Neural fields, also known as implicit neural representations, have emerged as a powerful
means to represent complex signals of various modalities. Based on this Dupont et al.(2022) …

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 …

In recent years, there have been attempts to increase the kernel size of Convolutional
Neural Nets (CNNs) to mimic the global receptive field of Vision Transformers'(ViTs) self …

Neural fields have emerged as a new data representation paradigm and have shown
remarkable success in various signal representations. Since they preserve signals in their …

Implicit neural fields, typically encoded by a multilayer perceptron (MLP) that maps from
coordinates (eg, xyz) to signals (eg, signed distances), have shown remarkable promise as …

Neural fields have emerged as a powerful and broadly applicable method for representing
signals. However in contrast to classical discrete digital signal processing the portfolio of …

The original ImageNet dataset is a popular large-scale benchmark for training Deep Neural
Networks. Since the cost of performing experiments (eg, algorithm design, architecture …

Dot-product attention mechanism plays a crucial role in modern deep architectures (eg,
Transformer) for sequence modeling, however, naïve exact computation of this model incurs …

Neural Representations have recently been shown to effectively reconstruct a wide range of
signals from 3D meshes and shapes to images and videos. We show that, when adapted …

Formula-driven supervised learning (FDSL) has been shown to be an effective method for
pre-training vision transformers, where ExFractalDB-21k was shown to exceed the pre …