Publications

Beyond Paraphrases: Generalization Across Task Formats in Parametric Knowledge Editing

Cheongwoong Kang, Jongeun Baek, Yeonjea Kim, Jaesik Choi

Findings of EMNLPExplainable AILanguage models

Diffusion Integrated Gradients: Controllable Path Generation for Flexible Feature Attribution

Soyeon Kim, Kyowoon Lee, and Jaesik Choi

European Conference on Computer Vision (ECCV) 2026.Explainable AIGenerative models

📝arXiv

SPICE: Simple Polysemantic Feature Interpretation via Clustering-based Explanations

Sehyun Lee, Dahee Kwon, Damin Lee, and Jaesik Choi

European Conference on Computer Vision (ECCV) 2026.Computer visionExplainable AI

Spectral Integrated Gradients for Coarse-to-Fine Feature Attribution

Soyeon Kim, Seongwoo Lim, Kyowoon Lee, and Jaesik Choi

Knowledge Discovery and Data Mining (KDD) 2026.Explainable AIData mining

📝arXiv

Manifold-Aligned Guided Integrated Gradients for Reliable Feature Attribution

Soyeon Kim, Seongwoo Lim, Kyowoon Lee, and Jaesik Choi

International Conference on Machine Learning (ICML), 2026.Explainable AI

📄Paper

Towards Transparent Time Series Analysis: Exploring Methods and Enhancing Interpretability

Youngjin Park, Anh Tong, Sehyun Lee, Jihyeon Seong, Qin Xie and Jaesik Choi

ACM Computing Surveys, 2026.Explainable AITime series

📄Paper

Unsupervised Concept Discovery for Deep Weather Forecast Models with High-Resolution Radar Data

Soyeon Kim, Junho Choi, Subeen Lee and Jaesik Choi

PLOS Climate, 2025.Explainable AIWeather and climate

📝arXiv

Granular Concept Circuits: Toward a Fine-Grained Circuit Discovery for Concept Representations

Dahee Kwon*, Sehyun Lee* and Jaesik Choi

International Conference on Computer Vision (ICCV), 2025.Explainable AI

📄Paper

Example-Based Concept Analysis Framework for Deep Weather Forecast Models

Soyeon Kim, Junho Choi, Subeen Lee and Jaesik Choi

Artificial Intelligence for the Earth Systems (AIES), 2025.Explainable AIWeather and climate

📄Paper

Counterfactual Activation Editing for Post-hoc Prosody and Mispronunciation Correction in TTS Models

Kyowoon Lee, Artyom Stitsyuk, Gunu Jho, Inchul Hwang and Jaesik Choi

Annual Conference of the International Speech Communication Association (Interspeech), 2025.Explainable AI

📄Paper

Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions

Luca Longo, Mario Brcic, Federico Cabitza, Jaesik Choi, Roberto Confalonieri, Javier Del Ser, Riccardo Guidotti, Yoichi Hayashi, Francisco Herrera, Andreas Holzinger, Richard Jiang, Hassan Khosravi, Freddy Lecue, Gianclaudio Malgieri, Andrés Páez, Wojciech Samek, Johannes Schneider, Timo Speith and Simone Stumpf

Information Fusion, 2024.Explainable AI

📄Paper

Understanding Distributed Representations of Concepts in Deep Neural Networks without Supervision

Wonjoon Chang, Dahee Kwon and Jaesik Choi

AAAI Conference on Artificial Intelligence (AAAI), 2024.Explainable AI

📄Paper

Explainable AI-Based Interface System for Weather Forecasting Model

Soyeon Kim, Junho Choi, Yeji Choi, Subeen Lee, Artyom Stitsyuk, Minkyoung Park, Seongyeop Jeong, Youhyun Baek and Jaesik Choi

International Conference on Human-Computer Interaction International (HCII), 2023.Explainable AIWeather and climate

📄Paper

Beyond Single Path Integrated Gradients for Reliable Input Attribution via Randomized Path Sampling

Giyoung Jeon, Haedong Jeong and Jaesik Choi

International Conference on Computer Vision (ICCV), 2023.Explainable AI

📄Paper

Adaptive and Explainable Deployment of Navigation Skills via Hierarchical Deep Reinforcement Learning

Kyowoon Lee*, Seongun Kim* and Jaesik Choi

International Conference on Robotics and Automation (ICRA), 2023 (* contributed equally) (pdf).Explainable AIReinforcement learning

📄Paper

Distilled Gradient Aggregation: Purify Features for Input Attribution in the Deep Neural Network

Giyoung Jeon*, Haedong Jeong* and Jaesik Choi

Conference on Neural Information Processing Systems (NeurIPS), 2022 (* contributed equally).Explainable AI

📄Paper

Predicting Potentially Hazardous Chemical Reactions Using Explainable Neural Network

Juhwan Kim, Geun Ho Gu, Juhwan Noh, Seongun Kim, Suji Gim, Jaesik Choi* and Yousung Jung*

Chemical Science (Chem), 2021.Explainable AI

📄Paper

Explaining the Decisions of Deep Policy Networks for Robotic Manipulations

Seongun Kim and Jaesik Choi

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021.Explainable AIReinforcement learning

📄Paper

Interpreting Internal Activation Patterns in Deep Temporal Neural Networks by Finding Prototypes

Sohee Cho*, Wonjoon Chang*, GinKyeng Lee and Jaesik Choi

ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021 (* contributed equally).Explainable AITime seriesData mining

📄Paper

Interpreting Deep Neural Networks with Relative Sectional Propagation by Analyzing Comparative Gradients and Hostile Activations

Woo Jeoung Nam, Jaesik Choi and Seong-Whan Lee

AAAI Conference on Artificial Intelligence (AAAI), 2021.Explainable AI

📄Paper

Interpreting and Explaining Deep Neural Networks: A Perspective on Time Series Data

Jaesik Choi

ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), 2020 [tutorial link, video]Explainable AITime seriesData mining

📄Paper

Relative Attributing Propagation: Interpreting the Comparative Contributions of Individual Units in Deep Neural Networks

Woo-Jeoung Nam, Shir Gur, Jaesik Choi, Lior Wolf and Seong-Whan Lee

AAAI Conference on Artificial Intelligence (AAAI), 2020.Explainable AI

📄Paper

XAI—Explainable artificial intelligence

David Gunning, Mark Stefik, Jaesik Choi, Timothy Miller, Simone Stumpf and Guang-Zhong Yang

Science Robotics, 4(37), 2019. (IF: 19.4)Explainable AIReinforcement learning

📄Paper