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 …
F Ballerini, PZ Ramirez, R Mirabella… - Proceedings of the …, 2024 - openaccess.thecvf.com
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 …
Recently, Neural Fields have emerged as a powerful modelling paradigm to represent continuous signals. In a conditional neural field, a field is represented by a latent variable …
Many algorithms and observed phenomena in deep learning appear to be affected by parameter symmetries--transformations of neural network parameters that do not change the …
Learning representations of well-trained neural network models holds the promise to provide an understanding of the inner workings of those models. However, previous work …
This paper pertains to an emerging machine learning paradigm: learning higher-order functions, ie functions whose inputs are functions themselves, $\textit {particularly when …
Multimodal Large Language Models (MLLMs) have demonstrated an excellent understanding of images and 3D data. However, both modalities have shortcomings in …
The rapid growth of neural network models shared on the internet has made model weights an important data modality. However, this information is underutilized as the weights are …