The recent success of neural networks as implicit representation of data has driven growing
interest in neural functionals: models that can process other neural networks as input by …

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

Our understanding of the generalization capabilities of neural networks NNs is still
incomplete. Prevailing explanations are based on implicit biases of gradient descent GD but …

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 …

Neural fields (NeFs) have recently emerged as a versatile method for modeling signals of
various modalities including images shapes and scenes. Subsequently a number of works …

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 …

Abstract Recent progress in Quality Diversity Reinforcement Learning (QD-RL) has enabled
learning a collection of behaviorally diverse, high performing policies. However, these …

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 …

Fleets of robots ingest massive amounts of streaming data generated by interacting with
their environments, far more than those that can be stored or transmitted with ease. At the …