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Learning from structured data is a core machine learning task.
Higher order learning with graphs
Sameer Agarwal, Kristin Branson, and Serge J. Belongie · 2006
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On the naturalness of software
Abram Hindle, Earl T Barr, Zhendong Su, Mark Gabel, and Premkumar Devanbu · 2012
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Translating embeddings for modeling multi-relational data
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Lei Jimmy Ba, Ryan Kiros, and Geoffrey E. Hinton · 2016
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Gated graph sequence neural networks
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On the representation and embedding of knowledge bases beyond binary relations
Jianfeng Wen, Jianxin Li, Yongyi Mao, Shini Chen, and Richong Zhang · 2016
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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End-to-end differentiable proving
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Attention is all you need
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DeepPath: A reinforcement learning method for knowledge graph reasoning
Wenhan Xiong, Thien Hoang, and William Yang Wang · 2017
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A syntactic neural model for general-purpose code generation
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Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabás Póczos, Ruslan Salakhutdinov, and Alexander J. Smola · 2017
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Relational inductive biases, deep learning, and graph networks
Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinícius Flores Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Çaglar Gülçehre, Francis Song, Andrew J. Ballard, Justin Gilmer, George E. Dahl, Ashish Vaswani, Kelsey Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matthew Botvinick, Oriol Vinyals, Yujia Li, and Razvan Pascanu · 2018
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Convolutional 2d knowledge graph embeddings
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Modeling relational data with graph convolutional network
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Graph Attention Networks
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Hypergraph neural networks
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Message passing for hyper-relational knowledge graphs
Mikhail Galkin, Priyansh Trivedi, Gaurav Maheshwari, Ricardo Usbeck, and Jens Lehmann · 2020
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Global relational models of source code
Vincent J Hellendoorn, Charles Sutton, Rishabh Singh, Petros Maniatis, and David Bieber · 2020
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Learning and evaluating contextual embedding of source code
Aditya Kanade, Petros Maniatis, Gogul Balakrishnan, and Kensen Shi · 2020
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Hypergraph learning with line expansion
Chaoqi Yang, Ruijie Wang, Shuochao Yao, and Tarek F. Abdelzaher · 2020
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Hyper-SAGNN: a self-attention based graph neural network for hypergraphs
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Structured neural summarization
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HpLapGCN: Hypergraph p -laplacian graph convolutional networks
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Link prediction on n-ary relational data
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Pytorch: An imperative style, high-performance deep learning library
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HyperGCN: A new method for training graph convolutional networks on hypergraphs
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Hypersage: Generalizing inductive representation learning on hypergraphs
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Hypergraph convolution and hypergraph attention
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Self-supervised bug detection and repair
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Program synthesis with large language models
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Evaluating large language models trained on code
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UniGNN: a unified framework for graph and hypergraph neural networks
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A survey on knowledge graphs: Representation, acquisition, and applications
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Improving hyper-relational knowledge graph completion
Donghan Yu and Yiming Yang · 2021
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Competition-level code generation with alphacode
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