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We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs.
SOTA for Graph Classification on IPC-lifted
We introduce an architecture to learn joint multilingual sentence representations for 93 languages, belonging to more than 30 different families and written in 28 different scripts.
CROSS-LINGUAL BITEXT MINING CROSS-LINGUAL DOCUMENT CLASSIFICATION CROSS-LINGUAL NATURAL LANGUAGE INFERENCE CROSS-LINGUAL TRANSFER DOCUMENT CLASSIFICATION JOINT MULTILINGUAL SENTENCE REPRESENTATIONS PARALLEL CORPUS MINING
In this technical report, we adapt whole word masking in Chinese text, that masking the whole word instead of masking Chinese characters, which could bring another challenge in Masked Language Model (MLM) pre-training task.
We present DeepWalk, a novel approach for learning latent representations of vertices in a network.
#3 best model for Node Classification on Wikipedia
We demonstrate that large gains on these tasks can be realized by generative pre-training of a language model on a diverse corpus of unlabeled text, followed by discriminative fine-tuning on each specific task.
#4 best model for Natural Language Inference on SNLI
We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations.
#2 best model for Skeleton Based Action Recognition on J-HMBD Early Action
This paper introduces Random Multimodel Deep Learning (RMDL): a new ensemble, deep learning approach for classification.