A Multidimensional Graph Fourier Transformation Neural Network for Vehicle Trajectory Prediction

Abstract

This work provides a comprehensive derivation of the parameter gradients for GATv2 [4], a widely used implementation of Graph Attention Networks (GATs). GATs have proven to be powerful frameworks for processing graph-structured data and, hence, have been used in a range of applications. However, the achieved performance by these attempts has been found to be inconsistent across different datasets and the reasons for this remains an open research question. As the gradient flow provides valuable insights into the training dynamics of statistically learning models, this work obtains the gradients for the trainable model parameters of GATv2. The gradient derivations supplement the efforts of [2], where potential pitfalls of GATv2 are investigated.

MolPROP: Molecular Property prediction with multimodal language and graph fusion

“…Overall, we find that deep learning models can benefit from representational fusion for classification tasks such as clinical toxicity, however, the guidelines for when and how representational fusion is beneficial remains an open question. Recent investigations have found that GNNs can be unstable when training on classifications tasks [ 39 – 43 ]. For example, GCNs have been reportedly unstable when the number of node features becomes too large [ 42 ] and GATv2 suffers from initialization instabilities [ 43 ].…”

“…Recent investigations have found that GNNs can be unstable when training on classifications tasks [ 39 – 43 ]. For example, GCNs have been reportedly unstable when the number of node features becomes too large [ 42 ] and GATv2 suffers from initialization instabilities [ 43 ]. These fundamental limitations of the GNNs may explain the more variable performance on classification tasks.…”

A Multidimensional Graph Fourier Transformation Neural Network for Vehicle Trajectory Prediction

“…To gain understanding of the training behavior of GATv2 [4], this work derives the gradients for its network parameters. This study is an addition to [2], in which potential drawbacks and issues of GATv2 are analyzed. Certain hypotheses of [2] are evidenced and supported upon the outcome of this study.…”

“…This study is an addition to [2], in which potential drawbacks and issues of GATv2 are analyzed. Certain hypotheses of [2] are evidenced and supported upon the outcome of this study.…”

“…As the gradient flow provides valuable insights into the training dynamics of statistically learning models, this work obtains the gradients for the trainable model parameters of GATv2. The gradient derivations supplement the efforts of [2], where potential pitfalls of GATv2 are investigated.…”