Factored Planning for Controlling a Robotic Arm. - Jaesik Choi; Eyal Amir (AAAI Fall Symposium: Integrating Reasoning into Everyday Applications, 2006)

Factor-guided motion planning for a robot arm. - Jaesik Choi; Eyal Amir (IROS, 2007)

Spatio-temporal pyramid matching for sports videos. - Jaesik Choi; Won J. Jeon; Sang-Chul Lee (Multimedia Information Retrieval, 2008)

Greedy Algorithms for Sequential Sensing Decisions. - Hannaneh Hajishirzi; Afsaneh Shirazi; Jaesik Choi; Eyal Amir (IJCAI, 2009)

Safe Navigation of a Mobile Robot Considering Visibility of Environment. - Woojin Chung; Seokgyu Kim; Minki Choi; Jaesik Choi; Hoyeon Kim; Chang-Bae Moon; Jae-Bok Song (IEEE Trans. Ind. Electron., 2009)

Lifted Inference for Relational Continuous Models. - Jaesik Choi; David J. Hill 002; Eyal Amir (StarAI@AAAI, 2010)

Combining Planning and Motion Planning. - Jaesik Choi; Eyal Amir (Cognitive Robotics, 2010)

Efficient Methods for Lifted Inference with Aggregate Factors. - Jaesik Choi; Rodrigo de Salvo Braz; Hung Hai Bui (AAAI, 2011)

Lifted Relational Kalman Filtering. - Jaesik Choi; Abner Guzmán-Rivera; Eyal Amir (IJCAI, 2011)

Nonparametric Relational Hybrid Models. - Jaesik Choi; Eyal Amir (StarAI@UAI, 2012)

Lifted Relational Variational Inference. - Jaesik Choi; Eyal Amir (UAI, 2012)

Lifted Inference for Relational Hybrid Models - Jaesik Choi (2012)

Lifted Inference on Transitive Relations. - Wen Pu; Jaesik Choi; Eyal Amir (StarAI@AAAI, 2013)

Fast Change Point Detection for electricity market analysis. - William Gu; Jaesik Choi; Ming Gu 002; Horst D. Simon; Kesheng Wu (IEEE BigData, 2013)

SecureCore: A multicore-based intrusion detection architecture for real-time embedded systems. - Man-Ki Yoon; Sibin Mohan; Jaesik Choi; Jung-Eun Kim; Lui Sha (IEEE Real-Time and Embedded Technology and Applications Symposium, 2013)

A spatio-temporal pyramid matching for video retrieval. - Jaesik Choi; Ziyu Wang 005; Sang-Chul Lee; Won J. Jeon (Comput. Vis. Image Underst., 2013)

Parameter Estimation for Relational Kalman Filtering. - Jaesik Choi; Eyal Amir; Tianfang Xu; Albert J. Valocchi (StarAI@AAAI, 2014)

Low complexity sensing for big spatio-temporal data. - Dongeun Lee 001; Jaesik Choi (IEEE BigData, 2014)

Low-complexity compressive sensing with downsampling. - Dongeun Lee 001; Jaesik Choi; Heonshik Shin (IEICE Electron. Express, 2014)

Learning Relational Kalman Filtering. - Jaesik Choi; Eyal Amir; Tianfang Xu; Albert J. Valocchi (AAAI, 2015)

Memory heat map: anomaly detection in real-time embedded systems using memory behavior. - Man-Ki Yoon; Lui Sha; Sibin Mohan; Jaesik Choi (DAC, 2015)

Reading Documents for Bayesian Online Change Point Detection. - Taehoon Kim; Jaesik Choi (EMNLP, 2015)

A Deterministic Partition Function Approximation for Exponential Random Graph Models. - Wen Pu; Jaesik Choi; Yunseong Hwang; Eyal Amir (IJCAI, 2015)

Learning Compressive Sensing Models for Big Spatio-Temporal Data. - Dongeun Lee 001; Jaesik Choi (SDM, 2015)

Extracting Baseline Electricity Usage Using Gradient Tree Boosting. - Taehoon Kim; Dongeun Lee 001; Jaesik Choi; Anna Spurlock; Alex Sim; Annika Todd; Kesheng Wu (SmartCity, 2015)

The Automatic Statistician: A Relational Perspective. - Yunseong Hwang; Jaesik Choi (CoRR, 2015)

Learning Dynamic Compressive Sensing Models. - Dongeun Lee 001; Jaesik Choi (CoRR, 2015)

Intrusion Detection Using Execution Contexts Learned from System Call Distributions of Real-Time Embedded Systems. - Man-Ki Yoon; Sibin Mohan; Jaesik Choi; Mihai Christodorescu; Lui Sha (CoRR, 2015)

Semi-Local Structure Patterns for Robust Face Detection. - Kyungjoong Jeong; Jaesik Choi; Gil-Jin Jang (IEEE Signal Process. Lett., 2015)

Global Deconvolutional Networks for Semantic Segmentation. - Vladimir Nekrasov; Janghoon Ju; Jaesik Choi (BMVC, 2016)

Automatic Construction of Nonparametric Relational Regression Models for Multiple Time Series. - Yunseong Hwang; Anh Tong; Jaesik Choi (ICML, 2016)

Novel Data Reduction Based on Statistical Similarity. - Dongeun Lee 001; Alex Sim; Jaesik Choi; Kesheng Wu (SSDBM, 2016)

SAIL (UNIST) team Participation in KBP 2016 Cold-Start Slot filling. - Seongwoo Lim; Jiyeon Han; Kyowoon Lee; Jaesik Choi (TAC, 2016)

Improving Imprecise Compressive Sensing Models. - Dongeun Lee 001; Rafael Lima; Jaesik Choi (UAI, 2016)

Searching for Topological Symmetry in Data Haystack. - Kallol Roy; Anh Tong; Jaesik Choi (CoRR, 2016)

A Robot Learning to Play a Mobile Game Under Unknown Dynamics. - Wonjun Yoon; Sol-A. Kim; Jaesik Choi (CoRR, 2016)

Expanding Statistical Similarity Based Data Reduction to Capture Diverse Patterns. - Dongeun Lee 001; Alex Sim; Jaesik Choi; Kesheng Wu (DCC, 2017)

Learning Execution Contexts from System Call Distribution for Anomaly Detection in Smart Embedded System. - Man-Ki Yoon; Sibin Mohan; Jaesik Choi; Mihai Christodorescu; Lui Sha (IoTDI, 2017)

Improving Statistical Similarity Based Data Reduction for Non-Stationary Data. - Dongeun Lee 001; Alex Sim; Jaesik Choi; Kesheng Wu (SSDBM, 2017)

UNIST SAIL System for TAC 2017 Cold Start Slot Filling. - Seongwoo Lim; Soonjae Kwon; Sehyun Lee; Jaesik Choi (TAC, 2017)

Automatic Decomposition of Self-Triggering Kernels of Hawkes Processes. - Rafael Lima; Jaesik Choi (CoRR, 2017)

Grouped Convolutional Neural Networks for Multivariate Time Series. - Subin Yi; Janghoon Ju; Man-Ki Yoon; Jaesik Choi (CoRR, 2017)

Layer-wise Learning of Stochastic Neural Networks with Information Bottleneck. - Thanh T. Nguyen; Jaesik Choi (CoRR, 2017)

Dynamic Online Performance Optimization in Streaming Data Compression. - J. Kade Gibson; Dongeun Lee 001; Jaesik Choi; Alex Sim (IEEE BigData, 2018)

Learning the Group Structure of Deep Neural Networks with an Expectation Maximization Method. - Subin Yi; Jaesik Choi (ICDM Workshops, 2018)

Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling. - Kyowoon Lee; Sol-A. Kim; Jaesik Choi; Seong-Whan Lee (ICML, 2018)

Predicting baseline for analysis of electricity pricing. - Taehoon Kim; Jaesik Choi; Dongeun Lee 001; Alex Sim; C. Anna Spurlock; Annika Todd; Kesheng Wu (Int. J. Big Data Intell., 2018)

Discovering Latent Covariance Structures for Multiple Time Series. - Anh Tong; Jaesik Choi (ICML, 2019)

Confirmatory Bayesian Online Change Point Detection in the Covariance Structure of Gaussian Processes. - Jiyeon Han; Kyowoon Lee; Anh Tong; Jaesik Choi (IJCAI, 2019)

Relative Attributing Propagation: Interpreting the Comparative Contributions of Individual Units in Deep Neural Networks. - Woo-Jeoung Nam; Jaesik Choi; Seong-Whan Lee (CoRR, 2019)

Why Do Masked Neural Language Models Still Need Common Sense Knowledge? - Sunjae Kwon; Cheongwoong Kang; Jiyeon Han; Jaesik Choi (CoRR, 2019)

IDEALEM: Statistical Similarity Based Data Reduction. - Dongeun Lee 001; Alex Sim; Jaesik Choi; Kesheng Wu (CoRR, 2019)

Markov Information Bottleneck to Improve Information Flow in Stochastic Neural Networks. - Thanh T. Nguyen; Jaesik Choi (Entropy, 2019)

XAI - Explainable artificial intelligence. - David Gunning; Mark Stefik; Jaesik Choi; Timothy Miller 001; Simone Stumpf; Guang-Zhong Yang (Sci. Robotics, 2019)

An Efficient Explorative Sampling Considering the Generative Boundaries of Deep Generative Neural Networks. - Giyoung Jeon; Haedong Jeong; Jaesik Choi (AAAI, 2020)

Relative Attributing Propagation: Interpreting the Comparative Contributions of Individual Units in Deep Neural Networks. - Woo-Jeoung Nam; Shir Gur; Jaesik Choi; Lior Wolf; Seong-Whan Lee (AAAI, 2020)

Improved predictive deep temporal neural networks with trend filtering. - Youngjin Park; Deokjun Eom; Byoungki Seo; Jaesik Choi (ICAIF, 2020)

A Single Multi-Task Deep Neural Network with Post-Processing for Object Detection with Reasoning and Robotic Grasp Detection. - Dongwon Park; Yonghyeok Seo; Dongju Shin; Jaesik Choi; Se Young Chun (ICRA, 2020)

Interpreting and Explaining Deep Neural Networks: A Perspective on Time Series Data. - Jaesik Choi (KDD, 2020)

HetPipe: Enabling Large DNN Training on (Whimpy) Heterogeneous GPU Clusters through Integration of Pipelined Model Parallelism and Data Parallelism. - Jay H. Park; Gyeongchan Yun; Chang M. Yi; Nguyen T. Nguyen; Seungmin Lee; Jaesik Choi; Sam H. Noh; Young-ri Choi (USENIX ATC, 2020)

Interpretation of Deep Temporal Representations by Selective Visualization of Internally Activated Units. - Sohee Cho; Ginkyeng Lee; Jaesik Choi (CoRR, 2020)

Interpreting Deep Neural Networks with Relative Sectional Propagation by Analyzing Comparative Gradients and Hostile Activations. - Woo-Jeoung Nam; Jaesik Choi; Seong-Whan Lee (AAAI, 2021)

Characterizing Deep Gaussian Processes via Nonlinear Recurrence Systems. - Anh Tong; Jaesik Choi (AAAI, 2021)

Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior. - Anh Tong; Toan M. Tran; Hung Bui; Jaesik Choi (AAAI, 2021)

Automatic Correction of Internal Units in Generative Neural Networks. - Ali Tousi; Haedong Jeong; Jiyeon Han; Hwanil Choi; Jaesik Choi (CVPR, 2021)

Conditional Temporal Neural Processes with Covariance Loss. - Boseon Yoo; Jiwoo Lee; Janghoon Ju; Seijun Chung; Soyeon Kim; Jaesik Choi (ICML, 2021)

Explaining the Decisions of Deep Policy Networks for Robotic Manipulations. - Seongun Kim; Jaesik Choi (IROS, 2021)

Interpreting Internal Activation Patterns in Deep Temporal Neural Networks by Finding Prototypes. - Sohee Cho; Wonjoon Chang; Ginkyeng Lee; Jaesik Choi (KDD, 2021)

An Unsupervised Way to Understand Artifact Generating Internal Units in Generative Neural Networks. - Haedong Jeong; Jiyeon Han; Jaesik Choi (AAAI, 2022)

Can We Find Neurons that Cause Unrealistic Images in Deep Generative Networks? - Hwanil Choi; Wonjoon Chang; Jaesik Choi (IJCAI, 2022)

Distilled Gradient Aggregation: Purify Features for Input Attribution in the Deep Neural Network. - Giyoung Jeon; Haedong Jeong; Jaesik Choi (NeurIPS, 2022)

Learning Fractional White Noises in Neural Stochastic Differential Equations. - Anh Tong; Thanh Nguyen-Tang; Toan M. Tran; Jaesik Choi (NeurIPS, 2022)

Variational Neural Temporal Point Process. - Deokjun Eom; Sehyun Lee; Jaesik Choi (CoRR, 2022)

On the Relationship Between Adversarial Robustness and Decision Region in Deep Neural Network. - Seongjin Park; Haedong Jeong; Giyoung Jeon; Jaesik Choi (CoRR, 2022)

Why Do Neural Language Models Still Need Commonsense Knowledge to Handle Semantic Variations in Question Answering? - Sunjae Kwon; Cheongwoong Kang; Jiyeon Han; Jaesik Choi (CoRR, 2022)

Explanation on Pretraining Bias of Finetuned Vision Transformer. - Bumjin Park; Jaesik Choi (CoRR, 2022)

Scheduling PID Attitude and Position Control Frequencies for Time-Optimal Quadrotor Waypoint Tracking under Unknown External Disturbances. - Cheongwoong Kang; Bumjin Park; Jaesik Choi (Sensors, 2022)

Semisupervised Training of Deep Generative Models for High-Dimensional Anomaly Detection. - Qin Xie; Peng Zhang 030; Boseon Yu; Jaesik Choi (IEEE Trans. Neural Networks Learn. Syst., 2022)

CR-COPEC: Causal Rationale of Corporate Performance Changes to learn from Financial Reports. - Ye Eun Chun; Sunjae Kwon; Kyunghwan Sohn; Nakwon Sung; Junyoup Lee; Byoung Seo; Kevin Compher; Seung-won Hwang; Jaesik Choi (EMNLP (Findings), 2023)

Impact of Co-occurrence on Factual Knowledge of Large Language Models. - Cheongwoong Kang; Jaesik Choi (EMNLP (Findings), 2023)

Explainable AI-Based Interface System for Weather Forecasting Model. - Soyeon Kim; Junho Choi; Yeji Choi; Subeen Lee; Artyom Stitsyuk; Minkyoung Park; Seongyeop Jeong; You-Hyun Baek; Jaesik Choi (HCI (53), 2023)

SigFormer: Signature Transformers for Deep Hedging. - Anh Tong; Thanh Nguyen-Tang; Dongeun Lee 001; Toan M. Tran; Jaesik Choi (ICAIF, 2023)

Beyond Single Path Integrated Gradients for Reliable Input Attribution via Randomized Path Sampling. - Giyoung Jeon; Haedong Jeong; Jaesik Choi (ICCV, 2023)

Rarity Score : A New Metric to Evaluate the Uncommonness of Synthesized Images. - Jiyeon Han; Hwanil Choi; Yunjey Choi; Junho Kim; Jung-Woo Ha 001; Jaesik Choi (ICLR, 2023)

Variational Curriculum Reinforcement Learning for Unsupervised Discovery of Skills. - Seongun Kim; Kyowoon Lee; Jaesik Choi (ICML, 2023)

Adaptive and Explainable Deployment of Navigation Skills via Hierarchical Deep Reinforcement Learning. - Kyowoon Lee; Seongun Kim; Jaesik Choi (ICRA, 2023)

Algorithmic Read Resistance Trim for Improving Yield and Reducing Test Time in MRAM. - Daehyun Chang; Youngdae Kim; Suksoo Pyo; Shin Hun; Daesop Lee; Sohee Hwang; Jaesik Choi; Siwoong Kim (ITC, 2023)

Refining Diffusion Planner for Reliable Behavior Synthesis by Automatic Detection of Infeasible Plans. - Kyowoon Lee; Seongun Kim; Jaesik Choi (NeurIPS, 2023)

Stress and Adaptation: Applying Anna Karenina Principle in Deep Learning for Image Classification. - Nesma Mahmoud; Hanna Antson; Jaesik Choi; Osamu Shimmi; Kallol Roy (CoRR, 2023)

The Disharmony Between BN and ReLU Causes Gradient Explosion, but is Offset by the Correlation Between Activations. - Inyoung Paik; Jaesik Choi (CoRR, 2023)

CR-COPEC: Causal Rationale of Corporate Performance Changes to Learn from Financial Reports. - Ye Eun Chun; Sunjae Kwon; Kyunghwan Sohn; Nakwon Sung; Junyoup Lee; Byungki Seo; Kevin Compher; Seung-won Hwang; Jaesik Choi (CoRR, 2023)

Understanding Distributed Representations of Concepts in Deep Neural Networks without Supervision. - Wonjoon Chang; Dahee Kwon; Jaesik Choi (AAAI, 2024)

Towards Diverse Perspective Learning with Selection over Multiple Temporal Poolings. - Jihyeon Seong; Jungmin Kim; Jaesik Choi (AAAI, 2024)

Pathwise Explanation of ReLU Neural Networks. - Seongwoo Lim; Won Jo; Joohyung Lee; Jaesik Choi (AISTATS, 2024)

CardioCaps: Attention-Based Capsule Network for Class-Imbalanced Echocardiogram Classification. - Hyunkyung Han; Jihyeon Seong; Jaesik Choi (BigComp, 2024)

Probing Network Decisions: Capturing Uncertainties and Unveiling Vulnerabilities Without Label Information. - Youngju Joung; Sehyun Lee; Jaesik Choi (ICPRAI (1), 2024)

Identifying the Source of Generation for Large Language Models. - Bumjin Park; Jaesik Choi (ICPRAI (2), 2024)

Memorizing Documents with Guidance in Large Language Models. - Bumjin Park; Jaesik Choi (IJCAI, 2024)

Towards Dynamic Trend Filtering through Trend Point Detection with Reinforcement Learning. - Jihyeon Seong; Sekwang Oh; Jaesik Choi (IJCAI, 2024)

Capsule Neural Networks as Noise Stabilizer for Time Series Data. - Soyeon Kim; Jihyeon Seong; Hyunkyung Han; Jaesik Choi (CoRR, 2024)

Optimal path for Biomedical Text Summarization Using Pointer GPT. - Hyunkyung Han; Jaesik Choi (CoRR, 2024)

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 001; Javier Del Ser; Riccardo Guidotti; Yoichi Hayashi; Francisco Herrera; Andreas Holzinger; Richard Jiang 001; Hassan Khosravi; et al. (Inf. Fusion, 2024)

Diverse Rare Sample Generation with Pretrained GANs. - Subeen Lee; Jiyeon Han; Soyeon Kim; Jaesik Choi (AAAI, 2025)

xPatch: Dual-Stream Time Series Forecasting with Exponential Seasonal-Trend Decomposition. - Artyom Stitsyuk; Jaesik Choi (AAAI, 2025)

Deontological Keyword Bias: The Impact of Modal Expressions on Normative Judgments of Language Models. - Bumjin Park; Leejinsil Leejinsil; Jaesik Choi (ACL (1), 2025)

Enhancing Creative Generation on Stable Diffusion-based Models. - Jiyeon Han; Dahee Kwon; Gayoung Lee; Junho Kim; Jaesik Choi (CVPR, 2025)

Rethinking Shapley Value for Negative Interactions in Non-convex Games. - Wonjoon Chang; Myeongjin Lee; Jaesik Choi (ICLR, 2025)

Neural ODE Transformers: Analyzing Internal Dynamics and Adaptive Fine-tuning. - Anh Tong; Thanh Nguyen-Tang; Dongeun Lee 001; Duc Nguyen; Toan M. Tran; David Leo Wright Hall; Cheongwoong Kang; Jaesik Choi (ICLR, 2025)

Context-Aware Negative Sampling for Sequential Recommendation. - Jinseok Seol; Jaesik Choi (IEEE Access, 2025)

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; Jaesik Choi (CoRR, 2025)

Example-Based Concept Analysis Framework for Deep Weather Forecast Models. - Soyeon Kim; Junho Choi; Subeen Lee; Jaesik Choi (CoRR, 2025)

PnPXAI: A Universal XAI Framework Providing Automatic Explanations Across Diverse Modalities and Models. - Seongun Kim; Sol A Kim; Geonhyeong Kim; Enver Menadjiev; Chanwoo Lee; Seongwook Chung; Nari Kim; Jaesik Choi (CoRR, 2025)

Counterfactual Activation Editing for Post-hoc Prosody and Mispronunciation Correction in TTS Models. - Kyowoon Lee; Artyom Stitsyuk; Gunu Jho; Inchul Hwang; Jaesik Choi (CoRR, 2025)

Local Manifold Approximation and Projection for Manifold-Aware Diffusion Planning. - Kyowoon Lee; Jaesik Choi (CoRR, 2025)

State-Covering Trajectory Stitching for Diffusion Planners. - Kyowoon Lee; Jaesik Choi (CoRR, 2025)

Deontological Keyword Bias: The Impact of Modal Expressions on Normative Judgments of Language Models. - Bumjin Park; Jinsil Lee; Jaesik Choi (CoRR, 2025)

Granular Concept Circuits: Toward a Fine-Grained Circuit Discovery for Concept Representations. - Dahee Kwon; Sehyun Lee; Jaesik Choi (CoRR, 2025)