Most existing geometry processing algorithms use meshes as the default shape
representation. Manipulating meshes, however, requires one to maintain high quality in the …

Neural fields have emerged as a new paradigm for representing signals, thanks to their
ability to do it compactly while being easy to optimize. In most applications, however, neural …

Abstract Deep Neural Networks (DNNs) have obtained impressive performance across
tasks, however they still remain as black boxes, eg, hard to theoretically analyze. At the …

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 …

Neural tangent kernel (NTK) is a powerful tool to analyze training dynamics of neural
networks and their generalization bounds. The study on NTK has been devoted to typical …

In this research, we present an innovative Quadratic Residual Multiplicative Filter Neural
Network (QRMFNN) to effectively learn extremely complex sensor signals as a low …

Self-attention mechanisms and non-local blocks have become crucial building blocks for
state-of-the-art neural architectures thanks to their unparalleled ability in capturing long …

A ReLU network is a piecewise linear function over polytopes. Figuring out the properties of
such polytopes is of fundamental importance for the research and development of neural …

Abstract Polynomial Networks (PNs) have demonstrated promising performance on face and
image recognition recently. However, robustness of PNs is unclear and thus obtaining …

In this article, we propose the concept of random polynomial neural networks (RPNNs)
realized based on the architecture of polynomial neural networks (PNNs) with random …