Update: We have now launched Phase 1 of the Challenge with $50,000 in prizes:
Score-based prizes: $25,000 / $10,000 / $5,000 for 1st / 2nd / 3rd place
Algorithmic contribution prize: $10,000
For Phase 1, we have increased the depth of the network from 8 to 32 hidden layers. Our existing algorithms scale poorly with depth, and so we expect there to be significant room for improvement. Phase 1 lasts until the end of July, after which Phase 2 begins. For Phase 2, there will be a prize pool of at least $100,000, and we may change the architectural parameters again.
The best-performing algorithms in the warm-up round appear to be variants on the factorized 3rd cumulant propagation algorithm we introduced in our paper, combined with learned networks that consume the cumulant estimates as features. Many of these submissions appear to have been produced with the help of LLMs, but we don’t know much about the extent of this. For Phase 1, we expect the increased depth to advantage approaches that go beyond basic cumulant propagation in their mechanistic analysis. We also expect to get more visibility into the design of top submissions, since we will be using accompanying technical write-ups to award the algorithmic contribution prize. For more information, please see the contest website.
Update: We have now launched Phase 1 of the Challenge with $50,000 in prizes:
Score-based prizes: $25,000 / $10,000 / $5,000 for 1st / 2nd / 3rd place
Algorithmic contribution prize: $10,000
For Phase 1, we have increased the depth of the network from 8 to 32 hidden layers. Our existing algorithms scale poorly with depth, and so we expect there to be significant room for improvement. Phase 1 lasts until the end of July, after which Phase 2 begins. For Phase 2, there will be a prize pool of at least $100,000, and we may change the architectural parameters again.
The best-performing algorithms in the warm-up round appear to be variants on the factorized 3rd cumulant propagation algorithm we introduced in our paper, combined with learned networks that consume the cumulant estimates as features. Many of these submissions appear to have been produced with the help of LLMs, but we don’t know much about the extent of this. For Phase 1, we expect the increased depth to advantage approaches that go beyond basic cumulant propagation in their mechanistic analysis. We also expect to get more visibility into the design of top submissions, since we will be using accompanying technical write-ups to award the algorithmic contribution prize. For more information, please see the contest website.