NeurIPS 2020 Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning Review 1 Summary and Contributions: [edit: see "additional feedback" section for response to author rebuttal] This paper considers the problem of learning representations of specific subgraphs of a larger graph, in such a way that subgraphs of non-isomorphic graphs should be assigned different representations. Specifically, they note that most GNNs can only distinguish things that the 1-WL test ca
Abstract page for arXiv paper 2005.08129: Neural Collaborative Reasoning
Graph Neural Networks (GNNs) show promising results for graph tasks. However, existing GNNs' generalization ability will degrade when there exist distribution shifts between testing and training graph data. The cardinal impetus underlying the severe degeneration is that the GNNs are architected predicated upon the I.I.D assumptions. In such a setting, GNNs are inclined to leverage imperceptible statistical correlations subsisting in the training set to predict, albeit it is a spurious correlation. In this p
In this video, we explain the concept of training an artificial neural network
What if neural networks moved nonlinearity from fixed node activations to learnable functions on edges? This explores whether such a structural redesign could improve accuracy, interpretability, and scaling compared to standard MLPs
Musings of a Computer Scientist.
Introduction In this tutorial, we will explore the implementation of a 5-input XOR function using pre-calculated weights and threshold in a neural
Part of the power of a recursive neural network is that the same framework can teach itself to generate text in a huge variety of styles. So far I’ve used it to generate things like recipes, Dr. Who episode titles, D&D spells, story titles, metal band names
← MUSCLE: A Model Update Strategy for Compatible LLM Evolution A Perspective on Foundation Models for the Electric Power Grid → # NeuFair: Neural Network Fairness Repair with Dropout この論文では、ディープ ニューラル ネットワーク (DNN) の後処理バイアス軽減策としてのニューロン ドロップアウトについて調査します。 ニューラル駆動のソフトウェア ソリューションは
Explains how power laws govern neural network scaling. Topics include log-log analysis, fitting techniques, and how to predict model performance at any scale