distill.00003
  Oct. 17, 2016
  Odena, Augustus and Dumoulin, Vincent and Olah, Chris
• How to Use t-SNE Effectively
  http://doi.org/10.23915/distill.00002
  Oct. 13, 2016
  Wattenberg, Martin and Viégas, Fernanda and Johnson, Ian

Vol 1, Issue 12

• Experiments in Handwriting with a Neural Network
  http://doi.org/10.23915/distill.00004
  Dec. 6, 2016
  Carter, Shan and Ha, David and Johnson, Ian and Olah, Chris

Vol 2, Issue 3

• Research Debt
  http://doi.org/10.23915/distill.00005
  March. 22, 2017
  Olah, Chris and Carter, Shan

Vol 2, Issue 4

• Why Momentum Really Works
  http://doi.org/10.23915/distill.00006
  April. 4, 2017
  Goh, Gabriel

Vol 2, Issue 11

• Sequence Modeling with CTC
  http://doi.org/10.23915/distill.00008
  Nov.. 27, 2017
  Hannun, Awni
• Feature Visualization
  http://doi.org/10.23915/distill.00007
  Nov.. 7, 2017
  Olah, Chris and Mordvintsev, Alexander and Schubert, Ludwig

Vol 2, Issue 12

• Using Artificial Intelligence to Augment Human Intelligence
  http://doi.org/10.23915/distill.00009
  Dec.. 4, 2017
  Carter, Shan and Nielsen, Michael

Vol 3, Issue 3

• The Building Blocks of Interpretability
  http://doi.org/10.23915/distill.00010
  March. 6, 2018
  Olah, Chris and Satyanarayan, Arvind and Johnson, Ian and Carter, Shan and Schubert, Ludwig and Ye, Katherine and Mordvintsev, Alexander

Vol 3, Issue 7

• Differentiable Image Parameterizations
  http://doi.org/10.23915/distill.00012
  July. 25, 2018
  Mordvintsev, Alexander and Pezzotti, Nicola and Schubert, Ludwig and Olah, Chris
• Feature-wise transformations
  http://doi.org/10.23915/distill.00011
  July. 9, 2018
  Dumoulin, Vincent and Perez, Ethan and Schucher, Nathan and Strub, Florian and Vries, Harm de and Courville, Aaron and Bengio, Yoshua

Vol 3, Issue 8

• Distill Update 2018
  http://doi.org/10.23915/distill.00013
  Aug.. 14, 2018
  Editors, Distill

Vol 4, Issue 2

• AI Safety Needs Social Scientists
  http://doi.org/10.23915/distill.00014
  Feb.. 19, 2019
  Irving, Geoffrey and Askell, Amanda

Vol 4, Issue 3

• Visualizing memorization in RNNs
  http://doi.org/10.23915/distill.00016
  March. 25, 2019
  Madsen, Andreas
• Activation Atlas
  http://doi.org/10.23915/distill.00015
  March. 6, 2019
  Carter, Shan and Armstrong, Zan and Schubert, Ludwig and Johnson, Ian and Olah, Chris

Vol 4, Issue 4

• Open Questions about Generative Adversarial Networks
  http://doi.org/10.23915/distill.00018
  April. 9, 2019
  Odena, Augustus
• A Visual Exploration of Gaussian Processes
  http://doi.org/10.23915/distill.00017
  April. 2, 2019
  Görtler, Jochen and Kehlbeck, Rebecca and Deussen, Oliver

Vol 4, Issue 8

• A Discussion of ‘Adversarial Examples Are Not Bugs, They Are Features’
  http://doi.org/10.23915/distill.00019
  Aug.. 6, 2019
  Engstrom, Logan and Gilmer, Justin and Goh, Gabriel and Hendrycks, Dan and Ilyas, Andrew and Madry, Aleksander and Nakano, Reiichiro and Nakkiran, Preetum and Santurkar, Shibani and Tran, Brandon and Tsipras, Dimitris and Wallace, Eric
• A Discussion of ‘Adversarial Examples Are Not Bugs, They Are Features’: Adversarial Example Researchers Need to Expand What is Meant by ‘Robustness’
  http://doi.org/10.23915/distill.00019.1
  Aug.. 6, 2019
  Gilmer, Justin and Hendrycks, Dan
• A Discussion of ‘Adversarial Examples Are Not Bugs, They Are Features’: Robust Feature Leakage
  http://doi.org/10.23915/distill.00019.2
  Aug.. 6, 2019
  Goh, Gabriel
• A Discussion of ‘Adversarial Examples Are Not Bugs, They Are Features’: Two Examples of Useful, Non-Robust Features
  http://doi.org/10.23915/distill.00019.3
  Aug.. 6, 2019
  Goh, Gabriel
• A Discussion of ‘Adversarial Examples Are Not Bugs, They Are Features’: Adversarially Robust Neural Style Transfer
  http://doi.org/10.23915/distill.00019.4
  Aug.. 6, 2019
  Nakano, Reiichiro
• A Discussion of ‘Adversarial Examples Are Not Bugs, They Are Features’: Adversarial Examples are Just Bugs, Too
  http://doi.org/10.23915/distill.00019.5
  Aug.. 6, 2019
  Nakkiran, Preetum
• A Discussion of ‘Adversarial Examples Are Not Bugs, They Are Features’: Learning from Incorrectly Labeled Data
  http://doi.org/10.23915/distill.00019.6
  Aug.. 6, 2019
  Wallace, Eric
• A Discussion of ‘Adversarial Examples Are Not Bugs, They Are Features’: Discussion and Author Responses
  http://doi.org/10.23915/distill.00019.7
  Aug.. 6, 2019
  Engstrom, Logan and Ilyas, Andrew and Madry, Aleksander and Santurkar, Shibani and Tran, Brandon and Tsipras, Dimitris

Vol 4, Issue 9

• The Paths Perspective on Value Learning
  http://doi.org/10.23915/distill.00020
  Sept.. 30, 2019
  Greydanus, Sam and Olah, Chris

Vol 4, Issue 11

• Computing Receptive Fields of Convolutional Neural Networks
  http://doi.org/10.23915/distill.00021
  Nov.. 4, 2019
  Araujo, André and Norris, Wade and Sim, Jack

Vol 5, Issue 1

• Visualizing the Impact of Feature Attribution Baselines
  http://doi.org/10.23915/distill.00022
  Jan.. 10, 2020
  Sturmfels, Pascal and Lundberg, Scott and Lee, Su-In

Vol 5, Issue 2

• Growing Neural Cellular Automata
  http://doi.org/10.23915/distill.00023
  Feb.. 11, 2020
  Mordvintsev, Alexander and Randazzo, Ettore and Niklasson, Eyvind and Levin, Michael

Vol 5, Issue 3

• Visualizing Neural Networks with the Grand Tour
  http://doi.org/10.23915/distill.00025
  March. 16, 2020
  Li, Mingwei and Zhao, Zhenge and Scheidegger, Carlos
• Thread: Circuits
  http://doi.org/10.23915/distill.00024
  March. 10, 2020
  Cammarata, Nick and Carter, Shan and Goh, Gabriel and Olah, Chris and Petrov, Michael and Schubert, Ludwig and Voss, Chelsea and Egan, Ben and Lim, Swee Kiat
• Zoom In: An Introduction to Circuits
  http://doi.org/10.23915/distill.00024.001
  March. 10, 2020
  Olah, Chris and Cammarata, Nick and Schubert, Ludwig and Goh, Gabriel and Petrov, Michael and Carter, Shan

Vol 5, Issue 4

• An Overview of Early Vision in InceptionV1
  http://doi.org/10.23915/distill.00024.002
  April. 1, 2020
  Olah, Chris and Cammarata, Nick and Schubert, Ludwig and Goh, Gabriel and Petrov, Michael and Carter, Shan

Vol 5, Issue 5

• Exploring Bayesian Optimization
  http://doi.org/10.23915/distill.00026
  May. 5, 2020
  Agnihotri, Apoorv and Batra, Nipun

Vol 5, Issue 6

• Curve Detectors
  http://doi.org/10.23915/distill.00024.003
  June. 17, 2020
  Cammarata, Nick and Goh, Gabriel and Carter, Shan and Schubert, Ludwig and Petrov, Michael and Olah, Chris

Vol 5, Issue 8

• Thread: Differentiable Self-organizing Systems
  http://doi.org/10.23915/distill.00027
  Aug.. 27, 2020
  Mordvintsev, Alexander and Randazzo, Ettore and Niklasson, Eyvind and Levin, Michael and Greydanus, Sam
• Self-classifying MNIST Digits
  http://doi.org/10.23915/distill.00027.002
  Aug.. 27, 2020
  Randazzo, Ettore and Mordvintsev, Alexander and Niklasson, Eyvind and Levin, Michael and Greydanus, Sam

Vol 5, Issue 9

• Communicating with Interactive Articles
  http://doi.org/10.23915/distill.00028
  Sept.. 11, 2020
  Hohman, Fred and Conlen, Matthew and Heer, Jeffrey and Chau, Duen Horng (Polo)

Vol 5, Issue 11

• Understanding RL Vision
  http://doi.org/10.23915/distill.00029
  Nov.. 17, 2020
  Hilton, Jacob and Cammarata, Nick and Carter, Shan and Goh, Gabriel and Olah, Chris

Vol 5, Issue 12

• Naturally Occurring Equivariance in Neural Networks
  http://doi.org/10.23915/distill.00024.004
  Dec.. 8, 2020
  Olah, Chris and Cammarata, Nick and Voss, Chelsea and Schubert, Ludwig and Goh, Gabriel

Vol 6, Issue 1

• Curve Circuits
  http://doi.org/10.23915/distill.00024.006
  Jan.. 30, 2021
  Cammarata, Nick and Goh, Gabriel and Carter, Shan and Voss, Chelsea and Schubert, Ludwig and Olah, Chris
• High-Low Frequency Detectors
  http://doi.org/10.23915/distill.00024.005
  Jan.. 27, 2021
  Schubert, Ludwig and Voss, Chelsea and Cammarata, Nick and Goh, Gabriel and Olah, Chris

Vol 6, Issue 2

• Self-Organising Textures
  http://doi.org/10.23915/distill.00027.003
  Feb.. 11, 2021
  Niklasson, Eyvind and Mordvintsev, Alexander and Randazzo, Ettore and Levin, Michael
• Visualizing Weights
  http://doi.org/10.23915/distill.00024.007
  Feb.. 4, 2021
  Voss, Chelsea and Cammarata, Nick and Goh, Gabriel and Petrov, Michael and Schubert, Ludwig and Egan, Ben and Lim, Swee Kiat and Olah, Chris

Vol 6, Issue 3

• Multimodal Neurons in Artificial Neural Networks
  http://doi.org/10.23915/distill.00030
  March. 4, 2021
  Goh, Gabriel and †, Nick Cammarata and †, Chelsea Voss and Carter, Shan and Petrov, Michael and Schubert, Ludwig and Radford, Alec and Olah, Chris

Vol 6, Issue 4

• Weight Banding
  http://doi.org/10.23915/distill.00024.009
  April. 8, 2021
  Petrov, Michael and Voss, Chelsea and Schubert, Ludwig and Cammarata, Nick and Goh, Gabriel and Olah, Chris
• Branch Specialization
  http://doi.org/10.23915/distill.00024.008
  April. 5, 2021
  Voss, Chelsea and Goh, Gabriel and Cammarata, Nick and Petrov, Michael and Schubert, Ludwig and Olah, Chris

Vol 6, Issue 5

• Adversarial Reprogramming of Neural Cellular Automata
  http://doi.org/10.23915/distill.00027.004
  May. 6, 2021
  Randazzo, Ettore and Mordvintsev, Alexander and Niklasson, Eyvind and Levin, Michael

Vol 6, Issue 7

• Distill Hiatus
  http://doi.org/10.23915/distill.00031
  July. 2, 2021
  Team, Editorial

Vol 6, Issue 9

• Understanding Convolutions on Graphs
  http://doi.org/10.23915/distill.00032
  Sept.. 2, 2021
  Daigavane, Ameya and Ravindran, Balaraman and Aggarwal, Gaurav
• A Gentle Introduction to Graph Neural Networks
  http://doi.org/10.23915/distill.00033
  Sept.. 2, 2021
  Sanchez-Lengeling, Benjamin and Reif, Emily and Pearce, Adam and Wiltschko, Alexander B.