The results? In medical imaging, Rate-In maintains sharp uncertainty estimates around critical anatomical boundaries, while traditional methods get fuzzy. We demonstrate superior performance across different noise levels and benchmarks!

Rate-In's approach: We dynamically adjust dropout rates by measuring information loss in each layer. Where features are critical, we preserve more; where they're redundant, we drop more. Like adaptive noise, guided by information theory!

Imagine you're a doctor looking at an MRI scan. Would you rather have an AI that:
A) Says "There's a tumor" with blind confidence
B) Points out exactly which areas it's uncertain about, helping focus your expertise.

A new paper 🥳🥳🥳
We present "Rate-In" - a technique that helps neural networks better express their uncertainty during inference, which is especially crucial for medical applications!
with Tal Zeevi, @yann-lecun.bsky.social , H. Stain Lawrence and John Onofrey

Apparently, I reached 4000 citations 🤓
Thank you all my collaborators! 🎉
In 5K, I will give my secret to amazing papers titles 😎

By attempt 482, the prize was $50K, and each try cost $450. Then someone cracked it with genius social engineering:

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Another paper -

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Looking at ICLR submissions with the lowest score - What a work of art! 🧵