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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
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.
Many deep learning architectures have been proposed to model the compositionality in text sequences, requiring a substantial number of parameters and expensive computations.
#4 best model for Text Classification on Yahoo! Answers