Jaesik Choi

ORCID

Publication Activity (10 Years)

Years Active: 2006-2024
Publications (10 Years): 105

Top Venues

Publications

Hyunkyung Han, Jihyeon Seong, Jaesik Choi
CardioCaps: Attention-Based Capsule Network for Class-Imbalanced Echocardiogram Classification. BigComp (2024)
Jihyeon Seong, Sekwang Oh, Jaesik Choi
Towards Dynamic Trend Filtering through Trend Point Detection with Reinforcement Learning. CoRR (2024)
Wonjoon Chang, Dahee Kwon, Jaesik Choi
Understanding Distributed Representations of Concepts in Deep Neural Networks without Supervision. AAAI (2024)
Bumjin Park, Jaesik Choi
Identifying the Source of Generation for Large Language Models. CoRR (2024)
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 Lécué, Gianclaudio Malgieri, Andrés Páez, Wojciech Samek, Johannes Schneider, Timo Speith, Simone Stumpf
Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions. Inf. Fusion 106 (2024)
Jihyeon Seong, Jungmin Kim, Jaesik Choi
Towards Diverse Perspective Learning with Selection over Multiple Temporal Poolings. AAAI (2024)
Jihyeon Seong, Jungmin Kim, Jaesik Choi
Towards Diverse Perspective Learning with Selection over Multiple Temporal Poolings. CoRR (2024)
Soyeon Kim, Jihyeon Seong, Hyunkyung Han, Jaesik Choi
Capsule Neural Networks as Noise Stabilizer for Time Series Data. CoRR (2024)
Bumjin Park, Jaesik Choi
Memorizing Documents with Guidance in Large Language Models. CoRR (2024)
Hyunkyung Han, Jihyeon Seong, Jaesik Choi
CardioCaps: Attention-based Capsule Network for Class-Imbalanced Echocardiogram Classification. CoRR (2024)
Seongwoo Lim, Won Jo, Joohyung Lee, Jaesik Choi
Pathwise Explanation of ReLU Neural Networks. AISTATS (2024)
Hyunkyung Han, Jaesik Choi
Optimal path for Biomedical Text Summarization Using Pointer GPT. CoRR (2024)
Seongun Kim, Kyowoon Lee, Jaesik Choi
Variational Curriculum Reinforcement Learning for Unsupervised Discovery of Skills. CoRR (2023)
Kyowoon Lee, Seongun Kim, Jaesik Choi
Refining Diffusion Planner for Reliable Behavior Synthesis by Automatic Detection of Infeasible Plans. NeurIPS (2023)
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 Lécué, Gianclaudio Malgieri, Andrés Páez, Wojciech Samek, Johannes Schneider, Timo Speith, Simone Stumpf
Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions. CoRR (2023)
Anh Tong, Thanh Nguyen-Tang, Dongeun Lee, Toan M. Tran, Jaesik Choi
SigFormer: Signature Transformers for Deep Hedging. ICAIF (2023)
Cheongwoong Kang, Jaesik Choi
Impact of Co-occurrence on Factual Knowledge of Large Language Models. EMNLP (Findings) (2023)
Kyowoon Lee, Seongun Kim, Jaesik Choi
Adaptive and Explainable Deployment of Navigation Skills via Hierarchical Deep Reinforcement Learning. ICRA (2023)
Seongun Kim, Kyowoon Lee, Jaesik Choi
Variational Curriculum Reinforcement Learning for Unsupervised Discovery of Skills. ICML (2023)
Ye Eun Chun, Sunjae Kwon, Kyunghwan Sohn, Nakwon Sung, Junyoup Lee, Byoung Seo, Kevin Compher, Seung-won Hwang, Jaesik Choi
CR-COPEC: Causal Rationale of Corporate Performance Changes to learn from Financial Reports. EMNLP (Findings) (2023)
Soyeon Kim, Junho Choi, Yeji Choi, Subeen Lee, Artyom Stitsyuk, Minkyoung Park, Seongyeop Jeong, You-Hyun Baek, Jaesik Choi
Explainable AI-Based Interface System for Weather Forecasting Model. HCI (53) (2023)
Anh Tong, Thanh Nguyen-Tang, Dongeun Lee, Toan M. Tran, Jaesik Choi
SigFormer: Signature Transformers for Deep Hedging. CoRR (2023)
Seongun Kim, Jaesik Choi
Explaining the Decisions of Deep Policy Networks for Robotic Manipulations. CoRR (2023)
Nesma Mahmoud, Hanna Antson, Jaesik Choi, Osamu Shimmi, Kallol Roy
Stress and Adaptation: Applying Anna Karenina Principle in Deep Learning for Image Classification. CoRR (2023)
Wonjoon Chang, Dahee Kwon, Jaesik Choi
Understanding Distributed Representations of Concepts in Deep Neural Networks without Supervision. CoRR (2023)
Ye Eun Chun, Sunjae Kwon, Kyunghwan Sohn, Nakwon Sung, Junyoup Lee, Byungki Seo, Kevin Compher, Seung-won Hwang, Jaesik Choi
CR-COPEC: Causal Rationale of Corporate Performance Changes to Learn from Financial Reports. CoRR (2023)
Daehyun Chang, Youngdae Kim, Suksoo Pyo, Shin Hun, Daesop Lee, Sohee Hwang, Jaesik Choi, Siwoong Kim
Algorithmic Read Resistance Trim for Improving Yield and Reducing Test Time in MRAM. ITC (2023)
Kyowoon Lee, Seongun Kim, Jaesik Choi
Adaptive and Explainable Deployment of Navigation Skills via Hierarchical Deep Reinforcement Learning. CoRR (2023)
Kyowoon Lee, Seongun Kim, Jaesik Choi
Refining Diffusion Planner for Reliable Behavior Synthesis by Automatic Detection of Infeasible Plans. CoRR (2023)
Inyoung Paik, Jaesik Choi
The Disharmony Between BN and ReLU Causes Gradient Explosion, but is Offset by the Correlation Between Activations. CoRR (2023)
Cheongwoong Kang, Jaesik Choi
Impact of Co-occurrence on Factual Knowledge of Large Language Models. CoRR (2023)
Jiyeon Han, Hwanil Choi, Yunjey Choi, Junho Kim, Jung-Woo Ha, Jaesik Choi
Rarity Score : A New Metric to Evaluate the Uncommonness of Synthesized Images. ICLR (2023)
Giyoung Jeon, Haedong Jeong, Jaesik Choi
Beyond Single Path Integrated Gradients for Reliable Input Attribution via Randomized Path Sampling. ICCV (2023)
Hwanil Choi, Wonjoon Chang, Jaesik Choi
Can We Find Neurons that Cause Unrealistic Images in Deep Generative Networks? CoRR (2022)
Bumjin Park, Jaesik Choi
Explanation on Pretraining Bias of Finetuned Vision Transformer. CoRR (2022)
Giyoung Jeon, Haedong Jeong, Jaesik Choi
Distilled Gradient Aggregation: Purify Features for Input Attribution in the Deep Neural Network. NeurIPS (2022)
Sunjae Kwon, Cheongwoong Kang, Jiyeon Han, Jaesik Choi
Why Do Neural Language Models Still Need Commonsense Knowledge to Handle Semantic Variations in Question Answering? CoRR (2022)
Haedong Jeong, Jiyeon Han, Jaesik Choi
An Unsupervised Way to Understand Artifact Generating Internal Units in Generative Neural Networks. AAAI (2022)
Cheongwoong Kang, Bumjin Park, Jaesik Choi
Scheduling PID Attitude and Position Control Frequencies for Time-Optimal Quadrotor Waypoint Tracking under Unknown External Disturbances. Sensors 22 (1) (2022)
Hwanil Choi, Wonjoon Chang, Jaesik Choi
Can We Find Neurons that Cause Unrealistic Images in Deep Generative Networks? IJCAI (2022)
Anh Tong, Thanh Nguyen-Tang, Toan M. Tran, Jaesik Choi
Learning Fractional White Noises in Neural Stochastic Differential Equations. NeurIPS (2022)
Jiyeon Han, Hwanil Choi, Yunjey Choi, Junho Kim, Jung-Woo Ha, Jaesik Choi
Rarity Score : A New Metric to Evaluate the Uncommonness of Synthesized Images. CoRR (2022)
Deokjun Eom, Sehyun Lee, Jaesik Choi
Variational Neural Temporal Point Process. CoRR (2022)
Seongjin Park, Haedong Jeong, Giyoung Jeon, Jaesik Choi
On the Relationship Between Adversarial Robustness and Decision Region in Deep Neural Network. CoRR (2022)
Qin Xie, Peng Zhang, Boseon Yu, Jaesik Choi
Semisupervised Training of Deep Generative Models for High-Dimensional Anomaly Detection. IEEE Trans. Neural Networks Learn. Syst. 33 (6) (2022)
Haedong Jeong, Jiyeon Han, Jaesik Choi
An Unsupervised Way to Understand Artifact Generating Internal Units in Generative Neural Networks. CoRR (2021)
Ali Tousi, Haedong Jeong, Jiyeon Han, Hwanil Choi, Jaesik Choi
Automatic Correction of Internal Units in Generative Neural Networks. CVPR (2021)
Seongun Kim, Jaesik Choi
Explaining the Decisions of Deep Policy Networks for Robotic Manipulations. IROS (2021)
Woo-Jeoung Nam, Jaesik Choi, Seong-Whan Lee
Interpreting Deep Neural Networks with Relative Sectional Propagation by Analyzing Comparative Gradients and Hostile Activations. AAAI (2021)
Anh Tong, Jaesik Choi
Characterizing Deep Gaussian Processes via Nonlinear Recurrence Systems. AAAI (2021)
Sohee Cho, Wonjoon Chang, Ginkyeng Lee, Jaesik Choi
Interpreting Internal Activation Patterns in Deep Temporal Neural Networks by Finding Prototypes. KDD (2021)
Ali Tousi, Haedong Jeong, Jiyeon Han, Hwanil Choi, Jaesik Choi
Automatic Correction of Internal Units in Generative Neural Networks. CoRR (2021)
Anh Tong, Toan M. Tran, Hung Bui, Jaesik Choi
Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior. AAAI (2021)
Boseon Yoo, Jiwoo Lee, Janghoon Ju, Seijun Chung, Soyeon Kim, Jaesik Choi
Conditional Temporal Neural Processes with Covariance Loss. ICML (2021)
Anh Tong, Toan M. Tran, Hung Bui, Jaesik Choi
Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior. CoRR (2020)
Jaesik Choi
Interpreting and Explaining Deep Neural Networks: A Perspective on Time Series Data. KDD (2020)
Youngjin Park, Deokjun Eom, Byoungki Seo, Jaesik Choi
Improved predictive deep temporal neural networks with trend filtering. ICAIF (2020)
Jay H. Park, Gyeongchan Yun, Chang M. Yi, Nguyen T. Nguyen, Seungmin Lee, Jaesik Choi, Sam H. Noh, Young-ri Choi
HetPipe: Enabling Large DNN Training on (Whimpy) Heterogeneous GPU Clusters through Integration of Pipelined Model Parallelism and Data Parallelism. CoRR (2020)
Woo-Jeoung Nam, Jaesik Choi, Seong-Whan Lee
Interpreting Deep Neural Networks with Relative Sectional Propagation by Analyzing Comparative Gradients and Hostile Activations. CoRR (2020)
Youngjin Park, Deokjun Eom, Byoungki Seo, Jaesik Choi
Improved Predictive Deep Temporal Neural Networks with Trend Filtering. CoRR (2020)
Dongwon Park, Yonghyeok Seo, Dongju Shin, Jaesik Choi, Se Young Chun
A Single Multi-Task Deep Neural Network with Post-Processing for Object Detection with Reasoning and Robotic Grasp Detection. ICRA (2020)
Giyoung Jeon, Haedong Jeong, Jaesik Choi
An Efficient Explorative Sampling Considering the Generative Boundaries of Deep Generative Neural Networks. AAAI (2020)
Jay H. Park, Gyeongchan Yun, Chang M. Yi, Nguyen T. Nguyen, Seungmin Lee, Jaesik Choi, Sam H. Noh, Young-ri Choi
HetPipe: Enabling Large DNN Training on (Whimpy) Heterogeneous GPU Clusters through Integration of Pipelined Model Parallelism and Data Parallelism. USENIX Annual Technical Conference (2020)
Woo-Jeoung Nam, Shir Gur, Jaesik Choi, Lior Wolf, Seong-Whan Lee
Relative Attributing Propagation: Interpreting the Comparative Contributions of Individual Units in Deep Neural Networks. AAAI (2020)
Sohee Cho, Ginkyeng Lee, Jaesik Choi
Interpretation of Deep Temporal Representations by Selective Visualization of Internally Activated Units. CoRR (2020)
Anh Tong, Jaesik Choi
Characterizing Deep Gaussian Processes via Nonlinear Recurrence Systems. CoRR (2020)
Jiyeon Han, Kyowoon Lee, Anh Tong, Jaesik Choi
Confirmatory Bayesian Online Change Point Detection in the Covariance Structure of Gaussian Processes. CoRR (2019)
Dongwon Park, Yonghyeok Seo, Dongju Shin, Jaesik Choi, Se Young Chun
A Single Multi-Task Deep Neural Network with Post-Processing for Object Detection with Reasoning and Robotic Grasp Detection. CoRR (2019)
Jiyeon Han, Kyowoon Lee, Anh Tong, Jaesik Choi
Confirmatory Bayesian Online Change Point Detection in the Covariance Structure of Gaussian Processes. IJCAI (2019)
Giyoung Jeon, Haedong Jeong, Jaesik Choi
An Efficient Explorative Sampling Considering the Generative Boundaries of Deep Generative Neural Networks. CoRR (2019)
Thanh T. Nguyen, Jaesik Choi
Markov Information Bottleneck to Improve Information Flow in Stochastic Neural Networks. Entropy 21 (10) (2019)
Sunjae Kwon, Cheongwoong Kang, Jiyeon Han, Jaesik Choi
Why Do Masked Neural Language Models Still Need Common Sense Knowledge? CoRR (2019)
Dongeun Lee, Alex Sim, Jaesik Choi, Kesheng Wu
IDEALEM: Statistical Similarity Based Data Reduction. CoRR (2019)
Woo-Jeoung Nam, Jaesik Choi, Seong-Whan Lee
Relative Attributing Propagation: Interpreting the Comparative Contributions of Individual Units in Deep Neural Networks. CoRR (2019)
David Gunning, Mark Stefik, Jaesik Choi, Timothy Miller, Simone Stumpf, Guang-Zhong Yang
XAI - Explainable artificial intelligence. Sci. Robotics 4 (37) (2019)
Anh Tong, Jaesik Choi
Discovering Latent Covariance Structures for Multiple Time Series. ICML (2019)
Kyowoon Lee, Sol-A. Kim, Jaesik Choi, Seong-Whan Lee
Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling. ICML (2018)
Taehoon Kim, Jaesik Choi, Dongeun Lee, Alex Sim, C. Anna Spurlock, Annika Todd, Kesheng Wu
Predicting baseline for analysis of electricity pricing. Int. J. Big Data Intell. 5 (1/2) (2018)
Subin Yi, Jaesik Choi
Learning the Group Structure of Deep Neural Networks with an Expectation Maximization Method. ICDM Workshops (2018)
J. Kade Gibson, Dongeun Lee, Jaesik Choi, Alex Sim
Dynamic Online Performance Optimization in Streaming Data Compression. IEEE BigData (2018)
Dongeun Lee, Alex Sim, Jaesik Choi, Kesheng Wu
Improving Statistical Similarity Based Data Reduction for Non-Stationary Data. SSDBM (2017)
Thanh T. Nguyen, Jaesik Choi
Layer-wise Learning of Stochastic Neural Networks with Information Bottleneck. CoRR (2017)
Man-Ki Yoon, Sibin Mohan, Jaesik Choi, Mihai Christodorescu, Lui Sha
Learning Execution Contexts from System Call Distribution for Anomaly Detection in Smart Embedded System. IoTDI (2017)
Dongeun Lee, Alex Sim, Jaesik Choi, Kesheng Wu
Expanding Statistical Similarity Based Data Reduction to Capture Diverse Patterns. DCC (2017)
Subin Yi, Janghoon Ju, Man-Ki Yoon, Jaesik Choi
Grouped Convolutional Neural Networks for Multivariate Time Series. CoRR (2017)
Seongwoo Lim, Soonjae Kwon, Sehyun Lee, Jaesik Choi
UNIST SAIL System for TAC 2017 Cold Start Slot Filling. TAC (2017)
Rafael Lima, Jaesik Choi
Automatic Decomposition of Self-Triggering Kernels of Hawkes Processes. CoRR (2017)
Kallol Roy, Anh Tong, Jaesik Choi
Searching for Topological Symmetry in Data Haystack. CoRR (2016)
Wonjun Yoon, Sol-A. Kim, Jaesik Choi
A Robot Learning to Play a Mobile Game Under Unknown Dynamics. CoRR (2016)
Vladimir Nekrasov, Janghoon Ju, Jaesik Choi
Global Deconvolutional Networks for Semantic Segmentation. CoRR (2016)
Dongeun Lee, Rafael Lima, Jaesik Choi
Improving Imprecise Compressive Sensing Models. UAI (2016)
Vladimir Nekrasov, Janghoon Ju, Jaesik Choi
Global Deconvolutional Networks for Semantic Segmentation. BMVC (2016)
Seongwoo Lim, Jiyeon Han, Kyowoon Lee, Jaesik Choi
SAIL (UNIST) team Participation in KBP 2016 Cold-Start Slot filling. TAC (2016)
Yunseong Hwang, Anh Tong, Jaesik Choi
Automatic Construction of Nonparametric Relational Regression Models for Multiple Time Series. ICML (2016)
Dongeun Lee, Alex Sim, Jaesik Choi, Kesheng Wu
Novel Data Reduction Based on Statistical Similarity. SSDBM (2016)
Kyungjoong Jeong, Jaesik Choi, Gil-Jin Jang
Semi-Local Structure Patterns for Robust Face Detection. IEEE Signal Process. Lett. 22 (9) (2015)
Taehoon Kim, Dongeun Lee, Jaesik Choi, Anna Spurlock, Alex Sim, Annika Todd, Kesheng Wu
Extracting Baseline Electricity Usage Using Gradient Tree Boosting. SmartCity (2015)
Wen Pu, Jaesik Choi, Yunseong Hwang, Eyal Amir
A Deterministic Partition Function Approximation for Exponential Random Graph Models. IJCAI (2015)
Dongeun Lee, Jaesik Choi
Learning Compressive Sensing Models for Big Spatio-Temporal Data. SDM (2015)
Man-Ki Yoon, Sibin Mohan, Jaesik Choi, Mihai Christodorescu, Lui Sha
Intrusion Detection Using Execution Contexts Learned from System Call Distributions of Real-Time Embedded Systems. CoRR (2015)

Jaesik Choi.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Jaesik Choi