Neural networks that process the parameters of other neural networks find applications in
domains as diverse as classifying implicit neural representations, generating neural network …

A challenging problem in many modern machine learning tasks is to process weight-space
features, ie, to transform or extract information from the weights and gradients of a neural …

Abstract Neural Radiance Fields (NeRFs) have emerged as a standard framework for
representing 3D scenes and objects introducing a novel data type for information exchange …

In recent years, Neural Fields (NF s) have emerged as an effective tool for encoding diverse
continuous signals such as images, videos, audio, and 3D shapes. When applied to 3D …

Permutation symmetries of deep networks make simple operations like model averaging
and similarity estimation challenging. In many cases, aligning the weights of the networks …

Learning in deep weight spaces (DWS), where neural networks process the weights of other
neural networks, is an emerging research direction, with applications to 2D and 3D neural …

In recent years, Neural Fields (NFs) have emerged as an effective tool for encoding diverse
continuous signals such as images, videos, audio, and 3D shapes. When applied to 3D …

Neural networks efficiently encode learned information within their parameters.
Consequently, many tasks can be unified by treating neural networks themselves as input …

Learning in weight spaces, where neural networks process the weights of other deep neural
networks, has emerged as a promising research direction with applications in various fields …

In this paper, a new variant of convolutional neural network (CNN) based on L 1
regularization is proposed. The main consideration is to generate a sparse weight matrix, ie …