Tommy_Xie

Figure: compression vs. correspondence. Each dot is a proof of the network's average loss. Further right means fewer model evaluations (more FLOPs). Higher means closer to the true loss. For a simple enough toy network [1], can we use a consumer-level agentic setting to independently find arguments that can be...

Figure: compression vs. correspondence. Each dot is a proof of the network's average loss. Further right means fewer model evaluations. Higher means closer to the true loss. Three AI agents proposed, implemented and checked a proof about a small neural network. The proof is an upper bound on the network's...