Jihoon Tack

Cited by

	All	Since 2020
Citations	1400	1398
h-index	10	10
i10-index	11	11

460

230

115

345

2020202120222023202420259 147 300 442 460 38

Co-authors

Jinwoo ShinKAIST Endowed Chair ProfessorVerified email at kaist.ac.kr
Jongheon JeongAssistant Professor, Korea UniversityVerified email at korea.ac.kr
Minseon KimPostdoctoral Researcher, Microsoft ResearchVerified email at microsoft.com
Sung Ju HwangKAIST, DeepAutoVerified email at kaist.ac.kr
Sihyun YuPh.D. student, KAISTVerified email at kaist.ac.kr
Jaeho LeePOSTECH; GoogleVerified email at postech.ac.kr
Jung-Woo HaResearch Fellow@NAVER AI Lab, Head of Future AI Center@NAVERVerified email at navercorp.com
Jonathan Richard SchwarzThomson ReutersVerified email at thomsonreuters.com
Jaehyun NamPh.D. student, KAISTVerified email at kaist.ac.kr
Junho KimNAVER AI LabVerified email at navercorp.com
Hyunsu KimBucketplaceVerified email at bucketplace.net
Jaehyung KimAssistant Professor at Yonsei UniversityVerified email at yonsei.ac.kr
Yee Whye TehProfessor of Statistical Machine Learning, Oxford, Research Scientist, DeepMindVerified email at stats.ox.ac.uk
Seong Hyeon ParkKAIST AIVerified email at kaist.ac.kr
Hankook LeeAssistant Professor, Department of Computer Science and Engineering at Sungkyunkwan UniversityVerified email at skku.edu
Woomin SongPh.D Student, KAISTVerified email at kaist.ac.kr
Namhoon LeePOSTECH; GoogleVerified email at postech.ac.kr
Kyungmin LeeKAISTVerified email at kaist.ac.kr
Sukmin YunAssistant Professor, Hanyang University - ERICAVerified email at hanyang.ac.kr
Subin KimKAIST AIVerified email at kaist.ac.kr

Jihoon Tack

Ph.D. student, KAIST

Verified email at kaist.ac.kr - Homepage

Machine Learning Deep Learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances J Tack, S Mo, J Jeong, J Shin NeurIPS, 2020	673	2020
Adversarial Self-Supervised Contrastive Learning M Kim, J Tack, SJ Hwang NeurIPS, 2020	292	2020
Generating Videos with Dynamics-aware Implicit Generative Adversarial Networks S Yu, J Tack, S Mo*, H Kim, J Kim, JW Ha, J Shin ICLR, 2022	211	2022
Consistency Regularization for Adversarial Robustness J Tack, S Yu, J Jeong, M Kim, SJ Hwang, J Shin AAAI, 2022	62	2022
Meta-Learning Sparse Implicit Neural Representations J Lee, J Tack, N Lee, J Shin NeurIPS, 2021	38	2021
STUNT: Few-shot Tabular Learning with Self-generated Tasks from Unlabeled Tables J Nam, J Tack, K Lee, H Lee, J Shin ICLR, 2023	30	2023
Modality-Agnostic Variational Compression of Implicit Neural Representations JR Schwarz, J Tack, YW Teh, J Lee, J Shin ICML, 2023	20	2023
K-centered Patch Sampling for Efficient Video Recognition SH Park, J Tack, B Heo, JW Ha, J Shin ECCV, 2022	17	2022
Online adaptation of language models with a memory of amortized contexts J Tack, J Kim, E Mitchell, J Shin, YW Teh, JR Schwarz NeurIPS, 2024	14	2024
Rethinking the Entropy of Instance in Adversarial Training M Kim, J Tack, J Shin, SJ Hwang IEEE SaTML, 2023	12*	2023
Tabular Transfer Learning via Prompting LLMs J Nam, W Song, SH Park, J Tack, S Yun, J Kim, KH Oh, J Shin COLM, 2024	10*	2024
Meta-Learning with Self-Improving Momentum Target J Tack, J Park, H Lee, J Lee, J Shin NeurIPS, 2022	9	2022
Learning Large-scale Neural Fields via Context Pruned Meta-Learning J Tack, S Kim, S Yu, J Lee, J Shin, JR Schwarz NeurIPS, 2023	8*	2023
Optimized Feature Generation for Tabular Data via LLMs with Decision Tree Reasoning J Nam, K Kim, S Oh, J Tack, J Kim, J Shin NeurIPS, 2024	3	2024
Modality-Agnostic Self-Supervised Learning with Meta-Learned Masked Auto-Encoder H Jang, J Tack, D Choi, J Jeong, J Shin NeurIPS, 2023	1	2023
Sparsified State-Space Models are Efficient Highway Networks W Song, J Tack, S Mo, S Oh, J Shin NeurIPS Workshop on Efficient Natural Language and Speech Processing, 2024		2024
ReMoDetect: Reward Models Recognize Aligned LLM's Generations H Lee, J Tack, J Shin NeurIPS, 2024		2024
Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts S Chen, J Tack, Y Yang, YW Teh, JR Schwarz, Y Wei ICML, 2024		2024

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