Jinheon Baek (백진헌) (jinheon.baek [at] kaist [dot] ac [dot] kr), and here is my CV (Curriculum Vitae)

I’m a Ph.D. student in the Graduate school of AI at KAIST (MLAI Lab), where I am fortunate to be advised by Prof. Sung Ju Hwang, and before that I received a M.S degree of Artifical Intelligence at KAIST in 2022 under the supervision of Prof. Sung Ju Hwang. Prior to studying at KAIST, I received my B.S. (Computer Science and Engineering) and B.E. (Software Technology and Enterprise Program) degrees at Korea University in 2020, where I studied Machine Learning under the guidance of Prof. Jaewoo Kang. This fall (Aug-Oct), I am going to work at the Alexa Knowledge team in Amazon Cambridge as an applied scientist intern.

My primary research interest lies in the area of machine learning for graphs. In particular, my goal of research is to build machine learning models and algorithms that can reason over the graphs: accurately representing, generating and retrieving the graph-structured data, from which exploiting the learned graph representations to the domains of computer vision and natural language understanding: interaction of structured (graphs) and unstructured (images and texts) knowledge for structured prediction.

My selected publications are listed below (* denotes the equal contribution):

  • Personalized Subgraph Federated Learning
    Jinheon Baek*, Wonyong Jeong*, Jiongdao Jin, Jaehong Yoon, and Sung Ju Hwang
    Under Review [paper]

  • Graph Self-supervised Learning with Accurate Discrepancy Learning
    Dongki Kim*, Jinheon Baek*, and Sung Ju Hwang
    Under Review [paper]

  • Knowledge-Consistent Dialogue Generation with Knowledge Graphs
    Minki Kang*, Jin Myung Kwak*, Jinheon Baek*, and Sung Ju Hwang
    Knowledge Retrieval and Language Models Workshop at International Conference on Machine Learning (KRLM@ICML), 2022 (To Appear)

  • KALA: Knowledge-Augmented Language Model Adaptation
    Minki Kang*, Jinheon Baek*, and Sung Ju Hwang
    Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2022 (Oral Presentation) [paper] [code]

  • Augmenting Document Representations for Dense Retrieval with Interpolation and Perturbation
    Soyeong Jeong, Jinheon Baek, Sukmin Cho, Sung Ju Hwang and Jong C. Park
    Annual Meeting of the Association for Computational Linguistics (ACL), 2022 (Oral Presentation) [paper] [code]

  • Toward Accurate Learning of Graph Representations
    Jinheon Baek
    Master’s Thesis, KAIST, 2022 [paper]

  • Edge Representation Learning with Hypergraphs
    Jaehyeong Jo*, Jinheon Baek*, Seul Lee*, Dongki Kim, Minki Kang, and Sung Ju Hwang
    Conference on Neural Information Processing Systems (NeurIPS), 2021 [paper] [code]

  • Task-Adaptive Neural Network Retrieval with Meta-Contrastive Learning
    Wonyong Jeong*, Hayeon Lee*, Gun Park*, Eunyoung Hyung, Jinheon Baek, and Sung Ju Hwang
    Conference on Neural Information Processing Systems (NeurIPS), 2021 (Spotlight Presentation) [paper] [code]

  • Unsupervised Document Expansion for Information Retrieval with Stochastic Text Generation
    Soyeong Jeong, Jinheon Baek, ChaeHun Park, and Jong C. Park
    Scholarly Document Processing at Conference of the North American Chapter of the Association for Computational Linguistics (SDP@NAACL), 2021 (Oral Presentation) [paper] [code]

  • Accurate Learning of Graph Representations with Graph Multiset Pooling
    Jinheon Baek*, MinKi Kang*, and Sung Ju Hwang
    International Conference on Learning Representations (ICLR), 2021 [paper] [code]

  • Exploring The Spatial Reasoning Ability of Neural Models in Human IQ Tests
    Hyunjae Kim*, Yookyung Koh*, Jinheon Baek, and Jaewoo Kang
    Neural Networks, 2021 [paper]

  • Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction
    Jinheon Baek, Dong Bok Lee, and Sung Ju Hwang
    Conference on Neural Information Processing Systems (NeurIPS), 2020 [paper] [code]

You can see more details about me in about page.

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