2 citations · 2 across the 3 of their papers we have counts for
3 papers
Towards Scalable and Versatile Weight Space Learning
Konstantin Schürholt, Michael W. Mahoney, Damian Borth
Learning representations of well-trained neural network models holds the promise to provide an understanding of the inner workings of those models. However, previous work has eithe…
Sparsified Model Zoo Twins: Investigating Populations of Sparsified Neural Network Models
Dominik Honegger, Konstantin Schürholt, Damian Borth
With growing size of Neural Networks (NNs), model sparsification to reduce the computational cost and memory demand for model inference has become of vital interest for both resear…
Hyper-Representations for Pre-Training and Transfer Learning
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…