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.
In this work, we propose CARLS, a novel framework for augmenting the capacity of existing deep learning frameworks by enabling multiple components -- model trainers, knowledge makers and knowledge banks -- to concertedly work together in an asynchronous fashion across hardware platforms.
Several machine learning models, including neural networks, consistently misclassify adversarial examples---inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed input results in the model outputting an incorrect answer with high confidence.
Ranked #57 on Image Classification on MNIST
In our experiments, we applied VAT to supervised and semi-supervised learning tasks on multiple benchmark datasets.
Learning image representations to capture fine-grained semantics has been a challenging and important task enabling many applications such as image search and clustering.
Ranked #11 on Image Classification on iNaturalist
However, existing hyperbolic embedding methods do not account for the rich logical patterns in KGs.
Ranked #5 on Link Prediction on YAGO3-10