ARC has teamed up with AIcrowd to launch the ARC White-Box Estimation Challenge, a contest to improve upon our estimation algorithms for random MLPs. The warm-up round begins this week, and later rounds will have a total prize pool of at least $100,000.

We are very grateful to Sharada Mohanty, Sneha Nanavati, Dipam Chakraborty and everyone else at AIcrowd for working with us to host this contest, as well as to Paul Rosu for testing the contest and to Harshita Khera for operational support.

Introduction to the Challenge

Our challenge follows the same setup as our recent paper on wide random MLPs: we consider MLPs with weights , defined by

To begin with, we are fixing the width and the number of hidden layers , but we expect to change this setup in future rounds.[1]

Contestants must design an algorithm that takes in a set of weights and produces an estimate for the expected output

Algorithms will be evaluated on MLPs with randomly-sampled Gaussian weights. The goal is to achieve as low mean squared error as possible, subject to certain computational [...]

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Outline:

(00:41) Introduction to the Challenge

(01:58) Why run this contest?

(03:39) Use of LLMs

The original text contained 4 footnotes which were omitted from this narration.

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