We're posting lots of papers written with GPT-6 Astra on generalization properties of bayesian neural net learning. [1] The following picture emerges: * For various reasonable priors on MLP weights, [2] the induced prior on functions is a circuit prior: for fixed depth, the ordering of functions by negative log...
Here are the slides of a talk Kaarel gave, presenting work with Dmitry establishing that (even arbitrarily overparametrized) neural net bayesian learning has a circuit prior — and thus, when learning a function which is implemented by some small circuit, only requires a small amount of training data to get...
This note discusses a (proto-)plan for [de[AGI-[x-risk]]]ing [1] (pdf version). Here's the plan: 1. You somehow make/find/identify an AI with the following properties: * the AI is human-level intelligent/capable; * however, it would be possible for the AI to quickly gain in intelligence/capabilities in some fairly careful self-guided way, sufficiently...
1. I was intending to write and post one (hypo)thesis [relating to thinking (with an eye toward alignment)] each day this Advent, starting on 24/12/01 and finishing on 24/12/24. 2. Ok, so that didn't happen, and it is now 25/03/17, but whatever — an advent of thought can happen whenever...
Background Lucius: I recently held a small talk presenting an idea for how and why deep learning generalises. It tried to reduce concepts from Singular Learning theory back to basic algorithmic information theory to sketch a unified picture that starts with Solomonoff induction and, with a lot of hand waving,...
Clément Dumas, Walter Laurito, Robert Klassert, Kaarel Hänni Epistemic Status: Initial Exploration The following is a status update of a project started as part of the SPAR program. We explored some initial directions and there are still a lot of low-hanging fruits to pick up. We might continue to work...
A short post laying out our reasoning for using integrated gradients as attribution method. It is intended as a stand-alone post based on our LIB papers [1] [2]. This work was produced at Apollo Research. Context Understanding circuits in neural networks requires understanding how features interact with other features. There's...