Model Parsing defines the task of predicting hyperparameters of the generative model (GM), given a GM-generated image as the input.

Graph Neural Networks (GNNs) are non-Euclidean deep learning models for graph-structured data.

In this work, inspired by the architectural similarities in standard DNNs and B-cos networks, we propose'B-cosification', a novel approach to transform existing pre-trained models to become

In many applications, we desire neural networks to exhibit invariance or equivari-ance to certain groups due to symmetries inherent in the data. Recently, frame-averaging methods emerged to be a unified framework for attaining symmetries efficiently by averaging over input-dependent subsets of the group, i.e., frames. What we currently lack is a principled understanding of the design of frames.

Our code is available at https://github.com/JHL-HUST/LMSPS . Utilizing long-range dependency is essential for graph representation learning.

Our results are based on a detailed non-asymptotic analysis of the dynamics of each hidden neuron throughout the training.

We refer the reader to the review papers [7, 47] for more details. Many recent results have improved the theoretical understanding of GNNs.

This approach enables evaluation of the model's performance within a time-sensitive

Our implementation is available online.