9 citations · 11 across the 4 of their papers we have counts for
4 papers
Can We Scale Transformers to Predict Parameters of Diverse ImageNet Models?
Boris Knyazev, Doha Hwang, Simon Lacoste-Julien
Pretraining a neural network on a large dataset is becoming a cornerstone in machine learning that is within the reach of only a few communities with large-resources. We aim at an…
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…
Pretraining a Neural Network before Knowing Its Architecture
Boris Knyazev
Training large neural networks is possible by training a smaller hypernetwork that predicts parameters for the large ones. A recently released Graph HyperNetwork (GHN) trained this…
On Evaluation Metrics for Graph Generative Models
Rylee Thompson, Boris Knyazev, Elahe Ghalebi +2
In image generation, generative models can be evaluated naturally by visually inspecting model outputs. However, this is not always the case for graph generative models (GGMs), mak…