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Found 3 papers, 1 papers with code
Randomness Is All You Need: Semantic Traversal of Problem-Solution Spaces with Large Language Models
We present a novel approach to exploring innovation problem and solution domains using LLM fine-tuning with a custom idea database.
Why Neural Networks Work
We argue that many properties of fully-connected feedforward neural networks (FCNNs), also called multi-layer perceptrons (MLPs), are explainable from the analysis of a single pair of operations, namely a random projection into a higher-dimensional space than the input, followed by a sparsification operation.
Reinforcement Learning for Standards Design
Communications standards are designed via committees of humans holding repeated meetings over months or even years until consensus is achieved.
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