1 code implementation • Distill 2021 • Gabriel Goh, Nick Cammarata, Chelsea Voss, Shan Carter, Michael Petrov, Ludwig Schubert, Alec Radford, Chris Olah
It’s the fact that you plug visual information into the rich tapestry of memory that brings it to life."
no code implementations • Distill 2021 • Michael Petrov, Chelsea Voss, Ludwig Schubert, Nick Cammarata, Gabriel Goh, Chris Olah
Open up any ImageNet conv net and look at the weights in the last layer.
no code implementations • Distill 2021 • Chelsea Voss, Nick Cammarata, Gabriel Goh, Michael Petrov, Ludwig Schubert, Ben Egan, Swee Kiat Lim, Chris Olah
Trying to understand artificial neural networks also has a lot in common with neuroscience, which tries to understand biological neural networks.
no code implementations • Distill 2021 • Ludwig Schubert, Chelsea Voss, Nick Cammarata, Gabriel Goh, Chris Olah
Yet, when systematically characterizing the early layers of InceptionV1, we found a full fifteen neurons of mixed3a that appear to detect a high frequency pattern on one side, and a low frequency pattern on the other.
no code implementations • Distill 2020 • Nick Cammarata, Gabriel Goh, Shan Carter, Ludwig Schubert, Michael Petrov, Chris Olah
Every vision model we've explored in detail contains neurons which detect curves.
no code implementations • Distill 2020 • Nick Cammarata, Shan Carter, Gabriel Goh, Chris Olah, Michael Petrov, Ludwig Schubert, Chelsea Voss, Ben Egan, Swee Kiat Lim
To facilitate exploration of this direction, Distill is inviting a “thread” of short articles on circuits, interspersed with critical commentary by experts in adjacent fields.
1 code implementation • Distill 2020 • Chris Olah, Alexander Mordvintsev, Ludwig Schubert
There is a growing sense that neural networks need to be interpretable to humans.
1 code implementation • Distill 2019 • Shan Carter, Zan Armstrong, Ludwig Schubert, Ian Johnson, Chris Olah
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 how the network typically represents some concepts.
1 code implementation • 17 Dec 2018 • Felipe Petroski Such, Vashisht Madhavan, Rosanne Liu, Rui Wang, Pablo Samuel Castro, Yulun Li, Jiale Zhi, Ludwig Schubert, Marc G. Bellemare, Jeff Clune, Joel Lehman
We lessen this friction, by (1) training several algorithms at scale and releasing trained models, (2) integrating with a previous Deep RL model release, and (3) releasing code that makes it easy for anyone to load, visualize, and analyze such models.
2 code implementations • Distill 2018 • Alexander Mordvintsev, Nicola Pezzotti, Ludwig Schubert, Chris Olah
Typically, we parameterize the input image as the RGB values of each pixel, but that isn’t the only way.
1 code implementation • Distill 2018 • Chris Olah, Arvind Satyanarayan, Ian Johnson, Shan Carter, Ludwig Schubert, Katherine Ye, Alexander Mordvintsev
In this article, we treat existing interpretability methods as fundamental and composable building blocks for rich user interfaces.