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๐Ÿฆ  DeepDecipher: An open source API to MLP neurons

Home Page: https://apartresearch.com

License: MIT License

Rust 76.92% Python 7.26% HTML 0.12% JavaScript 0.50% TypeScript 3.52% Svelte 9.46% Shell 1.79% Dockerfile 0.42%
academic api interpretability interpretability-jam interpretability-methods machine-learning mechanistic-interpretability research website

deepdecipher's Issues

Remove the model-service table

It is not necessary to explicitly store which services are available for which models.
Instead, this can be calculated from which data objects a model has available.
To do this, each service provider must provide a list of necessary data objects.
When receiving a service API call, first check that the model has the necessary data objects, and if not, respond with an error message saying which are missing.
When receiving an all API call, the response JSON should contain an object for all services in the database, with the before mentioned error message for the unavailable ones.

Rebuttal of article reviews

Deadline is 29/08/2023

  • Get the server up and running again.
  • Case studies.
  • Add Neuroscope data for all 25 neuroscope.io models.
  • Add Neuron2Graph data for as many models as possible.
  • Measure response time for single requests.
  • Stress test server against high traffic.
  • Have someone reproduce some of our data and compare it with what DeepDecipher shows.
  • Write deeper methodology section describing what we did and not only how to use it.
  • Stitch together rebuttal.
  • Submit rebuttal!

Basic UX testing

Get people with some mech-interp experience to take a look around the website and say what the like/don't like.
Show some people the index site and ask them to do specific things and think aloud while doing so.

Speed up backend

Currently, the backend can't take more than 20 requests a second, even for neuron pages without N2G. This is not nearly good enough.

Improve UX

Current frontend is very basic. One of our main selling points over e.g. Neuroscope is that our site is supposed to be nicer to look at and easier to use. Lets actually deliver on that.

Not much point in working on this before all (MVP) parts of the frontend are done, so depends on:

Add some interestingness measure for neurons

E.g. activation kurtosis

Create a script that takes a model (and probably some data) and calculates an interestingness measure for each neuron in the model and stores it as an array.

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