5 citations · 9 across the 3 of their papers we have counts for
3 papers
cs.LG2024
Hyper-Representations: Learning from Populations of Neural Networks
Konstantin Schürholt
This thesis addresses the challenge of understanding Neural Networks through the lens of their most fundamental component: the weights, which encapsulate the learned information an…
cs.LG2022★ 5 cited
Model Zoos: A Dataset of Diverse Populations of Neural Network Models
Konstantin Schürholt, Diyar Taskiran, Boris Knyazev +2
In the last years, neural networks (NN) have evolved from laboratory environments to the state-of-the-art for many real-world problems. It was shown that NN models (i.e., their wei…
cs.LG2022★ 4 cited
Hyper-Representations as Generative Models: Sampling Unseen Neural Network Weights
Konstantin Schürholt, Boris Knyazev, Xavier Giró-i-Nieto +1
Learning representations of neural network weights given a model zoo is an emerging and challenging area with many potential applications from model inspection, to neural architect…