  A good initialization of deep learning models is essential since it can help
them converge better and faster. However, pretraining large models is
unaffordable for many researchers, which makes a desired prediction for initial
parameters more necessary nowadays. Graph HyperNetworks (GHNs), one approach to
predicting model parameters, have recently shown strong performance in
initializing large vision models. Unfortunately, predicting parameters of very
wide networks relies on copying small chunks of parameters multiple times and
requires an extremely large number of parameters to support full prediction,
which greatly hinders its adoption in practice. To address this limitation, we
propose LoGAH (Low-rank GrAph Hypernetworks), a GHN with a low-rank parameter
decoder that expands to significantly wider networks without requiring as
excessive increase of parameters as in previous attempts. LoGAH allows us to
predict the parameters of 774-million large neural networks in a
memory-efficient manner. We show that vision and language models (i.e., ViT and
GPT-2) initialized with LoGAH achieve better performance than those initialized
randomly or using existing hypernetworks. Furthermore, we show promising
transfer learning results w.r.t. training LoGAH on small datasets and using the
predicted parameters to initialize for larger tasks. We provide the codes in
https://github.com/Blackzxy/LoGAH .
