Common Contracts
3 similar Graph Agreement contracts
Otilia Stretcu‡∗, Krishnamurthy Viswanathan†, Dana Movshovitz-Attias†, Emmanouil Antonios Platanios‡, Andrew Tomkins†, Sujith Ravi†Graph Agreement • September 26th, 2022
Graph-based algorithms are among the most successful paradigms for solving semi- supervised learning tasks. Recent work on graph convolutional networks and neural graph learning methods has successfully combined the expressiveness of neural networks with graph structures. We propose a technique that, when applied to these methods, achieves state-of-the-art results on semi-supervised learning datasets. Traditional graph-based algorithms, such as label propagation, were designed with the underlying assumption that the label of a node can be imputed from that of the neighboring nodes. However, real-world graphs are either noisy or have edges that do not correspond to label agreement. To address this, we propose Graph Agreement Models (GAM), which introduces an auxiliary model that predicts the probability of two nodes sharing the same label as a learned function of their features. The agreement model is used when training a node classification model by encouraging agreement only for the p
Otilia Stretcu‡∗, Krishnamurthy Viswanathan†, Dana Movshovitz-Attias†, Emmanouil Antonios Platanios‡, Andrew Tomkins†, Sujith Ravi†Graph Agreement • October 11th, 2020
Graph-based algorithms are among the most successful paradigms for solving semi- supervised learning tasks. Recent work on graph convolutional networks and neural graph learning methods has successfully combined the expressiveness of neural networks with graph structures. We propose a technique that, when applied to these methods, achieves state-of-the-art results on semi-supervised learning datasets. Traditional graph-based algorithms, such as label propagation, were designed with the underlying assumption that the label of a node can be imputed from that of the neighboring nodes. However, real-world graphs are either noisy or have edges that do not correspond to label agreement. To address this, we propose Graph Agreement Models (GAM), which introduces an auxiliary model that predicts the probability of two nodes sharing the same label as a learned function of their features. The agreement model is used when training a node classification model by encouraging agreement only for the p
Graph Agreement Models for Semi-Supervised LearningGraph Agreement • October 28th, 2019
Graph-based algorithms are among the most successful paradigms for solving semi- supervised learning tasks. Recent work on graph convolutional networks and neural graph learning methods has successfully combined the expressiveness of neural networks with graph structures. We propose a technique that, when applied to these methods, achieves state-of-the-art results on semi-supervised learning datasets. Traditional graph-based algorithms, such as label propagation, were designed with the underlying assumption that the label of a node can be imputed from that of the neighboring nodes. However, real-world graphs are either noisy or have edges that do not correspond to label agreement. To address this, we propose Graph Agreement Models (GAM), which introduces an auxiliary model that predicts the probability of two nodes sharing the same label as a learned function of their features. The agreement model is used when training a node classification model by encouraging agreement only for the p
