Neural functional networks (NFNs) have recently gained significant attention due to their
diverse applications, ranging from predicting network generalization and network editing to …

Low-rank adaptations (LoRAs) have revolutionized the finetuning of large foundation
models, enabling efficient adaptation even with limited computational resources. The …

Neural fields (NeFs) have recently emerged as a state-of-the-art method for encoding spatio-
temporal signals of various modalities. Despite the success of NeFs in reconstructing …

This paper systematically explores neural functional networks (NFN) for transformer
architectures. NFN are specialized neural networks that treat the weights, gradients, or …

Neural Functional Networks (NFNs) have gained increasing interest due to their wide range
of applications, including extracting information from implicit representations of data, editing …

Implicit neural representations (INRs) exhibit exceptional compression and generalisation
abilities that have enabled striking progress across a variety of applications. These …

The ongoing deep learning revolution of the last decade has brought about hundreds of
millions of neural networks (NNs) trained on diverse datasets. At the same time, the recent …