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Recent studies seek to provide Graph Neural Network (GNN) interpretability via multiple unsupervised learning models.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901
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Learning how to propagate messages in graph neural networks
Teng Xiao, Zhengyu Chen, Donglin Wang, and Suhang Wang. 2021 · 1903
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Shanshan Tang, Bo Li, and Haijun Yu. 2019 · 1911
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Graph wavenet for deep spatial-temporal graph modeling
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Modeling by shortest data description
Jorma Rissanen. 1978 · 1978
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Bayesian interpolation
David JC MacKay. 1992 · 1992
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Derivation and validation of toxicophores for mutagenicity prediction
Jeroen Kazius, Ross McGuire, and Roberta Bursi. 2005 · 2005
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The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. 2009 · 2009
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Practical variational inference for neural networks
Alex Graves. 2011 · 2011
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Spectral networks and locally connected networks on graphs
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. 2013 · 2013
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Convolutional networks on graphs for learning molecular fingerprints
David K Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P Adams. 2015 · 2015
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Bayesian convolutional neural networks with bernoulli approximate variational inference
Yarin Gal and Zoubin Ghahramani. 2015 · 2015
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Adding gradient noise improves learning for very deep networks
Arvind Neelakantan, Luke Vilnis, Quoc V Le, Ilya Sutskever, Lukasz Kaiser, Karol Kurach, and James Martens. 2015 · 2015
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Diffusion-convolutional neural networks
James Atwood and Don Towsley. 2016 · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling. 2016 · 2016
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
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Feature selection: A data perspective
Jundong Li, Kewei Cheng, Suhang Wang, Fred Morstatter, Robert P. Trevino, Jiliang Tang, and Huan Liu. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Modeling semantics with gated graph neural networks for knowledge base question answering
Daniil Sorokin and Iryna Gurevych. 2018 · 2018
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Explainability techniques for graph convolutional networks
Federico Baldassarre and Hossein Azizpour. 2019 · 2019
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Concrete autoencoders: Differentiable feature selection and reconstruction
Muhammed Fatih Balın, Abubakar Abid, and James Zou. 2019 · 2019
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Utilizing molecular network information via graph convolutional neural networks to predict metastatic event in breast cancer
Hryhorii Chereda, Annalen Bleckmann, Frank Kramer, Andreas Leha, and Tim Beissbarth. 2019 · 2019
Cited alongside, same era.
Molecular geometry prediction using a deep generative graph neural network
E. Mansimov, O. Mahmood, and S. Kang. 2019 · 2019
Cited alongside, same era.
Gnnexplainer: Generating explanations for graph neural networks
Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec. 2019 · 2019
Cited alongside, same era.
Parameterized explainer for graph neural network
Dongsheng Luo, Wei Cheng, Dongkuan Xu, Wenchao Yu, Bo Zong, Haifeng Chen, and Xiang Zhang. 2020 · 2020
Cited alongside, same era.
Evaluating attribution for graph neural networks
Benjamin Sanchez-Lengeling, Jennifer Wei, Brian Lee, Emily Reif, Peter Wang, Wesley Qian, Kevin McCloskey, Lucy Colwell, and Alexander Wiltschko. 2020 · 2020
Cited alongside, same era.
A survey of trustworthy graph learning: Reliability, explainability, and privacy protection
Bingzhe Wu, Jintang Li, Junchi Yu, Yatao Bian, Hengtong Zhang, CHaochao Chen, Chengbin Hou, Guoji Fu, Liang Chen, Tingyang Xu, et al. 2022 · 2022
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Explainability in graph neural networks: A taxonomic survey
Hao Yuan, Haiyang Yu, Shurui Gui, and Shuiwang Ji. 2022 · 2022
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Trustworthy graph neural networks: Aspects, methods and trends
He Zhang, Bang Wu, Xingliang Yuan, Shirui Pan, Hanghang Tong, and Jian Pei. 2022 · 2022
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Large language models are human-level prompt engineers
Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba. 2022 · 2022
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Sparks of artificial general intelligence: Early experiments with gpt-4
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Traffic flow prediction via spatial temporal graph neural network
Xiaoyang Wang, Yao Ma, Yiqi Wang, Wei Jin, Xin Wang, Jiliang Tang, Caiyan Jia, and Jian Yu. 2020 · 2020
Cited alongside, same era.
Towards self-explainable graph neural network
Enyan Dai and Suhang Wang. 2021 · 2021
Cited alongside, same era.
Spatial-temporal fusion graph neural networks for traffic flow forecasting
Mengzhang Li and Zhanxing Zhu. 2021 · 2021
Cited alongside, same era.
Stgsn — a spatial–temporal graph neural network framework for time-evolving social networks
Shengjie Min, Zhan Gao, Jing Peng, Liang Wang, Ke Qin, and Bo Fang. 2021 · 2021
Cited alongside, same era.
Transformers can do bayesian inference
Samuel Müller, Noah Hollmann, Sebastian Pineda Arango, Josif Grabocka, and Frank Hutter. 2021 · 2021
Cited alongside, same era.
Reinforcement learning enhanced explainer for graph neural networks
Caihua Shan, Yifei Shen, Yao Zhang, Xiang Li, and Dongsheng Li. 2021 · 2021
Cited alongside, same era.
Towards multi-grained explainability for graph neural networks
Xiang Wang, Ying-Xin Wu, An Zhang, Xiangnan He, and Tat-Seng Chua. 2021a · 2021
Cited alongside, same era.
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al. 2023 · 2023
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Deep evidential learning in diffusion convolutional recurrent neural network
Zhiyuan Feng, Kai Qi, Bin Shi, Hao Mei, Qinghua Zheng, and Hua Wei. 2023 · 2023
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Harnessing explanations: Llm-to-lm interpreter for enhanced text-attributed graph representation learning
Xiaoxin He, Xavier Bresson, Thomas Laurent, Adam Perold, Yann LeCun, and Bryan Hooi. 2023 · 2023
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Time-llm: Time series forecasting by reprogramming large language models
Ming Jin, Shiyu Wang, Lintao Ma, Zhixuan Chu, James Y Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, et al. 2023 · 2023
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Pearl: Personalizing large language model writing assistants with generation-calibrated retrievers
Sheshera Mysore, Zhuoran Lu, Mengting Wan, Longqi Yang, Steve Menezes, Tina Baghaee, Emmanuel Barajas Gonzalez, Jennifer Neville, and Tara Safavi. 2023 · 2023
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ZeroPrompt: Streaming Acoustic Encoders are Zero-Shot Masked LMs
Xingchen Song, Di Wu, Binbin Zhang, Zhendong Peng, Bo Dang, Fuping Pan, and Zhiyong Wu. 2023 · 2023
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Scalable inverse uncertainty quantification by hierarchical bayesian modeling and variational inference
Chen Wang, Xu Wu, Ziyu Xie, and Tomasz Kozlowski. 2023 · 2023
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Generating in-distribution proxy graphs for explaining graph neural networks
Zhuomin Chen, Jiaxing Zhang, Jingchao Ni, Xiaoting Li, Yuchen Bian, Md Mezbahul Islam, Ananda Mohan Mondal, Hua Wei, and Dongsheng Luo. 2024 · 2024
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Real-time pill identification for the visually impaired using deep learning
Bo Dang, Wenchao Zhao, Yufeng Li, Danqing Ma, Qixuan Yu, and Elly Yijun Zhu. 2024 · 2024
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Chenhao Fang, Xiaohan Li, Zezhong Fan, Jianpeng Xu, Kaushiki Nag, Evren Korpeoglu, Sushant Kumar, and Kannan Achan. 2024 · 2024
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Adaptive ensembles of fine-tuned transformers for llm-generated text detection
Zhixin Lai, Xuesheng Zhang, and Suiyao Chen. 2024 · 2024
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Jiayi Liu, Tinghan Yang, and Jennifer Neville. 2024 · 2024
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Imprint: Generative object compositing by learning identity-preserving representation
Yizhi Song, Zhifei Zhang, Zhe Lin, Scott Cohen, Brian Price, Jianming Zhang, Soo Ye Kim, He Zhang, Wei Xiong, and Daniel Aliaga. 2024 · 2024
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Tnt-llm: Text mining at scale with large language models
Mengting Wan, Tara Safavi, Sujay Kumar Jauhar, Yujin Kim, Scott Counts, Jennifer Neville, Siddharth Suri, Chirag Shah, Ryen W White, Longqi Yang, et al. 2024 · 2024
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Visionllm: Large language model is also an open-ended decoder for vision-centric tasks
Wenhai Wang, Zhe Chen, Xiaokang Chen, Jiannan Wu, Xizhou Zhu, Gang Zeng, Ping Luo, Tong Lu, Jie Zhou, Yu Qiao, et al. 2024 · 2024
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Switchtab: Switched autoencoders are effective tabular learners
Jing Wu, Suiyao Chen, Qi Zhao, Renat Sergazinov, Chen Li, Shengjie Liu, Chongchao Zhao, Tianpei Xie, Hanqing Guo, Cheng Ji, et al. 2024 · 2024
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