We propose a novel differentiable graph neural network simulator (GNS) by combining reverse mode automatic differentiation (AD) of graph neural networks with gradient-based optimization for solving ...

Enhancing Graph Classification with Edge-Node Attention-based Differentiable Pooling and Multi-Distance Graph Neural Networks GNNs News Hub. My account. Get into your ... Any cookies that may not be ...

Deep learning solutions have recently demonstrated remarkable performance in phase unwrapping by approaching the problem as a semantic segmentation task. However, these solutions lack explainability ...

A recent line of work has shown that end-to-end optimization of Bayesian filters can be used to learn state estimators for systems whose underlying models are difficult to hand-design or tune, while ...

In this paper we propose to solve this problem by first obtaining a differentiable rig function by training a multi layer perceptron to approximate the rig function. This differentiable rig function ...

Quasiconvex functions play an important role in several branches of applied mathematics (e.g. mathematical programming, minimax theory, games theory, etc.) and of economic analysis (production theory, ...