distill.pubadded August 2026
Distillby Chris Olah; Nick Cammarata; Ludwig Schubert; Gabriel Goh; Michael Petrov; Shan Carter10 Mar 2020
By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks.
Distillby Nick Cammarata; Shan Carter; Gabriel Goh; Chris Olah; Michael Petrov; Ludwig Schubert; Chelsea Voss; Ben Egan; Swee Kiat Lim10 Mar 2020
What can we learn if we invest heavily in reverse engineering a single neural network? In the original narrative of deep learning, each neuron builds progressively more abstract, meaningful...
Distillby Alexander Mordvintsev; Ettore Randazzo; Eyvind Niklasson; Michael Levin11 Feb 2020
Differentiable Model of Morphogenesis Most multicellular organisms begin their life as a single egg cell - a single cell whose progeny reliably self-assemble into highly complex anatomies with many...
Distillby Pascal Sturmfels; Scott Lundberg; Su-In Lee10 Jan 2020
Path attribution methods are a gradient-based way of explaining deep models. These methods require choosing a hyperparameter known as the baseline input.
Distillby Andr; Eacute; Araujo; Wade Norris; Jack Sim4 Nov 2019
Mathematical derivations and open-source library to compute receptive fields of convnets, enabling the mapping of extracted features to input signals.
Distillby Sam Greydanus; Chris Olah30 Sept 2019
A closer look at how Temporal Difference learning merges paths of experience for greater statistical efficiency.
Distillby Logan Engstrom; Andrew Ilyas; Aleksander Madry; Shibani Santurkar; Brandon Tran; Dimitris Tsipras6 Aug 2019
We want to thank all the commenters for the discussion and for spending time designing experiments analyzing, replicating, and expanding upon our results.
Distillby Eric Wallace6 Aug 2019
Section 3.2 of Ilyas et al. (2019) shows that training a model on only adversarial errors leads to non-trivial generalization on the original test set.
Distillby Preetum Nakkiran6 Aug 2019
Refining the source of adversarial examples We demonstrate that there exist adversarial examples which are just “bugs”: aberrations in the classifier that are not intrinsic properties of the data...
Distillby Reiichiro Nakano6 Aug 2019
A figure in Ilyas, et. al. One way to interpret this graph is that it shows how well a particular architecture is able to capture non-robust features in an image.
Distillby Gabriel Goh6 Aug 2019
Ilyas et al. its correlation with the label while under attack. Ilyas et al. Our search is simplified when we realize the following: non-robust features are not unique to the complex, nonlinear...
Distillby Gabriel Goh6 Aug 2019
Ilyas et al. We show that at least 23.5% (out of 88%) of the accuracy can be explained by robust features in .
Distillby Justin Gilmer; Dan Hendrycks6 Aug 2019
The hypothesis in Ilyas et. al. is a special case of a more general principle that is well accepted in the distributional robustness literature — models lack robustness to distribution shift...
Distillby Logan Engstrom; Justin Gilmer; Gabriel Goh; Dan Hendrycks; Andrew Ilyas; Aleksander Madry; Reiichiro Nakano; Preetum Nakkiran; Shibani Santurkar; Brandon Tran; Dimitris Tsipras; Eric Wallace6 Aug 2019
The original authors describe their takeaways and some clarifcations that resulted from the conversation. This article also contains their responses to each comment.
Distillby Augustus Odena9 Apr 2019
What we’d like to find out about GANs that we don’t know yet. By some metrics, research on Generative Adversarial Networks (GANs) has progressed substantially in the past 2 years.
Distillby Jochen Görtler; Rebecca Kehlbeck; Oliver Deussen2 Apr 2019
How to turn a collection of small building blocks into a versatile tool for solving regression problems.
Distillby Andreas Madsen25 Mar 2019
Inspecting gradient magnitudes in context can be a powerful tool to see when recurrent units use short-term or long-term contextual understanding.
Distillby Shan Carter; Zan Armstrong; Ludwig Schubert; Ian Johnson; Chris Olah6 Mar 2019
By using feature inversion to visualize millions of activations from an image classification network, we create an explorable activation atlas of features the network has learned which can reveal...
Distillby Geoffrey Irving; Amanda Askell19 Feb 2019
Properly aligning advanced AI systems with human values will require resolving many uncertainties related to the psychology of human rationality, emotion, and biases.
Distillby Distill Editors14 Aug 2018
A little over a year ago, we formally launched Distill as an open-access scientific journal. It’s been an exciting ride since then!
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