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Found 6 papers, 2 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.
General Information Bottleneck Objectives and their Applications to Machine Learning
Aided by this insight, we formulate a new IBO that accounts for this property of the parameters of the trained model, and derive and optimize a variational bound on this IBO.
Machine Learning using the Variational Predictive Information Bottleneck with a Validation Set
Zellner (1988) modeled statistical inference in terms of information processing and postulated the Information Conservation Principle (ICP) between the input and output of the information processing block, showing that this yielded Bayesian inference as the optimum information processing rule.
PPD: Permutation Phase Defense Against Adversarial Examples in Deep Learning
In this paper, Permutation Phase Defense (PPD), is proposed as a novel method to resist adversarial attacks.
